[ { "path": ".gitattributes", "content": "*.ipynb linguist-vendored" }, { "path": ".github/workflows/build.yml", "content": "# This workflow will install Python dependencies, run tests and lint with a variety of Python versions\n# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions\n\nname: build\n\non:\n push:\n branches: [ \"master\" ]\n pull_request:\n branches: [ \"master\" ]\n\njobs:\n build:\n\n runs-on: ubuntu-latest\n strategy:\n fail-fast: false\n matrix:\n python-version: [\"3.8\", \"3.9\", \"3.10\"]\n\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v3\n with:\n python-version: ${{ matrix.python-version }}\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n python -m pip install flake8 pytest\n if [ -f requirements.txt ]; then pip install -r requirements.txt; fi\n pip install -e .'[dev]'\n - name: Lint with flake8\n run: |\n # stop the build if there are Python syntax errors or undefined names\n flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics\n # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide\n flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics\n - name: Test with pytest\n run: |\n pip install pytest\n pip install pytest-cov\n pip install pytest-subtests\n pytest tests/ --doctest-modules --junitxml=junit/test-results.xml --cov=com --cov-report=xml --cov-report=html\n" }, { "path": ".github/workflows/documentation-links.yaml", "content": "# .github/workflows/documentation-links.yaml\n\nname: Read the Docs Pull Request Preview\non:\n pull_request_target:\n types:\n - opened\n\npermissions:\n pull-requests: write\n\njobs:\n documentation-links:\n runs-on: ubuntu-latest\n steps:\n - uses: readthedocs/actions/preview@v1\n with:\n project-slug: \"pymgrid\"\n" }, { "path": ".github/workflows/garage-compat.yml", "content": "# This workflow will install Python dependencies, run tests and lint with a variety of Python versions\n# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions\n\nname: garage-compat\n\non:\n push:\n branches: [ \"master\" ]\n pull_request:\n branches: [ \"master\" ]\n\njobs:\n build:\n\n runs-on: ubuntu-latest\n strategy:\n fail-fast: false\n matrix:\n python-version: [\"3.8\", \"3.9\"]\n\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v3\n with:\n python-version: ${{ matrix.python-version }}\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n python -m pip install flake8 pytest\n if [ -f requirements.txt ]; then pip install -r requirements.txt; fi\n pip install -e .'[dev]'\n - name: Get garage VERSION\n uses: wei/wget@v1\n with:\n args: -O VERSION https://raw.githubusercontent.com/rlworkgroup/garage/master/VERSION\n - name: Get garage README.md\n uses: wei/wget@v1\n with:\n args: -O README.md https://raw.githubusercontent.com/rlworkgroup/garage/master/README.md\n - name: Get garage setup.py\n uses: wei/wget@v1\n with:\n args: -O garage_setup.py https://raw.githubusercontent.com/rlworkgroup/garage/master/setup.py\n - name: Extract garage dependencies\n run: |\n python -m garage_setup egg_info\n in_all=false\n while IFS= read -r line; do\n if [[ \"$line\" == *\"[all]\"* ]]; then\n in_all=true\n elif [[ \"$line\" == \\[* ]]; then\n in_all=false\n elif [[ $in_all == true ]]; then\n echo $\"$line\" >> src/garage.egg-info/requires_all.txt\n fi\n done < \"src/garage.egg-info/requires.txt\"\n - name: Install garage dependencies\n run: |\n sudo apt install libopenmpi-dev\n cat src/garage.egg-info/requires_all.txt\n python -m pip install -r src/garage.egg-info/requires_all.txt \n - name: Lint with flake8\n run: |\n # stop the build if there are Python syntax errors or undefined names\n flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics\n # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide\n flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics\n - name: Test with pytest\n run: |\n pip install pytest\n pip install pytest-cov\n pip install pytest-subtests\n pytest tests/ --doctest-modules --junitxml=junit/test-results.xml --cov=com --cov-report=xml --cov-report=html\n" }, { "path": ".gitignore", "content": "#Ignore what is not python code\nnotebooks/\n.DS_STORE\n.idea\n*.pkl\npymgrid/__pycache__/\n*.ipynb\n.ipynb_checkpoints\n__pycache__/\n\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\ndocs/source/reference/api/\npip-wheel-metadata/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n*sandbox.py\n" }, { "path": ".readthedocs.yaml", "content": "# .readthedocs.yaml\n# Read the Docs configuration file\n# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details\n\n# Required\nversion: 2\n\n# Set the version of Python and other tools you might need\nbuild:\n os: ubuntu-20.04\n tools:\n python: \"3.8\"\n\nsphinx:\n configuration: docs/source/conf.py\n\npython:\n install:\n - method: pip\n path: .\n extra_requirements:\n - all\n\nformats:\n - pdf" }, { "path": "CHANGELOG.md", "content": "\n" }, { "path": "CONTRIBUTING.md", "content": "\n" }, { "path": "LICENSE", "content": " GNU LESSER GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. \n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n\n This version of the GNU Lesser General Public License incorporates\nthe terms and conditions of version 3 of the GNU General Public\nLicense, supplemented by the additional permissions listed below.\n\n 0. Additional Definitions.\n\n As used herein, \"this License\" refers to version 3 of the GNU Lesser\nGeneral Public License, and the \"GNU GPL\" refers to version 3 of the GNU\nGeneral Public License.\n\n \"The Library\" refers to a covered work governed by this License,\nother than an Application or a Combined Work as defined below.\n\n An \"Application\" is any work that makes use of an interface provided\nby the Library, but which is not otherwise based on the Library.\nDefining a subclass of a class defined by the Library is deemed a mode\nof using an interface provided by the Library.\n\n A \"Combined Work\" is a work produced by combining or linking an\nApplication with the Library. The particular version of the Library\nwith which the Combined Work was made is also called the \"Linked\nVersion\".\n\n The \"Minimal Corresponding Source\" for a Combined Work means the\nCorresponding Source for the Combined Work, excluding any source code\nfor portions of the Combined Work that, considered in isolation, are\nbased on the Application, and not on the Linked Version.\n\n The \"Corresponding Application Code\" for a Combined Work means the\nobject code and/or source code for the Application, including any data\nand utility programs needed for reproducing the Combined Work from the\nApplication, but excluding the System Libraries of the Combined Work.\n\n 1. Exception to Section 3 of the GNU GPL.\n\n You may convey a covered work under sections 3 and 4 of this License\nwithout being bound by section 3 of the GNU GPL.\n\n 2. Conveying Modified Versions.\n\n If you modify a copy of the Library, and, in your modifications, a\nfacility refers to a function or data to be supplied by an Application\nthat uses the facility (other than as an argument passed when the\nfacility is invoked), then you may convey a copy of the modified\nversion:\n\n a) under this License, provided that you make a good faith effort to\n ensure that, in the event an Application does not supply the\n function or data, the facility still operates, and performs\n whatever part of its purpose remains meaningful, or\n\n b) under the GNU GPL, with none of the additional permissions of\n this License applicable to that copy.\n\n 3. Object Code Incorporating Material from Library Header Files.\n\n The object code form of an Application may incorporate material from\na header file that is part of the Library. You may convey such object\ncode under terms of your choice, provided that, if the incorporated\nmaterial is not limited to numerical parameters, data structure\nlayouts and accessors, or small macros, inline functions and templates\n(ten or fewer lines in length), you do both of the following:\n\n a) Give prominent notice with each copy of the object code that the\n Library is used in it and that the Library and its use are\n covered by this License.\n\n b) Accompany the object code with a copy of the GNU GPL and this license\n document.\n\n 4. Combined Works.\n\n You may convey a Combined Work under terms of your choice that,\ntaken together, effectively do not restrict modification of the\nportions of the Library contained in the Combined Work and reverse\nengineering for debugging such modifications, if you also do each of\nthe following:\n\n a) Give prominent notice with each copy of the Combined Work that\n the Library is used in it and that the Library and its use are\n covered by this License.\n\n b) Accompany the Combined Work with a copy of the GNU GPL and this license\n document.\n\n c) For a Combined Work that displays copyright notices during\n execution, include the copyright notice for the Library among\n these notices, as well as a reference directing the user to the\n copies of the GNU GPL and this license document.\n\n d) Do one of the following:\n\n 0) Convey the Minimal Corresponding Source under the terms of this\n License, and the Corresponding Application Code in a form\n suitable for, and under terms that permit, the user to\n recombine or relink the Application with a modified version of\n the Linked Version to produce a modified Combined Work, in the\n manner specified by section 6 of the GNU GPL for conveying\n Corresponding Source.\n\n 1) Use a suitable shared library mechanism for linking with the\n Library. A suitable mechanism is one that (a) uses at run time\n a copy of the Library already present on the user's computer\n system, and (b) will operate properly with a modified version\n of the Library that is interface-compatible with the Linked\n Version.\n\n e) Provide Installation Information, but only if you would otherwise\n be required to provide such information under section 6 of the\n GNU GPL, and only to the extent that such information is\n necessary to install and execute a modified version of the\n Combined Work produced by recombining or relinking the\n Application with a modified version of the Linked Version. (If\n you use option 4d0, the Installation Information must accompany\n the Minimal Corresponding Source and Corresponding Application\n Code. If you use option 4d1, you must provide the Installation\n Information in the manner specified by section 6 of the GNU GPL\n for conveying Corresponding Source.)\n\n 5. Combined Libraries.\n\n You may place library facilities that are a work based on the\nLibrary side by side in a single library together with other library\nfacilities that are not Applications and are not covered by this\nLicense, and convey such a combined library under terms of your\nchoice, if you do both of the following:\n\n a) Accompany the combined library with a copy of the same work based\n on the Library, uncombined with any other library facilities,\n conveyed under the terms of this License.\n\n b) Give prominent notice with the combined library that part of it\n is a work based on the Library, and explaining where to find the\n accompanying uncombined form of the same work.\n\n 6. Revised Versions of the GNU Lesser General Public License.\n\n The Free Software Foundation may publish revised and/or new versions\nof the GNU Lesser General Public License from time to time. Such new\nversions will be similar in spirit to the present version, but may\ndiffer in detail to address new problems or concerns.\n\n Each version is given a distinguishing version number. If the\nLibrary as you received it specifies that a certain numbered version\nof the GNU Lesser General Public License \"or any later version\"\napplies to it, you have the option of following the terms and\nconditions either of that published version or of any later version\npublished by the Free Software Foundation. If the Library as you\nreceived it does not specify a version number of the GNU Lesser\nGeneral Public License, you may choose any version of the GNU Lesser\nGeneral Public License ever published by the Free Software Foundation.\n\n If the Library as you received it specifies that a proxy can decide\nwhether future versions of the GNU Lesser General Public License shall\napply, that proxy's public statement of acceptance of any version is\npermanent authorization for you to choose that version for the\nLibrary.\n" }, { "path": "MANIFEST.in", "content": "global-include *.csv\nglobal-include *.pkl\n" }, { "path": "README.md", "content": "# pymgrid\n\n## Important Notice\n\n### The person that has been maintaining pymgrid since 2020 has moved future development to [python-microgrid](https://github.com/ahalev/python-microgrid) for the foreseeable future, a drop in replacement for pymgrid, and pymgrid may not be receiving any future updates. Please open any new issues in python-microgrid.\n\n\n![Build](https://github.com/Total-RD/pymgrid/workflows/build/badge.svg?dummy=unused)\n\npymgrid (PYthon MicroGRID) is a python library to generate and simulate a large number of microgrids.\n\nFor more context, please see the [presentation](https://www.climatechange.ai/papers/neurips2020/3) done at Climate Change AI\nand the [documentation](https://pymgrid.readthedocs.io).\n\n## Installation\n\nThe easiest way to install pymgrid is with pip:\n\n`pip install -U pymgrid`\n\nAlternatively, you can install from source. First clone the repo:\n \n```bash\ngit clone https://github.com/Total-RD/pymgrid.git\n``` \nThen navigate to the root directory of pymgrid and call\n\n```bash\npip install .\n```\n## Getting Started\n\nMicrogrids are straightforward to generate from scratch. Simply define some modules and pass them\nto a microgrid:\n```python\nimport numpy as np\nfrom pymgrid import Microgrid\nfrom pymgrid.modules import GensetModule, BatteryModule, LoadModule, RenewableModule\n\n\ngenset = GensetModule(running_min_production=10,\n running_max_production=50,\n genset_cost=0.5)\n\nbattery = BatteryModule(min_capacity=0,\n max_capacity=100,\n max_charge=50,\n max_discharge=50,\n efficiency=1.0,\n init_soc=0.5)\n\n# Using random data\nrenewable = RenewableModule(time_series=50*np.random.rand(100))\n\nload = LoadModule(time_series=60*np.random.rand(100),\n loss_load_cost=10)\n\nmicrogrid = Microgrid([genset, battery, (\"pv\", renewable), load])\n```\n\nThis creates a microgrid with the modules defined above, as well as an unbalanced energy module -- \nwhich reconciles situations when energy demand cannot be matched to supply.\n\nPrinting the microgrid gives us its architecture:\n\n```python\n>> microgrid\n\nMicrogrid([genset x 1, load x 1, battery x 1, pv x 1, balancing x 1])\n```\n\nA microgrid is contained of fixed modules and flex modules. Some modules can be both -- `GridModule`, for example\n-- but not at the same time.\n\n\nA *fixed* module has requires a request of a certain amount of energy ahead of time, and then attempts to \nproduce or consume said amount. `LoadModule` is an example of this; you must tell it to consume a certain amount of energy\nand it will then do so.\n\n A *flex* module, on the other hand, is able to adapt to meet demand. `RenewableModule` is an example of this as\n it allows for curtailment of any excess renewable produced.\n \n A microgrid will tell you which modules are which:\n \n ```python\n>> microgrid.fixed_modules\n\n{\n \"genset\": \"[GensetModule(running_min_production=10, running_max_production=50, genset_cost=0.5, co2_per_unit=0, cost_per_unit_co2=0, start_up_time=0, wind_down_time=0, allow_abortion=True, init_start_up=True, raise_errors=False, provided_energy_name=genset_production)]\",\n \"load\": \"[LoadModule(time_series=, loss_load_cost=10, forecaster=NoForecaster, forecast_horizon=0, forecaster_increase_uncertainty=False, raise_errors=False)]\",\n \"battery\": \"[BatteryModule(min_capacity=0, max_capacity=100, max_charge=50, max_discharge=50, efficiency=1.0, battery_cost_cycle=0.0, battery_transition_model=None, init_charge=None, init_soc=0.5, raise_errors=False)]\"\n}\n\n>>microgrid.flex_modules\n\n{\n \"pv\": \"[RenewableModule(time_series=, raise_errors=False, forecaster=NoForecaster, forecast_horizon=0, forecaster_increase_uncertainty=False, provided_energy_name=renewable_used)]\",\n \"balancing\": \"[UnbalancedEnergyModule(raise_errors=False, loss_load_cost=10, overgeneration_cost=2)]\"\n}\n\n```\n\n\nRunning the microgrid is straightforward. Simply pass an action for each fixed module to `microgrid.run`. The microgrid\ncan also provide you a random action by calling `microgrid.sample_action.` Once the microgrid has been run for a\ncertain number of steps, results can be viewed by calling microgrid.get_log.\n\n```python\n>> for j in range(10):\n>> action = microgrid.sample_action(strict_bound=True)\n>> microgrid.run(action)\n\n>> microgrid.get_log(drop_singleton_key=True)\n\n genset ... balance\n reward ... fixed_absorbed_by_microgrid\n0 -5.000000 ... 10.672095\n1 -14.344353 ... 50.626726\n2 -5.000000 ... 17.538018\n3 -0.000000 ... 15.492778\n4 -0.000000 ... 35.748724\n5 -0.000000 ... 30.302300\n6 -5.000000 ... 36.451662\n7 -0.000000 ... 66.533872\n8 -0.000000 ... 20.645077\n9 -0.000000 ... 10.632957\n```\n\n## Benchmarking\n\n`pymgrid` also comes pre-packaged with a set of 25 microgrids for benchmarking.\nThe config files for these microgrids are available in `data/scenario/pymgrid25`.\nSimply deserialize one of the yaml files to load one of the saved microgrids; for example,\nto load the zeroth microgrid:\n\n```python\nimport yaml\nfrom pymgrid import PROJECT_PATH\n\nyaml_file = PROJECT_PATH / 'data/scenario/pymgrid25/microgrid_0/microgrid_0.yaml'\nmicrogrid = yaml.safe_load(yaml_file.open('r'))\n```\n\nAlternatively, `Microgrid.load(yaml_file.open('r'))` will perform the same deserialization.\n\n\n## Citation\n\nIf you use this package for your research, please cite the following paper:\n\n@misc{henri2020pymgrid,\n title={pymgrid: An Open-Source Python Microgrid Simulator for Applied Artificial Intelligence Research}, \n author={Gonzague Henri, Tanguy Levent, Avishai Halev, Reda Alami and Philippe Cordier},\n year={2020},\n eprint={2011.08004},\n archivePrefix={arXiv},\n primaryClass={cs.AI}\n}\n\nYou can find it on Arxiv here: https://arxiv.org/abs/2011.08004\n\n## Data\n\nData in pymgrid are based on TMY3 (data based on representative weather). The PV data comes from DOE/NREL/ALLIANCE (https://nsrdb.nrel.gov/about/tmy.html) and the load data comes from OpenEI (https://openei.org/doe-opendata/dataset/commercial-and-residential-hourly-load-profiles-for-all-tmy3-locations-in-the-united-states)\n\nThe CO2 data is from Jacque de Chalendar and his gridemissions API.\n\n## Contributing\nPull requests are welcome for bug fixes. For new features, please open an issue first to discuss what you would like to add.\n\nPlease make sure to update tests as appropriate.\n\n## License\n\nThis repo is under a GNU LGPL 3.0 (https://github.com/total-sa/pymgrid/edit/master/LICENSE)\n" }, { "path": "docs/Makefile", "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line, and also\n# from the environment for the first two.\nSPHINXOPTS ?=\nSPHINXBUILD ?= sphinx-build\nSOURCEDIR = source\nBUILDDIR = build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n" }, { "path": "docs/make.bat", "content": "@ECHO OFF\n\npushd %~dp0\n\nREM Command file for Sphinx documentation\n\nif \"%SPHINXBUILD%\" == \"\" (\n\tset SPHINXBUILD=sphinx-build\n)\nset SOURCEDIR=source\nset BUILDDIR=build\n\n%SPHINXBUILD% >NUL 2>NUL\nif errorlevel 9009 (\n\techo.\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\n\techo.installed, then set the SPHINXBUILD environment variable to point\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\n\techo.may add the Sphinx directory to PATH.\n\techo.\n\techo.If you don't have Sphinx installed, grab it from\n\techo.https://www.sphinx-doc.org/\n\texit /b 1\n)\n\nif \"%1\" == \"\" goto help\n\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\ngoto end\n\n:help\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\n\n:end\npopd\n" }, { "path": "docs/requirements.txt", "content": "nbsphinx==0.8.10\nnbsphinx-link==1.3.0\nnumpydoc==1.5.0\npydata_sphinx_theme==0.12.0\nSphinx==5.3.0\n" }, { "path": "docs/source/_templates/autosummary/base.rst", "content": "{{ objname | escape | underline }}\n\n.. currentmodule:: {{ module }}\n\n.. auto{{ objtype }}:: {{ objname }}\n" }, { "path": "docs/source/_templates/autosummary/class.rst", "content": "{{ fullname | escape | underline}}\n\n.. currentmodule:: {{ module }}\n\n.. autoclass:: {{ objname }}\n\n {% block methods %}\n\n {% if methods %}\n .. rubric:: {{ _('Methods') }}\n\n .. autosummary::\n :toctree: generated/\n\n {% for item in methods %}\n {% if item != \"__init__\" %}\n ~{{ name }}.{{ item }}\n {% endif %}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n\n {% block attributes %}\n {% if attributes %}\n .. rubric:: {{ _('Attributes') }}\n\n .. autosummary::\n :toctree: generated/\n\n {% for item in attributes %}\n ~{{ name }}.{{ item }}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n" }, { "path": "docs/source/conf.py", "content": "# Configuration file for the Sphinx documentation builder.\n#\n# For the full list of built-in configuration values, see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\nimport inspect\nimport os\nimport sys\n\nfrom copy import deepcopy\nfrom builtins import object\n\nimport pymgrid\n\n\n# -- Project information -----------------------------------------------------\n# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information\n\nproject = 'pymgrid'\ncopyright = '2022, TotalEnergies'\nauthor = 'Avishai Halev'\nrelease = pymgrid.__version__\nversion = pymgrid.__version__\n\n# -- General configuration ---------------------------------------------------\n# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration\n\nextensions = [\n 'sphinx.ext.duration',\n 'sphinx.ext.autodoc',\n 'sphinx.ext.coverage',\n 'sphinx.ext.autosummary',\n 'sphinx.ext.doctest',\n 'sphinx.ext.linkcode',\n 'sphinx.ext.intersphinx',\n 'sphinx.ext.mathjax',\n 'nbsphinx',\n 'nbsphinx_link',\n 'IPython.sphinxext.ipython_console_highlighting'\n]\n\ntemplates_path = ['_templates']\nexclude_patterns = []\n\n\n# -- Options for HTML output -------------------------------------------------\n# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output\n\nhtml_theme = 'pydata_sphinx_theme'\n\nhtml_theme_options = {\n \"primary_sidebar_end\": [\"indices.html\", \"sidebar-ethical-ads.html\"],\n\n \"logo\": {\n \"image_light\": \"logo-light.png\",\n \"image_dark\": \"logo-dark.png\",\n },\n\n \"icon_links\": [\n {\n \"name\": \"GitHub\",\n \"url\": \"https://github.com/Total-RD/pymgrid\",\n \"icon\": \"fa-brands fa-github\",\n },\n {\n \"name\": \"PyPI\",\n \"url\": \"https://pypi.org/project/pymgrid/\",\n \"icon\": \"fa-solid fa-box\",\n }\n ]\n}\n\n\nhtml_static_path = ['_static']\n\n\n# These are attributes that don't have a __doc__ attribute to read ':meta private:' from.\nskip_members = [\n 'yaml_flow_style',\n 'metadata',\n 'render_mode',\n 'reward_range',\n 'spec'\n ]\n\n\ndef autodoc_skip_member(app, what, name, obj, skip, options):\n if name in skip_members:\n return True\n\n try:\n doc = obj.__doc__\n except AttributeError:\n return None\n\n if doc is not None and ':meta private:' in doc:\n return True\n return None\n\n\ndef autodoc_process_signature(app, what, name, obj, options, signature, return_annotation):\n \"\"\"\n If a class signature is being read from cls.__new__, we want to replace it with the signature from cls.__init__.\n \"\"\"\n if what == 'class' and signature[1:] in str(inspect.signature(obj.__new__)):\n obj_copy = deepcopy(obj)\n obj_copy.__new__ = object.__new__\n signature = str(inspect.signature(obj_copy))\n return signature, return_annotation\n\n\ndef linkcode_resolve(domain, info):\n \"\"\"\n Determine the URL corresponding to Python object\n \"\"\"\n if domain != \"py\":\n return None\n\n modname = info[\"module\"]\n fullname = info[\"fullname\"]\n\n submod = sys.modules.get(modname)\n if submod is None:\n return None\n\n obj = submod\n for part in fullname.split(\".\"):\n try:\n obj = getattr(obj, part)\n except AttributeError:\n return None\n\n try:\n fn = inspect.getsourcefile(inspect.unwrap(obj))\n except TypeError:\n try: # property\n fn = inspect.getsourcefile(inspect.unwrap(obj.fget))\n except (AttributeError, TypeError):\n fn = None\n if not fn:\n return None\n\n try:\n source, lineno = inspect.getsourcelines(obj)\n except TypeError:\n try: # property\n source, lineno = inspect.getsourcelines(obj.fget)\n except (AttributeError, TypeError):\n lineno = None\n except OSError:\n lineno = None\n\n if lineno:\n linespec = f\"#L{lineno}-L{lineno + len(source) - 1}\"\n else:\n linespec = \"\"\n\n fn = os.path.relpath(fn, start=os.path.dirname(pymgrid.__file__))\n\n return f'https://github.com/Total-RD/pymgrid/tree/v{pymgrid.__version__}/src/pymgrid/{fn}{linespec}'\n\n\nintersphinx_mapping = {\n 'gym': ('https://www.gymlibrary.dev/', None)\n}\n\n\ndef setup(app):\n app.connect('autodoc-skip-member', autodoc_skip_member)\n app.connect('autodoc-process-signature', autodoc_process_signature)\n" }, { "path": "docs/source/examples/index.rst", "content": "Examples\n========\n\nTo run the examples, clone the repository:\n\n.. code-block:: bash\n\n $ git clone https://github.com/Total-RD/pymgrid.git\n\nand use `Jupyter `_ to open any notebook in\nthe :code:`notebooks` directory.\n\n.. toctree::\n :maxdepth: 2\n\n quick-start\n rbc-example\n mpc-example\n rl-example" }, { "path": "docs/source/examples/mpc-example.nblink", "content": "{\n \"path\": \"../../../notebooks/mpc-example.ipynb\"\n}" }, { "path": "docs/source/examples/quick-start.nblink", "content": "{\n \"path\": \"../../../notebooks/quick-start.ipynb\"\n}\n" }, { "path": "docs/source/examples/rbc-example.nblink", "content": "{\n \"path\": \"../../../notebooks/rbc-example.ipynb\"\n}\n" }, { "path": "docs/source/examples/rl-example.nblink", "content": "{\n \"path\": \"../../../notebooks/rl-example.ipynb\"\n}\n" }, { "path": "docs/source/getting_started.rst", "content": "Getting Started\n===============\n\n.. _installation:\n\nInstallation\n------------\n\nThe easiest way to install *pymgrid* is with pip:\n\n.. code-block:: console\n\n $ pip install -U pymgrid\n\nAlternatively, you can install from source. First clone the repo:\n\n.. code-block:: bash\n\n $ git clone https://github.com/Total-RD/pymgrid.git\n\nThen navigate to the root directory of pymgrid and call\n\n.. code-block:: bash\n\n $ pip install .\n\nAdvanced Installation\n---------------------\n\nTo use the included model predictive control algorithm on microgrids containing gensets,\nadditional dependencies are required as the optimization problem becomes mixed integer.\n\nThe packages MOSEK and CVXOPT can both handle this case; you can install both by calling\n\n.. code-block:: bash\n\n $ pip install pymgrid[genset_mpc]\n\nNote that MOSEK requires a license; see https://www.mosek.com/ for details.\nAcademic and trial licenses are available.\n\nSimple Example\n--------------\nSee :doc:`examples/quick-start` for a simple example to get started.\n" }, { "path": "docs/source/index.rst", "content": ".. pymgrid documentation master file, created by\n sphinx-quickstart on Sat Nov 19 12:49:18 2022.\n You can adapt this file completely to your liking, but it should at least\n contain the root `toctree` directive.\n\n*********************\npymgrid documentation\n*********************\n\nImportant Notice\n----------------\n\n**The person that has been maintaining pymgrid since 2020 has moved future development to**\n`python-microgrid `__\n**for the foreseeable future, a drop in replacement for pymgrid, and pymgrid may not be receiving any future updates.**\n**Please open any new issues in python-microgrid. You can also view python-microgrid's documentation**\n`at this link `__.\n\n**Version**: |version|\n\n*pymgrid* is a Python library to simulate tertiary control of electrical microgrids. *pymgrid* allows\nusers to create and customize microgrids of their choosing. These microgrids can then be controlled using a user-defined\nalgorithm or one of the control algorithms contained in *pymgrid*: rule-based control and model predictive control.\n\nEnvironments corresponding to the OpenAI-Gym API are also provided, with both continuous and discrete action space\nenvironments available. These environments can be used with your choice of reinforcement learning algorithm to train\na control algorithm.\n\n*pymgrid* attempts to offer the simplest and most intuitive API possible, allowing the user to\nfocus on their particular application.\n\nSee the :doc:`getting_started` section for further information, including instructions on how to\n:ref:`install ` the project.\n\n**Useful links**:\n`Binary Installers `__ |\n`Source Repository `__\n\n\n.. note::\n\n This project is under active development.\n\nContents\n========\n\n.. toctree::\n :maxdepth: 2\n\n getting_started\n examples/index\n reference/index\n\nIndices and tables\n==================\n\n* :ref:`genindex`\n* :ref:`modindex`\n* :ref:`search`\n" }, { "path": "docs/source/reference/algos/index.rst", "content": ".. _api.control:\n\nControl Algorithms\n==================\n\n.. currentmodule:: pymgrid.algos\n\nControl algorithms built into pymgrid, as well as references for external algorithms that can be deployed\n\nRule Based Control\n------------------\n\nHeuristic Algorithm that deploys modules via a priority list.\n\n.. autosummary::\n :toctree: ../api/algos/\n\n RuleBasedControl\n\n\nModel Predictive Control\n------------------------\n\nAlgorithm that depends on a future forecast as well as a model of state transitions to determine optimal controls.\n\n\n.. autosummary::\n :toctree: ../api/algos/\n\n ModelPredictiveControl\n\n\nReinforcement Learning\n----------------------\n\nAlgorithms that treat a microgrid as a Markov process, and train a black-box policy by repeated interactions with\nthe environment. See :doc:`here <../../examples/rl-example>` for an example of using\nreinforcement learning to train such an algorithm.\n\n\n\n..\n HACK -- the point here is that we don't want this to appear in the output, but the autosummary should still generate the pages.\n Copied from pandas docs.\n\n .. currentmodule:: pymgrid.algos.priority_list\n\n .. autosummary::\n :toctree: ../api/algos/priority_list/\n PriorityListElement" }, { "path": "docs/source/reference/envs/index.rst", "content": ".. _api.envs:\n\nReinforcement Learning (RL) Environments\n======================\n\n.. currentmodule:: pymgrid.envs\n\nEnvironment classes using the `OpenAI Gym API `_ for reinforcement learning.\n\nDiscrete\n--------\n\nEnvironment with a discrete action space.\n\n\n.. autosummary::\n :toctree: ../api/envs/\n\n DiscreteMicrogridEnv\n\nContinuous\n----------\n\nEnvironment with a discrete action space.\n\n\n.. autosummary::\n :toctree: ../api/envs/\n\n ContinuousMicrogridEnv\n" }, { "path": "docs/source/reference/forecast/index.rst", "content": ".. _api.forecast:\n\nForecasting\n===========\n\n.. currentmodule:: pymgrid.forecast\n\nClasses available to use for time-series forecasting, as well a class that allows users to define their own forecaster.\n\n.. autosummary::\n :toctree: ../api/forecast/\n\n get_forecaster\n OracleForecaster\n GaussianNoiseForecaster\n UserDefinedForecaster\n NoForecaster\n\n" }, { "path": "docs/source/reference/general/index.rst", "content": ".. _api.general:\n\nGeneral functions and objects\n=============================\n\n.. currentmodule:: pymgrid.modules\n\nModuleContainer\n---------------\n\nObject that store's a microgrid's modules.\n\n.. autosummary::\n :toctree: ../api/general/\n\n ModuleContainer\n\nModuleSpace\n-----------\n\nObject for module action and observation spaces.\n\n.. currentmodule:: pymgrid.utils.space\n\n.. autosummary::\n :toctree: ../api/general/\n\n ModuleSpace\n" }, { "path": "docs/source/reference/index.rst", "content": "API reference\n=============\n\nThis page contains an overview of all public *pymgrid* objects and functions.\n\n.. toctree::\n :maxdepth: 2\n\n microgrid\n modules/index\n forecast/index\n envs/index\n algos/index\n general/index" }, { "path": "docs/source/reference/microgrid.rst", "content": ".. _api.microgrid:\n\n\nMicrogrid\n=================\n\n.. currentmodule:: pymgrid\n\nConstructor\n-----------\n.. autosummary::\n :toctree: api/microgrid/\n\n Microgrid\n\nMethods\n-------\n.. autosummary::\n\n :toctree: api/microgrid/\n\n Microgrid.run\n Microgrid.reset\n Microgrid.sample_action\n Microgrid.get_log\n Microgrid.get_forecast_horizon\n Microgrid.get_empty_action\n\nSerialization/IO/Conversion\n---------------------------\n.. autosummary::\n\n :toctree: api/microgrid/\n\n Microgrid.load\n Microgrid.dump\n Microgrid.from_nonmodular\n Microgrid.from_scenario\n Microgrid.to_nonmodular" }, { "path": "docs/source/reference/modules/index.rst", "content": ".. _api.modules:\n\nModules\n=======\n\n.. currentmodule:: pymgrid.modules\n\nThe modules defined here are commonly found in microgrids.\nPass any combination of modules to :ref:`Microgrid ` to define and run a microgrid.\n\nTimeseries Modules\n------------------\n\nModules that are temporal in nature.\n\n\n\n.. autosummary::\n :toctree: ../api/modules/\n\n GridModule\n LoadModule\n RenewableModule\n\nNon-temporal Modules\n--------------------\n\nModules that do not depend on an underlying timeseries.\n\n.. autosummary::\n :toctree: ../api/modules/\n\n BatteryModule\n GensetModule\n\nHelper Module\n--------------\n\nA module that cleans up after all the other modules are deployed.\n\n.. autosummary::\n :toctree: ../api/modules/\n\n UnbalancedEnergyModule" }, { "path": "pyproject.toml", "content": "[tool.pytest.ini_options]\nlog_cli = true\nlog_cli_level = \"INFO\"\nlog_cli_format = \"%(asctime)s [%(levelname)8s] %(message)s (%(filename)s:%(lineno)s)\"\nlog_cli_date_format = \"%Y-%m-%d %H:%M:%S\"" }, { "path": "requirements.txt", "content": "cufflinks>=0.17.3\ncvxpy>=1.1.4\ngym>=0.15.7\nmatplotlib>=3.1.1\nnumpy>=1.19.5\npandas>=1.0.3\nplotly>=4.9.0\nscipy>=1.5.3\nstatsmodels>=0.11.1\ntqdm>=4.1.0\npyyaml>=1.5" }, { "path": "setup.cfg", "content": "[metadata]\ndescription-file = README.md\n\n[tool:pytest]\nnorecursedirs=tests/helpers" }, { "path": "setup.py", "content": "from pathlib import Path\nfrom setuptools import setup, find_packages\n\nv = {}\nexec(open('src/pymgrid/version.py').read(), v) # read __version__\nVERSION = v['__version__']\nDESCRIPTION = \"A simulator for tertiary control of electrical microgrids\"\nDOWNLOAD_URL = f\"https://github.com/Total-RD/pymgrid/archive/refs/tags/v{VERSION}.tar.gz\"\nMAINTAINER = \"Avishai Halev\"\nMAINTAINER_EMAIL = \"avishaihalev@gmail.com\"\nLICENSE = \"GNU LGPL 3.0\"\nPROJECT_URLS = {\"Source Code\": \"https://github.com/Total-RD/pymgrid\",\n \"Documentation\": \"https://pymgrid.readthedocs.io/en/latest/\"}\n\nEXTRAS = dict()\nEXTRAS[\"genset_mpc\"] = [\"Mosek\", \"cvxopt\"]\nEXTRAS[\"dev\"] = [\n \"pytest\",\n \"pytest-subtests\",\n \"flake8\",\n \"sphinx\",\n \"pydata_sphinx_theme\",\n \"numpydoc\",\n \"nbsphinx\",\n \"nbsphinx-link\",\n *EXTRAS[\"genset_mpc\"]]\n\nEXTRAS[\"rtd\"] = [\"ipython\"]\n\nEXTRAS[\"all\"] = list(set(sum(EXTRAS.values(), [])))\n\n\nsetup(\n name=\"pymgrid\",\n package_dir={\"\": \"src\"},\n packages=find_packages(\"src\"),\n python_requires=\">=3.6\",\n version=VERSION,\n maintainer=MAINTAINER,\n maintainer_email=MAINTAINER_EMAIL,\n download_url=DOWNLOAD_URL,\n project_urls=PROJECT_URLS,\n description=DESCRIPTION,\n license=LICENSE,\n long_description=(Path(__file__).parent / \"README.md\").read_text(),\n long_description_content_type=\"text/markdown\",\n include_package_data=True,\n install_requires=[\n \"pandas\",\n \"numpy\",\n \"cvxpy\",\n \"statsmodels\",\n \"matplotlib\",\n \"plotly\",\n \"cufflinks\",\n \"gym\",\n \"tqdm\",\n \"pyyaml\"\n ],\n extras_require=EXTRAS\n)\n" }, { "path": "src/__init__.py", "content": "" }, { "path": "src/pymgrid/MicrogridGenerator.py", "content": "\"\"\"\nCopyright 2020 Total S.A., Tanguy Levent all rights reserved,\nAuthors:Gonzague Henri , Tanguy Levent <>\nPermission to use, modify, and distribute this software is given under the\nterms of the pymgrid License.\nNO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.\n$Date: 2020/06/04 14:54 $\nGonzague Henri\n\"\"\"\n\"\"\"\n\nCopyright (C) <2020> \n\nThis program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\n\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.\n\nYou should have received a copy of the GNU Lesser General Public License along with this program. If not, see .\n\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom pymgrid import NonModularMicrogrid, Microgrid, PROJECT_PATH\nfrom os import listdir\nfrom os.path import isfile, join\nimport os\nimport sys\nimport pickle\nfrom IPython.display import display\nfrom pathlib import Path\n\n# MICROGRID_DEFAULT_CONFIG : {\n# 'load_type':'Folder', #or 'File'\n# 'load_path': 'default', # or a specific path\n# 'pv_type':'Folder', #or 'File'\n# 'pv_path': 'default', # or a specific path\n# 'co2_type':'Folder', #or 'File'\n# 'co2_path': 'default', # or a specific path\n\n\n# 'parameters':{\n\n# }, #Dictionary\n# 'df_actions':df_actions, #Dataframe\n# 'architecture':architecture, #Dictionary\n# 'df_status':df_status, #Dictionary\n# 'df_actual_generation':df_actual_production,#Dataframe\n# 'grid_spec':grid_spec, #value = 0\n# 'df_cost':df_cost, #Dataframe of 1 value = 0.0\n# 'df_co2': df_co2,\n# 'pv':pv, #Dataframe\n# 'load': load, #Dataframe\n# 'grid_ts':grid_ts, #Dataframe\n# 'control_dict': column_actions, #dictionnary\n# 'grid_price_import' : grid_price_import_ts,\n# 'grid_price_export' : grid_price_export_ts,\n# 'grid_co2': grid_co2_ts,\n# }\n\nclass MicrogridGenerator:\n \"\"\"\n The class MicrogridGenerator generates a number of microgrids with differerent and randomized paramters based on\n the load and renewable data files in the data folder.\n\n Parameters\n ----------\n nb_microgrid: int, optional\n Number representing the number of microgrid to be generated.\n random_seed: int, optional\n Seed to be used to generate the needed random numbers to size microgrids.\n timestep: int, optional\n Timestep to be used in the time series.\n path: string\n The path to the pymgrid folder, used to get the data files needed.\n\n Attributes\n ----------\n self.microgrids= [] # generate a list of microgrid object\n #self.annual_load\n self.nb_microgrids=nb_microgrid\n self.timestep=1\n self.path=path\n\n microgrids: list\n List that contains all the generated microgrids\n nb_microgrid: int, optional\n Number representing the number of microgrid to be generated.\n this microgrid has one of them\n timestep: int, optional\n Timestep to be used in the time series.\n path: string\n The path to the pymgrid folder, used to get the data files needed.\n\n Notes\n -----\n Due to the random nature of the implemented process, all the generated microgrids might not make the most sense\n economically or in term of generator sizing. The main idea is to generate realistic-ich microgrids to develop,\n test and compare control algorithms and advance AI research applied to microgrids.\n\n Examples\n --------\n To create microgrids through MicrogridGenerator:\n >>> m_gen=mg.MicrogridGenerator(nb_microgrid=10)\n >>> m_gen.generate_microgrid()\n\n To plot informations about the generated microgrids:\n >>> m_gen.print_mg_parameters()\n \"\"\"\n\n\n def __init__(self, nb_microgrid=10,\n random_seed=42,\n timestep=1,\n path=str(Path(__file__).parent)):\n \n np.random.seed(random_seed)\n self.microgrids= [] # generate a list of microgrid object\n #self.annual_load\n self.nb_microgrids=nb_microgrid\n self.timestep=1\n self.path=path\n\n\n ###########################################\n #utility functions\n ###########################################\n def _get_random_file(self, path):\n \"\"\" Based on a path, and a folder containing data files, return a file chosen randomly.\"\"\"\n from pathlib import Path\n _path = Path(path)\n data_files = list(_path.glob(\"*.csv\"))\n if not len(data_files):\n raise NameError(f\"Unable to find csv data files in {path}\")\n return pd.read_csv(np.random.choice(data_files))\n\n def _scale_ts(self, df_ts, size, scaling_method='sum'):\n \"\"\" Scales a time series based on either the sum or the maximum of the time series.\"\"\"\n\n actual_ratio=1\n if scaling_method =='sum':\n actual_ratio = size/df_ts.sum()#.values[0]\n\n if scaling_method == 'max':\n actual_ratio=size / df_ts.max()\n df_ts = df_ts * actual_ratio\n\n return df_ts\n\n def _resize_timeseries(self, timeseries, current_time_step, new_time_step):\n \"\"\" Change the frequency of a time series. \"\"\"\n\n index = pd.date_range('1/1/2015 00:00:00', freq=str(int(current_time_step * 60)) + 'Min',\n periods=(len(timeseries))) # , freq='0.9S')\n\n try:\n timeseries = timeseries.squeeze()\n except AttributeError:\n pass\n\n try:\n timeseries = timeseries.values\n except AttributeError:\n pass\n\n unsampled = pd.Series(timeseries, index=index)\n resampled = unsampled.resample(rule=str(int(new_time_step * 60)) + 'Min').mean().interpolate(method='linear')\n\n return resampled.values\n\n ###########################################\n # methods to generate timeseries\n ###########################################\n # def load_generator(self, shape, annual_consumption=1): #consumption inMWh\n # ts_load = np.random(shape)\n # annual_consumption = annual_consumption\n\n def _get_pv_ts(self):\n \"\"\" Function to get a random PV file.\"\"\"\n #open pv folder\n # get list of file\n # select randomly rank if file to select in the list\n\n path = self.path+'/data/pv/'\n return self._get_random_file(path)\n\n def _get_load_ts(self):\n \"\"\" Function to get a random load file. \"\"\"\n #open load folder\n # get list of file\n # select randomly rank if file to select in the list\n\n path = self.path+'/data/load/'\n return self._get_random_file(path)\n\n def _get_wind_ts(self):\n \"\"\" Function to get a random wind file. \"\"\"\n #open load folder\n # get list of file\n # select randomly rank if file to select in the list\n\n path = self.path+'/data/wind/'\n return self._get_random_file(path)\n\n def _get_co2_ts(self):\n \"\"\" Function to get a random wind file. \"\"\"\n # open load folder\n # get list of file\n # select randomly rank if file to select in the list\n\n path = self.path + '/data/co2/'\n return self._get_random_file(path)\n\n def _get_genset(self, rated_power=1000, pmax=0.9, pmin=0.05):\n \"\"\" Function generates a dictionnary with the genset information. \"\"\"\n\n polynom=[np.random.rand()*10, np.random.rand(), np.random.rand()/10] #fuel consumption\n\n genset={\n 'polynom':polynom,\n 'rated_power':rated_power,\n 'pmax':pmax,\n 'pmin':pmin,\n 'fuel_cost':0.4,\n 'co2':2,\n }\n\n return genset\n\n def _get_battery(self, capa=1000, duration=4, pcharge=100, pdischarge=100, soc_max=1, soc_min=0.2, efficiency=0.9):\n \"\"\" Function generates a dictionnary with the battery information. \"\"\"\n battery={\n 'capa':capa,\n 'pcharge':int(np.ceil(capa/duration)),\n 'pdischarge':int(np.ceil(capa/duration)),\n 'soc_max':soc_max,\n 'soc_min':soc_min,\n 'efficiency':efficiency,\n 'soc_0':min(max(np.random.randn(), soc_min),soc_max),\n 'cost_cycle':0.02\n\n }\n return battery\n\n\n def _get_grid_price_ts(self, nb_time_step_per_year, tou=0, rt=0, price=0):\n \"\"\" This functions is used to generate time series of import and export prices.\"\"\"\n if tou == 0 and rt ==0:\n price_ts = [price for i in range(nb_time_step_per_year)]\n\n return price_ts\n\n def _get_electricity_tariff(self, scenario):\n \"\"\"\n Function to generate price time series based on existing tariffs.\n scenario == 1 representes the TOU A-6 2020 summer from PG&E (https://www.pge.com/tariffs/electric.shtml)\n scenario == 2 represents the commercial tariff from France, with a Marseille TOU plage 5 (\n times: https://www.fournisseurs-electricite.com/edf/tarifs/heures-creuses-heures-pleines,\n prices: https://www.cre.fr/Electricite/marche-de-detail-de-l-electricite\n )\n \"\"\"\n price_import = []\n price_export = np.zeros((8760,))\n\n if scenario == 1: # PGE A-6 TOU 2020 summer\n\n for i in range(8760):\n if (i% 24 >= 12 and i%24 <18):\n price_import.append(0.59)\n elif (i% 24 < 8 or i%24 >=21):\n price_import.append(0.22)\n else:\n price_import.append(0.29)\n\n\n if scenario == 2: # France Commercial TOU Marseille plage 5\n for i in range(8760):\n if (i% 24 >= 0 and i%24 <5) or (i%24>=14 and i%24<17):\n price_import.append(0.08)\n else:\n price_import.append(0.11)\n\n # if scenario == 3: Belgium\n\n return price_import, price_export\n\n\n def _get_grid(self, rated_power=1000, weak_grid=0, pmin=0.2, price_scenario=0, price_export = 0, price_import =0.3):\n \"\"\" Function generates a dictionnary with the grid information. \"\"\"\n\n if weak_grid == 1:\n rand_outage_per_day = np.random.randn()*3/4 +0.25\n rand_duration = np.random.randint(low=1, high =8)\n grid_ts = self._generate_weak_grid_profile( rand_outage_per_day, rand_duration,8760/self.timestep)\n\n else:\n #grid_ts=pd.DataFrame([1+i*0 for i in range(int(np.floor(8760/self.timestep)))], columns=['grid_status'])\n grid_ts = pd.DataFrame(np.ones(int(np.floor(8760 / self.timestep))),\n columns=['grid_status'])\n\n # Make sure grid_ts is of length 8760\n grid_ts = grid_ts.iloc[:8760]\n\n # price_export = pd.DataFrame(self._get_grid_price_ts(price_export,8760),\n # columns=['grid_price_export'])\n # price_import = pd.DataFrame(self._get_grid_price_ts(price_import, 8760),\n # columns=['grid_price_import'])\n\n price_import, price_export = self._get_electricity_tariff(price_scenario)\n\n grid={\n 'grid_power_import':rated_power,\n 'grid_power_export':rated_power,\n 'grid_ts':grid_ts,\n 'grid_price_export':pd.DataFrame(price_export),\n 'grid_price_import': pd.DataFrame(price_import),\n }\n\n return grid\n\n def _generate_weak_grid_profile(self, outage_per_day, duration_of_outage,nb_time_step_per_year):\n \"\"\" Function generates an outage time series to be used in the microgrids with a weak grid. \"\"\"\n\n #weak_grid_timeseries = np.random.random_integers(0,1, int(nb_time_step_per_year+1) ) #for a number of time steps, value between 0 and 1\n #generate a timeseries of 8760/timestep points based on np.random seed\n #profile of ones and zeros\n weak_grid_timeseries = np.random.random(int(nb_time_step_per_year+1) ) #for a number of time steps, value between 0 and 1\n\n\n weak_grid_timeseries = [0 if weak_grid_timeseries[i] < outage_per_day/24 else 1 for i in range(len(weak_grid_timeseries))]\n\n timestep=8760/nb_time_step_per_year\n for i in range(len(weak_grid_timeseries)):\n if weak_grid_timeseries[i] == 0:\n for j in range(1, int(duration_of_outage/timestep)):\n if i-j > 0:\n weak_grid_timeseries[i-j] = 0\n #print weak_grid_timeseries\n\n return pd.DataFrame(weak_grid_timeseries, columns=['grid_status']) #[0 if weak_grid_timeseries[i] < h_outage_per_day/24 else 1 for i in range(len(weak_grid_timeseries))]\n\n\n ###########################################\n # sizing functions\n ###########################################\n def _size_mg(self, load, size_load=1):\n '''\n Function that returns a dictionnary with the size of each component of a microgrid. We chose to define PV\n penetration as defined by NREL (https://www.nrel.gov/docs/fy12osti/55094.pdf)\n PV penetration = peak PV power / peak load power\n '''\n # generate a list of size based on the number of architecture generated\n # 2 size the other generators based on the load\n\n #PV penetration definition by NREL: https: // www.nrel.gov/docs/fy12osti/55094.pdf\n # penetragion = peak pv / peak load\n pv=load.max().values[0]*(np.random.randint(low=30, high=151)/100)\n\n #battery_size = self._size_battery(load)\n # return a dataframe with the power of each generator, and if applicable the number of generator\n\n size={\n 'pv': pv,\n 'load': size_load,\n 'battery': self._size_battery(load),\n 'genset': self._size_genset(load),\n 'grid': int(max(load.values)*2),\n }\n\n return size\n\n def _size_genset(self, load, max_operating_loading = 0.9):\n \"\"\" Function that returns the maximum power a genset. \"\"\"\n #random number > 3 < 20\n # polynomial for fuel consumption\n\n _size_genset = int(np.ceil(np.max(load)/max_operating_loading))\n\n return _size_genset\n\n\n def _size_battery(self, load):\n \"\"\" Function that returns the capacity of the battery, equivalent to 3 to 5 hours of mean load. \"\"\"\n #energy duration\n battery = int(np.ceil(np.random.randint(low=3,high=6)*np.mean(load).item()))\n return battery\n\n\n ###########################################\n #generate the microgrid\n ###########################################\n\n def generate_microgrid(self, modular=True, verbose=False):\n \"\"\" Function used to generate the nb_microgrids to append them to the microgrids list. \"\"\"\n\n convert = lambda x: x.to_modular() if modular else x\n\n for i in range(self.nb_microgrids):\n #size=self._size_mg()\n self.microgrids.append(convert(self._create_microgrid()))\n \n if verbose and not modular:\n self.print_mg_parameters()\n\n return self\n\n @classmethod\n def load(cls, scenario):\n instance = cls()\n instance.microgrids = [\n Microgrid.load(\n (PROJECT_PATH/ f'data/scenario/{scenario}/microgrid_{j}/microgrid_{j}.yaml').open('r')\n ) for j in range(25)]\n\n return instance\n\n def _bin_genset_grid(self):\n rand = np.random.rand()\n bin_genset = 0\n bin_grid = 0\n\n if rand < 0.33:\n\n bin_genset = 1\n\n elif rand >= 0.33 and rand < 0.66:\n\n bin_grid = 1\n\n else:\n\n bin_genset = 1\n bin_grid = 1\n\n return bin_genset, bin_grid\n\n def _size_load(self, size_load=None):\n if size_load is None:\n return np.random.randint(low=100,high=100001)\n else:\n return size_load\n\n def _create_microgrid(self):\n \"\"\"\n Function used to create one microgrid. First selecting a load file, and a load size and a randome architecture\n and then size the other components of the microgrid depending on the load size. This function also initializes\n the tracking dataframes to be used in microgrid.\n \"\"\"\n\n # get the sizing data\n # create microgrid object and append\n # return the list\n\n bin_genset, bin_grid = self._bin_genset_grid()\n\n architecture = {'PV':1, 'battery':1, 'genset':bin_genset, 'grid':bin_grid}\n size_load = self._size_load()\n load = self._scale_ts(self._get_load_ts(), size_load, scaling_method='max') #obtain dataframe of loads\n size = self._size_mg(load, size_load) #obtain a dictionary of mg sizing components\n column_actions=[]\n column_actual_production=[]\n column_cost = []\n grid_ts=[]\n grid_price_export_ts = []\n grid_price_import_ts = []\n grid_co2_ts = []\n df_parameters = pd.DataFrame()\n # df_cost = {'cost':[]}\n df_status = {}\n df_co2 = {'co2':[]}\n\n df_parameters['load'] = [size_load]\n df_parameters['cost_loss_load'] = 10\n df_parameters['cost_overgeneration'] = 1\n df_parameters['cost_co2'] = 0.1\n #df_cost['cost'] = [0.0]\n df_status['load'] = [np.around(load.iloc[0,0],1)]# --> il y a doublon pour l'instant avec l'architecture PV, -> non si pas de pv la net load est juste la load\n df_status['hour'] = [0]\n column_actual_production.append('loss_load')\n column_actual_production.append('overgeneration')\n column_actions.append('load')\n column_cost.append('loss_load')\n column_cost.append('overgeneration')\n column_cost.append('co2')\n if architecture['PV'] == 1:\n\n df_parameters['PV_rated_power'] = np.around(size['pv'],2)\n column_actual_production.append('pv_consummed')\n column_actual_production.append('pv_curtailed')\n column_actions.append('pv_consummed')\n column_actions.append('pv_curtailed')\n column_actions.append('pv')\n pv = pd.DataFrame(self._scale_ts(self._get_pv_ts(), size['pv'], scaling_method='max'))\n df_status['pv'] = [np.around( pv.iloc[0].values[0],1)]\n\n if architecture['battery']==1:\n\n battery = self._get_battery(capa=size['battery']) #return a dictionary of battery characteristic\n df_parameters['battery_soc_0'] = battery['soc_0']\n df_parameters['battery_power_charge'] = battery['pcharge']\n df_parameters['battery_power_discharge'] = battery['pdischarge']\n df_parameters['battery_capacity'] = battery['capa']\n df_parameters['battery_efficiency'] = battery['efficiency']\n df_parameters['battery_soc_min'] = battery['soc_min']\n df_parameters['battery_soc_max'] = battery['soc_max']\n df_parameters['battery_cost_cycle'] = battery['cost_cycle']\n column_actual_production.append('battery_charge')\n column_actual_production.append('battery_discharge')\n column_actions.append('battery_charge')\n column_actions.append('battery_discharge')\n column_cost.append('battery')\n df_status['battery_soc'] = [battery['soc_0']]\n\n capa_to_charge = max(\n (df_parameters['battery_soc_max'].values[0] * df_parameters['battery_capacity'].values[0] -\n df_parameters['battery_soc_0'].iloc[-1] *\n df_parameters['battery_capacity'].values[0]\n ) / df_parameters['battery_efficiency'].values[0], 0)\n\n capa_to_discharge = max((df_parameters['battery_soc_0'].iloc[-1] *\n df_parameters['battery_capacity'].values[0]\n - df_parameters['battery_soc_min'].values[0] *\n df_parameters['battery_capacity'].values[0])\n * df_parameters['battery_efficiency'].values[0], 0)\n\n df_status['capa_to_charge'] = [np.around(capa_to_charge,1)]\n df_status['capa_to_discharge'] = [np.around(capa_to_discharge,1)]\n\n\n\n grid_spec=0\n\n if architecture['grid']==1:\n\n rand_weak_grid = np.random.randint(low=0, high=2)\n price_scenario = np.random.randint(low=1, high=3)\n if rand_weak_grid == 1:\n architecture['genset'] = 1\n grid = self._get_grid(rated_power=size['grid'], weak_grid=rand_weak_grid, price_scenario=price_scenario)\n df_parameters['grid_weak'] = rand_weak_grid\n df_parameters['grid_power_import'] = grid['grid_power_import']\n df_parameters['grid_power_export'] = grid['grid_power_export']\n grid_ts = grid['grid_ts']\n #df_parameters['grid_price_import'] = grid['grid_price_import']\n #df_parameters['grid_price_export'] = grid['grid_price_export']\n column_actual_production.append('grid_import')\n column_actual_production.append('grid_export')\n column_actions.append('grid_import')\n column_actions.append('grid_export')\n column_cost.append('grid_import')\n column_cost.append('grid_export')\n df_status['grid_status'] = [grid_ts.iloc[0,0]]\n #todo Switch back to random file to generate the new version of pymgrid25\n grid_co2_ts = self._get_co2_ts() \n df_status['grid_co2'] = [grid_co2_ts.iloc[0, 0]]\n\n grid_price_import_ts = grid['grid_price_import']\n grid_price_export_ts = grid['grid_price_export']\n df_status['grid_price_import'] = [grid_price_import_ts.iloc[0, 0]]\n df_status['grid_price_export'] = [grid_price_export_ts.iloc[0, 0]]\n\n if architecture['genset']==1:\n genset = self._get_genset(rated_power=size['genset'])\n df_parameters['genset_polynom_order'] = len(genset['polynom'])\n\n for i in range(len(genset['polynom'])):\n df_parameters['genset_polynom_'+str(i)]=genset['polynom'][i]\n\n df_parameters['genset_rated_power'] = genset['rated_power']\n df_parameters['genset_pmin'] = genset['pmin']\n df_parameters['genset_pmax'] = genset['pmax']\n df_parameters['fuel_cost'] = genset['fuel_cost']\n df_parameters['genset_co2'] = genset['co2']\n column_actual_production.append('genset')\n column_actions.append('genset')\n column_cost.append('genset')\n\n column_cost.append('total_cost')\n df_actions= {key:[] for key in column_actions}#pd.DataFrame(columns = column_actions, )\n df_actual_production = {key:[] for key in column_actual_production}#pd.DataFrame(columns=column_actual_production)\n df_cost = {key: [] for key in column_cost}\n\n microgrid_spec={\n 'parameters':df_parameters, #Dictionary\n 'df_actions':df_actions, #Dataframe\n 'architecture':architecture, #Dictionary\n 'df_status':df_status, #Dictionary\n 'df_actual_generation':df_actual_production,#Dataframe\n 'grid_spec':grid_spec, #value = 0\n 'df_cost':df_cost, #Dataframe of 1 value = 0.0\n 'df_co2': df_co2,\n 'pv':pv, #Dataframe\n 'load': load, #Dataframe\n 'grid_ts':grid_ts, #Dataframe\n 'control_dict': column_actions, #dictionnary\n 'grid_price_import' : grid_price_import_ts,\n 'grid_price_export' : grid_price_export_ts,\n 'grid_co2': grid_co2_ts,\n }\n\n microgrid = NonModularMicrogrid(microgrid_spec)\n\n return microgrid\n ########################################################\n # PRINT / PLOT FUNCTIONS\n ########################################################\n\n # function to plot the parameters of all the microgrid generated\n def print_mg_parameters(self, id='all'):\n \"\"\" This function is used to print the parameters of all the generated microgrids.\"\"\"\n\n if id == 'all':\n\n if self.microgrids != []:\n parameters = pd.DataFrame()\n for i in range(self.nb_microgrids):\n parameters = parameters.append(self.microgrids[i].parameters, ignore_index=True)\n\n pd.options.display.max_columns = None\n display(parameters)\n\n elif isinstance(id, int) and id < self.nb_microgrids:\n display(self.microgrids[id].parameters)\n\n def print_all_costs(self):\n\n #one column ID, one run cost, one rule based, one mpc\n #if train test split, for each train and test\n df_cost = pd.DataFrame()\n for i in range(self.nb_microgrids):\n\n if self.microgrids[i]._has_train_test_split == False:\n\n cost_run = self.microgrids[i]._df_record_cost.sum().values\n cost_mpc = np.nan\n cost_rule_based = np.nan\n\n if self.microgrids[i]._has_run_mpc_baseline == True:\n cost_mpc = np.around(self.microgrids[i]._baseline_linprog_cost.sum().values[0], 1)\n\n if self.microgrids[i]._has_run_rule_based_baseline == True:\n cost_rule_based = np.around(self.microgrids[i]._baseline_priority_list_cost.sum().values[0],1)\n\n\n else:\n cost_run = self.microgrids[i]._df_record_cost.sum().values\n cost_mpc = np.nan\n cost_rule_based = np.nan\n\n if self.microgrids[i]._has_run_mpc_baseline == True:\n cost_mpc = np.around(self.microgrids[i]._baseline_linprog_cost.iloc[self.microgrids[i]._limit_index:].sum().values[0],1)\n\n if self.microgrids[i]._has_run_rule_based_baseline == True:\n cost_rule_based = np.around(self.microgrids[i]._baseline_priority_list_cost.iloc[self.microgrids[i]._limit_index:].sum().values[0],1)\n\n df_cost =df_cost.append({'ID':i, 'Cost': cost_run, 'Cost (MPC)': cost_mpc, 'Cost (rule-based)':cost_rule_based}, ignore_index=True)\n\n display(df_cost)\n" }, { "path": "src/pymgrid/__init__.py", "content": "from pathlib import Path\nfrom .version import __version__\n\nPROJECT_PATH = Path(__file__).parent\n\nfrom ._deprecated.non_modular_microgrid import NonModularMicrogrid\nfrom .microgrid import Microgrid\nfrom .MicrogridGenerator import MicrogridGenerator\n\nfrom .utils import add_pymgrid_yaml_representers\n\nimport pymgrid.envs\n\n__all__ = [\n 'Microgrid',\n 'MicrogridGenerator',\n 'NonModularMicrogrid',\n 'envs'\n]" }, { "path": "src/pymgrid/_deprecated/Environments/Environment.py", "content": "\"\"\"\nCopyright 2020 Total S.A\nAuthors:Gonzague Henri \nPermission to use, modify, and distribute this software is given under the\nterms of the pymgrid License.\nNO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.\n$Date: 2020/10/21 07:43 $\nGonzague Henri\n\"\"\"\n\n\"\"\"\n\nCopyright (C) <2020> \nThis program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.\nYou should have received a copy of the GNU Lesser General Public License along with this program. If not, see .\n\"\"\"\n\nimport numpy as np\nimport gym\nfrom gym.utils import seeding\nfrom gym.spaces import Space, Discrete, Box\nfrom . import Preprocessing\nfrom pymgrid.algos.saa.saa import SampleAverageApproximation\n\nDEFAULT_CONFIG={\n 'microgrid': None, #need to be passed by user\n 'training_reward_smoothing':'sqrt', #'peak_load'\n 'resampling_on_reset':True,\n 'forecast_args':None, #used to init the SAA for resampling on reset\n 'baseline_sampling_args':None,\n}\n\ndef generate_sampler(microgrid, forecast_args):\n \"\"\"\n Generates an instance of SampleAverageApproximate to use in future sampling.\n :param microgrid:\n :param forecast_args:\n :return:\n \"\"\"\n if forecast_args is None:\n forecast_args = dict()\n\n return SampleAverageApproximation(microgrid, **forecast_args)\n\nclass Environment(gym.Env):\n \"\"\"\n Markov Decision Process associated to the microgrid.\n Parameters\n ----------\n microgrid: microgrid, mandatory\n The controlled microgrid.\n random_seed: int, optional\n Seed to be used to generate the needed random numbers to size microgrids.\n \"\"\"\n\n def __init__(self, env_config, seed = 42):\n # Set seed\n np.random.seed(seed)\n\n self.states_normalization = Preprocessing.normalize_environment_states(env_config['microgrid'])\n\n self.TRAIN = True\n # Microgrid\n self.env_config = env_config\n self.mg = env_config['microgrid']\n # State space\n self.mg.train_test_split()\n #np.zeros(2+self.mg.architecture['grid']*3+self.mg.architecture['genset']*1)\n # Number of states\n self.Ns = len(self.mg._df_record_state.keys())+1\n # Number of actions\n\n #training_reward_smoothing\n try:\n self.training_reward_smoothing = env_config['training_reward_smoothing']\n except:\n self.training_reward_smoothing = 'sqrt'\n\n try:\n self.resampling_on_reset = env_config['resampling_on_reset']\n except:\n self.resampling_on_reset = False\n \n if self.resampling_on_reset == True:\n self.forecast_args = env_config['forecast_args']\n self.baseline_sampling_args = env_config['baseline_sampling_args']\n self.saa = generate_sampler(self.mg, self.forecast_args)\n \n self.observation_space = Box(low=-1, high=np.float('inf'), shape=(self.Ns,), dtype=np.float)\n #np.zeros(len(self.mg._df_record_state.keys()))\n # Action space\n self.metadata = {\"render.modes\": [ \"human\"]}\n \n self.state, self.reward, self.done, self.info, self.round = None, None, None, None, None\n self.round = None\n\n # Start the first round\n self.seed()\n self.reset()\n \n\n try:\n assert (self.observation_space.contains(self.state))\n except AssertionError:\n print(\"ERROR : INVALID STATE\", self.state)\n\n def get_reward(self):\n if self.TRAIN == True:\n if self.training_reward_smoothing == 'sqrt':\n return -(self.mg.get_cost()**0.5)\n if self.training_reward_smoothing == 'peak_load':\n return -self.mg.get_cost()/self.mg.parameters['load'].values[0]\n return -self.mg.get_cost()\n\n def get_cost(self):\n return sum(self.mg._df_record_cost['cost'])\n\n\n\n def step(self, action):\n\n # CONTROL\n if self.done:\n print(\"WARNING : EPISODE DONE\") # should never reach this point\n return self.state, self.reward, self.done, self.info\n try:\n assert (self.observation_space.contains(self.state))\n except AssertionError:\n print(\"ERROR : INVALID STATE\", self.state)\n\n try:\n assert (self.action_space.contains(action))\n except AssertionError:\n print(\"ERROR : INVALD ACTION\", action)\n\n # UPDATE THE MICROGRID\n control_dict = self.get_action(action)\n self.mg.run(control_dict)\n\n # COMPUTE NEW STATE AND REWARD\n self.state = self.transition()\n self.reward = self.get_reward()\n self.done = self.mg.done\n self.info = {}\n self.round += 1\n\n return self.state, self.reward, self.done, self.info\n \n# control_dict = self.get_action(action)\n# self.mg.run(control_dict)\n# reward = self.reward()\n# s_ = self.transition()\n# self.state = s_\n# done = self.mg.done\n# self.round += 1\n# return s_, reward, done, {}\n\n def reset(self, testing=False):\n if \"testing\" in self.env_config:\n testing = self.env_config[\"testing\"]\n self.round = 1\n # Reseting microgrid\n self.mg.reset(testing=testing)\n if testing == True:\n self.TRAIN = False\n elif self.resampling_on_reset == True:\n Preprocessing.sample_reset(self.mg.architecture['grid'] == 1, self.saa, self.mg, sampling_args=self.sampling_args)\n \n \n self.state, self.reward, self.done, self.info = self.transition(), 0, False, {}\n \n return self.state\n\n\n def get_action(self, action):\n \"\"\"\n :param action: current action\n :return: control_dict : dicco of controls\n \"\"\"\n '''\n States are:\n binary variable whether charging or dischargin\n battery power, normalized to 1\n binary variable whether importing or exporting\n grid power, normalized to 1\n binary variable whether genset is on or off\n genset power, normalized to 1\n '''\n\n control_dict=[]\n\n return control_dict\n\n def states(self): # soc, price, load, pv 'df status?'\n observation_space = []\n return observation_space\n\n # Transition function\n def transition(self):\n # net_load = round(self.mg.load - self.mg.pv)\n # soc = round(self.mg.battery.soc,1)\n # s_ = (net_load, soc) # next state\n updated_values = self.mg.get_updated_values()\n updated_values = {x:float(updated_values[x])/self.states_normalization[x] for x in self.states_normalization} \n updated_values['hour_sin'] = np.sin(2*np.pi*updated_values['hour']) # the hour is already divided by 24 in the line above\n updated_values['hour_cos'] = np.cos(2*np.pi*updated_values['hour']) \n updated_values.pop('hour', None)\n\n s_ = np.array(list(updated_values.values()))\n #np.array(self.mg.get_updated_values().values)#.astype(np.float)#self.mg.get_updated_values()\n #s_ = [ s_[key] for key in s_.keys()]\n return s_\n \n def seed (self, seed=None):\n self.np_random, seed = seeding.np_random(seed)\n return [seed]\n \n def render(self, mode=\"human\"):\n txt = \"state: \" + str(self.state) + \" reward: \" + str(self.reward) + \" info: \" + str(self.info)\n print(txt)\n\n # Mapping between action and the control_dict\n def get_action_continuous(self, action):\n \"\"\"\n :param action: current action\n :return: control_dict : dicco of controls\n \"\"\"\n '''\n Actions are:\n binary variable whether charging or dischargin\n battery power, normalized to 1\n binary variable whether importing or exporting\n grid power, normalized to 1\n binary variable whether genset is on or off\n genset power, normalized to 1\n '''\n\n mg = self.mg\n pv = mg.pv\n load = mg.load\n net_load = load - pv\n capa_to_charge = mg.battery.capa_to_charge\n p_charge_max = mg.battery.p_charge_max\n p_charge = max(0, min(-net_load, capa_to_charge, p_charge_max))\n\n capa_to_discharge = mg.battery.capa_to_discharge\n p_discharge_max = mg.battery.p_discharge_max\n p_discharge = max(0, min(net_load, capa_to_discharge, p_discharge_max))\n\n control_dict = {}\n\n if mg.architecture['battery'] == 1:\n control_dict['battery_charge'] = max(0, action[0] * min(action[1] * mg.battery.capacity,\n mg.battery.capa_to_charge,\n mg.battery.p_charge_max))\n control_dict['battery_discharge'] = max(0, (1 - action[0]) * min(action[1] * mg.battery.capacity,\n mg.battery.capa_to_discharge,\n mg.battery.p_discharge_max))\n\n if mg.architecture['grid'] == 1:\n if mg.grid.status == 1:\n control_dict['grid_import'] = max(0, action[2] * min(action[3] * mg.grid.power_import,\n mg.grid.power_import))\n control_dict['grid_export'] = max(0, (1 - action[2]) * min(action[3] * mg.grid.power_export,\n mg.grid.power_export))\n else:\n # avoid warnings\n control_dict['grid_import'] = 0\n control_dict['grid_export'] = 0\n\n if mg.architecture['genset'] == 1:\n control_dict['genset'] = max(0, action[4] * min(action[5] * mg.genset.rated_power,\n mg.genset.rated_power))\n return control_dict\n\n def get_action_discrete(self, action):\n \"\"\"\n :param action: current action\n :return: control_dict : dicco of controls\n \"\"\"\n '''\n Actions are:\n binary variable whether charging or dischargin\n battery power, normalized to 1\n binary variable whether importing or exporting\n grid power, normalized to 1\n binary variable whether genset is on or off\n genset power, normalized to 1\n '''\n control_dict={}\n\n control_dict['pv_consumed'] = action[0]\n if self.mg.architecture['battery'] == 1:\n control_dict['battery_charge'] = action[1] * action[3]\n control_dict['battery_discharge'] = action[2] * (1- action[3])\n\n if self.mg.architecture['genset'] == 1:\n control_dict['genset'] = action[4]\n\n if self.mg.architecture['grid'] == 1:\n control_dict['grid_import'] = action[5] * action[7]\n control_dict['grid_export'] = action[6] * (1- action[7])\n\n elif self.mg.architecture['grid'] == 1:\n control_dict['grid_import'] = action[4] * action[6]\n control_dict['grid_export'] = action[5] * (1 - action[6])\n\n\n\n\n return control_dict\n\n # Mapping between action and the control_dict\n def get_action_priority_list(self, action):\n \"\"\"\n :param action: current action\n :return: control_dict : dicco of controls\n \"\"\"\n '''\n States are:\n binary variable whether charging or dischargin\n battery power, normalized to 1\n binary variable whether importing or exporting\n grid power, normalized to 1\n binary variable whether genset is on or off\n genset power, normalized to 1\n '''\n\n mg = self.mg\n pv = mg.pv\n load = mg.load\n net_load = load - pv\n capa_to_charge = mg.battery.capa_to_charge\n p_charge_max = mg.battery.p_charge_max\n p_charge = max(0, min(-net_load, capa_to_charge, p_charge_max))\n\n capa_to_discharge = mg.battery.capa_to_discharge\n p_discharge_max = mg.battery.p_discharge_max\n p_discharge = max(0, min(net_load, capa_to_discharge, p_discharge_max))\n\n control_dict = {}\n\n control_dict = self.actions_agent_discret(mg, action)\n\n return control_dict\n\n\n def actions_agent_discret(self, mg, action):\n if mg.architecture['genset'] == 1 and mg.architecture['grid'] == 1:\n control_dict = self.action_grid_genset(mg, action)\n\n elif mg.architecture['genset'] == 1 and mg.architecture['grid'] == 0:\n control_dict = self.action_genset(mg, action)\n\n else:\n control_dict = self.action_grid(mg, action)\n\n return control_dict\n\n def action_grid(self, mg, action):\n # slack is grid\n\n pv = mg.pv\n load = mg.load\n\n net_load = load - pv\n\n capa_to_charge = mg.battery.capa_to_charge\n p_charge_max = mg.battery.p_charge_max\n p_charge_pv = max(0, min(-net_load, capa_to_charge, p_charge_max))\n p_charge_grid = max(0, min( capa_to_charge, p_charge_max))\n\n capa_to_discharge = mg.battery.capa_to_discharge\n p_discharge_max = mg.battery.p_discharge_max\n p_discharge = max(0, min(net_load, capa_to_discharge, p_discharge_max))\n\n # Charge\n if action == 0:\n\n control_dict = {'pv_consummed': min(pv, load),\n 'battery_charge': p_charge_pv,\n 'battery_discharge': 0,\n 'grid_import': 0,\n 'grid_export': max(0, pv - min(pv, load) - p_charge_pv),\n 'genset': 0\n }\n \n if action == 4:\n load = load + p_charge_grid\n control_dict = {'pv_consummed': min(pv, load),\n 'battery_charge': p_charge_grid,\n 'battery_discharge': 0,\n 'grid_import': max(0, load - min(pv, load)),\n 'grid_export': max(0, pv - min(pv, load) - p_charge_grid) ,\n 'genset': 0\n }\n\n\n # décharger full\n elif action == 1:\n\n control_dict = {'pv_consummed': min(pv, load),\n 'battery_charge': 0,\n 'battery_discharge': p_discharge,\n 'grid_import': max(0, load - min(pv, load) - p_discharge),\n 'grid_export': 0,\n 'genset': 0\n }\n\n # Import\n elif action == 2:\n\n control_dict = {'pv_consummed': min(pv, load),\n 'battery_charge': 0,\n 'battery_discharge': 0,\n 'grid_import': max(0, net_load),\n 'grid_export': 0,\n 'genset': 0\n }\n # Export\n elif action == 3:\n\n control_dict = {'pv_consummed': min(pv, load),\n 'battery_charge': 0,\n 'battery_discharge': 0,\n 'grid_import': 0,\n 'grid_export': abs(min(net_load, 0)),\n 'genset': 0\n }\n\n return control_dict\n\n def action_grid_genset(self, mg, action):\n # slack is grid\n\n pv = mg.pv\n load = mg.load\n\n net_load = load - pv\n status = mg.grid.status # whether there is an outage or not\n capa_to_charge = mg.battery.capa_to_charge\n p_charge_max = mg.battery.p_charge_max\n p_charge_pv = max(0, min(-net_load, capa_to_charge, p_charge_max))\n p_charge_grid = max(0, min( capa_to_charge, p_charge_max))\n\n capa_to_discharge = mg.battery.capa_to_discharge\n p_discharge_max = mg.battery.p_discharge_max\n p_discharge = max(0, min(net_load, capa_to_discharge, p_discharge_max))\n\n capa_to_genset = mg.genset.rated_power * mg.genset.p_max\n p_genset = max(0, min(net_load, capa_to_genset))\n\n # Charge\n if action == 0:\n\n control_dict = {'pv_consummed': min(pv, load),\n 'battery_charge': p_charge_pv,\n 'battery_discharge': 0,\n 'grid_import': 0,\n 'grid_export': max(0, pv - min(pv, load) - p_charge_pv) * status,\n 'genset': 0\n }\n if action == 5:\n load = load+p_charge_grid\n\n control_dict = {'pv_consummed': min(pv, load),\n 'battery_charge': p_charge_grid,\n 'battery_discharge': 0,\n 'grid_import': max(0, load - min(pv, load)) * status,\n 'grid_export': max(0, pv - min(pv, load) - p_charge_grid) * status,\n 'genset': 0\n }\n\n\n # décharger full\n elif action == 1:\n\n control_dict = {'pv_consummed': min(pv, load),\n 'battery_charge': 0,\n 'battery_discharge': p_discharge,\n 'grid_import': max(0, load - min(pv, load) - p_discharge) * status,\n 'grid_export': 0,\n 'genset': 0\n }\n\n # Import\n elif action == 2:\n\n control_dict = {'pv_consummed': min(pv, load),\n 'battery_charge': 0,\n 'battery_discharge': 0,\n 'grid_import': max(0, net_load) * status,\n 'grid_export': 0,\n 'genset': 0\n }\n # Export\n elif action == 3:\n\n control_dict = {'pv_consummed': min(pv, load),\n 'battery_charge': 0,\n 'battery_discharge': 0,\n 'grid_import': 0,\n 'grid_export': abs(min(net_load, 0)) * status,\n 'genset': 0\n }\n # Genset\n elif action == 4:\n\n control_dict = {'pv_consummed': min(pv, load),\n 'battery_charge': 0,\n 'battery_discharge': 0,\n 'grid_import': 0,\n 'grid_export': 0,\n 'genset': max(net_load, 0)\n }\n\n elif action == 6:\n\n control_dict = {'pv_consummed': min(pv, load),\n 'battery_charge': 0,\n 'battery_discharge': p_discharge,\n 'grid_import': 0,\n 'grid_export': 0,\n 'genset': max(0, load - min(pv, load) - p_discharge),\n }\n\n return control_dict\n\n def action_genset(self, mg, action):\n # slack is genset\n\n pv = mg.pv\n load = mg.load\n\n net_load = load - pv\n\n capa_to_charge = mg.battery.capa_to_charge\n p_charge_max = mg.battery.p_charge_max\n p_charge = max(0, min(-net_load, capa_to_charge, p_charge_max))\n\n capa_to_discharge = mg.battery.capa_to_discharge\n p_discharge_max = mg.battery.p_discharge_max\n p_discharge = max(0, min(net_load, capa_to_discharge, p_discharge_max))\n\n capa_to_genset = mg.genset.rated_power * mg.genset.p_max\n p_genset = max(0, min(net_load, capa_to_genset))\n\n # Charge\n if action == 0:\n\n control_dict = {'pv_consummed': min(pv, load),\n 'battery_charge': p_charge,\n 'battery_discharge': 0,\n 'grid_import': 0,\n 'grid_export': 0,\n 'genset': 0\n }\n\n\n # décharger full\n elif action == 1:\n\n control_dict = {'pv_consummed': min(pv, load),\n 'battery_charge': 0,\n 'battery_discharge': p_discharge,\n 'grid_import': 0,\n 'grid_export': 0,\n 'genset': max(0, load - min(pv, load) - p_discharge)\n }\n\n # Genset\n elif action == 2:\n\n control_dict = {'pv_consummed': min(pv, load),\n 'battery_charge': 0,\n 'battery_discharge': 0,\n 'grid_import': 0,\n 'grid_export': 0,\n 'genset': max(0, load - min(pv, load))\n }\n\n return control_dict\n" }, { "path": "src/pymgrid/_deprecated/Environments/Preprocessing.py", "content": "import pandas as pd\n\ndef normalize_environment_states(mg):\n max_values = {}\n for keys in mg._df_record_state:\n if keys == 'hour':\n max_values[keys] = 24\n elif keys == 'capa_to_charge' or keys == 'capa_to_discharge' :\n max_values[keys] = mg.parameters.battery_capacity.values[0]\n elif keys == 'grid_status' or keys == 'battery_soc':\n max_values[keys] = 1\n elif keys == 'grid_co2':\n max_values[keys] = max(mg._grid_co2.values[0])\n elif keys == 'grid_price_import':\n max_values[keys] = max(mg._grid_price_import.values[0]) \n elif keys == 'grid_price_export':\n max_values[keys] = max(mg._grid_price_import.values[0]) \n elif keys == 'load':\n max_values[keys] = mg.parameters.load.values[0]\n elif keys == 'pv':\n max_values[keys] = mg.parameters.PV_rated_power.values[0]\n else:\n max_values[keys] = mg.parameters[keys].values[0] \n \n return max_values\n\ndef sample_reset(has_grid, saa, microgrid, sampling_args=None):\n \"\"\"\n Generates a new sample using an instance of SampleAverageApproximation and\n :param has_grid: bool, whether the microgrid has a grid.\n :param saa:, SampleAverageApproximation\n :param microgrid: Microgrid\n :param sampling_args: arguments to be passed to saa.sample_from_forecasts().\n :return:\n \"\"\"\n if sampling_args is None:\n sampling_args = dict()\n\n sample = saa.sample_from_forecasts(n_samples=1, **sampling_args)\n sample = sample[0]\n\n microgrid._load_ts = pd.DataFrame(sample['load'])\n microgrid._pv_ts = pd.DataFrame(sample['pv'])\n microgrid._df_record_state['load'] = [sample['load'].iloc[0].squeeze()]\n microgrid._df_record_state['pv'] = [sample['pv'].iloc[0].squeeze()]\n if has_grid:\n microgrid._grid_status_ts = pd.DataFrame(sample['grid'])\n microgrid._df_record_state['grid_status'] = [sample['grid'].iloc[0].squeeze()]" }, { "path": "src/pymgrid/_deprecated/Environments/__init__.py", "content": "\n" }, { "path": "src/pymgrid/_deprecated/Environments/pymgrid_csca.py", "content": "from abc import ABC\nimport gym, logging, numpy as np, pandas as pd\nfrom gym import Env\nfrom pymgrid._deprecated.non_modular_microgrid import NonModularMicrogrid\nfrom pymgrid.MicrogridGenerator import MicrogridGenerator\nfrom copy import deepcopy\nfrom pymgrid.algos.saa.saa import SampleAverageApproximation\nfrom pymgrid.algos import ModelPredictiveControl\n\nlogger = logging.getLogger(__name__)\nLOG = False\nDEBUG = True\n\n# If you get a JSON serializable error, turn this on:\nJSON_ERROR = False\n\n\ndef sample_reset(has_grid, saa, microgrid, sampling_args=None):\n \"\"\"\n Generates a new sample using an instance of SampleAverageApproximation and\n :param has_grid: bool, whether the microgrid has a grid.\n :param saa:, SampleAverageApproximation\n :param microgrid: Microgrid\n :param sampling_args: arguments to be passed to saa.sample_from_forecasts().\n :return:\n \"\"\"\n if sampling_args is None:\n sampling_args = dict()\n\n sample = saa.sample_from_forecasts(n_samples=1, **sampling_args)\n sample = sample[0]\n\n microgrid._load_ts = pd.DataFrame(sample['load'])\n microgrid._pv_ts = pd.DataFrame(sample['pv'])\n microgrid._df_record_state['load'] = [sample['load'].iloc[0].squeeze()]\n microgrid._df_record_state['pv'] = [sample['pv'].iloc[0].squeeze()]\n if has_grid:\n microgrid._grid_status_ts = pd.DataFrame(sample['grid'])\n microgrid._df_record_state['grid_status'] = [sample['grid'].iloc[0].squeeze()]\n\n\ndef generate_sampler(microgrid, forecast_args):\n \"\"\"\n Generates an instance of SampleAverageApproximate to use in future sampling.\n :param microgrid:\n :param forecast_args:\n :return:\n \"\"\"\n if forecast_args is None:\n forecast_args = dict()\n\n return SampleAverageApproximation(microgrid, **forecast_args)\n\n\nclass MicrogridEnv(Env, ABC):\n metadata = {'render.modes': ['human']}\n\n def __init__(self, microgrid, trajectory_len=None, max_episode_len=None):\n \"\"\"\n :param max_episode_len:\n :param microgrid: Microgrid, the underlying microgrid.\n :param trajectory_len: int, length of a trajectory (to be started at a random index).\n Default None, runs an entire year\n \"\"\"\n super().__init__()\n\n if isinstance(microgrid, int) and 0<=microgrid<=25:\n print('Initializing microgrid {} of 25 using 25 microgrids from MicrogridGenerator'.format(microgrid))\n m_gen = MicrogridGenerator(nb_microgrid=25)\n m_gen.generate_microgrid(verbose=False)\n self.microgrid = deepcopy(m_gen.microgrids[microgrid])\n\n elif isinstance(microgrid, NonModularMicrogrid):\n self.microgrid = deepcopy(microgrid)\n\n else:\n raise ValueError('microgrid must be of type Microgrid, is {}'.format(type(microgrid)))\n\n assert self.microgrid._data_length == 8760, 'Microgrid data length should be 8760, is {}'.format(self.microgrid._data_length)\n\n self.has_grid = self.microgrid.architecture['grid'] == 1\n self.has_genset = self.microgrid.architecture['genset'] == 1\n\n observation_dim = len(self.microgrid._df_record_state)\n self.observation_space = gym.spaces.Box(low=0, high=np.float('inf'), shape=(observation_dim,), dtype=np.float64)\n self.action_space = None\n\n self.current_action = None\n self.current_obs = None\n\n\n if max_episode_len is None:\n self.microgrid.horizon = 0\n else:\n self.microgrid.horizon = self.microgrid._data_length - max_episode_len\n\n self.trajectory_len = trajectory_len\n self._short_trajectory_set()\n\n\n def _short_trajectory_set(self):\n trajectory_len = self.trajectory_len\n if trajectory_len is not None:\n assert isinstance(trajectory_len, int)\n from numpy.random import randint\n high_range = self.microgrid._data_length - self.microgrid.horizon - trajectory_len\n\n start_index = randint(low=0, high=high_range)\n self.microgrid._tracking_timestep = start_index\n self.microgrid._data_length = start_index + trajectory_len + self.microgrid.horizon\n\n def reset(self):\n self.microgrid.reset()\n self._short_trajectory_set()\n\n initial_state = self.microgrid.get_updated_values()\n observations = np.array(list(initial_state.values()))\n\n self.current_obs = observations\n\n return observations\n\n def step(self, action, **kwargs):\n \"\"\"\n :param **kwargs:\n :param\n action:\n :return:\n observation, np.ndarray\n If self.has_grid, shape (10,), with values\n [load, hour, pv, battery_soc, capa_to_charge, capa_to_discharge,\n grid_status, grid_co2, grid_price_import, grid_price_export]\n Else, shape (6,), with values\n [load, hour, pv, battery_soc, capa_to_charge, capa_to_discharge]\n reward, float\n The reward (negative cost) of the step\n done, bool\n Whether the episode is complete\n info, dict\n Info\n\n \"\"\"\n control_dict = self.get_control_dict(action)\n observation = self.run_control(control_dict)\n reward = -1.0 * self.microgrid.get_cost()\n done = self.microgrid.done\n info = dict()\n\n self.current_obs = observation\n self.current_action = action\n\n return observation, reward, done, info\n\n def get_control_dict(self, action):\n \"\"\"\n A function that takes an action (discrete or continuous) and returns a control_dict.\n See ContinuousMicrogridEnv for an example.\n \"\"\"\n return NotImplemented\n\n def run_control(self, control_dict):\n \"\"\"\n Given a control_dict, calls microgrid.run(_) and returns the observations.\n :param control_dict:\n :return: observations, np.ndarray\n \"\"\"\n updated_vals = self.microgrid.run(control_dict)\n observations = np.array(list(updated_vals.values()))\n\n assert len(observations) == self.observation_space.shape[0]\n return observations\n\n\nclass ContinuousMicrogridEnv(MicrogridEnv):\n \"\"\"\n Class to run a Microgrid in the format of a gym env. Continuous states, continuous actions.\n \"\"\"\n def __init__(self, microgrid, standardization=True, trajectory_len=None, max_episode_len=None, **kwargs):\n \"\"\"\n :param microgrid: Microgrid, the underlying microgrid.\n :param standardization: bool, default True. whether to scale the actions to a factor determined by a run of MPC.\n \"\"\"\n super().__init__(microgrid, trajectory_len=trajectory_len, max_episode_len=max_episode_len)\n\n self.logger = kwargs['logger'] if 'logger' in kwargs else None\n\n action_dim = 5+self.has_genset\n upper_bound, lower_bound = self._get_action_ub_lb()\n\n self.action_space = gym.spaces.Box(low=lower_bound, high=upper_bound, shape=(action_dim,), dtype=np.float64)\n\n self.standardization = standardization\n if not JSON_ERROR and self.standardization:\n self.standardizations = self.pre_compute_standardizations()\n\n # Rescale the action space\n low_new = self.standardize(self.action_space.low, use_proxy='action')\n self.action_space.low = low_new\n high_new = self.standardize(self.action_space.high, use_proxy='action')\n high_new[1] = 0.1 # This is a hard-coded grid_export bound\n self.action_space.high = high_new\n else:\n self.standardizations = None\n\n def _get_action_ub_lb(self):\n # Upper Bound\n p_max_import = self.microgrid.parameters['grid_power_import'].values[0]\n p_max_export = self.microgrid.parameters['grid_power_export'].values[0]\n p_max_charge = self.microgrid.parameters['battery_power_charge'].values[0]\n p_max_discharge = self.microgrid.parameters['battery_power_discharge'].values[0]\n pv_max = self.microgrid.parameters.PV_rated_power.squeeze()\n\n upper_bound = [p_max_import, p_max_export, p_max_charge, p_max_discharge, pv_max]\n\n if self.has_genset:\n p_genset_max = self.microgrid.parameters['genset_rated_power'].values[0] * self.microgrid.parameters['genset_pmax'].values[0]\n upper_bound.insert(0,p_genset_max)\n\n upper_bound = np.array(upper_bound)\n\n # Lower Bound\n\n lower_bound = [0]*5\n\n if self.has_genset:\n action_dim = 6\n p_genset_min = self.microgrid.parameters['genset_rated_power'].values[0] * \\\n self.microgrid.parameters['genset_pmin'].values[0]\n lower_bound.insert(0, p_genset_min)\n\n lower_bound = np.array(lower_bound)\n\n return upper_bound, lower_bound\n\n\n\n def get_values(self, *value_names):\n # TODO Speed this up. Refactor standardizations. nan_to_num takes significant time.\n \"\"\"\n Helper function. Given a list of value names (e.g., the names of components of the state/actions),\n returns their values in the same order.\n Note: if the env standardizes values, this function returns the unstandarized values.\n :param value_names:\n :return:\n \"\"\"\n genset_actions = ['genset', 'grid_import', 'grid_export', 'battery_charge', 'battery_discharge', 'pv_consummed']\n no_genset_actions = ['grid_import', 'grid_export', 'battery_charge', 'battery_discharge', 'pv_consummed']\n\n grid_observations = ['load', 'hour', 'pv', 'battery_soc', 'capa_to_charge', 'capa_to_discharge',\n 'grid_status', 'grid_co2', 'grid_price_import', 'grid_price_export']\n no_grid_observations = ['load', 'hour', 'pv', 'battery_soc', 'capa_to_charge', 'capa_to_discharge']\n\n if self.current_action is None:\n print('Warning: current_action is None, should only happen on first iteration')\n if self.has_genset:\n self.current_action = np.array([0]*len(genset_actions))\n else:\n self.current_action = np.array([0]*len(no_genset_actions))\n\n action = self.current_action\n obs = self.current_obs\n\n elif self.standardization:\n obs_mean, obs_std, action_mean, action_std = self.standardizations\n action = self.standardize(self.current_action, action_mean, action_std, direction='backward')\n obs = self.standardize(self.current_obs, obs_mean, obs_std, direction='backward')\n else:\n action = self.current_action\n obs = self.current_obs\n\n if self.has_genset:\n actions_dict = dict(zip(genset_actions, action))\n else:\n actions_dict = dict(zip(no_genset_actions, action))\n if self.has_grid:\n obs_dict = dict(zip(grid_observations, obs))\n else:\n obs_dict = dict(zip(no_grid_observations, obs))\n\n values = []\n\n\n for name in value_names:\n if name in actions_dict.keys():\n values.append(actions_dict[name])\n elif name in obs_dict.keys():\n values.append(obs_dict[name])\n else:\n raise ValueError('Value \\'{}\\' not recognized with current architecture'.format(name))\n\n return values\n\n def reset(self):\n observation = super().reset()\n if self.standardization:\n obs_mean, obs_std, action_mean, action_std = self.standardizations\n observation = self.standardize(observation, obs_mean, obs_std, direction='forward')\n self.current_obs = observation\n\n return observation\n\n\n\n def step(self, action, **kwargs):\n \"\"\"\n :param **kwargs:\n :param\n action: np.ndarray\n If self.has_genset: shape (6,), with values\n [genset, grid_import, grid_export, battery_charge, battery_discharge, pv_consummed]\n Else: shape (5,), with values\n [grid_import, grid_export, battery_charge, battery_discharge, pv_consummed]\n :return:\n observation, np.ndarray\n If self.has_grid, shape (10,), with values\n [load, hour, pv, battery_soc, capa_to_charge, capa_to_discharge,\n grid_status, grid_co2, grid_price_import, grid_price_export]\n Else, shape (6,), with values\n [load, hour, pv, battery_soc, capa_to_charge, capa_to_discharge]\n reward, float\n The reward (negative cost) of the step\n done, bool\n Whether the episode is complete\n info, dict\n Info\n\n \"\"\"\n # Actions must be passed in order as defined in pymgrid25 paper\n assert isinstance(action, np.ndarray)\n\n unscaled_action = action.copy()\n\n if self.standardization:\n if not isinstance(action, np.ndarray):\n raise TypeError('action must be of type np.ndarray')\n\n obs_mean, obs_std, action_mean, action_std = self.standardizations\n\n action = self.standardize(action, action_mean, action_std, direction='backward')\n\n observation, reward, done, info = super().step(action)\n\n if self.standardization:\n observation = self.standardize(observation, obs_mean, obs_std, direction='forward')\n\n self.current_obs = observation\n self.current_action = unscaled_action\n\n # Do you want to deal w everything in normalized space or unnormalized space\n\n return observation, reward, done, info\n\n\n def standardize(self, data, mean_proxy=None, std_proxy=None, direction='forward', use_proxy=None):\n \"\"\"\n :param data: np.ndarray, shape (observation_dim,) or (action_dim), observation or action to rescale\n :param mean_proxy: np.ndarray, shape (observation_dim,) or (action_dim), mean to use in rescaling\n :param std_proxy: np.ndarray, shape (observation_dim,) or (action_dim), standard deviation to use in rescaling\n :param direction: str, default 'forward'. One of 'forward' or 'backward', whether to scale to or from standard normal\n :return: np.ndarray, rescaled values.\n \"\"\"\n\n if (mean_proxy is None and std_proxy is None and use_proxy is None) or (mean_proxy is not None and use_proxy is not None):\n raise ValueError('Must pass mean_proxy and std_proxy, or use_proxy must be a str in (\\'action\\', \\'obs\\'), but not both')\n if mean_proxy is None and std_proxy is None:\n if use_proxy == 'action':\n mean_proxy, std_proxy = [x for x in self.standardizations[2:]]\n elif use_proxy == 'obs':\n mean_proxy, std_proxy = [x for x in self.standardizations[:2]]\n else:\n raise NameError('Unable to recognize use_proxy {}, must be one of \\'action\\' or \\'obs\\''.format(use_proxy))\n\n\n names = ('data', 'mean_proxy', 'std_proxy')\n vals = (data, mean_proxy, std_proxy)\n dirs = ('forward', 'backward')\n\n for name, v in zip(names, vals):\n if not isinstance(v, np.ndarray):\n raise TypeError('{} must be of type numpy.ndarray, is {}'.format(name, type(v)))\n if not (data.shape == mean_proxy.shape and mean_proxy.shape == std_proxy.shape):\n raise ValueError('Incompatible shapes of data, mean_proxy, std_proxy. Must be equal, are: {}'.format(\n dict(zip(names, [v.shape for v in vals]))))\n\n if direction not in dirs:\n raise ValueError('direction must be one of {}'.format(dirs))\n\n if direction == 'forward':\n return (data-mean_proxy)/std_proxy\n else:\n return data*std_proxy+mean_proxy\n\n def pre_compute_standardizations(self,alg_to_use='mpc'):\n \"\"\"\n Runs a control algorithm to pre compute the standardizations for actions/observations to rescale to standard normal (ish).\n :param alg_to_use: str, default 'mpc'. What algorithm to run to compute the standardizations\n :return: tuple len(4,): obs_mean, obs_std, action_mean, action_std\n \"\"\"\n\n action_mean = [0]*self.action_space.shape[0]\n action_std = [0]*self.action_space.shape[0]\n obs_mean = [0]*self.observation_space.shape[0]\n obs_std = [0]*self.observation_space.shape[0]\n\n if alg_to_use == 'mpc':\n old_horizon = self.microgrid.horizon\n self.microgrid.horizon = 24\n MPC = ModelPredictiveControl(self.microgrid)\n mpc_output = MPC.run(max_steps=1000)\n self.microgrid.horizon = old_horizon\n if self.has_genset:\n\n action_keys = 'genset', 'grid_import, grid_export, battery_charge, battery_discharge, pv_consummed'\n\n for j, name in enumerate(action_keys):\n action_mean[j] = np.mean(mpc_output['action'][name])\n action_std[j] = np.std(mpc_output['action'][name])\n\n obs_keys = list(self.microgrid._df_record_state.keys())\n\n for j, name in enumerate(obs_keys):\n obs_mean[j] = np.mean(mpc_output['status'][name])\n obs_std[j] = np.std(mpc_output['status'][name])\n\n else:\n action_keys = 'grid_import', 'grid_export', 'battery_charge', 'battery_discharge', 'pv_consummed'\n\n for j, name in enumerate(action_keys):\n action_mean[j] = np.mean(mpc_output['action'][name])\n action_std[j] = np.std(mpc_output['action'][name])\n\n obs_keys = list(self.microgrid._df_record_state.keys())\n\n for j, name in enumerate(obs_keys):\n obs_mean[j] = np.mean(mpc_output['status'][name])\n obs_std[j] = np.std(mpc_output['status'][name])\n else:\n raise RuntimeError('algorithm name {} not currently supported'.format(alg_to_use))\n\n for j in range(len(obs_std)):\n if obs_std[j] < 1.0:\n obs_std[j] = 1.0\n for j in range(len(action_std)):\n if action_std[j] < 1.0:\n action_std[j] = 1.0\n\n names = ('obs_mean', 'obs_std', 'action_mean', 'action_std')\n outputs = obs_mean, obs_std, action_mean, action_std\n outputs = tuple(np.array(output) for output in outputs)\n\n for name, output in zip(names,outputs):\n if (output == 0).sum() != 0:\n for j,val in enumerate(output):\n if val == 0:\n print('Warning: Zero value in pos {} in {}, may not have been filled properly'.format(j,name))\n\n return outputs\n\n def get_control_dict(self, action):\n \"\"\"\n Given an np.ndarray of actions, parses into a control_dict.\n :param action: np.ndarray, shape (action_dim,)\n :return: dict, control_dict\n \"\"\"\n\n if not isinstance(action, np.ndarray):\n raise TypeError('action must be an ndarray, is {}'.format(type(action)))\n\n if self.has_genset:\n\n control_dict = {'battery_charge': action[3],\n 'battery_discharge': action[4],\n 'genset': action[0],\n 'grid_import': action[1],\n 'grid_export': action[2],\n 'pv_consummed': action[5]}\n else:\n control_dict = {'battery_charge': action[2],\n 'battery_discharge': action[3],\n 'grid_import': action[0],\n 'grid_export': action[1],\n 'pv_consummed': action[4]}\n\n return control_dict\n\n\nclass ContinuousMicrogridSampleEnv(ContinuousMicrogridEnv):\n \"\"\"\n Same as ContinuousMicrogridEnv but uses samples generated from SampleAverageApproximation as states.\n \"\"\"\n metadata = {'render.modes': ['human']}\n def __init__(self, microgrid, standardization=True,\n forecast_args=None, baseline_sampling_args=None, max_episode_len=None):\n\n super().__init__(microgrid, standardization=standardization, max_episode_len=max_episode_len)\n self.forecast_args = forecast_args\n self.baseline_sampling_args = baseline_sampling_args\n self.saa = generate_sampler(self.microgrid, forecast_args)\n\n def reset(self, sampling_args=None):\n \"\"\"\n Generates a new sample to use as load/pv/grid data. Then calls parent reset function.\n :param sampling_args:\n :return:\n \"\"\"\n sample_reset(self.has_grid, self.saa, self.microgrid, sampling_args=sampling_args)\n observations = super().reset()\n return observations\n\n\nclass SafeExpMicrogridEnv(ContinuousMicrogridEnv):\n \"\"\"\n ContinuousMicrogridEnv but with constraint functionality for safety layer.\n \"\"\"\n def __init__(self, microgrid,\n standardization=True,\n balance_tolerance=1.,\n scale_constraints=True,\n only_inequality_constr=True,\n trajectory_len=None,\n max_episode_len=None):\n\n super().__init__(microgrid,\n standardization=standardization,\n trajectory_len=trajectory_len,\n max_episode_len=max_episode_len)\n\n self.balance_tolerance=balance_tolerance\n self.scale_constraints = scale_constraints\n self.only_inequality_constr = only_inequality_constr\n\n self.n_constraints = 9 if self.has_genset else 7\n if only_inequality_constr:\n self.n_constraints -= 1\n\n def get_num_constraints(self):\n \"\"\"\n Two for energy balance (one equality)\n One each p_charge, p_discharge, p_import, p_export\n Two p_genset if genset\n TODO: do you need pv_curtail and loss_load\n :return:\n \"\"\"\n return self.n_constraints\n\n def get_constraint_values(self):\n \"\"\"\n All constraints are set up here such that we return c_i for constraints of the form c_i<0\n :return:\n \"\"\"\n inequality_constraints = self._get_inequality_constraints()\n energy_balance = self._get_energy_balance()\n\n if self.only_inequality_constr:\n constraints = inequality_constraints\n return constraints\n\n return np.append(inequality_constraints, energy_balance)\n\n def _get_energy_balance(self):\n if self.has_genset:\n p_import, p_export, p_charge, p_discharge, p_genset, load, pv, pv_consumed = \\\n self.get_values('grid_import','grid_export', 'battery_charge', 'battery_discharge','genset','load','pv','pv_consummed')\n else:\n p_import, p_export, p_charge, p_discharge, load, pv, pv_consumed = \\\n self.get_values('grid_import', 'grid_export', 'battery_charge', 'battery_discharge', 'load',\n 'pv', 'pv_consummed')\n\n p_genset = 0\n\n pv_curtailed = pv-pv_consumed\n\n energy_balance = np.array(p_import-p_export-p_charge+p_discharge+p_genset-pv_curtailed-load+pv)\n\n if self.scale_constraints:\n charge_scale_factor = float(self.microgrid.parameters.battery_capacity.squeeze())\n energy_balance /= charge_scale_factor\n\n return energy_balance\n\n def _get_inequality_constraints(self):\n constraints = []\n\n p_charge, p_discharge, p_max_charge, p_max_discharge = self.get_values('battery_charge','battery_discharge',\n 'capa_to_charge','capa_to_discharge')\n\n if self.scale_constraints:\n charge_scale_factor = float(self.microgrid.parameters.battery_capacity.squeeze())\n\n constraints.append((p_charge-p_max_charge)/charge_scale_factor)\n constraints.append((p_discharge-p_max_discharge)/charge_scale_factor)\n else:\n constraints.append(p_charge - p_max_charge)\n constraints.append(p_discharge - p_max_discharge)\n\n p_max_import = self.microgrid.parameters['grid_power_import'].values[0]\n p_max_export = self.microgrid.parameters['grid_power_export'].values[0]\n p_import, p_export, grid_status = self.get_values('grid_import','grid_export','grid_status')\n\n if self.scale_constraints:\n constraints.append((p_import - p_max_import * grid_status)/p_max_import)\n constraints.append((p_export - p_max_export * grid_status)/p_max_export)\n else:\n constraints.append(p_import-p_max_import*grid_status)\n constraints.append(p_export-p_max_export*grid_status)\n\n battery_max = self.microgrid.parameters['battery_soc_max'].values[0]\n battery_min = self.microgrid.parameters['battery_soc_min'].values[0]\n battery_soc, = self.get_values('battery_soc')\n\n if self.scale_constraints:\n constraints.append((battery_soc - battery_max)/battery_max)\n constraints.append((battery_min - battery_soc)/battery_min)\n else:\n constraints.append(battery_soc-battery_max)\n constraints.append(battery_min-battery_soc)\n\n if self.has_genset:\n\n p_genset_max = self.microgrid.parameters['genset_rated_power'].values[0] * self.microgrid.parameters['genset_pmax'].values[0]\n p_genset_min = self.microgrid.parameters['genset_rated_power'].values[0] * self.microgrid.parameters['genset_pmin'].values[0]\n p_genset, = self.get_values('genset')\n\n # TODO what if we want it to be off? For now, this:\n if p_genset<1:\n if self.scale_constraints:\n constraints.append((p_genset-1)/p_genset_max)\n constraints.append((-p_genset-self.balance_tolerance)/p_genset_max)\n else:\n constraints.append(p_genset - 1)\n constraints.append(-p_genset - self.balance_tolerance)\n else:\n if self.scale_constraints:\n constraints.append((p_genset - p_genset_max)/p_genset_max)\n constraints.append((p_genset_min - p_genset)/p_genset_min)\n else:\n constraints.append(p_genset-p_genset_max)\n constraints.append(p_genset_min-p_genset)\n\n constraints = np.array(constraints)\n\n return constraints\n\n\nclass SafeExpMicrogridSampleEnv(SafeExpMicrogridEnv):\n def __init__(self,\n microgrid,\n standardization=True,\n balance_tolerance=1.,\n scale_constraints=True,\n only_inequality_constr=True,\n forecast_args=None,\n baseline_sampling_args=None,\n trajectory_len=None,\n max_episode_len=None):\n super().__init__(microgrid,\n standardization=standardization,\n balance_tolerance=balance_tolerance,\n scale_constraints=scale_constraints,\n only_inequality_constr=only_inequality_constr,\n trajectory_len=trajectory_len,\n max_episode_len=max_episode_len)\n\n self.forecast_args = forecast_args\n self.forecast_args = forecast_args\n self.baseline_sampling_args = baseline_sampling_args\n self.saa = generate_sampler(self.microgrid, forecast_args)\n\n def reset(self, sampling_args=None):\n sample_reset(self.has_grid, self.saa, self.microgrid, sampling_args=sampling_args)\n observations = super().reset()\n return observations\n\n" }, { "path": "src/pymgrid/_deprecated/Environments/pymgrid_csca_old.py", "content": "from pymgrid.Environments.Environment import Environment\nimport numpy as np\nimport gym\nfrom gym.utils import seeding\nfrom gym.spaces import Space, Discrete, Box\n\n\nclass MicroGridEnv(Environment):\n \"\"\"\n Markov Decision Process associated to the microgrid.\n\n Parameters\n ----------\n microgrid: microgrid, mandatory\n The controlled microgrid.\n random_seed: int, optional\n Seed to be used to generate the needed random numbers to size microgrids.\n\n \"\"\"\n def __init__(self, env_config, seed=42):\n super().__init__(env_config, seed)\n self.Na = 4 + self.mg.architecture['grid'] * 3 + self.mg.architecture['genset'] * 1\n\n action_limits = [int(self.mg._pv_ts.max().values[0]),\n int(self.mg.parameters['battery_power_charge'].values[0]),\n int(self.mg.parameters['battery_power_discharge'].values[0]),\n 2,\n ]\n if self.mg.architecture['genset'] ==1:\n action_limits.append(int(self.mg.parameters['genset_rated_power'].values[0]* self.mg.parameters['genset_pmax'].values[0]))\n\n if self.mg.architecture['grid'] == 1:\n action_limits.append(int(self.mg.parameters['grid_power_import'].values[0]))\n action_limits.append(int(self.mg.parameters['grid_power_export'].values[0]))\n action_limits.append(2)\n\n self.action_space = gym.spaces.Tuple([gym.spaces.Discrete(x) for x in action_limits])\n\n\n def get_action(self, action):\n return self.get_action_continuous(action)" }, { "path": "src/pymgrid/_deprecated/Environments/pymgrid_csda.py", "content": "from pymgrid.Environments.Environment import Environment\nimport numpy as np\nimport gym\nfrom gym.utils import seeding\nfrom gym.spaces import Space, Discrete, Box\n\n\nclass MicroGridEnv(Environment):\n \"\"\"\n Markov Decision Process associated to the microgrid.\n\n Parameters\n ----------\n microgrid: microgrid, mandatory\n The controlled microgrid.\n random_seed: int, optional\n Seed to be used to generate the needed random numbers to size microgrids.\n\n \"\"\"\n def __init__(self, env_config, seed=42):\n super().__init__(env_config, seed)\n self.Na = 4 + self.mg.architecture['grid'] * 3 + self.mg.architecture['genset'] * 1\n\n action_limits = [int(self.mg._pv_ts.max().values[0]),\n int(self.mg.parameters['battery_power_charge'].values[0]),\n int(self.mg.parameters['battery_power_discharge'].values[0]),\n 2,\n ]\n if self.mg.architecture['genset'] ==1:\n action_limits.append(int(self.mg.parameters['genset_rated_power'].values[0]* self.mg.parameters['genset_pmax'].values[0]))\n\n if self.mg.architecture['grid'] == 1:\n action_limits.append(int(self.mg.parameters['grid_power_import'].values[0]))\n action_limits.append(int(self.mg.parameters['grid_power_export'].values[0]))\n action_limits.append(2)\n\n self.action_space = gym.spaces.Tuple([gym.spaces.Discrete(x) for x in action_limits])\n\n\n def get_action(self, action):\n return self.get_action_discrete(action)" }, { "path": "src/pymgrid/_deprecated/Environments/pymgrid_cspla.py", "content": "\"\"\"\nCopyright 2020 Total S.A\nAuthors:Gonzague Henri \nPermission to use, modify, and distribute this software is given under the\nterms of the pymgrid License.\nNO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.\n$Date: 2020/10/21 07:43 $\nGonzague Henri\n\"\"\"\n\n\"\"\"\n\nCopyright (C) <2020> \n\nThis program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\n\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.\n\nYou should have received a copy of the GNU Lesser General Public License along with this program. If not, see .\n\n\"\"\"\nfrom pymgrid.Environments.Environment import Environment\nimport numpy as np\nimport gym\nfrom gym.utils import seeding\nfrom gym.spaces import Space, Discrete, Box\n\nclass MicroGridEnv(Environment):\n \"\"\"\n Markov Decision Process associated to the microgrid.\n\n Parameters\n ----------\n microgrid: microgrid, mandatory\n The controlled microgrid.\n random_seed: int, optional\n Seed to be used to generate the needed random numbers to size microgrids.\n\n \"\"\"\n\n def __init__(self, env_config, seed=42):\n super().__init__(env_config, seed)\n self.Na = 2 + self.mg.architecture['grid'] * 3 + self.mg.architecture['genset'] * 1 \n if self.mg.architecture['grid'] == 1 and self.mg.architecture['genset'] == 1:\n self.Na += 1\n self.action_space = Discrete(self.Na)\n\n\n\n def get_action(self, action):\n return self.get_action_priority_list(action)\n\n \n\n\n" }, { "path": "src/pymgrid/_deprecated/__init__.py", "content": "" }, { "path": "src/pymgrid/_deprecated/non_modular_microgrid.py", "content": "\"\"\"\nCopyright 2020 Total S.A., Tanguy Levent all rights reserved,\nAuthors:Gonzague Henri , Tanguy Levent <>\nPermission to use, modify, and distribute this software is given under the\nterms of the pymgrid License.\nNO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.\n$Date: 2020/06/04 14:54 $\nGonzague Henri\n\"\"\"\n\n\"\"\"\n\nCopyright (C) <2020> \n\nThis program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\n\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.\n\nYou should have received a copy of the GNU Lesser General Public License along with this program. If not, see .\n\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nfrom copy import copy\nfrom plotly.offline import init_notebook_mode, iplot\nimport matplotlib.pyplot as plt\nfrom IPython.display import display\nfrom IPython import get_ipython\n\ndef in_ipynb():\n try:\n cfg = get_ipython().config\n if cfg['IPKernelApp']['parent_appname'] == 'ipython-notebook':\n return True\n else:\n return False\n except (NameError, AttributeError):\n return False\n\nif in_ipynb():\n init_notebook_mode(connected=False)\n\nnp.random.seed(123)\n\n#cf.set_config_file(offline=True, theme='pearl') #commented for now, issues with parallel processes\n\nDEFAULT_HORIZON = 24 #in hours\nDEFAULT_TIMESTEP = 1 #in hours\nZERO = 10**-5\n\n'''\nThe following classes are used to contain the information related to the different components\nof the microgrid. Their main use is for easy access in a notebook.\n'''\n\nclass Battery:\n\n \"\"\"\n The class battery is used to store the information related to the battery in a microgrid. One of the main use for\n this class is for an easy access to information in a notebook using the battery object contained in a microgrid.\n\n Parameters\n ----------\n param_battery : dataframe\n All the data to initialize the battery.\n capa_to_charge : float\n Represents the amount of energy that a battery can charge before being full.\n capa_to_discharge : float\n Represents the amount of energy available that a battery can discharge before being empty.\n\n Attributes\n ----------\n soc: float\n Value between 0 and 1 representing the state of charge of the battery (1 being full, 0 being empty)\n capacity: int\n Total energy capacity of the battery (kWh).\n soc_max: float\n Value representing the maximum SOC that a battery can reach\n soc_min: float\n Value representing the minimum SOC that a battery can reach\n p_charge_max: float\n Value representing the maximum charging rate of the battery (kW)\n p_discharge_max: float\n Value representing the maximum discharging rate of the battery (kW)\n efficiency: float\n Value between 0 and 1 representing a one-way efficiency of the battery (considering same efficiency for charging\n and discharging).\n cost_cycle: float\n Value representing the cost of using the battery in $/kWh.\n capa_to_charge : float\n Represents the amount of energy that a battery can charge before being full.\n capa_to_discharge : float\n Represents the amount of energy available that a battery can discharge before being empty.\n\n\n Notes\n -----\n Another way to use this information in a notebook is to use /tab/ after /microgrid.battery./ so you can see all the\n battery attributes.\n\n Examples\n --------\n >>> m_gen=mg.MicrogridGenerator(nb_microgrid=1,path='your_path')\n >>> m_gen.generate_microgrid()\n >>> m_gen.microgrids[0].battery\n You can then add a point and use tab to have suggestion of the different paramterers\n You can access state of charge for example with:\n >>> m_gen.microgrids[0].battery.soc\n \"\"\"\n\n def __init__(self, param_battery, capa_to_charge, capa_to_discharge):\n\n self.soc = param_battery['battery_soc_0'].values[0]\n self.capacity = param_battery['battery_capacity'].values[0]\n self.soc_max = param_battery['battery_soc_max'].values[0]\n self.soc_min = param_battery['battery_soc_min'].values[0]\n self.p_charge_max = param_battery['battery_power_charge'].values[0]\n self.p_discharge_max = param_battery['battery_power_discharge'].values[0]\n self.efficiency = param_battery['battery_efficiency'].values[0]\n self.cost_cycle = param_battery['battery_cost_cycle'].values[0]\n self.capa_to_charge = capa_to_charge\n self.capa_to_discharge = capa_to_discharge\n\n\n\nclass Genset:\n \"\"\"\n The class Genset is used to store the information related to the genset in a microgrid. One of the main use for\n this class is for an easy access to information in a notebook using the genset object contained in a microgrid.\n\n Parameters\n ----------\n param : dataframe\n All the data to initialize the genset.\n\n Attributes\n ----------\n rated_power: int\n Maximum rater power of the genset.\n p_min: float\n Value representing the minimum operating power of the genset (kW)\n p_max: float\n Value representing the maximum operating power of the genset (kW)\n fuel_cost: float\n Value representing the cost of using the genset in $/kWh.\n\n Notes\n -----\n Another way to use this information in a notebook is to use /tab/ after /microgrid.genset./ so you can see all the\n genset attributes.\n\n Examples\n --------\n >>> m_gen=mg.MicrogridGenerator(nb_microgrid=1,path='your_path')\n >>> m_gen.generate_microgrid()\n >>> m_gen.microgrids[0].genset\n You can then add a point and use tab to have suggestion of the different paramaterers\n You can access the maximum power max for example with:\n >>> m_gen.microgrids[0].genset.p_max\n \"\"\"\n def __init__(self, param):\n self.rated_power = param['genset_rated_power'].values[0]\n self.p_min = param['genset_pmin'].values[0]\n self.p_max = param['genset_pmax'].values[0]\n self.fuel_cost = param['fuel_cost'].values[0]\n self.co2 = param['genset_co2'].values[0]\n\n\nclass Grid:\n \"\"\"\n The class Grid is used to store the information related to the grid in a microgrid. One of the main use for\n this class is for an easy access to information in a notebook using the grid object contained in a microgrid.\n\n Parameters\n ----------\n param : dataframe\n All the data to initialize the grid.\n status: int\n Whether the grid is connected or not at the first time step.\n\n\n Attributes\n ----------\n power_export: float\n Value representing the maximum export power to the grid (kW)\n power_import: float\n Value representing the maximum import power from the grid (kW)\n price_export: float\n Value representing the cost of exporting to the grid in $/kWh.\n price_import: float\n Value representing the cost of importing to the grid in $/kWh.\n status: int, binary\n Binary value representing whether the grid is connected or not (for example 0 represent a black-out of the\n main grid).\n\n Notes\n -----\n Another way to use this information in a notebook is to use /tab/ after /microgrid.grid./ so you can see all the\n grid attributes.\n\n Examples\n --------\n >>> m_gen=mg.MicrogridGenerator(nb_microgrid=1,path='your_path')\n >>> m_gen.generate_microgrid()\n >>> m_gen.microgrids[0].grid\n You can then add a point and use tab to have suggestion of the different paramaterers\n You can access the status of the grid for example with:\n >>> m_gen.microgrids[0].grid.status\n \"\"\"\n def __init__(self, param, status, price_import, price_export, co2):\n self.power_export = param['grid_power_export'].values[0]\n self.power_import = param['grid_power_import'].values[0]\n self.price_export = price_export #param['grid_price_export'].values[0]\n self.price_import = price_import # param['grid_price_import'].values[0]\n self.status = status\n self.co2 = co2\n\n\nclass NonModularMicrogrid:\n\n \"\"\"\n The class microgrid implement a microgrid. It is also used to run the simulation and different benchmarks.\n\n Parameters\n ----------\n parameters : dataframe\n In parameters we find:\n -'parameters': a dataframe containing all the fixed (not changing with time ) parameters of the microgrid\n -'architecture': a dictionnary containing a binary variable for each possible generator and indicating if\n this microgrid has one of them\n -'load': the load time series\n -'pv': the pv time series\n -'grid_ts': a time series of 1 and 0 indicating whether the grid is available\n -'df_actions': an empty dataframe representing the actions that the microgrid can take\n -'df_status': a dataframe representing the parameters of the microgrid that change with time, with the\n information for the first time step\n -'df_actual_generation': an empty dataframe that is used to store what actually happens in the microgrid\n after control actions are taken\n -'df_cost': dataframe to track the cost of operating the microgrid at each time step\n -'control_dict': an example of the control dictionnary that needs to be passed in run to operate the\n microgrid\n horizon : int, optional\n The horizon considered to control the microgrid, mainly used in the MPC function and to return the forecasting\n values (in hour).\n timestep : int, optional\n Time step the microgrid is operating at (in hour).\n\n Attributes\n ----------\n parameters: dataframe\n A dataframe containing all the fixed (not changing with time ) parameters of the microgrid\n architecture : dictionary\n A dictionary containing a binary variable for each possible generator and indicating if\n this microgrid has one of them\n _load_ts: dataframe\n The time series of load\n _pv_ts: dataframe\n Time series of PV generation\n pv: float\n The PV production at _run_timestep\n load: float\n The load consumption at _run_timestep\n _next_pv: float\n The PV production at _run_timestep +1\n _next_load: float\n The load consumption at _run_timestep + 1\n _grid_status_ts: dataframe\n A timeseries of binary values indicating whether the grid is connected or not.\n _df_record_control_dict: dataframe\n This dataframe is used to record the control actions taked at each time step.\n _df_record_state : dataframe\n This dataframe is used to record the variable parameters of the microgrid at each time step.\n _df_record_actual_production : dataframe\n This dataframe is used to record the actual generation of the microgrid at each time step.\n _df_record_cost : dataframe\n This dataframe is used to record the cost of operating the microgrid at each time step.\n _df_cost_per_epochs : dataframe\n In the case we run the simulation through multiple epochs, this dataframe is used to record the cost of\n operating the microgrid at each time step of each epoch.\n horizon : int, optional\n The horizon considered to control the microgrid, mainly used in the MPC function and to return the forecasting\n values (in hour).\n _run_timestep : int\n Time step the microgrid is operating at (in hour).\n _data_length: int\n Represents the number of time steps in PV/Load files (minimum between the 2) that will be used to run the\n simulation.\n done: True or False\n Indicates whether a simulation is done or not\n _has_run_rule_based_baseline: True or False\n Indicates whether the rule based benchmark has already been run or not.\n _has_run_mpc_baseline: True or False\n Indicates whether the MPC benchmark has already been run or not.\n _epoch: int\n Tracks what epoch the microgrid is at\n _zero: float\n Approximate value to 0, used in some comparisons\n control_dict: dictionnary\n Represents the list of control actions to pass in the run function\n battery: object\n Represents all the parameter of the battery, including the value changing with time (in this case it is the\n value at _run_timestep).\n genset: object\n Represents all the parameter of the genset, including the value changing with time (in this case it is the\n value at _run_timestep).\n grid: object\n Represents all the parameter of the grid, including the value changing with time (in this case it is the\n value at _run_timestep).\n benchmarks: algos.Control.Benchmarks\n Benchmark object with the ability to run benchmark algorithms and store/print the results.\n\n Notes\n -----\n We are trying to keep hidden a lot of what is happening under the hood to simplify using this class for control or\n RL research at the maximum. A few notes, in this class parameters refer to the fixed parameters of the microgrid,\n meaning they don't vary with time. The varying parameters can be found in either the other classes or\n _df_record_state.\n\n Examples\n --------\n To create microgrids through MicrogridGenerator:\n >>> m_gen=mg.MicrogridGenerator(nb_microgrid=1,path='your_path')\n >>> m_gen.generate_microgrid()\n >>>microgrid = m_gen.microgrid[0]\n\n To plot informations about the microgrid:\n >>> microgrid.print_info()\n >>> microgrid.print_control_info()\n\n To compute the benchmarks:\n >>> microgrid.compute_benchmark() # to compute them all\n >>> microgrid.compute_benchmark('mpc_linprog') #to compute only the MPC\n\n For example, a simple control loop:\n >>> while m_gen.microgrids[0].done == False:\n >>> load = mg_data['load']\n >>> pv = mg_data['pv']\n >>> control_dict = {'battery_charge': 0, 'battery_discharge': 0,'grid_import': max(0, load-pv),'grid_export':0,'pv': min(pv, load),}\n >>> mg_data = m_gen.microgrids[0].run(control_dict)\n \"\"\"\n\n def __init__(self, parameters, horizon=DEFAULT_HORIZON, timestep=DEFAULT_TIMESTEP):\n #list of parameters\n #this is a static dataframe: parameters of the microgrid that do not change with time\n\n #self._param_check(parameters)\n\n self.parameters = parameters['parameters']\n self.architecture = parameters['architecture']\n #different timeseries\n self._load_ts=parameters['load']\n self._pv_ts=parameters['pv']\n\n self.pv = self._pv_ts.iloc[0,0]\n self.load = self._load_ts.iloc[0, 0]\n self._next_load = self._load_ts.iloc[1,0]\n self._next_pv = self._pv_ts.iloc[1,0]\n if parameters['architecture']['grid']==1:\n self._grid_status_ts=parameters['grid_ts'] #time series of outages\n #self.grid_status = self._grid_status_ts.iloc[0, 0]\n self._grid_price_import=parameters['grid_price_import']\n self._grid_price_export=parameters['grid_price_export']\n self._grid_co2 = parameters['grid_co2']\n\n self._next_grid_status = self._grid_status_ts.iloc[0, 0]\n self._next_grid_price_export = self._grid_price_export.iloc[0, 0]\n self._next_grid_price_import = self._grid_price_import.iloc[0, 0]\n self._next_grid_co2 = self._grid_co2.iloc[0, 0]\n\n # those dataframe record what is happening at each time step\n self._df_record_control_dict=parameters['df_actions']\n self._df_record_state = parameters['df_status']\n self._df_record_actual_production = parameters['df_actual_generation']\n self._df_record_cost = parameters['df_cost']\n self._df_record_co2 = parameters['df_co2']\n self._df_cost_per_epochs = []\n self.horizon = horizon\n self._tracking_timestep = 0\n self._data_length = min(self._load_ts.shape[0], self._pv_ts.shape[0])\n self.done = False\n self._has_run_rule_based_baseline = False\n self._has_run_mpc_baseline = False\n self._has_train_test_split = False\n self._epoch=0\n self._zero = ZERO\n self.control_dict = parameters['control_dict']\n self._data_set_to_use_default = 'all'\n self._data_set_to_use = 'all'\n\n if self.architecture['battery'] == 1:\n self.battery = Battery(self.parameters,\n self._df_record_state['capa_to_charge'][0],\n self._df_record_state['capa_to_discharge'][0])\n if self.architecture['genset'] == 1:\n self.genset = Genset(self.parameters)\n if self.architecture['grid'] == 1:\n self.grid = Grid(self.parameters, self._grid_status_ts.iloc[0,0],\n self._grid_price_import.iloc[0, 0],\n self._grid_price_export.iloc[0, 0],\n self._grid_co2.iloc[0, 0])\n\n def _param_check(self, parameters):\n \"\"\"Simple parameter checks\"\"\"\n\n # Check parameters\n if not isinstance(parameters, dict):\n raise TypeError('parameters must be of type dict, is ({})'.format(type(parameters)))\n\n\n # Check architecture\n try:\n architecture = parameters['architecture']\n except KeyError:\n print('Dict of parameters does not appear to contain architecture key')\n raise\n if not isinstance(architecture, dict):\n raise TypeError('parameters[\\'architecture\\'] must be of type dict, is ({})'.format(type(architecture)))\n\n for key, val in architecture.items():\n if isinstance(val,bool):\n continue\n elif isinstance(val,int) and (val == 0 or val == 1):\n continue\n else:\n raise TypeError('Value ({}) of key ({}) in architecture is of unrecognizable type, '\n 'must be bool or in {{0,1}}, is ({})'.format(val, key, type(val)))\n\n # Ensure various DataFrames exist and are in fact DataFrames\n\n keys = ('parameters', 'load', 'pv', 'df_actions', 'df_status', 'df_actual_generation', 'df_cost')\n\n for key in keys:\n try:\n df = parameters[key]\n except KeyError:\n print('Dict of parameters does not appear to contain {} key'.format(key))\n raise\n if not isinstance(df, pd.DataFrame):\n raise TypeError('parameters[\\'{}\\'] must be of type pd.DataFrame, is ({})'.format(key, type(df)))\n\n\n\n def set_horizon(self, horizon):\n \"\"\"Function used to change the horizon of the simulation.\"\"\"\n self.horizon = horizon\n\n def set_cost_co2(self, co2_cost):\n \"\"\"Function used to change the horizon of the simulation.\"\"\"\n self.parameters['cost_co2'] = co2_cost\n\n def get_data(self):\n \"\"\"Function to return the time series used in the microgrid\"\"\"\n return self._load_ts, self._pv_ts\n\n def get_training_testing_data(self):\n\n if self._has_train_test_split == True:\n\n return self._limit_index, self._load_train, self._pv_train, self._load_test, self._pv_test\n\n else:\n print('You have not split the dataset into training and testing sets')\n\n def get_control_dict(self):\n \"\"\" Function that returns the control_dict. \"\"\"\n return self.control_dict\n\n\n def get_parameters(self):\n \"\"\" Function that returns the parameters of the microgrid. \"\"\"\n return self.parameters\n\n\n def get_cost(self):\n \"\"\" Function that returns the cost associated the operation of the last time step. \"\"\"\n return self._df_record_cost['total_cost'][-1]\n\n def get_co2(self):\n \"\"\" Function that returns the co2 emissions associated to the operation of the last time step. \"\"\"\n return self._df_record_co2['co2'][-1]\n\n def get_updated_values(self):\n \"\"\"\n Function that returns microgrid parameters that change with time. Depending on the architecture we have:\n - PV production\n - Load\n - Battery state of charge\n - Battery capacity to charge\n - Battery capacity to discharge\n - Whether the grid is connected or not\n - CO2 intensity of the grid\n \"\"\"\n mg_data = {}\n\n for i in self._df_record_state:\n mg_data[i] = self._df_record_state[i][-1]\n\n return mg_data\n\n\n def forecast_all(self):\n \"\"\" Function that returns the PV, load and grid_status forecasted values for the next horizon. \"\"\"\n forecast = {\n 'pv': self.forecast_pv(),\n 'load': self.forecast_load(),\n }\n if self.architecture['grid'] == 1:\n forecast['grid_status'] = self.forecast_grid_status()\n forecast['grid_import'], forecast['grid_export'] = self.forecast_grid_prices()\n forecast['grid_co2'] = self.forecast_grid_co2()\n\n return forecast\n\n\n def forecast_pv(self):\n \"\"\" Function that returns the PV forecasted values for the next horizon. \"\"\"\n forecast = np.nan\n if self._data_set_to_use == 'training':\n forecast=self._pv_train.iloc[self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n\n if self._data_set_to_use == 'testing':\n forecast = self._pv_test.iloc[\n self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n\n if self._data_set_to_use == 'all':\n forecast = self._pv_ts.iloc[self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n\n return forecast\n\n\n def forecast_load(self):\n \"\"\" Function that returns the load forecasted values for the next horizon. \"\"\"\n forecast = np.nan\n if self._data_set_to_use == 'training':\n forecast = self._load_train.iloc[self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n\n if self._data_set_to_use == 'testing':\n forecast = self._load_test.iloc[self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n\n if self._data_set_to_use == 'all':\n forecast = self._load_ts.iloc[self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n\n return forecast\n\n def forecast_grid_status(self):\n \"\"\" Function that returns the grid_status forecasted values for the next horizon. \"\"\"\n forecast = np.nan\n if self._data_set_to_use == 'training':\n forecast = self._grid_status_train.iloc[\n self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n\n if self._data_set_to_use == 'testing':\n forecast = self._grid_status_test.iloc[\n self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n\n if self._data_set_to_use == 'all':\n forecast = self._grid_status_ts.iloc[\n self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n\n return forecast\n\n def forecast_grid_co2(self):\n \"\"\" Function that returns the grid_status forecasted values for the next horizon. \"\"\"\n forecast = np.nan\n if self._data_set_to_use == 'training':\n forecast = self._grid_co2_train.iloc[\n self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n\n if self._data_set_to_use == 'testing':\n forecast = self._grid_co2_test.iloc[\n self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n\n if self._data_set_to_use == 'all':\n forecast = self._grid_co2.iloc[\n self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n\n return forecast\n\n def forecast_grid_prices(self):\n \"\"\" Function that returns the forecasted import and export prices for the next horizon. \"\"\"\n forecast_import = np.nan\n forecast_export = np.nan\n if self._data_set_to_use == 'training':\n forecast_import = self._grid_price_import_train.iloc[\n self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n forecast_export = self._grid_price_export_train.iloc[\n self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n\n if self._data_set_to_use == 'testing':\n forecast_import = self._grid_price_import_test.iloc[\n self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n forecast_export = self._grid_price_export_test.iloc[\n self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n\n if self._data_set_to_use == 'all':\n forecast_import = self._grid_price_import.iloc[\n self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n forecast_export = self._grid_price_export.iloc[\n self._tracking_timestep:self._tracking_timestep + self.horizon].values.flatten()\n\n return forecast_import, forecast_export\n\n\n\n #if return whole pv and load ts, the time can be counted in notebook\n def run(self, control_dict):\n \"\"\"\n Function to 'run' the microgrid and iterate over the dataset.\n\n Parameters\n ----------\n control_dict : dictionnary\n Dictionnary containing the different control actions we want to apply to the microgrid. Its fields depend\n on the architecture of the microgrid\n\n Return\n ----------\n self.get_updated_values(): dictionnary\n Return all the parameters that change with time in the microgrid. CF this function for more details.\n\n Notes\n ----------\n This loop is the main connexion with a user in a notebook. That is where the simulation is ran and where the\n control actions are recorder and applied.\n\n \"\"\"\n\n control_dict['load'] = self.load\n control_dict['pv'] = self.pv\n\n self._df_record_control_dict = self._record_action(control_dict, self._df_record_control_dict)\n\n\n\n self._df_record_actual_production = self._record_production(control_dict,\n self._df_record_actual_production,\n self._df_record_state)\n\n if self.architecture['grid'] == 1:\n self._df_record_co2 = self._record_co2({ i:self._df_record_actual_production[i][-1] for i in self._df_record_actual_production},\n self._df_record_co2, self.grid.co2)\n\n self._df_record_cost = self._record_cost({ i:self._df_record_actual_production[i][-1] for i in self._df_record_actual_production},\n self._df_record_cost, self._df_record_co2, self.grid.price_import, self.grid.price_export)\n self._df_record_state = self._update_status({key: value[-1] for key, value in self._df_record_actual_production.items()},\n self._df_record_state, self._next_load, self._next_pv,\n self._next_grid_status, self._next_grid_price_import,\n self._next_grid_price_export, self._next_grid_co2)\n\n\n else:\n self._df_record_co2 = self._record_co2({ i:self._df_record_actual_production[i][-1] for i in self._df_record_actual_production},\n self._df_record_co2)\n\n self._df_record_cost = self._record_cost({ i:self._df_record_actual_production[i][-1] for i in self._df_record_actual_production},\n self._df_record_cost, self._df_record_co2)\n self._df_record_state = self._update_status(control_dict,\n self._df_record_state, self._next_load, self._next_pv)\n\n if self._tracking_timestep == self._data_length - self.horizon or self._tracking_timestep == self._data_length - 1:\n self.done = True\n return self.get_updated_values()\n\n self._tracking_timestep += 1\n self.update_variables()\n\n return self.get_updated_values()\n\n\n def train_test_split(self, train_size=0.67, shuffle = False, cancel=False):\n \"\"\"\n Function to split our data between a training and testing set.\n\n Parameters\n ----------\n train_size : float, optional\n Value between 0 and 1 reflecting the percentage of the dataset that should be in the training set.\n shuffle: boolean\n Variable to know if the training and testing sets should be shuffled or in the 'temporal' order\n Not implemented yet for shuffle = True\n cancel: boolean\n Variable indicating if the split needs to be reverted, and the data brought back into one dataset\n\n Attributes\n ----------\n _limit_index : int\n Index that delimit the training and testing sets in the time series\n load_train : dataframe\n Timeseries of load in training set\n pv_train: dataframe\n Timeseries of PV in training set\n load_test : dataframe\n Timeseries of load in testing set\n pv_test: dataframe\n Timeseries of PV in testing set\n grid_status_train: dataframe\n Timeseries of grid_status in training set\n grid_status_test: dataframe\n Timeseries of grid_status in testing set\n grid_price_import_train: dataframe\n Timeseries of price_import in training set\n grid_price_import_test: dataframe\n Timeseries of price_import in testing set\n grid_price_export_train: dataframe\n Timeseries of price_export in training set\n grid_price_export_test: dataframe\n Timeseries of price_export in testing set\n\n \"\"\"\n\n if self._has_train_test_split == False:\n self._limit_index = int(np.ceil(self._data_length*train_size))\n self._load_train = self._load_ts.iloc[:self._limit_index]\n self._pv_train = self._pv_ts.iloc[:self._limit_index]\n\n self._load_test = self._load_ts.iloc[self._limit_index:]\n self._pv_test = self._pv_ts.iloc[self._limit_index:]\n\n if self.architecture['grid'] == 1:\n self._grid_status_train = self._grid_status_ts.iloc[:self._limit_index]\n self._grid_status_test = self._grid_status_ts.iloc[self._limit_index:]\n\n self._grid_price_import_train = self._grid_price_import.iloc[:self._limit_index]\n self._grid_price_import_test = self._grid_price_import.iloc[self._limit_index:]\n\n self._grid_price_export_train = self._grid_price_export.iloc[:self._limit_index]\n self._grid_price_export_test = self._grid_price_export.iloc[self._limit_index:]\n\n self._grid_co2_train = self._grid_co2.iloc[:self._limit_index]\n self._grid_co2_test = self._grid_co2.iloc[self._limit_index:]\n\n self._has_train_test_split = True\n self._data_set_to_use_default = 'training'\n self._data_set_to_use = 'training'\n\n elif self._has_train_test_split == True and cancel == True:\n self._has_train_test_split = False\n self._data_set_to_use_default = 'all'\n self._data_set_to_use = 'all'\n\n self.reset()\n\n def update_variables(self):\n \"\"\" Function that updates the variablers containing the parameters of the microgrid changing with time. \"\"\"\n\n if self._data_set_to_use == 'training':\n self.pv = self._pv_train.iloc[self._tracking_timestep, 0]\n self.load = self._load_train.iloc[self._tracking_timestep, 0]\n\n self._next_pv = self._pv_train.iloc[self._tracking_timestep +1, 0]\n self._next_load = self._load_train.iloc[self._tracking_timestep+1, 0]\n\n\n if self._data_set_to_use == 'testing':\n self.pv = self._pv_test.iloc[self._tracking_timestep, 0]\n self.load = self._load_test.iloc[self._tracking_timestep, 0]\n\n self._next_pv = self._pv_test.iloc[self._tracking_timestep+1, 0]\n self._next_load = self._load_test.iloc[self._tracking_timestep+1, 0]\n\n if self._data_set_to_use == 'all':\n self.pv = self._pv_ts.iloc[self._tracking_timestep, 0]\n self.load = self._load_ts.iloc[self._tracking_timestep, 0]\n\n\n if self._tracking_timestep < self._data_length - 1:\n self._next_pv = self._pv_ts.iloc[self._tracking_timestep+1, 0]\n self._next_load = self._load_ts.iloc[self._tracking_timestep+1, 0]\n else:\n self._next_pv, self._next_load = None, None\n\n\n if self.architecture['grid']==1:\n if self._data_set_to_use == 'training':\n self.grid.status = self._grid_status_train.iloc[self._tracking_timestep, 0]\n self.grid.price_import = self._grid_price_import_train.iloc[self._tracking_timestep,0]\n self.grid.price_export = self._grid_price_export_train.iloc[self._tracking_timestep,0]\n self.grid.co2 = self._grid_co2_train.iloc[self._tracking_timestep, 0]\n\n self._next_grid_status = self._grid_status_train.iloc[self._tracking_timestep +1, 0]\n self._next_grid_price_import = self._grid_price_import_train.iloc[self._tracking_timestep +1, 0]\n self._next_grid_price_export = self._grid_price_export_train.iloc[self._tracking_timestep +1, 0]\n self._next_grid_co2 = self._grid_co2_train.iloc[self._tracking_timestep + 1, 0]\n\n if self._data_set_to_use == 'testing':\n self.grid.status = self._grid_status_test.iloc[self._tracking_timestep, 0]\n self.grid.price_import = self._grid_price_import_test.iloc[self._tracking_timestep, 0]\n self.grid.price_export = self._grid_price_export_test.iloc[self._tracking_timestep, 0]\n self.grid.co2 = self._grid_co2_test.iloc[self._tracking_timestep, 0]\n\n self._next_grid_status = self._grid_status_test.iloc[self._tracking_timestep + 1, 0]\n self._next_grid_price_import = self._grid_price_import_test.iloc[self._tracking_timestep + 1, 0]\n self._next_grid_price_export = self._grid_price_export_test.iloc[self._tracking_timestep + 1, 0]\n self._next_grid_co2 = self._grid_co2_test.iloc[self._tracking_timestep + 1, 0]\n\n\n if self._data_set_to_use == 'all':\n self.grid.status = self._grid_status_ts.iloc[self._tracking_timestep, 0]\n self.grid.price_import = self._grid_price_import.iloc[self._tracking_timestep, 0]\n self.grid.price_export = self._grid_price_export.iloc[self._tracking_timestep, 0]\n self.grid.co2 = self._grid_co2.iloc[self._tracking_timestep, 0]\n\n if self._tracking_timestep < self._data_length - 1:\n self._next_grid_status = self._grid_status_ts.iloc[self._tracking_timestep + 1, 0]\n self._next_grid_price_import = self._grid_price_import.iloc[self._tracking_timestep + 1, 0]\n self._next_grid_price_export = self._grid_price_export.iloc[self._tracking_timestep + 1, 0]\n self._next_grid_co2 = self._grid_co2.iloc[self._tracking_timestep + 1, 0]\n else:\n self._next_grid_status, self._next_grid_price_import, self._next_grid_price_export, \\\n self._next_grid_co2 = None, None, None, None\n\n if self.architecture['battery'] == 1:\n self.battery.soc = self._df_record_state['battery_soc'][-1]\n self.battery.capa_to_discharge = self._df_record_state['capa_to_discharge'][-1]\n self.battery.capa_to_charge = self._df_record_state['capa_to_charge'][-1]\n\n def reset(self, testing=False):\n \"\"\"This function is used to reset the dataframes that track what is happening in simulation. Mainly used in RL.\"\"\"\n if self._data_set_to_use == 'training':\n temp_cost = copy(self._df_record_cost)\n temp_cost['epoch'] = self._epoch\n self._df_cost_per_epochs.append(temp_cost)\n\n self._df_record_control_dict = {i:[] for i in self._df_record_control_dict}\n self._df_record_state = {i:[self._df_record_state[i][0]] for i in self._df_record_state}\n self._df_record_actual_production = {i:[] for i in self._df_record_actual_production}\n self._df_record_cost = {i:[] for i in self._df_record_cost}\n self._df_record_co2 = {i:[] for i in self._df_record_co2}\n\n self._tracking_timestep = 0\n\n if testing == True and self._data_set_to_use_default == 'training':\n self._data_set_to_use = 'testing'\n self._data_length = min(self._load_test.shape[0], self._pv_test.shape[0])\n else:\n self._data_set_to_use = self._data_set_to_use_default\n if self._data_set_to_use == 'training':\n self._data_length = min(self._load_train.shape[0], self._pv_train.shape[0])\n else:\n self._data_length = min(self._load_ts.shape[0], self._pv_ts.shape[0])\n\n self.update_variables()\n self.done = False\n\n\n\n self._epoch+=1\n\n\n ########################################################\n # FUNCTIONS TO UPDATE THE INTERNAL DICTIONARIES\n ########################################################\n\n\n def _record_action(self, control_dict, df):\n \"\"\" This function is used to record the actions taken, before being checked for feasability. \"\"\"\n if not isinstance(df, dict):\n raise TypeError('We know this should be named differently but df needs to be dict, is {}'.format(type(df)))\n for j in df:\n if j in control_dict.keys():\n df[j].append(control_dict[j])\n else:\n df[j].append({j:0})\n #df = df.append(control_dict,ignore_index=True)\n\n return df\n\n\n def _update_status(self, production_dict, df, next_load, next_pv, next_grid = 0, next_price_import =0, next_price_export = 0, next_co2 = 0):\n \"\"\" This function update the parameters of the microgrid that change with time. \"\"\"\n #self.df_status = self.df_status.append(self.new_row, ignore_index=True)\n\n if not isinstance(df, dict):\n raise TypeError('We know this should be named differently but df needs to be dict, is {}'.format(type(df)))\n\n new_dict = {\n 'load': next_load,\n 'pv': next_pv,\n 'hour':self._tracking_timestep%24,\n }\n new_soc =np.nan\n if self.architecture['battery'] == 1:\n new_soc = df['battery_soc'][-1] + (production_dict['battery_charge'] * self.parameters['battery_efficiency'].values[0]\n - production_dict['battery_discharge'] / self.parameters['battery_efficiency'].values[0]) / self.parameters['battery_capacity'].values[0]\n #if col == 'net_load':\n capa_to_charge = max(\n (self.parameters['battery_soc_max'].values[0] * self.parameters['battery_capacity'].values[0] -\n new_soc *\n self.parameters['battery_capacity'].values[0]\n ) * self.parameters['battery_efficiency'].values[0], 0)\n\n capa_to_discharge = max((new_soc *\n self.parameters['battery_capacity'].values[0]\n - self.parameters['battery_soc_min'].values[0] *\n self.parameters['battery_capacity'].values[0]\n ) * self.parameters['battery_efficiency'].values[0], 0)\n\n new_dict['battery_soc']=new_soc\n new_dict['capa_to_discharge'] = capa_to_discharge\n new_dict['capa_to_charge'] = capa_to_charge\n\n if self.architecture['grid'] == 1 :\n new_dict['grid_status'] = next_grid\n new_dict['grid_price_import'] = next_price_import\n new_dict['grid_price_export'] = next_price_export\n new_dict['grid_co2'] = next_co2\n\n for j in df:\n df[j].append(new_dict[j])\n\n #df = df.append(dict,ignore_index=True)\n\n\n\n return df\n\n\n #now we consider all the generators on all the time (mainly concern genset)\n\n def _check_constraints_genset(self, p_genset):\n \"\"\" This function checks that the constraints of the genset are respected.\"\"\"\n if p_genset < 0:\n p_genset =0\n print('error, genset power cannot be lower than 0')\n\n if p_genset < self.parameters['genset_rated_power'].values[0] * self.parameters['genset_pmin'].values[0] and p_genset >1:\n p_genset = self.parameters['genset_rated_power'].values[0] * self.parameters['genset_pmin'].values[0]\n\n if p_genset > self.parameters['genset_rated_power'].values[0] * self.parameters['genset_pmax'].values[0]:\n p_genset = self.parameters['genset_rated_power'].values[0] * self.parameters['genset_pmax'].values[0]\n\n return p_genset\n\n def _check_constraints_grid(self, p_import, p_export):\n \"\"\" This function checks that the constraints of the grid are respected.\"\"\"\n if p_import < 0:\n p_import = 0\n\n if p_export <0:\n p_export = 0\n\n if p_import > self._zero and p_export > self._zero:\n \tpass\n #print ('cannot import and export at the same time')\n #todo how to deal with that?\n\n if p_import > self.parameters['grid_power_import'].values[0]:\n p_import = self.parameters['grid_power_import'].values[0]\n\n if p_export > self.parameters['grid_power_export'].values[0]:\n p_export = self.parameters['grid_power_export'].values[0]\n\n return p_import, p_export\n\n def _check_constraints_battery(self, p_charge, p_discharge, status):\n \"\"\" This function checks that the constraints of the battery are respected.\"\"\"\n\n if p_charge < 0:\n p_charge = 0\n\n if p_discharge < 0:\n p_discharge = 0\n\n if p_charge > self._zero and p_discharge > self._zero:\n pass\n\n capa_to_charge = max(\n (self.parameters['battery_soc_max'].values[0] * self.parameters['battery_capacity'].values[0] -\n status['battery_soc'][-1] *\n self.parameters['battery_capacity'].values[0]\n ) * self.parameters['battery_efficiency'].values[0], 0)\n\n capa_to_discharge = max((status['battery_soc'][-1] *\n self.parameters['battery_capacity'].values[0]\n - self.parameters['battery_soc_min'].values[0] *\n self.parameters['battery_capacity'].values[0]\n ) * self.parameters['battery_efficiency'].values[0], 0)\n\n if p_charge > capa_to_charge or p_charge > self.parameters['battery_power_charge'].values[0]:\n p_charge = min (capa_to_charge, self.parameters['battery_power_charge'].values[0])\n\n\n if p_discharge > capa_to_discharge or p_discharge > self.parameters['battery_power_discharge'].values[0]:\n p_discharge = min (capa_to_discharge, self.parameters['battery_power_discharge'].values[0])\n\n return p_charge, p_discharge\n\n def _record_production(self, control_dict, production_dict, status):\n \"\"\"\n This function records the actual production occuring in the microgrid. Based on the control actions and the\n parameters of the microgrid. This function will check that the control actions respect the constraints of\n the microgrid and then record what generators have produced energy.\n\n Parameters\n ----------\n control_dict : dictionnary\n Dictionnary representing the control actions taken by an algorithm (either benchmark or in the run function).\n df: dataframe\n Previous version of the record_production dataframe (coming from the run loop, or benchmarks).\n status: dataframe\n One line dataframe representing the changing parameters of the microgrid.\n\n Notes\n -----\n The mechanism to incure a penalty in case of over-generation is not yet in its final version.\n \"\"\"\n assert isinstance(production_dict, dict)\n try:\n control_dict.pop('pv_consummed')\n except KeyError:\n pass\n\n\n has_grid = self.architecture['grid'] == 1\n has_genset = self.architecture['genset'] == 1\n has_battery = self.architecture['battery'] == 1\n\n sources = 0.0\n sinks = control_dict['load']\n\n # Battery\n if has_battery:\n p_charge, p_discharge = self._check_constraints_battery(control_dict['battery_charge'],\n control_dict['battery_discharge'],\n status)\n production_dict['battery_charge'].append(p_charge)\n production_dict['battery_discharge'].append(p_discharge)\n\n sources += p_discharge\n sinks += p_charge\n\n if has_grid:\n p_import, p_export = self._check_constraints_grid(control_dict['grid_import'],\n control_dict['grid_export'])\n production_dict['grid_import'].append(p_import)\n production_dict['grid_export'].append(p_export)\n\n sources += p_import\n sinks += p_export\n\n if has_genset:\n p_genset = self._check_constraints_genset(control_dict['genset'])\n production_dict['genset'].append(p_genset)\n sources += p_genset\n\n pv_required = sinks-sources\n pv_available = control_dict['pv']\n\n if np.abs(pv_required-pv_available) < 1e-3: # meeting demand\n pv_consumed = pv_available\n loss_load = 0\n pv_curtailed = 0\n overgeneration = 0\n\n elif pv_required > pv_available: # loss load\n pv_consumed = pv_available\n loss_load = pv_required-pv_available\n pv_curtailed = 0\n overgeneration = 0\n\n elif 0 < pv_required < pv_available: # curtail pv\n pv_consumed = pv_required\n loss_load = 0\n pv_curtailed = pv_available-pv_required\n overgeneration = 0\n\n else: # overgeneration. Requires NO pv whatsoever\n assert pv_required < 0\n pv_consumed = 0\n loss_load = 0\n pv_curtailed = pv_available if pv_available > 0 else 0\n overgeneration = -pv_required\n\n production_dict['pv_consummed'].append(pv_consumed)\n production_dict['loss_load'].append(loss_load)\n production_dict['pv_curtailed'].append(pv_curtailed)\n production_dict['overgeneration'].append(overgeneration)\n\n return production_dict\n\n def _record_co2(self, control_dict, df, grid_co2=0):\n \"\"\" This function record the cost of operating the microgrid at each time step.\"\"\"\n co2 = 0\n\n if self.architecture['genset'] == 1:\n co2 += control_dict['genset'] * self.parameters['genset_co2'].values[0]\n\n if self.architecture['grid'] == 1:\n co2 += grid_co2 * control_dict['grid_import']\n\n cost_dict = {'co2': co2}\n\n df['co2'].append( co2)\n\n return df\n\n def _record_cost(self, control_dict, df, df_co2, cost_import=0, cost_export=0):\n \"\"\" This function record the cost of operating the microgrid at each time step.\"\"\"\n\n if not isinstance(df, dict):\n raise TypeError('We know this should be named differently but df needs to be dict, is {}'.format(type(df)))\n\n cost_loss_load = control_dict['loss_load'] * self.parameters['cost_loss_load'].values[0]\n cost_overgeneration = control_dict['overgeneration'] * self.parameters['cost_overgeneration'].values[0]\n\n df['loss_load'].append(cost_loss_load)\n df['overgeneration'].append(cost_overgeneration)\n\n # cost += control_dict['loss_load'] * self.parameters['cost_loss_load'].values[0]\n # cost += control_dict['overgeneration'] * self.parameters['cost_overgeneration'].values[0]\n\n if self.architecture['genset'] == 1:\n genset_cost = control_dict['genset'] * self.parameters['fuel_cost'].values[0]\n df['genset'].append(genset_cost)\n\n if self.architecture['grid'] ==1:\n grid_import_cost = cost_import * control_dict['grid_import']\n grid_export_cost = - cost_export * control_dict['grid_export']\n df['grid_import'].append(grid_import_cost)\n df['grid_export'].append(grid_export_cost)\n\n\n if self.architecture['battery'] ==1 :\n battery_cost = (control_dict['battery_charge']+control_dict['battery_discharge'])*self.parameters['battery_cost_cycle'].values[0]\n df['battery'].append(battery_cost)\n\n co2_cost = self.parameters['cost_co2'].values[0] * df_co2['co2'][-1]\n df['co2'].append(co2_cost)\n\n total_cost = np.sum([val[-1] for key, val in df.items() if key != 'total_cost'])\n df['total_cost'].append(total_cost)\n\n return df\n\n ########################################################\n # PRINT FUNCTIONS\n ########################################################\n\n\n def print_load_pv(self):\n\n print('Load')\n fig1 = self._load_ts.iplot(asFigure=True)\n iplot(fig1)\n\n print('PV')\n fig2 =self._pv_ts.iplot(asFigure=True)\n iplot(fig2)\n\n def print_actual_production(self):\n if self._df_record_actual_production != type(pd.DataFrame()):\n df = pd.DataFrame(self._df_record_actual_production)\n fig1 = df.iplot(asFigure=True)\n iplot(fig1)\n else:\n fig1 = self._df_record_actual_production.iplot(asFigure=True)\n iplot(fig1)\n\n def print_control(self):\n if self._df_record_control_dict != type(pd.DataFrame()):\n df = pd.DataFrame(self._df_record_control_dict)\n fig1 = df.iplot(asFigure=True)\n iplot(fig1)\n else:\n fig1 = self._df_record_control_dict.iplot(asFigure=True)\n iplot(fig1)\n\n def print_co2(self):\n if self._df_record_co2 != type(pd.DataFrame()):\n df = pd.DataFrame(self._df_record_co2)\n fig1 = df.iplot(asFigure=True)\n iplot(fig1)\n else:\n fig1 = self._df_record_co2.iplot(asFigure=True)\n iplot(fig1)\n\n def print_cumsum_cost(self):\n if self._df_record_cost != type(pd.DataFrame()):\n df = pd.DataFrame(self._df_record_cost)\n plt.plot(df.cumsum())\n plt.show()\n else:\n plt.plot(self._df_record_cost.cumsum())\n plt.show()\n\n\n\n def print_benchmark_cost(self):\n \"\"\"\n This function prints the cumulative cost of the different benchmark ran and different part of the dataset\n depending on if split it in train/test or not.\n \"\"\"\n from pymgrid.algos.Control import Benchmarks\n benchmarks = Benchmarks(self)\n\n if len(benchmarks.outputs_dict) == 0:\n print('No benchmark algorithms have been run, running all.')\n #self.benchmarks.run_benchmarks()\n\n if self._has_train_test_split:\n benchmarks.describe_benchmarks(test_split=self._has_train_test_split, test_index=self._limit_index)\n\n else:\n benchmarks.describe_benchmarks(test_split=False)\n\n def print_info(self):\n \"\"\" This function prints the main information regarding the microgrid.\"\"\"\n\n print('Microgrid parameters')\n display(self.parameters)\n print('Architecture:')\n print(self.architecture)\n print('Actions: ')\n print(self._df_record_control_dict.keys())\n print('Control dictionnary:')\n print(self.control_dict)\n print('Status: ')\n print(self._df_record_state.keys())\n print('Has run mpc baseline:')\n print(self._has_run_mpc_baseline)\n print('Has run rule based baseline:')\n print(self._has_run_rule_based_baseline)\n\n def print_control_info(self):\n \"\"\" This function prints the control_dict that needs to be used to control the microgrid\"\"\"\n\n print('you should fill this dictionnary at each time step')\n print('it is included in the mg_data object')\n print('you can copy it by: ctrl = mg_data.control_dict')\n print('or you can use self.get_conrol_dict()')\n print('Control dictionnary:')\n print(self.control_dict)\n\n def print_updated_parameters(self):\n \"\"\" This function prints the last values for the parameters of the microgrid changing with time.\"\"\"\n state={}\n for i in self._df_record_state:\n state[i] = self._df_record_state[i][-1]\n\n print(state)\n\n ########################################################\n # RL UTILITY FUNCTIONS\n ########################################################\n #todo add a forecasting function that add noise to the time series\n #todo forecasting function can be used for both mpc benchmart and rl loop\n\n\n #todo verbose\n def penalty(self, coef = 1):\n \"\"\"Penalty that represents discrepancies between control dict and what really happens. \"\"\"\n penalty = 0\n for i in self._df_record_control_dict:\n penalty += abs(self._df_record_control_dict[i][-1] - self._df_record_actual_production[i][-1])\n\n return penalty*coef\n\n\n @classmethod\n def from_modular(cls, modular):\n from pymgrid.convert.convert import to_nonmodular\n return to_nonmodular(modular)\n\n def to_modular(self):\n from pymgrid.convert.convert import to_modular\n return to_modular(self)\n\n" }, { "path": "src/pymgrid/algos/Control.py", "content": "\n\"\"\"\nCopyright 2020 Total S.A.\nAuthors:Gonzague Henri , Avishai Halev <>\nPermission to use, modify, and distribute this software is given under the\nterms of the pymgrid License.\nNO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.\n$Date: 2020/08/27 08:04 $\nGonzague Henri\n\"\"\"\n\n\"\"\"\n\nCopyright (C) <2020> \n\nThis program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\n\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.\n\nYou should have received a copy of the GNU Lesser General Public License along with this program. If not, see .\n\n\"\"\"\nimport pandas as pd\nimport numpy as np\nfrom copy import deepcopy\n\n\nclass HorizonOutput:\n\n def __init__(self,control_dicts, microgrid, current_step):\n self.df = pd.DataFrame(control_dicts)\n self.microgrid = microgrid\n self.current_step = current_step\n self.cost = self.compute_cost_over_horizon(current_step)\n self.first_dict = control_dicts[0]\n\n def compute_cost_over_horizon(self, current_step):\n\n horizon = self.microgrid.horizon\n cost = 0.0\n\n cost += self.df['loss_load'].sum()*self.microgrid.parameters['cost_loss_load'].values[0] # loss load\n\n if self.microgrid.architecture['genset'] == 1:\n cost += self.df['genset'].sum() * self.microgrid.parameters['fuel_cost'].values[0]\n\n if self.microgrid.architecture['grid'] == 1:\n price_import = self.microgrid._grid_price_import.iloc[current_step:current_step + horizon].values\n price_export = self.microgrid._grid_price_export.iloc[current_step:current_step + horizon].values\n\n import_cost_vec = price_import.reshape(-1)*self.df['grid_import']\n export_cost_vec = price_export.reshape(-1)*self.df['grid_export']\n grid_cost = import_cost_vec.sum()-export_cost_vec.sum()\n\n cost += grid_cost\n\n return cost\n\n def __eq__(self, other):\n if type(self) != type(other):\n return NotImplemented\n return self.cost == other.cost\n\n def __lt__(self, other):\n if type(self) != type(other):\n return NotImplemented\n return self.cost < other.cost\n\n def __gt__(self, other):\n if type(self) != type(other):\n return NotImplemented\n return self.cost > other.cost\n\n\nclass ControlOutput(dict):\n \"\"\"\n Helper class that allows comparisons between controls by comparing the sum of their resultant costs\n Parameters:\n names: tuple, len 4\n names of each of the dataframes output in MPC\n dfs: tuple, len 4\n DataFrames of the outputs of MPC\n alg_name: str\n Name of the algorithm that produced the output\n Usage: dict-like, e.g.:\n\n >>> names = ('action', 'status', 'production', 'cost', 'co2')\n >>> dfs = (baseline_linprog_action, baseline_linprog_update_status,\n >>> baseline_linprog_record_production, baseline_linprog_cost) # From MPC\n >>> M = ControlOutput(names, dfs,'mpc')\n >>> actions = M['action'] # returns the dataframe baseline_linprog_action\n\n \"\"\"\n def __init__(self, names=None, dfs=None, alg_name=None, empty=False, microgrid=None):\n\n if not empty:\n if names is None:\n raise TypeError('names cannot be None unless initializing empty and empty=True')\n if dfs is None:\n raise TypeError('dfs cannot be None unless initializing empty and empty=True')\n if alg_name is None:\n raise TypeError('alg_name cannot be None unless initializing empty and empty=True')\n # else:\n # if not isinstance(microgrid,Microgrid.Microgrid):\n # raise TypeError('microgrid must be a Microgrid if empty is True')\n\n if not empty:\n names_needed = ('action', 'status', 'production', 'cost', 'co2')\n if any([needed not in names for needed in names_needed]):\n raise ValueError('Names must contain {}, currently contains {}'.format(names,names_needed))\n\n super(ControlOutput, self).__init__(zip(names, dfs))\n self.alg_name = alg_name\n self.microgrid = microgrid\n\n else:\n names = ('action', 'status', 'production', 'cost', 'co2')\n baseline_linprog_action = deepcopy(microgrid._df_record_control_dict)\n baseline_linprog_update_status = deepcopy(microgrid._df_record_state)\n baseline_linprog_record_production = deepcopy(microgrid._df_record_actual_production)\n baseline_linprog_cost = deepcopy(microgrid._df_record_cost)\n baseline_linprog_co2 = deepcopy(microgrid._df_record_co2)\n\n dfs = (baseline_linprog_action, baseline_linprog_update_status,\n baseline_linprog_record_production, baseline_linprog_cost, baseline_linprog_co2)\n\n super(ControlOutput, self).__init__(zip(names, dfs))\n self.alg_name = alg_name\n self.microgrid = microgrid\n\n def append(self, other_output, actual_load=None, actual_pv=None, actual_grid = None, slice_to_use=0):\n if isinstance(other_output, ControlOutput):\n for name in self.keys():\n if name not in other_output.keys():\n raise KeyError('name {} not founds in other_output keys'.format(name))\n\n self[name].append(other_output[name].iloc[slice_to_use], ignore_index=True)\n\n elif isinstance(other_output, HorizonOutput):\n action = self['action']\n production = self['production']\n cost = self['cost']\n status = self['status']\n co2 = self['co2']\n\n action = self.microgrid._record_action(other_output.first_dict, action)\n production = self.microgrid._record_production(other_output.first_dict, production, status)\n\n last_prod = dict([(key, production[key][-1]) for key in production])\n\n i = other_output.current_step\n\n if self.microgrid.architecture['grid'] == 1:\n co2 = self.microgrid._record_co2(\n last_prod,\n co2,\n self.microgrid._grid_co2.iloc[i].values[0]\n )\n\n status = self.microgrid._update_status(\n last_prod,\n status,\n actual_load,\n actual_pv,\n actual_grid,\n self.microgrid._grid_price_import.iloc[i + 1].values[0],\n self.microgrid._grid_price_export.iloc[i + 1].values[0],\n self.microgrid._grid_co2.iloc[i + 1].values[0]\n )\n\n cost = self.microgrid._record_cost(\n last_prod,\n cost,\n co2,\n self.microgrid._grid_price_import.iloc[i, 0],\n self.microgrid._grid_price_export.iloc[i, 0])\n else:\n\n co2 = self.microgrid._record_co2(\n last_prod,\n co2,\n )\n\n status = self.microgrid._update_status(\n last_prod,\n status,\n actual_load,\n actual_pv\n )\n cost = self.microgrid._record_cost(\n last_prod,\n cost,\n co2\n )\n\n self['action'] = action\n self['production'] = production\n self['cost'] = cost\n self['status'] = status\n self['co2'] = co2\n\n def to_frame(self):\n d = dict()\n max_len = -np.inf\n for k_1, v_1 in self.items():\n for k_2, v_2 in v_1.items():\n if len(v_2) > max_len:\n max_len = len(v_2)\n d[(k_1, k_2)] = v_2\n\n for _, v in d.items():\n if len(v) < max_len:\n v.extend([np.nan]*(max_len-len(v)))\n\n return pd.DataFrame(d)\n\n def __eq__(self, other):\n if type(self) != type(other):\n return NotImplemented\n return np.sum(self['cost']) == np.sum(other['cost'])\n\n def __lt__(self, other):\n if type(self) != type(other):\n return NotImplemented\n return np.sum(self['cost']) < np.sum(other['cost'])\n\n def __gt__(self, other):\n if type(self) != type(other):\n return NotImplemented\n return np.sum(self['cost']) > np.sum(other['cost'])\n\n\nclass Benchmarks:\n \"\"\"\n Class to run various control algorithms. Currently supports MPC and rule-based control.\n\n Parameters\n -----------\n microgrid: Microgrid.Microgrid\n microgrid on which to run the benchmarks\n\n Attributes\n -----------\n microgrid, Microgrid.Microgrid\n microgrid on which to run the benchmarks\n mpc_output: ControlOutput or None, default None\n output of MPC if it has been run, otherwise None\n outputs_dict: dict\n Dictionary of the outputs of all run algorithm. Keys are names of algorithms, any or all of 'mpc' or 'rbc' as of now.\n has_mpc_benchmark: bool, default False\n whether the MPC benchmark has been run or not\n rule_based_output: ControlOutput or None, default None\n output of rule basded control if it has been run, otherwise None\n has_rule_based_benchmark: bool, default False\n whether the rule based benchmark has been run or not\n\n \"\"\"\n def __init__(self, microgrid):\n # if not isinstance(microgrid, Microgrid.Microgrid):\n # raise TypeError('microgrid must be of type Microgrid, is {}'.format(type(microgrid)))\n\n self.microgrid = microgrid\n self.outputs_dict = dict()\n\n self.mpc_output = None\n self.has_mpc_benchmark = False\n self.rule_based_output = None\n self.has_rule_based_benchmark = False\n self.saa_output = None\n self.has_saa_benchmark = False\n\n def run_mpc_benchmark(self, verbose=False, **kwargs):\n \"\"\"\n Run the MPC benchmark and store the output in self.mpc_output\n :return:\n None\n \"\"\"\n from pymgrid.algos import ModelPredictiveControl\n MPC = ModelPredictiveControl(self.microgrid)\n self.mpc_output = MPC.run(verbose=verbose, **kwargs)\n self.has_mpc_benchmark = True\n self.outputs_dict[self.mpc_output.alg_name] = self.mpc_output\n\n def run_rule_based_benchmark(self):\n \"\"\"\n Run the rule based benchmark and store the output in self.rule_based_output\n :return:\n None\n \"\"\"\n from pymgrid.algos import RuleBasedControl\n RBC = RuleBasedControl(self.microgrid)\n self.rule_based_output = RBC.run_rule_based()\n self.has_rule_based_benchmark = True\n self.outputs_dict[self.rule_based_output.alg_name] = self.rule_based_output\n\n def run_saa_benchmark(self, preset_to_use=85, **kwargs):\n from pymgrid.algos.saa import SampleAverageApproximation\n SAA = SampleAverageApproximation(self.microgrid, preset_to_use=preset_to_use, **kwargs)\n self.saa_output = SAA.run(**kwargs)\n self.has_saa_benchmark = True\n self.outputs_dict[self.saa_output.alg_name] = self.saa_output\n\n def run_benchmarks(self, algo=None, verbose=False, preset_to_use=85, **kwargs):\n \"\"\"\n Runs both run_mpc_benchmark() and self.run_mpc_benchmark() and stores the results.\n :param verbose: bool, default False\n Whether to describe benchmarks after running.\n :return:\n None\n \"\"\"\n\n if algo == 'mpc':\n self.run_mpc_benchmark(verbose=verbose, **kwargs)\n elif algo == 'rbc':\n self.run_rule_based_benchmark()\n elif algo == 'saa':\n self.run_saa_benchmark(preset_to_use=preset_to_use, **kwargs)\n else:\n self.run_mpc_benchmark(verbose=verbose, **kwargs)\n self.run_rule_based_benchmark()\n self.run_saa_benchmark(preset_to_use=preset_to_use, **kwargs)\n\n if verbose:\n self.describe_benchmarks()\n\n def describe_benchmarks(self, test_split=False, test_ratio=None, test_index=None, algorithms=None):\n \"\"\"\n Prints the cost of any and all benchmarks that have been run.\n If test_split==True, must have either a test_ratio or a test_index but not both.\n\n :param test_split: bool, default False\n Whether to report the cost of the partial tail (e.g. the last third steps) or all steps.\n :param test_ratio: float, default None\n If test_split, the percentage of the data set to report on.\n :param test_index: int, default None\n If test_split, the index to split the data into train/test sets\n :return:\n None\n \"\"\"\n possible_benchmarks = ('saa', 'mpc', 'rbc')\n\n if algorithms is not None:\n if any([b_name not in possible_benchmarks for b_name in algorithms]):\n raise ValueError('Unable to recognize one or multiple of list_of_benchmarks: {}, can only contain {}'.format(\n algorithms, possible_benchmarks))\n else:\n algorithms = possible_benchmarks\n\n t_vals = []\n for key in self.outputs_dict:\n t_vals.append(len(self.outputs_dict[key]['cost']['total_cost']))\n\n if not all([t_val == t_vals[0] for t_val in t_vals]):\n raise ValueError('Outputs are of different lengths')\n\n T = t_vals[0]\n\n if test_split:\n if test_ratio is None and test_index is None:\n raise ValueError('If test_split, must have either a test_ratio or test_index')\n elif test_ratio is not None and test_index is not None:\n raise ValueError('Cannot have both test_ratio and test_split')\n elif test_ratio is not None and not (0 <= test_ratio <= 1):\n raise ValueError('test_ratio must be in [0,1], is {}'.format(test_ratio))\n elif test_index is not None and test_index > T:\n raise ValueError('test_index cannot be larger than length of output')\n\n if T != 8736:\n print('length of MPCOutput cost is {}, not 8736, may be invalid'.format(T))\n\n if not test_split or test_ratio is not None:\n if not test_split:\n test_ratio = 1\n\n steps = T - int(np.ceil(T * (1 - test_ratio)))\n percent = round(test_ratio * 100, 1)\n\n if self.has_mpc_benchmark and 'mpc' in algorithms:\n cost = round(np.sum(self.mpc_output['cost']['total_cost'][int(np.ceil(T*(1-test_ratio))):]), 2)\n print('Cost of the last {} steps ({} percent of all steps) using MPC: {}'.format(steps, percent, cost))\n\n if self.has_rule_based_benchmark and 'rbc' in algorithms:\n cost = round(np.sum(self.rule_based_output['cost']['total_cost'][int(np.ceil(T*(1-test_ratio))):]), 2)\n print('Cost of the last {} steps ({} percent of all steps) using rule-based control: {}'.format(steps, percent, cost))\n\n if self.has_saa_benchmark and 'saa' in algorithms:\n cost = round(np.sum(self.saa_output['cost']['total_cost'][int(np.ceil(T*(1-test_ratio))):]), 2)\n print('Cost of the last {} steps ({} percent of all steps) using sample-average MPC control: {}'.format(steps, percent, cost))\n\n else:\n\n if self.has_mpc_benchmark and 'mpc' in algorithms:\n cost_train = round(np.sum(self.mpc_output['cost']['total_cost'][:test_index]), 2)\n cost_test = round(np.sum(self.mpc_output['cost']['total_cost'][test_index:]), 2)\n\n print('Test set cost using MPC: {}'.format(cost_test))\n print('Train set cost using MPC: {}'.format(cost_train))\n\n if self.has_rule_based_benchmark and 'rbc' in algorithms:\n cost_train = round(np.sum(self.rule_based_output['cost']['total_cost'][:test_index]), 2)\n cost_test = round(np.sum(self.rule_based_output['cost']['total_cost'][test_index:]), 2)\n\n print('Test set cost using RBC: {}'.format(cost_test))\n print('Train set cost using RBC: {}'.format(cost_train))\n\n if self.has_saa_benchmark and 'saa' in algorithms:\n cost_train = round(np.sum(self.saa_output['cost']['total_cost'][:test_index]), 2)\n cost_test = round(np.sum(self.saa_output['cost']['total_cost'][test_index:]), 2)\n\n print('Test set cost using SAA: {}'.format(cost_test))\n print('Train set cost using SAA: {}'.format(cost_train))\n" }, { "path": "src/pymgrid/algos/__init__.py", "content": "from .mpc.mpc import ModelPredictiveControl\nfrom .rbc.rbc import RuleBasedControl\n" }, { "path": "src/pymgrid/algos/example.log", "content": "" }, { "path": "src/pymgrid/algos/mpc/__init__.py", "content": "" }, { "path": "src/pymgrid/algos/mpc/mpc.py", "content": "import time\nfrom copy import deepcopy\nfrom tqdm import tqdm\nimport cvxpy as cp\nimport numpy as np\nimport pandas as pd\nfrom warnings import warn\nfrom scipy.sparse import csr_matrix\n\ntry:\n import mosek\nexcept ImportError:\n mosek = None\n\nfrom pymgrid.algos.Control import ControlOutput, HorizonOutput\nfrom pymgrid.utils.DataGenerator import return_underlying_data\nimport logging\n\n\nlogger = logging.getLogger(__name__)\n\n\"\"\"\nAttributes:\n--------------\nmicrogrid: Union[Microgrid.Microgrid, modular_microgrid.ModularMicrogrid]\n The underlying microgrid\n\nhorizon: int\n The forecast horizon being used in MPC\n\nhas_genset: bool\n Whether the microgrid has a genset or not\n\np_vars: cvxpy.Variable, shape ((7+self.has_genset)*horizon,)\n Vector of all of the controls, at all timesteps. See P in pymgrid paper for details.\n\nu_genset: None or cvxpy.Variable, shape (self.horizon,)\n Boolean vector variable denoting the status of the genset (on or off) at each timestep if\n the genset exists. If not genset, u_genset = None.\n\ncosts: cvxpy.Parameter, shape ((7+self.has_genset)*self.horizon,)\n Parameter vector of all of the respective costs, at all timesteps. See C in pymgrid paper for details.\n\nequality_rhs: cvxpy.Parameter, shape (2 * self.horizon,)\n Parameter vector contraining the RHS of the equality constraint equation. See b in pymgrid paper for details.\n\ninequality_rhs: cvxpy.Parameter, shape (8 * self.horizon,)\n Parameter vector contraining the RHS of the inequality constraint equation. See d in pymgrid paper for details.\n\nproblem: cvxpy.problems.problem.Problem\n The constraint optimization problem to solve\n\"\"\"\n\n\nclass ModelPredictiveControl:\n \"\"\"\n Run a model predictive control algorithm on a microgrid.\n\n In model predictive control, a model of the microgrid is used to predict the microgrid's response to taking\n certain actions. Armed with this prediction model, we can predict the microgrid's response to simulating forward\n a certain number of steps (the forecast \"horizon\"). This results in an objective function -- with the objective\n being the cost of running the microgrid over the entire horizon.\n\n Given the solution of this optimization problem, we apply the control we found at the current step (ignoring the\n rest) and then repeat.\n\n The specifics of the model implementation can be seen in the accompanying paper.\n\n .. warning::\n This implementation of model predictive control does not support arbitrary microgrid components. One each\n of load, renewable, battery, grid, and genset are allowed. Microgrids are not required to have both grid and\n genset but they must have one; they also must have one each of load, renewable, and battery.\n\n Parameters\n ----------\n\n microgrid : :class:`pymgrid.Microgrid`\n Microgrid on which to run model predictive control.\n\n \"\"\"\n def __init__(self, microgrid, solver=None):\n self.microgrid, self.is_modular, self.microgrid_module_names = self._verify_microgrid(microgrid)\n self.horizon = self._get_horizon()\n\n if self.has_genset:\n self.p_vars = cp.Variable((8*self.horizon,), pos=True)\n self.u_genset = cp.Variable((self.horizon,), boolean=True)\n self.costs = cp.Parameter(8 * self.horizon)\n self.inequality_rhs = cp.Parameter(9 * self.horizon)\n\n else:\n self.p_vars = cp.Variable((7*self.horizon,), pos=True)\n self.u_genset = None\n self.costs = cp.Parameter(7 * self.horizon, nonneg=True)\n self.inequality_rhs = cp.Parameter(8 * self.horizon)\n\n self.equality_rhs = cp.Parameter(2 * self.horizon) # rhs\n\n parameters = self._parse_microgrid()\n\n self.problem = self._create_problem(*parameters)\n self._passed_solver = solver\n self._solver = self._get_solver()\n\n @property\n def has_genset(self):\n \"\"\"\n :meta private:\n \"\"\"\n if self.is_modular:\n return \"genset\" in self.microgrid_module_names.keys()\n else:\n return self.microgrid.architecture[\"genset\"] == 1\n\n def _verify_microgrid(self, microgrid):\n try:\n microgrid.to_modular()\n return microgrid, False, {}\n except AttributeError:\n try:\n microgrid.to_nonmodular()\n return microgrid, True, self._get_modules(microgrid)\n except Exception as e:\n if isinstance(e, AttributeError) and \"to_nonmodular\" in e.args[0]:\n raise TypeError(f\"Unable to verify microgrid as modular or nonmodular.\") from e\n\n raise ValueError(f\"Modular microgrid must be convertable to nonmodular. \"\n f\"Is not due to:\\n{type(e)}: {e}\") from e\n\n def _get_modules(self, modular_microgrid):\n def remove_suffix(s, suf):\n if suf and s.endswith(suf):\n return s[:-len(suf)]\n return s\n return {remove_suffix(module.item().__class__.__name__, \"Module\").lower(): name\n for name, module in modular_microgrid.modules.iterdict()}\n\n def _get_horizon(self):\n if self.is_modular:\n horizon = self.microgrid.get_forecast_horizon() + 1\n if horizon == 0:\n raise ValueError(\"Microgrid has horizon=0. Do your timeseries modules have a forecaster?\")\n return horizon\n\n return self.microgrid.horizon\n\n def _parse_microgrid(self):\n \"\"\"\n Protected helper function.\n Parses the microgrid in self.microgrid to extract the parameters necessary to run MPC.\n :return:\n eta: float\n battery efficiency\n battery_capacity: float\n battery capacity for normalization\n fuel_cost: float\n fuel cost for the genset\n cost_battery_cycle: float\n cost of cycling the battery\n cost_loss_load: float\n cost of loss load\n p_genset_min: float\n minimum production of the genset\n p_genset_max: float\n maximum production of the genset\n \"\"\"\n if self.is_modular:\n return self._parse_modular_microgrid()\n else:\n return self._parse_nonmodular_microgrid()\n\n def _parse_nonmodular_microgrid(self):\n parameters = self.microgrid.parameters\n\n eta = parameters['battery_efficiency'].values[0]\n battery_capacity = parameters['battery_capacity'].values[0]\n\n if self.microgrid.architecture['genset'] == 1:\n fuel_cost = parameters['fuel_cost'].values[0]\n else:\n fuel_cost = 0\n\n cost_battery_cycle = parameters['battery_cost_cycle'].values[0]\n cost_loss_load = parameters['cost_loss_load'].values[0]\n cost_co2 = parameters['cost_co2'].values[0]\n\n if self.has_genset:\n p_genset_min = parameters['genset_pmin'].values[0] * parameters['genset_rated_power'].values[0]\n p_genset_max = parameters['genset_pmax'].values[0] * parameters['genset_rated_power'].values[0]\n genset_co2 = parameters['genset_co2'].values[0]\n\n else:\n p_genset_min = 0\n p_genset_max = 0\n genset_co2 = 0\n\n return eta, battery_capacity, fuel_cost, cost_battery_cycle, cost_loss_load, p_genset_min, p_genset_max, cost_co2, genset_co2\n\n def _parse_modular_microgrid(self):\n battery = self.microgrid.battery.item()\n\n eta = battery.efficiency\n battery_capacity = battery.max_capacity\n cost_battery_cycle = battery.battery_cost_cycle\n\n cost_loss_load = self.microgrid.modules[self.microgrid_module_names[\"unbalancedenergy\"]].item().loss_load_cost\n\n if self.has_genset:\n genset = self.microgrid.modules[self.microgrid_module_names[\"genset\"]].item()\n fuel_cost = genset.genset_cost\n p_genset_min = genset.running_min_production\n p_genset_max = genset.running_max_production\n cost_co2 = genset.cost_per_unit_co2\n genset_co2 = genset.co2_per_unit\n\n else:\n fuel_cost, p_genset_min, p_genset_max, cost_co2, genset_co2 = 0, 0, 0, 0, 0\n\n return (\n eta,\n battery_capacity,\n fuel_cost,\n cost_battery_cycle,\n cost_loss_load,\n p_genset_min,\n p_genset_max,\n cost_co2,\n genset_co2\n )\n\n def _create_problem(self, eta, battery_capacity, fuel_cost, cost_battery_cycle, cost_loss_load,\n p_genset_min, p_genset_max, cost_co2, genset_co2):\n\n \"\"\"\n Protected, automatically called on initialization.\n\n Defines the constrainted optimization problem to be stored in self.problem.\n The parameters defined here do not change between timesteps.\n\n :param eta: float\n battery efficiency\n :param battery_capacity: float\n battery capacity for normalization\n :param fuel_cost: float\n fuel cost for the genset\n :param cost_battery_cycle: float\n cost of cycling the battery\n :param cost_loss_load: float\n cost of loss load\n :param p_genset_min: float\n minimum production of the genset\n :param p_genset_max: float\n maximum production of the genset\n :return :\n problem: cvxpy.problems.problem.Problem\n The constrainted optimization problem to be solved at each step of the MPC.\n \"\"\"\n\n delta_t = 1\n\n # Define matrix Y\n if self.has_genset:\n Y = np.zeros((self.horizon, self.horizon * 8))\n\n Y[0, 3] = -1.0 * eta * delta_t/battery_capacity\n Y[0, 4] = delta_t / (eta * battery_capacity)\n Y[0, 7] = 1\n\n gamma = np.zeros(16)\n gamma[7] = -1\n gamma[11] = -1.0 * eta * delta_t/battery_capacity\n gamma[12] = delta_t / (eta * battery_capacity)\n gamma[15] = 1\n\n for j in range(1, self.horizon):\n start = (j - 1) * 8\n\n Y[j, start:start + 16] = gamma\n else:\n Y = np.zeros((self.horizon, self.horizon * 7))\n Y[0, 2] = -1.0 * eta * delta_t / battery_capacity\n Y[0, 3] = delta_t / (eta * battery_capacity)\n Y[0, 6] = 1\n\n gamma = np.zeros(14)\n gamma[6] = -1\n gamma[9] = -1.0 * eta * delta_t/battery_capacity\n gamma[10] = delta_t / (eta * battery_capacity)\n gamma[13] = 1\n\n for j in range(1, self.horizon):\n start = (j - 1) * 7\n\n Y[j, start:start + 14] = gamma\n\n # done with Y\n if self.has_genset:\n X = np.zeros((self.horizon, self.horizon * 8))\n\n alpha = np.ones(8)\n alpha[2] = -1\n alpha[3] = -1\n alpha[5] = -1\n alpha[7] = 0\n\n for j in range(self.horizon):\n start = j * 8\n X[j, start:start + 8] = alpha\n\n else:\n\n X = np.zeros((self.horizon, self.horizon * 7))\n\n alpha = np.ones(7)\n alpha[1] = -1\n alpha[2] = -1\n alpha[4] = -1\n alpha[6] = 0\n\n for j in range(self.horizon):\n start = j * 7\n X[j, start:start + 7] = alpha\n\n A = np.concatenate((X, Y)) # lhs\n A = csr_matrix(A)\n\n # Define inequality constraints\n\n # Inequality lhs\n # This is for one timestep\n\n C_block = np.zeros((9, 8))\n C_block[0, 0] = 1\n C_block[1, 7] = 1\n C_block[2, 7] = -1\n C_block[3, 3] = 1\n C_block[4, 4] = 1\n C_block[5, 1] = 1\n C_block[6, 2] = 1\n C_block[7, 5] = 1\n C_block[8, 6] = 1\n\n if not self.has_genset: # drop the first column if no genset\n C_block = C_block[1:, 1:]\n\n # For all timesteps\n block_lists = [[C_block if i == j else np.zeros(C_block.shape) for i in range(self.horizon)] for j in\n range(self.horizon)]\n C = np.block(block_lists)\n C = csr_matrix(C)\n\n # Inequality rhs\n\n constraints = [A @ self.p_vars == self.equality_rhs, C @ self.p_vars <= self.inequality_rhs]\n\n if self.has_genset:\n constraints.extend((p_genset_min * self.u_genset <= self.p_vars[:: 8],\n self.p_vars[:: 8] <= p_genset_max * self.u_genset))\n\n # Define objective\n if self.has_genset:\n cost_vector = np.array([fuel_cost + cost_co2 * genset_co2, 0, 0,\n cost_battery_cycle, cost_battery_cycle, 0, cost_loss_load, 0])\n else:\n cost_vector = np.array([0, 0,\n cost_battery_cycle, cost_battery_cycle, 0, cost_loss_load, 0])\n\n costs_vector = np.concatenate([cost_vector] * self.horizon)\n\n self.costs.value = costs_vector\n\n objective = cp.Minimize(self.costs @ self.p_vars)\n\n return cp.Problem(objective, constraints)\n\n def _get_solver(self, mosek_failure=None):\n if self._passed_solver is not None:\n return self._passed_solver\n\n elif \"MOSEK\" in cp.installed_solvers() and mosek_failure is None:\n solver = cp.MOSEK\n elif \"GLPK_MI\" in cp.installed_solvers():\n solver = cp.GLPK_MI\n elif self.problem.is_mixed_integer():\n assert self.has_genset\n raise RuntimeError(\"If microgrid has a genset, the cvxpy problem becomes mixed integer. Either MOSEK or \"\n \"CVXOPT must be installed.\\n\"\n \"You can install both by calling pip install -e .'[genset_mpc]' in the root folder of \"\n \"pymgrid. Note that MOSEK requires a license; see https://www.mosek.com/ for details.\\n\"\n \"Academic and trial licenses are available.\")\n else:\n solver = None\n\n if mosek_failure is not None:\n logger.info(f\"MOSEK Solver failed due to {mosek_failure}. Retrying with solver={solver}\")\n else:\n logger.info(\"Using default solver.\" if solver is None else f\"Using {solver} solver.\")\n\n return solver\n\n def _set_parameters(self, load_vector, pv_vector, grid_vector, import_price, export_price,\n e_max, e_min, p_max_charge, p_max_discharge,\n p_max_import, p_max_export, soc_0, p_genset_max, cost_co2, grid_co2, genset_co2,):\n\n \"\"\"\n Protected, called by _set_and_solve.\n Sets the time-varying (and some static) parameters in the optimization problem at any given timestep.\n\n :param load_vector: np.ndarray, shape (self.horizon,)\n load values over the horizon\n :param pv_vector: np.ndarray, shape (self.horizon,)\n pv values over the horizon\n :param grid_vector: np.ndarray, shape (self.horizon,)\n grid values (boolean) over the horizon\n :param import_price: np.ndarray, shape (self.horizon,)\n import prices over the horizon\n :param export_price: np.ndarray, shape (self.horizon,)\n export prices over the horizon\n :param e_max: float\n maximum state of charge of the battery\n :param e_min: float\n minimum state of charge of the battery\n :param p_max_charge: float\n maximum amount of power the battery can charge in one timestep\n :param p_max_discharge: float\n maximum amount of power the battery can discharge in one timestep\n :param p_max_import: float\n maximum amount of power that can be imported in one timestep\n :param p_max_export: float\n maximum amount of power that can be exported in one timestep\n :param soc_0: float\n state of charge of the battery at the timestep just preceding the current horizon\n :return:\n None\n \"\"\"\n vector_dict = dict(load_vector=load_vector,\n pv_vector=pv_vector,\n grid_vector=grid_vector,\n import_price=import_price,\n export_price=export_price)\n\n for name, vector in vector_dict.items():\n if not isinstance(vector, np.ndarray):\n raise TypeError(f'Vector {name} must be ndarray, is {type(vector)}.')\n\n if len(vector.shape) != 1 and load_vector.shape[0] != self.horizon:\n raise ValueError(f'Invalid {name} shape {vector.shape}, must have shape ({self.horizon}, ).')\n\n # Set equality rhs\n equality_rhs_vals = np.zeros(self.equality_rhs.shape)\n equality_rhs_vals[:self.horizon] = load_vector-pv_vector\n equality_rhs_vals[self.horizon] = soc_0\n self.equality_rhs.value = equality_rhs_vals\n\n # Set inequality rhs\n if self.has_genset:\n inequality_rhs_block = np.array([p_genset_max, e_max, -e_min, p_max_charge, p_max_discharge,\n np.nan, np.nan, np.nan, np.nan])\n else:\n inequality_rhs_block = np.array([e_max, -e_min, p_max_charge, p_max_discharge,\n np.nan, np.nan, np.nan, np.nan])\n\n inequality_rhs_vals = np.concatenate([inequality_rhs_block]*self.horizon)\n\n # set d7-d10\n if self.has_genset:\n inequality_rhs_vals[5::9] = p_max_import * grid_vector\n inequality_rhs_vals[6::9] = p_max_export * grid_vector\n inequality_rhs_vals[7::9] = pv_vector\n inequality_rhs_vals[8::9] = load_vector\n\n else:\n inequality_rhs_vals[4::8] = p_max_import * grid_vector\n inequality_rhs_vals[5::8] = p_max_export * grid_vector\n inequality_rhs_vals[6::8] = pv_vector\n inequality_rhs_vals[7::8] = load_vector\n\n if np.isnan(inequality_rhs_vals).any():\n raise RuntimeError('There are still nan values in inequality_rhs_vals, something is wrong')\n\n self.inequality_rhs.value = inequality_rhs_vals\n\n # Set costs\n if self.has_genset:\n self.costs.value[1::8] = import_price.reshape(-1) + grid_co2.reshape(-1) * cost_co2\n self.costs.value[2::8] = export_price.reshape(-1)\n else:\n self.costs.value[0::7] = import_price.reshape(-1) + grid_co2.reshape(-1) * cost_co2\n self.costs.value[1::7] = export_price.reshape(-1)\n\n if np.isnan(self.costs.value).any():\n raise RuntimeError('There are still nan values in self.costs.value, something is wrong')\n\n def run(self, max_steps=None, verbose=False):\n \"\"\"\n Run the model prediction control algorithm.\n\n Parameters\n ---------\n max_steps : int or None, default None\n Maximum number of MPC steps. If None, run until the microgrid terminates.\n\n verbose : bool, default False\n Whether to display a progress bar.\n\n Returns\n -------\n log : pd.DataFrame\n Results of running the rule-based control algorithm.\n\n \"\"\"\n if self.is_modular:\n return self._run_mpc_on_modular(forecast_steps=max_steps, verbose=verbose)\n else:\n return self._run_mpc_on_nonmodular(forecast_steps=max_steps, verbose=verbose)\n\n def _run_mpc_on_nonmodular(self, forecast_steps=None, verbose=False):\n \"\"\"\n Function that allows MPC to be run on self.microgrid by first parsing its data\n\n :param forecast_steps: int, default None\n Number of steps to run MPC on. If None, runs over 8760-self.horizon steps\n :param verbose: bool\n Whether to discuss progress\n :return:\n output, ControlOutput\n dict-like containing the DataFrames ('action', 'status', 'production', 'cost'),\n but with an ordering defined via comparing the costs.\n \"\"\"\n sample = return_underlying_data(self.microgrid)\n sample = sample.reset_index(drop=True)\n return self._run_mpc_on_sample(sample, forecast_steps=forecast_steps, verbose=verbose)\n\n def _run_mpc_on_modular(self, forecast_steps=None, verbose=False):\n\n num_iter = self._get_num_iter(forecast_steps)\n self.microgrid.reset()\n\n for i in tqdm(range(num_iter), desc=\"MPC Progress\", disable=(not verbose)):\n control = self._set_and_solve(*self._get_modular_state_values(),\n iteration=i,\n total_iterations=num_iter,\n verbose=verbose>1)\n\n _, _, done, _ = self.microgrid.run(control, normalized=False)\n\n if done:\n break\n\n return self.microgrid.get_log()\n\n\n def _get_num_iter(self, forecast_steps=None):\n if forecast_steps is not None:\n assert forecast_steps <= len(self.microgrid), 'forecast steps cannot be longer than data length.'\n return forecast_steps\n\n elif not self.is_modular:\n return len(self.microgrid) - self.horizon\n\n return self.microgrid.final_step - self.microgrid.initial_step\n\n def _run_mpc_on_sample(self, sample, forecast_steps=None, verbose=False):\n \"\"\"\n Runs MPC on a sample over a number of iterations\n\n :param sample: pd.DataFrame, shape (8760,3)\n sample to run the MPC on. Must contain columns 'load', 'pv', and 'grid'.\n :param forecast_steps: int, default None\n Number of steps to run MPC on. If None, runs over 8760-self.horizon steps\n :param verbose: bool\n Whether to discuss progress\n :return:\n output, ControlOutput\n dict-like containing the DataFrames ('action', 'status', 'production', 'cost'),\n but with an ordering defined via comparing the costs.\n \"\"\"\n if not isinstance(sample, pd.DataFrame):\n raise TypeError('sample must be of type pd.DataFrame, is {}'.format(type(sample)))\n\n sample = sample.iloc[:self.microgrid._data_length]\n\n # dataframes, copied API from _baseline_linprog\n self.microgrid.reset()\n baseline_linprog_action = deepcopy(self.microgrid._df_record_control_dict)\n baseline_linprog_update_status = deepcopy(self.microgrid._df_record_state)\n baseline_linprog_record_production = deepcopy(self.microgrid._df_record_actual_production)\n baseline_linprog_cost = deepcopy(self.microgrid._df_record_cost)\n baseline_linprog_co2 = deepcopy(self.microgrid._df_record_co2)\n\n T = len(sample)\n horizon = self.microgrid.horizon\n\n if forecast_steps is None:\n num_iter = T - horizon\n else:\n assert forecast_steps <= T - horizon, 'forecast steps can\\'t look past horizon'\n num_iter = forecast_steps\n\n t0 = time.time()\n old_control_dict = None\n\n for i in tqdm(range(num_iter), desc=\"MPC Progress\", disable=(not verbose)):\n\n if self.microgrid.architecture['grid'] == 0:\n temp_grid = np.zeros(horizon)\n price_import = np.zeros(horizon)\n price_export = np.zeros(horizon)\n p_max_import = 0\n p_max_export = 0\n grid_co2 = np.zeros(horizon)\n else:\n temp_grid = sample.loc[i:i + horizon - 1, 'grid'].values\n price_import = self.microgrid._grid_price_import.iloc[i:i + horizon].values\n price_export = self.microgrid._grid_price_export.iloc[i:i + horizon].values\n grid_co2 = self.microgrid._grid_co2.iloc[i:i + horizon].values\n p_max_import = self.microgrid.parameters['grid_power_import'].values[0]\n p_max_export = self.microgrid.parameters['grid_power_export'].values[0]\n\n if temp_grid.shape != price_export.shape and price_export.shape != price_import.shape:\n raise RuntimeError('I think this is a problem')\n\n\n e_min = self.microgrid.parameters['battery_soc_min'].values[0]\n e_max = self.microgrid.parameters['battery_soc_max'].values[0]\n p_max_charge = self.microgrid.parameters['battery_power_charge'].values[0]\n p_max_discharge = self.microgrid.parameters['battery_power_discharge'].values[0]\n\n soc_0 = baseline_linprog_update_status['battery_soc'][-1]\n\n cost_co2 = self.microgrid.parameters['cost_co2'].values[0]\n\n if self.has_genset:\n p_genset_max = self.microgrid.parameters['genset_pmax'].values[0] *\\\n self.microgrid.parameters['genset_rated_power'].values[0]\n genset_co2 = self.microgrid.parameters['genset_co2'].values[0]\n else:\n p_genset_max = None\n genset_co2 = None\n\n # Solve one step of MPC\n control_dict = self._set_and_solve(sample.loc[i:i + horizon - 1, 'load'].values,\n sample.loc[i:i + horizon - 1, 'pv'].values, temp_grid, price_import,\n price_export, e_max, e_min, p_max_charge, p_max_discharge, p_max_import,\n p_max_export, soc_0, p_genset_max, cost_co2, grid_co2, genset_co2, iteration = i, total_iterations = num_iter)\n\n if control_dict is not None:\n baseline_linprog_action = self.microgrid._record_action(control_dict, baseline_linprog_action)\n baseline_linprog_record_production = self.microgrid._record_production(control_dict,\n baseline_linprog_record_production,\n baseline_linprog_update_status)\n old_control_dict = control_dict.copy()\n\n elif old_control_dict is not None:\n print('Using previous controls')\n baseline_linprog_action = self.microgrid._record_action(old_control_dict, baseline_linprog_action)\n baseline_linprog_record_production = self.microgrid._record_production(old_control_dict,\n baseline_linprog_record_production,\n baseline_linprog_update_status)\n else:\n raise RuntimeError('Fell through, was unable to solve for control_dict and could not find previous control dict')\n\n if self.microgrid.architecture['grid'] == 1:\n baseline_linprog_co2 = self.microgrid._record_co2(\n {i: baseline_linprog_record_production[i][-1] for i in baseline_linprog_record_production},\n baseline_linprog_co2,\n self.microgrid._grid_co2.iloc[i].values[0],\n )\n\n baseline_linprog_update_status = self.microgrid._update_status(\n {i: baseline_linprog_record_production[i][-1] for i in baseline_linprog_record_production},\n baseline_linprog_update_status,\n sample.at[i + 1, 'load'],\n sample.at[i + 1, 'pv'],\n sample.at[i + 1, 'grid'],\n self.microgrid._grid_price_import.iloc[i + 1].values[0],\n self.microgrid._grid_price_export.iloc[i + 1].values[0],\n self.microgrid._grid_co2.iloc[i + 1].values[0],\n )\n\n baseline_linprog_cost = self.microgrid._record_cost(\n {i: baseline_linprog_record_production[i][-1] for i in baseline_linprog_record_production},\n baseline_linprog_cost,\n baseline_linprog_co2,\n self.microgrid._grid_price_import.iloc[i, 0],\n self.microgrid._grid_price_export.iloc[i, 0])\n else:\n baseline_linprog_co2 = self.microgrid._record_co2(\n {i: baseline_linprog_record_production[i][-1] for i in baseline_linprog_record_production},\n baseline_linprog_co2,\n )\n\n baseline_linprog_update_status = self.microgrid._update_status(\n {i: baseline_linprog_record_production[i][-1] for i in baseline_linprog_record_production},\n baseline_linprog_update_status,\n sample.at[i + 1, 'load'],\n sample.at[i + 1, 'pv']\n )\n baseline_linprog_cost = self.microgrid._record_cost(\n {i: baseline_linprog_record_production[i][-1] for i in baseline_linprog_record_production},\n baseline_linprog_cost,\n baseline_linprog_co2,\n )\n\n names = ('action', 'status', 'production', 'cost', 'co2')\n\n dfs = (baseline_linprog_action, baseline_linprog_update_status,\n baseline_linprog_record_production, baseline_linprog_cost, baseline_linprog_co2)\n\n if verbose:\n print('Total time: {} minutes'.format(round((time.time()-t0)/60, 2)))\n\n return ControlOutput(names, dfs, 'mpc')\n\n def _set_and_solve(self,\n load_vector,\n pv_vector,\n grid_vector,\n import_price,\n export_price,\n e_max,\n e_min,\n p_max_charge,\n p_max_discharge,\n p_max_import,\n p_max_export,\n soc_0,\n p_genset_max,\n cost_co2,\n grid_co2,\n genset_co2,\n iteration=None,\n total_iterations=None,\n return_steps=0,\n verbose=False):\n \"\"\"\n Sets the parameters in the problem and then solves the problem.\n Specifically, sets the right-hand sides b and d from the paper of the\n equality and inequality equations, respectively, and the costs vector by calling _set_parameters, then\n solves the problem and returns a control dictionary\n\n\n :param load_vector: np.ndarray, shape (self.horizon,)\n load values over the horizon\n :param pv_vector: np.ndarray, shape (self.horizon,)\n pv values over the horizon\n :param grid_vector: np.ndarray, shape (self.horizon,)\n grid values (boolean) over the horizon\n :param import_price: np.ndarray, shape (self.horizon,)\n import prices over the horizon\n :param export_price: np.ndarray, shape (self.horizon,)\n export prices over the horizon\n :param e_max: float\n maximum state of charge of the battery\n :param e_min: float\n minimum state of charge of the battery\n :param p_max_charge: float\n maximum amount of power the battery can charge in one timestep\n :param p_max_discharge: float\n maximum amount of power the battery can discharge in one timestep\n :param p_max_import: float\n maximum amount of power that can be imported in one timestep\n :param p_max_export: float\n maximum amount of power that can be exported in one timestep\n :param soc_0: float\n state of charge of the battery at the timestep just preceding the current horizon\n :param p_genset_max: float\n maximum amount of production of the genset\n :param iteration: int\n Current iteration, used for verbosity\n :param total_iterations:\n Total iterations, used for verbosity\n :return:\n control_dict, dict\n dictionary of the controls of the first timestep, as MPC does.\n \"\"\"\n\n self._set_parameters(load_vector, pv_vector, grid_vector, import_price, export_price,\n e_max, e_min, p_max_charge, p_max_discharge,\n p_max_import, p_max_export, soc_0, p_genset_max, cost_co2, grid_co2, genset_co2,)\n\n if mosek is not None:\n try:\n self.problem.solve(warm_start=True, solver=self._solver)\n except mosek.Error as e:\n self._solver = self._get_solver(mosek_failure=e)\n self.problem.solve(warm_start=True, solver=self._solver)\n else:\n self.problem.solve(warm_start=True, solver=self._solver)\n\n if self.problem.status == 'infeasible':\n warn(\"Infeasible problem\")\n\n if self.is_modular:\n return self._extract_modular_control(load_vector, verbose)\n else:\n return self._extract_control_dict(return_steps, pv_vector, load_vector)\n\n def _extract_control_dict(self, return_steps, pv_vector, load_vector):\n if return_steps == 0:\n if self.has_genset:\n control_dict = {'battery_charge': self.p_vars.value[3],\n 'battery_discharge': self.p_vars.value[4],\n 'genset': self.p_vars.value[0],\n 'grid_import': self.p_vars.value[1],\n 'grid_export': self.p_vars.value[2],\n 'loss_load': self.p_vars.value[6],\n 'pv_consummed': pv_vector[0] - self.p_vars.value[5],\n 'pv_curtailed': self.p_vars.value[5],\n 'load': load_vector[0],\n 'pv': pv_vector[0]}\n else:\n control_dict = {'battery_charge': self.p_vars.value[2],\n 'battery_discharge': self.p_vars.value[3],\n 'grid_import': self.p_vars.value[0],\n 'grid_export': self.p_vars.value[1],\n 'loss_load': self.p_vars.value[5],\n 'pv_consummed': pv_vector[0] - self.p_vars.value[4],\n 'pv_curtailed': self.p_vars.value[4],\n 'load': load_vector[0],\n 'pv': pv_vector[0]}\n\n return control_dict\n\n else:\n if return_steps > self.horizon:\n raise ValueError('return_steps cannot be greater than horizon')\n\n control_dicts = []\n\n if self.has_genset:\n for j in range(return_steps):\n start_index = j*8\n\n control_dict = {'battery_charge': self.p_vars.value[start_index+3],\n 'battery_discharge': self.p_vars.value[start_index+4],\n 'genset': self.p_vars.value[start_index],\n 'grid_import': self.p_vars.value[start_index+1],\n 'grid_export': self.p_vars.value[start_index+2],\n 'loss_load': self.p_vars.value[start_index+6],\n 'pv_consummed': pv_vector[j] - self.p_vars.value[start_index+5],\n 'pv_curtailed': self.p_vars.value[start_index+5],\n 'load': load_vector[j],\n 'pv': pv_vector[j]}\n\n control_dicts.append(control_dict)\n\n else:\n for j in range(return_steps):\n start_index = j * 7\n\n control_dict = {'battery_charge': self.p_vars.value[start_index + 2],\n 'battery_discharge': self.p_vars.value[start_index + 3],\n 'grid_import': self.p_vars.value[start_index],\n 'grid_export': self.p_vars.value[start_index + 1],\n 'loss_load': self.p_vars.value[start_index + 5],\n 'pv_consummed': pv_vector[j] - self.p_vars.value[start_index + 4],\n 'pv_curtailed': self.p_vars.value[start_index + 4],\n 'load': load_vector[j],\n 'pv': pv_vector[j]}\n\n control_dicts.append(control_dict)\n\n return control_dicts\n\n def _extract_modular_control(self, load_vector, verbose):\n control = dict()\n control_vals = list(self.p_vars.value)\n\n if self.has_genset:\n genset = control_vals.pop(0)\n genset_status = self.u_genset.value[0]\n control[self.microgrid_module_names[\"genset\"]] = [np.array([genset_status, genset])]\n\n battery_charge, battery_discharge = control_vals[2:4]\n battery_diff = battery_discharge - battery_charge\n\n grid_import, grid_export = control_vals[0:2]\n grid_diff = grid_import - grid_export\n\n if battery_charge > 0 and battery_discharge > 0:\n if verbose and not np.isclose([battery_charge, battery_discharge], 0, atol=1e-4).any():\n warn(f\"battery_charge={battery_charge} and battery_discharge={battery_discharge} are both nonzero. \"\n f\"Flattening to the difference, leading to a {'discharge' if battery_diff > 0 else 'charge'} of {battery_diff}.\")\n\n if grid_import > 0 and grid_export > 0:\n if verbose and not np.isclose([grid_import, grid_export], 0, atol=1e-4).any():\n warn(f\"grid_import={grid_import} and grid_export={grid_export} are both nonzero. \"\n f\"Flattening to the difference, leading to a {'import' if grid_diff > 0 else 'export'} of {grid_diff}.\")\n\n if \"grid\" in self.microgrid_module_names.keys():\n control.update({self.microgrid_module_names[\"grid\"]: grid_diff})\n\n control.update({self.microgrid_module_names[\"battery\"]: battery_diff})\n\n return control\n\n def _get_modular_state_values(self):\n\n load_state = -1.0 * self.microgrid.modules[self.microgrid_module_names[\"load\"]].item().state # state is negative, want positive values.\n pv_state = self.microgrid.modules[self.microgrid_module_names[\"renewable\"]].item().state\n\n try:\n grid = self.microgrid.modules[self.microgrid_module_names[\"grid\"]].item()\n except KeyError:\n grid_status = np.zeros(self.horizon)\n price_import = np.zeros(self.horizon)\n price_export = np.zeros(self.horizon)\n grid_co2_per_kwh = np.zeros(self.horizon)\n cost_co2 = []\n\n grid_max_import, grid_max_export = 0, 0\n else:\n grid_status = np.ones(self.horizon)\n\n price_import = grid.import_price\n price_export = grid.export_price\n grid_co2_per_kwh = grid.co2_per_kwh\n cost_co2 = [grid.cost_per_unit_co2]\n\n grid_max_import, grid_max_export = grid.max_import, grid.max_export\n\n try:\n battery = self.microgrid.battery.item()\n except AttributeError:\n raise ValueError(f\"Microgrid {self.microgrid} has no battery.\")\n else:\n e_min = battery.min_soc\n e_max = battery.max_soc\n battery_max_charge = battery.max_charge\n battery_max_discharge = battery.max_discharge\n\n soc_0 = battery.soc\n\n try:\n genset = self.microgrid.modules[self.microgrid_module_names[\"genset\"]].item()\n except KeyError:\n genset_max_prod, genset_co2_per_kwh = None, None\n else:\n genset_max_prod = genset.running_max_production\n genset_co2_per_kwh = genset.co2_per_unit\n cost_co2.append(genset.cost_per_unit_co2)\n\n cost_co2 = np.mean(cost_co2)\n\n return (\n load_state,\n pv_state,\n grid_status,\n price_import,\n price_export,\n e_max,\n e_min,\n battery_max_charge,\n battery_max_discharge,\n grid_max_import,\n grid_max_export,\n soc_0,\n genset_max_prod,\n cost_co2,\n grid_co2_per_kwh,\n genset_co2_per_kwh\n )\n\n def mpc_single_step(self, sample, previous_output, current_step):\n \"\"\"\n :meta private:\n\n Parameters\n ----------\n sample\n previous_output\n current_step\n\n Returns\n -------\n\n \"\"\"\n\n if not isinstance(previous_output, ControlOutput):\n raise TypeError('previous_output must be ControlOutput, unless first_step is True')\n\n # baseline_linprog_update_status = pd.DataFrame(previous_output['status'].iloc[-1].squeeze()).transpose()\n\n horizon = self.microgrid.horizon\n\n if self.microgrid.architecture['grid'] == 0:\n temp_grid = np.zeros(horizon)\n price_import = np.zeros(horizon)\n price_export = np.zeros(horizon)\n grid_co2 = np.zeros(horizon)\n p_max_import = 0\n p_max_export = 0\n else:\n temp_grid = sample.loc[current_step:current_step + horizon - 1, 'grid'].values\n price_import = self.microgrid._grid_price_import.iloc[current_step:current_step + horizon].values\n price_export = self.microgrid._grid_price_export.iloc[current_step:current_step + horizon].values\n grid_co2 = self.microgrid._grid_co2.iloc[current_step:current_step + horizon].values\n p_max_import = self.microgrid.parameters['grid_power_import'].values[0]\n p_max_export = self.microgrid.parameters['grid_power_export'].values[0]\n\n if temp_grid.shape != price_export.shape and price_export.shape != price_import.shape:\n raise RuntimeError('I think this is a problem')\n\n e_min = self.microgrid.parameters['battery_soc_min'].values[0]\n e_max = self.microgrid.parameters['battery_soc_max'].values[0]\n p_max_charge = self.microgrid.parameters['battery_power_charge'].values[0]\n p_max_discharge = self.microgrid.parameters['battery_power_discharge'].values[0]\n soc_0 = previous_output['status']['battery_soc'][-1]\n\n cost_co2 = self.microgrid.parameters['cost_co2'].values[0]\n\n if self.has_genset:\n p_genset_max = self.microgrid.parameters['genset_pmax'].values[0] * \\\n self.microgrid.parameters['genset_rated_power'].values[0]\n genset_co2 = self.microgrid.parameters['genset_co2'].values[0]\n else:\n p_genset_max = None\n genset_co2 = 0\n\n # Solve one step of MPC\n control_dicts = self._set_and_solve(sample.loc[current_step:current_step + horizon - 1, 'load'].values,\n sample.loc[current_step:current_step + horizon - 1, 'pv'].values, temp_grid, price_import,\n price_export, e_max, e_min, p_max_charge, p_max_discharge, p_max_import,\n p_max_export, soc_0, p_genset_max, cost_co2, grid_co2, genset_co2, iteration=current_step, return_steps=self.microgrid.horizon)\n\n if any([d is None for d in control_dicts]):\n for j, d in enumerate(control_dicts):\n if d is None:\n raise TypeError('control_dict number {} is None'.format(j))\n\n return HorizonOutput(control_dicts, self.microgrid, current_step)" }, { "path": "src/pymgrid/algos/priority_list/__init__.py", "content": "from .priority_list_element import PriorityListElement\nfrom .priority_list import PriorityListAlgo\n" }, { "path": "src/pymgrid/algos/priority_list/priority_list.py", "content": "import numpy as np\nimport pandas as pd\n\nfrom abc import abstractmethod\nfrom itertools import permutations\n\n\nfrom gym.spaces import Discrete\n\nfrom pymgrid.algos.priority_list import PriorityListElement as Element\nfrom pymgrid.modules import GensetModule\n\n\nclass PriorityListAlgo:\n def get_priority_lists(self, remove_redundant_gensets):\n \"\"\"\n Get all of the priority lists for the microgrid.\n\n A priority list is an order in which to deploy all of the controllable modules of the microgrid.\n\n Returns\n -------\n priority_lists : list of list of :class:`.PriorityListElement`\n List of all priority lists.\n\n \"\"\"\n controllable_sources = [Element(module.name, module.action_space.shape[0], n_actions, module.marginal_cost)\n for module in self.modules.controllable.sources.iterlist()\n for n_actions in range(module.action_space.shape[0])]\n\n controllable_sources.extend([Element(module.name, module.action_space.shape[0], n_actions, module.marginal_cost)\n for module in self.modules.controllable.source_and_sinks.iterlist()\n for n_actions in range(module.action_space.shape[0])])\n\n all_permutations = permutations(controllable_sources)\n priority_lists = self._remove_redundant_actions(all_permutations, gensets=remove_redundant_gensets)\n\n return priority_lists\n\n def _remove_redundant_actions(self, priority_lists, gensets=False):\n pls = []\n for pl in priority_lists:\n is_redundant = pd.DataFrame(el.module for el in pl).duplicated()\n pls.append(tuple(el for j, el in enumerate(pl) if not is_redundant.iloc[j]))\n\n unique_pls = list(dict.fromkeys(pls))\n\n if gensets:\n unique_pls = self._remove_redundant_gensets(unique_pls)\n\n return unique_pls\n\n def _remove_redundant_gensets(self, priority_lists):\n redundant_genset_actions = []\n for module_name, module_list in self.modules.iterdict():\n for module_n, module in enumerate(module_list):\n if isinstance(module, GensetModule):\n if module.running_min_production == 0:\n removable_element = Element(\n module=(module_name, module_n),\n module_actions=2,\n action=0,\n marginal_cost=module.marginal_cost\n )\n redundant_genset_actions.append(removable_element)\n\n return [el for el in priority_lists if not any(redundant in el for redundant in redundant_genset_actions)]\n\n def _populate_action(self, priority_list):\n action = self.get_empty_action()\n loads, total_load = self._get_load()\n renewable = self._get_renewable()\n assert total_load >= 0 and renewable >= 0\n\n remaining_load = (total_load-renewable).item()\n\n for element in priority_list:\n module_name, module_number = element.module\n total_module_actions = element.module_actions\n module_action_number = element.action\n\n module_to_deploy = self.modules[module_name][module_number]\n\n if total_module_actions > 1:\n if action[module_name][module_number] is not None:\n # Already hit this module in the priority list (as it has multiple elements)\n continue\n else:\n action[module_name][module_number] = [module_action_number]\n\n if np.isclose(remaining_load, 0.0, atol=1e-4):\n # Don't need to do anything\n module_energy = 0.0\n elif remaining_load > 0:\n # Need to produce\n module_energy = self._produce_from_module(module_action_number, module_to_deploy, remaining_load)\n else:\n # Need to consume. These are sources and sources_and_sinks, so need to only use sources_and_sinks.\n module_energy = self._consume_in_module(module_to_deploy, remaining_load)\n\n try:\n action[module_name][module_number].append(module_energy)\n except AttributeError:\n action[module_name][module_number] = module_energy\n\n remaining_load -= module_energy\n\n if total_module_actions > 1:\n # If we have, e.g. a genset (with two actions)\n action[module_name][module_number] = np.array(action[module_name][module_number])\n\n bad_keys = [k for k, v in action.items() if v is None]\n if len(bad_keys):\n raise RuntimeError(f'None values found in action, corresponding to keys\\n\\t{bad_keys}')\n\n return action\n\n def _consume_in_module(self, module_to_deploy, remaining_load):\n module_max_consumption = module_to_deploy.max_consumption\n\n assert remaining_load <= 0.0\n\n if module_to_deploy.is_sink:\n assert module_max_consumption >= 0\n\n if -1 * remaining_load > module_to_deploy.max_consumption:\n # Can't consume it all\n module_consumption = -1.0 * module_to_deploy.max_consumption\n else:\n # Can consume all\n module_consumption = remaining_load\n else: # Not a sink\n module_consumption = 0.0\n\n assert module_consumption <= 0\n return module_consumption\n\n def _produce_from_module(self, module_action_number, module_to_deploy, remaining_load):\n try:\n max_production = module_to_deploy.next_max_production(module_action_number)\n min_production = module_to_deploy.next_min_production(module_action_number)\n except AttributeError:\n max_production, min_production = module_to_deploy.max_production, module_to_deploy.min_production\n if min_production <= remaining_load <= max_production:\n # Module can meet demand\n module_production = remaining_load\n elif remaining_load < min_production:\n # Module production too much\n module_production = min_production\n else:\n # Module production not enough\n module_production = max_production\n\n assert module_production >= 0\n return module_production\n\n def _get_load(self):\n loads = dict()\n total_load = 0.0\n for fixed_sink in self.fixed.sinks.iterlist():\n loads[fixed_sink.name] = fixed_sink.max_consumption\n total_load += fixed_sink.max_consumption\n\n return loads, total_load\n\n def _get_renewable(self):\n return np.sum([flex_source.max_production for flex_source in self.flex.sources.iterlist()])\n\n @property\n @abstractmethod\n def modules(self):\n pass\n\n @property\n @abstractmethod\n def fixed(self):\n pass\n\n @property\n @abstractmethod\n def flex(self):\n pass\n\n @abstractmethod\n def get_empty_action(self):\n pass\n" }, { "path": "src/pymgrid/algos/priority_list/priority_list_element.py", "content": "from dataclasses import dataclass\nfrom functools import total_ordering\nfrom typing import Tuple, Optional\n\n\n@total_ordering\n@dataclass(frozen=True)\nclass PriorityListElement:\n \"\"\"\n A dataclass that acts as a representation of a module's position in a deployment order.\n\n Used in :class:`.RuleBasedControl` and :class:`.DiscreteMicrogridEnv` to define actions given a specific order\n of module deployment.\n\n In :class:`.RuleBasedControl`, one list of these elements is used at every step to define actions.\n\n In :class:`.DiscreteMicrogridEnv`, each action in the environment's action space corresponds to a distinct priority\n list.\n\n See :attr:`.PriorityListElement.module_actions` and :attr:`.PriorityListElement.action` for further details\n of how these elements are incorporated in algorithms.\n\n \"\"\"\n\n module: Tuple[str, int]\n \"\"\"\n Module name.\n \"\"\"\n\n module_actions: int\n \"\"\"\n Number of possible action classes.\n \n * If one, actions generated with this element will be scalar.\n \n * If greater than one, actions generated this element will be a length-2 array. The first element\n is the action number (defined by `action`) and the second is a float corresponding to the scalar case.\n \n See :attr:`.PriorityListElement.action` for additional details.\n\n \"\"\"\n\n action: int\n \"\"\"\n Action number in {0, ..., :attr:`.PriorityListElement.module_actions`-1}.\n\n This is most relevant to :class:`GensetModule` and other elements with an action space\n with more than one dimension.\n \n Specifically, :class:`GensetModules` have a two-dimensional action space: \n the first element defines whether to turn the module on or off, and the second is an amount of energy to draw -- \n assuming the module is on. See :meth:`.GensetModule.step` and :meth:`.GensetModule.update_status` for details.\n \"\"\"\n\n marginal_cost: Optional[float] = None\n \"\"\"\n Marginal cost of using the module.\n \n Defines an ordering on elements.\n \"\"\"\n\n def __eq__(self, other):\n if type(self) != type(other):\n return NotImplemented\n\n return (\n self.module == other.module and\n self.module_actions == other.module_actions and\n self.action == other.action and\n self.marginal_cost == other.marginal_cost\n )\n\n def __lt__(self, other):\n if type(self) != type(other) or self.marginal_cost is None or other.marginal_cost is None:\n return NotImplemented\n\n return (\n self.marginal_cost < other.marginal_cost or\n (self.marginal_cost == other.marginal_cost and self.action > other.action)\n )\n" }, { "path": "src/pymgrid/algos/rbc/__init__.py", "content": "" }, { "path": "src/pymgrid/algos/rbc/_nonmodular_rbc.py", "content": "import operator\nimport sys\nfrom copy import deepcopy\n\nfrom pymgrid.algos.Control import ControlOutput\n\n\nclass NonModularRuleBasedControl:\n def __init__(self, microgrid):\n # if not isinstance(microgrid, Microgrid.Microgrid):\n # raise TypeError('microgrid must be of type Microgrid, is {}'.format(type(microgrid)))\n\n self.microgrid = microgrid\n\n def _generate_priority_list(self, architecture, parameters, grid_status=0, price_import=0, price_export=0):\n \"\"\"\n Depending on the architecture of the microgrid and grid related import/export costs, this function generates a\n priority list to be run in the rule based benchmark.\n \"\"\"\n # compute marginal cost of each resource\n # construct priority list\n # should receive fuel cost and cost curve, price of electricity\n if architecture['grid'] == 1:\n\n if price_export / (parameters['battery_efficiency'].values[0]**2) < price_import:\n\n # should return something like ['gen', starting at in MW]?\n priority_dict = {'PV': 1 * architecture['PV'],\n 'battery': 2 * architecture['battery'],\n 'grid': int(3 * architecture['grid'] * grid_status),\n 'genset': 4 * architecture['genset']}\n\n else:\n # should return something like ['gen', starting at in MW]?\n priority_dict = {'PV': 1 * architecture['PV'],\n 'battery': 3 * architecture['battery'],\n 'grid': int(2 * architecture['grid'] * grid_status),\n 'genset': 4 * architecture['genset']}\n\n else:\n priority_dict = {'PV': 1 * architecture['PV'],\n 'battery': 2 * architecture['battery'],\n 'grid': 0,\n 'genset': 4 * architecture['genset']}\n\n return priority_dict\n\n def _run_priority_based(self, load, pv, parameters, status, priority_dict):\n \"\"\"\n This function runs one loop of rule based control, based on a priority list, load and pv, dispatch the\n generators\n\n Parameters\n ----------\n load: float\n Demand value\n PV: float\n PV generation\n parameters: dataframe\n The fixed parameters of the mircrogrid\n status: dataframe\n The parameters of the microgrid changing with time.\n priority_dict: dictionnary\n Dictionnary representing the priority with which run each generator.\n\n \"\"\"\n\n temp_load = load\n excess_gen = 0\n\n p_charge = 0\n p_discharge = 0\n p_import = 0\n p_export = 0\n p_genset = 0\n load_not_matched = 0\n pv_not_curtailed = 0\n self_consumed_pv = 0\n\n\n sorted_priority = priority_dict\n min_load = 0\n if self.microgrid.architecture['genset'] == 1:\n #load - pv - min(capa_to_discharge, p_discharge) > 0: then genset on and min load, else genset off\n grid_first = 0\n capa_to_discharge = max(min((status['battery_soc'][-1] *\n parameters['battery_capacity'].values[0]\n - parameters['battery_soc_min'].values[0] *\n parameters['battery_capacity'].values[0]\n ) * parameters['battery_efficiency'].values[0], self.microgrid.battery.p_discharge_max), 0)\n\n if self.microgrid.architecture['grid'] == 1 and sorted_priority['grid'] < sorted_priority['genset'] and sorted_priority['grid']>0:\n grid_first=1\n\n if temp_load > pv + capa_to_discharge and grid_first ==0:\n\n min_load = self.microgrid.parameters['genset_rated_power'].values[0] * self.microgrid.parameters['genset_pmin'].values[0]\n if min_load <= temp_load:\n temp_load = temp_load - min_load\n else:\n temp_load = min_load\n priority_dict = {'PV': 0,\n 'battery': 0,\n 'grid': 0,\n 'genset': 1}\n\n sorted_priority = sorted(priority_dict.items(), key=operator.itemgetter(1))\n # for gen with prio i in 1:max(priority_dict)\n # we sort the priority list\n # probably we should force the PV to be number one, the min_power should be absorbed by genset, grid?\n # print (sorted_priority)\n for gen, priority in sorted_priority: # .iteritems():\n\n if priority > 0:\n\n if gen == 'PV':\n self_consumed_pv = min(temp_load, pv) # self.maximum_instantaneous_pv_penetration,\n temp_load = max(0, temp_load - self_consumed_pv)\n excess_gen = pv - self_consumed_pv\n pv_not_curtailed = pv_not_curtailed + pv - excess_gen\n\n if gen == 'battery':\n\n capa_to_charge = max(\n (parameters['battery_soc_max'].values[0] * parameters['battery_capacity'].values[0] -\n status['battery_soc'][-1] *\n parameters['battery_capacity'].values[0]\n ) / self.microgrid.parameters['battery_efficiency'].values[0], 0)\n capa_to_discharge = max((status['battery_soc'][-1] *\n parameters['battery_capacity'].values[0]\n - parameters['battery_soc_min'].values[0] *\n parameters['battery_capacity'].values[0]\n ) * parameters['battery_efficiency'].values[0], 0)\n if temp_load > 0:\n p_discharge = max(0, min(capa_to_discharge, parameters['battery_power_discharge'].values[0],\n temp_load))\n temp_load = temp_load - p_discharge\n\n elif excess_gen > 0:\n p_charge = max(0, min(capa_to_charge, parameters['battery_power_charge'].values[0],\n excess_gen))\n excess_gen = excess_gen - p_charge\n\n pv_not_curtailed = pv_not_curtailed + p_charge\n\n if gen == 'grid':\n if temp_load > 0:\n p_import = temp_load\n temp_load = 0\n\n\n\n elif excess_gen > 0:\n p_export = excess_gen\n excess_gen = 0\n\n pv_not_curtailed = pv_not_curtailed + p_export\n\n if gen == 'genset':\n if temp_load > 0:\n p_genset = temp_load + min_load\n temp_load = 0\n min_load = 0\n\n if temp_load > 0:\n load_not_matched = 1\n\n control_dict = {'battery_charge': p_charge,\n 'battery_discharge': p_discharge,\n 'genset': p_genset,\n 'grid_import': p_import,\n 'grid_export': p_export,\n 'loss_load': load_not_matched,\n 'pv_consummed': pv_not_curtailed,\n 'pv_curtailed': pv - pv_not_curtailed,\n 'load': load,\n 'pv': pv}\n\n return control_dict\n\n def run_rule_based(self, priority_list=0, length=None):\n\n \"\"\" This function runs the rule based benchmark over the datasets (load and pv profiles) in the microgrid.\"\"\"\n\n baseline_priority_list_action = deepcopy(self.microgrid._df_record_control_dict)\n baseline_priority_list_update_status = deepcopy(self.microgrid._df_record_state)\n baseline_priority_list_record_production = deepcopy(self.microgrid._df_record_actual_production)\n baseline_priority_list_cost = deepcopy(self.microgrid._df_record_cost)\n baseline_priority_list_co2 = deepcopy(self.microgrid._df_record_co2)\n\n if length is None or length >= self.microgrid._data_length:\n length = self.microgrid._data_length-1\n\n n = length - self.microgrid.horizon\n print_ratio = n/100\n\n for i in range(length - self.microgrid.horizon):\n\n e = i\n\n if e == (n-1):\n\n e = n\n\n e = e/print_ratio\n\n sys.stdout.write(\"\\rIn Progress %d%% \" % e)\n sys.stdout.flush()\n\n if e == 100:\n\n sys.stdout.write(\"\\nRules Based Calculation Finished\")\n sys.stdout.flush()\n sys.stdout.write(\"\\n\")\n\n\n if self.microgrid.architecture['grid'] == 1:\n priority_dict = self._generate_priority_list(self.microgrid.architecture, self.microgrid.parameters,\n self.microgrid._grid_status_ts.iloc[i].values[0],\n self.microgrid._grid_price_import.iloc[i].values[0],\n self.microgrid._grid_price_export.iloc[i].values[0])\n else:\n priority_dict = self._generate_priority_list(self.microgrid.architecture, self.microgrid.parameters)\n\n control_dict = self._run_priority_based(self.microgrid._load_ts.iloc[i].values[0], self.microgrid._pv_ts.iloc[i].values[0],\n self.microgrid.parameters,\n baseline_priority_list_update_status, priority_dict)\n\n baseline_priority_list_action = self.microgrid._record_action(control_dict,\n baseline_priority_list_action)\n\n baseline_priority_list_record_production = self.microgrid._record_production(control_dict,\n baseline_priority_list_record_production,\n baseline_priority_list_update_status)\n\n\n if self.microgrid.architecture['grid']==1:\n\n baseline_priority_list_co2 = self.microgrid._record_co2(\n {i: baseline_priority_list_record_production[i][-1] for i in baseline_priority_list_record_production},\n baseline_priority_list_co2,\n self.microgrid._grid_co2.iloc[i].values[0],\n )\n\n baseline_priority_list_update_status = self.microgrid._update_status(\n {i: baseline_priority_list_record_production[i][-1] for i in baseline_priority_list_record_production},\n baseline_priority_list_update_status, self.microgrid._load_ts.iloc[i + 1].values[0],\n self.microgrid._pv_ts.iloc[i + 1].values[0],\n self.microgrid._grid_status_ts.iloc[i + 1].values[0],\n self.microgrid._grid_price_import.iloc[i + 1].values[0],\n self.microgrid._grid_price_export.iloc[i + 1].values[0],\n self.microgrid._grid_co2.iloc[i + 1].values[0],\n )\n\n\n baseline_priority_list_cost = self.microgrid._record_cost(\n {i: baseline_priority_list_record_production[i][-1] for i in\n baseline_priority_list_record_production},\n baseline_priority_list_cost,\n baseline_priority_list_co2,\n self.microgrid._grid_price_import.iloc[i,0], self.microgrid._grid_price_export.iloc[i,0])\n else:\n\n baseline_priority_list_co2 = self.microgrid._record_co2(\n {i: baseline_priority_list_record_production[i][-1] for i in\n baseline_priority_list_record_production},\n baseline_priority_list_co2,\n )\n\n baseline_priority_list_update_status = self.microgrid._update_status(\n {i: baseline_priority_list_record_production[i][-1] for i in\n baseline_priority_list_record_production},\n baseline_priority_list_update_status, self.microgrid._load_ts.iloc[i + 1].values[0],\n self.microgrid._pv_ts.iloc[i + 1].values[0])\n\n baseline_priority_list_cost = self.microgrid._record_cost(\n {i: baseline_priority_list_record_production[i][-1] for i in\n baseline_priority_list_record_production},\n baseline_priority_list_cost,\n baseline_priority_list_co2)\n\n names = ('action', 'status', 'production', 'cost', 'co2')\n\n dfs = (baseline_priority_list_action, baseline_priority_list_update_status,\n baseline_priority_list_record_production, baseline_priority_list_cost, baseline_priority_list_co2)\n\n return ControlOutput(names, dfs, 'rbc')" }, { "path": "src/pymgrid/algos/rbc/rbc.py", "content": "from copy import deepcopy\nfrom tqdm import tqdm\n\nfrom pymgrid.algos.priority_list import PriorityListAlgo\n\n\nclass RuleBasedControl(PriorityListAlgo):\n \"\"\"\n\n Run a rule-based (heuristic) control algorithm on a microgrid.\n\n In rule-based control, modules are deployed in a preset order. You can either define this order by passing a\n priority list or the order will be defined automatically from the module with the lowest marginal cost to the\n highest.\n\n Parameters\n ----------\n microgrid : :class:`pymgrid.Microgrid`\n Microgrid on which to run rule-based control.\n\n priority_list : list of :class:`.PriorityListElement` or None, default None.\n Priority list to use. If None, the order will be defined automatically from the module with the lowest marginal\n cost to the highest.\n\n \"\"\"\n def __init__(self, microgrid, priority_list=None, remove_redundant_gensets=True):\n super().__init__()\n self._microgrid = deepcopy(microgrid)\n self._priority_list = self._get_priority_list(priority_list, remove_redundant_gensets)\n\n def _get_priority_list(self, priority_list, remove_redundant_gensets):\n \"\"\"\n Given a microgrid, return the optimal order of module deployment.\n \"\"\"\n priority_lists = self.get_priority_lists(remove_redundant_gensets=remove_redundant_gensets)\n\n if priority_list is None:\n return sorted(priority_lists[0])\n\n if priority_list not in priority_lists:\n raise ValueError('Invalid priority list. Use RuleBasedControl.get_priority_lists to view all '\n 'valid priority lists.')\n\n return priority_list\n\n def _get_action(self):\n \"\"\"\n Given the priority list, define an action.\n \"\"\"\n return self._populate_action(self._priority_list)\n\n def reset(self):\n \"\"\"\n Reset the underlying microgrid.\n\n Returns\n -------\n obs : dict[str, list[float]]\n Observations from resetting the modules as well as the flushed balance log.\n\n \"\"\"\n return self._microgrid.reset()\n\n def run(self, max_steps=None, verbose=False):\n \"\"\"\n Get the priority list and then deploy on the microgrid for some number of steps.\n\n Parameters\n ---------\n max_steps : int or None, default None\n Maximum number of RBC steps. If None, run until the microgrid terminates.\n\n verbose : bool, default False\n Whether to display a progress bar.\n\n Returns\n -------\n log : pd.DataFrame\n Results of running the rule-based control algorithm.\n\n \"\"\"\n if max_steps is None:\n max_steps = len(self.microgrid)\n\n self.reset()\n\n for _ in tqdm(range(max_steps), desc=\"RBC Progress\", disable=(not verbose)):\n action = self._get_action()\n _, _, done, _ = self._microgrid.run(action, normalized=False)\n if done:\n break\n\n return self._microgrid.get_log(as_frame=True)\n\n def get_empty_action(self):\n \"\"\"\n :meta private:\n \"\"\"\n return self._microgrid.get_empty_action()\n\n @property\n def microgrid(self):\n \"\"\"\n View of the microgrid.\n\n Returns\n -------\n microgrid : :class:`pymgrid.Microgrid`\n The microgrid that RBC is being run on.\n\n \"\"\"\n return self._microgrid\n\n @property\n def fixed(self):\n \"\"\":meta private:\"\"\"\n return self._microgrid.fixed\n\n @property\n def flex(self):\n \"\"\":meta private:\"\"\"\n return self._microgrid.flex\n\n @property\n def modules(self):\n \"\"\":meta private:\"\"\"\n return self._microgrid.modules\n\n @property\n def priority_list(self):\n \"\"\"\n Order in which to deploy controllable modules.\n\n Returns\n -------\n priority_list: list of :class:`.PriorityListElement`\n Priority list.\n\n \"\"\"\n return self._priority_list\n" }, { "path": "src/pymgrid/algos/saa/__init__.py", "content": "from .saa import SampleAverageApproximation" }, { "path": "src/pymgrid/algos/saa/saa.py", "content": "import time\n\nimport numpy as np\nimport pandas as pd\nfrom pymgrid.algos.Control import ControlOutput\nfrom pymgrid.utils.DataGenerator import SampleGenerator\nfrom pymgrid.algos import ModelPredictiveControl\n\n\nclass SampleAverageApproximation(SampleGenerator):\n \"\"\"\n A class to run a Sample Average Approximation version of Stochastic MPC.\n\n Parameters\n\n microgrid: pymgrid.Microgrid.Microgrid\n the underlying microgrid\n control_duration: int\n number of iterations to learn over\n\n Attributes:\n\n microgrid: pymgrid.Microgrid.Microgrid\n the underlying microgrid\n control_duration: int\n number of iterations to learn over\n mpc: algos.Control.ModelPredictiveControl\n An instance of MPC class to run MPC over for each sample\n NPV: utils.DataGenerator.NoisyPVData\n An instance of NoisyPVData to produce pv forecast and samples\n NL: utils.DataGenerator.NoisyLoadData\n An instance of NoisyLoadData to produce initial load forecast\n NG: utils.DataGenerator.NoisyGridData or None\n An instance of NoisyGridData to produce initial grid forecast. None if there is no grid\n forecasts: pd.DataFrame, shape (8760,3)\n load, pv, grid forecasts. See create_forecasts for details.\n samples: list of pd.DataFrame of shape (8760,3), or None\n list of samples created from sampling from distributions defined in forecasts.\n See sample_from_forecasts for details. None if sample_from_forecasts hasn't been called\n \"\"\"\n def __init__(self, microgrid, control_duration=8760, **forecast_args):\n if control_duration > 8760:\n raise ValueError('control_duration must be less than 8760')\n\n # if not isinstance(microgrid, Microgrid.Microgrid):\n # raise TypeError('microgrid must be of type \\'pymgrid.Microgrid.Microgrid\\', is {}'.format(type(microgrid)))\n\n super().__init__(microgrid, **forecast_args)\n self.control_duration = control_duration\n self.mpc = ModelPredictiveControl(self.microgrid)\n\n def run(self, n_samples=10, forecast_steps=None, optimal_percentile=0.5, use_previous_samples=True, verbose=False, **kwargs):\n \"\"\"\n Runs MPC over a number of samples for to average out for SAA\n :param n_samples: int, default 25\n number of samples to run\n :param forecast_steps: int or None, default None\n number of steps to use in forecast. If None, uses 8760-self.horizon\n :param use_previous_samples: bool, default True\n whether to use previous previous stored in self.samples if they are available\n :param verbose: bool, default False\n verbosity\n :return:\n outputs, list of ControlOutput\n list of ControlOutputs for each sample. See ControlOutput or run_mpc_on_sample for details.\n \"\"\"\n if self.samples is None or not use_previous_samples:\n self.samples = self.sample_from_forecasts(n_samples=n_samples, **kwargs)\n\n outputs = []\n\n t0 = time.time()\n\n output = self.run_mpc_on_group(self.samples, forecast_steps=forecast_steps,\n optimal_percentile=optimal_percentile, verbose=verbose)\n\n if verbose:\n print('Running time: {}'.format(round(time.time()-t0)))\n\n return output\n\n def determine_optimal_actions(self, outputs=None, percentile=0.5, verbose=False):\n \"\"\"\n Given a list of samples from run(), determines which one has cost at the percentile in percentile.\n\n :param outputs: list of ControlOutput\n list of ControlOutputs from run()\n :param percentile: float, default 0.5\n which percentile to return as optimal.\n :return:\n optimal_output, ControlOutput\n output at optimal percentile\n \"\"\"\n if percentile < 0. or percentile > 1.:\n raise ValueError('percentile must be in [0,1]')\n\n partition_val = int(np.floor(len(outputs)*percentile))\n partition = np.partition(outputs, partition_val)\n\n if verbose:\n sorted_outputs = np.sort(outputs)\n selected_output = partition[partition_val]\n print()\n for j, output in enumerate(sorted_outputs):\n print('Output {}, cost: {}, battery charge {}, discharge {}:'.format(\n j, round(output.cost,2) , round(output.first_dict['battery_charge'],2), round(output.first_dict['battery_discharge'],2)))\n if output is selected_output:\n print('Selected output {} with percentile {}'.format(j, percentile))\n\n return partition[partition_val]\n\n def run_mpc_on_group(self, samples, forecast_steps=None, optimal_percentile=0.5, verbose=False):\n columns_needed = ('pv', 'load', 'grid')\n\n output = ControlOutput(alg_name='saa', empty=True, microgrid=self.microgrid)\n\n T = min([len(sample) for sample in samples])\n if forecast_steps is None:\n forecast_steps = T-self.microgrid.horizon\n elif forecast_steps>T-self.microgrid.horizon:\n raise ValueError('forecast steps must be less than length of samples minus horizon')\n\n for j in range(forecast_steps):\n if verbose:\n print('iter {}'.format(j))\n\n horizon_outputs = []\n\n for sample in samples:\n if not isinstance(sample, pd.DataFrame):\n raise TypeError('samples must be pd.DataFrame')\n if not all([needed in sample.columns.values for needed in columns_needed]):\n raise KeyError('samples must contain columns {}, currently contains {}'.format(\n columns_needed, sample.columns.values))\n\n sample.iloc[j] = self.underlying_data.iloc[j] # overwrite with actual data\n\n horizon_output = self.mpc.mpc_single_step(sample, output, j)\n\n horizon_outputs.append(horizon_output)\n\n # return horizon_outputs\n\n optimal_output = self.determine_optimal_actions(outputs=horizon_outputs, percentile=optimal_percentile)\n output.append(optimal_output, actual_load=self.underlying_data.loc[j,'load'],\n actual_pv=self.underlying_data.loc[j,'pv'],\n actual_grid=self.underlying_data.loc[j,'grid'])\n\n return output\n\n def run_deterministic_on_forecast(self, forecast_steps=None, verbose=False):\n\n sample = self.forecasts.copy()\n output = ControlOutput(alg_name='mpc', empty=True, microgrid=self.microgrid)\n\n T = len(sample)\n\n if forecast_steps is None:\n forecast_steps = T - self.microgrid.horizon\n elif forecast_steps > T - self.microgrid.horizon:\n raise ValueError('forecast steps must be less than length of samples minus horizon')\n\n for j in range(forecast_steps):\n if verbose:\n print('iter {}'.format(j))\n\n sample.iloc[j] = self.underlying_data.iloc[j] # overwrite with actual data\n\n horizon_output = self.mpc.mpc_single_step(sample, output, j)\n\n output.append(horizon_output)\n\n return output" }, { "path": "src/pymgrid/convert/__init__.py", "content": "" }, { "path": "src/pymgrid/convert/convert.py", "content": "from pymgrid._deprecated.non_modular_microgrid import NonModularMicrogrid\nfrom pymgrid.microgrid.microgrid import Microgrid\nfrom pymgrid.convert.get_module import get_module\nfrom pymgrid.convert.to_nonmodular_ops import check_viability, add_params_from_module, get_empty_params, finalize_params\n\n\ndef to_modular(nonmodular, raise_errors=False):\n modules = [('load', get_module('load', nonmodular, raise_errors)),\n ('unbalanced_energy', get_module('unbalanced_energy', nonmodular, raise_errors))]\n for component, exists in nonmodular.architecture.items():\n if exists:\n module = get_module(component, nonmodular, raise_errors)\n modules.append((component, module))\n return Microgrid(modules, add_unbalanced_module=False)\n\n\ndef to_nonmodular(modular):\n \"\"\"\n microgrid_params needs to contain the following:\n parameters: ('DataFrame', (1, 16))\n df_actions: ('dict', {'load': [], 'pv_consummed': [], 'pv_curtailed': [], 'pv': [], 'battery_charge': [], 'battery_discharge': [], 'grid_import': [], 'grid_export': []})\n architecture: ('dict', {'PV': 1, 'battery': 1, 'genset': 0, 'grid': 1})\n df_status: ('dict', {'load': [304.4], 'hour': [0], 'pv': [0.0], 'battery_soc': [0.2], 'capa_to_charge': [1290.7], 'capa_to_discharge': [0.0], 'grid_status': [1.0], 'grid_co2': [0.23975790800000002], 'grid_price_import': [0.22], 'grid_price_export': [0.0]})\n df_actual_generation: ('dict', {'loss_load': [], 'overgeneration': [], 'pv_consummed': [], 'pv_curtailed': [], 'battery_charge': [], 'battery_discharge': [], 'grid_import': [], 'grid_export': []})\n grid_spec: ('int', 0)\n df_cost: ('dict', {'loss_load': [], 'overgeneration': [], 'co2': [], 'battery': [], 'grid_import': [], 'grid_export': [], 'total_cost': []})\n df_co2: ('dict', {'co2': []})\n pv: ('DataFrame', (8760, 1))\n load: ('DataFrame', (8760, 1))\n grid_ts: ('DataFrame', (8760, 1))\n control_dict: ('list', ['load', 'pv_consummed', 'pv_curtailed', 'pv', 'battery_charge', 'battery_discharge', 'grid_import', 'grid_export'])\n grid_price_import: ('DataFrame', (8760, 1))\n grid_price_export: ('DataFrame', (8760, 1))\n grid_co2: ('DataFrame', (8760, 1))\n :param modular:\n :return:\n \"\"\"\n check_viability(modular)\n microgrid_params = get_empty_params()\n for _, module_list in modular.modules.iterdict():\n add_params_from_module(module_list[0], microgrid_params)\n finalize_params(microgrid_params)\n return NonModularMicrogrid(parameters=microgrid_params, horizon=modular.get_forecast_horizon() + 1)\n" }, { "path": "src/pymgrid/convert/get_module.py", "content": "import pandas as pd\nfrom pymgrid.modules import LoadModule, RenewableModule, BatteryModule, GridModule, GensetModule, UnbalancedEnergyModule\n\n\ndef get_module(component, nonmodular, raise_errors):\n if component == 'load':\n return get_load_module(nonmodular, raise_errors)\n elif component == 'PV':\n return get_pv_module(nonmodular, raise_errors)\n elif component == 'battery':\n return get_battery_module(nonmodular, raise_errors)\n elif component == 'genset':\n return get_genset_module(nonmodular, raise_errors)\n elif component == 'grid':\n return get_grid_module(nonmodular, raise_errors)\n elif component == 'unbalanced_energy':\n return get_unbalanced_energy_module(nonmodular, raise_errors)\n else:\n raise ValueError(f'Cannot parse component {component}.')\n\n\ndef get_load_module(nonmodular, raise_errors):\n time_series = nonmodular._load_ts\n return LoadModule(time_series=time_series,\n forecaster='oracle',\n forecast_horizon=nonmodular.horizon-1,\n raise_errors=raise_errors)\n\n\ndef get_pv_module(nonmodular, raise_errors):\n time_series = nonmodular._pv_ts\n return RenewableModule(time_series=time_series,\n raise_errors=raise_errors,\n forecaster='oracle',\n forecast_horizon=nonmodular.horizon-1\n )\n\n\ndef get_battery_module(nonmodular, raise_errors):\n battery = nonmodular.battery\n max_capacity = battery.capacity\n min_capacity = max_capacity*battery.soc_min\n max_charge = battery.p_charge_max\n max_discharge = battery.p_discharge_max\n efficiency = battery.efficiency\n battery_cost_cycle = battery.cost_cycle\n init_soc = battery.soc\n return BatteryModule(min_capacity=min_capacity,\n max_capacity=max_capacity,\n max_charge=max_charge,\n max_discharge=max_discharge,\n efficiency=efficiency,\n battery_cost_cycle=battery_cost_cycle,\n init_soc=init_soc,\n raise_errors=raise_errors)\n\n\ndef get_genset_module(nonmodular, raise_errors):\n genset = nonmodular.genset\n min_production = genset.p_min*genset.rated_power\n max_production = genset.p_max*genset.rated_power\n genset_cost = genset.fuel_cost\n co2_per_unit = nonmodular.parameters.genset_co2.item()\n cost_per_unit_co2 = nonmodular.parameters.cost_co2.item()\n return GensetModule(running_min_production=min_production,\n running_max_production=max_production,\n genset_cost=genset_cost,\n co2_per_unit=co2_per_unit,\n cost_per_unit_co2=cost_per_unit_co2,\n start_up_time=0,\n wind_down_time=0,\n raise_errors=raise_errors)\n\n\ndef get_grid_module(nonmodular, raise_errors):\n max_import = nonmodular.grid.power_import\n max_export = nonmodular.grid.power_export\n cost_per_unit_co2 = nonmodular.parameters.cost_co2.item()\n\n cost_import = nonmodular._grid_price_import.squeeze()\n cost_import.name = 'cost_import'\n cost_export = nonmodular._grid_price_export.squeeze()\n cost_export.name = 'cost_export'\n co2_per_unit = nonmodular._grid_co2.squeeze()\n co2_per_unit.name = 'co2_per_unit_production'\n grid_status = nonmodular._grid_status_ts.squeeze()\n grid_status.name = 'grid_status'\n time_series = pd.concat([cost_import, cost_export, co2_per_unit, grid_status], axis=1)\n\n return GridModule(max_import=max_import,\n max_export=max_export,\n time_series=time_series,\n forecaster='oracle',\n forecast_horizon=nonmodular.horizon - 1,\n cost_per_unit_co2=cost_per_unit_co2,\n raise_errors=raise_errors)\n\n\ndef get_unbalanced_energy_module(nonmodular, raise_errors):\n return UnbalancedEnergyModule(raise_errors=raise_errors,\n loss_load_cost=nonmodular.parameters['cost_loss_load'].item(),\n overgeneration_cost=nonmodular.parameters['cost_overgeneration'].item()\n )" }, { "path": "src/pymgrid/convert/to_nonmodular_ops.py", "content": "from pymgrid.modules import LoadModule, RenewableModule, BatteryModule, GridModule, GensetModule, UnbalancedEnergyModule\nfrom copy import deepcopy\nimport pandas as pd\nimport numpy as np\nfrom warnings import warn\n\n_empty_params = dict(parameters=dict(),\n df_actions=dict(),\n architecture=dict(PV=0, battery=0, genset=0, grid=0),\n df_status=dict(hour=[0]),\n df_actual_generation=dict(loss_load=[], overgeneration=[]),\n df_cost=dict(loss_load=[], overgeneration=[], total_cost=[]),\n df_co2=dict(co2=[]),\n pv=None,\n load=None,\n grid_ts=None,\n control_dict=[],\n grid_price_import=None,\n grid_price_export=None,\n grid_co2=None,\n )\n\n\ndef get_empty_params():\n return deepcopy(_empty_params)\n\n\ndef check_viability(modular):\n classes = LoadModule, RenewableModule, BatteryModule, GridModule, GensetModule, UnbalancedEnergyModule\n classes_str = '\\n'.join([str(x) for x in classes])\n n_modules_by_cls = dict(zip(classes, [0]*len(classes)))\n\n for name, module_list in modular.modules.iterdict():\n if len(module_list) > 1:\n raise ValueError('Cannot convert modular microgrid with multiple modules of same type. '\n f'The following module name has multiple modules: {name}')\n\n module = module_list[0]\n if not isinstance(module, classes):\n raise TypeError(f'Unable to parse module {name} of type {module.__class__.__name__}.'\n f'Must be one of:\\n{classes_str}')\n\n n_modules_by_cls[module.__class__] += 1\n\n invalid = []\n for cls, n_modules in n_modules_by_cls.items():\n if n_modules > 1:\n invalid.append((cls, n_modules))\n\n if len(invalid):\n raise ValueError('Cannot convert modular microgrid with multiple modules of same type. '\n f'The following module types conflict: {invalid}')\n\n if n_modules_by_cls[LoadModule] != 1:\n raise ValueError('Cannot convert modular microgrid that has no LoadModule.')\n\n\ndef finalize_params(params_dict):\n params_dict['parameters'] = pd.DataFrame(params_dict['parameters'])\n\n\ndef add_params_from_module(module, params_dict):\n if isinstance(module, LoadModule):\n add_load_params(module, params_dict)\n elif isinstance(module, RenewableModule):\n add_pv_params(module, params_dict)\n elif isinstance(module, BatteryModule):\n add_battery_params(module, params_dict)\n elif isinstance(module, GridModule):\n add_grid_params(module, params_dict)\n elif isinstance(module, GensetModule):\n add_genset_params(module, params_dict)\n elif isinstance(module, UnbalancedEnergyModule):\n add_unbalanced_energy_params(module, params_dict)\n else:\n raise ValueError(f'Cannot parse module {module}.')\n\n\ndef add_load_params(load_module, params_dict):\n params_dict['load'] = pd.DataFrame(load_module.time_series)\n _add_to_parameters(params_dict,\n load=-1 * load_module.min_act)\n _add_to_control_dict(params_dict, 'load')\n _add_to_df_actual_generation(params_dict, 'loss_load')\n _add_to_df_actions(params_dict, 'load')\n _add_to_df_status(params_dict, load=round(load_module.current_load, 1))\n\n\ndef add_pv_params(pv_module, params_dict):\n params_dict['pv'] = pd.DataFrame(pv_module.time_series)\n _add_to_architecture(params_dict, 'PV')\n _add_to_parameters(params_dict, PV_rated_power=pv_module.max_act)\n _add_to_df_actions(params_dict, 'pv_consummed','pv_curtailed','pv')\n _add_to_df_status(params_dict, pv=[pv_module.current_renewable])\n _add_to_df_actual_generation(params_dict, 'pv_consummed','pv_curtailed')\n _add_to_control_dict(params_dict, 'pv_consummed', 'pv_curtailed', 'pv')\n\n\ndef add_battery_params(battery_module, params_dict):\n _add_to_architecture(params_dict, 'battery')\n _add_to_parameters(params_dict,\n battery_soc_0=battery_module.soc,\n battery_power_charge=battery_module.max_charge,\n battery_power_discharge=battery_module.max_discharge,\n battery_capacity=battery_module.max_capacity,\n battery_efficiency=battery_module.efficiency,\n battery_soc_min=battery_module.min_soc,\n battery_soc_max=battery_module.max_soc,\n battery_cost_cycle=battery_module.battery_cost_cycle)\n _add_to_df_actions(params_dict, 'battery_charge', 'battery_discharge')\n _add_to_df_status(params_dict,\n battery_soc=battery_module.soc,\n capa_to_charge=round((battery_module.max_soc-battery_module.soc) *\n battery_module.max_capacity/battery_module.efficiency, 1),\n capa_to_discharge=round((battery_module.soc-battery_module.min_soc) *\n battery_module.max_capacity/battery_module.efficiency, 1)\n )\n _add_to_df_actual_generation(params_dict, 'battery_charge', 'battery_discharge')\n _add_to_df_cost(params_dict, 'battery')\n _add_to_control_dict(params_dict, 'battery_charge','battery_discharge')\n\n\ndef add_grid_params(grid_module, params_dict):\n time_series_df = pd.DataFrame(grid_module.time_series,\n columns=['grid_price_import', 'grid_price_export', 'grid_co2', 'grid_status'])\n params_dict['grid_price_import'] = time_series_df['grid_price_import'].to_frame()\n params_dict['grid_price_export'] = time_series_df['grid_price_export'].to_frame()\n params_dict['grid_co2'] = time_series_df['grid_co2'].to_frame()\n params_dict['grid_ts'] = time_series_df['grid_status'].to_frame()\n _add_to_architecture(params_dict, 'grid')\n _add_to_parameters(params_dict,\n grid_weak=(time_series_df['grid_status'].min() < 1).item(),\n grid_power_import=grid_module.max_import,\n grid_power_export=grid_module.max_export)\n _add_to_df_actions(params_dict, 'grid_import','grid_export')\n _add_to_df_status(params_dict,\n grid_status=time_series_df['grid_status'].iloc[0],\n grid_co2=time_series_df['grid_co2'].iloc[0],\n grid_price_import=time_series_df['grid_price_import'].iloc[0],\n grid_price_export=time_series_df['grid_price_export'].iloc[0]\n )\n _add_to_df_actual_generation(params_dict, 'grid_import', 'grid_export')\n _add_to_df_cost(params_dict, 'grid_import', 'grid_export')\n _add_to_control_dict(params_dict, 'grid_import', 'grid_export')\n _add_cost_co2(params_dict, grid_module.cost_per_unit_co2)\n\n\ndef add_genset_params(genset_module, params_dict):\n warn('GensetModules does not contain separate rated_power and p_max information.'\n 'Assuming p_max=0.9.')\n genset_pmax=0.9\n genset_rated_power = genset_module.running_max_production/genset_pmax\n _add_to_architecture(params_dict, 'genset')\n _add_genset_polynom(params_dict)\n\n if genset_rated_power == 0:\n raise RuntimeError\n\n _add_to_parameters(params_dict,\n genset_rated_power=genset_rated_power,\n genset_pmin=genset_module.running_min_production/genset_rated_power,\n genset_pmax=genset_pmax,\n fuel_cost=genset_module.genset_cost,\n genset_co2=genset_module.co2_per_unit)\n _add_to_df_actions(params_dict, 'genset')\n _add_to_df_actual_generation(params_dict, 'genset')\n _add_to_df_cost(params_dict, 'genset')\n _add_to_control_dict(params_dict, 'genset')\n _add_cost_co2(params_dict, genset_module.cost_per_unit_co2)\n\n\ndef add_unbalanced_energy_params(unbalanced_energy_module, params_dict):\n _add_to_parameters(params_dict,\n cost_overgeneration=unbalanced_energy_module.overgeneration_cost,\n cost_loss_load=unbalanced_energy_module.loss_load_cost\n )\n _add_to_df_actual_generation(params_dict, 'overgeneration')\n _add_to_df_cost(params_dict, 'overgeneration')\n\n\ndef _add_empty(params_dict, subdict_name, *keys):\n params_dict[subdict_name].update({k: [] for k in keys})\n\n\ndef _add_to_architecture(params_dict, component):\n if component not in params_dict['architecture']:\n raise NameError(f'Component {component} not viable member of architecture')\n params_dict['architecture'][component] = 1\n\n\ndef _add_to_parameters(params_dict, **parameters):\n params_dict['parameters'].update({key: [value] if not isinstance(value, list) else value\n for key, value in parameters.items()})\n\n\ndef _add_to_df_actions(params_dict, *keys):\n _add_empty(params_dict, 'df_actions', *keys)\n\n\ndef _add_to_df_status(params_dict, **init_status_values):\n params_dict['df_status'].update({key: [value] if not isinstance(value, list) else value\n for key, value in init_status_values.items()})\n\n\ndef _add_to_df_actual_generation(params_dict, *keys):\n _add_empty(params_dict, 'df_actual_generation', *keys)\n\n\ndef _add_to_df_cost(params_dict, *keys):\n _add_empty(params_dict, 'df_cost', *keys)\n\n\ndef _add_to_control_dict(params_dict, *keys):\n params_dict['control_dict'].extend(list(keys))\n\n\ndef _add_cost_co2(params_dict, cost_co2):\n from warnings import warn\n if 'cost_co2' in params_dict['parameters']:\n existing_cost_co2 = params_dict['parameters']['cost_co2']\n if cost_co2 != existing_cost_co2:\n warn(f'cost_co2 value {cost_co2} being added is different from existing cost_co2 value {existing_cost_co2}. Using mean.')\n params_dict['parameters']['cost_co2'] = np.mean([cost_co2, existing_cost_co2])\n else:\n params_dict['parameters']['cost_co2'] = cost_co2\n _add_to_df_cost(params_dict, 'co2')\n\n\ndef _add_genset_polynom(params_dict):\n np.random.seed(0)\n warn('Getting genset_polynom parameters randomly')\n polynom = [np.random.rand() * 10, np.random.rand(), np.random.rand() / 10]\n\n to_add = dict(genset_polynom_order= len(polynom))\n to_add.update({f'genset_polynom_{i}': pn for i, pn in enumerate(polynom)})\n _add_to_parameters(params_dict, **to_add)\n" }, { "path": "src/pymgrid/data/__init__.py", "content": "" }, { "path": "src/pymgrid/data/co2/__init__.py", "content": "\n" }, { "path": "src/pymgrid/data/co2/co2_caiso.csv", "content": 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}, { "path": "src/pymgrid/data/load/RefBldgPrimarySchoolNew2004_v1.3_7.1_2A_USA_TX_HOUSTON.csv", "content": "Electricity:Facility 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}, { "path": "src/pymgrid/data/pv/__init__.py", "content": "" }, { "path": "src/pymgrid/data/scenario/__init__.py", "content": "\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_0/microgrid_0.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 1452\n max_charge: 363\n max_discharge: 363\n min_capacity: 290.40000000000003\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 290.40000000000003\n soc: 0.2\n- - grid\n - !GridModule\n cls_params:\n cost_per_unit_co2: 0.1\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n max_export: 1920\n max_import: 1920\n raise_errors: false\n time_series: !NDArray 'data/cls_params/GridModule/time_series.csv.gz'\n name:\n - grid\n - 0\n state:\n _current_step: 0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_1/microgrid_1.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 60363.0\n running_min_production: 3353.5\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 91274\n max_charge: 22819\n max_discharge: 22819\n min_capacity: 18254.8\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 18254.8\n soc: 0.19999999999999998\n- - grid\n - !GridModule\n cls_params:\n cost_per_unit_co2: 0.1\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n max_export: 120726\n max_import: 120726\n raise_errors: false\n time_series: !NDArray 'data/cls_params/GridModule/time_series.csv.gz'\n name:\n - grid\n - 0\n state:\n _current_step: 0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_10/microgrid_10.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 60144.3\n running_min_production: 3341.3500000000004\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 219201\n max_charge: 54801\n max_discharge: 54801\n min_capacity: 43840.200000000004\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 43840.200000000004\n soc: 0.2\n- - grid\n - !GridModule\n cls_params:\n cost_per_unit_co2: 0.1\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n max_export: 120288\n max_import: 120288\n raise_errors: false\n time_series: !NDArray 'data/cls_params/GridModule/time_series.csv.gz'\n name:\n - grid\n - 0\n state:\n _current_step: 0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_11/microgrid_11.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 1\n initial_step: 0\n max_capacity: 69594\n max_charge: 17399\n max_discharge: 17399\n min_capacity: 13918.800000000001\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 69594\n soc: 1.0\n- - grid\n - !GridModule\n cls_params:\n cost_per_unit_co2: 0.1\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n max_export: 55230\n max_import: 55230\n raise_errors: false\n time_series: !NDArray 'data/cls_params/GridModule/time_series.csv.gz'\n name:\n - grid\n - 0\n state:\n _current_step: 0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_12/microgrid_12.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 143726\n max_charge: 35932\n max_discharge: 35932\n min_capacity: 28745.2\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 28745.2\n soc: 0.2\n- - grid\n - !GridModule\n cls_params:\n cost_per_unit_co2: 0.1\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n max_export: 96428\n max_import: 96428\n raise_errors: false\n time_series: !NDArray 'data/cls_params/GridModule/time_series.csv.gz'\n name:\n - grid\n - 0\n state:\n _current_step: 0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_13/microgrid_13.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 19941.3\n running_min_production: 1107.8500000000001\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 47556\n max_charge: 11889\n max_discharge: 11889\n min_capacity: 9511.2\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 9511.2\n soc: 0.2\n- - grid\n - !GridModule\n cls_params:\n cost_per_unit_co2: 0.1\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n max_export: 39882\n max_import: 39882\n raise_errors: false\n time_series: !NDArray 'data/cls_params/GridModule/time_series.csv.gz'\n name:\n - grid\n - 0\n state:\n _current_step: 0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_14/microgrid_14.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 162570\n max_charge: 40643\n max_discharge: 40643\n min_capacity: 32514.0\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 32514.0\n soc: 0.2\n- - grid\n - !GridModule\n cls_params:\n cost_per_unit_co2: 0.1\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n max_export: 197286\n max_import: 197286\n raise_errors: false\n time_series: !NDArray 'data/cls_params/GridModule/time_series.csv.gz'\n name:\n - grid\n - 0\n state:\n _current_step: 0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_15/microgrid_15.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 17296.2\n running_min_production: 960.9000000000001\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 1\n initial_step: 0\n max_capacity: 26153\n max_charge: 6539\n max_discharge: 6539\n min_capacity: 5230.6\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 26153\n soc: 1.0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_16/microgrid_16.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.534118811938557\n initial_step: 0\n max_capacity: 60099\n max_charge: 15025\n max_discharge: 15025\n min_capacity: 12019.800000000001\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 32100.00647869534\n soc: 0.534118811938557\n- - grid\n - !GridModule\n cls_params:\n cost_per_unit_co2: 0.1\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n max_export: 97244\n max_import: 97244\n raise_errors: false\n time_series: !NDArray 'data/cls_params/GridModule/time_series.csv.gz'\n name:\n - grid\n - 0\n state:\n _current_step: 0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_17/microgrid_17.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 31257.0\n running_min_production: 1736.5\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 38636\n max_charge: 9659\n max_discharge: 9659\n min_capacity: 7727.200000000001\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 7727.200000000001\n soc: 0.2\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_18/microgrid_18.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 34418.700000000004\n running_min_production: 1912.15\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 1\n initial_step: 0\n max_capacity: 82080\n max_charge: 20520\n max_discharge: 20520\n min_capacity: 16416.0\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 82080\n soc: 1.0\n- - grid\n - !GridModule\n cls_params:\n cost_per_unit_co2: 0.1\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n max_export: 68836\n max_import: 68836\n raise_errors: false\n time_series: !NDArray 'data/cls_params/GridModule/time_series.csv.gz'\n name:\n - grid\n - 0\n state:\n _current_step: 0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_19/microgrid_19.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 1529.1000000000001\n running_min_production: 84.95\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 2506\n max_charge: 627\n max_discharge: 627\n min_capacity: 501.20000000000005\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 501.20000000000005\n soc: 0.2\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_2/microgrid_2.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 43725.6\n running_min_production: 2429.2000000000003\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 66116\n max_charge: 16529\n max_discharge: 16529\n min_capacity: 13223.2\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 13223.2\n soc: 0.2\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_20/microgrid_20.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 79186.5\n running_min_production: 4399.25\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 129765\n max_charge: 32442\n max_discharge: 32442\n min_capacity: 25953.0\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 25953.0\n soc: 0.2\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_21/microgrid_21.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 95435.1\n running_min_production: 5301.950000000001\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 170694\n max_charge: 42674\n max_discharge: 42674\n min_capacity: 34138.8\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 34138.8\n soc: 0.2\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_22/microgrid_22.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 80372.7\n running_min_production: 4465.150000000001\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 292924\n max_charge: 73231\n max_discharge: 73231\n min_capacity: 58584.8\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 58584.8\n soc: 0.2\n- - grid\n - !GridModule\n cls_params:\n cost_per_unit_co2: 0.1\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n max_export: 160744\n max_import: 160744\n raise_errors: false\n time_series: !NDArray 'data/cls_params/GridModule/time_series.csv.gz'\n name:\n - grid\n - 0\n state:\n _current_step: 0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_23/microgrid_23.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 9453.6\n running_min_production: 525.2\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 14294\n max_charge: 3574\n max_discharge: 3574\n min_capacity: 2858.8\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 2858.8\n soc: 0.2\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_24/microgrid_24.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 50157.0\n running_min_production: 2786.5\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 103328\n max_charge: 25832\n max_discharge: 25832\n min_capacity: 20665.600000000002\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 20665.600000000002\n soc: 0.2\n- - grid\n - !GridModule\n cls_params:\n cost_per_unit_co2: 0.1\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n max_export: 100314\n max_import: 100314\n raise_errors: false\n time_series: !NDArray 'data/cls_params/GridModule/time_series.csv.gz'\n name:\n - grid\n - 0\n state:\n _current_step: 0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_3/microgrid_3.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 78147.0\n running_min_production: 4341.5\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 102450\n max_charge: 25613\n max_discharge: 25613\n min_capacity: 20490.0\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 20490.0\n soc: 0.2\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_4/microgrid_4.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 65305\n max_charge: 16327\n max_discharge: 16327\n min_capacity: 13061.0\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 13061.0\n soc: 0.2\n- - grid\n - !GridModule\n cls_params:\n cost_per_unit_co2: 0.1\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n max_export: 99625\n max_import: 99625\n raise_errors: false\n time_series: !NDArray 'data/cls_params/GridModule/time_series.csv.gz'\n name:\n - grid\n - 0\n state:\n _current_step: 0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_5/microgrid_5.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 28560.600000000002\n running_min_production: 1586.7\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 1\n initial_step: 0\n max_capacity: 57581\n max_charge: 14396\n max_discharge: 14396\n min_capacity: 11516.2\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 57581\n soc: 1.0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_6/microgrid_6.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.960305168086668\n initial_step: 0\n max_capacity: 179373\n max_charge: 44844\n max_discharge: 44844\n min_capacity: 35874.6\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 172252.8189152099\n soc: 0.960305168086668\n- - grid\n - !GridModule\n cls_params:\n cost_per_unit_co2: 0.1\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n max_export: 98432\n max_import: 98432\n raise_errors: false\n time_series: !NDArray 'data/cls_params/GridModule/time_series.csv.gz'\n name:\n - grid\n - 0\n state:\n _current_step: 0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_7/microgrid_7.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 11798.1\n running_min_production: 655.45\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 1\n initial_step: 0\n max_capacity: 19334\n max_charge: 4834\n max_discharge: 4834\n min_capacity: 3866.8\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 19334\n soc: 1.0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_8/microgrid_8.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 64065.6\n running_min_production: 3559.2000000000003\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.2\n initial_step: 0\n max_capacity: 105583\n max_charge: 26396\n max_discharge: 26396\n min_capacity: 21116.600000000002\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 21116.600000000002\n soc: 0.2\n- - grid\n - !GridModule\n cls_params:\n cost_per_unit_co2: 0.1\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n max_export: 128130\n max_import: 128130\n raise_errors: false\n time_series: !NDArray 'data/cls_params/GridModule/time_series.csv.gz'\n name:\n - grid\n - 0\n state:\n _current_step: 0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/data/scenario/pymgrid25/microgrid_9/microgrid_9.yaml", "content": "!Microgrid\nfinal_step: 8759\ninitial_step: 0\nmodules:\n- - load\n - !LoadModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n raise_errors: false\n time_series: !NDArray 'data/cls_params/LoadModule/time_series.csv.gz'\n name:\n - load\n - 0\n state:\n _current_step: 0\n- - pv\n - !RenewableModule\n cls_params:\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n provided_energy_name: renewable_used\n raise_errors: false\n time_series: !NDArray 'data/cls_params/RenewableModule/time_series.csv.gz'\n name:\n - pv\n - 0\n state:\n _current_step: 0\n- - unbalanced_energy\n - !UnbalancedEnergyModule\n cls_params:\n initial_step: 0\n loss_load_cost: 10\n overgeneration_cost: 1\n raise_errors: false\n name:\n - unbalanced_energy\n - 0\n state:\n _current_step: 0\n- - genset\n - !Genset\n cls_params:\n allow_abortion: true\n co2_per_unit: 2\n cost_per_unit_co2: 0.1\n genset_cost: 0.4\n init_start_up: true\n initial_step: 0\n provided_energy_name: genset_production\n raise_errors: false\n running_max_production: 98996.40000000001\n running_min_production: 5499.8\n start_up_time: 0\n wind_down_time: 0\n name:\n - genset\n - 0\n state:\n _current_status: true\n _current_step: 0\n _goal_status: true\n _steps_until_down: 0\n _steps_until_up: 0\n- - battery\n - !BatteryModule\n cls_params:\n battery_cost_cycle: 0.02\n battery_transition_model: null\n efficiency: 0.9\n init_charge: null\n init_soc: 0.5374787046064285\n initial_step: 0\n max_capacity: 199587\n max_charge: 49897\n max_discharge: 49897\n min_capacity: 39917.4\n raise_errors: false\n name:\n - battery\n - 0\n state:\n _current_step: 0\n current_charge: 107273.76221628326\n soc: 0.5374787046064285\n- - grid\n - !GridModule\n cls_params:\n cost_per_unit_co2: 0.1\n final_step: 8759\n forecast_horizon: 23\n forecaster: oracle\n forecaster_increase_uncertainty: false\n forecaster_relative_noise: false\n initial_step: 0\n max_export: 197992\n max_import: 197992\n raise_errors: false\n time_series: !NDArray 'data/cls_params/GridModule/time_series.csv.gz'\n name:\n - grid\n - 0\n state:\n _current_step: 0\ntrajectory_func: null\n" }, { "path": "src/pymgrid/envs/__init__.py", "content": "from .discrete.discrete import DiscreteMicrogridEnv\nfrom .continuous.continuous import ContinuousMicrogridEnv\n" }, { "path": "src/pymgrid/envs/base/__init__.py", "content": "from .base import BaseMicrogridEnv\n" }, { "path": "src/pymgrid/envs/base/base.py", "content": "import pandas as pd\n\nfrom gym import Env\nfrom gym.spaces import Box, Dict, Tuple, flatten_space, flatten\nfrom abc import abstractmethod\n\nfrom pymgrid import NonModularMicrogrid, Microgrid\nfrom pymgrid.envs.base.skip_init import skip_init\n\n\nclass BaseMicrogridEnv(Microgrid, Env):\n \"\"\"\n Base class for all microgrid environments.\n\n Implements the `OpenAI Gym API `_ for a microgrid;\n inherits from both :class:`.Microgrid` and :class:`gym.Env`.\n\n Parameters\n ----------\n modules : list, Microgrid, NonModularMicrogrid, or int.\n The constructor can be called in three ways:\n\n 1. Passing a list of microgrid modules. This is identical to the :class:`.Microgrid` constructor.\n\n 2. Passing a :class:`.Microgrid` or :class:`.NonModularMicrogrid` instance.\n This will effectively wrap the microgrid instance with the Gym API.\n\n 3. Passing an integer in [0, 25).\n This will be result in loading the corresponding `pymgrid25` benchmark microgrids.\n\n add_unbalanced_module : bool, default True.\n Whether to add an unbalanced energy module to your microgrid. Such a module computes and attributes\n costs to any excess supply or demand.\n Set to True unless ``modules`` contains an :class:`.UnbalancedEnergyModule`.\n\n loss_load_cost : float, default 10.0\n Cost per unit of unmet demand. Ignored if ``add_unbalanced_module=False``.\n\n overgeneration_cost : float, default 2.0\n Cost per unit of excess generation. Ignored if ``add_unbalanced_module=False``.\n\n flat_spaces : bool, default True\n Whether the environment's spaces should be flat.\n\n If True, all continuous spaces are :class:`gym:gym.spaces.Box`.\n\n Otherwise, they are nested :class:`gym:gym.spaces.Dict` of :class:`gym:gym.spaces.Tuple`\n of :class:`gym:gym.spaces.Box`, corresponding to the structure of the ``control`` arg of :meth:`.Microgrid.run`.\n\n trajectory_func : callable or None, default None\n Callable that sets an initial and final step for an episode. ``trajectory_func`` must take two inputs:\n :attr:`.initial_step` and :attr:`.final_step`, and return two integers: the initial and final step for\n that particular episode, respectively. This function will be called every time :meth:`.reset` is called.\n\n If None, :attr:`.initial_step` and :attr:`.final_step` are used to define every episode.\n\n \"\"\"\n\n action_space = None\n 'Space object corresponding to valid actions.'\n\n observation_space = None\n 'Space object corresponding to valid observations.'\n\n def __new__(cls, modules, *args, **kwargs):\n if isinstance(modules, (NonModularMicrogrid, Microgrid)):\n import warnings\n warnings.warn('Initializing an environment with a microgrid will be deprecated in a future version.'\n 'Use from_microgrid() instead.', category=FutureWarning)\n\n instance = cls.from_microgrid(modules, **kwargs)\n\n elif isinstance(modules, int):\n import warnings\n warnings.warn('Initializing an environment with a scenario integer will be deprecated in a future version.'\n 'Use from_scenario() instead.', category=FutureWarning)\n instance = cls.from_scenario(modules, **kwargs)\n\n else:\n return super().__new__(cls)\n\n cls.__init__ = skip_init(cls, cls.__init__)\n return instance\n\n def __init__(self,\n modules,\n add_unbalanced_module=True,\n loss_load_cost=10,\n overgeneration_cost=2,\n reward_shaping_func=None,\n trajectory_func=None,\n flat_spaces=True,\n observation_keys=()\n ):\n\n super().__init__(modules,\n add_unbalanced_module=add_unbalanced_module,\n loss_load_cost=loss_load_cost,\n overgeneration_cost=overgeneration_cost,\n reward_shaping_func=reward_shaping_func,\n trajectory_func=trajectory_func)\n\n self._flat_spaces = flat_spaces\n self.observation_keys = self._validate_observation_keys(observation_keys)\n\n self.action_space = self._get_action_space()\n self.observation_space, self._nested_observation_space = self._get_observation_space()\n\n def _validate_observation_keys(self, keys):\n if not keys:\n return keys\n\n if isinstance(keys, str):\n keys = [keys]\n\n possible_keys = self.state_series().index.get_level_values(-1).unique()\n bad_keys = [key for key in keys if key not in possible_keys]\n\n if bad_keys:\n raise NameError(f'Keys {bad_keys} not found in state.')\n\n return keys\n\n @abstractmethod\n def _get_action_space(self, remove_redundant_actions=False):\n pass\n\n def _get_observation_space(self):\n obs_space = {}\n\n state_series = self.state_series()\n\n for name, module_list in self.modules.iterdict():\n tup = []\n for module_num, module in enumerate(module_list):\n normalized_space = module.observation_space['normalized']\n\n if not self.observation_keys:\n tup.append(normalized_space)\n else:\n try:\n relevant_state_idx = state_series.loc[pd.IndexSlice[name, module_num]].index\n except KeyError:\n continue\n\n locs = [\n relevant_state_idx.get_loc(key) for key in self.observation_keys if key in relevant_state_idx\n ]\n if locs:\n box_slice = Box(\n normalized_space.low[locs],\n normalized_space.high[locs],\n shape=(len(locs), ),\n dtype=normalized_space.dtype\n )\n\n tup.append(box_slice)\n if tup:\n obs_space[name] = Tuple(tup)\n\n obs_space = Dict(obs_space)\n\n return (flatten_space(obs_space) if self._flat_spaces else obs_space), obs_space\n\n def reset(self):\n obs = super().reset()\n return self._get_obs(obs)\n\n def step(self, action, normalized=True):\n \"\"\"\n Run one timestep of the environment's dynamics.\n\n When the end of the episode is reached, you are responsible for calling `reset()`\n to reset the environment's state.\n\n Accepts an action and returns a tuple (observation, reward, done, info).\n\n Parameters\n ----------\n action : dict[str, list[float]]\n An action provided by the agent.\n\n normalized : bool, default True\n Whether the passed action is normalized or not.\n\n Returns\n -------\n observation : dict[str, list[float]] or np.ndarray, shape self.observation_space.shape\n Observations of each module after using the passed ``action``.\n ``observation`` is a nested dict if :attr:`~.flat_spaces` is True and a one-dimensional numpy array\n otherwise.\n\n reward : float\n Reward/cost of running the microgrid. A positive value implies revenue while a negative\n value is a cost.\n\n done : bool\n Whether the microgrid terminates.\n\n info : dict\n Additional information from this step.\n\n \"\"\"\n\n obs, reward, done, info = self.run(action, normalized=normalized)\n\n obs = self._get_obs(obs)\n\n return obs, reward, done, info\n\n def _get_obs(self, obs):\n if self.observation_keys:\n obs = self.state_series(normalized=True).loc[pd.IndexSlice[:, :, self.observation_keys]]\n\n if self._flat_spaces:\n obs = obs.values\n else:\n obs = obs.to_frame().unstack(level=1).T.droplevel(level=1, axis=1).to_dict(orient='list')\n\n elif self._flat_spaces:\n obs = flatten(self._nested_observation_space, obs)\n\n return obs\n\n def render(self, mode=\"human\"):\n \"\"\":meta private:\"\"\"\n raise NotImplementedError\n\n @property\n def unwrapped(self):\n \"\"\":meta private:\"\"\"\n return super().unwrapped\n\n @property\n def flat_spaces(self):\n \"\"\"\n Whether the environment's spaces are flat.\n\n If True, all continuous spaces are :class:`gym:gym.spaces.Box`.\n\n Otherwise, they are nested :class:`gym:gym.spaces.Dict` of :class:`gym:gym.spaces.Tuple`\n of :class:`gym:gym.spaces.Box`, corresponding to the structure of the ``control`` arg of :meth:`Microgrid.run`.\n\n Returns\n -------\n flat_spaces : bool\n Whether the environment's spaces are flat.\n\n \"\"\"\n return self._flat_spaces\n\n @classmethod\n def from_microgrid(cls, microgrid, **kwargs):\n \"\"\"\n Construct an RL environment from a microgrid.\n\n Effectively wraps the microgrid with the environment API.\n\n .. warning::\n Any logs contained in the microgrid will not be ported over to the environment.\n\n Parameters\n ----------\n microgrid : :class:`pymgrid.Microgrid`\n Microgrid to wrap.\n\n Returns\n -------\n env\n The environment, suitable for reinforcement learning.\n\n \"\"\"\n try:\n modules = microgrid.modules\n except AttributeError:\n assert isinstance(microgrid, NonModularMicrogrid)\n return cls.from_nonmodular(microgrid, **kwargs)\n\n kwargs = kwargs.copy()\n\n kwargs['add_unbalanced_module'] = kwargs.pop('add_unbalanced_module', False)\n kwargs['reward_shaping_func'] = kwargs.pop('reward_shaping_func', microgrid.reward_shaping_func)\n kwargs['trajectory_func'] = kwargs.pop('trajectory_func', microgrid.trajectory_func)\n\n return cls(modules.to_tuples(), **kwargs)\n\n @classmethod\n def from_nonmodular(cls, nonmodular, **kwargs):\n microgrid = super().from_nonmodular(nonmodular)\n return cls.from_microgrid(microgrid, **kwargs)\n\n @classmethod\n def from_scenario(cls, microgrid_number=0, **kwargs):\n env = super().from_scenario(microgrid_number=microgrid_number)\n\n if kwargs:\n return cls.from_microgrid(env, **kwargs)\n\n return env\n\n @classmethod\n def load(cls, stream):\n return cls(super().load(stream))\n" }, { "path": "src/pymgrid/envs/base/skip_init.py", "content": "def skip_init(cls, init):\n \"\"\"\n Skip init once on cls, and then revert to original init.\n\n Parameters\n ----------\n cls : Type\n Class to skip init on.\n init : callable\n Original init.\n\n Returns\n -------\n skip_init : callable\n Callable that skips init once.\n\n \"\"\"\n def reset_init(*args, **kwargs):\n cls.__init__ = init\n return reset_init\n" }, { "path": "src/pymgrid/envs/continuous/__init__.py", "content": "" }, { "path": "src/pymgrid/envs/continuous/continuous.py", "content": "from gym.spaces import Dict, Tuple, flatten_space\nfrom warnings import warn\n\nfrom pymgrid.envs.base import BaseMicrogridEnv\n\n\nclass ContinuousMicrogridEnv(BaseMicrogridEnv):\n \"\"\"\n Microgrid environment with a continuous action space.\n\n Implements the `OpenAI Gym API `_ for a microgrid;\n inherits from both :class:`.Microgrid` and :class:`gym.Env`.\n\n Parameters\n ----------\n modules : list, Microgrid, NonModularMicrogrid, or int.\n The constructor can be called in three ways:\n\n 1. Passing a list of microgrid modules. This is identical to the :class:`.Microgrid` constructor.\n\n 2. Passing a :class:`.Microgrid` or :class:`.NonModularMicrogrid` instance.\n This will effectively wrap the microgrid instance with the Gym API.\n\n 3. Passing an integer in [0, 25).\n This will be result in loading the corresponding `pymgrid25` benchmark microgrids.\n\n add_unbalanced_module : bool, default True.\n Whether to add an unbalanced energy module to your microgrid. Such a module computes and attributes\n costs to any excess supply or demand.\n Set to True unless ``modules`` contains an :class:`.UnbalancedEnergyModule`.\n\n loss_load_cost : float, default 10.0\n Cost per unit of unmet demand. Ignored if ``add_unbalanced_module=False``.\n\n overgeneration_cost : float, default 2.0\n Cost per unit of excess generation. Ignored if ``add_unbalanced_module=False``.\n\n flat_spaces : bool, default True\n Whether the environment's spaces should be flat.\n\n If True, all continuous spaces are :class:`gym:gym.spaces.Box`.\n\n Otherwise, they are nested :class:`gym:gym.spaces.Dict` of :class:`gym:gym.spaces.Tuple`\n of :class:`gym:gym.spaces.Box`, corresponding to the structure of the ``control`` arg of :meth:`.Microgrid.run`.\n\n trajectory_func : callable or None, default None\n Callable that sets an initial and final step for an episode. ``trajectory_func`` must take two inputs:\n :attr:`.initial_step` and :attr:`.final_step`, and return two integers: the initial and final step for\n that particular episode, respectively. This function will be called every time :meth:`.reset` is called.\n\n If None, :attr:`.initial_step` and :attr:`.final_step` are used to define every episode.\n\n \"\"\"\n _nested_action_space = None\n\n def _get_nested_action_space(self):\n return Dict({name: Tuple([module.action_space['normalized'] for module in modules_list])\n for name, modules_list in self.fixed.iterdict() if modules_list[0].is_source})\n\n def _get_action_space(self):\n self._nested_action_space = self._get_nested_action_space()\n return flatten_space(self._nested_action_space) if self._flat_spaces else self._nested_action_space\n\n def _get_action(self, action):\n # Action does not have fixed sinks (loads); add those values.\n assert action in self._nested_action_space, 'Action is not in action space.'\n action = action.copy()\n for name, module_list in self.fixed.sinks.iterdict():\n action[name] = [module.to_normalized(-1 * module.max_consumption, act=True) for module in module_list]\n return action\n\n def step(self, action):\n action = self._get_action(action)\n return super().run(action)\n\n def run(self, action, normalized=True):\n warn('run() should not be called directly in environments.')\n return super().run(action, normalized=normalized)\n" }, { "path": "src/pymgrid/envs/discrete/__init__.py", "content": "" }, { "path": "src/pymgrid/envs/discrete/discrete.py", "content": "import yaml\n\nfrom gym.spaces import Discrete\nfrom warnings import warn\n\nfrom pymgrid.algos.priority_list import PriorityListAlgo\nfrom pymgrid.envs.base import BaseMicrogridEnv\n\n\nclass DiscreteMicrogridEnv(BaseMicrogridEnv, PriorityListAlgo):\n \"\"\"\n A discrete env that implements priority lists as actions on a microgrid.\n\n The environment deploys fixed controllable modules to the extent necessary to zero out the net load (load minus\n renewable generation).\n \"\"\"\n\n actions_list: list\n \"\"\"\n List of priority lists.\n \n Each element in this list corresponds to an action in the environment's action space, and defines an order \n in which to deploy fixed controllable modules. Specifically, each action corresponds to a unique priority list, \n itself containing :class:`PriorityListElements<.PriorityListElement>` that represents a particular module's position\n in the deployment order.\n \n Returns\n -------\n actions_list : list of list of :class:`.PriorityListElement`\n List of all priority lists.\n \n \"\"\"\n\n yaml_tag = u\"!DiscreteMicrogridEnv\"\n yaml_loader = yaml.SafeLoader\n yaml_dumper = yaml.SafeDumper\n\n def __init__(self,\n modules,\n add_unbalanced_module=True,\n loss_load_cost=10,\n overgeneration_cost=2,\n reward_shaping_func=None,\n trajectory_func=None,\n flat_spaces=True,\n observation_keys=None,\n remove_redundant_gensets=True\n ):\n super().__init__(modules,\n add_unbalanced_module=add_unbalanced_module,\n loss_load_cost=loss_load_cost,\n overgeneration_cost=overgeneration_cost,\n reward_shaping_func=reward_shaping_func,\n trajectory_func=trajectory_func,\n flat_spaces=flat_spaces,\n observation_keys=observation_keys)\n\n self.action_space, self.actions_list = self._get_action_space(remove_redundant_gensets)\n\n def _get_action_space(self, remove_redundant_gensets=False):\n \"\"\"\n An action here is a priority list - in what order to deploy controllable source modules.\n Compute the total expected load\n It exhausts the entire production of the 0th source module before moving on to the second and the third, etc.\n\n If there are n fixed source modules, then there are 2^{n-1} actions.\n :return:\n \"\"\"\n\n priority_lists = self.get_priority_lists(remove_redundant_gensets)\n\n n_actions = len(priority_lists)\n\n if n_actions > 1000:\n warn(f'Microgrid with {len(priority_lists[0])} fixed source modules defines large action space with '\n f'{n_actions} elements.')\n\n space = Discrete(n_actions)\n\n return space, priority_lists\n\n def _get_action(self, action_num):\n if action_num not in self.action_space:\n raise ValueError(f\" Action {action_num} not in action space {self.action_space}\")\n\n priority_list = list(self.actions_list[action_num])\n\n return self._populate_action(priority_list)\n\n def remove_action(self, action_number):\n \"\"\"\n Remove an action from the action space.\n\n Useful if two actions happen to be redundant in a particular use case.\n\n Parameters\n ----------\n action_number : int\n Index of the action to remove.\n\n \"\"\"\n\n if action_number not in self.action_space:\n raise ValueError('Cannot remove action that is not in the action space!')\n\n self.actions_list.pop(action_number)\n self.action_space = Discrete(self.action_space.n - 1)\n\n def step(self, action):\n \"\"\"\n Run one timestep of the environment's dynamics.\n\n When the end of the episode is reached, you are responsible for calling `reset()`\n to reset the environment's state.\n\n Accepts an action and returns a tuple (observation, reward, done, info).\n\n Parameters\n ----------\n action : int\n An action provided by the agent.\n\n Returns\n -------\n observation : dict[str, list[float]] or np.ndarray, shape self.observation_space.shape\n Observations of each module after using the passed ``action``.\n ``observation`` is a nested dict if :attr:`~.flat_spaces` is True and a one-dimensional numpy array\n otherwise.\n\n reward : float\n Reward/cost of running the microgrid. A positive value implies revenue while a negative\n value is a cost.\n\n done : bool\n Whether the microgrid terminates.\n\n info : dict\n Additional information from this step.\n\n \"\"\"\n self._microgrid_logger.log(action=action)\n microgrid_action = self._get_action(action)\n return super().step(microgrid_action, normalized=False)\n\n def sample_action(self, strict_bound=False, sample_flex_modules=False):\n return self.action_space.sample()\n\n def __repr__(self):\n return f\"DiscreteMicrogridEnv({super().__repr__()}\"\n\n def __str__(self):\n return self.__repr__()\n" }, { "path": "src/pymgrid/forecast/__init__.py", "content": "from .forecaster import get_forecaster, OracleForecaster, GaussianNoiseForecaster, UserDefinedForecaster, NoForecaster\n" }, { "path": "src/pymgrid/forecast/forecaster.py", "content": "import numpy as np\nfrom abc import abstractmethod\n\nfrom pandas.api.types import is_number, is_numeric_dtype\n\nfrom pymgrid.utils.ray import ray_decorator\nfrom pymgrid.utils.space import ModuleSpace\n\n\ndef get_forecaster(forecaster,\n observation_space,\n forecast_shape,\n time_series=None,\n increase_uncertainty=False,\n relative_noise=False):\n \"\"\"\n Get the forecasting function for the time series module.\n\n Parameters\n ----------\n forecaster : callable, float, \"oracle\", or None, default None.\n Function that gives a forecast n-steps ahead.\n\n * If ``callable``, must take as arguments ``(val_c: float, val_{c+n}: float, n: int)``, where\n\n * ``val_c`` is the current value in the time series: ``self.time_series[self.current_step]``\n\n * ``val_{c+n}`` is the value in the time series n steps in the future\n\n * n is the number of steps in the future at which we are forecasting.\n\n The output ``forecast = forecaster(val_c, val_{c+n}, n)`` must have the same sign\n as the inputs ``val_c`` and ``val_{c+n}``.\n\n * If ``float``, serves as a standard deviation for a mean-zero gaussian noise function\n that is added to the true value.\n\n * If ``\"oracle\"``, gives a perfect forecast.\n\n * If ``None``, no forecast.\n\n forecast_shape : int or tuple of int\n Expected shape of forecasts. If an integer, will return forecasts of shape (shape, 1).\n\n observation_space : :class:`ModuleSpace `\n Observation space; used to determine values to pad missing forecasts when we are forecasting past the\n end of the time series.\n\n time_series: ndarray[float] or None, default None.\n The underlying time series.\n\n * If ``callable(forecaster)``, used to validate :class:`.UserDefinedForecaster`.\n * If ``forecaster`` is numeric and ``relative_noise`` is true, the noise standard deviation\n will be defined relative to the mean of the time series.\n\n increase_uncertainty : bool, default False\n Whether to increase uncertainty for farther-out dates if using\n :class:`.GaussianNoiseForecaster`. Ignored otherwise.\n\n relative_noise : bool, default False\n Whether to define noise standard deviation relative to mean of time series if using\n :class:`.GaussianNoiseForecaster`. Ignored otherwise.\n\n\n Returns\n -------\n forecaster : :class:`.Forecaster`\n The forecasting function.\n\n \"\"\"\n\n if forecaster is None:\n return NoForecaster(observation_space, forecast_shape)\n elif isinstance(forecaster, (UserDefinedForecaster, OracleForecaster, GaussianNoiseForecaster)):\n return forecaster\n elif callable(forecaster):\n return UserDefinedForecaster(forecaster, observation_space, forecast_shape, time_series)\n elif forecaster == \"oracle\":\n return OracleForecaster(observation_space, forecast_shape)\n elif is_number(forecaster):\n return GaussianNoiseForecaster(forecaster,\n observation_space,\n forecast_shape,\n time_series=time_series,\n increase_uncertainty=increase_uncertainty,\n relative_noise=relative_noise)\n else:\n raise ValueError(f\"Unable to parse forecaster of type {type(forecaster)}\")\n\n\nclass Forecaster:\n def __init__(self, observation_space, forecast_shape):\n self._observation_space = observation_space\n self._forecast_shaped_space = self._get_forecast_shaped_space(forecast_shape)\n self._fill_arr = (self._observation_space.unnormalized.high + self._observation_space.unnormalized.low) / 2\n\n def _get_forecast_shaped_space(self, shape):\n if len(shape) == 1:\n shape = (*shape, 1)\n elif len(shape) > 2:\n raise ValueError(f'shape must be one- or two-dimensional.')\n\n n_in_forecast = shape[0]*shape[1]\n\n if n_in_forecast:\n unnormalized_low = self._observation_space.unnormalized.low[-n_in_forecast:]\n unnormalized_high = self._observation_space.unnormalized.high[-n_in_forecast:]\n else:\n unnormalized_low = np.array([])\n unnormalized_high = np.array([])\n\n return ModuleSpace(unnormalized_low=unnormalized_low.reshape(shape),\n unnormalized_high=unnormalized_high.reshape(shape),\n shape=shape)\n\n @abstractmethod\n def _forecast(self, val_c, val_c_n, n):\n pass\n\n def _pad(self, forecast, n):\n if forecast.shape[0] == n:\n return forecast\n else:\n pad_amount = n - forecast.shape[0]\n pad = self._fill_arr.reshape((-1, forecast.shape[1]))[-pad_amount:]\n\n if pad.shape[0] < pad_amount:\n raise RuntimeError(\"Attempting to pad a forecast to a value larger than the module's observation space \"\n \"implies.\")\n\n return np.concatenate((forecast, pad))\n\n def full_pad(self, shape, forecast_horizon):\n if forecast_horizon is None:\n return None\n empty_forecast = np.array([]).reshape((0, shape[1]))\n return self._pad(empty_forecast, forecast_horizon)\n\n @ray_decorator\n def _clip(self, forecast):\n lb = self._forecast_shaped_space.unnormalized.low[-forecast.shape[0]:]\n ub = self._forecast_shaped_space.unnormalized.high[-forecast.shape[0]:]\n lt_lb = forecast < lb\n gt_ub = forecast > ub\n\n forecast[lt_lb] = lb[lt_lb]\n forecast[gt_ub] = ub[gt_ub]\n\n return forecast\n\n @property\n def observation_space(self):\n return self._observation_space\n\n @observation_space.setter\n def observation_space(self, value):\n self._observation_space = value\n self._fill_arr = (self._observation_space.unnormalized.high + self._observation_space.unnormalized.low) / 2\n new_shape = (\n int((value.shape[0] - self._forecast_shaped_space.shape[1]) / self._forecast_shaped_space.shape[1]),\n self._forecast_shaped_space.shape[1]\n )\n self._forecast_shaped_space = self._get_forecast_shaped_space(new_shape)\n\n def __eq__(self, other):\n if type(self) != type(other):\n return NotImplemented\n\n return (self._fill_arr == other._fill_arr).all() and \\\n all(v == other.__dict__[k] for k, v in self.__dict__.items() if k != '_fill_arr')\n\n def __call__(self, val_c, val_c_n, n):\n if len(val_c_n.shape) == 1:\n val_c_n = val_c_n.reshape((-1, 1))\n\n if val_c_n.shape[0] > self._forecast_shaped_space.shape[0]:\n raise RuntimeError(f'val_c_n shape {val_c_n.shape} is too large for space {self._forecast_shaped_space.shape}')\n\n forecast = self._forecast(val_c, val_c_n, n)\n\n if forecast is None:\n return None\n else:\n forecast = self._pad(forecast, n)\n forecast = self._clip(forecast)\n assert forecast.shape == (n, val_c_n.shape[1])\n return forecast\n\n def __repr__(self):\n return self.__class__.__name__\n\n\nclass UserDefinedForecaster(Forecaster):\n def __init__(self, forecaster_function, observation_space, forecast_shape, time_series):\n self.is_vectorized_forecaster, self.cast_to_arr = \\\n _validate_callable_forecaster(forecaster_function, time_series)\n\n if not self.is_vectorized_forecaster:\n forecaster_function = vectorize_scalar_forecaster(forecaster_function)\n\n self._forecaster = forecaster_function\n\n super().__init__(observation_space, forecast_shape)\n\n def _cast_to_arr(self, forecast, val_c_n):\n if self.cast_to_arr:\n return np.array(forecast.reshape(val_c_n.shape))\n return forecast\n\n def _forecast(self, val_c, val_c_n, n):\n forecast = self._forecaster(val_c, val_c_n, n)\n return self._cast_to_arr(forecast, val_c_n)\n\n\nclass OracleForecaster(Forecaster):\n def _forecast(self, val_c, val_c_n, n):\n return val_c_n\n\n\nclass GaussianNoiseForecaster(Forecaster):\n def __init__(self,\n noise_std,\n observation_space,\n forecast_shape,\n time_series=None,\n increase_uncertainty=False,\n relative_noise=False):\n super().__init__(observation_space, forecast_shape)\n\n self.input_noise_std = noise_std\n self.increase_uncertainty = increase_uncertainty\n self.relative_noise = relative_noise\n\n self._noise_size = self._forecast_shaped_space.shape\n self._noise_std = self._get_noise_std(time_series)\n\n def _get_noise_std(self, time_series):\n scalar_val = self.input_noise_std\n if self.relative_noise:\n if time_series is None:\n raise ValueError('Must pass time_series if relative_noise is True.')\n scalar_val *= np.abs(time_series.mean())\n\n if self.increase_uncertainty:\n return scalar_val * np.outer(\n 1 + np.log(1 + np.arange(self._noise_size[0])),\n np.ones(self._noise_size[-1])\n )\n else:\n return scalar_val\n\n def _get_noise(self, size):\n try:\n return np.random.normal(scale=self._noise_std, size=size)\n except ValueError:\n noise_std = self._noise_std[:size[0], :]\n if noise_std.shape != size:\n raise RuntimeError(f'Cannot broadcast shapes {self._noise_std.shape} and {size}.')\n\n return np.random.normal(scale=noise_std, size=size)\n\n def _forecast(self, val_c, val_c_n, n):\n return val_c_n + self._get_noise(val_c_n.shape).reshape(val_c_n.shape)\n\n @property\n def noise_std(self):\n return self._noise_std\n\n @noise_std.setter\n def noise_std(self, value):\n pass\n\n def __repr__(self):\n return f'GaussianNoiseForecaster(noise_std={self.input_noise_std}, ' \\\n f'increase_uncertainty={self.increase_uncertainty})'\n\n\nclass NoForecaster(Forecaster):\n def _forecast(self, val_c, val_c_n, n):\n return None\n\n\ndef _validate_callable_forecaster(forecaster, time_series):\n val_c = time_series[0]\n n = np.random.randint(2, len(time_series))\n vector_true_forecast = time_series[:n]\n try:\n cast_to_arr = _validate_vectorized_forecaster(forecaster, val_c, vector_true_forecast, n)\n is_vectorized_forecaster = True\n except NotImplementedError:\n scalar_true_forecast = vector_true_forecast[-1]\n _validate_scalar_forecaster(forecaster, val_c, scalar_true_forecast, n)\n is_vectorized_forecaster = False\n cast_to_arr = False\n\n return is_vectorized_forecaster, cast_to_arr\n\n\ndef _validate_vectorized_forecaster(forecaster, val_c, vector_true_forecast, n):\n try:\n vectorized_forecast = forecaster(val_c, vector_true_forecast, n)\n except Exception as e:\n raise NotImplementedError(\"Unable to call forecaster with vector inputs. \"\n f\"\\nFunc call forecaster(val_c={val_c}, val_c_n={vector_true_forecast}, n={n})\"\n f\"\\nraised {type(e).__name__}: {e}\") from e\n else:\n # vectorized function call succeeded\n if not hasattr(vectorized_forecast, 'size'):\n vectorized_forecast = np.array(vectorized_forecast)\n cast_to_arr = True\n else:\n cast_to_arr = False\n try:\n vectorized_forecast = vectorized_forecast.reshape(vector_true_forecast.shape)\n except ValueError:\n raise ValueError(f\"Forecaster output of shape {vectorized_forecast.shape} cannot be casted to \"\n f\"necessary forecast shape {vector_true_forecast.shape}\")\n\n for i, (forecast, true_forecast) in enumerate(zip(vectorized_forecast, vector_true_forecast)):\n try:\n _validate_forecasted_value(forecast, true_forecast, val_c, n)\n except Exception as e:\n raise type(e)(f\"Failed validating forecast at position {i} due to exception {e}\") from e\n\n return cast_to_arr\n\n\ndef _validate_scalar_forecaster(forecaster, val_c, scalar_true_forecast, n):\n try:\n scalar_forecast = forecaster(val_c, scalar_true_forecast, n)\n except Exception as e_scalar:\n raise ValueError(\"Unable to call forecaster with scalar inputs. \"\n f\"\\nFunc call forecaster(val_c={val_c}, val_c_plus_n={scalar_true_forecast}, n={n})\"\n f\"\\nraised {type(e_scalar).__name__}: {e_scalar}\") from e_scalar\n else: # scalar function call succeeded\n # check shape\n try:\n assert is_number(scalar_forecast)\n scalar_forecast_item = scalar_forecast\n except AssertionError:\n try:\n scalar_forecast_item = scalar_forecast.item()\n except (ValueError, AttributeError):\n raise ValueError(\"Unable to validate forecaster. Forecaster must return scalar output with scalar \"\n f\"input but returned {scalar_forecast}\")\n\n _validate_forecasted_value(scalar_forecast_item, scalar_true_forecast, val_c, n)\n\n\ndef _validate_forecasted_value(forecaster_output, true_forecast, val_c, n):\n if not is_numeric_dtype(np.array(forecaster_output)):\n raise TypeError(\n \"Unable to validate forecaster. Forecaster must return numeric np.ndarray or number but returned \"\n f\"output of type {np.array(forecaster_output).dtype}: {forecaster_output}\")\n elif not (forecaster_output * true_forecast >= 0):\n raise ValueError(\n \"Unable to validate forecaster. Forecaster must return output of same sign (or zero) as \"\n f\"input but returned output {forecaster_output} with inputs \"\n f\"val_c={val_c}, val_c_plus_n={true_forecast}, n={n}\")\n\n\ndef vectorize_scalar_forecaster(forecaster):\n def vectorized(val_c, val_c_n, n):\n if n != len(val_c_n):\n raise ValueError(f\"Incompatible true values length ({val_c_n}) to forecast {n}-steps ahead.\")\n vectorized_output = np.array([forecaster(val_c, val_c_n_i, n_i) for n_i, val_c_n_i in enumerate(val_c_n)])\n try:\n shape = (-1, vectorized_output.shape[1])\n except IndexError:\n shape = (-1, 1)\n return vectorized_output.reshape(shape)\n\n return vectorized\n" }, { "path": "src/pymgrid/microgrid/__init__.py", "content": "DEFAULT_HORIZON = 23\n\nfrom .microgrid import Microgrid\n" }, { "path": "src/pymgrid/microgrid/microgrid.py", "content": "import numpy as np\nimport pandas as pd\nimport yaml\n\nfrom copy import deepcopy\nfrom warnings import warn\n\nfrom pymgrid.microgrid import DEFAULT_HORIZON\nfrom pymgrid.modules import ModuleContainer, UnbalancedEnergyModule\nfrom pymgrid.utils.logger import ModularLogger\nfrom pymgrid.microgrid.utils.step import MicrogridStep\nfrom pymgrid.utils.serialize import add_numpy_pandas_representers, add_numpy_pandas_constructors, dump_data\nfrom pymgrid.utils.space import MicrogridSpace\n\nclass Microgrid(yaml.YAMLObject):\n \"\"\"\n Microgrid class, used to define and simulate an environment with a variety of modules.\n\n Parameters\n ----------\n modules : List[Union[Tuple[str, BaseMicrogridModule], BaseMicrogridModule]]\n List of modules that define the microgrid. The list can contain either/both microgrid modules -- subclasses of\n ``BaseMicrogridModule`` -- and tuples of length two, which must contain a string defining the name of the module\n followed by the module.\n\n ``Microgrid`` groups modules into lists based on their names. If no name is given (e.g. an element in ``modules``\n is a subclass of ``BaseMicrogridModule`` and not a tuple, then the name is defined to be\n ``module.__class__.name[0]``. Modules are then exposed (within lists) by name as attributes to the microgrid.\n See below for an example.\n\n .. note::\n The constructor copies modules passed to it.\n\n add_unbalanced_module : bool, default True.\n Whether to add an unbalanced energy module to your microgrid. Such a module computes and attributes\n costs to any excess supply or demand.\n Set to True unless ``modules`` contains an ``UnbalancedEnergyModule``.\n\n loss_load_cost : float, default 10.0\n Cost per unit of unmet demand. Ignored if ``add_unbalanced_module=False``.\n\n overgeneration_cost : float, default 2.0\n Cost per unit of excess generation. Ignored if ``add_unbalanced_module=False``.\n\n reward_shaping_func : callable or None, default None\n Function that allows for custom definition of the microgrid's reward/cost.\n If None, the cost of each step is simply the total cost of all modules.\n For example, you may define a function that defines the cost as only being the loss load, or only\n the pv curtailment.\n\n trajectory_func : callable or None, default None\n Callable that sets an initial and final step for an episode. ``trajectory_func`` must take two inputs:\n :attr:`.initial_step` and :attr:`.final_step`, and return two integers: the initial and final step for\n that particular episode, respectively. This function will be called every time :meth:`.reset` is called.\n\n If None, :attr:`.initial_step` and :attr:`.final_step` are used to define every episode.\n\n\n Examples\n --------\n >>> from pymgrid import Microgrid\n >>> from pymgrid.modules import LoadModule, RenewableModule, GridModule, BatteryModule\n >>> timesteps = 10\n >>> load = LoadModule(10*np.random.rand(timesteps), loss_load_cost=10.)\n >>> pv = RenewableModule(10*np.random.rand(timesteps))\n >>> grid = GridModule(max_import=100, max_export=10, time_series=np.random.rand(timesteps, 3))\n >>> battery_0 = BatteryModule(min_capacity=0, \\\n max_capacity=100, \\\n max_charge=1,\\\n max_discharge=10, \\\n efficiency=0.9, \\\n init_soc=0.5)\n >>> battery_1 = BatteryModule(min_capacity=1, \\\n max_capacity=20, \\\n max_charge=5, \\\n max_discharge=10, \\\n efficiency=0.9, \\\n init_soc=0.5)\n\n >>> microgrid = Microgrid(modules=[load, ('pv', pv), grid, battery_0, battery_1])\n >>> # The modules are now available as attributes. The exception to this is `load`, which is an exposed method.\n >>> print(microgrid.pv)\n [RenewableModule(time_series=, raise_errors=False, forecaster=NoForecaster, forecast_horizon=0, forecaster_increase_uncertainty=False, provided_energy_name=renewable_used)]\n >>> print(microgrid.grid)\n [GridModule(max_import=100, max_export=10)]\n >>> print(microgrid.grid.item()) # Return the module instead of a list containing the module, if list has one item.\n GridModule(max_import=100, max_export=10)\n >>> for j, battery in enumerate(microgrid.battery):\n >>> print(f\"Battery {j}: {battery}\")\n Battery 0: BatteryModule(min_capacity=0, max_capacity=100, max_charge=1, max_discharge=10, efficiency=0.9, battery_cost_cycle=0.0, battery_transition_model=None, init_charge=None, init_soc=0.5, raise_errors=False)\n Battery 1: BatteryModule(min_capacity=1, max_capacity=20, max_charge=5, max_discharge=10, efficiency=0.9, battery_cost_cycle=0.0, battery_transition_model=None, init_charge=None, init_soc=0.5, raise_errors=False)\n\n \"\"\"\n\n yaml_tag = u\"!Microgrid\"\n \"\"\"Tag used for yaml serialization.\"\"\"\n yaml_dumper = yaml.SafeDumper\n yaml_loader = yaml.SafeLoader\n\n def __init__(self,\n modules,\n add_unbalanced_module=True,\n loss_load_cost=10.,\n overgeneration_cost=2.,\n reward_shaping_func=None,\n trajectory_func=None):\n\n self._modules = self._get_module_container(modules,\n add_unbalanced_module,\n loss_load_cost,\n overgeneration_cost)\n\n # TODO (ahalev) transform envs to wrappers, and remove microgrid from attr names)\n self.microgrid_action_space = MicrogridSpace(\n self._modules.get_attrs('action_space', 'module_type', as_pandas=False), 'act'\n )\n self.microgrid_observation_space = MicrogridSpace(\n self._modules.get_attrs('observation_space', as_pandas=False), 'obs'\n )\n\n self._initial_step = self._get_module_initial_step()\n self._final_step = self._get_module_final_step()\n\n self.reward_shaping_func = reward_shaping_func\n self.trajectory_func = self._check_trajectory_func(trajectory_func)\n\n self._balance_logger = ModularLogger()\n self._microgrid_logger = ModularLogger() # log additional information.\n\n def _get_unbalanced_energy_module(self,\n loss_load_cost,\n overgeneration_cost):\n\n return UnbalancedEnergyModule(raise_errors=False,\n loss_load_cost=loss_load_cost,\n overgeneration_cost=overgeneration_cost\n )\n\n def _get_module_container(self, modules, add_unbalanced_module, loss_load_cost, overgeneration_cost):\n \"\"\"\n Types of _modules:\n Fixed source: provides energy to the microgrid.\n When queried, the microgrid must absorb said energy.\n Example: battery (when discharging), grid (when importing from)\n\n Flex source: provides energy to the microgrid.\n Can retain excess energy and not send to microgrid.\n Queried last, in the hopes of balancing other _modules.\n Example: pv with ability to curtail pv\n\n Fixed sink: absorbs energy from the microgrid.\n When queried, the microgrid must send it said energy.\n Example: load, grid (when exporting to)\n\n Flexible sink: absorbs energy from the microgrid.\n Can absorb excess energy from the microgrid.\n Queried last, in the hopes of balancing other _modules.\n Example: dispatchable load\n\n Note that _modules can act as both sources and sinks (batteries, grid), but cannot be both\n fixed and flexible.\n\n :return:\n \"\"\"\n modules = deepcopy(modules)\n\n if not pd.api.types.is_list_like(modules):\n raise TypeError(\"modules must be list-like of modules.\")\n\n if add_unbalanced_module:\n modules.append(self._get_unbalanced_energy_module(loss_load_cost, overgeneration_cost))\n\n return ModuleContainer(modules)\n\n def _check_trajectory_func(self, trajectory_func):\n if trajectory_func is None:\n return trajectory_func\n\n if not callable(trajectory_func):\n raise TypeError('trajectory_func must be callable.')\n\n output = trajectory_func(self._initial_step, self._final_step)\n\n try:\n initial_step, final_step = output\n if not (isinstance(initial_step, int) and isinstance(final_step, int)):\n raise ValueError\n except (TypeError, ValueError):\n raise TypeError(f'trajectory func must return two integer values, not {output}')\n\n if initial_step < self._initial_step:\n raise ValueError(f'trajectory_func returned initial_step value ({initial_step}) less than env\\'s initial '\n f'step: ({self._initial_step})')\n\n if final_step > self._final_step:\n raise ValueError(f'trajectory_func returned final_step value ({final_step}) greater than env\\'s final step:'\n f' ({self._final_step})')\n\n if initial_step >= final_step:\n raise ValueError(f'trajectory_func returned values ({initial_step}, {final_step}) such that initial_step'\n f'was greater than or equal to final_step.')\n\n return trajectory_func\n\n def reset(self):\n \"\"\"\n Reset the microgrid and flush the log.\n\n Returns\n -------\n dict[str, list[float]]\n Observations from resetting the modules as well as the flushed balance log.\n \"\"\"\n self._set_trajectory()\n return {\n **{name: [module.reset() for module in module_list] for name, module_list in self.modules.iterdict()},\n **{\"balance\": self._balance_logger.flush(),\n \"other\": self._microgrid_logger.flush()}\n }\n\n def _set_trajectory(self):\n if self.trajectory_func is not None:\n initial_step, final_step = self.trajectory_func(self._initial_step, self._final_step)\n self._set_initial_step(initial_step, modules_only=True)\n self._set_final_step(final_step, modules_only=True)\n\n def run(self, control, normalized=True):\n \"\"\"\n\n Run the microgrid for a single step.\n\n Parameters\n ----------\n control : dict[str, list[float]]\n Actions to pass to each fixed module.\n normalized : bool, default True\n Whether ``control`` is a normalized value or not. If not, each module de-normalizes its respective action.\n\n Returns\n -------\n observation : dict[str, list[float]]\n Observations of each module after using the passed ``control``.\n reward : float\n Reward/cost of running the microgrid. A positive value implies revenue while a negative\n value is a cost.\n done : bool\n Whether the microgrid terminates.\n info : dict\n Additional information from this step.\n\n \"\"\"\n control_copy = control.copy()\n microgrid_step = MicrogridStep(reward_shaping_func=self.reward_shaping_func, cost_info=self.get_cost_info())\n\n for name, modules in self.fixed.iterdict():\n for module in modules:\n microgrid_step.append(name, *module.step(0.0, normalized=False))\n\n fixed_provided, fixed_consumed, _, _ = microgrid_step.balance()\n log_dict = self._get_log_dict(fixed_provided, fixed_consumed, prefix='fixed')\n\n for name, modules in self.controllable.iterdict():\n try:\n module_controls = control_copy.pop(name)\n except KeyError:\n raise ValueError(f'Control for module \"{name}\" not found. Available controls:\\n\\t{control.keys()}')\n else:\n try:\n _zip = zip(modules, module_controls)\n except TypeError:\n _zip = zip(modules, [module_controls])\n\n for module, _control in _zip:\n module_step = module.step(_control, normalized=normalized) # obs, reward, done, info.\n microgrid_step.append(name, *module_step)\n\n provided, consumed, _, _ = microgrid_step.balance()\n difference = provided - consumed # if difference > 0, have an excess. Try to use flex sinks to dissapate\n # otherwise, insufficient. Use flex sources to make up\n\n log_dict = self._get_log_dict(provided-fixed_provided, consumed-fixed_consumed, log_dict=log_dict, prefix='controllable')\n\n if len(control_copy) > 0:\n warn(f'\\nIgnoring the following keys in passed control:\\n {list(control_copy.keys())}')\n\n if difference > 0:\n energy_excess = difference\n for name, modules in self.flex.iterdict():\n for module in modules:\n if not module.is_sink:\n sink_amt = 0.0\n elif module.max_consumption < energy_excess: # module cannot dissapate all excess energy\n sink_amt = -1.0*module.max_consumption\n else:\n sink_amt = -1.0 * energy_excess\n\n module_step = module.step(sink_amt, normalized=False)\n microgrid_step.append(name, *module_step)\n energy_excess += sink_amt\n\n else:\n energy_needed = - difference\n for name, modules in self.flex.iterdict():\n for module in modules:\n if not module.is_source:\n source_amt = 0.0\n elif module.max_production < energy_needed: # module cannot provide sufficient energy\n source_amt = module.max_production\n else:\n source_amt = energy_needed\n\n module_step = module.step(source_amt, normalized=False)\n microgrid_step.append(name, *module_step)\n energy_needed -= source_amt\n\n provided, consumed, reward, shaped_reward = microgrid_step.balance()\n log_dict = self._get_log_dict(provided, consumed, log_dict=log_dict, prefix='overall')\n\n self._balance_logger.log(reward=reward, shaped_reward=shaped_reward, **log_dict)\n\n if not np.isclose(provided, consumed):\n raise RuntimeError('Microgrid modules unable to balance energy production with consumption.\\n'\n '')\n\n return microgrid_step.output()\n\n def _get_log_dict(self, provided_energy, absorbed_energy, log_dict=None, prefix=None):\n _log_dict = dict(provided_to_microgrid=provided_energy, absorbed_from_microgrid=absorbed_energy)\n _log_dict = {(prefix + '_' + k if prefix is not None else k): v for k, v in _log_dict.items()}\n if log_dict:\n _log_dict.update(log_dict)\n return _log_dict\n\n def get_cost_info(self):\n return self._modules.get_attrs('production_marginal_cost', 'absorption_marginal_cost', as_pandas=False)\n\n def sample_action(self, strict_bound=False, sample_flex_modules=False):\n \"\"\"\n Get a random action within the microgrid's action space.\n\n Parameters\n ----------\n strict_bound : bool, default False\n If True, choose actions that is guaranteed to satisfy self.max_consumption and\n self.max_production bounds. Otherwise selects action from min_act and min_act, which may not satisfy\n instantaneous bounds.\n sample_flex_modules : bool, default false\n Whether to sample the flex modules in the microgrid.\n ``run`` does not expect actions for flex modules.\n\n Returns\n -------\n\n dict[str, list[float]]\n Random action in the action space.\n\n \"\"\"\n\n module_iterator = self._modules.to_dict() if sample_flex_modules else self._modules.controllable.to_dict()\n return {module_name: [module.sample_action(strict_bound=strict_bound) for module in module_list]\n for module_name, module_list in module_iterator.items()\n if module_list[0].action_space.shape[0]}\n\n def get_empty_action(self, sample_flex_modules=False):\n \"\"\"\n Get an action for the microgrid with no values set.\n\n Values are all ``None``; every ``None`` value should be replaced before passing an action to ``run``.\n\n Parameters\n ----------\n sample_flex_modules : bool, default false\n Whether to sample the flex modules in the microgrid.\n ``run`` does not expect actions for flex modules.\n\n Returns\n -------\n\n dict[str, list[None]]\n Empty action.\n\n \"\"\"\n module_iterator = self._modules.to_dict() if sample_flex_modules else self._modules.controllable.to_dict()\n\n return {module_name: [None]*len(module_list) for module_name, module_list in module_iterator.items()\n if module_list[0].action_space.shape[0]}\n\n def to_normalized(self, data_dict, act=False, obs=False):\n \"\"\"\n Normalize an action or observation.\n\n Parameters\n ----------\n data_dict : dict[str, list[int]]\n Action or observation to normalize. Dictionary keys are names of the modules while dictionary values\n are lists containing an action corresponding to all modules with that name.\n act : bool, default False\n Set to True if you are normalizing an action.\n obs : bool, default False\n Set to True if you are normalizing an observation.\n\n Returns\n -------\n dict[str, list[float]]\n Normalized action.\n \"\"\"\n assert act + obs == 1, 'One of act or obs must be True but not both.'\n return {module_name: [module.to_normalized(value, act=act, obs=obs) for module, value in zip(module_list, data_dict[module_name])]\n for module_name, module_list in self._modules.iterdict() if module_name in data_dict}\n\n def from_normalized(self, data_dict, act=False, obs=False):\n \"\"\"\n De-normalize an action or observation.\n\n Parameters\n ----------\n data_dict : dict[str, list[int]]\n Action or observation to de-normalize. Dictionary keys are names of the modules while dictionary values\n are lists containing an action corresponding to all modules with that name.\n act : bool, default False\n Set to True if you are de-normalizing an action.\n obs : bool, default False\n Set to True if you are de-normalizing an observation.\n\n Returns\n -------\n dict[str, list[float]]\n De-normalized action.\n \"\"\"\n assert act + obs == 1, 'One of act or obs must be True but not both.'\n return {module_name: [module.from_normalized(value, act=act, obs=obs) for module, value in zip(module_list, data_dict[module_name])]\n for module_name, module_list in self._modules.iterdict() if module_name in data_dict}\n\n def get_log(self, as_frame=True, drop_singleton_key=False):\n \"\"\"\n\n Collect a log of controls and responses of the microgrid.\n\n Parameters\n ----------\n as_frame : bool, default True\n Whether to return the log as a pd.DataFrame. If False, returns a nested dict.\n drop_singleton_key : bool, default False\n Whether to drop index level enumerating the modules by name if each module name has only one module.\n Ignored otherwise.\n\n Returns\n -------\n pd.DataFrame or dict\n\n \"\"\"\n _log_dict = dict()\n for name, modules in self._modules.iterdict():\n for j, module in enumerate(modules):\n for key, value in module.log_dict().items():\n _log_dict[(name, j, key)] = value\n\n for key, value in self._balance_logger.to_dict().items():\n _log_dict[('balance', 0, key)] = value\n\n for key, value in self._microgrid_logger.items():\n _log_dict[(key, 0, '')] = value\n\n col_names = ['module_name', 'module_number', 'field']\n\n df = pd.DataFrame(_log_dict, index=pd.RangeIndex(start=self.initial_step, stop=self.current_step))\n df.columns = pd.MultiIndex.from_tuples(df.columns.to_list(), names=col_names)\n\n if drop_singleton_key:\n df.columns = df.columns.remove_unused_levels()\n\n if as_frame:\n return df\n\n return df.to_dict()\n\n def set_forecaster(self,\n forecaster,\n forecast_horizon=DEFAULT_HORIZON,\n forecaster_increase_uncertainty=False,\n forecaster_relative_noise=False):\n \"\"\"\n Set the forecaster for timeseries modules in the microgrid.\n\n You may either pass in a single value for ``forecaster`` to apply the same forecasting logic to all timeseries\n modules, or pass key-value pairs in a ``dict`` to set the forecaster for specific modules. In the latter case,\n you may also set different forecasters for each named module. See :meth:`.get_forecaster` for additional details\n on setting forecasters.\n\n forecaster : callable, float, \"oracle\", None, or dict.\n Function that gives a forecast n-steps ahead.\n\n * If ``callable``, must take as arguments ``(val_c: float, val_{c+n}: float, n: int)``, where\n\n * ``val_c`` is the current value in the time series: ``self.time_series[self.current_step]``\n\n * ``val_{c+n}`` is the value in the time series n steps in the future\n\n * n is the number of steps in the future at which we are forecasting.\n\n The output ``forecast = forecaster(val_c, val_{c+n}, n)`` must have the same sign\n as the inputs ``val_c`` and ``val_{c+n}``.\n\n * If ``float``, serves as a standard deviation for a mean-zero gaussian noise function\n that is added to the true value.\n\n * If ``\"oracle\"``, gives a perfect forecast.\n\n * If ``None``, no forecast.\n\n * If ``dict``, must contain key-value pairs of the form ``module_name: forecaster``.\n Will set the forecaster of the module corresponding to ``module_name`` using the logic above.\n\n\n forecast_horizon : int\n Number of steps in the future to forecast. If forecaster is None, this parameter is ignored and the resultant\n horizon will be zero.\n\n forecaster_increase_uncertainty : bool, default False\n Whether to increase uncertainty for farther-out dates if using a GaussianNoiseForecaster. Ignored otherwise.\n\n forecaster_relative_noise : bool, default False\n Whether to define noise standard deviation relative to mean of time series if using\n :class:`.GaussianNoiseForecaster`. Ignored otherwise.\n\n \"\"\"\n if isinstance(forecaster, dict):\n for module_name, _forecaster in forecaster.items():\n if module_name not in self._modules.names():\n raise NameError(f'Unrecognized module {module_name}.')\n\n try:\n self._modules[module_name].set_forecaster(\n _forecaster,\n forcast_horizon=forecast_horizon,\n forecaster_increase_uncertainty=forecaster_increase_uncertainty,\n forecaster_relative_noise=forecaster_relative_noise\n )\n except AttributeError:\n pass\n else:\n for module in self._modules.iterlist():\n try:\n module.set_forecaster(\n forecaster,\n forecast_horizon=forecast_horizon,\n forecaster_increase_uncertainty=forecaster_increase_uncertainty,\n forecaster_relative_noise=forecaster_relative_noise\n )\n except AttributeError:\n pass\n\n def get_forecast_horizon(self):\n \"\"\"\n Get the forecast horizon of timeseries modules contained in the microgrid.\n\n Returns\n -------\n int\n The forecast horizon.\n\n Raises\n ------\n ValueError\n If horizons between modules are inconsistent.\n\n \"\"\"\n horizons = []\n for module in self._modules.iterlist():\n try:\n horizons.append(module.forecast_horizon)\n except AttributeError:\n pass\n\n if len(horizons) == 0:\n warn(f\"No forecast horizon found in microgrid.modules. Using default horizon {DEFAULT_HORIZON}\")\n return DEFAULT_HORIZON\n elif not np.min(horizons) == np.max(horizons):\n raise ValueError(f\"Mismatched forecast_horizons found: {horizons}\")\n\n return horizons[0]\n\n def set_module_attr(self, attr_name, value):\n \"\"\"\n Set the value of an attribute in all modules containing that attribute.\n\n Does nothing in modules not already containing the attribute.\n\n Parameters\n ----------\n attr_name : str\n Name of the module attribute.\n value : any\n Value to set the attribute to.\n\n Raises\n ------\n AttributeError\n If no module has the attribute.\n\n \"\"\"\n set_at_least_one = False\n\n for module in self._modules.iterlist():\n if not hasattr(module, attr_name):\n continue\n\n setattr(module, attr_name, value)\n set_at_least_one = True\n\n if not set_at_least_one:\n raise AttributeError(f\"No module has attribute '{attr_name}'.\")\n\n @property\n def current_step(self):\n \"\"\"\n Current step of underlying modules.\n\n Returns\n -------\n current_step : int\n Current step.\n \"\"\"\n return self._modules.get_attrs('current_step', unique=True).item()\n\n @property\n def initial_step(self):\n \"\"\"\n Initial step at which to start underlying timeseries data.\n\n The step to which :attr:`.current_step` is reset to when calling :meth:`.reset`.\n\n Returns\n -------\n initial_step : int\n Initial step.\n \"\"\"\n return self._initial_step\n\n def _get_module_initial_step(self):\n initial_step = self.modules.get_attrs('initial_step', unique=True)\n try:\n return initial_step.item()\n except ValueError:\n if initial_step.empty:\n return 0\n\n @initial_step.setter\n def initial_step(self, value):\n self._set_initial_step(value)\n\n def _set_initial_step(self, value, modules_only=False):\n self.set_module_attr('initial_step', value)\n if not modules_only:\n self._initial_step = self._get_module_initial_step()\n\n @property\n def final_step(self):\n \"\"\"\n Final step of underlying timeseries data.\n\n Returns\n -------\n final_step : int\n Final step.\n \"\"\"\n return self._final_step\n\n def _get_module_final_step(self):\n final_step = self.modules.get_attrs('final_step', unique=True)\n try:\n return final_step.item()\n except ValueError:\n if final_step.empty:\n return np.inf\n\n @final_step.setter\n def final_step(self, value):\n self._set_final_step(value)\n\n def _set_final_step(self, value, modules_only=False):\n self.set_module_attr('final_step', value)\n if not modules_only:\n self._final_step = self._get_module_final_step()\n\n @property\n def modules(self):\n \"\"\"\n View of the module container.\n\n Returns\n -------\n modules : :class:`pymgrid.modules.module_container.ModuleContainer`\n View of the container.\n\n \"\"\"\n return self._modules\n\n def state_dict(self, normalized=False):\n \"\"\"\n State of the microgrid as a dict.\n\n Keys are module names and values are lists of state dicts for all modules with said name.\n\n Parameters\n ----------\n normalized : bool, default False\n Whether to return a dict of normalized values.\n\n Returns\n -------\n state_dict : dict[str, list[dict]]\n State of the microgrid as a nested dict.\n\n \"\"\"\n return {name: [\n module.state_dict(normalized=normalized) for module in modules\n ] for name, modules in self._modules.iterdict()}\n\n @property\n def log(self):\n \"\"\"\n Microgrid's log as a DataFrame.\n\n This is equivalent to `:meth:`get_log`.\n\n Returns\n -------\n log : pd.DataFrame\n The log of the microgrid.\n\n \"\"\"\n return self.get_log()\n\n def state_series(self, normalized=False):\n \"\"\"\n State of the microgrid as a pandas Series.\n\n Three are three levels in the MultiIndex: ``microgrid_name``, ``microgrid_number``\n (relative to each ``microgrid_name``) and state key name.\n\n Parameters\n ----------\n normalized : bool, default False\n Whether to return a Series of normalized values.\n\n Returns\n -------\n state : pd.Series\n State of the microgrid as a pandas Series..\n\n \"\"\"\n return pd.Series(\n {\n (name, num, key): value\n for name, sd_list in self.state_dict(normalized=normalized).items()\n for num, sd in enumerate(sd_list)\n for key, value in sd.items()\n }\n )\n\n @property\n def fixed(self):\n \"\"\"\n List of all fixed modules in the microgrid.\n\n Returns\n -------\n list of modules\n \"\"\"\n return self._modules.fixed\n\n @property\n def flex(self):\n \"\"\"\n List of all flex modules in the microgrid.\n\n Returns\n -------\n list\n The list of modules\n \"\"\"\n return self._modules.flex\n\n @property\n def controllable(self):\n \"\"\"\n List of all controllable modules in the microgrid.\n\n Returns\n -------\n list of modules\n \"\"\"\n return self._modules.controllable\n\n @property\n def module_list(self):\n \"\"\"\n List of all modules in the microgrid.\n\n Returns\n -------\n list\n The list of modules\n\n \"\"\"\n return self._modules.to_list()\n\n @property\n def n_modules(self):\n \"\"\"\n Number of modules in the microgrid.\n\n Returns\n -------\n int\n \"\"\"\n return len(self._modules)\n\n def dump(self, stream=None):\n \"\"\"\n Save a microgrid to a YAML buffer.\n\n Supports both strings of YAML or storing YAML in a path-like object.\n\n Parameters\n ----------\n stream : file-like object or None, default None\n Stream to save the YAML document. If None, returns the document instead.\n\n Returns\n -------\n str or None :\n Returns the YAMl document as a string if ``stream=None``. Returns None otherwise\n\n .. note::\n\n ``dump`` handles the serialization of array-like objects (e.g. time series and logs) differently depending\n on the value of ``stream``. If ``stream is None``, array-like objects are serialized inline. If ``stream`` is\n a stream to a file-like object, however, array-like objects will be serialized as `.csv.gz` files in a\n directory relative to ``stream``, and the relative locations stored inline in the YAML file. For an example of\n this behavior, see `data/scenario/pymgrid25/microgrid_0`.\n\n \"\"\"\n return yaml.safe_dump(self, stream=stream)\n\n @classmethod\n def load(cls, stream):\n \"\"\"\n Load a microgrid from a yaml buffer.\n\n Supports both strings of YAML or YAML stored in a path-like object.\n\n Parameters\n ----------\n stream : str or file-like object\n YAML document. Can be either a string of loaded YAML or a stream to a local file containing a YAML document.\n\n Returns\n -------\n Microgrid : the loaded microgrid.\n\n \"\"\"\n return yaml.safe_load(stream)\n\n @classmethod\n def to_yaml(cls, dumper, data):\n \"\"\"\n :meta private:\n \"\"\"\n add_numpy_pandas_representers()\n return dumper.represent_mapping(cls.yaml_tag, data.serialize(dumper.stream), flow_style=cls.yaml_flow_style)\n\n @classmethod\n def from_yaml(cls, loader, node):\n \"\"\"\n :meta private:\n \"\"\"\n add_numpy_pandas_constructors()\n mapping = loader.construct_mapping(node, deep=True)\n\n if 'scenario' in mapping:\n microgrid_number = mapping.pop('scenario')\n if len(mapping):\n warn(f'Ignoring keys {mapping.keys()} when loading from scenario.')\n return cls.from_scenario(microgrid_number)\n\n instance = cls(mapping[\"modules\"], add_unbalanced_module=False)\n instance._balance_logger = instance._balance_logger.from_raw(mapping.get(\"balance_log\"))\n instance.trajectory_func = mapping.get('trajectory_func', None)\n instance._initial_step = mapping.get('initial_step', instance.initial_step)\n instance._final_step = mapping.get('final_step', instance.final_step)\n return instance\n\n def serialize(self, dumper_stream):\n \"\"\"\n :meta private:\n \"\"\"\n return dump_data(self._serialization_data(), dumper_stream, self.yaml_tag)\n\n def _serialization_data(self):\n return {\n \"modules\": self._modules.to_tuples(),\n 'trajectory_func': self.trajectory_func,\n 'initial_step': self.initial_step,\n 'final_step': self.final_step,\n **self._balance_logger.serialize(\"balance_log\")\n }\n\n @classmethod\n def from_nonmodular(cls, nonmodular):\n \"\"\"\n Convert to Microgrid from old-style NonModularMicrogrid.\n\n Parameters\n ----------\n nonmodular : pymgrid.NonModularMicrogrid\n Non-modular (old-style) microgrid to be converted.\n\n Returns\n -------\n converted : pymgrid.Microgrid\n New-style modular microgrid.\n\n See Also\n --------\n pymgrid.Microgrid.to_nonmodular : Converter from new-style to old-style.\n\n .. warning::\n\n Any logs that have accumulated will be lost in conversion.\n\n \"\"\"\n from pymgrid.convert.convert import to_modular\n return to_modular(nonmodular)\n\n def to_nonmodular(self):\n \"\"\"\n Convert Microgrid to old-style NonModularMicrogrid.\n\n Returns\n -------\n converted : pymgrid.NonModularMicrogrid\n Old-style microgrid.\n\n See Also\n --------\n :meth:`Microgrid.to_modular` : Converter from old-style to new-style.\n\n .. warning::\n\n Any logs that have accumulated will be lost in conversion.\n\n \"\"\"\n from pymgrid.convert.convert import to_nonmodular\n return to_nonmodular(self)\n\n @classmethod\n def from_scenario(cls, microgrid_number=0):\n \"\"\"\n Load one of the *pymgrid25* benchmark microgrids.\n\n Parameters\n ----------\n microgrid_number : int, default 0\n Number of the microgrid to return. ``0<=microgrid_number<25``.\n\n Returns\n -------\n scenario : pymgrid.Microgrid\n The loaded microgrid.\n \"\"\"\n from pymgrid import PROJECT_PATH\n n = microgrid_number\n\n if n not in np.arange(25):\n raise TypeError(f'Invalid microgrid_number {n}, must be an integer in the range [0, 25).')\n\n with open(PROJECT_PATH / f\"data/scenario/pymgrid25/microgrid_{n}/microgrid_{n}.yaml\", \"r\") as f:\n return cls.load(f)\n\n def _dir_additions(self):\n return {\n x for x in dir(self._modules) if\n not x.startswith('_') and not callable(getattr(self._modules, x)) and x in self._modules\n }\n\n def __dir__(self):\n rv = set(super().__dir__())\n rv = rv | self._dir_additions()\n return sorted(rv)\n\n def __getnewargs__(self):\n return (self.modules.to_tuples(), )\n\n def __len__(self):\n \"\"\"\n Length of available underlying data.\n \"\"\"\n l = []\n for module in self.modules.iterlist():\n try:\n l.append(len(module))\n except TypeError:\n pass\n\n return min(l)\n\n def __eq__(self, other):\n if type(self) != type(other):\n return NotImplemented\n\n return all([\n self.modules == other.modules,\n self._balance_logger == other._balance_logger,\n self.trajectory_func == other.trajectory_func])\n\n def __repr__(self):\n module_str = [name + ' x ' + str(len(modules)) for name, modules in self._modules.iterdict()]\n module_str = ', '.join(module_str)\n return f'Microgrid([{module_str}])'\n\n def __getattr__(self, item):\n if item.startswith(\"__\") or item == \"_modules\":\n raise AttributeError\n\n if item in self._modules:\n return self._modules[item]\n\n return object.__getattribute__(self, item)\n" }, { "path": "src/pymgrid/microgrid/reward_shaping/__init__.py", "content": "from .pv_curtailment_shaper import PVCurtailmentShaper\nfrom .battery_discharge_shaper import BatteryDischargeShaper\n" }, { "path": "src/pymgrid/microgrid/reward_shaping/base.py", "content": "import yaml\n\nfrom abc import abstractmethod\n\n\nclass BaseRewardShaper(yaml.YAMLObject):\n yaml_dumper = yaml.SafeDumper\n yaml_loader = yaml.SafeLoader\n\n @staticmethod\n def sum_module_val(info, module_name, module_attr):\n try:\n module_info = info[module_name]\n return sum([d[module_attr] for d in module_info])\n except KeyError:\n return 0.0\n\n @abstractmethod\n def __call__(self, step_info, cost_info):\n pass\n\n def __repr__(self):\n return f'{self.__class__.__name__}()'\n" }, { "path": "src/pymgrid/microgrid/reward_shaping/battery_discharge_shaper.py", "content": "import numpy as np\n\nfrom pymgrid.microgrid.reward_shaping.base import BaseRewardShaper\n\n\nclass BatteryDischargeShaper(BaseRewardShaper):\n \"\"\"\n Reward is the percentage of load that is met by battery discharging.\n\n Return a value in [-1, 1]. Value of -1 implies that all load was loss load. Value of 1 implies all load\n was met by battery.\n\n Use in a config with\n\n microgrid:\n attributes:\n reward_shaping_func: !BatteryDischargeShaper {}\n\n \"\"\"\n yaml_tag = u\"!BatteryDischargeShaper\"\n\n def __call__(self, step_info, cost_info):\n battery_discharge = self.sum_module_val(step_info, 'battery', 'provided_energy')\n load = self.sum_module_val(step_info, 'load', 'absorbed_energy')\n loss_load = self.sum_module_val(step_info, 'unbalanced_energy', 'provided_energy')\n\n # battery_discharge is in [0, load-loss_load]; loss_load is in [0, load].\n try:\n percent_battery = (battery_discharge - loss_load) / load\n except ZeroDivisionError:\n return 0.0\n\n assert -1 <= percent_battery <= 1 or np.isclose(percent_battery, 1) or np.isclose(percent_battery, 0)\n return percent_battery\n" }, { "path": "src/pymgrid/microgrid/reward_shaping/pv_curtailment_shaper.py", "content": "from pymgrid.microgrid.reward_shaping.base import BaseRewardShaper\n\n\nclass PVCurtailmentShaper(BaseRewardShaper):\n \"\"\"\n Use in a config with \n \n microgrid:\n attributes:\n reward_shaping_func: !PVCurtailmentShaper {}\n \"\"\"\n\n yaml_tag = u\"!PVCurtailmentShaper\"\n\n def __call__(self, step_info, cost_info):\n pv_curtailment = self.sum_module_val(step_info, 'pv', 'curtailment')\n return -1.0 * pv_curtailment\n" }, { "path": "src/pymgrid/microgrid/trajectory/__init__.py", "content": "from .deterministic import DeterministicTrajectory\nfrom .stochastic import StochasticTrajectory, FixedLengthStochasticTrajectory\n" }, { "path": "src/pymgrid/microgrid/trajectory/base.py", "content": "import inspect\nimport yaml\n\nfrom abc import abstractmethod\n\n\nclass BaseTrajectory(yaml.YAMLObject):\n yaml_dumper = yaml.SafeDumper\n yaml_loader = yaml.SafeLoader\n\n @abstractmethod\n def __call__(self, initial_step, final_step):\n pass\n\n def __repr__(self):\n params = inspect.signature(self.__init__).parameters\n formatted_params = ', '.join([f'{p}={getattr(self, p)}' for p in params])\n return f'{self.__class__.__name__}({formatted_params})'\n\n def __eq__(self, other):\n if type(self) != type(other):\n return NotImplemented\n return repr(self) == repr(other)\n" }, { "path": "src/pymgrid/microgrid/trajectory/deterministic.py", "content": "from pymgrid.microgrid.trajectory.base import BaseTrajectory\n\n\nclass DeterministicTrajectory(BaseTrajectory):\n yaml_tag = u\"!DeterministicTrajectory\"\n\n def __init__(self, initial_step, final_step):\n self.initial_step = initial_step\n self.final_step = final_step\n\n def __call__(self, initial_step, final_step):\n return self.initial_step, self.final_step\n" }, { "path": "src/pymgrid/microgrid/trajectory/stochastic.py", "content": "import numpy as np\n\nfrom pymgrid.microgrid.trajectory.base import BaseTrajectory\n\n\nclass StochasticTrajectory(BaseTrajectory):\n yaml_tag = u\"!StochasticTrajectory\"\n\n def __call__(self, initial_step, final_step):\n\n initial = np.random.randint(initial_step, final_step-2)\n final = np.random.randint(initial, final_step)\n\n return initial, final\n\n\nclass FixedLengthStochasticTrajectory(BaseTrajectory):\n yaml_tag = u\"!FixedLengthStochasticTrajectory\"\n\n def __init__(self, trajectory_length):\n self.trajectory_length = trajectory_length\n\n def __call__(self, initial_step, final_step):\n if final_step - initial_step < self.trajectory_length:\n raise ValueError(f'Cannot create a trajectory of length {self.trajectory_length}'\n f'between initial_step ({initial_step}) and final_step ({final_step})')\n\n initial = np.random.randint(initial_step, final_step-self.trajectory_length)\n\n return initial, initial + self.trajectory_length\n" }, { "path": "src/pymgrid/microgrid/utils/__init__.py", "content": "" }, { "path": "src/pymgrid/microgrid/utils/step.py", "content": "import numpy as np\n\n\nclass MicrogridStep:\n def __init__(self, reward_shaping_func=None, cost_info=None):\n self._reward_shaping_func = reward_shaping_func\n self.cost_info = cost_info\n self._obs = dict()\n self._reward = 0.0\n self._done = False\n self._info = dict(absorbed_energy=[], provided_energy=[])\n\n def append(self, module_name, obs, reward, done, info):\n try:\n self._obs[module_name].append(obs)\n except KeyError:\n self._obs[module_name] = [obs]\n self._reward += reward\n if done:\n self._done = True\n\n try:\n self._info[module_name].append(info)\n except KeyError:\n self._info[module_name] = [info]\n\n for key, value in info.items():\n try:\n self._info[key].append(value)\n except KeyError:\n pass\n\n def balance(self):\n provided_energy = np.sum(self._info['provided_energy'])\n absorbed_energy = np.sum(self._info['absorbed_energy'])\n return provided_energy, absorbed_energy, self._reward, self.shaped_reward()\n\n def output(self):\n return self._obs, self.shaped_reward(), self._done, self._output_info()\n\n def shaped_reward(self):\n if self._reward_shaping_func is not None:\n assert isinstance(self.cost_info, dict)\n return self._reward_shaping_func(self._output_info(), self.cost_info)\n\n return self._reward\n\n def _output_info(self):\n return {k: v for k, v in self._info.items() if k not in ('absorbed_energy', 'provided_energy')}\n\n @property\n def obs(self):\n return self._obs\n\n @property\n def reward(self):\n return self._reward\n\n @property\n def done(self):\n return self._done\n\n @property\n def info(self):\n return self._info" }, { "path": "src/pymgrid/modules/__init__.py", "content": "from .battery_module import BatteryModule\nfrom .genset_module import GensetModule\nfrom .grid_module import GridModule\nfrom .load_module import LoadModule\nfrom .renewable_module import RenewableModule\nfrom .unbalanced_energy_module import UnbalancedEnergyModule\n\nfrom .module_container import ModuleContainer\n" }, { "path": "src/pymgrid/modules/base/__init__.py", "content": "from .base_module import BaseMicrogridModule\nfrom .timeseries.base_timeseries_module import BaseTimeSeriesMicrogridModule\n" }, { "path": "src/pymgrid/modules/base/base_module.py", "content": "from abc import abstractmethod\nimport inspect\nimport logging\nimport yaml\nimport numpy as np\n\nfrom warnings import warn\n\nfrom pymgrid.utils.logger import ModularLogger\nfrom pymgrid.utils.space import ModuleSpace\nfrom pymgrid.utils.serialize import add_numpy_pandas_representers, add_numpy_pandas_constructors, dump_data\n\n\nscript_logger = logging.getLogger(__name__)\n\n\nclass BaseMicrogridModule(yaml.YAMLObject):\n \"\"\"\n Base class for all microgrid _modules.\n All values passed to step(self) that result in non-negative\n \"\"\"\n module_type = None\n \"\"\"\n Type of the module.\n\n Returns : tuple[str, {'fixed', 'flex', 'controllable'}]\n length-two tuple denoting the name of the module class and whether it is a fixed, flex, or controllable module.\n\n \"\"\"\n yaml_tag = None\n \"\"\"\n Tag used for yaml serialization.\n \"\"\"\n\n _energy_pos = 0\n\n def __init__(self,\n raise_errors,\n initial_step=0,\n provided_energy_name='provided_energy',\n absorbed_energy_name='absorbed_energy'\n ):\n\n self.raise_errors = raise_errors\n self.initial_step = initial_step\n self._current_step = initial_step\n self._action_space = self._get_action_spaces()\n self._observation_space = self._get_observation_spaces()\n self.provided_energy_name, self.absorbed_energy_name = provided_energy_name, absorbed_energy_name\n self._logger = ModularLogger()\n self.name = (None, None) # set by ModularMicrogrid\n\n def _get_action_spaces(self):\n unnormalized_low = self.min_act if isinstance(self.min_act, np.ndarray) else np.array([self.min_act])\n unnormalized_high = self.max_act if isinstance(self.max_act, np.ndarray) else np.array([self.max_act])\n return ModuleSpace(unnormalized_low=unnormalized_low,\n unnormalized_high=unnormalized_high)\n\n def _get_observation_spaces(self):\n unnormalized_low = self.min_obs if isinstance(self.min_obs, np.ndarray) else np.array([self.min_obs])\n unnormalized_high = self.max_obs if isinstance(self.max_obs, np.ndarray) else np.array([self.max_obs])\n return ModuleSpace(unnormalized_low=unnormalized_low,\n unnormalized_high=unnormalized_high)\n\n def reset(self):\n \"\"\"\n Reset the module to step zero and flush the log.\n\n Returns\n -------\n np.ndarray\n Normalized observation after resetting.\n\n \"\"\"\n self._update_step(reset=True)\n self._logger.flush()\n return self.to_normalized(self.state, obs=True)\n\n def _raise_error(self, ask_value, available_value, as_source=False, as_sink=False, lower_bound=False):\n assert as_source + as_sink == 1, 'Must act as either source or sink but not both or neither.'\n name = self.__class__.__name__\n ask_v = round(ask_value, 2)\n available_v = round(available_value, 2)\n\n if as_source:\n if lower_bound:\n raise ValueError(f'Module {name} unable to supply requested value {ask_v} as a source. '\n f'Must supply at least: {available_v}.')\n raise ValueError(f'Module {name} unable to supply requested value {ask_v} as a source. '\n f'Max currently available: {available_v}.')\n else:\n raise ValueError(f'Module {name} unable to absorb requested value {ask_v} as a sink. '\n f'Max currently capable of absorbing: {available_v}.')\n\n def step(self, action, normalized=True):\n \"\"\"\n Take one step in the module, attempting to draw or send ``action`` amount of energy.\n\n Parameters\n ----------\n action : float or np.ndarray, shape (1,)\n The amount of energy to draw or send.\n\n If ``normalized``, the action is assumed to be normalized and is un-normalized into the range\n [:attr:`.BaseModule.min_act`, :attr:`.BaseModule.max_act`].\n\n If the **unnormalized** action is positive, the module acts as a source and provides energy to the\n microgrid. Otherwise, the module acts as a sink and absorbs energy.\n\n If the unnormalized action implies acting as a sink and ``is_sink`` is False -- or the converse -- an\n ``AssertionError`` is raised.\n\n normalized : bool, default True\n Whether ``action`` is normalized. If True, action is assumed to be normalized and is un-normalized into the\n range [:attr:`.BaseModule.min_act`, :attr:`.BaseModule.max_act`].\n\n Raises\n ------\n AssertionError\n If action implies acting as a source and module is not a source. Likewise if action implies acting as a\n sink and module is not a sink.\n\n Returns\n -------\n observation : np.ndarray\n State of the module after taking action ``action``.\n reward : float\n Reward/cost after taking the action.\n done : bool\n Whether the module terminates.\n info : dict\n Additional information from this step.\n Will include either``provided_energy`` or ``absorbed_energy`` as a key, denoting the amount of energy\n this module provided to or absorbed from the microgrid.\n\n \"\"\"\n\n denormalized_action = self._action_space.denormalize(action) if normalized else action\n\n try:\n denormalized_action = denormalized_action[self._energy_pos]\n except (IndexError, TypeError):\n if not isinstance(denormalized_action, (float, int)):\n try:\n flat_dim = np.product(denormalized_action.shape)\n assert flat_dim == 0\n except (AttributeError, AssertionError):\n raise ValueError(f'Bad action {denormalized_action}')\n else:\n denormalized_action = 0.0\n\n state_dict = self.state_dict()\n reward, done, info = self._unnormalized_step(denormalized_action)\n self._log(state_dict, reward=reward, **info)\n self._update_step()\n\n obs = self.to_normalized(self.state, obs=True)\n\n return obs, reward, done, info\n\n def _unnormalized_step(self, unnormalized_action):\n if unnormalized_action > 0:\n return self.as_source(unnormalized_action)\n elif unnormalized_action < 0:\n return self.as_sink(-1.0*unnormalized_action)\n else:\n if self.is_source:\n return self.as_source(unnormalized_action)\n else:\n assert self.is_sink\n return self.as_sink(-1.0 * unnormalized_action)\n\n def as_source(self, energy_demand):\n \"\"\"\n Act as an energy source to the microgrid.\n\n Microgrid will attempt to provide ``energy_demand`` amount of energy.\n Examples of this include discharging a battery, importing from a grid, or using renewables.\n\n It is assumed that ``energy_demand>=0``.\n\n Parameters\n ----------\n energy_demand : float\n Amount of energy that the microgrid is requesting. Must be non-negative.\n\n Returns\n -------\n reward : float\n Reward/cost after attempting the satisfy the energy demand.\n done : bool\n Whether the module terminates.\n info : dict\n Additional information from this step.\n Will include``provided_energy`` as a key, denoting the amount of energy\n this module provided to the microgrid.\n\n Raises\n ------\n AssertionError\n If ``energy_demand<0`` or the module is not a source.\n\n \"\"\"\n\n assert energy_demand >= 0\n assert self.is_source, f'step() was called with positive energy (source) for module {self} but ' \\\n f'module is not a source and ' \\\n f'can only be called with negative energy.'\n\n if self.module_type[-1] == 'fixed':\n return self.update(None, as_source=True)\n\n if energy_demand > self.max_production:\n if self.raise_errors:\n self._raise_error(energy_demand, self.max_production, as_source=True)\n provided_energy = self.max_production\n\n elif energy_demand < self.min_production:\n if self.raise_errors:\n self._raise_error(energy_demand, self.min_production, as_source=True, lower_bound=True)\n provided_energy = self.min_production\n\n else:\n provided_energy = energy_demand\n\n return self.update(provided_energy, as_source=True)\n\n def as_sink(self, energy_excess):\n \"\"\"\n Act as an energy sink to the microgrid.\n\n Microgrid will attempt to provide ``energy_excess`` amount of energy.\n Examples of this include charging a battery, exporting from a grid, or meeting a load.\n\n It is assumed that ``energy_excess>=0``.\n\n Parameters\n ----------\n energy_excess : float\n Amount of energy that the microgrid is attempting to dissipate. Must be non-negative.\n\n Returns\n -------\n reward : float\n Reward/cost after attempting the absorb the energy excess.\n done : bool\n Whether the module terminates.\n info : dict\n Additional information from this step.\n Will include``absorbed_energy`` as a key, denoting the amount of energy\n this module provided to the microgrid.\n\n Raises\n ------\n AssertionError\n If ``energy_excess<0`` or the module is not a sink.\n\n \"\"\"\n\n assert energy_excess >= 0\n\n if self.module_type[-1] == 'fixed':\n return self.update(None, as_sink=True)\n\n if energy_excess > self.max_consumption:\n if self.raise_errors:\n self._raise_error(energy_excess, self.max_consumption, as_sink=True)\n absorbed_energy = self.max_consumption\n else:\n absorbed_energy = energy_excess\n\n assert absorbed_energy >= 0\n\n return self.update(absorbed_energy, as_sink=True)\n\n def _log(self, state_dict_pre_step, provided_energy=None, absorbed_energy=None, **info):\n _info = info.copy()\n\n if self.provided_energy_name is not None:\n _info[self.provided_energy_name] = provided_energy if provided_energy is not None else 0.0\n else:\n assert provided_energy is None, 'Cannot log provided_energy with NoneType provided_energy_name.'\n\n if self.absorbed_energy_name is not None:\n _info[self.absorbed_energy_name] = absorbed_energy if absorbed_energy is not None else 0.0\n else:\n assert absorbed_energy is None, 'Cannot log absorbed_energy with NoneType absorbed_energy_name.'\n\n _info.update(state_dict_pre_step)\n self._logger.log(**_info)\n\n def _update_step(self, reset=False):\n if reset:\n self._current_step = self.initial_step\n else:\n self._current_step += 1\n\n @abstractmethod\n def update(self, external_energy_change, as_source=False, as_sink=False):\n \"\"\"\n Update the state of the module given an energy request.\n\n Parameters\n ----------\n external_energy_change : float or None\n Amount of energy to provide or absorb.\n as_source : bool\n Whether the module is acting as a source.\n as_sink\n Whether the module is acting as a sink.\n\n Returns\n -------\n reward : float\n Reward/cost after attempting the absorb the energy excess.\n done : bool\n Whether the module terminates.\n info : dict\n Additional information from this step.\n Will include``absorbed_energy`` as a key, denoting the amount of energy\n this module provided to the microgrid.\n \"\"\"\n\n pass\n\n def sample_action(self, strict_bound=False):\n \"\"\"\n Sample an action from the module's action space.\n\n Parameters\n ----------\n strict_bound : bool, default False\n If True, choose action that is guaranteed to satisfy self.max_consumption and\n self.max_production bounds. Otherwise select action from min_act and min_act, which may not satisfy\n instantaneous bounds.\n\n Returns\n -------\n float\n An action within the action space for this module.\n\n \"\"\"\n\n min_bound, max_bound = 0, 1\n\n if strict_bound:\n if self.is_sink:\n min_bound = self._action_space.normalize(-1 * self.max_consumption)\n if np.isnan(min_bound):\n min_bound = 0\n\n if self.is_source:\n max_bound = self._action_space.normalize(self.max_production)\n if np.isnan(max_bound):\n max_bound = 0\n return np.random.rand()*(max_bound-min_bound) + min_bound\n\n def to_normalized(self, value, act=False, obs=False):\n \"\"\"\n Normalize an action or observation.\n\n Parameters\n ----------\n value : scalar or array-like\n Action or observation to normalize.\n act : bool, default False\n Set to True if you are normalizing an action.\n obs : bool, default False\n Set to True if you are normalizing an observation.\n\n Returns\n -------\n np.ndarray\n Normalized action.\n\n \"\"\"\n assert act + obs == 1, 'One of act or obs must be True but not both.'\n if act:\n return self._action_space.normalize(value)\n else:\n return self._observation_space.normalize(value)\n\n def from_normalized(self, value, act=False, obs=False):\n \"\"\"\n Un-normalize an action or observation.\n\n Parameters\n ----------\n value : scalar or array-like\n Action or observation to un-normalize.\n act : bool, default False\n Set to True if you are un-normalizing an action.\n obs : bool, default False\n Set to True if you are un-normalizing an observation.\n\n Returns\n -------\n np.ndarray\n Un-normalized action.\n\n \"\"\"\n assert act + obs == 1, 'One of act or obs must be True but not both.'\n if act:\n return self._action_space.denormalize(value)\n if obs:\n return self._observation_space.denormalize(value)\n\n def log_dict(self):\n \"\"\"\n Module's log as a dict.\n\n Returns\n -------\n dict\n\n \"\"\"\n return self._logger.to_dict()\n\n def log_frame(self):\n \"\"\"\n Module's log as a DataFrame.\n\n Returns\n -------\n log : pd.DataFrame\n\n \"\"\"\n return self._logger.to_frame()\n\n @property\n def log(self):\n \"\"\"\n Module's log as a DataFrame.\n\n Equivalent to :meth:`log_frame`.\n\n Returns\n -------\n log : pd.DataFrame\n\n \"\"\"\n return self.log_frame()\n\n @property\n def logger(self):\n \"\"\"\n The module's logger.\n\n Returns\n -------\n logger : ModularLogger\n\n \"\"\"\n return self._logger\n\n @property\n def logger_last(self):\n \"\"\"\n The most recent entry in the log.\n\n Returns\n -------\n entry : dict\n\n \"\"\"\n return {k: v[-1] for k, v in self._logger}\n\n @logger.setter\n def logger(self, logger):\n assert isinstance(logger, ModularLogger)\n self._logger = logger\n\n def state_dict(self, normalized=False):\n \"\"\"\n Current state of the module as a dictionary.\n\n Parameters\n ----------\n normalized : bool, default False\n Whether to return a dict of normalized values.\n\n Returns\n -------\n state_dict : dict\n\n \"\"\"\n if normalized:\n return dict(zip(self.state_dict().keys(), self._observation_space.normalize(self.state)))\n\n return self._state_dict()\n\n @abstractmethod\n def _state_dict(self):\n \"\"\"\n Unnormalized state_dict.\n\n Returns\n -------\n state_dict : dict\n The return value of :meth:`.state_dict` if ``normalized=False``.\n\n \"\"\"\n pass\n\n @property\n def state(self):\n \"\"\"\n Current state of the module as a vector.\n\n Equivalent to the values of ``state_dict``.\n\n Returns\n -------\n np.ndarray\n\n \"\"\"\n return np.array([*self.state_dict().values()])\n\n @property\n def current_step(self):\n \"\"\"\n Current step of the module.\n\n Returns\n -------\n int\n\n \"\"\"\n return self._current_step\n\n @current_step.setter\n def current_step(self, value):\n self._current_step = value\n\n @property\n @abstractmethod\n def min_obs(self):\n \"\"\"\n Minimum observation that the module gives.\n\n Used in normalization and to define observation spaces.\n\n Returns\n -------\n float or np.ndarray\n Scalar or vector minimum observation.\n\n \"\"\"\n raise NotImplementedError('Must define min_obs (along with the other three bounds) '\n 'before calling super().__init__().')\n\n @property\n @abstractmethod\n def max_obs(self):\n \"\"\"\n Maximum observation that the module gives.\n\n Used in normalization and to define observation spaces.\n\n Returns\n -------\n float or np.ndarray\n Scalar or vector maximum observation.\n\n \"\"\"\n raise NotImplementedError('Must define max_obs (along with the other three bounds) '\n 'before calling super().__init__().')\n\n @property\n @abstractmethod\n def min_act(self):\n \"\"\"\n Minimum action that the module allows.\n\n Used in normalization and to define action spaces.\n\n Returns\n -------\n float or np.ndarray\n Scalar or vector minimum action.\n\n \"\"\"\n raise NotImplementedError('Must define min_act (along with the other three bounds) '\n 'before calling super().__init__().')\n\n @property\n @abstractmethod\n def max_act(self):\n \"\"\"\n Maximum action that the module allows.\n\n Used in normalization and to define action spaces.\n\n Returns\n -------\n float or np.ndarray\n Scalar or vector maximum action.\n\n \"\"\"\n raise NotImplementedError('Must define max_act (along with the other three bounds) '\n 'before calling super().__init__().')\n\n @property\n def min_production(self):\n \"\"\"\n Minimum amount of production at the current time step.\n\n In general, this value is zero. Some modules, such as ``GensetModule``,\n must produce a minimum amount of energy in some cases, and this value will be positive.\n\n Must be defined in any child class that is a source.\n If the module is not a source, this value is irrelevant.\n\n Returns\n -------\n float\n\n \"\"\"\n return 0\n\n @property\n def max_production(self):\n \"\"\"\n Maximum amount of production at the current time step.\n\n Must be defined in any child class that is a source.\n If the module is not a source, this value is irrelevant.\n\n Returns\n -------\n float\n\n \"\"\"\n return NotImplemented\n\n @property\n def max_consumption(self):\n \"\"\"\n Maximum amount of consumption at the current time step.\n\n Must be defined in any child class that is a sink.\n If the module is not a sink, this value is irrelevant.\n\n Returns\n -------\n float\n\n \"\"\"\n return NotImplemented\n\n @property\n def marginal_cost(self):\n \"\"\"\n Average marginal cost of producing with the module.\n\n Returns\n -------\n marginal_cost : float\n Average marginal cost.\n\n \"\"\"\n return self.production_marginal_cost\n\n @property\n def production_marginal_cost(self):\n return 0.0\n\n @property\n def absorption_marginal_cost(self):\n return 0.0\n\n @property\n def action_space(self):\n \"\"\"\n Action spaces of the module.\n\n Contains both normalized and un-normalized action space.\n\n Returns\n -------\n action_space : :class:`ModuleSpace `\n The action space.\n \"\"\"\n return self._action_space\n\n @property\n def observation_space(self):\n \"\"\"\n Observation space of the module.\n\n Contains both normalized and un-normalized observation spaces.\n\n Returns\n -------\n observation_space : :class:`ModuleSpace `\n The observation space.\n \"\"\"\n return self._observation_space\n\n @property\n def is_source(self):\n \"\"\"\n Whether the module is a source.\n\n Returns\n -------\n bool\n\n \"\"\"\n return False\n\n @property\n def is_sink(self):\n \"\"\"\n Whether the module is a sink.\n\n Returns\n -------\n bool\n\n \"\"\"\n return False\n\n def dump(self, stream=None):\n \"\"\"\n Save a module to a YAML buffer.\n\n Supports both strings of YAML or storing YAML in a path-like object.\n\n Parameters\n ----------\n stream : file-like object or None, default None\n Stream to save the YAML document. If None, returns the document instead.\n\n Returns\n -------\n str or None :\n Returns the YAMl document as a string if ``stream=None``. Otherwise, returns None.\n\n .. note::\n\n ``dump`` handles the serialization of array-like objects (e.g. time series and logs) differently depending\n on the value of ``stream``. If ``stream is None``, array-like objects are serialized inline. If ``stream`` is\n a stream to a file-like object, however, array-like objects will be serialized as `.csv.gz` files in a\n directory relative to ``stream``, and the relative locations stored inline in the YAML file. For an example of\n this behavior, see `data/scenario/pymgrid25/microgrid_0`.\n\n \"\"\"\n return yaml.safe_dump(self, stream=stream)\n\n @classmethod\n def load(cls, stream):\n \"\"\"\n Load a module from yaml representation.\n\n Equivalent to ``yaml.safe_load(stream)``.\n\n Parameters\n ----------\n stream : str or file-like object\n Stream from which to read yaml representation of a module.\n\n Returns\n -------\n BaseMicrogridModule or child class of BaseMicrogridModule\n Deserialized module, populated with the state it possessed upon serialization.\n\n \"\"\"\n return yaml.safe_load(stream)\n\n @classmethod\n def from_yaml(cls, loader, node):\n \"\"\"\n Convert a yaml representation of a module to a module.\n\n Part of the ``load`` and equivalently the ``yaml.safe_load`` procedures.\n Should not be called directly.\n\n :meta private:\n\n Parameters\n ----------\n loader : yaml.SafeLoader\n The yaml loader.\n node : yaml.node.MappingNode\n yaml node representation of the module.\n\n Returns\n -------\n BaseMicrogridModule or child class of BaseMicrogridModule\n Deserialized module, populated with the state it possessed upon serialization.\n\n \"\"\"\n add_numpy_pandas_constructors()\n mapping = loader.construct_mapping(node, deep=True)\n instance = cls.deserialize_instance(mapping[\"cls_params\"])\n instance.logger = instance.logger.from_raw(mapping.get(\"log\"))\n instance.name = tuple(mapping[\"name\"])\n return instance.deserialize(mapping[\"state\"])\n\n @classmethod\n def to_yaml(cls, dumper, data):\n \"\"\"\n Convert a module to a yaml representation node.\n\n Part of the ``dump`` and equivalently the ``yaml.safe_dump`` procedures.\n Should not be called directly.\n\n :meta private:\n\n Parameters\n ----------\n dumper : yaml.SafeDumper\n The yaml dumper.\n data : BaseMicrogridModule or child class of BaseMicrogridModule\n Module to be serialized\n\n Returns\n -------\n yaml.node.MappingNode\n\n \"\"\"\n add_numpy_pandas_representers()\n return dumper.represent_mapping(cls.yaml_tag, data.serialize(dumper.stream), flow_style=cls.yaml_flow_style)\n\n def serialize(self, dumper_stream):\n \"\"\"\n Serialize module. The result is passed to a YAML dumper.\n\n :meta private:\n\n Parameters\n ----------\n dumper_stream : file-like object or None.\n The stream that the object will be dumped to.\n\n Returns\n -------\n dict\n The serialized module.\n\n \"\"\"\n data = {\n \"name\": self.name,\n \"cls_params\": self._serialize_cls_params(),\n \"state\": self._serialize_state_attributes(),\n **self._logger.serialize(\"log\")\n }\n\n return dump_data(data, dumper_stream, self.yaml_tag)\n\n def serializable_state_attributes(self):\n \"\"\"\n\n Return the attributes of the module that represent the module's current state for serialization.\n\n :meta private:\n\n Returns\n -------\n list[str]\n List of attribute names.\n\n \"\"\"\n return [\"_current_step\", *self.state_dict().keys()]\n\n def _serialize_state_attributes(self):\n return {attr_name: getattr(self, attr_name) for attr_name in self.serializable_state_attributes()}\n\n def _serialize_cls_params(self):\n serialized_args = {}\n cls_params = inspect.signature(self.__init__).parameters\n\n for p_name in cls_params.keys():\n try:\n serialized_args[p_name] = (getattr(self, p_name))\n except AttributeError:\n raise AttributeError(f\"Module {self.__class__.__name__} must have attribute/property '{p_name}' corresponding to \"\n f\"class parameter of the same name.\")\n\n return serialized_args\n\n @classmethod\n def deserialize_instance(cls, param_dict):\n \"\"\"\n Generate an instance of this module with the arguments in param_dict.\n\n Part of the ``load`` and ``yaml.safe_load`` methods. Should not be called directly.\n\n :meta private:\n\n Parameters\n ----------\n param_dict : dict\n Class arguments.\n\n Returns\n -------\n BaseMicrogridModule or child class of BaseMicrogridModule\n The module instance.\n\n \"\"\"\n param_dict = param_dict.copy()\n cls_params = inspect.signature(cls).parameters\n\n cls_kwargs = {}\n missing_params, default_params = [], []\n\n for p_name, p_value in cls_params.items():\n try:\n cls_kwargs[p_name] = param_dict.pop(p_name)\n except KeyError:\n if p_value.default is p_value.empty:\n missing_params.append(p_name)\n else:\n cls_kwargs[p_name] = p_value.default\n default_params.append(p_name)\n\n if len(default_params):\n warn(f'Missing parameter values {default_params} for {cls}. Using available default values.')\n\n if len(missing_params):\n raise KeyError(f\"Missing parameter values {missing_params} for {cls} with no default values available.\")\n\n return cls(**cls_kwargs)\n\n def deserialize(self, serialized_dict):\n \"\"\"\n Populate the attributes in ``self.serializable_state_attributes``.\n\n Part of the ``load`` and ``yaml.safe_load`` methods. Should not be called directly.\n\n :meta private:\n\n Parameters\n ----------\n serialized_dict : dict\n Serialized state attributes\n\n Returns\n -------\n BaseMicrogridModule or child of BaseMicrogridModule\n The module instance.\n\n \"\"\"\n serialized_dict = serialized_dict.copy()\n for attr_name in self.serializable_state_attributes():\n if not hasattr(self, attr_name):\n raise ValueError(f\"Key {attr_name} is not an attribute of module {self} and cannot be set.\")\n try:\n setattr(self, attr_name, serialized_dict.pop(attr_name))\n except KeyError:\n raise KeyError(f\"Missing key {attr_name} in deserialized dict.\")\n\n if len(serialized_dict):\n warn(f\"Unused keys in serialized_dict: {list(serialized_dict.keys())}\")\n return self\n\n def __eq__(self, other):\n if type(self) != type(other):\n return NotImplemented\n\n diff = [(k1, v1, v2) for (k1, v1), (k2, v2) in zip(self.__dict__.items(), other.__dict__.items()) if\n ((hasattr(v1, \"any\") and not np.allclose(v1, v2)) or (not hasattr(v1, \"any\") and v1 != v2))]\n\n return len(diff) == 0\n\n def __repr__(self):\n param_repr = {p: getattr(self, p) for p in inspect.signature(self.__init__).parameters}\n param_repr = [f'{p}={type(v) if hasattr(v, \"__len__\") and not isinstance(v, str) else v}' for p, v in param_repr.items()]\n param_repr = ', '.join(param_repr)\n return f'{self.__class__.__name__}(' \\\n f'{param_repr})'\n" }, { "path": "src/pymgrid/modules/base/timeseries/__init__.py", "content": "" }, { "path": "src/pymgrid/modules/base/timeseries/base_timeseries_module.py", "content": "import numpy as np\n\nfrom pymgrid.microgrid import DEFAULT_HORIZON\nfrom pymgrid.modules.base import BaseMicrogridModule\nfrom pymgrid.forecast.forecaster import get_forecaster, OracleForecaster, NoForecaster\n\n\nclass BaseTimeSeriesMicrogridModule(BaseMicrogridModule):\n\n state_components = None\n \"\"\"\n Labels of the components of each entry in the module's time series.\n\n Column labels of self.time_series.\n\n Returns\n -------\n state_components : np.ndarray[str], shape (self.time_series.shape[1], )\n The state components.\n \"\"\"\n\n def __init__(self,\n time_series,\n raise_errors,\n forecaster=None,\n forecast_horizon=DEFAULT_HORIZON,\n forecaster_increase_uncertainty=False,\n forecaster_relative_noise=False,\n initial_step=0,\n final_step=-1,\n provided_energy_name='provided_energy',\n absorbed_energy_name='absorbed_energy',\n normalize_pos=...):\n\n self._time_series = self._set_time_series(time_series)\n self._min_obs, self._max_obs, self._min_act, self._max_act = self._get_bounds()\n\n self.final_step = final_step\n\n self._forecast_param = forecaster\n self._forecast_horizon = forecast_horizon * (forecaster is not None)\n self._forecaster = get_forecaster(forecaster,\n self._get_observation_spaces(),\n forecast_shape=(self.forecast_horizon, len(self.state_components)),\n time_series=self.time_series[initial_step:self.final_step, :],\n increase_uncertainty=forecaster_increase_uncertainty,\n relative_noise=forecaster_relative_noise)\n\n self._state_dict_keys = self._set_state_dict_keys()\n\n super().__init__(raise_errors,\n initial_step=initial_step,\n provided_energy_name=provided_energy_name,\n absorbed_energy_name=absorbed_energy_name)\n\n self._current_forecast = self.forecast()\n\n def _set_time_series(self, time_series):\n _time_series = np.array(time_series)\n try:\n shape = (-1, _time_series.shape[1])\n except IndexError:\n shape = (-1, 1)\n _time_series = _time_series.reshape(shape)\n assert len(_time_series) == len(time_series)\n return self._sign_check(_time_series)\n\n def _sign_check(self, time_series):\n if self.is_source and self.is_sink:\n return time_series\n\n if not ((np.sign(time_series) <= 0).all() or (np.sign(time_series) >= 0).all()):\n raise ValueError('time_series cannot contain both positive and negative values unless it is both '\n 'a source and a sink.')\n\n if self.is_source:\n return np.abs(time_series)\n else:\n return -np.abs(time_series)\n\n def _get_bounds(self):\n _min, _max = np.min(self._time_series), np.max(self._time_series)\n if _min > 0:\n _min = 0\n elif _max < 0:\n _max = 0\n\n return _min, _max, _min, _max\n\n def _set_state_dict_keys(self):\n return {\n \"current\": [f\"{component}_current\" for component in self.state_components],\n \"forecast\": [\n f\"{component}_forecast_{j}\"\n for j in range(self._forecast_horizon) for component in self.state_components\n ]\n }\n\n def _update_step(self, reset=False):\n super()._update_step(reset=reset)\n self._current_forecast = self.forecast()\n\n def forecast(self):\n \"\"\"\n Forecast the module's time series from the current state.\n\n Returns\n -------\n forecast : None or np.ndarray, shape (n, len(self.state_components))\n The forecasted time series.\n \"\"\"\n val_c_n = self.time_series[1+self.current_step:1+self.current_step+self.forecast_horizon, :]\n try:\n val_c = self.time_series[self.current_step, :]\n except IndexError:\n forecast = self._forecaster.full_pad(self.time_series.shape, self._forecast_horizon)\n else:\n forecast = self._forecaster(val_c=val_c,\n val_c_n=val_c_n,\n n=self.forecast_horizon)\n\n return None if forecast is None else forecast\n\n def _done(self):\n return self._current_step >= self._final_step - 1\n\n @property\n def current_obs(self):\n \"\"\"\n Current observation.\n\n Returns\n -------\n obs : np.ndarray, shape (len(self.state_components), )\n The observation.\n \"\"\"\n try:\n return self.time_series[self.current_step, :]\n except IndexError:\n return self._forecaster.full_pad(self.time_series.shape, 1).reshape(-1)\n\n @property\n def time_series(self):\n \"\"\"\n View of the module's time series.\n\n Returns\n -------\n time_series : np.ndarray, shape (len(self), len(self.state_components))\n The underlying time series.\n\n \"\"\"\n return self._time_series\n\n @time_series.setter\n def time_series(self, value):\n self._time_series = self._set_time_series(value)\n self._min_obs, self._max_obs, self._min_act, self._max_act = self._get_bounds()\n self._action_space = self._get_action_spaces()\n self._observation_space = self._get_observation_spaces()\n\n @property\n def min_obs(self):\n # TODO find a better solution\n return np.repeat(np.array(self._min_obs).reshape((-1, 1)), 1+self._forecast_horizon, axis=1).T.reshape(-1)\n\n @property\n def max_obs(self):\n # TODO find a better solution\n return np.repeat(np.array(self._max_obs).reshape((-1, 1)), 1+self._forecast_horizon, axis=1).T.reshape(-1)\n\n @property\n def min_act(self):\n return self._min_act\n\n @property\n def max_act(self):\n return self._max_act\n\n @property\n def forecaster(self):\n \"\"\"\n View of the forecaster.\n\n Returns\n -------\n forecaster : :class:`.Forecaster`\n The module's forecaster.\n\n \"\"\"\n return self._forecaster\n\n def set_forecaster(self,\n forecaster,\n forecast_horizon=DEFAULT_HORIZON,\n forecaster_increase_uncertainty=False,\n forecaster_relative_noise=False):\n \"\"\"\n Set the forecaster for this module.\n\n Sets the forecaster with the same logic as upon initialization.\n\n forecaster : callable, float, \"oracle\", or None, default None.\n Function that gives a forecast n-steps ahead.\n\n * If ``callable``, must take as arguments ``(val_c: float, val_{c+n}: float, n: int)``, where\n\n * ``val_c`` is the current value in the time series: ``self.time_series[self.current_step]``\n\n * ``val_{c+n}`` is the value in the time series n steps in the future\n\n * n is the number of steps in the future at which we are forecasting.\n\n The output ``forecast = forecaster(val_c, val_{c+n}, n)`` must have the same sign\n as the inputs ``val_c`` and ``val_{c+n}``.\n\n * If ``float``, serves as a standard deviation for a mean-zero gaussian noise function\n that is added to the true value.\n\n * If ``\"oracle\"``, gives a perfect forecast.\n\n * If ``None``, no forecast.\n\n forecast_horizon : int\n Number of steps in the future to forecast. If forecaster is None, this parameter is ignored and the resultant\n horizon will be zero.\n\n forecaster_increase_uncertainty : bool, default False\n Whether to increase uncertainty for farther-out dates if using a GaussianNoiseForecaster. Ignored otherwise.\n\n forecaster_relative_noise : bool, default False\n Whether to define noise standard deviation relative to mean of time series if using\n :class:`.GaussianNoiseForecaster`. Ignored otherwise.\n\n \"\"\"\n\n self.forecast_horizon = forecast_horizon * (forecaster is not None)\n\n self._forecaster = get_forecaster(forecaster,\n self._observation_space,\n (self.forecast_horizon, len(self.state_components)),\n self.time_series[self.initial_step:self._final_step, :],\n increase_uncertainty=forecaster_increase_uncertainty,\n relative_noise=forecaster_relative_noise)\n\n @property\n def forecast_horizon(self):\n \"\"\"\n The number of steps until which the module forecasts.\n\n Returns\n -------\n forecast_horizon : int\n The forecast horizon.\n\n \"\"\"\n return self._forecast_horizon\n\n @forecast_horizon.setter\n def forecast_horizon(self, value):\n\n self._forecast_horizon = value\n self._state_dict_keys = self._set_state_dict_keys()\n self._observation_space = self._get_observation_spaces()\n\n if value > 0 and isinstance(self._forecaster, NoForecaster):\n from warnings import warn\n warn(\"Setting forecast_horizon requires a non-null forecaster. Implementing OracleForecaster.\")\n self._forecaster = OracleForecaster(self._observation_space,\n forecast_shape=(value, len(self.state_components))\n )\n\n self._forecaster.observation_space = self._observation_space\n\n @property\n def forecaster_increase_uncertainty(self):\n \"\"\"\n View of :class:`pymgrid.forecast.GaussianNoiseForecaster``.increase_uncertainty`.\n\n Required for serialization as a mirror to the class parameter.\n Will only ever be True if ``self.forecaster`` is a ``GaussianNoiseForecaster``.\n\n Returns\n -------\n forecaster_increase_uncertainty : bool\n Associated attribute of ``self.forecaster``.\n\n \"\"\"\n try:\n return self._forecaster.increase_uncertainty\n except AttributeError:\n return False\n\n @property\n def forecaster_relative_noise(self):\n \"\"\"\n View of :class:`pymgrid.forecast.GaussianNoiseForecaster``.forecaster_relative_noise`.\n\n Required for serialization as a mirror to the class parameter.\n Will only ever be True if ``self.forecaster`` is a ``GaussianNoiseForecaster``.\n\n Returns\n -------\n forecaster_relative_noise : bool\n Associated attribute of ``self.forecaster``.\n\n \"\"\"\n try:\n return self._forecaster.relative_noise\n except AttributeError:\n return False\n\n @property\n def final_step(self):\n return self._final_step\n\n @final_step.setter\n def final_step(self, value):\n if not value // 1 == value:\n raise ValueError('final_step value must be an integer.')\n\n if value <= 0:\n self._final_step = len(self)\n else:\n self._final_step = value\n try:\n if self._final_step <= self.initial_step:\n raise ValueError('final_step value must be greater than initial_step')\n except AttributeError:\n pass\n\n def _state_dict(self):\n state_dict = dict(zip(self._state_dict_keys['current'], self.current_obs))\n\n if self._current_forecast is not None:\n state_dict.update(zip(self._state_dict_keys['forecast'], self._current_forecast.reshape(-1)))\n\n return state_dict\n\n def serialize(self, dumper_stream):\n data = super().serialize(dumper_stream)\n data[\"cls_params\"][\"forecaster\"] = self._forecast_param\n return data\n\n def serializable_state_attributes(self):\n return [\"_current_step\"]\n\n def __len__(self):\n return self._time_series.shape[0]\n" }, { "path": "src/pymgrid/modules/battery_module.py", "content": "from pymgrid.modules.base import BaseMicrogridModule\nimport numpy as np\nimport yaml\nfrom warnings import warn\n\n\nclass BatteryModule(BaseMicrogridModule):\n \"\"\"\n A battery module.\n\n Battery modules are fixed: when calling ``Microgrid.run``, you must pass a control for batteries.\n\n Parameters\n ----------\n min_capacity : float\n Minimum energy that must be contained in the battery.\n\n max_capacity : float\n Maximum energy that can be contained in the battery.\n If ``soc=1``, capacity is at this maximum.\n\n max_charge : float\n Maximum amount the battery can be charged in one step.\n\n max_discharge : float\n Maximum amount the battery can be discharged in one step.\n\n efficiency : float\n Efficiency of the battery.\n See :meth:`BatteryModule.model_transition` for details.\n\n battery_cost_cycle : float, default 0.0\n Marginal cost of charging and discharging.\n\n battery_transition_model : callable or None, default None\n Function to model the battery's transition.\n If None, :meth:`BatteryModule.default_transition_model` is used.\n\n .. note::\n If you define a battery_transition_model, it must be YAML-serializable if you plan to serialize\n your battery module or any microgrid containing your battery.\n\n For example, you can define it as a class with a ``__call__`` method and ``yaml.YAMLObject`` as its metaclass.\n See the `PyYAML documentation `_ for details.\n\n init_charge : float or None, default None\n Initial charge of the battery.\n One of ``init_charge`` or ``init_soc`` must be passed, else an exception is raised.\n If both are passed, ``init_soc`` is ignored and ``init_charge`` is used.\n\n init_soc : float or None, default None\n Initial state of charge of the battery.\n One of ``init_charge`` or ``init_soc`` must be passed, else an exception is raised.\n If both are passed, ``init_soc`` is ignored and ``init_charge`` is used.\n\n raise_errors : bool, default False\n Whether to raise errors if bounds are exceeded in an action.\n If False, actions are clipped to the limit possible.\n\n \"\"\"\n module_type = ('battery', 'controllable')\n yaml_tag = f\"!BatteryModule\"\n yaml_dumper = yaml.SafeDumper\n yaml_loader = yaml.SafeLoader\n\n def __init__(self,\n min_capacity,\n max_capacity,\n max_charge,\n max_discharge,\n efficiency,\n battery_cost_cycle=0.0,\n battery_transition_model=None,\n init_charge=None,\n init_soc=None,\n initial_step=0,\n raise_errors=False):\n assert 0 < efficiency <= 1\n self.min_capacity = min_capacity\n self.max_capacity = max_capacity\n self.max_charge = max_charge\n self.max_discharge = max_discharge\n self.efficiency = efficiency\n self.battery_transition_model = battery_transition_model\n self.battery_cost_cycle = battery_cost_cycle\n\n self.min_soc, self.max_soc = min_capacity/max_capacity, 1\n self.init_charge, self.init_soc = init_charge, init_soc\n self._current_charge, self._soc = self._init_battery(init_charge, init_soc)\n self.name = ('battery', None)\n super().__init__(raise_errors,\n initial_step=initial_step,\n provided_energy_name='discharge_amount',\n absorbed_energy_name='charge_amount')\n\n def _init_battery(self, init_charge, init_soc):\n if init_charge is not None:\n if init_soc is not None:\n warn('Passed both init_capacity and init_soc. Using init_charge and ignoring init_soc')\n init_soc = init_charge / self.max_capacity\n elif init_soc is not None:\n init_charge = init_soc * self.max_capacity\n else:\n raise ValueError(\"Must set one of init_charge and init_soc.\")\n\n return init_charge, init_soc\n\n def update(self, external_energy_change, as_source=False, as_sink=False):\n assert as_source + as_sink == 1, 'Must act as either source or sink but not both or neither.'\n\n if as_source:\n info_key = 'provided_energy'\n internal_energy_change = self.model_transition(-1.0 * external_energy_change)\n assert internal_energy_change <= 0\n else:\n info_key = 'absorbed_energy'\n internal_energy_change = self.model_transition(external_energy_change)\n assert internal_energy_change >= 0\n\n self._update_state(internal_energy_change)\n reward = -1.0 * self.get_cost(internal_energy_change)\n info = {info_key: external_energy_change}\n return reward, False, info\n\n def _update_state(self, energy_change):\n self._current_charge += energy_change\n if self._current_charge < self.min_capacity:\n assert np.isclose(self._current_charge, self.min_capacity)\n self._current_charge = self.min_capacity\n self._soc = self._current_charge/self.max_capacity\n\n def get_cost(self, energy_change):\n \"\"\"\n Get the cost of charging or discharging.\n\n Parameters\n ----------\n energy_change : float\n Internal energy change.\n\n Returns\n -------\n cost : float\n Cost of charging or discharging.\n\n \"\"\"\n return np.abs(energy_change)*self.battery_cost_cycle\n\n def model_transition(self, energy):\n \"\"\"\n Convert an external energy request to a change in internal energy.\n\n This function uses the class argument ``battery_transition_model`` if one was passed.\n\n ``battery_transition_model`` must use the following api:\n\n .. code-block:: bash\n\n internal_energy_change = battery_transition_model(\n external_energy_change,\n min_capacity,\n max_capacity,\n max_charge,\n max_discharge,\n efficiency,\n battery_cost_cycle,\n max_production,\n max_consumption,\n state_dict\n )\n\n The return value ``internal_energy_change`` must be a float.\n See :meth:`transition_kwargs` and :meth:`battery_transition_model` for details on these parameters;\n all parameters are passed as keyword arguments.\n\n Parameters\n ----------\n energy : float\n External energy change.\n\n Returns\n -------\n internal_energy : float\n Amount of energy that the battery must use or will retain given the external amount of energy.\n\n \"\"\"\n if self.battery_transition_model is None:\n return self.default_transition_model(external_energy_change=energy, **self.transition_kwargs())\n return self.battery_transition_model(external_energy_change=energy, **self.transition_kwargs())\n\n def transition_kwargs(self):\n \"\"\"\n Values passed to transition models.\n\n Keys\n ----\n min_capacity : float\n Minimum energy that must be contained in the battery.\n\n max_capacity : float\n Maximum energy that can be contained in the battery.\n If ``soc=1``, capacity is at this maximum.\n\n max_charge : float\n Maximum amount the battery can be charged in one step.\n\n max_discharge : float\n Maximum amount the battery can be discharged in one step.\n\n efficiency : float\n Efficiency of the battery.\n\n battery_cost_cycle : float\n Marginal cost of charging and discharging.\n\n max_production : float\n Maximum amount of production, which is the lower of the maximum discharge and the discharge that would\n send the battery to ``min_capacity``.\n\n max_consumption : float\n Maximum amount of consumption, which is the lower of the maximum charge and the charge that would send\n the battery to ``max_capacity``.\n\n state_dict : dict\n State dictionary, with state of charge and current capacity information.\n\n Returns\n -------\n kwargs : dict\n Transition keyword arguments.\n\n \"\"\"\n return dict(min_capacity=self.min_capacity,\n max_capacity=self.max_capacity,\n max_charge=self.max_charge,\n max_discharge=self.max_discharge,\n efficiency=self.efficiency,\n battery_cost_cycle=self.battery_cost_cycle,\n max_production=self.max_production,\n max_consumption=self.max_consumption,\n state_dict=self.state_dict()\n )\n\n @staticmethod\n def default_transition_model(external_energy_change, efficiency, **transition_kwargs):\n \"\"\"\n A simple battery transition model.\n\n In this model, the amount of energy retained is given by ``efficiency``.\n\n For example, if a microgrid requests 100 kWh of energy and ``efficiency=0.5``, the battery must use\n 200 kWh of energy. Alternatively, if a microgrid sends a battery 100 kWh of energy and ``efficiency=0.5``,\n the battery's charge will increase by 50 kWh.\n\n Parameters\n ----------\n external_energy_change : float\n Amount of energy that is being requested externally.\n If ``energy > 0``, it is energy that is absorbed by the battery -- a charge.\n If ``energy < 0``, it is energy provided by the battery: a discharge.\n\n efficiency : float\n Battery efficiency.\n\n transition_kwargs : dict\n State transition values given by :meth:`BatteryModule.transition_kwargs`.\n\n Returns\n -------\n internal_energy : float\n Amount of energy that the battery must use or will retain given the external amount of energy.\n\n \"\"\"\n\n if external_energy_change < 0:\n return external_energy_change / efficiency\n else:\n return external_energy_change * efficiency\n\n def _state_dict(self):\n return dict(zip(('soc', 'current_charge'), [self._soc, self._current_charge]))\n\n @property\n def max_production(self):\n # Max discharge\n return min(self.max_discharge, self._current_charge-self.min_capacity) * self.efficiency\n\n @property\n def max_consumption(self):\n # Max charge\n return min(self.max_charge, self.max_capacity - self._current_charge) / self.efficiency\n\n @property\n def current_charge(self):\n \"\"\"\n Battery charge.\n\n Level of charge of the battery.\n\n Returns\n -------\n current_charge : float\n Charge.\n\n \"\"\"\n return self._current_charge\n\n @property\n def soc(self):\n \"\"\"\n Battery state of charge.\n\n Level of charge of the battery relative to its capacity.\n\n Returns\n -------\n soc : float\n State of charge. In the range [0, 1].\n\n \"\"\"\n return self._soc\n\n @property\n def min_obs(self):\n # Min charge amount, min soc\n return np.array([self.min_soc, self.min_capacity])\n\n @property\n def max_obs(self):\n return np.array([self.max_soc, self.max_capacity])\n\n @property\n def min_act(self):\n return -self.max_discharge / self.efficiency\n\n @property\n def max_act(self):\n return self.max_charge * self.efficiency\n\n @property\n def production_marginal_cost(self):\n return self.battery_cost_cycle\n\n @property\n def absorption_marginal_cost(self):\n return self.battery_cost_cycle\n\n @property\n def is_source(self):\n return True\n\n @property\n def is_sink(self):\n return True\n\n @soc.setter\n def soc(self, value):\n self._current_charge, self._soc = self._init_battery(None, value)\n\n @current_charge.setter\n def current_charge(self, value):\n self._current_charge, self._soc = self._init_battery(value, None)\n" }, { "path": "src/pymgrid/modules/genset_module.py", "content": "import yaml\nimport numpy as np\nfrom warnings import warn\n\nfrom pymgrid.modules.base import BaseMicrogridModule\n\n\nclass GensetModule(BaseMicrogridModule):\n \"\"\"\n A genset/generator module.\n\n This module is a controllable source module; when used as a module in a microgrid, you must pass it an energy production\n request.\n\n Parameters\n ----------\n running_min_production : float\n Minimum production of the genset when it is running.\n\n running_max_production : float\n Maximum production of the genset when it is running.\n\n genset_cost : float or callable\n * If float, the marginal cost of running the genset: ``total_cost = genset_cost * production``.\n\n * If callable, a function that takes the genset production as an argument and returns the genset cost.\n\n co2_per_unit : float, default 0.0\n Carbon dioxide production per unit energy production.\n\n cost_per_unit_co2 : float, default 0.0\n Carbon dioxide cost per unit carbon dioxide production.\n\n start_up_time : int, default 0\n Number of steps it takes to turn on the genset.\n\n wind_down_time : int, default 0\n Number of steps it takes to turn off the genset.\n\n allow_abortion : bool, default True\n Whether the genset is able to remain shut down while in the process of starting up and vice versa.\n\n init_start_up : bool, default True\n Whether the genset is running upon reset.\n\n raise_errors : bool, default False\n Whether to raise errors if bounds are exceeded in an action.\n If False, actions are clipped to the limit possible.\n\n provided_energy_name : str, default \"genset_production\"\n Name of the energy provided by this module, to be used in logging.\n\n \"\"\"\n module_type = 'genset', 'controllable'\n yaml_tag = f\"!Genset\"\n yaml_dumper = yaml.SafeDumper\n yaml_loader = yaml.SafeLoader\n\n _energy_pos = 1\n\n def __init__(self,\n running_min_production,\n running_max_production,\n genset_cost,\n co2_per_unit=0.0,\n cost_per_unit_co2=0.0,\n start_up_time=0,\n wind_down_time=0,\n allow_abortion=True,\n init_start_up=True,\n initial_step=0,\n raise_errors=False,\n provided_energy_name='genset_production'):\n\n if running_min_production > running_max_production:\n raise ValueError('parameter min_production must not be greater than parameter max_production.')\n\n if not allow_abortion:\n warn('Gensets that do not allow abortions are not fully tested, setting allow_abortion=False '\n 'may lead to unexpected behavior.')\n\n self.running_min_production, self.running_max_production = running_min_production, running_max_production\n self.co2_per_unit, self.cost_per_unit_co2 = co2_per_unit, cost_per_unit_co2\n\n self.genset_cost = genset_cost\n self.start_up_time = start_up_time\n self.wind_down_time = wind_down_time\n self.allow_abortion = allow_abortion\n self.init_start_up = init_start_up\n\n self._current_status, self._goal_status = int(init_start_up), int(init_start_up)\n self._steps_until_up, self._steps_until_down = self._reset_up_down_times()\n self.name = ('genset', None)\n\n super().__init__(raise_errors,\n initial_step=initial_step,\n provided_energy_name=provided_energy_name,\n absorbed_energy_name=None)\n\n def step(self, action, normalized=True):\n \"\"\"\n Take one step in the module, attempting to draw a certain amount of energy from the genset.\n\n Parameters\n ----------\n action : float or np.ndarray, shape (2,)\n Two-dimensional vector containing two values. The first value is used to passed to\n :meth:`.GensetModule.update_status` while the second is the amount of energy to draw from the genset.\n\n If ``normalized``, the amount of energy is assumed to be normalized and is un-normalized into the range\n [:attr:`.GensetModule.min_act`, :attr:`.GensetModule.max_act`].\n\n If the **unnormalized** action is positive, the module acts as a source and provides energy to the\n microgrid. Otherwise, the module acts as a sink and absorbs energy.\n\n If the unnormalized action implies acting as a sink and ``is_sink`` is False -- or the converse -- an\n ``AssertionError`` is raised.\n\n .. warning::\n The first element in ``action`` is not denormalized before being passed to\n :meth:`.GensetModule.update_status`, regardless of the value of ``normalized``.\n\n normalized : bool, default True\n Whether ``action`` is normalized. If True, action is assumed to be normalized and is un-normalized into the\n range [:attr:`.GensetModule.min_act`, :attr:`.GensetModule.max_act`].\n\n Raises\n ------\n AssertionError\n If action implies acting as a sink, or ``action[0]`` in outside of ``[0, 1]``.\n\n Returns\n -------\n observation : np.ndarray\n State of the module after taking action ``action``.\n reward : float\n Reward/cost after taking the action.\n done : bool\n Whether the module terminates.\n info : dict\n Additional information from this step.\n Will include either `provided_energy` or `absorbed_energy` as a key, denoting the amount of energy\n this module provided to or absorbed from the microgrid.\n\n \"\"\"\n goal_status = action[0]\n assert 0 <= goal_status <= 1\n self.update_status(goal_status)\n return super().step(action, normalized=normalized)\n\n def get_co2(self, production):\n \"\"\"\n Carbon dioxide emissions of energy production.\n\n Parameters\n ----------\n production : float\n Energy production.\n Returns\n -------\n co2 : float\n Carbon dioxide production.\n\n \"\"\"\n return self.co2_per_unit*production\n\n def get_co2_cost(self, production):\n \"\"\"\n Carbon dioxide production cost.\n\n Parameters\n ----------\n production : float\n Energy production.\n Returns\n -------\n co2_cost : float\n Carbon dioxide cost.\n\n \"\"\"\n return self.cost_per_unit_co2 * self.get_co2(production)\n\n def _get_fuel_cost(self, production):\n if callable(self.genset_cost):\n return self.genset_cost(production)\n return self.genset_cost*production\n\n def get_cost(self, production):\n \"\"\"\n Total cost of energy production.\n\n Includes both fuel and carbon dioxide costs.\n\n Parameters\n ----------\n production : float\n Energy production.\n\n Returns\n -------\n cost : float\n Total cost.\n\n \"\"\"\n return self._get_fuel_cost(production) + self.get_co2_cost(production)\n\n def update(self, external_energy_change, as_source=False, as_sink=False):\n assert as_source, 'This module may only act as a source.'\n\n reward = -1.0 * self.get_cost(external_energy_change)\n info = {'provided_energy': external_energy_change,\n 'co2_production': self.get_co2(external_energy_change)}\n\n return reward, False, info\n\n def _reset_up_down_times(self):\n if self._goal_status != self._current_status:\n raise RuntimeError('Attempting to reset up and down times with status change in progress.')\n\n if self._current_status:\n self._steps_until_up = 0\n self._steps_until_down = self.wind_down_time\n else:\n self._steps_until_down = 0\n self._steps_until_up = self.start_up_time\n\n return self._steps_until_up, self._steps_until_down\n\n def _update_up_down_times(self):\n if self._goal_status == 0: # Turning it off\n self._steps_until_down -= 1\n else:\n self._steps_until_up -= 1\n\n def update_status(self, goal_status):\n \"\"\"\n Update the status of the microgrid.\n\n The status and goal status are updated, taking into account any in-progress status change as well as\n ``goal_status``. This method updates the internal properties ``self.current_status``, ``self.goal_status``,\n ``self.steps_until_up``, and ``self.steps_until_down`` as follows:\n\n 1. If ``steps_until_up == 0`` or ``steps_until_down == 0``, the status is changed to on and off, respectively.\n The following steps are then executed.\n \n 2. If ``goal_status == self.current_status == self.goal_status``, the genset is in equilibrium and its status\n does not change.\n\n In this case, one of ``self.steps_until_up``/``self.steps_until_down`` should be zero -- the former\n if ``self.current_status`` and the latter if not -- and the other should be\n ``self.start_up_time``/``self.wind_down_time``, respectively.\n\n 3. If ``goal_status == self.current_status != self.goal_status``, we are trying to abort a status change.\n\n * If ``self.allow_abortion``, the abortion can succeed. ``self.goal_status`` changes to ``goal_status``\n and ``steps_until_up``/``steps_until_down`` are reset (one to zero, one to\n the corresponding ``self.start_up_time``/``self.wind_down_time``).\n\n * Otherwise, we proceed with an in-progress status change, and the corresponding\n ``steps_until_up``/``steps_until_down`` is incremented. This is identical to the case below.\n\n 4. If ``goal_status == self.goal_status != self.current_status``, a previously requested status change is\n being continued, and the corresponding ``steps_until_up``/``steps_until_down`` is incremented.\n\n .. note::\n Steps 2, 3, and 4 are mutually exclusive, while step 1 is not and will be executed before the relevant\n step 2, 3 or 4.\n\n Parameters\n ----------\n goal_status : float in [0, 1].\n Goal status as defined by an external action.\n\n Will be rounded to 0 or 1 to define the goal status.\n \"\"\"\n assert self._steps_until_down >= 0 and self._steps_until_up >= 0\n\n if not 0 <= goal_status <= 1:\n raise ValueError(f\"Invalid goal_status value {goal_status}, must be in [0, 1].\")\n\n goal_status = round(goal_status)\n next_prediction = self.next_status(goal_status)\n\n if goal_status == self._current_status == self._goal_status:\n # Everything is hunky-dory\n assert self._steps_until_down == 0 or self._steps_until_up == 0\n return\n\n instant_up = self.start_up_time == 0 and goal_status == 1\n instant_down = self.wind_down_time == 0 and goal_status == 0\n if goal_status != self._goal_status and (self.allow_abortion or instant_up or instant_down):\n self._goal_status = goal_status\n\n finished_change = self._finish_in_progress_change()\n\n if not finished_change:\n self._non_instantaneous_update(goal_status)\n\n if not self._current_status == next_prediction:\n raise ValueError('self.next_status working incorrectly.')\n\n def _finish_in_progress_change(self):\n if self._steps_until_up == 0 and self._goal_status == 1:\n self._current_status = 1\n self._reset_up_down_times()\n return True\n elif self._steps_until_down == 0 and self._goal_status == 0:\n self._current_status = 0\n self._reset_up_down_times()\n return True\n return False\n\n def _instant_up(self):\n self._goal_status = 1\n\n if not self._current_status:\n self._current_status = 1\n self._reset_up_down_times()\n\n def _instant_down(self):\n self._goal_status = 0\n\n if self._current_status:\n self._current_status = 0\n self._reset_up_down_times()\n\n def _non_instantaneous_update(self, goal_status):\n if (goal_status == self._current_status != self._goal_status) and self.allow_abortion:\n # First case: aborting an in-progress status change\n self._goal_status = goal_status\n self._reset_up_down_times()\n elif self._current_status == self._goal_status != goal_status:\n # Second case: new status change request\n self._reset_up_down_times()\n self._goal_status = goal_status\n\n if self._goal_status != self._current_status:\n \"\"\"\n Current status is not equal to status goal; thus a status change is in progress and the relevant\n incrementer should be positive.\n \"\"\"\n if self._goal_status:\n assert self._steps_until_up > 0\n else:\n assert self._steps_until_down > 0\n self._update_up_down_times()\n\n def sample_action(self, strict_bound=False, **kwargs):\n return np.array([np.random.rand(), super().sample_action(strict_bound=strict_bound)])\n\n def _raise_error(self, ask_value, available_value, as_source=False, as_sink=False, lower_bound=False):\n try:\n super()._raise_error(ask_value, available_value, as_source=as_source, as_sink=as_sink, lower_bound=lower_bound)\n except ValueError as e:\n if not self._current_status:\n raise ValueError(f'{e}\\n This may be because this genset module is not currently running.') from e\n else:\n raise ValueError(f'{e}\\n This is despite the fact this genset module is currently running.') from e\n\n def next_status(self, goal_status):\n \"\"\"\n Predict the next status of the genset given a goal status.\n\n Does not modify the genset in any way.\n\n Parameters\n ----------\n goal_status : {0, 1}\n Goal status.\n\n Returns\n -------\n next_status : {0, 1}\n The next status given the current status and the goal status.\n\n \"\"\"\n if goal_status:\n if self._current_status:\n return 1\n elif self._steps_until_up == 0:\n return 1\n else:\n return 0\n else:\n if not self._current_status:\n return 0\n elif self._steps_until_down == 0:\n return 0\n else:\n return 1\n\n def next_max_production(self, goal_status):\n \"\"\"\n Maximum production given a goal status.\n\n Parameters\n ----------\n goal_status : {0, 1}\n A goal status.\n\n Returns\n -------\n next_max_production : float\n Maximum production given a goal status.\n\n \"\"\"\n return self.next_status(goal_status) * self.running_max_production\n\n def next_min_production(self, goal_status):\n \"\"\"\n Minimum production given a goal status.\n\n Parameters\n ----------\n goal_status : {0, 1}\n A goal status.\n\n Returns\n -------\n next_min_production : float\n Minimum production given a goal status.\n\n \"\"\"\n return self.next_status(goal_status) * self.running_min_production\n\n def serializable_state_attributes(self):\n return [\"_current_step\"] + [f\"_{key}\" for key in self.state_dict().keys()]\n\n def _state_dict(self):\n return {'current_status': self._current_status,\n 'goal_status': self._goal_status,\n 'steps_until_up': self._steps_until_up,\n 'steps_until_down': self._steps_until_down}\n\n @property\n def current_status(self):\n \"\"\"\n Status of the genset.\n\n On or off.\n\n Returns\n -------\n status : {0, 1}\n Integer value denoting the genset's current status.\n\n \"\"\"\n return self._current_status\n\n @property\n def goal_status(self):\n \"\"\"\n Goal of the genset.\n\n Whether the genset is trying to turn -- or keep -- itself on or off.\n\n Returns\n -------\n status : {0, 1}\n Integer value denoting the genset's goal status.\n\n \"\"\"\n return self._goal_status\n\n @property\n def max_production(self):\n \"\"\"\n Maximum amount of production at the current time step.\n\n .. warning::\n This value is aware of the genset's current status, but does not know if you're planning on turning\n it off at this step.\n\n This consideration is only relevant if ``start_up_time==0`` or ``wind_down_time==0``.\n\n Returns\n -------\n max_production : float\n Current maximum production.\n\n \"\"\"\n return self._current_status * self.running_max_production\n\n @property\n def min_production(self):\n \"\"\"\n Minimum amount of production at the current time step.\n\n .. warning::\n This value is aware of the genset's current status, but does not know if you're planning on turning\n it off at this step.\n\n This consideration is only relevant if ``start_up_time==0`` or ``wind_down_time==0``.\n\n Returns\n -------\n min_production : float\n Current minimum production.\n\n \"\"\"\n return self._current_status * self.running_min_production\n\n @property\n def min_obs(self):\n return np.array([0, 0, 0, 0])\n\n @property\n def max_obs(self):\n return np.array([1, 1, self.start_up_time, self.wind_down_time])\n\n @property\n def min_act(self):\n return np.array([0, 0])\n\n @property\n def max_act(self):\n return np.array([1, self.running_max_production])\n\n @property\n def production_marginal_cost(self):\n return self.get_cost(1.0)\n\n @property\n def is_source(self):\n return True\n" }, { "path": "src/pymgrid/modules/grid_module.py", "content": "import numpy as np\nimport yaml\n\nfrom pymgrid.microgrid import DEFAULT_HORIZON\nfrom pymgrid.modules.base import BaseTimeSeriesMicrogridModule\n\n\nclass GridModule(BaseTimeSeriesMicrogridModule):\n \"\"\"\n An electrical grid module.\n\n By default, ``GridModule`` is a *fixed* module; it can be transformed to a flex module with ``GridModule.as_flex``.\n ``GridModule`` is the only built-in module that can be both a fixed and flex module.\n\n Parameters\n ----------\n max_import : float\n Maximum import at any time step.\n\n max_export : float\n Maximum export at any time step.\n\n time_series : array-like, shape (n_features, n_steps), n_features = {3, 4}\n If n_features=3, time series of ``(import_price, export_price, co2_per_kwH)`` in each column, respectively.\n Grid is assumed to have no outages.\n If n_features=4, time series of ``(import_price, export_price, co2_per_kwH, grid_status)``\n in each column, respectively. ``time_series[:, -1]`` -- the grid status -- must be binary.\n\n forecaster : callable, float, \"oracle\", or None, default None.\n Function that gives a forecast n-steps ahead.\n\n * If ``callable``, must take as arguments ``(val_c: float, val_{c+n}: float, n: int)``, where\n\n * ``val_c`` is the current value in the time series: ``self.time_series[self.current_step]``\n\n * ``val_{c+n}`` is the value in the time series n steps in the future\n\n * n is the number of steps in the future at which we are forecasting.\n\n The output ``forecast = forecaster(val_c, val_{c+n}, n)`` must have the same sign\n as the inputs ``val_c`` and ``val_{c+n}``.\n\n * If ``float``, serves as a standard deviation for a mean-zero gaussian noise function\n that is added to the true value.\n\n * If ``\"oracle\"``, gives a perfect forecast.\n\n * If ``None``, no forecast.\n\n forecast_horizon : int.\n Number of steps in the future to forecast. If forecaster is None, ignored and 0 is returned.\n\n forecaster_increase_uncertainty : bool, default False\n Whether to increase uncertainty for farther-out dates if using a GaussianNoiseForecaster. Ignored otherwise.\n\n cost_per_unit_co2 : float, default 0.0\n Marginal cost of grid co2 production.\n\n raise_errors : bool, default False\n Whether to raise errors if bounds are exceeded in an action.\n If False, actions are clipped to the limit possible.\n\n \"\"\"\n\n module_type = ('grid', 'controllable')\n\n yaml_tag = u\"!GridModule\"\n yaml_loader = yaml.SafeLoader\n yaml_dumper = yaml.SafeDumper\n\n state_components = np.array(['import_price', 'export_price', 'co2_per_kwh', 'grid_status'], dtype=object)\n\n def __init__(self,\n max_import,\n max_export,\n time_series,\n forecaster=None,\n forecast_horizon=DEFAULT_HORIZON,\n forecaster_increase_uncertainty=False,\n forecaster_relative_noise=False,\n initial_step=0,\n final_step=-1,\n cost_per_unit_co2=0.0,\n raise_errors=False):\n\n time_series = self._check_params(max_import, max_export, time_series)\n self.max_import, self.max_export = max_import, max_export\n self.cost_per_unit_co2 = cost_per_unit_co2\n self.name = ('grid', None)\n super().__init__(\n time_series,\n raise_errors,\n forecaster=forecaster,\n forecast_horizon=forecast_horizon,\n forecaster_increase_uncertainty=forecaster_increase_uncertainty,\n forecaster_relative_noise=forecaster_relative_noise,\n initial_step=initial_step,\n final_step=final_step,\n provided_energy_name='grid_import',\n absorbed_energy_name='grid_export'\n )\n\n def _check_params(self, max_import, max_export, time_series):\n if max_import < 0:\n raise ValueError('parameter max_import must be non-negative.')\n if max_export < 0:\n raise ValueError('parameter max_export must be non-negative.')\n if time_series.shape[1] not in [3, 4]:\n raise ValueError('Time series must be two dimensional with three or four columns.'\n 'See docstring for details.')\n\n if time_series.shape[1] == 4:\n if not ((np.array(time_series)[:, -1] == 0) | (np.array(time_series)[:, -1] == 1)).all():\n raise ValueError(\"Last column (grid status) must contain binary values.\")\n else:\n new_ts = np.ones((time_series.shape[0], 4))\n new_ts[:, :3] = time_series\n time_series = new_ts\n\n if (time_series < 0).any().any():\n raise ValueError('Time series must be non-negative.')\n\n return time_series\n\n def _get_bounds(self):\n min_obs = self._time_series.min(axis=0)\n max_obs = self._time_series.max(axis=0)\n assert len(min_obs) in (3, 4)\n\n min_act, max_act = -1 * self.max_export, self.max_import\n\n return min_obs, max_obs, min_act, max_act\n\n def update(self, external_energy_change, as_source=False, as_sink=False):\n assert as_source + as_sink == 1, 'Must act as either source or sink but not both or neither.'\n reward = self.get_cost(external_energy_change, as_source, as_sink)\n info_key = 'provided_energy' if as_source else 'absorbed_energy'\n info = {info_key: external_energy_change,\n 'co2_production': self.get_co2_production(external_energy_change, as_source, as_sink)}\n\n return reward, self._done(), info\n\n def get_cost(self, import_export, as_source, as_sink):\n \"\"\"\n Current cost of the grid's usage.\n\n Includes both the cost of importing/exporting as well as the cost of carbon dioxide production.\n Note that the \"cost\" of exporting may be negative as the module may receive revenue in exchange for\n energy export.\n If the module is exporting to the grid, co2 production cost will be zero.\n\n Parameters\n ----------\n import_export : float\n Amount of energy that is imported or exported.\n as_source : bool\n Whether the grid is acting as a source.\n as_sink : bool\n Whether the grid is acting as a sink.\n\n Returns\n -------\n cost : float\n Cost of using the grid.\n\n \"\"\"\n if as_source: # Import\n import_cost = self._time_series[self.current_step, 0]\n return -1 * import_cost*import_export + self.get_co2_cost(import_export, as_source, as_sink)\n elif as_sink: # Export\n export_cost = self._time_series[self.current_step, 1]\n return export_cost * import_export + self.get_co2_cost(import_export, as_source, as_sink)\n else:\n raise RuntimeError\n\n def get_co2_cost(self, import_export, as_source, as_sink):\n \"\"\"\n Current cost of the carbon dioxide production of the grid's usage.\n\n If the module is exporting to the grid, co2 production cost will be zero.\n\n Parameters\n ----------\n import_export : float\n Amount of energy that is imported or exported.\n as_source : bool\n Whether the grid is acting as a source.\n as_sink : bool\n Whether the grid is acting as a sink.\n\n Returns\n -------\n co2_cost : float\n Cost of carbon dioxide production.\n\n \"\"\"\n return -1.0 * self.cost_per_unit_co2*self.get_co2_production(import_export, as_source, as_sink)\n\n def get_co2_production(self, import_export, as_source, as_sink):\n \"\"\"\n\n Current carbon dioxide production of the grid's usage.\n\n If the module is exporting to the grid, co2 production will be zero.\n\n Parameters\n ----------\n import_export : float\n Amount of energy that is imported or exported.\n as_source : bool\n Whether the grid is acting as a source.\n as_sink : bool\n Whether the grid is acting as a sink.\n\n Returns\n -------\n co2_production : float\n Carbon dioxide production.\n\n \"\"\"\n if as_source: # Import\n co2_prod_per_kWh = self._time_series[self.current_step, 2]\n co2 = import_export*co2_prod_per_kWh\n return co2\n elif as_sink: # Export\n return 0.0\n else:\n raise RuntimeError\n\n def as_flex(self):\n \"\"\"\n Convert the module to a flex module.\n\n Flex modules do not require a control to be passed, and are deployed as necessary to balance load and demand.\n\n \"\"\"\n self.__class__.module_type = (self.__class__.module_type[0], 'flex')\n\n def as_fixed(self):\n \"\"\"\n Convert the module to a fixed module.\n\n Flex modules require a control to be passed.\n\n \"\"\"\n self.__class__.module_type = (self.__class__.module_type[0], 'fixed')\n\n @property\n def import_price(self):\n \"\"\"\n Current and forecasted import prices.\n\n Returns\n -------\n prices : np.ndarray, shape (forecast_horizon, )\n prices[0] gives the current import price while prices[1:] gives forecasted import prices.\n\n \"\"\"\n return self.state[::4]\n\n @property\n def export_price(self):\n \"\"\"\n Current and forecasted export prices.\n\n Returns\n -------\n prices : np.ndarray, shape (forecast_horizon, )\n prices[0] gives the current export price while prices[1:] gives forecasted export prices.\n\n \"\"\"\n return self.state[1::4]\n\n @property\n def co2_per_kwh(self):\n \"\"\"\n Current and forecasted carbon dioxide production per kWh.\n\n Returns\n -------\n marginal_production : np.ndarray, shape (forecast_horizon, )\n marginal_production[0] gives the current production per kWh while\n marginal_production[1:] gives forecasted production per kWh.\n\n \"\"\"\n return self.state[2::4]\n\n @property\n def grid_status(self):\n \"\"\"\n Current and forecasted grid status.\n\n Returns\n -------\n status : np.ndarray, shape (forecast_horizon, )\n status[0] gives the current status of the grid while status[1:] gives forecasted status.\n\n \"\"\"\n return self.state[3::4]\n\n @property\n def current_status(self):\n \"\"\"\n Current status of the grid.\n\n Returns\n -------\n status : {0, 1}\n Current status.\n\n \"\"\"\n return self.grid_status[0]\n\n @property\n def max_production(self):\n return self.max_import * self.current_status\n\n @property\n def max_consumption(self):\n return self.max_export * self.current_status\n\n @property\n def production_marginal_cost(self):\n return self.import_price[0]\n\n @property\n def absorption_marginal_cost(self):\n return self.export_price[0]\n\n @property\n def is_source(self):\n return True\n\n @property\n def is_sink(self):\n return True\n\n @property\n def weak_grid(self):\n \"\"\"\n Whether the grid has outages or not.\n\n Returns\n -------\n weak_grid : bool\n True if the grid has outages.\n\n \"\"\"\n return self._time_series[:, -1].min() < 1\n\n def __repr__(self):\n return f'GridModule(max_import={self.max_import}, max_export={self.max_export})'\n" }, { "path": "src/pymgrid/modules/load_module.py", "content": "import numpy as np\nimport yaml\n\nfrom pymgrid.microgrid import DEFAULT_HORIZON\nfrom pymgrid.modules.base import BaseTimeSeriesMicrogridModule\n\n\nclass LoadModule(BaseTimeSeriesMicrogridModule):\n \"\"\"\n A renewable energy module.\n\n The classic examples of renewables are photovoltaics (PV) and wind turbines.\n\n Parameters\n ----------\n time_series : array-like, shape (n_steps, )\n Time series of load demand.\n\n forecaster : callable, float, \"oracle\", or None, default None.\n Function that gives a forecast n-steps ahead.\n\n * If ``callable``, must take as arguments ``(val_c: float, val_{c+n}: float, n: int)``, where\n\n * ``val_c`` is the current value in the time series: ``self.time_series[self.current_step]``\n\n * ``val_{c+n}`` is the value in the time series n steps in the future\n\n * n is the number of steps in the future at which we are forecasting.\n\n The output ``forecast = forecaster(val_c, val_{c+n}, n)`` must have the same sign\n as the inputs ``val_c`` and ``val_{c+n}``.\n\n * If ``float``, serves as a standard deviation for a mean-zero gaussian noise function\n that is added to the true value.\n\n * If ``\"oracle\"``, gives a perfect forecast.\n\n * If ``None``, no forecast.\n\n forecast_horizon : int.\n Number of steps in the future to forecast. If forecaster is None, ignored and 0 is returned.\n\n forecaster_increase_uncertainty : bool, default False\n Whether to increase uncertainty for farther-out dates if using a GaussianNoiseForecaster. Ignored otherwise..\n\n raise_errors : bool, default False\n Whether to raise errors if bounds are exceeded in an action.\n If False, actions are clipped to the limit possible.\n\n \"\"\"\n module_type = ('load', 'fixed')\n yaml_tag = u\"!LoadModule\"\n yaml_dumper = yaml.SafeDumper\n yaml_loader = yaml.SafeLoader\n\n state_components = np.array([\"load\"], dtype=object)\n\n def __init__(self,\n time_series,\n forecaster=None,\n forecast_horizon=DEFAULT_HORIZON,\n forecaster_increase_uncertainty=False,\n forecaster_relative_noise=False,\n initial_step=0,\n final_step=-1,\n raise_errors=False):\n super().__init__(\n time_series,\n raise_errors=raise_errors,\n forecaster=forecaster,\n forecast_horizon=forecast_horizon,\n forecaster_increase_uncertainty=forecaster_increase_uncertainty,\n forecaster_relative_noise=forecaster_relative_noise,\n initial_step=initial_step,\n final_step=final_step,\n provided_energy_name=None,\n absorbed_energy_name='load_met'\n )\n\n self.name = ('load', None)\n\n def _get_bounds(self):\n _min_obs, _max_obs, _, _ = super()._get_bounds()\n return _min_obs, _max_obs, np.array([]), np.array([])\n\n def update(self, external_energy_change, as_source=False, as_sink=False):\n assert as_sink, f'Class {self.__class__.__name__} is a sink.'\n\n info = {'absorbed_energy': self.current_load}\n\n return 0.0, self._done(), info\n\n def sample_action(self, strict_bound=False):\n return np.array([])\n\n @property\n def max_consumption(self):\n return self.current_load\n\n @property\n def current_load(self):\n \"\"\"\n Current load.\n\n Returns\n -------\n load : float\n Current load demand.\n\n \"\"\"\n return -1 * self._time_series[self._current_step].item()\n\n @property\n def is_sink(self):\n return True\n" }, { "path": "src/pymgrid/modules/module_container.py", "content": "import json\nimport pandas as pd\n\nfrom collections import UserDict, UserList\nfrom pymgrid.modules.base import BaseMicrogridModule\n\n\nclass Container(UserDict):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.dir_additions = self.dir_additions()\n\n @property\n def containers(self):\n \"\"\"\n View of this container's containers.\n\n Returns\n -------\n containers : dict-like\n View of containers.\n \"\"\"\n return self\n\n def to_list(self):\n \"\"\"\n Get the modules as a list.\n\n Returns\n -------\n l : list of modules\n List of modules\n\n \"\"\"\n l = []\n for _, raw_container in self.containers.items():\n l.extend(raw_container.to_list())\n return l\n\n def to_dict(self):\n \"\"\"\n Get the modules as a dictionary.\n\n Returns\n -------\n d : dict[str, list[module]]\n Dictionary with module names as keys, modules as values.\n\n \"\"\"\n d = dict()\n for k, raw_container in self.containers.items():\n d.update(raw_container)\n return d\n\n def to_tuples(self):\n \"\"\"\n Get the modules in (name, module) pairs.\n\n Returns\n -------\n tups : list of tuples: (name, module)\n Module names and modules.\n\n \"\"\"\n l = []\n for name, modules in self.iterdict():\n tups = list(zip([name] * len(modules), modules))\n l.extend(tups)\n return l\n\n def iterlist(self):\n \"\"\"\n Iterable of the container's modules as a list.\n\n Returns\n -------\n iter : generator\n Iterator of modules.\n\n \"\"\"\n for module in self.to_list():\n yield module\n\n def iterdict(self):\n \"\"\"\n Iterable of the container's modules as a dict.\n\n Returns\n -------\n iter : generator\n Iterator of (name, module) pairs.\n\n \"\"\"\n for name, modules in self.to_dict().items():\n yield name, modules\n\n def get_attrs(self, *attrs, unique=False, as_pandas=True):\n \"\"\"\n Get module attributes as a dictionary or pandas object.\n\n If ``unique``, checks that the value is unique for all modules and returns only the unique value.\n Otherwise, returns the values for all modules.\n\n If ``as_pandas``, returns either a pd.Series (if ``unique``) or pd.DataFrame of attributes.\n\n Parameters\n ----------\n attrs : str\n Names of module attributes to return.\n\n unique : bool, default False\n Whether to check for and return a single, unique value for an attribute.\n If different modules have different values, a ``ValueError`` will be raised.\n\n as_pandas : bool, default True\n Whether to return either a pd.Series or pd.DataFrame.\n If True, the return value will be a pd.Series if ``unique`` and a pd.DataFrame otherwise.\n If False, returns a dict.\n\n .. note::\n If only some modules have a particular attribute, ``get_attrs`` will not raise an error.\n\n If ``unique``, the unique value of the modules containing the value will be returned.\n Otherwise, ``NotImplemented`` will fill in missing values.\n\n Returns\n -------\n d : dict or pd.DataFrame or pd.Series\n * Returns dict if ``as_pandas`` is False.\n\n * Otherwise, returns a pd.Series if ``unique`` and a pd.DataFrame otherwise.\n\n Raises\n ------\n ValueError\n * If ``attrs`` is empty, or\n * ``unique`` is True and non-unique values are found.\n\n AttributeError\n If no module has the particular attribute.\n\n .. warning::\n This check is not performed if both ``unique`` and ``as_pandas`` are False.\n\n \"\"\"\n if not attrs:\n raise ValueError('Missing attrs to get.')\n\n d = dict()\n for k, raw_container in self.containers.items():\n d.update({\n name: [{attr: getattr(module, attr, NotImplemented) for attr in attrs} for module in module_list]\n for name, module_list in raw_container.items()\n })\n\n if not (unique or as_pandas):\n return d\n\n d_df = pd.DataFrame({(name, num): subdict for name, module_list in d.items()\n for num, subdict in enumerate(module_list)}).T\n\n bad_keys = []\n uniques, nonuniques = {}, []\n\n for k, v in d_df.items():\n not_notimplemented = v[v != NotImplemented]\n unique_items = not_notimplemented.drop_duplicates().values\n\n try:\n unique_item = unique_items.item()\n except ValueError:\n if len(unique_items) == 0:\n # Only values were NotImplemented\n bad_keys.append(k)\n else:\n nonuniques.append(k)\n else:\n uniques[k] = unique_item\n\n if len(bad_keys):\n raise AttributeError(f'No values found for key(s) {bad_keys}')\n\n if unique:\n if len(nonuniques):\n raise ValueError(f\"Attribute(s) {nonuniques} have non-unique values, cannot return single unique value.\")\n\n if not as_pandas:\n return uniques\n\n return pd.Series(uniques, dtype=None if len(uniques) else float)\n\n if as_pandas:\n return d_df\n\n return d\n\n def dir_additions(self):\n \"\"\"\n :meta private:\n \"\"\"\n additions = set(self.keys())\n for x in self.values():\n try:\n additions.update(x.dir_additions)\n except AttributeError:\n pass\n return additions\n\n def __getitem__(self, item):\n if item == 'data' or item == 'module_dict':\n raise KeyError(item)\n try:\n return self.data[item]\n except KeyError:\n try:\n return self.to_dict()[item]\n except KeyError:\n raise KeyError(item)\n\n def __getattr__(self, item):\n if item == 'data' or item.startswith('__') or item not in dir(self):\n raise AttributeError(item)\n try:\n return self[item]\n except KeyError:\n raise AttributeError(item)\n\n def __len__(self):\n return sum(len(v) for k, v in self.containers.items())\n\n def __repr__(self):\n try:\n return json.dumps(self.to_dict(), indent=2, default=str)\n except TypeError:\n return super().__repr__()\n\n def __dir__(self):\n rv = set(super().__dir__())\n rv = rv | self.dir_additions\n return sorted(rv)\n\n def __contains__(self, item):\n return item in self.data.keys() or item in self.dir_additions\n\n\nclass ModuleContainer(Container):\n \"\"\"\n Container of modules.\n\n Allows for indexing and viewing of a microgrids module's in various ways.\n \"\"\"\n \"\"\"\n Container of modules. Allows for indexing/getting of the modules in various ways.\n Modules are stored at the lowest level: self._containers[('fixed', 'source')] = [Genset, Grid], for example.\n These modules, however, can be accessed in many different ways:\n\n container.fixed\n All fixed modules\n container.flex\n All flex modules\n container.sources\n ll source modules\n container.sinks\n All sink modules\n container.fixed.sources\n All fixed source modules\n container.sources.fixed\n Same as above\n container.fixed.sinks\n All fixed sink modules\n container.sinks.fixed\n Same as above\n\n Modules can also be accessed directly:\n container.genset\n All modules named genset\n\n container.big_battery\n Modules passed with custom name big_battery\n\n Each level can be iterated on, both by calling .items() or by iterating through the modules directly with .iterlist()\n For example, container.sinks.iterlist() returns an iterator of all the sinks, without their names.\n\n \"\"\"\n def __init__(self, modules):\n \"\"\"\n\n :param modules: list-like. List of _modules or tuples. Latter case: tup(str, Module); str to define name of module\n and second element is the module.\n\n \"\"\"\n self._containers = get_subcontainers(modules)\n midlevels = self._set_midlevel()\n self._types_by_name = self._get_types_by_name()\n super().__init__(**midlevels)\n\n def _get_types_by_name(self):\n return {name: container_type for container_type, container in self._containers.items() for name in container}\n\n def _set_midlevel(self):\n midlevels = dict()\n for key, subcontainer in self._containers.items():\n fixed_or_flex, source_sink_both = key\n\n if fixed_or_flex in midlevels:\n midlevels[fixed_or_flex][source_sink_both] = subcontainer\n else:\n midlevels[fixed_or_flex] = {source_sink_both: subcontainer}\n\n if source_sink_both in midlevels:\n midlevels[source_sink_both][fixed_or_flex] = subcontainer\n else:\n midlevels[source_sink_both] = {fixed_or_flex: subcontainer}\n midlevels = {k: Container(**v) for k, v in midlevels.items()}\n return midlevels\n\n def names(self):\n return list(self._types_by_name.keys())\n\n @property\n def containers(self):\n return self._containers\n\n\nclass ModuleList(UserList):\n def item(self):\n \"\"\"\n Get the value of a singleton list.\n\n Returns\n -------\n module : BaseMicrogridModule\n Item in a singleton list.\n\n Raises\n ------\n ValueError :\n If there is more than one item in the list.\n\n \"\"\"\n if len(self) != 1:\n raise ValueError(\"Can only convert a ModuleList of length one to a scalar\")\n return self[0]\n\n def to_list(self):\n \"\"\"\n :meta private:\n\n Function to be compatible with Container API.\n\n \"\"\"\n return self\n\n\ndef get_subcontainers(modules):\n \"\"\"\n :meta private:\n \"\"\"\n source_sink_keys = ('sources', 'sinks', 'source_and_sinks')\n fixed = {k: dict() for k in source_sink_keys}\n flex = {k: dict() for k in source_sink_keys}\n controllable = {k: dict() for k in source_sink_keys}\n\n module_names = dict()\n\n for module in modules:\n try: # module is a tuple\n module_name, module = module\n fixed_flex_controllable = module.__class__.module_type[1]\n except TypeError: # module is a module\n try:\n module_name, fixed_flex_controllable = module.__class__.module_type\n except TypeError:\n raise NotImplementedError(\n f'Must define the class attribute module_type for class {module.__class__.__name__}')\n\n assert isinstance(module, BaseMicrogridModule), 'Module must inherit from BaseMicrogridModule.'\n assert module.is_sink or module.is_source, 'Module must be sink or source (or both).'\n\n source_sink_both = 'source_and_sinks' if module.is_sink and module.is_source else \\\n 'sources' if module.is_source else 'sinks'\n\n if fixed_flex_controllable == 'fixed':\n d = fixed\n elif fixed_flex_controllable == 'flex':\n d = flex\n elif fixed_flex_controllable == 'controllable':\n d = controllable\n else:\n raise TypeError(f'Cannot parse fixed_flex_controllable from module type {module.__class__.module_type}')\n\n try:\n module_names[module_name] = (fixed_flex_controllable, source_sink_both)\n except KeyError:\n raise NameError(\n f'Attempted to add module {module_name} of type {(fixed_flex_controllable, source_sink_both)}, '\n f'but there is an identically named module of type {module_names[module_name]}.')\n\n try:\n d[source_sink_both][module_name].append(module)\n except KeyError:\n d[source_sink_both][module_name] = ModuleList([module])\n module.name = (module_name, len(d[source_sink_both][module_name]) - 1)\n\n modules_dict = dict(fixed=fixed,\n flex=flex,\n controllable=controllable)\n\n containers = {(ffs, source_sink_both): Container(modules_dict[ffs][source_sink_both])\n for ffs in ('fixed', 'flex', 'controllable')\n for source_sink_both in source_sink_keys}\n\n return containers\n" }, { "path": "src/pymgrid/modules/renewable_module.py", "content": "import numpy as np\nimport yaml\n\nfrom pymgrid.microgrid import DEFAULT_HORIZON\nfrom pymgrid.modules.base import BaseTimeSeriesMicrogridModule\n\n\nclass RenewableModule(BaseTimeSeriesMicrogridModule):\n \"\"\"\n A renewable energy module.\n\n The classic examples of renewables are photovoltaics (PV) and wind turbines.\n\n Parameters\n ----------\n time_series : array-like, shape (n_steps, )\n Time series of renewable production.\n\n forecaster : callable, float, \"oracle\", or None, default None.\n Function that gives a forecast n-steps ahead.\n\n * If ``callable``, must take as arguments ``(val_c: float, val_{c+n}: float, n: int)``, where\n\n * ``val_c`` is the current value in the time series: ``self.time_series[self.current_step]``\n\n * ``val_{c+n}`` is the value in the time series n steps in the future\n\n * n is the number of steps in the future at which we are forecasting.\n\n The output ``forecast = forecaster(val_c, val_{c+n}, n)`` must have the same sign\n as the inputs ``val_c`` and ``val_{c+n}``.\n\n * If ``float``, serves as a standard deviation for a mean-zero gaussian noise function\n that is added to the true value.\n\n * If ``\"oracle\"``, gives a perfect forecast.\n\n * If ``None``, no forecast.\n\n forecast_horizon : int.\n Number of steps in the future to forecast. If forecaster is None, ignored and 0 is returned.\n\n forecaster_increase_uncertainty : bool, default False\n Whether to increase uncertainty for farther-out dates if using a GaussianNoiseForecaster. Ignored otherwise.\n\n provided_energy_name: str, default \"renewable_used\"\n Name of the energy provided by this module, to be used in logging.\n\n raise_errors : bool, default False\n Whether to raise errors if bounds are exceeded in an action.\n If False, actions are clipped to the limit possible.\n\n \"\"\"\n module_type = ('renewable', 'flex')\n yaml_tag = u\"!RenewableModule\"\n yaml_loader = yaml.SafeLoader\n yaml_dumper = yaml.SafeDumper\n\n state_components = np.array([\"renewable\"], dtype=object)\n\n def __init__(self,\n time_series,\n raise_errors=False,\n forecaster=None,\n forecast_horizon=DEFAULT_HORIZON,\n forecaster_increase_uncertainty=False,\n forecaster_relative_noise=False,\n initial_step=0,\n final_step=-1,\n provided_energy_name='renewable_used'):\n super().__init__(\n time_series,\n raise_errors,\n forecaster=forecaster,\n forecast_horizon=forecast_horizon,\n forecaster_increase_uncertainty=forecaster_increase_uncertainty,\n forecaster_relative_noise=forecaster_relative_noise,\n initial_step=initial_step,\n final_step=final_step,\n provided_energy_name=provided_energy_name,\n absorbed_energy_name=None\n )\n\n self.name = ('renewable', None)\n\n def update(self, external_energy_change, as_source=False, as_sink=False):\n assert as_source, f'Class {self.__class__.__name__} can only be used as a source.'\n assert external_energy_change <= self.current_renewable, f'Cannot provide more than {self.current_renewable}'\n\n info = {'provided_energy': external_energy_change,\n 'curtailment': self.current_renewable-external_energy_change}\n\n return 0.0, self._done(), info\n\n @property\n def max_production(self):\n return self.current_renewable\n\n @property\n def current_renewable(self):\n \"\"\"\n Current renewable production.\n\n Returns\n -------\n renewable : float\n Renewable production.\n\n \"\"\"\n return self._time_series[self._current_step].item()\n\n @property\n def is_source(self):\n return True\n" }, { "path": "src/pymgrid/modules/unbalanced_energy_module.py", "content": "import numpy as np\nimport yaml\n\nfrom pymgrid.modules.base import BaseMicrogridModule\n\n\nclass UnbalancedEnergyModule(BaseMicrogridModule):\n module_type = ('balancing', 'flex')\n yaml_tag = u\"!UnbalancedEnergyModule\"\n yaml_dumper = yaml.SafeDumper\n yaml_loader = yaml.SafeLoader\n\n def __init__(self,\n raise_errors,\n initial_step=0,\n loss_load_cost=10,\n overgeneration_cost=2.0\n ):\n\n super().__init__(raise_errors,\n initial_step=initial_step,\n provided_energy_name='loss_load',\n absorbed_energy_name='overgeneration')\n\n self.loss_load_cost, self.overgeneration_cost = loss_load_cost, overgeneration_cost\n self.name = ('unbalanced_energy', None)\n\n def update(self, external_energy_change, as_source=False, as_sink=False):\n assert as_source + as_sink == 1, 'Must act as either source or sink but not both or neither.'\n\n info_key = 'provided_energy' if as_source else 'absorbed_energy'\n reward = -1.0 * self.get_cost(external_energy_change, as_source, as_sink)\n assert reward <= 0\n info = {info_key: external_energy_change}\n\n return reward, False, info\n\n def get_cost(self, energy_amount, as_source, as_sink):\n \"\"\"\n Get the cost of unmet load or excess production.\n\n Parameters\n ----------\n energy_amount : float>=0\n Amount of unmet load or excess production.\n\n as_source : bool\n Whether the energy is unmet load.\n\n as_sink : bool\n Whether the energy is excess production.\n\n Returns\n -------\n cost : float\n\n Raises\n ------\n TypeError\n If both as_source and as_sink are True or neither are.\n\n \"\"\"\n if as_source and as_sink:\n raise TypeError(\"as_source and as_sink cannot both be True.\")\n if as_source: # loss load\n return self.loss_load_cost*energy_amount\n elif as_sink:\n return self.overgeneration_cost*energy_amount\n else:\n raise TypeError(\"One of as_source or as_sink must be True.\")\n\n def _state_dict(self):\n return dict()\n\n @property\n def state(self):\n return np.array([])\n\n @property\n def min_obs(self):\n return np.array([])\n\n @property\n def max_obs(self):\n return np.array([])\n\n @property\n def min_act(self):\n return -np.inf\n\n @property\n def max_act(self):\n return np.inf\n\n @property\n def max_production(self):\n return np.inf\n\n @property\n def max_consumption(self):\n return np.inf\n\n @property\n def is_source(self):\n return True\n\n @property\n def is_sink(self):\n return True\n\n @property\n def production_marginal_cost(self):\n return self.loss_load_cost\n\n @property\n def absorption_marginal_cost(self):\n return self.overgeneration_cost\n" }, { "path": "src/pymgrid/utils/DataGenerator.py", "content": "\"\"\"\nCopyright 2020 Total S.A.\nAuthors:Gonzague Henri , Avishai Halev <>\nPermission to use, modify, and distribute this software is given under the\nterms of the pymgrid License.\nNO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.\n$Date: 2020/08/27 08:04 $\nGonzague Henri\n\"\"\"\n\"\"\"\n\nCopyright (C) <2020> \n\nThis program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\n\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.\n\nYou should have received a copy of the GNU Lesser General Public License along with this program. If not, see .\n\n\"\"\"\nimport sys\nimport unittest\nimport pandas as pd\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom scipy.interpolate import interp1d\nimport statsmodels.regression.quantile_regression as quantile_regression\nfrom IPython.display import display\n\ndef return_underlying_data(microgrid):\n \"\"\"\n Returns the pv, load, and grid data from the microgrid in the same format as samples.\n :param microgrid, pymgrid.Microgrid.Microgrid\n microgrid to reformat underlying data for\n :return:\n data: pd.DataFrame, shape (8760,3)\n DataFrame with columns 'pv', 'load', 'grid', values of these respectively at each timestep.\n \"\"\"\n pv_data = microgrid._pv_ts\n load_data = microgrid._load_ts\n\n pv_data = pv_data[pv_data.columns[0]]\n load_data = load_data[load_data.columns[0]]\n pv_data.name = 'pv'\n load_data.name = 'load'\n\n if microgrid.architecture['grid'] != 0:\n grid_data = microgrid._grid_status_ts\n if isinstance(grid_data, pd.DataFrame):\n grid_data = grid_data[grid_data.columns[0]]\n grid_data.name = 'grid'\n elif isinstance(grid_data, pd.Series):\n grid_data.name = 'grid'\n else:\n raise RuntimeError('Unable to handle microgrid._grid_status_ts of type {}.'.format(type(grid_data)))\n else:\n grid_data = pd.Series(data=[0] * len(microgrid._load_ts), index=pv_data.index, name='grid')\n\n return pd.concat([pv_data, load_data, grid_data], axis=1)\n\n\n\nclass NoisyPVData:\n def __init__(self, pv_data = None,file_name = None):\n if pv_data is not None:\n if isinstance(pv_data,pd.Series):\n self.unmunged_data = pv_data.to_frame()\n self.data = pv_data.to_frame()\n elif not isinstance(pv_data, pd.DataFrame):\n raise TypeError('known_data must be of type pd.DataFrame or pd.Series, is ({})'.format(type(pv_data)))\n else:\n self.unmunged_data = pv_data.copy()\n self.data = pv_data.copy()\n\n elif file_name is not None:\n self.data, self.unmunged_data = self.import_file(file_name)\n\n else:\n raise RuntimeError('Unable to initialize data')\n\n self.num_hours = len(self.data)\n self.munged = False\n self.interpolated = False\n self.daily_maxes = None\n self.feature_functions = None\n self.feature_names = None\n self.interpolated_coef = None\n self.parabolic_baseline = None\n self.distribution_bounds = None\n\n def import_file(self,file_name):\n return pd.read_csv(file_name), pd.read_csv(file_name)\n\n def data_munge(self, verbose=False):\n\n # Get column name of pv values\n if len(self.data.columns.values)!=1:\n print('Warning multiple columns in pv_data, attempting to use \\'GH illum (lx)\\' as column name')\n col_name = 'GH illum (lx)'\n else:\n col_name = self.data.columns[0]\n\n hours = [j % 24 for j in range(self.num_hours)]\n day = [int(np.floor(j / 24)) for j in range(self.num_hours)]\n\n self.data['hour'] = pd.Series(data=hours)\n self.data['day'] = pd.Series(data=day)\n self.data = self.data.pivot(index='hour', columns='day', values=col_name)\n\n indices_of_max = self.data.idxmax(axis=0)\n maxes = self.data.max(axis=0)\n indices_of_max.name = 'time_of_max'\n maxes.name = 'max_GHI'\n self.daily_maxes = pd.concat([indices_of_max, maxes], axis=1)\n self.daily_maxes['cumulative_hr'] = self.daily_maxes['time_of_max'] + self.daily_maxes.index.values * 24\n\n self.munged = True\n\n if verbose:\n print('Munging completed.')\n print(self.data.describe())\n print(self.daily_maxes.describe())\n\n def _add_feature_columns(self, num_feature_functions=1, period_scale=1., ):\n feature_names = []\n feature_funcs = {}\n\n if not self.munged:\n raise RuntimeError('Data must be munged before adding feature columns or curve interpolation. '\n 'Call data_munge first')\n name = 'ones'\n self.daily_maxes[name] = pd.Series(data = [1.0]*len(self.daily_maxes['cumulative_hr']))\n\n def f(x):\n if isinstance(x, int) or isinstance(x, float):\n return 1.0\n else:\n return pd.Series(data=[1.0] * len(x))\n\n feature_names.append(name)\n feature_funcs[name] = f\n\n for k in range(num_feature_functions):\n if k % 2 == 0:\n num = int(np.floor((k + 2) / 2))\n name = 'cos' + str(num) + 'x'\n self.daily_maxes[name] = np.cos(\n 2 * num * np.pi / 8760. * period_scale * (self.daily_maxes['cumulative_hr'] - 173 * 24))\n\n def f(x):\n return np.cos(2 * num * np.pi / 8760. * period_scale * (x - 173 * 24))\n\n assert all((f(self.daily_maxes['cumulative_hr']) == self.daily_maxes[name])), \\\n 'Function declaration failed'\n\n elif k % 2 == 1:\n num = int(np.floor((k + 1) / 2))\n name = 'sin' + str(num) + 'x'\n self.daily_maxes[name] = np.sin(\n 2 * num * np.pi / 8760. * period_scale * (self.daily_maxes['cumulative_hr'] - 173 * 24))\n\n def f(x):\n return np.sin(2 * num * np.pi / 8760. * period_scale * (x - 173 * 24))\n\n else:\n raise RuntimeError('Should not be here')\n\n feature_funcs[name] = f\n feature_names.append(name)\n\n for name in feature_names:\n assert all((feature_funcs[name](self.daily_maxes['cumulative_hr']) == self.daily_maxes[name])), \\\n 'Something wrong with feature functions'\n\n self.feature_functions = feature_funcs\n self.feature_names = feature_names\n\n def max_min_curve_interpolate(self, num_feature_functions=1,\n percentile=0.8,\n plot_curve=False,\n use_preset_params = True,\n params = 'sf'):\n \"\"\"\n Interpolates the upper bound curve using cos/sin features so that at least\n percentile points are below the curve\n \"\"\"\n sf_presets = {'period_scale': 0.8,\n 'q_max': 0.9,\n 'q_min': 0.25}\n houston_presets = {'period_scale':0.8,\n 'q_max': 0.9,\n 'q_min':0.05}\n\n presets = {'sf' : sf_presets, 'houston' : houston_presets}\n\n if use_preset_params:\n if params in presets.keys():\n parameters = presets[params]\n period_scale = parameters['period_scale']\n q_max = parameters['q_max']\n q_min = parameters['q_min']\n else:\n raise NameError('If use_preset_params is True, params must be one of {\\'sf\\',\\'houston\\' '\n 'denoting preset parameters')\n else:\n if not isinstance(params,dict):\n raise TypeError('params must be a dict of parameters, not ({})'.format(params))\n period_scale = params['period_scale']\n q_max = params['q_max']\n q_min = params['q_min']\n\n if not (1.0 >= percentile >= 0.0):\n raise ValueError('percentile must be in [0,1], is ({})'.format(percentile))\n\n self._add_feature_columns(num_feature_functions=num_feature_functions, period_scale=period_scale)\n\n x_vars = self.daily_maxes[self.feature_names]\n\n quantile_reg_model = quantile_regression.QuantReg(self.daily_maxes['max_GHI'], x_vars)\n results = quantile_reg_model.fit(q=q_max)\n max_coef = results.params\n max_curve = max_coef['ones']*x_vars['ones']\n\n for p_name in self.feature_names:\n if p_name == 'ones':\n continue\n else:\n max_curve+=max_coef[p_name]*x_vars[p_name]\n\n results = quantile_reg_model.fit(q=q_min)\n min_coef = results.params\n min_curve = min_coef['ones'] * x_vars['ones']\n\n for p_name in self.feature_names:\n if p_name == 'ones':\n continue\n else:\n min_curve += min_coef[p_name] * x_vars[p_name]\n\n if plot_curve:\n plt.scatter(self.daily_maxes['cumulative_hr']/24, self.daily_maxes['max_GHI'], color='r', marker='.')\n plt.plot(self.daily_maxes['cumulative_hr']/24, max_curve, label='{}th percentile'.format(int(100 * q_max)))\n plt.plot(self.daily_maxes['cumulative_hr']/24,\n min_curve, color='k', label='{}th percentile'.format(int(100 * q_min)))\n plt.xlabel('Day')\n plt.legend()\n plt.title('Maximum daily PV upper/lower bounds')\n plt.show()\n\n self.interpolated_coef = {'max': max_coef, 'min': min_coef}\n self.interpolated = True\n\n def most_light_curve_eval(self, max_min, cumulative_hours=None, day_hour_pairs=None):\n \"\"\"\n Evaluates the interpolated upper bound curve at the time values in val\n \"\"\"\n to_return = []\n if max_min =='max':\n interpolated_coef = self.interpolated_coef['max']\n elif max_min == 'min':\n interpolated_coef = self.interpolated_coef['min']\n else:\n raise ValueError('max_min must be one of \\'max\\' or \\'min\\', is {}',format(max_min))\n\n if cumulative_hours is not None:\n try:\n cumul_times = pd.Series(data=cumulative_hours)\n y = pd.Series(data=[0] * len(cumul_times), name='Upper Bound Values')\n except TypeError:\n cumul_times = cumulative_hours\n y = interpolated_coef['ones']\n\n for j, name in enumerate(self.feature_names):\n feature_function = self.feature_functions[name]\n y += interpolated_coef[name] * feature_function(cumul_times)\n\n if len(y) == 1:\n y = y[0]\n to_return.append(y)\n\n if day_hour_pairs is not None:\n cumul_times = []\n for pair in day_hour_pairs:\n if len(pair) != 2:\n raise ValueError('pairs must be array-like of length two, containing days and hours')\n if pair[1] < 0 or pair[1] >= 24:\n raise ValueError('hour must be in [0,23], is ({})'.format(pair[1]))\n\n cumul_times.append(pair[0] * 24 + pair[1])\n\n x = pd.Series(data=cumul_times)\n y_pairs = pd.Series(data=[0] * len(cumul_times), name='Upper Bound Values')\n for j, name in enumerate(self.feature_names):\n feature_function = self.feature_functions[name]\n y_pairs += interpolated_coef[name] * feature_function(x)\n\n if len(y_pairs) == 1:\n y_pairs = y_pairs[0]\n\n to_return.append(y_pairs)\n\n if len(to_return) == 1:\n to_return = to_return[0]\n\n return to_return\n\n def _sample_parabola(self,noise_type, noise_parameters, verbose, push_peak_val=False, push_peak_ratio=0.5):\n noisy_data = self.data.copy() # Columns are days, index is hours\n\n # Need three points for interpolation: two zeros\n # Get points for each day:\n\n lower_distribution_bounds = []\n upper_distribution_bounds = []\n\n for day in noisy_data.columns:\n if noisy_data[day][0] != 0:\n raise RuntimeError('It appears that it is sunny at midnight of day ({}). No good.'.format(day))\n if noisy_data[day][23] != 0:\n raise RuntimeError('It appears that it is sunny at 11PM of day ({}). No good.'.format(day))\n\n night_hours = np.where(noisy_data[day] == 0)[0]\n next_night_hours = np.roll(night_hours, -1)\n index_of_dawn = np.where(night_hours + 1 != next_night_hours)[0][0]\n dawn_time = night_hours[index_of_dawn] # Gives a zero: (dawn_time,0)\n dusk_time = night_hours[index_of_dawn + 1] # Another zero: (dusk_time,0)\n\n # time_of_most_light = self.daily_maxes.loc[day, 'time_of_max']\n time_of_most_light = (dawn_time + dusk_time) / 2.0\n interpolated_least_light = self.most_light_curve_eval(max_min='min',\n day_hour_pairs=((day, time_of_most_light),))\n interpolated_most_light = self.most_light_curve_eval(max_min='max',\n day_hour_pairs=((day, time_of_most_light),))\n\n # Check if these bounds are negative.\n if interpolated_least_light<0:\n if interpolated_most_light<0: # This is dumb, but it flips the negative bounds\n most_light = -min(interpolated_least_light, interpolated_most_light)\n least_light = -max(interpolated_least_light, interpolated_most_light)\n interpolated_most_light = most_light\n interpolated_least_light = least_light\n else:\n interpolated_least_light = 0\n\n lower_b = interpolated_least_light\n upper_b = interpolated_most_light\n spread = upper_b - lower_b\n\n if noise_type == 'uniform':\n low = lower_b + noise_parameters['lower'] * spread\n high = upper_b + (noise_parameters['upper'] - 1) * spread\n lower_distribution_bounds.append(low)\n upper_distribution_bounds.append(high)\n peak_val = np.random.uniform(low=low, high=high)\n\n if verbose:\n print('Day {}'.format(day))\n print('Using uniform distribution between {} and {}'.format(round(low, 1),\n round(high, 1)))\n print('Unscaled bounds: [{},{}]'.format(round(lower_b, 1), round(upper_b, 1)))\n print('Selected daily peak value {}'.format(peak_val))\n\n elif noise_type == 'triangular':\n low = lower_b + noise_parameters['lower'] * spread\n high = upper_b + (noise_parameters['upper'] - 1) * spread\n if 'mode' in noise_parameters.keys():\n mode_param = noise_parameters['mode']\n if not 0 <= mode_param <= 1:\n raise ValueError(\n 'mode parameter ({}) invalid, must be scale value in [0,1]'.format(mode_param))\n mode = spread * mode_param + lower_b\n assert high >= mode >= low, 'mode computation did not work'\n else:\n mode = 0.5 * (lower_b + upper_b)\n\n lower_distribution_bounds.append(low)\n upper_distribution_bounds.append(high)\n\n peak_val = np.random.triangular(left=low, mode=mode, right=high)\n\n if verbose:\n print('Day {}'.format(day))\n print('using triangular distribution with low {}, mode {}, high {}'.format(\n round(low, 1), round(mode, 1), round(high, 1)))\n print('Unscaled bounds: [{},{}]'.format(round(lower_b, 1), round(upper_b, 1)))\n print('Selected daily peak value {}'.format(peak_val))\n\n else:\n raise RuntimeError('Fell through in noise_types, unable to recognize ({})'.format(noise_type))\n\n if push_peak_val:\n peak_val = peak_val+push_peak_ratio*(self.daily_maxes.loc[day, 'max_GHI']-peak_val)\n\n daytime_x = np.array([dawn_time, time_of_most_light, dusk_time])\n daytime_y = np.array([0, peak_val, 0])\n if any(np.diff(daytime_x) <= 0):\n raise RuntimeError('Something is wrong in interpolating daily curves, '\n 'have dawn/peak/dusk times as ({}), not in order'.format(daytime_x))\n\n # Interpolate that\n f = interp1d(daytime_x, daytime_y, kind='quadratic', bounds_error=False, fill_value=0)\n noisy_data[day] = f(noisy_data.index)\n\n self.parabolic_baseline = noisy_data.copy()\n self.distribution_bounds = (lower_distribution_bounds, upper_distribution_bounds)\n\n return noisy_data, lower_distribution_bounds, upper_distribution_bounds\n\n def sample(self,\n noise_types=('uniform', 'gaussian'),\n noise_params=({'lower': 0, 'upper': 1}, {'std_ratio': 0.05}),\n return_stacked_data = True,\n plot_noisy=False,\n days_to_plot=(0, 10),\n verbose=False,\n push_peak_val=False,\n push_peak_ratio=0.5,\n push_individual_vals=False,\n push_individual_ratio=0.5,\n **kwargs\n ):\n\n # TODO add param to push peak toward actual peak\n\n potential_noises = {0: (None, 'uniform', 'triangular'),\n 1: (None, 'gaussian')}\n\n noise_parameters = ({'lower': 0, 'upper': 1, 'mode':0.5}, {'std_ratio': 0.05})\n\n for j, noise in enumerate(noise_types):\n if noise not in potential_noises[j]:\n raise ValueError('Noise ({}) not recognized in position ({}), must be one of {}'.format(\n noise, j, potential_noises[j]))\n\n if not self.munged:\n self.data_munge()\n\n if not self.interpolated:\n self.max_min_curve_interpolate()\n\n if not self.interpolated:\n raise RuntimeError('Must have an interpolating curve before adding noise. '\n 'Call max_min_curve_interpolate first.')\n if len(noise_params) != 2:\n raise TypeError('Unable to parse noise_params, must be array-like length 2')\n\n for j, v in enumerate(noise_params):\n if v is not None and not isinstance(v, dict):\n raise TypeError('Element ({}) in noise_params must be None or dict, is {}'.format(j, type(v)))\n elif v is not None:\n for key in noise_parameters[j].keys():\n if key in v.keys():\n noise_parameters[j][key] = v[key]\n\n if noise_types[0] is None:\n if self.parabolic_baseline is None:\n raise ValueError('noise_types[0] is None, but there is no stored baseline')\n else:\n noisy_data = self.parabolic_baseline.copy()\n lower_distribution_bounds, upper_distribution_bounds = self.distribution_bounds\n else:\n noisy_data, lower_distribution_bounds, \\\n upper_distribution_bounds = self._sample_parabola(noise_types[0], noise_parameters[0], verbose,\n push_peak_val=push_peak_val, push_peak_ratio=push_peak_ratio)\n\n if noise_types[1] == 'gaussian':\n noisy_data += np.random.normal(scale=noise_parameters[1]['std_ratio'] * noisy_data)\n\n if plot_noisy or return_stacked_data:\n stacked_data = noisy_data.transpose().stack()\n stacked_data = stacked_data.reset_index()\n stacked_data = stacked_data.drop(columns=['hour', 'day'])\n\n assert len(stacked_data.columns)==1, 'stacked data should only have one column here'\n\n for name in stacked_data.columns:\n stacked_data.rename(columns={name:'pv'},inplace=True)\n\n if plot_noisy:\n\n if 'plot_ub_lb' in kwargs.keys() and kwargs['plot_ub_lb']:\n plot_upper_lower_bounds=True\n else:\n plot_upper_lower_bounds=False\n\n if 'plot_points_of_dist' in kwargs.keys() and kwargs['plot_points_of_dist']:\n plot_points_of_distribution = True\n else:\n plot_points_of_distribution = False\n\n self.plot(stacked_data, days_to_plot=days_to_plot, plot_original=True,\n plot_upper_lower_bounds=plot_upper_lower_bounds,\n plot_points_of_distribution=plot_points_of_distribution)\n\n if return_stacked_data:\n stacked_data = self._check_sample(stacked_data, verbose=verbose)\n\n if push_individual_vals:\n stacked_data['pv'] += push_individual_ratio*(self.unmunged_data['GH illum (lx)']-stacked_data['pv'])\n\n return stacked_data\n\n return noisy_data\n\n def _check_sample(self, stacked_data, verbose=False):\n temp_data = stacked_data.copy()\n temp_data = temp_data.squeeze()\n if not isinstance(temp_data, pd.Series):\n raise ValueError('stacked_data needs to be a series or a single column DataFrame, has shape {}'.format(\n stacked_data.shape))\n\n negative_indices = temp_data < 0\n\n if negative_indices.sum() > 0 and verbose:\n print('Found {} negative values in pv_data sample, forcing them to be 0'.format(\n negative_indices.sum()))\n\n value = 0\n\n temp_data.loc[negative_indices] = value\n\n assert (temp_data >= 0).all(), 'There are still negative numbers in temp_data when checking sample of pv_data'\n\n if isinstance(stacked_data, pd.Series):\n return temp_data\n\n elif isinstance(stacked_data, pd.DataFrame):\n new_stacked_data = stacked_data.copy()\n new_stacked_data[new_stacked_data.columns[0]] = temp_data\n return new_stacked_data\n\n def plot(self, stacked_data, days_to_plot=(0,10),\n plot_sample=True, plot_original=True, plot_upper_lower_bounds=True, plot_points_of_distribution=False,\n plot_daily_maxes=False, plot_parabolas=False, month_xticks=False):\n\n if isinstance(stacked_data, pd.DataFrame):\n stacked_data = stacked_data.squeeze()\n\n if not isinstance(stacked_data, pd.Series):\n if plot_sample:\n print('Warning: stacked_data is an arbitrary sample. Must be pd.Series to plot passed stacked_data, is '\n '{}'.format((type(stacked_data))))\n stacked_data = self.sample()\n\n indices = slice(24 * days_to_plot[0], 24 * days_to_plot[1])\n daily_slice = slice(*days_to_plot)\n if plot_sample:\n plt.plot(stacked_data[indices].index, stacked_data[indices].values, label='Sample')\n\n if plot_original:\n plt.plot(stacked_data[indices].index, self.unmunged_data[indices].values, label='Original')\n\n if plot_upper_lower_bounds:\n plt.plot(stacked_data[indices].index,\n self.most_light_curve_eval('max', cumulative_hours=stacked_data[indices].index), color='k',\n label='Parabola distribution UB')\n\n plt.plot(stacked_data[indices].index,\n self.most_light_curve_eval('min', cumulative_hours=stacked_data[indices].index), color='c',\n label='Parabola distribution LB')\n\n if plot_points_of_distribution:\n if self.distribution_bounds is None:\n raise RuntimeError('Could not find distribution bounds, must call \\'sample\\' at least once to plot')\n else:\n lower_distribution_bounds, upper_distribution_bounds = self.distribution_bounds\n\n plt.scatter(self.daily_maxes['cumulative_hr'].iloc[daily_slice], lower_distribution_bounds[daily_slice],\n marker='.', color='r')\n plt.scatter(self.daily_maxes['cumulative_hr'].iloc[daily_slice], upper_distribution_bounds[daily_slice],\n marker='.', color='r')\n\n if plot_daily_maxes:\n plt.scatter(self.daily_maxes['cumulative_hr'].iloc[daily_slice],self.daily_maxes['max_GHI'].iloc[daily_slice],\n marker='.', color='r', label='Underlying daily max pv')\n if plot_parabolas:\n parabolas = self.parabolic_baseline.transpose().stack().squeeze()\n print(parabolas)\n plt.plot(stacked_data[indices].index, parabolas.iloc[indices],color='c',label='Parabolic Baseline')\n if month_xticks:\n print(plt.xticks())\n locs = [j*30*24 for j in range(13) if j*12>=days_to_plot[0] and j*12<=days_to_plot[1]]\n ticks = [j for j in range(13) if j*12>=days_to_plot[0] and j*12<=days_to_plot[1]]\n print(locs)\n print(ticks)\n plt.xticks(locs, ticks)\n\n plt.xlabel('Month')\n plt.ylabel('PV')\n plt.legend(loc='lower right')\n plt.savefig('daily max distribution.png', bbox_inches='tight')\n plt.show()\n\n\nclass NoisyLoadData:\n def __init__(self, load_data=None, file_name=None):\n if load_data is not None:\n if isinstance(load_data,pd.Series):\n self.unmunged_data = load_data.to_frame()\n self.data = load_data.to_frame()\n\n elif not isinstance(load_data, pd.DataFrame):\n raise TypeError('known_data must be of type pd.DataFrame or pd.Series, is ({})'.format(type(load_data)))\n else:\n self.unmunged_data = load_data.copy()\n self.data = load_data.copy()\n\n elif file_name is not None:\n self.data, self.unmunged_data = self._import_file(file_name)\n\n else:\n raise RuntimeError('Unable to initialize data, either load_data or file_name must not be None')\n\n # Cut load data to the correct size\n self.data = self.data.iloc[:8760]\n self.unmunged_data = self.unmunged_data.iloc[:8760]\n\n self.num_hours = len(load_data)\n self.munged = False\n self.interpolated = False\n\n def _import_file(self, file_name):\n return pd.read_csv(file_name), pd.read_csv(file_name)\n\n def data_munge(self, verbose=False):\n\n # Get column name of pv values\n if len(self.data.columns.values)!=1:\n print('Warning multiple columns in load_data, attempting to use \\'Electricity:Facility [kW](Hourly)\\' as column name')\n col_name = 'Electricity:Facility [kW](Hourly)'\n else:\n col_name = self.data.columns[0]\n\n hours = [j % 24 for j in range(self.num_hours)]\n day = [int(np.floor(j / 24)) for j in range(self.num_hours)]\n\n self.data['hour'] = pd.Series(data=hours)\n self.data['day'] = pd.Series(data=day)\n self.data = self.data.pivot(index='day', columns='hour', values=col_name)\n self.data['day_of_week'] = self.data.index % 7\n\n self.load_mean = self.data.groupby(['day_of_week']).mean()\n self.load_std = self.data.groupby(['day_of_week']).std().fillna(value=0)\n\n self.munged = True\n\n def sample(self, distribution='gaussian', load_variance_scale=1., return_stacked = True, verbose=False, **kwargs):\n\n if not self.munged:\n self.data_munge()\n\n possible_distributions = ('gaussian',)\n if distribution not in possible_distributions:\n raise ValueError(\n 'distribution {} not recognized, must be one of ({})'.format(distribution, possible_distributions))\n\n if distribution == 'gaussian':\n copied_mean = self.data.copy()\n copied_mean = copied_mean.set_index([copied_mean.index, 'day_of_week'])\n copied_std = copied_mean.copy()\n\n\n\n for ind in copied_mean.index:\n copied_mean.loc[ind] = self.load_mean.loc[ind[1]]\n copied_std.loc[ind] = self.load_std.loc[ind[1]]\n\n else:\n raise RuntimeError('Unsupported')\n\n data_sample = pd.DataFrame(data=np.random.normal(loc=copied_mean, scale=load_variance_scale * copied_std),\n index=self.data.index,\n columns=self.data.columns[:-1])\n if return_stacked:\n stacked_data = data_sample.stack()\n stacked_data = stacked_data.reset_index()\n stacked_data = stacked_data.drop(columns=['day', 'hour'])\n\n assert len(stacked_data.columns) == 1, 'stacked data should only have one column here'\n\n for name in stacked_data.columns:\n stacked_data.rename(columns={name: 'load'}, inplace=True)\n\n stacked_data = self._check_sample(stacked_data, verbose=verbose)\n\n return stacked_data\n\n return data_sample\n\n def _check_sample(self,stacked_data,verbose=False):\n temp_data = stacked_data.copy()\n temp_data = temp_data.squeeze()\n if not isinstance(temp_data,pd.Series):\n raise ValueError('stacked_data needs to be a series or a single column DataFrame, has shape {}'.format(stacked_data.shape))\n\n negative_indices = temp_data < 0\n\n if negative_indices.sum() > 0 and verbose:\n print('Found {} negative values in load_data, forcing them to be min of underlying data'.format(negative_indices.sum()))\n\n value = self.unmunged_data.min().squeeze()\n\n temp_data.loc[negative_indices] = value\n\n assert (temp_data>=0).all(), 'There are still negative numbers in temp_data when checking load_data sample'\n\n if isinstance(stacked_data, pd.Series):\n return temp_data\n\n elif isinstance(stacked_data, pd.DataFrame):\n new_stacked_data = stacked_data.copy()\n new_stacked_data[new_stacked_data.columns[0]] = temp_data\n return new_stacked_data\n\n def plot(self, sample, days_to_plot=(0, 10)):\n\n if not sample.shape[1]==1:\n raise ValueError('sample must be in stacked form')\n\n plt.plot(sample[24 * days_to_plot[0]:24 * days_to_plot[1]].values, label='sample')\n plt.plot(self.unmunged_data[24 * days_to_plot[0]:24 * days_to_plot[1]].values, label='original')\n plt.title('Load Sample')\n plt.legend()\n plt.show()\n\n\nclass NoisyGridData:\n def __init__(self,grid_data,dist_type = 'markov'):\n\n if not (isinstance(grid_data,pd.DataFrame) or isinstance(grid_data,pd.Series)):\n raise TypeError('grid_data must be of type pd.DataFrame, is {}'.format(type(grid_data)))\n\n if not ((grid_data==1) | (grid_data==0)).all().item():\n raise ValueError('Non-binary values found in grid_data')\n\n possible_dist_types = ('naive','markov')\n if dist_type not in possible_dist_types:\n raise TypeError('dist type ({}) not recognized, must be one of {}'.format(dist_type, possible_dist_types))\n\n self.dist_type = dist_type\n self.data = grid_data.copy()\n self.unmunged_data = grid_data.copy()\n self.has_distribution = False\n self.transition_prob_matrix = None\n self.occurrences = None\n\n def learn_distribution(self):\n\n if self.dist_type == 'naive':\n transition_prob_matrix = np.zeros(2)\n probability_of_one = self.data.mean()\n transition_prob_matrix[0] = 1-probability_of_one\n transition_prob_matrix[1] = probability_of_one\n\n elif self.dist_type == 'markov':\n grid_vals = self.data.values\n transition_prob_matrix = np.zeros((2, 2))\n occurrences = np.zeros(2)\n for j, val in enumerate(grid_vals):\n if j < len(grid_vals) - 1: # One less than length b/c we are counting transitions\n transition_prob_matrix[int(val), int(grid_vals[j + 1])] += 1\n occurrences[int(val)] += 1\n\n if occurrences[0] > 0:\n transition_prob_matrix[0, :] /= occurrences[0]\n else:\n transition_prob_matrix[0, 0] = 1\n\n if occurrences[1] > 0:\n transition_prob_matrix[1, :] /= occurrences[1]\n else:\n transition_prob_matrix[1, 1] = 1\n\n self.occurrences = occurrences\n\n assert all([np.sum(transition_prob_matrix[j, :]) == 1 for j in range(2)]), \\\n 'transition prob matrix invalid: {}'.format(transition_prob_matrix)\n\n else:\n raise RuntimeError('Should not be here')\n\n self.transition_prob_matrix = transition_prob_matrix\n self.has_distribution = True\n\n def sample(self):\n\n if not self.has_distribution:\n self.learn_distribution()\n\n if self.dist_type =='naive':\n generated_sample = np.random.choice([0, 1], size = len(self.data), p=self.transition_prob_matrix)\n generated_sample = pd.Series(data=generated_sample, name='grid')\n\n elif self.dist_type == 'markov':\n generated_sample = np.zeros(len(self.data))\n\n probs = self.occurrences/np.sum(self.occurrences)\n generated_sample[0] = np.random.choice([0,1], p=probs)\n\n for j in range(1,len(self.data)):\n probs = self.transition_prob_matrix[int(generated_sample[j-1]),:]\n generated_sample[j] = np.random.choice([0,1], p=probs)\n\n generated_sample = pd.Series(data=generated_sample, name='grid')\n\n else:\n raise RuntimeError('Should not be here')\n\n return generated_sample\n\nclass SampleGenerator:\n def __init__(self, microgrid, **forecast_args):\n self.microgrid = microgrid\n self.NPV = NoisyPVData(pv_data=self.microgrid._pv_ts)\n self.NL = NoisyLoadData(load_data=self.microgrid._load_ts)\n\n if self.microgrid.architecture['grid'] != 0:\n self.NG = NoisyGridData(grid_data=self.microgrid._grid_status_ts)\n else:\n self.NG = None\n\n self.underlying_data = return_underlying_data(self.microgrid)\n self.forecasts = self.create_forecasts(**forecast_args)\n self.samples = None\n\n def create_forecasts(self, pv_args=None, load_args=None, preset_to_use=None, print_mape=False, **forecast_args):\n \"\"\"\n Creates pv, load, and grid forecasts that are then used to create samples.\n\n :return:\n df, pd.DataFrame, shape (8760,3)\n DataFrame with columns of 'pv', 'load', and 'grid', containing values for each at all 8760 timesteps\n \"\"\"\n if pv_args is None and load_args is None and preset_to_use is not None:\n print('Using preset forecast arguments')\n args = ForecastArgSet(preset_to_use=preset_to_use)\n pv_args = args['pv_args']\n load_args = args['load_args']\n else:\n if pv_args is None:\n pv_args = dict()\n if load_args is None:\n load_args = dict()\n\n pv_forecast = self.NPV.sample(**pv_args)\n load_forecast = self.NL.sample(**load_args)\n\n if self.microgrid.architecture['grid'] != 0:\n grid_forecast = self.NG.sample()\n else:\n grid_forecast = pd.Series(data=[0] * len(self.microgrid._load_ts), name='grid')\n\n forecast = pd.concat([pv_forecast, load_forecast, grid_forecast], axis=1)\n\n if print_mape:\n mape = self.validate_forecasts(forecasts=forecast, aggregate=True)\n print('MAPE: {}'.format(mape))\n\n if hasattr(self, 'forecasts'):\n self.forecasts = forecast\n else:\n return forecast\n\n def test_args(self, iters_per_set = 3):\n num_pv_noise_params_0 = 3\n num_pv_std_ratio = 3\n num_load_variance_scale = 3\n max_load_var_scale = 2.\n max_pv_std_ratio = 0.5\n num_push_peak_ratio = 3\n num_push_individual_ratio = 3\n\n forecast_args = ForecastArgs(num_pv_noise_params_0, num_pv_std_ratio, num_load_variance_scale,\n num_push_peak_ratio, num_push_individual_ratio,\n max_load_var_scale=max_load_var_scale, max_pv_std_ratio=max_pv_std_ratio)\n\n for j, arg_set in enumerate(forecast_args.param_sets):\n for k in range(iters_per_set):\n print('iter {}.{}'.format(j, k))\n forecasts = self.create_forecasts(**arg_set)\n mape = self.validate_forecasts(forecasts, aggregate=True)\n arg_set.update_with_mape(mape)\n\n return forecast_args\n\n def validate_forecasts(self, forecasts=None, aggregate=False):\n\n if forecasts is None:\n forecasts = self.forecasts\n\n mape_vals = dict()\n\n for col in ('pv', 'load'):\n mape_vals[col] = self.mape(self.underlying_data[col], forecasts[col])\n\n if aggregate:\n return np.sqrt(np.mean(np.array(list(mape_vals.values()))**2))\n return mape_vals\n\n def mape(self, actual_vals, forecast_vals):\n if isinstance(actual_vals.squeeze(), pd.Series):\n actual_vals = actual_vals.to_numpy()\n elif isinstance(actual_vals, np.ndarray):\n pass\n else:\n raise TypeError('actual_vals must be squeezable to single column, has shape {}'.format(actual_vals.shape))\n if isinstance(forecast_vals.squeeze(), pd.Series):\n forecast_vals = forecast_vals.to_numpy()\n elif isinstance(forecast_vals, np.ndarray):\n pass\n else:\n raise TypeError('forecast_vals must be squeezable to single column, has shape {}'.format(forecast_vals.shape))\n\n ratios = np.abs(((actual_vals-forecast_vals)/actual_vals))\n mape = np.mean(ratios[~np.isnan(ratios)])\n\n return mape\n\n def sample_from_forecasts(self, n_samples=10, **sampling_args):\n \"\"\"\n Generates samples of load, grid, pv data by sampling from the distributions defined by using self.forecasts\n as a baseline in NoisyLoadData, NoisyPVData, NoisyGridData.\n\n :param n_samples: int, default 100\n Number of samples to generate\n :param sampling_args: dict\n Sampling arguments to be passed to NPV.sample() and NL.sample()\n :return:\n samples: list of pd.DataFrame of shape (8760,3)\n list of samples created from sampling from distributions defined in forecasts.\n \"\"\"\n NPV = self.NPV\n NL = NoisyLoadData(load_data=self.forecasts['load'])\n NG = NoisyGridData(grid_data=self.forecasts['grid'])\n\n samples = []\n\n if 'noise_types' not in sampling_args.keys():\n sampling_args['noise_types'] = (None, 'gaussian')\n\n for j in range(n_samples):\n print('Creating sample {}'.format(j))\n pv_forecast = NPV.sample(**sampling_args)\n load_forecast = NL.sample(**sampling_args)\n\n grid_forecast = NG.sample()\n\n sample = pd.concat([pv_forecast, load_forecast, grid_forecast], axis=1)\n\n truncated_index = min(len(NPV.unmunged_data), len(NL.unmunged_data), len(NG.unmunged_data))\n sample = sample.iloc[:truncated_index]\n samples.append(sample)\n\n self.samples = samples\n return samples\n\n def plot(self, var='load', days_to_plot=(0, 10), original=True, forecast=True, samples=True):\n \"\"\"\n Function to plot the load, pv, or grid data versus the forecast or original data\n :param var: str, default 'load'\n one of 'load', 'pv', 'grid', which variable to plot\n :param days_to_plot: tuple, len 2. default (0,10)\n defines the days to plot. Plots all hours from days_to_plot[0] to days_to_plot[1]\n :param original: bool, default True\n whether to plot the underlying microgrid data\n :param forecast: bool, default True\n whether to plot the forecast stored in self.forecast\n :param samples: bool, default True\n whether to plot the samples stored in self.samples\n :return:\n None\n \"\"\"\n\n if var not in self.forecasts.columns:\n raise ValueError('Cound not find var {} in self.forecasts, should be one of {}'.format(var, self.forecasts.columns))\n\n indices = slice(24 * days_to_plot[0], 24 * days_to_plot[1])\n\n if original:\n plt.plot(self.underlying_data.loc[indices, var].index, self.underlying_data.loc[indices, var].values,\n label='original', color='b')\n if forecast:\n plt.plot(self.forecasts.loc[indices, var].index, self.forecasts.loc[indices, var].values, label='forecast',\n color='r')\n if samples:\n for sample in self.samples:\n plt.plot(sample.loc[indices, var].index, sample.loc[indices, var].values, color='k')\n\n plt.legend()\n plt.show()\n\n\nclass ForecastArgSet(dict):\n def __init__(self, pv_param_set=None, load_param_set=None, preset_to_use=None):\n\n if pv_param_set is None and load_param_set is None and preset_to_use is not None:\n saved_dict = self.get_preset(preset_to_use)\n super(ForecastArgSet, self).__init__(saved_dict)\n\n elif pv_param_set is not None and load_param_set is not None and preset_to_use is None:\n super(ForecastArgSet, self).__init__(pv_args=pv_param_set, load_args=load_param_set)\n\n else:\n raise KeyError('Unable to parse inputs')\n\n self.mape_vals = []\n self.mape_mean = None\n self.mape_std = None\n\n def update_with_mape(self, mape):\n\n self.mape_vals.append(mape)\n self.mape_mean = np.mean(self.mape_vals)\n self.mape_std = np.std(self.mape_vals)\n\n def get_preset(self,forecast_accuracy=50):\n potential_forecast_accuracies = (50, 70, 85)\n if forecast_accuracy not in potential_forecast_accuracies:\n raise ValueError('do not have relevant sampling parameters for forecast accuracy {}, must be one of {}'.format(\n forecast_accuracy, potential_forecast_accuracies))\n\n if forecast_accuracy == 50:\n return {'pv_args': {'noise_params': ({'lower': 0.0, 'upper': 0.5},\n {'std_ratio': 0.25}),\n 'push_peak_val': True,\n 'push_peak_ratio': 0.0,\n 'push_individual_vals': True,\n 'push_individual_ratio': 0.5},\n 'load_args': {'load_variance_scale': 2.0}}\n\n if forecast_accuracy == 70:\n return {'pv_args': {'noise_params': ({'lower': 0.0, 'upper': 0.5},\n {'std_ratio': 0.25}),\n 'push_peak_val': True,\n 'push_peak_ratio': 0.0,\n 'push_individual_vals': True,\n 'push_individual_ratio': 0.65},\n 'load_args': {'load_variance_scale': 2.0}}\n\n elif forecast_accuracy == 85:\n return {'pv_args': {'noise_params': ({'lower': 0.0, 'upper': 0.5},\n {'std_ratio': 0.25}),\n 'push_peak_val': True,\n 'push_peak_ratio': 0.0,\n 'push_individual_vals': True,\n 'push_individual_ratio': 1.0},\n 'load_args': {'load_variance_scale': 2.0}}\n\n\n def __eq__(self, other):\n if type(self) != type(other):\n return NotImplemented\n\n return self.mape_mean == other.mape_mean\n\n def __lt__(self, other):\n if type(self) != type(other):\n return NotImplemented\n\n return self.mape_mean < other.mape_mean\n\n def __gt__(self, other):\n if type(self) != type(other):\n return NotImplemented\n\n return self.mape_mean > other.mape_mean\n\nclass ForecastArgs:\n def __init__(self, num_pv_noise_params_0, num_pv_std_ratio, num_load_variance_scale, num_push_peak_ratio,\n num_push_individual_ratio, max_load_var_scale=2., max_pv_std_ratio=0.5):\n\n pv_params = self.pv_parameters(num_pv_noise_params_0, num_pv_std_ratio, num_push_peak_ratio, num_push_individual_ratio,\n max_std_ratio=max_pv_std_ratio)\n load_params = self.load_parameters(num_load_variance_scale, max_load_var_scale=max_load_var_scale)\n\n self.param_sets = self.combine_sets(pv_params, load_params)\n\n def pv_parameters(self,num_noise_params_0, num_std_ratio, num_push_peak_ratio, num_push_individual_ratio,\n max_std_ratio=0.5):\n\n pv_params = []\n for individual_ratio in np.linspace(0,1,num_push_individual_ratio):\n for peak_ratio in np.linspace(0,1,num_push_peak_ratio):\n for std_ratio in np.linspace(0,max_std_ratio, num_std_ratio):\n for lower in np.linspace(0,1,num_noise_params_0):\n for upper in np.linspace(1,lower,num_noise_params_0):\n if upper >= lower:\n pv_params.append(dict(noise_params=(dict(lower=lower, upper=upper), dict(std_ratio=std_ratio)),\n push_peak_val=True, push_peak_ratio=peak_ratio,\n push_individual_vals=True, push_individual_ratio=individual_ratio))\n else:\n print('upper not geq lower')\n\n return pv_params\n\n def load_parameters(self,num_load_variance_scale, max_load_var_scale=2.):\n load_params = []\n for var_scale in np.linspace(0,max_load_var_scale,num_load_variance_scale):\n load_params.append(dict(load_variance_scale=var_scale))\n\n return load_params\n\n def combine_sets(self, pv_params, load_params):\n\n sets = []\n\n for pv_param in pv_params:\n for load_param in load_params:\n sets.append(ForecastArgSet(pv_param_set = pv_param, load_param_set = load_param))\n\n return sets\n" }, { "path": "src/pymgrid/utils/__init__.py", "content": "from .serialize import add_pymgrid_yaml_representers\n" }, { "path": "src/pymgrid/utils/logger.py", "content": "from collections import UserDict\n\nimport numpy as np\nimport pandas as pd\n\n\nclass ModularLogger(UserDict):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self._log_length = max(len(v) for _, v in self.items()) if len(self.data) else 0\n\n def flush(self):\n d = self.data.copy()\n self.clear()\n self._log_length = 0\n return d\n\n def log(self, **log_dict):\n for key, value in log_dict.items():\n try:\n self[key].append(value.item())\n except AttributeError:\n self[key].append(value)\n except KeyError:\n self[key] = [np.nan]*self._log_length\n self[key].append(value)\n\n self._log_length += 1\n\n def to_dict(self):\n return self.data.copy()\n\n def raw(self):\n return {k: list(map(float, v)) for k, v in self.data.items()}\n\n def to_frame(self):\n return pd.DataFrame(self.data)\n\n def serialize(self, key):\n return {key: self.to_frame()} if len(self) > 0 else {}\n\n def __len__(self):\n return self._log_length\n\n @classmethod\n def from_raw(cls, raw):\n if raw is None:\n return cls()\n elif isinstance(raw, str):\n raw = pd.read_csv(raw).to_dict()\n return cls(raw)\n" }, { "path": "src/pymgrid/utils/ray.py", "content": "import functools\nfrom copy import copy\n\n\ndef ray_decorator(func):\n \"\"\"\n ray raises an error when assigning values after ray.get to variables defined before ray.get.\n Easiest solution is to copy the values and try again.\n\n :meta private:\n\n \"\"\"\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n try:\n return func(*args, **kwargs)\n except ValueError as e:\n if 'assignment destination is read-only' not in e.args[0]:\n raise\n else:\n return func(*(copy(a) for a in args), **{k: copy(v) for k, v in kwargs.items()})\n return wrapper\n" }, { "path": "src/pymgrid/utils/serialize.py", "content": "import numpy as np\nimport pandas as pd\nimport yaml\n\nfrom pathlib import Path\n\nTO_CSV_TYPES = np.ndarray, pd.core.generic.NDFrame\n\n\ndef add_pymgrid_yaml_representers():\n add_numpy_pandas_representers()\n from pymgrid.microgrid.trajectory import (\n DeterministicTrajectory,\n StochasticTrajectory,\n FixedLengthStochasticTrajectory\n )\n\n from pymgrid.microgrid.reward_shaping import (\n PVCurtailmentShaper,\n BatteryDischargeShaper\n )\n\n\ndef dump_data(data_dict, stream, yaml_tag):\n if not hasattr(stream, \"name\"):\n return data_dict\n\n path = Path(stream.name).parent / \"data\"\n return add_path_to_arr_like(data_dict, path, yaml_tag)\n\n\ndef add_path_to_arr_like(data_dict, path, yaml_tag):\n for key, value in data_dict.items():\n if isinstance(value, dict):\n data_dict[key] = add_path_to_arr_like(value, path / key, yaml_tag)\n elif isinstance(value, TO_CSV_TYPES):\n if isinstance(value, np.ndarray):\n value = NDArraySubclass(value)\n value.path = path / f'{yaml_tag.lstrip(\"!\")}/{key}.csv.gz'\n data_dict[key] = value\n\n return data_dict\n\n\ndef add_numpy_pandas_representers():\n yaml.SafeDumper.add_representer(pd.DataFrame, _pandas_df_representer)\n yaml.SafeDumper.add_multi_representer(np.ndarray, _numpy_arr_representer)\n yaml.SafeDumper.add_multi_representer(np.floating, _numpy_represent_floating)\n yaml.SafeDumper.add_multi_representer(np.integer, _numpy_represent_int)\n\n\ndef add_numpy_pandas_constructors():\n yaml.SafeLoader.add_constructor('!NDArray', _numpy_arr_constructor)\n yaml.SafeLoader.add_constructor('!DataFrame', _pandas_df_constructor)\n\n\ndef _numpy_represent_floating(dumper, data):\n return dumper.represent_float(data.item())\n\n\ndef _numpy_represent_int(dumper, data):\n return dumper.represent_int(data.item())\n\n\ndef _numpy_arr_representer(dumper, data):\n return _arr_representer(dumper, data, 'NDArray')\n\n\ndef _pandas_df_representer(dumper, data):\n return _arr_representer(dumper, data, 'DataFrame')\n\n\ndef _arr_representer(dumper, data, r_type):\n if hasattr(data, \"path\"):\n rel_path = _dump_representation(data, data.path, Path(dumper.stream.name).parent)\n return dumper.represent_scalar(f'!{r_type}', rel_path)\n\n try:\n return dumper.represent_mapping(f'!{r_type}', data.to_dict())\n except AttributeError:\n return dumper.represent_sequence(f'!{r_type}', data.tolist())\n\n\ndef _dump_representation(data, path, stream_loc):\n if not path.exists():\n path.parent.mkdir(parents=True, exist_ok=True)\n pd.DataFrame(data).to_csv(path)\n return str(path.relative_to(stream_loc))\n\n\ndef _pandas_df_constructor(loader, node):\n if isinstance(node, yaml.MappingNode):\n return pd.DataFrame(loader.construct_mapping(node))\n\n data_path = Path(loader.construct_scalar(node))\n\n if not data_path.is_absolute():\n try:\n stream_name = loader.stream.name\n except AttributeError:\n raise ValueError(f\"Path {data_path} must be absolute if yaml stream has no 'name'.\")\n\n data_path = Path(stream_name).parent / data_path\n\n return pd.read_csv(data_path, index_col=0)\n\n\ndef _numpy_arr_constructor(loader, node):\n if isinstance(node, yaml.SequenceNode):\n return np.array(loader.construct_sequence(node))\n\n return _pandas_df_constructor(loader, node).values\n\n\nclass NDArraySubclass(np.ndarray):\n \"\"\"\n A simple python class that allows a 'path' attribute for serialization.\n `path` may be lost if object is manipulated.\n \"\"\"\n def __new__(cls, input_array, path=None):\n obj = np.asarray(input_array).view(cls)\n obj.path = path\n return obj\n\n def __array_finalize__(self, obj):\n if obj is None:\n return\n self.path = getattr(obj, 'path', None)\n" }, { "path": "src/pymgrid/utils/space.py", "content": "import operator\nimport numpy as np\nimport warnings\n\nfrom abc import abstractmethod\nfrom gym.spaces import Box, Dict, Space, Tuple\nfrom typing import Union\n\n\nclass _PymgridDict(Dict):\n def __init__(self, d, act_or_obs, normalized=False, seed=None):\n builtins = self._extract_builtins(d, act_or_obs=act_or_obs, normalized=normalized)\n try:\n super().__init__(builtins, seed=seed)\n except AssertionError:\n import gym\n warnings.warn(f\"gym.Space does not accept argument 'seed' in version {gym.__version__}; this argument will \"\n f\"be ignored. Upgrade your gym version with 'pip install -U gym' to use this functionality.\")\n\n super().__init__(builtins.spaces)\n\n def _extract_builtins(self, d, act_or_obs='act', normalized=False):\n if act_or_obs == 'act':\n spaces = self._extract_action_spaces(d)\n elif act_or_obs == 'obs':\n spaces = self._extract_observation_spaces(d)\n else:\n raise NameError(act_or_obs)\n\n return self._transform_builtins(spaces, normalized=normalized)\n\n def _extract_action_spaces(self, d):\n controllable = {}\n for module_name, module_list in d.items():\n controllable_spaces = [v['action_space'] for v in module_list if 'controllable' in v['module_type']]\n if controllable_spaces:\n controllable[module_name] = controllable_spaces\n\n return controllable\n\n def _extract_observation_spaces(self, d):\n obs_spaces = {}\n for module_name, module_list in d.items():\n spaces = [v['observation_space'] for v in module_list]\n if spaces:\n obs_spaces[module_name] = spaces\n\n return obs_spaces\n\n def _transform_builtins(self, d, normalized=False):\n space_key = 'normalized' if normalized else 'unnormalized'\n\n transformed = {}\n\n if isinstance(d, dict):\n transformed = {}\n for k, v in d.items():\n if isinstance(v, Space):\n transformed[k] = v[space_key]\n else:\n transformed[k] = self._transform_builtins(v, normalized=normalized)\n\n transformed = Dict(transformed)\n\n elif isinstance(d, list):\n transformed = []\n for v in d:\n if isinstance(v, Space):\n transformed.append(v[space_key])\n else:\n transformed.append(self._transform_builtins(v, normalized=normalized))\n\n transformed = Tuple(transformed)\n\n return transformed\n\n def get_attr(self, attr):\n return {k: [getattr(v, attr) for v in tup] for k, tup in self.items()}\n\n @property\n def low(self):\n return self.get_attr('low')\n\n @property\n def high(self):\n return self.get_attr('high')\n\n @property\n def shape(self):\n return self.get_attr('shape')\n\n @shape.setter\n def shape(self, value):\n \"\"\"\n Necessary for compatability with gym<0.21.\n \"\"\"\n assert value is None\n\n def __getattr__(self, item):\n \"\"\"\n Necessary for compatability with gym<0.21, where gym.spaces.Dict did not inherit from collections.Mapping.\n \"\"\"\n if item == 'spaces':\n raise AttributeError('spaces')\n try:\n return getattr(self.spaces, item)\n except AttributeError:\n raise AttributeError(item)\n\n\nclass _PymgridSpace(Space):\n _unnormalized: Union[Box, _PymgridDict]\n _normalized: Union[Box, _PymgridDict]\n _spread: Union[np.ndarray, dict]\n\n def contains(self, x):\n \"\"\"\n Check if `x` is a valid member of the space.\n\n .. note::\n This method checks if `x` is a valid member of the space. Use :meth:`.ModuleSpace.normalized.contains` to\n check if a value is a member of the normalized space.\n\n Parameters\n ----------\n x : scalar or array-like\n Value to check membership.\n\n Returns\n -------\n containment : bool\n Whether `x` is a valid member of the space.\n\n \"\"\"\n return x in self._unnormalized\n\n def sample(self, normalized=False):\n if normalized:\n return self._normalized.sample()\n return self._unnormalized.sample()\n\n def _shape_check(self, val, func):\n low = self._unnormalized.low\n if hasattr(val, '__len__') and len(val) != len(low):\n raise TypeError(f'Unable to {func} value of length {len(val)}, expected {len(low)}')\n elif isinstance(val, (int, float)) and len(low) != 1:\n raise TypeError(f'Unable to {func} scalar value, expected array-like of shape {len(low)}')\n\n @abstractmethod\n def normalize(self, val):\n pass\n\n @abstractmethod\n def denormalize(self, val):\n pass\n\n @property\n def normalized(self):\n return self._normalized\n\n @property\n def unnormalized(self):\n return self._unnormalized\n\n def __getitem__(self, item):\n if item == 'normalized':\n return self._normalized\n elif item == 'unnormalized':\n return self._unnormalized\n\n raise KeyError(item)\n\n def __repr__(self):\n return f'ModuleSpace{repr(self._unnormalized).replace(\"Box\", \"\")}'\n\n def __eq__(self, other):\n if type(self) != type(other):\n return NotImplemented\n\n return self.unnormalized == other.unnormalized\n\n\nclass ModuleSpace(_PymgridSpace):\n def __init__(self, unnormalized_low, unnormalized_high, shape=None, dtype=np.float64, seed=None):\n\n low = np.float64(unnormalized_low) if np.isscalar(unnormalized_low) else unnormalized_low.astype(np.float64)\n high = np.float64(unnormalized_high) if np.isscalar(unnormalized_high) else unnormalized_high.astype(np.float64)\n\n self._unnormalized = Box(low=low,\n high=high,\n shape=shape,\n dtype=dtype)\n\n self._normalized = Box(low=0, high=1, shape=self._unnormalized.shape, dtype=dtype)\n\n try:\n super().__init__(shape=self._unnormalized.shape, dtype=self._unnormalized.dtype, seed=seed)\n except TypeError:\n super().__init__(shape=self._unnormalized.shape, dtype=self._unnormalized.dtype)\n import gym\n warnings.warn(f\"gym.Space does not accept argument 'seed' in version {gym.__version__}; this argument will \"\n f\"be ignored. Upgrade your gym version with 'pip install -U gym' to use this functionality.\")\n\n self._spread = self._unnormalized.high - self._unnormalized.low\n self._spread[self._spread == 0] = 1\n\n def normalize(self, val):\n low, high = self._unnormalized.low, self._unnormalized.high\n\n self._shape_check(val, 'normalize')\n self._bounds_check(val, low, high)\n\n normalized = (val - low) / self._spread\n\n try:\n return normalized.item()\n except (AttributeError, ValueError):\n return normalized\n\n def denormalize(self, val):\n low, high = self._unnormalized.low, self._unnormalized.high\n\n self._shape_check(val, 'denormalize')\n self._bounds_check(val, 0, 1)\n\n denormalized = low + self._spread * val\n\n try:\n return denormalized.item()\n except (AttributeError, ValueError):\n return denormalized\n\n def _bounds_check(self, val, low, high):\n array_like_check = hasattr(val, '__len__') and \\\n not (all((low <= val) & (val <= high)) or np.allclose(val, low) or np.allclose(val, high))\n scalar_check = not hasattr(val, '__len__') and not low <= val <= high\n\n if array_like_check or scalar_check:\n warnings.warn(f'Value {val} resides out of expected bounds of value to be normalized: [{low}, {high}].')\n\n\nclass MicrogridSpace(_PymgridSpace):\n def __init__(self, module_space_dict, act_or_obs, seed=None):\n\n self._unnormalized = _PymgridDict(module_space_dict, act_or_obs)\n self._normalized = _PymgridDict(module_space_dict, act_or_obs, normalized=True)\n\n try:\n super().__init__(shape=None, seed=seed)\n\n except TypeError:\n super().__init__(shape=None)\n import gym\n warnings.warn(f\"gym.Space does not accept argument 'seed' in version {gym.__version__}; this argument will \"\n f\"be ignored. Upgrade your gym version with 'pip install -U gym' to use this functionality.\")\n\n self._spread = self._get_spread()\n\n def _get_spread(self):\n spread = {}\n\n for k, high_list in self._unnormalized.high.items():\n low_list = self._unnormalized.low[k]\n s = [h-l for h, l in zip(high_list, low_list)]\n for s_val in s:\n s_val[s_val == 0] = 1\n\n spread[k] = s\n\n return spread\n\n def normalize(self, val):\n low, high = self._unnormalized.low, self._unnormalized.high\n\n self._shape_check(val, 'normalize')\n val_minus_low = self.dict_op(val, low, operator.sub)\n normalized = self.dict_op(val_minus_low, self._spread, operator.truediv)\n\n # normalized = (val - low) / self._spread\n\n return normalized\n\n def denormalize(self, val):\n low, high = self._unnormalized.low, self._unnormalized.high\n\n self._shape_check(val, 'denormalize')\n\n val_times_spread = self.dict_op(val, self._spread, operator.mul)\n denormalized = self.dict_op(val_times_spread, low, operator.add)\n\n return denormalized\n\n @staticmethod\n def dict_op(first, second, op):\n out = {}\n for k, first_list in first.items():\n second_list = second[k]\n out[k] = [op(f, s) for f, s in zip(first_list, second_list)]\n return out\n" }, { "path": "src/pymgrid/version.py", "content": "__version__ = '1.2.2'\n" }, { "path": "tests/__init__.py", "content": "" }, { "path": "tests/conftest.py", "content": "" }, { "path": "tests/control/__init__.py", "content": "" }, { "path": "tests/control/data_generation/__init__.py", "content": "" }, { "path": "tests/control/data_generation/test_data_generator.py", "content": "import unittest\nfrom pymgrid.utils.DataGenerator import *\nfrom pandas import Series\nfrom pandas.testing import assert_frame_equal, assert_series_equal\nfrom numpy.testing import assert_array_equal, assert_array_almost_equal\nfrom matplotlib import pyplot as plt\n\ndef create_pv_test_set():\n test_set = np.zeros(48)\n test_set[7:20] = np.arange(7,20)\n test_set[7:20] = -20*(test_set[7:20]-13)**2 + 30*-1**(np.arange(7,20) % 2) + 5*(np.arange(6,19) %5)\n test_set[7:20] += -1*np.min(test_set[7:20])\n\n n = 24\n test_set[7+n:20+n] = np.arange(7,20)\n test_set[7+n:20+n] = -20*(test_set[7+n:20+n]-13)**2 + 20*-1**(np.arange(6,19) % 2) + 4*(np.arange(6,19) %3)\n test_set[7+n:20+n] += -1*np.min(test_set[7+n:20+n])\n\n return test_set/10\n\n\nclass TestNoisyPV(unittest.TestCase):\n def setUp(self):\n\n self.test_data = create_pv_test_set()\n self.test_series = Series(data = self.test_data)\n\n\n def test_init(self):\n NPV = NoisyPVData(pv_data = self.test_series)\n df = pd.DataFrame(self.test_data)\n assert_frame_equal(NPV.unmunged_data, df)\n assert_frame_equal(NPV.data, df)\n\n def test_data_munge(self):\n NPV = NoisyPVData(pv_data=self.test_series)\n NPV.data_munge()\n\n # Assertions:\n assert_array_equal(NPV.data.values[:,0],self.test_data[:24])\n assert_array_equal(NPV.data.values[:,1],self.test_data[24:])\n assert_array_equal(NPV.daily_maxes['time_of_max'].values, np.array([13,13]))\n assert_array_equal(NPV.daily_maxes['cumulative_hr'], np.array([13, 37]))\n self.assertTrue(NPV.munged)\n\n def test_add_feature_columns(self):\n NPV = NoisyPVData(pv_data=self.test_series)\n NPV.data_munge()\n\n num_feature_functions = 1\n period_scale = 0.8\n\n NPV._add_feature_columns(num_feature_functions=num_feature_functions, period_scale=period_scale)\n\n self.assertIn('ones', NPV.daily_maxes.columns.values)\n self.assertIn('cos1x', NPV.daily_maxes.columns.values)\n assert_array_equal(NPV.daily_maxes['ones'], np.array([1,1]))\n cos1x = np.cos(\n 2 * np.pi / 8760. * period_scale * (NPV.daily_maxes['cumulative_hr'] - 173 * 24))\n assert_array_equal(NPV.daily_maxes['cos1x'], cos1x)\n\n self.assertListEqual(NPV.feature_names, ['ones', 'cos1x'])\n\n for name in NPV.feature_names:\n assert_array_equal(NPV.feature_functions[name](NPV.daily_maxes['cumulative_hr']).values, NPV.daily_maxes[name].values)\n\n\nclass TestNoisyGrid(unittest.TestCase):\n\n def setUp(self) -> None:\n always_on = np.ones(48)\n self.always_on = pd.Series(always_on)\n self.with_outages = self.always_on.copy()\n self.with_outages.iloc[3:6] = 0\n self.with_outages.iloc[40:47] = 0\n self.with_outages_data = dict(naive_probabilities = np.array([10/48, 38/48]),\n occurences=np.array([10,37]),\n transition_prob_matrix = np.array([\n [8 / 10, 2 / 10],\n [2/37, 35/37]\n ]))\n self.dist_types = ('naive', 'markov')\n\n def test_init(self):\n\n for dist_type in self.dist_types:\n for data in self.always_on, self.with_outages:\n NGD = NoisyGridData(data,dist_type=dist_type)\n assert_series_equal(data, NGD.data)\n assert_series_equal(data, NGD.unmunged_data)\n\n def test_bad_grid_data(self):\n grid_data = self.with_outages.copy()\n grid_data[5] = -3\n try:\n NoisyGridData(grid_data)\n except ValueError:\n pass\n except Exception:\n self.fail('unexpected exception raised')\n else:\n self.fail('ValueError not raised')\n grid_data[5] = 1.1\n try:\n NoisyGridData(grid_data)\n except ValueError:\n pass\n except Exception:\n self.fail('unexpected exception raised')\n else:\n self.fail('ValueError not raised')\n\n def test_learn_distribution_always_on_naive(self):\n NGD = NoisyGridData(self.always_on, dist_type='naive')\n self.assertFalse(NGD.has_distribution)\n NGD.learn_distribution()\n self.assertTrue(NGD.has_distribution)\n\n assert_array_equal(NGD.transition_prob_matrix, np.array([0, 1]))\n\n def test_learn_distribution_always_on_markov(self):\n NGD = NoisyGridData(self.always_on, dist_type='markov')\n self.assertFalse(NGD.has_distribution)\n NGD.learn_distribution()\n self.assertTrue(NGD.has_distribution)\n\n assert_array_equal(NGD.occurrences, np.array([0, 47]))\n assert_array_equal(NGD.transition_prob_matrix, np.array([[1,0],[0,1]]))\n\n def test_learn_distribution_with_outages_naive(self):\n NGD = NoisyGridData(self.with_outages, dist_type='naive')\n self.assertFalse(NGD.has_distribution)\n NGD.learn_distribution()\n self.assertTrue(NGD.has_distribution)\n\n assert_array_almost_equal(NGD.transition_prob_matrix, self.with_outages_data['naive_probabilities'])\n\n def test_learn_distribution_with_outages_markov(self):\n NGD = NoisyGridData(self.with_outages, dist_type='markov')\n self.assertFalse(NGD.has_distribution)\n NGD.learn_distribution()\n self.assertTrue(NGD.has_distribution)\n\n assert_array_almost_equal(NGD.occurrences, self.with_outages_data['occurences'])\n assert_array_almost_equal(NGD.transition_prob_matrix, self.with_outages_data['transition_prob_matrix'])\n\n def test_sample_always_on_naive(self):\n NGD = NoisyGridData(self.always_on, dist_type='naive')\n NGD.learn_distribution()\n sample = NGD.sample()\n assert_array_equal(sample, np.ones(48))\n\n def test_sample_always_on_markov(self):\n NGD = NoisyGridData(self.always_on, dist_type='markov')\n NGD.learn_distribution()\n sample = NGD.sample()\n assert_array_equal(sample, np.ones(48))\n\n def test_sample_with_outages_naive(self):\n # This is a ridiculous unit test All it does is check that the data generated from the probability distribution\n # matches the distribution. Thus, can fail randomly.\n np.random.seed(0)\n num_tests = 50\n\n NGD = NoisyGridData(self.with_outages, dist_type='naive')\n NGD.learn_distribution()\n\n probs_list = []\n for j in range(num_tests):\n sample = NGD.sample()\n new_NGD = NoisyGridData(sample, dist_type='naive')\n new_NGD.learn_distribution()\n probs_list.append(new_NGD.transition_prob_matrix)\n\n transition_prob_mean = np.mean(np.array(probs_list), axis=0)\n assert_array_almost_equal(self.with_outages_data['naive_probabilities'], transition_prob_mean, decimal=2)\n\n def test_sample_with_outages_markov(self):\n \"\"\"\n This is also a ridiculous unit test. All it does is check that the data generated from the probability distribution\n matches the distribution. Thus, can fail randomly.\n :return:\n \"\"\"\n\n np.random.seed(0)\n num_tests = 50\n\n NGD = NoisyGridData(self.with_outages, dist_type='markov')\n NGD.learn_distribution()\n\n probs_list = []\n for j in range(num_tests):\n sample = NGD.sample()\n new_NGD = NoisyGridData(sample, dist_type='markov')\n new_NGD.learn_distribution()\n probs_list.append(new_NGD.transition_prob_matrix)\n\n transition_prob_mean = np.mean(np.array(probs_list), axis=0)\n assert_array_almost_equal(self.with_outages_data['transition_prob_matrix'], transition_prob_mean, decimal=1)\n\n\nclass TestNoisyLoad(unittest.TestCase):\n def setUp(self) -> None:\n self.n_days = 12\n\n load_data = np.array([304, 205, 200, 200, 202, 306, 524, 611, 569, 466, 571, 579, 569, 470, 466, 465, 597, 625, 620, 525, 521, 524, 522, 531, 305, 200, 199, 200, 202, 306, 524, 611, 568, 466, 568, 579, 569, 467, 467, 466, 597, 626, 626, 525, 525, 524, 522, 533])\n load_data = np.concatenate([load_data + j % 5 for j in range(int(self.n_days/2))])\n\n self.load_data = pd.Series(data = load_data)\n\n def test_init(self):\n NLD = NoisyLoadData(load_data=self.load_data)\n assert_frame_equal(NLD.data, self.load_data.to_frame())\n assert_frame_equal(NLD.unmunged_data, self.load_data.to_frame())\n\n def test_data_munge(self):\n NLD = NoisyLoadData(load_data=self.load_data)\n\n self.assertFalse(NLD.munged)\n NLD.data_munge()\n self.assertTrue(NLD.munged)\n\n self.assertTupleEqual(NLD.load_mean.shape, (7,24))\n self.assertTupleEqual(NLD.load_std.shape, (7,24))\n\n self.assertEqual(NLD.data.shape[0], self.n_days)\n self.assertFalse(np.isnan(NLD.load_mean).any(axis=None))\n self.assertFalse(np.isnan(NLD.load_std).any(axis=None))\n\n for j in range(7):\n NLD_computed_avg = NLD.load_mean.iloc[j,:].values\n NLD_computed_std = NLD.load_std.iloc[j,:].values\n\n\n slice = self.load_data[24*j:24*(j+1)].values\n\n for k in range(1, (self.n_days-j) // 7 + 1):\n if (j+k*7) >= self.n_days:\n continue\n slice = np.stack((slice,self.load_data[24*(j+k*7):24*(j+k*7+1)]))\n\n if len(slice.shape) == 1:\n slice = slice.reshape((1, 24))\n assert_array_almost_equal(NLD_computed_avg, np.mean(slice, axis=0))\n else:\n assert_array_almost_equal(NLD_computed_avg, np.mean(slice, axis=0))\n assert_array_almost_equal(NLD_computed_std, np.std(slice, axis=0, ddof=1))\n\nif __name__ == '__main__':\n unittest.main()\n\n" }, { "path": "tests/control/test_control.py", "content": "import unittest\n\n" }, { "path": "tests/control/test_mpc.py", "content": "import numpy as np\n\nfrom tests.helpers.test_case import TestCase\nfrom tests.helpers.modular_microgrid import get_modular_microgrid\n\nfrom pymgrid.algos import ModelPredictiveControl\n\n\nclass TestMPC(TestCase):\n def test_init(self):\n microgrid = get_modular_microgrid()\n mpc = ModelPredictiveControl(microgrid)\n self.assertTrue(mpc.is_modular)\n self.assertEqual(mpc.horizon, 1)\n\n def test_run_with_load_pv_battery_grid(self):\n from pymgrid.modules import RenewableModule, LoadModule\n\n max_steps = 10\n pv_const = 50\n load_const = 60\n pv = RenewableModule(time_series=pv_const*np.ones(100))\n load = LoadModule(time_series=load_const*np.ones(100))\n\n microgrid = get_modular_microgrid(remove_modules=[\"renewable\", \"load\", \"genset\"], additional_modules=[pv, load])\n\n mpc = ModelPredictiveControl(microgrid)\n mpc_output = mpc.run(max_steps=max_steps)\n self.assertEqual(mpc_output.shape[0], max_steps)\n self.assertEqual(mpc_output[(\"grid\", 0, \"grid_import\")].values +\n mpc_output[(\"battery\", 0, \"discharge_amount\")].values +\n mpc_output[(\"renewable\", 0, \"renewable_used\")].values,\n [load_const] * mpc_output.shape[0]\n )\n\n def test_run_with_load_pv_battery_genset(self):\n from pymgrid.modules import RenewableModule, LoadModule\n\n max_steps = 10\n pv_const = 50\n load_const = 60\n pv = RenewableModule(time_series=pv_const*np.ones(100))\n load = LoadModule(time_series=load_const*np.ones(100))\n\n microgrid = get_modular_microgrid(remove_modules=[\"renewable\", \"load\", \"grid\"], additional_modules=[pv, load])\n\n mpc = ModelPredictiveControl(microgrid)\n mpc_output = mpc.run(max_steps=max_steps)\n self.assertEqual(mpc_output.shape[0], max_steps)\n\n self.assertEqual(mpc_output[(\"load\", 0, \"load_met\")].values, [60.]*mpc_output.shape[0])\n self.assertEqual(mpc_output[(\"genset\", 0, \"genset_production\")].values +\n mpc_output[(\"battery\", 0, \"discharge_amount\")].values,\n [10.] * mpc_output.shape[0])\n\n def test_run_twice_with_load_pv_battery_genset(self):\n from pymgrid.modules import RenewableModule, LoadModule\n\n max_steps = 10\n pv_const = 50\n load_const = 60\n pv = RenewableModule(time_series=pv_const*np.ones(100))\n load = LoadModule(time_series=load_const*np.ones(100))\n\n microgrid = get_modular_microgrid(remove_modules=[\"renewable\", \"load\", \"grid\"], additional_modules=[pv, load])\n\n mpc = ModelPredictiveControl(microgrid)\n mpc_output = mpc.run(max_steps=max_steps)\n\n self.assertEqual(mpc_output.shape[0], max_steps)\n self.assertEqual(mpc_output[(\"load\", 0, \"load_met\")].values, [60.] * mpc_output.shape[0])\n self.assertEqual(mpc_output[(\"genset\", 0, \"genset_production\")].values +\n mpc_output[(\"battery\", 0, \"discharge_amount\")].values,\n [10.] * mpc_output.shape[0])\n\n mpc_output = mpc.run(max_steps=max_steps)\n\n self.assertEqual(mpc_output.shape[0], max_steps)\n self.assertEqual(mpc_output[(\"load\", 0, \"load_met\")].values, [60.] * mpc_output.shape[0])\n self.assertEqual(mpc_output[(\"genset\", 0, \"genset_production\")].values, [10.] * mpc_output.shape[0])\n\n def test_run_with_load_pv_battery_grid_different_names(self):\n from pymgrid.modules import RenewableModule, LoadModule\n\n max_steps = 10\n pv_const = 50\n load_const = 60\n pv = RenewableModule(time_series=pv_const*np.ones(100))\n load = LoadModule(time_series=load_const*np.ones(100))\n\n microgrid = get_modular_microgrid(remove_modules=[\"renewable\", \"load\", \"genset\"],\n additional_modules=[(\"pv_with_name\", pv), (\"load_with_name\", load)])\n\n mpc = ModelPredictiveControl(microgrid)\n mpc_output = mpc.run(max_steps=max_steps)\n self.assertEqual(mpc_output.shape[0], max_steps)\n self.assertEqual(mpc_output[(\"load_with_name\", 0, \"load_met\")].values, [load_const]*mpc_output.shape[0])\n self.assertEqual(mpc_output[(\"grid\", 0, \"grid_import\")].values +\n mpc_output[(\"battery\", 0, \"discharge_amount\")].values +\n mpc_output[(\"pv_with_name\", 0, \"renewable_used\")].values,\n [load_const] * mpc_output.shape[0]\n )\n self.assertEqual(mpc_output[(\"load_with_name\", 0, \"load_met\")].values, [load_const]*mpc_output.shape[0])" }, { "path": "tests/control/test_rbc.py", "content": "from copy import deepcopy\n\nfrom tests.helpers.test_case import TestCase\nfrom tests.helpers.modular_microgrid import get_modular_microgrid\n\nfrom pymgrid.algos import RuleBasedControl\n\n\nclass TestRBC(TestCase):\n def setUp(self) -> None:\n self.rbc = RuleBasedControl(get_modular_microgrid())\n\n def test_init(self):\n microgrid = get_modular_microgrid()\n self.assertEqual(microgrid, self.rbc.microgrid)\n self.assertEqual(microgrid, deepcopy(self.rbc).microgrid)\n\n def test_priority_list(self):\n rbc = deepcopy(self.rbc)\n\n for j, (element_1, element_2) in enumerate(zip(rbc.priority_list[:-1], rbc.priority_list[1:])):\n with self.subTest(testing=f'element_{j}<=element_{j+1}'):\n self.assertLessEqual(element_1.marginal_cost, element_2.marginal_cost)\n\n def test_run_once(self):\n rbc = deepcopy(self.rbc)\n\n self.assertEqual(len(rbc.microgrid.log), 0)\n\n n_steps = 10\n\n log = rbc.run(n_steps)\n\n self.assertEqual(len(log), n_steps)\n self.assertEqual(log, rbc.microgrid.log)\n return rbc\n\n def test_reset_after_run(self):\n rbc = self.test_run_once()\n rbc.reset()\n self.assertEqual(len(rbc.microgrid.log), 0)" }, { "path": "tests/envs/__init__.py", "content": "" }, { "path": "tests/envs/test_discrete.py", "content": "from copy import deepcopy\nfrom math import factorial\n\nfrom tests.helpers.test_case import TestCase\nfrom tests.helpers.modular_microgrid import get_modular_microgrid\n\nfrom pymgrid.envs import DiscreteMicrogridEnv\n\n\nclass TestDiscreteEnv(TestCase):\n\n def test_init_from_microgrid(self):\n microgrid = get_modular_microgrid()\n env = DiscreteMicrogridEnv(microgrid)\n\n self.assertEqual(env.modules, microgrid.modules)\n self.assertIsNot(env.modules.to_tuples(), microgrid.modules.to_tuples())\n\n n_obs = sum([x.observation_space['normalized'].shape[0] for x in microgrid.modules.to_list()])\n\n self.assertEqual(env.observation_space.shape, (n_obs,))\n\n def test_init_from_modules(self):\n microgrid = get_modular_microgrid()\n env = DiscreteMicrogridEnv(microgrid.modules.to_tuples(), add_unbalanced_module=False)\n\n self.assertEqual(env.modules, microgrid.modules)\n self.assertIsNot(env.modules.to_tuples(), microgrid.modules.to_tuples())\n\n n_obs = sum([x.observation_space['normalized'].shape[0] for x in microgrid.modules.to_list()])\n\n self.assertEqual(env.observation_space.shape, (n_obs,))\n\n\nclass TestDiscreteEnvScenario(TestCase):\n microgrid_number = 0\n\n def setUp(self) -> None:\n self.env = DiscreteMicrogridEnv.from_scenario(microgrid_number=self.microgrid_number)\n\n def test_run_once(self):\n env = deepcopy(self.env)\n # sample environment then get log\n self.assertEqual(len(env.log), 0)\n for j in range(10):\n with self.subTest(step=j):\n action = env.sample_action(strict_bound=True)\n env.step(action)\n self.assertEqual(len(env.log), j+1)\n\n def test_reset_after_run(self):\n env = deepcopy(self.env)\n env.step(env.sample_action(strict_bound=True))\n env.reset()\n self.assertEqual(len(env.log), 0)\n\n def test_run_again_after_reset(self):\n env = deepcopy(self.env)\n env.step(env.sample_action(strict_bound=True))\n\n self.assertEqual(len(env.log), 1)\n\n env.reset()\n\n self.assertEqual(len(env.log), 0)\n\n for j in range(10):\n with self.subTest(step=j):\n action = env.sample_action(strict_bound=True)\n env.step(action)\n self.assertEqual(len(env.log), j+1)\n\n def test_action_space(self):\n env = deepcopy(self.env)\n\n n_action_modules = len(env.modules.controllable.sources) + len(env.modules.controllable.source_and_sinks)\n genset_modules = len(env.modules.genset) if hasattr(env.modules, 'genset') else 0\n\n n_actions = factorial(n_action_modules) * (2 ** genset_modules)\n self.assertEqual(env.action_space.n, n_actions)\n\n def test_simple_observation_keys(self):\n keys_in_all_scenarios = ['load_current', 'renewable_current']\n\n env = DiscreteMicrogridEnv.from_scenario(microgrid_number=self.microgrid_number,\n observation_keys=keys_in_all_scenarios)\n\n obs, _, _, _ = env.step(env.action_space.sample())\n\n expected_obs = [\n env.modules['load'].item().state_dict(normalized=True)['load_current'],\n env.modules['pv'].item().state_dict(normalized=True)['renewable_current']\n ]\n\n self.assertEqual(obs.tolist(), expected_obs)\n\n\nclass TestDiscreteEnvScenario1(TestDiscreteEnvScenario):\n microgrid_number = 1\n\n\nclass TestDiscreteEnvScenario2(TestDiscreteEnvScenario):\n microgrid_number = 2\n\n\nclass TestDiscreteEnvScenario3(TestDiscreteEnvScenario):\n microgrid_number = 3\n\n\nclass TestDiscreteEnvScenario4(TestDiscreteEnvScenario):\n microgrid_number = 4\n\n\nclass TestDiscreteEnvScenario5(TestDiscreteEnvScenario):\n microgrid_number = 5\n\n\nclass TestDiscreteEnvScenario6(TestDiscreteEnvScenario):\n microgrid_number = 6\n\n\nclass TestDiscreteEnvScenario47(TestDiscreteEnvScenario):\n microgrid_number = 7\n\n\nclass TestDiscreteEnvScenario8(TestDiscreteEnvScenario):\n microgrid_number = 8\n\n\nclass TestDiscreteEnvScenario9(TestDiscreteEnvScenario):\n microgrid_number = 9\n\n\nclass TestDiscreteEnvScenario10(TestDiscreteEnvScenario):\n microgrid_number = 10\n\n\nclass TestDiscreteEnvScenario11(TestDiscreteEnvScenario):\n microgrid_number = 11\n\n\nclass TestDiscreteEnvScenario12(TestDiscreteEnvScenario):\n microgrid_number = 12\n\n\nclass TestDiscreteEnvScenario13(TestDiscreteEnvScenario):\n microgrid_number = 13\n\n\nclass TestDiscreteEnvScenario14(TestDiscreteEnvScenario):\n microgrid_number = 14\n \n \nclass TestDiscreteEnvScenario15(TestDiscreteEnvScenario):\n microgrid_number = 15\n \n\nclass TestDiscreteEnvScenario16(TestDiscreteEnvScenario):\n microgrid_number = 16\n \n\nclass TestDiscreteEnvScenario17(TestDiscreteEnvScenario):\n microgrid_number = 17\n \n\nclass TestDiscreteEnvScenario18(TestDiscreteEnvScenario):\n microgrid_number = 18\n \n\nclass TestDiscreteEnvScenario19(TestDiscreteEnvScenario):\n microgrid_number = 19\n \n\nclass TestDiscreteEnvScenario20(TestDiscreteEnvScenario):\n microgrid_number = 20\n\n\nclass TestDiscreteEnvScenario21(TestDiscreteEnvScenario):\n microgrid_number = 21\n\n\nclass TestDiscreteEnvScenario22(TestDiscreteEnvScenario):\n microgrid_number = 22\n\n\nclass TestDiscreteEnvScenario23(TestDiscreteEnvScenario):\n microgrid_number = 23\n\n\nclass TestDiscreteEnvScenario24(TestDiscreteEnvScenario):\n microgrid_number = 24\n" }, { "path": "tests/envs/test_trajectory.py", "content": "import numpy as np\n\nfrom tests.helpers.test_case import TestCase\nfrom tests.helpers.modular_microgrid import get_modular_microgrid\n\nfrom pymgrid.envs import DiscreteMicrogridEnv\n\n\nclass TestTrajectory(TestCase):\n\n def check_initial_final_steps(self,\n env,\n expected_env_initial,\n expected_env_final,\n expected_module_initial,\n expected_module_final):\n\n self.assertEqual(env.initial_step, expected_env_initial)\n self.assertEqual(env.final_step, expected_env_final)\n\n env.reset()\n\n self.assertEqual(env.initial_step, expected_env_initial)\n self.assertEqual(env.final_step, expected_env_final)\n\n self.assertEqual(env.modules.get_attrs('initial_step', unique=True).item(), expected_module_initial)\n self.assertEqual(env.modules.get_attrs('final_step', unique=True).item(), expected_module_final)\n\n def test_none_trajectory(self):\n timeseries_length = 100\n modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True)\n env = DiscreteMicrogridEnv(modules, trajectory_func=None)\n self.check_initial_final_steps(env, 0, timeseries_length, 0, timeseries_length)\n\n def test_deterministic_trajectory(self):\n deterministic_initial, deterministic_final = 10, 20\n\n def trajectory_func(initial_step, final_step):\n return deterministic_initial, deterministic_final\n\n timeseries_length = 100\n modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True)\n env = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func)\n\n self.check_initial_final_steps(env, 0, timeseries_length, deterministic_initial, deterministic_final)\n\n def test_stochastic_trajectory(self):\n def trajectory_func(initial_step, final_step):\n initial = np.random.randint(low=initial_step+1, high=final_step-2)\n final = np.random.randint(low=initial, high=final_step)\n return initial, final\n\n timeseries_length = 100\n modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True)\n env = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func)\n\n self.assertEqual(env.initial_step, 0)\n self.assertEqual(env.final_step, timeseries_length)\n\n env.reset()\n\n self.assertEqual(env.initial_step, 0)\n self.assertEqual(env.final_step, timeseries_length)\n\n self.assertGreater(env.modules.get_attrs('initial_step', unique=True).item(), 0)\n self.assertLess(env.modules.get_attrs('initial_step', unique=True).item(), timeseries_length)\n\n self.assertGreater(env.modules.get_attrs('final_step', unique=True).item(), 0)\n self.assertLess(env.modules.get_attrs('final_step', unique=True).item(), timeseries_length)\n\n self.assertLess(env.modules.get_attrs('initial_step', unique=True).item(),\n env.modules.get_attrs('final_step', unique=True).item())\n\n def test_bad_trajectory_out_of_range(self):\n def trajectory_func(initial_step, final_step):\n return 10, 110\n\n timeseries_length = 100\n modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True)\n\n with self.assertRaises(ValueError):\n _ = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func)\n\n def test_bad_trajectory_bad_signature(self):\n def trajectory_func(initial_step):\n return 10, 110\n\n timeseries_length = 100\n modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True)\n\n with self.assertRaises(TypeError):\n _ = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func)\n\n def test_bad_trajectory_initial_gt_final(self):\n def trajectory_func(initial_step, final_step):\n return 20, 10\n\n timeseries_length = 100\n modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True)\n\n with self.assertRaises(ValueError):\n _ = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func)\n\n def test_bad_trajectory_scalar_output(self):\n def trajectory_func(initial_step, final_step):\n return 20\n\n timeseries_length = 100\n modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True)\n\n with self.assertRaises(TypeError):\n _ = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func)\n\n def test_bad_trajectory_too_many_outputs(self):\n def trajectory_func(initial_step, final_step):\n return 10, 20, 30\n\n timeseries_length = 100\n modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True)\n\n with self.assertRaises(TypeError):\n _ = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func)\n\n def test_bad_trajectory_wrong_output_types(self):\n def trajectory_func(initial_step, final_step):\n return 'abc', 10.0\n\n timeseries_length = 100\n modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True)\n\n with self.assertRaises(TypeError):\n _ = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func)\n\n def test_correct_trajectory_length(self):\n\n def trajectory_func(initial_step, final_step):\n trajectory_func.n_resets += 1\n return 10, 11+trajectory_func.n_resets\n\n trajectory_func.n_resets = 0\n\n timeseries_length = 100\n modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True)\n env = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func)\n\n for correct_trajectory_length in range(3, 7):\n with self.subTest(correct_trajectory_length=correct_trajectory_length):\n env.reset()\n n_steps = 0\n done = False\n\n while not done:\n _, _, done, _ = env.step(env.action_space.sample())\n n_steps += 1\n\n self.assertEqual(n_steps, correct_trajectory_length)\n\n def test_trajectory_serialization(self):\n import yaml\n from pymgrid.microgrid.trajectory import DeterministicTrajectory\n\n trajectory_func = DeterministicTrajectory(10, 20)\n\n timeseries_length = 100\n modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True)\n\n env = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func)\n env.reset()\n loaded_env = yaml.safe_load(yaml.safe_dump(env))\n\n self.assertIsNotNone(env.trajectory_func)\n self.assertEqual(env, loaded_env)\n" }, { "path": "tests/helpers/__init__.py", "content": "" }, { "path": "tests/helpers/genset_module_testing_utils.py", "content": "from pymgrid.modules import GensetModule\n\n\ndefault_params = dict(running_min_production=10,\n running_max_production=100,\n genset_cost=1,\n start_up_time=0,\n wind_down_time=0,\n allow_abortion=True,\n init_start_up=True,\n raise_errors=True)\n\n\ndef get_genset(default_parameters=None, **new_params):\n params = default_parameters.copy() if default_parameters is not None else default_params.copy()\n params.update(new_params)\n return GensetModule(**params), params\n\n\ndef normalize_production(production, max_production=None):\n max_production = max_production if max_production else default_params['running_max_production']\n return production/max_production\n" }, { "path": "tests/helpers/modular_microgrid.py", "content": "import numpy as np\n\nfrom pymgrid import Microgrid\n\nfrom pymgrid.modules import (\n BatteryModule,\n GensetModule,\n GridModule,\n LoadModule,\n RenewableModule\n)\n\n\ndef get_modular_microgrid(remove_modules=(),\n retain_only=None,\n additional_modules=None,\n add_unbalanced_module=True,\n timeseries_length=100,\n modules_only=False):\n\n modules = dict(\n genset=GensetModule(running_min_production=10, running_max_production=50, genset_cost=0.5),\n\n battery=BatteryModule(min_capacity=0,\n max_capacity=100,\n max_charge=50,\n max_discharge=50,\n efficiency=1.0,\n init_soc=0.5),\n\n renewable=RenewableModule(time_series=50*np.ones(timeseries_length)),\n\n load=LoadModule(time_series=60*np.ones(timeseries_length)),\n\n grid=GridModule(max_import=100, max_export=0, time_series=np.ones((timeseries_length, 3)), raise_errors=True)\n )\n\n if retain_only is not None:\n modules = {k: v for k, v in modules.items() if k in retain_only}\n if remove_modules:\n raise RuntimeError('Can pass either remove_modules or retain_only, but not both.')\n else:\n for module in remove_modules:\n try:\n modules.pop(module)\n except KeyError:\n raise NameError(f\"Module {module} not one of default modules {list(modules.keys())}.\")\n\n modules = list(modules.values())\n modules.extend(additional_modules if additional_modules else [])\n\n if modules_only:\n return modules\n\n return Microgrid(modules, add_unbalanced_module=add_unbalanced_module)\n" }, { "path": "tests/helpers/test_case.py", "content": "import unittest\nimport numpy as np\nfrom warnings import warn\n\n\nclass TestCase(unittest.TestCase):\n def assertEqual(self, first, second, msg=None) -> None:\n try:\n super().assertEqual(first, second, msg=msg)\n except (ValueError, AssertionError):\n # array-like or pandas obj\n try:\n # convert pandas obj\n first, second = first.values, second.values\n except AttributeError:\n # not a pandas obj\n pass\n\n try:\n np.testing.assert_equal(first, second, err_msg=msg if msg else '')\n except AssertionError as e:\n try:\n np.testing.assert_allclose(first, second, rtol=1e-7, atol=1e-10, err_msg=msg if msg else '')\n except TypeError:\n raise e\n" }, { "path": "tests/microgrid/__init__.py", "content": "" }, { "path": "tests/microgrid/modules/__init__.py", "content": "" }, { "path": "tests/microgrid/modules/container_tests/__init__.py", "content": "" }, { "path": "tests/microgrid/modules/container_tests/test_container.py", "content": "from tests.helpers.modular_microgrid import get_modular_microgrid\nfrom tests.helpers.test_case import TestCase\n\n\nclass TestContainer(TestCase):\n def test_container_init(self):\n microgrid = get_modular_microgrid()\n self.assertTrue(len(microgrid.controllable.sources))\n self.assertTrue(len(microgrid.controllable.source_and_sinks))\n action = microgrid.sample_action()\n" }, { "path": "tests/microgrid/modules/conversion_test/__init__.py", "content": "" }, { "path": "tests/microgrid/modules/conversion_test/test_modular_conversion.py", "content": "import numpy as np\n\nfrom tests.helpers.test_case import TestCase\n\n\nclass TestToModular(TestCase):\n def setUp(self) -> None:\n from pymgrid.MicrogridGenerator import MicrogridGenerator\n mgen = MicrogridGenerator()\n mgen.generate_microgrid(modular=False)\n self.weak_grids = [microgrid for microgrid in mgen.microgrids if self.is_weak_grid(microgrid)]\n self.genset_only = [microgrid for microgrid in mgen.microgrids if not microgrid.architecture[\"grid\"]]\n self.strong_grid_only = [microgrid for microgrid in mgen.microgrids if\n (not microgrid.architecture[\"genset\"]) and self.is_strong_grid(microgrid)]\n self.strong_grid_and_genset = [microgrid for microgrid in mgen.microgrids if\n microgrid.architecture[\"genset\"] and self.is_strong_grid(microgrid)]\n\n @staticmethod\n def is_weak_grid(microgrid):\n return microgrid.architecture[\"grid\"] and microgrid._grid_status_ts.min().item() < 1\n\n @staticmethod\n def is_strong_grid(microgrid):\n return microgrid.architecture[\"grid\"] and microgrid._grid_status_ts.min().item() == 1\n\n def test_weak_grid_conversion_success(self):\n for microgrid in self.weak_grids:\n modular_microgrid = microgrid.to_modular()\n self.assertTrue(modular_microgrid.grid.item().weak_grid)\n\n def test_genset_only(self):\n for microgrid in self.genset_only:\n modular = microgrid.to_modular()\n self.assertTrue(len(modular.genset) == 1)\n\n genset_module = modular.genset[0]\n self.assertEqual(microgrid.genset.fuel_cost, genset_module.genset_cost)\n self.assertEqual(microgrid.genset.co2, genset_module.co2_per_unit)\n self.assertEqual(microgrid.genset.rated_power*microgrid.genset.p_max, genset_module.max_production)\n\n with self.assertRaises(AttributeError):\n _ = modular.grid" }, { "path": "tests/microgrid/modules/forecaster_tests/__init__.py", "content": "" }, { "path": "tests/microgrid/modules/forecaster_tests/test_forecaster.py", "content": "import numpy as np\nfrom tests.helpers.test_case import TestCase\nfrom pymgrid.forecast import (\n get_forecaster, OracleForecaster, GaussianNoiseForecaster, UserDefinedForecaster, NoForecaster)\nfrom pymgrid.utils.space import ModuleSpace\n\nSTATE_COMPONENTS = np.random.randint(low=1, high=10)\nFORECAST_HORIZON = np.random.randint(low=2, high=10)\n\nPOSITIVE_OBSERVATION_SPACE = ModuleSpace(\n unnormalized_low=0,\n unnormalized_high=10,\n shape=(STATE_COMPONENTS*(FORECAST_HORIZON+1),)\n )\n\nNEGATIVE_OBSERVATION_SPACE = ModuleSpace(\n unnormalized_low=-10,\n unnormalized_high=0,\n shape=(STATE_COMPONENTS*(FORECAST_HORIZON+1),)\n )\n\n\ndef get_test_inputs(n=None, state_components=None, negative=False):\n state_components = state_components if state_components else STATE_COMPONENTS\n n = n if n else FORECAST_HORIZON\n val_c_n = POSITIVE_OBSERVATION_SPACE.unnormalized.high[0] * np.random.rand(n, state_components)\n val_c = val_c_n[0, :]\n # val_c_n = val_c_n.reshape((FORECAST_HORIZON, STATE_COMPONENTS))\n if negative:\n return -val_c, -val_c_n, n\n else:\n return val_c, val_c_n, n\n\n\nclass TestOracleForecaster(TestCase):\n def setUp(self) -> None:\n self.forecaster = OracleForecaster(observation_space=POSITIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS))\n\n def test_positive_inputs(self):\n val_c, val_c_n, n = get_test_inputs()\n forecast = self.forecaster(val_c, val_c_n, n)\n self.assertEqual(forecast, val_c_n)\n\n def test_negative_inputs(self):\n val_c, val_c_n, n = get_test_inputs(negative=True)\n forecast = self.forecaster(val_c, val_c_n, n)\n self.assertEqual(forecast, val_c_n)\n\n\nclass TestGaussianNoiseForecaster(TestCase):\n def test_single_forecast_positive(self):\n noise_std = 1\n forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=POSITIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS),\n increase_uncertainty=False)\n val_c, val_c_n, n = get_test_inputs()\n forecast = forecaster(val_c, val_c_n, n)\n self.assertEqual(noise_std, forecaster.noise_std)\n self.assertTrue((forecast >= 0).all())\n\n def test_single_forecast_positive_high_std(self):\n noise_std = 100\n forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=POSITIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS),\n increase_uncertainty=False)\n val_c, val_c_n, n = get_test_inputs()\n forecast = forecaster(val_c, val_c_n, n)\n self.assertEqual(noise_std, forecaster.noise_std)\n self.assertTrue((forecast >= 0).all())\n\n def test_single_forecast_negative(self):\n noise_std = 1\n forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=NEGATIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS),\n increase_uncertainty=False)\n val_c, val_c_n, n = get_test_inputs(negative=True)\n forecast = forecaster(val_c, val_c_n, n)\n self.assertEqual(noise_std, forecaster.noise_std)\n self.assertTrue((forecast <= 0).all())\n\n def test_single_forecast_negative_high_std(self):\n noise_std = 100\n forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=NEGATIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS),\n increase_uncertainty=False)\n val_c, val_c_n, n = get_test_inputs(negative=True)\n forecast = forecaster(val_c, val_c_n, n)\n self.assertEqual(noise_std, forecaster.noise_std)\n self.assertTrue((forecast <= 0).all())\n\n def test_multiple_forecast_positive(self):\n noise_std = 1\n forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=POSITIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS),\n increase_uncertainty=False)\n n = None\n for _ in range(2):\n val_c, val_c_n, n = get_test_inputs(n=n)\n forecast = forecaster(val_c, val_c_n, n)\n self.assertEqual(noise_std, forecaster.noise_std)\n self.assertTrue((forecast >= 0).all())\n\n def test_multiple_forecast_positive_high_std(self):\n noise_std = 100\n forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=POSITIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS),\n increase_uncertainty=False)\n n = None\n for _ in range(2):\n val_c, val_c_n, n = get_test_inputs(n=n)\n forecast = forecaster(val_c, val_c_n, n)\n self.assertEqual(noise_std, forecaster.noise_std)\n self.assertTrue((forecast >= 0).all())\n\n def test_multiple_forecast_negative(self):\n noise_std = 1\n forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=NEGATIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS),\n increase_uncertainty=False)\n n = None\n for _ in range(2):\n val_c, val_c_n, n = get_test_inputs(n=n, negative=True)\n forecast = forecaster(val_c, val_c_n, n)\n self.assertEqual(noise_std, forecaster.noise_std)\n self.assertTrue((forecast <= 0).all())\n\n def test_multiple_forecast_negative_high_std(self):\n noise_std = 100\n forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=NEGATIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS),\n increase_uncertainty=False)\n n = None\n for _ in range(2):\n val_c, val_c_n, n = get_test_inputs(n=n, negative=True)\n forecast = forecaster(val_c, val_c_n, n)\n self.assertEqual(noise_std, forecaster.noise_std)\n self.assertTrue((forecast <= 0).all())\n\n def test_increasing_uncertainty_positive(self):\n noise_std = 1\n forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=POSITIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS),\n increase_uncertainty=True)\n val_c, val_c_n, n = get_test_inputs()\n expected_noise_std = np.outer(noise_std*(1+np.log(1+np.arange(len(val_c_n)))), np.ones(STATE_COMPONENTS))\n\n forecast = forecaster(val_c, val_c_n, n)\n self.assertTrue((noise_std != forecaster.noise_std).any())\n self.assertEqual(expected_noise_std, forecaster.noise_std)\n self.assertTrue((forecast >= 0).all())\n\n def test_increasing_uncertainty_negative(self):\n noise_std = 1\n forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=NEGATIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS),\n increase_uncertainty=True)\n\n val_c, val_c_n, n = get_test_inputs(negative=True)\n expected_noise_std = np.outer(noise_std*(1+np.log(1+np.arange(len(val_c_n)))), np.ones(STATE_COMPONENTS))\n\n forecast = forecaster(val_c, val_c_n, n)\n self.assertTrue((noise_std != forecaster.noise_std).any())\n self.assertEqual(expected_noise_std, forecaster.noise_std)\n self.assertTrue((forecast <= 0).all())\n\n def test_bad_shape(self):\n noise_std = 1\n forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=POSITIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS),\n increase_uncertainty=False)\n\n n = np.random.randint(FORECAST_HORIZON, 2*FORECAST_HORIZON)\n val_c, val_c_n, n = get_test_inputs(n=n)\n with self.assertRaises(RuntimeError):\n _ = forecaster(val_c, val_c_n, n)\n # self.assertEqual(noise_std, forecaster.noise_std)\n # val_c, val_c_n, n = get_test_inputs(n=n_vals[1])\n # with self.assertRaises(ValueError):\n # _ = forecaster(val_c, val_c_n, n)\n\n\nclass TestUserDefinedForecaster(TestCase):\n def setUp(self) -> None:\n self.simple_time_series = np.arange(FORECAST_HORIZON).reshape((-1, 1))\n\n @staticmethod\n def oracle_scalar_forecaster(val_c, val_c_n, n):\n return val_c_n.item()\n\n def get_oracle_forecaster(self, negative=False):\n return OracleForecaster(observation_space=self.get_obs_space(negative=negative),\n forecast_shape=(FORECAST_HORIZON,))\n\n def get_obs_space(self, negative=False):\n if negative:\n low = -10\n high = 0\n else:\n low = 0\n high = 10\n\n return ModuleSpace(unnormalized_low=low, unnormalized_high=high, shape=(10, ))\n\n def test_user_defined_oracle_positive(self):\n forecaster = UserDefinedForecaster(forecaster_function=self.get_oracle_forecaster(),\n observation_space=self.get_obs_space(), forecast_shape=(FORECAST_HORIZON,),\n time_series=self.simple_time_series)\n val_c, val_c_n, n = get_test_inputs(state_components=1)\n forecast = forecaster(val_c, val_c_n, n)\n self.assertEqual(forecast, val_c_n)\n\n def test_user_defined_oracle_negative(self):\n forecaster = UserDefinedForecaster(forecaster_function=self.get_oracle_forecaster(negative=True),\n observation_space=self.get_obs_space(negative=True),\n forecast_shape=(FORECAST_HORIZON,), time_series=self.simple_time_series)\n val_c, val_c_n, n = get_test_inputs(state_components=1, negative=True)\n forecast = forecaster(val_c, val_c_n, n)\n self.assertEqual(forecast, val_c_n)\n\n def test_scalar_forecaster(self):\n forecaster = UserDefinedForecaster(forecaster_function=self.oracle_scalar_forecaster,\n observation_space=self.get_obs_space(negative=True),\n forecast_shape=(FORECAST_HORIZON,), time_series=self.simple_time_series)\n val_c, val_c_n, n = get_test_inputs(state_components=1, negative=True)\n forecast = forecaster(val_c, val_c_n, n)\n self.assertEqual(forecast, val_c_n)\n\n def test_vectorized_forecaster_bad_output_shape(self):\n bad_output_shape_forecaster = lambda val_c, val_c_n, n: np.append(val_c_n, [0])\n with self.assertRaisesRegex(ValueError, \"Forecaster output of shape (.*) \"\n \"cannot be casted to necessary forecast shape (.*, 1)\"):\n _ = UserDefinedForecaster(forecaster_function=bad_output_shape_forecaster,\n observation_space=POSITIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS),\n time_series=self.simple_time_series)\n\n def test_vectorized_forecaster_bad_output_type(self):\n bad_output_type_forecaster = lambda val_c, val_c_n, n: np.array([str(x) for x in val_c_n]).reshape((-1, 1))\n with self.assertRaisesRegex(TypeError, \"Forecaster must return numeric np.ndarray or number but returned \"\n \"output of type\"):\n _ = UserDefinedForecaster(forecaster_function=bad_output_type_forecaster,\n observation_space=POSITIVE_OBSERVATION_SPACE, forecast_shape=(FORECAST_HORIZON,),\n time_series=self.simple_time_series)\n\n def test_vectorized_forecaster_bad_output_signs(self):\n def bad_output_type_forecaster(val_c, val_c_n, n):\n out = val_c_n.copy()\n pos = np.random.randint(low=1, high=len(out))\n out[pos] *= -1\n return out\n\n with self.assertRaisesRegex(ValueError, \"Forecaster must return output of same \"\n \"sign \\(or zero\\) as input but returned output\"):\n _ = UserDefinedForecaster(forecaster_function=bad_output_type_forecaster,\n observation_space=POSITIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS),\n time_series=self.simple_time_series)\n\n def test_bad_forecaster(self):\n bad_forecaster = lambda val_c, val_c_n, n: 0/0\n\n with self.assertRaisesRegex(ValueError, \"Unable to call forecaster with scalar inputs.\"):\n _ = UserDefinedForecaster(forecaster_function=bad_forecaster, observation_space=POSITIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS),\n time_series=self.simple_time_series)\n\n def test_scalar_forecaster_bad_output_shape(self):\n def bad_output_shape_forecaster(val_c, val_c_n, n):\n if hasattr(val_c_n, '__len__') and len(val_c_n) > 1:\n raise RuntimeError\n return [val_c_n]*2\n\n with self.assertRaisesRegex(ValueError, \"Forecaster must return scalar output with scalar input but returned.\"):\n _ = UserDefinedForecaster(forecaster_function=bad_output_shape_forecaster,\n observation_space=POSITIVE_OBSERVATION_SPACE,\n forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS),\n time_series=self.simple_time_series)\n\n\nclass TestGetForecaster(TestCase):\n def setUp(self) -> None:\n self.simple_time_series = np.arange(10).reshape((-1, 1))\n self.forecaster_horizon = 24\n\n def test_user_defined_forecaster(self):\n user_defined_forecaster = lambda val_c, val_c_n, n: val_c_n\n forecaster = get_forecaster(user_defined_forecaster,\n POSITIVE_OBSERVATION_SPACE,\n (FORECAST_HORIZON, STATE_COMPONENTS),\n time_series=self.simple_time_series)\n self.assertIsInstance(forecaster, UserDefinedForecaster)\n\n def test_oracle_forecaster(self):\n forecaster = get_forecaster(\"oracle\", POSITIVE_OBSERVATION_SPACE, (FORECAST_HORIZON, STATE_COMPONENTS))\n self.assertIsInstance(forecaster, OracleForecaster)\n\n def test_no_forecaster(self):\n forecaster = get_forecaster(None, POSITIVE_OBSERVATION_SPACE, (FORECAST_HORIZON, STATE_COMPONENTS))\n self.assertIsInstance(forecaster, NoForecaster)\n\n def test_gaussian_noise_forecaster_init(self):\n noise_std = 0.5\n forecaster = get_forecaster(noise_std, POSITIVE_OBSERVATION_SPACE, (FORECAST_HORIZON, STATE_COMPONENTS))\n self.assertIsInstance(forecaster, GaussianNoiseForecaster)\n self.assertEqual(forecaster.input_noise_std, noise_std)\n\n def test_gaussian_noise_forecaster_increase_uncertainty_init(self):\n noise_std = 0.5\n forecaster = get_forecaster(noise_std, POSITIVE_OBSERVATION_SPACE, (FORECAST_HORIZON, STATE_COMPONENTS), increase_uncertainty=True)\n self.assertIsInstance(forecaster, GaussianNoiseForecaster)\n self.assertEqual(forecaster.input_noise_std, noise_std)\n self.assertTrue((forecaster.noise_std != noise_std).any())\n\n def test_gaussian_noise_forecaster_correct_size(self):\n noise_std = 0.5\n forecaster = get_forecaster(noise_std, POSITIVE_OBSERVATION_SPACE, (FORECAST_HORIZON, STATE_COMPONENTS))\n\n val_c, val_c_n, n = get_test_inputs()\n forecast = forecaster(val_c, val_c_n, n)\n\n self.assertEqual(forecast.shape, val_c_n.shape)\n self.assertEqual(forecast.shape, (FORECAST_HORIZON, STATE_COMPONENTS))\n self.assertTrue((forecast.reshape(-1) >= 0).all())\n\n def test_gaussian_noise_forecaster_insufficient_true_vals(self):\n noise_std = 0.5\n forecaster = get_forecaster(noise_std, POSITIVE_OBSERVATION_SPACE, (FORECAST_HORIZON, STATE_COMPONENTS))\n\n val_c, val_c_n, _ = get_test_inputs(n=FORECAST_HORIZON-2)\n forecast = forecaster(val_c, val_c_n, FORECAST_HORIZON)\n\n self.assertEqual(forecast.shape, (FORECAST_HORIZON, STATE_COMPONENTS))\n self.assertTrue((forecast.reshape(-1) >= 0).all())\n\n def test_gaussian_noise_forecaster_insufficient_true_vals_increasing_uncertainty(self):\n noise_std = 0.5\n forecaster = get_forecaster(noise_std,\n POSITIVE_OBSERVATION_SPACE,\n (FORECAST_HORIZON, STATE_COMPONENTS),\n increase_uncertainty=True)\n\n val_c, val_c_n, _ = get_test_inputs(n=FORECAST_HORIZON-2)\n forecast = forecaster(val_c, val_c_n, FORECAST_HORIZON)\n\n self.assertEqual(forecast.shape, (FORECAST_HORIZON, STATE_COMPONENTS))\n self.assertTrue((forecast.reshape(-1) >= 0).all())" }, { "path": "tests/microgrid/modules/module_tests/test_genset_long_status_changes.py", "content": "from tests.helpers.genset_module_testing_utils import get_genset, normalize_production\nfrom tests.helpers.test_case import TestCase\nimport numpy as np\nfrom copy import deepcopy\nfrom itertools import product\n\n\nclass TestGensetStartUp2WindDown3OnAtStartUp(TestCase):\n def setUp(self) -> None:\n self.genset, self.default_params = get_genset(init_start_up=True, start_up_time=2, wind_down_time=3)\n\n def get_genset(self, **new_params):\n if len(new_params) == 0:\n return deepcopy(self.genset), self.default_params\n return get_genset(default_parameters=self.default_params, **new_params)\n\n def turn_on(self, genset, unnormalized_production=0.):\n # Take a step, ask genset to turn on.\n action = np.array([1.0, normalize_production(unnormalized_production)])\n obs, reward, done, info = genset.step(action)\n return obs, reward, done, info\n\n def turn_off(self, genset, unnormalized_production=50.):\n # Take a step, ask genset to turn on.\n action = np.array([0.0, normalize_production(unnormalized_production)])\n obs, reward, done, info = genset.step(action)\n return obs, reward, done, info\n\n def test_on_at_start_up(self):\n genset, _ = self.get_genset()\n self.assertTrue(genset.current_status)\n self.assertEqual(genset.goal_status, 1)\n self.assertEqual(genset.state, np.array([1, 1, 0, 3]))\n\n def test_turn_off_step_1(self):\n genset, params = self.get_genset()\n unnormalized_production = 50.\n obs, reward, done, info = self.turn_off(genset, unnormalized_production)\n self.assertEqual(reward, -1.0*params['genset_cost']*unnormalized_production)\n self.assertTrue(genset.current_status)\n self.assertEqual(genset.goal_status, 0)\n self.assertEqual(genset.state, np.array([1, 0, 0, 2]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], unnormalized_production)\n\n def test_turn_off_step_2(self):\n genset, params = self.get_genset()\n unnormalized_production = 50.\n\n for j in range(2):\n obs, reward, done, info = self.turn_off(genset, unnormalized_production)\n\n self.assertEqual(reward, -1.0*params['genset_cost']*unnormalized_production)\n self.assertTrue(genset.current_status)\n self.assertEqual(genset.goal_status, 0)\n self.assertEqual(genset.state, np.array([1, 0, 0, 1]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], unnormalized_production)\n\n def test_turn_off_step_3(self):\n genset, params = self.get_genset()\n unnormalized_production = 50.\n\n for j in range(3):\n obs, reward, done, info = self.turn_off(genset, unnormalized_production)\n\n self.assertEqual(reward, -1.0*params['genset_cost']*unnormalized_production)\n self.assertTrue(genset.current_status)\n self.assertEqual(genset.goal_status, 0)\n self.assertEqual(genset.state, np.array([1, 0, 0, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], unnormalized_production)\n\n def test_turn_off_step_4_final(self):\n genset, params = self.get_genset()\n unnormalized_production = 50.\n\n for j in range(3):\n self.turn_off(genset, unnormalized_production)\n\n unnormalized_production = 0\n obs, reward, done, info = self.turn_off(genset, unnormalized_production)\n\n self.assertEqual(reward, 0)\n self.assertFalse(genset.current_status)\n self.assertEqual(genset.goal_status, 0)\n self.assertEqual(genset.state, np.array([0, 0, 2, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], 0)\n\n def test_turn_on_after_turn_off_step_1(self):\n genset, params = self.get_genset()\n unnormalized_production = 50.\n\n for j in range(3):\n self.turn_off(genset, unnormalized_production)\n\n # Step 4, should be off.\n unnormalized_production = 0\n obs, reward, done, info = self.turn_off(genset, unnormalized_production)\n\n self.assertEqual(reward, 0)\n self.assertFalse(genset.current_status)\n self.assertEqual(genset.goal_status, 0)\n self.assertEqual(genset.state, np.array([0, 0, 2, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], 0)\n\n unnormalized_production = 0\n obs, reward, done, info = self.turn_on(genset, unnormalized_production)\n self.assertEqual(reward, 0)\n self.assertFalse(genset.current_status)\n self.assertEqual(genset.goal_status, 1)\n self.assertEqual(genset.state, np.array([0, 1, 1, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], 0)\n\n def test_turn_on_after_turn_off_step_2(self):\n genset, params = self.get_genset()\n unnormalized_production = 50.\n\n for j in range(3):\n self.turn_off(genset, unnormalized_production)\n\n # Step 4, should be off.\n unnormalized_production = 0\n obs, reward, done, info = self.turn_off(genset, unnormalized_production)\n\n self.assertEqual(reward, 0)\n self.assertFalse(genset.current_status)\n self.assertEqual(genset.goal_status, 0)\n self.assertEqual(genset.state, np.array([0, 0, 2, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], 0)\n\n # Turning back on\n unnormalized_production = 0\n for j in range(2):\n obs, reward, done, info = self.turn_on(genset, unnormalized_production)\n self.assertEqual(reward, 0)\n self.assertFalse(genset.current_status)\n self.assertEqual(genset.goal_status, 1)\n self.assertEqual(genset.state, np.array([0, 1, 0, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], 0)\n\n def test_turn_on_after_turn_off_final(self):\n genset, params = self.get_genset()\n unnormalized_production = 50.\n\n for j in range(3):\n self.turn_off(genset, unnormalized_production)\n\n # Step 4, should be off.\n unnormalized_production = 0\n obs, reward, done, info = self.turn_off(genset, unnormalized_production)\n\n self.assertEqual(reward, 0)\n self.assertFalse(genset.current_status)\n self.assertEqual(genset.goal_status, 0)\n self.assertEqual(genset.state, np.array([0, 0, 2, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], 0)\n\n # Turning back on\n unnormalized_production = 0\n for j in range(2):\n self.turn_on(genset, unnormalized_production)\n\n unnormalized_production = 50.\n obs, reward, done, info = self.turn_on(genset, unnormalized_production)\n\n self.assertEqual(reward, -1.0*unnormalized_production*params['genset_cost'])\n self.assertTrue(genset.current_status)\n self.assertEqual(genset.goal_status, 1)\n self.assertEqual(genset.state, np.array([1, 1, 0, 3]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], unnormalized_production)\n\n def test_turn_off_abortion(self):\n genset, params = self.get_genset()\n unnormalized_production = 50.\n\n for j in range(2):\n self.turn_off(genset, unnormalized_production)\n\n self.assertEqual(genset.state, np.array([1, 0, 0, 1]))\n\n # Step 3: abort!\n obs, reward, done, info = self.turn_on(genset, unnormalized_production)\n\n self.assertEqual(reward, -1.0*unnormalized_production*params['genset_cost'])\n self.assertTrue(genset.current_status)\n self.assertEqual(genset.goal_status, 1)\n self.assertEqual(genset.state, np.array([1, 1, 0, 3]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], unnormalized_production)\n\n def test_turn_on_abortion(self):\n genset, params = self.get_genset(init_start_up=False)\n unnormalized_production = 0.\n\n self.turn_on(genset, unnormalized_production)\n self.assertEqual(genset.state, np.array([0, 1, 1, 0]))\n\n # Step 3: abort!\n obs, reward, done, info = self.turn_off(genset, unnormalized_production)\n\n self.assertEqual(reward, 0)\n self.assertFalse(genset.current_status)\n self.assertEqual(genset.goal_status, 0)\n self.assertEqual(genset.state, np.array([0, 0, 2, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], 0)\n\n\nclass TestManyStatusChanges(TestCase):\n\n def test_many_status_changes(self):\n\n n_steps = 5\n\n def next_status(genset, goal_status):\n if goal_status:\n if genset.current_status:\n return 1\n elif genset._steps_until_up == 0:\n return 1\n else:\n return 0\n else:\n if not genset.current_status:\n return 0\n elif genset._steps_until_down == 0:\n return 0\n else:\n return 1\n\n for _running in True, False:\n for start_up_time in range(0, n_steps):\n for wind_down_time in range(0, n_steps):\n for goal_status in 0, 1:\n\n intermediate_goal_statuses = product([0, 1], repeat=n_steps - 1)\n\n for steps in intermediate_goal_statuses:\n genset, _ = get_genset(init_start_up=_running,\n start_up_time=start_up_time,\n wind_down_time=wind_down_time)\n\n _s = (goal_status, *steps)\n for j, sub_goal_status in enumerate(_s):\n with self.subTest(_running=_running,\n _steps_until_up=start_up_time,\n _steps_until_down=wind_down_time,\n goal_status=goal_status,\n step_combination=_s,\n step=j,\n goal_status_at_step=sub_goal_status):\n predicted_status = next_status(genset, sub_goal_status)\n genset.update_status(goal_status=sub_goal_status)\n self.assertEqual(predicted_status, genset.current_status)\n\n" }, { "path": "tests/microgrid/modules/module_tests/test_genset_module.py", "content": "import numpy as np\n\nfrom tests.helpers.genset_module_testing_utils import default_params, get_genset, normalize_production\nfrom tests.helpers.test_case import TestCase\n\n\nclass TestGensetModule(TestCase):\n def setUp(self) -> None:\n np.random.seed(0)\n self.default_params = default_params.copy()\n\n def get_genset(self, **new_params):\n return get_genset(**new_params)\n\n def test_init_start_up(self):\n genset, _ = self.get_genset()\n self.assertTrue(genset.current_status)\n genset, _ = self.get_genset(init_start_up=False)\n self.assertFalse(genset.current_status)\n\n def test_get_cost_linear(self):\n genset_cost = np.random.rand()\n genset, params = self.get_genset(genset_cost=genset_cost)\n\n production = params['running_min_production'] + (params['running_max_production']-params['running_min_production'])*np.random.rand()\n production_cost = production*genset_cost\n\n self.assertEqual(genset.get_cost(production), production_cost)\n\n def test_get_cost_callable(self):\n genset_cost = lambda x: x**2\n genset, params = self.get_genset(genset_cost=genset_cost)\n\n production = params['running_min_production'] + (params['running_max_production']-params['running_min_production'])*np.random.rand()\n production_cost = genset_cost(production)\n\n self.assertEqual(genset.get_cost(production), production_cost)\n\n def test_step_out_of_range_goal_status(self):\n genset, _ = self.get_genset()\n action = np.array([-0.5, 0.5])\n\n with self.assertRaises(AssertionError):\n genset.step(action)\n\n def test_step_out_of_normalized_range_production(self):\n genset, _ = self.get_genset()\n\n with self.assertRaises(AssertionError):\n action = np.array([-0.5, 2])\n genset.step(action)\n\n def test_step_incorrect_action_shape(self):\n genset, _ = self.get_genset()\n\n with self.assertRaises(TypeError):\n action = 0.5\n genset.step(action)\n\n with self.assertRaises(TypeError):\n action = np.ones(3)\n genset.step(action)\n\n def test_step_unnormalized_production(self):\n genset, _ = self.get_genset()\n\n action = np.array([1.0, 50])\n # try:\n obs, reward, done, info = genset.step(action, normalized=False)\n\n self.assertEqual(reward, -1.0 * default_params['genset_cost']*action[1])\n self.assertTrue(genset.current_status)\n self.assertEqual(genset.goal_status, 1)\n self.assertEqual(obs, np.array([1, 1, 0, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], action[1])\n\n def test_step_normalized_production(self):\n genset, params = self.get_genset()\n\n unnormalized_production = 50\n action = np.array([1.0, normalize_production(unnormalized_production)])\n # try:\n obs, reward, done, info = genset.step(action, normalized=True)\n\n self.assertEqual(reward, -1.0 * params['genset_cost']*unnormalized_production)\n self.assertTrue(genset.current_status)\n self.assertEqual(genset.goal_status, 1)\n self.assertEqual(obs, np.array([1, 1, 0, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], unnormalized_production)\n\n def test_step_immediate_status_change(self):\n genset, params = self.get_genset()\n\n unnormalized_production = 0\n action = np.array([0.0, normalize_production(unnormalized_production)])\n\n self.assertTrue(genset.current_status)\n\n obs, reward, done, info = genset.step(action, normalized=True)\n\n self.assertEqual(reward, 0)\n self.assertFalse(genset.current_status)\n self.assertEqual(genset.goal_status, 0)\n self.assertEqual(obs, np.array([0, 0, 0, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], unnormalized_production)\n\n def test_step_genset_off_production_request_error_raise(self):\n genset, _ = self.get_genset()\n\n unnormalized_production = 50\n action = np.array([0.0, normalize_production(unnormalized_production)])\n\n # Genset starts on\n self.assertTrue(genset.current_status)\n self.assertEqual(genset.goal_status, 1)\n\n # Turn genset off (wind_down_time=0), and then ask for production. (no-no).\n with self.assertRaises(ValueError):\n genset.step(action, normalized=True)\n\n def test_step_genset_off_production_request_no_error_raise(self):\n genset, _ = self.get_genset(raise_errors=False)\n\n unnormalized_production = 50\n action = np.array([0.0, normalize_production(unnormalized_production)])\n\n obs, reward, done, info = genset.step(action)\n self.assertEqual(reward, 0)\n self.assertFalse(genset.current_status)\n self.assertEqual(genset.goal_status, 0)\n self.assertEqual(obs, np.array([0, 0, 0, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], 0)\n\n def test_step_genset_production_request_out_of_range_no_error_raise(self):\n genset, params = self.get_genset(raise_errors=False)\n\n requested_possible_warn = [(params['running_min_production']*np.random.rand(), params['running_min_production'], False),\n (params['running_max_production'] * (1+np.random.rand()), params['running_max_production'], True)]\n\n\n # First requested value is below min_production, second is above max_production.\n # Second should raise a warning.\n\n for requested, possible, raises_warn in requested_possible_warn:\n with self.subTest(requested_production=requested, possible_production=possible):\n action = np.array([1.0, normalize_production(requested)])\n\n if raises_warn:\n with self.assertWarns(Warning):\n obs, reward, done, info = genset.step(action)\n else:\n obs, reward, done, info = genset.step(action)\n\n self.assertEqual(reward, -1.0 * params['genset_cost']*possible)\n self.assertTrue(genset.current_status)\n self.assertEqual(genset.goal_status, 1)\n self.assertEqual(obs, np.array([1, 1, 0, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], possible)\n" }, { "path": "tests/microgrid/modules/module_tests/test_genset_module_start_up_1_wind_down_1.py", "content": "from tests.helpers.genset_module_testing_utils import get_genset, normalize_production\nfrom tests.helpers.test_case import TestCase\nimport numpy as np\nfrom copy import deepcopy\n\n\nclass TestGensetStartUp1WindDown0OffAtStartUp(TestCase):\n def setUp(self) -> None:\n self.genset, self.default_params = get_genset(init_start_up=False, start_up_time=1)\n self.warm_up(self.genset)\n\n def get_genset(self, new=False, **new_params):\n if len(new_params) == 0 and not new:\n return deepcopy(self.genset), self.default_params\n\n genset, params = get_genset(default_parameters=self.default_params, **new_params)\n if not new:\n self.warm_up(genset)\n return genset, params\n\n\n def warm_up(self, genset):\n # Take a step, ask genset to turn on. Warm-up takes one step so genset is still off at this point.\n unnormalized_production = 0\n action = np.array([1.0, normalize_production(unnormalized_production)])\n obs, reward, done, info = genset.step(action)\n return obs, reward, done, info\n\n def test_off_at_start_up(self):\n genset, _ = self.get_genset(new=True)\n self.assertFalse(genset.current_status)\n self.assertEqual(genset.goal_status, 0)\n self.assertEqual(genset.state, np.array([0, 0, 1, 0]))\n\n def test_warm_up(self):\n # Take a step, ask genset to turn on. Warm-up takes one step so genset is still off at this point.\n genset,_ = self.get_genset(new=True)\n obs, reward, done, info = self.warm_up(genset)\n self.assertEqual(reward, 0)\n self.assertFalse(genset.current_status)\n self.assertEqual(genset.goal_status, 1)\n self.assertEqual(obs, np.array([0, 1, 0, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], 0)\n\n def test_step_start_up_1_exception(self):\n # Assert that exception is thrown when production is requested while genset is off\n genset, _ = self.get_genset(new=True)\n\n self.assertFalse(genset.current_status)\n self.assertEqual(genset.goal_status, 0)\n\n unnormalized_production = 50\n action = np.array([1.0, normalize_production(unnormalized_production)])\n\n with self.assertRaises(ValueError) as e:\n genset.step(action)\n err_msg = e.exception.args[0]\n self.assertTrue('This may be because this genset module is not currently running.' in err_msg)\n\n def test_step_start_up_1_no_exception(self):\n # Genset is on now. Should be able to request production.\n\n genset, params = self.get_genset()\n\n unnormalized_production = 50\n action = np.array([1.0, normalize_production(unnormalized_production)])\n obs, reward, done, info = genset.step(action)\n self.assertEqual(reward, -1.0*params['genset_cost']*unnormalized_production)\n self.assertTrue(genset.current_status)\n self.assertEqual(genset.goal_status, 1)\n self.assertEqual(obs, np.array([1, 1, 0, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], unnormalized_production)\n\n def test_start_up_1_request_below_min_exception_raise(self):\n genset, params = self.get_genset()\n\n # Genset is on now. Requesting below min production.\n unnormalized_production = params['running_min_production']*np.random.rand()\n action = np.array([1.0, normalize_production(unnormalized_production)])\n with self.assertRaises(ValueError):\n genset.step(action)\n\n def test_start_up_1_request_below_min_no_exception(self):\n # Genset is on, requesting production less than the min should return min production.\n genset, params = self.get_genset(raise_errors=False)\n\n # Genset is on now. Requesting below min production.\n unnormalized_production = params['running_min_production']*np.random.rand()\n action = np.array([1.0, normalize_production(unnormalized_production)])\n obs, reward, done, info = genset.step(action)\n self.assertEqual(reward, -1.0*params['genset_cost']*params['running_min_production'])\n self.assertTrue(genset.current_status)\n self.assertEqual(genset.goal_status, 1)\n self.assertEqual(obs, np.array([1, 1, 0, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], params['running_min_production'])\n\n def test_start_up_1_then_shut_down_exception_raise(self):\n # Genset is on, requesting production less than the min should return min production.\n genset, params = self.get_genset()\n\n # Genset is on now. Requesting below min production.\n unnormalized_production = 50.\n action = np.array([0.1, normalize_production(unnormalized_production)])\n with self.assertRaises(ValueError):\n genset.step(action)\n\n def test_start_up_1_then_shut_down_no_exception(self):\n # Genset is on, requesting production less than the min should return min production.\n genset, params = self.get_genset(raise_errors=False)\n\n # Genset is on now. Requesting below min production.\n unnormalized_production = 50.\n action = np.array([0.1, normalize_production(unnormalized_production)])\n obs, reward, done, info = genset.step(action)\n self.assertEqual(reward, 0.0)\n self.assertFalse(genset.current_status)\n self.assertEqual(genset.goal_status, 0)\n self.assertEqual(obs, np.array([0, 0, params['start_up_time'], 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], 0.0)\n\n\nclass TestGensetStartUp1WindDown0OnAtStartUp(TestCase):\n def setUp(self) -> None:\n self.genset, self.default_params = get_genset(init_start_up=True, start_up_time=1)\n self.warm_up(self.genset)\n\n def get_genset(self, new=False, **new_params):\n if len(new_params) == 0 and not new:\n return deepcopy(self.genset), self.default_params\n\n genset, params = get_genset(default_parameters=self.default_params, **new_params)\n if not new:\n self.warm_up(genset)\n return genset, params\n\n def warm_up(self, genset):\n # Take a step, ask genset to turn on. Genset begins on so should be on at this point.\n unnormalized_production = self.default_params['running_min_production']\n action = np.array([1.0, normalize_production(unnormalized_production)])\n obs, reward, done, info = genset.step(action)\n return obs, reward, done, info\n\n def test_on_at_start_up(self):\n genset, _ = self.get_genset(new=True)\n self.assertTrue(genset.current_status)\n self.assertEqual(genset.goal_status, 1)\n self.assertEqual(genset.state, np.array([1, 1, 0, 0]))\n\n def test_warm_up(self):\n # Take a step, ask genset to turn on. Genset begins on so should be on at this point.\n genset, params = self.get_genset(new=True)\n obs, reward, done, info = self.warm_up(genset)\n self.assertEqual(reward, -1.0*params['genset_cost']*params['running_min_production'])\n self.assertTrue(genset.current_status)\n self.assertEqual(genset.goal_status, 1)\n self.assertEqual(obs, np.array([1, 1, 0, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], params['running_min_production'])\n\n def test_shut_down(self):\n genset, _ = self.get_genset()\n unnormalized_production = 0\n action = np.array([0.0, normalize_production(unnormalized_production)])\n obs, reward, done, info = genset.step(action)\n self.assertEqual(reward, 0)\n self.assertFalse(genset.current_status)\n self.assertEqual(genset.goal_status, 0)\n self.assertEqual(obs, np.array([0, 0, 1, 0]))\n self.assertFalse(done)\n self.assertEqual(info['provided_energy'], 0)" }, { "path": "tests/microgrid/modules/module_tests/test_load_module.py", "content": "import numpy as np\n\nfrom pymgrid.modules import LoadModule\n\nfrom tests.microgrid.modules.module_tests.timeseries_modules import (\n TestTimeseriesModuleForecasting,\n TestTimeseriesModuleNoForecasting,\n TestTimeSeriesModuleForecastingNegativeVals,\n TestTimeSeriesModuleNoForecastingNegativeVals\n)\n\n\nclass TestLoadModuleNoForecasting(TestTimeseriesModuleNoForecasting):\n __test__ = True\n negative_time_series = True\n action_space_dim = 0\n\n\n def get_module(self):\n return LoadModule(self.module_time_series)\n\n def test_init_current_load(self):\n load_module = self.get_module()\n self.assertEqual(load_module.current_load, -1 * self.time_series[0])\n\n def test_step(self):\n load_module = self.get_module()\n self.assertEqual(load_module.current_load, -1 * self.time_series[0])\n\n obs, reward, done, info = load_module.step(np.array([]))\n obs = load_module.from_normalized(obs, obs=True)\n self.assertEqual(obs, self.time_series[1])\n self.assertEqual(reward, 0)\n self.assertFalse(done)\n self.assertEqual(info[\"absorbed_energy\"], -1 * self.time_series[0])\n\n\nclass TestLoadModuleForecasting(TestTimeseriesModuleForecasting):\n __test__ = True\n negative_time_series = True\n action_space_dim = 0\n\n def get_module(self):\n return LoadModule(self.module_time_series, forecaster=\"oracle\", forecast_horizon=self.forecast_horizon)\n\n def test_step(self):\n load_module = self.get_module()\n self.assertEqual(load_module.current_load, -1 * self.time_series[0])\n\n action = load_module.to_normalized(np.array([]), act=True)\n obs, reward, done, info = load_module.step(action)\n obs = load_module.from_normalized(obs, obs=True)\n self.assertEqual(obs, self.time_series[1:self.forecast_horizon+2])\n self.assertEqual(reward, 0)\n self.assertFalse(done)\n self.assertEqual(info[\"absorbed_energy\"], -1 * self.time_series[0])\n\n\nclass TestLoadModuleForecastingNegativeVals(TestTimeSeriesModuleForecastingNegativeVals,\n TestLoadModuleForecasting):\n pass\n\n\nclass TestLoadModuleNoForecastingNegativeVals(TestTimeSeriesModuleNoForecastingNegativeVals,\n TestLoadModuleNoForecasting):\n pass\n\n" }, { "path": "tests/microgrid/modules/module_tests/test_renewable_module.py", "content": "\nfrom pymgrid.modules import RenewableModule\n\nfrom tests.microgrid.modules.module_tests.timeseries_modules import (\n TestTimeseriesModuleForecasting,\n TestTimeseriesModuleNoForecasting,\n TestTimeSeriesModuleForecastingNegativeVals,\n TestTimeSeriesModuleNoForecastingNegativeVals\n)\n\n\nclass TestRenewableModuleNoForecasting(TestTimeseriesModuleNoForecasting):\n __test__ = True\n action_space_dim = 1\n\n def get_module(self):\n return RenewableModule(self.module_time_series)\n\n def test_init_current_renewable(self):\n renewable_module = self.get_module()\n self.assertEqual(renewable_module.current_renewable, self.time_series[0])\n\n def test_step(self):\n renewable_module = self.get_module()\n self.assertEqual(renewable_module.current_renewable, self.time_series[0])\n\n unnormalized_action = 1\n action = renewable_module.to_normalized(unnormalized_action, act=True)\n obs, reward, done, info = renewable_module.step(action)\n obs = renewable_module.from_normalized(obs, obs=True)\n self.assertEqual(obs, self.time_series[1])\n self.assertEqual(reward, 0)\n self.assertFalse(done)\n self.assertEqual(info[\"provided_energy\"], unnormalized_action)\n self.assertEqual(info[\"curtailment\"], 0)\n\n\nclass TestRenewableModuleForecasting(TestTimeseriesModuleForecasting):\n __test__ = True\n action_space_dim = 1\n\n def get_module(self):\n return RenewableModule(self.module_time_series, forecaster=\"oracle\", forecast_horizon=self.forecast_horizon)\n\n def test_step(self):\n renewable_module = self.get_module()\n self.assertEqual(renewable_module.current_renewable, self.time_series[0])\n\n unnormalized_action = 1\n action = renewable_module.to_normalized(unnormalized_action, act=True)\n obs, reward, done, info = renewable_module.step(action)\n obs = renewable_module.from_normalized(obs, obs=True)\n self.assertEqual(obs, self.time_series[1:self.forecast_horizon+2])\n self.assertEqual(reward, 0)\n self.assertFalse(done)\n self.assertEqual(info[\"provided_energy\"], unnormalized_action)\n self.assertEqual(info[\"curtailment\"], 0)\n\n\nclass TestRenewableModuleForecastingNegativeVals(TestTimeSeriesModuleForecastingNegativeVals,\n TestRenewableModuleForecasting):\n pass\n\n\nclass TestRenewableModuleNoForecastingNegativeVals(TestTimeSeriesModuleNoForecastingNegativeVals,\n TestRenewableModuleNoForecasting):\n pass\n" }, { "path": "tests/microgrid/modules/module_tests/timeseries_modules.py", "content": "import numpy as np\nfrom abc import abstractmethod\nfrom gym.spaces import Box\n\nfrom pymgrid.utils.space import ModuleSpace\n\nfrom tests.helpers.test_case import TestCase\n\n\nclass TestTimeseriesModule(TestCase):\n __test__ = False\n negative_time_series = False\n forecast_horizon: int\n action_space_dim: int\n\n def setUp(self) -> None:\n self.module_time_series = self._get_module_time_series()\n self.time_series = self._get_time_series()\n\n def _get_module_time_series(self):\n return self._get_time_series()\n\n def _get_time_series(self):\n sign = -1 if self.negative_time_series else 1\n return sign * (2 - np.cos(np.pi * np.arange(100) / 2))\n\n @abstractmethod\n def get_module(self):\n return NotImplemented\n\n def test_action_space(self):\n module = self.get_module()\n normalized_action_space = module.action_space[\"normalized\"]\n unnormalized_action_space = module.action_space[\"unnormalized\"]\n\n self.assertEqual(normalized_action_space, Box(low=0, high=1, shape=(self.action_space_dim, )))\n self.assertEqual(unnormalized_action_space, Box(low=min(0, self.time_series.min()),\n high=max(0, self.time_series.max()),\n shape=(self.action_space_dim, )))\n\n def test_observation_space(self):\n module = self.get_module()\n normalized_obs_space = module.observation_space[\"normalized\"]\n unnormalized_obs_space = module.observation_space[\"unnormalized\"]\n\n self.assertEqual(normalized_obs_space, Box(low=0, high=1, shape=(1+self.forecast_horizon,)))\n self.assertEqual(unnormalized_obs_space, Box(low=min(0, self.time_series.min()),\n high=max(0, self.time_series.max()),\n shape=(1+self.forecast_horizon,)))\n\n\n def test_observations_in_observation_space(self):\n module = self.get_module()\n\n observation_space = ModuleSpace(\n unnormalized_low=min(0, self.time_series.min()),\n unnormalized_high=max(0, self.time_series.max()),\n shape=(1 + module.forecast_horizon,)\n )\n\n self.assertEqual(module.observation_space, observation_space)\n\n done = False\n while not done:\n obs, reward, done, info = module.step(module.action_space.sample(), normalized=False)\n if np.isscalar(obs):\n obs = np.array([obs])\n self.assertIn(obs, observation_space['normalized'])\n self.assertIn(module.state, observation_space['unnormalized'])\n\n\nclass TestTimeseriesModuleNoForecasting(TestTimeseriesModule):\n forecast_horizon = 0\n\n def test_init(self):\n module = self.get_module()\n self.assertIsNone(module.forecast())\n self.assertEqual(module.state, self.time_series[0])\n self.assertEqual(len(module.state_dict()), 1+self.forecast_horizon)\n\n\nclass TestTimeseriesModuleForecasting(TestTimeseriesModule):\n forecast_horizon = 24\n\n def test_init(self):\n module = self.get_module()\n self.assertIsNotNone(module.forecast())\n self.assertEqual(module.forecast(), self.time_series[1:1 + self.forecast_horizon].reshape((-1, 1)))\n self.assertEqual(module.state, self.time_series[:1 + self.forecast_horizon])\n self.assertEqual(len(module.state_dict()), 1 + self.forecast_horizon)\n\n\nclass TestTimeSeriesModuleNoForecastingNegativeVals(TestTimeseriesModuleNoForecasting):\n def _get_module_time_series(self):\n return -1 * self._get_time_series()\n\n\nclass TestTimeSeriesModuleForecastingNegativeVals(TestTimeseriesModuleForecasting):\n def _get_module_time_series(self):\n return -1 * self._get_time_series()\n" }, { "path": "tests/microgrid/serialize/test_microgrid_serialization.py", "content": "import numpy as np\n\nfrom pymgrid import Microgrid\n\nfrom tests.helpers.modular_microgrid import get_modular_microgrid\nfrom tests.helpers.test_case import TestCase\n\n\nclass TestMicrogridSerialization(TestCase):\n def test_serialize_no_modules(self):\n microgrid = Microgrid([], add_unbalanced_module=False)\n dump = microgrid.dump()\n loaded = Microgrid.load(dump)\n\n self.assertEqual(microgrid, loaded)\n\n def test_serialize_with_renewable(self):\n microgrid = get_modular_microgrid(remove_modules=[\"genset\", \"battery\", \"load\", \"grid\"],\n add_unbalanced_module=False)\n\n self.assertEqual(len(microgrid.modules), 1)\n self.assertEqual(microgrid, Microgrid.load(microgrid.dump()))\n" }, { "path": "tests/microgrid/test_microgrid.py", "content": "import numpy as np\nimport pandas as pd\n\n\nfrom pymgrid import Microgrid\nfrom pymgrid.modules import LoadModule, RenewableModule\n\nfrom tests.helpers.modular_microgrid import get_modular_microgrid\nfrom tests.helpers.test_case import TestCase\n\n\nclass TestMicrogrid(TestCase):\n def test_from_scenario(self):\n for j in range(25):\n with self.subTest(microgrid_number=j):\n microgrid = Microgrid.from_scenario(j)\n self.assertTrue(hasattr(microgrid, \"load\"))\n self.assertTrue(hasattr(microgrid, \"pv\"))\n self.assertTrue(hasattr(microgrid, \"battery\"))\n self.assertTrue(hasattr(microgrid, \"grid\") or hasattr(microgrid, \"genset\"))\n\n def test_empty_action_without_load(self):\n microgrid = get_modular_microgrid(remove_modules=('load', ))\n action = microgrid.get_empty_action()\n\n self.assertIn('battery', action)\n self.assertIn('genset', action)\n self.assertIn('grid', action)\n\n self.assertTrue(all(v == [None] for v in action.values()))\n\n def test_empty_action_with_load(self):\n microgrid = get_modular_microgrid()\n action = microgrid.get_empty_action()\n\n self.assertIn('battery', action)\n self.assertIn('genset', action)\n self.assertIn('grid', action)\n self.assertNotIn('load', action)\n\n self.assertTrue(all(v == [None] for v in action.values()))\n\n def test_action_space(self):\n microgrid = get_modular_microgrid()\n action = microgrid.microgrid_action_space.sample()\n\n for module_name, module_list in microgrid.modules.iterdict():\n for module_num, module in enumerate(module_list):\n if 'controllable' in module.module_type:\n with self.subTest(module_name=module_name, module_num=module_num):\n self.assertIn(action[module_name][module_num], module.action_space)\n\n def test_action_space_normalize(self):\n microgrid = get_modular_microgrid()\n action = microgrid.microgrid_action_space.sample()\n normalized = microgrid.microgrid_action_space.normalize(action)\n\n for module_name, module_list in microgrid.modules.iterdict():\n for module_num, module in enumerate(module_list):\n if 'controllable' in module.module_type:\n with self.subTest(module_name=module_name, module_num=module_num):\n self.assertIn(normalized[module_name][module_num], module.action_space.normalized)\n\n def test_action_space_denormalize(self):\n microgrid = get_modular_microgrid()\n action = microgrid.microgrid_action_space.sample(normalized=True)\n denormalized = microgrid.microgrid_action_space.denormalize(action)\n\n for module_name, module_list in microgrid.modules.iterdict():\n for module_num, module in enumerate(module_list):\n if 'controllable' in module.module_type:\n with self.subTest(module_name=module_name, module_num=module_num):\n self.assertIn(denormalized[module_name][module_num], module.action_space.unnormalized)\n\n def test_sample_action(self):\n microgrid = get_modular_microgrid()\n action = microgrid.sample_action()\n\n for module_name, action_list in action.items():\n for module_num, _act in enumerate(action_list):\n with self.subTest(module_name=module_name, module_num=module_num):\n action_arr = np.array(_act)\n if not action_arr.shape:\n action_arr = np.array([_act])\n self.assertTrue(microgrid.modules[module_name][module_num].action_space.shape[0])\n self.assertIn(action_arr, microgrid.modules[module_name][module_num].action_space.normalized)\n\n def test_sample_action_all_modules_populated(self):\n microgrid = get_modular_microgrid()\n action = microgrid.sample_action()\n\n for module_name, module_list in microgrid.fixed.iterdict():\n for module_num, module in enumerate(module_list):\n with self.subTest(module_name=module_name, module_num=module_num):\n empty_action_space = module.action_space.shape == (0, )\n try:\n _ = action[module_name][module_num]\n has_corresponding_action = True\n except KeyError:\n has_corresponding_action = False\n\n self.assertTrue(empty_action_space != has_corresponding_action) # XOR\n\n def test_current_step(self):\n microgrid = get_modular_microgrid()\n\n self.assertEqual(microgrid.current_step, 0)\n\n for j in range(4):\n with self.subTest(step=j):\n microgrid.run(microgrid.sample_action())\n self.assertEqual(microgrid.current_step, j+1)\n\n def test_current_step_after_reset(self):\n microgrid = get_modular_microgrid()\n self.assertEqual(microgrid.current_step, 0)\n\n microgrid.run(microgrid.sample_action())\n self.assertEqual(microgrid.current_step, 1)\n\n microgrid.reset()\n self.assertEqual(microgrid.current_step, 0)\n\n def test_set_module_attr_forecast_horizon(self):\n forecast_horizon = 50\n\n microgrid = get_modular_microgrid()\n microgrid.set_module_attr('forecast_horizon', forecast_horizon)\n\n microgrid_fh = [module.forecast_horizon for module in microgrid.modules.iterlist()\n if hasattr(module, 'forecast_horizon')]\n\n self.assertEqual(min(microgrid_fh), max(microgrid_fh))\n\n self.assertEqual(min(microgrid_fh), forecast_horizon)\n\n def test_set_module_attr_bad_attr_name(self):\n microgrid = get_modular_microgrid()\n\n with self.assertRaises(AttributeError):\n microgrid.set_module_attr('blah', 'blah')\n\n def test_get_cost_info(self):\n modules = 'genset', 'battery', 'renewable', 'load', 'grid', 'balancing'\n\n microgrid = get_modular_microgrid()\n cost_info = microgrid.get_cost_info()\n\n for module in modules:\n with self.subTest(info_of_module=cost_info):\n self.assertIn(module, cost_info.keys())\n self.assertEqual(len(cost_info[module]), 1)\n self.assertIsInstance(cost_info[module][0], dict)\n\n self.assertIn('production_marginal_cost', cost_info[module][0])\n self.assertIn('absorption_marginal_cost', cost_info[module][0])\n self.assertEqual(len(cost_info[module][0]), 2)\n\n self.assertTrue(pd.api.types.is_number(cost_info[module][0]['production_marginal_cost']))\n self.assertTrue(pd.api.types.is_number(cost_info[module][0]['absorption_marginal_cost']))\n\n def test_set_initial_step(self):\n microgrid = get_modular_microgrid()\n\n self.assertEqual(microgrid.initial_step, 0)\n\n for module_name, module in microgrid.modules.iterdict():\n with self.subTest(module_name=module_name):\n try:\n initial_step = module.initial_step\n except AttributeError:\n continue\n\n self.assertEqual(initial_step, 0)\n\n microgrid.initial_step = 1\n\n for module_name, module in microgrid.modules.iterdict():\n with self.subTest(module_name=module_name):\n try:\n initial_step = module.initial_step\n except AttributeError:\n continue\n\n self.assertEqual(initial_step, 1)\n\n\nclass TestMicrogridLoadPV(TestCase):\n def setUp(self):\n self.load_ts, self.pv_ts = self.set_ts()\n self.microgrid, self.n_loads, self.n_pvs = self.set_microgrid()\n self.n_modules = 1 + self.n_loads + self.n_pvs\n\n def set_ts(self):\n ts = 10 * np.random.rand(100)\n return ts, ts\n\n def set_microgrid(self):\n load = LoadModule(time_series=self.load_ts, raise_errors=True)\n pv = RenewableModule(time_series=self.pv_ts, raise_errors=True)\n return Microgrid([load, pv]), 1, 1\n\n def test_populated_correctly(self):\n self.assertTrue(hasattr(self.microgrid.modules, 'load'))\n self.assertTrue(hasattr(self.microgrid.modules, 'renewable'))\n self.assertEqual(len(self.microgrid.modules), self.n_modules) # load, pv, unbalanced\n\n def test_current_load_correct(self):\n try:\n current_load = self.microgrid.modules.load.item().current_load\n except ValueError:\n # More than one load module\n current_load = sum(load.current_load for load in self.microgrid.modules.load)\n self.assertEqual(current_load, self.load_ts[0])\n\n def test_current_pv_correct(self):\n try:\n current_renewable = self.microgrid.modules.renewable.item().current_renewable\n except ValueError:\n # More than one load module\n current_renewable = sum(renewable.current_renewable for renewable in self.microgrid.modules.renewable)\n self.assertEqual(current_renewable, self.pv_ts[0])\n\n def test_sample_action(self):\n sampled_action = self.microgrid.sample_action()\n self.assertEqual(len(sampled_action), 0)\n\n def test_sample_action_with_flex(self):\n sampled_action = self.microgrid.sample_action(sample_flex_modules=True)\n self.assertEqual(len(sampled_action), 2)\n self.assertIn('renewable', sampled_action)\n self.assertIn('balancing', sampled_action)\n self.assertEqual(len(sampled_action['renewable']), self.n_pvs)\n\n def test_state_dict(self):\n sd = self.microgrid.state_dict()\n self.assertIn('load', sd)\n self.assertIn('renewable', sd)\n self.assertIn('balancing', sd)\n self.assertEqual(len(sd['load']), self.n_loads)\n self.assertEqual(len(sd['balancing']), 1)\n\n def test_state_series(self):\n ss = self.microgrid.state_series()\n self.assertEqual({'load', 'renewable'}, set(ss.index.get_level_values(0)))\n self.assertEqual(ss['load'].index.get_level_values(0).nunique(), self.n_loads)\n self.assertEqual(ss['renewable'].index.get_level_values(0).nunique(), self.n_pvs)\n self.assertEqual(ss['load'].index.get_level_values(0).nunique(), self.n_loads)\n\n def test_to_nonmodular(self):\n if self.n_pvs > 1 or self.n_loads > 1:\n with self.assertRaises(ValueError) as e:\n self.microgrid.to_nonmodular()\n self.assertIn(\"Cannot convert modular microgrid with multiple modules of same type\", e)\n\n else:\n nonmodular = self.microgrid.to_nonmodular()\n self.assertTrue(nonmodular.architecture['PV'])\n self.assertFalse(nonmodular.architecture['battery'])\n self.assertFalse(nonmodular.architecture['grid'])\n self.assertFalse(nonmodular.architecture['genset'])\n\n def check_step(self, microgrid, step_number=0):\n\n control = microgrid.get_empty_action()\n self.assertEqual(len(control), 0)\n\n obs, reward, done, info = microgrid.run(control)\n loss_load = self.load_ts[step_number]-self.pv_ts[step_number]\n loss_load_cost = self.microgrid.modules.balancing[0].loss_load_cost * max(loss_load, 0)\n\n self.assertEqual(loss_load_cost, -1*reward)\n\n self.assertEqual(len(microgrid.log), step_number + 1)\n self.assertTrue(all(module in microgrid.log for module in microgrid.modules.names()))\n\n load_met = min(self.load_ts[step_number], self.pv_ts[step_number])\n loss_load = max(self.load_ts[step_number] - load_met, 0)\n pv_curtailment = max(self.pv_ts[step_number]-load_met, 0)\n\n # Checking the log populated correctly.\n\n log_row = microgrid.log.iloc[step_number]\n log_entry = lambda module, entry: log_row.loc[pd.IndexSlice[module, :, entry]].sum()\n\n # Check that there are log entries for all modules of each name\n self.assertEqual(log_row['load'].index.get_level_values(0).nunique(), self.n_loads)\n\n self.assertEqual(log_entry('load', 'load_current'), -1 * self.load_ts[step_number])\n self.assertEqual(log_entry('load', 'load_met'), self.load_ts[step_number])\n\n if loss_load == 0:\n self.assertEqual(log_entry('load', 'load_met'), load_met)\n\n self.assertEqual(log_entry('renewable', 'renewable_current'), self.pv_ts[step_number])\n self.assertEqual(log_entry('renewable', 'renewable_used'), load_met)\n self.assertEqual(log_entry('renewable', 'curtailment'), pv_curtailment)\n\n self.assertEqual(log_entry('balancing', 'loss_load'), loss_load)\n\n self.assertEqual(log_entry('balance', 'reward'), -1 * loss_load_cost)\n self.assertEqual(log_entry('balance', 'overall_provided_to_microgrid'), self.load_ts[step_number])\n self.assertEqual(log_entry('balance', 'overall_absorbed_from_microgrid'), self.load_ts[step_number])\n self.assertEqual(log_entry('balance', 'fixed_provided_to_microgrid'), 0.0)\n self.assertEqual(log_entry('balance', 'fixed_absorbed_from_microgrid'), self.load_ts[step_number])\n self.assertEqual(log_entry('balance', 'controllable_absorbed_from_microgrid'), 0.0)\n self.assertEqual(log_entry('balance', 'controllable_provided_to_microgrid'), 0.0)\n\n return microgrid\n\n def test_run_one_step(self):\n microgrid = self.microgrid\n self.check_step(microgrid=microgrid, step_number=0)\n\n def test_run_n_steps(self):\n microgrid = self.microgrid\n for step in range(len(self.load_ts)):\n with self.subTest(step=step):\n microgrid = self.check_step(microgrid=microgrid, step_number=step)\n\n\nclass TestMicrogridLoadExcessPV(TestMicrogridLoadPV):\n # Same as above but pv is greater than load.\n def set_ts(self):\n load_ts = 10*np.random.rand(100)\n pv_ts = load_ts + 5*np.random.rand(100)\n return load_ts, pv_ts\n\n\nclass TestMicrogridPVExcessLoad(TestMicrogridLoadPV):\n # Load greater than PV.\n def set_ts(self):\n pv_ts = 10 * np.random.rand(100)\n load_ts = pv_ts + 5 * np.random.rand(100)\n return load_ts, pv_ts\n\n\nclass TestMicrogridTwoLoads(TestMicrogridLoadPV):\n def set_microgrid(self):\n load_1_ts = self.load_ts*(1-np.random.rand(*self.load_ts.shape))\n load_2_ts = self.load_ts - load_1_ts\n\n assert all(load_1_ts > 0)\n assert all(load_2_ts > 0)\n\n load_1 = LoadModule(time_series=load_1_ts, raise_errors=True)\n load_2 = LoadModule(time_series=load_2_ts, raise_errors=True)\n pv = RenewableModule(time_series=self.pv_ts, raise_errors=True)\n return Microgrid([load_1, load_2, pv]), 2, 1\n\n\nclass TestMicrogridTwoPV(TestMicrogridLoadPV):\n def set_microgrid(self):\n pv_1_ts = self.pv_ts*(1-np.random.rand(*self.pv_ts.shape))\n pv_2_ts = self.pv_ts - pv_1_ts\n\n assert all(pv_1_ts > 0)\n assert all(pv_2_ts > 0)\n\n load = LoadModule(time_series=self.load_ts, raise_errors=True)\n pv_1 = RenewableModule(time_series=pv_1_ts, raise_errors=True)\n pv_2 = RenewableModule(time_series=pv_2_ts)\n return Microgrid([load, pv_1, pv_2]), 1, 2\n\n\nclass TestMicrogridTwoEach(TestMicrogridLoadPV):\n def set_microgrid(self):\n load_1_ts = self.load_ts*(1-np.random.rand(*self.load_ts.shape))\n load_2_ts = self.load_ts - load_1_ts\n\n pv_1_ts = self.pv_ts*(1-np.random.rand(*self.pv_ts.shape))\n pv_2_ts = self.pv_ts - pv_1_ts\n\n assert all(load_1_ts > 0)\n assert all(load_2_ts > 0)\n assert all(pv_1_ts > 0)\n assert all(pv_2_ts > 0)\n\n load_1 = LoadModule(time_series=load_1_ts, raise_errors=True)\n load_2 = LoadModule(time_series=load_2_ts, raise_errors=True)\n pv_1 = RenewableModule(time_series=pv_1_ts, raise_errors=True)\n pv_2 = RenewableModule(time_series=pv_2_ts)\n\n return Microgrid([load_1, load_2, pv_1, pv_2]), 2, 2\n\n\nclass TestMicrogridManyEach(TestMicrogridLoadPV):\n def set_microgrid(self):\n n_loads = np.random.randint(3, 10)\n n_pvs = np.random.randint(3, 10)\n\n load_ts = [self.load_ts * (1 - np.random.rand(*self.load_ts.shape))]\n pv_ts = [self.pv_ts * (1 - np.random.rand(*self.pv_ts.shape))]\n\n for ts_list, ts_sum, n_modules in zip(\n [load_ts, pv_ts],\n [self.load_ts, self.pv_ts],\n [n_loads, n_pvs]\n ):\n remaining = ts_sum-ts_list[0]\n\n for j in range(1, n_modules-1):\n ts_list.append(remaining*(1-np.random.rand(*ts_sum.shape)))\n assert all(ts_list[-1] > 0)\n remaining -= ts_list[-1]\n\n assert all(remaining > 0)\n ts_list.append(remaining)\n\n load_modules = [LoadModule(time_series=ts) for ts in load_ts]\n pv_modules = [RenewableModule(time_series=ts) for ts in pv_ts]\n\n return Microgrid([*load_modules, *pv_modules]), n_loads, n_pvs\n\n\nclass TestMicrogridManyEachExcessPV(TestMicrogridManyEach):\n def set_ts(self):\n load_ts = 10*np.random.rand(100)\n pv_ts = load_ts + 5*np.random.rand(100)\n return load_ts, pv_ts\n\n\nclass TestMicrogridManyEachExcessLoad(TestMicrogridManyEach):\n def set_ts(self):\n pv_ts = 10*np.random.rand(100)\n load_ts = pv_ts + 5*np.random.rand(100)\n return load_ts, pv_ts\n\n\nclass TestMicrogridRewardShaping(TestMicrogridLoadPV):\n def set_microgrid(self):\n original_microgrid, n_loads, n_pvs = super().set_microgrid()\n new_microgrid = Microgrid(original_microgrid.modules.to_tuples(),\n add_unbalanced_module=False,\n reward_shaping_func=self.reward_shaping_func)\n\n return new_microgrid, n_loads, n_pvs\n\n @staticmethod\n def reward_shaping_func(energy_info, cost_info):\n total = 0\n for module_name, info_list in energy_info.items():\n for module_info in info_list:\n for j, (energy_type, energy_amount) in enumerate(module_info.items()):\n if energy_type == 'absorbed_energy':\n marginal_cost = cost_info[module_name][j]['absorption_marginal_cost']\n elif energy_type == 'provided_energy':\n marginal_cost = cost_info[module_name][j]['production_marginal_cost']\n else:\n # Some other key\n continue\n total += energy_amount * marginal_cost\n\n return total\n" }, { "path": "tests/test_microgridgenerator.py", "content": "\"\"\"\nCopyright 2020 Total S.A.\nAuthors:Gonzague Henri \nPermission to use, modify, and distribute this software is given under the\nterms of the pymgrid License.\nNO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.\n$Date: 2020/08/27 08:04 $\nGonzague Henri\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom numpy.testing import assert_allclose\n\n\nimport os, sys\nsys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))\n\nfrom pymgrid.MicrogridGenerator import MicrogridGenerator\n\nimport unittest\n\n\nclass TestMicogridGenerator(unittest.TestCase):\n\n def setUp(self):\n self.mgen = MicrogridGenerator()\n\n def test_get_random_file(self):\n import inspect, pymgrid\n from pathlib import Path\n\n path = Path(inspect.getfile(pymgrid)).parent\n path = path / 'data/pv'\n data = self.mgen._get_random_file(path)\n\n self.assertEqual(len(data), 8760)\n\n def test_scale_ts(self):\n ts = pd.DataFrame( [i for i in range(10)])\n factor = 4\n scaled = self.mgen._scale_ts(ts, factor)\n assert_allclose(ts/ts.sum()*factor, scaled)\n\n def test_get_genset(self):\n genset = self.mgen._get_genset()\n self.assertEqual (1000, genset['rated_power'])\n\n\n def test_get_battery(self):\n battery = self.mgen._get_battery()\n self.assertEqual (1000, battery['capa'])\n\n def test_get_grid_price_ts(self):\n price = self.mgen._get_grid_price_ts(10, price=0.2)\n self.assertTrue(all([p == 0.2 for p in price]))\n\n def test_get_grid(self):\n grid = self.mgen._get_grid()\n self.assertEqual(1000, grid['grid_power_import'])\n\n def test_size_mg(self):\n ts = pd.DataFrame([i for i in range(10)])\n mg = self.mgen._size_mg(ts, 10)\n\n self.assertEqual(18, mg['grid'])\n\n def test_size_genset(self):\n self.assertEqual(int(np.ceil(10/0.9)), self.mgen._size_genset([10, 10, 10]))\n\n def test_size_battery(self):\n size = self.mgen._size_battery([10, 10, 10])\n self.assertLessEqual(30, size)\n self.assertGreaterEqual(50, size)\n\n def test_generate_microgrid(self):\n microgrids = self.mgen.generate_microgrid().microgrids\n\n self.assertEqual(self.mgen.nb_microgrids, len(microgrids))\n\n def test_create_microgrid(self):\n mg = self.mgen._create_microgrid()\n\n self.assertEqual(1, mg.architecture['battery'])\n\nif __name__ == '__main__':\n unittest.main()\n" }, { "path": "tests/test_nonmodular_microgrid.py", "content": "\"\"\"\nCopyright 2020 Total S.A.\nAuthors:Gonzague Henri \nPermission to use, modify, and distribute this software is given under the\nterms of the pymgrid License.\nNO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.\n$Date: 2020/08/27 08:04 $\nGonzague Henri\n\"\"\"\nimport unittest\nimport numpy as np\n\nfrom pymgrid.MicrogridGenerator import MicrogridGenerator\n\n\nclass TestNonmodularMicrogrid(unittest.TestCase):\n def setUp(self):\n mgen = MicrogridGenerator()\n self.mg = mgen._create_microgrid()\n\n @staticmethod\n def random_control():\n return dict(pv_consummed=np.random.rand(),\n battery_charge=np.random.rand(),\n battery_discharge=np.random.rand(),\n grid_import=np.random.rand(),\n grid_export=np.random.rand()\n )\n\n def test_set_horizon(self):\n self.mg.set_horizon(25)\n self.assertEqual(25, self.mg.horizon)\n\n def test_get_updated_values(self):\n mg_data = self.mg.get_updated_values()\n self.assertEqual(0, mg_data['pv'])\n\n def test_forecast_all(self):\n self.mg.set_horizon(24)\n forecast = self.mg.forecast_all()\n self.assertEqual(24, len(forecast['load']))\n\n def test_forecast_pv(self):\n self.mg.set_horizon(24)\n forecast = self.mg.forecast_pv()\n self.assertEqual (24, len(forecast))\n\n def test_forecast_load(self):\n self.mg.set_horizon(24)\n forecast = self.mg.forecast_load()\n self.assertEqual (24, len(forecast))\n\n def test_run(self):\n pv1 = self.mg.forecast_pv()[1]\n self.mg.run(self.random_control())\n pv2 = self.mg.pv\n self.assertEqual(pv1, pv2)\n\n def test_train_test_split(self):\n self.mg.train_test_split()\n self.assertEqual('training',self.mg._data_set_to_use)\n\n def test_reset(self):\n self.mg.run(self.random_control())\n self.mg.reset()\n self.assertEqual (0, self.mg._tracking_timestep)\n\n\nif __name__ == '__main__':\n unittest.main()\n" } ]