diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index 9030923a..00000000 --- a/.gitattributes +++ /dev/null @@ -1 +0,0 @@ -*.ipynb linguist-vendored \ No newline at end of file diff --git a/.gitignore b/.gitignore index 2215c23f..c61680c9 100644 --- a/.gitignore +++ b/.gitignore @@ -1,10 +1,9 @@ #Ignore what is not python code -notebooks/ .DS_STORE .idea *.pkl pymgrid/__pycache__/ -*.ipynb +#*.ipynb .ipynb_checkpoints __pycache__/ @@ -29,3 +28,7 @@ share/python-wheels/ *.egg MANIFEST *sandbox.py +.venv/ + +# Data +data/ \ No newline at end of file diff --git a/.readthedocs.yaml b/.readthedocs.yaml deleted file mode 100644 index 79d39f7b..00000000 --- a/.readthedocs.yaml +++ /dev/null @@ -1,25 +0,0 @@ -# .readthedocs.yaml -# Read the Docs configuration file -# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details - -# Required -version: 2 - -# Set the version of Python and other tools you might need -build: - os: ubuntu-20.04 - tools: - python: "3.8" - -sphinx: - configuration: docs/source/conf.py - -python: - install: - - method: pip - path: . - extra_requirements: - - all - -formats: - - pdf \ No newline at end of file diff --git a/CHANGELOG.md b/CHANGELOG.md deleted file mode 100644 index 8b137891..00000000 --- a/CHANGELOG.md +++ /dev/null @@ -1 +0,0 @@ - diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md deleted file mode 100644 index 8b137891..00000000 --- a/CONTRIBUTING.md +++ /dev/null @@ -1 +0,0 @@ - diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 0a041280..00000000 --- a/LICENSE +++ /dev/null @@ -1,165 +0,0 @@ - GNU LESSER GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - - This version of the GNU Lesser General Public License incorporates -the terms and conditions of version 3 of the GNU General Public -License, supplemented by the additional permissions listed below. - - 0. Additional Definitions. - - As used herein, "this License" refers to version 3 of the GNU Lesser -General Public License, and the "GNU GPL" refers to version 3 of the GNU -General Public License. - - "The Library" refers to a covered work governed by this License, -other than an Application or a Combined Work as defined below. - - An "Application" is any work that makes use of an interface provided -by the Library, but which is not otherwise based on the Library. -Defining a subclass of a class defined by the Library is deemed a mode -of using an interface provided by the Library. - - A "Combined Work" is a work produced by combining or linking an -Application with the Library. The particular version of the Library -with which the Combined Work was made is also called the "Linked -Version". - - The "Minimal Corresponding Source" for a Combined Work means the -Corresponding Source for the Combined Work, excluding any source code -for portions of the Combined Work that, considered in isolation, are -based on the Application, and not on the Linked Version. - - The "Corresponding Application Code" for a Combined Work means the -object code and/or source code for the Application, including any data -and utility programs needed for reproducing the Combined Work from the -Application, but excluding the System Libraries of the Combined Work. - - 1. Exception to Section 3 of the GNU GPL. - - You may convey a covered work under sections 3 and 4 of this License -without being bound by section 3 of the GNU GPL. - - 2. Conveying Modified Versions. - - If you modify a copy of the Library, and, in your modifications, a -facility refers to a function or data to be supplied by an Application -that uses the facility (other than as an argument passed when the -facility is invoked), then you may convey a copy of the modified -version: - - a) under this License, provided that you make a good faith effort to - ensure that, in the event an Application does not supply the - function or data, the facility still operates, and performs - whatever part of its purpose remains meaningful, or - - b) under the GNU GPL, with none of the additional permissions of - this License applicable to that copy. - - 3. Object Code Incorporating Material from Library Header Files. - - The object code form of an Application may incorporate material from -a header file that is part of the Library. You may convey such object -code under terms of your choice, provided that, if the incorporated -material is not limited to numerical parameters, data structure -layouts and accessors, or small macros, inline functions and templates -(ten or fewer lines in length), you do both of the following: - - a) Give prominent notice with each copy of the object code that the - Library is used in it and that the Library and its use are - covered by this License. - - b) Accompany the object code with a copy of the GNU GPL and this license - document. - - 4. Combined Works. - - You may convey a Combined Work under terms of your choice that, -taken together, effectively do not restrict modification of the -portions of the Library contained in the Combined Work and reverse -engineering for debugging such modifications, if you also do each of -the following: - - a) Give prominent notice with each copy of the Combined Work that - the Library is used in it and that the Library and its use are - covered by this License. - - b) Accompany the Combined Work with a copy of the GNU GPL and this license - document. - - c) For a Combined Work that displays copyright notices during - execution, include the copyright notice for the Library among - these notices, as well as a reference directing the user to the - copies of the GNU GPL and this license document. - - d) Do one of the following: - - 0) Convey the Minimal Corresponding Source under the terms of this - License, and the Corresponding Application Code in a form - suitable for, and under terms that permit, the user to - recombine or relink the Application with a modified version of - the Linked Version to produce a modified Combined Work, in the - manner specified by section 6 of the GNU GPL for conveying - Corresponding Source. - - 1) Use a suitable shared library mechanism for linking with the - Library. A suitable mechanism is one that (a) uses at run time - a copy of the Library already present on the user's computer - system, and (b) will operate properly with a modified version - of the Library that is interface-compatible with the Linked - Version. - - e) Provide Installation Information, but only if you would otherwise - be required to provide such information under section 6 of the - GNU GPL, and only to the extent that such information is - necessary to install and execute a modified version of the - Combined Work produced by recombining or relinking the - Application with a modified version of the Linked Version. (If - you use option 4d0, the Installation Information must accompany - the Minimal Corresponding Source and Corresponding Application - Code. If you use option 4d1, you must provide the Installation - Information in the manner specified by section 6 of the GNU GPL - for conveying Corresponding Source.) - - 5. Combined Libraries. - - You may place library facilities that are a work based on the -Library side by side in a single library together with other library -facilities that are not Applications and are not covered by this -License, and convey such a combined library under terms of your -choice, if you do both of the following: - - a) Accompany the combined library with a copy of the same work based - on the Library, uncombined with any other library facilities, - conveyed under the terms of this License. - - b) Give prominent notice with the combined library that part of it - is a work based on the Library, and explaining where to find the - accompanying uncombined form of the same work. - - 6. Revised Versions of the GNU Lesser General Public License. - - The Free Software Foundation may publish revised and/or new versions -of the GNU Lesser General Public License from time to time. Such new -versions will be similar in spirit to the present version, but may -differ in detail to address new problems or concerns. - - Each version is given a distinguishing version number. If the -Library as you received it specifies that a certain numbered version -of the GNU Lesser General Public License "or any later version" -applies to it, you have the option of following the terms and -conditions either of that published version or of any later version -published by the Free Software Foundation. If the Library as you -received it does not specify a version number of the GNU Lesser -General Public License, you may choose any version of the GNU Lesser -General Public License ever published by the Free Software Foundation. - - If the Library as you received it specifies that a proxy can decide -whether future versions of the GNU Lesser General Public License shall -apply, that proxy's public statement of acceptance of any version is -permanent authorization for you to choose that version for the -Library. diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index e263e044..00000000 --- a/MANIFEST.in +++ /dev/null @@ -1 +0,0 @@ -graft src/pymgrid/data \ No newline at end of file diff --git a/README.md b/README.md index 8a8ebf38..ff768171 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,6 @@ +![image](https://github.com/user-attachments/assets/1c50aa6f-a8db-49b6-971d-794b2977eae3) + + # python-microgrid ![Build](https://github.com/ahalev/python-microgrid/workflows/build/badge.svg?dummy=unused) diff --git a/database.db b/database.db new file mode 100644 index 00000000..aaf7ac06 Binary files /dev/null and b/database.db differ diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index d0c3cbf1..00000000 --- a/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = source -BUILDDIR = build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index dc1312ab..00000000 --- a/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=source -set BUILDDIR=build - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.https://www.sphinx-doc.org/ - exit /b 1 -) - -if "%1" == "" goto help - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd diff --git a/docs/requirements.txt b/docs/requirements.txt deleted file mode 100644 index 6ad7903c..00000000 --- a/docs/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -nbsphinx==0.8.10 -nbsphinx-link==1.3.0 -numpydoc==1.5.0 -pydata_sphinx_theme==0.12.0 -Sphinx==5.3.0 diff --git a/docs/source/_templates/autosummary/base.rst b/docs/source/_templates/autosummary/base.rst deleted file mode 100644 index 3fe9858d..00000000 --- a/docs/source/_templates/autosummary/base.rst +++ /dev/null @@ -1,5 +0,0 @@ -{{ objname | escape | underline }} - -.. currentmodule:: {{ module }} - -.. auto{{ objtype }}:: {{ objname }} diff --git a/docs/source/_templates/autosummary/class.rst b/docs/source/_templates/autosummary/class.rst deleted file mode 100644 index 5a47cff2..00000000 --- a/docs/source/_templates/autosummary/class.rst +++ /dev/null @@ -1,34 +0,0 @@ -{{ fullname | escape | underline}} - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - - {% block methods %} - - {% if methods %} - .. rubric:: {{ _('Methods') }} - - .. autosummary:: - :toctree: generated/ - - {% for item in methods %} - {% if item != "__init__" %} - ~{{ name }}.{{ item }} - {% endif %} - {%- endfor %} - {% endif %} - {% endblock %} - - {% block attributes %} - {% if attributes %} - .. rubric:: {{ _('Attributes') }} - - .. autosummary:: - :toctree: generated/ - - {% for item in attributes %} - ~{{ name }}.{{ item }} - {%- endfor %} - {% endif %} - {% endblock %} diff --git a/docs/source/conf.py b/docs/source/conf.py deleted file mode 100644 index 3d8f13f6..00000000 --- a/docs/source/conf.py +++ /dev/null @@ -1,144 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# For the full list of built-in configuration values, see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -import inspect -import os -import sys - -from copy import deepcopy -from builtins import object - -import pymgrid - - -# -- Project information ----------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information - -project = 'pymgrid' -copyright = '2022, TotalEnergies' -author = 'Avishai Halev' -release = pymgrid.__version__ -version = pymgrid.__version__ - -# -- General configuration --------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration - -extensions = [ - 'sphinx.ext.duration', - 'sphinx.ext.autodoc', - 'sphinx.ext.coverage', - 'sphinx.ext.autosummary', - 'sphinx.ext.doctest', - 'sphinx.ext.linkcode', - 'sphinx.ext.intersphinx', - 'sphinx.ext.mathjax', - 'nbsphinx', - 'nbsphinx_link', - 'IPython.sphinxext.ipython_console_highlighting' -] - -templates_path = ['_templates'] -exclude_patterns = [] - - -# -- Options for HTML output ------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output - -html_theme = 'pydata_sphinx_theme' - -html_theme_options = { - "primary_sidebar_end": ["indices.html", "sidebar-ethical-ads.html"], - - "logo": { - "image_light": "logo-light.png", - "image_dark": "logo-dark.png", - }, - - "icon_links": [ - { - "name": "GitHub", - "url": "https://github.com/ahalev/python-microgrid/", - "icon": "fa-brands fa-github", - }, - { - "name": "PyPI", - "url": "https://pypi.org/project/python-microgrid/", - "icon": "fa-solid fa-box", - } - ] -} - - -html_static_path = ['_static'] - - -# These are attributes that don't have a __doc__ attribute to read ':meta private:' from. -skip_members = [ - 'yaml_flow_style', - 'metadata', - 'render_mode', - 'reward_range', - 'spec' - ] - - -def autodoc_skip_member(app, what, name, obj, skip, options): - if name in skip_members: - return True - - try: - doc = obj.__doc__ - except AttributeError: - return None - - if doc is not None and ':meta private:' in doc: - return True - return None - - -def autodoc_process_signature(app, what, name, obj, options, signature, return_annotation): - """ - If a class signature is being read from cls.__new__, we want to replace it with the signature from cls.__init__. - """ - if what == 'class' and signature[1:] in str(inspect.signature(obj.__new__)): - obj_copy = deepcopy(obj) - obj_copy.__new__ = object.__new__ - signature = str(inspect.signature(obj_copy)) - return signature, return_annotation - - -def linkcode_resolve(domain, info): - """ - Determine the URL corresponding to Python object - """ - if domain != "py": - return None - - modname = info["module"] - fullname = info["fullname"] - - submod = sys.modules.get(modname) - if submod is None: - return None - - obj = submod - for part in fullname.split("."): - try: - obj = getattr(obj, part) - except AttributeError: - return None - - return pymgrid.utils.obj_linkcode(obj) - - - -intersphinx_mapping = { - 'gym': ('https://www.gymlibrary.dev/', None) -} - - -def setup(app): - app.connect('autodoc-skip-member', autodoc_skip_member) - app.connect('autodoc-process-signature', autodoc_process_signature) diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst deleted file mode 100644 index 05796f0f..00000000 --- a/docs/source/examples/index.rst +++ /dev/null @@ -1,19 +0,0 @@ -Examples -======== - -To run the examples, clone the repository: - -.. code-block:: bash - - $ git clone https://github.com/Total-RD/pymgrid.git - -and use `Jupyter `_ to open any notebook in -the :code:`notebooks` directory. - -.. toctree:: - :maxdepth: 2 - - quick-start - rbc-example - mpc-example - rl-example \ No newline at end of file diff --git a/docs/source/examples/mpc-example.nblink b/docs/source/examples/mpc-example.nblink deleted file mode 100644 index efc5cf41..00000000 --- a/docs/source/examples/mpc-example.nblink +++ /dev/null @@ -1,3 +0,0 @@ -{ - "path": "../../../notebooks/mpc-example.ipynb" -} \ No newline at end of file diff --git a/docs/source/examples/quick-start.nblink b/docs/source/examples/quick-start.nblink deleted file mode 100644 index 12a56ab4..00000000 --- a/docs/source/examples/quick-start.nblink +++ /dev/null @@ -1,3 +0,0 @@ -{ - "path": "../../../notebooks/quick-start.ipynb" -} diff --git a/docs/source/examples/rbc-example.nblink b/docs/source/examples/rbc-example.nblink deleted file mode 100644 index f815a548..00000000 --- a/docs/source/examples/rbc-example.nblink +++ /dev/null @@ -1,3 +0,0 @@ -{ - "path": "../../../notebooks/rbc-example.ipynb" -} diff --git a/docs/source/examples/rl-example.nblink b/docs/source/examples/rl-example.nblink deleted file mode 100644 index ab9aa101..00000000 --- a/docs/source/examples/rl-example.nblink +++ /dev/null @@ -1,3 +0,0 @@ -{ - "path": "../../../notebooks/rl-example.ipynb" -} diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst deleted file mode 100644 index a2dbfcf7..00000000 --- a/docs/source/getting_started.rst +++ /dev/null @@ -1,44 +0,0 @@ -Getting Started -=============== - -.. _installation: - -Installation ------------- - -The easiest way to install *pymgrid* is with pip: - -.. code-block:: console - - $ pip install -U pymgrid - -Alternatively, you can install from source. First clone the repo: - -.. code-block:: bash - - $ git clone https://github.com/Total-RD/pymgrid.git - -Then navigate to the root directory of pymgrid and call - -.. code-block:: bash - - $ pip install . - -Advanced Installation ---------------------- - -To use the included model predictive control algorithm on microgrids containing gensets, -additional dependencies are required as the optimization problem becomes mixed integer. - -The packages MOSEK and CVXOPT can both handle this case; you can install both by calling - -.. code-block:: bash - - $ pip install pymgrid[genset_mpc] - -Note that MOSEK requires a license; see https://www.mosek.com/ for details. -Academic and trial licenses are available. - -Simple Example --------------- -See :doc:`examples/quick-start` for a simple example to get started. diff --git a/docs/source/index.rst b/docs/source/index.rst deleted file mode 100644 index 9ef87bff..00000000 --- a/docs/source/index.rst +++ /dev/null @@ -1,53 +0,0 @@ -.. python-microgrid documentation master file, created by - sphinx-quickstart on Sat Nov 19 12:49:18 2022. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -********************* -python-microgrid documentation -********************* - -**Version**: |version| - -**Maintainer**: Avishai Halev - -*python-microgrid* is a Python library to simulate tertiary control of electrical microgrids. -It is an extension of TotalEnergies' [pymgrid](https://github.com/Total-RD/pymgrid). *python-microgrid* allows -users to create and customize microgrids of their choosing. These microgrids can then be controlled using a user-defined -algorithm or one of the control algorithms contained in *python-microgrid*: rule-based control and model predictive control. - -Environments corresponding to the OpenAI-Gym API are also provided, with both continuous and discrete action space -environments available. These environments can be used with your choice of reinforcement learning algorithm to train -a control algorithm. - -*python-microgrid* attempts to offer the simplest and most intuitive API possible, allowing the user to -focus on their particular application. - -See the :doc:`getting_started` section for further information, including instructions on how to -:ref:`install ` the project. - -**Useful links**: -`Binary Installers `__ | -`Source Repository `__ - - -.. note:: - - This project is under active development. - -Contents -======== - -.. toctree:: - :maxdepth: 2 - - getting_started - examples/index - reference/index - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/source/reference/algos/index.rst b/docs/source/reference/algos/index.rst deleted file mode 100644 index 479fb6f4..00000000 --- a/docs/source/reference/algos/index.rst +++ /dev/null @@ -1,50 +0,0 @@ -.. _api.control: - -Control Algorithms -================== - -.. currentmodule:: pymgrid.algos - -Control algorithms built into pymgrid, as well as references for external algorithms that can be deployed - -Rule Based Control ------------------- - -Heuristic Algorithm that deploys modules via a priority list. - -.. autosummary:: - :toctree: ../api/algos/ - - RuleBasedControl - - -Model Predictive Control ------------------------- - -Algorithm that depends on a future forecast as well as a model of state transitions to determine optimal controls. - - -.. autosummary:: - :toctree: ../api/algos/ - - ModelPredictiveControl - - -Reinforcement Learning ----------------------- - -Algorithms that treat a microgrid as a Markov process, and train a black-box policy by repeated interactions with -the environment. See :doc:`here <../../examples/rl-example>` for an example of using -reinforcement learning to train such an algorithm. - - - -.. - HACK -- the point here is that we don't want this to appear in the output, but the autosummary should still generate the pages. - Copied from pandas docs. - - .. currentmodule:: pymgrid.algos.priority_list - - .. autosummary:: - :toctree: ../api/algos/priority_list/ - PriorityListElement \ No newline at end of file diff --git a/docs/source/reference/envs/index.rst b/docs/source/reference/envs/index.rst deleted file mode 100644 index 3d18ed37..00000000 --- a/docs/source/reference/envs/index.rst +++ /dev/null @@ -1,30 +0,0 @@ -.. _api.envs: - -Reinforcement Learning (RL) Environments -====================== - -.. currentmodule:: pymgrid.envs - -Environment classes using the `OpenAI Gym API `_ for reinforcement learning. - -Discrete --------- - -Environment with a discrete action space. - - -.. autosummary:: - :toctree: ../api/envs/ - - DiscreteMicrogridEnv - -Continuous ----------- - -Environment with a discrete action space. - - -.. autosummary:: - :toctree: ../api/envs/ - - ContinuousMicrogridEnv diff --git a/docs/source/reference/forecast/index.rst b/docs/source/reference/forecast/index.rst deleted file mode 100644 index 7dcaef6c..00000000 --- a/docs/source/reference/forecast/index.rst +++ /dev/null @@ -1,18 +0,0 @@ -.. _api.forecast: - -Forecasting -=========== - -.. currentmodule:: pymgrid.forecast - -Classes available to use for time-series forecasting, as well a class that allows users to define their own forecaster. - -.. autosummary:: - :toctree: ../api/forecast/ - - get_forecaster - OracleForecaster - GaussianNoiseForecaster - UserDefinedForecaster - NoForecaster - diff --git a/docs/source/reference/general/index.rst b/docs/source/reference/general/index.rst deleted file mode 100644 index 4f960011..00000000 --- a/docs/source/reference/general/index.rst +++ /dev/null @@ -1,28 +0,0 @@ -.. _api.general: - -General functions and objects -============================= - -.. currentmodule:: pymgrid.modules - -ModuleContainer ---------------- - -Object that store's a microgrid's modules. - -.. autosummary:: - :toctree: ../api/general/ - - ModuleContainer - -ModuleSpace ------------ - -Object for module action and observation spaces. - -.. currentmodule:: pymgrid.utils.space - -.. autosummary:: - :toctree: ../api/general/ - - ModuleSpace diff --git a/docs/source/reference/index.rst b/docs/source/reference/index.rst deleted file mode 100644 index 3c83f6a4..00000000 --- a/docs/source/reference/index.rst +++ /dev/null @@ -1,14 +0,0 @@ -API reference -============= - -This page contains an overview of all public *pymgrid* objects and functions. - -.. toctree:: - :maxdepth: 2 - - microgrid - modules/index - forecast/index - envs/index - algos/index - general/index \ No newline at end of file diff --git a/docs/source/reference/microgrid.rst b/docs/source/reference/microgrid.rst deleted file mode 100644 index b914ef16..00000000 --- a/docs/source/reference/microgrid.rst +++ /dev/null @@ -1,39 +0,0 @@ -.. _api.microgrid: - - -Microgrid -================= - -.. currentmodule:: pymgrid - -Constructor ------------ -.. autosummary:: - :toctree: api/microgrid/ - - Microgrid - -Methods -------- -.. autosummary:: - - :toctree: api/microgrid/ - - Microgrid.run - Microgrid.reset - Microgrid.sample_action - Microgrid.get_log - Microgrid.get_forecast_horizon - Microgrid.get_empty_action - -Serialization/IO/Conversion ---------------------------- -.. autosummary:: - - :toctree: api/microgrid/ - - Microgrid.load - Microgrid.dump - Microgrid.from_nonmodular - Microgrid.from_scenario - Microgrid.to_nonmodular \ No newline at end of file diff --git a/docs/source/reference/modules/battery_transition_models/index.rst b/docs/source/reference/modules/battery_transition_models/index.rst deleted file mode 100644 index f2837501..00000000 --- a/docs/source/reference/modules/battery_transition_models/index.rst +++ /dev/null @@ -1,16 +0,0 @@ -.. _api.battery_transition_models: - -Battery Transition Models -========================= - -.. currentmodule:: pymgrid.modules.battery.transition_models - -Various battery transition models. - -.. autosummary:: - :toctree: ../../api/battery_transition_models/ - - BatteryTransitionModel - BiasedTransitionModel - DecayTransitionModel - diff --git a/docs/source/reference/modules/index.rst b/docs/source/reference/modules/index.rst deleted file mode 100644 index 26c75602..00000000 --- a/docs/source/reference/modules/index.rst +++ /dev/null @@ -1,62 +0,0 @@ -.. _api.modules: - -Modules -======= - -.. currentmodule:: pymgrid.modules - -The modules defined here are commonly found in microgrids. -Pass any combination of modules to :ref:`Microgrid ` to define and run a microgrid. - -Timeseries Modules ------------------- - -Modules that are temporal in nature. - - - -.. autosummary:: - :toctree: ../api/modules/ - - GridModule - LoadModule - RenewableModule - -Non-temporal Modules --------------------- - -Modules that do not depend on an underlying timeseries. - -.. autosummary:: - :toctree: ../api/modules/ - - BatteryModule - GensetModule - -Helper Module --------------- - -A module that cleans up after all the other modules are deployed. - -.. autosummary:: - :toctree: ../api/modules/ - - UnbalancedEnergyModule - - -Module Functions -================ - -Battery Transition Models -------------------------- - -Various battery transition models. - -.. currentmodule:: pymgrid.modules.battery.transition_models - -.. autosummary:: - :toctree: ../api/battery_transition_models/ - - BatteryTransitionModel - BiasedTransitionModel - DecayTransitionModel diff --git a/logs/log-2025-03-24_14-44-49.csv b/logs/log-2025-03-24_14-44-49.csv new file mode 100644 index 00000000..eea6ddd5 --- /dev/null +++ b/logs/log-2025-03-24_14-44-49.csv @@ -0,0 +1,27 @@ +balancing,balancing,balancing,battery,battery,battery,battery,battery,node,node,node,pv_spain,pv_spain,pv_spain,pv_spain,balance,balance,balance,balance,balance,balance,balance,balance,balance,balance +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loss_load,overgeneration,reward,charge_amount,current_charge,discharge_amount,reward,soc,load_met,node_current,reward,curtailment,renewable_current,renewable_used,reward,reward,shaped_reward,overall_provided_to_microgrid,overall_absorbed_from_microgrid,flex_provided_to_microgrid,flex_absorbed_from_microgrid,controllable_provided_to_microgrid,controllable_absorbed_from_microgrid,fixed_provided_to_microgrid,fixed_absorbed_from_microgrid +0.0,25.0,-50.0,0.0,50.0,50.0,-0.0,0.5,25.0,-32.91337944374272,0.0,0.0,0.0,0.0,0.0,-50.0,-50.0,50.0,50.0,0.0,25.0,50.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-39.23960507316958,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-25.53548709368051,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-59.824809923236,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-7.973485964160614,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-12.344175991180135,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-6.271852515159728,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-33.9908755686652,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.967457663710153,0.0,-249.67457663710152,0.0,0.0,0.0,-0.0,0.0,25.0,-55.435023913048866,0.0,0.0,0.0325423362898458,0.0325423362898458,0.0,-249.67457663710152,-249.67457663710152,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.864345471691777,0.0,-248.64345471691777,0.0,0.0,0.0,-0.0,0.0,25.0,-55.680322947349794,0.0,0.0,0.135654528308223,0.135654528308223,0.0,-248.64345471691777,-248.64345471691777,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.7317076789276,0.0,-247.31707678927597,0.0,0.0,0.0,-0.0,0.0,25.0,-58.154283750735765,0.0,0.0,0.268292321072402,0.268292321072402,0.0,-247.31707678927597,-247.31707678927597,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.62790269439436,0.0,-246.27902694394362,0.0,0.0,0.0,-0.0,0.0,25.0,-8.305620947876788,0.0,0.0,0.37209730560564,0.37209730560564,0.0,-246.27902694394362,-246.27902694394362,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.571802221269618,0.0,-245.71802221269618,0.0,0.0,0.0,-0.0,0.0,25.0,-3.3298400867402322,0.0,0.0,0.428197778730383,0.428197778730383,0.0,-245.71802221269618,-245.71802221269618,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.581853415542664,0.0,-245.81853415542665,0.0,0.0,0.0,-0.0,0.0,25.0,-56.1997766652032,0.0,0.0,0.418146584457336,0.418146584457336,0.0,-245.81853415542665,-245.81853415542665,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.61201764305202,0.0,-246.1201764305202,0.0,0.0,0.0,-0.0,0.0,25.0,-14.593621213596801,0.0,0.0,0.387982356947981,0.387982356947981,0.0,-246.1201764305202,-246.1201764305202,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.67006373999728,0.0,-246.70063739997278,0.0,0.0,0.0,-0.0,0.0,25.0,-13.672469805324884,0.0,0.0,0.32993626000272,0.32993626000272,0.0,-246.70063739997278,-246.70063739997278,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.823577712482585,0.0,-248.23577712482586,0.0,0.0,0.0,-0.0,0.0,25.0,-44.10809465315699,0.0,0.0,0.176422287517416,0.176422287517416,0.0,-248.23577712482586,-248.23577712482586,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-27.40109402216017,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-32.70938052475366,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-45.257445487966244,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-13.195832312150147,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-34.09782225735759,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-5.388919175187549,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-43.02479348517267,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 diff --git a/logs/log-2025-03-24_14-46-32.csv b/logs/log-2025-03-24_14-46-32.csv new file mode 100644 index 00000000..03a11028 --- /dev/null +++ b/logs/log-2025-03-24_14-46-32.csv @@ -0,0 +1,27 @@ +balancing,balancing,balancing,battery,battery,battery,battery,battery,node,node,node,pv_spain,pv_spain,pv_spain,pv_spain,balance,balance,balance,balance,balance,balance,balance,balance,balance,balance +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loss_load,overgeneration,reward,charge_amount,current_charge,discharge_amount,reward,soc,load_met,node_current,reward,curtailment,renewable_current,renewable_used,reward,reward,shaped_reward,overall_provided_to_microgrid,overall_absorbed_from_microgrid,flex_provided_to_microgrid,flex_absorbed_from_microgrid,controllable_provided_to_microgrid,controllable_absorbed_from_microgrid,fixed_provided_to_microgrid,fixed_absorbed_from_microgrid +52.19617932351274,0.0,-521.9617932351274,27.196179323512737,50.0,0.0,-0.0,0.5,25.0,-32.91337944374272,0.0,0.0,0.0,0.0,0.0,-521.9617932351274,-521.9617932351274,52.19617932351274,52.19617932351274,52.19617932351274,0.0,0.0,27.196179323512737,0.0,25.0 +47.80382067648726,0.0,-478.0382067648726,22.803820676487263,77.19617932351274,0.0,-0.0,0.7719617932351274,25.0,-39.23960507316958,0.0,0.0,0.0,0.0,0.0,-478.0382067648726,-478.0382067648726,47.80382067648726,47.80382067648726,47.80382067648726,0.0,0.0,22.803820676487263,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-25.53548709368051,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-59.824809923236,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-7.973485964160614,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-12.344175991180135,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-6.271852515159728,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-33.9908755686652,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.967457663710153,0.0,-249.67457663710152,0.0,100.0,0.0,-0.0,1.0,25.0,-55.435023913048866,0.0,0.0,0.0325423362898458,0.0325423362898458,0.0,-249.67457663710152,-249.67457663710152,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.864345471691777,0.0,-248.64345471691777,0.0,100.0,0.0,-0.0,1.0,25.0,-55.680322947349794,0.0,0.0,0.135654528308223,0.135654528308223,0.0,-248.64345471691777,-248.64345471691777,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.7317076789276,0.0,-247.31707678927597,0.0,100.0,0.0,-0.0,1.0,25.0,-58.154283750735765,0.0,0.0,0.268292321072402,0.268292321072402,0.0,-247.31707678927597,-247.31707678927597,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.62790269439436,0.0,-246.27902694394362,0.0,100.0,0.0,-0.0,1.0,25.0,-8.305620947876788,0.0,0.0,0.37209730560564,0.37209730560564,0.0,-246.27902694394362,-246.27902694394362,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.571802221269618,0.0,-245.71802221269618,0.0,100.0,0.0,-0.0,1.0,25.0,-3.3298400867402322,0.0,0.0,0.428197778730383,0.428197778730383,0.0,-245.71802221269618,-245.71802221269618,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.581853415542664,0.0,-245.81853415542665,0.0,100.0,0.0,-0.0,1.0,25.0,-56.1997766652032,0.0,0.0,0.418146584457336,0.418146584457336,0.0,-245.81853415542665,-245.81853415542665,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.61201764305202,0.0,-246.1201764305202,0.0,100.0,0.0,-0.0,1.0,25.0,-14.593621213596801,0.0,0.0,0.387982356947981,0.387982356947981,0.0,-246.1201764305202,-246.1201764305202,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.67006373999728,0.0,-246.70063739997278,0.0,100.0,0.0,-0.0,1.0,25.0,-13.672469805324884,0.0,0.0,0.32993626000272,0.32993626000272,0.0,-246.70063739997278,-246.70063739997278,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.823577712482585,0.0,-248.23577712482586,0.0,100.0,0.0,-0.0,1.0,25.0,-44.10809465315699,0.0,0.0,0.176422287517416,0.176422287517416,0.0,-248.23577712482586,-248.23577712482586,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-27.40109402216017,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-32.70938052475366,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-45.257445487966244,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-13.195832312150147,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-34.09782225735759,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-5.388919175187549,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-43.02479348517267,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 diff --git a/logs/log-2025-03-24_14-47-15.csv b/logs/log-2025-03-24_14-47-15.csv new file mode 100644 index 00000000..03a11028 --- /dev/null +++ b/logs/log-2025-03-24_14-47-15.csv @@ -0,0 +1,27 @@ +balancing,balancing,balancing,battery,battery,battery,battery,battery,node,node,node,pv_spain,pv_spain,pv_spain,pv_spain,balance,balance,balance,balance,balance,balance,balance,balance,balance,balance +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loss_load,overgeneration,reward,charge_amount,current_charge,discharge_amount,reward,soc,load_met,node_current,reward,curtailment,renewable_current,renewable_used,reward,reward,shaped_reward,overall_provided_to_microgrid,overall_absorbed_from_microgrid,flex_provided_to_microgrid,flex_absorbed_from_microgrid,controllable_provided_to_microgrid,controllable_absorbed_from_microgrid,fixed_provided_to_microgrid,fixed_absorbed_from_microgrid +52.19617932351274,0.0,-521.9617932351274,27.196179323512737,50.0,0.0,-0.0,0.5,25.0,-32.91337944374272,0.0,0.0,0.0,0.0,0.0,-521.9617932351274,-521.9617932351274,52.19617932351274,52.19617932351274,52.19617932351274,0.0,0.0,27.196179323512737,0.0,25.0 +47.80382067648726,0.0,-478.0382067648726,22.803820676487263,77.19617932351274,0.0,-0.0,0.7719617932351274,25.0,-39.23960507316958,0.0,0.0,0.0,0.0,0.0,-478.0382067648726,-478.0382067648726,47.80382067648726,47.80382067648726,47.80382067648726,0.0,0.0,22.803820676487263,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-25.53548709368051,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-59.824809923236,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-7.973485964160614,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-12.344175991180135,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-6.271852515159728,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-33.9908755686652,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.967457663710153,0.0,-249.67457663710152,0.0,100.0,0.0,-0.0,1.0,25.0,-55.435023913048866,0.0,0.0,0.0325423362898458,0.0325423362898458,0.0,-249.67457663710152,-249.67457663710152,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.864345471691777,0.0,-248.64345471691777,0.0,100.0,0.0,-0.0,1.0,25.0,-55.680322947349794,0.0,0.0,0.135654528308223,0.135654528308223,0.0,-248.64345471691777,-248.64345471691777,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.7317076789276,0.0,-247.31707678927597,0.0,100.0,0.0,-0.0,1.0,25.0,-58.154283750735765,0.0,0.0,0.268292321072402,0.268292321072402,0.0,-247.31707678927597,-247.31707678927597,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.62790269439436,0.0,-246.27902694394362,0.0,100.0,0.0,-0.0,1.0,25.0,-8.305620947876788,0.0,0.0,0.37209730560564,0.37209730560564,0.0,-246.27902694394362,-246.27902694394362,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.571802221269618,0.0,-245.71802221269618,0.0,100.0,0.0,-0.0,1.0,25.0,-3.3298400867402322,0.0,0.0,0.428197778730383,0.428197778730383,0.0,-245.71802221269618,-245.71802221269618,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.581853415542664,0.0,-245.81853415542665,0.0,100.0,0.0,-0.0,1.0,25.0,-56.1997766652032,0.0,0.0,0.418146584457336,0.418146584457336,0.0,-245.81853415542665,-245.81853415542665,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.61201764305202,0.0,-246.1201764305202,0.0,100.0,0.0,-0.0,1.0,25.0,-14.593621213596801,0.0,0.0,0.387982356947981,0.387982356947981,0.0,-246.1201764305202,-246.1201764305202,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.67006373999728,0.0,-246.70063739997278,0.0,100.0,0.0,-0.0,1.0,25.0,-13.672469805324884,0.0,0.0,0.32993626000272,0.32993626000272,0.0,-246.70063739997278,-246.70063739997278,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.823577712482585,0.0,-248.23577712482586,0.0,100.0,0.0,-0.0,1.0,25.0,-44.10809465315699,0.0,0.0,0.176422287517416,0.176422287517416,0.0,-248.23577712482586,-248.23577712482586,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-27.40109402216017,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-32.70938052475366,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-45.257445487966244,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-13.195832312150147,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-34.09782225735759,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-5.388919175187549,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,100.0,0.0,-0.0,1.0,25.0,-43.02479348517267,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 diff --git a/logs/log-2025-03-24_14-49-37.csv b/logs/log-2025-03-24_14-49-37.csv new file mode 100644 index 00000000..7d00324e --- /dev/null +++ b/logs/log-2025-03-24_14-49-37.csv @@ -0,0 +1,27 @@ +balancing,balancing,balancing,battery,battery,battery,battery,battery,node,node,node,pv_spain,pv_spain,pv_spain,pv_spain,balance,balance,balance,balance,balance,balance,balance,balance,balance,balance +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loss_load,overgeneration,reward,charge_amount,current_charge,discharge_amount,reward,soc,load_met,node_current,reward,curtailment,renewable_current,renewable_used,reward,reward,shaped_reward,overall_provided_to_microgrid,overall_absorbed_from_microgrid,flex_provided_to_microgrid,flex_absorbed_from_microgrid,controllable_provided_to_microgrid,controllable_absorbed_from_microgrid,fixed_provided_to_microgrid,fixed_absorbed_from_microgrid +277.19617932351275,0.0,-2771.9617932351275,27.196179323512737,50.0,0.0,-0.0,0.5,250.0,-32.91337944374272,0.0,0.0,0.0,0.0,0.0,-2771.9617932351275,-2771.9617932351275,277.19617932351275,277.19617932351275,277.19617932351275,0.0,0.0,27.19617932351275,0.0,250.0 +272.80382067648725,0.0,-2728.0382067648725,22.803820676487263,77.19617932351274,0.0,-0.0,0.7719617932351274,250.0,-39.23960507316958,0.0,0.0,0.0,0.0,0.0,-2728.0382067648725,-2728.0382067648725,272.80382067648725,272.80382067648725,272.80382067648725,0.0,0.0,22.80382067648725,0.0,250.0 +250.0,0.0,-2500.0,0.0,100.0,0.0,-0.0,1.0,250.0,-25.53548709368051,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,100.0,0.0,-0.0,1.0,250.0,-59.824809923236,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,100.0,0.0,-0.0,1.0,250.0,-7.973485964160614,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,100.0,0.0,-0.0,1.0,250.0,-12.344175991180135,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,100.0,0.0,-0.0,1.0,250.0,-6.271852515159728,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,100.0,0.0,-0.0,1.0,250.0,-33.9908755686652,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.96745766371015,0.0,-2499.6745766371014,0.0,100.0,0.0,-0.0,1.0,250.0,-55.435023913048866,0.0,0.0,0.0325423362898458,0.0325423362898458,0.0,-2499.6745766371014,-2499.6745766371014,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.86434547169176,0.0,-2498.6434547169174,0.0,100.0,0.0,-0.0,1.0,250.0,-55.680322947349794,0.0,0.0,0.135654528308223,0.135654528308223,0.0,-2498.6434547169174,-2498.6434547169174,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.7317076789276,0.0,-2497.317076789276,0.0,100.0,0.0,-0.0,1.0,250.0,-58.154283750735765,0.0,0.0,0.268292321072402,0.268292321072402,0.0,-2497.317076789276,-2497.317076789276,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.62790269439435,0.0,-2496.2790269439433,0.0,100.0,0.0,-0.0,1.0,250.0,-8.305620947876788,0.0,0.0,0.37209730560564,0.37209730560564,0.0,-2496.2790269439433,-2496.2790269439433,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.57180222126962,0.0,-2495.7180222126963,0.0,100.0,0.0,-0.0,1.0,250.0,-3.3298400867402322,0.0,0.0,0.428197778730383,0.428197778730383,0.0,-2495.7180222126963,-2495.7180222126963,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.58185341554267,0.0,-2495.8185341554267,0.0,100.0,0.0,-0.0,1.0,250.0,-56.1997766652032,0.0,0.0,0.418146584457336,0.418146584457336,0.0,-2495.8185341554267,-2495.8185341554267,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.612017643052,0.0,-2496.12017643052,0.0,100.0,0.0,-0.0,1.0,250.0,-14.593621213596801,0.0,0.0,0.387982356947981,0.387982356947981,0.0,-2496.12017643052,-2496.12017643052,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.6700637399973,0.0,-2496.700637399973,0.0,100.0,0.0,-0.0,1.0,250.0,-13.672469805324884,0.0,0.0,0.32993626000272,0.32993626000272,0.0,-2496.700637399973,-2496.700637399973,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.82357771248257,0.0,-2498.2357771248257,0.0,100.0,0.0,-0.0,1.0,250.0,-44.10809465315699,0.0,0.0,0.176422287517416,0.176422287517416,0.0,-2498.2357771248257,-2498.2357771248257,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,100.0,0.0,-0.0,1.0,250.0,-27.40109402216017,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,100.0,0.0,-0.0,1.0,250.0,-32.70938052475366,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,100.0,0.0,-0.0,1.0,250.0,-45.257445487966244,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,100.0,0.0,-0.0,1.0,250.0,-13.195832312150147,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,100.0,0.0,-0.0,1.0,250.0,-34.09782225735759,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,100.0,0.0,-0.0,1.0,250.0,-5.388919175187549,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,100.0,0.0,-0.0,1.0,250.0,-43.02479348517267,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 diff --git a/logs/log-2025-03-24_14-51-02.csv b/logs/log-2025-03-24_14-51-02.csv new file mode 100644 index 00000000..1906c825 --- /dev/null +++ b/logs/log-2025-03-24_14-51-02.csv @@ -0,0 +1,27 @@ +balancing,balancing,balancing,battery,battery,battery,battery,battery,node,node,node,pv_spain,pv_spain,pv_spain,pv_spain,balance,balance,balance,balance,balance,balance,balance,balance,balance,balance +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loss_load,overgeneration,reward,charge_amount,current_charge,discharge_amount,reward,soc,load_met,node_current,reward,curtailment,renewable_current,renewable_used,reward,reward,shaped_reward,overall_provided_to_microgrid,overall_absorbed_from_microgrid,flex_provided_to_microgrid,flex_absorbed_from_microgrid,controllable_provided_to_microgrid,controllable_absorbed_from_microgrid,fixed_provided_to_microgrid,fixed_absorbed_from_microgrid +200.0,0.0,-2000.0,0.0,50.0,50.0,-0.0,0.5,250.0,-32.91337944374272,0.0,0.0,0.0,0.0,0.0,-2000.0,-2000.0,250.0,250.0,200.0,0.0,50.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-39.23960507316958,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-25.53548709368051,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-59.824809923236,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-7.973485964160614,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-12.344175991180135,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-6.271852515159728,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-33.9908755686652,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.96745766371015,0.0,-2499.6745766371014,0.0,0.0,0.0,-0.0,0.0,250.0,-55.435023913048866,0.0,0.0,0.0325423362898458,0.0325423362898458,0.0,-2499.6745766371014,-2499.6745766371014,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.86434547169176,0.0,-2498.6434547169174,0.0,0.0,0.0,-0.0,0.0,250.0,-55.680322947349794,0.0,0.0,0.135654528308223,0.135654528308223,0.0,-2498.6434547169174,-2498.6434547169174,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.7317076789276,0.0,-2497.317076789276,0.0,0.0,0.0,-0.0,0.0,250.0,-58.154283750735765,0.0,0.0,0.268292321072402,0.268292321072402,0.0,-2497.317076789276,-2497.317076789276,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.62790269439435,0.0,-2496.2790269439433,0.0,0.0,0.0,-0.0,0.0,250.0,-8.305620947876788,0.0,0.0,0.37209730560564,0.37209730560564,0.0,-2496.2790269439433,-2496.2790269439433,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.57180222126962,0.0,-2495.7180222126963,0.0,0.0,0.0,-0.0,0.0,250.0,-3.3298400867402322,0.0,0.0,0.428197778730383,0.428197778730383,0.0,-2495.7180222126963,-2495.7180222126963,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.58185341554267,0.0,-2495.8185341554267,0.0,0.0,0.0,-0.0,0.0,250.0,-56.1997766652032,0.0,0.0,0.418146584457336,0.418146584457336,0.0,-2495.8185341554267,-2495.8185341554267,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.612017643052,0.0,-2496.12017643052,0.0,0.0,0.0,-0.0,0.0,250.0,-14.593621213596801,0.0,0.0,0.387982356947981,0.387982356947981,0.0,-2496.12017643052,-2496.12017643052,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.6700637399973,0.0,-2496.700637399973,0.0,0.0,0.0,-0.0,0.0,250.0,-13.672469805324884,0.0,0.0,0.32993626000272,0.32993626000272,0.0,-2496.700637399973,-2496.700637399973,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.82357771248257,0.0,-2498.2357771248257,0.0,0.0,0.0,-0.0,0.0,250.0,-44.10809465315699,0.0,0.0,0.176422287517416,0.176422287517416,0.0,-2498.2357771248257,-2498.2357771248257,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-27.40109402216017,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-32.70938052475366,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-45.257445487966244,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-13.195832312150147,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-34.09782225735759,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-5.388919175187549,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-43.02479348517267,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 diff --git a/logs/log-2025-03-25_11-15-14.csv b/logs/log-2025-03-25_11-15-14.csv new file mode 100644 index 00000000..1728488a --- /dev/null +++ b/logs/log-2025-03-25_11-15-14.csv @@ -0,0 +1,27 @@ +balancing,balancing,balancing,battery,battery,battery,battery,battery,node,node,node,pv_spain,pv_spain,pv_spain,pv_spain,balance,balance,balance,balance,balance,balance,balance,balance,balance,balance +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loss_load,overgeneration,reward,charge_amount,current_charge,discharge_amount,reward,soc,load_met,node_current,reward,curtailment,renewable_current,renewable_used,reward,reward,shaped_reward,overall_provided_to_microgrid,overall_absorbed_from_microgrid,flex_provided_to_microgrid,flex_absorbed_from_microgrid,controllable_provided_to_microgrid,controllable_absorbed_from_microgrid,fixed_provided_to_microgrid,fixed_absorbed_from_microgrid +272.7541860481051,0.0,-2727.541860481051,22.754186048105097,50.0,0.0,-0.0,0.5,250.0,-32.91337944374272,0.0,0.0,0.0,0.0,0.0,-2727.541860481051,-2727.541860481051,272.7541860481051,272.7541860481051,272.7541860481051,0.0,0.0,22.75418604810511,0.0,250.0 +228.1214311345833,0.0,-2281.2143113458333,0.0,72.7541860481051,21.878568865416696,-0.0,0.727541860481051,250.0,-39.23960507316958,0.0,0.0,0.0,0.0,0.0,-2281.2143113458333,-2281.2143113458333,250.0,250.0,228.1214311345833,0.0,21.878568865416696,0.0,0.0,250.0 +263.944736506872,0.0,-2639.44736506872,13.944736506872033,50.8756171826884,0.0,-0.0,0.508756171826884,250.0,-25.53548709368051,0.0,0.0,0.0,0.0,0.0,-2639.44736506872,-2639.44736506872,263.944736506872,263.944736506872,263.944736506872,0.0,0.0,13.944736506872005,0.0,250.0 +210.12153604016032,0.0,-2101.215360401603,0.0,64.82035368956043,39.87846395983969,-0.0,0.6482035368956044,250.0,-59.824809923236,0.0,0.0,0.0,0.0,0.0,-2101.215360401603,-2101.215360401603,250.0,250.0,210.12153604016032,0.0,39.87846395983969,0.0,0.0,250.0 +229.4079195708707,0.0,-2294.0791957087067,0.0,24.941889729720742,20.59208042912931,-0.0,0.24941889729720743,250.0,-7.973485964160614,0.0,0.0,0.0,0.0,0.0,-2294.0791957087067,-2294.0791957087067,250.0,250.0,229.4079195708707,0.0,20.59208042912931,0.0,0.0,250.0 +259.39648117727967,0.0,-2593.9648117727966,9.39648117727969,4.349809300591431,0.0,-0.0,0.04349809300591431,250.0,-12.344175991180135,0.0,0.0,0.0,0.0,0.0,-2593.9648117727966,-2593.9648117727966,259.39648117727967,259.39648117727967,259.39648117727967,0.0,0.0,9.396481177279668,0.0,250.0 +270.41417263790805,0.0,-2704.1417263790804,20.414172637908052,13.74629047787112,0.0,-0.0,0.1374629047787112,250.0,-6.271852515159728,0.0,0.0,0.0,0.0,0.0,-2704.1417263790804,-2704.1417263790804,270.41417263790805,270.41417263790805,270.41417263790805,0.0,0.0,20.414172637908052,0.0,250.0 +235.014844713354,0.0,-2350.14844713354,0.0,34.16046311577917,14.985155286646005,-0.0,0.3416046311577917,250.0,-33.9908755686652,0.0,0.0,0.0,0.0,0.0,-2350.14844713354,-2350.14844713354,250.0,250.0,235.014844713354,0.0,14.985155286646005,0.0,0.0,250.0 +248.3948234125293,0.0,-2483.948234125293,0.0,19.175307829133168,1.572634251180851,-0.0,0.19175307829133167,250.0,-55.435023913048866,0.0,0.0,0.0325423362898458,0.0325423362898458,0.0,-2483.948234125293,-2483.948234125293,250.0,250.0,248.42736574881914,0.0,1.572634251180851,0.0,0.0,250.0 +247.74248814974362,0.0,-2477.424881497436,0.0,17.602673577952316,2.121857321948127,-0.0,0.17602673577952316,250.0,-55.680322947349794,0.0,0.0,0.135654528308223,0.135654528308223,0.0,-2477.424881497436,-2477.424881497436,249.99999999999997,250.0,247.87814267805186,0.0,2.121857321948127,0.0,0.0,250.0 +251.47673931628913,0.0,-2514.7673931628915,1.745031637361535,15.48081625600419,0.0,-0.0,0.1548081625600419,250.0,-58.154283750735765,0.0,0.0,0.268292321072402,0.268292321072402,0.0,-2514.7673931628915,-2514.7673931628915,251.74503163736154,251.74503163736154,251.74503163736154,0.0,0.0,1.745031637361535,0.0,250.0 +294.3592317844217,0.0,-2943.592317844217,44.73132909002734,17.225847893365724,0.0,-0.0,0.17225847893365726,250.0,-8.305620947876788,0.0,0.0,0.37209730560564,0.37209730560564,0.0,-2943.592317844217,-2943.592317844217,294.7313290900273,294.7313290900273,294.7313290900273,0.0,0.0,44.731329090027316,0.0,250.0 +269.9147516832149,0.0,-2699.147516832149,20.342949461945288,61.95717698339306,0.0,-0.0,0.6195717698339306,250.0,-3.3298400867402322,0.0,0.0,0.428197778730383,0.428197778730383,0.0,-2699.147516832149,-2699.147516832149,270.3429494619453,270.3429494619453,270.3429494619453,0.0,0.0,20.342949461945295,0.0,250.0 +213.32065199637393,0.0,-2133.206519963739,0.0,82.30012644533835,36.26120141916873,-0.0,0.8230012644533835,250.0,-56.1997766652032,0.0,0.0,0.418146584457336,0.418146584457336,0.0,-2133.206519963739,-2133.206519963739,250.0,250.0,213.73879858083126,0.0,36.26120141916873,0.0,0.0,250.0 +218.51155944277593,0.0,-2185.1155944277593,0.0,46.03892502616962,31.100458200276066,-0.0,0.4603892502616962,250.0,-14.593621213596801,0.0,0.0,0.387982356947981,0.387982356947981,0.0,-2185.1155944277593,-2185.1155944277593,249.99999999999997,250.0,218.89954179972392,0.0,31.100458200276066,0.0,0.0,250.0 +282.93872316405947,0.0,-2829.3872316405946,33.26865942406219,14.938466825893556,0.0,-0.0,0.14938466825893557,250.0,-13.672469805324884,0.0,0.0,0.32993626000272,0.32993626000272,0.0,-2829.3872316405946,-2829.3872316405946,283.2686594240622,283.2686594240622,283.2686594240622,0.0,0.0,33.26865942406221,0.0,250.0 +211.51157716304448,0.0,-2115.1157716304447,0.0,48.20712624995575,38.31200054943811,-0.0,0.4820712624995575,250.0,-44.10809465315699,0.0,0.0,0.176422287517416,0.176422287517416,0.0,-2115.1157716304447,-2115.1157716304447,250.0,250.0,211.6879994505619,0.0,38.31200054943811,0.0,0.0,250.0 +292.1507878343237,0.0,-2921.507878343237,42.150787834323694,9.895125700517639,0.0,-0.0,0.09895125700517639,250.0,-27.40109402216017,0.0,0.0,0.0,0.0,0.0,-2921.507878343237,-2921.507878343237,292.1507878343237,292.1507878343237,292.1507878343237,0.0,0.0,42.150787834323694,0.0,250.0 +240.5092011810477,0.0,-2405.092011810477,0.0,52.04591353484133,9.490798818952292,-0.0,0.5204591353484134,250.0,-32.70938052475366,0.0,0.0,0.0,0.0,0.0,-2405.092011810477,-2405.092011810477,250.0,250.0,240.5092011810477,0.0,9.490798818952292,0.0,0.0,250.0 +269.33543959543266,0.0,-2693.3543959543267,19.335439595432657,42.55511471588904,0.0,-0.0,0.4255511471588904,250.0,-45.257445487966244,0.0,0.0,0.0,0.0,0.0,-2693.3543959543267,-2693.3543959543267,269.33543959543266,269.33543959543266,269.33543959543266,0.0,0.0,19.33543959543266,0.0,250.0 +218.662750220509,0.0,-2186.62750220509,0.0,61.890554311321694,31.337249779491017,-0.0,0.6189055431132169,250.0,-13.195832312150147,0.0,0.0,0.0,0.0,0.0,-2186.62750220509,-2186.62750220509,250.0,250.0,218.662750220509,0.0,31.337249779491017,0.0,0.0,250.0 +295.94492345345463,0.0,-2959.449234534546,45.94492345345464,30.553304531830676,0.0,-0.0,0.3055330453183068,250.0,-34.09782225735759,0.0,0.0,0.0,0.0,0.0,-2959.449234534546,-2959.449234534546,295.94492345345463,295.94492345345463,295.94492345345463,0.0,0.0,45.944923453454635,0.0,250.0 +268.8551933434315,0.0,-2688.551933434315,18.855193343431516,76.49822798528533,0.0,-0.0,0.7649822798528533,250.0,-5.388919175187549,0.0,0.0,0.0,0.0,0.0,-2688.551933434315,-2688.551933434315,268.8551933434315,268.8551933434315,268.8551933434315,0.0,0.0,18.855193343431495,0.0,250.0 +224.40174281201172,0.0,-2244.017428120117,0.0,95.35342132871685,25.598257187988295,-0.0,0.9535342132871685,250.0,-43.02479348517267,0.0,0.0,0.0,0.0,0.0,-2244.017428120117,-2244.017428120117,250.0,250.0,224.40174281201172,0.0,25.598257187988295,0.0,0.0,250.0 diff --git a/logs/log-2025-03-25_11-15-40.csv b/logs/log-2025-03-25_11-15-40.csv new file mode 100644 index 00000000..1906c825 --- /dev/null +++ b/logs/log-2025-03-25_11-15-40.csv @@ -0,0 +1,27 @@ +balancing,balancing,balancing,battery,battery,battery,battery,battery,node,node,node,pv_spain,pv_spain,pv_spain,pv_spain,balance,balance,balance,balance,balance,balance,balance,balance,balance,balance +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loss_load,overgeneration,reward,charge_amount,current_charge,discharge_amount,reward,soc,load_met,node_current,reward,curtailment,renewable_current,renewable_used,reward,reward,shaped_reward,overall_provided_to_microgrid,overall_absorbed_from_microgrid,flex_provided_to_microgrid,flex_absorbed_from_microgrid,controllable_provided_to_microgrid,controllable_absorbed_from_microgrid,fixed_provided_to_microgrid,fixed_absorbed_from_microgrid +200.0,0.0,-2000.0,0.0,50.0,50.0,-0.0,0.5,250.0,-32.91337944374272,0.0,0.0,0.0,0.0,0.0,-2000.0,-2000.0,250.0,250.0,200.0,0.0,50.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-39.23960507316958,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-25.53548709368051,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-59.824809923236,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-7.973485964160614,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-12.344175991180135,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-6.271852515159728,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-33.9908755686652,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.96745766371015,0.0,-2499.6745766371014,0.0,0.0,0.0,-0.0,0.0,250.0,-55.435023913048866,0.0,0.0,0.0325423362898458,0.0325423362898458,0.0,-2499.6745766371014,-2499.6745766371014,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.86434547169176,0.0,-2498.6434547169174,0.0,0.0,0.0,-0.0,0.0,250.0,-55.680322947349794,0.0,0.0,0.135654528308223,0.135654528308223,0.0,-2498.6434547169174,-2498.6434547169174,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.7317076789276,0.0,-2497.317076789276,0.0,0.0,0.0,-0.0,0.0,250.0,-58.154283750735765,0.0,0.0,0.268292321072402,0.268292321072402,0.0,-2497.317076789276,-2497.317076789276,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.62790269439435,0.0,-2496.2790269439433,0.0,0.0,0.0,-0.0,0.0,250.0,-8.305620947876788,0.0,0.0,0.37209730560564,0.37209730560564,0.0,-2496.2790269439433,-2496.2790269439433,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.57180222126962,0.0,-2495.7180222126963,0.0,0.0,0.0,-0.0,0.0,250.0,-3.3298400867402322,0.0,0.0,0.428197778730383,0.428197778730383,0.0,-2495.7180222126963,-2495.7180222126963,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.58185341554267,0.0,-2495.8185341554267,0.0,0.0,0.0,-0.0,0.0,250.0,-56.1997766652032,0.0,0.0,0.418146584457336,0.418146584457336,0.0,-2495.8185341554267,-2495.8185341554267,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.612017643052,0.0,-2496.12017643052,0.0,0.0,0.0,-0.0,0.0,250.0,-14.593621213596801,0.0,0.0,0.387982356947981,0.387982356947981,0.0,-2496.12017643052,-2496.12017643052,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.6700637399973,0.0,-2496.700637399973,0.0,0.0,0.0,-0.0,0.0,250.0,-13.672469805324884,0.0,0.0,0.32993626000272,0.32993626000272,0.0,-2496.700637399973,-2496.700637399973,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +249.82357771248257,0.0,-2498.2357771248257,0.0,0.0,0.0,-0.0,0.0,250.0,-44.10809465315699,0.0,0.0,0.176422287517416,0.176422287517416,0.0,-2498.2357771248257,-2498.2357771248257,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-27.40109402216017,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-32.70938052475366,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-45.257445487966244,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-13.195832312150147,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-34.09782225735759,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-5.388919175187549,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 +250.0,0.0,-2500.0,0.0,0.0,0.0,-0.0,0.0,250.0,-43.02479348517267,0.0,0.0,0.0,0.0,0.0,-2500.0,-2500.0,250.0,250.0,250.0,0.0,0.0,0.0,0.0,250.0 diff --git a/logs/log-2025-03-25_11-16-23.csv b/logs/log-2025-03-25_11-16-23.csv new file mode 100644 index 00000000..eea6ddd5 --- /dev/null +++ b/logs/log-2025-03-25_11-16-23.csv @@ -0,0 +1,27 @@ +balancing,balancing,balancing,battery,battery,battery,battery,battery,node,node,node,pv_spain,pv_spain,pv_spain,pv_spain,balance,balance,balance,balance,balance,balance,balance,balance,balance,balance +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loss_load,overgeneration,reward,charge_amount,current_charge,discharge_amount,reward,soc,load_met,node_current,reward,curtailment,renewable_current,renewable_used,reward,reward,shaped_reward,overall_provided_to_microgrid,overall_absorbed_from_microgrid,flex_provided_to_microgrid,flex_absorbed_from_microgrid,controllable_provided_to_microgrid,controllable_absorbed_from_microgrid,fixed_provided_to_microgrid,fixed_absorbed_from_microgrid +0.0,25.0,-50.0,0.0,50.0,50.0,-0.0,0.5,25.0,-32.91337944374272,0.0,0.0,0.0,0.0,0.0,-50.0,-50.0,50.0,50.0,0.0,25.0,50.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-39.23960507316958,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-25.53548709368051,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-59.824809923236,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-7.973485964160614,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-12.344175991180135,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-6.271852515159728,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-33.9908755686652,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.967457663710153,0.0,-249.67457663710152,0.0,0.0,0.0,-0.0,0.0,25.0,-55.435023913048866,0.0,0.0,0.0325423362898458,0.0325423362898458,0.0,-249.67457663710152,-249.67457663710152,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.864345471691777,0.0,-248.64345471691777,0.0,0.0,0.0,-0.0,0.0,25.0,-55.680322947349794,0.0,0.0,0.135654528308223,0.135654528308223,0.0,-248.64345471691777,-248.64345471691777,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.7317076789276,0.0,-247.31707678927597,0.0,0.0,0.0,-0.0,0.0,25.0,-58.154283750735765,0.0,0.0,0.268292321072402,0.268292321072402,0.0,-247.31707678927597,-247.31707678927597,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.62790269439436,0.0,-246.27902694394362,0.0,0.0,0.0,-0.0,0.0,25.0,-8.305620947876788,0.0,0.0,0.37209730560564,0.37209730560564,0.0,-246.27902694394362,-246.27902694394362,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.571802221269618,0.0,-245.71802221269618,0.0,0.0,0.0,-0.0,0.0,25.0,-3.3298400867402322,0.0,0.0,0.428197778730383,0.428197778730383,0.0,-245.71802221269618,-245.71802221269618,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.581853415542664,0.0,-245.81853415542665,0.0,0.0,0.0,-0.0,0.0,25.0,-56.1997766652032,0.0,0.0,0.418146584457336,0.418146584457336,0.0,-245.81853415542665,-245.81853415542665,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.61201764305202,0.0,-246.1201764305202,0.0,0.0,0.0,-0.0,0.0,25.0,-14.593621213596801,0.0,0.0,0.387982356947981,0.387982356947981,0.0,-246.1201764305202,-246.1201764305202,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.67006373999728,0.0,-246.70063739997278,0.0,0.0,0.0,-0.0,0.0,25.0,-13.672469805324884,0.0,0.0,0.32993626000272,0.32993626000272,0.0,-246.70063739997278,-246.70063739997278,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +24.823577712482585,0.0,-248.23577712482586,0.0,0.0,0.0,-0.0,0.0,25.0,-44.10809465315699,0.0,0.0,0.176422287517416,0.176422287517416,0.0,-248.23577712482586,-248.23577712482586,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-27.40109402216017,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-32.70938052475366,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-45.257445487966244,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-13.195832312150147,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-34.09782225735759,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-5.388919175187549,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 +25.0,0.0,-250.0,0.0,0.0,0.0,-0.0,0.0,25.0,-43.02479348517267,0.0,0.0,0.0,0.0,0.0,-250.0,-250.0,25.0,25.0,25.0,0.0,0.0,0.0,0.0,25.0 diff --git a/logs/log-2025-03-25_11-17-12.csv b/logs/log-2025-03-25_11-17-12.csv new file mode 100644 index 00000000..3bd9da77 --- /dev/null +++ b/logs/log-2025-03-25_11-17-12.csv @@ -0,0 +1,27 @@ +balancing,balancing,balancing,battery,battery,battery,battery,battery,node,node,node,pv_spain,pv_spain,pv_spain,pv_spain,balance,balance,balance,balance,balance,balance,balance,balance,balance,balance +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loss_load,overgeneration,reward,charge_amount,current_charge,discharge_amount,reward,soc,load_met,node_current,reward,curtailment,renewable_current,renewable_used,reward,reward,shaped_reward,overall_provided_to_microgrid,overall_absorbed_from_microgrid,flex_provided_to_microgrid,flex_absorbed_from_microgrid,controllable_provided_to_microgrid,controllable_absorbed_from_microgrid,fixed_provided_to_microgrid,fixed_absorbed_from_microgrid +0.0,47.5,-95.0,0.0,50.0,50.0,-0.0,0.5,2.5,-32.91337944374272,0.0,0.0,0.0,0.0,0.0,-95.0,-95.0,50.0,50.0,0.0,47.5,50.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-39.23960507316958,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-25.53548709368051,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-59.824809923236,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-7.973485964160614,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-12.344175991180135,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-6.271852515159728,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-33.9908755686652,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.4674576637101544,0.0,-24.674576637101545,0.0,0.0,0.0,-0.0,0.0,2.5,-55.435023913048866,0.0,0.0,0.0325423362898458,0.0325423362898458,0.0,-24.674576637101545,-24.674576637101545,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.364345471691777,0.0,-23.64345471691777,0.0,0.0,0.0,-0.0,0.0,2.5,-55.680322947349794,0.0,0.0,0.135654528308223,0.135654528308223,0.0,-23.64345471691777,-23.64345471691777,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.231707678927598,0.0,-22.31707678927598,0.0,0.0,0.0,-0.0,0.0,2.5,-58.154283750735765,0.0,0.0,0.268292321072402,0.268292321072402,0.0,-22.31707678927598,-22.31707678927598,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.12790269439436,0.0,-21.2790269439436,0.0,0.0,0.0,-0.0,0.0,2.5,-8.305620947876788,0.0,0.0,0.37209730560564,0.37209730560564,0.0,-21.2790269439436,-21.2790269439436,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.0718022212696168,0.0,-20.71802221269617,0.0,0.0,0.0,-0.0,0.0,2.5,-3.3298400867402322,0.0,0.0,0.428197778730383,0.428197778730383,0.0,-20.71802221269617,-20.71802221269617,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.081853415542664,0.0,-20.81853415542664,0.0,0.0,0.0,-0.0,0.0,2.5,-56.1997766652032,0.0,0.0,0.418146584457336,0.418146584457336,0.0,-20.81853415542664,-20.81853415542664,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.112017643052019,0.0,-21.12017643052019,0.0,0.0,0.0,-0.0,0.0,2.5,-14.593621213596801,0.0,0.0,0.387982356947981,0.387982356947981,0.0,-21.12017643052019,-21.12017643052019,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.17006373999728,0.0,-21.700637399972802,0.0,0.0,0.0,-0.0,0.0,2.5,-13.672469805324884,0.0,0.0,0.32993626000272,0.32993626000272,0.0,-21.700637399972802,-21.700637399972802,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.323577712482584,0.0,-23.235777124825837,0.0,0.0,0.0,-0.0,0.0,2.5,-44.10809465315699,0.0,0.0,0.176422287517416,0.176422287517416,0.0,-23.235777124825837,-23.235777124825837,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-27.40109402216017,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-32.70938052475366,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-45.257445487966244,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-13.195832312150147,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-34.09782225735759,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-5.388919175187549,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-43.02479348517267,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 diff --git a/logs/log-2025-03-25_11-18-19.csv b/logs/log-2025-03-25_11-18-19.csv new file mode 100644 index 00000000..33cf1067 --- /dev/null +++ b/logs/log-2025-03-25_11-18-19.csv @@ -0,0 +1,27 @@ +balancing,balancing,balancing,battery,battery,battery,battery,battery,node,node,node,pv_spain,pv_spain,pv_spain,pv_spain,balance,balance,balance,balance,balance,balance,balance,balance,balance,balance +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loss_load,overgeneration,reward,charge_amount,current_charge,discharge_amount,reward,soc,load_met,node_current,reward,curtailment,renewable_current,renewable_used,reward,reward,shaped_reward,overall_provided_to_microgrid,overall_absorbed_from_microgrid,flex_provided_to_microgrid,flex_absorbed_from_microgrid,controllable_provided_to_microgrid,controllable_absorbed_from_microgrid,fixed_provided_to_microgrid,fixed_absorbed_from_microgrid +0.0,47.5,-95.0,0.0,50.0,50.0,-0.0,0.5,2.5,-32.91337944374272,0.0,0.0,0.0,0.0,0.0,-95.0,-95.0,50.0,50.0,0.0,47.5,50.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-39.23960507316958,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-25.53548709368051,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-59.824809923236,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-7.973485964160614,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-12.344175991180135,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-6.271852515159728,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-33.9908755686652,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.4674576637101544,0.0,-24.674576637101545,0.0,0.0,0.0,-0.0,0.0,2.5,-55.435023913048866,0.0,0.0,0.0325423362898458,0.0325423362898458,0.0,-24.674576637101545,-24.674576637101545,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.364345471691777,0.0,-23.64345471691777,0.0,0.0,0.0,-0.0,0.0,2.5,-55.680322947349794,0.0,0.0,0.135654528308223,0.135654528308223,0.0,-23.64345471691777,-23.64345471691777,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +52.2317076789276,0.0,-522.317076789276,50.0,0.0,0.0,-0.0,0.0,2.5,-58.154283750735765,0.0,0.0,0.268292321072402,0.268292321072402,0.0,-522.317076789276,-522.317076789276,52.5,52.5,52.5,0.0,0.0,50.0,0.0,2.5 +52.12790269439436,0.0,-521.2790269439436,50.0,50.0,0.0,-0.0,0.5,2.5,-8.305620947876788,0.0,0.0,0.37209730560564,0.37209730560564,0.0,-521.2790269439436,-521.2790269439436,52.5,52.5,52.5,0.0,0.0,50.0,0.0,2.5 +2.0718022212696168,0.0,-20.71802221269617,0.0,100.0,0.0,-0.0,1.0,2.5,-3.3298400867402322,0.0,0.0,0.428197778730383,0.428197778730383,0.0,-20.71802221269617,-20.71802221269617,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.081853415542664,0.0,-20.81853415542664,0.0,100.0,0.0,-0.0,1.0,2.5,-56.1997766652032,0.0,0.0,0.418146584457336,0.418146584457336,0.0,-20.81853415542664,-20.81853415542664,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.112017643052019,0.0,-21.12017643052019,0.0,100.0,0.0,-0.0,1.0,2.5,-14.593621213596801,0.0,0.0,0.387982356947981,0.387982356947981,0.0,-21.12017643052019,-21.12017643052019,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.17006373999728,0.0,-21.700637399972802,0.0,100.0,0.0,-0.0,1.0,2.5,-13.672469805324884,0.0,0.0,0.32993626000272,0.32993626000272,0.0,-21.700637399972802,-21.700637399972802,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +0.0,21.077712482584005,-42.15542496516801,0.0,100.0,23.577712482584005,-0.0,1.0,2.5,-44.10809465315699,0.0,0.176422287517416,0.176422287517416,0.0,0.0,-42.15542496516801,-42.15542496516801,23.577712482584005,23.577712482584005,0.0,21.077712482584005,23.577712482584005,0.0,0.0,2.5 +0.0,47.5,-95.0,0.0,76.422287517416,50.0,-0.0,0.76422287517416,2.5,-27.40109402216017,0.0,0.0,0.0,0.0,0.0,-95.0,-95.0,50.0,50.0,0.0,47.5,50.0,0.0,0.0,2.5 +0.0,23.922287517415995,-47.84457503483199,0.0,26.422287517415995,26.422287517415995,-0.0,0.26422287517415993,2.5,-32.70938052475366,0.0,0.0,0.0,0.0,0.0,-47.84457503483199,-47.84457503483199,26.422287517415995,26.422287517415995,0.0,23.922287517415995,26.422287517415995,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-45.257445487966244,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-13.195832312150147,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-34.09782225735759,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-5.388919175187549,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 +2.5,0.0,-25.0,0.0,0.0,0.0,-0.0,0.0,2.5,-43.02479348517267,0.0,0.0,0.0,0.0,0.0,-25.0,-25.0,2.5,2.5,2.5,0.0,0.0,0.0,0.0,2.5 diff --git a/logs/log-2025-03-25_14-52-13.csv b/logs/log-2025-03-25_14-52-13.csv new file mode 100644 index 00000000..a050223d --- /dev/null +++ b/logs/log-2025-03-25_14-52-13.csv @@ -0,0 +1,27 @@ +balancing,balancing,balancing,battery,battery,battery,battery,battery,node,node,node,pv_spain,pv_spain,pv_spain,pv_spain,balance,balance,balance,balance,balance,balance,balance,balance,balance,balance +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loss_load,overgeneration,reward,charge_amount,current_charge,discharge_amount,reward,soc,load_met,node_current,reward,curtailment,renewable_current,renewable_used,reward,reward,shaped_reward,overall_provided_to_microgrid,overall_absorbed_from_microgrid,flex_provided_to_microgrid,flex_absorbed_from_microgrid,controllable_provided_to_microgrid,controllable_absorbed_from_microgrid,fixed_provided_to_microgrid,fixed_absorbed_from_microgrid +0.0,42.5,-85.0,0.0,50.0,50.0,-0.0,0.5,7.5,-41.7881511358717,0.0,0.0,0.0,0.0,0.0,-85.0,-85.0,50.0,50.0,0.0,42.5,50.0,0.0,0.0,7.5 +7.5,0.0,-75.0,0.0,0.0,0.0,-0.0,0.0,7.5,-17.168360097022767,0.0,0.0,0.0,0.0,0.0,-75.0,-75.0,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.5,0.0,-75.0,0.0,0.0,0.0,-0.0,0.0,7.5,-13.611087213852187,0.0,0.0,0.0,0.0,0.0,-75.0,-75.0,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.5,0.0,-75.0,0.0,0.0,0.0,-0.0,0.0,7.5,-33.078886144973474,0.0,0.0,0.0,0.0,0.0,-75.0,-75.0,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.5,0.0,-75.0,0.0,0.0,0.0,-0.0,0.0,7.5,-43.168138187133785,0.0,0.0,0.0,0.0,0.0,-75.0,-75.0,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.5,0.0,-75.0,0.0,0.0,0.0,-0.0,0.0,7.5,-25.386387607467658,0.0,0.0,0.0,0.0,0.0,-75.0,-75.0,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.5,0.0,-75.0,0.0,0.0,0.0,-0.0,0.0,7.5,-58.84585190307693,0.0,0.0,0.0,0.0,0.0,-75.0,-75.0,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.5,0.0,-75.0,0.0,0.0,0.0,-0.0,0.0,7.5,-41.0897843150918,0.0,0.0,0.0,0.0,0.0,-75.0,-75.0,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.467457663710154,0.0,-74.67457663710154,0.0,0.0,0.0,-0.0,0.0,7.5,-28.855914089061656,0.0,0.0,0.0325423362898458,0.0325423362898458,0.0,-74.67457663710154,-74.67457663710154,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.364345471691777,0.0,-73.64345471691777,0.0,0.0,0.0,-0.0,0.0,7.5,-23.52705109164903,0.0,0.0,0.135654528308223,0.135654528308223,0.0,-73.64345471691777,-73.64345471691777,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.231707678927598,0.0,-72.31707678927597,0.0,0.0,0.0,-0.0,0.0,7.5,-20.590680969052165,0.0,0.0,0.268292321072402,0.268292321072402,0.0,-72.31707678927597,-72.31707678927597,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.1279026943943595,0.0,-71.27902694394359,0.0,0.0,0.0,-0.0,0.0,7.5,-43.7429824430425,0.0,0.0,0.37209730560564,0.37209730560564,0.0,-71.27902694394359,-71.27902694394359,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.071802221269617,0.0,-70.71802221269617,0.0,0.0,0.0,-0.0,0.0,7.5,-26.314334680777463,0.0,0.0,0.428197778730383,0.428197778730383,0.0,-70.71802221269617,-70.71802221269617,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.081853415542664,0.0,-70.81853415542665,0.0,0.0,0.0,-0.0,0.0,7.5,-3.580673796574101,0.0,0.0,0.418146584457336,0.418146584457336,0.0,-70.81853415542665,-70.81853415542665,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.112017643052019,0.0,-71.12017643052019,0.0,0.0,0.0,-0.0,0.0,7.5,-23.882655319825886,0.0,0.0,0.387982356947981,0.387982356947981,0.0,-71.12017643052019,-71.12017643052019,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.17006373999728,0.0,-71.7006373999728,0.0,0.0,0.0,-0.0,0.0,7.5,-44.27972434392214,0.0,0.0,0.32993626000272,0.32993626000272,0.0,-71.7006373999728,-71.7006373999728,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.323577712482584,0.0,-73.23577712482584,0.0,0.0,0.0,-0.0,0.0,7.5,-10.949503827209998,0.0,0.0,0.176422287517416,0.176422287517416,0.0,-73.23577712482584,-73.23577712482584,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.5,0.0,-75.0,0.0,0.0,0.0,-0.0,0.0,7.5,-10.527105368849552,0.0,0.0,0.0,0.0,0.0,-75.0,-75.0,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.5,0.0,-75.0,0.0,0.0,0.0,-0.0,0.0,7.5,-31.8930824305103,0.0,0.0,0.0,0.0,0.0,-75.0,-75.0,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.5,0.0,-75.0,0.0,0.0,0.0,-0.0,0.0,7.5,-31.909655225811964,0.0,0.0,0.0,0.0,0.0,-75.0,-75.0,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.5,0.0,-75.0,0.0,0.0,0.0,-0.0,0.0,7.5,-38.06405751307926,0.0,0.0,0.0,0.0,0.0,-75.0,-75.0,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.5,0.0,-75.0,0.0,0.0,0.0,-0.0,0.0,7.5,-50.965907644667375,0.0,0.0,0.0,0.0,0.0,-75.0,-75.0,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.5,0.0,-75.0,0.0,0.0,0.0,-0.0,0.0,7.5,-43.467319491638115,0.0,0.0,0.0,0.0,0.0,-75.0,-75.0,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 +7.5,0.0,-75.0,0.0,0.0,0.0,-0.0,0.0,7.5,-36.66141064065497,0.0,0.0,0.0,0.0,0.0,-75.0,-75.0,7.5,7.5,7.5,0.0,0.0,0.0,0.0,7.5 diff --git a/logs/log-2025-03-27_09-47-31.csv b/logs/log-2025-03-27_09-47-31.csv new file mode 100644 index 00000000..6c1a6fde --- /dev/null +++ b/logs/log-2025-03-27_09-47-31.csv @@ -0,0 +1,27 @@ +balancing,balancing,balancing,battery,battery,battery,battery,battery,node,node,node,pv_spain,pv_spain,pv_spain,pv_spain,balance,balance,balance,balance,balance,balance,balance,balance,balance,balance +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loss_load,overgeneration,reward,charge_amount,current_charge,discharge_amount,reward,soc,load_met,node_current,reward,curtailment,renewable_current,renewable_used,reward,reward,shaped_reward,overall_provided_to_microgrid,overall_absorbed_from_microgrid,flex_provided_to_microgrid,flex_absorbed_from_microgrid,controllable_provided_to_microgrid,controllable_absorbed_from_microgrid,fixed_provided_to_microgrid,fixed_absorbed_from_microgrid +0.0,35.0,-70.0,0.0,50.0,50.0,-0.0,0.5,15.0,-41.7881511358717,0.0,0.0,0.0,0.0,0.0,-70.0,-70.0,50.0,50.0,0.0,35.0,50.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-17.168360097022767,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-13.611087213852187,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-33.078886144973474,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-43.168138187133785,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-25.386387607467658,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-58.84585190307693,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-41.0897843150918,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.967457663710155,0.0,-149.67457663710155,0.0,0.0,0.0,-0.0,0.0,15.0,-28.855914089061656,0.0,0.0,0.0325423362898458,0.0325423362898458,0.0,-149.67457663710155,-149.67457663710155,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.864345471691777,0.0,-148.64345471691777,0.0,0.0,0.0,-0.0,0.0,15.0,-23.52705109164903,0.0,0.0,0.135654528308223,0.135654528308223,0.0,-148.64345471691777,-148.64345471691777,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.731707678927599,0.0,-147.31707678927597,0.0,0.0,0.0,-0.0,0.0,15.0,-20.590680969052165,0.0,0.0,0.268292321072402,0.268292321072402,0.0,-147.31707678927597,-147.31707678927597,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.62790269439436,0.0,-146.2790269439436,0.0,0.0,0.0,-0.0,0.0,15.0,-43.7429824430425,0.0,0.0,0.37209730560564,0.37209730560564,0.0,-146.2790269439436,-146.2790269439436,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.571802221269618,0.0,-145.71802221269618,0.0,0.0,0.0,-0.0,0.0,15.0,-26.314334680777463,0.0,0.0,0.428197778730383,0.428197778730383,0.0,-145.71802221269618,-145.71802221269618,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.581853415542664,0.0,-145.81853415542665,0.0,0.0,0.0,-0.0,0.0,15.0,-3.580673796574101,0.0,0.0,0.418146584457336,0.418146584457336,0.0,-145.81853415542665,-145.81853415542665,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.612017643052019,0.0,-146.1201764305202,0.0,0.0,0.0,-0.0,0.0,15.0,-23.882655319825886,0.0,0.0,0.387982356947981,0.387982356947981,0.0,-146.1201764305202,-146.1201764305202,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.67006373999728,0.0,-146.7006373999728,0.0,0.0,0.0,-0.0,0.0,15.0,-44.27972434392214,0.0,0.0,0.32993626000272,0.32993626000272,0.0,-146.7006373999728,-146.7006373999728,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.823577712482583,0.0,-148.23577712482583,0.0,0.0,0.0,-0.0,0.0,15.0,-10.949503827209998,0.0,0.0,0.176422287517416,0.176422287517416,0.0,-148.23577712482583,-148.23577712482583,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-10.527105368849552,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-31.8930824305103,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-31.909655225811964,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-38.06405751307926,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-50.965907644667375,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-43.467319491638115,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-36.66141064065497,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 diff --git a/logs/log-2025-03-27_09-50-54.csv b/logs/log-2025-03-27_09-50-54.csv new file mode 100644 index 00000000..6c1a6fde --- /dev/null +++ b/logs/log-2025-03-27_09-50-54.csv @@ -0,0 +1,27 @@ +balancing,balancing,balancing,battery,battery,battery,battery,battery,node,node,node,pv_spain,pv_spain,pv_spain,pv_spain,balance,balance,balance,balance,balance,balance,balance,balance,balance,balance +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loss_load,overgeneration,reward,charge_amount,current_charge,discharge_amount,reward,soc,load_met,node_current,reward,curtailment,renewable_current,renewable_used,reward,reward,shaped_reward,overall_provided_to_microgrid,overall_absorbed_from_microgrid,flex_provided_to_microgrid,flex_absorbed_from_microgrid,controllable_provided_to_microgrid,controllable_absorbed_from_microgrid,fixed_provided_to_microgrid,fixed_absorbed_from_microgrid +0.0,35.0,-70.0,0.0,50.0,50.0,-0.0,0.5,15.0,-41.7881511358717,0.0,0.0,0.0,0.0,0.0,-70.0,-70.0,50.0,50.0,0.0,35.0,50.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-17.168360097022767,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-13.611087213852187,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-33.078886144973474,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-43.168138187133785,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-25.386387607467658,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-58.84585190307693,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-41.0897843150918,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.967457663710155,0.0,-149.67457663710155,0.0,0.0,0.0,-0.0,0.0,15.0,-28.855914089061656,0.0,0.0,0.0325423362898458,0.0325423362898458,0.0,-149.67457663710155,-149.67457663710155,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.864345471691777,0.0,-148.64345471691777,0.0,0.0,0.0,-0.0,0.0,15.0,-23.52705109164903,0.0,0.0,0.135654528308223,0.135654528308223,0.0,-148.64345471691777,-148.64345471691777,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.731707678927599,0.0,-147.31707678927597,0.0,0.0,0.0,-0.0,0.0,15.0,-20.590680969052165,0.0,0.0,0.268292321072402,0.268292321072402,0.0,-147.31707678927597,-147.31707678927597,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.62790269439436,0.0,-146.2790269439436,0.0,0.0,0.0,-0.0,0.0,15.0,-43.7429824430425,0.0,0.0,0.37209730560564,0.37209730560564,0.0,-146.2790269439436,-146.2790269439436,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.571802221269618,0.0,-145.71802221269618,0.0,0.0,0.0,-0.0,0.0,15.0,-26.314334680777463,0.0,0.0,0.428197778730383,0.428197778730383,0.0,-145.71802221269618,-145.71802221269618,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.581853415542664,0.0,-145.81853415542665,0.0,0.0,0.0,-0.0,0.0,15.0,-3.580673796574101,0.0,0.0,0.418146584457336,0.418146584457336,0.0,-145.81853415542665,-145.81853415542665,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.612017643052019,0.0,-146.1201764305202,0.0,0.0,0.0,-0.0,0.0,15.0,-23.882655319825886,0.0,0.0,0.387982356947981,0.387982356947981,0.0,-146.1201764305202,-146.1201764305202,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.67006373999728,0.0,-146.7006373999728,0.0,0.0,0.0,-0.0,0.0,15.0,-44.27972434392214,0.0,0.0,0.32993626000272,0.32993626000272,0.0,-146.7006373999728,-146.7006373999728,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.823577712482583,0.0,-148.23577712482583,0.0,0.0,0.0,-0.0,0.0,15.0,-10.949503827209998,0.0,0.0,0.176422287517416,0.176422287517416,0.0,-148.23577712482583,-148.23577712482583,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-10.527105368849552,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-31.8930824305103,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-31.909655225811964,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-38.06405751307926,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-50.965907644667375,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-43.467319491638115,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-36.66141064065497,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 diff --git a/logs/log-2025-03-27_09-59-17.csv b/logs/log-2025-03-27_09-59-17.csv new file mode 100644 index 00000000..6c1a6fde --- /dev/null +++ b/logs/log-2025-03-27_09-59-17.csv @@ -0,0 +1,27 @@ +balancing,balancing,balancing,battery,battery,battery,battery,battery,node,node,node,pv_spain,pv_spain,pv_spain,pv_spain,balance,balance,balance,balance,balance,balance,balance,balance,balance,balance +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loss_load,overgeneration,reward,charge_amount,current_charge,discharge_amount,reward,soc,load_met,node_current,reward,curtailment,renewable_current,renewable_used,reward,reward,shaped_reward,overall_provided_to_microgrid,overall_absorbed_from_microgrid,flex_provided_to_microgrid,flex_absorbed_from_microgrid,controllable_provided_to_microgrid,controllable_absorbed_from_microgrid,fixed_provided_to_microgrid,fixed_absorbed_from_microgrid +0.0,35.0,-70.0,0.0,50.0,50.0,-0.0,0.5,15.0,-41.7881511358717,0.0,0.0,0.0,0.0,0.0,-70.0,-70.0,50.0,50.0,0.0,35.0,50.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-17.168360097022767,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-13.611087213852187,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-33.078886144973474,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-43.168138187133785,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-25.386387607467658,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-58.84585190307693,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-41.0897843150918,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.967457663710155,0.0,-149.67457663710155,0.0,0.0,0.0,-0.0,0.0,15.0,-28.855914089061656,0.0,0.0,0.0325423362898458,0.0325423362898458,0.0,-149.67457663710155,-149.67457663710155,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.864345471691777,0.0,-148.64345471691777,0.0,0.0,0.0,-0.0,0.0,15.0,-23.52705109164903,0.0,0.0,0.135654528308223,0.135654528308223,0.0,-148.64345471691777,-148.64345471691777,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.731707678927599,0.0,-147.31707678927597,0.0,0.0,0.0,-0.0,0.0,15.0,-20.590680969052165,0.0,0.0,0.268292321072402,0.268292321072402,0.0,-147.31707678927597,-147.31707678927597,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.62790269439436,0.0,-146.2790269439436,0.0,0.0,0.0,-0.0,0.0,15.0,-43.7429824430425,0.0,0.0,0.37209730560564,0.37209730560564,0.0,-146.2790269439436,-146.2790269439436,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.571802221269618,0.0,-145.71802221269618,0.0,0.0,0.0,-0.0,0.0,15.0,-26.314334680777463,0.0,0.0,0.428197778730383,0.428197778730383,0.0,-145.71802221269618,-145.71802221269618,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.581853415542664,0.0,-145.81853415542665,0.0,0.0,0.0,-0.0,0.0,15.0,-3.580673796574101,0.0,0.0,0.418146584457336,0.418146584457336,0.0,-145.81853415542665,-145.81853415542665,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.612017643052019,0.0,-146.1201764305202,0.0,0.0,0.0,-0.0,0.0,15.0,-23.882655319825886,0.0,0.0,0.387982356947981,0.387982356947981,0.0,-146.1201764305202,-146.1201764305202,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.67006373999728,0.0,-146.7006373999728,0.0,0.0,0.0,-0.0,0.0,15.0,-44.27972434392214,0.0,0.0,0.32993626000272,0.32993626000272,0.0,-146.7006373999728,-146.7006373999728,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +14.823577712482583,0.0,-148.23577712482583,0.0,0.0,0.0,-0.0,0.0,15.0,-10.949503827209998,0.0,0.0,0.176422287517416,0.176422287517416,0.0,-148.23577712482583,-148.23577712482583,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-10.527105368849552,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-31.8930824305103,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-31.909655225811964,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-38.06405751307926,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-50.965907644667375,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-43.467319491638115,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 +15.0,0.0,-150.0,0.0,0.0,0.0,-0.0,0.0,15.0,-36.66141064065497,0.0,0.0,0.0,0.0,0.0,-150.0,-150.0,15.0,15.0,15.0,0.0,0.0,0.0,0.0,15.0 diff --git a/logs/log-2025-03-27_12-48-42.csv b/logs/log-2025-03-27_12-48-42.csv new file mode 100644 index 00000000..d427a02c --- /dev/null +++ b/logs/log-2025-03-27_12-48-42.csv @@ -0,0 +1,27 @@ +balancing,balancing,balancing,battery,battery,battery,battery,battery,node,node,node,pv_source,pv_source,pv_source,pv_source,balance,balance,balance,balance,balance,balance,balance,balance,balance,balance +0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +loss_load,overgeneration,reward,charge_amount,current_charge,discharge_amount,reward,soc,load_met,node_current,reward,curtailment,renewable_current,renewable_used,reward,reward,shaped_reward,overall_provided_to_microgrid,overall_absorbed_from_microgrid,flex_provided_to_microgrid,flex_absorbed_from_microgrid,controllable_provided_to_microgrid,controllable_absorbed_from_microgrid,fixed_provided_to_microgrid,fixed_absorbed_from_microgrid +50.0,0.0,-500.0,50.0,50.0,0.0,-0.0,0.5,0.0,-42.87479974214829,0.0,0.0,0.0,0.0,0.0,-500.0,-500.0,50.0,50.0,50.0,0.0,0.0,50.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-19.370407524795734,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-55.629357857209605,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-20.138280775757423,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-10.421511191331943,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-10.062158037504448,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-56.97003502033187,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-42.43681101350396,0.0,0.105675139344046,0.105675139344046,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-20.000029028154636,0.0,0.271582372664065,0.271582372664065,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-27.10304998465167,0.0,0.32015892189185,0.32015892189185,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-12.060813833525874,0.0,0.379473923382826,0.379473923382826,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-48.97834851630727,0.0,0.487642039586702,0.487642039586702,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-1.6601004728002966,0.0,0.457425473848596,0.457425473848596,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-5.977871611209096,0.0,0.337864091087416,0.337864091087416,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-45.87819344988104,0.0,0.18106638524248,0.18106638524248,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-29.188992739104037,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-23.880950158948128,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-46.72447144110434,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-55.7627716336709,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-46.16422526783202,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-4.861241275622,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-12.225932965664303,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-5.841549334149438,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +0.0,0.0,-0.0,0.0,100.0,0.0,-0.0,1.0,0.0,-30.82203352228881,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/notebooks/mpc-example.ipynb b/notebooks/mpc-example.ipynb deleted file mode 100644 index 0b583680..00000000 --- a/notebooks/mpc-example.ipynb +++ /dev/null @@ -1,43 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c9464f3b", - "metadata": {}, - "source": [ - "### Model Predictive Control\n", - "\n", - "Coming Soon." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "25adf05f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/quick-start.ipynb b/notebooks/quick-start.ipynb deleted file mode 100644 index ba4ae057..00000000 --- a/notebooks/quick-start.ipynb +++ /dev/null @@ -1,886 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6266a327", - "metadata": {}, - "source": [ - "## Quick Start\n", - "\n", - "To get started with *pymgrid*, first [clone or install](../../html/getting_started.html) the package.\n", - "\n", - "This notebook shows how to define a simple microgrid, create actions to control it, and read the results. Microgrids can be defined by either defining a set of modules and then passing them to the ``Microgrid`` constructor or by a YAML config file. We detail the first case here." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "6516d304", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "np.random.seed(0)\n", - "\n", - "from pymgrid import Microgrid\n", - "from pymgrid.modules import (\n", - " BatteryModule,\n", - " LoadModule,\n", - " RenewableModule,\n", - " GridModule)" - ] - }, - { - "cell_type": "markdown", - "id": "00673154", - "metadata": {}, - "source": [ - "### Defining a Microgrid" - ] - }, - { - "cell_type": "markdown", - "id": "d57b8952", - "metadata": {}, - "source": [ - "We can then define some components of our microgrid. We will define two batteries, one with a capacity of 100 kWh and another with a slower charging rate and lower efficiency but a capacity of 1000 kWh." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "53c9221a", - "metadata": {}, - "outputs": [], - "source": [ - "small_battery = BatteryModule(min_capacity=10,\n", - " max_capacity=100,\n", - " max_charge=50,\n", - " max_discharge=50,\n", - " efficiency=0.9,\n", - " init_soc=0.2)\n", - "\n", - "large_battery = BatteryModule(min_capacity=10,\n", - " max_capacity=1000,\n", - " max_charge=10,\n", - " max_discharge=10,\n", - " efficiency=0.7,\n", - " init_soc=0.2)" - ] - }, - { - "cell_type": "markdown", - "id": "f3b248b0", - "metadata": {}, - "source": [ - "We will also define a load and photovoltaic (pv) module -- the latter representing, say, a solar farm -- with some random data. We will define 90 days worth of data, in hourly increments." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "75d6d6df", - "metadata": {}, - "outputs": [], - "source": [ - "load_ts = 100+100*np.random.rand(24*90) # random load data in the range [100, 200].\n", - "pv_ts = 200*np.random.rand(24*90) # random pv data in the range [0, 200].\n", - "\n", - "load = LoadModule(time_series=load_ts)\n", - "\n", - "pv = RenewableModule(time_series=pv_ts)" - ] - }, - { - "cell_type": "markdown", - "id": "c1739938", - "metadata": {}, - "source": [ - "Finally, we define an external electrical grid to fill any energy gaps. \n", - "\n", - "The grid time series must contain three or four columns. The first three denote import price, export price, and carbon dioxide production per kWh. If a fourth column exists, it denotes the grid status (as a bool); if it does not, the grid is assumed to always be up and running." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2c250ad4", - "metadata": {}, - "outputs": [], - "source": [ - "grid_ts = [0.2, 0.1, 0.5] * np.ones((24*90, 3))\n", - "\n", - "grid = GridModule(max_import=100,\n", - " max_export=100,\n", - " time_series=grid_ts)" - ] - }, - { - "cell_type": "markdown", - "id": "2c0c94b0", - "metadata": {}, - "source": [ - "We can then pass these to the ``Microgrid`` constructor to define a microgrid. Here, we give our renewable module the name \"pv\"." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3f584c56", - "metadata": {}, - "outputs": [], - "source": [ - "modules = [\n", - " small_battery, \n", - " large_battery,\n", - " ('pv', pv),\n", - " load,\n", - " grid]\n", - "\n", - "microgrid = Microgrid(modules)" - ] - }, - { - "cell_type": "markdown", - "id": "c9b42b83", - "metadata": {}, - "source": [ - "Printing the microgrid will tell us the modules contained in that microgrid. By default a ``balancing`` module is added to keep track of any unmet demand or excess production. This can be disabled by passing ``unbalanced_energy_module=False``, but is not recommended." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e893b0a4", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Microgrid([load x 1, pv x 1, balancing x 1, battery x 2, grid x 1])\n" - ] - } - ], - "source": [ - "print(microgrid)" - ] - }, - { - "cell_type": "markdown", - "id": "bf68ba4e", - "metadata": {}, - "source": [ - "We can then access the modules in the microgrid by name or by key:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d45899bd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[RenewableModule(time_series=, raise_errors=False, forecaster=NoForecaster, forecast_horizon=0, forecaster_increase_uncertainty=False, provided_energy_name=renewable_used)]\n" - ] - } - ], - "source": [ - "print(microgrid.modules.pv)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6bcf9b54", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n" - ] - } - ], - "source": [ - "print(microgrid.modules.grid is microgrid.modules['grid'])" - ] - }, - { - "cell_type": "markdown", - "id": "e3eb58cd", - "metadata": {}, - "source": [ - "### Controlling a microgrid\n", - "\n", - "You must pass an action for each ``controllable`` module to control the microgrid. The fixed modules are stored in the attribute ``microgrid.controllable``:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a360f3dc", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{\n", - " \"battery\": \"[BatteryModule(min_capacity=10, max_capacity=100, max_charge=50, max_discharge=50, efficiency=0.9, battery_cost_cycle=0.0, battery_transition_model=None, init_charge=None, init_soc=0.2, raise_errors=False), BatteryModule(min_capacity=10, max_capacity=1000, max_charge=10, max_discharge=10, efficiency=0.7, battery_cost_cycle=0.0, battery_transition_model=None, init_charge=None, init_soc=0.2, raise_errors=False)]\",\n", - " \"grid\": \"[GridModule(max_import=100, max_export=100)]\"\n", - "}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "microgrid.controllable" - ] - }, - { - "cell_type": "markdown", - "id": "08cd3053", - "metadata": {}, - "source": [ - "As we can see, our \"load\", \"battery\", and \"grid\" modules are fixed.\n", - "\n", - "We can also view what modules we need to pass an action for by getting an empty action from the microgrid. Here, all ``None`` values must be replaced to pass this action as a control." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2d94930f", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'battery': [None, None], 'grid': [None]}\n" - ] - } - ], - "source": [ - "print(microgrid.get_empty_action())" - ] - }, - { - "cell_type": "markdown", - "id": "d3337709", - "metadata": {}, - "source": [ - "We can then simply define a control for these modules. Before doing so, we will reset our microgrid and then check its current state." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "117a7ae8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
0
load0load_current-169.646919
pv0renewable_current65.358668
battery0soc0.200000
current_charge20.000000
1soc0.200000
current_charge200.000000
grid0import_price_current0.200000
export_price_current0.100000
co2_per_kwh_current0.500000
grid_status_current1.000000
\n", - "
" - ], - "text/plain": [ - " 0\n", - "load 0 load_current -169.646919\n", - "pv 0 renewable_current 65.358668\n", - "battery 0 soc 0.200000\n", - " current_charge 20.000000\n", - " 1 soc 0.200000\n", - " current_charge 200.000000\n", - "grid 0 import_price_current 0.200000\n", - " export_price_current 0.100000\n", - " co2_per_kwh_current 0.500000\n", - " grid_status_current 1.000000" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "microgrid.reset()\n", - "microgrid.state_series.to_frame()" - ] - }, - { - "cell_type": "markdown", - "id": "5b4d2638", - "metadata": {}, - "source": [ - "We will attempt to meet our load demand of 169.646919 by using our available renewable and then discharging our batteries:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "c2c64cfa", - "metadata": {}, - "outputs": [], - "source": [ - "load = -1.0 * microgrid.modules.load.item().current_load\n", - "pv = microgrid.modules.pv.item().current_renewable" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "79b1ef8a", - "metadata": {}, - "outputs": [], - "source": [ - "net_load = load + pv # negative if load demand exceeds pv\n", - "\n", - "if net_load > 0:\n", - " net_load = 0.0" - ] - }, - { - "cell_type": "markdown", - "id": "190fe39c", - "metadata": {}, - "source": [ - "For our batteries, we will attempt to generate the lower of the excess load and the maximum production." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "8424911a", - "metadata": {}, - "outputs": [], - "source": [ - "battery_0_discharge = min(-1*net_load, microgrid.modules.battery[0].max_production)\n", - "net_load += battery_0_discharge\n", - "\n", - "battery_1_discharge = min(-1*net_load, microgrid.modules.battery[1].max_production)\n", - "net_load += battery_1_discharge" - ] - }, - { - "cell_type": "markdown", - "id": "90700c09", - "metadata": {}, - "source": [ - "Finally, we will let our grid clean up the rest -- or as must as it can." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "6aa7b085", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "grid_import = min(-1*net_load, microgrid.modules.grid.item().max_production)" - ] - }, - { - "cell_type": "markdown", - "id": "027419ac", - "metadata": {}, - "source": [ - "Putting this together, we have our control. \n", - "\n", - "**Note that positive values denote energy moving into the microgrid and negative values denote energy leaving the microgrid.**" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "e2ed2c6f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'battery': [9.0, 7.0], 'grid': [88.28825060785877]}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "control = {\"battery\" : [battery_0_discharge, battery_1_discharge],\n", - " \"grid\": [grid_import]}\n", - "\n", - "control" - ] - }, - { - "cell_type": "markdown", - "id": "36681d36", - "metadata": {}, - "source": [ - "We can then run the microgrid with this control. Since this control is not normalized, we pass ``normalized=False``." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "d2212f7b", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "obs, reward, done, info = microgrid.run(control, normalized=False)" - ] - }, - { - "cell_type": "markdown", - "id": "df844499", - "metadata": {}, - "source": [ - "### Analyzing Results\n", - "\n", - "After passing a control to the microgrid, we can view the results by viewing the microgrid's -- or any of the modules -- logs. \n", - "\n", - "The microgrid's log has one row for each action taken. There are values for both the actions -- e.g. the amount of load met -- as well as the state: for example, the current load. \n", - "\n", - "**Note that the state values are the values of the state from *before* the action was taken.**\n", - "\n", - "The columns are a ``pd.MultiIndex``, with three levels: module names, module name enumeration (e.g. which number of each module name we are in) and property.\n", - "\n", - "For example, since there is one ``load``, all of its log entries will be available under the key ``('load', 0)``. Since there are two batteries, there will be both ``(battery, 0)`` and ``(battery, 1)``." - ] - }, - { - "cell_type": "markdown", - "id": "c5f570ef", - "metadata": {}, - "source": [ - "We can see that we met all of our load:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "26121b4f", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
module_nameload
module_number0
fieldrewardload_metload_current
00.0169.646919-169.646919
\n", - "
" - ], - "text/plain": [ - "module_name load \n", - "module_number 0 \n", - "field reward load_met load_current\n", - "0 0.0 169.646919 -169.646919" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "microgrid.log.loc[:, pd.IndexSlice['load', 0, :]]" - ] - }, - { - "cell_type": "markdown", - "id": "56e7e758", - "metadata": {}, - "source": [ - "And consumed all available pv, even though we did not define a control for it:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "71ac1673", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
module_namepv
module_number0
fieldrewardcurtailmentrenewable_usedrenewable_current
00.00.065.35866865.358668
\n", - "
" - ], - "text/plain": [ - "module_name pv \n", - "module_number 0 \n", - "field reward curtailment renewable_used renewable_current\n", - "0 0.0 0.0 65.358668 65.358668" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "microgrid.log.loc[:, pd.IndexSlice['pv', 0, :]]" - ] - }, - { - "cell_type": "markdown", - "id": "b0ddf53e", - "metadata": {}, - "source": [ - "Since we have two batteries, we will have log entries for each of them:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "7c032938", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
module_number01
fieldrewarddischarge_amountcharge_amountsoccurrent_chargerewarddischarge_amountcharge_amountsoccurrent_charge
0-0.09.00.00.220.0-0.07.00.00.2200.0
\n", - "
" - ], - "text/plain": [ - "module_number 0 \\\n", - "field reward discharge_amount charge_amount soc current_charge \n", - "0 -0.0 9.0 0.0 0.2 20.0 \n", - "\n", - "module_number 1 \n", - "field reward discharge_amount charge_amount soc current_charge \n", - "0 -0.0 7.0 0.0 0.2 200.0 " - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "microgrid.log.loc[:, 'battery']" - ] - }, - { - "cell_type": "markdown", - "id": "6402dc5c", - "metadata": {}, - "source": [ - "### Plotting Results\n", - "\n", - "We can also utilize pandas plotting functionality to view results. To illustrate this, we will run the microgrid for an additional ten steps with randomly sampled actions." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "298cbc0a", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "for _ in range(10):\n", - " microgrid.run(microgrid.sample_action(strict_bound=True))" - ] - }, - { - "cell_type": "markdown", - "id": "1a5c2a92", - "metadata": {}, - "source": [ - "Here we will plot the load met, pv consumed and loss load:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "603c4f14", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "microgrid.log[[('load', 0, 'load_met'), \n", - " ('pv', 0, 'renewable_used'),\n", - " ('balancing', 0, 'loss_load')]].droplevel(axis=1, level=1).plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46e9b61b", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/notebooks/rbc-example.ipynb b/notebooks/rbc-example.ipynb deleted file mode 100644 index f44fdada..00000000 --- a/notebooks/rbc-example.ipynb +++ /dev/null @@ -1,327 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "38527853", - "metadata": {}, - "source": [ - "## Rule Based Control\n", - "\n", - "This example displays how to use rule-based control (RBC) to control a simple microgrid.\n", - "\n", - "In rule-based control, modules are deployed in a preset order. You can either define this order by passing a priority list or the order will be defined automatically from the module with the lowest marginal cost to the highest." - ] - }, - { - "cell_type": "markdown", - "id": "023e3f88", - "metadata": {}, - "source": [ - "### Setting up the algorithm\n", - "\n", - "Setting up a rule-based control algorithm in straightforward. Simply define your microgrid and pass it to the [pymgrid.algos.RuleBasedControl](../reference/api/algos/pymgrid.algos.RuleBasedControl.rst) class." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "4da1e23d", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "from matplotlib import pyplot as plt\n", - "\n", - "from pymgrid import Microgrid\n", - "from pymgrid.algos import RuleBasedControl" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d13ff855", - "metadata": {}, - "outputs": [], - "source": [ - "microgrid = Microgrid.from_scenario(microgrid_number=0)\n", - "rbc = RuleBasedControl(microgrid)" - ] - }, - { - "cell_type": "markdown", - "id": "d22769b2", - "metadata": {}, - "source": [ - "Running the algorithm is straightforward:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c429687e", - "metadata": {}, - "outputs": [], - "source": [ - "rbc.reset()\n", - "rbc_result = rbc.run()" - ] - }, - { - "cell_type": "markdown", - "id": "3c6ee1a4", - "metadata": {}, - "source": [ - "### Investigating the results\n", - "\n", - "At this point, all the results are stored in the DataFrame `rbc_result`. We can investigate the costs of running the microgrid, the usages of the various modules, and so on.\n", - "\n", - "Most of the cost of running the microgrid is from grid usage:" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "2287505f", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rbc_result.loc[:, pd.IndexSlice[:, :, 'reward']].cumsum().plot()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "fcec5baa", - "metadata": {}, - "source": [ - "As we would hope, there are no excess costs due to overgeneration or loss load:" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "a6393673", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total overgeneration or loss load costs over the course of the year:\n", - " -3.0811264650765224e-10\n" - ] - } - ], - "source": [ - "print(f\"Total overgeneration or loss load costs over the course of the year:\\n\\\n", - " {rbc_result.loc[:, pd.IndexSlice['unbalanced_energy', :, 'reward']].sum().item()}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "aaec2407", - "metadata": {}, - "outputs": [], - "source": [ - "days_in_month = [\n", - " ('January', 31),\n", - " ('February', 28),\n", - " ('March', 31),\n", - " ('April', 30),\n", - " ('May', 31),\n", - " ('June', 30),\n", - " ('July', 31),\n", - " ('August', 31),\n", - " ('September', 30),\n", - " ('October', 31),\n", - " ('November', 30),\n", - " ('December', 31)\n", - "]\n", - "\n", - "month_start_end_dates = {days_in_month[0][0]: [0, 24 * days_in_month[0][1]]}\n", - "\n", - "for month_n, (month, days_in) in enumerate(days_in_month[1:], start=1):\n", - " last_end = month_start_end_dates[days_in_month[month_n-1][0]][-1]\n", - " month_start_end_dates[month] = [last_end, 24 * days_in + last_end]" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "ba469727", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "load_less_renewable_available = rbc_result[('load', 0, 'load_met')] - rbc_result[('pv', 0, 'renewable_current')]\n", - "grid_import = rbc_result[('grid', 0, 'grid_import')]\n", - "\n", - "for month, (start_hour, end_hour) in month_start_end_dates.items():\n", - "\n", - " pd.concat([load_less_renewable_available, grid_import], \n", - " keys=['Net load', 'Grid import'], \n", - " axis=1).iloc[start_hour:end_hour].plot(alpha=0.5, title=f'Net load vs grid import in {month}')\n", - "\n", - " plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/rl-example.ipynb b/notebooks/rl-example.ipynb deleted file mode 100644 index f8065b77..00000000 --- a/notebooks/rl-example.ipynb +++ /dev/null @@ -1,159 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a12bb3ff", - "metadata": {}, - "source": [ - "## Reinforcement Learning\n", - "\n", - "This example displays how to use reinforcement learning (RL) to train a policy to control a simple microgrid. We will train and deploy a simple Discrete Q-Network (DQN) policy on one of the *pymgrid25* benchmark microgrids.\n", - "\n", - "Algorithms for reinforcement learning are not built into *pymgrid*, nor are they a dependency. We recommend using one of [RLlib](https://docs.ray.io/en/latest/rllib/index.html) and [garage](https://garage.readthedocs.io/en/latest/); RLlib is better supported and has a wider variety of algorithms but can be less developer-friendly in some scenarios. This example will use garage; the API for RLlib is similar.\n", - "\n", - "To install garage, see the [garage documentation](https://garage.readthedocs.io/en/latest/user/installation.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "230beca4", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "from pymgrid.envs import DiscreteMicrogridEnv" - ] - }, - { - "cell_type": "markdown", - "id": "92121e85", - "metadata": {}, - "source": [ - "### Defining the Environment\n", - "\n", - "Defining an RL environment is extremely straightforward. To define an environment on one of the benchmark microgrids, we simply call ``from_scenario`` on our choice of the ``DiscreteMicrogridEnv`` and the ``ContinuousMicrogridEnv``. \n", - "\n", - "Here, we will use the discrete environment and train a DQN on it." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ca0707cf", - "metadata": {}, - "outputs": [], - "source": [ - "env = DiscreteMicrogridEnv.from_scenario(microgrid_number=0)" - ] - }, - { - "cell_type": "markdown", - "id": "1453812d", - "metadata": {}, - "source": [ - "Environments subclass [pymgrid.Microgrid](../reference/microgrid.rst) and thus have the same attribute and logging functionality:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a9d10e03", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LoadModule(time_series=, forecaster=OracleForecaster, forecast_horizon=23, forecaster_increase_uncertainty=False, raise_errors=False)\n", - "\n", - "RenewableModule(time_series=, raise_errors=False, forecaster=OracleForecaster, forecast_horizon=23, forecaster_increase_uncertainty=False, provided_energy_name=renewable_used)\n", - "\n", - "UnbalancedEnergyModule(raise_errors=False, loss_load_cost=10, overgeneration_cost=1)\n", - "\n", - "BatteryModule(min_capacity=290.40000000000003, max_capacity=1452, max_charge=363, max_discharge=363, efficiency=0.9, battery_cost_cycle=0.02, battery_transition_model=None, init_charge=None, init_soc=0.2, raise_errors=False)\n", - "\n", - "GridModule(max_import=1920, max_export=1920)\n", - "\n" - ] - } - ], - "source": [ - "for module in env.modules.module_list():\n", - " print(f'{module}\\n')" - ] - }, - { - "cell_type": "markdown", - "id": "64eda080", - "metadata": {}, - "source": [ - "### Setting Up the RL Algorithm\n", - "\n", - "As we mentioned, we are planning on deploying a simple DQN in this case.\n", - "\n", - "For ease of use, we will employ a simple ``LocalSampler`` that does not parallelize sampling. We will also use an ``EpsilonGreedyPolicy`` for exploration." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "1d174e6c", - "metadata": {}, - "outputs": [], - "source": [ - "from garage.experiment.deterministic import set_seed\n", - "\n", - "from garage.np.exploration_policies import EpsilonGreedyPolicy\n", - "\n", - "from garage.replay_buffer import PathBuffer\n", - "\n", - "from garage.sampler import LocalSampler, RaySampler\n", - "\n", - "from garage.torch.algos.dqn import DQN\n", - "from garage.torch.policies import DiscreteQFArgmaxPolicy\n", - "from garage.torch.q_functions import DiscreteMLPQFunction\n", - "\n", - "from garage.trainer import Trainer" - ] - }, - { - "cell_type": "markdown", - "id": "ace8f2ae", - "metadata": {}, - "source": [ - "Remainder Coming Soon." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c56b3d25", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index ec33f447..00000000 --- a/pyproject.toml +++ /dev/null @@ -1,5 +0,0 @@ -[tool.pytest.ini_options] -log_cli = true -log_cli_level = "INFO" -log_cli_format = "%(asctime)s [%(levelname)8s] %(message)s (%(filename)s:%(lineno)s)" -log_cli_date_format = "%Y-%m-%d %H:%M:%S" \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index f9dfee0f..96479209 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,11 +1,13 @@ -cufflinks>=0.17.3 -cvxpy>=1.1.4 -gym>=0.15.7 -matplotlib>=3.1.1 -numpy>=1.19.5 -pandas>=1.0.3 -plotly>=4.9.0 -scipy>=1.5.3 -statsmodels>=0.11.1 -tqdm>=4.1.0 -pyyaml>=1.5 \ No newline at end of file +cvxpy==1.6.1 +gym==0.26.2 +ipython==8.12.3 +matplotlib==3.10.0 +mosek==11.0.8 +numpy==2.2.3 +pandas==2.2.3 +plotly==6.0.0 +PyYAML==6.0.2 +PyYAML==6.0.2 +scipy==1.15.2 +statsmodels==0.14.4 +tqdm==4.66.1 diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index b7569e67..00000000 --- a/setup.cfg +++ /dev/null @@ -1,5 +0,0 @@ -[metadata] -description-file = README.md - -[tool:pytest] -norecursedirs=tests/helpers \ No newline at end of file diff --git a/setup.py b/setup.py deleted file mode 100644 index 9a1d80dc..00000000 --- a/setup.py +++ /dev/null @@ -1,61 +0,0 @@ -from pathlib import Path -from setuptools import setup, find_packages - -v = {} -exec(open('src/pymgrid/version.py').read(), v) # read __version__ -VERSION = v['__version__'] -DESCRIPTION = "A simulator for tertiary control of electrical microgrids" -DOWNLOAD_URL = f"https://github.com/ahalev/python-microgrid/archive/refs/tags/v{VERSION}.tar.gz" -MAINTAINER = "Avishai Halev" -MAINTAINER_EMAIL = "avishaihalev@gmail.com" -LICENSE = "GNU LGPL 3.0" -PROJECT_URLS = {"Source Code": "https://github.com/ahalev/python-microgrid", - "Documentation": "https://python-microgrid.readthedocs.io/"} - -EXTRAS = dict() -EXTRAS["genset_mpc"] = ["Mosek", "cvxopt"] -EXTRAS["dev"] = [ - "pytest", - "pytest-subtests", - "flake8", - "sphinx", - "pydata_sphinx_theme", - "numpydoc", - "nbsphinx", - "nbsphinx-link", - *EXTRAS["genset_mpc"]] - -EXTRAS["rtd"] = ["ipython"] - -EXTRAS["all"] = list(set(sum(EXTRAS.values(), []))) - - -setup( - name="python-microgrid", - package_dir={"": "src"}, - packages=find_packages("src"), - python_requires=">=3.6", - version=VERSION, - maintainer=MAINTAINER, - maintainer_email=MAINTAINER_EMAIL, - download_url=DOWNLOAD_URL, - project_urls=PROJECT_URLS, - description=DESCRIPTION, - license=LICENSE, - long_description=(Path(__file__).parent / "README.md").read_text(), - long_description_content_type="text/markdown", - include_package_data=True, - install_requires=[ - "pandas", - "numpy", - "cvxpy", - "statsmodels", - "matplotlib", - "plotly", - "cufflinks", - "gym", - "tqdm", - "pyyaml" - ], - extras_require=EXTRAS -) diff --git a/src/api.py b/src/api.py new file mode 100644 index 00000000..317a1a8a --- /dev/null +++ b/src/api.py @@ -0,0 +1,44 @@ +from flask import Flask, jsonify, request, g +import sqlite3 + +app = Flask(__name__) + +conn = sqlite3.connect("database.db") + +state_object = None + + +@app.route("/soc", methods=["GET"]) +def soc(): + if state_object is None: + return jsonify({"error": "No data available"}), 404 + return jsonify({"state": state_object}) + +@app.route("/insert", methods=["POST"]) +def insert(): + global state_object + if not request.json or "data" not in request.json: + return jsonify({"error": "Invalid request"}), 400 + + state_object = request.json["data"] + return jsonify({"message": "Inserted!"}), 201 + +@app.route("/schedule-job", methods=["POST"]) +def schedule_job(): + body = request.get_json() + + if not body or "Gridname" not in body or "Load" not in body: + return jsonify({"error": "Missing Gridname or Load"}), 400 + + print(body) + + conn = sqlite3.connect("database.db") + cursor = conn.cursor() + cursor.execute( + "INSERT INTO microgrids (Gridname, Load) VALUES (?, ?)", (body["Gridname"], body["Load"]) + ) + cursor.close() + conn.commit() + conn.close() + + return jsonify({"message": "Scheduled!"}), 201 \ No newline at end of file diff --git a/src/app.py b/src/app.py new file mode 100644 index 00000000..7d12e377 --- /dev/null +++ b/src/app.py @@ -0,0 +1,227 @@ +import numpy as np +import time +import sqlite3 +import pandas as pd +import requests +from datetime import datetime +from pymgrid import Microgrid +from pymgrid.modules import ( + GensetModule, + BatteryModule, + LoadModule, + RenewableModule, + NodeModule, +) + + +def get_column_names(dataframe: pd.DataFrame): + column_names = dataframe.columns[1:].tolist() + + return column_names + + +def db_load_retrieve(last_timestamp: str): + current_timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + + connection = sqlite3.connect("database.db") + cursor = connection.execute( + "SELECT Gridname, SUM(Load) " + "FROM microgrids " + "WHERE Timestamp BETWEEN ? AND ? " + "GROUP BY Gridname", + (last_timestamp, current_timestamp), + ) + rows = cursor.fetchall() + cursor.close() + connection.close() + + return rows + + +def grid_initial_load(c_names: list): + grid_dict = {name: 0.0 for name in c_names} + + return grid_dict + + +def update_grid_load(grid_dict: dict, rows: list): + for grid_name, load_value in rows: + if grid_name in grid_dict: + grid_dict[grid_name] = load_value + + +def generate_battery_modules(c_names: list): + battery_modules = {} + + for name in c_names: + battery = BatteryModule( + min_capacity=0, + max_capacity=100, + max_charge=50, + max_discharge=50, + efficiency=1.0, + init_soc=0.5, + ) + battery_modules[name] = battery + + return battery_modules + + +def generate_node_modules(c_names: list, final_step: int, grid_dict: dict): + node_modules = {} + + for name in c_names: + node = NodeModule( + time_series=60 + * np.random.rand( + final_step + ), # Denne parameter bliver overridet på klassen af vores load, så den er ligegyldig (orker ikke at rode med at fjerne den fra klassen) + final_step=final_step, + load=grid_dict[name], + ) + node_modules[name] = node + + return node_modules + + +def generate_renewable_modules( + c_names: list, final_step: int, renewable_data: pd.DataFrame +): + renewable_modules = {} + + for name in c_names: + renewable = RenewableModule( + time_series=renewable_data[name], + final_step=final_step, + ) + renewable_modules[name] = renewable + + return renewable_modules + + +def generate_microgrids(c_names: list, batteries: dict, nodes: dict, renewables: dict): + microgrids = {} + + for name in c_names: + microgrid = Microgrid( + [batteries[name], ("pv_source", renewables[name]), nodes[name]] + ) + microgrid.grid_name = name + microgrids[name] = microgrid + + return microgrids + + +def calculate_final_step(dataframe: pd.DataFrame): + print("length is ", len(dataframe)) + + return len(dataframe) - 1 + + +def main(): + # Load the solar data and setup variables for microgrid setup + df_solar = pd.read_csv("data/solarPV.csv") + column_names = get_column_names(df_solar) + print(column_names) + final_step = calculate_final_step(df_solar) + + # Create the initial grid load dictionary, with everything set to 0.0 + grid_dict = grid_initial_load(column_names) + print(grid_dict) + + # Generate the battery, node, renewable and microgrid modules + batteries = generate_battery_modules(column_names) + nodes = generate_node_modules(column_names, final_step, grid_dict) + renewables = generate_renewable_modules(column_names, final_step, df_solar) + microgrids = generate_microgrids(column_names, batteries, nodes, renewables) + + # Create the empty action, which will be updated with the load values + custom_action = microgrids["ES10"].get_empty_action() + print(custom_action) + + # + # The actual simulation with updating loads, actions and logging + # + + # update_grid_load(grid_dict=grid_dict, rows=rows) + # print(grid_dict["ES10"], grid_dict["PT02"], grid_dict["ES12"]) + + + # while True: + # microgrid.step(microgrid.sample_action()) + # print(microgrid.get_log()) + # time.sleep(wait_time - ((time.monotonic() - starttime) % wait_time)) + + # microgrid.reset() + + # timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + timestamp = "2025-03-25 13:00:19" + wait_time = 5.0 + starttime = time.monotonic() + + state_of_charge = [] + + while True: + #for j in range(24): + #time.sleep(wait_time - ((time.monotonic() - starttime) % wait_time)) + time.sleep(wait_time) + + state_of_charge.clear() + rows = db_load_retrieve(timestamp) + print("Selected rows ", rows) + update_grid_load(grid_dict=grid_dict, rows=rows) + + for microgrid in microgrids.values(): + print(microgrid.grid_name) + + microgrid.modules.node[0].update_current_load( + grid_dict[microgrid.grid_name] + ) + + print("Load ", microgrid.modules.node[0].current_load) + print("Grid dict load ", grid_dict[microgrid.grid_name]) + print("Renewable ", microgrid.modules.pv_source[0].current_renewable) + + custom_action.update( + { + "battery": [ + microgrid.modules.node[0].current_load + - (microgrid.modules.pv_source[0].current_renewable * 10) + ] + } + ) + + microgrid.step(custom_action) + + state_of_charge.append( + { + "Timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "SOC": microgrid.modules.battery[0].soc, + "Current_renewable": microgrid.modules.pv_source[ + 0 + ].current_renewable, + "Current_load": microgrid.compute_net_load(), + "Gridname": microgrid.grid_name, + } + ) + #print(shared_state.state_of_charge) + + # API STUFF + url = "http://127.0.0.1:5000/insert" + data = {"data": state_of_charge} + response = requests.post(url, json=data) + print(response.status_code, response.json()) + + + # Logging and visualization of the data + + # df = microgrid.get_log() + # compute_net_load + + # timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + # filename = f"logs/log-{timestamp}.csv" + # df.to_csv(filename, index=False) + + +if __name__ == "__main__": + main() diff --git a/tests/__init__.py b/src/database.db similarity index 100% rename from tests/__init__.py rename to src/database.db diff --git a/src/database.py b/src/database.py new file mode 100644 index 00000000..841a2a9d --- /dev/null +++ b/src/database.py @@ -0,0 +1,33 @@ +import sqlite3 + +conn = sqlite3.connect("database.db") +cursor = conn.cursor() +print("Created / Opened database successfully") + +cursor.execute( + """CREATE TABLE IF NOT EXISTS microgrids + (ID INTEGER PRIMARY KEY AUTOINCREMENT, + Timestamp DATETIME DEFAULT CURRENT_TIMESTAMP, + Gridname text, + Load real)""" +) + +cursor.execute( + """CREATE TABLE IF NOT EXISTS state_of_charge + (ID INTEGER PRIMARY KEY AUTOINCREMENT, + Timestamp DATETIME DEFAULT CURRENT_TIMESTAMP, + Gridname text, + SOC real)""" +) + +print("Table(s) created successfully") + +cursor.execute("INSERT INTO microgrids (Gridname, Load) VALUES ('ES10', 7.5)") +cursor.execute("INSERT INTO microgrids (Gridname, Load) VALUES ('PT02', 5.0)") +cursor.execute("INSERT INTO microgrids (Gridname, Load) VALUES ('ES12', 2.5)") + +cursor.close() + +conn.commit() +conn.close() +print("Connection closed") diff --git a/src/graph.ipynb b/src/graph.ipynb new file mode 100644 index 00000000..817e5ace --- /dev/null +++ b/src/graph.ipynb @@ -0,0 +1,284 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load and import stuff" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 0.5\n", + "3 0.0\n", + "4 0.0\n", + "5 0.0\n", + "6 0.0\n", + "7 0.0\n", + "8 0.0\n", + "9 0.0\n", + "10 0.0\n", + "11 0.0\n", + "12 0.0\n", + "13 0.0\n", + "14 0.0\n", + "15 0.0\n", + "16 0.0\n", + "17 0.0\n", + "18 0.0\n", + "19 0.0\n", + "20 0.0\n", + "21 0.0\n", + "22 0.0\n", + "23 0.0\n", + "24 0.0\n", + "25 0.0\n", + "Name: battery.4, dtype: float64\n", + "float64\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Load the CSV file into a pandas DataFrame\n", + "df = pd.read_csv('../logs/log-2025-03-25_14-52-13.csv')\n", + "\n", + "df_soc = df['battery.4']\n", + "df_soc = df_soc.tail(df_soc.shape[0] - 2)\n", + "\n", + "df_soc = df_soc.astype(float)\n", + "\n", + "df_node = df['node.1']\n", + "df_node = df_node.tail(df_node.shape[0] - 2)\n", + "df_node = df_node.astype(float)\n", + "\n", + "df_node2 = df['node']\n", + "df_node2 = df_node2.tail(df_node2.shape[0] - 2)\n", + "df_node2 = df_node2.astype(float)\n", + "\n", + "df_pv = df['pv_spain.2']\n", + "df_pv = df_pv.tail(df_pv.shape[0] - 2)\n", + "df_pv = df_pv.astype(float)\n", + "\n", + "print(df_soc)\n", + "print(df_soc.dtypes)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " balancing balancing.1 balancing.2 battery battery.1 \\\n", + "0 0 0 0 0 0 \n", + "1 loss_load overgeneration reward charge_amount current_charge \n", + "2 0.0 42.5 -85.0 0.0 50.0 \n", + "3 7.5 0.0 -75.0 0.0 0.0 \n", + "4 7.5 0.0 -75.0 0.0 0.0 \n", + "\n", + " battery.2 battery.3 battery.4 node node.1 ... \\\n", + "0 0 0 0 0 0 ... \n", + "1 discharge_amount reward soc load_met node_current ... \n", + "2 50.0 -0.0 0.5 7.5 -41.7881511358717 ... \n", + "3 0.0 -0.0 0.0 7.5 -17.168360097022767 ... \n", + "4 0.0 -0.0 0.0 7.5 -13.611087213852187 ... \n", + "\n", + " balance balance.1 balance.2 \\\n", + "0 0 0 0 \n", + "1 reward shaped_reward overall_provided_to_microgrid \n", + "2 -85.0 -85.0 50.0 \n", + "3 -75.0 -75.0 7.5 \n", + "4 -75.0 -75.0 7.5 \n", + "\n", + " balance.3 balance.4 \\\n", + "0 0 0 \n", + "1 overall_absorbed_from_microgrid flex_provided_to_microgrid \n", + "2 50.0 0.0 \n", + "3 7.5 7.5 \n", + "4 7.5 7.5 \n", + "\n", + " balance.5 balance.6 \\\n", + "0 0 0 \n", + "1 flex_absorbed_from_microgrid controllable_provided_to_microgrid \n", + "2 42.5 50.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "\n", + " balance.7 balance.8 \\\n", + "0 0 0 \n", + "1 controllable_absorbed_from_microgrid fixed_provided_to_microgrid \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "\n", + " balance.9 \n", + "0 0 \n", + "1 fixed_absorbed_from_microgrid \n", + "2 7.5 \n", + "3 7.5 \n", + "4 7.5 \n", + "\n", + "[5 rows x 25 columns]\n" + ] + } + ], + "source": [ + "print(df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot battery charge\n", + "# 1. Battery State of Charge (SOC) over Time\n", + "plt.figure(figsize=(10, 6)) # Adjust figure size for better readability\n", + "plt.plot(df_soc.index, df_soc)\n", + "plt.xlabel('Time')\n", + "plt.ylabel('State of Charge (SOC)')\n", + "plt.title('Battery SOC over Time')\n", + "plt.legend()\n", + "plt.grid(True) # Add a grid for better readability\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# plt.figure(figsize=(10, 6)) # Adjust figure size for better readability\n", + "# plt.plot(df_node.index, df_node)\n", + "# plt.xlabel('Time')\n", + "# plt.ylabel('Node Consumption over Time')\n", + "# plt.title('Node Consumption (kWh)')\n", + "# plt.legend()\n", + "# plt.grid(True) # Add a grid for better readability\n", + "# plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6)) # Adjust figure size for better readability\n", + "plt.plot(df_node2.index, df_node2)\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Node Consumption over Time')\n", + "plt.title('Node Consumption Constant (kWh)')\n", + "plt.legend()\n", + "plt.grid(True) # Add a grid for better readability\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6)) # Adjust figure size for better readability\n", + "plt.plot(df_pv.index, df_pv)\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Power Generation (kWh)')\n", + "plt.title('Renewable Current (kWh)')\n", + "plt.legend()\n", + "plt.grid(True) # Add a grid for better readability\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/pymgrid/README.md b/src/pymgrid/README.md new file mode 100644 index 00000000..4d5e81ec --- /dev/null +++ b/src/pymgrid/README.md @@ -0,0 +1,21 @@ +![image](https://media.discordapp.net/attachments/311213361125916672/1355122972750512228/gaylol.jpg?ex=67e7c81d&is=67e6769d&hm=a4bbb0ed48ed36ae2498c86dd3bce700b7a9b66f473a4fcf362fc09b69f26983&=&format=webp) + +# How to run the simulation + +Before getting started, create the database by running the following command: + +```bash +python src/database.py +``` + +To run the simulation, you will need two terminals. Open the first terminal in the *python-microgrid-realtime* directory and run the following command: + +```bash +flask --app src/api run +``` + +Likewise, open the second terminal in the *python-microgrid-realtime* directory and run the following command: + +```bash +python src/app.py +``` \ No newline at end of file diff --git a/src/pymgrid/modules/__init__.py b/src/pymgrid/modules/__init__.py index b262b248..fdd21bbe 100644 --- a/src/pymgrid/modules/__init__.py +++ b/src/pymgrid/modules/__init__.py @@ -2,6 +2,7 @@ from .genset_module import GensetModule from .grid_module import GridModule from .load_module import LoadModule +from .node_module import NodeModule from .renewable_module import RenewableModule from .unbalanced_energy_module import UnbalancedEnergyModule diff --git a/src/pymgrid/modules/node_module.py b/src/pymgrid/modules/node_module.py new file mode 100644 index 00000000..0093ba71 --- /dev/null +++ b/src/pymgrid/modules/node_module.py @@ -0,0 +1,140 @@ +import numpy as np +import yaml + +from pymgrid.microgrid import DEFAULT_HORIZON +from pymgrid.modules.base import BaseTimeSeriesMicrogridModule + + +class NodeModule(BaseTimeSeriesMicrogridModule): + """ + A renewable energy module. + + The classic examples of renewables are photovoltaics (PV) and wind turbines. + + Parameters + ---------- + time_series : array-like, shape (n_steps, ) + Time series of load demand. + + forecaster : callable, float, "oracle", or None, default None. + Function that gives a forecast n-steps ahead. + + * If ``callable``, must take as arguments ``(val_c: float, val_{c+n}: float, n: int)``, where + + * ``val_c`` is the current value in the time series: ``self.time_series[self.current_step]`` + + * ``val_{c+n}`` is the value in the time series n steps in the future + + * n is the number of steps in the future at which we are forecasting. + + The output ``forecast = forecaster(val_c, val_{c+n}, n)`` must have the same sign + as the inputs ``val_c`` and ``val_{c+n}``. + + * If ``float``, serves as a standard deviation for a mean-zero gaussian noise function + that is added to the true value. + + * If ``"oracle"``, gives a perfect forecast. + + * If ``None``, no forecast. + + forecast_horizon : int. + Number of steps in the future to forecast. If forecaster is None, ignored and 0 is returned. + + forecaster_increase_uncertainty : bool, default False + Whether to increase uncertainty for farther-out dates if using a GaussianNoiseForecaster. Ignored otherwise. + + normalized_action_bounds : tuple of int or float, default (0, 1). + Bounds of normalized actions. + Change to (-1, 1) for e.g. an RL policy with a Tanh output activation. + + raise_errors : bool, default False + Whether to raise errors if bounds are exceeded in an action. + If False, actions are clipped to the limit possible. + + """ + + module_type = ("node", "fixed") + # module_type = ('node', 'controllable') + yaml_tag = "!NodeModule" + yaml_dumper = yaml.SafeDumper + yaml_loader = yaml.SafeLoader + + state_components = np.array(["node"], dtype=object) + + def __init__( + self, + time_series, + load, + forecaster=None, + forecast_horizon=DEFAULT_HORIZON, + forecaster_increase_uncertainty=False, + forecaster_relative_noise=False, + initial_step=0, + final_step=-1, + normalized_action_bounds=(0, 1), + raise_errors=False, + ): + super().__init__( + time_series, + raise_errors=raise_errors, + forecaster=forecaster, + forecast_horizon=forecast_horizon, + forecaster_increase_uncertainty=forecaster_increase_uncertainty, + forecaster_relative_noise=forecaster_relative_noise, + initial_step=initial_step, + final_step=final_step, + normalized_action_bounds=normalized_action_bounds, + provided_energy_name=None, + absorbed_energy_name="load_met", + ) + self._load = load + + def _get_bounds(self): + _min_obs, _max_obs, _, _ = super()._get_bounds() + return _min_obs, _max_obs, np.array([]), np.array([]) + + def update(self, external_energy_change, as_source=False, as_sink=False): + assert as_sink or external_energy_change == 0.0, ( + f"step() was called with positive energy (source) for " + f"module {self} but module is not a source and " + f"can only be called with negative energy." + ) + + info = {"absorbed_energy": self.current_load} + + return 0.0, self._done(), info + + def sample_action(self, strict_bound=False): + return np.array([]) + + def update_current_load(self, load: float): + self._load = load + + @property + def max_consumption(self): + return self.current_load + + """ + This function should be modified in such a way, + that instead of looking at the current timestep, + it simply fetches the latest consumption and returns that. + """ + + @property + def current_load(self): + """ + Current load. + + Returns + ------- + load : float + Current load demand. + + """ + # print(self._time_series[self._current_step].item()) + # return -1 * self._time_series[self._current_step].item() + return self._load + + @property + def is_sink(self): + return True diff --git a/src/pymgrid/modules/renewable_module.py b/src/pymgrid/modules/renewable_module.py index 12388bf3..73576ab0 100644 --- a/src/pymgrid/modules/renewable_module.py +++ b/src/pymgrid/modules/renewable_module.py @@ -55,24 +55,27 @@ class RenewableModule(BaseTimeSeriesMicrogridModule): If False, actions are clipped to the limit possible. """ - module_type = ('renewable', 'flex') - yaml_tag = u"!RenewableModule" + + module_type = ("renewable", "flex") + yaml_tag = "!RenewableModule" yaml_loader = yaml.SafeLoader yaml_dumper = yaml.SafeDumper state_components = np.array(["renewable"], dtype=object) - def __init__(self, - time_series, - raise_errors=False, - forecaster=None, - forecast_horizon=DEFAULT_HORIZON, - forecaster_increase_uncertainty=False, - forecaster_relative_noise=False, - initial_step=0, - final_step=-1, - normalized_action_bounds=(0, 1), - provided_energy_name='renewable_used'): + def __init__( + self, + time_series, + raise_errors=False, + forecaster=None, + forecast_horizon=DEFAULT_HORIZON, + forecaster_increase_uncertainty=False, + forecaster_relative_noise=False, + initial_step=0, + final_step=-1, + normalized_action_bounds=(0, 1), + provided_energy_name="renewable_used", + ): super().__init__( time_series, raise_errors, @@ -84,15 +87,21 @@ def __init__(self, final_step=final_step, normalized_action_bounds=normalized_action_bounds, provided_energy_name=provided_energy_name, - absorbed_energy_name=None + absorbed_energy_name=None, ) def update(self, external_energy_change, as_source=False, as_sink=False): - assert as_source, f'Class {self.__class__.__name__} can only be used as a source.' - assert external_energy_change <= self.current_renewable, f'Cannot provide more than {self.current_renewable}' - - info = {'provided_energy': external_energy_change, - 'curtailment': self.current_renewable-external_energy_change} + assert ( + as_source + ), f"Class {self.__class__.__name__} can only be used as a source." + assert ( + external_energy_change <= self.current_renewable + ), f"Cannot provide more than {self.current_renewable}" + + info = { + "provided_energy": external_energy_change, + "curtailment": self.current_renewable - external_energy_change, + } return 0.0, self._done(), info @@ -111,6 +120,7 @@ def current_renewable(self): Renewable production. """ + # print(self._time_series[self._current_step].item(), "Current step: ", self._current_step) return self._time_series[self._current_step].item() @property diff --git a/tests/conftest.py b/tests/conftest.py deleted file mode 100644 index ef4c48cb..00000000 --- a/tests/conftest.py +++ /dev/null @@ -1,21 +0,0 @@ -import pytest - - -def pytest_addoption(parser): - parser.addoption( - "--run-slow", action="store_true", default=False, help="run slow tests" - ) - - -def pytest_configure(config): - config.addinivalue_line("markers", "slow: mark test as slow to run") - - -def pytest_collection_modifyitems(config, items): - if config.getoption("--run-slow"): - # --runslow given in cli: do not skip slow tests - return - skip_slow = pytest.mark.skip(reason="need --run-slow option to run") - for item in items: - if "slow" in item.keywords: - item.add_marker(skip_slow) \ No newline at end of file diff --git a/tests/control/__init__.py b/tests/control/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/control/data_generation/__init__.py b/tests/control/data_generation/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/control/data_generation/test_data_generator.py b/tests/control/data_generation/test_data_generator.py deleted file mode 100644 index 520d3bae..00000000 --- a/tests/control/data_generation/test_data_generator.py +++ /dev/null @@ -1,251 +0,0 @@ -import unittest -from pymgrid.utils.DataGenerator import * -from pandas import Series -from pandas.testing import assert_frame_equal, assert_series_equal -from numpy.testing import assert_array_equal, assert_array_almost_equal -from matplotlib import pyplot as plt - -def create_pv_test_set(): - test_set = np.zeros(48) - test_set[7:20] = np.arange(7,20) - test_set[7:20] = -20*(test_set[7:20]-13)**2 + 30*-1**(np.arange(7,20) % 2) + 5*(np.arange(6,19) %5) - test_set[7:20] += -1*np.min(test_set[7:20]) - - n = 24 - test_set[7+n:20+n] = np.arange(7,20) - 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) - test_set[7+n:20+n] += -1*np.min(test_set[7+n:20+n]) - - return test_set/10 - - -class TestNoisyPV(unittest.TestCase): - def setUp(self): - - self.test_data = create_pv_test_set() - self.test_series = Series(data = self.test_data) - - - def test_init(self): - NPV = NoisyPVData(pv_data = self.test_series) - df = pd.DataFrame(self.test_data) - assert_frame_equal(NPV.unmunged_data, df) - assert_frame_equal(NPV.data, df) - - def test_data_munge(self): - NPV = NoisyPVData(pv_data=self.test_series) - NPV.data_munge() - - # Assertions: - assert_array_equal(NPV.data.values[:,0],self.test_data[:24]) - assert_array_equal(NPV.data.values[:,1],self.test_data[24:]) - assert_array_equal(NPV.daily_maxes['time_of_max'].values, np.array([13,13])) - assert_array_equal(NPV.daily_maxes['cumulative_hr'], np.array([13, 37])) - self.assertTrue(NPV.munged) - - def test_add_feature_columns(self): - NPV = NoisyPVData(pv_data=self.test_series) - NPV.data_munge() - - num_feature_functions = 1 - period_scale = 0.8 - - NPV._add_feature_columns(num_feature_functions=num_feature_functions, period_scale=period_scale) - - self.assertIn('ones', NPV.daily_maxes.columns.values) - self.assertIn('cos1x', NPV.daily_maxes.columns.values) - assert_array_equal(NPV.daily_maxes['ones'], np.array([1,1])) - cos1x = np.cos( - 2 * np.pi / 8760. * period_scale * (NPV.daily_maxes['cumulative_hr'] - 173 * 24)) - assert_array_equal(NPV.daily_maxes['cos1x'], cos1x) - - self.assertListEqual(NPV.feature_names, ['ones', 'cos1x']) - - for name in NPV.feature_names: - assert_array_equal(NPV.feature_functions[name](NPV.daily_maxes['cumulative_hr']).values, NPV.daily_maxes[name].values) - - -class TestNoisyGrid(unittest.TestCase): - - def setUp(self) -> None: - always_on = np.ones(48) - self.always_on = pd.Series(always_on) - self.with_outages = self.always_on.copy() - self.with_outages.iloc[3:6] = 0 - self.with_outages.iloc[40:47] = 0 - self.with_outages_data = dict(naive_probabilities = np.array([10/48, 38/48]), - occurences=np.array([10,37]), - transition_prob_matrix = np.array([ - [8 / 10, 2 / 10], - [2/37, 35/37] - ])) - self.dist_types = ('naive', 'markov') - - def test_init(self): - - for dist_type in self.dist_types: - for data in self.always_on, self.with_outages: - NGD = NoisyGridData(data,dist_type=dist_type) - assert_series_equal(data, NGD.data) - assert_series_equal(data, NGD.unmunged_data) - - def test_bad_grid_data(self): - grid_data = self.with_outages.copy() - grid_data[5] = -3 - try: - NoisyGridData(grid_data) - except ValueError: - pass - except Exception: - self.fail('unexpected exception raised') - else: - self.fail('ValueError not raised') - grid_data[5] = 1.1 - try: - NoisyGridData(grid_data) - except ValueError: - pass - except Exception: - self.fail('unexpected exception raised') - else: - self.fail('ValueError not raised') - - def test_learn_distribution_always_on_naive(self): - NGD = NoisyGridData(self.always_on, dist_type='naive') - self.assertFalse(NGD.has_distribution) - NGD.learn_distribution() - self.assertTrue(NGD.has_distribution) - - assert_array_equal(NGD.transition_prob_matrix, np.array([0, 1])) - - def test_learn_distribution_always_on_markov(self): - NGD = NoisyGridData(self.always_on, dist_type='markov') - self.assertFalse(NGD.has_distribution) - NGD.learn_distribution() - self.assertTrue(NGD.has_distribution) - - assert_array_equal(NGD.occurrences, np.array([0, 47])) - assert_array_equal(NGD.transition_prob_matrix, np.array([[1,0],[0,1]])) - - def test_learn_distribution_with_outages_naive(self): - NGD = NoisyGridData(self.with_outages, dist_type='naive') - self.assertFalse(NGD.has_distribution) - NGD.learn_distribution() - self.assertTrue(NGD.has_distribution) - - assert_array_almost_equal(NGD.transition_prob_matrix, self.with_outages_data['naive_probabilities']) - - def test_learn_distribution_with_outages_markov(self): - NGD = NoisyGridData(self.with_outages, dist_type='markov') - self.assertFalse(NGD.has_distribution) - NGD.learn_distribution() - self.assertTrue(NGD.has_distribution) - - assert_array_almost_equal(NGD.occurrences, self.with_outages_data['occurences']) - assert_array_almost_equal(NGD.transition_prob_matrix, self.with_outages_data['transition_prob_matrix']) - - def test_sample_always_on_naive(self): - NGD = NoisyGridData(self.always_on, dist_type='naive') - NGD.learn_distribution() - sample = NGD.sample() - assert_array_equal(sample, np.ones(48)) - - def test_sample_always_on_markov(self): - NGD = NoisyGridData(self.always_on, dist_type='markov') - NGD.learn_distribution() - sample = NGD.sample() - assert_array_equal(sample, np.ones(48)) - - def test_sample_with_outages_naive(self): - # This is a ridiculous unit test All it does is check that the data generated from the probability distribution - # matches the distribution. Thus, can fail randomly. - np.random.seed(0) - num_tests = 50 - - NGD = NoisyGridData(self.with_outages, dist_type='naive') - NGD.learn_distribution() - - probs_list = [] - for j in range(num_tests): - sample = NGD.sample() - new_NGD = NoisyGridData(sample, dist_type='naive') - new_NGD.learn_distribution() - probs_list.append(new_NGD.transition_prob_matrix) - - transition_prob_mean = np.mean(np.array(probs_list), axis=0) - assert_array_almost_equal(self.with_outages_data['naive_probabilities'], transition_prob_mean, decimal=2) - - def test_sample_with_outages_markov(self): - """ - This is also a ridiculous unit test. All it does is check that the data generated from the probability distribution - matches the distribution. Thus, can fail randomly. - :return: - """ - - np.random.seed(0) - num_tests = 50 - - NGD = NoisyGridData(self.with_outages, dist_type='markov') - NGD.learn_distribution() - - probs_list = [] - for j in range(num_tests): - sample = NGD.sample() - new_NGD = NoisyGridData(sample, dist_type='markov') - new_NGD.learn_distribution() - probs_list.append(new_NGD.transition_prob_matrix) - - transition_prob_mean = np.mean(np.array(probs_list), axis=0) - assert_array_almost_equal(self.with_outages_data['transition_prob_matrix'], transition_prob_mean, decimal=1) - - -class TestNoisyLoad(unittest.TestCase): - def setUp(self) -> None: - self.n_days = 12 - - 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]) - load_data = np.concatenate([load_data + j % 5 for j in range(int(self.n_days/2))]) - - self.load_data = pd.Series(data = load_data) - - def test_init(self): - NLD = NoisyLoadData(load_data=self.load_data) - assert_frame_equal(NLD.data, self.load_data.to_frame()) - assert_frame_equal(NLD.unmunged_data, self.load_data.to_frame()) - - def test_data_munge(self): - NLD = NoisyLoadData(load_data=self.load_data) - - self.assertFalse(NLD.munged) - NLD.data_munge() - self.assertTrue(NLD.munged) - - self.assertTupleEqual(NLD.load_mean.shape, (7,24)) - self.assertTupleEqual(NLD.load_std.shape, (7,24)) - - self.assertEqual(NLD.data.shape[0], self.n_days) - self.assertFalse(np.isnan(NLD.load_mean).any(axis=None)) - self.assertFalse(np.isnan(NLD.load_std).any(axis=None)) - - for j in range(7): - NLD_computed_avg = NLD.load_mean.iloc[j,:].values - NLD_computed_std = NLD.load_std.iloc[j,:].values - - - slice = self.load_data[24*j:24*(j+1)].values - - for k in range(1, (self.n_days-j) // 7 + 1): - if (j+k*7) >= self.n_days: - continue - slice = np.stack((slice,self.load_data[24*(j+k*7):24*(j+k*7+1)])) - - if len(slice.shape) == 1: - slice = slice.reshape((1, 24)) - assert_array_almost_equal(NLD_computed_avg, np.mean(slice, axis=0)) - else: - assert_array_almost_equal(NLD_computed_avg, np.mean(slice, axis=0)) - assert_array_almost_equal(NLD_computed_std, np.std(slice, axis=0, ddof=1)) - -if __name__ == '__main__': - unittest.main() - diff --git a/tests/control/test_control.py b/tests/control/test_control.py deleted file mode 100644 index c5c2eee9..00000000 --- a/tests/control/test_control.py +++ /dev/null @@ -1,2 +0,0 @@ -import unittest - diff --git a/tests/control/test_mpc.py b/tests/control/test_mpc.py deleted file mode 100644 index d92d1adc..00000000 --- a/tests/control/test_mpc.py +++ /dev/null @@ -1,134 +0,0 @@ -import numpy as np - -from tests.helpers.test_case import TestCase -from tests.helpers.modular_microgrid import get_modular_microgrid - -from pymgrid.algos import ModelPredictiveControl - - -class TestMPC(TestCase): - def test_init(self): - microgrid = get_modular_microgrid() - mpc = ModelPredictiveControl(microgrid) - self.assertTrue(mpc.is_modular) - self.assertEqual(mpc.horizon, 1) - - def test_run_with_load_pv_battery_grid(self): - from pymgrid.modules import RenewableModule, LoadModule - - max_steps = 10 - pv_const = 50 - load_const = 60 - pv = RenewableModule(time_series=pv_const*np.ones(100)) - load = LoadModule(time_series=load_const*np.ones(100)) - - microgrid = get_modular_microgrid(remove_modules=["renewable", "load", "genset"], additional_modules=[pv, load]) - - mpc = ModelPredictiveControl(microgrid) - mpc_output = mpc.run(max_steps=max_steps) - self.assertEqual(mpc_output.shape[0], max_steps) - self.assertEqual(mpc_output[("grid", 0, "grid_import")].values + - mpc_output[("battery", 0, "discharge_amount")].values + - mpc_output[("renewable", 0, "renewable_used")].values, - [load_const] * mpc_output.shape[0] - ) - - def test_run_with_load_pv_battery_genset(self): - from pymgrid.modules import RenewableModule, LoadModule - - max_steps = 10 - pv_const = 50 - load_const = 60 - pv = RenewableModule(time_series=pv_const*np.ones(100)) - load = LoadModule(time_series=load_const*np.ones(100)) - - microgrid = get_modular_microgrid(remove_modules=["renewable", "load", "grid"], additional_modules=[pv, load]) - - mpc = ModelPredictiveControl(microgrid) - mpc_output = mpc.run(max_steps=max_steps) - self.assertEqual(mpc_output.shape[0], max_steps) - - self.assertEqual(mpc_output[("load", 0, "load_met")].values, [60.]*mpc_output.shape[0]) - self.assertEqual(mpc_output[("genset", 0, "genset_production")].values + - mpc_output[("battery", 0, "discharge_amount")].values, - [10.] * mpc_output.shape[0]) - - def test_run_twice_with_load_pv_battery_genset_without_reset(self): - from pymgrid.modules import RenewableModule, LoadModule - - max_steps = 10 - pv_const = 50 - load_const = 60 - pv = RenewableModule(time_series=pv_const*np.ones(100)) - load = LoadModule(time_series=load_const*np.ones(100)) - - microgrid = get_modular_microgrid(remove_modules=["renewable", "load", "grid"], additional_modules=[pv, load]) - - mpc = ModelPredictiveControl(microgrid) - mpc_output = mpc.run(max_steps=max_steps) - - self.assertEqual(mpc_output.shape[0], max_steps) - self.assertEqual(mpc_output[("load", 0, "load_met")].values, [60.] * mpc_output.shape[0]) - self.assertEqual(mpc_output[("genset", 0, "genset_production")].values + - mpc_output[("battery", 0, "discharge_amount")].values, - [10.] * mpc_output.shape[0]) - - mpc_output = mpc.run(max_steps=max_steps) - - self.assertEqual(mpc_output.shape[0], 2 * max_steps) - self.assertEqual(mpc_output[("load", 0, "load_met")].values, [60.] * mpc_output.shape[0]) - self.assertEqual(mpc_output[("genset", 0, "genset_production")].values + - mpc_output[("battery", 0, "discharge_amount")].values, - [10.] * mpc_output.shape[0]) - - def test_run_twice_with_load_pv_battery_genset_with_reset(self): - from pymgrid.modules import RenewableModule, LoadModule - - max_steps = 10 - pv_const = 50 - load_const = 60 - pv = RenewableModule(time_series=pv_const*np.ones(100)) - load = LoadModule(time_series=load_const*np.ones(100)) - - microgrid = get_modular_microgrid(remove_modules=["renewable", "load", "grid"], additional_modules=[pv, load]) - - mpc = ModelPredictiveControl(microgrid) - mpc_output = mpc.run(max_steps=max_steps) - - self.assertEqual(mpc_output.shape[0], max_steps) - self.assertEqual(mpc_output[("load", 0, "load_met")].values, [60.] * mpc_output.shape[0]) - self.assertEqual(mpc_output[("genset", 0, "genset_production")].values + - mpc_output[("battery", 0, "discharge_amount")].values, - [10.] * mpc_output.shape[0]) - - mpc.reset() - mpc_output = mpc.run(max_steps=max_steps) - - self.assertEqual(mpc_output.shape[0], max_steps) - self.assertEqual(mpc_output[("load", 0, "load_met")].values, [60.] * mpc_output.shape[0]) - self.assertEqual(mpc_output[("genset", 0, "genset_production")].values + - mpc_output[("battery", 0, "discharge_amount")].values, - [10.] * mpc_output.shape[0]) - - def test_run_with_load_pv_battery_grid_different_names(self): - from pymgrid.modules import RenewableModule, LoadModule - - max_steps = 10 - pv_const = 50 - load_const = 60 - pv = RenewableModule(time_series=pv_const*np.ones(100)) - load = LoadModule(time_series=load_const*np.ones(100)) - - microgrid = get_modular_microgrid(remove_modules=["renewable", "load", "genset"], - additional_modules=[("pv_with_name", pv), ("load_with_name", load)]) - - mpc = ModelPredictiveControl(microgrid) - mpc_output = mpc.run(max_steps=max_steps) - self.assertEqual(mpc_output.shape[0], max_steps) - self.assertEqual(mpc_output[("load_with_name", 0, "load_met")].values, [load_const]*mpc_output.shape[0]) - self.assertEqual(mpc_output[("grid", 0, "grid_import")].values + - mpc_output[("battery", 0, "discharge_amount")].values + - mpc_output[("pv_with_name", 0, "renewable_used")].values, - [load_const] * mpc_output.shape[0] - ) - self.assertEqual(mpc_output[("load_with_name", 0, "load_met")].values, [load_const]*mpc_output.shape[0]) diff --git a/tests/control/test_mpc_scenarios.py b/tests/control/test_mpc_scenarios.py deleted file mode 100644 index d8b85cc7..00000000 --- a/tests/control/test_mpc_scenarios.py +++ /dev/null @@ -1,171 +0,0 @@ -import pytest - - -from tests.helpers.test_case import TestCase -from tests.helpers.modular_microgrid import get_modular_microgrid - -from pymgrid import Microgrid -from pymgrid.algos import ModelPredictiveControl -from pymgrid.modules.base import BaseTimeSeriesMicrogridModule -from pymgrid.forecast import OracleForecaster, GaussianNoiseForecaster - - -@pytest.mark.slow -class MPCScenario(TestCase): - microgrid_number: int - - def setUp(self) -> None: - microgrid = Microgrid.from_scenario(microgrid_number=self.microgrid_number) - self.mpc = ModelPredictiveControl(microgrid) - - def test_correct_forecasts_oracle_forecaster(self): - self.mpc.microgrid.set_forecaster(forecaster='oracle', forecast_horizon=23) - self.mpc.run() - - for module in self.mpc.microgrid.modules.iterlist(): - if not isinstance(module, BaseTimeSeriesMicrogridModule): - continue - - self.assertIsInstance(module.forecaster, OracleForecaster) - - for state_component in module.state_components: - current_value_log = module.log[f'{state_component}_current'] - - for forecast_step in range(module.forecast_horizon): - forecast_value_log = module.log[f'{state_component}_forecast_{forecast_step}'] - shifted_forecast = forecast_value_log.shift(forecast_step+1) - - with self.subTest( - module_name=module.name, - state_component=state_component, - forecast_step=forecast_step - ): - self.assertEqual(current_value_log.iloc[forecast_step:], shifted_forecast.iloc[forecast_step:]) - - self.assertTrue(False) - - def test_correct_forecasts_gaussian_forecaster_zero_noise(self): - self.microgrid.set_forecaster(forecaster=0.0, forecast_horizon=23) - self.mpc.run() - - for module in self.microgrid.modules.iterlist(): - if not isinstance(module, BaseTimeSeriesMicrogridModule): - continue - - self.assertIsInstance(module.forecaster, GaussianNoiseForecaster) - - for state_component in module.state_components: - current_value_log = module.log[f'{state_component}_current'] - - for forecast_step in range(module.forecast_horizon): - forecast_value_log = module.log[f'{state_component}_forecast_{forecast_step}'] - shifted_forecast = forecast_value_log.shift(forecast_step + 1) - - with self.subTest( - module_name=module.name, - state_component=state_component, - forecast_step=forecast_step - ): - self.assertEqual(current_value_log.iloc[forecast_step:], shifted_forecast.iloc[forecast_step:]) - - self.assertTrue(False) - - -class TestMPCScenario0(MPCScenario): - microgrid_number = 1 - - -class TestMPCScenario1(MPCScenario): - microgrid_number = 1 - - -class TestMPCScenario2(MPCScenario): - microgrid_number = 2 - - -class TestMPCScenario3(MPCScenario): - microgrid_number = 3 - - -class TestMPCScenario4(MPCScenario): - microgrid_number = 4 - - -class TestMPCScenario5(MPCScenario): - microgrid_number = 5 - - -class TestMPCScenario6(MPCScenario): - microgrid_number = 6 - - -class TestMPCScenario47(MPCScenario): - microgrid_number = 7 - - -class TestMPCScenario8(MPCScenario): - microgrid_number = 8 - - -class TestMPCScenario9(MPCScenario): - microgrid_number = 9 - - -class TestMPCScenario10(MPCScenario): - microgrid_number = 10 - - -class TestMPCScenario11(MPCScenario): - microgrid_number = 11 - - -class TestMPCScenario12(MPCScenario): - microgrid_number = 12 - - -class TestMPCScenario13(MPCScenario): - microgrid_number = 13 - - -class TestMPCScenario14(MPCScenario): - microgrid_number = 14 - - -class TestMPCScenario15(MPCScenario): - microgrid_number = 15 - - -class TestMPCScenario16(MPCScenario): - microgrid_number = 16 - - -class TestMPCScenario17(MPCScenario): - microgrid_number = 17 - - -class TestMPCScenario18(MPCScenario): - microgrid_number = 18 - - -class TestMPCScenario19(MPCScenario): - microgrid_number = 19 - - -class TestMPCScenario20(MPCScenario): - microgrid_number = 20 - - -class TestMPCScenario21(MPCScenario): - microgrid_number = 21 - - -class TestMPCScenario22(MPCScenario): - microgrid_number = 22 - - -class TestMPCScenario23(MPCScenario): - microgrid_number = 23 - - -class TestMPCScenario24(MPCScenario): - microgrid_number = 24 diff --git a/tests/control/test_rbc.py b/tests/control/test_rbc.py deleted file mode 100644 index e7baf546..00000000 --- a/tests/control/test_rbc.py +++ /dev/null @@ -1,41 +0,0 @@ -from copy import deepcopy - -from tests.helpers.test_case import TestCase -from tests.helpers.modular_microgrid import get_modular_microgrid - -from pymgrid.algos import RuleBasedControl - - -class TestRBC(TestCase): - def setUp(self) -> None: - self.rbc = RuleBasedControl(get_modular_microgrid()) - - def test_init(self): - microgrid = get_modular_microgrid() - self.assertEqual(microgrid, self.rbc.microgrid) - self.assertEqual(microgrid, deepcopy(self.rbc).microgrid) - - def test_priority_list(self): - rbc = deepcopy(self.rbc) - - for j, (element_1, element_2) in enumerate(zip(rbc.priority_list[:-1], rbc.priority_list[1:])): - with self.subTest(testing=f'element_{j}<=element_{j+1}'): - self.assertLessEqual(element_1.marginal_cost, element_2.marginal_cost) - - def test_run_once(self): - rbc = deepcopy(self.rbc) - - self.assertEqual(len(rbc.microgrid.log), 0) - - n_steps = 10 - - log = rbc.run(n_steps) - - self.assertEqual(len(log), n_steps) - self.assertEqual(log, rbc.microgrid.log) - return rbc - - def test_reset_after_run(self): - rbc = self.test_run_once() - rbc.reset() - self.assertEqual(len(rbc.microgrid.log), 0) \ No newline at end of file diff --git a/tests/control/test_rbc_scenarios.py b/tests/control/test_rbc_scenarios.py deleted file mode 100644 index e3e5b889..00000000 --- a/tests/control/test_rbc_scenarios.py +++ /dev/null @@ -1,167 +0,0 @@ -import pytest - -from tests.helpers.test_case import TestCase - -from pymgrid import Microgrid -from pymgrid.algos import RuleBasedControl -from pymgrid.modules.base import BaseTimeSeriesMicrogridModule -from pymgrid.forecast import OracleForecaster, GaussianNoiseForecaster - - -@pytest.mark.slow -class TestRBCScenario0(TestCase): - microgrid_number = 0 - - def setUp(self) -> None: - microgrid = Microgrid.from_scenario(microgrid_number=self.microgrid_number) - self.rbc = RuleBasedControl(microgrid) - - def test_correct_forecasts_oracle_forecaster(self): - self.rbc.microgrid.set_forecaster(forecaster='oracle', forecast_horizon=23) - self.rbc.run() - - for module in self.rbc.microgrid.modules.iterlist(): - if not isinstance(module, BaseTimeSeriesMicrogridModule): - continue - - self.assertIsInstance(module.forecaster, OracleForecaster) - - for state_component in module.state_components: - current_value_log = module.log[f'{state_component}_current'] - - for forecast_step in range(module.forecast_horizon): - forecast_value_log = module.log[f'{state_component}_forecast_{forecast_step}'] - shifted_forecast = forecast_value_log.shift(forecast_step + 1) - - with self.subTest( - module_name=module.name, - state_component=state_component, - forecast_step=forecast_step - ): - self.assertEqual( - current_value_log.iloc[forecast_step+1:], - shifted_forecast.iloc[forecast_step+1:] - ) - - def test_correct_forecasts_gaussian_forecaster_zero_noise(self): - self.rbc.microgrid.set_forecaster(forecaster=0.0, forecast_horizon=5) - self.rbc.run() - - for module in self.rbc.microgrid.modules.iterlist(): - if not isinstance(module, BaseTimeSeriesMicrogridModule): - continue - - self.assertIsInstance(module.forecaster, GaussianNoiseForecaster) - - for state_component in module.state_components: - current_value_log = module.log[f'{state_component}_current'] - - for forecast_step in range(module.forecast_horizon): - forecast_value_log = module.log[f'{state_component}_forecast_{forecast_step}'] - shifted_forecast = forecast_value_log.shift(forecast_step + 1) - - with self.subTest( - module_name=module.name, - state_component=state_component, - forecast_step=forecast_step - ): - self.assertEqual( - current_value_log.iloc[forecast_step+1:], - shifted_forecast.iloc[forecast_step+1:] - ) - - -class TestRBCScenario1(TestRBCScenario0): - microgrid_number = 1 - - -class TestRBCScenario2(TestRBCScenario0): - microgrid_number = 2 - - -class TestRBCScenario3(TestRBCScenario0): - microgrid_number = 3 - - -class TestRBCScenario4(TestRBCScenario0): - microgrid_number = 4 - - -class TestRBCScenario5(TestRBCScenario0): - microgrid_number = 5 - - -class TestRBCScenario6(TestRBCScenario0): - microgrid_number = 6 - - -class TestRBCScenario47(TestRBCScenario0): - microgrid_number = 7 - - -class TestRBCScenario8(TestRBCScenario0): - microgrid_number = 8 - - -class TestRBCScenario9(TestRBCScenario0): - microgrid_number = 9 - - -class TestRBCScenario10(TestRBCScenario0): - microgrid_number = 10 - - -class TestRBCScenario11(TestRBCScenario0): - microgrid_number = 11 - - -class TestRBCScenario12(TestRBCScenario0): - microgrid_number = 12 - - -class TestRBCScenario13(TestRBCScenario0): - microgrid_number = 13 - - -class TestRBCScenario14(TestRBCScenario0): - microgrid_number = 14 - - -class TestRBCScenario15(TestRBCScenario0): - microgrid_number = 15 - - -class TestRBCScenario16(TestRBCScenario0): - microgrid_number = 16 - - -class TestRBCScenario17(TestRBCScenario0): - microgrid_number = 17 - - -class TestRBCScenario18(TestRBCScenario0): - microgrid_number = 18 - - -class TestRBCScenario19(TestRBCScenario0): - microgrid_number = 19 - - -class TestRBCScenario20(TestRBCScenario0): - microgrid_number = 20 - - -class TestRBCScenario21(TestRBCScenario0): - microgrid_number = 21 - - -class TestRBCScenario22(TestRBCScenario0): - microgrid_number = 22 - - -class TestRBCScenario23(TestRBCScenario0): - microgrid_number = 23 - - -class TestRBCScenario24(TestRBCScenario0): - microgrid_number = 24 diff --git a/tests/envs/__init__.py b/tests/envs/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/envs/test_base.py b/tests/envs/test_base.py deleted file mode 100644 index a3a1382a..00000000 --- a/tests/envs/test_base.py +++ /dev/null @@ -1,248 +0,0 @@ -import functools -import numpy as np -import pandas as pd - -from copy import deepcopy - -from tests.helpers.test_case import TestCase -from tests.helpers.modular_microgrid import get_modular_microgrid - -from pymgrid.modules import BatteryModule -from pymgrid.envs import DiscreteMicrogridEnv, ContinuousMicrogridEnv, NetLoadContinuousMicrogridEnv -from pymgrid.envs.base import BaseMicrogridEnv - - -def pass_if_parent(func): - @functools.wraps(func) - def wrapper(self, *args, **kwargs): - if self.env_class is not None: - return func(self, *args, **kwargs) - - return wrapper - - -class Parent(TestCase): - env_class: BaseMicrogridEnv = None - observation_keys = () - - @pass_if_parent - def setUp(self) -> None: - self.env = self.env_class.from_microgrid(get_modular_microgrid(), observation_keys=self.observation_keys) - - @pass_if_parent - def test_reset_obs_in_obs_space(self): - env = deepcopy(self.env) - obs = env.reset() - - self.assertIn(obs, env.observation_space) - - @pass_if_parent - def test_pre_reset_state_series_invariant_to_observation_keys(self): - env = deepcopy(self.env) - - self.assertEqual(env.state_series().shape, (13, )) - - @pass_if_parent - def test_flattened_state_dict_is_state_series(self): - env = deepcopy(self.env) - - state_dict = env.state_dict(normalized=True, as_run_output=True) - flattened_state_dict = flatten_nested_dict(state_dict) - - self.assertEqual(flattened_state_dict, env.state_series(normalized=True).values) - - @pass_if_parent - def test_pre_reset_state_dict_invariant_to_observation_keys(self): - env = deepcopy(self.env) - state_dict = env.state_dict() - - n_state_dict_values = functools.reduce(lambda x, y: x+len(y[0]), state_dict.values(), 0) - - self.assertEqual(n_state_dict_values, 13) - - @pass_if_parent - def test_obs_values_after_reset(self): - env = deepcopy(self.env) - obs = env.reset() - - if self.observation_keys: - expected_obs = env.state_series(normalized=True).loc[pd.IndexSlice[:, :, self.observation_keys]].values - else: - expected_obs = env.state_series(normalized=True).values - - self.assertEqual(obs, expected_obs) - - @pass_if_parent - def test_state_series_values(self): - env = deepcopy(self.env) - - expected_state_series = np.array([10., -60., 50., 1., 1., 0., 0., 0.5, 50., 1., 1., 1., 1.]) - self.assertEqual(env.state_series(normalized=False).values, expected_state_series) - - @pass_if_parent - def test_state_series_values_normalized(self): - env = deepcopy(self.env) - - expected_state_series = np.array([1/6., 0., 1., 1., 1., 0., 0., 0.5, 0.5, 0., 0., 0., 0.]) - self.assertEqual(env.state_series(normalized=True).values, expected_state_series) - - @pass_if_parent - def test_get_obs(self): - env = deepcopy(self.env) - obs = env._get_obs() - - if self.observation_keys: - expected_obs = env.state_series(normalized=True).loc[pd.IndexSlice[:, :, self.observation_keys]].values - else: - expected_obs = env.state_series(normalized=True).values - - self.assertEqual(obs, expected_obs) - - @pass_if_parent - def test_steps(self): - env = deepcopy(self.env) - - env.reset() - - for j in range(10): - with self.subTest(step=j): - obs, _, _, _ = env.step(env.action_space.sample()) - self.assertTrue((obs >= 0).all()) - self.assertTrue((obs <= 1).all()) - - -class ObsKeysNoNetLoadParent(Parent): - observation_keys = ['soc', 'import_price_current', 'goal_status', 'load_current', 'renewable_current'] - - @pass_if_parent - def test_get_obs_correct_keys_in_modules(self): - env = deepcopy(self.env) - obs = env._get_obs() - - for module in env.modules.iterlist(): - module_state_dict = module.state_dict(normalized=True) - matching_keys = [obs_key for obs_key in self.observation_keys if obs_key in module.state_dict().keys()] - matching_values = [module_state_dict[k] for k in matching_keys] - - with self.subTest(module=module.name, keys=matching_keys): - self.assertEqual(obs[np.isin(self.observation_keys, matching_keys)], matching_values) - - -class ObsKeysWithNetLoadParent(ObsKeysNoNetLoadParent): - observation_keys = ['net_load', 'soc', 'load_current', 'export_price_current'] - - -class ObsKeysDuplicateKeysParent(ObsKeysNoNetLoadParent): - observation_keys = ['net_load', 'soc', 'load_current', 'load_current', 'export_price_current'] - - @pass_if_parent - def test_get_obs_correct_keys_in_modules(self): - env = deepcopy(self.env) - obs = env._get_obs() - - unique_obs_keys = pd.Index(self.observation_keys).drop_duplicates().tolist() - - for module in env.modules.iterlist(): - module_state_dict = module.state_dict(normalized=True) - matching_keys = [obs_key for obs_key in unique_obs_keys if obs_key in module.state_dict().keys()] - matching_values = [module_state_dict[k] for k in matching_keys] - - with self.subTest(module=module.name, keys=matching_keys): - self.assertEqual(obs[np.isin(unique_obs_keys, matching_keys)], matching_values) - - -class ObsKeysDuplicateModulesParent(Parent): - - @pass_if_parent - def setUp(self) -> None: - second_battery = BatteryModule( - min_capacity=0, - max_capacity=1000, - max_charge=500, - max_discharge=500, - efficiency=1.0, - init_soc=0.5, - normalized_action_bounds=(0, 1)) - - microgrid = get_modular_microgrid( - additional_modules=[second_battery], - ) - - self.env = self.env_class.from_microgrid(microgrid, observation_keys=self.observation_keys) - - @pass_if_parent - def test_pre_reset_state_series_invariant_to_observation_keys(self): - env = deepcopy(self.env) - - self.assertEqual(env.state_series().shape, (15, )) - - @pass_if_parent - def test_state_series_values(self): - env = deepcopy(self.env) - - expected_state_series = np.array([10., -60., 50., 1., 1., 0., 0., 0.5, 50., 0.5, 500, 1., 1., 1., 1.]) - self.assertEqual(env.state_series(normalized=False).values, expected_state_series) - - @pass_if_parent - def test_state_series_values_normalized(self): - env = deepcopy(self.env) - - expected_state_series = np.array([1/6., 0., 1., 1., 1., 0., 0., 0.5, 0.5, 0.5, 0.5, 0., 0., 0., 0.]) - self.assertEqual(env.state_series(normalized=True).values, expected_state_series) - - -class TestDiscrete(Parent): - env_class = DiscreteMicrogridEnv - - -class TestContinuous(Parent): - env_class = ContinuousMicrogridEnv - - -class TestNetLoadContinuous(Parent): - env_class = NetLoadContinuousMicrogridEnv - - -class TestDiscreteObsKeysNoNetLoad(ObsKeysNoNetLoadParent): - env_class = DiscreteMicrogridEnv - - -class TestContinuousObsKeysNoNetLoad(ObsKeysNoNetLoadParent): - env_class = ContinuousMicrogridEnv - - -class TestNetLoadContinuousObsKeysNoNetLoad(ObsKeysNoNetLoadParent): - env_class = NetLoadContinuousMicrogridEnv - - -class TestDiscreteObsDuplicateKeys(ObsKeysDuplicateKeysParent): - env_class = DiscreteMicrogridEnv - - -class TestContinuousObsDuplicateKeys(ObsKeysDuplicateKeysParent): - env_class = ContinuousMicrogridEnv - - -class TestNetLoadContinuousObsDuplicateKeys(ObsKeysDuplicateKeysParent): - env_class = NetLoadContinuousMicrogridEnv - - -class TestDiscreteDuplicateModules(ObsKeysDuplicateModulesParent): - env_class = DiscreteMicrogridEnv - - -class TestContinuousDuplicateModules(ObsKeysDuplicateModulesParent): - env_class = ContinuousMicrogridEnv - - -class TestNetLoadContinuousDuplicateModules(ObsKeysDuplicateModulesParent): - env_class = NetLoadContinuousMicrogridEnv - - -def flatten_nested_dict(nested_dict): - def extract_list(l): - # assert len(l) == 1, 'reduction only works with length 1 lists' - # return l[0].tolist() - return sum([_l.tolist() for _l in l], []) - - return functools.reduce(lambda x, y: x + extract_list(y), nested_dict.values(), []) diff --git a/tests/envs/test_continuous_net_load.py b/tests/envs/test_continuous_net_load.py deleted file mode 100644 index fbbc8099..00000000 --- a/tests/envs/test_continuous_net_load.py +++ /dev/null @@ -1,740 +0,0 @@ -import numpy as np - -from copy import deepcopy -from gym.spaces import Box - -from tests.helpers.test_case import TestCase -from tests.helpers.modular_microgrid import get_modular_microgrid - -from tests.envs.test_discrete import TestDiscreteEnvScenario - -from pymgrid.envs import NetLoadContinuousMicrogridEnv -from pymgrid.modules import RenewableModule, BatteryModule -from pymgrid import Microgrid - - -class TestNetLoadContinuousEnv(TestCase): - def test_init_from_microgrid(self): - microgrid = get_modular_microgrid() - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid) - - self.assertEqual(env.modules, microgrid.modules) - self.assertIsNot(env.modules.to_tuples(), microgrid.modules.to_tuples()) - - # add one for net load - n_obs = 1 + sum([x.observation_space['normalized'].shape[0] for x in microgrid.modules.to_list()]) - - self.assertEqual(env.observation_space.shape, (n_obs,)) - - def test_action_space(self): - microgrid = get_modular_microgrid() - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid) - - n_actions = len(env.modules.controllable) - - if 'genset' in env.modules: - n_actions += len(env.modules.genset) - - self.assertEqual(env.action_space, Box(low=0, high=1, shape=(n_actions, ))) - - def test_net_load(self): - microgrid = get_modular_microgrid() - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid) - - net_load = 10 - - self.assertEqual( - microgrid.modules.load.item().current_load-microgrid.modules.renewable.item().current_renewable, net_load) - - self.assertEqual(env.compute_net_load(), net_load) - - pass - - def test_convert_action_to_absolute(self): - microgrid = get_modular_microgrid() - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid) - - expected_absolute_action = { - 'battery': [np.array([5])], - 'genset': [np.array([0, 2.5])], - 'grid': [np.array([2.5])] - } - - relative_action = np.array([0.5, 0, 0.25, 0.25]) - - absolute_action = env.convert_action(relative_action) - - for module_name, action_list in expected_absolute_action.items(): - for module_num, act in enumerate(action_list): - with self.subTest(module_name=module_name, module_num=module_num): - self.assertEqual(act, absolute_action[module_name][module_num]) - - def test_convert_action_to_relative(self): - microgrid = get_modular_microgrid() - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid) - - expected_relative_action = np.array([0.5, 0, 0.25, 0.25]) - - absolute_action = { - 'battery': [np.array([5])], - 'genset': [np.array([0, 2.5])], - 'grid': [np.array([2.5])] - } - - relative_action = env.convert_action(absolute_action, to_microgrid=False) - self.assertEqual(relative_action, expected_relative_action) - - def test_convert_action_to_absolute_zero_net_load(self): - new_renewable_module = RenewableModule(time_series=60*np.ones(100)) - microgrid = get_modular_microgrid(remove_modules=['renewable'], additional_modules=[new_renewable_module]) - - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid) - - self.assertEqual(env.compute_net_load(), 0.0) - - expected_absolute_action = { - 'battery': [np.array([0])], - 'genset': [np.array([0, 0])], - 'grid': [np.array([0])] - } - - relative_action = np.array([0.5, 0, 0.25, 0.25]) - - absolute_action = env.convert_action(relative_action) - - for module_name, action_list in expected_absolute_action.items(): - for module_num, act in enumerate(action_list): - with self.subTest(module_name=module_name, module_num=module_num): - self.assertEqual(act, absolute_action[module_name][module_num]) - - def test_convert_action_to_relative_zero_net_load(self): - new_renewable_module = RenewableModule(time_series=60 * np.ones(100)) - microgrid = get_modular_microgrid(remove_modules=['renewable'], additional_modules=[new_renewable_module]) - - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid) - - self.assertEqual(env.compute_net_load(), 0.0) - - expected_relative_action = np.array([0.0, 0.0, 0.0, 0.0]) - - absolute_action = { - 'battery': [np.array([5])], - 'genset': [np.array([0, 2.5])], - 'grid': [np.array([2.5])] - } - - relative_action = env.convert_action(absolute_action, to_microgrid=False) - self.assertEqual(relative_action, expected_relative_action) - - def test_convert_action_to_absolute_negative_net_load_clip_actions(self): - new_renewable_module = RenewableModule(time_series=70*np.ones(100)) - microgrid = get_modular_microgrid(remove_modules=['renewable'], additional_modules=[new_renewable_module]) - - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid) - - self.assertEqual(env.compute_net_load(), -10.0) - - expected_absolute_action = { - 'battery': [np.array([-5])], - 'genset': [np.array([0, 0.0])], - 'grid': [np.array([-2.5])] - } - - relative_action = np.array([0.5, 0, 0.25, 0.25]) - absolute_action = env.convert_action(relative_action) - - for module_name, action_list in expected_absolute_action.items(): - for module_num, act in enumerate(action_list): - with self.subTest(module_name=module_name, module_num=module_num): - self.assertEqual(act, absolute_action[module_name][module_num]) - - def test_convert_action_to_relative_negative_net_load(self): - new_renewable_module = RenewableModule(time_series=70*np.ones(100)) - microgrid = get_modular_microgrid(remove_modules=['renewable'], additional_modules=[new_renewable_module]) - - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid) - - self.assertEqual(env.compute_net_load(), -10.0) - - expected_relative_action = np.array([0.5, 0, 0.25, 0.25]) - - absolute_action = { - 'battery': [np.array([-5])], - 'genset': [np.array([0, -2.5])], - 'grid': [np.array([-2.5])] - } - - relative_action = env.convert_action(absolute_action, to_microgrid=False) - self.assertEqual(relative_action, expected_relative_action) - - def test_convert_action_to_absolute_negative_net_load_no_clip_actions(self): - new_renewable_module = RenewableModule(time_series=70*np.ones(100)) - microgrid = get_modular_microgrid(remove_modules=['renewable'], additional_modules=[new_renewable_module]) - - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, clip_actions=False) - - self.assertEqual(env.compute_net_load(), -10.0) - - expected_absolute_action = { - 'battery': [np.array([-5])], - 'genset': [np.array([0, -2.5])], - 'grid': [np.array([-2.5])] - } - - relative_action = np.array([0.5, 0, 0.25, 0.25]) - absolute_action = env.convert_action(relative_action) - - for module_name, action_list in expected_absolute_action.items(): - for module_num, act in enumerate(action_list): - with self.subTest(module_name=module_name, module_num=module_num): - self.assertEqual(act, absolute_action[module_name][module_num]) - - def test_clip_action(self): - battery = BatteryModule( - min_capacity=0, - max_capacity=100, - max_charge=50, - max_discharge=50, - efficiency=1.0, - init_soc=0.8, - ) - - microgrid = get_modular_microgrid(remove_modules=['battery'], additional_modules=[battery]) - - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, clip_actions=True) - - out_of_range_action = { - 'battery': [np.array([-30])], - 'genset': [np.array([0, 0])], - 'grid': [np.array([0.0])] - } - - expected_clipped_action = { - 'battery': [np.array([-20])], - 'genset': [np.array([0, 0])], - 'grid': [np.array([0.0])] - } - - clipped_action = env.clip_action(out_of_range_action) - - self.assertEqual(clipped_action, expected_clipped_action) - - -class TestNetLoadContinuousEnvSlackModule(TestCase): - def test_init_from_microgrid(self): - microgrid = get_modular_microgrid() - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, slack_module=('grid', 0)) - - self.assertEqual(env.modules, microgrid.modules) - self.assertIsNot(env.modules.to_tuples(), microgrid.modules.to_tuples()) - - self.assertEqual(env.slack_module, ('grid', 0)) - - try: - action_space_keys = list(env._nested_action_space.keys()) - except AttributeError: # Dict object does not subclass mapping in old version of gym - action_space_keys = list(env._nested_action_space.spaces.keys()) - - self.assertIn('battery', action_space_keys) - self.assertIn('genset', action_space_keys) - self.assertNotIn('grid', action_space_keys) - - # add one for net load - n_obs = 1 + sum([x.observation_space['normalized'].shape[0] for x in microgrid.modules.to_list()]) - - self.assertEqual(env.observation_space.shape, (n_obs,)) - - def test_action_space(self): - microgrid = get_modular_microgrid() - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, slack_module=('grid', 0)) - - n_actions = len(env.modules.controllable) - 1 # subtract grid, not in action space - - if 'genset' in env.modules: - n_actions += len(env.modules.genset) - - self.assertEqual(env.action_space, Box(low=0, high=1, shape=(n_actions, ))) - - def test_net_load(self): - microgrid = get_modular_microgrid() - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, slack_module=('grid', 0)) - - net_load = 10 - - self.assertEqual( - microgrid.modules.load.item().current_load - microgrid.modules.renewable.item().current_renewable, net_load) - - self.assertEqual(env.compute_net_load(), net_load) - - def test_convert_action_to_absolute(self): - microgrid = get_modular_microgrid() - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, slack_module=('grid', 0)) - - expected_absolute_action = { - 'battery': [np.array([5])], - 'genset': [np.array([0, 2.5])], - 'grid': [np.array([2.5])] - } - - relative_action = np.array([0.5, 0, 0.25]) - - absolute_action = env.convert_action(relative_action) - - for module_name, action_list in expected_absolute_action.items(): - for module_num, act in enumerate(action_list): - with self.subTest(module_name=module_name, module_num=module_num): - self.assertEqual(act, absolute_action[module_name][module_num]) - - def test_convert_action_to_relative(self): - microgrid = get_modular_microgrid() - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, slack_module=('grid', 0)) - - expected_relative_action = np.array([0.5, 0, 0.25]) - - absolute_action = { - 'battery': [np.array([5])], - 'genset': [np.array([0, 2.5])], - 'grid': [np.array([2.5])] - } - - relative_action = env.convert_action(absolute_action, to_microgrid=False) - self.assertEqual(relative_action, expected_relative_action) - - def test_convert_action_to_absolute_zero_net_load(self): - new_renewable_module = RenewableModule(time_series=60 * np.ones(100)) - microgrid = get_modular_microgrid(remove_modules=['renewable'], additional_modules=[new_renewable_module]) - - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, slack_module=('grid', 0)) - - self.assertEqual(env.compute_net_load(), 0.0) - - expected_absolute_action = { - 'battery': [np.array([0])], - 'genset': [np.array([0, 0])], - 'grid': [np.array([0])] - } - - relative_action = np.array([0.5, 0, 0.25]) - - absolute_action = env.convert_action(relative_action) - - for module_name, action_list in expected_absolute_action.items(): - for module_num, act in enumerate(action_list): - with self.subTest(module_name=module_name, module_num=module_num): - self.assertEqual(act, absolute_action[module_name][module_num]) - - def test_convert_action_to_relative_zero_net_load(self): - new_renewable_module = RenewableModule(time_series=60 * np.ones(100)) - microgrid = get_modular_microgrid(remove_modules=['renewable'], additional_modules=[new_renewable_module]) - - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, slack_module=('grid', 0)) - - self.assertEqual(env.compute_net_load(), 0.0) - - expected_relative_action = np.array([0.0, 0.0, 0.0]) - - absolute_action = { - 'battery': [np.array([5])], - 'genset': [np.array([0, 2.5])], - 'grid': [np.array([2.5])] - } - - relative_action = env.convert_action(absolute_action, to_microgrid=False) - self.assertEqual(relative_action, expected_relative_action) - - def test_convert_action_to_absolute_negative_net_load_with_clip(self): - new_renewable_module = RenewableModule(time_series=70*np.ones(100)) - microgrid = get_modular_microgrid(remove_modules=['renewable'], additional_modules=[new_renewable_module]) - - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, slack_module=('grid', 0)) - - self.assertEqual(env.compute_net_load(), -10.0) - - expected_absolute_action = { - 'battery': [np.array([-5.])], - 'genset': [np.array([0, 0])], - 'grid': [np.array([-5.])] - } - - relative_action = np.array([0.5, 0, 0.25]) - - absolute_action = env.convert_action(relative_action) - - for module_name, action_list in expected_absolute_action.items(): - for module_num, act in enumerate(action_list): - with self.subTest(module_name=module_name, module_num=module_num): - self.assertEqual(act, absolute_action[module_name][module_num]) - - def test_convert_action_to_absolute_negative_net_load_with_clip_out_of_range_genset_status(self): - new_renewable_module = RenewableModule(time_series=70*np.ones(100)) - microgrid = get_modular_microgrid(remove_modules=['renewable'], additional_modules=[new_renewable_module]) - - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, slack_module=('grid', 0)) - - self.assertEqual(env.compute_net_load(), -10.0) - - expected_absolute_action = { - 'battery': [np.array([-5.])], - 'genset': [np.array([1.0, 0])], - 'grid': [np.array([-5.])] - } - relative_action = np.array([0.5, 1.1, 0.25]) - - absolute_action = env.convert_action(relative_action) - - for module_name, action_list in expected_absolute_action.items(): - for module_num, act in enumerate(action_list): - with self.subTest(module_name=module_name, module_num=module_num): - self.assertEqual(act, absolute_action[module_name][module_num]) - - def test_convert_action_to_absolute_negative_net_load_no_clip(self): - new_renewable_module = RenewableModule(time_series=70*np.ones(100)) - microgrid = get_modular_microgrid(remove_modules=['renewable'], additional_modules=[new_renewable_module]) - - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, slack_module=('grid', 0), clip_actions=False) - - self.assertEqual(env.compute_net_load(), -10.0) - - expected_absolute_action = { - 'battery': [np.array([-5.])], - 'genset': [np.array([0, -2.5])], - 'grid': [np.array([-2.5])] - } - - relative_action = np.array([0.5, 0, 0.25]) - - absolute_action = env.convert_action(relative_action) - - for module_name, action_list in expected_absolute_action.items(): - for module_num, act in enumerate(action_list): - with self.subTest(module_name=module_name, module_num=module_num): - self.assertEqual(act, absolute_action[module_name][module_num]) - - def test_convert_action_to_relative_negative_net_load(self): - new_renewable_module = RenewableModule(time_series=70*np.ones(100)) - microgrid = get_modular_microgrid(remove_modules=['renewable'], additional_modules=[new_renewable_module]) - - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, slack_module=('grid', 0)) - - self.assertEqual(env.compute_net_load(), -10.0) - - expected_relative_action = np.array([0.5, 0, 0.25]) - - absolute_action = { - 'battery': [np.array([-5])], - 'genset': [np.array([0, -2.5])], - 'grid': [np.array([-2.5])] - } - - relative_action = env.convert_action(absolute_action, to_microgrid=False) - self.assertEqual(relative_action, expected_relative_action) - - def test_convert_action_to_absolute_different_signs_with_clip(self): - microgrid = get_modular_microgrid() - - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, slack_module=('grid', 0)) - - self.assertEqual(env.compute_net_load(), 10.0) - - expected_absolute_action = { - 'battery': [np.array([5.0])], - 'genset': [np.array([1, 0.0])], - 'grid': [np.array([5.0])] - } - - relative_action = np.array([0.5, 1, -0.25]) - - absolute_action = env.convert_action(relative_action) - - for module_name, action_list in expected_absolute_action.items(): - for module_num, act in enumerate(action_list): - with self.subTest(module_name=module_name, module_num=module_num): - self.assertEqual(act, absolute_action[module_name][module_num]) - - def test_convert_action_to_absolute_different_signs_no_clip(self): - microgrid = get_modular_microgrid() - - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, slack_module=('grid', 0), clip_actions=False) - - self.assertEqual(env.compute_net_load(), 10.0) - - expected_absolute_action = { - 'battery': [np.array([5.0])], - 'genset': [np.array([1, -2.5])], - 'grid': [np.array([7.5])] - } - - relative_action = np.array([0.5, 1, -0.25]) - - absolute_action = env.convert_action(relative_action) - - for module_name, action_list in expected_absolute_action.items(): - for module_num, act in enumerate(action_list): - with self.subTest(module_name=module_name, module_num=module_num): - self.assertEqual(act, absolute_action[module_name][module_num]) - - def test_convert_action_to_relative_different_signs(self): - microgrid = get_modular_microgrid() - - env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, slack_module=('grid', 0)) - - self.assertEqual(env.compute_net_load(), 10.0) - - expected_relative_action = np.array([0.5, 1, -0.25]) - - - absolute_action = { - 'battery': [np.array([5.0])], - 'genset': [np.array([1, -2.5])], - 'grid': [np.array([7.5])] - } - - relative_action = env.convert_action(absolute_action, to_microgrid=False) - self.assertEqual(relative_action, expected_relative_action) - - -class TestNetLoadContinuousEnvScenario(TestDiscreteEnvScenario): - microgrid_number = 0 - - def setUp(self) -> None: - self.env = NetLoadContinuousMicrogridEnv.from_scenario(microgrid_number=self.microgrid_number) - - def test_action_space(self): - from gym.spaces import Box - - env = deepcopy(self.env) - - controllable = len(env.modules.controllable) - genset_modules = len(env.modules.genset) if hasattr(env.modules, 'genset') else 0 - - action_dim = controllable + genset_modules - - self.assertEqual(env.action_space, Box(low=0, high=1, shape=(action_dim, ))) - - -class TestNetLoadContinuousEnvScenario1(TestNetLoadContinuousEnvScenario): - microgrid_number = 1 - - -class TestNetLoadContinuousEnvScenario2(TestNetLoadContinuousEnvScenario): - microgrid_number = 2 - - -class TestNetLoadContinuousEnvScenario3(TestNetLoadContinuousEnvScenario): - microgrid_number = 3 - - -class TestNetLoadContinuousEnvScenario4(TestNetLoadContinuousEnvScenario): - microgrid_number = 4 - - -class TestNetLoadContinuousEnvScenario5(TestNetLoadContinuousEnvScenario): - microgrid_number = 5 - - -class TestNetLoadContinuousEnvScenario6(TestNetLoadContinuousEnvScenario): - microgrid_number = 6 - - -class TestNetLoadContinuousEnvScenario7(TestNetLoadContinuousEnvScenario): - microgrid_number = 7 - - -class TestNetLoadContinuousEnvScenario8(TestNetLoadContinuousEnvScenario): - microgrid_number = 8 - - -class TestNetLoadContinuousEnvScenario9(TestNetLoadContinuousEnvScenario): - microgrid_number = 9 - - -class TestNetLoadContinuousEnvScenario10(TestNetLoadContinuousEnvScenario): - microgrid_number = 10 - - -class TestNetLoadContinuousEnvScenario11(TestNetLoadContinuousEnvScenario): - microgrid_number = 11 - - -class TestNetLoadContinuousEnvScenario12(TestNetLoadContinuousEnvScenario): - microgrid_number = 12 - - -class TestNetLoadContinuousEnvScenario13(TestNetLoadContinuousEnvScenario): - microgrid_number = 13 - - -class TestNetLoadContinuousEnvScenario14(TestNetLoadContinuousEnvScenario): - microgrid_number = 14 - - -class TestNetLoadContinuousEnvScenario15(TestNetLoadContinuousEnvScenario): - microgrid_number = 15 - - -class TestNetLoadContinuousEnvScenario16(TestNetLoadContinuousEnvScenario): - microgrid_number = 16 - - -class TestNetLoadContinuousEnvScenario17(TestNetLoadContinuousEnvScenario): - microgrid_number = 17 - - -class TestNetLoadContinuousEnvScenario18(TestNetLoadContinuousEnvScenario): - microgrid_number = 18 - - -class TestNetLoadContinuousEnvScenario19(TestNetLoadContinuousEnvScenario): - microgrid_number = 19 - - -class TestNetLoadContinuousEnvScenario20(TestNetLoadContinuousEnvScenario): - microgrid_number = 20 - - -class TestNetLoadContinuousEnvScenario21(TestNetLoadContinuousEnvScenario): - microgrid_number = 21 - - -class TestNetLoadContinuousEnvScenario22(TestNetLoadContinuousEnvScenario): - microgrid_number = 22 - - -class TestNetLoadContinuousEnvScenario23(TestNetLoadContinuousEnvScenario): - microgrid_number = 23 - - -class TestNetLoadContinuousEnvScenario24(TestNetLoadContinuousEnvScenario): - microgrid_number = 24 - - -class TestNetLoadContinuousEnvSlackScenario(TestDiscreteEnvScenario): - microgrid_number = 0 - - def setUp(self) -> None: - microgrid = Microgrid.from_scenario(self.microgrid_number) - self.slack_module = ('grid', 0) if hasattr(microgrid.modules, 'grid') else ('genset', 0) - - self.env = NetLoadContinuousMicrogridEnv.from_microgrid(microgrid, slack_module=self.slack_module) - - def test_module_existence(self): - try: - grid = self.env.modules.grid.item() - except AttributeError: - pass - else: - if grid.weak_grid: - self.assertTrue(hasattr(self.env.modules, 'genset')) - - if hasattr(self.env.modules, 'genset'): - self.assertLessEqual(grid.marginal_cost, self.env.modules.genset.item().marginal_cost) - - - def test_action_space(self): - from gym.spaces import Box - - env = deepcopy(self.env) - - controllable = len(env.modules.controllable) - genset_modules = len(env.modules.genset) if hasattr(env.modules, 'genset') else 0 - - action_dim = controllable + genset_modules - - if 'genset' in self.slack_module: - action_dim -= 2 - else: - action_dim -= 1 - - self.assertEqual(env.action_space, Box(low=0, high=1, shape=(action_dim, ))) - - -class TestNetLoadContinuousEnvSlackScenario2(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 2 - - -class TestNetLoadContinuousEnvSlackScenario3(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 3 - - -class TestNetLoadContinuousEnvSlackScenario4(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 4 - - -class TestNetLoadContinuousEnvSlackScenario5(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 5 - - -class TestNetLoadContinuousEnvSlackScenario6(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 6 - - -class TestNetLoadContinuousEnvSlackScenario7(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 7 - - -class TestNetLoadContinuousEnvSlackScenario8(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 8 - - -class TestNetLoadContinuousEnvSlackScenario9(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 9 - - -class TestNetLoadContinuousEnvSlackScenario10(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 10 - - -class TestNetLoadContinuousEnvSlackScenario11(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 11 - - -class TestNetLoadContinuousEnvSlackScenario12(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 12 - - -class TestNetLoadContinuousEnvSlackScenario13(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 13 - - -class TestNetLoadContinuousEnvSlackScenario14(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 14 - - -class TestNetLoadContinuousEnvSlackScenario15(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 15 - - -class TestNetLoadContinuousEnvSlackScenario16(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 16 - - -class TestNetLoadContinuousEnvSlackScenario17(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 17 - - -class TestNetLoadContinuousEnvSlackScenario18(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 18 - - -class TestNetLoadContinuousEnvSlackScenario19(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 19 - - -class TestNetLoadContinuousEnvSlackScenario20(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 20 - - -class TestNetLoadContinuousEnvSlackScenario21(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 21 - - -class TestNetLoadContinuousEnvSlackScenario22(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 22 - - -class TestNetLoadContinuousEnvSlackScenario23(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 23 - - -class TestNetLoadContinuousEnvSlackScenario24(TestNetLoadContinuousEnvSlackScenario): - microgrid_number = 24 diff --git a/tests/envs/test_discrete.py b/tests/envs/test_discrete.py deleted file mode 100644 index e4c773a4..00000000 --- a/tests/envs/test_discrete.py +++ /dev/null @@ -1,264 +0,0 @@ -from copy import deepcopy -from math import factorial - -from tests.helpers.test_case import TestCase -from tests.helpers.modular_microgrid import get_modular_microgrid - -from pymgrid.envs import DiscreteMicrogridEnv - - -class TestDiscreteEnv(TestCase): - - def test_init_from_microgrid(self): - microgrid = get_modular_microgrid() - env = DiscreteMicrogridEnv.from_microgrid(microgrid) - - self.assertEqual(env.modules, microgrid.modules) - self.assertIsNot(env.modules.to_tuples(), microgrid.modules.to_tuples()) - - # Add one for net load - n_obs = 1 + sum([x.observation_space['normalized'].shape[0] for x in microgrid.modules.to_list()]) - - self.assertEqual(env.observation_space.shape, (n_obs,)) - - def test_init_from_modules(self): - microgrid = get_modular_microgrid() - env = DiscreteMicrogridEnv(microgrid.modules.to_tuples(), add_unbalanced_module=False) - - self.assertEqual(env.modules, microgrid.modules) - self.assertIsNot(env.modules.to_tuples(), microgrid.modules.to_tuples()) - - # Add one for net load - n_obs = 1 + sum([x.observation_space['normalized'].shape[0] for x in microgrid.modules.to_list()]) - - self.assertEqual(env.observation_space.shape, (n_obs,)) - - -class TestDiscreteEnvScenario(TestCase): - microgrid_number = 0 - - def setUp(self) -> None: - self.env = DiscreteMicrogridEnv.from_scenario(microgrid_number=self.microgrid_number) - - def test_run_once(self): - env = deepcopy(self.env) - # sample environment then get log - self.assertEqual(len(env.log), 0) - for j in range(10): - with self.subTest(step=j): - action = env.sample_action(strict_bound=True) - env.step(action) - self.assertEqual(len(env.log), j+1) - - def test_reset_after_run(self): - env = deepcopy(self.env) - env.step(env.sample_action(strict_bound=True)) - env.reset() - self.assertEqual(len(env.log), 0) - - def test_run_again_after_reset(self): - env = deepcopy(self.env) - env.step(env.sample_action(strict_bound=True)) - - self.assertEqual(len(env.log), 1) - - env.reset() - - self.assertEqual(len(env.log), 0) - - for j in range(10): - with self.subTest(step=j): - action = env.sample_action(strict_bound=True) - env.step(action) - self.assertEqual(len(env.log), j+1) - - def test_action_space(self): - env = deepcopy(self.env) - - n_action_modules = len(env.modules.controllable.sources) + len(env.modules.controllable.source_and_sinks) - genset_modules = len(env.modules.genset) if hasattr(env.modules, 'genset') else 0 - - n_actions = factorial(n_action_modules) * (2 ** genset_modules) - self.assertEqual(env.action_space.n, n_actions) - - def test_simple_observation_keys(self): - keys_in_all_scenarios = ['load_current', 'renewable_current'] - - env = DiscreteMicrogridEnv.from_scenario(microgrid_number=self.microgrid_number, - observation_keys=keys_in_all_scenarios) - - obs, _, _, _ = env.step(env.action_space.sample()) - - expected_obs = [ - env.modules['load'].item().state_dict(normalized=True)['load_current'], - env.modules['pv'].item().state_dict(normalized=True)['renewable_current'] - ] - - self.assertEqual(obs.tolist(), expected_obs) - - def test_observation_keys_net_load_unnormalized(self): - keys_in_all_scenarios = ['net_load'] - - env = DiscreteMicrogridEnv.from_scenario(microgrid_number=self.microgrid_number, - observation_keys=keys_in_all_scenarios) - - for j in range(3): - with self.subTest(step=j): - obs, _, _, _ = env.step(env.action_space.sample()) - - load = env.modules['load'].item().state_dict(normalized=True)['load_current'] - renewable = env.modules['pv'].item().state_dict(normalized=True)['renewable_current'] - - expected_obs = [(load-renewable) / load] - - self.assertEqual(obs.tolist(), expected_obs) - - def test_observation_keys_net_load_and_load_pv_unnormalized(self): - keys_in_all_scenarios = ['renewable_current', 'net_load', 'load_current'] - - env = DiscreteMicrogridEnv.from_scenario(microgrid_number=self.microgrid_number, - observation_keys=keys_in_all_scenarios) - - for j in range(3): - with self.subTest(step=j): - obs, _, _, _ = env.step(env.action_space.sample()) - - load = env.modules['load'].item().state_dict(normalized=True)['load_current'] - renewable = env.modules['pv'].item().state_dict(normalized=True)['renewable_current'] - - expected_obs = [(load - renewable) / load, renewable, load] - - self.assertEqual(obs.tolist(), expected_obs) - - def test_set_initial_step(self): - env = DiscreteMicrogridEnv.from_scenario(self.microgrid_number) - env = deepcopy(env) - - self.assertEqual(env.initial_step, 0) - - self.assertEqual(env.initial_step, 0) - self.assertEqual(env.modules.get_attrs('initial_step', unique=True, as_pandas=False), 0) - - for module_name, module_list in env.modules.iterdict(): - for n, module in enumerate(module_list): - with self.subTest(module_name=module_name, module_num=n): - try: - initial_step = module.initial_step - except AttributeError: - continue - - self.assertEqual(initial_step, 0) - - env = deepcopy(env) - - env.initial_step = 1 - - self.assertEqual(env.initial_step, 1) - self.assertEqual(env.modules.get_attrs('initial_step', unique=True, as_pandas=False), 1) - - for module_name, module_list in env.modules.iterdict(): - for n, module in enumerate(module_list): - with self.subTest(module_name=module_name, module_num=n): - try: - initial_step = module.initial_step - except AttributeError: - continue - - self.assertEqual(initial_step, 1) - - - -class TestDiscreteEnvScenario1(TestDiscreteEnvScenario): - microgrid_number = 1 - - -class TestDiscreteEnvScenario2(TestDiscreteEnvScenario): - microgrid_number = 2 - - -class TestDiscreteEnvScenario3(TestDiscreteEnvScenario): - microgrid_number = 3 - - -class TestDiscreteEnvScenario4(TestDiscreteEnvScenario): - microgrid_number = 4 - - -class TestDiscreteEnvScenario5(TestDiscreteEnvScenario): - microgrid_number = 5 - - -class TestDiscreteEnvScenario6(TestDiscreteEnvScenario): - microgrid_number = 6 - - -class TestDiscreteEnvScenario47(TestDiscreteEnvScenario): - microgrid_number = 7 - - -class TestDiscreteEnvScenario8(TestDiscreteEnvScenario): - microgrid_number = 8 - - -class TestDiscreteEnvScenario9(TestDiscreteEnvScenario): - microgrid_number = 9 - - -class TestDiscreteEnvScenario10(TestDiscreteEnvScenario): - microgrid_number = 10 - - -class TestDiscreteEnvScenario11(TestDiscreteEnvScenario): - microgrid_number = 11 - - -class TestDiscreteEnvScenario12(TestDiscreteEnvScenario): - microgrid_number = 12 - - -class TestDiscreteEnvScenario13(TestDiscreteEnvScenario): - microgrid_number = 13 - - -class TestDiscreteEnvScenario14(TestDiscreteEnvScenario): - microgrid_number = 14 - - -class TestDiscreteEnvScenario15(TestDiscreteEnvScenario): - microgrid_number = 15 - - -class TestDiscreteEnvScenario16(TestDiscreteEnvScenario): - microgrid_number = 16 - - -class TestDiscreteEnvScenario17(TestDiscreteEnvScenario): - microgrid_number = 17 - - -class TestDiscreteEnvScenario18(TestDiscreteEnvScenario): - microgrid_number = 18 - - -class TestDiscreteEnvScenario19(TestDiscreteEnvScenario): - microgrid_number = 19 - - -class TestDiscreteEnvScenario20(TestDiscreteEnvScenario): - microgrid_number = 20 - - -class TestDiscreteEnvScenario21(TestDiscreteEnvScenario): - microgrid_number = 21 - - -class TestDiscreteEnvScenario22(TestDiscreteEnvScenario): - microgrid_number = 22 - - -class TestDiscreteEnvScenario23(TestDiscreteEnvScenario): - microgrid_number = 23 - - -class TestDiscreteEnvScenario24(TestDiscreteEnvScenario): - microgrid_number = 24 diff --git a/tests/envs/test_trajectory.py b/tests/envs/test_trajectory.py deleted file mode 100644 index 829857e0..00000000 --- a/tests/envs/test_trajectory.py +++ /dev/null @@ -1,172 +0,0 @@ -import numpy as np - -from tests.helpers.test_case import TestCase -from tests.helpers.modular_microgrid import get_modular_microgrid - -from pymgrid.envs import DiscreteMicrogridEnv - - -class TestTrajectory(TestCase): - - def check_initial_final_steps(self, - env, - expected_env_initial, - expected_env_final, - expected_module_initial, - expected_module_final): - - self.assertEqual(env.initial_step, expected_env_initial) - self.assertEqual(env.final_step, expected_env_final) - - env.reset() - - self.assertEqual(env.initial_step, expected_env_initial) - self.assertEqual(env.final_step, expected_env_final) - - self.assertEqual(env.modules.get_attrs('initial_step', unique=True), expected_module_initial) - self.assertEqual(env.modules.get_attrs('final_step', unique=True), expected_module_final) - - def test_none_trajectory(self): - timeseries_length = 100 - modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True) - env = DiscreteMicrogridEnv(modules, trajectory_func=None) - self.check_initial_final_steps(env, 0, timeseries_length, 0, timeseries_length) - - def test_deterministic_trajectory(self): - deterministic_initial, deterministic_final = 10, 20 - - def trajectory_func(initial_step, final_step): - return deterministic_initial, deterministic_final - - timeseries_length = 100 - modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True) - env = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func) - - self.check_initial_final_steps(env, 0, timeseries_length, deterministic_initial, deterministic_final) - - def test_stochastic_trajectory(self): - def trajectory_func(initial_step, final_step): - initial = np.random.randint(low=initial_step+1, high=final_step-2) - final = np.random.randint(low=initial, high=final_step) - return initial, final - - timeseries_length = 100 - modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True) - env = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func) - - self.assertEqual(env.initial_step, 0) - self.assertEqual(env.final_step, timeseries_length) - - env.reset() - - self.assertEqual(env.initial_step, 0) - self.assertEqual(env.final_step, timeseries_length) - - self.assertGreater(env.modules.get_attrs('initial_step', unique=True), 0) - self.assertLess(env.modules.get_attrs('initial_step', unique=True), timeseries_length) - - self.assertGreater(env.modules.get_attrs('final_step', unique=True), 0) - self.assertLess(env.modules.get_attrs('final_step', unique=True), timeseries_length) - - self.assertLess(env.modules.get_attrs('initial_step', unique=True), - env.modules.get_attrs('final_step', unique=True)) - - def test_bad_trajectory_out_of_range(self): - def trajectory_func(initial_step, final_step): - return 10, 110 - - timeseries_length = 100 - modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True) - - with self.assertRaises(ValueError): - _ = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func) - - def test_bad_trajectory_bad_signature(self): - def trajectory_func(initial_step): - return 10, 110 - - timeseries_length = 100 - modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True) - - with self.assertRaises(TypeError): - _ = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func) - - def test_bad_trajectory_initial_gt_final(self): - def trajectory_func(initial_step, final_step): - return 20, 10 - - timeseries_length = 100 - modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True) - - with self.assertRaises(ValueError): - _ = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func) - - def test_bad_trajectory_scalar_output(self): - def trajectory_func(initial_step, final_step): - return 20 - - timeseries_length = 100 - modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True) - - with self.assertRaises(TypeError): - _ = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func) - - def test_bad_trajectory_too_many_outputs(self): - def trajectory_func(initial_step, final_step): - return 10, 20, 30 - - timeseries_length = 100 - modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True) - - with self.assertRaises(TypeError): - _ = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func) - - def test_bad_trajectory_wrong_output_types(self): - def trajectory_func(initial_step, final_step): - return 'abc', 10.0 - - timeseries_length = 100 - modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True) - - with self.assertRaises(TypeError): - _ = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func) - - def test_correct_trajectory_length(self): - - def trajectory_func(initial_step, final_step): - trajectory_func.n_resets += 1 - return 10, 11+trajectory_func.n_resets - - trajectory_func.n_resets = 0 - - timeseries_length = 100 - modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True) - env = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func) - - for correct_trajectory_length in range(3, 7): - with self.subTest(correct_trajectory_length=correct_trajectory_length): - env.reset() - n_steps = 0 - done = False - - while not done: - _, _, done, _ = env.step(env.action_space.sample()) - n_steps += 1 - - self.assertEqual(n_steps, correct_trajectory_length) - - def test_trajectory_serialization(self): - import yaml - from pymgrid.microgrid.trajectory import DeterministicTrajectory - - trajectory_func = DeterministicTrajectory(10, 20) - - timeseries_length = 100 - modules = get_modular_microgrid(timeseries_length=timeseries_length, modules_only=True) - - env = DiscreteMicrogridEnv(modules, trajectory_func=trajectory_func) - env.reset() - loaded_env = yaml.safe_load(yaml.safe_dump(env)) - - self.assertIsNotNone(env.trajectory_func) - self.assertEqual(env, loaded_env) diff --git a/tests/helpers/__init__.py b/tests/helpers/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/helpers/genset_module_testing_utils.py b/tests/helpers/genset_module_testing_utils.py deleted file mode 100644 index f8a44138..00000000 --- a/tests/helpers/genset_module_testing_utils.py +++ /dev/null @@ -1,22 +0,0 @@ -from pymgrid.modules import GensetModule - - -default_params = dict(running_min_production=10, - running_max_production=100, - genset_cost=1, - start_up_time=0, - wind_down_time=0, - allow_abortion=True, - init_start_up=True, - raise_errors=True) - - -def get_genset(default_parameters=None, **new_params): - params = default_parameters.copy() if default_parameters is not None else default_params.copy() - params.update(new_params) - return GensetModule(**params), params - - -def normalize_production(production, max_production=None): - max_production = max_production if max_production else default_params['running_max_production'] - return production/max_production diff --git a/tests/helpers/modular_microgrid.py b/tests/helpers/modular_microgrid.py deleted file mode 100644 index faf9390b..00000000 --- a/tests/helpers/modular_microgrid.py +++ /dev/null @@ -1,66 +0,0 @@ -import numpy as np - -from pymgrid import Microgrid - -from pymgrid.modules import ( - BatteryModule, - GensetModule, - GridModule, - LoadModule, - RenewableModule -) - - -def get_modular_microgrid(remove_modules=(), - retain_only=None, - additional_modules=None, - add_unbalanced_module=True, - timeseries_length=100, - modules_only=False, - normalized_action_bounds=(0, 1)): - - modules = dict( - genset=GensetModule(running_min_production=10, - running_max_production=50, - genset_cost=0.5, - normalized_action_bounds=normalized_action_bounds), - - battery=BatteryModule(min_capacity=0, - max_capacity=100, - max_charge=50, - max_discharge=50, - efficiency=1.0, - init_soc=0.5, - normalized_action_bounds=normalized_action_bounds), - - renewable=RenewableModule(time_series=50*np.ones(timeseries_length), - normalized_action_bounds=normalized_action_bounds), - - load=LoadModule(time_series=60*np.ones(timeseries_length), - normalized_action_bounds=normalized_action_bounds), - - grid=GridModule(max_import=100, - max_export=100, - time_series=np.ones((timeseries_length, 3)), - normalized_action_bounds=normalized_action_bounds, - raise_errors=True) - ) - - if retain_only is not None: - modules = {k: v for k, v in modules.items() if k in retain_only} - if remove_modules: - raise RuntimeError('Can pass either remove_modules or retain_only, but not both.') - else: - for module in remove_modules: - try: - modules.pop(module) - except KeyError: - raise NameError(f"Module {module} not one of default modules {list(modules.keys())}.") - - modules = list(modules.values()) - modules.extend(additional_modules if additional_modules else []) - - if modules_only: - return modules - - return Microgrid(modules, add_unbalanced_module=add_unbalanced_module) diff --git a/tests/helpers/test_case.py b/tests/helpers/test_case.py deleted file mode 100644 index b7520e30..00000000 --- a/tests/helpers/test_case.py +++ /dev/null @@ -1,36 +0,0 @@ -import unittest -import numpy as np -import pandas as pd - -from unittest.util import safe_repr - -class TestCase(unittest.TestCase): - def assertEqual(self, first, second, msg=None) -> None: - try: - super().assertEqual(first, second, msg=msg) - except (ValueError, AssertionError): - # array-like or pandas obj - # convert pandas obj - if isinstance(first, (pd.DataFrame, pd.Series)): - first, second = first.values, second.values - - try: - np.testing.assert_equal(first, second, err_msg=msg if msg else '') - except AssertionError as e: - try: - np.testing.assert_allclose(first, second, rtol=1e-7, atol=1e-10, err_msg=msg if msg else '') - except TypeError: - raise e - - def assertNotEqual(self, first, second, msg=None) -> None: - try: - super().assertNotEqual(first, second, msg=msg) - except ValueError as e: - try: - self.assertEqual(first, second) - except AssertionError: - pass - else: - msg = self._formatMessage(msg, '%s == %s' % (safe_repr(first), - safe_repr(second))) - raise self.failureException(msg) diff --git a/tests/microgrid/__init__.py b/tests/microgrid/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/microgrid/modules/__init__.py b/tests/microgrid/modules/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/microgrid/modules/container_tests/__init__.py b/tests/microgrid/modules/container_tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/microgrid/modules/container_tests/test_container.py b/tests/microgrid/modules/container_tests/test_container.py deleted file mode 100644 index 26aca1c6..00000000 --- a/tests/microgrid/modules/container_tests/test_container.py +++ /dev/null @@ -1,10 +0,0 @@ -from tests.helpers.modular_microgrid import get_modular_microgrid -from tests.helpers.test_case import TestCase - - -class TestContainer(TestCase): - def test_container_init(self): - microgrid = get_modular_microgrid() - self.assertTrue(len(microgrid.controllable.sources)) - self.assertTrue(len(microgrid.controllable.source_and_sinks)) - action = microgrid.sample_action() diff --git a/tests/microgrid/modules/conversion_test/__init__.py b/tests/microgrid/modules/conversion_test/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/microgrid/modules/conversion_test/test_modular_conversion.py b/tests/microgrid/modules/conversion_test/test_modular_conversion.py deleted file mode 100644 index 153cfe23..00000000 --- a/tests/microgrid/modules/conversion_test/test_modular_conversion.py +++ /dev/null @@ -1,42 +0,0 @@ -import numpy as np - -from tests.helpers.test_case import TestCase - - -class TestToModular(TestCase): - def setUp(self) -> None: - from pymgrid.MicrogridGenerator import MicrogridGenerator - mgen = MicrogridGenerator() - mgen.generate_microgrid(modular=False) - self.weak_grids = [microgrid for microgrid in mgen.microgrids if self.is_weak_grid(microgrid)] - self.genset_only = [microgrid for microgrid in mgen.microgrids if not microgrid.architecture["grid"]] - self.strong_grid_only = [microgrid for microgrid in mgen.microgrids if - (not microgrid.architecture["genset"]) and self.is_strong_grid(microgrid)] - self.strong_grid_and_genset = [microgrid for microgrid in mgen.microgrids if - microgrid.architecture["genset"] and self.is_strong_grid(microgrid)] - - @staticmethod - def is_weak_grid(microgrid): - return microgrid.architecture["grid"] and microgrid._grid_status_ts.min().item() < 1 - - @staticmethod - def is_strong_grid(microgrid): - return microgrid.architecture["grid"] and microgrid._grid_status_ts.min().item() == 1 - - def test_weak_grid_conversion_success(self): - for microgrid in self.weak_grids: - modular_microgrid = microgrid.to_modular() - self.assertTrue(modular_microgrid.grid.item().weak_grid) - - def test_genset_only(self): - for microgrid in self.genset_only: - modular = microgrid.to_modular() - self.assertTrue(len(modular.genset) == 1) - - genset_module = modular.genset[0] - self.assertEqual(microgrid.genset.fuel_cost, genset_module.genset_cost) - self.assertEqual(microgrid.genset.co2, genset_module.co2_per_unit) - self.assertEqual(microgrid.genset.rated_power*microgrid.genset.p_max, genset_module.max_production) - - with self.assertRaises(AttributeError): - _ = modular.grid \ No newline at end of file diff --git a/tests/microgrid/modules/forecaster_tests/__init__.py b/tests/microgrid/modules/forecaster_tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/microgrid/modules/forecaster_tests/test_forecaster.py b/tests/microgrid/modules/forecaster_tests/test_forecaster.py deleted file mode 100644 index 2f1b3ed4..00000000 --- a/tests/microgrid/modules/forecaster_tests/test_forecaster.py +++ /dev/null @@ -1,347 +0,0 @@ -import numpy as np -from tests.helpers.test_case import TestCase -from pymgrid.forecast import ( - get_forecaster, OracleForecaster, GaussianNoiseForecaster, UserDefinedForecaster, NoForecaster) -from pymgrid.utils.space import ModuleSpace - -STATE_COMPONENTS = np.random.randint(low=1, high=10) -FORECAST_HORIZON = np.random.randint(low=2, high=10) - -POSITIVE_OBSERVATION_SPACE = ModuleSpace( - unnormalized_low=0, - unnormalized_high=10, - shape=(STATE_COMPONENTS*(FORECAST_HORIZON+1),) - ) - -NEGATIVE_OBSERVATION_SPACE = ModuleSpace( - unnormalized_low=-10, - unnormalized_high=0, - shape=(STATE_COMPONENTS*(FORECAST_HORIZON+1),) - ) - - -def get_test_inputs(n=None, state_components=None, negative=False): - state_components = state_components if state_components else STATE_COMPONENTS - n = n if n else FORECAST_HORIZON - val_c_n = POSITIVE_OBSERVATION_SPACE.unnormalized.high[0] * np.random.rand(n, state_components) - val_c = val_c_n[0, :] - # val_c_n = val_c_n.reshape((FORECAST_HORIZON, STATE_COMPONENTS)) - if negative: - return -val_c, -val_c_n, n - else: - return val_c, val_c_n, n - - -class TestOracleForecaster(TestCase): - def setUp(self) -> None: - self.forecaster = OracleForecaster(observation_space=POSITIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS)) - - def test_positive_inputs(self): - val_c, val_c_n, n = get_test_inputs() - forecast = self.forecaster(val_c, val_c_n, n) - self.assertEqual(forecast, val_c_n) - - def test_negative_inputs(self): - val_c, val_c_n, n = get_test_inputs(negative=True) - forecast = self.forecaster(val_c, val_c_n, n) - self.assertEqual(forecast, val_c_n) - - -class TestGaussianNoiseForecaster(TestCase): - def test_single_forecast_positive(self): - noise_std = 1 - forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=POSITIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS), - increase_uncertainty=False) - val_c, val_c_n, n = get_test_inputs() - forecast = forecaster(val_c, val_c_n, n) - self.assertEqual(noise_std, forecaster.noise_std) - self.assertTrue((forecast >= 0).all()) - - def test_single_forecast_positive_high_std(self): - noise_std = 100 - forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=POSITIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS), - increase_uncertainty=False) - val_c, val_c_n, n = get_test_inputs() - forecast = forecaster(val_c, val_c_n, n) - self.assertEqual(noise_std, forecaster.noise_std) - self.assertTrue((forecast >= 0).all()) - - def test_single_forecast_negative(self): - noise_std = 1 - forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=NEGATIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS), - increase_uncertainty=False) - val_c, val_c_n, n = get_test_inputs(negative=True) - forecast = forecaster(val_c, val_c_n, n) - self.assertEqual(noise_std, forecaster.noise_std) - self.assertTrue((forecast <= 0).all()) - - def test_single_forecast_negative_high_std(self): - noise_std = 100 - forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=NEGATIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS), - increase_uncertainty=False) - val_c, val_c_n, n = get_test_inputs(negative=True) - forecast = forecaster(val_c, val_c_n, n) - self.assertEqual(noise_std, forecaster.noise_std) - self.assertTrue((forecast <= 0).all()) - - def test_multiple_forecast_positive(self): - noise_std = 1 - forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=POSITIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS), - increase_uncertainty=False) - n = None - for _ in range(2): - val_c, val_c_n, n = get_test_inputs(n=n) - forecast = forecaster(val_c, val_c_n, n) - self.assertEqual(noise_std, forecaster.noise_std) - self.assertTrue((forecast >= 0).all()) - - def test_multiple_forecast_positive_high_std(self): - noise_std = 100 - forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=POSITIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS), - increase_uncertainty=False) - n = None - for _ in range(2): - val_c, val_c_n, n = get_test_inputs(n=n) - forecast = forecaster(val_c, val_c_n, n) - self.assertEqual(noise_std, forecaster.noise_std) - self.assertTrue((forecast >= 0).all()) - - def test_multiple_forecast_negative(self): - noise_std = 1 - forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=NEGATIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS), - increase_uncertainty=False) - n = None - for _ in range(2): - val_c, val_c_n, n = get_test_inputs(n=n, negative=True) - forecast = forecaster(val_c, val_c_n, n) - self.assertEqual(noise_std, forecaster.noise_std) - self.assertTrue((forecast <= 0).all()) - - def test_multiple_forecast_negative_high_std(self): - noise_std = 100 - forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=NEGATIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS), - increase_uncertainty=False) - n = None - for _ in range(2): - val_c, val_c_n, n = get_test_inputs(n=n, negative=True) - forecast = forecaster(val_c, val_c_n, n) - self.assertEqual(noise_std, forecaster.noise_std) - self.assertTrue((forecast <= 0).all()) - - def test_increasing_uncertainty_positive(self): - noise_std = 1 - forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=POSITIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS), - increase_uncertainty=True) - val_c, val_c_n, n = get_test_inputs() - expected_noise_std = np.outer(noise_std*(1+np.log(1+np.arange(len(val_c_n)))), np.ones(STATE_COMPONENTS)) - - forecast = forecaster(val_c, val_c_n, n) - self.assertTrue((noise_std != forecaster.noise_std).any()) - self.assertEqual(expected_noise_std, forecaster.noise_std) - self.assertTrue((forecast >= 0).all()) - - def test_increasing_uncertainty_negative(self): - noise_std = 1 - forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=NEGATIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS), - increase_uncertainty=True) - - val_c, val_c_n, n = get_test_inputs(negative=True) - expected_noise_std = np.outer(noise_std*(1+np.log(1+np.arange(len(val_c_n)))), np.ones(STATE_COMPONENTS)) - - forecast = forecaster(val_c, val_c_n, n) - self.assertTrue((noise_std != forecaster.noise_std).any()) - self.assertEqual(expected_noise_std, forecaster.noise_std) - self.assertTrue((forecast <= 0).all()) - - def test_bad_shape(self): - noise_std = 1 - forecaster = GaussianNoiseForecaster(noise_std=noise_std, observation_space=POSITIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS), - increase_uncertainty=False) - - n = np.random.randint(FORECAST_HORIZON, 2*FORECAST_HORIZON) - val_c, val_c_n, n = get_test_inputs(n=n) - with self.assertRaises(RuntimeError): - _ = forecaster(val_c, val_c_n, n) - # self.assertEqual(noise_std, forecaster.noise_std) - # val_c, val_c_n, n = get_test_inputs(n=n_vals[1]) - # with self.assertRaises(ValueError): - # _ = forecaster(val_c, val_c_n, n) - - -class TestUserDefinedForecaster(TestCase): - def setUp(self) -> None: - self.simple_time_series = np.arange(FORECAST_HORIZON).reshape((-1, 1)) - - @staticmethod - def oracle_scalar_forecaster(val_c, val_c_n, n): - return val_c_n.item() - - def get_oracle_forecaster(self, negative=False): - return OracleForecaster(observation_space=self.get_obs_space(negative=negative), - forecast_shape=(FORECAST_HORIZON,)) - - def get_obs_space(self, negative=False): - if negative: - low = -10 - high = 0 - else: - low = 0 - high = 10 - - return ModuleSpace(unnormalized_low=low, unnormalized_high=high, shape=(10, )) - - def test_user_defined_oracle_positive(self): - forecaster = UserDefinedForecaster(forecaster_function=self.get_oracle_forecaster(), - observation_space=self.get_obs_space(), forecast_shape=(FORECAST_HORIZON,), - time_series=self.simple_time_series) - val_c, val_c_n, n = get_test_inputs(state_components=1) - forecast = forecaster(val_c, val_c_n, n) - self.assertEqual(forecast, val_c_n) - - def test_user_defined_oracle_negative(self): - forecaster = UserDefinedForecaster(forecaster_function=self.get_oracle_forecaster(negative=True), - observation_space=self.get_obs_space(negative=True), - forecast_shape=(FORECAST_HORIZON,), time_series=self.simple_time_series) - val_c, val_c_n, n = get_test_inputs(state_components=1, negative=True) - forecast = forecaster(val_c, val_c_n, n) - self.assertEqual(forecast, val_c_n) - - def test_scalar_forecaster(self): - forecaster = UserDefinedForecaster(forecaster_function=self.oracle_scalar_forecaster, - observation_space=self.get_obs_space(negative=True), - forecast_shape=(FORECAST_HORIZON,), time_series=self.simple_time_series) - val_c, val_c_n, n = get_test_inputs(state_components=1, negative=True) - forecast = forecaster(val_c, val_c_n, n) - self.assertEqual(forecast, val_c_n) - - def test_vectorized_forecaster_bad_output_shape(self): - bad_output_shape_forecaster = lambda val_c, val_c_n, n: np.append(val_c_n, [0]) - with self.assertRaisesRegex(ValueError, "Forecaster output of shape (.*) " - "cannot be casted to necessary forecast shape (.*, 1)"): - _ = UserDefinedForecaster(forecaster_function=bad_output_shape_forecaster, - observation_space=POSITIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS), - time_series=self.simple_time_series) - - def test_vectorized_forecaster_bad_output_type(self): - bad_output_type_forecaster = lambda val_c, val_c_n, n: np.array([str(x) for x in val_c_n]).reshape((-1, 1)) - with self.assertRaisesRegex(TypeError, "Forecaster must return numeric np.ndarray or number but returned " - "output of type"): - _ = UserDefinedForecaster(forecaster_function=bad_output_type_forecaster, - observation_space=POSITIVE_OBSERVATION_SPACE, forecast_shape=(FORECAST_HORIZON,), - time_series=self.simple_time_series) - - def test_vectorized_forecaster_bad_output_signs(self): - def bad_output_type_forecaster(val_c, val_c_n, n): - out = val_c_n.copy() - pos = np.random.randint(low=1, high=len(out)) - out[pos] *= -1 - return out - - with self.assertRaisesRegex(ValueError, "Forecaster must return output of same " - "sign \(or zero\) as input but returned output"): - _ = UserDefinedForecaster(forecaster_function=bad_output_type_forecaster, - observation_space=POSITIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS), - time_series=self.simple_time_series) - - def test_bad_forecaster(self): - bad_forecaster = lambda val_c, val_c_n, n: 0/0 - - with self.assertRaisesRegex(ValueError, "Unable to call forecaster with scalar inputs."): - _ = UserDefinedForecaster(forecaster_function=bad_forecaster, observation_space=POSITIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS), - time_series=self.simple_time_series) - - def test_scalar_forecaster_bad_output_shape(self): - def bad_output_shape_forecaster(val_c, val_c_n, n): - if hasattr(val_c_n, '__len__') and len(val_c_n) > 1: - raise RuntimeError - return [val_c_n]*2 - - with self.assertRaisesRegex(ValueError, "Forecaster must return scalar output with scalar input but returned."): - _ = UserDefinedForecaster(forecaster_function=bad_output_shape_forecaster, - observation_space=POSITIVE_OBSERVATION_SPACE, - forecast_shape=(FORECAST_HORIZON, STATE_COMPONENTS), - time_series=self.simple_time_series) - - -class TestGetForecaster(TestCase): - def setUp(self) -> None: - self.simple_time_series = np.arange(10).reshape((-1, 1)) - self.forecaster_horizon = 24 - - def test_user_defined_forecaster(self): - user_defined_forecaster = lambda val_c, val_c_n, n: val_c_n - forecaster = get_forecaster(user_defined_forecaster, - POSITIVE_OBSERVATION_SPACE, - (FORECAST_HORIZON, STATE_COMPONENTS), - time_series=self.simple_time_series) - self.assertIsInstance(forecaster, UserDefinedForecaster) - - def test_oracle_forecaster(self): - forecaster = get_forecaster("oracle", POSITIVE_OBSERVATION_SPACE, (FORECAST_HORIZON, STATE_COMPONENTS)) - self.assertIsInstance(forecaster, OracleForecaster) - - def test_no_forecaster(self): - forecaster = get_forecaster(None, POSITIVE_OBSERVATION_SPACE, (FORECAST_HORIZON, STATE_COMPONENTS)) - self.assertIsInstance(forecaster, NoForecaster) - - def test_gaussian_noise_forecaster_init(self): - noise_std = 0.5 - forecaster = get_forecaster(noise_std, POSITIVE_OBSERVATION_SPACE, (FORECAST_HORIZON, STATE_COMPONENTS)) - self.assertIsInstance(forecaster, GaussianNoiseForecaster) - self.assertEqual(forecaster.input_noise_std, noise_std) - - def test_gaussian_noise_forecaster_increase_uncertainty_init(self): - noise_std = 0.5 - forecaster = get_forecaster(noise_std, POSITIVE_OBSERVATION_SPACE, (FORECAST_HORIZON, STATE_COMPONENTS), increase_uncertainty=True) - self.assertIsInstance(forecaster, GaussianNoiseForecaster) - self.assertEqual(forecaster.input_noise_std, noise_std) - self.assertTrue((forecaster.noise_std != noise_std).any()) - - def test_gaussian_noise_forecaster_correct_size(self): - noise_std = 0.5 - forecaster = get_forecaster(noise_std, POSITIVE_OBSERVATION_SPACE, (FORECAST_HORIZON, STATE_COMPONENTS)) - - val_c, val_c_n, n = get_test_inputs() - forecast = forecaster(val_c, val_c_n, n) - - self.assertEqual(forecast.shape, val_c_n.shape) - self.assertEqual(forecast.shape, (FORECAST_HORIZON, STATE_COMPONENTS)) - self.assertTrue((forecast.reshape(-1) >= 0).all()) - - def test_gaussian_noise_forecaster_insufficient_true_vals(self): - noise_std = 0.5 - forecaster = get_forecaster(noise_std, POSITIVE_OBSERVATION_SPACE, (FORECAST_HORIZON, STATE_COMPONENTS)) - - val_c, val_c_n, _ = get_test_inputs(n=FORECAST_HORIZON-2) - forecast = forecaster(val_c, val_c_n, FORECAST_HORIZON) - - self.assertEqual(forecast.shape, (FORECAST_HORIZON, STATE_COMPONENTS)) - self.assertTrue((forecast.reshape(-1) >= 0).all()) - - def test_gaussian_noise_forecaster_insufficient_true_vals_increasing_uncertainty(self): - noise_std = 0.5 - forecaster = get_forecaster(noise_std, - POSITIVE_OBSERVATION_SPACE, - (FORECAST_HORIZON, STATE_COMPONENTS), - increase_uncertainty=True) - - val_c, val_c_n, _ = get_test_inputs(n=FORECAST_HORIZON-2) - forecast = forecaster(val_c, val_c_n, FORECAST_HORIZON) - - self.assertEqual(forecast.shape, (FORECAST_HORIZON, STATE_COMPONENTS)) - self.assertTrue((forecast.reshape(-1) >= 0).all()) \ No newline at end of file diff --git a/tests/microgrid/modules/module_tests/test_battery_module.py b/tests/microgrid/modules/module_tests/test_battery_module.py deleted file mode 100644 index ed3e7f56..00000000 --- a/tests/microgrid/modules/module_tests/test_battery_module.py +++ /dev/null @@ -1,258 +0,0 @@ -from tests.helpers.test_case import TestCase - -from pymgrid.modules import BatteryModule -from pymgrid.modules.battery.transition_models import BiasedTransitionModel, DecayTransitionModel - -DEFAULT_PARAMS = { - 'min_capacity': 0, - 'max_capacity': 100, - 'max_charge': 50, - 'max_discharge': 50, - 'efficiency': 0.5, - 'battery_cost_cycle': 0.0, - 'battery_transition_model': None, - 'init_soc': 0.5 - } - - -def get_battery(**params): - p = DEFAULT_PARAMS.copy() - p.update(params) - - if 'init_charge' in params and 'init_soc' not in params: - p.pop('init_soc') - - return BatteryModule(**p) - - -class TestBatteryModule(TestCase): - def test_min_act(self): - params = { - 'init_soc': 0, - 'efficiency': 0.5, - 'max_charge': 40, - 'max_discharge': 60 - } - - battery = get_battery(**params) - expected_min_act = -1 * params['max_charge'] / params['efficiency'] - - self.assertEqual(battery.soc, 0) - self.assertEqual(battery.current_charge, 0) - self.assertEqual(battery.min_act, expected_min_act) - - obs, reward, done, info = battery.step(expected_min_act, normalized=False) - - self.assertEqual(info['absorbed_energy'], -1 * expected_min_act) - self.assertEqual(battery.current_charge, params['max_charge']) - - def test_max_act(self): - params = { - 'init_soc': 1, - 'efficiency': 0.5, - 'max_charge': 40, - 'max_discharge': 60 - } - - battery = get_battery(**params) - expected_max_act = params['max_discharge'] * params['efficiency'] - - self.assertEqual(battery.soc, 1) - self.assertEqual(battery.current_charge, DEFAULT_PARAMS['max_capacity']) - self.assertEqual(battery.max_act, expected_max_act) - - obs, reward, done, info = battery.step(expected_max_act, normalized=False) - - self.assertEqual(info['provided_energy'], expected_max_act) - self.assertEqual(battery.current_charge, 100 - params['max_discharge']) - - def test_max_consumption_max_charge(self): - params = { - 'init_soc': 0, - 'efficiency': 0.5, - 'max_charge': 40, - 'max_discharge': 60 - } - - battery = get_battery(**params) - - self.assertEqual(battery.max_consumption, params['max_charge'] / params['efficiency']) - - def test_max_consumption_nonmax_charge(self): - params = { - 'init_charge': 80, - 'efficiency': 0.5, - 'max_charge': 40, - 'max_discharge': 60 - } - - battery = get_battery(**params) - - self.assertEqual( - battery.max_consumption, - (DEFAULT_PARAMS['max_capacity']-params['init_charge']) / params['efficiency'] - ) - - def test_max_production_max_discharge(self): - params = { - 'init_soc': 1, - 'efficiency': 0.5, - 'max_charge': 40, - 'max_discharge': 60 - } - - battery = get_battery(**params) - - self.assertEqual(battery.max_production, params['max_discharge'] * params['efficiency']) - - def test_max_production_nonmax_discharge(self): - params = { - 'init_charge': 20, - 'efficiency': 0.5, - 'max_charge': 40, - 'max_discharge': 60 - } - - battery = get_battery(**params) - - self.assertEqual( - battery.max_production, - (params['init_charge']-DEFAULT_PARAMS['min_capacity']) * params['efficiency'] - ) - - -class TestBiasedBatteryModule(TestCase): - def test_single_step_charge(self): - true_efficiency = 0.6 - init_soc = 1.0 - - battery_transition_model = BiasedTransitionModel(true_efficiency=true_efficiency) - battery = get_battery(battery_transition_model=battery_transition_model, init_soc=init_soc) - - self.assertEqual(battery.battery_transition_model.true_efficiency, true_efficiency) - self.assertEqual(battery.battery_transition_model, battery_transition_model) - self.assertNotEqual(battery.efficiency, true_efficiency) - - _, _, _, info = battery.step(battery.max_act, normalized=False) - - self.assertEqual(info['provided_energy'], true_efficiency * DEFAULT_PARAMS['max_discharge']) - - def test_single_step_discharge(self): - true_efficiency = 0.6 - init_soc = 0.0 - - battery_transition_model = BiasedTransitionModel(true_efficiency=true_efficiency) - battery = get_battery(battery_transition_model=battery_transition_model, init_soc=init_soc) - - self.assertEqual(battery.battery_transition_model.true_efficiency, true_efficiency) - self.assertEqual(battery.battery_transition_model, battery_transition_model) - self.assertNotEqual(battery.efficiency, true_efficiency) - - _, _, _, info = battery.step(battery.min_act, normalized=False) - - self.assertEqual(info['absorbed_energy'], DEFAULT_PARAMS['max_discharge'] / true_efficiency) - - -class TestDecayBatteryModule(TestCase): - def test_single_step_discharge(self): - init_soc = 1.0 - efficiency = 1.0 - decay_rate = 0.5 - - battery_transition_model = DecayTransitionModel(decay_rate=decay_rate) - battery = get_battery(battery_transition_model=battery_transition_model, init_soc=init_soc, efficiency=efficiency) - - self.assertEqual(battery.battery_transition_model.decay_rate, decay_rate) - self.assertEqual(battery.battery_transition_model, battery_transition_model) - self.assertEqual(battery.efficiency, efficiency) - - self.assertEqual(battery.max_act, DEFAULT_PARAMS['max_discharge']) - - _, _, _, info = battery.step(battery.max_act, normalized=False) - - self.assertEqual(battery.max_act, DEFAULT_PARAMS['max_discharge']) - - self.assertEqual(info['provided_energy'], DEFAULT_PARAMS['max_discharge']) - - def test_single_step_charge(self): - init_soc = 0.0 - efficiency = 1.0 - decay_rate = 0.5 - - battery_transition_model = DecayTransitionModel(decay_rate=decay_rate) - battery = get_battery(battery_transition_model=battery_transition_model, init_soc=init_soc, efficiency=efficiency) - - self.assertEqual(battery.battery_transition_model.decay_rate, decay_rate) - self.assertEqual(battery.battery_transition_model, battery_transition_model) - self.assertEqual(battery.efficiency, efficiency) - - self.assertEqual(battery.min_act, -1 * DEFAULT_PARAMS['max_charge']) - - _, _, _, info = battery.step(battery.min_act, normalized=False) - - self.assertEqual(battery.min_act, -1 * DEFAULT_PARAMS['max_charge']) - - self.assertEqual(info['absorbed_energy'], DEFAULT_PARAMS['max_charge']) - - def test_two_step_discharge(self): - init_soc = 1.0 - efficiency = 1.0 - decay_rate = 0.5 - - battery_transition_model = DecayTransitionModel(decay_rate=decay_rate) - battery = get_battery(battery_transition_model=battery_transition_model, init_soc=init_soc, efficiency=efficiency) - - self.assertEqual(battery.max_act, DEFAULT_PARAMS['max_discharge']) - - _, _, _, info = battery.step(battery.max_act, normalized=False) - _, _, _, info = battery.step(battery.max_act, normalized=False) - - self.assertEqual(battery.max_act, DEFAULT_PARAMS['max_discharge']) - self.assertEqual(info['provided_energy'], 0.5 * DEFAULT_PARAMS['max_discharge']) - - def test_three_step_discharge(self): - efficiency = 1.0 - decay_rate = 0.5 - energy_amount = 100.0 - - battery_transition_model = DecayTransitionModel(decay_rate=decay_rate) - - for j in range(3): - with self.subTest(step=j): - transition = battery_transition_model.transition(energy_amount, efficiency, j) - self.assertEqual(transition, energy_amount * (decay_rate ** j)) - - def test_multiple_steps_ahead(self): - init_soc = 1.0 - efficiency = 1.0 - decay_rate = 0.5 - - battery_transition_model = DecayTransitionModel(decay_rate=decay_rate) - battery = get_battery(battery_transition_model=battery_transition_model, init_soc=init_soc, efficiency=efficiency) - - battery_transition_model._previous_step = 5 - self.assertEqual(battery_transition_model._current_efficiency(efficiency=1.0, current_step=6), 0.5**6) - - self.assertEqual(battery.max_act, DEFAULT_PARAMS['max_discharge']) - - def test_decay_transition_reset_after_decay_jump_back(self): - decay_rate = 0.5 - energy_amount = 50 - - battery_transition_model = DecayTransitionModel(decay_rate=decay_rate) - battery_transition_model.initial_step = 0 - battery_transition_model._previous_step = 5 - - transition = battery_transition_model.transition(energy_amount, 1.0, current_step=2) - self.assertEqual(transition, energy_amount) - - def test_decay_transition_reset_after_decay_jump_forward(self): - decay_rate = 0.5 - energy_amount = 50 - - battery_transition_model = DecayTransitionModel(decay_rate=decay_rate) - battery_transition_model.initial_step = 0 - battery_transition_model._previous_step = 5 - - transition = battery_transition_model.transition(energy_amount, 1.0, current_step=10) - self.assertEqual(transition, energy_amount) diff --git a/tests/microgrid/modules/module_tests/test_genset_long_status_changes.py b/tests/microgrid/modules/module_tests/test_genset_long_status_changes.py deleted file mode 100644 index a4ab1834..00000000 --- a/tests/microgrid/modules/module_tests/test_genset_long_status_changes.py +++ /dev/null @@ -1,263 +0,0 @@ -from tests.helpers.genset_module_testing_utils import get_genset, normalize_production -from tests.helpers.test_case import TestCase -import numpy as np -from copy import deepcopy -from itertools import product - - -class TestGensetStartUp2WindDown3OnAtStartUp(TestCase): - def setUp(self) -> None: - self.genset, self.default_params = get_genset(init_start_up=True, start_up_time=2, wind_down_time=3) - - def get_genset(self, **new_params): - if len(new_params) == 0: - return deepcopy(self.genset), self.default_params - return get_genset(default_parameters=self.default_params, **new_params) - - def turn_on(self, genset, unnormalized_production=0.): - # Take a step, ask genset to turn on. - action = np.array([1.0, normalize_production(unnormalized_production)]) - obs, reward, done, info = genset.step(action) - return obs, reward, done, info - - def turn_off(self, genset, unnormalized_production=50.): - # Take a step, ask genset to turn on. - action = np.array([0.0, normalize_production(unnormalized_production)]) - obs, reward, done, info = genset.step(action) - return obs, reward, done, info - - def test_on_at_start_up(self): - genset, _ = self.get_genset() - self.assertTrue(genset.current_status) - self.assertEqual(genset.goal_status, 1) - self.assertEqual(genset.state, np.array([1, 1, 0, 3])) - - def test_turn_off_step_1(self): - genset, params = self.get_genset() - unnormalized_production = 50. - obs, reward, done, info = self.turn_off(genset, unnormalized_production) - self.assertEqual(reward, -1.0*params['genset_cost']*unnormalized_production) - self.assertTrue(genset.current_status) - self.assertEqual(genset.goal_status, 0) - self.assertEqual(genset.state, np.array([1, 0, 0, 2])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], unnormalized_production) - - def test_turn_off_step_2(self): - genset, params = self.get_genset() - unnormalized_production = 50. - - for j in range(2): - obs, reward, done, info = self.turn_off(genset, unnormalized_production) - - self.assertEqual(reward, -1.0*params['genset_cost']*unnormalized_production) - self.assertTrue(genset.current_status) - self.assertEqual(genset.goal_status, 0) - self.assertEqual(genset.state, np.array([1, 0, 0, 1])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], unnormalized_production) - - def test_turn_off_step_3(self): - genset, params = self.get_genset() - unnormalized_production = 50. - - for j in range(3): - obs, reward, done, info = self.turn_off(genset, unnormalized_production) - - self.assertEqual(reward, -1.0*params['genset_cost']*unnormalized_production) - self.assertTrue(genset.current_status) - self.assertEqual(genset.goal_status, 0) - self.assertEqual(genset.state, np.array([1, 0, 0, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], unnormalized_production) - - def test_turn_off_step_4_final(self): - genset, params = self.get_genset() - unnormalized_production = 50. - - for j in range(3): - self.turn_off(genset, unnormalized_production) - - unnormalized_production = 0 - obs, reward, done, info = self.turn_off(genset, unnormalized_production) - - self.assertEqual(reward, 0) - self.assertFalse(genset.current_status) - self.assertEqual(genset.goal_status, 0) - self.assertEqual(genset.state, np.array([0, 0, 2, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], 0) - - def test_turn_on_after_turn_off_step_1(self): - genset, params = self.get_genset() - unnormalized_production = 50. - - for j in range(3): - self.turn_off(genset, unnormalized_production) - - # Step 4, should be off. - unnormalized_production = 0 - obs, reward, done, info = self.turn_off(genset, unnormalized_production) - - self.assertEqual(reward, 0) - self.assertFalse(genset.current_status) - self.assertEqual(genset.goal_status, 0) - self.assertEqual(genset.state, np.array([0, 0, 2, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], 0) - - unnormalized_production = 0 - obs, reward, done, info = self.turn_on(genset, unnormalized_production) - self.assertEqual(reward, 0) - self.assertFalse(genset.current_status) - self.assertEqual(genset.goal_status, 1) - self.assertEqual(genset.state, np.array([0, 1, 1, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], 0) - - def test_turn_on_after_turn_off_step_2(self): - genset, params = self.get_genset() - unnormalized_production = 50. - - for j in range(3): - self.turn_off(genset, unnormalized_production) - - # Step 4, should be off. - unnormalized_production = 0 - obs, reward, done, info = self.turn_off(genset, unnormalized_production) - - self.assertEqual(reward, 0) - self.assertFalse(genset.current_status) - self.assertEqual(genset.goal_status, 0) - self.assertEqual(genset.state, np.array([0, 0, 2, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], 0) - - # Turning back on - unnormalized_production = 0 - for j in range(2): - obs, reward, done, info = self.turn_on(genset, unnormalized_production) - self.assertEqual(reward, 0) - self.assertFalse(genset.current_status) - self.assertEqual(genset.goal_status, 1) - self.assertEqual(genset.state, np.array([0, 1, 0, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], 0) - - def test_turn_on_after_turn_off_final(self): - genset, params = self.get_genset() - unnormalized_production = 50. - - for j in range(3): - self.turn_off(genset, unnormalized_production) - - # Step 4, should be off. - unnormalized_production = 0 - obs, reward, done, info = self.turn_off(genset, unnormalized_production) - - self.assertEqual(reward, 0) - self.assertFalse(genset.current_status) - self.assertEqual(genset.goal_status, 0) - self.assertEqual(genset.state, np.array([0, 0, 2, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], 0) - - # Turning back on - unnormalized_production = 0 - for j in range(2): - self.turn_on(genset, unnormalized_production) - - unnormalized_production = 50. - obs, reward, done, info = self.turn_on(genset, unnormalized_production) - - self.assertEqual(reward, -1.0*unnormalized_production*params['genset_cost']) - self.assertTrue(genset.current_status) - self.assertEqual(genset.goal_status, 1) - self.assertEqual(genset.state, np.array([1, 1, 0, 3])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], unnormalized_production) - - def test_turn_off_abortion(self): - genset, params = self.get_genset() - unnormalized_production = 50. - - for j in range(2): - self.turn_off(genset, unnormalized_production) - - self.assertEqual(genset.state, np.array([1, 0, 0, 1])) - - # Step 3: abort! - obs, reward, done, info = self.turn_on(genset, unnormalized_production) - - self.assertEqual(reward, -1.0*unnormalized_production*params['genset_cost']) - self.assertTrue(genset.current_status) - self.assertEqual(genset.goal_status, 1) - self.assertEqual(genset.state, np.array([1, 1, 0, 3])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], unnormalized_production) - - def test_turn_on_abortion(self): - genset, params = self.get_genset(init_start_up=False) - unnormalized_production = 0. - - self.turn_on(genset, unnormalized_production) - self.assertEqual(genset.state, np.array([0, 1, 1, 0])) - - # Step 3: abort! - obs, reward, done, info = self.turn_off(genset, unnormalized_production) - - self.assertEqual(reward, 0) - self.assertFalse(genset.current_status) - self.assertEqual(genset.goal_status, 0) - self.assertEqual(genset.state, np.array([0, 0, 2, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], 0) - - -class TestManyStatusChanges(TestCase): - - def test_many_status_changes(self): - - n_steps = 5 - - def next_status(genset, goal_status): - if goal_status: - if genset.current_status: - return 1 - elif genset._steps_until_up == 0: - return 1 - else: - return 0 - else: - if not genset.current_status: - return 0 - elif genset._steps_until_down == 0: - return 0 - else: - return 1 - - for _running in True, False: - for start_up_time in range(0, n_steps): - for wind_down_time in range(0, n_steps): - for goal_status in 0, 1: - - intermediate_goal_statuses = product([0, 1], repeat=n_steps - 1) - - for steps in intermediate_goal_statuses: - genset, _ = get_genset(init_start_up=_running, - start_up_time=start_up_time, - wind_down_time=wind_down_time) - - _s = (goal_status, *steps) - for j, sub_goal_status in enumerate(_s): - with self.subTest(_running=_running, - _steps_until_up=start_up_time, - _steps_until_down=wind_down_time, - goal_status=goal_status, - step_combination=_s, - step=j, - goal_status_at_step=sub_goal_status): - predicted_status = next_status(genset, sub_goal_status) - genset.update_status(goal_status=sub_goal_status) - self.assertEqual(predicted_status, genset.current_status) - diff --git a/tests/microgrid/modules/module_tests/test_genset_module.py b/tests/microgrid/modules/module_tests/test_genset_module.py deleted file mode 100644 index 4b6c5180..00000000 --- a/tests/microgrid/modules/module_tests/test_genset_module.py +++ /dev/null @@ -1,164 +0,0 @@ -import numpy as np - -from tests.helpers.genset_module_testing_utils import default_params, get_genset, normalize_production -from tests.helpers.test_case import TestCase - - -class TestGensetModule(TestCase): - def setUp(self) -> None: - np.random.seed(0) - self.default_params = default_params.copy() - - def get_genset(self, **new_params): - return get_genset(**new_params) - - def test_init_start_up(self): - genset, _ = self.get_genset() - self.assertTrue(genset.current_status) - genset, _ = self.get_genset(init_start_up=False) - self.assertFalse(genset.current_status) - - def test_get_cost_linear(self): - genset_cost = np.random.rand() - genset, params = self.get_genset(genset_cost=genset_cost) - - production = params['running_min_production'] + (params['running_max_production']-params['running_min_production'])*np.random.rand() - production_cost = production*genset_cost - - self.assertEqual(genset.get_cost(production), production_cost) - - def test_get_cost_callable(self): - genset_cost = lambda x: x**2 - genset, params = self.get_genset(genset_cost=genset_cost) - - production = params['running_min_production'] + (params['running_max_production']-params['running_min_production'])*np.random.rand() - production_cost = genset_cost(production) - - self.assertEqual(genset.get_cost(production), production_cost) - - def test_step_out_of_range_goal_status(self): - genset, _ = self.get_genset() - action = np.array([-0.5, 0.5]) - - with self.assertRaises(ValueError): - genset.step(action) - - def test_step_out_of_normalized_range_production(self): - genset, _ = self.get_genset() - - with self.assertRaises(ValueError): - action = np.array([-0.5, 2]) - genset.step(action) - - def test_step_incorrect_action_shape(self): - genset, _ = self.get_genset() - - with self.assertRaises(TypeError): - action = 0.5 - genset.step(action) - - with self.assertRaises(TypeError): - action = np.ones(3) - genset.step(action) - - def test_step_unnormalized_production(self): - genset, _ = self.get_genset() - - action = np.array([1.0, 50]) - # try: - obs, reward, done, info = genset.step(action, normalized=False) - - self.assertEqual(reward, -1.0 * default_params['genset_cost']*action[1]) - self.assertTrue(genset.current_status) - self.assertEqual(genset.goal_status, 1) - self.assertEqual(obs, np.array([1, 1, 0, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], action[1]) - - def test_step_normalized_production(self): - genset, params = self.get_genset() - - unnormalized_production = 50 - action = np.array([1.0, normalize_production(unnormalized_production)]) - # try: - obs, reward, done, info = genset.step(action, normalized=True) - - self.assertEqual(reward, -1.0 * params['genset_cost']*unnormalized_production) - self.assertTrue(genset.current_status) - self.assertEqual(genset.goal_status, 1) - self.assertEqual(obs, np.array([1, 1, 0, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], unnormalized_production) - - def test_step_immediate_status_change(self): - genset, params = self.get_genset() - - unnormalized_production = 0 - action = np.array([0.0, normalize_production(unnormalized_production)]) - - self.assertTrue(genset.current_status) - - obs, reward, done, info = genset.step(action, normalized=True) - - self.assertEqual(reward, 0) - self.assertFalse(genset.current_status) - self.assertEqual(genset.goal_status, 0) - self.assertEqual(obs, np.array([0, 0, 0, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], unnormalized_production) - - def test_step_genset_off_production_request_error_raise(self): - genset, _ = self.get_genset() - - unnormalized_production = 50 - action = np.array([0.0, normalize_production(unnormalized_production)]) - - # Genset starts on - self.assertTrue(genset.current_status) - self.assertEqual(genset.goal_status, 1) - - # Turn genset off (wind_down_time=0), and then ask for production. (no-no). - with self.assertRaises(ValueError): - genset.step(action, normalized=True) - - def test_step_genset_off_production_request_no_error_raise(self): - genset, _ = self.get_genset(raise_errors=False) - - unnormalized_production = 50 - action = np.array([0.0, normalize_production(unnormalized_production)]) - - obs, reward, done, info = genset.step(action) - self.assertEqual(reward, 0) - self.assertFalse(genset.current_status) - self.assertEqual(genset.goal_status, 0) - self.assertEqual(obs, np.array([0, 0, 0, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], 0) - - def test_step_genset_production_request_out_of_range_no_error_raise(self): - genset, params = self.get_genset(raise_errors=False) - genset.action_space.verbose = True - - requested_possible_warn = [(params['running_min_production']*np.random.rand(), params['running_min_production'], False), - (params['running_max_production'] * (1+np.random.rand()), params['running_max_production'], True)] - - - # First requested value is below min_production, second is above max_production. - # Second should raise a warning. - - for requested, possible, raises_warn in requested_possible_warn: - with self.subTest(requested_production=requested, possible_production=possible): - action = np.array([1.0, normalize_production(requested)]) - - if raises_warn: - with self.assertWarns(Warning): - obs, reward, done, info = genset.step(action) - else: - obs, reward, done, info = genset.step(action) - - self.assertEqual(reward, -1.0 * params['genset_cost']*possible) - self.assertTrue(genset.current_status) - self.assertEqual(genset.goal_status, 1) - self.assertEqual(obs, np.array([1, 1, 0, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], possible) diff --git a/tests/microgrid/modules/module_tests/test_genset_module_start_up_1_wind_down_1.py b/tests/microgrid/modules/module_tests/test_genset_module_start_up_1_wind_down_1.py deleted file mode 100644 index 48f799fe..00000000 --- a/tests/microgrid/modules/module_tests/test_genset_module_start_up_1_wind_down_1.py +++ /dev/null @@ -1,174 +0,0 @@ -from tests.helpers.genset_module_testing_utils import get_genset, normalize_production -from tests.helpers.test_case import TestCase -import numpy as np -from copy import deepcopy - - -class TestGensetStartUp1WindDown0OffAtStartUp(TestCase): - def setUp(self) -> None: - self.genset, self.default_params = get_genset(init_start_up=False, start_up_time=1) - self.warm_up(self.genset) - - def get_genset(self, new=False, **new_params): - if len(new_params) == 0 and not new: - return deepcopy(self.genset), self.default_params - - genset, params = get_genset(default_parameters=self.default_params, **new_params) - if not new: - self.warm_up(genset) - return genset, params - - - def warm_up(self, genset): - # Take a step, ask genset to turn on. Warm-up takes one step so genset is still off at this point. - unnormalized_production = 0 - action = np.array([1.0, normalize_production(unnormalized_production)]) - obs, reward, done, info = genset.step(action) - return obs, reward, done, info - - def test_off_at_start_up(self): - genset, _ = self.get_genset(new=True) - self.assertFalse(genset.current_status) - self.assertEqual(genset.goal_status, 0) - self.assertEqual(genset.state, np.array([0, 0, 1, 0])) - - def test_warm_up(self): - # Take a step, ask genset to turn on. Warm-up takes one step so genset is still off at this point. - genset,_ = self.get_genset(new=True) - obs, reward, done, info = self.warm_up(genset) - self.assertEqual(reward, 0) - self.assertFalse(genset.current_status) - self.assertEqual(genset.goal_status, 1) - self.assertEqual(obs, np.array([0, 1, 0, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], 0) - - def test_step_start_up_1_exception(self): - # Assert that exception is thrown when production is requested while genset is off - genset, _ = self.get_genset(new=True) - - self.assertFalse(genset.current_status) - self.assertEqual(genset.goal_status, 0) - - unnormalized_production = 50 - action = np.array([1.0, normalize_production(unnormalized_production)]) - - with self.assertRaises(ValueError) as e: - genset.step(action) - err_msg = e.exception.args[0] - self.assertTrue('This may be because this genset module is not currently running.' in err_msg) - - def test_step_start_up_1_no_exception(self): - # Genset is on now. Should be able to request production. - - genset, params = self.get_genset() - - unnormalized_production = 50 - action = np.array([1.0, normalize_production(unnormalized_production)]) - obs, reward, done, info = genset.step(action) - self.assertEqual(reward, -1.0*params['genset_cost']*unnormalized_production) - self.assertTrue(genset.current_status) - self.assertEqual(genset.goal_status, 1) - self.assertEqual(obs, np.array([1, 1, 0, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], unnormalized_production) - - def test_start_up_1_request_below_min_exception_raise(self): - genset, params = self.get_genset() - - # Genset is on now. Requesting below min production. - unnormalized_production = params['running_min_production']*np.random.rand() - action = np.array([1.0, normalize_production(unnormalized_production)]) - with self.assertRaises(ValueError): - genset.step(action) - - def test_start_up_1_request_below_min_no_exception(self): - # Genset is on, requesting production less than the min should return min production. - genset, params = self.get_genset(raise_errors=False) - - # Genset is on now. Requesting below min production. - unnormalized_production = params['running_min_production']*np.random.rand() - action = np.array([1.0, normalize_production(unnormalized_production)]) - obs, reward, done, info = genset.step(action) - self.assertEqual(reward, -1.0*params['genset_cost']*params['running_min_production']) - self.assertTrue(genset.current_status) - self.assertEqual(genset.goal_status, 1) - self.assertEqual(obs, np.array([1, 1, 0, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], params['running_min_production']) - - def test_start_up_1_then_shut_down_exception_raise(self): - # Genset is on, requesting production less than the min should return min production. - genset, params = self.get_genset() - - # Genset is on now. Requesting below min production. - unnormalized_production = 50. - action = np.array([0.1, normalize_production(unnormalized_production)]) - with self.assertRaises(ValueError): - genset.step(action) - - def test_start_up_1_then_shut_down_no_exception(self): - # Genset is on, requesting production less than the min should return min production. - genset, params = self.get_genset(raise_errors=False) - - # Genset is on now. Requesting below min production. - unnormalized_production = 50. - action = np.array([0.1, normalize_production(unnormalized_production)]) - obs, reward, done, info = genset.step(action) - self.assertEqual(reward, 0.0) - self.assertFalse(genset.current_status) - self.assertEqual(genset.goal_status, 0) - self.assertEqual(obs, np.array([0, 0, params['start_up_time'], 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], 0.0) - - -class TestGensetStartUp1WindDown0OnAtStartUp(TestCase): - def setUp(self) -> None: - self.genset, self.default_params = get_genset(init_start_up=True, start_up_time=1) - self.warm_up(self.genset) - - def get_genset(self, new=False, **new_params): - if len(new_params) == 0 and not new: - return deepcopy(self.genset), self.default_params - - genset, params = get_genset(default_parameters=self.default_params, **new_params) - if not new: - self.warm_up(genset) - return genset, params - - def warm_up(self, genset): - # Take a step, ask genset to turn on. Genset begins on so should be on at this point. - unnormalized_production = self.default_params['running_min_production'] - action = np.array([1.0, normalize_production(unnormalized_production)]) - obs, reward, done, info = genset.step(action) - return obs, reward, done, info - - def test_on_at_start_up(self): - genset, _ = self.get_genset(new=True) - self.assertTrue(genset.current_status) - self.assertEqual(genset.goal_status, 1) - self.assertEqual(genset.state, np.array([1, 1, 0, 0])) - - def test_warm_up(self): - # Take a step, ask genset to turn on. Genset begins on so should be on at this point. - genset, params = self.get_genset(new=True) - obs, reward, done, info = self.warm_up(genset) - self.assertEqual(reward, -1.0*params['genset_cost']*params['running_min_production']) - self.assertTrue(genset.current_status) - self.assertEqual(genset.goal_status, 1) - self.assertEqual(obs, np.array([1, 1, 0, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], params['running_min_production']) - - def test_shut_down(self): - genset, _ = self.get_genset() - unnormalized_production = 0 - action = np.array([0.0, normalize_production(unnormalized_production)]) - obs, reward, done, info = genset.step(action) - self.assertEqual(reward, 0) - self.assertFalse(genset.current_status) - self.assertEqual(genset.goal_status, 0) - self.assertEqual(obs, np.array([0, 0, 1, 0])) - self.assertFalse(done) - self.assertEqual(info['provided_energy'], 0) \ No newline at end of file diff --git a/tests/microgrid/modules/module_tests/test_load_module.py b/tests/microgrid/modules/module_tests/test_load_module.py deleted file mode 100644 index 7bbed81b..00000000 --- a/tests/microgrid/modules/module_tests/test_load_module.py +++ /dev/null @@ -1,67 +0,0 @@ -import numpy as np - -from pymgrid.modules import LoadModule - -from tests.microgrid.modules.module_tests.timeseries_modules import ( - TestTimeseriesModuleForecasting, - TestTimeseriesModuleNoForecasting, - TestTimeSeriesModuleForecastingNegativeVals, - TestTimeSeriesModuleNoForecastingNegativeVals -) - - -class TestLoadModuleNoForecasting(TestTimeseriesModuleNoForecasting): - __test__ = True - negative_time_series = True - action_space_dim = 0 - - - def get_module(self): - return LoadModule(self.module_time_series) - - def test_init_current_load(self): - load_module = self.get_module() - self.assertEqual(load_module.current_load, -1 * self.time_series[0]) - - def test_step(self): - load_module = self.get_module() - self.assertEqual(load_module.current_load, -1 * self.time_series[0]) - - obs, reward, done, info = load_module.step(np.array([])) - obs = load_module.from_normalized(obs, obs=True) - self.assertEqual(obs, self.time_series[1]) - self.assertEqual(reward, 0) - self.assertFalse(done) - self.assertEqual(info["absorbed_energy"], -1 * self.time_series[0]) - - -class TestLoadModuleForecasting(TestTimeseriesModuleForecasting): - __test__ = True - negative_time_series = True - action_space_dim = 0 - - def get_module(self): - return LoadModule(self.module_time_series, forecaster="oracle", forecast_horizon=self.forecast_horizon) - - def test_step(self): - load_module = self.get_module() - self.assertEqual(load_module.current_load, -1 * self.time_series[0]) - - action = load_module.to_normalized(np.array([]), act=True) - obs, reward, done, info = load_module.step(action) - obs = load_module.from_normalized(obs, obs=True) - self.assertEqual(obs, self.time_series[1:self.forecast_horizon+2]) - self.assertEqual(reward, 0) - self.assertFalse(done) - self.assertEqual(info["absorbed_energy"], -1 * self.time_series[0]) - - -class TestLoadModuleForecastingNegativeVals(TestTimeSeriesModuleForecastingNegativeVals, - TestLoadModuleForecasting): - pass - - -class TestLoadModuleNoForecastingNegativeVals(TestTimeSeriesModuleNoForecastingNegativeVals, - TestLoadModuleNoForecasting): - pass - diff --git a/tests/microgrid/modules/module_tests/test_renewable_module.py b/tests/microgrid/modules/module_tests/test_renewable_module.py deleted file mode 100644 index 0bff0e10..00000000 --- a/tests/microgrid/modules/module_tests/test_renewable_module.py +++ /dev/null @@ -1,67 +0,0 @@ - -from pymgrid.modules import RenewableModule - -from tests.microgrid.modules.module_tests.timeseries_modules import ( - TestTimeseriesModuleForecasting, - TestTimeseriesModuleNoForecasting, - TestTimeSeriesModuleForecastingNegativeVals, - TestTimeSeriesModuleNoForecastingNegativeVals -) - - -class TestRenewableModuleNoForecasting(TestTimeseriesModuleNoForecasting): - __test__ = True - action_space_dim = 1 - - def get_module(self): - return RenewableModule(self.module_time_series) - - def test_init_current_renewable(self): - renewable_module = self.get_module() - self.assertEqual(renewable_module.current_renewable, self.time_series[0]) - - def test_step(self): - renewable_module = self.get_module() - self.assertEqual(renewable_module.current_renewable, self.time_series[0]) - - unnormalized_action = 1 - action = renewable_module.to_normalized(unnormalized_action, act=True) - obs, reward, done, info = renewable_module.step(action) - obs = renewable_module.from_normalized(obs, obs=True) - self.assertEqual(obs, self.time_series[1]) - self.assertEqual(reward, 0) - self.assertFalse(done) - self.assertEqual(info["provided_energy"], unnormalized_action) - self.assertEqual(info["curtailment"], 0) - - -class TestRenewableModuleForecasting(TestTimeseriesModuleForecasting): - __test__ = True - action_space_dim = 1 - - def get_module(self): - return RenewableModule(self.module_time_series, forecaster="oracle", forecast_horizon=self.forecast_horizon) - - def test_step(self): - renewable_module = self.get_module() - self.assertEqual(renewable_module.current_renewable, self.time_series[0]) - - unnormalized_action = 1 - action = renewable_module.to_normalized(unnormalized_action, act=True) - obs, reward, done, info = renewable_module.step(action) - obs = renewable_module.from_normalized(obs, obs=True) - self.assertEqual(obs, self.time_series[1:self.forecast_horizon+2]) - self.assertEqual(reward, 0) - self.assertFalse(done) - self.assertEqual(info["provided_energy"], unnormalized_action) - self.assertEqual(info["curtailment"], 0) - - -class TestRenewableModuleForecastingNegativeVals(TestTimeSeriesModuleForecastingNegativeVals, - TestRenewableModuleForecasting): - pass - - -class TestRenewableModuleNoForecastingNegativeVals(TestTimeSeriesModuleNoForecastingNegativeVals, - TestRenewableModuleNoForecasting): - pass diff --git a/tests/microgrid/modules/module_tests/timeseries_modules.py b/tests/microgrid/modules/module_tests/timeseries_modules.py deleted file mode 100644 index 52e77ab8..00000000 --- a/tests/microgrid/modules/module_tests/timeseries_modules.py +++ /dev/null @@ -1,100 +0,0 @@ -import numpy as np -from abc import abstractmethod -from gym.spaces import Box - -from pymgrid.utils.space import ModuleSpace - -from tests.helpers.test_case import TestCase - - -class TestTimeseriesModule(TestCase): - __test__ = False - negative_time_series = False - forecast_horizon: int - action_space_dim: int - - def setUp(self) -> None: - self.module_time_series = self._get_module_time_series() - self.time_series = self._get_time_series() - - def _get_module_time_series(self): - return self._get_time_series() - - def _get_time_series(self): - sign = -1 if self.negative_time_series else 1 - return sign * (2 - np.cos(np.pi * np.arange(100) / 2)) - - @abstractmethod - def get_module(self): - return NotImplemented - - def test_action_space(self): - module = self.get_module() - normalized_action_space = module.action_space["normalized"] - unnormalized_action_space = module.action_space["unnormalized"] - - self.assertEqual(normalized_action_space, Box(low=0, high=1, shape=(self.action_space_dim, ))) - self.assertEqual(unnormalized_action_space, Box(low=min(0, self.time_series.min()), - high=max(0, self.time_series.max()), - shape=(self.action_space_dim, ))) - - def test_observation_space(self): - module = self.get_module() - normalized_obs_space = module.observation_space["normalized"] - unnormalized_obs_space = module.observation_space["unnormalized"] - - self.assertEqual(normalized_obs_space, Box(low=0, high=1, shape=(1+self.forecast_horizon,))) - self.assertEqual(unnormalized_obs_space, Box(low=min(0, self.time_series.min()), - high=max(0, self.time_series.max()), - shape=(1+self.forecast_horizon,))) - - - def test_observations_in_observation_space(self): - module = self.get_module() - - observation_space = ModuleSpace( - unnormalized_low=min(0, self.time_series.min()), - unnormalized_high=max(0, self.time_series.max()), - shape=(1 + module.forecast_horizon,) - ) - - self.assertEqual(module.observation_space, observation_space) - - done = False - while not done: - obs, reward, done, info = module.step(module.action_space.sample(), normalized=False) - if np.isscalar(obs): - obs = np.array([obs]) - self.assertIn(obs, observation_space['normalized']) - self.assertIn(module.state, observation_space['unnormalized']) - - -class TestTimeseriesModuleNoForecasting(TestTimeseriesModule): - forecast_horizon = 0 - - def test_init(self): - module = self.get_module() - self.assertIsNone(module.forecast()) - self.assertEqual(module.state, self.time_series[0]) - self.assertEqual(len(module.state_dict()), 1+self.forecast_horizon) - - -class TestTimeseriesModuleForecasting(TestTimeseriesModule): - forecast_horizon = 24 - - def test_init(self): - module = self.get_module() - self.assertIsNotNone(module.forecast()) - self.assertEqual(module.forecast(), self.time_series[1:1 + self.forecast_horizon].reshape((-1, 1))) - self.assertEqual(module.state, self.time_series[:1 + self.forecast_horizon]) - self.assertEqual(len(module.state_dict()), 1 + self.forecast_horizon) - - -class TestTimeSeriesModuleNoForecastingNegativeVals(TestTimeseriesModuleNoForecasting): - def _get_module_time_series(self): - return -1 * self._get_time_series() - - -class TestTimeSeriesModuleForecastingNegativeVals(TestTimeseriesModuleForecasting): - def _get_module_time_series(self): - return -1 * self._get_time_series() diff --git a/tests/microgrid/serialize/test_microgrid_serialization.py b/tests/microgrid/serialize/test_microgrid_serialization.py deleted file mode 100644 index 4d6a961a..00000000 --- a/tests/microgrid/serialize/test_microgrid_serialization.py +++ /dev/null @@ -1,22 +0,0 @@ -import numpy as np - -from pymgrid import Microgrid - -from tests.helpers.modular_microgrid import get_modular_microgrid -from tests.helpers.test_case import TestCase - - -class TestMicrogridSerialization(TestCase): - def test_serialize_no_modules(self): - microgrid = Microgrid([], add_unbalanced_module=False) - dump = microgrid.dump() - loaded = Microgrid.load(dump) - - self.assertEqual(microgrid, loaded) - - def test_serialize_with_renewable(self): - microgrid = get_modular_microgrid(remove_modules=["genset", "battery", "load", "grid"], - add_unbalanced_module=False) - - self.assertEqual(len(microgrid.modules), 1) - self.assertEqual(microgrid, Microgrid.load(microgrid.dump())) diff --git a/tests/microgrid/space/__init__.py b/tests/microgrid/space/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/microgrid/space/test_microgrid_space.py b/tests/microgrid/space/test_microgrid_space.py deleted file mode 100644 index 0461c682..00000000 --- a/tests/microgrid/space/test_microgrid_space.py +++ /dev/null @@ -1,9 +0,0 @@ -import numpy as np - -from pymgrid.utils.space import ModuleSpace -from tests.helpers.test_case import TestCase - - -class TestMicrogridSpace(TestCase): - # Placeholder - pass diff --git a/tests/microgrid/space/test_module_space.py b/tests/microgrid/space/test_module_space.py deleted file mode 100644 index d878d82d..00000000 --- a/tests/microgrid/space/test_module_space.py +++ /dev/null @@ -1,125 +0,0 @@ -import numpy as np - -from pymgrid.utils.space import ModuleSpace -from tests.helpers.test_case import TestCase - - -class TestModuleSpace(TestCase): - - @staticmethod - def get_space(unnormalized_low, unnormalized_high, normalized_bounds=(0, 1)): - return ModuleSpace(unnormalized_low=unnormalized_low, - unnormalized_high=unnormalized_high, - normalized_bounds=normalized_bounds) - - def test_normalize(self): - unnorm_low = np.zeros(2) - unnorm_high = np.array([1, 2]) - - space = self.get_space(unnorm_low, unnorm_high) - - vals_to_normalize = [np.zeros(2), np.array([0.5, 1]), np.array([1, 2])] - expected_normalized_vals = [np.zeros(2), np.array([0.5, 0.5]), np.array([1, 1])] - - for val_to_normalize, expected_normalized_val in zip(vals_to_normalize, expected_normalized_vals): - with self.subTest(val_to_normalize=val_to_normalize, expected_normalized_val=expected_normalized_val): - normalized = space.normalize(val_to_normalize) - self.assertEqual(normalized, expected_normalized_val) - - def test_denormalize(self): - unnorm_low = np.zeros(2) - unnorm_high = np.array([1, 2]) - - space = self.get_space(unnorm_low, unnorm_high) - - vals_to_denormalize = [np.zeros(2), np.array([0.5, 0.5]), np.array([1, 1])] - expected_denormalized_vals = [np.zeros(2), np.array([0.5, 1]), np.array([1, 2])] - - for val_to_denormalize, expected_denormalized_val in zip(vals_to_denormalize, expected_denormalized_vals): - with self.subTest(val_to_denormalize=val_to_denormalize, expected_denormalized_val=expected_denormalized_val): - denormalized = space.denormalize(val_to_denormalize) - self.assertEqual(denormalized, expected_denormalized_val) - - def test_normalize_different_normalized_bounds(self): - unnorm_low = np.zeros(2) - unnorm_high = np.array([1, 2]) - normalized_bounds = [-3, 2] - - space = self.get_space(unnorm_low, unnorm_high, normalized_bounds=normalized_bounds) - - vals_to_normalize = [np.zeros(2), np.array([0.5, 1]), np.array([1, 2])] - expected_normalized_vals = [np.array([-3, -3]), np.array([-0.5, -0.5]), np.array([2, 2])] - - for val_to_normalize, expected_normalized_val in zip(vals_to_normalize, expected_normalized_vals): - with self.subTest(val_to_normalize=val_to_normalize, expected_normalized_val=expected_normalized_val): - normalized = space.normalize(val_to_normalize) - self.assertEqual(normalized, expected_normalized_val) - - def test_denormalize_different_normalized_bounds(self): - unnorm_low = np.zeros(2) - unnorm_high = 2 * np.arange(2) - normalized_bounds = [-3, 2] - - space = self.get_space(unnorm_low, unnorm_high, normalized_bounds=normalized_bounds) - - vals_to_denormalize = [np.array([-3, -3]), np.array([-0.5, -0.5]), np.array([2, 2])] - expected_denormalized_vals = [np.zeros(2), np.array([0.5, 1]), np.array([1, 2])] - - for val_to_denormalize, expected_denormalized_val in zip(vals_to_denormalize, expected_denormalized_vals): - with self.subTest(val_to_denormalize=val_to_denormalize, expected_denormalized_val=expected_denormalized_val): - denormalized = space.denormalize(val_to_denormalize) - self.assertEqual(denormalized, expected_denormalized_val) - - def test_clip_no_clip_normalized(self): - unnorm_low = np.zeros(2) - unnorm_high = 2 * np.arange(2) - normalized_bounds = [0, 2] - - space = self.get_space(unnorm_low, unnorm_high, normalized_bounds=normalized_bounds) - - val = (normalized_bounds[1] - normalized_bounds[0]) * np.random.rand(2) + normalized_bounds[0] - - self.assertIn(val, space.normalized) - self.assertEqual(val, space.clip(val, normalized=True)) - - def test_clip_yes_clip_normalized(self): - unnorm_low = np.zeros(2) - unnorm_high = 2 * np.arange(2) - normalized_bounds = [0, 2] - - space = self.get_space(unnorm_low, unnorm_high, normalized_bounds=normalized_bounds) - - val = (normalized_bounds[1] - normalized_bounds[0]) * np.random.rand(2) + normalized_bounds[1] - - clipped = space.clip(val, normalized=True) - - self.assertNotIn(val, space.normalized) - self.assertNotEqual(val, clipped) - self.assertEqual(clipped, space.normalized.high) - - def test_clip_no_clip_unnormalized(self): - unnorm_low = np.zeros(2) - unnorm_high = 2 * np.arange(2) - normalized_bounds = [0, 2] - - space = self.get_space(unnorm_low, unnorm_high, normalized_bounds=normalized_bounds) - - val = (unnorm_high - unnorm_low) * np.random.rand(2) + unnorm_low - - self.assertIn(val, space.unnormalized) - self.assertEqual(val, space.clip(val, normalized=False)) - - def test_clip_yes_clip_unnormalized(self): - unnorm_low = np.zeros(2) - unnorm_high = 2 * np.arange(2) - normalized_bounds = [0, 2] - - space = self.get_space(unnorm_low, unnorm_high, normalized_bounds=normalized_bounds) - - val = (unnorm_high - unnorm_low) * np.random.rand(2) + unnorm_high - - clipped = space.clip(val, normalized=False) - - self.assertNotIn(val, space.unnormalized) - self.assertNotEqual(val, clipped) - self.assertEqual(clipped, space.unnormalized.high) diff --git a/tests/microgrid/test_microgrid.py b/tests/microgrid/test_microgrid.py deleted file mode 100644 index d2f5ab13..00000000 --- a/tests/microgrid/test_microgrid.py +++ /dev/null @@ -1,590 +0,0 @@ -import numpy as np -import pandas as pd - - -from pymgrid import Microgrid -from pymgrid.modules import LoadModule, RenewableModule - -from tests.helpers.modular_microgrid import get_modular_microgrid -from tests.helpers.test_case import TestCase - - -class TestMicrogrid(TestCase): - def test_from_scenario(self): - for j in range(25): - with self.subTest(microgrid_number=j): - microgrid = Microgrid.from_scenario(j) - self.assertTrue(hasattr(microgrid, "load")) - self.assertTrue(hasattr(microgrid, "pv")) - self.assertTrue(hasattr(microgrid, "battery")) - self.assertTrue(hasattr(microgrid, "grid") or hasattr(microgrid, "genset")) - - def test_empty_action_without_load(self): - microgrid = get_modular_microgrid(remove_modules=('load', )) - action = microgrid.get_empty_action() - - self.assertIn('battery', action) - self.assertIn('genset', action) - self.assertIn('grid', action) - - self.assertTrue(all(v == [None] for v in action.values())) - - def test_empty_action_with_load(self): - microgrid = get_modular_microgrid() - action = microgrid.get_empty_action() - - self.assertIn('battery', action) - self.assertIn('genset', action) - self.assertIn('grid', action) - self.assertNotIn('load', action) - - self.assertTrue(all(v == [None] for v in action.values())) - - def test_action_space(self): - microgrid = get_modular_microgrid() - action = microgrid.microgrid_action_space.sample() - - for module_name, module_list in microgrid.modules.iterdict(): - for module_num, module in enumerate(module_list): - if 'controllable' in module.module_type: - with self.subTest(module_name=module_name, module_num=module_num): - self.assertIn(action[module_name][module_num], module.action_space) - - def test_action_space_normalize(self): - microgrid = get_modular_microgrid() - action = microgrid.microgrid_action_space.sample() - normalized = microgrid.microgrid_action_space.normalize(action) - - for module_name, module_list in microgrid.modules.iterdict(): - for module_num, module in enumerate(module_list): - if 'controllable' in module.module_type: - with self.subTest(module_name=module_name, module_num=module_num): - self.assertIn(normalized[module_name][module_num], module.action_space.normalized) - - def test_action_space_denormalize(self): - microgrid = get_modular_microgrid() - action = microgrid.microgrid_action_space.sample(normalized=True) - denormalized = microgrid.microgrid_action_space.denormalize(action) - - for module_name, module_list in microgrid.modules.iterdict(): - for module_num, module in enumerate(module_list): - if 'controllable' in module.module_type: - with self.subTest(module_name=module_name, module_num=module_num): - self.assertIn(denormalized[module_name][module_num], module.action_space.unnormalized) - - def test_action_space_normalize_different_bounds(self): - microgrid = get_modular_microgrid(normalized_action_bounds=[-2, 3]) - - actions = [ - {'battery': [-50], 'genset': [np.array([0,0])], 'grid': [-100]}, - {'battery': [0], 'genset': [np.array([0.5, 25])], 'grid': [0]}, - {'battery': [50], 'genset': [np.array([1, 50])], 'grid': [100]}, - {'battery': [50], 'genset': [np.array([0, 50])], 'grid': [100]} - ] - - normalized_actions = [ - {'battery': [np.array([-2])], 'genset': [np.array([-2, -2])], 'grid': [np.array([-2])]}, - {'battery': [np.array([0.5])], 'genset': [np.array([0.5, 0.5])], 'grid': [np.array([0.5])]}, - {'battery': [np.array([3])], 'genset': [np.array([3, 3])], 'grid': [np.array([3])]}, - {'battery': [np.array([3])], 'genset': [np.array([-2, 3])], 'grid': [np.array([3])]} - ] - - for action, expected_normalized_action in zip(actions, normalized_actions): - with self.subTest(action=action, expected_normalized_action=expected_normalized_action): - normalized = microgrid.microgrid_action_space.normalize(action) - for k, v in expected_normalized_action.items(): - self.assertEqual(v, normalized[k]) - - def test_action_space_denormalize_different_bounds(self): - microgrid = get_modular_microgrid(normalized_action_bounds=[-2, 3]) - - actions = [ - {'battery': [np.array([-2])], 'genset': [np.array([-2, -2])], 'grid': [np.array([-2])]}, - {'battery': [np.array([0.5])], 'genset': [np.array([0.5, 0.5])], 'grid': [np.array([0.5])]}, - {'battery': [np.array([3])], 'genset': [np.array([3, 3])], 'grid': [np.array([3])]}, - {'battery': [np.array([3])], 'genset': [np.array([-2, 3])], 'grid': [np.array([3])]} - ] - - denormalized_actions = [ - {'battery': [-50], 'genset': [np.array([0,0])], 'grid': [-100]}, - {'battery': [0], 'genset': [np.array([0.5, 25])], 'grid': [0]}, - {'battery': [50], 'genset': [np.array([1, 50])], 'grid': [100]}, - {'battery': [50], 'genset': [np.array([0, 50])], 'grid': [100]} - ] - - for action, expected_denormalized_action in zip(actions, denormalized_actions): - with self.subTest(action=action, expected_normalized_action=expected_denormalized_action): - denormalized = microgrid.microgrid_action_space.denormalize(action) - for k, v in expected_denormalized_action.items(): - self.assertEqual(v, denormalized[k]) - - def test_action_space_clip_no_clip_normalized(self): - microgrid = get_modular_microgrid(normalized_action_bounds=[0, 2]) - action = {'battery': [np.array([1])], 'genset': [np.array([1, 1])], 'grid': [np.array([1])]} - - self.assertIn(action, microgrid.microgrid_action_space.normalized) - self.assertEqual(action, microgrid.microgrid_action_space.clip(action, normalized=True)) - - def test_action_space_clip_down_normalized(self): - microgrid = get_modular_microgrid(normalized_action_bounds=[0, 2]) - action = {'battery': [np.array([2.1])], 'genset': [np.array([2.1, 2.3])], 'grid': [np.array([2.5])]} - - clipped = microgrid.microgrid_action_space.clip(action, normalized=True) - - self.assertNotIn(action, microgrid.microgrid_action_space.normalized) - - self.assertNotEqual(action, clipped) - self.assertEqual(clipped, microgrid.microgrid_action_space.normalized.high) - - def test_action_space_clip_up_normalized(self): - microgrid = get_modular_microgrid(normalized_action_bounds=[0, 2]) - action = {'battery': [np.array([-0.1])], 'genset': [np.array([-0.2, -1])], 'grid': [np.array([-2])]} - - clipped = microgrid.microgrid_action_space.clip(action, normalized=True) - - self.assertNotIn(action, microgrid.microgrid_action_space.normalized) - - self.assertNotEqual(action, clipped) - self.assertEqual(clipped, microgrid.microgrid_action_space.normalized.low) - - def test_action_space_clip_no_clip_unnormalized(self): - microgrid = get_modular_microgrid(normalized_action_bounds=[0, 2]) - action = {'battery': [np.array([0])], 'genset': [np.array([0.5, 25])], 'grid': [np.array([50])]} - - self.assertIn(action, microgrid.microgrid_action_space.unnormalized) - self.assertEqual(action, microgrid.microgrid_action_space.clip(action, normalized=False)) - - def test_action_space_clip_down_unnormalized(self): - microgrid = get_modular_microgrid(normalized_action_bounds=[0, 2]) - action = {'battery': [np.array([60])], 'genset': [np.array([1.1, 60])], 'grid': [np.array([110])]} - - clipped = microgrid.microgrid_action_space.clip(action, normalized=False) - - self.assertNotIn(action, microgrid.microgrid_action_space.unnormalized) - - self.assertNotEqual(action, clipped) - self.assertEqual(clipped, microgrid.microgrid_action_space.unnormalized.high) - - def test_action_space_clip_up_unnormalized(self): - microgrid = get_modular_microgrid(normalized_action_bounds=[0, 2]) - - action = {'battery': [np.array([-60])], 'genset': [np.array([-0.1, -0.1])], 'grid': [np.array([-110])]} - - clipped = microgrid.microgrid_action_space.clip(action, normalized=False) - - self.assertNotIn(action, microgrid.microgrid_action_space.unnormalized) - - self.assertNotEqual(action, clipped) - self.assertEqual(clipped, microgrid.microgrid_action_space.unnormalized.low) - - def test_action_space_clip_both_ways_unnormalized(self): - microgrid = get_modular_microgrid(normalized_action_bounds=[0, 2]) - - action = {'battery': [np.array([60])], 'genset': [np.array([-0.1, 60])], 'grid': [np.array([-10])]} - - clipped = microgrid.microgrid_action_space.clip(action, normalized=False) - - self.assertNotIn(action, microgrid.microgrid_action_space.unnormalized) - - self.assertNotEqual(action, clipped) - self.assertIn(clipped, microgrid.microgrid_action_space.unnormalized) - - def test_action_space_clip_define_low_high(self): - microgrid = get_modular_microgrid(normalized_action_bounds=[0, 2]) - - action = {'battery': [np.array([60])], 'genset': [np.array([-0.1, 60])], 'grid': [np.array([-10])]} - - clipped = microgrid.microgrid_action_space.clip(action, - low=microgrid.microgrid_action_space.unnormalized.low, - high=microgrid.microgrid_action_space.unnormalized.high) - - self.assertNotIn(action, microgrid.microgrid_action_space.unnormalized) - - self.assertNotEqual(action, clipped) - self.assertIn(clipped, microgrid.microgrid_action_space.unnormalized) - - def test_sample_action(self): - microgrid = get_modular_microgrid() - action = microgrid.sample_action() - - for module_name, action_list in action.items(): - for module_num, _act in enumerate(action_list): - with self.subTest(module_name=module_name, module_num=module_num): - action_arr = np.array(_act) - if not action_arr.shape: - action_arr = np.array([_act]) - self.assertTrue(microgrid.modules[module_name][module_num].action_space.shape[0]) - self.assertIn(action_arr, microgrid.modules[module_name][module_num].action_space.normalized) - - def test_sample_action_all_modules_populated(self): - microgrid = get_modular_microgrid() - action = microgrid.sample_action() - - for module_name, module_list in microgrid.fixed.iterdict(): - for module_num, module in enumerate(module_list): - with self.subTest(module_name=module_name, module_num=module_num): - empty_action_space = module.action_space.shape == (0, ) - try: - _ = action[module_name][module_num] - has_corresponding_action = True - except KeyError: - has_corresponding_action = False - - self.assertTrue(empty_action_space != has_corresponding_action) # XOR - - def test_current_step(self): - microgrid = get_modular_microgrid() - - self.assertEqual(microgrid.current_step, 0) - - for j in range(4): - with self.subTest(step=j): - microgrid.step(microgrid.sample_action()) - self.assertEqual(microgrid.current_step, j+1) - - def test_current_step_after_reset(self): - microgrid = get_modular_microgrid() - self.assertEqual(microgrid.current_step, 0) - - microgrid.step(microgrid.sample_action()) - self.assertEqual(microgrid.current_step, 1) - - microgrid.reset() - self.assertEqual(microgrid.current_step, 0) - - def test_set_module_attr_forecast_horizon(self): - forecast_horizon = 50 - - microgrid = get_modular_microgrid() - microgrid.set_module_attrs(forecast_horizon=forecast_horizon) - - microgrid_fh = [module.forecast_horizon for module in microgrid.modules.iterlist() - if hasattr(module, 'forecast_horizon')] - - self.assertEqual(min(microgrid_fh), max(microgrid_fh)) - - self.assertEqual(min(microgrid_fh), forecast_horizon) - - def test_set_module_attr_bad_attr_name(self): - microgrid = get_modular_microgrid() - - with self.assertRaises(AttributeError): - microgrid.set_module_attrs(blah='blah') - - def test_get_cost_info(self): - modules = 'genset', 'battery', 'renewable', 'load', 'grid', 'balancing' - - microgrid = get_modular_microgrid() - cost_info = microgrid.get_cost_info() - - for module in modules: - with self.subTest(info_of_module=cost_info): - self.assertIn(module, cost_info.keys()) - self.assertEqual(len(cost_info[module]), 1) - self.assertIsInstance(cost_info[module][0], dict) - - self.assertIn('production_marginal_cost', cost_info[module][0]) - self.assertIn('absorption_marginal_cost', cost_info[module][0]) - self.assertEqual(len(cost_info[module][0]), 2) - - self.assertTrue(pd.api.types.is_number(cost_info[module][0]['production_marginal_cost'])) - self.assertTrue(pd.api.types.is_number(cost_info[module][0]['absorption_marginal_cost'])) - - def test_set_initial_step(self): - microgrid = get_modular_microgrid() - - self.assertEqual(microgrid.initial_step, 0) - - for module_name, module_list in microgrid.modules.iterdict(): - for n, module in enumerate(module_list): - with self.subTest(module_name=module_name, module_num=n): - try: - initial_step = module.initial_step - except AttributeError: - continue - - self.assertEqual(initial_step, 0) - - microgrid.initial_step = 1 - - self.assertEqual(microgrid.initial_step, 1) - self.assertEqual(microgrid.modules.get_attrs('initial_step', unique=True, as_pandas=False), 1) - - for module_name, module_list in microgrid.modules.iterdict(): - for n, module in enumerate(module_list): - with self.subTest(module_name=module_name, module_num=n): - try: - initial_step = module.initial_step - except AttributeError: - continue - - self.assertEqual(initial_step, 1) - - -class TestMicrogridLoadPV(TestCase): - def setUp(self): - self.load_ts, self.pv_ts = self.set_ts() - self.microgrid, self.n_loads, self.n_pvs = self.set_microgrid() - self.n_modules = 1 + self.n_loads + self.n_pvs - - def set_ts(self): - ts = 10 * np.random.rand(100) - return ts, ts - - def set_microgrid(self): - load = LoadModule(time_series=self.load_ts, raise_errors=True) - pv = RenewableModule(time_series=self.pv_ts, raise_errors=True) - return Microgrid([load, pv]), 1, 1 - - def test_populated_correctly(self): - self.assertTrue(hasattr(self.microgrid.modules, 'load')) - self.assertTrue(hasattr(self.microgrid.modules, 'renewable')) - self.assertEqual(len(self.microgrid.modules), self.n_modules) # load, pv, unbalanced - - def test_current_load_correct(self): - try: - current_load = self.microgrid.modules.load.item().current_load - except ValueError: - # More than one load module - current_load = sum(load.current_load for load in self.microgrid.modules.load) - self.assertEqual(current_load, self.load_ts[0]) - - def test_current_pv_correct(self): - try: - current_renewable = self.microgrid.modules.renewable.item().current_renewable - except ValueError: - # More than one load module - current_renewable = sum(renewable.current_renewable for renewable in self.microgrid.modules.renewable) - self.assertEqual(current_renewable, self.pv_ts[0]) - - def test_sample_action(self): - sampled_action = self.microgrid.sample_action() - self.assertEqual(len(sampled_action), 0) - - def test_sample_action_with_flex(self): - sampled_action = self.microgrid.sample_action(sample_flex_modules=True) - self.assertEqual(len(sampled_action), 2) - self.assertIn('renewable', sampled_action) - self.assertIn('balancing', sampled_action) - self.assertEqual(len(sampled_action['renewable']), self.n_pvs) - - def test_state_dict(self): - sd = self.microgrid.state_dict() - self.assertIn('load', sd) - self.assertIn('renewable', sd) - self.assertIn('balancing', sd) - self.assertEqual(len(sd['load']), self.n_loads) - self.assertEqual(len(sd['balancing']), 1) - - def test_state_series(self): - ss = self.microgrid.state_series() - self.assertEqual({'load', 'renewable'}, set(ss.index.get_level_values(0))) - self.assertEqual(ss['load'].index.get_level_values(0).nunique(), self.n_loads) - self.assertEqual(ss['renewable'].index.get_level_values(0).nunique(), self.n_pvs) - self.assertEqual(ss['load'].index.get_level_values(0).nunique(), self.n_loads) - - def test_to_nonmodular(self): - if self.n_pvs > 1 or self.n_loads > 1: - with self.assertRaises(ValueError) as e: - self.microgrid.to_nonmodular() - self.assertIn("Cannot convert modular microgrid with multiple modules of same type", e) - - else: - nonmodular = self.microgrid.to_nonmodular() - self.assertTrue(nonmodular.architecture['PV']) - self.assertFalse(nonmodular.architecture['battery']) - self.assertFalse(nonmodular.architecture['grid']) - self.assertFalse(nonmodular.architecture['genset']) - - def check_step(self, microgrid, step_number=0): - - control = microgrid.get_empty_action() - self.assertEqual(len(control), 0) - - obs, reward, done, info = microgrid.step(control) - loss_load = self.load_ts[step_number]-self.pv_ts[step_number] - loss_load_cost = self.microgrid.modules.balancing[0].loss_load_cost * max(loss_load, 0) - - self.assertEqual(loss_load_cost, -1*reward) - - self.assertEqual(len(microgrid.log), step_number + 1) - self.assertTrue(all(module in microgrid.log for module in microgrid.modules.names())) - - load_met = min(self.load_ts[step_number], self.pv_ts[step_number]) - loss_load = max(self.load_ts[step_number] - load_met, 0) - pv_curtailment = max(self.pv_ts[step_number]-load_met, 0) - - # Checking the log populated correctly. - - log_row = microgrid.log.iloc[step_number] - log_entry = lambda module, entry: log_row.loc[pd.IndexSlice[module, :, entry]].sum() - - # Check that there are log entries for all modules of each name - self.assertEqual(log_row['load'].index.get_level_values(0).nunique(), self.n_loads) - - self.assertEqual(log_entry('load', 'load_current'), -1 * self.load_ts[step_number]) - self.assertEqual(log_entry('load', 'load_met'), self.load_ts[step_number]) - - if loss_load == 0: - self.assertEqual(log_entry('load', 'load_met'), load_met) - - self.assertEqual(log_entry('renewable', 'renewable_current'), self.pv_ts[step_number]) - self.assertEqual(log_entry('renewable', 'renewable_used'), load_met) - self.assertEqual(log_entry('renewable', 'curtailment'), pv_curtailment) - - self.assertEqual(log_entry('balancing', 'loss_load'), loss_load) - - self.assertEqual(log_entry('balance', 'reward'), -1 * loss_load_cost) - self.assertEqual(log_entry('balance', 'overall_provided_to_microgrid'), self.load_ts[step_number]) - self.assertEqual(log_entry('balance', 'overall_absorbed_from_microgrid'), self.load_ts[step_number]) - self.assertEqual(log_entry('balance', 'fixed_provided_to_microgrid'), 0.0) - self.assertEqual(log_entry('balance', 'fixed_absorbed_from_microgrid'), self.load_ts[step_number]) - self.assertEqual(log_entry('balance', 'controllable_absorbed_from_microgrid'), 0.0) - self.assertEqual(log_entry('balance', 'controllable_provided_to_microgrid'), 0.0) - - return microgrid - - def test_run_one_step(self): - microgrid = self.microgrid - self.check_step(microgrid=microgrid, step_number=0) - - def test_run_n_steps(self): - microgrid = self.microgrid - for step in range(len(self.load_ts)): - with self.subTest(step=step): - microgrid = self.check_step(microgrid=microgrid, step_number=step) - - -class TestMicrogridLoadExcessPV(TestMicrogridLoadPV): - # Same as above but pv is greater than load. - def set_ts(self): - load_ts = 10*np.random.rand(100) - pv_ts = load_ts + 5*np.random.rand(100) - return load_ts, pv_ts - - -class TestMicrogridPVExcessLoad(TestMicrogridLoadPV): - # Load greater than PV. - def set_ts(self): - pv_ts = 10 * np.random.rand(100) - load_ts = pv_ts + 5 * np.random.rand(100) - return load_ts, pv_ts - - -class TestMicrogridTwoLoads(TestMicrogridLoadPV): - def set_microgrid(self): - load_1_ts = self.load_ts*(1-np.random.rand(*self.load_ts.shape)) - load_2_ts = self.load_ts - load_1_ts - - assert all(load_1_ts > 0) - assert all(load_2_ts > 0) - - load_1 = LoadModule(time_series=load_1_ts, raise_errors=True) - load_2 = LoadModule(time_series=load_2_ts, raise_errors=True) - pv = RenewableModule(time_series=self.pv_ts, raise_errors=True) - return Microgrid([load_1, load_2, pv]), 2, 1 - - -class TestMicrogridTwoPV(TestMicrogridLoadPV): - def set_microgrid(self): - pv_1_ts = self.pv_ts*(1-np.random.rand(*self.pv_ts.shape)) - pv_2_ts = self.pv_ts - pv_1_ts - - assert all(pv_1_ts > 0) - assert all(pv_2_ts > 0) - - load = LoadModule(time_series=self.load_ts, raise_errors=True) - pv_1 = RenewableModule(time_series=pv_1_ts, raise_errors=True) - pv_2 = RenewableModule(time_series=pv_2_ts) - return Microgrid([load, pv_1, pv_2]), 1, 2 - - -class TestMicrogridTwoEach(TestMicrogridLoadPV): - def set_microgrid(self): - load_1_ts = self.load_ts*(1-np.random.rand(*self.load_ts.shape)) - load_2_ts = self.load_ts - load_1_ts - - pv_1_ts = self.pv_ts*(1-np.random.rand(*self.pv_ts.shape)) - pv_2_ts = self.pv_ts - pv_1_ts - - assert all(load_1_ts > 0) - assert all(load_2_ts > 0) - assert all(pv_1_ts > 0) - assert all(pv_2_ts > 0) - - load_1 = LoadModule(time_series=load_1_ts, raise_errors=True) - load_2 = LoadModule(time_series=load_2_ts, raise_errors=True) - pv_1 = RenewableModule(time_series=pv_1_ts, raise_errors=True) - pv_2 = RenewableModule(time_series=pv_2_ts) - - return Microgrid([load_1, load_2, pv_1, pv_2]), 2, 2 - - -class TestMicrogridManyEach(TestMicrogridLoadPV): - def set_microgrid(self): - n_loads = np.random.randint(3, 10) - n_pvs = np.random.randint(3, 10) - - load_ts = [self.load_ts * (1 - np.random.rand(*self.load_ts.shape))] - pv_ts = [self.pv_ts * (1 - np.random.rand(*self.pv_ts.shape))] - - for ts_list, ts_sum, n_modules in zip( - [load_ts, pv_ts], - [self.load_ts, self.pv_ts], - [n_loads, n_pvs] - ): - remaining = ts_sum-ts_list[0] - - for j in range(1, n_modules-1): - ts_list.append(remaining*(1-np.random.rand(*ts_sum.shape))) - assert all(ts_list[-1] > 0) - remaining -= ts_list[-1] - - assert all(remaining > 0) - ts_list.append(remaining) - - load_modules = [LoadModule(time_series=ts) for ts in load_ts] - pv_modules = [RenewableModule(time_series=ts) for ts in pv_ts] - - return Microgrid([*load_modules, *pv_modules]), n_loads, n_pvs - - -class TestMicrogridManyEachExcessPV(TestMicrogridManyEach): - def set_ts(self): - load_ts = 10*np.random.rand(100) - pv_ts = load_ts + 5*np.random.rand(100) - return load_ts, pv_ts - - -class TestMicrogridManyEachExcessLoad(TestMicrogridManyEach): - def set_ts(self): - pv_ts = 10*np.random.rand(100) - load_ts = pv_ts + 5*np.random.rand(100) - return load_ts, pv_ts - - -class TestMicrogridRewardShaping(TestMicrogridLoadPV): - def set_microgrid(self): - original_microgrid, n_loads, n_pvs = super().set_microgrid() - new_microgrid = Microgrid(original_microgrid.modules.to_tuples(), - add_unbalanced_module=False, - reward_shaping_func=self.reward_shaping_func) - - return new_microgrid, n_loads, n_pvs - - @staticmethod - def reward_shaping_func(original_reward, energy_info, cost_info): - total = 0 - for module_name, info_list in energy_info.items(): - for module_info in info_list: - for j, (energy_type, energy_amount) in enumerate(module_info.items()): - if energy_type == 'absorbed_energy': - marginal_cost = cost_info[module_name][j]['absorption_marginal_cost'] - elif energy_type == 'provided_energy': - marginal_cost = cost_info[module_name][j]['production_marginal_cost'] - else: - # Some other key - continue - total += energy_amount * marginal_cost - - return total diff --git a/tests/test_microgridgenerator.py b/tests/test_microgridgenerator.py deleted file mode 100644 index bbe6ffa2..00000000 --- a/tests/test_microgridgenerator.py +++ /dev/null @@ -1,87 +0,0 @@ -""" -Copyright 2020 Total S.A. -Authors:Gonzague Henri -Permission to use, modify, and distribute this software is given under the -terms of the pymgrid License. -NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK. -$Date: 2020/08/27 08:04 $ -Gonzague Henri -""" - -import numpy as np -import pandas as pd -from numpy.testing import assert_allclose - - -import os, sys -sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))) - -from pymgrid.MicrogridGenerator import MicrogridGenerator - -import unittest - - -class TestMicogridGenerator(unittest.TestCase): - - def setUp(self): - self.mgen = MicrogridGenerator() - - def test_get_random_file(self): - import inspect, pymgrid - from pathlib import Path - - path = Path(inspect.getfile(pymgrid)).parent - path = path / 'data/pv' - data = self.mgen._get_random_file(path) - - self.assertEqual(len(data), 8760) - - def test_scale_ts(self): - ts = pd.DataFrame( [i for i in range(10)]) - factor = 4 - scaled = self.mgen._scale_ts(ts, factor) - assert_allclose(ts/ts.sum()*factor, scaled) - - def test_get_genset(self): - genset = self.mgen._get_genset() - self.assertEqual (1000, genset['rated_power']) - - - def test_get_battery(self): - battery = self.mgen._get_battery() - self.assertEqual (1000, battery['capa']) - - def test_get_grid_price_ts(self): - price = self.mgen._get_grid_price_ts(10, price=0.2) - self.assertTrue(all([p == 0.2 for p in price])) - - def test_get_grid(self): - grid = self.mgen._get_grid() - self.assertEqual(1000, grid['grid_power_import']) - - def test_size_mg(self): - ts = pd.DataFrame([i for i in range(10)]) - mg = self.mgen._size_mg(ts, 10) - - self.assertEqual(18, mg['grid']) - - def test_size_genset(self): - self.assertEqual(int(np.ceil(10/0.9)), self.mgen._size_genset([10, 10, 10])) - - def test_size_battery(self): - size = self.mgen._size_battery([10, 10, 10]) - self.assertLessEqual(30, size) - self.assertGreaterEqual(50, size) - - def test_generate_microgrid(self): - microgrids = self.mgen.generate_microgrid().microgrids - - self.assertEqual(self.mgen.nb_microgrids, len(microgrids)) - - def test_create_microgrid(self): - mg = self.mgen._create_microgrid() - - self.assertEqual(1, mg.architecture['battery']) - -if __name__ == '__main__': - unittest.main() diff --git a/tests/test_nonmodular_microgrid.py b/tests/test_nonmodular_microgrid.py deleted file mode 100644 index a99fcb63..00000000 --- a/tests/test_nonmodular_microgrid.py +++ /dev/null @@ -1,70 +0,0 @@ -""" -Copyright 2020 Total S.A. -Authors:Gonzague Henri -Permission to use, modify, and distribute this software is given under the -terms of the pymgrid License. -NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK. -$Date: 2020/08/27 08:04 $ -Gonzague Henri -""" -import unittest -import numpy as np - -from pymgrid.MicrogridGenerator import MicrogridGenerator - - -class TestNonmodularMicrogrid(unittest.TestCase): - def setUp(self): - mgen = MicrogridGenerator() - self.mg = mgen._create_microgrid() - - @staticmethod - def random_control(): - return dict(pv_consummed=np.random.rand(), - battery_charge=np.random.rand(), - battery_discharge=np.random.rand(), - grid_import=np.random.rand(), - grid_export=np.random.rand() - ) - - def test_set_horizon(self): - self.mg.set_horizon(25) - self.assertEqual(25, self.mg.horizon) - - def test_get_updated_values(self): - mg_data = self.mg.get_updated_values() - self.assertEqual(0, mg_data['pv']) - - def test_forecast_all(self): - self.mg.set_horizon(24) - forecast = self.mg.forecast_all() - self.assertEqual(24, len(forecast['load'])) - - def test_forecast_pv(self): - self.mg.set_horizon(24) - forecast = self.mg.forecast_pv() - self.assertEqual (24, len(forecast)) - - def test_forecast_load(self): - self.mg.set_horizon(24) - forecast = self.mg.forecast_load() - self.assertEqual (24, len(forecast)) - - def test_run(self): - pv1 = self.mg.forecast_pv()[1] - self.mg.run(self.random_control()) - pv2 = self.mg.pv - self.assertEqual(pv1, pv2) - - def test_train_test_split(self): - self.mg.train_test_split() - self.assertEqual('training',self.mg._data_set_to_use) - - def test_reset(self): - self.mg.run(self.random_control()) - self.mg.reset() - self.assertEqual (0, self.mg._tracking_timestep) - - -if __name__ == '__main__': - unittest.main()