Metadata-Version: 2.1
Name: microgrids
Version: 0.1.0
Summary: Operational & economic simulation of Microgrid projects
Project-URL: Homepage, https://github.com/Microgrids-X/Microgrids.py
Project-URL: Bug Tracker, https://github.com/Microgrids-X/Microgrids.py/issues
Author-email: Pierre <pierre.haessig@centralesupelec.fr>
Maintainer-email: Pierre <pierre.haessig@centralesupelec.fr>
License: MIT License
        
        Copyright © 2022 by Evelise de G. Antunes, Nabil Sadou and Pierre Haessig
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.9
Requires-Dist: matplotlib
Requires-Dist: numpy
Description-Content-Type: text/markdown

# Microgrids.py

This package allows simulating the energetic operation of an isolated microgrid,
returning economic and operation indicators.

Installation with `pip`:

```
pip install -U microgrids
```

## Documentation

See the [Microgrid_py_PV_BT_DG.ipynb](examples/Microgrid_py_PV_BT_DG.ipynb)
notebook example which walks through:
1. the main data structure to describe a Microgrid project
2. the main function to simulate it and display the results

You can have a live demo of this notebook right in your browser:
[Microgrid_py_PV_BT_DG.ipynb web demo](https://microgrids-x.github.io/Microgrids.web/lab?path=Microgrid_py_PV_BT_DG.ipynb)
(from [Microgrids.web](https://github.com/Microgrids-X/Microgrids.web/) repository).


## Ackowledgements

The development of Microgrids.jl (sibling package in Julia) was lead by
Evelise de Godoy Antunes. She was financed in part by
the Coordenação de Aperfeiçoamento de Pessoal de Nı́vel Superior - Brasil (CAPES) – Finance Code 001,
by Conselho Nacional de Desenvolvimento Cientı́fico e Tecnológico - Brasil (CNPq)
and by the grant “Accélérer le dimensionnement des systèmes énergétiques avec
la différentiation automatique” from [GdR SEEDS (CNRS, France)](https://seeds.cnrs.fr/).


## Other microgrids-related packages in Python

Found by searching for ["microgrid"](https://pypi.org/search/?q=microgrid) on PyPI:

- [PyEPLAN](https://pypi.org/project/pyeplan/):  a free software toolbox for designing resilient mini-grids in developing countries. From Leeds, CUT, ICL.
- [OpenModelica Microgrid Gym](https://pypi.org/project/openmodelica-microgrid-gym/) (OMG):
  a software toolbox for the simulation and control optimization of microgrids
  based on energy conversion by power electronic converters.
    - “The main characteristics of the toolbox are the plug-and-play grid design and simulation in OpenModelica as well as the ready-to-go approach of intuitive reinforcement learning (RL) approaches through a Python interface.”
    - “The OMG toolbox is built upon the OpenAI Gym environment definition framework. Therefore, the toolbox is specifically designed for running reinforcement learning algorithms to train agents controlling power electronic converters in microgrids. Nevertheless, also arbritary classical control approaches can be combined and tested using the OMG interface.”
- [CVXMG](https://pypi.org/project/cvxmg/): Planning of Microgrids considering Demand Side Management Strategies using Disciplined Convex Deterministic and Stochastic Programming
