easygrid

easygrid is an OpenAI Gym environment of simple microgrid simulation for Reinforcement Learning applications. Its goal is to provide an environment that allows for somewhat realistic simulation of controling a microgrid with :

• A battery.
• A connection to the global grid.
• Some PV production.
• Some local demand

while also allowing for customization of the environment to test various RL approach that may not be easy to test on a realistic setting. Such customization includes the capacity to easily adapt the amount of power produced locally as well as electricity prices.

It is losely based on pymgrid (https://github.com/Total-RD/pymgrid) and should allow for a simpler framework and understanding, and thus easier customization.

The goal is to help build easy to track RL experiments and thus follow this philosophy :

• 100% deterministic.
• parsable configurations defining all the important parameters
• simple modifications of parameters for diverse scenarios
• efficient computation (at the expense of some accuracy)
• extensive logging of parameters to monitor the microgrid In addition :
• we chose to remove the forecast part for simplicity and performance
• simpler dependencies for easier integration in a lot of environments

Installation

This package uses poetry if you wish to build it yourself.

git clone https://github.com/YannBerthelot/ easygrid.git
cd easygrid
poetry install

otherwise, using pip and Test PyPi (not yet released on regular pypi)

pip install -i https://test.pypi.org/simple/easygrid

Usage

To run the example:

python -m easygrid

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT

To-do

• Extend usage in readme.
• Extend and test logging system to track results
• Add more tests to the simulation
• Add CLI parsing (if necessary)
• Optimize efficiency of code for fast simulations
• Add timeseries creators.