pypownet stands for Python Power Network, which is a simulator for power (electrical) networks.
The simulator is able to emulate a power grid (of any size or characteristics) subject to a set of temporal injections (productions and consumptions) for discretized timesteps. Loadflow computations relies on Matpower and can be run under the AC or DC models. The simulator is able to simulate cascading failures, where successively overflowed lines are switched off and a loadflow is computed on the subsequent grid.
Illustration of a running power grid with our renderer on the default IEEE14 grid environment. NB: the renderer drastically slows the performance of pypownet: it takes ~40s to compute 1000 timesteps without renderer mode with this environment.
The simulator comes with an Reinforcement Learning-focused environment, which implements states (observations), actions (reduced to node-splitting and line status switches) as well as a reward signal. Finally, a renderer is available, such that the observations of the network can be plotted in real-time (synchronized with the game time).
Official documentation: https://pypownet.readthedocs.io/
- 1 Installation
- 2 Basic usage
- 3 Main features of pypownet
- 4 Generate the documentation
- 5 License information
Retrieve the Docker image:
sudo docker pull marvinler/pypownet:2.2.8-light
- Python >= 3.6
For Octave backend (default is Python backend):
- Octave >= 4.0.6
- Matpower >= 6.0
These instructions allow to run the simulator with a Python backend; for Octave backend, please refer to the documentation for installation instructions.
sudo apt-get update
sudo apt-get install python3.6
If you have any trouble with this step, please refer to the official webpage of Python.
virtualenv -p python3.6 --system-site-packages venv
source venv/bin/activate
git clone https://github.com/MarvinLer/pypownet
This should create a folder pypownet with the current sources.
Finally, run the following Python command to install the current simulator (including the Python libraries dependencies):
cd pypownet/
python3.6 setup.py install
After this, this simulator is available under the name pypownet (e.g. import pypownet).
Experiments can be conducted using the CLI.
CLI can be used to run simulations:
python -m pypownet.main -v
You can use python -m pypownet.main --help for further information about these runners arguments. Example running 1000 iterations (here, ~40 days) of the do-nothing (default) agent on a grid with 14 substations:
python -m pypownet.main --parameters parameters/default14 --niter 1000 --verbose --render
With this default14/ parameters (emulates a grid with 14 substations, 5 productions, 11 consumptions and 20 lines), it takes ~100 seconds to run 1000 timesteps (old i5).
You can use the command line of the image with shared display (for running the renderer):
sudo docker run -it --privileged --net=host --env="DISPLAY" --volume="$HOME/.Xauthority:/root/.Xauthority:rw" marvinler/pypownet:2.2.0 sh
This will open a terminal of the image. The usage is then identical to without docker, by doing the steps within this terminal.
pypownet is a power grid simulator, that emulates a power grid that is subject to pre-computed injections, planned maintenance as well as random external hazards. Here is a list of pypownet main features:
- emulates a grid of any size and electrical properties in a game discretized in timesteps of any (fixed) size
- computes and apply cascading failure process: at each timestep, overflowed lines with certain conditions are switched off, with a consequent loadflow computation to retrieve the new grid steady-state, and reiterating the process
- has an RL-focused interface, where players or controlers can play actions (node-splitting or line status switches) on the current grid, based on a partial observation of the grid (high dimension), with a customable reward signal (and game over options)
- has a renderer that enables the user to see the grid evolving in real-time, as well as the actions of the controler currently playing and further grid state details (works only for pypownet official grid cases)
- has a runner that enables to use pypownet fully by simply coding an agent (with a method act(observation))
- possess some baselines models (including treesearches) illustrating how to use the furnished environment
- can be launched with CLI with the possibility of managing certain parameters (such as renderer toggling or the agent to be played)
- functions on both DC and AC mode
- has a set of parameters that can be customized (including AC or DC mode, or hard-overflow coefficient), associated with sets of injections, planned maintenance and random hazards of the various chronics
- handles node-splitting (at the moment only max 2 nodes per substation) and lines switches off for topology management
The stable official documentation is available at https://pypownet.readthedocs.io/. Alternatively, a copy of the master documentation can be computed: you will need Sphinx, a Documentation building tool, and a nice-looking custom Sphinx theme similar to the one of readthedocs.io:
pip install sphinx sphinx_rtd_theme
This installs both the Sphinx package and the custom template. Then:
cd doc
sphinx-build -b html ./source ./build
The html will be available within the folder doc/build.
pypownet is provided with series of tests developped by @ZergD and RTE. These tests are designed to verify some behavior of the game as a whole, including some expected grid values based on perfectly controlled injections/topology. Tests can be run with pytest in the current directory.
(Here)[tests/README.md] for more information about the testing module.
Copyright 2017-2019 RTE and INRIA (France)
RTE: http://www.rte-france.com
INRIA: https://www.inria.fr/
This Source Code is subject to the terms of the GNU Lesser General Public License v3.0. If a copy of the LGPL-v3 was not distributed with this file, You can obtain one at https://www.gnu.org/licenses/lgpl-3.0.fr.html.
If you use this repo or find it useful, please consider citing:
@article{lerousseau2021design,
title={Design and implementation of an environment for Learning to Run a Power Network (L2RPN)},
author={Lerousseau, Marvin},
journal={arXiv preprint arXiv:2104.04080},
year={2021}
}