RLGC

Repo of the Reinforcement Learning for Grid Control (RLGC) Project

We explore to use deep reinforcement learning methods for control and decision-making problems in power systems.

NOTE: RLGC is under active development and may change at any time. Feel free to provide feedback and comments.

Environment setup

To run the training, you need python 3.5 or above and Java 8. Unix-based OS is recommended. We suggest using Anaconda to create virtual environment from the yaml file we provided.

To clone our project

git clone https://github.com/RLGC-Project/RLGC.git

To create the virtual environment
```
cd RLGC    
conda env create --name <your-env-name>  
```
If you get errors about OpenAI gym you probably need to install cmake and zlib1g-dev. For example on Ubuntu machine, do the following command.
```
sudo apt-get upgrade
sudo apt-get install cmake
sudo apt-get install zlib1g-dev
```
After creating environment , you can activate the virtual environment and do development under this environment.
To activate virtual environment
```
source activate <your-env-name>  
```
To deactivate virtual environment
```
source deactivate
```

Training

First launch java server which is used to simulate the power grid system. Then launch your training in the virtual environment. We provide two power grid systems in the examples. RLGCJavaServer0.72.jar is the latest release and can be used for the IEEE 39-bus system and RLGCJavaServerSimple.jaris only used for the Kundur 2-area system.

To launch the java server, open a new terminal

cd ~
cd RLGC/lib  
java -jar RLGCJavaServer0.72.jar 25001

The last parameter is the communication port number between grid system and the training agent. You can switch the port number if necessary.

To launch the training, you need first activate the virtual environment. Then run the following scripts. trainKundur2areaGenBrakingAgent.py is used for training the generator braking agent for the Kundur 2-area system and trainIEEE39LoadSheddingAgent.py is used for training an agent for regional load shedding in IEEE 39-bus system

source activate <your-env-name> 
cd RLGC/src/py  
python trainIEEE39LoadSheddingAgent.py

During the training the screen will dump out the training log. After training, you can deactivate the virtual environment by

source deactivate

Check training results and test trained model

Two Jupyter notebooks (with Linux and Windows versions-- directory paths are specified differently) are provided as examples for checking training results and testing trained RL model.

Citation

If you use this code please cite it as:

@article{huang2019adaptive,
  title={Adaptive Power System Emergency Control using Deep Reinforcement Learning},
  author={Huang, Qiuhua and Huang, Renke and Hao, Weituo and Tan, Jie and Fan, Rui and Huang, Zhenyu},
  journal={IEEE Transactions on Smart Grid},
  year={2019},
  publisher={IEEE}
}

Communication

If you spot a bug or have a problem running the code, please open an issue.