Grid2Op Reinforcement Learning Project

This project explores the implementation of reinforcement learning algorithms, specifically the Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO), within the Grid2Op environment. The primary focus is on optimizing the flow of power in the l2rpn_case14_sandbox grid while adhering to the N-1 reliability criterion.

Table of Contents

Authors

Installation

Dependencies

Usage

Project Structure

Results

Evaluation

License

Authors

Thapelo Mafabatho Harvey (1744565)

Rita Jmanagile (2094913)

Nthabiseng Mabetlela (1828559)

Installation

To run this project, clone the repository and install the required dependencies using pip.

git clone https://github.com/ThapeloGithub/Grid2Op-Reinforcement-Learning-Project
cd Grid2Op-Reinforcement-Learning-Project
pip install -r requirements.txt

Dependencies

The following packages are required to run this project:

torch

torchvision

numpy

gymnasium

grid2op

matplotlib

stable-baselines3 [extra]

lightsim2grid

Usage

The code is designed to be run in a Google Colab notebook. Make sure to upload the necessary scripts and data files to your Colab environment before executing the code.

Project structure

ThapeloGithub/Grid2Op-Reinforcement-Learning-Project

Project Directory Structure

SAC&PPO
├── plots
└── files
    ├── sac&ppo.ipynb
    ├── env.py
    └── run_sac_ppo.py
└── requirements.txt  # List of required Python packages

Running the SAC & PPO Script

To run the sac&ppo.ipynb notebook programmatically, you can use the following command:

python SAC&PPO/files/run_sac_ppo.py

Evaluation

The performance of the agents will be evaluated based on the Combined Reward, which aims to maximize the flow of power through the grid while ensuring the N-1 reliability criterion is met.

License

This project is available for public use. Feel free to use, modify, and distribute it.