Energy Optimization in Smart Grids using Reinforcement Learning

This project implements Reinforcement Learning (RL) algorithms to optimize energy usage and minimize electricity wastage in smart grids.

Project Structure

• microgrid/ - Core simulation environment for the smart grid
• agents/ - RL agents implementation
• models/ - Trained models and utilities
• notebooks/ - Jupyter notebooks for analysis and visualization
• experiments/ - Experiment configurations and results
• utils/ - Utility functions

Getting Started

1. Install the required dependencies:

   pip install -r requirements.txt
   

2. Run the basic simulation:

   python main.py
   

3. Experiment with different RL algorithms in the notebooks.

Features

• Smart grid simulation environment with renewable energy sources
• Energy storage systems and load management
• Multiple RL algorithms for optimization
• Performance metrics and visualization tools