Reinforcement Learning for Grid Optimization

Overview

The integration of renewable energy sources presents complex optimization challenges that traditional control systems cannot efficiently solve. This talk examines how AI agents could manage the dynamic complexity of modern electrical grids, making real-time decisions about power distribution and renewable integration. We explore multi-agent systems coordinating across grid components, deep learning for renewable energy prediction, and AI-driven virtual power plants. The session covers implementing distributed AI agents for grid optimization and using generative models to simulate various grid scenarios for testing and planning.