Reinforcement Learning for Smart Grid Optimization in Energy Systems

Abstract

The rapid evolution of energy demands and the integration of renewable energy sources have necessitated the development of intelligent energy management systems. Reinforcement Learning (RL), a subset of machine learning, has emerged as a promising tool for optimizing operations within smart grids. This paper presents a comprehensive analysis of RL algorithms applied to various smart grid functions, including load balancing, demand response, and storage management. By interacting with dynamic environments, RL agents learn optimal policies that improve grid efficiency, reduce operational costs, and enhance stability. The paper also discusses challenges and opportunities for real-world deployment, supported by a performance comparison of key RL models.