A Review on Leveraging Reinforcement Learning for Enhanced Decision-Making in Power and Energy Systems

Reinforcement learning (RL) is a potential method for tackling difficult decision-making problems in power and energy systems. This review paper presents a thorough examination of the utilization of reinforcement learning (RL) methods in many areas of power and energy systems. These areas encompass demand-side management, energy optimization, grid control, and the integration of renewable energy. Through the utilization of RL algorithms, researchers and practitioners have successfully created intelligent and adaptable solutions that improve the efficiency, dependability, and sustainability of systems. This work analyses the theoretical underpinnings of reinforcement learning (RL), investigates its implementation in several domains of power and energy systems, deliberates on the obstacles and constraints, and proposes potential avenues for further research. This paper emphasizes the significant impact of Reinforcement Learning (RL) on the future of power and energy systems. By examining case studies and current breakthroughs, it showcases how RL has the ability to revolutionize these systems, making them more efficient and sustainable.