Reinforcement Learning in Microgrid Energy Management: Review, Methods and Prospects

ABSTRACT

A rapid integration of distributed energy resources and electrification has increased the need for intelligent energy management in microgrids under grid‐connected and islanded operation. The variability of renewables, energy storage system cycle limits and market interactions are primary drivers for learning‐based coordination in microgrids. Therefore, this papers presents a comprehensive review of reinforcement learning and deep reinforcement learning methods for microgrid energy management. The comparison of RL and DRL methods is provided for MG EMS applications. Multiagent DRL frameworks and constrained safe RL architectures are also discussed. The states, actions, rewards, evaluation metric and grid‐connectivity are also presented for reinforcement learning based microgrid energy management systems. Finally, limitations of existing methods are identified, including challenges related to reward function design, partial observability, uncertainty management, cybersecurity and real‐world deployment along with future research directions.