Autonomous Microgrids Optimization Using Reinforcement Learning: Applications, Challenges and Prospects

This research investigates integrating reinforcement learning (RL) algorithms to optimize microgrid operations autonomously. Microgrids, as decentralized energy systems, pose unique challenges in adapting to dynamic energy sources and consumption patterns. By investigating applications, challenges, and prospects within this domain, we explore how RL algorithms enable microgrids to autonomously adapt and optimize their operations in response to dynamic energy conditions. The applications encompass a spectrum of scenarios, including smart grid optimization, demand-side management, and integration of renewable energy sources. Despite the promising applications, challenges arise in balancing the intricacies of RL algorithms with the need for interpretability and scalability within microgrid environments. The study navigates these challenges and envisions prospects for refining RL approaches, paving the way for resilient, efficient, and sustainable autonomous microgrid systems.