Abstract Microgrids are being considered to be very crucial in enhancing the involvement of renewable energy sources (RESs) in electrical grids and also improving their overall sustainability and resilience. Modern day control techniques are getting attention by researchers for optimal control and management of microgrids, as it is found in many articles that classical control techniques are short-falling in adaptability in different environments, data handling and data driven decision making. This article provides systematic review to follow a thorough evaluation of the present status of research on reinforcement learning (RL)-based microgrid control. The description of microgrid systems, their components, control and management challenges are also provided in this survey. Further, majorly implemented RL-based algorithms for microgrid control are discussed. Summary and critical assessment of main findings and contributions in this field are tabulated through methodical categorization of selected articles. Emerging algorithms for microgrid control are also discussed thoroughly. A comparative analysis based on performance of RL algorithms, and RL based control with other types of control frameworks is also systematically presented. Further safety and deploying challenges, along with ongoing trends in RL implementation for microgrids, are elaborated. This work will be helpful for practitioners and researchers who want to investigate the best way to design microgrid systems, especially when it comes to using RL algorithms for microgrid control systems and management.
Waghmare et al. (2025) studied this question.