A review of control strategies for optimized microgrid operations

Microgrids (MGs) have emerged as a promising solution for providing reliable and sustainable electricity, particularly in underserved communities and remote areas. Integrating diverse renewable energy sources into the grid has further emphasized the need for effective management and sophisticated control strategies. This review explores the crucial role of control strategies in optimizing MG operations and ensuring efficient utilization of distributed energy resources, storage systems, networks, and loads. To maximize energy source utilization and overall system performance, various control strategies are implemented, including demand response, energy storage management, data management, and generation‐load management. Employing artificial intelligence (AI) and optimization techniques further enhances these strategies, leading to improved energy management and performance in MGs. The review delves into the control strategies and their architectures, and highlights the significant contributions of AI and emerging technologies in advancing MG energy management. Microgrids (MGs) are gaining traction as a sustainable and reliable power solution, particularly in remote areas. Efficient and intelligent control strategies are crucial for optimizing MG operations and maximizing the utilization of distributed energy resources, storage systems, networks, and loads. This review examines various control strategies, including demand response, energy storage management, data management, and load management, and highlights the potential of artificial intelligence (AI) and optimization techniques to enhance these strategies and improve MG energy management.