The increasing emphasis on sustainable and decentralized energy has elevated microgrids as a central element of modern power systems. By integrating renewable energy sources, advanced energy storage technologies, and intelligent control strategies, microgrids enhance efficiency, stability, and flexibility and play a vital role in creating resilient and adaptable energy networks. This review provides a comprehensive analysis of Energy Management Systems (EMSs) in microgrids, distinguishing between planning-oriented tools for techno-economic evaluation and control-oriented platforms for real-time operation and optimization. Hierarchical control architectures spanning primary, secondary, and tertiary levels are examined, highlighting their roles in frequency and voltage regulation, load sharing, and economic dispatch. Optimization techniques for EMSs are analyzed across deterministic, stochastic, metaheuristic, and artificial intelligence/machine learning methods, addressing objectives, constraints, uncertainties, and multi-timeframe decision-making. AI-based methods, including supervised learning, deep learning, and reinforcement learning, are highlighted for their ability to enhance predictive control, system autonomy, and operational efficiency, despite their computational demands. Future trends emphasize AI-based predictive control, deep learning for energy forecasting, multi-microgrid coordination, hybrid energy storage management, and cybersecurity enhancements. Overall, an intelligent EMS, combined with innovative technologies, is critical for developing resilient, scalable, and sustainable microgrid solutions that meet the evolving demands of modern energy systems.
Bentata et al. (Wed,) studied this question.
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