Integrated Energy Management Systems for Resilient Community Microgrids: A Comprehensive Review

Abstract

The increasing penetration of distributed energy resources, the variability of renewables, and climate disruptions make community microgrids key to modern low-voltage distribution networks. Integrated energy management systems coordinate generation, storage, and loads to enhance resilience, sustainability, and efficiency. In this paper, we summarize significant technological and methodological advances in integrated energy management systems for community microgrids, covering conceptual foundations, architectural models, and enabling technologies. We compare centralized, decentralized, and hybrid-hierarchical control frameworks using quantitative criteria, including computational complexity, communication overhead, failure-recovery time, and the demonstrated maximum distributed energy resources count. We also examine the convergence of supervisory control and data acquisition, energy management systems, and distributed energy resource management systems platforms over the IEC 61850 and IEEE 2030.5 communication protocols. We review AI methods for forecasting, optimization, and control, including long short-term memory, convolutional neural networks, and hybrid models that reduce mean absolute percentage error. We examine model predictive control and reinforcement learning for decision-making. In addition, we examine the challenges of integrating renewable energy sources and storage solutions. Resilience mechanisms are reviewed within a unified framework covering voltage and frequency stability, black-start, islanding, and cyber-resilience against false-data-injection and denial-of-service attacks. Through this study, we identify key research gaps and suggest future directions for scalability, interoperability, cybersecurity, and sociotechnical integration that will help researchers, practitioners, and policymakers to develop resilient and sustainable community microgrids.