Network-Constrained Multi-Objective Optimization for Integrated Microgrids with Renewable and EV Integration: A Systematic Review
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Maw, Theint Theint
Zhou, Shuai
Lie, Tek Tjing
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Tech Science Press
Abstract
The rapid deployment of distributed energy resources (DERs), including photovoltaic (PV) generation, wind turbines (WT), battery energy storage systems (BESS), and electric vehicles (EVs), is transforming modern distribution networks by introducing bidirectional power flows, voltage variations, and increased operational complexity, thereby require enhanced system resilience. This paper presents a systematic review of multi-objective optimization approaches for interconnected multi-microgrid (MMG) systems with explicit consideration of resilience, following the PRISMA 2020 guidelines. A structured literature search and screening process was conducted across major databases, including IEEE Xplore, Scopus, and ScienceDirect, covering publications from 2015 to 2026. The selected studies are synthesised based on modelling frameworks, power flow formulations, resilience metrics, and optimization strategies. The review identifies key trends, including the growing adoption of distributed coordination schemes and advanced optimization techniques to address uncertainty and scalability. However, a critical gap is observed in the integration of resilience objectives with detailed network-constrained modelling, which limits practical applicability in real-world MMG systems. Finally, key research gaps are highlighted, and future research directions are proposed to support the development of unified, scalable, and resilient optimization frameworks for high-DER MMG systems.
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40 Engineering, 4008 Electrical Engineering, 13 Climate Action, 7 Affordable and Clean Energy, 0914 Resources Engineering and Extractive Metallurgy, Energy, Distributed energy resources, interconnected multi-microgrids, power flow modelling, resilience, multi-objective optimization, hybrid optimization, distribution networks
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Energy Engineering, ISSN: 0199-8595 (Print); 1546-0118 (Online), Tech Science Press, 0(0), 1-10. doi: 10.32604/ee.2026.081744
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Copyright © 2026 The Authors. Published by Tech Science Press. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright © 2026 The Authors. Published by Tech Science Press. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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