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Network-Constrained Multi-Objective Optimization for Integrated Microgrids with Renewable and EV Integration: A Systematic Review

aut.relation.endpage10
aut.relation.issue0
aut.relation.journalEnergy Engineering
aut.relation.startpage1
aut.relation.volume0
dc.contributor.authorMaw, Theint Theint
dc.contributor.authorZhou, Shuai
dc.contributor.authorLie, Tek Tjing
dc.date.accessioned2026-07-17T01:49:19Z
dc.date.issued2026-07-09
dc.description.abstractThe 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.
dc.identifier.citationEnergy Engineering, ISSN: 0199-8595 (Print); 1546-0118 (Online), Tech Science Press, 0(0), 1-10. doi: 10.32604/ee.2026.081744
dc.identifier.doi10.32604/ee.2026.081744
dc.identifier.issn0199-8595
dc.identifier.issn1546-0118
dc.identifier.urihttp://hdl.handle.net/10292/21591
dc.languageen
dc.publisherTech Science Press
dc.relation.urihttps://www.techscience.com/energy/online/detail/27510
dc.rightsCC-BY
dc.rightsCopyright © 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.
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject40 Engineering
dc.subject4008 Electrical Engineering
dc.subject13 Climate Action
dc.subject7 Affordable and Clean Energy
dc.subject0914 Resources Engineering and Extractive Metallurgy
dc.subjectEnergy
dc.subjectDistributed energy resources
dc.subjectinterconnected multi-microgrids
dc.subjectpower flow modelling
dc.subjectresilience
dc.subjectmulti-objective optimization
dc.subjecthybrid optimization
dc.subjectdistribution networks
dc.titleNetwork-Constrained Multi-Objective Optimization for Integrated Microgrids with Renewable and EV Integration: A Systematic Review
dc.typeJournal Article
pubs.elements-id769454

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