Open Access

REVIEW

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

Theint Theint Maw, Shuai Zhou^*, Tek Tjing Lie

Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland, New Zealand

* Corresponding Author: Shuai Zhou. Email:

(This article belongs to the Special Issue: AI in Green Energy Technologies and Their Applications)

Energy Engineering 2026, 123(9), 3 https://doi.org/10.32604/ee.2026.081744

Received 08 March 2026; Accepted 22 April 2026; Issue published 06 August 2026

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.

---

Keywords

---