Year: 2024
Type: article
Abstract: The multi‐microgrid network structure optimisation problem (MNSDOP) aims to minimise the circuit lengths while maximising power transmission stability, a crucial aspect for system robustness enhancement. While existing literature primarily focuses on achieving Pareto optimal solutions there is an overlooked emphasis on solution diversity. In situations like MNSDOPs, several optimal solutions might possess identical or comparable objective values, classifying them under multimodal multi‐objective optimisation roblems (MMOPs). Offering an array of optimal solutions equips decision‐makers with a holistic understanding of the problem, aiding in the identification of preferred solutions. Motivated by this gap, we introduce a multimodal multi‐objective evolutionary algorithm tailored for MNSDOPs, termed MMO‐BM. We also propose a diversity evaluation metric specifically for binary optimisation problems, substantially amplifying the exploration capabilities of evolutionary algorithms. In addition, a novel matrix‐oriented DE operator is proposed to accerate the convergence process. Experimental evaluations attest to our method's prowess in yielding diverse and high‐quality solutions.
Source: Energy internet.
DOI: 10.1049/ein2.12018
Authors: Tao Zhang, Xingyi Yao, Wenhua Li, Rui Wang
Institution: National University of Defense Technology
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G2047166748, G8476579440
OpenAlex ID: W4404764371