Paper Publications

Impact Factor:1.6

DOI number:10.3934/jimo.2026065

Journal:Journal of Industrial and Management Optimization

Key Words:adaptive particle swarm optimization, energy storage system, optimal configuration, wind power integration, wind power generation

Abstract:In wind power, energy storage systems (ESSs) are widely used to address power fluctuations and grid connection risks. However, optimizing the configuration of these systems presents a significant challenge. This paper introduces a novel approach, namely the adaptive particle swarm optimization (APSO), which offers several key advancements. First, it introduces a novel linear inertia weight mechanism based on the sine function, which dynamically adjusts the search behavior of particles across different optimization stages. Second, it incorporates a hybrid update strategy that integrates Levy flight, quasi-opposition-based learning, and global best guidance, enabling the algorithm to adaptively switch among different search modes. Third, an immigration operator is designed to facilitate information exchange between two subpopulations, enhancing thr search diversity and preventing premature convergence. Additionally, the algorithm is applied to both the optimal configuration and scheduling models of ESSs, demonstrating its effectiveness in reducing the system's costs, stabilizing wind power output, and mitigating grid connection risks. The proposed APSO is validated against several established algorithms using a comprehensive test suite comprising 92 benchmarks, showing competitive or superior performance in most cases and confirming its practical value in ESS optimization.

Indexed by:Journal paper

Discipline:interdisciplinary subject

Document Type:J

Volume:22

Issue:4

Page Number:1758-1788

ISSN No.:1553-166X

Translation or Not:no

Date of Publication:2026-03-12

Included Journals:SCI

First Author:Chia-Hung Wang

Co-author:Hongzhen Yan,Haitao Liu,Qigen Zhao,Xiaojing Wu