An adaptive PSO-based framework for energy storage efficiency and reliability maximization in wind power grid integration

Chia-Hung Wang; Hongzhen Yan; Haitao Liu; Qigen Zhao; Xiaojing Wu; Chia-Hung Wang; Hongzhen Yan; Haitao Liu; Qigen Zhao; Xiaojing Wu

doi:10.3934/jimo.2026065

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 4: 1758-1788. doi: 10.3934/jimo.2026065

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Research article

An adaptive PSO-based framework for energy storage efficiency and reliability maximization in wind power grid integration

1.
College of Computer Science and Mathematics, Fujian University of Technology, Fujian 350118, China
2.
Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian 350118, China
3.
School of Big Data, Fuzhou University of International Studies and Trade, Fujian 350202, China
4.
Shenzhen Megmeet Electrical Co., Ltd, Guangdong 518057, China
5.
College of Electronics, Electrical Engineering and Physics, Fujian University of Technology, Fujian 350118, China

Received: 23 October 2025 Revised: 31 January 2026 Accepted: 25 February 2026 Published: 11 March 2026
68T05, 90C26

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.
- adaptive particle swarm optimization,
- energy storage system,
- optimal configuration,
- wind power integration,
- wind power generation
Citation: Chia-Hung Wang, Hongzhen Yan, Haitao Liu, Qigen Zhao, Xiaojing Wu. An adaptive PSO-based framework for energy storage efficiency and reliability maximization in wind power grid integration[J]. Journal of Industrial and Management Optimization, 2026, 22(4): 1758-1788. doi: 10.3934/jimo.2026065

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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.

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