Capacity Configuration Optimization of Wind–Light–Load Storage Based on Improved PSO

Benhong Wang, Ligui Wu, Peng Zhang, Yifeng Gu, Fangqing Zhang () and Jiang Guo
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Benhong Wang: China Yangtze Power Co., Ltd., Yichang 443000, China
Ligui Wu: China Yangtze Power Co., Ltd., Yichang 443000, China
Peng Zhang: China Yangtze Power Co., Ltd., Yichang 443000, China
Yifeng Gu: School of Power and Mechanical Engineering, Wuhan University, Wuhan 430000, China
Fangqing Zhang: School of Power and Mechanical Engineering, Wuhan University, Wuhan 430000, China
Jiang Guo: School of Power and Mechanical Engineering, Wuhan University, Wuhan 430000, China

Energies, 2025, vol. 18, issue 19, 1-13

Abstract: To improve the economy and stability of data center green power direct supply, the capacity configuration optimization of wind–light–load storage based on improved particle swarm optimization (PSO) is conducted. According to wind speed, the Weibull distribution of wind output is established, while the Beta distribution of solar output is established according to light intensity. Furthermore, by conducting the correlation analysis, it is indicated that there is a negative correlation between wind and solar output, which is helpful to optimize the mix of wind and solar output. To minimize the yearly average cost of wind–light–load storage, the capacity configuration optimization model is established, where the constraints include wind and solar output, energy storage capacity, balance between wind and solar output and data center load. To solve the capacity configuration optimization model, the improved PSO is adopted, compared to other optimization algorithms, like differential evolution (DE), genetic algorithm (GA) and grey wolf optimizer (GWO); by adjusting the inertia weight factor dynamically, the improved PSO is more likely to escape the local optimal solution. To validate the feasibility of data center green power direct supply with wind–light–load storage, a case study is conducted. By solving the capacity configuration optimization model of wind–light–load storage with the improved PSO, the balance rate between wind–solar output and data center load is improved by 12.5%, while the rate of abandoned wind and solar output is reduced by 17.5%, which is helpful to improve the economy and stability of data center green power direct supply.

Keywords: wind–light–load storage; capacity configuration optimization; improved particle swarm optimization; correlation analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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