Economic Optimization of Hybrid Energy Storage Capacity for Wind Power Based on Coordinated SGMD and PSO

Kai Qi, Keqilao Meng (), Xiangdong Meng, Fengwei Zhao and Yuefei Lü
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Kai Qi: School of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
Keqilao Meng: School of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
Xiangdong Meng: Huaneng Wulatezhongqi New Energy Power Generation Co., Ltd., Bayannur 015200, China
Fengwei Zhao: Huaneng Wulatezhongqi New Energy Power Generation Co., Ltd., Bayannur 015200, China
Yuefei Lü: Huaneng Wulatezhongqi New Energy Power Generation Co., Ltd., Bayannur 015200, China

Energies, 2025, vol. 18, issue 10, 1-14

Abstract: Under the dual carbon objectives, wind power penetration has accelerated markedly. However, the inherent volatility and insufficient peak regulation capability in energy storage allocation hamper efficient grid integration. To address these challenges, this paper presents a hybrid storage capacity configuration method that combines Symplectic Geometry Mode Decomposition (SGMD) with Particle Swarm Optimization (PSO). SGMD provides fine-grained, multi-scale decomposition of load–power curves to reduce modal aliasing, while PSO determines globally optimal ESS capacities under peak-shaving constraints. Case-study simulations showed a 25.86% reduction in the storage investment cost compared to EMD-based baselines, maintenance of the state of charge (SOC) within 0.3–0.6, and significantly enhanced overall energy management efficiency. The proposed framework thus offers a cost-effective and robust solution for energy storage at renewable energy plants.

Keywords: peak regulation capability; symplectic geometric mode decomposition; hybrid energy storage capacity configuration; particle swarm optimization (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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