Power Allocation and Capacity Optimization Configuration of Hybrid Energy Storage Systems in Microgrids Using RW-GWO-VMD

Honghui Liu, Donghui Li (), Zhong Xiao (), Qiansheng Qiu, Xinjie Tao, Qifeng Qian, Mengxin Jiang and Wei Yu
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Honghui Liu: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Donghui Li: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Zhong Xiao: School of Chemical Safety, North China Institute of Science and Technology, Langfang 065201, China
Qiansheng Qiu: Zhejiang Sunoren Solar Technology Co., Ltd., Jiaxing 314400, China
Xinjie Tao: School of Chemical Safety, North China Institute of Science and Technology, Langfang 065201, China
Qifeng Qian: Zhejiang Sunoren Solar Technology Co., Ltd., Jiaxing 314400, China
Mengxin Jiang: School of Chemical Safety, North China Institute of Science and Technology, Langfang 065201, China
Wei Yu: Chemical and Materials Engineering Department, University of Auckland, Auckland 1010, New Zealand

Energies, 2025, vol. 18, issue 16, 1-25

Abstract: Optimizing the power allocation and capacity configuration of hybrid energy storage systems (HESS) is crucial for enhancing grid stability, power quality and renewable energy utilization in wind–solar complementary microgrids. However, the conventional configuration methods exhibit inaccuracy and low reliability. To achieve the optimal capacity configuration of HESS in wind–solar complementary microgrids, a power allocation strategy and a capacity optimization configuration model for HESS consisting of vanadium redox flow batteries (VRBs) and supercapacitors (SCs) were proposed based on parameter-optimized variational mode decomposition (VMD). Firstly, the number of mode decomposition (K) and the penalty factor ( α ) of VMD were optimized using the random walk grey wolf optimizer (RW-GWO) algorithm, and the HESS power signal was decomposed by RW-GWO-VMD. Secondly, an optimal capacity configuration model was formulated, taking into account the whole life cycle cost of HESS, and particle swarm optimization (PSO) algorithm was applied to optimize HESS capacity while satisfying operational constraints on charge/discharge power, state of charge (SOC) range, and permissible rates of load deficit and energy loss. Thirdly, the optimal capacity allocation was obtained by minimizing the whole life cycle cost of HESS, with the frequency division threshold N serving as the optimization parameter. Finally, comprehensive comparison and analysis of proposed methods were conducted through simulation experiments. The results demonstrated that the whole life cycle cost of RW-GWO-VMD was 7.44% lower than that of EMD, 1.00% lower than that of PSO-VMD, 0.72% lower than that of AOA-VMD, and 0.27% lower than that of GWO-VMD.

Keywords: hybrid energy storage system (HESS); variational mode decomposition (VMD); random walk grey wolf optimizer (RW-GWO); power allocation; capacity configuration (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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