Title: Capacity configuration method for new energy storage system based on segmented peak shaving

Authors: Zesen Li; Bingjie Li; Guojing Liu

Addresses: Economics and Technological Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing, Jiangsu, China ' Economics and Technological Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing, Jiangsu, China ' Economics and Technological Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing, Jiangsu, China

Abstract: To overcome the problems of low accuracy in capacity estimation, low balancing degree and low utilisation rate in traditional methods, a capacity configuration method for new energy storage system based on segmented peak shaving is proposed. The battery's internal resistance and terminal voltage signals of the new energy storage system are taken as inputs, and the capacity estimation is the output. A capacity estimation model based on an improved fuzzy neural network is established. The capacity configuration objective function is constructed by combining segmented peak shaving and economic cost. Hybrid frog-leaping algorithm is used to obtain the optimal parameters for segmented peak shaving and economic cost through population initialisation, position updates and frog swarm sorting to determine the optimal configuration scheme. Experimental results show that the average accuracy of capacity estimation using this method is 97.31%, the maximum balancing degree is 0.98 and the minimum utilisation rate is only 90.9%.

Keywords: segmented peak shaving; new energy storage system; capacity configuration; improved fuzzy neural network; hybrid frog-leaping algorithm.

DOI: 10.1504/IJGEI.2025.147240

International Journal of Global Energy Issues, 2025 Vol.47 No.4/5, pp.418 - 435

Received: 04 Aug 2023
Accepted: 22 Nov 2023
Published online: 14 Jul 2025 *

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