Title: Optimal planning of charging station for electric vehicle based on hybrid RODDPSO and K-means algorithm

Authors: Birong Huang; Huahao Zhou; Fangbai Liu; Yuhang Zhu; Peng Geng; Xiaoyan Zhao

Addresses: Yancheng Power Supply Branch, State Grid Jiangsu Electric Power Co., Ltd, Yancheng 224000, China ' Yancheng Power Supply Branch, State Grid Jiangsu Electric Power Co., Ltd, Yancheng 224000, China ' Yancheng Power Supply Branch, State Grid Jiangsu Electric Power Co., Ltd, Yancheng 224000, China ' School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing 211167, China ' School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing 211167, China ' School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing 211167, China

Abstract: This research proposes a hybrid approach combining K-means with randomly occurring distributedly delayed particle swarm optimisation (RODDPSO) to strategically locate electric vehicle charging stations (EVCS). The method is structured in three phases: initial regional clustering for demand analysis, refined selection of charging pile locations within these regions, and consolidation into efficient charging stations. The approach enhances the traditional K-means by optimising initial centroids with RODDPSO, mitigating the risk of suboptimal solutions due to local minima. The Yancheng ride-hailing dataset is employed to validate the model, showcasing a significant improvement in utilisation rates and operational efficiency compared to the standard K-means algorithm. The findings underscore the hybrid method's potential to optimise EVCS placement for enhanced service coverage and economic viability.

Keywords: randomly occurring distributedly delayed particle swarm optimisation; RODDPSO; K-means; cluster centre optimisation; optimal planning of charging station.

DOI: 10.1504/IJADS.2025.148234

International Journal of Applied Decision Sciences, 2025 Vol.18 No.5, pp.575 - 601

Received: 11 Mar 2024
Accepted: 19 May 2024
Published online: 01 Sep 2025 *

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