Optimization of public charging station site selection by integrating <italic style="font-style: italic">K</italic>-Means and variation RODDPSO

GENG Peng; LIU Yan; ZHU Yuhang

doi:10.11959/j.issn.2096-3750.2025.00423

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Optimization of public charging station site selection by integrating K-Means and variation RODDPSO

Theory and Technology | 更新时间：2025-09-12

- Optimization of public charging station site selection by integrating K-Means and variation RODDPSO
- Chinese Journal on Internet of Things Vol. 9, Issue 2, Pages: 202-213(2025)
- 作者机构：
  
  1.南京工程学院通信与人工智能学院、集成电路学院，江苏南京 211167
  2.南京工程学院数理学院，江苏南京 211167
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(41972111);The Jiangsu Science and Technology Think Tank Program (Youth) Project(JSKX24085)
- DOI：10.11959/j.issn.2096-3750.2025.00423
  CLC： TP181
- Received：05 July 2024，
  
  Revised：2024-09-13，
  
  Published：10 June 2025
- 稿件说明：
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耿鹏,柳艳,朱宇航.融合K-Means与变异RODDPSO的公共充电站优化选址[J].物联网学报,2025,09(02):202-213.

GENG Peng,LIU Yan,ZHU Yuhang.Optimization of public charging station site selection by integrating K-Means and variation RODDPSO[J].Chinese Journal on Internet of Things,2025,09(02):202-213.
耿鹏,柳艳,朱宇航.融合K-Means与变异RODDPSO的公共充电站优化选址[J].物联网学报,2025,09(02):202-213. DOI： 10.11959/j.issn.2096-3750.2025.00423.

GENG Peng,LIU Yan,ZHU Yuhang.Optimization of public charging station site selection by integrating K-Means and variation RODDPSO[J].Chinese Journal on Internet of Things,2025,09(02):202-213. DOI： 10.11959/j.issn.2096-3750.2025.00423.

摘要

为优化电动汽车公共充电站的选址问题，以

-均值（

-Means

-means clustering algorithm）和随机分布式延迟粒子群优化（RODDPSO

randomly occurring distributedly delayed particle swarm optimization）算法为基础，根据电动汽车充电需求，提出了一种融合

-Means与变异随机分布式延迟粒子群优化（VRODDPSO

variation randomly occurring distributedly delayed particle swarm optimization）算法的电动汽车充电站选址优化方法，以确定最佳的充电站位置。首先，改进了RODDPSO算法，增加了自适应变异。其次，引入VRODDPSO算法对

-Means的聚类中心位置进行优化，使用聚类完成后各个区域的聚类中心点作为充电站的最佳选址。相比仅使用

-Means算法进行3次聚类，改进后的聚类模型能够有效地解决

-Means算法中不恰当的初始聚类中心点可能导致算法陷入局部最小值、产生不理想的聚类的问题。最后，在南京市公共充电站优化选址的实证研究中，提出了一种新的衡量方法，能够根据现实充电站的综合利用率来评价不同算法下充电站的选址优劣。分析结果证实了使用

-Means与VRODDPSO算法融合的方法能够有效地优化聚类后的聚类中心位置，即充电桩和充电站的选址。

Abstract

To optimize the siting of public charging stations for electric vehicles

a method based on the

-means clustering algorithm (

-Means) and randomly occurring distributedly delayed particle swarm optimization (RODDPSO) algorithm was proposed. By integrating

-Means with a variation randomly occurring distributedly delayed particle swarm optimization (VRODDPSO) algorit

the approach could determine the optimal locations for charging stations according to the charging demands of electric vehicles. Firstly

the RODDPSO algorithm was enhanced by incorporating an adaptive variation strategy. Then

the VRODDPSO algorithm was used to optimize the positions of the

-Means clustering centers. After clustering

the center points of each region were considered as the optimal locations for charging stations. Compared with using the

-Means algorithm alone for three iterations

the improved clustering model effectively addressed the issue of the

-Means algorithm potentially falling into local minima due to inappropriate initial cluster centers

which could result in suboptimal clustering. Finally

in an empirical study on the optimization of public charging station locations in Nanjing

a new evaluation method was proposed. This method assessed the siting quality of charging stations based on their comprehensive utilization rates. The analysis confirms that the integrated

-Means and VRODDPSO algorithm effectively optimizes the positions of the clustered centers

i.e.

the locations of charging piles and stations.

关键词

Keywords

references

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Optimization of public charging station site selection by integrating K-Means and variation RODDPSO

DOI：10.11959/j.issn.2096-3750.2025.00423

摘要

Abstract

关键词

Keywords

references