Location and Capacity of Electric Vehicle Charging Station Based on  Voronoi Diagram and Improved Gravity Search Algorithm

Zhao Bing-yao; Chen Jing-hua; Guo Jing-tao; Chen You-peng; Zhang Zhao-xuan

doi:10.12052/gdutxb.200095

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Location and Capacity of Electric Vehicle Charging Station Based on Voronoi Diagram and Improved Gravity Search Algorithm

更新时间：2026-05-27

- Location and Capacity of Electric Vehicle Charging Station Based on Voronoi Diagram and Improved Gravity Search Algorithm
- Vol. 38, Issue 3, Pages: 72-78(2021)
- 作者机构：
  
  1. 广东工业大学自动化学院,广东,广州,510006
  2. 中国能源建设集团广东省电力设计研究院有限公司,广东,广州,510663
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.200095
  CLC： TM743
- Online First：10 May 2021，
  
  Published：2021
- 稿件说明：
移动端阅览
赵炳耀, 陈璟华, 郭经韬, 陈友鹏, 张兆轩. 基于Voronoi图和改进引力搜索算法的电动汽车充电站选址定容[J]. 广东工业大学学报, 2021, 38(3): 72-78.

Zhao Bing-yao, Chen Jing-hua, Guo Jing-tao, et al. Location and Capacity of Electric Vehicle Charging Station Based on Voronoi Diagram and Improved Gravity Search Algorithm[J]. 2021, 38(3): 72-78.
赵炳耀, 陈璟华, 郭经韬, 陈友鹏, 张兆轩. 基于Voronoi图和改进引力搜索算法的电动汽车充电站选址定容[J]. 广东工业大学学报, 2021, 38(3): 72-78. DOI： 10.12052/gdutxb.200095.

Zhao Bing-yao, Chen Jing-hua, Guo Jing-tao, et al. Location and Capacity of Electric Vehicle Charging Station Based on Voronoi Diagram and Improved Gravity Search Algorithm[J]. 2021, 38(3): 72-78. DOI： 10.12052/gdutxb.200095.

摘要

电动汽车充电站的选址定容属于多变量、多约束及高维度的非线性数学问题

针对该问题提出一种基于Voronoi图和改进引力搜索算法(Improved Gravity Search Algorithm

IGSA)的选址定容方法。综合考虑主观权值和客观权值

运用层次分析熵权法

建立以建设运行成本、用户充电时间成本及配网损耗成本为目标的电动汽车充电站选址定容多目标决策模型。针对标准引力搜索算法(Gravity Search Algorithm

GSA)收敛速度慢、求解高维度问题精度不足等问题

在粒子初始化和更新阶段引入混沌映射

增加算法遍历性; 同时

引入全局最优点引导速度更新公式

提高算法跳出局部最优的能力。利用Voronoi图划分充电站服务区域

提出Voronoi图与IGSA联合求解流程。仿真结果证明了所建模型和算法的可行性和实用性。

Abstract

Considering the characteristics of location and capacity problems such as multi-variable

multi-constrained

high-dimensional and nonlinear

a method based on Voronoi diagram and improved gravitational search algorithm (IGSA) is proposed. Considering both subjective and objective weights

a multi-objective decision model considering construction costs

operation costs

user charging time costs and distribution network loss costs is established. In order to solve the slow convergence and the inadequate accuracy of standard gravitational search algorithm (GSA)

a chaotic map is introduced in the particle initialization and updating phase to enhance the ergodicity of the algorithm. At the same time

a global optimal point to guide speed updating formula is introduced to improve the ability of the algorithm to jump out of the local optimal. Voronoi diagram is used to divide service area of charging station

and a joint solution process of Voronoi diagram and IGSA is proposed. The simulation results prove the feasibility and validity of the model.

关键词

Keywords

references

安锋, 康利平, 秦兰芝, 等. 中国传统燃油车退出时间表研究[R]. 北京: 能源与交通创新中心, 2019.

张帆, 李康, 秦雪亮, 等. 2019—2020年度中国充电基础设施发展年度报告[R]. 北京: 中国电动汽车充电基础设施促进联盟, 2020.

LI Y X, LIN Y, YANG J. Research on multi-scenario planning method for EV charging network based on improved weighted voronoi diagram [J]. Guangdong Electric Power, 2019, 32(8): 43-50.

DING D J, DAI K, ZHANG X S, et al . Network planning for electric vehicle charging based on fuzzy multi-objective optimization [J ] . Power System Protection and Control, 2018, 46(2): 43-50.

LUO Q Y, TIAN W L, JIA H F. Location and capacity model of electric vehicle charging station considering commuting demand [J]. Journal of Jilin University (Engineering Edition), 2019, 49(5): 1471-1477.

ZHU S Y, YANG B, ZHU X L. Location and capacity of electric vehicle charging station considering deviation path [J]. Journal of Shanghai Maritime University, 2019, 40(1): 44-50.

CHEN J P, AI Q, XIAO F. EV charging station planning based on travel demand [J]. Electric Power Automation Equipment, 2016, 36(6): 34-39.

CHEN G, MAO Z L, LI J Y, et al . Multi-objective planning of electric vehicle charging stations considering carbon emissions [J ] . Automation of Electric Power Systems, 2014, 20(17): 49-53.

ZHAO S Q, LI Z W, DANG L. Optimal EV charging station siting and sizing based on urban traffic network information [J]. Electric Power Automation Equipment, 2016, 36(10): 8-15.

AI X, LI Y Z, WANG K Y, et al . Locating and sizing of electric vehicle charging station based on chaotic simulated annealing particle swarm optimization algorithm [J ] . Electric Power Automation Equipment, 2018, 38(9): 9-14.

PEI W J, WANG F, TAN Y H, et al . Chance-constrained programming for electric vehicle charging stations in distribution network containing photovoltaic distributed generations [J ] . Journal of Electric Power System and Automation, 2017, 29(6): 45-52.

XIONG H, XIANG T Y, ZHU Y G, et al . Electric vehicle public charging station location optimal planning [J ] . Automation of Electric Power Systems, 2012(23): 65-70.

YAN T Z, QIU X Y, LIU Y B, et al . Optimal planning of electric charging station based on PSOSA algorithm [J ] . Electric Measurement and Instrumentation, 2017, 54(6): 11-16.

张娟. 电动汽车有序充电策略及充电设施规划研究[D]. 太原: 太原理工大学, 2018: 52-53.

叶珍. 基于AHP的模糊综合评价方法研究及应用[D]. 广州: 华南理工大学, 2010.

范炜锋. 万有引力搜索算法的分析与改进[D]. 广州: 广东工业大学, 2014.

TANG J J, CHEN J H, QIU M J. Multi-objective dispatch of microgrid based on dynamic fuzzy chaotic particle swarm algorithm [J]. Journal of Guangdong University of Technology, 2018, 35(3): 100-106.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Locating and Sizing Planning of Battery Swapping Stations Considering Centralized Charging Station

The Liquid Cooling System of the Ni-MH Battery Pack for Hybrid Electric Vehicles

Fully Digitized Control of Electric Vehicle Permanent Magnet Brushless DC Electrical Machines

A Resource Allocation Optimization Method for Multi-UAV Integrated Sensing, Computing, and Communication Towards Disaster Relief

An Optimal Operation Strategy for Community Microgrid Alliances Considering Power Exchange under Multi-package Shared Energy Storage Model

Related Author

Chen Jing-hua

Wu Zhong-jie

Zhang Guo-qing

Hu Wei

HE Hong-shu

Related Institution

No data

AI问答

⁰