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

赵炳耀; 陈璟华; 郭经韬; 陈友鹏; 张兆轩

doi:10.12052/gdutxb.200095

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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

更新时间：2026-05-27

- 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
- Location and Capacity of Electric Vehicle Charging Station Based on Voronoi Diagram and Improved Gravity Search Algorithm
- 广东工业大学学报 2021年38卷第3期页码：72-78
- 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：
  
  赵炳耀(1995– )，男，硕士研究生，主要研究方向为电力系统优化规划、电动汽车充电站选址定容、智能算法
  陈璟华(1974–)，女，副教授，博士，主要研究方向为电力系统安全运行及人工智能技术在电力系统中的应用，E-mail：43884010@qq.com
- 基金信息：
  
  中央财政支持地方高校发展专项资金资助项目([2016]202号)
- DOI：10.12052/gdutxb.200095
  中图分类号： TM743
- 网络首发：2021-05-10，
  
  纸质出版：2021
- 稿件说明：
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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

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