考虑充电负荷时空分布特性的EV充电站规划  

EV charging station planning considering spatial-temporal distribution characteristics of charging load

作　　者：左逸凡 李伟豪 杨伟[1] ZUO Yifan;LI Weihao;YANG Wei(School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学自动化学院,南京210094

出　　处：《电测与仪表》2025年第3期1-9,共9页Electrical Measurement & Instrumentation

基　　金：国家电网公司科技项目(JSDL-XLFW-SQ-2016-10-092)。

摘　　要：针对电动汽车(electric vehicle,EV)充电站选址定容问题,提出了一种考虑充电负荷时空分布特性的EV充电站规划模型。首先,通过动态Floyd算法结合拉丁超立方抽样法(latin hypercube sampling,LHS)建立了EV的时空充电负荷预测模型。其次,从用户满意度的角度出发,以EV充电站和用户双方的成本最小为目标,采用Voronoi图与自适应模拟退火粒子群优化(adaptive simulated annealing particle swarm optimiza-tion,ASAPSO)算法确定充电站的服务范围、最优数量/位置以及各站点快充/慢充充电桩配置数目,建立了EV充电站选址定容模型。最后,通过对北方某市的部分城区进行规划,验证了模型的有效性。Aiming at the problem of location and volume of electric vehicle(EV)charging stations,a planning model of EV charging station considering spatial-temporal distribution characteristics of charging load is proposed.Firstly,the spatial-temporal charging load prediction model of EV is established by dynamic Floyd algorithm com-bined with Latin hypercube sampling(LHS).Afterwards,from the perspective of considering user satisfaction and aiming at the minimum cost of both EV charging stations and users,Voronoi diagram and an adaptive simulated an-nealing particle swarm optimization algorithm(ASAPSO)are used to determine the service range,optimal number and location of charging stations,as well as the number of fast charging and slow charging pile configurations of each station.The EV charging station location and volume model is established.Finally,the effectiveness of the model is verified by planning some urban areas of a city in north China.

关 键 词：EV充电站 时空充电负荷预测 选址定容 自适应模拟退火粒子群优化算法 

分 类 号：TM721[电气工程—电力系统及自动化]