随机充电需求下城市电动汽车充电站选址优化  

Location Optimization of Urban Electric Vehicle Charging Station under Random Charging Demand

作　　者：冯春[1,2] 陈木泉 蒋雪 FENG Chun;CHEN Mu-quan;JIANG Xue(School of Transportation and Logistics,Southwest Jiaotong University,Chengdu Sichuan 610031,China;National United Engineering Laboratory of Integrated and Intelligent Transportation,Southwest Jiaotong University,Chengdu Sichuan 610031,China)

机构地区：[1]西南交通大学交通运输与物流学院,四川成都610031 [2]西南交通大学综合交通运输智能化国家地方联合工程实验室,四川成都610031

出　　处：《计算机仿真》2022年第11期193-198,442,共7页Computer Simulation

摘　　要：针对电动汽车充电需求和用户充电行为的随机性进行充电站合理规划,对于减少驾驶员里程焦虑,提高出行满意度及促进电动汽车推广具有重要作用。利用网络Voronoi图能模拟设施沿道路网传播的特性划分备选充电站覆盖范围,考虑用户目的地停留时间和荷电状态的随机分布特性,以出行链上充电站捕获的总充电需求流量最大化为目标,建立基于情景的充电站选址两阶段随机规划模型,以确定充电站选址位置和充电需求流量捕获情况,并应用样本平均近似法求解模型。基于厦门市岛内充电站选址为例进行仿真,结果表明仅使用少数几个充电站和有限数量的充电桩即可满足大部分电动汽车充电需求,而随着充电可用性提高,充电站的利用率就会降低。According to the randomness of electric vehicle charging demand and user charging behavior,reasonable planning of charging station plays an important role in reducing driver’s mileage anxiety,improving travel satisfaction and promoting the promotion of electric vehicles.The network Voronoi diagram can simulate the propagation characteristics of facilities along the road network and divide the coverage of alternative charging stations.Considering the random distribution characteristics of user’s residence time and state of charge,a two-stage stochastic programming model of charging station location based on scenario was established to maximize the total charging demand flow captured by charging stations in the travel chain,so as to determine the charging station location and charging capacity The sample average approximation method was used to solve the model.Taking Xiamen Island as an example,the simulation results show that only a few charging stations and a limited number of charging piles can meet the charging demand of most electric vehicles,and with the improvement of charging availability,the utilization rate of charging stations will decrease.

关 键 词：充电站选址 随机需求 流量捕获 两阶段随机规划 样本平均近似法 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]