“车—路—网”协同优化下的电动汽车有序充电引导策略

Orderly charging guidance strategies for electric vehicles under EVs-Traffic-Distribution collaborative optimization

大规模电动汽车无序充电可能引发路网道路堵塞、配电网节点电压偏低等问题,为此分析“车—路—网”的交互特性,构建“车—路—网”协同优化架构。首先,分析电动汽车的行为特性,得到出行需求与充电需求模型;其次,基于Floyd算法提出满足出行需求的最短路径、最优时间与最少能耗线路;然后,基于BP神经网络模型滚动预测节点充电负荷并制定实时电价;最后,基于3种线路与实时电价提出满足充电需求的有序充电引导策略。仿真结果表明,所提引导策略能够降低电动汽车充电成本,同时有效减轻路网道路拥堵及配电网电压偏低的问题。

Disorderly charging of a large number of electric vehicles(EVs)can lead to issues such as traffic congestion on road networks and low voltage at distribution network nodes.To address this,an analysis of the EVs-Traffic-Distribution(ETD)interaction characteristics is conducted,leading to the development of a collaborative optimization framework for ETD.Firstly,travel and charging demand models are derived through analyzing the behavior characteristics of EVs.Next,the shortest path,optimal time,and minimal energy consumption routes that satisfy travel demand are proposed based on the Floyd algorithm.Then,node charging load is forcasted by rolling prediction and real-time electricity prices are formulated,both based on BP neural network models.Finally,an orderly charging guidance strategy is proposed to meet the charging demand based on three types of routes and real-time electricity prices.The simulation results show that the proposed guidance strategy can reduce EVs charging costs while effectively mitigating road congestion and low voltage problems.