大规模电动汽车集群分层实时优化调度

Hierarchical Real-time Optimized Dispatching for Large-scale Clusters of Electric Vehicles

为解决电动汽车的大规模实时优化调度问题,根据接入电动汽车不同的期望充电完成时间,将其划分为若干个不同优先级的电动汽车集群,在满足车主充电需求、配电网安全运行的同时,建立了考虑电动汽车充放电的大规模集群实时优化调度模型。该调度模型主要分为两个层次:首先,采用灰狼优化(GWO)算法对上层调度进行求解,从而获得各个电动汽车集群的充放电策略;然后,利用提出的能量缓冲一致性算法,制定出集群内的各辆电动汽车的底层充放电策略。仿真算例表明:所搭建的集群优化模型能明显降低电动汽车的大规模实时优化调度难度,同时,GWO算法和能量缓冲一致性算法在求解电动汽车的大规模优化调度问题上,更具有实用性和快速性。

This paper presents a real-time optimized dispatching model for large-scale cluster electric vehicles（EVs）to achieve the charging demand and safe operation of distribution network.For each new optimization scenario,the accessed EVs can cluster according to their desired completion time.The entire dispatching process for charging/discharging strategy can be divided into two steps,i.e.,the upper dispatching based on grey wolf optimization（GWO）algorithm for each cluster,and the bottom layer based on energy buffer consensus for each EV in the corresponding cluster.Simulation results demonstrate that the proposed model can significantly facilitate the real-time large-scale optimal dispatching of EVs,while GWO algorithm and energy buffer consensus are suitable to solve large-scale optimal dispatching with superior performance on availability and convergence rate.