两阶段机场多特种车辆协同充电调度策略

Two-stage collaborative charging scheduling strategy for airport multiple special vehicles

在机场区域内,新能源特种车辆充电具有很大的随机性,且不同种类特种车辆充电情况各不相同,造成飞行区内各充电桩利用率相差过大,影响机场配电网的健康运行。针对上述现象,设计了十一车型两阶段特种车辆协同充电调度策略。第一阶段通过分析不同车辆对航班的保障流程,以同一车辆对相邻航班保障起始时间差值最小为目标,生成存在充电需求的车辆序列。第二阶段以减小飞行区各区充电桩时间利用率方差和车辆充电排队时间为目标,在上一阶段车辆序列基础上采用改进的自适应变异粒子群算法进行模型求解,并以国内某枢纽机场的实际车辆充电数据进行对比验证。实验表明,采用该算法后,车辆充电时的等待时间降低了93.5%、飞行区充电桩时间利用率的整体方差下降了88.7%,达到了均衡使用充电桩的目标。

In the airport area,the charging behavior of new energy special vehicles has great randomness,and the charging conditions of different types of special vehicles are different,which results in a large difference in the utilization rate of each charging pile in the airport area,affecting the healthy operation of electric distribution network of the airport.In response to the above phenomenon,this paper designed a two-stage collaborative charging scheduling strategy for eleven types of special vehicles.In the first stage,this paper analyzed the service process of different vehicles for flights,with the goal of minimizing the difference value in the start time of service for adjacent flights by the same vehicle,and generated a sequence of vehicles with charging needs.In the second stage,the strategy aimed to reduce the variance of time utilization of charging piles and vehicle charging queue time in each zone of the airport area.Based on sequence of vehicles in the previous stage,this paper used an improved adaptive mutation particle swarm optimization algorithm to solve this model,and validated the strategy by comparing it with actual vehicle charging data of a domestic hub airport.The experiment shows that this algorithm can achieve a 93.5%reduction in the waiting time for vehicle charging,and it reduces the overall variance of time utilization of charging piles in the airport area by 88.7%.Finally,this strategy achieves the goal of balanced use of charging piles.