含大规模电动汽车接入的主动配电网多目标优化调度方法

针对大规模电动汽车接入配电网无序充电带来的负荷峰值增加等问题,提出一种含大规模电动汽车接入的主动配电网多目标优化调度方法。首先基于蒙特卡洛抽样方法分析了大规模电动汽车的充电负荷需求;然后,以含大规模电动汽车接入的主动配电网运行成本最小化和负荷曲线方差最小化为优化目标,综合考虑电动汽车的充电需求和配电网的运行约束,构建含规模化电动汽车接入的主动配电网多目标优化调度模型,采用带精英策略的非支配排序遗传算法(NSGA-II)对多目标优化模型进行求解,针对多目标优化得到的帕累托(Pareto)最优解集规模大,蕴含信息丰富,导致运行人员难以决策的问题,提出一种基于模糊聚类的方法对多目标Pareto最优解集进行筛选。通过改进的IEEE 34节点算例的多场景对比分析,结果表明:所提出的模型和方法可在保证系统经济运行的同时,有效利用电动汽车的优化充电降低系统负荷峰谷差。

Multi-Objective Optimization Scheduling Method for Active Distribution Network with Large Scale Electric Vehicles

Aimed at the problem of increasing peak load caused by random charging of large scale electric vehicle, a multi-objective optimization scheduling method for active distribution network with large scale electric vehicle access is proposed. firstly, the charging demand of large scale electric vehicle is analyzed based on Monte Carlo sampling method, then, take the minimum operation cost and the minimum load curve variance of active distribution network as optimization objectives, considering the charging demand of electric vehicle and the operation constraints of distribution network, a multi-objective optimization scheduling model for active distribution network with large-scale electric vehicles assess is proposed, NSGA-Ⅱ optimization algorithm is applied to solve the multi-objective model. For the Pareto optimal solution set is large and contains abundant information, it is difficult for the operators to make decision, a method based on fuzzy clustering is proposed to select the optimal solution from Pareto optimal solution set. The simulation test is carried out on the modified IEEE 34-node distribution system, the results shows that the method proposed in this paper can not only ensure the economic operation of the system, but also can reduce the load peak and valley difference of the system by using the optimized charging of electric vehicle.