考虑时序性和相关性的EVCS在配电网中的最优规划

Optimal Planning of EVCS in Distribution Network Considering Sequential Feature and Correlation

随着电动汽车的日益增加,电动汽车充电站(EVCS)的规划也迫在眉睫。由于交通流量和车主出行情况具有随机性,导致EVCS充电负荷也具有不确定性,因此EVCS在电网中的电气接入点和容量会影响配电网经济性和安全性。考虑EVCS负荷以及典型分布式能源的时序性和相关性,通过拉丁超立方抽样(LHS)技术生成大量场景并引入边距排序因子改进K-means聚类算法缩减场景,以配电网有功网损最小和电压偏差最小构成的综合评价指标为目标函数构建优化模型,最后利用IEEE33节点系统进行算例分析,得到EVCS的最优接入点及容量。

With the increasing number of electric vehicles,the planning of electric vehicle charging station(EVCS)is extremely urgent.Due to the randomness of traffic flow and vehicle owner’s travel situation,the EVCS charging load is also uncertain,so the electrical connecting nodes and capacity of the EVCS in power grid will affect the economy and safety of the distribution network.Based on the sequential feature and correlation of the EVCS charging load and several typical distributed energy resources,a large number of scenarios are generated by Latin hypercube sampling(LHS),and reduced scenarios are obtained by K-means clustering algorithm which introduces a margin ranking factor.In addition,an optimization model is established by using the comprehensive evaluation index composed of the minimum active power loss and voltage deviation of the distribution network as an objective function.Finally,IEEE 33-bus system is used to analyze the examples,obtaining the optimal electrical connecting nodes and capacity of the EVCS.