基于聚类算法的电动汽车充放电分时电价优化

The time-of-use price optimization of electric vehicle charging and discharging based on clustering algorithm

随着电动汽车的增多,电动汽车无序充电会给电网运行带来较大的负面影响。同时,随着电力市场改革,实施分时电价是必然的选择,通过合理设置充放电分时电价能够引导电动汽车用户的有序充放电。本文以私家车为研究对象,根据用户的充电持续时间、充电开始时间特征进行K-均值聚类分析得到用户日常的充电规律,建立减少电网波动及减少用户用电成本的目标函数,最后通过布谷鸟搜索算法进行最佳分时电价的求解。通过小区实际负荷实例验证此分时电价调度策略能够有效的减少用户用电成本,改善电网运行状况。

With the increase of electric vehicles,uncontrolled charging of electric vehicle will bring greater negative influence to the power system operation.At the same time,along with the electricity market reformation,the implementation of TOU price is the inevitable choice.Through a reasonable set of charging and discharging TOU price can guide the electric vehicle users.In this paper,private cars are taken as the research object.According to the user's charging duration and charging start time characteristics,the K-means clustering analysis is performed to obtain the user's daily charging rules.Therefore,an objective function for reducing grid fluctuations and the user's electricity cost is established.Finally,the optimal time-of-use price is solved by cuckoo search(CS)algorithm.Through the example of the actual load of a community,the scheduling strategy can effectively reduce user electricity cost and improve the operation of the power grid.