基于一致性K均值聚类的电动汽车充电负荷建模方法

A Modeling Method for Electric Vehicle Charging Load Based on Consensus K-means Clustering

准确预测电动汽车充电负荷是供电规划优化设计的基础,提出一种基于聚类分析的电动汽车充电负荷预测方法。将牵制一致性控制引入充电负荷数据聚类分析中,提出基于一致性理论的k均值聚类方法,利用当前时段与相邻时段充电负荷数据的不相似性度量,迭代更新聚类状态,准确计算聚类中心,完成电动汽车充电概率与充电起始时间概率分布函数的快速求取;根据识别出的电动汽车充电行为特性参数,通过求解非线性规划函数,准确预测充电高峰时期聚集负荷模型。结合典型场景下杭州电动汽车充电实际案例,验证了所提方法具有计算简单、聚类快速、建模准确的特点。

Accurate prediction of electric vehicle(abbr.EV)charging load is the basis of optimization design for power supply planning.A clustering analysis-based EV charging load prediction method was proposed.The pinning consensus control was led in the clustering analysis of charging load data,and a consistency theory-based k-means clustering method was proposed.By use of dissimilarity measure of charging load data in current period and adjacent periods,the clustering state was iterated and updated and the clustering center was accurately calculated to complete the fast calculation of the probability distribution function of EV charging probability and starting charging time of EV.According to the identified characteristic parameters of EV charging behavior and by means of solving nonlinear planning function,the aggregated load model at charging peak time could be accurately predicted.Combining with practical case of Hangzhou EV charging,It is verified that the proposed method possesses the advantage of simple calculation,fast clustering and accurate modeling.