全球能源互联网（英文）2023，Vol.6Issue(5)：542-553,12.DOI:10.1016/j.gloei.2023.10.003
基于改进k均值算法平抑风电波动的电动汽车动态分组控制策略
Dynamic grouping control of electric vehicles based on improved k-means algorithm for wind power fluctuations suppression
摘要
Abstract
To address the significant lifecycle degradation and inadequate state of charge(SOC)balance of electric vehicles(EVs)when mitigating wind power fluctuations,a dynamic grouping control strategy is proposed for EVs based on an improved k-means algorithm.First,a swing door trending(SDT)algorithm based on compression result feedback was designed to extract the feature data points of wind power.The gating coefficient of the SDT was adjusted based on the compression ratio and deviation,enabling the acquisition of grid-connected wind power signals through linear interpolation.Second,a novel algorithm called IDOA-KM is proposed,which utilizes the Improved Dingo Optimization Algorithm(IDOA)to optimize the clustering centers of the k-means algorithm,aiming to address its dependence and sensitivity on the initial centers.The EVs were categorized into priority charging,standby,and priority discharging groups using the IDOA-KM.Finally,an two-layer power distribution scheme for EVs was devised.The upper layer determines the charging/discharging sequences of the three EV groups and their corresponding power signals.The lower layer allocates power signals to each EV based on the maximum charging/discharging power or SOC equalization principles.The simulation results demonstrate the effectiveness of the proposed control strategy in accurately tracking grid power signals,smoothing wind power fluctuations,mitigating EV degradation,and enhancing the SOC balance.关键词
电动汽车/平抑风电波动/改进k-means算法/功率分配/旋转门算法Key words
Electric vehicles/Wind power fluctuation smoothing/Improved k-means/Power allocation/Swing door trending引用本文复制引用
余洋,刘霡,陈东阳,霍宇航,陆文韬..基于改进k均值算法平抑风电波动的电动汽车动态分组控制策略[J].全球能源互联网（英文）,2023,6(5):542-553,12.基金项目
This study was supported by the National Key Research and Development Program of China(No.2018YFE0122200),National Natural Science Foundation of China(No.52077078),and Fundamental Research Funds for the Central Universities(No.2020MS090). （No.2018YFE0122200）
