Dynamic grouping control of electric vehicles based on improved k-means algorithm for wind power fluctuations suppression  

作　　者：Yang Yu Mai Liu Dongyang Chen Yuhang Huo Wentao Lu 

机构地区：[1]State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,North China Electric Power University(Baoding),Baoding 071003,P.R.China [2]Key Laboratory of Distributed Energy Storage and Microgrid of Hebei Province,North China Electric Power University(Baoding),Baoding 071003,P.R.China

出　　处：《Global Energy Interconnection》2023年第5期542-553,共12页全球能源互联网（英文版）

基　　金：This study was supported by the National Key Research and Development Program of China(No.2018YFE0122200);National Natural Science Foundation of China(No.52077078);Fundamental Research Funds for the Central Universities(No.2020MS090).

摘　　要：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：Electric vehicles Wind power fluctuation smoothing Improved k-means Power allocation Swing door trending 

分 类 号：TM614[电气工程—电力系统及自动化] U491.8[交通运输工程—交通运输规划与管理] TP273[交通运输工程—道路与铁道工程]