Data-driven Reliability Assessment of an Electric Vehicle Penetrated Grid Utilizing the Diffusion Estimator and Slice Sampling  

作　　者：Songyu Huang Chengjin Ye Si Liu Wei Zhang Yi Ding Ruoyun Hu Jianbai Li 

机构地区：[1]College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China [2]State Grid Zhejiang Electric Power Research Institute,Hangzhou 310014,China

出　　处：《CSEE Journal of Power and Energy Systems》2023年第5期1845-1853,共9页中国电机工程学会电力与能源系统学报（英文）

基　　金：supported by the National Science Foundation for Distinguished Young Scholars of China under Grant(52125702).

摘　　要：Due to the stochasticity of charging behaviors of electric vehicles(EVs),it is difficult to anticipate when charging load demand will be densely concentrated.If massive charging loads and the system peak profile appear at the same time,it may pose a risk to the reliable operation of power grids.For a system integrated with renewable energies,this risk can be much higher because of their unsteady power output.With load measurements more widely collected,this paper presents a data-driven framework to assess the reliability of a power grid considering charging EVs.Specifically,the diffusion estimator is firstly applied to estimate the probability density function of EV charging loads,which possesses both regional adaptivity and good boundary estimation performance.Then,charging load samples are produced through slice sampling.It is capable of sampling from irregularly-shaped distributions with high accuracy.The proposed approach is verified by the numerical results from the simulations on a modified IEEE 30-bus test system based on real measurement data.

关 键 词：Charging loads DATA-DRIVEN diffusion estimator electric vehicles slice sampling system reliability 

分 类 号：TM73[电气工程—电力系统及自动化]