电力建设2025，Vol.46Issue(8)：54-66,13.DOI:10.12204/j.issn.1000-7229.2025.08.006
计及充电排队与实时SOC的电动汽车充电负荷时空分布预测
Spatial-Temporal Distribution Prediction of Electric Vehicle Charging Load Considering Charging Behavior and Real-Time SOC
摘要
Abstract
[Objective]To address the uncertainty of the travel mode and charging demand of electric vehicle(EV)users,we propose a spatial-temporal distribution prediction method for EV charging load based on the charging queue and real-time state of charge(SOC).[Methods]The influence of traffic conditions and ambient temperature on EV energy consumption and charging behavior is analyzed,and road traffic network and comprehensive energy consumption models are established.Based on the user's travel chain,the user's travel characteristics are analyzed,the shortest time method is used to plan the driving path,and a spatial-temporal distribution prediction model of the EV charging load is built considering the charging queue time and real-time SOC.Finally,the Monte Carlo method is used to verify the actual network structure and IEEE33-node distribution system.[Results]The analysis demonstrates that peak-hour charging queue durations exceeding 30 min induce partial user migration to off-peak periods,resulting in peak load reduction and off-peak load elevation compared with queuing-free models.Compared with the model that do not consider the charging queue,the peak load decreases,and the off-peak load increases.In addition,a significant time difference occur between the charging load during holidays and on working days.Moreover,as the penetration rate of EVs increases,the overall charging load continues to increase.The significant impact of the large-scale integration of EVs on the power grid was verified.[Conclusions]The proposed method can fully consider the interaction of the road network,EV,and user charging behavior and accurately predict the spatial-temporal distribution characteristics of EV charging loads.关键词
电动汽车/充电负荷/负荷预测/时空分布/实时荷电状态(SOC)Key words
electric vehicles/charging load/load prediction/spatial-temporal distribution/real-time state of charge(SOC)分类
信息技术与安全科学引用本文复制引用
张琳娟,李文峰,许长清,郭建宇,张夏韦,袁嘉,王要强..计及充电排队与实时SOC的电动汽车充电负荷时空分布预测[J].电力建设,2025,46(8):54-66,13.基金项目
国网河南省电力公司科技项目(5217L024000U) This work is supported by the Science and Technology Project of State Grid Henan Electric Power Company(No.5217L024000U). （5217L024000U）
