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Spatial-Temporal Distribution Prediction of Electric Vehicle Charging Load Considering Charging Behavior and Real-Time SOC
ZHANG Linjuan, LI Wenfeng, XU Changqing, GUO Jianyu, ZHANG Xiawei, YUAN Jia, WANG Yaoqiang
Electric Power Construction ›› 2025, Vol. 46 ›› Issue (8) : 54-66.
PDF(5011 KB)
PDF(5011 KB)
Spatial-Temporal Distribution Prediction of Electric Vehicle Charging Load Considering Charging Behavior and Real-Time SOC
[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.
electric vehicles / charging load / load prediction / spatial-temporal distribution / real-time state of charge（SOC）
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