Spatial-Temporal Distribution Prediction of Electric Vehicle Charging Load Based on Dynamic Travel Simulation

ZHANG Linjuan; LI Wenfeng; XU Changqing; GUO Jianyu; ZHANG Xiawei; YUAN Jia; WANG Yaoqiang

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Electric Power Construction ›› 2025

Spatial-Temporal Distribution Prediction of Electric Vehicle Charging Load Based on Dynamic Travel Simulation

ZHANG Linjuan¹, LI Wenfeng¹, XU Changqing¹, GUO Jianyu¹, ZHANG Xiawei^2,3, YUAN Jia^3,4, WANG Yaoqiang^3,4

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Abstract

[Objective] Aiming at the uncertainty of electric vehicle users' travel mode and charging demand, a spatial-temporal distribution prediction method of electric vehicle charging load based on charging queue and real-time SOC is proposed. [Methods] The influence of traffic conditions and ambient temperature on EV energy consumption and charging behavior is analyzed, and the road traffic network model and comprehensive energy consumption model 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 the spatial-temporal distribution prediction model of EV charging load is built taking into account the charging queue time and real-time SOC. Finally, 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 surpassing 30 minutes induce partial user migration to off-peak periods, resulting in peak load reduction and off-peak load elevation compared to queuing-free models. Compared with the model without considering the charging queue, the peak load decreases and the off-peak load increases. In addition, there is a significant time difference between the charging load during holidays and that on working days. Moreover, as the penetration rate of electric vehicles increases, the overall charging load keeps growing. The significant impact of the large-scale integration of electric vehicles on the power grid has been verified. [Conclusions] The results show that the proposed method can fully consider the interaction of road network, EV and user charging behavior, and accurately predict the spatial-temporal distribution characteristics of EV charging load.

Key words

electric vehicles / charging load / load prediction / spatial-temporal distribution / travel chain

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ZHANG Linjuan, LI Wenfeng, XU Changqing, GUO Jianyu, ZHANG Xiawei, YUAN Jia, WANG Yaoqiang. Spatial-Temporal Distribution Prediction of Electric Vehicle Charging Load Based on Dynamic Travel Simulation[J]. Electric Power Construction. 2025

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