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

张琳娟; 李文峰; 许长清; 郭建宇; 张夏韦; 袁嘉; 王要强

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电力建设 ›› 2025

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

张琳娟¹, 李文峰¹, 许长清¹, 郭建宇¹, 张夏韦^2,3, 袁嘉^3,4, 王要强^3,4

作者信息 +

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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文章历史 +

摘要

【目的】针对电动汽车用户出行方式和充电需求的不确定性问题,提出一种计及充电排队与实时荷电状态（state of charge,SOC）的电动汽车充电负荷时空分布预测方法。【方法】首先,分析交通路况和环境温度对电动汽车能耗及充电行为的影响,建立道路交通路网模型和综合能耗模型;其次,基于用户出行链分析用户出行特征,采用最短时间法规划行驶路径,计及充电排队时间和实时SOC构建电动汽车充电负荷时空分布预测模型;最后,采用蒙特卡洛方法以实际路网结构及IEEE 33节点配电系统为例进行验证。【结果】结果表明,高峰时段充电排队时长超过30 min,促使部分用户转向低峰时段充电,与未考虑充电排队模型相比,高峰负荷减少,低峰负荷增加。此外,节假日充电负荷与工作日充电负荷存在明显时间差异,且随着电动汽车渗透率的升高,总体充电负荷不断增大。验证了电动汽车规模化接入对电网的显著影响。【结论】所提出的方法能够充分考虑路网、电动汽车及用户充电行为的相互影响,准确预测出电动汽车充电负荷时空分布特性。

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.

导出引用

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

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

中图分类号： TM73

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