Prediction of Electric Vehicle Charging Load Considering Interaction Between Road Network and Power Grid and User’s Psychology

Xuemei LONG; Jun YANG; Fuzhang WU; Xiangpeng ZHAN; Yangjia LIN; Jian XU

doi:10.7500/AEPS20191011008

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Prediction of Electric Vehicle Charging Load Considering Interaction Between Road Network and Power Grid and User’s Psychology

Basic Research | 更新时间：2025-06-16

- Prediction of Electric Vehicle Charging Load Considering Interaction Between Road Network and Power Grid and User’s Psychology
- Automation of Electric Power Systems Vol. 44, Issue 14, Pages: 86-93(2020)
- 作者机构：
  
  武汉大学电气与自动化学院，湖北省武汉市 430072
- 作者简介：
- 基金信息：
  
  National Key R&D Program of China;Projects of Humanities and Social Sciences of the Ministry of Education;National Natural Science Foundation of China
- DOI：10.7500/AEPS20191011008
  CLC：
- Received：11 October 2019，
  
  Revised：2020-03-24，
  
  Published Online：13 May 2020，
  
  Published：25 July 2020
- 稿件说明：
移动端阅览
龙雪梅,杨军,吴赋章等.考虑路网-电网交互和用户心理的电动汽车充电负荷预测[J].电力系统自动化,2020,44(14):86-93.

LONG Xuemei,YANG Jun,WU Fuzhang,et al.Prediction of Electric Vehicle Charging Load Considering Interaction Between Road Network and Power Grid and User’s Psychology[J].Automation of Electric Power Systems,2020,44(14):86-93.
龙雪梅,杨军,吴赋章等.考虑路网-电网交互和用户心理的电动汽车充电负荷预测[J].电力系统自动化,2020,44(14):86-93. DOI： 10.7500/AEPS20191011008.

LONG Xuemei,YANG Jun,WU Fuzhang,et al.Prediction of Electric Vehicle Charging Load Considering Interaction Between Road Network and Power Grid and User’s Psychology[J].Automation of Electric Power Systems,2020,44(14):86-93. DOI： 10.7500/AEPS20191011008.

摘要

文中提出了一种考虑路网-电网信息交互和用户心理的电动汽车充电负荷预测框架。首先，利用出行链和原点-终点(OD)矩阵得到电动汽车出行目的地；然后，考虑行驶、排队时间和充电电价，提出基于后悔理论的充电站选择模型；接着，基于跟驰模型对车辆在路网中的行驶过程进行微观交通分析，建立基于电价驱动的路网-电网交互式负荷预测框架；最后，采用蒙特卡洛方法模拟电动汽车的出行和充电情况，以预测电动汽车充电负荷时空分布。通过在中国北京市三环路网和相应电网上的仿真，验证了所提电动汽车充电负荷预测框架的有效性。仿真结果也表明路网和电网通过电价相互作用，使得电动私家车和出租车的充电负荷在时间与空间上分布差异明显。

Abstract

A prediction framework of electric vehicle (EV) charging load is proposed

which considers user’s psychology and information interaction between road network and power grid. Firstly

the destination of EV is obtained through trip chain and origin-destination (OD) matrix. Secondly

considering driving time

queuing time and charging price

a model of choosing charging station based on regret theory is proposed. Thirdly

based on the following model

the microscopic traffic analysis on vehicle driving process in the road network is carried out

and the framework of charging load prediction considering the interaction between road network and power grid driven by charging price is established. Finally

Monte Carlo method is used to simulate the travelling and charging situations of EVs

so as to predict the time-space distribution of EV charging load. Through the simulation on the Third Ring Road Network in Beijing

China and the corresponding power grid

the effectiveness of the proposed EV charging load prediction framework is verified. The simulation results also show that the interaction between road network and power grid through charging price make the charging load distribution of electric private cars and taxis significantly different in time and space.

关键词

Keywords

references

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