考虑路网-电网交互和用户心理的电动汽车充电负荷预测

龙雪梅; 杨军; 吴赋章; 詹祥澎; 林洋佳; 徐箭

doi:10.7500/AEPS20191011008

您当前的位置：

首页 >

文章列表页 >

考虑路网-电网交互和用户心理的电动汽车充电负荷预测

学术研究 | 更新时间：2025-06-16

- 考虑路网-电网交互和用户心理的电动汽车充电负荷预测
- Prediction of Electric Vehicle Charging Load Considering Interaction Between Road Network and Power Grid and User’s Psychology
- 电力系统自动化-方正平台 2020年44卷第14期页码：86-93
- 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：
  
  龙雪梅(1996—)，女，硕士研究生，主要研究方向：电动汽车与交通路网。E-mail：2014302540112@whu.edu.cn
  杨军(1977—)，男，通信作者，博士，教授，博士生导师，主要研究方向：电动汽车与电网互动、电网安全运行等。E-mail：JYang@whu.edu.cn
  吴赋章(1992—)，男，博士研究生，主要研究方向：新能源接入、电动汽车运行调度。E-mail：wufuzhangwfz@163.com
- 基金信息：
  
  国家重点研发计划资助项目(2017YFB0902900);教育部人文社会科学研究规划项目(17YJCZH212);国家自然科学基金资助项目(51977154)
- DOI：10.7500/AEPS20191011008
  中图分类号：
- 收稿日期：2019-10-11，
  
  修回日期：2020-03-24，
  
  网络出版日期：2020-05-13，
  
  纸质出版日期：2020-07-25
- 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

陈丽丹 , 张尧 , Antonio FIGUEIREDO . 电动汽车充放电负荷预测研究综述 [J]. 电力系统自动化 , 2019 , 43 ( 10 ): 177 - 197 .

CHEN Lidan , ZHANG Yao , FIGUEIREDO A . Overview of charging and discharging load forcasting for electric vehicles [J]. Automation of Electric Power Systems , 2019 , 43 ( 10 ): 177 - 197 .

赵书强 , 周靖仁 , 李志伟 , 等 . 基于出行链理论的电动汽车充电需求分析方法 [J]. 电力自动化设备 , 2017 , 37 ( 8 ): 105 - 112 .

ZHAO Shuqiang , ZHOU Jingren , LI Zhiwei , et al . EV charging demand analysis based on trip chain theory [J]. Electric Power Automation Equipment , 2017 , 37 ( 8 ): 105 - 112 .

陈丽丹 , 聂涌泉 , 钟庆 . 基于出行链的电动汽车充电负荷预测模型 [J]. 电工技术学报 , 2015 , 30 ( 4 ): 216 - 225 .

CHEN Lidan , NIE Yongquan , ZHONG Qing . A model for electric vehicle charging load forecasting based on trip chains [J]. Transactions of China Electrotechnical Society , 2015 , 30 ( 4 ): 216 - 225 .

TANG D , WANG P . Probabilistic modeling of nodal charging demand based on spatial-temporal dynamics of moving electric vehicles [J]. IEEE Transactions on Smart Grid , 2015 , 7 ( 2 ): 627 - 636 .

张谦 , 王众 , 谭维玉 , 等 . 基于MDP随机路径模拟的电动汽车充电负荷时空分布预测 [J]. 电力系统自动化 , 2018 , 42 ( 20 ): 65 - 72 .

ZHANG Qian , WANG Zhong , TAN Weiyu , et al . Spatial-temporal distribution prediction of charging load for electric vehicle based on MDP random path simulation [J]. Automation of Electric Power Systems , 2018 , 42 ( 20 ): 65 - 72 .

ASHTARI A , BIBEAU E , SHAHIDINEJAD S , et al . PEV charging profile prediction and analysis based on vehicle usage data [J]. IEEE Transactions on Smart Grid , 2012 , 3 ( 1 ): 341 - 350 .

LI M , LENZEN M , KECH F , et al . GIS-based probabilistic modeling of BEV charging load for Australia [J]. IEEE Transactions on Smart Grid , 2019 , 10 ( 4 ): 3525 - 3534 .

王浩林 , 张勇军 , 毛海鹏 . 基于时刻充电概率的电动汽车充电负荷预测方法 [J]. 电力自动化设备 , 2019 , 39 ( 3 ): 213 - 219 .

WANG Haolin , ZHANG Yongjun , MAO Haipeng . Charging load forecasting method based on instantaneous charging probability for electric vehicles [J]. Electric Power Automation Equipment , 2019 , 39 ( 3 ): 213 - 219 .

BAE S , KWASINSKI A . Spatial and temporal model of electric vehicle charging demand [J]. IEEE Transactions on Smart Grid , 2012 , 3 ( 1 ): 394 - 403 .

李含玉 , 杜兆斌 , 陈丽丹 , 等 . 基于出行模拟的电动汽车充电负荷预测模型及V2G评估 [J]. 电力系统自动化 , 2019 , 43 ( 21 ): 88 - 102 .

LI Hanyu , DU Zhaobin , CHEN Lidan , et al . Trip simulation based charging load forecasting model and vehicle-to-grid evaluation of electric vehicles [J]. Automation of Electric Power Systems , 2019 , 43 ( 21 ): 88 - 102 .

YI Z , BAUER P H . Spatiotemporal energy demand models for electric vehicles [J]. IEEE Transactions on Vehicular Technology , 2016 , 65 ( 3 ): 1030 - 1042 .

陈丽丹 , 张尧 , Antonio FIGUEIREDO . 融合多源信息的电动汽车充电负荷预测及其对配电网的影响 [J]. 电力自动化设备 , 2018 , 38 ( 12 ): 1 - 10 .

CHEN Lidan , ZHANG Yao , FIGUEIREDO A . Charging load forecasting of electric vehicles based on multi-source information fusion and its influence on distribution network [J]. Electric Power Automation Equipment , 2018 , 38 ( 12 ): 1 - 10 .

吴赋章 , 杨军 , 林洋佳 , 等 . 考虑用户有限理性的电动汽车时空行为特性 [J]. 电工技术学报 , 2020 , 35 ( 7 ): 1563 - 1574 .

WU Fuzhang , YANG Jun , LIN Yangjia , et al . Research on spatiotemporal behavior of electric vehicles considering the users’ bounded rationality [J]. Transactions of China Electrotechnical Society , 2020 , 35 ( 7 ): 1563 - 1574 .

邵尹池 , 穆云飞 , 余晓丹 , 等 . “车-路-网”模式下电动汽车充电负荷时空预测及其对配电网潮流的影响 [J]. 中国电机工程学报 , 2017 , 37 ( 18 ): 5207 - 5219 .

SHAO Yinchi , MU Yunfei , YU Xiaodan , et al . A spatial-temporal charging load forecast and impact analysis method for distribution network using EVs-traffic-distribution model [J]. Proceedings of the CSEE , 2017 , 37 ( 18 ): 5207 - 5219 .

苏舒 , 林湘宁 , 张宏志 , 等 . 电动汽车充电需求时空分布动态演化模型 [J]. 中国电机工程学报 , 2017 , 37 ( 16 ): 4618 - 4629 .

SU Shu , LIN Xiangning , ZHANG Hongzhi , et al . Spatial and temporal distribution model of electric vehicle charging demand [J]. Proceedings of the CSEE , 2017 , 37 ( 16 ): 4618 - 4629 .

陈静鹏 , 艾芊 , 肖斐 . 基于用户出行需求的电动汽车充电站规划 [J]. 电力自动化设备 , 2016 , 36 ( 6 ): 34 - 39 .

CHEN Jingpeng , AI Qian , XIAO Fei . EV charging station planning based on travel demand [J]. Electric Power Automation Equipment , 2016 , 36 ( 6 ): 34 - 39 .

U . S . Department of Transportation . 2009 national household travel survey [EB/OL]. [ 2019-04-24 ]. http://nhts.ornl.gov/2009/pub/stt.pdf http://nhts.ornl.gov/2009/pub/stt.pdf ．

张晨彧 , 丁明 , 张晶晶 . 基于交通出行矩阵的私家车充电负荷时空分布预测 [J]. 电工技术学报 , 2017 ， 32 ( 1 ): 82 - 91 .

ZHANG Chenyu , DING Ming , ZHANG Jingjing . A temporal and spatial distribution forecasting of private car charging load based on origin-destination matrix [J]. Transactions of China Electrotechnical Society , 2017 , 32 ( 1 ): 82 - 91 .

李梦 , 黄海军 . 基于后悔理论的出行路径选择行为研究 [J]. 管理科学学报 , 2017 , 20 ( 11 ): 1 - 9 .

LI Meng , HUANG Haijun . Modeling route choice behavior based on regret theory [J]. Journal of Management Sciences in China , 2017 , 20 ( 11 ): 1 - 9 .

章恒全 , 涂俊玮 . 基于后悔理论的模糊多准则群决策方法 [J]. 统计与决策 , 2018 , 34 ( 7 ): 46 - 50 .

ZHANG Hengquan , TU Junwei . Fuzzy multi-criterion group decision making method based on regret theory [J]. Statistics & Decision , 2018 , 34 ( 7 ): 46 - 50 .

《中国公路学报》编辑部 . 中国交通工程学术研究综述·2016 [J]. 中国公路学报 , 2016 , 29 ( 6 ): 1 - 161 .

Editorial Department of China Journal of Highway and Transport . Review on China’s traffic engineering research progress: 2016 [J]. China Journal of Highway and Transport , 2016 , 29 ( 6 ): 1 - 161 .

SUMO( 2001 — 2009 ) SUMO web pages [EB/OL]. [ 2019-04-24 ]. http://sumo.sf.net http://sumo.sf.net .

KRAJZEWICZ D . Traffic simulation with SUMO—simulation of urban mobility [J]. Fundamentals of Traffic Simulation , 2010 , 145 : 269 - 293 .

ANDRÉS F A , ESPINOSA J E , ESPINOSA J . TraCI4Matlab: enabling the integration of the SUMO road traffic simulator and Matlab® through a software re-engineering process [M]. Berlin , Germany: Spring , 2015 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向电力-交通系统协同运行的建模、求解与应用评述

支持时变需求响应激励价格的电动汽车优化充电策略

电力-交通融合研究综述：模型、算法与关键问题