Layout Planning Method of Emergency Charging Facilities for Electric Vehicles

GUO Dong; LI Jiaojiao; ZHENG Chunyan; MA Pengcheng; ZHANG Tongqing; LI Chaochao

doi:10.7500/AEPS20221216001

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Layout Planning Method of Emergency Charging Facilities for Electric Vehicles

Basic Research | 更新时间：2025-06-16

- Layout Planning Method of Emergency Charging Facilities for Electric Vehicles
- Automation of Electric Power Systems Vol. 47, Issue 16, Pages: 66-75(2023)
- 作者机构：
  
  1.山东理工大学交通与车辆工程学院，山东省淄博市 255000
  2.山东理工大学管理学院，山东省淄博市 255000
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(51905320);Shandong Provincial Natural Science Foundation of China(ZR2021MG012)
- DOI：10.7500/AEPS20221216001
  CLC：
- Received：16 December 2022，
  
  Revised：2023-04-26，
  
  Published Online：17 July 2023，
  
  Published：25 August 2023
- 稿件说明：
移动端阅览
郭栋,李娇娇,郑春燕等.电动汽车应急充电设施布局规划方法[J].电力系统自动化,2023,47(16):66-75.

GUO Dong,LI Jiaojiao,ZHENG Chunyan,et al.Layout Planning Method of Emergency Charging Facilities for Electric Vehicles[J].Automation of Electric Power Systems,2023,47(16):66-75.
郭栋,李娇娇,郑春燕等.电动汽车应急充电设施布局规划方法[J].电力系统自动化,2023,47(16):66-75. DOI： 10.7500/AEPS20221216001.

GUO Dong,LI Jiaojiao,ZHENG Chunyan,et al.Layout Planning Method of Emergency Charging Facilities for Electric Vehicles[J].Automation of Electric Power Systems,2023,47(16):66-75. DOI： 10.7500/AEPS20221216001.

摘要

当前电动汽车充电设施发展不完善，无法针对性满足用户紧急、短时、便捷等特殊充电需求与多样化的充电场景。为此，提出一种精准配送与设施布局优化结合的应急充电方案，以满足电动汽车应急充电需求、偏远地区零散充电需求以及短时便捷补电需求，与充电站相协调形成优化的电动汽车充电网络。首先，考虑电动汽车出行规律、行驶规律和驾驶员充电决策，预测应急充电需求时空分布；然后，采用配送服务，考虑现有充电站分布，建立以用户等待成本和建设运行成本最低的应急充电设施布局模型；最后，算例分析表明，布局规划后所加入的充电设施与原有充电设施形成新的充电网络并重新模拟电动汽车出行过程后，应急充电需求满足率达到98.15%。

Abstract

The electric vehicle (EV) charging facilities are not well developed currently and cannot meet the special charging demands of users such as emergency

short-time

and convenient charging and diversified charging scenarios. This paper proposes an emergency charging scheme combining precise distribution and facility layout optimization to meet the EV demands of emergency charging

scattered charging in remote areas

and the short-term convenient power supply. This scheme coordinates with charging stations to form an optimized EV charging network. Considering the travel rules

driving rules

and driver charging decisions of EVs

the spatial and temporal distribution of emergency charging demand is predicted. By using the distribution services

considering the distribution of existing charging stations

an emergency charging facility layout model with the lowest user waiting cost and construction and operation cost is established. Finally

the case analysis shows that

after the layout planning

the added charging facilities form a new charging network with the original charging facilities and re-simulate the electric vehicle travel process

and the emergency charging demand satisfaction rate reaches 98.15%.

关键词

Keywords

references

国务院办公厅 . 国务院办公厅关于印发新能源汽车产业发展规划（ 2021 — 2035 年）的通知［EB/OL］.（ 2020-11-02 ）［ 2022-12-15 ］. https：//www.gov.cn/zhengce/content/2020-11/02/content_5556716.htm https://www.gov.cn/zhengce/content/2020-11/02/content_5556716.htm .

General Office of the State Council of the People’s Republic of China . Notice of General Office of the State Council on development plan of new energy automobile industry （ 2021-2035 ）. ［EB/OL］. （ 2020-11-02 ）［ 2022-12-15 ］. https：//www.gov.cn/zhengce/content/2020-11/02/content_5556716.htm https://www.gov.cn/zhengce/content/2020-11/02/content_5556716.htm .

艾瑞咨询 . 中国公共充电桩行业研究报告（ 2020 年）［R/OL］.（ 2020-06-05 ）［ 2022-12-15 ］ https：//report.iresearch.cn/report/202006/3583.shtml https://report.iresearch.cn/report/202006/3583.shtml .

IResearch . Research report on China’s public charging pile industry （ 2020 ）［R/OL］. （ 2020-06-05 ）［ 2022-12-15 ］. https：//report.iresearch.cn/report/202006/3583.shtml https://report.iresearch.cn/report/202006/3583.shtml .

AFSHAR M ， MOHAMMADI M R ， ABEDINI M . A novel spatial-temporal model for charging plug hybrid electrical vehicles based on traffic-flow analysis and Monte Carlo method ［J］. ISA Transactions ， 2021 ， 114 ： 263 - 276 .

LIN H Y ， FU K ， WANG Y ， et al . Characteristics of electric vehicle charging demand at multiple types of location—application of an agent-based trip chain model ［J］. Energy ， 2019 ， 188 ： 116122 .

LI Y J ， SU S ， LIU B ， et al . Trajectory-driven planning of electric taxi charging stations based on cumulative prospect theory ［J］. Sustainable Cities and Society ， 2022 ， 86 ： 104125 .

GAO K ， SUN L J ， YANG Y ， et al . Cumulative prospect theory coupled with multi-attribute decision making for modeling travel behavior ［J］. Transportation Research Part A： Policy and Practice ， 2021 ， 148 ： 1 - 21 .

YI Z Y ， LIU X C ， WEI R ， et al . Electric vehicle charging demand forecasting using deep learning model ［J］. Journal of Intelligent Transportation Systems ， 2022 ， 26 （ 6 ）： 690 - 703 .

YAN Q Y ， LIN H Y ， LI J M ， et al . Many-objective charging optimization for electric vehicles considering demand response and multi-uncertainties based on Markov chain and information gap decision theory ［J］. Sustainable Cities and Society ， 2022 ， 78 ： 103652 .

盛裕杰，郭庆来，刘梦洁，等 . 多源数据融合的用户充电行为分析与充电设施规划实践［J］. 电力系统自动化， 2022 ， 46 （ 12 ）： 151 - 162 .

SHENG Yujie ， GUO Qinglai ， LIU Mengjie ， et al . User charging behavior analysis and charging facility planning practice based on multi-source data fusion ［J］. Automation of Electric Power Systems ， 2022 ， 46 （ 12 ）： 151 - 162 .

张美霞，徐立成，杨秀 . 基于城市网格属性划分的电动汽车充电需求预测［J/OL］. 电测与仪表： 1 - 12 ［ 2023-02-15 ］. http：//kns.cnki.net/kcms/detail/23.1202.TH.20220607.1822.015.html http://kns.cnki.net/kcms/detail/23.1202.TH.20220607.1822.015.html .

ZHANG Meixia ， XU Licheng ， YANG Xiu . EV charging demand prediction based on city grid attribute division ［J/OL］. Electrical Measurement & Instrumentation ： 1 - 12 ［ 2023-02-15 ］. http：//kns.cnki.net/kcms/detail/23.1202.TH.20220607.1822.015.html http://kns.cnki.net/kcms/detail/23.1202.TH.20220607.1822.015.html .

杨扬，张天雨，朱宇婷，等 . 考虑建设时序和动态需求的城际公路充电设施优化布局［J］. 清华大学学报（自然科学版）， 2022 ， 62 （ 7 ）： 1163 - 1177 .

YANG Yang ， ZHANG Tianyu ， ZHU Yuting ， et al . Optimizing the deployment of charging systems on an expressway network considering the construction time sequence and the dynamic charging demand ［J］. Journal of Tsinghua University （Science and Technology）， 2022 ， 62 （ 7 ）： 1163 - 1177 .

ZHAO S X ， WANG Y C ， JIANG Z Y ， et al . Research on emergency distribution optimization of mobile power for electric vehicle in photovoltaic-energy storage-charging supply chain under the energy blockchain ［J］. Energy Reports ， 2022 ， 8 ： 6815 - 6825 .

CUI S H ， YAO B Z ， CHEN G ， et al . The multi-mode mobile charging service based on electric vehicle spatiotemporal distribution ［J］. Energy ， 2020 ， 198 ： 117302 .

TANG P ， HE F ， LIN X ， et al . Online-to-offline mobile charging system for electric vehicles： strategic planning and online operation ［J］. Transportation Research Part D： Transport and Environment ， 2020 ， 87 ： 102522 .

程杉，尚冬冬，魏昭彬，等 . 基于纳什议价博弈的微电网一体化电站分布式协调优化［J］. 电机与控制学报， 2022 ， 26 （ 5 ）： 86 - 95 .

CHENG Shan ， SHANG Dongdong ， WEI Zhaobin ， et al . Hierarchical and distributed coordination and optimization of microgrid with CSSIS based on Nash bargaining game ［J］. Electric Machines and Control ， 2022 ， 26 （ 5 ）： 86 - 95 .

刘丽军，吴桐，陈贤达，等 . 基于时空特性以及需求响应的DG和EV充电站多目标优化配置［J］. 电力自动化设备， 2021 ， 41 （ 11 ）： 48 - 56 .

LIU Lijun ， WU Tong ， CHEN Xianda ， et al . Multi-objective optimal allocation of DG and EV charging station based on space-time characteristics and demand response ［J］. Electric Power Automation Equipment ， 2021 ， 41 （ 11 ）： 48 - 56 .

侯慧，王逸凡，黄亮，等 . 满足电动汽车快充需求的含储能复合型充电站充电优化策略［J］. 电力自动化设备， 2022 ， 42 （ 1 ）： 65 - 71 .

HOU Hui ， WANG Yifan ， HUANG Liang ， et al . Charging optimization strategy of composite charging station with energy storage to meet fast charging demand of electric vehicles ［J］. Electric Power Automation Equipment ， 2022 ， 42 （ 1 ）： 65 - 71 .

HE Y ， GUO S ， ZHOU J X ， et al . The quantitative techno-economic comparisons and multi-objective capacity optimization of wind-photovoltaic hybrid power system considering different energy storage technologies ［J］. Energy Conversion and Management ， 2021 ， 229 ： 113779 .

WANG J S ， XUE K ， GUO Y J ， et al . Multi-objective capacity programming and operation optimization of an integrated energy system considering hydrogen energy storage for collective energy communities ［J］. Energy Conversion and Management ， 2022 ， 268 ： 116057 .

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 .

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 .

李映炼 . 基于电动汽车出行链的城市充电站布局研究［D］. 北京：北京交通大学， 2018 .

LI Yinglian . Study on the layout of urban charging stations based on the travel chain of electric vehicles ［D］. Beijing ： Beijing Jiaotong University ， 2018 .

MU Y F ， WU J Z ， JENKINS N ， et al . A spatial-temporal model for grid impact analysis of plug-in electric vehicles ［J］. Applied Energy ， 2014 ， 114 ： 456 - 465 .

温剑锋，陶顺，肖湘宁，等 . 基于出行链随机模拟的电动汽车充电需求分析［J］. 电网技术， 2015 ， 39 （ 6 ）： 1477 - 1484 .

WEN Jianfeng ， TAO Shun ， XIAO Xiangning ， et al . Analysis on charging demand of EV based on stochastic simulation of trip chain ［J］. Power System Technology ， 2015 ， 39 （ 6 ）： 1477 - 1484 .

宋媛媛 . 基于行驶工况的纯电动汽车能耗建模及续驶里程估算研究［D］. 北京：北京交通大学， 2014 .

SONG Yuanyuan . Research on energy consumption modeling and driving range estimation of pure electric vehicles based on driving conditions ［D］. Beijing ： Beijing Jiaotong University ， 2014 .

ZHANG Y Y ， LUO X ， QIU Y S ， et al . Understanding the generation mechanism of BEV drivers' charging demand： an exploration of the relationship between charging choice and complexity of trip chaining patterns ［J］. Transportation Research Part A： Policy and Practice ， 2022 ， 158 ： 110 - 126 .

麻秀范，李颖，王皓，等 . 基于电动汽车出行随机模拟的充电桩需求研究［J］. 电工技术学报， 2017 ， 32 （增刊2 ）： 190 - 202 .

MA Fanxiu ， LI Ying ， WANG Hao ， et al . Research on demand of charging piles based on stochastic simulation of EV trip chain ［J］. Transactions of China Electrotechnical Society ， 2017 ， 32 （ Supplement 2 ）： 190 - 202 .

王元，刘华，李航 . 基于有限元网格划分的城市道路网建模［J］. 图学学报， 2016 ， 37 （ 3 ）： 377 - 385 .

WANG Yuan ， LIU Hua ， LI Hang . Urban Road network modeling based on finite element mesh generation ［J］. Journal of Graphics ， 2016 ， 37 （ 3 ）： 377 - 385 .

BAO H B ， GUO X X ， LIANG J J ， et al . Relevance vector machine with optimal hybrid kernel function for electric vehicles ownership forecasting： the case of China ［J］. Energy Reports ， 2022 ， 8 ： 988 - 997 .

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