Identifying optimal locations for community electric vehicle charging

被引：49

作者：

Charly, Anna ^{[1
]}

Thomas, Nikita Jayan ^{[1
]}

Foley, Aoife ^{[2
,3
]}

Caulfield, Brian ^{[1
]}

机构：

[1] Univ Dublin, Trinity Coll Dublin, Ctr Transport Res, Dept Civil Struct & Environm Engn, Dublin, Ireland

[2] Univ Manchester, Sch Engn, Oxford Rd, Manchester M13 9PL, England

[3] Queens Univ Belfast, Sch Mech & Aerosp Engn, Belfast, North Ireland

来源：

SUSTAINABLE CITIES AND SOCIETY | 2023年 / 94卷

基金：

爱尔兰科学基金会;

关键词：

Electric vehicles; EV charging; Community charging; GIS-based analysis; QGIS; RANGE ANXIETY; INFRASTRUCTURE; STATIONS; FUTURE; IMPACT;

D O I：

10.1016/j.scs.2023.104573

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

This research seeks to identify suitable locations for deploying community Electric Vehicle charging points using a Geographic Information System (GIS)-based approach. The charging infrastructure is classified into shared-residential, en-route, and destination charging types, and each type's selection criteria are chosen according to the characteristics of targeted end-users. The investigation identified 770 ideal locations in Dublin that may be given priority for the initial installation of charging infrastructure. Further, 3080 suitable sites were identified for later implementation to satisfy the charging requirements forecasted by the Dublin Local Authority for 2030. The population served by the proposed residential charging points is determined while considering accessibility by five-minute walking or five-minute cycling. Results from the study can be helpful for practitioners while deploying charging stations in the region. The proposed methodology utilises an open-source GIS-supported approach that can be adapted to similar cities worldwide.

引用

页数：14

共 74 条

[51] Dynamic Response Characteristics of Fast Charging Station-EVs on Interaction of Multiple Vehicles [J].