Too much or not enough? Planning electric vehicle charging infrastructure: A review of modeling options

软件部署 电动汽车 温室气体 服务(商务) 订单(交换) 运输工程 风险分析(工程) 关系(数据库) 计算机科学 环境经济学 运筹学 业务 工程类 营销 经济 操作系统 功率(物理) 物理 财务 生物 数据库 量子力学 生态学
作者
Marc Olivier Metais,Oualid Jouini,Yannick Pérez,Jaâfar Berrada,Emilia Suomalainen
出处
期刊:Renewable & Sustainable Energy Reviews [Elsevier BV]
卷期号:153: 111719-111719 被引量:315
标识
DOI:10.1016/j.rser.2021.111719
摘要

While the transportation sector is responsible for a growing share of greenhouse gas emissions, electric vehicles (EVs) offer solutions for greener mobility. The proportion of electric vehicles in transportation fleets is increasing, but wider adoption will not be possible without an appropriate charging infrastructure. The deployment of such infrastructure should follow a strategy that considers both the environment in which it is deployed and the behavior patterns of electric vehicle users. If these aspects are not taken into consideration, there is a risk of failing to meet users’ needs and generating additional costs. Here we review the literature on location problems for electric vehicle charging stations. We aim to draw up a comparative overview of approaches that have been used up to 2020 for optimizing the locations of charging infrastructure. We first briefly review the issues raised by the deployment of charging infrastructure, namely technical, economic and user acceptance concerns. We then look at the goals of the infrastructure location models in the literature. Schematically, those goals fall into two categories: minimizing the cost of charging infrastructure for a given level of service, or maximizing the service provided for a given cost. Finally, we focus on the approaches used to achieve these goals. Three categories of approaches are identified: node, path, and tour- or activity-based approaches. We then discuss these approaches in relation to technical, economic and user acceptance factors in order to provide a comprehensive analysis for stakeholders involved in EV charging infrastructure planning. Directions are given for future research to develop models that better reflect the real-world picture.
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