Despite the environmental and economic benefits of Electric Vehicles (EVs), distribution network operators will need to understand the location where the charging infrastructure will be placed to ensure EV users’ needs are met. In this sense, this work proposes a methodology to define the optimal location of EV semi-fast charging stations (CS) at a neighborhood level, through a multi-objective approach. It applies a hierarchical clustering method to define CS service zones, considering both technical and mobility aspects. Besides, it considers uncertainties related to the EV load profile to determine the CS capacity, based on the user's charging behavior. A Pareto Frontier method is deployed to support the decision-making process on CS optimal location, considering utility and EV users’ preferences. The results indicate that the best CS locations for mid-term EV penetration can also fit into long-term planning, with higher EV charging demand. Thus, these locations would be good candidates for the power utility to make initial investments, regarding both planning horizons. A real distribution system case is used to demonstrate the applicability of the results.

尽管电动汽车 (EV) 具有环境和经济效益，但配电网络运营商仍需要了解充电基础设施的放置位置，以确保满足 EV 用户的需求。从这个意义上说，这项工作提出了一种方法，通过多目标方法在社区级别定义电动汽车半快速充电站 (CS) 的最佳位置。它应用分层聚类方法来定义 CS 服务区，同时考虑技术和移动性方面。此外，它会根据用户的充电行为，考虑与 EV 负载曲线相关的不确定性来确定 CS 容量。考虑效用和电动汽车用户的偏好，部署了帕累托前沿方法来支持 CS 最佳位置的决策过程。结果表明，电动汽车中期渗透的最佳 CS 位置也适合长期规划，具有更高的电动汽车充电需求。因此，这些位置将是电力公司进行初始投资的良好候选者，无论是在两个规划范围内。一个真实的配电系统案例被用来证明结果的适用性。