The transition from conventional mobility to electromobility largely depends on charging infrastructure availability and optimal placement.This paper examines the optimal placement of charging stations in urban areas. We maximise the charging infrastructure supply over the area and minimise waiting, travel, and charging times while setting budget constraints. Moreover, we include the possibility of charging vehicles at home to obtain a more refined estimation of the actual charging demand throughout the urban area. We formulate the Placement of Charging Stations problem as a non-linear integer optimisation problem that seeks the optimal positions for charging stations and the optimal number of charging piles of different charging types. We design a novel Deep Reinforcement Learning approach to solve the charging station placement problem (PCRL). Extensive experiments on real-world datasets show how the PCRL reduces the waiting and travel time while increasing the benefit of the charging plan compared to five baselines. Compared to the existing infrastructure, we can reduce the waiting time by up to 97% and increase the benefit up to 497%.

翻译：从常规机动性向电动性过渡主要取决于对基础设施的收费和最佳配置。本文件审视了城市地区充电站的最佳位置。我们尽量扩大对该地区基础设施的收费,并在设定预算限制时尽量减少等候、旅行和收费时间。此外,我们包括了在家中充电车辆的可能性,以便更精确地估计整个城市地区的实际充电需求。我们把充电站的定位问题描述为一个非线性整型优化化问题,寻求充电站的最佳位置和不同收费种类的充电堆的最佳数量。我们设计了新的深加学习方法来解决充电站的配置问题。我们设计了一个新的深加学习方法(PCRL ) 。 现实世界数据集的广泛实验显示,PCRL如何减少等候和差旅时间,同时将充电计划的效益提高到5个基线。与现有基础设施相比,我们可以将等待时间减少97%,将效益提高到497%。