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%.

本文使用深度强化学习方法针对城市区域优化充电站的安置位置与数量，以达到最大化电动车充电设施供应，最小化等待、行程和充电时间，并考虑在家中充电，通过实例实验证明本文方法可以增加电动汽车充电计划的效益，减少等待时间，提高收益，相较于现有基础设施，等待时间可以降低到 97％，收益可以增加达到 497％。