Reinforcement Learning-based Placement of Charging Stations in Urban Road Networks

• URL: http://arxiv.org/abs/2206.06011v1
• Date: Mon, 13 Jun 2022 10:03:32 GMT
• Title: Reinforcement Learning-based Placement of Charging Stations in Urban Road Networks
• Authors: Leonie von Wahl (1), Nicolas Tempelmeier (1), Ashutosh Sao (2) and Elena Demidova (3) ((1) Volkswagen Group, (2) L3S Research Center, University of Hannover, (3) Data Science & Intelligent Systems Group (DSIS), University of Bonn)
• Abstract summary: We design a novel Deep Reinforcement Learning approach to solve the charging station placement problem (PCRL) Compared to the existing infrastructure, we can reduce the waiting time by up to 97% and increase the benefit up to 497%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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