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

TL;DR

This paper introduces a deep reinforcement learning method for optimally placing charging stations in urban areas, significantly reducing wait times and improving infrastructure benefits.

Contribution

It presents a novel Deep Reinforcement Learning approach for the complex non-linear placement problem, incorporating home charging options for better demand estimation.

Findings

Reduces waiting time by up to 97%

Increases planning benefit up to 497%

Outperforms five baseline methods

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%.