A DQN-based Two-Stage Scheduling Method for Real-Time Large-Scale EVs Charging Service

• Tianyang Li,
• Yingnan Han,
• Xiaolong Li,

Vol. 18, No. 3, March 31, 2024
10.3837/tiis.2024.03.002, Download Paper (Free):

• charging service
• Cyber-Physical System
• Deep Reinforcement Learning
• EV charging scheduling algorithm
• surge demands.

Abstract

With the rapid development of electric vehicles (EVs) industry, EV charging service becomes more and more important. Especially, in the case of suddenly drop of air temperature or open holidays that large-scale EVs seeking for charging devices (CDs) in a short time. In such scenario, inefficient EV charging scheduling algorithm might lead to a bad service quality, for example, long queueing times for EVs and unreasonable idling time for charging devices. To deal with this issue, this paper propose a Deep-Q-Network (DQN) based two-stage scheduling method for the large-scale EVs charging service. Fine-grained states with two delicate neural networks are proposed to optimize the sequencing of EVs and charging station (CS) arrangement. Two efficient algorithms are presented to obtain the optimal EVs charging scheduling scheme for large-scale EVs charging demand. Three case studies show the superiority of our proposal, in terms of a high service quality (minimized average queuing time of EVs and maximized charging performance at both EV and CS sides) and achieve greater scheduling efficiency. The code and data are available at THE CODE AND DATA.

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