• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.551-569
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• 2024

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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.8
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• pp.98-105
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• 2014

In this paper, we have analyzed the power consumption property of EVs(Electric Vehicles) chargers established in a public place, proposed reasonable demand factors by the number of established EV chargers and its control method in apartments. The optimization of power system and the suppression of the peak load can be controlled through the proposed demand factors and charging scheduling control algorithm. In this paper, electrical design and an case analysis were carried out on a sample apartment complex to prove the effectiveness of the power system. As a result, emergency power transformer capacity has been reduced by approximately 25%, and we have confirmed that the electric rates saving and the control of peak load value is possible.