EV charging bidding by multi-DQN reinforcement learning in electricity auction market

doi:10.1016/j.neucom.2019.08.106

CORC > 兰州理工大学 > 兰州理工大学

	EV charging bidding by multi-DQN reinforcement learning in electricity auction market
	Zhang, Yang 2; Zhang, Zhengfeng 3; Yang, Qingyu 2,4; An, Dou 2,4; Li, Donghe 2; Li, Ce 1
刊名	Neurocomputing
	2020-07-15
卷号	397 页码:404-414
关键词	Charging (batteries) Commerce Economic and social effects Auction mechanisms Bidding strategy Charging station Economic benefits Electric Vehicles (EVs) Electricity auction market Optimal bidding strategy Value evaluations
ISSN号	09252312
DOI	10.1016/j.neucom.2019.08.106
英文摘要	In this paper, we address the issue of optimal bidding strategy selection for Electric Vehicles (EVs) charging in an auction market. The problem of EV charging has attracted growing attention as EVs become more and more popular. We consider the scenario that EV owners submit their bids for charging to the charging station, and then charging station determines the winning EVs who are admitted to charge and the payments based on an online continuous progressive second price (OCPSP) auction mechanism. In light of this, how to formulate optimal bidding strategy and maximize the economic benefits is crucial for EV owners. To this end, we propose a Multi-Deep-Q-Network (Multi-DQN) reinforcement learning bidding strategy, in which, a value evaluation network and a target network are proposed for each agent to learn the optimal bidding strategy. The extensive experimental results show that our bidding strategy can achieve better economic benefits and help EV owners spend less time on charging compared to the Q-learning based approach and the random approach. © 2020 Elsevier B.V.
WOS研究方向	Computer Science
语种	英语
出版者	Elsevier B.V., Netherlands
WOS记录号	WOS:000535918100003
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/151052]
专题	兰州理工大学
作者单位	1.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China 2.School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an; 710049, China; 3.Shaanxi Shangluo Power Plant Co., LTD., Shangluo; 726000, China; 4.SKLMSE lab, MOE Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an; 710049, China;
推荐引用方式 GB/T 7714	Zhang, Yang,Zhang, Zhengfeng,Yang, Qingyu,et al. EV charging bidding by multi-DQN reinforcement learning in electricity auction market[J]. Neurocomputing,2020,397:404-414.
APA	Zhang, Yang,Zhang, Zhengfeng,Yang, Qingyu,An, Dou,Li, Donghe,&Li, Ce.(2020).EV charging bidding by multi-DQN reinforcement learning in electricity auction market.Neurocomputing,397,404-414.
MLA	Zhang, Yang,et al."EV charging bidding by multi-DQN reinforcement learning in electricity auction market".Neurocomputing 397(2020):404-414.