Nash Q-learning based equilibrium transfer for integrated energy management game with We-Energy

doi:10.1016/j.neucom.2019.01.109

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 复杂系统智能机理与平行控制团队

	Nash Q-learning based equilibrium transfer for integrated energy management game with We-Energy
	Yang, Lingxiao 1; Sun, Qiuye 1; Ma, Dazhong 1; Wei, Qinglai2
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2020-07-05
卷号	396 页码:216-223
通讯作者	Yang, Lingxiao(ylxiao66@163.com)
摘要	This paper proposes an innovative energy interacting unit ("We-Energy") with the characteristic of full duplex trading mode. In order to manage all the We-Energies in an optimal way, a new integrated energy management framework based on a noncooperative game is performed so as to allocate the energy demands of each WE such that the benefit of each WE can be maximized. To overcome the impact of the randomness and inaccurate information of renewable energy sources, Nash Q-learning algorithm is applied for computation of game equilibrium under the unknown environment. The novelty of the proposed algorithms is related to the incorporation of the continuous action space into the discrete adaptive action set and combined the equilibrium transfer to improve the efficiency of the algorithm. Simulation studies of modified IMS confirm that it has a better performance with the desired equilibrium strategy and convergence speed. (C) 2019 Elsevier B.V. All rights reserved.
关键词	Nash Q-learning Integrated energy management game Interconnected multicarrier systems Equilibrium transfer We-Energy
DOI	10.1016/j.neucom.2019.01.109
关键词[WOS]	REINFORCEMENT ; MARKET
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61573094] ; National Natural Science Foundation of China[61433004] ; Fundamental Research Funds for the Central Universities[N170405002]
项目资助者	National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000536806600001
出版者	ELSEVIER
引用统计	被引频次：22[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/39561
专题	多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队
通讯作者	Yang, Lingxiao
作者单位	1.Northeastern Univ, Sch Informat Sci & Engn, Shenyang, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Yang, Lingxiao,Sun, Qiuye,Ma, Dazhong,et al. Nash Q-learning based equilibrium transfer for integrated energy management game with We-Energy[J]. NEUROCOMPUTING,2020,396:216-223.
APA	Yang, Lingxiao,Sun, Qiuye,Ma, Dazhong,&Wei, Qinglai.(2020).Nash Q-learning based equilibrium transfer for integrated energy management game with We-Energy.NEUROCOMPUTING,396,216-223.
MLA	Yang, Lingxiao,et al."Nash Q-learning based equilibrium transfer for integrated energy management game with We-Energy".NEUROCOMPUTING 396(2020):216-223.