Dynamic Energy Management of a Microgrid using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning

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	Dynamic Energy Management of a Microgrid using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning
	Zeng P(曾鹏)2; Li SH(李署辉)1; He, Haibo 3; Li H(李鹤鹏)2; Liu XY(刘晓源); Liu JG(刘金国); Li Z(李正); Chi HD(迟浩东)
刊名	IEEE Transactions on Smart Grid
	2018
关键词	microgrid dynamic energy management system approximate dynamic programming recurrent neural network deep learning
ISSN号	1949-3053
通讯作者	He, Haibo
产权排序	1
中文摘要	This paper focuses on economical operation of a microgrid (MG) in real-time. A novel dynamic energy management system (EMS) is developed to incorporate efficient management of energy storage system (ESS) into MG real-time dispatch while considering power flow constraints and uncertainties in load, renewable generation and real-time electricity price. The developed dynamic energy management mechanism does not require long-term forecast and optimization or distribution knowledge of the uncertainty, but can still optimize the long-term operational costs of MGs. First, the real-time scheduling problem is modeled as a finite-horizon Markov decision process (MDP) over a day. Then, approximate dynamic programming (ADP) and deep recurrent neural network (RNN) learning are employed to derive a near optimal real-time scheduling policy. Last, using real power grid data from California Independent System Operator (CAISO), a detailed simulation study is carried out to validate the effectiveness of the proposed method.
收录类别	EI
语种	英语
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/22402]
专题	沈阳自动化研究所_空间自动化技术研究室
作者单位	1.Department of Electrical Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487 USA 2.Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016 China 3.Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881 USA
推荐引用方式 GB/T 7714	Zeng P,Li SH,He, Haibo,et al. Dynamic Energy Management of a Microgrid using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning[J]. IEEE Transactions on Smart Grid,2018.
APA	Zeng P.,Li SH.,He, Haibo.,Li H.,刘晓源.,...&迟浩东.(2018).Dynamic Energy Management of a Microgrid using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning.IEEE Transactions on Smart Grid.
MLA	Zeng P,et al."Dynamic Energy Management of a Microgrid using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning".IEEE Transactions on Smart Grid (2018).