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Dynamic Energy Management of a Microgrid using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning
Zeng P(曾鹏)1; Li H(李鹤鹏)1; He, Haibo2; Li SH(李署辉)3
作者部门工业控制网络与系统研究室
关键词microgrid dynamic energy management system approximate dynamic programming recurrent neural network deep learning
发表期刊IEEE Transactions on Smart Grid
ISSN1949-3053
2018
收录类别EI
EI收录号20183105632226
产权排序1
资助机构Office of Naval Research under award number N00014-18-1-2396.
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.
语种英语
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/22344
专题工业控制网络与系统研究室
通讯作者He, Haibo
作者单位1.Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016 China
2.Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881 USA
3.Department of Electrical Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487 USA
推荐引用方式
GB/T 7714
Zeng P,Li H,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 H,He, Haibo,&Li SH.(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).
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