SIA OpenIR  > 工业控制网络与系统研究室
Dynamic Energy Management of a Microgrid using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning
Zeng P(曾鹏)1; Li HP(李鹤鹏)1; He, Haibo2; Li SH(李署辉)3
Department工业控制网络与系统研究室
Source PublicationIEEE Transactions on Smart Grid
ISSN1949-3053
2018
Indexed ByEI
EI Accession number20183105632226
Contribution Rank1
Funding OrganizationOffice of Naval Research under award number N00014-18-1-2396.
KeywordMicrogrid Dynamic Energy Management System Approximate Dynamic Programming Recurrent Neural Network Deep Learning
Abstract

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.

Language英语
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/22344
Collection工业控制网络与系统研究室
Corresponding AuthorHe, Haibo
Affiliation1.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
Recommended Citation
GB/T 7714
Zeng P,Li HP,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 HP,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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