Dynamic Energy Management of a Microgrid using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning | |
Zeng P(曾鹏)3; He HB(何海波)2; Li HP(李鹤鹏)3; Li SH(李署辉)1 | |
刊名 | IEEE Transactions on Smart Grid |
2019 | |
卷号 | 10期号:4页码:4435-4445 |
关键词 | Microgrid Dynamic Energy Management System Approximate Dynamic Programming Recurrent Neural Network Deep Learning |
ISSN号 | 1949-3053 |
产权排序 | 1 |
英文摘要 | 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. |
资助项目 | National Natural Science Foundation of China[61533015] ; Office of Naval Research[N00014-18-1-2396] |
WOS关键词 | MODEL-PREDICTIVE CONTROL ; OPERATION MANAGEMENT ; ECONOMIC-DISPATCH ; OPTIMIZATION ; INTEGRATION ; GENERATION ; SYSTEMS |
WOS研究方向 | Engineering |
语种 | 英语 |
WOS记录号 | WOS:000472577500083 |
内容类型 | 期刊论文 |
源URL | [http://119.78.100.139/handle/173321/22344] |
专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
通讯作者 | He HB(何海波) |
作者单位 | 1.Department of Electrical Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487 USA 2.Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881 USA 3.Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016 China |
推荐引用方式 GB/T 7714 | Zeng P,He HB,Li HP,et al. Dynamic Energy Management of a Microgrid using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning[J]. IEEE Transactions on Smart Grid,2019,10(4):4435-4445. |
APA | Zeng P,He HB,Li HP,&Li SH.(2019).Dynamic Energy Management of a Microgrid using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning.IEEE Transactions on Smart Grid,10(4),4435-4445. |
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 10.4(2019):4435-4445. |
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