Dynamic Energy Management of a Microgrid Using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning
This paper focuses on economical operation of a microgrid (MG) in real-time. A novel dynamic energy management system is developed to incorporate efficient management of energy storage system into MG real-time dispatch while considering power flow constraints and uncertainties in load, renewable gen...
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| Vydáno v: | IEEE transactions on smart grid Ročník 10; číslo 4; s. 4435 - 4445 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Piscataway
IEEE
01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 1949-3053, 1949-3061 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | This paper focuses on economical operation of a microgrid (MG) in real-time. A novel dynamic energy management system is developed to incorporate efficient management of energy storage system 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 over a day. Then, approximate dynamic programming and deep recurrent neural network learning are employed to derive a near optimal real-time scheduling policy. Last, using real power grid data from California independent system operator, a detailed simulation study is carried out to validate the effectiveness of the proposed method. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1949-3053 1949-3061 |
| DOI: | 10.1109/TSG.2018.2859821 |