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
Hlavní autoři:	Zeng, Peng, Li, Hepeng, He, Haibo, Li, Shuhui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.07.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	approximate dynamic programming Computer simulation deep learning Distributed generation dynamic energy management system Dynamic programming Electric power transmission Electricity distribution Electricity pricing Energy management Energy storage Learning Markov chains Microgrid Neural networks Operating costs Optimization Power flow Reactive power Real-time systems recurrent neural network Recurrent neural networks Scheduling Uncertainty
ISSN:	1949-3053, 1949-3061
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Abstract	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.
AbstractList	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.
Author	Li, Shuhui He, Haibo Zeng, Peng Li, Hepeng
Author_xml	– sequence: 1 givenname: Peng orcidid: 0000-0001-7863-3260 surname: Zeng fullname: Zeng, Peng email: zp@sia.cn organization: Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China – sequence: 2 givenname: Hepeng surname: Li fullname: Li, Hepeng email: cn.h.li@ieee.org organization: Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China – sequence: 3 givenname: Haibo orcidid: 0000-0002-5247-9370 surname: He fullname: He, Haibo email: he@ele.uri.edu organization: Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA – sequence: 4 givenname: Shuhui orcidid: 0000-0001-6754-8976 surname: Li fullname: Li, Shuhui email: sli@eng.ua.edu organization: Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL, USA
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SubjectTerms	approximate dynamic programming Computer simulation deep learning Distributed generation dynamic energy management system Dynamic programming Electric power transmission Electricity distribution Electricity pricing Energy management Energy storage Learning Markov chains Microgrid Neural networks Operating costs Optimization Power flow Reactive power Real-time systems recurrent neural network Recurrent neural networks Scheduling Uncertainty
Title	Dynamic Energy Management of a Microgrid Using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning
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