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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Veröffentlicht in:	IEEE transactions on smart grid Jg. 10; H. 4; S. 4435 - 4445
Hauptverfasser:	Zeng, Peng, Li, Hepeng, He, Haibo, Li, Shuhui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.07.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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