Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 10, Issue 4, Pages 4435-4445Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2018.2859821
Keywords
Microgrid; dynamic energy management system; approximate dynamic programming; recurrent neural network; deep learning
Categories
Funding
- Office of Naval Research [N00014-18-1-2396]
- National Natural Science Foundation of China [61533015]
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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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