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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Bibliographic Details
Published in:IEEE transactions on smart grid Vol. 10; no. 4; pp. 4435 - 4445
Main Authors: Zeng, Peng, Li, Hepeng, He, Haibo, Li, Shuhui
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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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ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2018.2859821