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Neural Computing and Applications
Published in: Neural Computing and Applications 7/2019

19-10-2017 | Original Article

Neural networks for power management optimal strategy in hybrid microgrid

Authors: Tiancai Wang, Xing He, Ting Deng

Published in: Neural Computing and Applications | Issue 7/2019

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Abstract

This paper proposes a more reasonable objective function for combined economic emission dispatch problem. To solve it, Lagrange programming neural network (LPNN) is utilized to obtain optimal scheduling of a hybrid microgrid, which includes power generation resources, variable demands and energy storage system for energy storing and supplying. Combining variable neurons with Lagrange neurons, the LPNN aims to minimize the cost function and maximize the power generated by the renewable sources. The asymptotic stability condition of the neurodynamic model is analyzed, and simulation results show that optimal power of each component with certain time interval can be obtained. In addition, a new method by radial basis function neural network is proposed to predict the power values of renewable energy and load demand, which are used as the input values in the optimal process.
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Appendix
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Metadata
Title
Neural networks for power management optimal strategy in hybrid microgrid
Authors
Tiancai Wang
Xing He
Ting Deng
Publication date
19-10-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 7/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3219-x

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