A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids

Fei Chen, Zhiyang Wang and Yu He ()
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Fei Chen: China Southern Power Grid Guizhou Power Grid Co., Ltd., Guiyang 550003, China
Zhiyang Wang: School of Electrical Engineering, Guizhou University, Guiyang 550025, China
Yu He: School of Electrical Engineering, Guizhou University, Guiyang 550025, China

Energies, 2023, vol. 16, issue 22, 1-17

Abstract: With the rapid growth in the proportion of renewable energy access and the structural complexity of distributed energy systems, traditional microgrid (MG) scheduling methods that rely on mathematical optimization models and expert experience are facing significant challenges. Therefore, it is essential to present a novel scheduling technique with high intelligence and fast decision-making capacity to realize MGs’ automatic operation and regulation. This paper proposes an optimal scheduling decision-making method for MGs based on deep neural networks (DNN). Firstly, a typical mathematical scheduling model used for MG operation is introduced, and the limitations of current methods are analyzed. Then, a two-stage optimal scheduling framework comprising day-ahead and intra-day stages is presented. The day-ahead part is solved by mixed integer linear programming (MILP), and the intra-day part uses a convolutional neural network (CNN)—bidirectional long short-term memory (Bi LSTM) for high-speed rolling decision making, with the outputs adjusted by a power correction balance algorithm. Finally, the validity of the model and algorithm of this paper are verified by arithmetic case analysis.

Keywords: microgrid; optimal dispatch; convolutional neural network; deep bidirectional long-short memory neural network; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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