LAPSE:2023.29265
Published Article
LAPSE:2023.29265
Optimal Scheduling of Microgrid Based on Deep Deterministic Policy Gradient and Transfer Learning
April 13, 2023
Microgrid has flexible composition, a complex operation mechanism, and a large amount of data while operating. However, optimization methods of microgrid scheduling do not effectively accumulate and utilize the scheduling knowledge at present. This paper puts forward a microgrid optimal scheduling method based on Deep Deterministic Policy Gradient (DDPG) and Transfer Learning (TL). This method uses Reinforcement Learning (RL) to learn the scheduling strategy and accumulates the corresponding scheduling knowledge. Meanwhile, the DDPG model is introduced to extend the microgrid scheduling strategy action from the discrete action space to the continuous action space. On this basis, this paper holds that a microgrid optimal scheduling TL algorithm on the strength of the actual supply and demand similarity is proposed with a purpose of making use of the existing scheduling knowledge effectively. The simulation results indicate that this paper can provide optimal scheduling strategy for microgrid with complex operation mechanism flexibly and efficiently through the effective accumulation of scheduling knowledge and the utilization of scheduling knowledge through TL.
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Keywords
microgrid, optimal scheduling, reinforcement learning, transfer learning
Subject
Suggested Citation
Fan L, Zhang J, He Y, Liu Y, Hu T, Zhang H. Optimal Scheduling of Microgrid Based on Deep Deterministic Policy Gradient and Transfer Learning. (2023). LAPSE:2023.29265
Author Affiliations
Fan L: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Zhang J: College of Electrical Engineering, Guizhou University, Guiyang 550025, China [ORCID]
He Y: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Liu Y: Power Grid Planning Research Center of Guizhou Power Grid Corporation, Guiyang 550002, China
Hu T: Guizhou Power Grid Corporation, Guiyang 550002, China
Zhang H: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Zhang J: College of Electrical Engineering, Guizhou University, Guiyang 550025, China [ORCID]
He Y: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Liu Y: Power Grid Planning Research Center of Guizhou Power Grid Corporation, Guiyang 550002, China
Hu T: Guizhou Power Grid Corporation, Guiyang 550002, China
Zhang H: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Journal Name
Energies
Volume
14
Issue
3
First Page
584
Year
2021
Publication Date
2021-01-23
Published Version
ISSN
1996-1073
Version Comments
Original Submission
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PII: en14030584, Publication Type: Journal Article
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LAPSE:2023.29265
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doi:10.3390/en14030584
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[v1] (Original Submission)
Apr 13, 2023
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Apr 13, 2023
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