Optimal Scheduling of Microgrid Based on Deep Deterministic Policy Gradient and Transfer Learning

• Published in: Energies (Basel) Vol. 14; no. 3; p. 584
• Main Authors: Fan, Luqin, Zhang, Jing, He, Yu, Liu, Ying, Hu, Tao, Zhang, Heng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2021
• Subjects: Algorithms; Alternative energy sources; Artificial intelligence; Distributed generation; Electricity distribution; Heuristic; Knowledge; microgrid; Neural networks; Operating costs; optimal scheduling; Optimization; reinforcement learning; Scheduling; Solar energy; Strategy; Supply & demand; Transfer learning; User behavior; Wind power
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en14030584

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.