Optimal Scheduling of Microgrid Based on Deep Deterministic Policy Gradient and Transfer Learning
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 m...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/3/584 |
| _version_ | 1797408006998065152 |
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| author | Luqin Fan Jing Zhang Yu He Ying Liu Tao Hu Heng Zhang |
| author_facet | Luqin Fan Jing Zhang Yu He Ying Liu Tao Hu Heng Zhang |
| author_sort | Luqin Fan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T03:51:05Z |
| format | Article |
| id | doaj.art-02a1f80b61bf47e0ad24e049dd5187f2 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T03:51:05Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-02a1f80b61bf47e0ad24e049dd5187f22023-12-03T14:26:28ZengMDPI AGEnergies1996-10732021-01-0114358410.3390/en14030584Optimal Scheduling of Microgrid Based on Deep Deterministic Policy Gradient and Transfer LearningLuqin Fan0Jing Zhang1Yu He2Ying Liu3Tao Hu4Heng Zhang5College of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaCollege of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaCollege of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaPower Grid Planning Research Center of Guizhou Power Grid Corporation, Guiyang 550002, ChinaGuizhou Power Grid Corporation, Guiyang 550002, ChinaCollege of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaMicrogrid 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.https://www.mdpi.com/1996-1073/14/3/584microgridoptimal schedulingreinforcement learningtransfer learning |
| spellingShingle | Luqin Fan Jing Zhang Yu He Ying Liu Tao Hu Heng Zhang Optimal Scheduling of Microgrid Based on Deep Deterministic Policy Gradient and Transfer Learning Energies microgrid optimal scheduling reinforcement learning transfer learning |
| title | Optimal Scheduling of Microgrid Based on Deep Deterministic Policy Gradient and Transfer Learning |
| title_full | Optimal Scheduling of Microgrid Based on Deep Deterministic Policy Gradient and Transfer Learning |
| title_fullStr | Optimal Scheduling of Microgrid Based on Deep Deterministic Policy Gradient and Transfer Learning |
| title_full_unstemmed | Optimal Scheduling of Microgrid Based on Deep Deterministic Policy Gradient and Transfer Learning |
| title_short | Optimal Scheduling of Microgrid Based on Deep Deterministic Policy Gradient and Transfer Learning |
| title_sort | optimal scheduling of microgrid based on deep deterministic policy gradient and transfer learning |
| topic | microgrid optimal scheduling reinforcement learning transfer learning |
| url | https://www.mdpi.com/1996-1073/14/3/584 |
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