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

Author

Listed:
  • Luqin Fan

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Jing Zhang

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Yu He

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Ying Liu

    (Power Grid Planning Research Center of Guizhou Power Grid Corporation, Guiyang 550002, China)

  • Tao Hu

    (Guizhou Power Grid Corporation, Guiyang 550002, China)

  • Heng Zhang

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

Abstract

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.

Suggested Citation

  • Luqin Fan & Jing Zhang & Yu He & Ying Liu & Tao Hu & Heng Zhang, 2021. "Optimal Scheduling of Microgrid Based on Deep Deterministic Policy Gradient and Transfer Learning," Energies, MDPI, vol. 14(3), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:584-:d:485925
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    2. Zhang, Xiaoshun & Bao, Tao & Yu, Tao & Yang, Bo & Han, Chuanjia, 2017. "Deep transfer Q-learning with virtual leader-follower for supply-demand Stackelberg game of smart grid," Energy, Elsevier, vol. 133(C), pages 348-365.
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    Cited by:

    1. Bingyin Lei & Yue Ren & Huiyu Luan & Ruonan Dong & Xiuyuan Wang & Junli Liao & Shu Fang & Kaiye Gao, 2023. "A Review of Optimization for System Reliability of Microgrid," Mathematics, MDPI, vol. 11(4), pages 1-30, February.
    2. Coraci, Davide & Brandi, Silvio & Hong, Tianzhen & Capozzoli, Alfonso, 2023. "Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings," Applied Energy, Elsevier, vol. 333(C).
    3. Bing Liu & Bowen Xu & Tong He & Wei Yu & Fanghong Guo, 2022. "Hybrid Deep Reinforcement Learning Considering Discrete-Continuous Action Spaces for Real-Time Energy Management in More Electric Aircraft," Energies, MDPI, vol. 15(17), pages 1-21, August.
    4. Ying Ji & Jianhui Wang & Jiacan Xu & Donglin Li, 2021. "Data-Driven Online Energy Scheduling of a Microgrid Based on Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-19, April.

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