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Research on Data-Driven Optimal Scheduling of Power System

Author

Listed:
  • Jianxun Luo

    (School of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)

  • Wei Zhang

    (School of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)

  • Hui Wang

    (Department of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Wenmiao Wei

    (Automation Academy, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Jinpeng He

    (School of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)

Abstract

The uncertainty of output makes it difficult to effectively solve the economic security dispatching problem of the power grid when a high proportion of renewable energy generating units are integrated into the power grid. Based on the proximal policy optimization (PPO) algorithm, a safe and economical grid scheduling method is designed. First, constraints on the safe and economical operation of renewable energy power systems are defined. Then, the quintuple of Markov decision process is defined under the framework of deep reinforcement learning, and the dispatching optimization problem is transformed into Markov decision process. To solve the problem of low sample data utilization in online reinforcement learning strategies, a PPO optimization algorithm based on the Kullback–Leibler (KL) divergence penalty factor and importance sampling technique is proposed, which transforms on-policy into off-policy and improves sample utilization. Finally, the simulation analysis of the example shows that in a power system with a high proportion of renewable energy generating units connected to the grid, the proposed scheduling strategy can meet the load demand under different load trends. In the dispatch cycle with different renewable energy generation rates, renewable energy can be absorbed to the maximum extent to ensure the safe and economic operation of the grid.

Suggested Citation

  • Jianxun Luo & Wei Zhang & Hui Wang & Wenmiao Wei & Jinpeng He, 2023. "Research on Data-Driven Optimal Scheduling of Power System," Energies, MDPI, vol. 16(6), pages 1-15, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2926-:d:1104578
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    References listed on IDEAS

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    1. White, Chelsea C. & White, Douglas J., 1989. "Markov decision processes," European Journal of Operational Research, Elsevier, vol. 39(1), pages 1-16, March.
    2. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    3. Xiang, Yue & Lu, Yu & Liu, Junyong, 2023. "Deep reinforcement learning based topology-aware voltage regulation of distribution networks with distributed energy storage," Applied Energy, Elsevier, vol. 332(C).
    4. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
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    Cited by:

    1. Gabriel Pesántez & Wilian Guamán & José Córdova & Miguel Torres & Pablo Benalcazar, 2024. "Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies," Energies, MDPI, vol. 17(9), pages 1-25, May.
    2. Rui Wang & Zhanqiang Zhang & Keqilao Meng & Pengbing Lei & Kuo Wang & Wenlu Yang & Yong Liu & Zhihua Lin, 2024. "Research on Energy Scheduling Optimization Strategy with Compressed Air Energy Storage," Sustainability, MDPI, vol. 16(18), pages 1-18, September.

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