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Power System Dispatch Based on Improved Scenario Division with Physical and Data-Driven Features

Author

Listed:
  • Wenqi Huang

    (Southern Power Grid Digital Grid Research Institute, Guangzhou 510000, China)

  • Shang Cao

    (Southern Power Grid Digital Grid Research Institute, Guangzhou 510000, China)

  • Lingyu Liang

    (Southern Power Grid Digital Grid Research Institute, Guangzhou 510000, China)

  • Huanming Zhang

    (Southern Power Grid Digital Grid Research Institute, Guangzhou 510000, China)

  • Xiangyu Zhao

    (Southern Power Grid Digital Grid Research Institute, Guangzhou 510000, China)

  • Hanju Li

    (Southern Power Grid Digital Grid Research Institute, Guangzhou 510000, China)

  • Jie Ren

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Liang Che

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

Abstract

In power systems with high penetration of renewable energy, traditional physical model-based optimal dispatch methods suffer from modeling difficulties and poor adaptability, while data-driven dispatch methods, represented by reinforcement learning, have the advantage of fast decision making and reflecting long-term benefits. However, the performances of data-driven methods are much limited by the problem of distribution shift under insufficient power system scenario samples in the training. To address this issue, this paper proposes an improved scenario division method by integrating the power system’s key physical features and the data-driven variational autoencoder (VAE)-generated features. Next, based on the scenario division results, a multi-scenario data-driven dispatch model is established. The effectiveness of the proposed method is verified by a simulation conducted on a real power system model in a province of China.

Suggested Citation

  • Wenqi Huang & Shang Cao & Lingyu Liang & Huanming Zhang & Xiangyu Zhao & Hanju Li & Jie Ren & Liang Che, 2023. "Power System Dispatch Based on Improved Scenario Division with Physical and Data-Driven Features," Energies, MDPI, vol. 16(22), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7520-:d:1277942
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