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Dynamic Equivalent Modeling of a Large Renewable Power Plant Using a Data-Driven Degree of Similarity Method

Author

Listed:
  • Mengjun Liao

    (Electric Power Research Institute of China Southern Power Grid, Guangzhou 510663, China)

  • Lin Zhu

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China)

  • Yonghao Hu

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China)

  • Yang Liu

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China)

  • Yue Wu

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China)

  • Leke Chen

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China)

Abstract

This paper aims to develop a novel method for the dynamic equivalence of a renewable power plant, ultimately contributing to power system modeling and enhancing the integration of renewable energy sources. In order to address the challenge posed by clusters of renewable generation units during the equivalence process, the paper introduces the degree of similarity to assess similarity features under data. After leveraging the degree of similarity in conjunction with data-driven techniques, the proposed method efficiently entails dividing numerous units in a large-scale plant into distinct clusters. Additionally, the paper adopts practical algorithms to determine the parameters for each aggregated cluster and streamline the intricate collector network within the renewable power plant. The equivalent model of a renewable power plant is thereby conclusively derived. Comprehensive case studies are conducted within a practical offshore wind plant setting. These case studies are accompanied by simulations, highlighting the advantages and effectiveness of the proposed method, offering an accurate representation of the renewable power plant under diverse operating conditions.

Suggested Citation

  • Mengjun Liao & Lin Zhu & Yonghao Hu & Yang Liu & Yue Wu & Leke Chen, 2023. "Dynamic Equivalent Modeling of a Large Renewable Power Plant Using a Data-Driven Degree of Similarity Method," Energies, MDPI, vol. 16(19), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6934-:d:1252850
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    References listed on IDEAS

    as
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