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Research on Dynamic Modeling and Parameter Identification of the Grid-Connected PV Power Generation System

Author

Listed:
  • Kezhen Liu

    (Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Yumin Mao

    (Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Xueou Chen

    (Training and Evaluation Center of Yunnan Power Grid Co., Ltd., Kunming 650106, China)

  • Jiedong He

    (Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Min Dong

    (Faculty of Information and Intelligent Engineering, Yunnan College of Business Management, Kunming 650304, China)

Abstract

With the increasing proportion of renewable energy in the new power system, the grid-connected capacity of photovoltaic (PV) units shows an obvious upward trend, but its dynamic behavior under different penetration rates significantly affects the transient stability of the power system, so it is crucial to establish a dynamic model that meets the actual working conditions and select a suitable parameter identification method. Therefore, in this paper, based on the electromechanical transient characteristics of the grid-connected PV power generation system, the corresponding dynamic discrete equivalent model is established, and the simulation platform of the grid-connected PV power generation system is built in MATLAB/Simulink to study the adaptability of the dynamic discrete equivalent model of the grid-connected PV power generation system from the single and multiple scenarios using the ordinary least squares (OLS) and bat algorithm (BA) while comparing the generalization ability of the parameters identified by the two methods to the model. The simulation results show that the generalization ability of the parameters identified by the OLS and BA for the model in the single scenario is better, indicating that the model has good adaptability; the generalization ability of a set of general parameters identified by the BA for the model in the multiple scenarios is better than that of the OLS, indicating that the parameters identified by the BA have better adaptability. In conclusion, the dynamic discrete equivalent model of the grid-connected PV power generation system proposed in this paper can accurately reflect the dynamic characteristics of the grid-connected PV power generation system, and the parameters identified by the BA are more generalized than the OLS.

Suggested Citation

  • Kezhen Liu & Yumin Mao & Xueou Chen & Jiedong He & Min Dong, 2023. "Research on Dynamic Modeling and Parameter Identification of the Grid-Connected PV Power Generation System," Energies, MDPI, vol. 16(10), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4152-:d:1149381
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    References listed on IDEAS

    as
    1. Chaabane Bouali & Horst Schulte & Abdelkader Mami, 2019. "A High Performance Optimizing Method for Modeling Photovoltaic Cells and Modules Array Based on Discrete Symbiosis Organism Search," Energies, MDPI, vol. 12(12), pages 1-32, June.
    2. Nouha Mansouri & Abderezak Lashab & Dezso Sera & Josep M. Guerrero & Adnen Cherif, 2019. "Large Photovoltaic Power Plants Integration: A Review of Challenges and Solutions," Energies, MDPI, vol. 12(19), pages 1-16, October.
    3. Fiedler, T., 2019. "Simulation of a power system with large renewable penetration," Renewable Energy, Elsevier, vol. 130(C), pages 319-328.
    4. Long, Wen & Jiao, Jianjun & Liang, Ximing & Xu, Ming & Tang, Mingzhu & Cai, Shaohong, 2022. "Parameters estimation of photovoltaic models using a novel hybrid seagull optimization algorithm," Energy, Elsevier, vol. 249(C).
    5. Aryuanto Soetedjo & Irrine Budi Sulistiawati, 2020. "Implementing Discrete Model of Photovoltaic System on the Embedded Platform for Real-Time Simulation," Energies, MDPI, vol. 13(17), pages 1-22, August.
    6. Lee, Yu-Wei & Kuo, Chung-Feng Jeffrey & Weng, Wei-Han & Huang, Chao-Yang & Peng, Cheng-Yu, 2017. "Dynamic modeling and entity validation of a photovoltaic system," Applied Energy, Elsevier, vol. 200(C), pages 370-382.
    7. Shair, Jan & Li, Haozhi & Hu, Jiabing & Xie, Xiaorong, 2021. "Power system stability issues, classifications and research prospects in the context of high-penetration of renewables and power electronics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    8. Gao, Yuan & Dong, Jianfei & Isabella, Olindo & Santbergen, Rudi & Tan, Hairen & Zeman, Miro & Zhang, Guoqi, 2019. "Modeling and analyses of energy performances of photovoltaic greenhouses with sun-tracking functionality," Applied Energy, Elsevier, vol. 233, pages 424-442.
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