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High Proportion of Distributed PV Reliability Planning Method Based on Big Data

Author

Listed:
  • Hualiang Fang

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Lei Shang

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Xuzhu Dong

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Ye Tian

    (Electric Power Research Institute, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110006, China)

Abstract

The higher proportion of distributed photovoltaic and lower fossil energy integrated into the power network brings huge challenges in power supply reliability and planning. The distributed photovoltaic planning method based on big data is proposed. According to the impact of stochastic photovoltaics and loads on reliability planning, the probability model of distributed photovoltaic and load is analyzed, and the dynamic capacity–load ratios are presented based on big data. The multi-scenario generation and reduction algorithm of stochastic distributed photovoltaic and load planning is studied, and a source–load scenario matching model is proposed based on big data. According to the big data scenario of source–load, the reliability indexes and dynamic capacity–load ratio may be obtained. Finally, the IEEE 33-bus system is used as an example, and the results show that distributed photovoltaic planning methods based on big data can improve photovoltaic utilization and power supply reliability.

Suggested Citation

  • Hualiang Fang & Lei Shang & Xuzhu Dong & Ye Tian, 2023. "High Proportion of Distributed PV Reliability Planning Method Based on Big Data," Energies, MDPI, vol. 16(23), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7692-:d:1284676
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    References listed on IDEAS

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    1. Viana, Matheus Sabino & Manassero, Giovanni & Udaeta, Miguel E.M., 2018. "Analysis of demand response and photovoltaic distributed generation as resources for power utility planning," Applied Energy, Elsevier, vol. 217(C), pages 456-466.
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