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A Mixed Uncertainty Power Flow Algorithm-Based Centralized Photovoltaic (PV) Cluster

Author

Listed:
  • Hao Wu

    (School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Lin Zhou

    (School of Electrical Engineering, Chongqing University, Chongqing 400044, China
    State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Yihao Wan

    (School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Qiang Liu

    (School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Siyu Zhou

    (School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

Abstract

With the large-scale centralized PV clusters connected to grid, the grid power flow has certain randomness. Considering the fluctuation of PV output, an improved Krawczyk-Moore algorithm in a mixed coordinate system is proposed to solve the uncertain power flow problem. Firstly, aiming at the special structure of a centralized PV cluster with only load node and no generator node, this paper proposes a power flow calculation in the mixed power flow coordinate, and then the Krawczyk-Moore operator is used to combine interval and affine arithmetic to overcome the shortcoming of over-conservative interval algorithm. Finally, the voltage operating condition under different volatility and different partial shading conditions is studied through the simulation of a practical example, and the out-of-limit voltage problem inside the centralized PV cluster is analyzed. Meanwhile, the effectiveness of the proposed algorithm is verified.

Suggested Citation

  • Hao Wu & Lin Zhou & Yihao Wan & Qiang Liu & Siyu Zhou, 2019. "A Mixed Uncertainty Power Flow Algorithm-Based Centralized Photovoltaic (PV) Cluster," Energies, MDPI, vol. 12(20), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:4008-:d:278900
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    References listed on IDEAS

    as
    1. Qi Wang & Ping Chang & Runqing Bai & Wenfei Liu & Jianfeng Dai & Yi Tang, 2019. "Mitigation Strategy for Duck Curve in High Photovoltaic Penetration Power System Using Concentrating Solar Power Station," Energies, MDPI, vol. 12(18), pages 1-16, September.
    2. Alonso Gutiérrez Galeano & Michael Bressan & Fernando Jiménez Vargas & Corinne Alonso, 2018. "Shading Ratio Impact on Photovoltaic Modules and Correlation with Shading Patterns," Energies, MDPI, vol. 11(4), pages 1-26, April.
    3. Liu, Dunnan & Liu, Mingguang & Xu, Erfeng & Pang, Bo & Guo, Xiaodan & Xiao, Bowen & Niu, Dongxiao, 2018. "Comprehensive effectiveness assessment of renewable energy generation policy: A partial equilibrium analysis in China," Energy Policy, Elsevier, vol. 115(C), pages 330-341.
    4. Qiang Zhao & Shuai Shao & Lingxing Lu & Xin Liu & Honglu Zhu, 2018. "A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm," Energies, MDPI, vol. 11(1), pages 1-21, January.
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    Cited by:

    1. Bin Ouyang & Lu Qu & Qiyang Liu & Baoye Tian & Zhichang Yuan & Peiqian Guo & Hongyi Dai & Weikun He, 2021. "Calculation and Analysis of the Interval Power Flow for Distributed Energy System Based on Affine Algorithm," Energies, MDPI, vol. 14(3), pages 1-14, January.
    2. Mengmeng Xiao & Shaorong Wang & Zia Ullah, 2021. "D-PMU and 5G-Network-Based Coordination Control Method for Three-Phase Imbalance Mitigation Units in the LVDN," Energies, MDPI, vol. 14(10), pages 1-12, May.

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