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A Vine-Copula Based Voltage State Assessment with Wind Power Integration

Author

Listed:
  • Xiaolu Chen

    (State Grid East Inner Mongolia Electric Power Company Limited Electric Power Research Institute, Hohhot 010020, China)

  • Ji Han

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Tingting Zheng

    (State Grid East Inner Mongolia Electric Power Company Limited Electric Power Research Institute, Hohhot 010020, China)

  • Ping Zhang

    (State Grid East Inner Mongolia Electric Power Company Limited Electric Power Research Institute, Hohhot 010020, China)

  • Simo Duan

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Shihong Miao

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

With the increasing rate of wind power installed capacity, voltage state assessment with large-scale wind power integration is of great significance. In this paper, a vine-copula based voltage state assessment method with large-scale wind power integration is proposed. Firstly, the nonparametric kernel density estimation is used to fit the wind speed distribution, and vine-copula is used to construct the wind speed joint distribution model of multiple regions. In order to obtain voltage distribution characteristics, probabilistic load flow based on the semi-invariant method and wind speed independent transformation based on the Rosenblatt transformation are described. On this basis, a voltage state assessment index is established for the more comprehensive evaluation of voltage characteristics, and a voltage state assessment procedure is proposed. Taking actual wind speed as an example, the case study of the IEEE 24-node power system and the east Inner Mongolia power system for voltage state assessment with large-scale wind power integration are studied. The simulation results verify the effectiveness of the proposed voltage state assessment method.

Suggested Citation

  • Xiaolu Chen & Ji Han & Tingting Zheng & Ping Zhang & Simo Duan & Shihong Miao, 2019. "A Vine-Copula Based Voltage State Assessment with Wind Power Integration," Energies, MDPI, vol. 12(10), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:2019-:d:234495
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    References listed on IDEAS

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    1. Xiaojun Shen & Chongcheng Zhou & Xuejiao Fu, 2018. "Study of Time and Meteorological Characteristics of Wind Speed Correlation in Flat Terrains Based on Operation Data," Energies, MDPI, vol. 11(1), pages 1-16, January.
    2. Yan Li & Ming Zhou & Dawei Wang & Yuehui Huang & Zifen Han, 2017. "Universal Generating Function Based Probabilistic Production Simulation Approach Considering Wind Speed Correlation," Energies, MDPI, vol. 10(11), pages 1-15, November.
    3. Ping He & Seyed Ali Arefifar & Congshan Li & Fushuan Wen & Yuqi Ji & Yukun Tao, 2019. "Enhancing Oscillation Damping in an Interconnected Power System with Integrated Wind Farms Using Unified Power Flow Controller," Energies, MDPI, vol. 12(2), pages 1-16, January.
    4. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    5. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2019. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction," Energies, MDPI, vol. 12(2), pages 1-42, January.
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    Cited by:

    1. Johannes Kaufmann & Philipp Artur Kienscherf & Wolfgang Ketter, 2020. "Modeling and Managing Joint Price and Volumetric Risk for Volatile Electricity Portfolios," Energies, MDPI, vol. 13(14), pages 1-19, July.

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