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Universal Generating Function Based Probabilistic Production Simulation Approach Considering Wind Speed Correlation

Author

Listed:
  • Yan Li

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Ming Zhou

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Dawei Wang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Yuehui Huang

    (China Electric Power Research Institute (CEPRI), Beijing 100192, China)

  • Zifen Han

    (State Grid Gansu Electric Power Company, Lanzhou 730070, China)

Abstract

Due to the volatile and correlated nature of wind speed, a high share of wind power penetration poses challenges to power system production simulation. Existing power system probabilistic production simulation approaches are in short of considering the time-varying characteristics of wind power and load, as well as the correlation between wind speeds at the same time, which brings about some problems in planning and analysis for the power system with high wind power penetration. Based on universal generating function (UGF), this paper proposes a novel probabilistic production simulation approach considering wind speed correlation. UGF is utilized to develop the chronological models of wind power that characterizes wind speed correlation simultaneously, as well as the chronological models of conventional generation sources and load. The supply and demand are matched chronologically to not only obtain generation schedules, but also reliability indices both at each simulation interval and the whole period. The proposed approach has been tested on the improved IEEE-RTS 79 test system and is compared with the Monte Carlo approach and the sequence operation theory approach. The results verified the proposed approach with the merits of computation simplicity and accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1786-:d:117779
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    References listed on IDEAS

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    1. Ekström, Jussi & Koivisto, Matti & Mellin, Ilkka & Millar, John & Saarijärvi, Eero & Haarla, Liisa, 2015. "Assessment of large scale wind power generation with new generation locations without measurement data," Renewable Energy, Elsevier, vol. 83(C), pages 362-374.
    2. Gregory Levitin, 2005. "The Universal Generating Function in Reliability Analysis and Optimization," Springer Series in Reliability Engineering, Springer, number 978-1-84628-245-4, June.
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    Cited by:

    1. Ma, Ye & Chi, Yuanying & Wu, Di & Peng, Rui & Wu, Shaomin, 2021. "Reliability of integrated electricity and gas supply system with performance substitution and sharing mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    2. 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.
    3. Fernando Manuel Carvalho da Silva Santos & Leonardo Elizeire Bremermann & Tadeu Da Mata Medeiros Branco & Diego Issicaba & Mauro Augusto da Rosa, 2018. "Impact Evaluation of Wind Power Geographic Dispersion on Future Operating Reserve Needs," Energies, MDPI, vol. 11(11), pages 1-13, October.
    4. Daw Saleh Sasi Mohammed & Muhammad Murtadha Othman & Ahmed Elbarsha, 2020. "A Modified Artificial Bee Colony for Probabilistic Peak Shaving Technique in Generators Operation Planning: Optimal Cost–Benefit Analysis," Energies, MDPI, vol. 13(12), pages 1-23, June.
    5. Guo, Zheyu & Zheng, Yanan & Li, Gengyin, 2020. "Power system flexibility quantitative evaluation based on improved universal generating function method: A case study of Zhangjiakou," Energy, Elsevier, vol. 205(C).

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