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Probabilistic Power Flow for Hybrid AC/DC Grids with Ninth-Order Polynomial Normal Transformation and Inherited Latin Hypercube Sampling

Author

Listed:
  • Sui Peng

    (Grid Planning and Research Center, Guangdong Power Grid Corporation, CSG, Guangzhou 510080, China)

  • Huixiang Chen

    (Grid Planning and Research Center, Guangdong Power Grid Corporation, CSG, Guangzhou 510080, China)

  • Yong Lin

    (Grid Planning and Research Center, Guangdong Power Grid Corporation, CSG, Guangzhou 510080, China)

  • Tong Shu

    (Power and Energy Reliability Research Center, State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Xingyu Lin

    (Power and Energy Reliability Research Center, State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Junjie Tang

    (Power and Energy Reliability Research Center, State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Wenyuan Li

    (Power and Energy Reliability Research Center, State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Weijie Wu

    (Grid Planning and Research Center, Guangdong Power Grid Corporation, CSG, Guangzhou 510080, China)

Abstract

This paper proposes a new probabilistic power flow method for the hybrid AC/VSC-MTDC (Voltage Source Control-Multiple Terminal Direct Current) grids, which is based on the combination of ninth-order polynomial normal transformation (NPNT) and inherited Latin hypercube sampling (ILHS) techniques. NPNT is utilized to directly handle historical records of uncertain sources to build the accurate probability model of random inputs, and ILHS is adopted to propagate the randomness from inputs to target outputs. Regardless of whether the underlying probability distribution is known or unknown, the proposed method has the ability to adaptively evaluate the sample size according to a specific operational scenario of the power systems, thus achieving a good balance between computational accuracy and speed. Meanwhile, the frequency histograms, probability distributions, and some more statistics of the results can be accurately and efficiently estimated as well. The modified IEEE 118-bus system, together with the realistic data of wind speeds and diverse consumer behaviors following irregular distributions, is used to demonstrate the effectiveness and superiority of the proposed method.

Suggested Citation

  • Sui Peng & Huixiang Chen & Yong Lin & Tong Shu & Xingyu Lin & Junjie Tang & Wenyuan Li & Weijie Wu, 2019. "Probabilistic Power Flow for Hybrid AC/DC Grids with Ninth-Order Polynomial Normal Transformation and Inherited Latin Hypercube Sampling," Energies, MDPI, vol. 12(16), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3088-:d:256625
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    References listed on IDEAS

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    1. Ziwei Zhu & Shifan Lu & Sui Peng, 2018. "An Improved Stochastic Response Surface Method Based Probabilistic Load Flow for Studies on Correlated Wind Speeds in the AC/DC Grid," Energies, MDPI, vol. 11(12), pages 1-14, December.
    2. Shargh, S. & Khorshid ghazani, B. & Mohammadi-ivatloo, B. & Seyedi, H. & Abapour, M., 2016. "Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties," Renewable Energy, Elsevier, vol. 94(C), pages 10-21.
    3. Prusty, B Rajanarayan & Jena, Debashisha, 2017. "A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1286-1302.
    4. Tong, Charles, 2006. "Refinement strategies for stratified sampling methods," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1257-1265.
    5. Sallaberry, C.J. & Helton, J.C. & Hora, S.C., 2008. "Extension of Latin hypercube samples with correlated variables," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 1047-1059.
    6. Abdullah, M.A. & Agalgaonkar, A.P. & Muttaqi, K.M., 2013. "Probabilistic load flow incorporating correlation between time-varying electricity demand and renewable power generation," Renewable Energy, Elsevier, vol. 55(C), pages 532-543.
    7. Headrick, Todd C., 2002. "Fast fifth-order polynomial transforms for generating univariate and multivariate nonnormal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 40(4), pages 685-711, October.
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    Cited by:

    1. Yue Chen & Zhizhong Guo & Hongbo Li & Yi Yang & Abebe Tilahun Tadie & Guizhong Wang & Yingwei Hou, 2020. "Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources," Sustainability, MDPI, vol. 12(2), pages 1-19, January.
    2. Antonio T. Alexandridis, 2020. "Modern Power System Dynamics, Stability and Control," Energies, MDPI, vol. 13(15), pages 1-8, July.

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