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An Improved Stochastic Response Surface Method Based Probabilistic Load Flow for Studies on Correlated Wind Speeds in the AC/DC Grid

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  • Ziwei Zhu

    (Information Engineering College, Nanchang University, Nanchang 330031, Jiangxi, China)

  • Shifan Lu

    (Information Engineering College, Nanchang University, Nanchang 330031, Jiangxi, China)

  • Sui Peng

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

Abstract

This paper proposed a probabilistic load flow technique of AC/VSC-MTDC (Alternate Current/Voltage Source Control-Multiple Terminal Direct Current) hybrid grids based on an improved stochastic response surface method. The applied traditional stochastic response surface method is inherent with the capability to tackle correlated normal variables; however, the accuracy is poor in the case of correlated diverse distributions. To address this issue, NATAF transformation was adopted to transform the correlated wind speeds and loads following arbitrary distributions into the variables that are subject to standard normal distributions. The collection points could be selected to establish the polynomial relationship among the independent standard normal variables and the output responses. Then, the probability distributions and statistics of the responses could be accurately and efficiently estimated. The modified IEEE 14-bus system, involving an additional VSC-MTDC system, wind speeds following various distributions, and diverse consumer behaviors, was used to demonstrate the validity and capability of the proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3501-:d:190839
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    References listed on IDEAS

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    1. Xuexia Zhang & Zhiqi Guo & Weirong Chen, 2017. "Probabilistic Power Flow Method Considering Continuous and Discrete Variables," Energies, MDPI, vol. 10(5), pages 1-17, April.
    2. Yanbo Che & Wenxun Li & Xialin Li & Jinhuan Zhou & Shengnan Li & Xinze Xi, 2017. "An Improved Coordinated Control Strategy for PV System Integration with VSC-MVDC Technology," Energies, MDPI, vol. 10(10), pages 1-14, October.
    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. González-Aparicio, I. & Zucker, A., 2015. "Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain," Applied Energy, Elsevier, vol. 159(C), pages 334-349.
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    Cited by:

    1. 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.

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