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Probabilistic load flow with detailed wind generator models considering correlated wind generation and correlated loads

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  • Gupta, Neeraj

Abstract

The enhancement in the penetration of intermittent generation necessitates the need to include uncertain behaviour in the conventional power flow programs. In this paper, four different wind generation models have been incorporated in probabilistic load flow for calculating the probability distribution of the reactive power consumed by the wind generators for three different scenarios; i) uncorrelated wind and uncorrelated loads ii) uncorrelated wind and correlated loads and iii) correlated wind and correlated loads The above mentioned scenarios have been implemented in probabilistic load flow using point estimate method in the IEEE-118 bus test system and accuracy of the results have been validated by comparing these results with those obtained by Monte Carlo simulation studies.

Suggested Citation

  • Gupta, Neeraj, 2016. "Probabilistic load flow with detailed wind generator models considering correlated wind generation and correlated loads," Renewable Energy, Elsevier, vol. 94(C), pages 96-105.
    • Handle: RePEc:eee:renene:v:94:y:2016:i:c:p:96-105
    DOI: 10.1016/j.renene.2016.03.030
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    1. Allen C. Miller, III & Thomas R. Rice, 1983. "Discrete Approximations of Probability Distributions," Management Science, INFORMS, vol. 29(3), pages 352-362, March.
    2. Morales, J.M. & Mínguez, R. & Conejo, A.J., 2010. "A methodology to generate statistically dependent wind speed scenarios," Applied Energy, Elsevier, vol. 87(3), pages 843-855, March.
    3. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
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    Cited by:

    1. Xiao, Qing & Zhou, Shaowu, 2018. "Probabilistic power flow computation considering correlated wind speeds," Applied Energy, Elsevier, vol. 231(C), pages 677-685.
    2. Samet, Haidar & Khorshidsavar, Morteza, 2018. "Analytic time series load flow," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3886-3899.
    3. Benjamin Böckl & Matthias Greiml & Lukas Leitner & Patrick Pichler & Lukas Kriechbaum & Thomas Kienberger, 2019. "HyFlow—A Hybrid Load Flow-Modelling Framework to Evaluate the Effects of Energy Storage and Sector Coupling on the Electrical Load Flows," Energies, MDPI, vol. 12(5), pages 1-25, March.
    4. Prusty, B. Rajanarayan & Jena, Debashisha, 2018. "An over-limit risk assessment of PV integrated power system using probabilistic load flow based on multi-time instant uncertainty modeling," Renewable Energy, Elsevier, vol. 116(PA), pages 367-383.

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