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Probabilistic Optimal Power Flow With Correlated Wind Power Uncertainty via Markov Chain Quasi-Monte-Carlo Sampling

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  • Additional Information
    • Publication Information:
      USA: IEEE
    • Publication Date:
      2019
    • Abstract:
      The irregular and truncated probabilistic characteristics of wind power uncertainty lead to unknown influences on the power system operation. In this article, we propose a new probabilistic optimal power flow (POPF) framework, which can cope with such uncertainties, while taking into account the correlations among the wind generation power in multiple wind farms. A truncated multivariate Gaussian mixture model (Trun-MultiGMM) is designed to describe the irregular and multimodal wind power distributions with its typical truncation feature. Then an efficient Markov chain quasi-Monte-Carlo (MCQMC) sampler is developed to deliver wind power samples from the customized Trun-MultiGMM. Numerical simulations are conducted on the publicly available wind generation datasets and multiple benchmark power systems. The results have verified the effectiveness and efficiency of Trun-MultiGMM as well as the proposed POPF framework with MCQMC sampler.
    • Author Affiliations:
      School of Artificial Intelligence and Automation, the State Key Lab of Digital Manufacturing Equipment and Technology, and the Key Lab of Imaging Processing and Intelligence Control, Huazhong University of Science and Technology, Wuhan, China
      School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW, Australia
    • ISSN:
      1551-3203
      1941-0050
    • Accession Number:
      10.1109/TII.2019.2928054
    • Rights:
      Copyright 2005-2012, IEEE
    • AMSID:
      8760433
    • Date of Current Version:
      2019
    • Document Subtype:
      IEEE Transaction
    • Sponsored by:
      IEEE Industrial Electronics Society
    • Accession Number:
      edseee.8760433
  • Citations
    • ABNT:
      SUN, W. et al. Probabilistic Optimal Power Flow With Correlated Wind Power Uncertainty via Markov Chain Quasi-Monte-Carlo Sampling. IEEE Transactions on Industrial Informatics, Industrial Informatics, IEEE Transactions on, IEEE Trans. Ind. Inf, [s. l.], v. 15, n. 11, p. 6058–6069, 2019. DOI 10.1109/TII.2019.2928054. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edseee&AN=edseee.8760433&custid=s8280428. Acesso em: 3 dez. 2020.
    • AMA:
      Sun W, Zamani M, Zhang H, Li Y. Probabilistic Optimal Power Flow With Correlated Wind Power Uncertainty via Markov Chain Quasi-Monte-Carlo Sampling. IEEE Transactions on Industrial Informatics, Industrial Informatics, IEEE Transactions on, IEEE Trans Ind Inf. 2019;15(11):6058-6069. doi:10.1109/TII.2019.2928054
    • APA:
      Sun, W., Zamani, M., Zhang, H., & Li, Y. (2019). Probabilistic Optimal Power Flow With Correlated Wind Power Uncertainty via Markov Chain Quasi-Monte-Carlo Sampling. IEEE Transactions on Industrial Informatics, Industrial Informatics, IEEE Transactions on, IEEE Trans. Ind. Inf, 15(11), 6058–6069. https://doi.org/10.1109/TII.2019.2928054
    • Chicago/Turabian: Author-Date:
      Sun, W., M. Zamani, H. Zhang, and Y. Li. 2019. “Probabilistic Optimal Power Flow With Correlated Wind Power Uncertainty via Markov Chain Quasi-Monte-Carlo Sampling.” IEEE Transactions on Industrial Informatics, Industrial Informatics, IEEE Transactions on, IEEE Trans. Ind. Inf 15 (11): 6058–69. doi:10.1109/TII.2019.2928054.
    • Harvard:
      Sun, W. et al. (2019) ‘Probabilistic Optimal Power Flow With Correlated Wind Power Uncertainty via Markov Chain Quasi-Monte-Carlo Sampling’, IEEE Transactions on Industrial Informatics, Industrial Informatics, IEEE Transactions on, IEEE Trans. Ind. Inf, 15(11), pp. 6058–6069. doi: 10.1109/TII.2019.2928054.
    • Harvard: Australian:
      Sun, W, Zamani, M, Zhang, H & Li, Y 2019, ‘Probabilistic Optimal Power Flow With Correlated Wind Power Uncertainty via Markov Chain Quasi-Monte-Carlo Sampling’, IEEE Transactions on Industrial Informatics, Industrial Informatics, IEEE Transactions on, IEEE Trans. Ind. Inf, vol. 15, no. 11, pp. 6058–6069, viewed 3 December 2020, .
    • MLA:
      Sun, W., et al. “Probabilistic Optimal Power Flow With Correlated Wind Power Uncertainty via Markov Chain Quasi-Monte-Carlo Sampling.” IEEE Transactions on Industrial Informatics, Industrial Informatics, IEEE Transactions on, IEEE Trans. Ind. Inf, vol. 15, no. 11, Nov. 2019, pp. 6058–6069. EBSCOhost, doi:10.1109/TII.2019.2928054.
    • Chicago/Turabian: Humanities:
      Sun, W., M. Zamani, H. Zhang, and Y. Li. “Probabilistic Optimal Power Flow With Correlated Wind Power Uncertainty via Markov Chain Quasi-Monte-Carlo Sampling.” IEEE Transactions on Industrial Informatics, Industrial Informatics, IEEE Transactions on, IEEE Trans. Ind. Inf 15, no. 11 (November 1, 2019): 6058–69. doi:10.1109/TII.2019.2928054.
    • Vancouver/ICMJE:
      Sun W, Zamani M, Zhang H, Li Y. Probabilistic Optimal Power Flow With Correlated Wind Power Uncertainty via Markov Chain Quasi-Monte-Carlo Sampling. IEEE Transactions on Industrial Informatics, Industrial Informatics, IEEE Transactions on, IEEE Trans Ind Inf [Internet]. 2019 Nov 1 [cited 2020 Dec 3];15(11):6058–69. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edseee&AN=edseee.8760433&custid=s8280428