A Data-Driven Nonparametric Approach for Probabilistic Load-Margin Assessment Considering Wind Power Penetration

Xu, Yijun; Mili, Lamine; Korkali, Mert; Karra, Kiran; Zheng, Zongsheng; Chen, Xiao

doi:10.1109/tpwrs.2020.2987900

Title: A Data-Driven Nonparametric Approach for Probabilistic Load-Margin Assessment Considering Wind Power Penetration

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Abstract

A modern power system is characterized by an increasing penetration of wind power, which results in large uncertainties in its states. These uncertainties must be quantified properly; otherwise, the system security may be threatened. Facing this challenge, here we propose a cost-effective, data-driven approach to assessing a power system's load margin probabilistically. Using actual wind data, a kernel density estimator is applied to infer the nonparametric wind speed distributions, which are further merged into the framework of a vine copula. The latter enables us to simulate complex multivariate and highly dependent model inputs with a variety of bivariate copulae that precisely represent the tail dependence in the correlated samples. Furthermore, to reduce the prohibitive computational time of traditional Monte-Carlo simulations that process a large amount of samples, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model through a Bayesian-learning framework. To accelerate the convergence rate of this Bayesian algorithm, a truncated polynomial chaos surrogate, which serves as a highly efficient, parametric Bayesian prior, is developed. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. Results of simulations that are performed onmore »« less

Authors:

^[1];

^[2]; Karra, Kiran ^[3]; Zheng, Zongsheng ^[1];

^[2]

Virginia Tech, Northern Virginia Center, Falls Church, VA (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Johns Hopkins Univ., Baltimore, MD (United States)

Publication Date:: Mon Apr 20 00:00:00 EDT 2020

Research Org.:: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF); USDOE Office of Electricity (OE)

OSTI Identifier:: 1698284

Report Number(s):: LLNL-JRNL-799106; TPWRS-01860-2019
Journal ID: ISSN 0885-8950; 1002026

Grant/Contract Number:: AC52-07NA27344; 1917308

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Power Systems

Additional Journal Information:: Journal Volume: 35; Journal Issue: 6; Journal ID: ISSN 0885-8950

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 42 ENGINEERING; 24 POWER TRANSMISSION AND DISTRIBUTION; 97 MATHEMATICS AND COMPUTING; 17 WIND ENERGY; probabilistic load margin; data-driven nonparametric model; reduced-order modeling; dependence; vine copula; uncertainty

Citation Formats


                    Xu, Yijun, Mili, Lamine, Korkali, Mert, Karra, Kiran, Zheng, Zongsheng, and Chen, Xiao. A Data-Driven Nonparametric Approach for Probabilistic Load-Margin Assessment Considering Wind Power Penetration.  United States: N. p., 2020. 
Web.  doi:10.1109/tpwrs.2020.2987900.

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                    Xu, Yijun, Mili, Lamine, Korkali, Mert, Karra, Kiran, Zheng, Zongsheng, & Chen, Xiao. A Data-Driven Nonparametric Approach for Probabilistic Load-Margin Assessment Considering Wind Power Penetration.  United States.  https://doi.org/10.1109/tpwrs.2020.2987900

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                    Xu, Yijun, Mili, Lamine, Korkali, Mert, Karra, Kiran, Zheng, Zongsheng, and Chen, Xiao. Mon .  
"A Data-Driven Nonparametric Approach for Probabilistic Load-Margin Assessment Considering Wind Power Penetration".  United States.  https://doi.org/10.1109/tpwrs.2020.2987900.  https://www.osti.gov/servlets/purl/1698284.

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@article{osti_1698284,

  title        = {A Data-Driven Nonparametric Approach for Probabilistic Load-Margin Assessment Considering Wind Power Penetration},

  author       = {Xu, Yijun and Mili, Lamine and Korkali, Mert and Karra, Kiran and Zheng, Zongsheng and Chen, Xiao},

  abstractNote = {A modern power system is characterized by an increasing penetration of wind power, which results in large uncertainties in its states. These uncertainties must be quantified properly; otherwise, the system security may be threatened. Facing this challenge, here we propose a cost-effective, data-driven approach to assessing a power system's load margin probabilistically. Using actual wind data, a kernel density estimator is applied to infer the nonparametric wind speed distributions, which are further merged into the framework of a vine copula. The latter enables us to simulate complex multivariate and highly dependent model inputs with a variety of bivariate copulae that precisely represent the tail dependence in the correlated samples. Furthermore, to reduce the prohibitive computational time of traditional Monte-Carlo simulations that process a large amount of samples, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model through a Bayesian-learning framework. To accelerate the convergence rate of this Bayesian algorithm, a truncated polynomial chaos surrogate, which serves as a highly efficient, parametric Bayesian prior, is developed. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. Results of simulations that are performed on several test systems reveal the impressive performance of the proposed method in the probabilistic load-margin assessment.},

  doi          = {10.1109/tpwrs.2020.2987900},

  journal      = {IEEE Transactions on Power Systems},

  number       = 6,

  volume       = 35,

  place        = {United States},

  year         = {Mon Apr 20 00:00:00 EDT 2020},

  month        = {Mon Apr 20 00:00:00 EDT 2020}

}

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