A Generalized Computationally Efficient Copula-Polynomial Chaos Framework for Probabilistic Power Flow Considering Nonlinear Correlations of PV Injections

Ye, Ketian; Zhao, Junbo; Zhang, Yingchen; Liu, Xiaodong; Zhang, Hongming

doi:10.1016/j.ijepes.2021.107727

Title: A Generalized Computationally Efficient Copula-Polynomial Chaos Framework for Probabilistic Power Flow Considering Nonlinear Correlations of PV Injections

Journal Article · Mon Nov 01 00:00:00 EDT 2021 · International Journal of Electrical Power & Energy Systems

DOI:https://doi.org/10.1016/j.ijepes.2021.107727· OSTI ID:1833353

Ye, Ketian; Zhao, Junbo;

; Liu, Xiaodong; Zhang, Hongming

This paper develops a general computationally efficient copula-polynomial chaos expansion (copula-PCE) framework for power system probabilistic power flow that can handle both linear and nonlinear correlations of uncertain power injections, such as wind and PVs. A data-driven copula statistical model is used to capture both nonlinear and linear correlations of uncertain power injections. This allows us to resort to the Rosenblatt transformation to transform correlated variables into independent ones while preserving the dependence structure. It paves the way for leveraging PCE for surrogate modeling and uncertainty quantification of power flow results. To further improve the computational efficiency of the proposed method without sacrificing accuracy, two strategies are developed and unified together. Specifically, the branch flow sensitivity analysis (BFSA) is used to select an appropriate number of outputs that need PCE surrogate modeling while the analysis of variance (ANOVA) scheme enables us to reduce the number of basis functions and coefficients evaluations for each PCE surrogate. Simulations carried out on the IEEE 57-bus and 118-bus and PEGASE 1354-bus systems show that the proposed framework can obtain better accuracy and computational efficiency than the existing PCE-based methods with linear correlation assumption and other Monte Carlo-based methods. The sensitivities to different copula types and parameters are also investigated.

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Research Organization:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind Energy Technologies Office (EE-4W); USDOE Grid Modernization Laboratory Consortium

DOE Contract Number:: AC36-08GO28308

OSTI ID:: 1833353

Report Number(s):: NREL/JA-5D00-81311; MainId:82084; UUID:381fcc73-0471-4329-a78f-7c2aa4481f23; MainAdminID:63380

Journal Information:: International Journal of Electrical Power & Energy Systems, Vol. 136

Country of Publication:: United States

Language:: English

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Title: A Generalized Computationally Efficient Copula-Polynomial Chaos Framework for Probabilistic Power Flow Considering Nonlinear Correlations of PV Injections

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