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

Ketian Ye, Junbo Zhao, Rui Yang, Yingchen Zhang, Xiaodong Liu

Research output: Contribution to conferencePaperpeer-review

4 Scopus Citations

Abstract

This paper develops a generalized Copula-polynomial chaos expansion (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 the 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. This paves the way of leveraging the PCE for surrogate modeling and uncertainty quantification of power flow results, i.e., achieving the probabilistic distributions of power flows. Simulations carried out on the IEEE 57-bus system show that the proposed framework can get much more accurate results than other alternatives with different linear and nonlinear power injection correlations.

Original languageAmerican English
Number of pages6
DOIs
StatePublished - 11 Apr 2021
Event52nd North American Power Symposium, NAPS 2020 - Tempe, United States
Duration: 11 Apr 202113 Apr 2021

Conference

Conference52nd North American Power Symposium, NAPS 2020
Country/TerritoryUnited States
CityTempe
Period11/04/2113/04/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

NREL Publication Number

  • NREL/CP-5D00-80938

Keywords

  • copula
  • nonlinear correlations
  • polynomial chaos
  • Probabilistic power flow
  • uncertainty quantification

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