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

Conference · Mon Jun 21 04:00:00 EDT 2021

DOI:https://doi.org/10.1109/NAPS50074.2021.9449783· OSTI ID:1820589

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

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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1820589
• Report Number(s): NREL/CP-5D00-80938; MainId:78716; UUID:787e4692-7130-461e-a09c-498ae5999411; MainAdminID:61792
• Country of Publication: United States
• Language: English

Related Subjects

POWER TRANSMISSION AND DISTRIBUTION
copula
nonlinear correlations
polynomial chaos
probabilistic power flow
uncertainty quantification