Robust Data-Driven Linear Power Flow Model With Probability Constrained Worst-Case Errors

doi:10.1109/TPWRS.2022.3189543

Robust Data-Driven Linear Power Flow Model With Probability Constrained Worst-Case Errors

2022-09-01

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DOI：10.1109/TPWRS.2022.3189543

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To limit the probability of unacceptable worst-case linearization errors that might yield risks for power system operations, this letter proposes a robust data-driven linear power flow (RD-LPF) model. It applies to both transmission and distribution systems and can achieve better robustness than the recent data-driven models. The key idea is to probabilistically constrain the worst-case errors through distributionally robust chance-constrained programming. It also allows guaranteeing the linearization accuracy for a chosen operating point. Comparison results with three recent LPF models demonstrate that the worst-case error of the RD-LPF model is significantly reduced over 2- to 70-fold while reducing the average error. A compromise between computational efficiency and accuracy can be achieved through different ambiguity sets and conversion methods.

Keywords:

Mathematical models

Computational modeling

Probability distribution

Load flow

Data models

Uncertainty

Programming

Data-driven

distributionally robust

linear power flow

worst-case errors

Robust Data-Driven Linear Power Flow Model With Probability Constrained Worst-Case Errors

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