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

TL;DR

This paper introduces a robust data-driven linear power flow model that probabilistically constrains worst-case errors, significantly improving accuracy and robustness for power system analysis.

Contribution

It proposes a novel distributionally robust chance-constrained approach to limit worst-case errors in linear power flow models, enhancing robustness over existing methods.

Findings

Worst-case error reduced by 2 to 70 times

Average error decreased compared to previous models

Flexible trade-off between accuracy and computational efficiency

Abstract

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 is applicable 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.