  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.
