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To limit the probability of unacceptable worst-case linearization errors thatmight yield risks for power system operations, this letter proposes a robustdata-driven linear power flow (RD-LPF) model. It is applicable to bothtransmission and distribution systems and can achieve better robustness thanthe recent data-driven models. The key idea is to probabilistically constrainthe worst-case errors through distributionally robust chance-constrainedprogramming. It also allows guaranteeing the linearization accuracy for achosen operating point. Comparison results with three recent LPF modelsdemonstrate that the worst-case error of the RD-LPF model is significantlyreduced over 2- to 70-fold while reducing the average error. A compromisebetween computational efficiency and accuracy can be achieved through differentambiguity sets and conversion methods.

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