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

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 flow (RD-LPF) model. It applies to both transmission and distribution systems can achieve better robustness than recent models. The key idea is probabilistically constrain through distributionally chance-constrained programming. also allows guaranteeing accuracy chosen operating point. Comparison results with three LPF models demonstrate error RD-LPF model significantly reduced over 2- 70-fold while reducing average error. A compromise between computational efficiency be achieved different ambiguity sets conversion methods.