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.

中文翻译：

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具有概率约束最坏情况误差的鲁棒数据驱动线性潮流模型


为了限制可能给电力系统运行带来风险的不可接受的最坏情况线性化误差的可能性，这封信提出了一种稳健的数据驱动的线性功率流（RD-LPF）模型。它适用于输电和配电系统，并且比最近的数据驱动模型能够实现更好的鲁棒性。关键思想是通过分布稳健的机会约束编程来概率地限制最坏情况的错误。它还可以保证所选工作点的线性化精度。与最近的三个 LPF 模型的比较结果表明，RD-LPF 模型的最坏情况误差显着降低了 2 至 70 倍，同时降低了平均误差。通过不同的模糊度集和转换方法可以实现计算效率和精度之间的折衷。