The linearized power flow (PF) model is mainly used to make the optimal power flow (OPF) problem convex. However, existing data-driven linear PF models are not applicable for OPF calculation since the Kirchhoff's law (KCL) constraints are neglected. In this letter, we propose a data-driven linear PF model incorporating the KCL constraints and can be embedded in OPF for distribution networks (DNs). By combining the support vector regression (SVR) and ridge regression (RR) algorithms, the proposed method is robust against bad data in measurements. Numerical tests show that the proposed model has much higher accuracy than the existing linear models, especially for OPF calculation.

中文翻译：

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一种数据驱动的配电网线性最优潮流模型

线性化潮流（PF）模型主要用于使最优潮流（OPF）问题凸化。然而，现有的数据驱动线性 PF 模型不适用于 OPF 计算，因为忽略了基尔霍夫定律 (KCL) 约束。在这封信中，我们提出了一个包含 KCL 约束的数据驱动线性 PF 模型，并且可以嵌入到配电网络 (DN) 的 OPF 中。通过结合支持向量回归 (SVR) 和岭回归 (RR) 算法，所提出的方法对测量中的不良数据具有鲁棒性。数值试验表明，所提出的模型比现有的线性模型具有更高的精度，特别是对于OPF计算。