A Data-Driven Linear Optimal Power Flow Model for Distribution Networks

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.