Physics-Data-Driven Power Flow Linearization Considering Topological Remedial Actions

This paper proposes a novel physics-data-driven method for the linearization of AC power flow equations in the context of topological remedial actions. As opposed to physics-based works, the proposed method does not require the re-establishment of the linear model to follow the system changes induced by remedial actions, such as line switching and phase shifting angle variations. By ingeniously leveraging physics-driven aspects, such as hierarchical topology clustering, as well as data-driven regression, the proposed approach can establish a linear mapping between power flows and remedial actions and estimate the new operating point in the event of a remedial action with high accuracy and computational efficiency. Numerical results on a real-world European power network demonstrate the effectiveness of the proposed physics-data-driven linear power flow method in a practical setting. Particularly, the proposed method outperforms purely physics-driven and data-driven methods when a topological remedial action occurs.