Data-Driven Successive Linearization for Optimal Voltage Control
This addresses voltage regulation challenges for power distribution systems, particularly under heavy renewable injection, with an incremental improvement over existing linear approximation methods.
The paper tackles voltage fluctuations in power distribution systems caused by renewable generation and varying loads by proposing a data-driven successive linearization approach for voltage control under nonlinear power flow constraints, achieving fast convergence and quick adaptation to load changes.
Power distribution systems are increasingly exposed to large voltage fluctuations driven by intermittent renewable generation and time varying loads (e.g., electric vehicles and storage). To address this challenge, a number of advanced controllers have been proposed for voltage regulation. However, these controllers typically rely on fixed linear approximations of voltage dynamics. As a result, the solutions may become infeasible when applied to the actual voltage behavior governed by nonlinear power flow equations, particularly under heavy power injection from distributed energy resources. This paper proposes a data-driven successive linearization approach for voltage control under nonlinear power flow constraints. By leveraging the fact that the deviation between the nonlinear power flow solution and its linearization is bounded by the distance from the operating point, we perform data-driven linearization around the most recent operating point. Convergence of the proposed method to a neighborhood of KKT points is established by exploiting the convexity of the objective function and structural properties of the nonlinear constraints. Case studies show that the proposed approach achieves fast convergence and adapts quickly to changes in net load.