Data-Driven Successive Linearization for Optimal Voltage Control

TL;DR

This paper introduces a data-driven successive linearization method for voltage control in power systems, effectively handling nonlinear power flow constraints and adapting quickly to load changes.

Contribution

It proposes a novel data-driven linearization approach that improves voltage regulation by ensuring convergence near optimal solutions under nonlinear power flow conditions.

Findings

Achieves fast convergence in voltage control tasks

Adapts quickly to changing net loads

Ensures solutions remain feasible under nonlinear dynamics

Abstract

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.