Learning Local Volt/Var Controllers Towards Efficient Network Operation with Stability Guarantees

TL;DR

This paper introduces a data-driven local Volt/Var control framework for distribution networks that learns optimal equilibrium points and guarantees stability, improving voltage regulation efficiency.

Contribution

It proposes a novel two-step approach combining data-driven equilibrium learning with a stable control scheme for voltage regulation.

Findings

The control scheme maintains voltage within limits in simulations.

Theoretical stability guarantees are established.

The approach effectively guides the network to optimal operating points.

Abstract

This paper considers the problem of voltage regulation in distribution networks. The primary motivation is to keep voltages within preassigned operating limits by commanding the reactive power output of distributed energy resources (DERs) deployed in the grid. We develop a framework for developing local Volt/Var control that comprises two main steps. In the first, by exploiting historical data and for each DER, we learn a function representing the desirable equilibrium points for the power network. These points approximate solutions of an Optimal Power Flow (OPF) problem. In the second, we propose a control scheme for steering the network towards these favorable configurations. Theoretical conditions are derived to formally guarantee the stability of the developed control scheme, and numerical simulations illustrate the effectiveness of the proposed approach.