Stability Constrained Voltage Control in Distribution Grids with Arbitrary Communication Infrastructure

TL;DR

This paper introduces a unified framework for designing learning-based voltage controllers in distribution grids that leverage arbitrary communication infrastructure to enhance stability and performance.

Contribution

It proposes a novel design method for ICNN-based controllers that incorporate communication infrastructure, extending beyond decentralized approaches and ensuring stability.

Findings

Controllers improve voltage regulation performance.

Communication infrastructure enhances control effectiveness.

Simulation confirms stability and efficiency gains.

Abstract

We consider the problem of designing learning-based reactive power controllers that perform voltage regulation in distribution grids while ensuring closed-loop system stability. In contrast to existing methods, where the provably stable controllers are restricted to be decentralized, we propose a unified design framework that enables the controllers to take advantage of an arbitrary communication infrastructure on top of the physical power network. This allows the controllers to incorporate information beyond their local bus, covering existing methods as a special case and leading to less conservative constraints on the controller design. We then provide a design procedure to construct input convex neural network (ICNN) based controllers that satisfy the identified stability constraints by design under arbitrary communication scenarios, and train these controllers using supervised learning. Simulation results on the the University of California, San Diego (UCSD) microgrid testbed illustrate the effectiveness of the framework and highlight the role of communication in improving control performance.