Learning Invariant Stabilizing Controllers for Frequency Regulation under Variable Inertia

TL;DR

This paper introduces a learning-based, invariant controller design for frequency regulation in power systems with variable inertia, ensuring stability without mode knowledge and demonstrating effectiveness through simulations.

Contribution

It proposes a novel, interpretable linear invariant controller learned from LQR data that stabilizes systems with switching inertia modes without requiring mode information.

Findings

Controller guarantees stability across inertia modes

Effective stabilization demonstrated in 12-bus network simulations

Communication-free local implementation is feasible

Abstract

Declines in cost and concerns about the environmental impact of traditional generation have boosted the penetration of renewables and non-conventional distributed energy resources into the power grid. The intermittent availability of these resources causes the inertia of the power system to vary over time. As a result, there is a need to go beyond traditional controllers designed to regulate frequency under the assumption of invariant dynamics. This paper presents a learning-based framework for the design of stable controllers based on imitating datasets obtained from linear-quadratic regulator (LQR) formulations for different switching sequences of inertia modes. The proposed controller is linear and invariant, thereby interpretable, does not require the knowledge of the current operating mode, and is guaranteed to stabilize the switching power dynamics. We also show that it is always possible to stabilize the switched system using a communication-free local controller, whose implementation only requires each node to use its own state. Simulations on a 12-bus 3-region network illustrate our results.