Stability-Constrained Learning for Frequency Regulation in Power Grids with Variable Inertia

TL;DR

This paper introduces a stability-constrained neural network controller for inverter-based frequency regulation in power grids with variable inertia, achieving faster response and lower costs while maintaining real-time applicability.

Contribution

It develops a novel combined linear and neural network controller with stability guarantees for systems with time-varying inertia modeled as a hybrid system.

Findings

Achieves over 50% reduction in control cost compared to optimized linear controller.

Faster mean settling time in frequency regulation.

Comparable performance to LQR without requiring full system knowledge.

Abstract

The increasing penetration of converter-based renewable generation has resulted in faster frequency dynamics, and low and variable inertia. As a result, there is a need for frequency control methods that are able to stabilize a disturbance in the power system at timescales comparable to the fast converter dynamics. This paper proposes a combined linear and neural network controller for inverter-based primary frequency control that is stable at time-varying levels of inertia. We model the time-variance in inertia via a switched affine hybrid system model. We derive stability certificates for the proposed controller via a quadratic candidate Lyapunov function. We test the proposed control on a 12-bus 3-area test network, and compare its performance with a base case linear controller, optimized linear controller, and finite-horizon Linear Quadratic Regulator (LQR). Our proposed controller achieves faster mean settling time and over 50% reduction in average control cost across $100$ inertia scenarios compared to the optimized linear controller. Unlike LQR which requires complete knowledge of the inertia trajectories and system dynamics over the entire control time horizon, our proposed controller is real-time tractable, and achieves comparable performance to LQR.