Data-Driven Koopman Predictive Control for Frequency Regulation of Power Systems using Black-Box IBRs

TL;DR

This paper introduces a data-driven Koopman predictive control method for frequency regulation in inverter-based power systems, effectively handling model uncertainties without requiring explicit system models.

Contribution

It develops a novel data-driven control framework using Koopman theory and Willems' lemma, enabling model-free frequency regulation for complex inverter-based resources.

Findings

Effective frequency control demonstrated in numerical simulations.

Outperforms traditional data-enabled predictive control (DeePC).

Shows a tunable trade-off between tracking performance and control effort.

Abstract

Model uncertainty of inverter-based resources (IBRs) presents significant challenges for power system control and stability. This work studies secondary frequency regulation in inverter-based power systems using a Data-driven Koopman Predictive Control (DKPC) framework. The method employs Koopman theory to lift the nonlinear system dynamics into a higher-dimensional space where they can be approximated as linear. Based on Willems' fundamental lemma, a behavioral model is constructed directly from lifted input-output data. A receding-horizon predictive control formulation is then provided that operates entirely using observed data, without requiring a parametric model, while satisfying explicit constraints on the control input and system output. The proposed approach is particularly suited for IBRs with complex or uncertain dynamics. Numerical results demonstrate its effectiveness for frequency control as benchmarked against the Data-enabled Predictive Control (DeePC). The trade-off between tracking performance and control effort is illustrated through tuning of the weighting parameters.