Data-Driven Koopman Predictive Control for Frequency Regulation of Power Systems using Black-Box IBRs
This addresses frequency stability challenges in power systems with black-box inverters, representing an incremental improvement over existing data-driven methods.
The paper tackles frequency regulation in power systems with uncertain inverter-based resources by proposing a Data-driven Koopman Predictive Control framework, which achieves effective control as demonstrated in numerical benchmarks against Data-enabled Predictive Control.
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.