  With the increasing penetration of renewable energy, frequency response and
its security are of significant concerns for reliable power system operations.
Frequency-constrained unit commitment (FCUC) is proposed to address this
challenge. Despite existing efforts in modeling frequency characteristics in
unit commitment (UC), current strategies can only handle oversimplified
low-order frequency response models and do not consider wide-range operating
conditions. This paper presents a generic data-driven framework for FCUC under
high renewable penetration. Deep neural networks (DNNs) are trained to predict
the frequency response using real data or high-fidelity simulation data. Next,
the DNN is reformulated as a set of mixed-integer linear constraints to be
incorporated into the ordinary UC formulation. In the data generation phase,
all possible power injections are considered, and a region-of-interests active
sampling is proposed to include power injection samples with frequency nadirs
closer to the UFLC threshold, which significantly enhances the accuracy of
frequency constraints in FCUC. The proposed FCUC is verified on the the IEEE
39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies
the effectiveness of FCUC in frequency-secure generator commitments.
