  Conventional synchronous generators are gradually being re-placed by
low-inertia inverter-based resources. Such transition introduces more
complicated operation conditions, frequency deviation stability and
rate-of-change-of-frequency (RoCoF) security are becoming great challenges.
This paper presents an active linearized sparse neural network (ALSNN) based
frequency-constrained unit commitment (ALSNN-FCUC) model to guarantee frequency
stability following the worst generator outage case while ensuring operational
efficiency. A generic data-driven predictor is first trained to predict maximal
frequency deviation and the highest locational RoCoF simultaneously based on a
high-fidelity simulation dataset, and then incorporated into ALSNN-FCUC model.
Sparse computation is introduced to avoid dense matrix multiplications. An
active data sampling method is proposed to maintain the bindingness of the
frequency related constraints. Besides, an active ReLU linearization method is
implemented to further improve the algorithm efficiency while retaining
solution quality. The effectiveness of proposed ALSNN-FCUC model is
demonstrated on the IEEE 24-bus system by conducting time domain simulations
using PSS/E.
