  The fast growth of inverter-based resources such as wind plants and solar
farms will largely replace and reduce conventional synchronous generators in
the future renewable energy-dominated power grid. Such transition will make the
system operation and control much more complicated; and one key challenge is
the low inertia issue that has been widely recognized. However, locational
post-contingency rate of change of frequency (RoCoF) requirements to
accommodate significant inertia reduction has not been fully investigated in
the literature. This paper presents a convolutional neural network (CNN) based
RoCoF-constrained unit commitment (CNN-RCUC) model to guarantee RoCoF stability
following the worst generator outage event while ensuring operational
efficiency. A generic CNN based predictor is first trained to track the highest
locational RoCoF based on a high-fidelity simulation dataset. The RoCoF
predictor is then formulated as MILP constraints into the unit commitment
model. Case studies are carried out on the IEEE 24-bus system, and simulation
results obtained with PSS/E indicate that the proposed method can ensure
locational post-contingency RoCoF stability without conservativeness.
