Transient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning

This paper presents a framework for addressing the critical loss of transient stability resulting from reduced inertia in grids with high penetration of inverter-based resources (IBRs). The proposed method integrates a predictive deep learning model with information gap decision theory to create a risk-averse dispatch strategy. By reformulating the conventional virtual inertia scheduling (VIS) problem, the framework uses early predictions of postfault dynamics to proactively redispatch resources, ensuring the system's center of inertia remains stable under worst-case contingencies. Validated on the IEEE 39-bus system with a 70% IBR penetration, the proposed approach prevents system collapse where a conventional VIS strategy fails, ensuring frequency stability at a cost increase of only 5%.