  Prompt and effective corrective actions in response to unexpected
contingencies are crucial for improving power system resilience and preventing
cascading blackouts. The optimal load shedding (OLS) accounting for network
limits has the potential to address the diverse system-wide impacts of
contingency scenarios as compared to traditional local schemes. However, due to
the fast cascading propagation of initial contingencies, real-time OLS
solutions are challenging to attain in large systems with high computation and
communication needs. In this paper, we propose a decentralized design that
leverages offline training of a neural network (NN) model for individual load
centers to autonomously construct the OLS solutions from locally available
measurements. Our learning-for-OLS approach can greatly reduce the computation
and communication needs during online emergency responses, thus preventing the
cascading propagation of contingencies for enhanced power grid resilience.
Numerical studies on both the IEEE 118-bus system and a synthetic Texas
2000-bus system have demonstrated the efficiency and effectiveness of our
scalable OLS learning design for timely power system emergency operations.
