  Effective and timely responses to unexpected contingencies are crucial for
enhancing the resilience of power grids. Given the fast, complex process of
cascading propagation, corrective actions such as optimal load shedding (OLS)
are difficult to attain in large-scale networks due to the computation
complexity and communication latency issues. This work puts forth an innovative
learning-for-OLS approach by constructing the optimal decision rules of load
shedding under a variety of potential contingency scenarios through offline
neural network (NN) training. Notably, the proposed NN-based OLS decisions are
fully decentralized, enabling individual load centers to quickly react to the
specific contingency using readily available local measurements. Numerical
studies on the IEEE 14-bus system have demonstrated the effectiveness of our
scalable OLS design for real-time responses to severe grid emergency events.
