Scalable Learning for Optimal Load Shedding Under Power Grid Emergency Operations. (arXiv:2111.11980v2 [cs.LG] UPDATED)

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 …

arxiv emergency learning operations power