  This paper proposes a novel machine-learning approach for predicting AC-OPF
solutions that features a fast and scalable training. It is motivated by the
two critical considerations: (1) the fact that topology optimization and the
stochasticity induced by renewable energy sources may lead to fundamentally
different AC-OPF instances; and (2) the significant training time needed by
existing machine-learning approaches for predicting AC-OPF. The proposed
approach is a 2-stage methodology that exploits a spatial decomposition of the
power network that is viewed as a set of regions. The first stage learns to
predict the flows and voltages on the buses and lines coupling the regions, and
the second stage trains, in parallel, the machine-learning models for each
region. Experimental results on the French transmission system (up to 6,700
buses and 9,000 lines) demonstrate the potential of the approach. Within a
short training time, the approach predicts AC-OPF solutions with very high
fidelity and minor constraint violations, producing significant improvements
over the state-of-the-art. The results also show that the predictions can seed
a load flow optimization to return a feasible solution within 0.03% of the
AC-OPF objective, while reducing running times significantly.
