  With increasing share of renewables in power generation mix, system operators
would need to run Optimal Power Flow (OPF) problems closer to real-time to
better manage uncertainty. Given that OPF is an expensive optimization problem
to solve, shifting computational effort away from real-time to offline training
by machine learning techniques has become an intense research area. In this
paper, we introduce a method for solving OPF problems, which can substantially
reduce solve times of the two-step hybrid techniques that comprise of a neural
network with a subsequent OPF step guaranteeing optimal solutions. A neural
network that predicts the binding status of constraints of the system is used
to generate an initial reduced OPF problem, defined by removing the predicted
non-binding constraints. This reduced model is then extended in an iterative
manner until guaranteeing an optimal solution to the full OPF problem. The
classifier is trained using a meta-loss objective, defined by the total
computational cost of solving the reduced OPF problems constructed during the
iterative procedure. Using a wide range of DC- and AC-OPF problems, we
demonstrate that optimizing this meta-loss objective results in a classifier
that significantly outperforms conventional loss functions used to train neural
network classifiers. We also provide an extensive analysis of the investigated
grids as well as an empirical limit of performance of machine learning
techniques providing optimal OPF solutions.
