  Increasing levels of renewable generation motivate a growing interest in
data-driven approaches for AC optimal power flow (AC OPF) to manage
uncertainty; however, a lack of disciplined dataset creation and benchmarking
prohibits useful comparison among approaches in the literature. To instill
confidence, models must be able to reliably predict solutions across a wide
range of operating conditions. This paper develops the OPF-Learn package for
Julia and Python, which uses a computationally efficient approach to create
representative datasets that span a wide spectrum of the AC OPF feasible
region. Load profiles are uniformly sampled from a convex set that contains the
AC OPF feasible set. For each infeasible point found, the convex set is reduced
using infeasibility certificates, found by using properties of a relaxed
formulation. The framework is shown to generate datasets that are more
representative of the entire feasible space versus traditional techniques seen
in the literature, improving machine learning model performance.
