TITLE: OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets 

ABSTRACT: 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.
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.
OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets
http://arxiv.org/abs/2111.01228v2