OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets
This addresses the problem of unreliable data-driven approaches for AC OPF in power systems, but it is incremental as it focuses on dataset creation rather than a new method for solving AC OPF.
The paper tackles the lack of disciplined dataset creation for AC optimal power flow (AC OPF) by developing the OPF-Learn package, which generates more representative datasets that improve machine learning model performance compared to traditional techniques.
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.