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

• URL: http://arxiv.org/abs/2111.01228v2
• Date: Wed, 3 Nov 2021 18:18:00 GMT
• Title: OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets
• Authors: Trager Joswig-Jones, Kyri Baker, Ahmed S. Zamzam
• Abstract summary: This paper develops the OPF-Learn package for Julia and Python, which uses a computationally efficient approach to create representative datasets. The framework is shown to generate datasets that are more representative of the entire feasible space versus traditional techniques seen in the literature.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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