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Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition

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arxiv 2206.07843 v1 pith:UCBY35OP submitted 2022-06-15 math.OC

Ignacio Aravena , Daniel K. Molzahn , Shixuan Zhang , Cosmin G. Petra , Frank E. Curtis , Shenyinying Tu , Andreas Wächter , Ermin Wei
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Elizabeth Wong Amin Gholami Kaizhao Sun Xu Andy Sun Stephen T. Elbert Jesse T. Holzer Arun Veeramany
This is my paper · ORCID
classification math.OC
keywords powercompetitionelectricflowproblemsc-ac-opfalgorithmschallenge
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The optimal power flow problem is central to many tasks in the design and operation of electric power grids. This problem seeks the minimum cost operating point for an electric power grid while satisfying both engineering requirements and physical laws describing how power flows through the electric network. By additionally considering the possibility of component failures and using an accurate AC power flow model of the electric network, the security-constrained AC optimal power flow (SC-AC-OPF) problem is of paramount practical relevance. To assess recent progress in solution algorithms for SC-AC-OPF problems and spur new innovations, the U.S. Department of Energy's Advanced Research Projects Agency--Energy (ARPA-E) organized Challenge 1 of the Grid Optimization (GO) competition. This paper describes the SC-AC-OPF problem formulation used in the competition, overviews historical developments and the state of the art in SC-AC-OPF algorithms, discusses the competition, and summarizes the algorithms used by the top three teams in Challenge 1 of the GO Competition (Teams gollnlp, GO-SNIP, and GMI-GO).

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Optimization Learning

    math.OC 2025-01 conditional novelty 4.0 of 10

    Neural proxies with repair and completion layers can learn to solve parametric optimization problems in milliseconds, returning feasible solutions with empirically tight dual bounds on large power-grid instances.

  2. Large-scale Grid Optimization: The Workhorse of Future Grid Computations

    eess.SY 2025-01 unverdicted novelty 2.0 of 10

    A review paper that categorizes large-scale power grid optimization methods and reports that physics-based solvers dominate while physics-constrained machine learning is emerging.

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