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

Intro to the ARPA-E Grid Optimization Competition In “Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition,” we review the state of the art in practical algorithms for scheduling power-systems operations in the short term and the results of the recent competition organized by the U.S. Advanced Research Projects Agency–Energy. We explain the mixed-integer nonlinear formulation used in the competition for nonspecialists in electrical engineering, the context and organization of the competition, and the performance of competitors. We find that the collective approaches and results of competitors provide support for efforts to move nonlinear optimization techniques into industrial applications, as they have proven to be a robust and efficient alternative to current linear approximation techniques. 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 travels through the electric network. By additionally considering the possibility of component failures and using an accurate alternating current (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 organized Challenge 1 of the Grid Optimization (GO) competition. This special issue includes papers authored by the top three teams in Challenge 1 of the GO Competition (Teams gollnlp, GO-SNIP, and GMI-GO). To introduce these papers and provide context about the 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 these three teams. History: This paper has been accepted for the Operations Research Special Issue on Computational Advances in Short-Term Power System Operations. Funding: I. Aravena and C. G. Petra contributed to this work under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, with support of the Exascale Computing Project [17-SC-20-SC], a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. The contributions of D. K. Molzhan, F. E. Curtis, S. Tu, A. Wächter, E. Wei, and E. Wong were supported by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy [Grant DE-AR0001073]. X. Andy Sun acknowledges the continued support of ARPA-E [Award DE-AR0001089] and the National Science Foundation [Award 1751747]. The Pacific Northwest National Laboratory (PNNL) information, data, or work presented herein was funded in part by ARPA-E under Award 13/CJ000/09/03. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/opre.2022.0315 .