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

• 成果类型: Article
• 署名作者: Aravena, Ignacio; Molzahn, Daniel K.; Zhang, Shixuan; Petra, Cosmin G.; Curtis, Frank E.; Tu, Shenyinying; Wachter, Andreas; Wei, Ermin; Wong, Elizabeth; Gholami, Amin; Sun, Kaizhao; Sun, Xu Andy; Elbert, Stephen T.; Holzer, Jesse T.; Veeramany, Arun
• 署名单位: United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University System of Georgia; Georgia Institute of Technology; Lehigh University; Northwestern University; University of California System; University of California San Diego; Massachusetts Institute of Technology (MIT); United States Department of Energy (DOE); Pacific Northwest National Laboratory
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.0315
• 发表日期: 2023
• 关键词: part i; algorithm; dispatch

摘要

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.