{"ID":2855093,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.13333","arxiv_id":"2510.13333","title":"An Augmented Lagrangian Method on GPU for Security-Constrained AC Optimal Power Flow","abstract":"We present a new algorithm for solving large-scale security-constrained optimal power flow in polar form (AC-SCOPF). The method builds on Nonlinearly Constrained augmented Lagrangian (NCL), an augmented Lagrangian method in which the subproblems are solved using an interior-point method. NCL has two key advantages for large-scale SC-OPF. First, NCL handles difficult problems such as infeasible ones or models with complementarity constraints. Second, the augmented Lagrangian term naturally regularizes the Newton linear systems within the interior-point method, enabling to solve the Newton systems with a pivoting-free factorization that can be efficiently parallelized on GPUs. We assess the performance of our implementation, called MadNCL, on large-scale corrective AC-SCOPFs, with complementarity constraints modeling the corrective actions. Numerical results show that MadNCL can solve AC-SCOPF with 500 buses and 256 contingencies fully on the GPU in less than 3 minutes, whereas Knitro takes more than 3 hours to find an equivalent solution.","short_abstract":"We present a new algorithm for solving large-scale security-constrained optimal power flow in polar form (AC-SCOPF). The method builds on Nonlinearly Constrained augmented Lagrangian (NCL), an augmented Lagrangian method in which the subproblems are solved using an interior-point method. NCL has two key advantages for...","url_abs":"https://arxiv.org/abs/2510.13333","url_pdf":"https://arxiv.org/pdf/2510.13333v1","authors":"[\"François Pacaud\",\"Armin Nurkanović\",\"Anton Pozharskiy\",\"Alexis Montoison\",\"Sungho Shin\"]","published":"2025-10-15T09:17:35Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}