{"ID":2831963,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06715","arxiv_id":"2512.06715","title":"GPU-Accelerated Optimization Solver for Unit Commitment in Large-Scale Power Grids","abstract":"This work presents a GPU-accelerated solver for the unit commitment (UC) problem in large-scale power grids. The solver uses the Primal-Dual Hybrid Gradient (PDHG) algorithm to efficiently solve the relaxed linear subproblem, achieving faster bound estimation and improved crossover and branch-and-bound convergence compared to conventional CPU-based methods. These improvements significantly reduce the total computation time for the mixed-integer linear UC problem. The proposed approach is validated on large-scale systems, including 4224-, 6049-, and 6717-bus networks with long control horizons and computationally intensive problems, demonstrating substantial speed-ups while maintaining solution quality.","short_abstract":"This work presents a GPU-accelerated solver for the unit commitment (UC) problem in large-scale power grids. The solver uses the Primal-Dual Hybrid Gradient (PDHG) algorithm to efficiently solve the relaxed linear subproblem, achieving faster bound estimation and improved crossover and branch-and-bound convergence comp...","url_abs":"https://arxiv.org/abs/2512.06715","url_pdf":"https://arxiv.org/pdf/2512.06715v1","authors":"[\"Hussein Sharadga\",\"Javad Mohammadi\"]","published":"2025-12-07T08:09:12Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"cs.AR\"]","methods":"[]","has_code":false}