GPU-Accelerated Optimization Solver for Unit Commitment in Large-Scale Power Grids
Optimization and Control
2025-12-09 v1 Hardware Architecture
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.
Keywords
Cite
@article{arxiv.2512.06715,
title = {GPU-Accelerated Optimization Solver for Unit Commitment in Large-Scale Power Grids},
author = {Hussein Sharadga and Javad Mohammadi},
journal= {arXiv preprint arXiv:2512.06715},
year = {2025}
}