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.

翻译：本研究提出了一种用于大规模电网机组组合问题的GPU加速求解器。该求解器采用原始-对偶混合梯度算法高效求解松弛线性子问题，相比传统基于CPU的方法，实现了更快的边界估计以及改进的交叉与分支定界收敛性。这些改进显著降低了混合整数线性机组组合问题的总计算时间。所提方法在包括4224、6049和6717节点网络的大规模系统中进行了验证，这些系统具有长控制时域和计算密集型问题，结果表明在保持解质量的同时实现了显著的加速效果。