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arXiv · 2512.06715

GPU-Accelerated Optimization Solver for Unit Commitment in Large-Scale Power Grids

Hussein Sharadga·Javad Mohammadi
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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.

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Hussein Sharadga, Javad Mohammadi. 2025-12-07. GPU-Accelerated Optimization Solver for Unit Commitment in Large-Scale Power Grids. https://arxiv.org/abs/2512.06715

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