GPU-Accelerated Optimization Solver for Unit Commitment in Large-Scale Power Grids
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911306291871744 |
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| author | Sharadga, Hussein Mohammadi, Javad |
| author_facet | Sharadga, Hussein Mohammadi, Javad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06715 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPU-Accelerated Optimization Solver for Unit Commitment in Large-Scale Power Grids Sharadga, Hussein Mohammadi, Javad Optimization and Control Hardware Architecture 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. |
| title | GPU-Accelerated Optimization Solver for Unit Commitment in Large-Scale Power Grids |
| topic | Optimization and Control Hardware Architecture |
| url | https://arxiv.org/abs/2512.06715 |