ExaModelsPower.jl: A GPU-Compatible Modeling Library for Nonlinear Power System Optimization
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918385532534784 |
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| author | Johnson, Sanjay Lauinger, Dirk Shin, Sungho Pacaud, François |
| author_facet | Johnson, Sanjay Lauinger, Dirk Shin, Sungho Pacaud, François |
| contents | As GPU-accelerated mathematical programming techniques mature, there is growing interest in utilizing them to address the computational challenges of power system optimization. This paper introduces ExaModelsPower.jl, an open-source modeling library for creating GPU-compatible nonlinear AC optimal power flow models. Built on ExaModels.jl, ExaModelsPower.jl provides a high-level interface that automatically generates all necessary callback functions for GPU solvers. The library is designed for large-scale problem instances, which may include multiple time periods and security constraints. Using ExaModelsPower.jl, we benchmark GPU and CPU solvers on open-source test cases. Our results show that GPU solvers can deliver up to two orders of magnitude speedups compared to alternative tools on CPU for problems with more than 20,000 variables and a solution precision of up to $10^{-4}$, while performance for smaller instances or tighter tolerances may vary. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_12897 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ExaModelsPower.jl: A GPU-Compatible Modeling Library for Nonlinear Power System Optimization Johnson, Sanjay Lauinger, Dirk Shin, Sungho Pacaud, François Systems and Control As GPU-accelerated mathematical programming techniques mature, there is growing interest in utilizing them to address the computational challenges of power system optimization. This paper introduces ExaModelsPower.jl, an open-source modeling library for creating GPU-compatible nonlinear AC optimal power flow models. Built on ExaModels.jl, ExaModelsPower.jl provides a high-level interface that automatically generates all necessary callback functions for GPU solvers. The library is designed for large-scale problem instances, which may include multiple time periods and security constraints. Using ExaModelsPower.jl, we benchmark GPU and CPU solvers on open-source test cases. Our results show that GPU solvers can deliver up to two orders of magnitude speedups compared to alternative tools on CPU for problems with more than 20,000 variables and a solution precision of up to $10^{-4}$, while performance for smaller instances or tighter tolerances may vary. |
| title | ExaModelsPower.jl: A GPU-Compatible Modeling Library for Nonlinear Power System Optimization |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.12897 |