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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.23196 |
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| _version_ | 1866912682882367488 |
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| author | Giraud, Bastien Nellikath, Rahul Vorwerk, Johanna Alowaifeer, Maad Chatzivasileiadis, Spyros |
| author_facet | Giraud, Bastien Nellikath, Rahul Vorwerk, Johanna Alowaifeer, Maad Chatzivasileiadis, Spyros |
| contents | The AC Optimal Power Flow (AC-OPF) problem is central to power system operation but challenging to solve efficiently due to its nonconvex and nonlinear nature. Neural networks (NNs) offer fast surrogates, yet their black-box behavior raises concerns about constraint violations that can compromise safety. We propose a verification-informed NN framework that incorporates worst-case constraint violations directly into training, producing models that are both accurate and provably safer. Through post-hoc verification, we achieve substantial reductions in worst-case violations and, for the first time, verify all operational constraints of large-scale AC-OPF proxies. Practical feasibility is further enhanced via restoration and warm-start strategies for infeasible operating points. Experiments on systems ranging from 57 to 793 buses demonstrate scalability, speed, and reliability, bridging the gap between ML acceleration and safe, real-time deployment of AC-OPF solutions - and paving the way toward data-driven optimal control. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23196 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Networks for AC Optimal Power Flow: Improving Worst-Case Guarantees during Training Giraud, Bastien Nellikath, Rahul Vorwerk, Johanna Alowaifeer, Maad Chatzivasileiadis, Spyros Systems and Control The AC Optimal Power Flow (AC-OPF) problem is central to power system operation but challenging to solve efficiently due to its nonconvex and nonlinear nature. Neural networks (NNs) offer fast surrogates, yet their black-box behavior raises concerns about constraint violations that can compromise safety. We propose a verification-informed NN framework that incorporates worst-case constraint violations directly into training, producing models that are both accurate and provably safer. Through post-hoc verification, we achieve substantial reductions in worst-case violations and, for the first time, verify all operational constraints of large-scale AC-OPF proxies. Practical feasibility is further enhanced via restoration and warm-start strategies for infeasible operating points. Experiments on systems ranging from 57 to 793 buses demonstrate scalability, speed, and reliability, bridging the gap between ML acceleration and safe, real-time deployment of AC-OPF solutions - and paving the way toward data-driven optimal control. |
| title | Neural Networks for AC Optimal Power Flow: Improving Worst-Case Guarantees during Training |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.23196 |