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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/2511.15624 |
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| _version_ | 1866909005811548160 |
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| author | Tekeler, Eren Zhong, Xiangru Zhang, Huan Chevalier, Samuel |
| author_facet | Tekeler, Eren Zhong, Xiangru Zhang, Huan Chevalier, Samuel |
| contents | Security-Constrained DC Optimal Power Flow (SC DCOPF) is an important tool for transmission system operators, enabling economically efficient and physically secure dispatch decisions. Although CPU-based commercial solvers (e.g., Gurobi) can efficiently solve SC-DCOPF problems with a reasonable number of security constraints, their performance degrades rapidly as both system size and the number of contingencies grow into thousands. In this paper, we design a computational graph representation of the SC-DCOPF-based market-clearing problem, inspired by the third ARPA-E Grid Optimization Competition. Using a tool from the neural network verification community known as Interval Bound Propagation (IBP), we quickly compute bounds on the optimal objective across the full set of N-1 contingencies. Our results demonstrate that IBP can compute certified bounds with mean optimal solution gaps below 3.98% on small cases, and it can efficiently scale up to 8,316 bus systems with thousands of contingencies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15624 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast and Certified Bounding of Security-Constrained DCOPF via Interval Bound Propagation Tekeler, Eren Zhong, Xiangru Zhang, Huan Chevalier, Samuel Systems and Control Security-Constrained DC Optimal Power Flow (SC DCOPF) is an important tool for transmission system operators, enabling economically efficient and physically secure dispatch decisions. Although CPU-based commercial solvers (e.g., Gurobi) can efficiently solve SC-DCOPF problems with a reasonable number of security constraints, their performance degrades rapidly as both system size and the number of contingencies grow into thousands. In this paper, we design a computational graph representation of the SC-DCOPF-based market-clearing problem, inspired by the third ARPA-E Grid Optimization Competition. Using a tool from the neural network verification community known as Interval Bound Propagation (IBP), we quickly compute bounds on the optimal objective across the full set of N-1 contingencies. Our results demonstrate that IBP can compute certified bounds with mean optimal solution gaps below 3.98% on small cases, and it can efficiently scale up to 8,316 bus systems with thousands of contingencies. |
| title | Fast and Certified Bounding of Security-Constrained DCOPF via Interval Bound Propagation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.15624 |