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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.11627 |
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| _version_ | 1866908572539944960 |
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| author | Chen, Shuyi Zhu, Shixiang Sioshansi, Ramteen |
| author_facet | Chen, Shuyi Zhu, Shixiang Sioshansi, Ramteen |
| contents | Extreme weather is straining electricity systems, exposing the limitations of reactive responses, and prompting the need for proactive resilience planning. Most existing approaches to enhance electricity system resilience employ simplified uncertainty models and decouple proactive and reactive decisions. This paper proposes a novel tri-level optimization model that integrates proactive actions, adversarial disruptions, and reactive responses. Conformal prediction is used to construct distribution-free system-disruption uncertainty sets with coverage guarantees. The tri-level problem is solved by using duality theory to derive a bi-level reformulation and employing Bender's decomposition. Numerical experiments demonstrate that our approach outperforms conventional robust and two-stage methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11627 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Electricity-System Resilience with Adaptive Robust Optimization and Conformal Uncertainty Characterization Chen, Shuyi Zhu, Shixiang Sioshansi, Ramteen Machine Learning Extreme weather is straining electricity systems, exposing the limitations of reactive responses, and prompting the need for proactive resilience planning. Most existing approaches to enhance electricity system resilience employ simplified uncertainty models and decouple proactive and reactive decisions. This paper proposes a novel tri-level optimization model that integrates proactive actions, adversarial disruptions, and reactive responses. Conformal prediction is used to construct distribution-free system-disruption uncertainty sets with coverage guarantees. The tri-level problem is solved by using duality theory to derive a bi-level reformulation and employing Bender's decomposition. Numerical experiments demonstrate that our approach outperforms conventional robust and two-stage methods. |
| title | Enhancing Electricity-System Resilience with Adaptive Robust Optimization and Conformal Uncertainty Characterization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.11627 |