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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2603.13508 |
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| _version_ | 1866917340880306176 |
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| author | Kwon, Taehyeon Subramanyam, Anirudh |
| author_facet | Kwon, Taehyeon Subramanyam, Anirudh |
| contents | Capacity expansion planning under uncertainty requires selecting a scenario count and representative operational horizon to estimate average production costs. Small choices risk unreliable plans, while large choices become intractable. We propose AutoSCEP, an automated, statistically grounded procedure that, for a fixed plan, selects the minimum sufficient scenario count and horizon length to estimate production costs to a given precision. Using these estimates, we train linear and neural surrogates to approximate expected production costs for arbitrary plans, and embed the surrogates within the planning model. On the continental-scale EMPIRE system, AutoSCEP attains 2% optimality gap on a reduced model and 8% gap on a large model, outperforming parallel progressive hedging under equal wall-clock budgets that include data generation, training, and solve times. Where the reduced model's optimum is available, investment patterns broadly align with the benchmark. Our approach enables high-resolution uncertainty modeling at realistic system scales. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13508 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Enabled Large-Scale Capacity Expansion Planning under Uncertainty Kwon, Taehyeon Subramanyam, Anirudh Optimization and Control Capacity expansion planning under uncertainty requires selecting a scenario count and representative operational horizon to estimate average production costs. Small choices risk unreliable plans, while large choices become intractable. We propose AutoSCEP, an automated, statistically grounded procedure that, for a fixed plan, selects the minimum sufficient scenario count and horizon length to estimate production costs to a given precision. Using these estimates, we train linear and neural surrogates to approximate expected production costs for arbitrary plans, and embed the surrogates within the planning model. On the continental-scale EMPIRE system, AutoSCEP attains 2% optimality gap on a reduced model and 8% gap on a large model, outperforming parallel progressive hedging under equal wall-clock budgets that include data generation, training, and solve times. Where the reduced model's optimum is available, investment patterns broadly align with the benchmark. Our approach enables high-resolution uncertainty modeling at realistic system scales. |
| title | Machine Learning-Enabled Large-Scale Capacity Expansion Planning under Uncertainty |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2603.13508 |