Constructing Deployment Scenarios for Reserve Deliverability via Adaptive Robust Optimization
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911523237003264 |
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| author | Van Caelenberg, Guillaume Stratigakos, Akylas Spyrou, Elina |
| author_facet | Van Caelenberg, Guillaume Stratigakos, Akylas Spyrou, Elina |
| contents | Network congestion often hinders the deployment of reserves needed to balance forecast errors during real-time operations. A pertinent idea to tackle this challenge involves adding deployment scenarios of spatial distributions of forecast errors as contingencies to the day-ahead problem. However, current approaches disregard the effect of grid topology and the day-ahead schedule on the induced congestion and, consequently, reserve deliverability. In this work, we formulate a two-stage adaptive robust optimization problem to jointly consider interactions between day-ahead and real-time operations and forecast errors. Using a column-and-constraint algorithm, we iteratively construct deployment scenarios by finding the worst-case forecast error for reserve deliverability. Simulations on the RTS-GMLC system show that adding these scenarios to the day-ahead problem significantly reduces the frequency of congestion-driven reserve undeliverability. Notably, the choice and number of scenarios dynamically adapt to the day-ahead schedule. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16752 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Constructing Deployment Scenarios for Reserve Deliverability via Adaptive Robust Optimization Van Caelenberg, Guillaume Stratigakos, Akylas Spyrou, Elina Optimization and Control Network congestion often hinders the deployment of reserves needed to balance forecast errors during real-time operations. A pertinent idea to tackle this challenge involves adding deployment scenarios of spatial distributions of forecast errors as contingencies to the day-ahead problem. However, current approaches disregard the effect of grid topology and the day-ahead schedule on the induced congestion and, consequently, reserve deliverability. In this work, we formulate a two-stage adaptive robust optimization problem to jointly consider interactions between day-ahead and real-time operations and forecast errors. Using a column-and-constraint algorithm, we iteratively construct deployment scenarios by finding the worst-case forecast error for reserve deliverability. Simulations on the RTS-GMLC system show that adding these scenarios to the day-ahead problem significantly reduces the frequency of congestion-driven reserve undeliverability. Notably, the choice and number of scenarios dynamically adapt to the day-ahead schedule. |
| title | Constructing Deployment Scenarios for Reserve Deliverability via Adaptive Robust Optimization |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2603.16752 |