Constructing deployment scenarios for reserve deliverability via adaptive robust optimization
File(s)
Published version
Author(s)
Van Caelenberg, Guillaume
Stratigakos, Akylas
Spyrou, Elina
Type
Journal Article
Abstract
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 characteristics 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 adapts to the day-ahead schedule.
Date Issued
2027-02-01
Date Acceptance
2026-06-22
Citation
Electric Power Systems Research, 2027, 263
ISSN
0378-7796
Publisher
Elsevier BV
Journal / Book Title
Electric Power Systems Research
Volume
263
Copyright Statement
© 2026 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
113632
Date Publish Online
2026-07-07