In this section
@article{Van:2027:10.1016/j.epsr.2026.113632,
author = {Van, Caelenberg G and Stratigakos, A and Spyrou, E},
doi = {10.1016/j.epsr.2026.113632},
journal = {Electric Power Systems Research},
title = {Constructing deployment scenarios for reserve deliverability via adaptive robust optimization},
url = {http://dx.doi.org/10.1016/j.epsr.2026.113632},
volume = {263},
year = {2027}
}
TY - JOUR
AB - 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.
AU - Van,Caelenberg G
AU - Stratigakos,A
AU - Spyrou,E
DO - 10.1016/j.epsr.2026.113632
PY - 2027///
SN - 0378-7796
TI - Constructing deployment scenarios for reserve deliverability via adaptive robust optimization
T2 - Electric Power Systems Research
UR - http://dx.doi.org/10.1016/j.epsr.2026.113632
UR - https://doi.org/10.1016/j.epsr.2026.113632
VL - 263
ER -
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