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@ARTICLE{Mayer:1051587,
author = {Mayer, Pablo and Hartmann, Carsten and Cramer, Eike and
Dahmen, Manuel and Witthaut, Dirk},
title = {{P}robabilistic {P}rediction of the {A}rea {C}ontrol
{E}rror {U}sing {N}ormalizing {F}lows},
journal = {ACM SIGEnergy energy informatics review},
volume = {5},
number = {3},
issn = {2770-5331},
address = {New York, NY},
publisher = {ACM},
reportid = {FZJ-2026-00515},
pages = {66 - 76},
year = {2025},
abstract = {Balancing generation and load is a central challenge in
power systems, particularly those with a high share of
renewable generation. The area control error (ACE)
quantifies the current power mismatch in a certain area of
the power grid and thus provides a central input for
balancing and control. Accurate forecasting of this quantity
can facilitate rapid control actions and thus improve grid
stability. In this contribution, we introduce a
probabilistic forecasting model for the ACE using a deep
generative neural network model called normalizing flow. Our
model generates scenarios for every quarter hour of the day
using conditional features such as the generation schedules.
We demonstrate that the generative model outperforms
elementary benchmark models.},
cin = {ICE-1},
ddc = {333.7},
cid = {I:(DE-Juel1)ICE-1-20170217},
pnm = {1121 - Digitalization and Systems Technology for
Flexibility Solutions (POF4-112)},
pid = {G:(DE-HGF)POF4-1121},
typ = {PUB:(DE-HGF)16},
doi = {10.1145/3777518.3777524},
url = {https://juser.fz-juelich.de/record/1051587},
}