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@ARTICLE{Kruse:1019421,
author = {Kruse, Johannes and Cramer, Eike and Schäfer, Benjamin and
Witthaut, Dirk},
title = {{P}hysics-{I}nformed {M}achine {L}earning for {P}ower
{G}rid {F}requency {M}odeling},
journal = {PRX energy},
volume = {2},
number = {4},
address = {College Park, MD},
publisher = {American Physical Society},
reportid = {FZJ-2023-05378},
pages = {043003},
year = {2023},
abstract = {The operation of power systems is affected by diverse
technical, economic, and social factors. Social behavior
determines load patterns, electricity markets regulate the
generation, and weather-dependent renewables introduce power
fluctuations. Thus, power system dynamics must be regarded
as a nonautonomous system whose parameters vary strongly
with time. However, the external driving factors are usually
only available on coarse scales and the actual dependencies
of the dynamic system parameters are generally unknown.
Here, we propose a physics-informed machine learning model
that bridges the gap between large-scale drivers and
short-term dynamics of the power system. Integrating
stochastic differential equations and artificial neural
networks, we construct a probabilistic model of the power
grid frequency dynamics in continental Europe. Its
probabilistic prediction outperforms the daily average
profile, which is an important benchmark, on a time horizon
of 15 min. Using the integrated model, we identify and
explain the parameters of the dynamical system from the
data, which reveal their strong time-dependence and their
relation to external drivers such as wind power feed-in and
fast generation ramps. Finally, we generate synthetic time
series from the model, which successfully reproduce central
characteristics of the grid frequency such as their
heavy-tailed distribution. All in all, our work emphasizes
the importance of modeling power system dynamics as a
stochastic nonautonomous system with both intrinsic dynamics
and external drivers.},
cin = {IEK-10},
ddc = {530},
cid = {I:(DE-Juel1)IEK-10-20170217},
pnm = {1122 - Design, Operation and Digitalization of the Future
Energy Grids (POF4-112) / CoNDyNet 2 - Kollektive
Nichtlineare Dynamik Komplexer Stromnetze (BMBF-03EK3055B) /
HDS LEE - Helmholtz School for Data Science in Life, Earth
and Energy (HDS LEE) (HDS-LEE-20190612)},
pid = {G:(DE-HGF)POF4-1122 / G:(DE-JUEL1)BMBF-03EK3055B /
G:(DE-Juel1)HDS-LEE-20190612},
typ = {PUB:(DE-HGF)16},
doi = {10.1103/PRXEnergy.2.043003},
url = {https://juser.fz-juelich.de/record/1019421},
}