001019421 001__ 1019421
001019421 005__ 20240712112852.0
001019421 0247_ $$2doi$$a10.1103/PRXEnergy.2.043003
001019421 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-05378
001019421 037__ $$aFZJ-2023-05378
001019421 082__ $$a530
001019421 1001_ $$0P:(DE-Juel1)179250$$aKruse, Johannes$$b0
001019421 245__ $$aPhysics-Informed Machine Learning for Power Grid Frequency Modeling
001019421 260__ $$aCollege Park, MD$$bAmerican Physical Society$$c2023
001019421 3367_ $$2DRIVER$$aarticle
001019421 3367_ $$2DataCite$$aOutput Types/Journal article
001019421 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1706509937_21453
001019421 3367_ $$2BibTeX$$aARTICLE
001019421 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001019421 3367_ $$00$$2EndNote$$aJournal Article
001019421 520__ $$aThe operation of power systems is affected by diverse technical, economic, and social factors. Social behavior determines load patterns, electricity markets regulate the generation, and weather-dependent renewables introduce power fluctuations. Thus, power system dynamics must be regarded as a nonautonomous system whose parameters vary strongly with time. However, the external driving factors are usually only available on coarse scales and the actual dependencies of the dynamic system parameters are generally unknown. Here, we propose a physics-informed machine learning model that bridges the gap between large-scale drivers and short-term dynamics of the power system. Integrating stochastic differential equations and artificial neural networks, we construct a probabilistic model of the power grid frequency dynamics in continental Europe. Its probabilistic prediction outperforms the daily average profile, which is an important benchmark, on a time horizon of 15 min. Using the integrated model, we identify and explain the parameters of the dynamical system from the data, which reveal their strong time-dependence and their relation to external drivers such as wind power feed-in and fast generation ramps. Finally, we generate synthetic time series from the model, which successfully reproduce central characteristics of the grid frequency such as their heavy-tailed distribution. All in all, our work emphasizes the importance of modeling power system dynamics as a stochastic nonautonomous system with both intrinsic dynamics and external drivers.
001019421 536__ $$0G:(DE-HGF)POF4-1122$$a1122 - Design, Operation and Digitalization of the Future Energy Grids (POF4-112)$$cPOF4-112$$fPOF IV$$x0
001019421 536__ $$0G:(DE-JUEL1)BMBF-03EK3055B$$aCoNDyNet 2 - Kollektive Nichtlineare Dynamik Komplexer Stromnetze (BMBF-03EK3055B)$$cBMBF-03EK3055B$$x1
001019421 536__ $$0G:(DE-Juel1)HDS-LEE-20190612$$aHDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)$$cHDS-LEE-20190612$$x2
001019421 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001019421 7001_ $$0P:(DE-Juel1)179591$$aCramer, Eike$$b1
001019421 7001_ $$00000-0003-1607-9748$$aSchäfer, Benjamin$$b2
001019421 7001_ $$0P:(DE-Juel1)162277$$aWitthaut, Dirk$$b3$$eCorresponding author$$ufzj
001019421 773__ $$0PERI:(DE-600)3105425-0$$a10.1103/PRXEnergy.2.043003$$gVol. 2, no. 4, p. 043003$$n4$$p043003$$tPRX energy$$v2$$y2023
001019421 8564_ $$uhttps://juser.fz-juelich.de/record/1019421/files/PRXEnergy.2.043003.pdf$$yOpenAccess
001019421 8564_ $$uhttps://juser.fz-juelich.de/record/1019421/files/PRXEnergy.2.043003.gif?subformat=icon$$xicon$$yOpenAccess
001019421 8564_ $$uhttps://juser.fz-juelich.de/record/1019421/files/PRXEnergy.2.043003.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
001019421 8564_ $$uhttps://juser.fz-juelich.de/record/1019421/files/PRXEnergy.2.043003.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
001019421 8564_ $$uhttps://juser.fz-juelich.de/record/1019421/files/PRXEnergy.2.043003.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
001019421 909CO $$ooai:juser.fz-juelich.de:1019421$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
001019421 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179250$$aForschungszentrum Jülich$$b0$$kFZJ
001019421 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)162277$$aForschungszentrum Jülich$$b3$$kFZJ
001019421 9131_ $$0G:(DE-HGF)POF4-112$$1G:(DE-HGF)POF4-110$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1122$$aDE-HGF$$bForschungsbereich Energie$$lEnergiesystemdesign (ESD)$$vDigitalisierung und Systemtechnik$$x0
001019421 9141_ $$y2023
001019421 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001019421 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001019421 920__ $$lno
001019421 9201_ $$0I:(DE-Juel1)IEK-10-20170217$$kIEK-10$$lModellierung von Energiesystemen$$x0
001019421 9801_ $$aFullTexts
001019421 980__ $$ajournal
001019421 980__ $$aVDB
001019421 980__ $$aUNRESTRICTED
001019421 980__ $$aI:(DE-Juel1)IEK-10-20170217
001019421 981__ $$aI:(DE-Juel1)ICE-1-20170217