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001051587 1001_ $$0P:(DE-HGF)0$$aMayer, Pablo$$b0
001051587 245__ $$aProbabilistic Prediction of the Area Control Error Using Normalizing Flows
001051587 260__ $$aNew York, NY$$bACM$$c2025
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001051587 520__ $$aBalancing 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.
001051587 536__ $$0G:(DE-HGF)POF4-1121$$a1121 - Digitalization and Systems Technology for Flexibility Solutions (POF4-112)$$cPOF4-112$$fPOF IV$$x0
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001051587 7001_ $$0P:(DE-Juel1)200120$$aHartmann, Carsten$$b1$$ufzj
001051587 7001_ $$0P:(DE-HGF)0$$aCramer, Eike$$b2
001051587 7001_ $$0P:(DE-Juel1)172097$$aDahmen, Manuel$$b3$$ufzj
001051587 7001_ $$0P:(DE-Juel1)162277$$aWitthaut, Dirk$$b4$$eCorresponding author$$ufzj
001051587 773__ $$0PERI:(DE-600)3128672-0$$a10.1145/3777518.3777524$$gVol. 5, no. 3, p. 66 - 76$$n3$$p66 - 76$$tACM SIGEnergy energy informatics review$$v5$$x2770-5331$$y2025
001051587 8564_ $$uhttps://juser.fz-juelich.de/record/1051587/files/ACE_prediction_NF.pdf$$yRestricted
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