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001052747 0247_ $$2doi$$a10.48550/ARXIV.2601.16028
001052747 037__ $$aFZJ-2026-01104
001052747 1001_ $$0P:(DE-Juel1)194737$$aWedemeyer, Moritz$$b0$$ufzj
001052747 245__ $$aData-Driven Conditional Flexibility Index
001052747 260__ $$barXiv$$c2026
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001052747 520__ $$aWith the increasing flexibilization of processes, determining robust scheduling decisions has become an important goal. Traditionally, the flexibility index has been used to identify safe operating schedules by approximating the admissible uncertainty region using simple admissible uncertainty sets, such as hypercubes. Presently, available contextual information, such as forecasts, has not been considered to define the admissible uncertainty set when determining the flexibility index. We propose the conditional flexibility index (CFI), which extends the traditional flexibility index in two ways: by learning the parametrized admissible uncertainty set from historical data and by using contextual information to make the admissible uncertainty set conditional. This is achieved using a normalizing flow that learns a bijective mapping from a Gaussian base distribution to the data distribution. The admissible latent uncertainty set is constructed as a hypersphere in the latent space and mapped to the data space. By incorporating contextual information, the CFI provides a more informative estimate of flexibility by defining admissible uncertainty sets in regions that are more likely to be relevant under given conditions. Using an illustrative example, we show that no general statement can be made about data-driven admissible uncertainty sets outperforming simple sets, or conditional sets outperforming unconditional ones. However, both data-driven and conditional admissible uncertainty sets ensure that only regions of the uncertain parameter space containing realizations are considered. We apply the CFI to a security-constrained unit commitment example and demonstrate that the CFI can improve scheduling quality by incorporating temporal information.
001052747 536__ $$0G:(DE-HGF)POF4-1121$$a1121 - Digitalization and Systems Technology for Flexibility Solutions (POF4-112)$$cPOF4-112$$fPOF IV$$x0
001052747 536__ $$0G:(DE-Juel1)HDS-LEE-20190612$$aHDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)$$cHDS-LEE-20190612$$x1
001052747 588__ $$aDataset connected to DataCite
001052747 650_7 $$2Other$$aMachine Learning (cs.LG)
001052747 650_7 $$2Other$$aFOS: Computer and information sciences
001052747 7001_ $$0P:(DE-Juel1)179591$$aCramer, Eike$$b1
001052747 7001_ $$0P:(DE-Juel1)172025$$aMitsos, Alexander$$b2$$ufzj
001052747 7001_ $$0P:(DE-Juel1)172097$$aDahmen, Manuel$$b3$$eCorresponding author$$ufzj
001052747 773__ $$a10.48550/ARXIV.2601.16028
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001052747 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)194737$$aForschungszentrum Jülich$$b0$$kFZJ
001052747 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)194737$$aRWTH Aachen$$b0$$kRWTH
001052747 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)179591$$a University College London$$b1
001052747 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172025$$aForschungszentrum Jülich$$b2$$kFZJ
001052747 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)172025$$aRWTH Aachen$$b2$$kRWTH
001052747 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172097$$aForschungszentrum Jülich$$b3$$kFZJ
001052747 9131_ $$0G:(DE-HGF)POF4-112$$1G:(DE-HGF)POF4-110$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1121$$aDE-HGF$$bForschungsbereich Energie$$lEnergiesystemdesign (ESD)$$vDigitalisierung und Systemtechnik$$x0
001052747 9141_ $$y2026
001052747 920__ $$lyes
001052747 9201_ $$0I:(DE-Juel1)ICE-1-20170217$$kICE-1$$lModellierung von Energiesystemen$$x0
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