000911687 001__ 911687
000911687 005__ 20250317091733.0
000911687 0247_ $$2Handle$$a2128/32753
000911687 037__ $$aFZJ-2022-04941
000911687 1001_ $$0P:(DE-Juel1)138707$$aBreuer, Thomas$$b0$$eCorresponding author
000911687 1112_ $$aThe International Conference for High Performance Computing, Networking, Storage, and Analysis$$cDallas$$d2022-11-13 - 2022-11-18$$gSC22$$wUSA
000911687 245__ $$aEnabling energy systems research on HPC
000911687 260__ $$c2022
000911687 3367_ $$033$$2EndNote$$aConference Paper
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000911687 520__ $$aEnergy systems research strongly relies on large modeling frameworks. Many of them use linear optimization approaches to calculate blueprints for ideal future energy systems, which become increasingly complex, as do the models. The state of the art is to compute them with shared-memory computers combined with approaches to reduce the model size. We overcome this and implement a fully automated workflow on HPC using a newly developed solver for distributed memory architectures. Moreover, we address the challenge of uncertainty in scenario analysis by performing sophisticated parameter variations for large-scale power system models, which cannot be solved in the conventional way. Preliminary results show that we are able to identify clusters of future energy system designs, which perform well from different perspectives of energy system research and also consider disruptive events. Furthermore, we also observe that our approach provides the most insights when being applied to complex rather than simple models.
000911687 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000911687 536__ $$0G:(BMWi)03EI1004F$$aVerbundvorhaben: UNSEEN ' Bewertung der Unsicherheiten in linear optimierenden Energiesystem-Modellen unter Zuhilfenahme Neuronaler Netze, Teilvorhaben: Entwicklung einer integrierten HPC-Workflow Umgebung zur Kopplung von Optimierungsmethoden mit Methode (03EI1004F)$$c03EI1004F$$x1
000911687 536__ $$0G:(DE-Juel-1)ATMLAO$$aATMLAO - ATML Application Optimization and User Service Tools (ATMLAO)$$cATMLAO$$x2
000911687 7001_ $$0P:(DE-HGF)0$$aCao, Karl-Kiên$$b1
000911687 7001_ $$0P:(DE-HGF)0$$aWetzel, Manuel$$b2
000911687 7001_ $$0P:(DE-HGF)0$$aFrey, Ulrich$$b3
000911687 7001_ $$0P:(DE-HGF)0$$aSasanpour, Shima$$b4
000911687 7001_ $$0P:(DE-HGF)0$$aBuschmann, Jan$$b5
000911687 7001_ $$0P:(DE-HGF)0$$aBöhme, Aileen$$b6
000911687 7001_ $$0P:(DE-HGF)0$$aVanaret, Charlie$$b7
000911687 8564_ $$uhttps://juser.fz-juelich.de/record/911687/files/UNSEEN_SC_2022_Poster.pdf$$yOpenAccess
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000911687 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138707$$aForschungszentrum Jülich$$b0$$kFZJ
000911687 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
000911687 9141_ $$y2022
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000911687 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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