001007797 001__ 1007797
001007797 005__ 20250317091734.0
001007797 037__ $$aFZJ-2023-02192
001007797 1001_ $$0P:(DE-Juel1)138707$$aBreuer, Thomas$$b0$$eCorresponding author$$ufzj
001007797 1112_ $$aThe International Conference for High Performance Computing, Networking, Storage, and Analysis$$cDallas$$d2022-11-13 - 2022-11-18$$gSC22$$wUSA
001007797 245__ $$aEnabling energy systems research on HPC
001007797 260__ $$bSC22 Supercomputing conference$$c2022
001007797 29510 $$aSC Technical Program Archives - Research posters
001007797 300__ $$a3 pp.
001007797 3367_ $$2ORCID$$aCONFERENCE_PAPER
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001007797 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1687163278_9014
001007797 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$mcontb
001007797 520__ $$aEnergy systems research strongly relies on large modeling frameworks. Many of them use linear optimization approaches to calculate blueprints for ideal future energy systems, which become increasingly complex, as do the models. The state of the art is to compute them with shared-memory computers combined with approaches to reduce the model size. We overcome this and implement a fully automated workflow on HPC using a newly developed solver for distributed memory architectures. Moreover, we address the challenge of uncertainty in scenario analysis by performing sophisticated parameter variations for large-scale power system models, which cannot be solved in the conventional way. Preliminary results show that we are able to identify clusters of future energy system designs, which perform well from different perspectives of energy system research and also consider disruptive events. Furthermore, we also observe that our approach provides the most insights when being applied to complex rather than simple models.
001007797 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001007797 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
001007797 536__ $$0G:(DE-Juel-1)ATMLAO$$aATMLAO - ATML Application Optimization and User Service Tools (ATMLAO)$$cATMLAO$$x2
001007797 7001_ $$0P:(DE-HGF)0$$aCao, Karl-Kiên$$b1
001007797 7001_ $$0P:(DE-HGF)0$$aWetzel, Manuel$$b2
001007797 7001_ $$0P:(DE-HGF)0$$aFrey, Ulrich$$b3
001007797 7001_ $$0P:(DE-HGF)0$$aSasanpour, Shima$$b4
001007797 7001_ $$0P:(DE-HGF)0$$aBuschman, Jan$$b5
001007797 7001_ $$0P:(DE-HGF)0$$aBöhme, Aileen$$b6
001007797 7001_ $$0P:(DE-HGF)0$$aVanaret, Charlie$$b7
001007797 8564_ $$uhttps://sc22.supercomputing.org/proceedings/tech_poster/tech_poster_pages/rpost109.html
001007797 8564_ $$uhttps://juser.fz-juelich.de/record/1007797/files/UNSEEN_SC_Poster_Summary.pdf$$yRestricted
001007797 909CO $$ooai:juser.fz-juelich.de:1007797$$pVDB
001007797 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138707$$aForschungszentrum Jülich$$b0$$kFZJ
001007797 9131_ $$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
001007797 9141_ $$y2023
001007797 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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