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| 001 | 911687 | ||
| 005 | 20250317091733.0 | ||
| 024 | 7 | _ | |a 2128/32753 |2 Handle |
| 037 | _ | _ | |a FZJ-2022-04941 |
| 100 | 1 | _ | |a Breuer, Thomas |0 P:(DE-Juel1)138707 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a The International Conference for High Performance Computing, Networking, Storage, and Analysis |g SC22 |c Dallas |d 2022-11-13 - 2022-11-18 |w USA |
| 245 | _ | _ | |a Enabling energy systems research on HPC |
| 260 | _ | _ | |c 2022 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1669203524_4739 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a Energy 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. |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a Verbundvorhaben: 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) |0 G:(BMWi)03EI1004F |c 03EI1004F |x 1 |
| 536 | _ | _ | |0 G:(DE-Juel-1)ATMLAO |a ATMLAO - ATML Application Optimization and User Service Tools (ATMLAO) |c ATMLAO |x 2 |
| 700 | 1 | _ | |a Cao, Karl-Kiên |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Wetzel, Manuel |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Frey, Ulrich |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Sasanpour, Shima |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Buschmann, Jan |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Böhme, Aileen |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Vanaret, Charlie |0 P:(DE-HGF)0 |b 7 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/911687/files/UNSEEN_SC_2022_Poster.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:911687 |p openaire |p open_access |p VDB |p driver |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)138707 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 914 | 1 | _ | |y 2022 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a poster |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
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