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@INPROCEEDINGS{Breuer:1007797,
      author       = {Breuer, Thomas and Cao, Karl-Kiên and Wetzel, Manuel and
                      Frey, Ulrich and Sasanpour, Shima and Buschman, Jan and
                      Böhme, Aileen and Vanaret, Charlie},
      title        = {{E}nabling energy systems research on {HPC}},
      publisher    = {SC22 Supercomputing conference},
      reportid     = {FZJ-2023-02192},
      pages        = {3 pp.},
      year         = {2022},
      comment      = {SC Technical Program Archives - Research posters},
      booktitle     = {SC Technical Program Archives -
                       Research posters},
      abstract     = {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.},
      month         = {Nov},
      date          = {2022-11-13},
      organization  = {The International Conference for High
                       Performance Computing, Networking,
                       Storage, and Analysis, Dallas (USA), 13
                       Nov 2022 - 18 Nov 2022},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / 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) / ATMLAO - ATML Application Optimization and
                      User Service Tools (ATMLAO)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(BMWi)03EI1004F /
                      G:(DE-Juel-1)ATMLAO},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      url          = {https://juser.fz-juelich.de/record/1007797},
}