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@INPROCEEDINGS{Breuer:1007796,
      author       = {Breuer, Thomas and Cao, Karl-Kiên and Wetzel, Manuel and
                      Frey, Ulrich and Sasanpour, Shima and Buschmann, Jan and von
                      Krbek, Kai and Böhme, Aileen and Vanaret, Charlie},
      title        = {{T}ackling challenges in energy system research with {HPC}},
      reportid     = {FZJ-2023-02191},
      year         = {2023},
      abstract     = {Energy system optimization models are one of the central
                      instruments for the successful realization of the energy
                      transition towards renewable sources. We have identified
                      three major challenges to overcome the current limitations
                      in energy system research. First, studying the future is
                      subject to large uncertainties and these uncertainties are
                      usually tackled with modeling of just a small subset of all
                      possible scenarios. This has proven to be inadequate since
                      most models are highly sensitive to input data. Second, the
                      widely-used commercial solvers show poor scalability and are
                      limited to single shared-memory compute nodes. Thus, models
                      are defined with a lower resolution and technological
                      diversity than necessary. The third challenge is that single
                      models usually tend to investigate only certain aspects of
                      an energy system, which do not cover all parts of future
                      pathways. To overcome those limitations, we inspect the
                      conceivable parameter space by using a hitherto unattained
                      number of model-based scenarios. Therefore, we have
                      implemented an automated parameter sampling based on a broad
                      literature review, and a self-developed distributed-memory
                      solver that outperforms commercial solvers. In addition, we
                      have coupled different types of models in an automated,
                      parallelized workflow. We use this workflow for a case study
                      of the German power system. By evaluating more than 3600
                      scenarios, we observe a clear dominance of photovoltaics in
                      future system designs. Efficiently leveraging the capability
                      of HPC by combining those approaches could be a game changer
                      for the energy-system analysis community and could ensure a
                      better applicability for real world policy support.},
      month         = {May},
      date          = {2023-05-21},
      organization  = {ISC High Performance 2023, Hamburg
                       (Germany), 21 May 2023 - 25 May 2023},
      subtyp        = {After Call},
      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)24},
      url          = {https://juser.fz-juelich.de/record/1007796},
}