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@INPROCEEDINGS{Breuer:911684,
      author       = {Breuer, Thomas and Cao, Karl-Kiên and Fiand, Fred and
                      Fuchs, Benjamin and Koch, Thorsten and Vanaret, Charlie and
                      Wetzel, Manuel},
      title        = {{E}valuation of uncertainties in linear energy system
                      optimization models using {HPC} and neural networks},
      reportid     = {FZJ-2022-04939},
      year         = {2022},
      abstract     = {Within the interdisciplinary BMWK-funded project UNSEEN,
                      experts from High Performance Computing, mathematical
                      optimization and energy systems analysis combine strengths
                      to evaluate uncertainties in modeling and planning future
                      energy systems with the aid of High Performance Computing
                      (HPC) and neural networks. Energy System Models (ESM) are
                      central instruments for realizing the energy transition.
                      These models try to optimize complex energy systems in order
                      to ensure security of supply while minimizing costs for
                      power production and transmission. In order to derive
                      reliable and robust policy advice for decision makers,
                      hundreds or even thousands of ESM problems need to be solved
                      in order to address uncertainties in a given model and
                      dataset.Mixed-integer linear programs (MIPs), a direct
                      extension of Linear programs (LPs), can be used to formulate
                      and compute more concrete and realistic energy systems.
                      Since the availability of fast LP solvers is a major
                      prerequisite for optimizing MIPs, the development of an
                      open-source scalable distributed-memory LP solver, called
                      PIPS-IPM++, was started in a preceding project and can
                      already outperform state-of-the-art solvers. A second
                      prerequisite for efficient MIP solving is the availability
                      of MIP heuristics. For this purpose, we develop a generic
                      MIP framework including reinforcement learning methods.
                      Moreover, we aim to implement an efficient automated HPC
                      workflow for generating, solving, and postprocessing
                      numerous ESM problems with a special structure in order to
                      develop new tools for better predictions about the future of
                      our energy system. This novel approach couples multiple
                      existing and new software packages to achieve the project
                      goals.},
      month         = {May},
      date          = {2022-05-29},
      organization  = {ISC High Performance 2022, Hamburg
                       (Germany), 29 May 2022 - 2 Jun 2022},
      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/911684},
}