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@ARTICLE{Kotzur:894712,
      author       = {Kotzur, Leander and Nolting, Lars and Hoffmann, Maximilian
                      and Groß, Theresa and Smolenko, Andreas and Priesmann, Jan
                      and Büsing, Henrik and Beer, Robin and Kullmann, Felix and
                      Singh, Bismark and Praktiknjo, Aaron and Stolten, Detlef and
                      Robinius, Martin},
      title        = {{A} modeler's guide to handle complexity in energy systems
                      optimization},
      journal      = {Advances in applied energy},
      volume       = {4},
      issn         = {2666-7924},
      address      = {[Amsterdam]},
      publisher    = {Elsevier ScienceDirect},
      reportid     = {FZJ-2021-03364},
      pages        = {100063 -},
      year         = {2021},
      abstract     = {Determining environmentally- and economically-optimal
                      energy systems designs and operations is complex. In
                      particular, the integration of weather-dependent renewable
                      energy technologies into energy system optimization models
                      presents new challenges to computational tractability that
                      cannot only be solved by advancements in computational
                      resources. In consequence, energy system modelers must
                      tackle the complexity of their models by applying various
                      methods to manipulate the underlying data and model
                      structure, with the ultimate goal of finding optimal
                      solutions. As which complexity reduction method is suitable
                      for which research question is often unclear, herein we
                      review different approaches for handling complexity. We
                      first analyze the determinants of complexity and note that
                      many drivers of complexity could be avoided a priori with a
                      tailored model design. Second, we conduct a review of
                      systematic complexity reduction methods for energy system
                      optimization models, which can range from simple
                      linearization performed by modelers to sophisticated
                      multi-level approaches combining aggregation and
                      decomposition methods. Based on this overview, we develop a
                      guide for energy system modelers who encounter computational
                      limitations.},
      cin          = {IEK-3 / JSC},
      ddc          = {333.7},
      cid          = {I:(DE-Juel1)IEK-3-20101013 / I:(DE-Juel1)JSC-20090406},
      pnm          = {1111 - Effective System Transformation Pathways (POF4-111)
                      / 1112 - Societally Feasible Transformation Pathways
                      (POF4-111) / 5112 - Cross-Domain Algorithms, Tools, Methods
                      Labs (ATMLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-1111 / G:(DE-HGF)POF4-1112 /
                      G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001022694400008},
      doi          = {10.1016/j.adapen.2021.100063},
      url          = {https://juser.fz-juelich.de/record/894712},
}