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@ARTICLE{Behrens:1032430,
      author       = {Behrens, Johannes and Zeyen, Elisabeth and Hoffmann,
                      Maximilian and Stolten, Detlef and Weinand, Jann M.},
      title        = {{R}eviewing the complexity of endogenous technological
                      learning for energy system modeling},
      journal      = {Advances in applied energy},
      volume       = {16},
      issn         = {2666-7924},
      address      = {[Amsterdam]},
      publisher    = {Elsevier ScienceDirect},
      reportid     = {FZJ-2024-06237},
      pages        = {100192 -},
      year         = {2024},
      abstract     = {Energy system components like renewable energy technologies
                      or electrolyzers are subject to decreasing investment costs
                      driven by technological progress. Various methods have been
                      developed in the literature to capture model-endogenous
                      technological learning. This review demonstrates the
                      non-linear relationship between investment costs and
                      production volume, resulting in non-convex optimization
                      problems and discuss concepts to account for technological
                      progress. While iterative solution methods tend to find
                      future energy system designs that rely on suboptimal
                      technology mixes, exact solutions leading to global
                      optimality are computationally demanding. Most studies omit
                      important system aspects such as sector integration, or a
                      detailed spatial, temporal, and technological resolution to
                      maintain model solvability, which likewise distorts the
                      impact of technological learning. This can be improved by
                      the application of methods such as temporal or spatial
                      aggregation, decomposition methods, or the clustering of
                      technologies. This review reveals the potential of those
                      methods and points out important considerations for
                      integrating endogenous technological learning. We propose a
                      more integrated approach to handle computational complexity
                      when integrating technological learning, that aims to
                      preserve the model's feasibility. Furthermore, we identify
                      significant gaps in current modeling practices and suggest
                      future research directions to enhance the accuracy and
                      utility of energy system models.},
      cin          = {ICE-2},
      ddc          = {333.7},
      cid          = {I:(DE-Juel1)ICE-2-20101013},
      pnm          = {1111 - Effective System Transformation Pathways (POF4-111)
                      / 1112 - Societally Feasible Transformation Pathways
                      (POF4-111) / 110 - Energiesystemdesign (ESD) (POF4-100)},
      pid          = {G:(DE-HGF)POF4-1111 / G:(DE-HGF)POF4-1112 /
                      G:(DE-HGF)POF4-110},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001352873600001},
      doi          = {10.1016/j.adapen.2024.100192},
      url          = {https://juser.fz-juelich.de/record/1032430},
}