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@ARTICLE{Langiu:891106,
      author       = {Langiu, Marco and Shu, David Yang and Baader, Florian and
                      Hering, Dominik and Bau, Uwe and Xhonneux, André and
                      Müller, Dirk and Bardow, André and Mitsos, Alexander and
                      Dahmen, Manuel},
      title        = {{COMANDO}: {A} {N}ext-{G}eneration {O}pen-{S}ource
                      {F}ramework for {E}nergy {S}ystems {O}ptimization},
      journal      = {Anatomical science international},
      issn         = {-},
      reportid     = {FZJ-2021-01369},
      year         = {2021},
      note         = {28 pages, 1 graphical abstract, 13 figures},
      abstract     = {Existing open-source modeling frameworks dedicated to
                      energy systems optimization typically utilize
                      (mixed-integer) linear programming ((MI)LP) formulations,
                      which lack modeling freedom for technical system design and
                      operation. We present COMANDO, an open-source Python package
                      for component-oriented modeling and optimization for
                      nonlinear design and operation of integrated energy systems.
                      COMANDO allows to assemble system models from component
                      models including nonlinear, dynamic and discrete
                      characteristics. Based on a single system model, different
                      deterministic and stochastic problem formulations can be
                      obtained by varying objective function and underlying data,
                      and by applying automatic or manual reformulations. The
                      flexible open-source implementation allows for the
                      integration of customized routines required to solve
                      challenging problems, e.g., initialization, problem
                      decomposition, or sequential solution strategies. We
                      demonstrate features of COMANDO via case studies, including
                      automated linearization, dynamic optimization, stochastic
                      programming, and the use of nonlinear artificial neural
                      networks as surrogate models in a reduced-space formulation
                      for deterministic global optimization.},
      cin          = {IEK-10},
      ddc          = {610},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {112 - Digitalisierung und Systemtechnik (POF4-112) / ES2050
                      - Energie System 2050 (ES2050)},
      pid          = {G:(DE-HGF)POF4-112 / G:(DE-HGF)ES2050},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2102.02057},
      howpublished = {arXiv:2102.02057},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2102.02057;\%\%$},
      url          = {https://juser.fz-juelich.de/record/891106},
}