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@ARTICLE{Baumgrtner:889896,
      author       = {Baumgärtner, Nils and Shu, David Yang and Bahl, Björn and
                      Hennen, Maike and Hollermann, Dinah Elena and Bardow,
                      André},
      title        = {{D}e{L}oop: {D}ecomposition-based {L}ong-term operational
                      optimization of energy systems with time-coupling
                      constraints},
      journal      = {Energy},
      volume       = {198},
      issn         = {0360-5442},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2021-00504},
      pages        = {117272 -},
      year         = {2020},
      abstract     = {Long-term operational optimization of energy systems
                      results in challenging, large-scale problems. These
                      large-scale problems can be directly decomposed into smaller
                      subproblems, in the absence of time-coupling constraints and
                      variables. However, time-coupling is common in energy
                      systems, e. g. due to (seasonal) energy storage and
                      peak-power prices. To solve time-coupled long-term
                      operational optimization problems, we propose the method
                      DeLoop for the Decomposition-based Long-term operational
                      optimization of energy systems with time-coupling. DeLoop
                      calculates feasible solutions (upper bounds) by decomposing
                      the operational optimization problem into smaller
                      subproblems. The solutions of these subproblems are
                      recombined to obtain a feasible solution for the original
                      long-term problem. To evaluate the quality of the feasible
                      solutions, DeLoop computes lower bounds by linear
                      programming relaxation. DeLoop iteratively decreases the
                      number of subproblems and employs the Branch-and-Cut
                      procedure to tighten the bounds. In a case study of an
                      energy system, DeLoop converges fast, outperforming a
                      commercial state-of-the-art solver by a factor of 32.},
      cin          = {IEK-10},
      ddc          = {600},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {153 - Assessment of Energy Systems – Addressing Issues of
                      Energy Efficiency and Energy Security (POF3-153)},
      pid          = {G:(DE-HGF)POF3-153},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000527569500029},
      doi          = {10.1016/j.energy.2020.117272},
      url          = {https://juser.fz-juelich.de/record/889896},
}