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@INPROCEEDINGS{Baumgrtner:877624,
      author       = {Baumgärtner, Nils and Shu, David Yang and Bahl, Björn and
                      Hennen, Maike and Bardow, André},
      title        = {{F}rom peak power prices to seasonal storage: {L}ong-term
                      operational optimization of energy systems by time-series
                      decomposition},
      volume       = {46},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2020-02339},
      series       = {Computer Aided Chemical Engineering},
      pages        = {703 - 708},
      year         = {2019},
      abstract     = {Long-term operation of energy systems is a complex
                      optimization task. Often, such long-term operational
                      optimizations are solved by direct decomposing the problem
                      into smaller subproblems. However, direct decomposition is
                      not possible for problems with time-coupling constraints and
                      variables. Such time-coupling is common in energy systems,
                      e.g., due to peak power prices and (seasonal) energy
                      storage. To efficiently solve coupled long-term operational
                      optimization problems, we propose a time-series
                      decomposition method. The proposed method calculates lower
                      and upper bounds to obtain a feasible solution of the
                      original problem with known quality. We compute lower bounds
                      by the Branch-and-Cut algorithm. For the upper bound, we
                      decompose complicating constraints and variables into
                      smaller subproblems. The solution of these subproblems are
                      recombined to obtain a feasible solution for the long-term
                      operational optimization. To tighten the upper bound, we
                      iteratively decrease the number of subproblems. In a case
                      study for an industrial energy system, we show that the
                      proposed time-series decomposition method converges fast,
                      outperforming a commercial state-of-the-art solver.},
      month         = {Jun},
      date          = {2019-06-16},
      organization  = {29th European Symposium on Computer
                       Aided Process Engineering, Eindhoven
                       (The Netherlands), 16 Jun 2019 - 19 Jun
                       2019},
      cin          = {IEK-10},
      ddc          = {660},
      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)8 / PUB:(DE-HGF)7},
      UT           = {WOS:000495447200118},
      doi          = {10.1016/B978-0-12-818634-3.50118-1},
      url          = {https://juser.fz-juelich.de/record/877624},
}