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@ARTICLE{Teichgraeber:872879,
      author       = {Teichgraeber, Holger and Lindenmeyer, Constantin P. and
                      Baumgärtner, Nils and Kotzur, Leander and Stolten, Detlef
                      and Robinius, Martin and Bardow, André and Brandt, Adam R.},
      title        = {{E}xtreme {E}vents as {P}art of {T}ime {S}eries
                      {A}ggregation: {A} {C}ase {S}tudy for the {O}ptimization of
                      {R}esidential {E}nergy {S}upply {S}ystems},
      journal      = {Applied energy},
      volume       = {275},
      issn         = {0360-5442},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2020-00344},
      pages        = {115223 -},
      year         = {2020},
      abstract     = {To account for volatile renewable energy supply, energy
                      systems optimization problems require high temporal
                      resolution. Many models use time-series clustering to find
                      representative periods to reduce the amount of time-series
                      input data and make the optimization problem computationally
                      tractable. However, clustering methods remove peaks and
                      other extreme events, which are important to achieve robust
                      system designs. This work addresses the challenge of
                      including extreme events. We present a general decision
                      framework to include extreme events in a set of
                      representative periods. We introduce a method to find
                      extreme periods based on the slack variables of the
                      optimization problem itself. Our method is evaluated and
                      benchmarked with other extreme period inclusion methods from
                      the literature for a design and operations optimization
                      problem: a residential energy supply system. Our method
                      ensures feasibility over the full input data of the
                      residential energy supply system although the design
                      optimization is performed on the reduced data set.We show
                      that using extreme periods as part of representative periods
                      improves the accuracy of the optimization results by $3\%$
                      to more than $75\%$ depending on system constraints compared
                      to results with clustering only, and thus reduces system
                      cost and enhances system reliability.},
      cin          = {IEK-3 / IEK-10},
      ddc          = {600},
      cid          = {I:(DE-Juel1)IEK-3-20101013 / I:(DE-Juel1)IEK-10-20170217},
      pnm          = {134 - Electrolysis and Hydrogen (POF3-134)},
      pid          = {G:(DE-HGF)POF3-134},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000565604700001},
      doi          = {10.1016/j.apenergy.2020.115223},
      url          = {https://juser.fz-juelich.de/record/872879},
}