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@ARTICLE{Teichgraeber:889921,
      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 events in time series aggregation: {A} case study
                      for optimal residential energy supply systems},
      reportid     = {FZJ-2021-00529},
      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. 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-10},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {899 - ohne Topic (POF3-899)},
      pid          = {G:(DE-HGF)POF3-899},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2002.03059},
      howpublished = {arXiv:2002.03059},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2002.03059;\%\%$},
      url          = {https://juser.fz-juelich.de/record/889921},
}