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@ARTICLE{Pesch:190181,
      author       = {Pesch, T. and Schröders, S. and Allelein, H. J. and Hake,
                      J. F.},
      title        = {{A} new {M}arkov-chain-related statistical approach for
                      modelling synthetic wind power time series},
      journal      = {New journal of physics},
      volume       = {17},
      number       = {5},
      issn         = {1367-2630},
      address      = {[Bad Honnef]},
      publisher    = {Dt. Physikalische Ges.},
      reportid     = {FZJ-2015-03109},
      pages        = {055001},
      year         = {2015},
      abstract     = {The integration of rising shares of volatile wind power in
                      the generation mix is a major challenge forthe future energy
                      system. To address the uncertainties involved in wind power
                      generation, modelsanalysing and simulating the stochastic
                      nature of this energy source are becoming
                      increasinglyimportant. One statistical approach that has
                      been frequently used in the literature is the Markov
                      chainapproach. Recently, the method was identified as being
                      of limited use for generating wind time serieswith time
                      steps shorter than 15–40 min as it is not capable of
                      reproducing the autocorrelationcharacteristics accurately.
                      This paper presents a new Markov-chain-related statistical
                      approach that iscapable of solving this problem by
                      introducing a variable second lag. Furthermore, additional
                      featuresare presented that allow for the further adjustment
                      of the generated synthetic time series. Theinfluences of the
                      model parameter settings are examined by meaningful
                      parameter variations. Thesuitability of the approach is
                      demonstrated by an application analysis with the example of
                      the windfeed-in in Germany. It shows that—in contrast to
                      conventional Markov chain approaches—thegenerated
                      synthetic time series do not systematically underestimate
                      the required storage capacity tobalance wind power
                      fluctuation.},
      cin          = {IEK-STE},
      ddc          = {530},
      cid          = {I:(DE-Juel1)IEK-STE-20101013},
      pnm          = {153 - Assessment of Energy Systems – Addressing Issues of
                      Energy Efficiency and Energy Security (POF3-153) /
                      VH-NG-1025 - Helmholtz Young Investigators Group
                      "Efficiency, Emergence and Economics of future supply
                      networks" $(VH-NG-1025_20112014)$},
      pid          = {G:(DE-HGF)POF3-153 / $G:(HGF)VH-NG-1025_20112014$},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000355277200001},
      doi          = {10.1088/1367-2630/17/5/055001},
      url          = {https://juser.fz-juelich.de/record/190181},
}