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@ARTICLE{Kruse:897203,
      author       = {Kruse, Johannes and Witthaut, Dirk and Schäfer, Benjamin},
      title        = {{R}evealing drivers and risks for power grid frequency
                      stability with explainable {AI}},
      journal      = {Patterns},
      volume       = {2},
      number       = {11},
      issn         = {2666-3899},
      address      = {[Amsterdam]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2021-03669},
      pages        = {100365 -},
      year         = {2021},
      abstract     = {Stable operation of an electric power system requires
                      strict operational limits for the grid frequency.
                      Fluctuations and external impacts can cause large frequency
                      deviations and increased control efforts. Although these
                      complex interdependencies can be modeled using machine
                      learning algorithms, the black box character of many models
                      limits insights and applicability. In this article, we
                      introduce an explainable machine learning model that
                      accurately predicts frequency stability indicators for three
                      European synchronous areas. Using Shapley additive
                      explanations, we identify key features and risk factors for
                      frequency stability. We show how load and generation ramps
                      determine frequency gradients, and we identify three classes
                      of generation technologies with converse impacts. Control
                      efforts vary strongly depending on the grid and time of day
                      and are driven by ramps as well as electricity prices.
                      Notably, renewable power generation is central only in the
                      British grid, while forecasting errors play a major role in
                      the Nordic grid.},
      cin          = {IEK-STE},
      ddc          = {004},
      cid          = {I:(DE-Juel1)IEK-STE-20101013},
      pnm          = {1112 - Societally Feasible Transformation Pathways
                      (POF4-111) / HDS LEE - Helmholtz School for Data Science in
                      Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) /
                      CoNDyNet 2 - Kollektive Nichtlineare Dynamik Komplexer
                      Stromnetze (BMBF-03EK3055B)},
      pid          = {G:(DE-HGF)POF4-1112 / G:(DE-Juel1)HDS-LEE-20190612 /
                      G:(DE-JUEL1)BMBF-03EK3055B},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {WOS:000719722100009},
      UT           = {WOS:000719722100009},
      doi          = {10.1016/j.patter.2021.100365},
      url          = {https://juser.fz-juelich.de/record/897203},
}