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@ARTICLE{Kruse:909513,
      author       = {Kruse, Johannes and Schäfer, Benjamin and Witthaut, Dirk},
      title        = {{S}econdary control activation analysed and predicted with
                      explainable {AI}},
      journal      = {Electric power systems research},
      volume       = {212},
      issn         = {0378-7796},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2022-03219},
      pages        = {108489 -},
      year         = {2022},
      abstract     = {The transition to a renewable energy system challenges
                      power grid operation and stability. Secondary control is key
                      in restoring the power system to its reference following a
                      disturbance. Underestimating the necessary control capacity
                      may require emergency measures, such that a solid
                      understanding of its predictability and driving factors is
                      needed. Here, we establish an explainable machine learning
                      model for the analysis of secondary control power in
                      Germany. Training gradient boosted trees, we obtain an
                      accurate ex-post description of control activation. Our
                      explainable model demonstrates the strong impact of external
                      drivers such as forecasting errors and the generation mix,
                      while daily patterns in the reserve activation play a minor
                      role. Training a prototypical forecasting model, we identify
                      forecast error estimates as crucial to improve
                      predictability. Generally, input data and model training
                      have to be carefully adapted to serve the different purposes
                      of either ex-post analysis or forecasting and reserve
                      sizing.},
      cin          = {IEK-STE},
      ddc          = {620},
      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) /
                      Verbundvorhaben CoNDyNet2: Kollektive nichtlineare Dynamik
                      komplexer Stromnetze (03EK3055B)},
      pid          = {G:(DE-HGF)POF4-1112 / G:(DE-Juel1)HDS-LEE-20190612 /
                      G:(BMBF)03EK3055B},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000856623900017},
      doi          = {10.1016/j.epsr.2022.108489},
      url          = {https://juser.fz-juelich.de/record/909513},
}