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@ARTICLE{Trebbien:1005788,
      author       = {Trebbien, Julius and Rydin Gorjão, Leonardo and
                      Praktiknjo, Aaron and Schäfer, Benjamin and Witthaut, Dirk},
      title        = {{U}nderstanding electricity prices beyond the merit order
                      principle using explainable {AI}},
      journal      = {Energy and AI},
      volume       = {13},
      issn         = {2666-5468},
      address      = {Amsterdam},
      publisher    = {Elsevier ScienceDirect},
      reportid     = {FZJ-2023-01633},
      pages        = {100250 -},
      year         = {2023},
      abstract     = {Electricity prices in liberalized markets are determined by
                      the supply and demand for electric power, which are in turn
                      driven by various external influences that vary strongly in
                      time. In perfect competition, the merit order principle
                      describes that dispatchable power plants enter the market in
                      the order of their marginal costs to meet the residual load,
                      i.e. the difference of load and renewable generation.
                      Various market models are based on this principle when
                      attempting to predict electricity prices, yet the principle
                      is fraught with assumptions and simplifications and thus is
                      limited in accurately predicting prices. In this article, we
                      present an explainable machine learning model for the
                      electricity prices on the German day-ahead market which
                      foregoes of the aforementioned assumptions of the merit
                      order principle. Our model is designed for an ex-post
                      analysis of prices and builds on various external features.
                      Using SHapley Additive exPlanation (SHAP) values we
                      disentangle the role of the different features and quantify
                      their importance from empiric data, and therein circumvent
                      the limitations inherent to the merit order principle. We
                      show that load, wind and solar generation are the central
                      external features driving prices, as expected, wherein wind
                      generation affects prices more than solar generation.
                      Similarly, fuel prices also highly affect prices, and do so
                      in a nontrivial manner. Moreover, large generation ramps are
                      correlated with high prices due to the limited flexibility
                      of nuclear and lignite plants. Overall, we offer a model
                      that describes the influence of the main drivers of
                      electricity prices in Germany, taking us a step beyond the
                      limited merit order principle in explaining the drivers of
                      electricity prices and their relation to each other.},
      cin          = {IEK-STE},
      ddc          = {624},
      cid          = {I:(DE-Juel1)IEK-STE-20101013},
      pnm          = {1112 - Societally Feasible Transformation Pathways
                      (POF4-111) / HGF-ZT-I-0029 - Helmholtz UQ: Uncertainty
                      Quantification - from data to reliable knowledge
                      (HGF-ZT-I-0029)},
      pid          = {G:(DE-HGF)POF4-1112 / G:(DE-Ds200)HGF-ZT-I-0029},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001146215000001},
      doi          = {10.1016/j.egyai.2023.100250},
      url          = {https://juser.fz-juelich.de/record/1005788},
}