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@ARTICLE{Titz:1018615,
      author       = {Titz, Maurizio and Pütz, Sebastian and Witthaut, Dirk},
      title        = {{I}dentifying drivers and mitigators for congestion and
                      redispatch in the {G}erman electric power system with
                      explainable {AI}},
      journal      = {Applied energy},
      volume       = {356},
      issn         = {0306-2619},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2023-04930},
      pages        = {122351},
      year         = {2024},
      abstract     = {The transition to a sustainable energy supply challenges
                      the operation of electric power systems in various ways.
                      Transmission grid loads increase as wind and solar power is
                      often installed far away from the consumers. System
                      operators resolve grid congestion via countertrading or
                      redispatch to ensure grid stability. While some drivers of
                      congestion are known, the magnitude of their impact is
                      unclear, and other factors might still be unidentified.In
                      this study, we conduct a data-driven investigation of
                      congestion in the German transmission grid that reveals
                      drivers and mitigators and quantifies their impact ex-post.
                      Specifically, we used Gradient Boosted Trees and SHAP values
                      to develop an explainable machine learning model for the
                      hourly volume of redispatch and countertrade. As expected,
                      wind power generation in northern Germany emerged as the
                      main driver. Cross-border electricity trading, especially
                      with Denmark, also plays an important role. German solar
                      power has very little effect. Furthermore, our results
                      suggest that run-of-river generation in the alpine region
                      has a strong mitigating effect. Our results support the idea
                      that market design changes, e.g., a bidding zone split,
                      could contribute to congestion prevention.},
      cin          = {IEK-10},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {1122 - Design, Operation and Digitalization of the Future
                      Energy Grids (POF4-112) / HDS LEE - Helmholtz School for
                      Data Science in Life, Earth and Energy (HDS LEE)
                      (HDS-LEE-20190612)},
      pid          = {G:(DE-HGF)POF4-1122 / G:(DE-Juel1)HDS-LEE-20190612},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001127131000001},
      doi          = {10.1016/j.apenergy.2023.122351},
      url          = {https://juser.fz-juelich.de/record/1018615},
}