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@ARTICLE{Cramer:917557,
      author       = {Cramer, Eike and Paeleke, Leonard and Mitsos, Alexander and
                      Dahmen, Manuel},
      title        = {{N}ormalizing {F}low-based {D}ay-{A}head {W}ind {P}ower
                      {S}cenario {G}eneration for {P}rofitable and {R}eliable
                      {D}elivery {C}ommitments by {W}ind {F}arm {O}perators},
      publisher    = {arXiv},
      reportid     = {FZJ-2023-00759},
      year         = {2022},
      abstract     = {We present a specialized scenario generation method that
                      utilizes forecast information to generate scenarios for
                      day-ahead scheduling problems. In particular, we use
                      normalizing flows to generate wind power scenarios by
                      sampling from a conditional distribution that uses wind
                      speed forecasts to tailor the scenarios to a specific day.
                      We apply the generated scenarios in a stochastic day-ahead
                      bidding problem of a wind electricity producer and analyze
                      whether the scenarios yield profitable decisions. Compared
                      to Gaussian copulas and Wasserstein-generative adversarial
                      networks, the normalizing flow successfully narrows the
                      range of scenarios around the daily trends while maintaining
                      a diverse variety of possible realizations. In the
                      stochastic day-ahead bidding problem, the conditional
                      scenarios from all methods lead to significantly more stable
                      profitable results compared to an unconditional selection of
                      historical scenarios. The normalizing flow consistently
                      obtains the highest profits, even for small sets scenarios.},
      keywords     = {Optimization and Control (math.OC) (Other) / Machine
                      Learning (cs.LG) (Other) / FOS: Mathematics (Other) / FOS:
                      Computer and information sciences (Other)},
      cin          = {IEK-10},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {1121 - Digitalization and Systems Technology for
                      Flexibility Solutions (POF4-112) / HDS LEE - Helmholtz
                      School for Data Science in Life, Earth and Energy (HDS LEE)
                      (HDS-LEE-20190612)},
      pid          = {G:(DE-HGF)POF4-1121 / G:(DE-Juel1)HDS-LEE-20190612},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2204.02242},
      url          = {https://juser.fz-juelich.de/record/917557},
}