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We present a specialized scenario generation method that utilizes forecastinformation to generate scenarios for day-ahead scheduling problems. Inparticular, we use normalizing flows to generate wind power scenarios bysampling from a conditional distribution that uses wind speed forecasts totailor the scenarios to a specific day. We apply the generated scenarios in astochastic day-ahead bidding problem of a wind electricity producer and analyzewhether the scenarios yield profitable decisions. Compared to Gaussian copulasand Wasserstein-generative adversarial networks, the normalizing flowsuccessfully narrows the range of scenarios around the daily trends whilemaintaining a diverse variety of possible realizations. In the stochasticday-ahead bidding problem, the conditional scenarios from all methods lead tosignificantly more stable profitable results compared to an unconditionalselection of historical scenarios. The normalizing flow consistently obtainsthe highest profits, even for small sets scenarios.

Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators