We present a specialized scenario generation method that utilizes forecast
information to generate scenarios for the particular usage in day-ahead
scheduling problems. In particular, we use normalizing flows to generate wind
power generation scenarios by sampling from a conditional distribution that
uses day-ahead wind speed forecasts to tailor the scenarios to the specific
day. We apply the generated scenarios in a simple stochastic day-ahead bidding
problem of a wind electricity producer and run a statistical analysis focusing
on whether the scenarios yield profitable and reliable decisions. Compared to
conditional scenarios generated from Gaussian copulas and
Wasserstein-generative adversarial networks, the normalizing flow scenarios
identify the daily trends more accurately and with a lower spread while
maintaining a diverse variety. In the stochastic day-ahead bidding problem, the
conditional scenarios from all methods lead to significantly more profitable
and reliable results compared to an unconditional selection of historical
scenarios. The obtained profits using the normalizing flow scenarios are
consistently closest to the perfect foresight solution, in particular, for
small sets of only five scenarios.Comment: manuscript (17 pages, 7 figures, 5 tables), supporting information (2
pages, 1 figure, 1 table
