  This paper develops a feature-driven model for hybrid power plants, enabling
them to exploit available contextual information such as historical forecasts
of wind power, and make optimal wind power and hydrogen trading decisions in
the day-ahead stage. For that, we develop different variations of
feature-driven linear policies, including a variation where policies depend on
price domains, resulting in a price-quantity bidding curve. In addition, we
propose a real-time adjustment strategy for hydrogen production. Our numerical
results show that the final profit obtained from our proposed feature-driven
trading mechanism in the day-ahead stage together with the real-time adjustment
strategy is very close to that in an ideal benchmark with perfect information.
