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This paper develops a feature-driven model for hybrid power plants, enablingthem to exploit available contextual information such as historical forecastsof day-ahead prices and wind power, and make optimal wind power and hydrogentrading decisions in the day-ahead stage. For that, we develop differentvariations of feature-driven linear policies. In addition, we propose areal-time adjustment strategy for hydrogen production. Our numerical resultsshow that the final profit obtained from our proposed feature-driven tradingmechanism in the day-ahead stage together with the real-time adjustmentstrategy is very close to that in an ideal benchmark with perfect information.

Feature-Driven Strategies for Trading Wind Power and Hydrogen