Feature-Driven Strategies for Trading Wind Power and Hydrogen

Research output: Contribution to conferencePaperResearchpeer-review

Abstract

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.
Original languageEnglish
Publication date2024
Number of pages8
Publication statusPublished - 2024
Event23rd Power Systems Computation Conference - Paris-Saclay, France
Duration: 4 Jun 20247 Jun 2024

Conference

Conference23rd Power Systems Computation Conference
Country/TerritoryFrance
CityParis-Saclay
Period04/06/202407/06/2024

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