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1319. Feature-driven strategies for trading wind power and hydrogen
Invited abstract in session WB-9: Production Optimization and Supply Chain Management of Green Hydrogen under Uncertainties, stream Energy Markets.
Wednesday, 10:30-12:00Room: 10 (building: 116)
Authors (first author is the speaker)
| 1. | Lesia Mitridati
|
| Technical University of Denmark (DTU) |
Abstract
This talk presents a feature-driven model for hybrid power plants, enabling them to exploit available contextual information such as historical forecasts of day-ahead prices and wind power, and make optimal wind power and hydrogen trading decisions in the day-ahead stage. For that, we define and compare different linear and piece-wise linear decision rules. 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.
Keywords
- Electricity Markets
- OR in Energy
Status: accepted
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