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Electrical Engineering and Systems Science > Systems and Control

arXiv:2310.01385 (eess)
[Submitted on 2 Oct 2023 (v1), last revised 29 Mar 2024 (this version, v2)]

Title:Feature-Driven Strategies for Trading Wind Power and Hydrogen

Authors:Emil Helgren, Jalal Kazempour, Lesia Mitridati
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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.
Subjects: Systems and Control (eess.SY)
Cite as: arXiv:2310.01385 [eess.SY]
  (or arXiv:2310.01385v2 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2310.01385

Submission history

From: Emil Helgren [view email]
[v1] Mon, 2 Oct 2023 17:45:11 UTC (447 KB)
[v2] Fri, 29 Mar 2024 11:12:31 UTC (572 KB)
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