Betting vs. Trading: Learning a Linear Decision Policy for Selling Wind Power and Hydrogen

Research output: Contribution to conferencePaperResearchpeer-review

Abstract

We develop a bidding strategy for a hybrid power plant combining co-located wind turbines and an electrolyzer, constructing a price-quantity bidding curve for the day-ahead electricity market while optimally scheduling hydrogen production. Without risk management, single imbalance pricing leads to an all-or-nothing trading strategy, which we term “betting”. To address this, we propose a data-driven, pragmatic approach that leverages contextual information to train linear decision policies for both power bidding and hydrogen scheduling. By introducing explicit risk constraints to limit imbalances, we move from the all-or-nothing approach to a “trading” strategy, where the plant diversifies its power trading decisions. We evaluate the model under three scenarios: when the plant is either conditionally allowed, always allowed, or not allowed to buy power from the grid, which impacts the green certification of the hydrogen produced. Comparing our data-driven strategy with an oracle model that has perfect foresight, we show that the risk-constrained, data-driven approach delivers satisfactory performance.
Original languageEnglish
Publication date2025
Number of pages10
Publication statusAccepted/In press - 2025
Event12th Bulk Power System Dynamics and Control Symposium - Sorrento, Italy
Duration: 22 Jun 202527 Jun 2025

Conference

Conference12th Bulk Power System Dynamics and Control Symposium
Country/TerritoryItaly
CitySorrento
Period22/06/202527/06/2025

Keywords

  • Hybrid power plant
  • Single imbalance price
  • Linear decision policies
  • Bidding curve
  • Hydrogen

Fingerprint

Dive into the research topics of 'Betting vs. Trading: Learning a Linear Decision Policy for Selling Wind Power and Hydrogen'. Together they form a unique fingerprint.

Cite this