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

arXiv: https://arxiv.org/abs/2412.18479 · HTML: https://mlsanity.com/paper/2412.18479/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles](https://mlsanity.com/paper/2602.13010/) (2026)

## Abstract (arXiv)

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.