Risk-Constrained Bidding for Wind Power and Hydrogen Production

Summary

This study develops a day-ahead bidding and operating policy for a hybrid plant that combines wind generation with an electrolyzer. The policy sets power bids and schedules hydrogen production using contextual information, with linear decision rules learned from data. It addresses how single imbalance pricing can make an unprotected strategy effectively all-or-nothing, exposing the plant to large imbalances.

The proposed approach adds explicit risk constraints to limit imbalances and diversify trading decisions. It is evaluated under three grid-purchasing rules: purchases are conditionally allowed, always allowed, or prohibited. These rules matter because grid power can affect whether the resulting hydrogen qualifies as green. The study compares its data-driven strategy with an oracle that has perfect foresight and reports satisfactory performance for the risk-constrained approach. The summary provides no numerical performance results, detailed market assumptions, or evidence about how the policy performs outside the modeled setting.

Key ideas

• Single imbalance pricing can make unprotected plant bidding an all-or-nothing decision.
• Linear decision policies use contextual information to set electricity bids and hydrogen production schedules.
• Explicit risk constraints limit imbalances and encourage more diversified power trading.
• Rules governing grid purchases affect the green certification of produced hydrogen.
• The data-driven strategy is compared with a perfect-foresight oracle, but the supplied summary gives no numerical results.

Tags

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


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









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.