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

TL;DR

This paper presents a data-driven linear decision policy for a hybrid wind and hydrogen plant to optimize day-ahead bidding and scheduling, incorporating risk constraints to transition from all-or-nothing betting to diversified trading strategies.

Contribution

It introduces a pragmatic, risk-aware, data-driven approach for joint power bidding and hydrogen scheduling in renewable energy plants, moving beyond traditional all-or-nothing strategies.

Findings

Risk constraints improve trading diversification.

Data-driven policies perform close to oracle models.

Scenario analysis shows impact of grid purchase permissions.

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.