Feature-Driven Strategies for Trading Wind Power and Hydrogen

TL;DR

This paper introduces a feature-driven model for hybrid power plants that optimizes wind power and hydrogen trading decisions using contextual information, achieving near-ideal profits in simulations.

Contribution

It presents novel feature-driven linear policies and a real-time adjustment strategy for hydrogen production, enhancing trading efficiency in hybrid power plants.

Findings

Profit close to ideal benchmark with perfect information

Effective use of historical forecasts for decision-making

Improved trading strategies for wind and hydrogen markets

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.