Presentations

Abstract

Hybrid wind-hydrogen plants have multiple revenue sources, subject to uncertainties and trade-offs. Since electrical power is typically traded ahead of the actual power generation, weather forecasts play a crucial role in the power trading strategy. Additionally, the trading and control strategies of other market players influence the imbalance of the public grid and, as a result, have an impact on the opportunity to generate extra revenue by providing grid balancing services. To maximize the total profit of a hybrid plant, we propose a novel methodology based on a multi-agent reinforcement learning (RL) system with two RL agents: one for day-ahead power trading, and a second for real-time power control of the wind farm and hydrogen production unit. The RL system is trained with real-world data from a large offshore wind farm in the Belgian North Sea. Results demonstrate that the RL agents effectively learn to maximize jointly the total operational profit of the hybrid plant, achieving a significantly higher profitability compared to conventional algorithms.