As the global energy transition accelerates, offshore wind energy is emerging as a crucial component of a resilient and low-carbon electricity supply. Beyond power generation, offshore wind farms have the potential to enhance grid stability by participating in ancillary service markets, particularly reserve markets. By dynamically adjusting their power output, wind turbines can help balance supply and demand, reduce frequency deviations, and improve overall system reliability.

However, such market participation introduces additional mechanical loads on turbine structures, influencing their aging process, fatigue, and long-term structural integrity. Understanding these effects is essential for optimizing both economic performance and operational reliability. This research aims to evaluate the impact of reserve market participation on wind turbine lifetime by analyzing the mechanical stresses induced by different operational scenarios.

Leveraging high-fidelity numerical simulations, structural health monitoring data, and machine learning-based predictive models, this study seeks to integrate structural aging into the decision-making process for offshore wind turbines. The ultimate goal is to develop strategies that enable market participation while ensuring turbine durability, improving bid formulation strategies, and enhancing long-term asset management in renewable energy markets.