The global energy transition is one of the most urgent technological and societal challenges of the 21st century. To achieve climate goals, it is essential to reduce the use of fossil fuels and replace them with sustainable alternatives. In this context, green hydrogen is receiving increasing attention, particularly as asolution for sectors where direct electrification is difficult or unfeasible, and as a means to balance the intermittency of renewable energy sources. However, hydrogen production via water electrolysis remains energy-intensive and costly, particularly when powered by fluctuating sources like wind. To improve economic viability, system-level optimisation must account for technical phenomena such as dynamic efficiency, degradation, gas crossover, and the performance of power electronics.
This thesis investigates the dynamic behaviour and optimal sizing of a directly coupled wind-powered alkaline electrolyser system, with the aim of minimising the Levelized Cost of Hydrogen (LCOH). Although static operating conditions are often assumed in existing models, this work addresses gaps in the literature by developing a time-resolved simulation model that includes wind variability, dynamic efficiency, degradation effects, realistic operational limits of the electrolyser, and time-dependent efficiency of power electronics.
An electrolyser model was developed and implemented in Python using an Electrical Equivalent Circuit (EEC) approach. An initial analysis compared intra-hour wind power fluctuations with hourly averages, revealing that the impact on hydrogen conversion efficiency was negligible (<0.06% over three 3-hour periods). Given this minimal difference and the widespread availability of hourly wind data across numerous locations, hourly wind data was deemed sufficiently accurate for system-level analysis.
The model was subsequently applied to evaluate system performance across 38 onshore European locations using 2015 wind data, assuming a constant configuration of a 2MW wind turbine coupled to a 1380kW electrolyser (69% ratio). For each location, the wind turbine and electrolyser capacity factors were calculated to assess the geographical variability in system utilisation. In addition, two Dutch sites, one coastal and one inland, were studied in greater detail to analyse annual operational behaviour, power electronics impact, conversion efficiency, hydrogen yield, and degradation patterns. Finally, lifetime simulations over 20 years were performed to evaluate system economics under varying electrolyser sizes, three cost scenarios, and two discount rates. Results showed that optimal electrolyser sizing is highly location-dependent and influenced by design objectives: the size yielding the highest hydrogen production is not necessarily the one that results in the lowest LCOH. In fact, the LCOH-optimal size was consistently smaller. Moreover, cost scenarios affected optimal sizing, with higher capital costs favouring slightly larger systems to offset investment through increased hydrogen output.
Time-resolved modelling further revealed the importance of minimum load constraints (to avoid gas crossover) and degradation effects, which influence system utilisation and stack replacement timing. While lifetime hydrogen production estimates from the dynamic model did not deviate significantly from those based on static assumptions, the dynamic approach enabled more accurate performance forecasting and degradation tracking. This research highlights the necessity of time-resolved modelling for techno-economic assessment of wind-powered hydrogen systems. The developed framework provides a comprehensive foundation for future optimisation studies and supports more accurate design and investment decisions for renewable hydrogen deployment.