As the global energy transition accelerates, transitioning away from fossil fuels has become critical to assist the mitigation of climate change. Hydrogen, with its dual role as both an energy carrier and fuel, holds great potential for this transition. However, its large-scale production, particularly for industries like chemicals and transportation, still relies heavily on fossil fuels, posing a challenge for sustainable development.

This research focuses on advancing green hydrogen production, specifically through alkaline water electrolysis, a technology associated with zero greenhouse gas emissions. A key aspect of this work is addressing a gap in large-scale electrolysis modeling, with an emphasis on a modular system design. Modularity, as opposed to traditional single-unit scaling, offers improved operational flexibility and safety. This approach is especially relevant when electrolysis systems are powered by renewable energy sources, another critical component of the energy transition.

This thesis presents an investigation into the performance optimization of a modular alkaline water electrolysis system, designed to handle fluctuating renewable energy inputs. A physics-based numerical model was developed in Python, to simulate a large-scale AWE system composed of multiple modular units, capturing critical parameters such as temperature evolution, gas purity, and energy losses. The model was built progressively, starting from the cell level, incorporating a thermal model for temperature development and a mass transfer model for gas purity estimation. These combined elements formed a robust tool for simulating and optimizing the performance of modular electrolysis systems.

After validating the model against existing numerical and experimental data at the cell level, it demonstrated a strong ability to accurately capture the behavior of a single-cell system across all modeled parameters, including cell potential, temperature, and gas impurities. The differences between the simulated values and experimental data were minimal, further confirming the model's accuracy and reliability. This validation provided confidence in the model's predictive capabilities and laid the foundation for extending it for a larger modular system.

Following this, a scaling analysis was conducted to evaluate the model's performance when applied to a modular system. The simulations were carried out under both steady and varying power inputs, reflecting realistic operational conditions, particularly when coupled with renewable energy sources. The results highlighted the model's capacity to predict temperature evolution and gas impurity levels in such a scaled system. These findings indicated that the model not only captured the thermal and mass transfer behavior but also provided valuable insights into the effect of system scaling on overall performance and safety.

The outcomes of this research demonstrate that the developed model could act as a valuable tool for optimizing the performance of modular alkaline water electrolysis systems. The model successfully predicts the thermal behavior, gas purity, and energy losses across a range of operational conditions, including fluctuating power inputs typical of renewable energy sources. By enabling the fine-tuning of operational parameters prior to system deployment, this model provides a significant advantage in designing safe, efficient, and scalable hydrogen production systems. Future work could extend the model's capabilities by incorporating additional factors such as degradation mechanisms and detailed component-level interactions, ensuring even more robust predictions over long-term operation.