Low-temperature electrolysis model optimized for improved performance in dynamic simulations

Traditional hydrogen production methods contribute significantly to greenhouse gas emissions, making technological change essential. Water electrolysis powered by renewable energy offers a promising route for sustainable hydrogen production. However, the variability of renewables challenges efficient electrolyzer operation, often requiring detailed simulations with long time series and high computational effort. We present a dynamic model of a low-temperature electrolyzer system optimized for computational efficiency. The model increases computational speed by a factor of 1000 compared to full-balance models while maintaining accuracy above 99% in hydrogen prediction. The simplified model is used to investigate the benefits of modularization in large-scale hydrogen plants. Results show that modularization has a minor effect on overall output but significantly increases operational time. This extended production window can reduce the need for large-scale hydrogen storage, especially when integrated with a continuous hydrogen consumer. • Dynamic electrolysis stack model with electrochemical and cross-over description. • Study analyzing the influence of pressure, temperature, and current on efficiency. • Performance optimized stack model with 1000x speed-up at > 99% accuracy. • Quantification of modularization benefits in large-scale plants show clear limitation.