This study presents a mathematics-based simulation model for designing, analyzing, and optimizing offshore green hydrogen stations powered by solar photovoltaic systems, applicable to any location worldwide. Developed in Python, the model integrates environmental, physical, and technological parameters to simulate and forecast hydrogen production via water electrolysis using ALK or PEM electrolyzers, combined with an adiabatic compressor that enhances energy storage and facilitates integration into smart grids.

The five-phase modular methodology includes timeframe definition; estimation of solar electricity generation based on solar trajectory and the geographic orientation of photovoltaic panels; performance modeling of electrolyzers and compressors; and the integration of all components into a cohesive system. A case study demonstrates the model’s real-world applicability.

Results based on 2025 efficiency projections for ALK and PEM technologies show a substantial increase in solar energy capture in offshore environments, due to reduced atmospheric pollution and sea-surface reflectivity. Reflectivity is modeled as a function of sea surface flatness. A 20% increase in electrolyzer efficiency improves production by 32.28%, and the same efficiency gain in the compressor adds 0.81%. These impacts directly correlate with proportional reductions in the photovoltaic panel surface area required—greater electricity generation capacity translates into smaller infrastructure needs.

The model enables quantitative evaluation of trade-offs among solar irradiance, component performance, and system design. It supports cost reduction through optimized sizing and improved integration. This approach contributes to lowering the Levelized Cost of Electricity (LCOE) and promoting the viability of marine-based green hydrogen deployment.