Experimental and simulation analysis of green hydrogen production with integration of solar cell and electrolyser systems

• Developed and validated a solar-powered PEM electrolyser model for hydrogen flow rate predictions. • Implemented Multi-Criteria Decision Making (MCDM) to identify the most suitable solar PV panel. • Conducted a techno-economic assessment integrating system efficiency with hydrogen production costs. • Proposed a scalable predictive tool combining experimental and real-world datasets for green hydrogen. The growing demand for clean energy and carbon emission reduction has driven interest in green hydrogen production. While prior studies on photovoltaic-proton exchange membrane (PV-PEM) integration have focused mainly on simulations or small-scale experiments, they often lack real-world variability and comprehensive economic feasibility analysis. The novelty of this study lies in developing and validating a solar-powered PEM electrolyser model that accurately predicts hydrogen flow rates under dynamic solar conditions. The model is validated using experimental hydrogen generation data. Optimal solar PV panel selection is performed using a Multi-Criteria Decision Making (MCDM) approach. Ten solar panels are evaluated through system performance and techno-economic assessment, including Levelized Cost of Electricity (LCOE) and Levelized Cost of Hydrogen (LCOH). Panel 3 (US-Solar USS-QCMI-2-54-200) is recommended, offering 22 % system efficiency, an LCOE of ∼ 1.6 $/kWh, and an LCOH of ∼ 6.6 $/kg. By integrating experimental validation, computational modeling, and economic analysis, this study bridges the gap between simulations and real-world PV-PEM applications for green hydrogen. The developed tool is reliable and easy to use for predicting hydrogen production, which is important for supporting the clean energy from different power sources.