Python–Simulink simulation of DC-coupled PV–PEM electrolyzer systems under minute-level solar variability

Abstract

Directly coupling photovoltaic (PV) arrays to proton exchange membrane (PEM) electrolysers is among the cheapest routes to green hydrogen, but without a power converter the operating point is mismatched to the solar maximum-power point, and that mismatch shifts minute-to-minute with cloud cover. Which coupling strategy maximizes hydrogen yield under realistic solar variability, and whether the answer holds across climates, has remained unresolved, because dynamic PV-array reconfiguration and sub-second electrochemical transients have never been modelled together. This work addresses both within a single Python–Simulink co-simulation of seven DC-coupled PV–PEM architectures, spanning battery-less and battery-integrated designs, driven by minute-level irradiance from a hybrid K-nearest-neighbours downscaling algorithm and evaluated across four sites from polar Svalbard to the Namib Desert. The dominant lever on hydrogen output is found to be array reconfiguration rather than added power electronics: string switching alone raises solar-to-hydrogen efficiency to 14.9%, matching converter-based maximum-power-point tracking without a converter, and, combined with active battery management, gives the highest yield of all seven topologies. Temporal resolution proves decisive, as hourly-averaged data suppress almost 200 daily reconfiguration events and misrank the topologies. Crucially, the performance hierarchy is invariant from pole to desert, yet the benefit of reconfiguration is strongly climate-dependent, falling from ＋24% in the low, variable light of the polar site to ＋4% under the abundant, steady irradiance of the desert. The framework provides a validated, climate-aware benchmark for PV–PEM system design.