Floating photovoltaic systems represent a promising pathway for low-carbon hydrogen production, especially in water-constrained regions where land availability is limited. Researchers have now demonstrated that advanced predictive control strategies can significantly improve the performance and stability of these integrated solar-hydrogen systems.
A team investigated a coordinated control framework based on Graph Neural Predictive Control (GNPC) for a floating photovoltaic array coupled with an electrolyzer and reservoir system at Morocco's Martil Dam. Unlike conventional maximum power point tracking methods—such as Incremental Conductance or fuzzy logic control—the GNPC approach captures dynamic interactions among all major components, optimizing power extraction, DC-bus voltage regulation, and hydrogen production simultaneously within a finite-horizon optimization window.
Simulation results were compelling. The controller achieved a tracking efficiency of 98.9%, delivering nearly 79.1 kilowatts from an 80 kilowatt photovoltaic array with a settling time of just 75 milliseconds. Under the thermal conditions tested, cumulative power reduction remained below 3 percent, demonstrating stable performance across a realistic operating range. Using measured irradiance data from Tetouan, the system maintained smooth transient behavior and consistent hydrogen output of approximately 5.9 × 10⁻⁴ moles per second.
To validate practical implementation feasibility, the researchers deployed the control algorithm on a Raspberry Pi-based embedded platform connected to a laboratory photovoltaic experimental setup. Real-time results confirmed rapid convergence and minimal steady-state oscillations, proving that the approach can function reliably on low-cost hardware without requiring expensive specialized controllers.
These findings carry significant implications for distributed hydrogen production in regions combining abundant solar resources and water infrastructure. The coordinated predictive approach addresses a key challenge in renewable hydrogen systems: maintaining stable, efficient operation across varying environmental conditions while managing multiple interdependent components. This work suggests that advanced control methods, combined with accessible embedded platforms, can accelerate deployment of sustainable hydrogen systems at scale.
