Adaptive and Enhanced Hydrogen Production Using Dynamic Particle Swarm Optimization for PV-PEM Systems

This study addresses the inefficiencies of static models in solar-driven hydrogen production by proposing a dynamic Particle Swarm Optimization (PSO) framework for a 2.88 kW photovoltaic (PV)-proton exchange membrane (PEM) electrolyzer system under extreme summer conditions. Unlike static models with fixed parameters, the PSO-based approach dynamically optimizes electrolyzer input power in real time, adapting to fluctuating solar irradiance up to 1000 W/m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and ambient temperatures ranging from 25°C to 48°C. Simulation results demonstrate that the dynamic model outperforms static counterparts, achieving 11.63% higher total hydrogen production, with 28.06 kg and 25.14 kg for dynamic and static models, respectively, and 7.76% greater average hourly output. System efficiency improved from 64.97% to 69.29%, with penalty mechanisms ensuring operational stability by preventing voltage and thermal violations during rapid environmental shifts. This work uniquely addresses nonlinear efficiency losses caused by PV temperature degradation and electrolyzer power mismatches in harsh climates. The findings validate the superiority of real-time metaheuristic optimization, offering a scalable solution to enhance renewable energy utilization and grid resilience.