Capacity Optimization Method for Photovoltaic Hydrogen Production Systems Based on Multi-Objective Particle Swarm Algorithm

[Objective] The hydrogen production system utilizes solar energy to convert water into hydrogen, aiming to reduce carbon emissions and improve the efficiency of renewable energy utilization. However, the randomness and volatility of photovoltaic power output severely impact the stable hydrogen supply of the system. [Method] This paper proposed a capacity optimization method for photovoltaic hydrogen production systems based on a multi-objective particle swarm algorithm. In the photovoltaic hydrogen production system, chemical energy battery packs and hydrogen storage tanks were integrated to construct a photovoltaic hydrogen production-storage-supply model. A system operation strategy prioritizing hydrogen storage was designed. [Result] Taking the economic cost of the system, curtailment rate of solar power, and electricity purchase rate as optimization objectives, the multi-objective particle swarm algorithm was employed to solve the capacity configuration of the system components. The optimization results demonstrated that, while ensuring a continuous and stable hydrogen supply, the economic cost, curtailment rate, and electricity purchase rates of the system were effectively reduced. [Conclusion] The results of case analysis indicate that the proposed capacity optimization method can effectively reduce the economic costs of the system, decrease curtailment and electricity purchases, and significantly enhance the operational stability.