Research on Data-Driven Optimization Model of Fuel Cell Hydrogen Supply Network

This paper proposes a data-driven optimization model for fuel cell hydrogen supply network, aiming to improve the overall efficiency of the supply network by analyzing actual data. The model uses a hybrid heuristic algorithm to optimize each node in the network, taking into account multiple variables in the supply chain, such as hydrogen production, transportation costs, and demand fluctuations. Through algorithm design and model simulation, this paper conducts an in-depth study on network optimization under different configurations, and verifies the effectiveness of the model with precise data analysis. The findings indicate that within limitations, the proposed model successfully attains an optimal balance between the effectiveness and cost-efficiency of hydrogen distribution. The developed optimization framework demonstrates its capability to significantly lower supply chain expenditures, enhance the dependability and robustness of the system, and furnish a strong foundation for the extensive deployment of fuel cells. The optimization model devised in this study, along with its simulated outcomes, offers substantial reference value for the design and enhancement of prospective hydrogen supply infrastructures.