Multi-objective optimization of a hydrogen supply chain network: Wind and solid biomass as primary energy sources for the public transport in Sicily

Abstract

Europe’s heavy reliance on diesel power for nearly half of its railway lines, for both goods and passengers, has significant implications for carbon emissions. To address this challenge, the European Union advocates for a shift towards hydrogen-based mobility, necessitating the development of robust and cost-effective hydrogen supply chains at regional and national levels. Leveraging renewable energy sources such as wind farms and solid biomass could foster the transition to sustainable hydrogen-based transportation. In this study, a mixed-integer linear programming approach integrated with an external heavy-duty refueling station analysis model is employed to address the optimal design of a new hydrogen supply chain. Through multi-objective optimization, this study aimed to minimize the overall daily costs and emissions of the supply chain. By applying the model to a case study in Sicily, different scenarios with varying supply chain configurations and wind curtailment factors were explored. The findings revealed that increasing the wind curtailment factor from 1% to 2% led to reductions of 12% and 15% in the total daily emission costs and network costs, respectively. Additionally, centralized biomass-based plants dominated hydrogen production, accounting for 96% and 94% of the total production under 1% and 2% wind curtailment factors, respectively. Furthermore, transporting gaseous hydrogen via tube trailers proved more cost effective than using tanker trucks for liquid hydrogen when compressed gaseous hydrogen is required at the dispenser of forecourt refueling stations. Finally, the breakdown of the levelized cost for the hydrogen refuelling station strongly depends on the form of hydrogen received at the gate, namely, liquid or gaseous. Specifically, for the former, the dispenser accounts for 60% of the total cost, whereas for the latter, the compressor is responsible for 58% of the total cost. This study highlights the importance of preliminary and quantitative analyses of new hydrogen supply chains through model-based optimization.