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.

中文翻译：

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氢供应链网络的多目标优化：风能和固体生物质作为西西里岛公共交通的主要能源


欧洲近一半的铁路线（无论是货物还是乘客）严重依赖柴油动力，这对碳排放产生了重大影响。为了应对这一挑战，欧盟主张转向以氢为基础的交通，需要在区域和国家层面发展强大且具有成本效益的氢供应链。利用风电场和固体生物质等可再生能源可以促进向可持续氢交通的过渡。在本研究中，采用混合整数线性规划方法与外部重型加氢站分析模型相结合来解决新氢气供应链的优化设计。通过多目标优化，本研究旨在最大限度地减少供应链的总体日常成本和排放。通过将该模型应用于西西里岛的案例研究，探讨了具有不同供应链配置和弃风因素的不同场景。研究结果显示，将弃风系数从 1% 提高到 2% 可使每日总排放成本和网络成本分别降低 12% 和 15%。此外，集中式生物质工厂在氢气生产中占主导地位，在1%和2%的弃风系数下分别占总产量的96%和94%。此外，当前院加油站的加油机需要压缩气态氢时，事实证明，通过长管拖车运输气态氢比使用液氢罐车运输更具成本效益。最后，加氢站平准化成本的细目很大程度上取决于在门口接收的氢气的形式，即液态或气态。 具体来说，对于前者，分配器占总成本的60%，而对于后者，压缩机占总成本的58%。这项研究强调了通过基于模型的优化对新氢供应链进行初步和定量分析的重要性。