铁路氢燃料电池升级与电气化的局部和全局敏感性分析

Yizhe Zhang , Zhongbei Tian , Kangrui Jiang , Stuart Hillmansen , Clive Roberts
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引用次数: 0

摘要

在轨道交通领域,英国交通部表示,到2050年将实现英国铁路的全面改造,放弃传统的柴油列车,升级为新型环保列车。目前主流的升级方式是电气化和氢燃料电池。综合升级成本高,选择最优的有轨电车和干线铁路升级方式至关重要。如果不进行敏感性分析,我们很难确定各参数与成本之间的影响关系,导致在选择线路重建方法时成本的浪费。此外,通过分析不同参数对成本的敏感性,确定优化的首要方向,以降低成本。全局高阶灵敏度分析能够量化参数相互作用,显示参数之间的非加性效应。选取影响改造成本的主要参数,通过局部和全局敏感性分析方法,对有轨电车和干线铁路两种升级方式的改造成本进行分析。分析结果表明,考虑到目前英国的铁路系统,选择电动有轨电车和氢动力干线列车更为经济。对于有轨电车来说,列车运行的速度对最终成本的影响最大。通过灵敏度分析,为当前铁路升级改造方案提供有效的数据参考,为下一步列车参数优化提供理论依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Local and global sensitivity analysis for railway upgrading between hydrogen fuel cell and electrification
In the field of rail transit, the UK Department of Transport stated that it will realize a comprehensive transformation of UK railways by 2050, abandoning traditional diesel trains and upgrading them to new environmentally friendly trains. The current mainstream upgrade methods are electrification and hydrogen fuel cells. Comprehensive upgrades are costly, and choosing the optimal upgrade method for trams and mainline railways is critical. Without a sensitivity analysis, it is difficult for us to determine the influence relationship between each parameter and cost, resulting in a waste of cost when choosing a line reconstruction method. In addition, by analyzing the sensitivity of different parameters to the cost, the primary optimization direction can be determined to reduce the cost. Global higher-order sensitivity analysis enables quantification of parameter interactions, showing non-additive effects between parameters. This paper selects the main parameters that affect the retrofit cost and analyzes the retrofit cost of the two upgrade methods in the case of trams and mainline railways through local and global sensitivity analysis methods. The results of the analysis show that, given the current UK rail system, it is more economical to choose electric trams and hydrogen mainline trains. For trams, the speed at which the train travels has the greatest impact on the final cost. Through the sensitivity analysis, this paper provides an effective data reference for the current railway upgrading and reconstruction plan and provides a theoretical basis for the next step of train parameter optimization.
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