(501d) Techno-Economic Analysis with Uncertainties for Alternative Fuels Enabling Near and Mid-Term U.S. Rail Sector Decarbonization

The Federal Highway Administration (FRA) expects a 47% increase in U.S. freight shipments and challenges the rail industry to achieve net-zero greenhouse gas (GHG) emissions by 2050 and meet the U.S. long-term decarbonization target (Hwang, 2022). While working towards complete electrification, finding alternative low-carbon fuels is crucial for near- and mid-term rail sector decarbonization, as they can be used as direct drop in or only require minor retrofitting of existing infrastructure. Various alternative fuel pathways have been explored in the open literature, but with different technology readiness level (TRL), model fidelities and economic assumptions, and therefore the results are not directly comparable for identifying top options. In addition, for emerging technologies, existing techno-economic analyses were often not based on the most up-to-date performance or optimized for the rail sector application.

To provide basis for screening alternative fuel options for rail sector decarbonizing, in this study, techno-economic analyses were conducted for 16 alternative fuel pathways with harmonized economic assumptions. The selected pathways include 7 fuel types of interest to U.S. rail sector (biodiesel, renewable diesel, bio-oils, ethanol, methanol, dimethyl ether, and green ammonia) and 4 feedstock types (1^st generation, organic waste, cellulosic biomass, and renewable hydrogen). Technologies are either fully commercialized or at least demonstrated in bench scale. For emerging technologies, process data were generated from Aspen Plus or ChemCAD models, while for commercial technologies, subscribed databases (i.e., PEP Yearbook) were used. Monte Carlo simulation was also conducted to capture the uncertainties from technology maturity, knowledge gaps, model fidelities, and economic assumptions. Results were reported in minimum fuel selling price with uncertainty range and detailed cost breakdown.

References

Hwang H, Lim H, Chin S, Uddin M, Biehl A, Xie F, Hargrove S, Liu Y, Wang C. 2022. ‘Freight analysis framework version 5 (FAF5) base year 2017 data development technical report.’ Oak Ridge National Laboratory, Oak Ridge, Tennessee. [Data available online, accessed 6/30/2022: https://faf.ornl.gov/faf5/]