On Jan. 15, 2025, the Federal Railroad Administration released on its website the final report titled Cost and Benefit Risk Framework for Modern Railway Electrification Options (download below). The report is based on FRA’s Office of Research, Development & Test (RD&T) award of a contract in 2023 to the University of Texas at Austin for an updated re-examination of freight railway electrification, with the following tasks:
- Develop updated costs and benefits for electrification of rail transportation considering traditional and modern innovative methods.
- Review past and current electrification studies and identify technologies and strategic operation and implementation approaches to improve benefits and reduce capital, operating costs and risk.
- Provide a risk-based framework that can aid railroads in evaluating electrification investments, and guide future supplier research and development, given current technological limitations and possible solutions to infrastructure and operational challenges.
Note that there is a separate, non-integrated report on the public policy needs assessment for railroad electrification. That report was released in December 2024 by the U.S. Department of Energy, with some participation by FRA and USDOT. That report is not a toolkit and is much more conclusion-oriented than this FRA report.
The FRA report has two major topical sections. The first is an intensive historical study of past reports and plans for possible railroad electrification in different regions of North America looking back as far as the 1960s. The University of Texas project authors (C. Tyler Dick, Rydell D. Walthall, Michael E. Iden, James R. Blaze) discuss in detail what the prospects and challenges were. The team examined possible reasons with the benefit of time and business/political circumstances as to why rail freight electrification was not extensively pursued.
The second topical part of the report involves the team’s development and then a tested use of an economic feasibility spreadsheet tool called the CURRENT (Costs, Uncertainties, & Risks of Rail Electrification with New Technologies) model that allows readers and a wide variety of interested parties to test various variables that help compute an estimated economic benefit return of such new lower carbon platforms against the mature diesel-electric locomotive fleet now in broad use. The model is equipped to allow parties to test different capital and operating and public pollution, and helps monetize those assumed changeable values and variables (scenarios).
These design and use (substitutes) include:
- Overhead Catenary System (OCS) electrification.
- Hybrid electrified locomotives.
- Discontinuous catenary construction.
- Selected other non-diesel-electric locomotive power supplies for moving freight trains.
Technical References
The authors provide an excel spreadsheet user’s manual as a separate document, with a summary in the broad report text. However, there are plenty of details about the model coding and structure design which potential users can find in the first part overall text section. For example, the risk analysis and the impact upon the cost-benefit analysis plus the use of a Monte Carlo Risk Simulation Test are described in Chapter 2. The description of the model’s train performance function calculations (written in Python) is found in Chapter 3, which also shows how the team estimated the energy consumption of the competing fuel sources, including diesel-electric, straight electric locomotives, or battery hybrid electric trains operating along each examined railway corridor.
Within the main report, Chapter 11 describes the model in greater detail, including assumptions, limitations and methods behind the calculations. Chapters 12 and 13 describe two toute geography case studies utilizing the model. Each of the two case studies examined about four different electrification characteristics against standard diesel-electric locomotive use. The spreadsheet, once accessed, is not completely hard-wired as to critical variable cell metrics or cell monetized cost figures. Therefore, over time, a user may obtain better variable assumption numbers and decide to use them for their feasibility project assessments on “a different railroad line of their choosing.” That flexibility was built in.
The University team, with technical outsider review by several subject-matter experts, eventually decided within the FRA budget to evaluate two different geographic corridors as Test Cases.
- Test Case #1 in northern Minnesota, with a focused port export resource commodity.
- Test Case #2 across the Midwest plains between Kansas City and Clovis, N.Mex., part of the Southern TransCon mixed-freight corridor owned and operated by BNSF.
The pro forma project returns and risks were only performed against those two corridors. No Class I freight railroad was asked to participate in the University’s independent project. The team instead used publicly available technical profile data for both routes to populate the feasibility simulation model and test up to four different electrification DBO (design-build-operate) electrified concept plans across those now diesel-electric routes. The published tests and results are therefore the sole conceptual modeling approach and results analysis of the four University of Texas team authors. FRA staff also did not participate in the analysis or the model building.
This team also did not test for any potential freight business volume increases that some might believe could be diverted from highway trucks because of a shift to electrified rail freight. Examining a possible change in rail freight modal split was not the team’s task. Others that follow us might decide differently and still could use this computer tool as a variable business volume tool for their future studies. That would require just limited spreadsheet coding for adjusted commerce volume.
Readers seeking more direct access to the CURRENT model spreadsheet tool should contact Rydell Walthall, C. Tyler Dick, Mike Iden, Mellisa Shurland (FRA) or Jim Blaze. On behalf of the team, I want to acknowledge the independent oversight and ideas contributed to the report by an all-volunteer advisory group. They did, however, clearly challenge our business propositions and metrics over several drafts. Please note that they did not individually or collectively author or review the final report and the team’s conclusions. To my knowledge, only now are they seeing the final report and its assessment and conclusions. They are:
- Lou Thompson, former World Bank, and now an independent consultant
- Cory Davis, CSX
- Devin Sprinkle, AARSteve Griffith, National Electrical Manufacturers Association
- James Hoecker, Senior Counsel and Energy Strategist, Husch Blackwell
- Richard Cogswell, Former FRA
- John Signor, Southern Pacific Technical & Historical Society
Below are selected input/output model pages as reference info, along with a few important findings and conclusions by the team:
Our team’s engineering assumptions as to locomotive efficiency over the next seven or more years is illustrated in this graph below, illustrated by our colleague Mike Iden. Yes, some of the assumptions in this graphic probably influenced our team’s fuel and pollution metric change outlook as we ran our version of the two case study corridors.
Yet you—the potential follow-on user—do not have to accept our inputs. R&D prototype testing, for example, might very well change hydrogen’s economic efficiency as a manufactured fuel. You are free as prospective investors (users) to make your own assumptions. By design, our team model will in most of the critical “cells” accept your risk and success cost assumptions. If not, then our leading model creator Rydell Walthall could be contacted directly to help your organization make a change.
Several unique revenue cost benefit opportunities vetted with multiple parties during the one-year team study are highlighted below. They are discussed in detail within the various chapters.
Projecting alternate possible pro forma cash flows over time produces this summation graphic:
The following few slides (as imaged text directly from the report) illustrate the variability of economic/financial returns using just a few variables like the cost of money to different “players” and different risks when using a Monte Carlo technique to test for possible future extreme events. These are exhibits from three different research and technical organization presentations given by some of our team members during the period October – December 2024 and allowed by the FRA while they internally reviewed the submitted report.
From the Test Geographic Corridor Case #2, across a high volume mixed traffic corridor:
Note in the report’s Table 41, the very high public value benefit of the Test Case #2 corridor has a much lower cost basis per unit of traffic and a generally better financing private railroad company pro forma regime “cost of financing” and project valuation discount rate as an NPV outlook, vs. the projected results for Test Case #1 in northern Minnessota. Table 41 illustrates the differences with a changed list of optional scenarios:
Note also the high 18% discount rate assumed by our team when having to rely on private capital from the rail freight business since Class I railroads often have large yet annually unfunded other “opportunity projects” that will compete with electrification as a business reality. The lower the discount rate because of such conditions (vs. the standard assumed federal discounting for government agency public projects—often at just 3%) drives the low cost/benefit ratio for the private industry sector as shown to the far right.
CONCLUSIONS
My economic takeaways for Railway Age readers:
- There is still a lot of work and research to be done before we collectively have a better idea of the opportunities and risks—and how to finance the choices eventually made—for each potential rail corridor’s geography.
- As completed so far, this report provides a structure but not the perfect solution. That task is up to others.
- Regardless of the choices made and the path forward, how long are railroad electrification project changes going to take? Probably longer than many outsiders think, because there is still a lot of engineering and design testing to be undertaken. For example, the time-to-market (build and fully test) a reliable commercially vetted locomotive fleet from today’s locomotive platform technology readiness scale of reliability can require a meaningful fleet size of perhaps 15 or more new prototype locomotives units under continuous testing, rather than just one to four units. And such real-world operations testing might take three or more years before the railroad companies see a meaningful set of fleet prototype unit performance records. That’s required before they mortgage for the huge required locomotive replacement plan to go for big-time electrification. Therefore, please note the graphic below as a suggested locomotive fleet improvement initial testing timeline (Figure 43 in the report):
Negotiating a complex new shared right-of-way agreement among parties might take two or more years, and then another three to four years to execute a corridor buildout. However, that is likely better than doing it alone and then financing a long buildout period for the required electrical grid network and the OCS wires below it or near the grid feed.
Time is money. Three to four added years that one can save on construction makes a difference on the project return pro formas. A negotiated PPP deal might save three or more years on a ten-year scope. That’s a big financial benefit in the assessment model when we run the CURRENT scenarios. But what are the real time savings going to be? That’s uncertain, but it will need to be monetized. Do we collectively agree?
The report’s Table 26 is what strategic planners call the SWOT (Strengths, Weaknesses, Opportunities and Threats), illustrated rather simply blow. Time and failures vs. faster and an improved success rate can and will have direct pro forma cash flow impact on the project feasibility scorecard.
Before undertaking a big-time, multi-billion-dollar power conversion program, investors at several different layers are going to want to see these words in the SWOT matrix above turned into believable financial projections. We collectively are as an industry just getting started, compared to highway trucking, with investment in a lower carbon alternative that has significant access to lower-cost-of-capital (discounted rate) government funding.
I leave this introductory summation with my own commentary as a Railway Age independent source. I’m not speaking for my teammates in this closing statement.
First, a question: What’s your level of confidence in my readers on the limited SWOT above? 50% as an assumption within each matrix shown above isn’t good enough for investors at most railroad levels. So how do you all get to at least about 80% confidence? How much more ticking of the clock before we have a meaningful consensus? Can we improve our level of confidence to 70% within three years, and within how many of the matrix cells?
Is this model potentially helpful to you that have the decision task ahead as your primary assignment? Can it be used to test how the change in financing risk and shared projects with some lower 3% discounting as public benefits-backed funding changes? The Texas team’s spreadsheet and calculations are a first-pass look. How much P3 (public/private partnership) participation improvement might we expect, say by 2028? Would we collectively need to reach a 60% or, more likely, a 75% level of confidence in the funding sector of the matrix in order to execute? Finally, given the rail freight market share loss pattern, how much of the 90,000 miles of the existing Class I rail network really should be the target? Our team report didn’t answer these questions. We just set up a discussion framework.There is plenty of work ahead for you younger readers.
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