Neha Rustagi, Hydrogen and Fuel Cell Technologies Office: Thank you, Eric. So the Systems Analysis program is meant to inform the office's RD&D activities. So within our portfolio we find analyses that identify the value proposition of hydrogen fuel cells in different sectors and also what the cost of these technologies has to be to be competitive.  

So our work starts with foundational work, where we assess the cost of individual specific technologies like an electrolyzer or a fuel cell or a truck, and then that information feeds models of market dynamics and supply chain, where we basically look at the overall cost and emissions of a particular system, like for example, a steel plant that uses hydrogen, or the likelihood of a customer purchasing a fuel cell truck versus the dealer one if the given price point is reached. And then with that information we inform the offices RD&D activities, much of which are executed by the sub-programs that spoke before me. And then using real-world data from these activities we calibrate our models to ensure a higher fidelity analysis going forward.

So this year our key focus is in the role that hydrogen can play in sectors that are otherwise hard to decarbonize. So one way to visualize this is with greenhouse gas abatement curves. So there's many of these out there. The one that's on the screen is from Goldman Sachs. What it shows on the x-axis is global CO2 emissions, and on the y-axis the cost of abating those emissions. So what you see is that the first 50-percent of emissions are relatively low-cost to abate using conventional technologies like renewable power, electrification, and then some use of hydrogen and fuel cells.

The second half of those emissions is where abatement becomes exponentially more expensive using current technologies. And so these spaces include generation of high-temperature heat for industrial processes or for buildings, long-duration energy storage to support large scale deployment of renewables, and medium and heavy-duty transportation.

So within systems analysis we're focused on understanding how low the cost of hydrogen and fuel cell technologies has to be to be competitive in these sectors and then also how much abatement is possible if we were to deploy these technologies at scale.

So our budget for the past couple years has been $3 million a year, and you can think of it kind of in three buckets. So the first bucket is the development of tools that characterize cost and emissions, and typically we make these tools publicly available and then we also use them in our analyses. The second bucket is actual cost and life cycle analysis of a particular system, like for example in synthetic fuel facility. And then the third bucket is scenario analysis, where we look at the cost and benefits of deploying hydrogen in a particular large-scale system, like for example, a particular region of the country or a particular demonstration site.

And then going forward we're also planning analysis to better understand the environmental justice and sustainability impacts of large-scale hydrogen employment and also the impact on job creation.

So one of our key accomplishments in 2020 was the release of three reports that characterized the supply and demand potential for hydrogen within the US. So the first of these reports was the resource report, and it depicted the locations in the country where renewable power generation [distorted audio] and also our quantities of fossil and uranium resource, and then used that information to estimate how much hydrogen we could produce domestically if we were to exploit those resources fully. And that repot affirmed that we do have significant resource within the US to support aggressive growth in hydrogen demand.

The second report characterized the markets in which hydrogen could be consumed and the price points that it would have to achieve to be competitive in those markets. And then the third report integrated information from the first two to estimate the technical and economic potential of hydrogen in the US.

So this year we're building upon that work to understand what the potential is for hydrogen growth given drivers for decarbonization. So our preliminary analysis indicates that that potential for growth is two to five times current consumption, where our current consumption is 2 million metric tons per year.

So to build upon that preliminary estimate we're working on updating hydrogen pathways within the Global Change Analysis Model, also known as GCAM. So GCAM is a model that estimates greenhouse gas emissions globally under various assumptions of technology costs, energy prices, and policy drivers, and then also taking into account changes in land use and water use. And it's actually used by modelers around the world, including by the IPCC, but the hydrogen pathways within the model actually haven't been updated in ten years, and so this year we're planning to incorporate current costs and performance assumptions of hydrogen within this model to be able to understand the market share that hydrogen and fuel cells can have and some of the hard to decarbonize sectors in the future, the degree to which we can reduce emissions, and then also the relative cost of abating emissions with hydrogen pathways versus with many of the competing alternatives.

So this is an example of an output from GCAM. So it depicts US CO2 emissions over time before any updates have been made to include hydrogen pathways. And you see that some of the hardest to decarbonize sectors are power generation and transportation. And so once we incorporate hydrogen fuel cell technologies into this model we'll be able to see how they can transform this curve.

Another area of focus for us is better understanding the role of hydrogen and long-duration energy storage. So this past year DOE's Strategic Analysis team worked with our office along with offices of Solar and Wind Energy to develop an assessment of the cost of ten different energy storage systems. So this project was led by NREL, and the first step of it was to characterize the capacity factors of energy storage systems on the grid. So to do this they generated hourly models of electricity prices in a grid that was simulated to have 85-percent renewable and located on the West Coast. This simulation was actually informed by EPRI along with five of their member utilities. Then they made estimates of how frequently these energy storage systems would be exercised and coupled those with estimates of technology costs to ultimately derive estimates of the levelized cost of energy storage with ten different systems including hydrogen pathways, thermal energy storage, caves, ethanol turbines, pumped hydro, and flow batteries.

And they did this for a few different scenarios, and the scenario you see on the screen is the Monte Carlo analysis of 120-hour energy storage assuming future costs of technology that would be realized at economies of scale that facilitate cost reduction. And what you see is that in this scenario hydrogen pathways with fuel cells or combustion turbines are among the top five lowest cost options for long-duration energy storage. This work also resulted in the creation of the Store Fast model, which is now online on the NREL website.

And then we complemented this by funding another model, actually at Pacific Northwest National Lab. So the second model incorporated the value that grid services can supply to an energy storage system. So in this model, which is also now online, it's a web-based easy-to-use interface. You can see a little video of it on the screen. So basically a user can input their own values for a system cost, like an n-system size, so the size of the electrolyzer, the size of the fuel cell storage system, everything within your system. And then also the revenue streams that are accessible to that system. So value from grid services, from selling hydrogen into a regional market, from hydrogen blending, and also hydrogen fueling into vehicles.

And then based on that information the model of output and an estimate of the total cost of that system over its lifetime, and then also the total revenue that it will generate over its lifetime. And so the benefit of this approach is that it's easy to use and you can iteratively run scenarios to see how low your technology cost has to be or how it has to be sized for it to be profitable. So this model is now online and in beta testing and it was co-funded with the DOE's Office of Electricity.

So another integration that we looked at this year was with the Office of Nuclear Energy and looked at hybrid energy systems with nuclear power plants. So folks – we spoke to this a little bit earlier, but basically a hybrid energy system is one where you integrate an electrolyzer with a nuclear power plant such that the plant is supplying electricity and heat to that electrolyzer in addition to supplying to the grid. And the benefit of doing this is that you basically have access to an additional revenue stream over the course of the year.

So in this project NREL, Idaho National Lab, and Argonne National Lab worked with Xcel Energy to simulate operations of the Prairie Island Nuclear Power Plant in Minnesota, both with and without electrolyzer integration. So they estimated group prices and then also the amount of revenue that would be generated if hydrogen was sold into that specific regional market.

So the next step with this project is to now do a sensitivity analysis and see how low does the cost of specific systems have to be, like for example the hydrogen storage, for this to be viable. And then also how big the regional market size has to be and how that market could evolve if there were drivers to decarbonize in that particular region.

So speaking of hydrogen markets, one of the market areas that we're focused on right now is industrial applications for hydrogen. So one example would be steel-making. So steel-making accounts for about 8-percent of global CO2 emissions. And part of those emissions come from the iron refining step. So in producing steel 30-percent of the feedstock in the US is iron, which is refined from iron ore. And so traditionally that refining utilizes coke from coal or natural gas. So an alternative is to use high concentrations of hydrogen and natural gas and preliminary analysis from Argonne indicates that that can reduce overall life cycle emissions of steel-making by anywhere from 30 to 50-percent.

Another area of focus is ammonia production. So this is work that Argonne actually did for RPE. So they characterized the life cycle emissions of conventional ammonia production, which relies on hydrogen from steam methane reforming, along with emissions associated with systems that use clean hydrogen produced from either electrolysis or as a byproduct of other industrial processes. And what they found is that these alternative pathways that use clean hydrogen achieve over 80-percent reduction in emissions relative to the incumbent.

So another area of focus is liquid fuels. So liquid fuels are particularly of interest in applications where you basically rely on combustion for the application. So an example would be long-distance aviation or long-distance shipping. And so we're working with our Bio Energy Technologies Office to evaluate different pathways to liquid fuel production. One of the pathways that we've looked at is combining concentrated CO2 with clean hydrogen to produce synthetic Fischer-Tropsch diesel. And so Argonne evaluated a few different systems that can produce this synthetic Fischer-Tropsch diesel, including ones with both recycling that same gas and not recycling, and found that you can achieve over 70-percent lower emissions with a Fischer-Tropsch synthetic process versus diesel from petroleum.

They coupled this with cost analysis, which indicated that in this pathway hydrogen is actually the largest driver of cost. And so you do need really low-cost hydrogen at $1.00 a kilogram for Fischer-Tropsch diesel to be competitive with diesel from petroleum on the basis of cost alone. And the Bio Energy Office is funding complementary work looking at pathways that actually don't require exogenous hydrogen for comparison as well.

So in the transportation space our analysis is focused on medium and heavy-duty transportation. So NREL engaged in that project this past year – for the past couple of years, that looked at the value proposition of hydrogen fuel cells, batteries, and conventional combustion engine vehicles in class 4 and class 8 vehicles with ranges ranging from 120 miles up to 750 miles. And what was really unique about this project is that they also looked at the impact that operational constraints can have on total cost of ownership.

So for example, if your fleet needs to be designed to be able to operate 24/7 with very limited time for refueling, what would the TCO of that fleet be versus if you had time to, for example, fill overnight. And what their analysis indicated was that batteries were more competitive at the lower weight classes, like for example, class 4, and also at the lower ranges, so for example, class 8 300-mile truck. And fuel cells were more competitive at the longer ranges, so for example, about 500 miles, as well as the scenarios where time was limited. So if your fleet had to be designed to be operating 24/7, that was a scenario where fuel cells had a strong value proposition.

Also in the transportation space one of our efforts this past year was the release of the annual Technology Baseline. So this was a project that was led by DOE Strategic Analysis team along with our office and the Vehicle and Bio Energies Technology Offices. This project was basically the release of a website that hosts data regarding the current cost of ten different light-duty vehicle power trains and also the emissions, and then shows a trajectory of how those costs and emissions change over time as DOE R&D targets are achieved.

The idea behind this website was to basically take data that's being generated and published in other DOE reports and make it more accessible by putting it on an easy-to-use website. And this year is also going to be expanded to include medium and heavy-duty vehicles and aviation.

So our work is generally conducted in great collaboration with stakeholders across the modeling community, and many of the tools that we developed are made publicly available and used by stakeholders externally as well. So a good example would be the SERA model. So this is a model at NREL that depicts a regional rollout of hydrogen infrastructure. So for example, where pipelines might be located and where they would be routed to in a particular scenario. And so SERA is currently being used in a project with UC Davis and several industry partners to model hydrogen rollout along the West Coast.

Another example would be the ReEDS, PLEXOS, and RODeO models. So those are all state-of-the-art grid models at NREL that are also used in several projects with industry to depict regional grids changing over time.

Another example would be the GREET model, which currently has over 14,000 users worldwide, including policymakers and regulators and depicts life cycle emissions of many different fuels, including hydrogen pathways.

And then our projects, we generally conduct them in coordination with other modelers and then also oftentimes co-fund them with other DOE offices to allow for leveraging and critical review. So one example of the platform that we use is the Stanford Energy Modeling Form. Another example would be co-funding of a project like GCAM, which is currently being co-funded by our office and three other DOE offices and other federal agencies.

And then a third good example would be the IPHE Hydrogen Production Task Force. So Cindy spoke to this a little in her talk. This is basically a taskforce including representatives from 13 countries and the European Commission. And the goal behind it is to develop mutually agreed upon methods to conduct life cycle analysis of hydrogen production with the intent of informing global trade so that everybody's doing their missions analysis in a consistent agreed-upon way and we're actively engaged in that effort as well.

So to put this in perspective we currently fund five national labs and two universities alongside nine DOE offices. We have several projects that include CRADAs that allow for direct collaboration with industry in addition to cost-share. And our work informs several cross-cutting DOE initiatives in energy storage and decarbonization. And we're also working in coordination with several federal agencies, like for example, the EPA.

And so I'm going to close by putting our program's evolution into perspective. So in 2019 is when we were beginning to sort of expand beyond analysis of transportation. So one of our big accomplishments that year was to complete analysis that showed the impact that innovative methods of hydrogen storage onboard vehicles could have on the cost of hydrogen fueling. And then that year we also kicked off new analysis looking at hybrid energy nuclear power plant systems along with long-duration energy storage.

In 2020 we released three H2 at scale reports that characterize hydrogen supply and demand, including in six industrial sectors. In 2021 our focus is on developing decarbonization strategy through efforts like the GCAM updates I mentioned, and then also collaboration with IPHE's Taskforce on emissions methods. And then in 2022 we're building on our existing work to better address environmental justice and jobs impacts of hydrogen and fuel cell deployment.

So with that I want to thank my colleagues, Marc Melaina and Masha Koleva, who provided invaluable technical insight and guidance behind a lot of the projects that I just mentioned. And then also our very dedicated team of researchers, whose work I provided at _____ will be presenting themselves tomorrow morning. And so I encourage you to check out their talks if you'd like more information.

And I want to thank you for your time.

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