Berkeley Lab team building new total cost of ownership model for fuel cells; intrinsic and external benefits
Funded by a $2-million grant from the US Department of Energy, a team of scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) led by Eric Masanet is building a sophisticated cost model for fuel cells that will will take into account the total cost of ownership.
The project will cover two types of fuel cells—solid oxide and proton-exchange-membrane—in systems of up to 250 kW. While vehicles are a common application for fuel cells, there is demand for stationary applications such as primary and backup power for commercial and residential buildings. This project will look at several specific stationary markets, including backup power, baseload power and combined heat and power, as well as forklifts. Success with forklifts could lead to bigger transportation markets.
The team will perform a detailed assessment of fuel cell design and manufacturing taking into account both intrinsic and external benefits. The aim of the five-year project is to quantify not only traditional manufacturing costs but also benefits that may previously have been overlooked and may ultimately bring down the cost of fuel cells.
There are, for example, human health benefits associated with fuel cells replacing fossil fuels, but they are traditionally not accounted for, especially not by the company making the fuel cells. But they are a benefit that society enjoys. So we will be using modeling techniques we’ve developed to see if there are optimal design and manufacturing strategies for different markets.—Berkeley Lab scientist Eric Masanet
|The technology underlying Analytica is based on a decade of research by decision analysts, computer scientists, and user-interface designers at Carnegie Mellon University, with support from the National Science Foundation.|
|Analytica was designed from the start to combine several key decision-technologies into a fully integrated package. These include: visual influence diagrams to define and organize complex models into comprehensible modules; intelligent arrays to make it easy to manage models with multiple dimensions; Monte Carlo simulation; and optimization, including linear and nonlinear programming.|
The result is intended to be not just a static cost analysis but a public tool that can be used by designers, manufacturers and building owners as they make decisions around producing and implementing fuel cells. The tool will be based on the Analytica software platform. Because it is modular, it will allow the user easily to change inputs, such as design and manufacturing costs and energy sources, and replace parts of the process.
The tool will be able to be updated over time with new proceses, designs and cost data, Masanet noted.
Another part of the cost equation normally not considered by manufacturers is that fuel cells can often be used to heat hot water in a building, which could save building owners on expenses such as hot water heaters and natural gas use.
Our model will give an idea of the big picture savings, and whether there are incentives that can be provided to make sure the savings are captured. Maybe building owners might be willing to accept a higher price if there are other advantages that manufacturers and policy makers can quantify. And when it comes to health benefits, policy makers can often provide incentives to accelerate technology adoption if they can quantify the greater benefits to society.—Eric Masanet
Masanet and his team—including Berkeley Lab scientists Jim McMahon, Adam Weber, Chris Marnay and Max Wei—will also consider geographic factors. For example, are the benefits of fuel cells the same in Washington state, which gets much of its power from hydroelectricity, as in West Virginia, which is dominated by coal? Cost benefits and pollution benefits might differ depending on where fuel cells are deployed, Masanet said, noting that there might be places where it’s more or less cost effective depending on external benefits.
Ballard Power Systems, a major fuel cell manufacturer, and two groups at the University of California Berkeley—the Laboratory for Manufacturing and Sustainability and the Transportation Sustainability Research Center—are subcontractors on the project.
A working tool should be available in three years, and Berkeley Lab scientists will continue to update the model for another two years. The analytical tools and techniques used in this project were largely developed over the past year in a project for Berkeley Lab’s Carbon Cycle 2.0 initiative.