Jessika Trancik is a professor at the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. Her research examines the dynamic costs, performance, and environmental impacts of energy systems to inform climate policy and accelerate beneficial and equitable technology innovation. Her projects focus on all energy services — including electricity, transportation, heating, and industrial processes. This work spans solar energy, wind energy, energy storage, low-carbon fuels, electric vehicles, and nuclear fission, among other technologies. She is also an external professor at the Santa Fe Institute, and was formerly at Columbia University’s Earth Institute and at WSP International/UNOPS (now Interpeace) in Geneva.
She will be the plenary speaker at the upcoming UIUC-UIC collaborative workshop, “Envisioning Equitable Transitions to Sustainable Transportation Systems,” on May 16-17, 2024, in Chicago. Ahead of this event, iSEE Communications Specialist April Wendling sat down with her to discuss her work.
April Wendling: Could you tell me a bit about your area of study?
Jessika Trancik: My work focuses on evaluating different potential climate solutions with a particular focus on energy solutions. I develop data-informed models to understand the impacts of those solutions and which ones might be most promising to invest in developing further. The idea is to use data-informed models to anticipate what sorts of energy solutions can be used to achieve the best outcomes. And information from these models can allow decision-makers to be deliberate about the investments they’re making in this transition, given the limited time to mitigate climate change and the finite financial resources available.
AW: What do these models look like?
JT: One example we’ve worked on quite a bit is modeling optimal locations for electric vehicle charging stations. One of the research questions we asked was: where should we place chargers so that people can conveniently charge their vehicles? Also, what is the rate of charging that would be needed in each of those different locations? There are a lot of factors that you need to take into consideration for a model like this. There’s the capacity of the batteries and range of the vehicles, where do people naturally stop and for how long, and how predictable are those behavioral patterns?
And what we find is that if you just haphazardly install chargers at, for example, shopping centers or malls, rather than in deliberate locations based on our understanding of how people use their vehicles, you end up with huge inefficiencies in your system. And inefficiencies prevent the system from working well for the people using it.
It’s really important to consider the variability of different people’s travel patterns and where they might park and where charging stations can be installed, and overall, one can design infrastructure to save people time, which ultimately would allow more people to adopt electric vehicles if they want to.
We also do a lot of work on comparing the costs and the emissions of different vehicle options, and then just generally in the transportation space, there’s a lot of questions at the intersection of technology, performance, and behavior. How good are technologies today, how much might they improve, how can we improve them, and then how does that fit in with people’s behaviors and what people want. So this research spans engineering and human behavior.
AW: How do you keep track of all these people using electric vehicles?
JT: We study not just the electric vehicle owners of today, but also people who may be electric vehicle owners in the future. The people that have already adopted electric vehicles in this country have primarily been wealthier individuals — they may be more likely to have off street parking spots, be able to install chargers at home, and have more than one car. And all of that is not really a model for a future equitable transition to sustainable transportation. It’s really important that the data covers the populations overall, not just early electric vehicle adopters.
We draw on a number of different datasets, and part of the modeling is to develop ways to match information across these datasets, so you can probabilistically match detailed data on a given trip with a less detailed but broader dataset covering an entire population that looks at how many trips they take per day, and their start times and end times and so on. We’ve worked mostly with publicly available data at various resolutions. And we’ve also done some data collection ourselves.
AW: I bet what you find from these models is very different depending on where you’re looking, right?
JT: There are differences, but there are some ways in which the results were more similar across urban and rural areas, and across different cities, than we expected. One of those results was from a paper we published back in 2016. We asked what percentage of vehicles on the road could be replaced by a low-cost electric vehicle without having to recharge during the day.
We looked across the entire country, and the answers weren’t as different as you might expect. It doesn’t mean that the cities are the same — some cities are much more car dependent than others. But when people do drive, there’s a certain similarity in the energy use. And our results indicated that across many different kinds of cities, there was a much larger adoption potential than one might have expected. Even at that time, close to 90% of vehicles could be replaced with these low-cost electric vehicles even if they could only charge overnight.
AW: What are some key areas in the coming years where you think we need to devote a lot of thought?
JT: Finding out what people want from climate solutions and what fits in with their lifestyles is what’s crucial. Many people do want to address climate change. There are many different opinions on how to do that and how urgent it is, but overall, people do want solutions, and many of these solutions provide other benefits, like cleaner air or more convenience. But it’s important to understand people’s varying preferences and to develop solutions that account for them.
One other thing I want to say is if we’re talking about reducing emissions from transportation, this is a very substantial change: It’s going to require a lot of investment. In this country and a number of others, we need to look at the challenges people face in accessing transportation resources. There are many people who don’t have access at all to a high-quality transportation service. And any time you’re talking about this major transition, those issues really need to be addressed, because this is going to require such substantial change and investment. I just think we have to remember that transportation is really about providing a service, and right now that service is unevenly available to people. That needs to be a central part of this overall effort.
AW: Could you tell me about the other research you do?
JT: I work across all different energy services, and I look at these questions about how to be deliberate about developing and investing in green technologies. That work involves developing both data-driven models and mechanistic models. And we’re working across industrial energy services, electricity, transportation, and heating, so we’re not just focused on one energy service. Some of the work I do is look at how technologies change over time — the rates of change, the drivers of that change.
AW: Could you give an example of these models?
JT: We have something we’ve developed called the Sustainable Energy Systems Model that allows us to look at the electric power system. We use a cost minimization framework to ask the question, if you want to minimize the overall cost of electricity and incorporate renewables, but also reduce emissions and also provide a high-quality service, how much solar and wind capacity might you want, how much storage, and can those options be complemented by other sources of power?
One question of particular interest is the role of hydrogen fuel and the different cost drivers for producing it. We are interested in how to reduce the cost of green hydrogen.