Electrification, shared mobility, and automation shape not only consumer choice but also the decisions utilities, city developers, service providers and other stakeholders make each day. In turn, where they decide to invest and locate their services, circles back to affect future consumer choice.
Consider electric vehicles (EVs). Consumers’ deciding to adopt them in any given area could lead companies to install more charging stations in that same region. This could in turn entice even more people in the neighborhood to buy and EV, and so on. As more charging infrastructure is put in place, utilities must adapt their grid to manage the increased load.
Over time, the web of influence between utilities, technology vendors, and consumers can have long-lasting and large-scale effects on how and where technologies multiply. Yet utilities and other service providers today have limited tools to guide them in this type of decision-making.
Researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory are addressing this issue through Compass, a large-scale computational model that captures the interactions between consumer choices and stakeholder decisions.
Compass uses ‘agent-based modeling’ to capture these interactions at a large, metropolitan scale. Agents represent an individual, organization or group whose decisions impacts a system. Agents could include a person commuting to work, a stay-at-home parent, a group of people riding an Uber or Lyft, etc.
Compass builds on the work Argonne and Exelon did building the Agent-based Transportation Analysis model (ATEAM). ATEAM was developed in 2018 and 2019 to help Exelon understand how EV adoption in the city of Chicago would co-evolve with the growth of new charging infrastructure over time.
Compass extends ATEAM to incorporate other models, including a model of fuel-cell vehicle adoption and is designed to support more types of users, including city planners and manufacturers building infrastructure or delivering services that support e-mobility.
“With Compass, we’re expanding on what we learned from building ATEAM in order to help a greater variety of people who are responsible for smart infrastructure investment decisions,” said Yan (Joann) Zhou, group manager of Vehicle and Energy Technology and Mobile Analysis at Argonne.
According to Zhou, a lot of the existing models that industry or transportation departments use today focus primarily on the behavior of the consumer, and less on the network of interactions among utilities, vendors and consumers. But accounting for and understanding these relationships is critical because they affect long-term growth and adoption of technology.
“As you can imagine the decisions stakeholders make affect consumer agent’s decisions and likewise, and those interactions change over time and space,” Zhou said.
Unlike other models, Compass focuses on the choices of both consumers and stakeholders and how they interact to illuminate the process that leads to a certain desired goal.
“Say we want to see a certain percentage of vehicles electrified by 2030. The more important question for the decisionmaker in this scenario is ‘What’s the process? What should be happening first to get us from 2020 to 2030?’” said Zhou. “The sequence of what should happen will really depend on what your ultimate goal is for the future.”
Goals affect use
Because different decisionmakers have different goals, the way they will use Compass will vary. For example, utilities may be motivated to increase EV adoption in order to sell more power, while city planners may be motivated to increase EV adoption to achieve specific sustainability goals. Their different goals will shape they kinds of decisions they make, and thus scenarios they model with Compass.
“For cities looking to achieve certain sustainability goals by increasing EV adoption, we could, for example, simulate how we would reach the goals by having more charging infrastructure installed in the places where it is needed,” Zhou said.
Compass is large-scale and capable of simulating events in an entire metropolitan area. Because it is built on an open-source framework, users can adapt the code according to the scenarios they wish to analyze.
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