Moving Towards Hydrogen:
ESE Divisions Work on a Predictive Model

Decision Flow in Agent-Based Modeling of a Hydrogen Transition

What will it take to move to hydrogen-based transportation? Agent-based models to simulate hydrogen vehicle adoption and use, investment behavior, and infrastructure evolution within the framework of a complex adaptive system could tell us. Staff from the Center for Energy, Environmental, and Economic Systems Analysis (CEEESA) within the Decision and Information Sciences Division (DIS) and from the Center for Transportation Research (CTR) within the Energy Systems Division (ES) are working together to develop this framework.

For this model, two agents are required: consumers and investors. Consumers decide whether to buy a hydrogen-powered vehicle, and when and where to refuel it. Their purchase decisions are based on their assessment of hydrogen fuel availability, the inconvenience of refueling and their risk of running out of fuel, the difference in cost between hydrogen and gasoline vehicles and use, their knowledge of hydrogen vehicles, their desire to imitate the behavior of others, their attitudes toward environmentally “greener” fuels, their socio-demographic characteristics (e.g., income, location, miles-driven), and their general willingness to adopt new technologies.

In a similar way, investors (such as car manufacturers and energy providers) decide what type of supply infrastructure to invest in, how much to support, and where to locate it. Investors try to maximize their return but are constrained by the cost of funds for risky investments and their willingness to take such risks.  Investors make decisions under uncertain demand and competition, and commit to provide facilities based on their expectations about both future events and consumer behavior.

The key feature of a hydrogen transition that lends itself to this agent-based modeling framework is the presence of multiple stakeholders (both consumers and investors) with different strategies, risk preferences, and objectives. Stakeholders make multiple decisions based on imperfect knowledge and a mix of private and public information. Although stakeholders attempt to achieve their own objectives, goals may conflict and strategies may change as they learn and adapt to real or perceived changes in the behavior of other stakeholders or in the real environment.

This three-year project is but one of many examples of ESE divisional collaboration on crosscutting research. ES brings domain expertise on hydrogen production and delivery infrastructure, conventional and fuel cell vehicles, transportation demand modeling, driver behavior and vehicle choice. DIS brings expertise on decision tools, agent-based modeling and simulation, the modeling of complex adaptive systems, and spatial analyses with geographic information systems. Together, these divergent insights and tool sets evolve into new products and processes.

November 2008