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Research Areas:

   Energy, Environment, and

   National and Homeland

   Infrastructure Assurance

   Emergency Preparedness

   Social Dynamics

   Policy Analysis

Core Capabilities:

   Systems Analysis

   Modeling, Simulation, and

   Complex Adaptive Systems

   Decision Support and Risk

   Information Sciences

Multi-Agent Simulation of Hydrogen Transition Issues

In collaboration with RCF Consulting, Ford Motor Company, BP, and others, Argonne is applying agent-based modeling and simulation techniques to analyze the early transition phase to a potential hydrogen-based transportation system. This spatially explicit software has several thousand agents and is currently implemented for the greater Los Angeles area with several extensions under way.

Argonne’s model uses the road topology of a real metropolitan area, the Los Angeles basin. The driver agents vary in income, live and work in the LA area, and have attributes such as “greenness.” Further, they are influenced in their purchase decisions not only by their personal experience and a general “belief space” that reflects the attitude of society in general toward hydrogen, but also by their interactions with peers. Their decision to purchase is implemented in the form of a utility function that computes the sum of all adoption incentives less the disincentives of inconvenience and worry about running out of fuel. It also considers their willingness to pay more or suffer more inconvenience to operate a vehicle that has desirable environmental characteristics.

The investor agents in the model decide the type of supply infrastructure and how much and where to locate it. These investment decisions are made with imperfect knowledge of driver vehicle purchase and fueling behavior, the expected penetration of hydrogen, the plans of competitors, and the total demand for fuel. Thus, investors need to develop expectations that can be used as a basis for their decisions. We model investors as agents that are trying to do the best they can, given the uncertainties they face. Our investors may make nonoptimal decisions and may learn from their experience. The model distinguishes between different types of investor agents—from risk prone to risk averse—with different relative risk aversion coefficients.

For more information, contact:
Guenter Conzelmann
Energy Systems Division
Argonne National Laboratory
9700 South Cass Ave., Bldg. 202
Argonne, IL, 60439
Phone: 630-252-7173
Fax: 630-252-9868
E-mail: Guenter Conzelmann

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