Principal Investigator: Dr. Margaret Eppstein (UVM Department of Computer Science)
Co-Investigators: Dr. Jeff Marshall (UVM School of Engineering) and Dr. Donna Rizzo (UVM School of Engineering)
Funding Agency: US DOT
Partners: Environmental Law Program, Vermont Law School (VLS)
Our current regulatory scheme for energy may not be viable as we turn to new energy sources including plug-in hybrid vehicles. By understanding the multitude of factors influencing market forces, this project will help assure that the regulatory actions by federal, state and local governments play a positive role in influencing the transportation energy market.
Achievement of federal targets for alternative energy use will require large-scale infrastructure investment in:
The exact type of investment, and the utility and life-span of the resulting infrastructure, is sensitive to a wide variety of dynamic factors, including supply and usage of different alternative fuels, weather fluctuations, production of biofuel feedstocks, prices and supply of traditional fossil fuels, and public perception of environmental concerns. All of these various factors are closely integrated and are ultimately regulated by market forces. Regulatory actions by federal, state and local governments can play a critical role in influencing the transportation energy market, but because of the high degree of interdependency between the various factors that govern this market, it is difficult to predict the market consequences and sensitivity to any given regulatory change.
The proposed research will develop an agent-based complex systems model for transportation energy usage. This model is intended to be used for development of optimal regulatory approaches for control of alternative energy usage and infrastructure investment. Two scales of modeling are considered - a city scale, in which the actions of agents represent choices made by individual users, and a national scale, in which agents represent the aggregate population of a town or city and the agent choices are made subject to a probability distribution representative of the choices of the city population.
Project Overview (PDF, March 2010)