Concerns about green house gases, pollution, and limited supply of oil are forcing nations to cut back on fossil fuel consumption. One of the ways to combat these challenges is the use of alternative energy sources like hydrogen, electricity, bio-fuels and natural gas to power the automobiles. Use of compressed hydrogen for powering fuel cell vehicles has seen some infrastructure development in California and upper Midwest (Kuby and Lim 2007). Recent advances in battery technology have enabled the development of reliable plug-in hybrid electric vehicles and pure electric vehicles.
However, since these vehicles have a limited range, an important requirement for their wide spread adoption is convenient access to refueling facilities. Petroleum based fuels still have a much higher energy density than competing alternative fuels (Chalk and Miller 2006) which leads to a shorter range for non-conventional fuel vehicles. After over a century of automobile use gasoline stations are ubiquitous, but refueling stations for most alternative fuels are few and far in between. Clearly, it is important to direct refueling infrastructure investment decisions for maximum impact.
Our work deals with the use of mathematical programming for determining the best locations for establishing alternative fuel refueling stations. The objective was to site the refueling stations at locations which maximize the number of vehicles served, while staying within budget constraints. The model we have used is a modified form of the flow interception facility location model (Berman, Larson et al. 1992). For the case study we used the transportation network of Alexandria, Virginia as a test bed for our model. Origin-Destination travel demand data for this city is simulated through an agent-based transportation simulator TRANSIMS (Smith, Beckman et al. 1995) to determine the routes taken by individual vehicles. The results are then compared with the service level offered by conventional gasoline refueling stations already located in the city. This work integrates the use of transportation modeling with mathematical programming for the solution of a complex large-scale problem on a real-life network.
Berman, O., R. C. Larson, et al. (1992). "Optimal Location of Discretionary Service Facilities." TRANSPORTATION SCIENCE 26(3): 201-211.
Chalk, S. G. and J. F. Miller (2006). "Key challenges and recent progress in batteries, fuel cells, and hydrogen storage for clean energy systems." Journal of Power Sources 159(1): 73-80.
Kuby, M. and S. Lim (2007). "Location of Alternative-Fuel Stations Using the Flow-Refueling Location Model and Dispersion of Candidate Sites on Arcs." Networks and Spatial Economics 7(2): 129-152.
Smith, L., R. Beckman, et al. (1995). TRANSIMS: TRansportation ANalysis and SIMulation System.