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Estimating Hydrogen Demand Distribution Using Geographic Information Systems (GIS)


Presentation Series

Hydrogen Pathways Program

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Suggested Citation:
Ni, Meng-Cheng, Nils Johnson, Joan M. Ogden, Christopher Yang, Joshua Johnson (2005) Estimating Hydrogen Demand Distribution Using Geographic Information Systems (GIS). Institute of Transportation Studies, University of California, Davis, Presentation Series UCD-ITS-RP-05-10

Presented at the National Hydrogen Association Annual Hydrogen Conference (NHA 2005), Washington, DC, March 29 - April 1, 2005

Understanding the evolution of a hydrogen fuel delivery infrastructure depends on the spatial characteristics of the hydrogen demand. We have developed a GIS-based method to model the magnitude and spatial distribution of hydrogen demand based on exogenously-derived market penetration rates and population data. This approach is applied to a study of the state of Ohio, but can be applied to any region of interest.

Our methodology is based upon population density, which is mapped at the census-block level and used to calculate hydrogen demand density based on per-capita vehicle ownership, projections for daily hydrogen use per vehicle, and market penetration levels or profiles. Various methods (including buffers and thresholds) are used to identify and aggregate high demand density areas into demand clusters, since only those areas with sufficient hydrogen demand are assumed to be viable locations for refueling stations. The resulting demand clusters (or demand centers) represent the potential areas in which investment in hydrogen infrastructure may be warranted and can be fed into a supply infrastructure model.

Sensitivity analyses were conducted to test the impact on hydrogen demand of different market penetration levels, thresholds, and buffer sizes. (i.e. different scenarios) The results allow one to examine the tradeoff between meeting hydrogen demand and the associated projected infrastructure costs. Although this demand model contains many simplifying assumptions, it provides a means for identifying potentially viable locations for hydrogen infrastructure investment at various scenarios.