Anelia Milbrandt

Solar and Wind Resource Assessments for Afghanistan and Pakistan

The U.S. National Renewable Energy Laboratory (NREL) has recently completed the production of high-resolution wind and solar energy resource maps and related data products for Afghanistan and Pakistan. The resource data have been incorporated into a geospatial toolkit (GsT), which allows the user to manipulate the resource information along with country-specific geospatial information such as highway networks, power facilities, transmission corridors, protected land areas, etc. The toolkit allows users to then transfer resource data for specific locations into NREL’s micropower optimization model known as HOMER.

Analysis of the Transition to Hydrogen Fuel Cell Vehicles and the Potential Hydrogen Energy Infrastructure Requirements, March 2008

Achieving a successful transition to hydrogen-powered vehicles in the U.S. automotive market will require strong and sustained commitment by hydrogen producers, vehicle manufacturers, transporters and retailers, consumers, and governments. The interaction of these agents in the marketplace will determine the real costs and benefits of early market transformation policies, and ultimately the success of the transition itself.

Adapting Existing Spatial Data Sets to New Uses: An Example from Energy Modeling

ABSTRACT Energy modeling and analysis often relies on data collected for other purposes such as census counts, atmospheric and air quality observations, and economic projections. These data are available at various spatial and temporal scales, which may be different from those needed by the energy modeling community. If the translation from the original format to the format required by the energy researcher is incorrect, then resulting models can produce misleading conclusions. This is of increasing importance because of the fine resolution data required by models for new alternative energy sources such as wind and distributed generation. This paper addresses the matter by applying spatial statistical techniques which improve the usefulness of spatial data sets (maps) that do not initially meet the spatial and/or temporal requirements of energy models. In particular, we focus on (1) aggregation and disaggregation of spatial data, (2) imputing missing data and (3) merging spatial data sets.