GSA Annual Meeting in Denver, Colorado, USA - 2016

Paper No. 242-6
Presentation Time: 9:00 AM-6:30 PM


PELLICCIA, Skye A., Department of Geology and Environmental Geosciences, College of Charleston, Charleston, SC 29404; geoCAS, University of the Virgin Islands, Charlotte Amalie West, PR 00802,

In 2009 the USVI legislature voted to require that “30% of WAPA's (Water and Power Authority) ‘peak demanded generating capacity’ be from renewable sources by 2025.” As of 2016, USVI solar power generated via rooftops provides about 13% of the WAPA’s daytime peak demand alone (that is, 43% of the island’s goal and 1.7% of WAPA’s electric power in its entirety). Since 2013, the average price of a completed commercial PV project has dropped by nearly 30%” making solar energy more accessible for a greater variety of incomes. Due to the fact that 95.9% of the island’s population is urban, rooftop solar paneling is the most reasonable form of renewable energy since it does not require any additional acreage than what the owner already possesses.

Each rooftop has its own “solar potential” for rooftop solar paneling based on multiple variables that directly contribute to its efficiency in terms of cost and energy. Using LiDAR data and ArcGIS software, the solar potential of each rooftop in St. Thomas was calculated, allowing owners’ to visualize, both visually and statistically, how efficient rooftop solar paneling would be for their own home. Each rooftop on the island was manually digitized in ArcGIS using images from 2010 at a 1:500 zoom. Using first return topographic LiDAR data, a 1 meter resolution DSM (Digital Surface Model) was generated via ArcGIS. The DSM raster was then ran through the Solar Radiation tool in ArcGIS which “accounts for atmospheric effects, site latitude and elevation, slope, and compass direction (aspect), daily and seasonal shifts of the sun angle, and effects of shadows cast by surrounding topography.” By clipping the entire solar radiation analyses to the previously created rooftop shapefile layer the solar potential of each individual rooftop over the span of 1 year was then analyzed in units of watt hours per square meter (WH/m2).

The outcome of this analysis allows owners to visualize their property’s solar potential and thus encourage them to invest in solar energy, benefiting not only themselves economically, but environmentally. This project demonstrates the usefulness of a LiDAR dataset in terms of solar potential and the processes leading to its data has potential to be used in future applications not only constrained to St. Thomas, but in practically any region with a topographic LiDAR dataset.