Team Location: Langley Research Center, Hampton, Virginia
Joanna Furst (Christopher Newport University)
Vivek Hebbar (University of California Berkeley)
Jacob Hope (University of Virginia)
Manisha Iruvanti (Tabb High School, Yorktown, Virginia)
Michael Kane (Poquoson High School, Poquoson, Virginia)
John Lingenfelser (Gloucester High School, Gloucester, Virginia)
Anthony Pototzky (Old Dominion University)
Radha Venkatesan (Grafton High School, Yorktown, Virginia)
Kenton Ross, Ph.D. (NASA, DEVELOP National Science Advisor)
Solar energy has become increasingly prominent in the Mid-Atlantic region of the United States due to a reduction in cost and a rise in demand. Many recent government policies also reflect the rising acceptance of solar energy as one of the most abundant renewable resources, as exemplified by Maryland’s Renewable Portfolio Standards (RPS). The RPS requires at least 2 percent of Maryland’s electricity to be generated from solar energy.
Unfortunately, current methods for siting potential solar farms are not as refined as they could be; improved methods of determining potential sites for solar energy must be investigated in order to complement the current siting methods and accommodate the rising demand for solar power. This project focused on identifying areas with the most advantageous climate and topography for placing solar panels to harness solar energy efficiently. This process involved the use of NASA’s Earth observing satellites and sensors such as: the Clouds and the Earth’s Radiant Energy System (CERES) on board Terra and Aqua, Visible/Infrared Imager/Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (NPP), the Moderate-resolution Imaging Spectroradiometer (MODIS) sensors of Terra and Aqua, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor on board Terra.
Using satellite-derived data, Global Horizontal Irradiance (GHIs) and average cloud coverage maps were created. The maps were then synthesized into a third map that takes into account which areas receive the most solar irradiation and which terrain is most suitable for absorbing solar energy. Thus, the final map reflects the suitability of an area to support solar energy production. The suitability factors can then be determined using data obtained from the maps, allowing policymakers and our own partners to more easily evaluate the potential of an area for efficient solar energy production.