NREL Raises Rooftop Photovoltaic Estimate
The current estimate is significantly greater than a previous NREL analysis
WASHINGTON — Analysts at the U.S. Department of Energy (DOE) National Renewable Energy Laboratory (NREL) have used detailed light detection and ranging (LiDAR) data for 128 cities nationwide along with improved data analysis methods and simulation tools to update its estimate of total U.S. technical potential for rooftop photovoltaic (PV) systems. The analysis reveals a technical potential of 1,118 gigawatts (GW) of capacity and 1,432 terawatt-hours (TWh) of annual energy generation, equivalent to 39 percent of the nation’s electricity sales.
This current estimate is significantly greater than that of a previous NREL analysis, which estimated 664 GW of installed capacity and 800 TWh of annual energy generation. Analysts attribute the new findings to increases in module power density, improved estimation of building suitability, higher estimates of the total number of buildings, and improvements in PV performance-simulation tools.
The analysis appears in “Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment,” which quantifies the technical potential for rooftop PV in the U.S. and estimates how much energy could be generated if PV systems were installed on all suitable roof areas.
“This report is the culmination of a three-year research effort and represents a significant advancement in our understanding of the potential for rooftop PV to contribute to meeting U.S. electricity demand,” said Robert Margolis, NREL senior energy analyst and co-author of the report. “It’s important to note this report only estimates the potential from existing, suitable rooftops and does not consider the immense potential of ground-mounted PV. Actual generation from PV in urban areas could exceed these estimates by installing systems on less suitable roof space by mounting PV on canopies over open spaces, such as parking lots, or by integrating PV into building facades. Further, the results are sensitive to assumptions about module performance, which are expected to continue improving over time.”
Publication date: 4/25/2016