Rooftop Solar Photovoltaic Technical Potential in the United States. A Detailed Assessment
Citations Over Time
Abstract
How much energy could be generated if PV modules were installed on all of the suitable roof area in the nation? To answer this question, we first use GIS methods to process a lidar dataset and determine the amount of roof area that is suitable for PV deployment in 128 cities nationwide, containing 23% of U.S. buildings, and provide PV-generation results for a subset of those cities. We then extend the insights from that analysis to the entire continental United States. We develop two statistical models--one for small buildings and one for medium and large buildings--and populate them with geographic variables that correlate with rooftop's suitability for PV. We simulate the productivity of PV installed on the suitable roof area, and present the technical potential of PV on both small buildings and medium/large buildings for every state in the continental US. Within the 128 cities covered by lidar data, 83% of small buildings have a location suitable for a PV installation, but only 26% of the total rooftop area of small buildings is suitable for development. The sheer number of buildings in this class, however, gives small buildings the greatest technical potential. Small building rooftops could accommodate 731 GW of PV capacity and generate 926 TWh/year of PV energy, approximately 65% of rooftop PV's total technical potential. We conclude by summing the PV-generation results for all building sizes and therefore answering our original question, estimating that the total national technical potential of rooftop PV is 1,118 GW of installed capacity and 1,432 TWh of annual energy generation. This equates to 39% of total national electric-sector sales.
Related Papers
- → TOF Lidar Development in Autonomous Vehicle(2018)106 cited
- → Comparative Analysis of SLAM Algorithms for Mechanical LiDAR and Solid-State LiDAR(2023)42 cited
- → Indoor and Outdoor Building Modeling Based on Point Cloud Data Convergence using Drones and Terrestrial LiDAR(2022)2 cited
- → Figures of Merit, Testing, and Calibration for LiDAR(2019)
- → Extracting and assessing plant features spatio-temporally by means of 3D sensing in apple trees(2021)