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Title: Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling

Abstract

We provide a detailed estimate of the technical potential of rooftop solar photovoltaic (PV) electricity generation throughout the contiguous United States. This national estimate is based on an analysis of select US cities that combines light detection and ranging (lidar) data with a validated analytical method for determining rooftop PV suitability employing geographic information systems. We use statistical models to extend this analysis to estimate the quantity and characteristics of roofs in areas not covered by lidar data. Finally, we model PV generation for all rooftops to yield technical potential estimates. At the national level, 8.13 billion m 2 of suitable roof area could host 1118 GW of PV capacity, generating 1432 TWh of electricity per year. This would equate to 38.6% of the electricity that was sold in the contiguous United States in 2013. This estimate is substantially higher than a previous estimate made by the National Renewable Energy Laboratory. The difference can be attributed to increases in PV module power density, improved estimation of building suitability, higher estimates of total number of buildings, and improvements in PV performance simulation tools that previously tended to underestimate productivity. Also notable, the nationwide percentage of buildings suitable for at least somemore » PV deployment is high—82% for buildings smaller than 5000 ft 2 and over 99% for buildings larger than that. In most states, rooftop PV could enable small, mostly residential buildings to offset the majority of average household electricity consumption. Even in some states with a relatively poor solar resource, such as those in the Northeast, the residential sector has the potential to offset around 100% of its total electricity consumption with rooftop PV.« less

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1420352
Alternate Identifier(s):
OSTI ID: 1423546
Report Number(s):
NREL/JA-6A20-70760
Journal ID: ISSN 1748-9326
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article: Published Article
Journal Name:
Environmental Research Letters
Additional Journal Information:
Journal Volume: 13; Journal Issue: 2; Journal ID: ISSN 1748-9326
Publisher:
IOP Publishing
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 29 ENERGY PLANNING, POLICY, AND ECONOMY; rooftop PV; distributed; technical potential; lidar; GIS

Citation Formats

Gagnon, Pieter, Margolis, Robert, Melius, Jennifer, Phillips, Caleb, and Elmore, Ryan. Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling. United States: N. p., 2018. Web. doi:10.1088/1748-9326/aaa554.
Gagnon, Pieter, Margolis, Robert, Melius, Jennifer, Phillips, Caleb, & Elmore, Ryan. Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling. United States. doi:10.1088/1748-9326/aaa554.
Gagnon, Pieter, Margolis, Robert, Melius, Jennifer, Phillips, Caleb, and Elmore, Ryan. Fri . "Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling". United States. doi:10.1088/1748-9326/aaa554.
@article{osti_1420352,
title = {Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling},
author = {Gagnon, Pieter and Margolis, Robert and Melius, Jennifer and Phillips, Caleb and Elmore, Ryan},
abstractNote = {We provide a detailed estimate of the technical potential of rooftop solar photovoltaic (PV) electricity generation throughout the contiguous United States. This national estimate is based on an analysis of select US cities that combines light detection and ranging (lidar) data with a validated analytical method for determining rooftop PV suitability employing geographic information systems. We use statistical models to extend this analysis to estimate the quantity and characteristics of roofs in areas not covered by lidar data. Finally, we model PV generation for all rooftops to yield technical potential estimates. At the national level, 8.13 billion m2 of suitable roof area could host 1118 GW of PV capacity, generating 1432 TWh of electricity per year. This would equate to 38.6% of the electricity that was sold in the contiguous United States in 2013. This estimate is substantially higher than a previous estimate made by the National Renewable Energy Laboratory. The difference can be attributed to increases in PV module power density, improved estimation of building suitability, higher estimates of total number of buildings, and improvements in PV performance simulation tools that previously tended to underestimate productivity. Also notable, the nationwide percentage of buildings suitable for at least some PV deployment is high—82% for buildings smaller than 5000 ft2 and over 99% for buildings larger than that. In most states, rooftop PV could enable small, mostly residential buildings to offset the majority of average household electricity consumption. Even in some states with a relatively poor solar resource, such as those in the Northeast, the residential sector has the potential to offset around 100% of its total electricity consumption with rooftop PV.},
doi = {10.1088/1748-9326/aaa554},
journal = {Environmental Research Letters},
number = 2,
volume = 13,
place = {United States},
year = {Fri Jan 05 00:00:00 EST 2018},
month = {Fri Jan 05 00:00:00 EST 2018}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1088/1748-9326/aaa554

Citation Metrics:
Cited by: 1 work
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