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Title: Using GIS-based methods and lidar data to estimate rooftop solar technical potential in US cities

Abstract

Here, we estimate the technical potential of rooftop solar photovoltaics (PV) for select US cities by combining light detection and ranging (lidar) data, a validated analytical method for determining rooftop PV suitability employing geographic information systems, and modeling of PV electricity generation. We find that rooftop PV's ability to meet estimated city electricity consumption varies widely - from meeting 16% of annual consumption (in Washington, DC) to meeting 88% (in Mission Viejo, CA). Important drivers include average rooftop suitability, household footprint/per-capita roof space, the quality of the solar resource, and the city's estimated electricity consumption. In addition to city-wide results, we also estimate the ability of aggregations of households to offset their electricity consumption with PV. In a companion article, we will use statistical modeling to extend our results and estimate national rooftop PV technical potential. In addition, our publically available data and methods may help policy makers, utilities, researchers, and others perform customized analyses to meet their specific needs.

Authors:
 [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)
OSTI Identifier:
1368609
Alternate Identifier(s):
OSTI ID: 1373083
Report Number(s):
NREL/JA-6A20-68943
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: 12; Journal Issue: 7; 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; photovoltaic; technical potential; GIS; lidar; energy generation

Citation Formats

Margolis, Robert, Gagnon, Pieter, Melius, Jennifer, Phillips, Caleb, and Elmore, Ryan. Using GIS-based methods and lidar data to estimate rooftop solar technical potential in US cities. United States: N. p., 2017. Web. doi:10.1088/1748-9326/aa7225.
Margolis, Robert, Gagnon, Pieter, Melius, Jennifer, Phillips, Caleb, & Elmore, Ryan. Using GIS-based methods and lidar data to estimate rooftop solar technical potential in US cities. United States. doi:10.1088/1748-9326/aa7225.
Margolis, Robert, Gagnon, Pieter, Melius, Jennifer, Phillips, Caleb, and Elmore, Ryan. Thu . "Using GIS-based methods and lidar data to estimate rooftop solar technical potential in US cities". United States. doi:10.1088/1748-9326/aa7225.
@article{osti_1368609,
title = {Using GIS-based methods and lidar data to estimate rooftop solar technical potential in US cities},
author = {Margolis, Robert and Gagnon, Pieter and Melius, Jennifer and Phillips, Caleb and Elmore, Ryan},
abstractNote = {Here, we estimate the technical potential of rooftop solar photovoltaics (PV) for select US cities by combining light detection and ranging (lidar) data, a validated analytical method for determining rooftop PV suitability employing geographic information systems, and modeling of PV electricity generation. We find that rooftop PV's ability to meet estimated city electricity consumption varies widely - from meeting 16% of annual consumption (in Washington, DC) to meeting 88% (in Mission Viejo, CA). Important drivers include average rooftop suitability, household footprint/per-capita roof space, the quality of the solar resource, and the city's estimated electricity consumption. In addition to city-wide results, we also estimate the ability of aggregations of households to offset their electricity consumption with PV. In a companion article, we will use statistical modeling to extend our results and estimate national rooftop PV technical potential. In addition, our publically available data and methods may help policy makers, utilities, researchers, and others perform customized analyses to meet their specific needs.},
doi = {10.1088/1748-9326/aa7225},
journal = {Environmental Research Letters},
number = 7,
volume = 12,
place = {United States},
year = {Thu Jul 06 00:00:00 EDT 2017},
month = {Thu Jul 06 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1088/1748-9326/aa7225

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