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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 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 PVmore » 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.« less

Authors:
ORCiD logo; ; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
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:
Published Article
Journal Name:
Environmental Research Letters
Additional Journal Information:
Journal Name: Environmental Research Letters Journal Volume: 13 Journal Issue: 2; Journal ID: ISSN 1748-9326
Publisher:
IOP Publishing
Country of Publication:
United Kingdom
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 Kingdom: N. p., 2018. Web. doi:10.1088/1748-9326/aaa554.
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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 Kingdom. https://doi.org/10.1088/1748-9326/aaa554
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Gagnon, Pieter, Margolis, Robert, Melius, Jennifer, Phillips, Caleb, and Elmore, Ryan. Tue . "Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling". United Kingdom. https://doi.org/10.1088/1748-9326/aaa554.
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@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 Kingdom},
year = {2018},
month = {2}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1088/1748-9326/aaa554
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Works referenced in this record:

Using GIS-based methods and lidar data to estimate rooftop solar technical potential in US cities
journal, July 2017

  • Margolis, Robert; Gagnon, Pieter; Melius, Jennifer
  • Environmental Research Letters, Vol. 12, Issue 7
  • DOI: 10.1088/1748-9326/aa7225

Modelling solar potential in the urban environment: State-of-the-art review
journal, January 2015

Quantifying rooftop solar photovoltaic potential for regional renewable energy policy
journal, July 2010

An automated model for rooftop PV systems assessment in ArcGIS using LIDAR
journal, January 2015

Technical-economic potential of PV systems on Brazilian rooftops
journal, March 2015

Potential of distributed photovoltaics in urban Chile
journal, October 2016

Photovoltaic potential in a Lisbon suburb using LiDAR data
journal, January 2012

Assessment of the photovoltaic potential at urban level based on 3D city models: A case study and new methodological approach
journal, April 2017

Quantifying rooftop photovoltaic solar energy potential: A machine learning approach
journal, January 2017

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    Works referencing / citing this record:

    A data mining approach to estimating rooftop photovoltaic potential in the US
    journal, July 2018

    Solar PV as a mitigation strategy for the US education sector
    journal, March 2019

    • Hanus, Nichole L.; Wong-Parodi, Gabrielle; Vaishnav, Parth T.
    • Environmental Research Letters, Vol. 14, Issue 4
    • DOI: 10.1088/1748-9326/aafbcf

    Powering a Sustainable and Circular Economy—An Engineering Approach to Estimating Renewable Energy Potentials within Earth System Boundaries
    journal, December 2019

    • Desing, Harald; Widmer, Rolf; Beloin-Saint-Pierre, Didier
    • Energies, Vol. 12, Issue 24
    • DOI: 10.3390/en12244723

    The Application of LiDAR Data for the Solar Potential Analysis Based on Urban 3D Model
    journal, October 2019

    • Prieto, Iñaki; Izkara, Jose Luis; Usobiaga, Elena
    • Remote Sensing, Vol. 11, Issue 20
    • DOI: 10.3390/rs11202348
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      Figures / Tables found in this record:

      Figure 1 (p. 3)figure
      Figure 2 (p. 3)figure
      Table 1 (p. 4)table
      Table 2 (p. 4)table
      Figure 3 (p. 5)figure
      Figure 4 (p. 5)figure
      Figure 5 (p. 6)figure
      Figure 6 (p. 6)figure
      Table 3 (p. 7)table
      Figure 7 (p. 8)figure
      Figure 8 (p. 8)figure
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