Estimating Rooftop Solar Electricity
Potential in Seattle from LiDAR Data
Ryan M. Liddell
Faculty advisor: Dr. Joe Bishop
Photo Copyright H Brothers Inc; used by permission.
Interest in PV for Seattle
• Black & Veatch Renewable Energy group
• Personal interest in sustainability
• Considering PV for my roof
Image courtesy of
Presentation Outline
• Project Objectives & Timeline
• PV feasibility in Seattle
• Workflow for GIS-based Estimate of Capacity
• Questions
Project Objectives
• Examine feasibility of photovoltaic (PV)
systems in Seattle
• Generate urban 3D model of Seattle
• Identify rooftops suitable for PV installations
• Estimate total solar electricity production
capacity for the City of Seattle
Project Timeline
• Examine feasibility of PV in Seattle: Complete
• Generate urban 3D model: July-August
• Identify suitable rooftops: August
• Estimate total PV production capacity for the
City of Seattle: September
How Photovoltaic Systems Work
Image: Clean Energy Associates
Technical Feasibility of PV in Seattle
• Solar insolation
– Latitude:
• Short winter days
• Long summer days
– Local weather, especially cloud cover
• Temperature – cooler is more efficient
• Germany produced 6,200 GWh in 2009*
*Source: "Development of Renewable Energy Sources in Germany 2009". Federal Ministry for Environment, Nature
Conservation and Nuclear Safety. http://www.erneuerbareenergien.de/files/pdfs/allgemein/application/pdf/ee_in_deutschland_graf_tab_2009_en.pdf.
Economic Feasibility in Seattle
Nearly 90% of electricity from hydropower
– $$$ is 30% less than the national average
– Winter
• High demand, lower supply
• City Light buys cheap electricity on market
– Summer
• Low demand, high supply
• City light sells at high price on market
Sources: Seattle City Light; U.S. Energy Information Administration Independent Statistics and Analysis
Economic Incentives
– 30% federal tax credit for PV system
cost
– No Washington sales tax
– Washington State 6170 program:
• Purchases solar generated electricity
• Starts at 15¢ per kWh
• Up to 54¢ per kWh
• Max: $5000 per year
– Net Metering through Seattle City Light
Potential Effects of Climate Change
– Reduced snowpack
– Peak stream flows earlier in year
– Winter
• Decreased demand for electricity (heating)
• Increased supply of hydro power
– Summer
• Increased demand (Air Conditioning)
• Decreased supply of hydro power
– Changes in Water Management for Salmon
Source: Washington Economic Steering Committee and the Climate Leadership Initiative Institute for a
Sustainable Environment
Estimating PV Production Capacity
Estimating PV Production Capacity
Airborne LiDAR Basics
From: http://www.dot.state.oh.us/Divisions/ProdMgt/Aerial/Pages/LiDARBasicS.aspx
Available LiDAR Data
Puget Sound LiDAR Consortium
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Flown in 2000 & 2002
Nominal 1 pulse per m2
Bare Earth and Top Surface DEMs: 6ft res
All-Returns ASCII files
Source: Puget Sound LiDAR Consortium.
Available LiDAR Data
King County GIS
• Digital Ground Model (DGM) TIN
• Digital Surface Model (DSM) TIN
• For both, nodes provide same level of
control as ASCII point files.
• Intensity data
Source: King County GIS Center.
Hillshade derived from KC DSM nodes
Estimating PV Production Capacity
Extraction of Buildings from LiDAR Data
Lots of research over the past 10 years
1) Priestnall, et al. 2000. Extracting Urban Features from LiDAR Digital Surface Models.
2) Haithcoat, et al. 2001. Building Footprint Extraction and 3-D Reconstruction from
LiDAR Data.
3) Elaksher and Bethel. 2002. Reconstructing 3D Buildings from LiDAR Data.
4) Rottensteiner. 2003. Automatic Generation of High-quality Building Models from LiDAR
Data.
5) Vosselman, et al. 2005. The Utilization of Airborne Laser Scanning for Mapping.
6) Verma, et al. 2006. 3D Building Detection and Modeling from Aerial LiDAR Data.
7) Sampath and Shan. 2007. Building Boundary Tracing and Regularization from Airborne
Lidar Point Clouds.
8) Q.-Y. Zhou and U. Neumann. 2008. Fast and Extensible Building Modeling from
Airborne LiDAR Data.
9) Vu, et al. 2009. Multi-scale Solution for Building Extraction from LiDAR and Image
Data.
and many more…
Building Extraction Algorithms
From: Q.-Y. Zhou and U. Neumann. Fast and Extensible Building Modeling from Airborne LiDAR Data. 2008.
Some LiDAR Software with Feature
Extraction Capabilities
From: G. Zhou, et al. Urban 3D GIS From LiDAR and digital aerial images. 2003.
Estimating PV Production Capacity
Goals for 3D Urban Model
• Successful segmentation of features
• Realistic modeling of rooftop geometry
• Accurate representation of tree canopy height
• Accurate representation of terrain
Estimating PV Production Capacity
Anlaysis of 3D Urban Model
Easier, static
• Rooftop Size
• Rooftop Aspect
• Rooftop Slope
Anlaysis of 3D Urban Model
• More Difficult, temporal in nature
–Rooftop Shading
–Rooftop Insolation
Estimating PV Production Capacity
Estimating PV Production
• Quantities of Suitable Rooftop Areas
• PV Module Performance Data
• Input from local PV contractors
• Advice from renewable energy experts at
Black & Veatch
References
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“2000-2005 Lower Puget Sound Projects”. Puget Sound LiDAR Consortium. Retrieved May 3,
2010. From http://pugetsoundlidar.ess.washington.edu/lidardata/restricted/projects/200005lowerpugetsound.html
"Development of Renewable Energy Sources in Germany 2009". Federal Ministry for
Environment, Nature Conservation and Nuclear Safety. http://www.erneuerbareenergien.de/files/pdfs/allgemein/application/pdf/ee_in_deutschland_graf_tab_2009_en.pdf.
“Fuel Mix: How Seattle City Light electricity is generated”. Seattle City Light. Retrieved May 3,
2010. From http://www.cityofseattle.net/light/FuelMix/
“Impacts of Climate Change on Washington’s Economy: A Preliminary Assessment of Risks and
Opportunities”. 2006. Washington Economic Steering Committee and the Climate Leadership
Initiative Institute for a Sustainable Environment. Written for State of Washington Department of
Ecology and Department of Community, Trade, and Economic Development. Retrieved June 5,
2010. From http://www.ecy.wa.gov/pubs/0701010.pdf
“LiDAR Basics”. Ohio Department of Transportation. Retrieved Juen 14, 2010. From
http://www.dot.state.oh.us/Divisions/ProdMgt/Aerial/Pages/LiDARBasicS.aspx
“State Electricity Profiles” U.S. Energy Information Administration Independent Statistics and
Analysis. Retrieved May 3, 2010. From
http://www.eia.doe.gov/electricity/st_profiles/e_profiles_sum.html
Vu, et al. 2009. Multi-scale Solution for Building Extraction from LiDAR and Image Data.
G. Zhou, et al. 2003. Urban 3D GIS from LiDAR and Digital Aerial Images. Computers &
Geosciences 30 (2004) 345-353.
Q.-Y. Zhou and U. Neumann. 2008. Fast and Extensible Building Modeling from Airborne LiDAR
Data. Retrieved May 3, 2010. From http://graphics.usc.edu/~qianyizh/papers/modeling_gis.pdf
Questions