1. Estimating Rooftop Solar Electricity Potential in Seattle from LiDAR Data Ryan M. Liddell Joseph A. Bishop, Ph.D. Photo Copyright H Brothers Inc; used by permission. 1

2. www.personal.psu.edu/rul135 Ryan M. Liddell www.bv.com 2

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4. Joe Bishop Research Associate - Geospatial Coordinator Riparia, a Center where science informs policy and practice. Department of Geography – Penn State University 4

5. Presentation Outline Project Objectives Workflow Results Discussion Questions 5

6. Project Objectives Experience utilizing LiDAR data and associated software for analysis Estimate total rooftop solar electricity potential in Seattle using LiDAR data as only input Develop methodology that could be used anywhere LiDAR data is available 6

7. Estimating PV Production Potential 7

8. Software ESRI ArcGIS 10 Desktop (ArcInfo) ESRI ArcGIS 10 extensions: 3D Analyst Spatial Analyst LiDAR Analyst 5.0 for ArcGIS by Overwatch Geospatial 8

9. Computer Specs HP Pavilion dv7 Notebook PC Intel® Core™ i5 CPU M 480@2.67Ghz 6GB RAM Windows 7 (64-bit) Home Premium 700 GB hard drive Filled ≈ 220 GB during project 9

10. Estimating PV Production Potential 10

11. Public Domain LiDAR Data for the City of Seattle Puget Sound LiDAR Consortium (PSLC) Flown in 2000 & 2002 Nominal pulse rate: 1 per m 2 Bare Earth and Top Surface DEMs: 6ft res All-Returns ASCII files Source: Puget Sound LiDAR Consortium. 11

12. Processing PSLC ASCII Files 270 text files covering the City of Seattle Divided City into 9 subareas Converted to shapefiles using ArcGIS 3D Analyst (ASCII 3D to Feature Class) Shapefiles converted to LAS using LiDAR Analyst extension for ArcGIS from Overwatch Geospatial LAS files converted to All-Returns DEMs using LiDAR Analyst 12

13. Estimating PV Production Potential 13

14. Extract Bare Earth from All-Returns DEM 14

15. Extract Buildings from All-Returns DEM 15

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18. Estimating PV Production Potential 18

19. Area Solar Radiation from All-Returns DEM 19

20. Determining Tool Parameters National Renewable Energy Laboratory (NREL) www.nrel.gov/ National Solar Radiation Database PVWatts System Advisor Model (SAM) https://www.nrel.gov/analysis/sam/ 20

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23. Estimating PV Production Potential 23

24. Estimate of Rooftop Solar Electricity Potential for Seattle 26,094,106,258 kWh per year Assume 30% of rooftop space is unusable Efficiency of energy conversion – 18% DC to AC derate factor: 0.77 2,531,650 Mwh 24

25. Discussion Method for identifying unsuitable rooftop space using higher res LiDAR Advancements in PV technologies to capture solar energy Limitations of infrastructure and energy storage – how can we best utilize solar resources? 25

26. Special Thanks Overwatch Geospatial Eric Thomas at Solar Epiphany LLC 26

27. References 1)“2000-2005 Lower Puget Sound Projects”. Puget Sound LiDAR Consortium. Retrieved May 3, 2010. From http://pugetsoundlidar.ess.washington.edu/lidardata/restricted/projects/2000- 05lowerpugetsound.html 2)"Development of Renewable Energy Sources in Germany 2009". Federal Ministry for Environment, Nature Conservation and Nuclear Safety. http://www.erneuerbare- energien.de/files/pdfs/allgemein/application/pdf/ee_in_deutschland_graf_tab_2009_en.pdf. 3)“Fuel Mix: How Seattle City Light electricity is generated”. Seattle City Light. Retrieved May 3, 2010. From http://www.cityofseattle.net/light/FuelMix/ 4)“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 5)“LiDAR Basics”. Ohio Department of Transportation. Retrieved Juen 14, 2010. From http://www.dot.state.oh.us/Divisions/ProdMgt/Aerial/Pages/LiDARBasicS.aspx 6)“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 7)Vu, et al. 2009. Multi-scale Solution for Building Extraction from LiDAR and Image Data. 8)G. Zhou, et al. 2003. Urban 3D GIS from LiDAR and Digital Aerial Images. Computers & Geosciences 30 (2004) 345-353. 9)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 27

28. Questions 28