MassCEC Rooftop Solar Map

Transcription

1 MassCEC Rooftop Solar Map Data and Methods Summary Critigen, LLC

2 Overview The detailed analysis of solar rooftop potential is a multi-step workflow with many facets and input parameters to the analysis to determine solar potential at a structure s unique location. The process requires LiDAR data and rooftop boundaries as input datasets, which will be used by Critigen s Solar Shading Model, Roof Parameter Analysis, and calculation of PV production estimates. The Solar Model analysis workflow is as follows: 1) Generate derived elevation products from LiDAR 2) Solar Model Processing a. Solar Duration & Solar Insolation Raster datasets 3) Roof Parameter Analysis Multi-step process a. Identifies unique roof surfaces from LiDAR data b. Identifies suitable areas for PV panel placement, looking at acceptable roof slope, roof azimuth, and roof size. 4) PV Production Estimates a. Calculates solar statistics including: i. Average annual daily hours of sunlight ii. kw (based on roof size) iii. kwh (based on kw, roof slope, roof aspect) iv. Roof aspect v. Roof slope vi. PV usable roof area vii. PV placement score (actual panel placement area vs. optimized panel placement area b. Summarize Solar Statistics by buildings and parcels Elevation Products from LiDAR Upon project kickoff, MassCEC supplied LAS files, and ASCII point files were used to generate the initial project elevation datasets. Elevation raster datasets for both First Return and Last Return datasets were generated by converting the appropriate LAS classification to a multipoint data file, then converting to a terrain dataset, and finally converting to a seamless raster. First Return raster datasets were used for Solar Modeling purposes, while Last Return datasets were used for rooftop analysis and assessments. Details to the source of this LiDAR data are provide in Appendix A Solar Model Processing The input LiDAR data provided by MassCEC was obtained from a variety of sources, which are detailed in Appendix A. This data was used for Critigen s Solar Modeling process. All shading accuracy was dependent on the accuracy and shadow casting features of the LiDAR data. The output data generated from the model were annual sunlight hours, solar insolation (kwh/m 2 ), and radiation (kwh/m 2 ). The Solar Model operates similar to common tools used by solar contractors in the field, such as a solar pathfinder, and can be thought of as digital version of this.

3 Roof Parameter Analysis The input LiDAR data provided by MassCEC was also used in the Roof Parameter Analysis where the structure s unique roof faces and obstructions are spatially analyzed to determine how favorable the physical parameters of a particular roof face are to solar PV technologies. The primary parameters the analysis looked at are: Pitch Azimuth Area Obstruction and roof edge setback of three feet were used as input parameters to account for local fire codes and other restrictions. Further spatial analysis assessed the dimensions of the roof areas to determine if the area is not large enough for PV panels or the dimensions are such that PV panels cannot be placed there. Roof areas that did not meet the analysis criteria were then removed from the analysis process. Unusable areas are typically defined by the following parameters (each bullet represents an input parameter in the analysis and these settings can be adjusted): Less than 36 square feet* Length-to-width dimension that does not accommodate PV panels Northerly azimuth value o With the exception of areas with a pitch less than 8 degrees, these are kept as large roofs typically have a small pitch to them to allow for water runoff and minor PV panel racking can correct the azimuth. These areas attributed as flat in the analysis. Solar shading values in the roof area that do not meet the minimum threshold of 2.5kW system size for the structures Latitude/location Slope value greater than the structure s Latitude (modified to include roof slopes to 46 degrees). The output of the Roof Parameter Analysis is unique roof areas that were determined suitable for solar PV, attributed with their unique pitch, azimuth, total area, and PV usable area. The PV usable area was used to calculate the PV system size (kw) for each roof face. The PV system size was calculated based on the PV Panel efficiency and PV usable area. Specific PV panels were not identified so an 18 watts/square foot efficiency was used. The PV system size was also calculated and applied as an attribute for each unique roof area. PV Production Estimates The PV usable roof face attributes from the Roof Parameter Analysis were used to determine the PV Production Estimates. Each usable roof face was analyzed using a web service called PVWatts, (Provided by the US EPA - which is considered to be the industry standard for solar PV production estimates where the specific pitch, azimuth, and PV system size information is provided as an input. The pitch and azimuth attribute is set to determine the PV panel pitch and azimuth. For flat roof areas (pitch less than eight degrees) the PV panel pitch and azimuth settings were optimized. Typically, optimized pitch and azimuth values are a pitch value equal to the structure s Latitude and an azimuth of 180 degrees. Flat areas are the only areas considered for optimization as PV panels need to be optimized to some degree on flat areas for

4 better energy production and racking costs are less on flat roof areas. The output of the PV production estimates make up the Roof Face Potential report and the aggregated sum of these values make the up Building Potential report. Data Dictionary 1. Solar Duration and Insolation raster Coverage: o Datasets cover the entire community of interest Attribute information and unit description: o Solar_Duration_[COMMUNITY] Raster values are annual sunlight hours viewable by an upward facing, hemispherical viewshed. o Solar_Global_[COMMUNITY] Raster values are watt/hrs/m 2 Methods overview: Esri Area Solar Radiation tool iteratively ran across gridded First Return LiDAR raster data. Individual.tif output files are mosaicked into a continuous, community wide seamless dataset, for both Global (Insolation), and Duration (hours) outputs. 2. Solar Duration and Insolation raster datasets trimmed to building Coverage: o Datasets cover building footprints of communities of interest Attribute information and unit description: o Solar_Duration_Building_[COMMUNITY] Raster values are annual sunlight hours viewable by an upward facing, hemispherical viewshed. o Solar_Global_Building_[COMMUNITY] Raster values are watt/hrs/m 2 Methods overview: o Esri Area Solar Radiation tool iteratively ran across gridded First Return LiDAR raster data. Individual.tif output files are mosaicked into a continuous, community wide seamless dataset, for both Global (Insoloation), and Duration (hours) outputs. Datasets trimmed to building footprints supplied by MassCEC and MassGIS 3. Solar Potential database (spatial and tabular) at the property level Coverage: o Datasets cover building footprints of communities of interest Attribute information and unit description: The following attributes were appended to MassCEC-supplied Building Footprint, and MassCEC-supplied community vector datasets. Original attribute fields in the source data are not covered here. Attribute data was first generated at the building level, then summarized to the parcel level using the following methods. This it account for instances of parcels containing multiple PV suitable rooftops.

5 Field Name PV_AREA_FT AVG_SOLAR KW KWH Field Description Area (square feet) of rooftop meeting PV analysis thresholds of adequate slope, azimuth, kw potential, and sunlight hours. Annual daily average (365 day year) of sunlight visible hours. Kilowatt potential of roof area based on PV_AREA, and assumed panel area efficiency of 18w/m 2. PV_AREA is reduced by 25% to account for panel racking, spacing, offsets, and other installation factors. Kilowatt hour production potential based on KW, roof slope, and roof aspect. Parameters are passed through PV Watts v2 API web service (standard 0.77 derate factor) to return KWH value. Field Name PV_AREA_FT AVG_SOLAR KW KWH Field Merge Method SUM MAXIMUM SUM SUM Methods overview: ESRI Area Solar Radiation tool iteratively ran across gridded First Return LiDAR raster data. Individual.tif output files are mosaicked into a continuous, community wide seamless dataset, for both Global (Insoloation), and Duration (hours) outputs. Datasets trimmed to building footprints supplied by MassCEC and MassGIS. Resulting raster datasets ran through multiple ArcGIS geoprocessing tools, and custom Critigen scripts to generate solar statistics at roof panel, building, and parcel levels. Statistics are generated at the highest resolution level (roof panel), then summarized through more granular datsets (building, parcel)

6 Appendix A: Metadata Descriptions 2011 Lidar for North East Massachusetts The LiDAR for the North East Project was designed to provide more accurate floodplain mapping in the North East representing the start of a regional LiDAR collection program that served as a test case for a national elevation program. Led by the United States Geological Survey's (USGS) National Geospatial Program Office and the State of Maine's Office of GIS with participation by other federal, state and local agencies resulted in LiDAR acquisition and processing of over 8,000 sq. miles of data of the coastal zone spanning six North Eastern states, including Maine, New Hampshire, Massachusetts, Connecticut, Rhode Island, and New York. USGS's National Geospatial Technical Operations Center (USGS NGTOC) provided project management and quality control oversight which consisted of two Task Orders issued to GMR Aerial Surveys inc. d/b/a Photo Science (contractor). Task Order specifications included state/area specific vertical accuracy, nominal post spacing and tide coordinated acquisition requirements. In Massachusetts, LiDAR was collected in the Winter and Spring of 2011 at a 1 meter or better nominal post spacing (1m GSD) for approximately 2,022 square miles. LiDAR data acquired along the Massachusetts coast was flown at Daily Predicted Low Tide plus or minus 90 minutes. LiDAR was flown, controlled, processed and classified to meet a bare earth Fundamental Vertical Accuracy (FVA) of 30 cm at a 95% confidence level, derived according to NSSDA, i.e., based on vrmse of 15 cm in the "open terrain" land cover category. Barnstable County was flown and processed to meet a bare earth Fundamental Vertical Accuracy (FVA) of cm at a 95% confidence level, derived according to NSSDA, i.e., based on vrmse of 9.25 cm in the "open terrain" land cover category. Massachusetts data was developed based on a horizontal projection/datum of UTM NAD83 (2007), UTM Zone 19 meters and a vertical datum of NAVD1988 (GEOID09) meters. LiDAR data was processed to create Classified LAS 1.2 files formatted to 2997 individual 1500m x 1500m tiles, as well as 1.0 meter raster DEM Files tiled to the same schema. The James W. Sewall Company, established a total of 60 control points that were used to calibrate the LIDAR to known ground locations throughout Massachusetts. Additionally, Sewall established twenty (20) quality control "blind" check points using survey grade, dual frequency GPS receivers and supplied the coordinate and elevation data values for each point to USGS to independently validate the required vertical accuracies. These points were located on relatively flat terrain on surfaces that generally consisted of grass, gravel or bare earth. They were not used by the Photo Science production team during any phase of the project. Classified LAS files show the manually-reviewed bare earth surface. Hydro Flattened Breaklines are used to provide consistent elevations to a bare earth surface model. Raster DEM files are used to show the Digital Elevation Model of the LAS Class 2 surface.

7 Users should be aware that temporal changes may have occurred since this data set was collected and that some parts of these data may no longer represent actual surface conditions. Users should not use this data for critical applications without a full awareness of its limitations. Photo Science, Inc. flew the LiDAR and processed the data. Northrop Grumman/3001 helped with the acquisition. Software used: MicroStation Version 8; TerraScan Version 11; ALS Post Processor 2.70 Build#15; TerraModeler Version 11; GeoCue Version ; ESRI ArcGIS 10.0; Global Mapper 13; Optech DashMAP ; ALS Post Processor 2.70 Build #15; GeoCue Version ; Windows XP Operating System The project required LiDAR to be collected at 1.0 meter GSD or better and processed to meet a bare earth vertical accuracy of 15.0 centimeters RMSEz or better with the exception of Barnstable County which was flown and processed to meet a bare earth vertical accuracy of 9.25cm RMSEz or better. Classified LAS files, Breaklines, and Raster DEMs were tested by Photo Science for both vertical and horizontal accuracy. All data are seamless from one tile to the next. There are no gaps or NoData areas. The vertical unit of the data files is decimal meters with 2 decimal point precision. Vertical Accuracy: meters RMSEz. This value was calculated from the ground (ASPRS Class 2) data in the final Classified LAS file. See metadata for more detailed information regarding the Control, Raw Flight Line, Classified LAS, Hydro Flattening Breakline, and Raster DEM Processes. Acknowledgement of the U.S. Geological Survey would be appreciated for products derived from these data.