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War & Environment Worst Case Scenarios: Terrorism & industrial chemicals. Redwood Ecotours: Explore California's Redwood North Coast. Download Free Renewable Energy ArcGIS Shapefiles <-- Free Shapefiles <-- Green Energy Maps & Shapefiles New to GIS? More Articles - Related Reading U.S. Biomass Potential, details U.S. Wind Density and Speed, details U.S. Hydrogen Potential, details U.S. Electric Transmission, details U.S. Solar Potential, details International Wind Density and Speed, details Biomass Useful for alternative and renewable green energy research and education. Biomass resource potential for the lower 48 states of the United States of America.Provide information on the biomass resource potential for the United States of America lower 48 states. Estimates technical biomass resources available in the United States by county. The following feedstock categories are considered for this study: crop residues, methane emissions from manure management, methane emissions from landfills and wastewater treatment facilities, forest residues, primary and secondary mill residues, urban wood waste, and dedicated energy crops. Wind The national wind resource assessment of the United States was created for the U.S. Department of Energy in 1986 by the Pacific Northwest Laboratory and is documented in the Wind Energy Resource Atlas of the United States, October 1986. The wind resource assessment was based on surface wind data, coastal marine area data and upper-air data, where applicable. In data-sparse areas, three qualitative indicators of wind speed or power were used when applicable: topographic/meteorological indicators (e.g. gorges, mountain summits, sheltered valleys); wind deformed vegetation; and eolian landforms (e.g. playas, sand dunes). The data was evaluated at a regional level to produce 12 regional wind resource assessments, the regional assessments were then incorporated into the national wind resource assessment. The conterminous United States was divided into grid cells 1/4 degree of latitude by 1/3 degree of longitude. Each grid cell was assigned a wind power class ranging from 1 to 6, with 6 being the windiest. The wind power density limits for each wind power class is shown in Table 1-1. Each grid cell contains sites of varying power class. The assigned wind power class is representative of the range of wind power densities likely to occur at exposed sites within the grid cell. Hilltops, ridge crests, mountain summits, large clearings, and other locations free of local obstruction to the wind are expected to be well exposed to the wind. In contrast, locations in narrow valleys and canyons, downwind of hills or obstructions, or in forested or urban areas are likely to have poor wind exposure. Hydrogen This study was conducted to estimate the potential for producing hydrogen from key renewable resources (onshore wind, solar photovoltaic, and biomass) by county in the United States and to create maps that allow the reader to easily visualize the results. To accomplish this objective, the authors analyzed renewable resource data both statistically and graphically utilizing a state-of-the-art Geographic Information System (GIS), a computer-based information system used to create and visualize geographic information. Land-use and environmental exclusions were applied to represent the most viable resources across the country. While wind, solar, and biomass are considered major renewable resources, other renewable energy resources could also be used for hydrogen production, thus contributing to hydrogen development locally and regionally. These additional resources include offshore wind, concentrating solar power, geothermal, hydropower, photoelectrochemical, and photobiological resources. This study found that approximately 1 billion metric tons of hydrogen could be produced annually from wind, solar, and biomass resources in the United States. The greatest potential for producing hydrogen from these key renewable resources is in the Great Plains region. In addition, this research suggests that renewable hydrogen has the potential to displace gasoline consumption in most states if and when a number of technical and scientific barriers can be overcome. Electric Transmission NREL received this data from the Federal Emergency Management Agency (FEMA) sometime around 1993. It is our understanding that the data represents a schematic of transmission line connectivity. This is based on conversations with people at FEMA, utility companies, and others familiar with actual transmission routing. The data can appropriately be used to show whether there is or is not a transmission line of some stated voltage in some general area. The user should not use the data for analysis that would require actual knowledge of easement locations. This coverage is undocumented and has not been updated. Solar Hawaii Monthly and annual average solar resource potential for the state of Hawaii. Provides information on the solar resource potential for the state of Hawaii. The insolation values represent the average solar energy available to a concentrating collector on a 2-axis tracker, such as a dish or a power tower. This data provides monthly average and annual average daily total solar resource averaged over surface cells of 0.1 degrees in both latitude and longitude, or about 10 km in size. This data was developed using the State University of New York/Albany satellite radiation model. This model was developed by Dr. Richard Perez and collaborators at the National Renewable Energy Laboratory and other universities for the U.S. Department of Energy. Specific information about this model can be found in Perez, et al. (2002). This model uses hourly radiance images from geostationary weather satellites, daily snow cover data, and monthly averages of atmospheric water vapor, trace gases, and the amount of aerosols in the atmosphere to calculate the hourly total insolation (sun and sky) falling on a horizontal surface. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. A modified Bird model is used to calculate clear sky direct normal (DNI). This is then adjusted as a function of the ratio of clear sky global horizontal (GHI) and the model predicted GHI. Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalable at a 10km resolution. As a result, it is believed that the modeled values are accurate to approximately 15% of a true measured value within the grid cell. Due to terrain effects and other microclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.SUNY Albany and NRELMarch 24, 2007 Lower 48 This data provides monthly average and annual average daily total solar resource averaged over surface cells of 0.1 degrees in both latitude and longitude, or about 10 km in size. This data was developed using the State University of New York/Albany satellite radiation model. This model was developed by Dr. Richard Perez and collaborators at the National Renewable Energy Laboratory and other universities for the U.S. Department of Energy. Specific information about this model can be found in Perez, et al. (2002). This model uses hourly radiance images from geostationary weather satellites, daily snow cover data, and monthly averages of atmospheric water vapor, trace gases, and the amount of aerosols in the atmosphere to calculate the hourly total insolation (sun and sky) falling on a horizontal surface. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. A modified Bird model is used to calculate clear sky direct normal (DNI). This is then adjusted as a function of the ratio of clear sky global horizontal (GHI) and the model predicted GHI. Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalable at a 10km resolution. As a result, it is believed that the modeled values are accurate to approximately 15% of a true measured value within the grid cell. Due to terrain effects and other microclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. International Wind Annual average wind resource potential for Countries at a 50 meter height. Provides information on the wind resource development potential. This data set has been validated by NREL and wind energy meteorological consultants. However, the data is not suitable for micro-siting potential development projects. The wind power resource estimates were produced by TrueWind Solutions using their MesoMap system and historical weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants. File Information The SHP file contains the geospatial data. The SHX file contains the index of the geospatial data. The DBF file contains the attribute data in dBASE format. The PRJ file contains the coordinate system information (optional). The AVL file contains the legend information (optional). The SBN and SBX files contain the spatial index of the geospatial data (optional). The XML file (*.shp.xml) contains the metadata describing the data set (optional). New to GIS? Read a brief summary about GIS and check out our free Learn2map GIS Tutorial and Atlas. Related Articles and Reports Renewable Energy Potential Maps of the United States Oil and Gas Field, Renewable Energy Potential and Residential Energy Consumption Maps in ArcGIS Shapefile and PDF Formats. Residential Energy Consumption Maps by U.S. Census Divisions. Renewable Energy Atlas of the West. <-- Free Shapefiles <-- Green Energy Maps & Shapefiles About Quality, Accuracy and Suitability This data may come from a variety of U.S. government and self-reporting private sources. While we try to assure the accuracy of this material, we cannot promise that it is absolutely accurate. We do promise that using the map layer will be fun, entertaining or educational. Beyond this, we make no guarantee as to its suitability for any purpose. We assume no liability or responsibility for errors or inaccuracies. Please understand that you use these map layers at your own risk. MapCruzin.com is an independent firm specializing in the publication of educational and research resources. 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