Solar Footprints in CaliforniaThis GIS dataset consists of polygons that represent the footprints of solar powered electric generation facilities and related infrastructure in California called Solar Footprints. The location of solar footprints was identified using other existing solar footprint datasets from various sources along with imagery interpretation. CEC staff reviewed footprints identified with imagery and digitized polygons to match the visual extent of each facility. Previous datasets of existing solar footprints used to locate solar facilities include: GIS Layers: (1) California Solar Footprints, (2) UC Berkeley Solar Points, (3) Kruitwagen et al. 2021, (4) BLM Renewable Project Facilities, (5) Quarterly Fuel and Energy Report (QFER)Imagery Datasets: Esri World Imagery, USGS National Agriculture Imagery Program (NAIP), 2020 SENTINEL 2 Satellite Imagery, 2023Solar facilities with large footprints such as parking lot solar, large rooftop solar, and ground solar were included in the solar footprint dataset. Small scale solar (approximately less than 0.5 acre) and residential footprints were not included. No other data was used in the production of these shapes. Definitions for the solar facilities identified via imagery are subjective and described as follows: Rooftop Solar: Solar arrays located on rooftops of large buildings. Parking lot Solar: Solar panels on parking lots roughly larger than 1 acre, or clusters of solar panels in adjacent parking lots. Ground Solar: Solar panels located on ground roughly larger than 1 acre, or large clusters of smaller scale footprints. Once all footprints identified by the above criteria were digitized for all California counties, the features were visually classified into ground, parking and rooftop categories. The features were also classified into rural and urban types using the 42 U.S. Code § 1490 definition for rural. In addition, the distance to the closest substation and the percentile category of this distance (e.g. 0-25th percentile, 25th-50th percentile) was also calculated. The coverage provided by this data set should not be assumed to be a complete accounting of solar footprints in California. Rather, this dataset represents an attempt to improve upon existing solar feature datasets and to update the inventory of "large" solar footprints via imagery, especially in recent years since previous datasets were published. This procedure produced a total solar project footprint of 150,250 acres. Attempts to classify these footprints and isolate the large utility-scale projects from the smaller rooftop solar projects identified in the data set is difficult. The data was gathered based on imagery, and project information that could link multiple adjacent solar footprints under one larger project is not known. However, partitioning all solar footprints that are at least partly outside of the techno-economic exclusions and greater than 7 acres yields a total footprint size of 133,493 acres. These can be approximated as utility-scale footprints. Metadata: (1) CBI Solar FootprintsAbstract: Conservation Biology Institute (CBI) created this dataset of solar footprints in California after it was found that no such dataset was publicly available at the time (Dec 2015-Jan 2016). This dataset is used to help identify where current ground based, mostly utility scale, solar facilities are being constructed and will be used in a larger landscape intactness model to help guide future development of renewable energy projects. The process of digitizing these footprints first began by utilizing an excel file from the California Energy Commission with lat/long coordinates of some of the older and bigger locations. After projecting those points and locating the facilities utilizing NAIP 2014 imagery, the developed area around each facility was digitized. While interpreting imagery, there were some instances where a fenced perimeter was clearly seen and was slightly larger than the actual footprint. For those cases the footprint followed the fenced perimeter since it limits wildlife movement through the area. In other instances, it was clear that the top soil had been scraped of any vegetation, even outside of the primary facility footprint. These footprints included the areas that were scraped within the fencing since, especially in desert systems, it has been near permanently altered. Other sources that guided the search for solar facilities included the Energy Justice Map, developed by the Energy Justice Network which can be found here:https://www.energyjustice.net/map/searchobject.php?gsMapsize=large&giCurrentpageiFacilityid;=1&gsTable;=facility&gsSearchtype;=advancedThe Solar Energy Industries Association’s “Project Location Map” which can be found here: https://www.seia.org/map/majorprojectsmap.phpalso assisted in locating newer facilities along with the "Power Plants" shapefile, updated in December 16th, 2015, downloaded from the U.S. Energy Information Administration located here:https://www.eia.gov/maps/layer_info-m.cfmThere were some facilities that were stumbled upon while searching for others, most of these are smaller scale sites located near farm infrastructure. Other sites were located by contacting counties that had solar developments within the county. Still, others were located by sleuthing around for proposals and company websites that had images of the completed facility. These helped to locate the most recently developed sites and these sites were digitized based on landmarks such as ditches, trees, roads and other permanent structures.Metadata: (2) UC Berkeley Solar PointsUC Berkeley report containing point location for energy facilities across the United States.2022_utility-scale_solar_data_update.xlsm (live.com)Metadata: (3) Kruitwagen et al. 2021Abstract: Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since 2009. Energy system projections that mitigate climate change and aid universal energy access show a nearly ten-fold increase in PV solar energy generating capacity by 2040. Geospatial data describing the energy system are required to manage generation intermittency, mitigate climate change risks, and identify trade-offs with biodiversity, conservation and land protection priorities caused by the land-use and land-cover change necessary for PV deployment. Currently available inventories of solar generating capacity cannot fully address these needs. Here we provide a global inventory of commercial-, industrial- and utility-scale PV installations (that is, PV generating stations in excess of 10 kilowatts nameplate capacity) by using a longitudinal corpus of remote sensing imagery, machine learning and a large cloud computation infrastructure. We locate and verify 68,661 facilities, an increase of 432 per cent (in number of facilities) on previously available asset-level data. With the help of a hand-labelled test set, we estimate global installed generating capacity to be 423 gigawatts (−75/+77 gigawatts) at the end of 2018. Enrichment of our dataset with estimates of facility installation date, historic land-cover classification and proximity to vulnerable areas allows us to show that most of the PV solar energy facilities are sited on cropland, followed by arid lands and grassland. Our inventory could aid PV delivery aligned with the Sustainable Development GoalsEnergy Resource Land Use Planning - Kruitwagen_etal_Nature.pdf - All Documents (sharepoint.com)Metadata: (4) BLM Renewable ProjectTo identify renewable energy approved and pending lease areas on BLM administered lands. To provide information about solar and wind energy applications and completed projects within the State of California for analysis and display internally and externally. This feature class denotes "verified" renewable energy projects at the California State BLM Office, displayed in GIS. The term "Verified" refers to the GIS data being constructed at the California State Office, using the actual application/maps with legal descriptions obtained from the renewable energy company. https://www.blm.gov/wo/st/en/prog/energy/renewable_energy https://www.blm.gov/style/medialib/blm/wo/MINERALS_REALTY_AND_RESOURCE_PROTECTION_/energy/solar_and_wind.Par.70101.File.dat/Public%20Webinar%20Dec%203%202014%20-%20Solar%20and%20Wind%20Regulations.pdfBLM CA Renewable Energy Projects | BLM GBP Hub (arcgis.com)Metadata: (5) Quarterly Fuel and Energy Report (QFER) California Power Plants - Overview (arcgis.com)

This dataset was produced by Vibrant Clean Energy and is licensed to the public under the Creative Commons Attribution 4.0 International license (CC-BY-4.0). The data consists of hourly, county-level renewable generation profiles in the continental United States and was compiled based on outputs from the NOAA HRRR weather model. Profiles are stated as a capacity factor (a percentage of nameplate capacity) and exist for onshore wind, offshore wind, and fixed-tilt solar generation types.

Archived by Catalyst Cooperative from data provided directly from the dataset's creator. For more information, see https://vibrantcleanenergy.com/products/datasets/

This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. It is organized into Frictionless Data Packages. For additional information about this data and PUDL, see the following resources:

• The PUDL Repository on GitHub
• PUDL Documentation
• Other Catalyst Cooperative data archives

New York Electric Generation By Fuel Type, GWh dataset provides data on total electricity requirements and in-state generation for New York State in giga-watt hours. Sources of electricity include coal, natural gas, petroleum products, hydro, nuclear, waste, landfill gas, wood, wind, solar, and net imports of electricity. How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

Power Plants in the U.S.This feature layer, utilizing data from the Energy Information Administration (EIA), depicts all operable electric generating plants by energy source in the U.S. This includes plants that are operating, on standby, or short- or long-term out of service. The data covers all plants with a combined nameplate capacity of 1 MW (Megawatt) or more.Per EIA, "The United States uses many different energy sources and technologies to generate electricity. The sources and technologies have changed over time, and some are used more than others. The three major categories of energy for electricity generation are fossil fuels (coal, natural gas, and petroleum), nuclear energy, and renewable energy sources. Most electricity is generated with steam turbines using fossil fuels, nuclear, biomass, geothermal, and solar thermal energy. Other major electricity generation technologies include gas turbines, hydro turbines, wind turbines, and solar photovoltaics."Madison Gas & Electric Company, Sycamore Power PlantData currency: This cached Esri service is checked monthly for updates from its federal source (Power Plants)Data modification: NoneFor more information, please visit:Electricity ExplainedEIA-860, Annual Electric Generator ReportEIA-860M, Monthly Update to the Annual Electric Generator ReportEIA-923, Power Plant Operations ReportSupport documentation: MetadataFor feedback: ArcGIScomNationalMaps@esri.comEnergy Information AdministrationPer EIA, "The U.S. Energy Information Administration (EIA) collects, analyzes, and disseminates independent and impartial energy information to promote sound policymaking, efficient markets, and public understanding of energy and its interaction with the economy and the environment."

This dataset provides future supply curves representing the total resource potential for land-based wind and solar photovoltaic (PV) deployment in the conterminous United States after accounting for the impact of land-use and land-cover change (LULC). We use LULC projections from 2010 to 2050 developed based on the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios. The LULC projections are subsequently fed into the renewable energy potential model (reV), which estimates total available wind and solar capacity after excluding non-developable land. Supply curves are provided for four IPCC scenarios in 2050: A1B, A2, B1, and B2. As a baseline, we also provide the supply curve from the B2 scenario in 2010.

In addition to the supply curves, we also provide representative wind and solar generation profiles for each supply curve point. These generation profiles are provided as capacity factors and are based on a 2012 weather year using NSRDB and WindToolkit resource data.

For more details on the supply curve and profile datasets included here please refer to README. Additional information on the supply curves and the LULC projections used to generate them, as well as an analysis of their impact on wind and solar deployment under decarbonization can be found in the publication linked below: "U.S. Wind and Solar PV Supply Curves with Future Land-use Change Publication".

This dataset includes information on completed and pipeline (not yet installed) solar electric projects supported by the New York State Energy Research and Development Authority (NYSERDA). Blank cells represent data that were not required or are not currently available. Contractor data is provided for completed projects only, except for Community Distributed Generation projects. Pipeline projects are subject to change. The interactive map at https://data.ny.gov/Energy-Environment/Solar-Electric-Programs-Reported-by-NYSERDA-Beginn/3x8r-34rs provides information on solar photovoltaic (PV) installations supported by NYSERDA throughout New York State since 2000 by county, region, or statewide. Updated monthly, the graphs show the number of projects, expected production, total capacity, and annual trends.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

This deposit combines data from https://doi.org/10.3886/E146782V1 and https://doi.org/10.3886/E146801V1 to produce files containing the hourly generation, costs, and capacities of virtually all power plants in the lower 48 United States between 1999-2012 for their use in "Data and Code for: Imperfect Markets versus Imperfect Regulation in U.S. Electricity Generation" (https://doi.org/10.3886/E115467V1).

This dataset contains hourly capacity factors for each renewable resource class and region (in this case, county). Technologies like large-scale utility PV (UPV), onshore wind, offshore wind, and concentrating solar power (CSP) are included. The dataset contains 7 years of hourly weather data (2007-2013) for different sites across the US and is used as one of the inputs to the ReEDS-2.0 model (see the "ReEDS 2.0 GitHub Repository" resource link below), developed by NREL. The weather profiles apply to any capacity that exists or is built in each region and class. This helps calculate the generation that can be provided using these resources.

Open, reference, and limited are 3 scenarios based on land-use allowance, derived from the Renewable Energy Potential (reV) model developed by NREL, which helps generate supply curves for renewable technologies and assess the maximum potential of renewable resources in a designated area. Each zipped file in this dataset corresponds to a technology and contains the respective land-use scenario files required to run that technology in ReEDS.

To use this dataset, download and place the extracted files in the locally cloned ReEDS repository inside one of the folders (inputs/variability/multi_year). After completing this copy, upon running the ReEDS model at the county-level spatial resolution for respective analysis purposes, the program will detect the presence of these files and will not fail.

This data set comes from a monthly publication of recent and historical energy statistics produced by the US Energy Information Administration, part of the Department of Energy. Renewable energy production includes hydroelectric, geothermal, solar, wind, and biomass, and consumption includes residential, commercial, industrial, transportation, and electric power consumption.

Solar Energy Index fell to 36.76 USD on July 31, 2025, down 1.45% from the previous day. Over the past month, Solar Energy Index's price has risen 3.32%, but it is still 12.46% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Solar Energy Index.

The New York State Energy Research and Development Authority (NYSERDA) hosts a web-based Distributed Energy Resources (DER) integrated data system at https://der.nyserda.ny.gov/. This site provides information on DERs that are funded by and report performance data to NYSERDA. Information is incorporated on more diverse DER technology as it becomes available. Distributed energy resources (DER) are technologies that generate or manage the demand of electricity at different points of the grid, such as at homes and businesses, instead of exclusively at power plants, and includes Combined Heat and Power (CHP) Systems, Anaerobic Digester Gas (ADG)-to-Electricity Systems, Fuel Cell Systems, Energy Storage Systems, and Large Photovoltaic (PV) Solar Electric Systems (larger than 50 kW). Historical databases with hourly readings for each system are updated each night to include data from the previous day. The web interface allows users to view, plot, analyze, and download performance data from one or several different DER sites. Energy storage systems include all operational systems in New York including projects not funded by NYSERDA. Only NYSERDA-funded energy storage systems will have performance data available. The database is intended to provide detailed, accurate performance data that can be used by potential users, developers, and other stakeholders to understand the real-world performance of these technologies. For NYSERDA’s performance-based programs, these data provide the basis for incentive payments to these sites.

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on Twitter, Facebook, YouTube, or Instagram.

Abstract: Monthly and annual average solar resource potential for the lower 48 states of the United States of America.

Purpose: Provide information on the solar resource potential for the for the lower 48 states of the United States of America.

Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximatley 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). 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 avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate 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.

Other Citation Details: George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME.

License Info

This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country.

The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023).

Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).

Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.

Folder (3) provides the corresponding shapefiles.

Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 3, 5 or 10 depending on country size.

Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).

Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).

Countries/territories included in the dataset:

ArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaCubaDominican RepublicEcuadorEl SalvadorFrench GuianaGuatemalaGuyanaHaitiHondurasJamaicaNicaraguaPanamaParaguayPeruSurinameUruguayVenezuela

References

Sterl, S., Hussain, B., Miketa, A. et al. An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. Sci Data 9, 664 (2022). https://doi.org/10.1038/s41597-022-01786-5

Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14870967

The National Solar Radiation Database (NSRDB) was produced by the National Renewable Energy Laboratory under the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy. The 1991-2010 NSRDB is an update of the 1991-2005 NSRDB released in 2006 and archived at NCDC. The serially complete hourly data provided in the NSRDB update are provided in two output formats: 1) ground-based solar and meteorological dataset, and 2) 10 km gridded output produced by the SUNY model. The 10 km gridded output is from the State University of New York/Albany (SUNY) satellite radiation model developed by Richard Perez and Clean Power Research. Data in the NSRDB are a slightly modified version of the SolarAnywhere dataset distributed by Clear Power Research. The modifications are detailed in the NSRDB User's Manual. The model uses hourly radiance images estimated from Geostationary Operational Environmental Satellite (GOES) imagery, 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 irradiance (sun and sky) falling on a horizontal surface. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. In simple terms, this satellite model uses the inverse relationship between reflected irradiance (that reflected by clouds and atmosphere back to space and the satellite sensor) and ground irradiance (that transmitted through the atmosphere to the Earth's surface). The high-resolution 10-km gridded data set from the SUNY model provides a consistency in modeled output data for its period of record for the years 1998 to 2009, the period for which necessary GOES imagery was available for the project. The SUNY model produces estimates of global and direct irradiance at hourly intervals on the 10-km grid for 49 states, excluding Alaska, where the geostationary satellites cannot resolve cloud cover with necessary detail. Although GOES images provide up to 1-km resolution, in the SUNY model, these data are down-sampled to 10-km resolution (0.1 degree x 0.1 degree). This resolution is adequate for most solar radiation resource applications and represents a practical trade-off between resolution and processing and data storage considerations. The model uses both GOES-East and GOES-West satellites for complete spatial coverage of the United States.

Geoscience Australia and Monash University have produced a series of renewable energy capacity factor maps of Australia. Solar photovoltaic, concentrated solar power, wind (150 metre hub height) and hybrid wind and solar capacity factor maps are included in this dataset. All maps are available for download in geotiff format.

Solar Photovoltaic capacity factor map The minimum capacity factor is <10% and the maximum is 25%. The map is derived from Bureau of Meteorology (2020) data. The scientific colour map is sourced from Crameri (2018).

Concentrated Solar Power capacity factor map The minimum capacity factor is 52% and the maximum is 62%. The map is derived from Bureau of Meteorology (2020) data. Minimum exposure cut-off values used are from International Renewable Energy Agency (2012) and Wang (2019). The scientific colour map is sourced from Crameri (2018).

Wind (150 m hub height) capacity factor map The minimum capacity factor is <15% and the maximum is 42%. The map is derived from Global Modeling and Assimilation Office (2015) and DNV GL (2016) data. The scientific colour map is sourced from Crameri (2018).

Hybrid Wind and Solar capacity factor maps Nine hybrid wind and solar maps are available, divided into 10% intervals of wind to solar ratio (eg. (wind 40% : solar 60%), (wind 50% : solar 50%), (wind 60% : solar 40%) etc.). The maps show the capacity factor available for electrolysis. Wind and solar plants might be oversized to increase the overall running time of the hydrogen plant allowing the investor to reduce electrolyser capital expenditures for the same amount of output. Calculations also include curtailment (or capping) of excess electricity when more electricity is generated than required to operate the electrolyser. The minimum and maximum capacity factors vary relative to a map’s specified wind to solar ratio. A wind to solar ratio of 50:50 produces the highest available capacity factor of 64%. The maps are derived from Global Modeling and Assimilation Office (2015), DNV GL (2016) and Bureau of Meteorology (2020) data. The scientific colour map is sourced from Crameri (2018).

See the ‘Downloads' tab for the full list of references.

Disclaimer The capacity factor maps are derived from modelling output and not all locations are validated. Geoscience Australia does not guarantee the accuracy of the maps, data, and visualizations presented, and accepts no responsibility for any consequence of their use. Capacity factor values shown in the maps should not be relied upon in an absolute sense when making a commercial decision. Rather they should be strictly interpreted as indicative. Users are urged to exercise caution when using the information and data contained. If you have found an error in this dataset, please let us know by contacting clientservices@ga.gov.au.

This dataset is published with the permission of the CEO, Geoscience Australia.

This spatial vector dataset shows areas of identified high quality potential for Concentrating Solar Power (CSP) development divided into large contiguous areas called "zones." This dataset shows all zones in the Eastern Africa Power Pool (EAPP) region. This is one of many products resulting from a study led by the International Renewable Energy Agency (IRENA) and the Lawrence Berkeley National Laboratory (LBNL) identifying wind and solar renewable energy zones for the Africa Clean Energy Corridor (ACEC). For each zone identified, multiple siting criteria were estimated, including the total and component levelized cost of electricity (LCOE), average capacity factor, distance to nearest grid infrastructure, distance to the nearest load center, average population density. For full documentation of the methods and descriptions of the attributes, please refer to the report and attribute information in the interactive PDF map. They can be found on the irena.org/Publications and mapre.lbl.gov websites. The information provided is meant to inform high-level policy debate (identification of opportunity areas for further prospection, preliminary assessment of technical potentials), or to perform market screening (cross referencing the resource information with policy information). It is suitable for decision-making activities, excluding financial commitments. By using this dataset, the user accepts IRENA and LBNL's Terms and Conditions shown here: IRENA: The designations employed and the presentation of materials herein do not imply the expression of any opinion whatsoever on the part of the International Renewable Energy Agency concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. While this publication promotes the adoption and use of renewable energy, the International Renewable Energy Agency does not endorse any particular project, product or service provider. LBNL: This document was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or the Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.

Energy Information Administration. State Energy Data System: Renewable Energy Consumption | Indicator: Renewable energy total consumption., 2014. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 004-012-043 Dataset: Presents estimates of renewable energy consumption for the United States as a whole and for individual states and Washington, DC, as available. Renewable energy includes energy resources that are naturally replenishing but flow-limited, ie, limited in the amount of energy that is available per unit of time. Renewable energy sources include energy include biomass, hydroelectric power, geothermal, solar, and wind. The State Energy Data System (SEDS) is maintained and operated by the United States Energy Information Administration (EIA). The goal in maintaining SEDS is to create historical time series of energy production, consumption, prices, and expenditures by state that are defined as consistently as possible over time and across sectors. SEDS is used primarily to provide (1) state energy production, consumption, price, and expenditure estimates to Members of Congress, federal and state agencies, and the general public; and (2) the historical time series necessary to develop EIA’s energy models. Efforts are made to ensure that the sums of the state estimates equal the national totals as closely as possible for each energy type and end-use sector as published in other EIA publications. SEDS state energy consumption estimates are generally comparable to the statistics in EIA's Annual Energy Review and Monthly Energy Review consumption tables. Although SEDS incorporates the most consistent series and procedures possible, users of this report should recognize the limitations of the data that are due to changing and inadequate data sources. See the technical documentation for information on data inconsistencies. http://www.eia.gov/state/seds/seds-data-complete.cfm Category: Energy Resources and Industries Subject: Energy Consumption, Renewable Energy Source: Energy Information Administration The Energy Information Administration (EIA), created by Congress in 1977, is an independent statistical and analytical agency within the United States Department of Energy. Its mission is to provide policy-independent data, forecasts, and analyses to promote sound policy making, efficient markets, and public understanding regarding energy and its interaction with the economy and the environment. http://www.eia.doe.gov/

Mapping Resources on energy infrastructure and potential implemented as part of the North American Cooperation on Energy Information (NACEI) between the Department of Energy of the United States of America, the Department of Natural Resources of Canada, and the Ministry of Energy of the United Mexican States. Natural Gas Processing Plants: Facilities designed to recover natural gas liquids from a stream of natural gas. These facilities control the quality of the natural gas to be marketed. Refineries: Facilities that separate and convert crude oil or other feedstock into liquid petroleum products, including upgraders and asphalt refineries. Liquefied Natural Gas Terminals: Natural gas onshore facilities used to receive, unload, load, store, gasify, liquefy, process and transport by ship, natural gas that is imported from a foreign country, exported to a foreign country, or for interior commerce. Power Plants, 100 MW or more: Stations containing prime movers, electric generators, and auxiliary equipment for converting mechanical, chemical, and/or fission energy into electric energy with an installed capacity of 100 megawatts or more. Renewable Power Plants, 1 MW or more: Stations containing prime movers, electric generators, and auxiliary equipment for converting mechanical, chemical into electric energy with an installed capacity of 1 Megawatt or more generated from renewable energy, including biomass, hydroelectric, pumped-storage hydroelectric, geothermal, solar, and wind. Natural Gas Underground Storage: Sub-surface facilities used for storing natural gas. The facilities are usually hollowed-out salt domes, geological reservoirs (depleted oil or gas field) or water bearing sands (called aquifers) topped by an impermeable cap rock. Border Crossings: Electric transmission lines, liquids pipelines and gas pipelines. Solar Resource, NSRDB PSM Global Horizontal Irradiance (GHI): Average of the hourly Global Horizontal Irradiance (GHI) over 17 years (1998-2014). Data extracted from the National Solar Radiation Database (NSRDB) developed using the Physical Solar Model (PSM) by National Renewable Energy Laboratory ("NREL"), Alliance for Sustainable Energy, LLC, U.S. Department of Energy ("DOE"). Solar Resource, NSRDB PSM Direct Normal Irradiance (DNI): Average of the hourly Direct Normal Irradiance (DNI) over 17 years (1998-2014). Data extracted from the National Solar Radiation Database (NSRDB) developed using the Physical Solar Model (PSM) by National Renewable Energy Laboratory ("NREL"), Alliance for Sustainable Energy, LLC, U.S. Department of Energy ("DOE"). The participating Agencies and Institutions shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics, if available, are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time and may differ from other official information. The Agencies and Institutions participants give no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data.

Federal and state decarbonization goals have led to numerous financial incentives and policies designed to increase access and adoption of renewable energy systems. In combination with the declining cost of both solar photovoltaic and battery energy storage systems and rising electric utility rates, residential renewable adoption has become more favorable than ever. However, not all states provide the same opportunity for cost recovery, and the complicated and changing policy and utility landscape can make it difficult for households to make an informed decision on whether to install a renewable system. This paper is intended to provide a guide to households considering renewable adoption by introducing relevant factors that influence renewable system performance and payback, summarized in a state lookup table for quick reference. Five states are chosen as case studies to perform economic optimizations based on net metering policy, utility rate structure, and average electric utility price; these states are selected to be representative of the possible combinations of factors to aid in the decision-making process for customers in all states. The results of this analysis highlight the dual importance of both state support for renewables and price signals, as the benefits of residential renewable systems are best realized inmore » states with net metering policies facing the challenge of above-average electric utility rates. This dataset is intended to allow readers to reproduce and customize the analysis performed in this work to their benefit. Suggested modifications include: location, household load profile, rate tariff structure, and renewable energy system design.« less

The solar array is located on Fire Station 3, located at 1021 Harris Mill Road, Morrisville, NC, 27560. The solar array has produced a peak power of 81.31 kWp This data is updated automatically via an API pull from our selected solar inverter provider daily.