Energy and utilities data from the Alaska Energy Authority, Alaska Energy Data Gateway. Includes: - Hydroelectric - Hydrokinetic - Wind Power - Thermal Areas - Hot Springs - Sawmills - Energy Regions - Electric Utility Lines - TAPS Pipeline - Volanoes and Vents - Solar PowerSource: Alaska Energy AuthorityThis data is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Energy Data Gateway

CDFW BIOS GIS Dataset, Contact: BLM Bureau of Land Management, Description: To 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.

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.

CDFW BIOS GIS Dataset, Contact: CEC California Energy Commission (CEC), Description: Locations of proposed Solar thermal energy projects for the Renewable Energy Transmission Initiative (RETI), from the California Energy Commission.

Data compiled from California Energy Commission staff from georeferenced electric territory maps and the United States Department of Homeland Security, Homeland Infrastructure Foundation-Level Data (HIFILD), https://hifld-geoplatform.opendata.arcgis.com/datasets/geoplatform::electric-retail-service-territories-2/aboutCommunity Choice Aggregation information provided by Cal-CCA.Boundaries are approximate, for absolute territory information, contact the appropriate load serving entity. Not all electric load serving entities are represented, if you have information on missing territory locations, please contact GIS@energy.ca.gov.For more information on California Load Serving Entities visit this website: https://www.energy.ca.gov/data-reports/energy-almanac/california-electricity-data/electric-load-serving-entities-lses

The power plant locations and characteristics are part of the California Energy Commission’s (CEC) California Energy Infrastructure geospatial data sets. The data is derived from the CEC’s QFER-1304 Power Plant Owner Reporting Database and is updated annually. Among other information, a number of identifying attributes are given for each power plant as well as the generator units at each plant, their energy type, the total nameplate capacity, and their owners and operators. This California Power Plants data set has identical information to the many tables making up the QFER data set, however this single feature layer is derived by condensing several QFER tables into one. Some fields of the original tables have been omitted, and point geometries, determined by each plants’ address fields, have been appended for geospatial display. Four new fields have been compiled from QFER’s Annual Generation Table. These are listed and defined as:Nameplate Capacity (MW): The total nameplate capacity from every unit that makes up the power plant, regardless of status Units: List of the unit names at each power plant Primary Energy Source: A list of the primary energy sources used by every generator at the plantLast Reported Year: The last year that the power plant was recorded in the Annual Generation Table.Primary Energy Source Descriptions: Source Type Description AB

IMPORTANT NOTICE This item has moved to a new organization and entered Mature Support on February 3rd, 2025. This item is scheduled to be Retired and removed from ArcGIS Online on July 30th, 2025. We encourage you to switch to using the item on the new organization as soon as possible to avoid any disruptions within your workflows. If you have any questions, please feel free to leave a comment below or email our Living Atlas Curator (livingatlascurator@esri.ca) The new version of this item can be found here 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, wind, and tidal.Mapping Resources 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.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.Maintenance and Update Frequency: As Needed For more information visit Renewable Energy Power Plants

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."

The BEPS Program was created by Title III of the Clean Energy DC Omnibus Act of 2018. The BEPS is a minimum threshold of energy performance that will be no lower than the local median ENERGY STAR score by property type (or equivalent metric). The standards were created to drive energy performance in existing buildings to help meet the energy and climate goals of the Sustainable DC plan — to reduce greenhouse gas emissions and energy consumption by 50% by 2032. DOEE established the first set of Standards on January 1, 2021. Standards will then be set every 6 years, creating BEPS Periods (BEPS Period 1, BEPS Period 2, etc.). The 2021 Building Energy Performance Standards and a Guide to the 2021 BEPS are available for viewing on DOEE’s website.To improve transparency and help building owners understand how their building performs relative to the BEPS, DOEE is publishing this BEPS Disclosure that compares a building’s benchmarking data with the BEPS and provides an estimate of the building’s distance from the standard and estimated performance requirement.Please note that this dataset is based on information currently available to DOEE using calendar year 2019 benchmarking data provided by the building owner. Some buildings are still being evaluated and therefore have been designated as “Under Review” in this dataset. Building owners that believe their 2019 calendar year data is incorrect should contact the Benchmarking Help Center (info.benchmark@dc.gov). Additionally, buildings that meet certain criteria may request a variance from the standards by submitting a variance request form on the DOEE website.

This dataset was developed by the National Renewable Energy Laboratory (NREL) for the U.S. Agency for International Development's (USAID) South Asia Regional Initiative for Energy Cooperation (SARI/E). The dataset contains Wind Power Density at 50-m Above Ground Level in the form of a GIS shapefile. The data were output in Geographic Information Systems (GIS) format and incorporated into a Geospatial Toolkit (GsT) which is provided in data resources. The GsT allows the user to examine the resource data in a geospatial context along with other key information relevant to renewable energy development, such as transportation networks, transmission corridors, existing power facilities, load centers, terrain conditions, and land use.

From Databasin: https://drecp.databasin.org/datasets/c61b0e256e494fc5b6958d6c3999a19a/Development Focus Areas (DFAs). Designation on BLM-administered lands within which solar, wind, and geothermal renewable energy development and associated activities are allowable uses and that have been determined to be of low or lower resource conflict. The intent is to incentivize and streamline such development in these areas.Variance Process Lands (VPLs). Designation on BLM-administered lands that are available for solar, wind, and geothermal renewable energy development. Renewable energy projects on VPLs have minimal streamlining and must comply with a specific set of CMAs. Renewable energy applications in VPLs will follow the variance process described in the Western Solar Plan ROD.

This data was aggregated by Sustainable Jersey in cooperation with the seven investor-owned utility companies in New Jersey and represents the total amount of electricity and natural gas purchased in each municipality by sector in each year beginning in 2015. Electricity purchased is shown in kilowatt-hours (kWh) and natural gas purchased is shown in therms for the residential, commercial & industrial, and street lighting sectors for each of these utilities. The raw data and more information can be found here: https://www.sustainablejersey.com/resources/data-center/sustainable-jersey-data-resources/

MetadataThese data represent the offshore wind leases within the U.S. Outer Continental Shelf (OCS) - federally managed waters within the U.S. Exclusive Economic Zone (EEZ) - managed by the Bureau of Ocean Energy Management. State leases are managed by individual state leasing authorities. The data show Individual blocks and sub-blocks, commercial, research, and right of way lease areas. Leases are considered provisional after auction, prior to signatures from BOEM and the Lessee.

Abstract: Monthly and annual average solar resource potential for Hawaii.

Purpose: Provide information on the solar resource potential for Hawaii. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented due south at an angle from horizontal equal to the latitude of the collector location.

Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximately 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. Units are in watt hours.

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.

Maxwell, E, R. George and S. Wilcox, "A Climatological Solar Radiation Model", Proceedings of the 1998 Annual Conference, American Solar Energy Society, Albuquerque NM.

License Info

DISCLAIMER NOTICE 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.

The global market for solar resource assessment software is experiencing robust growth, driven by the increasing demand for renewable energy and the need for efficient solar power plant development. The market size in 2025 is estimated at $250 million, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors: the expanding solar energy sector globally, stringent government regulations promoting renewable energy adoption, advances in software capabilities offering more accurate and detailed assessments, and the decreasing costs of solar technology making it more accessible. The segment for paid, commercial applications currently dominates the market share, reflecting the preference of large-scale solar developers for sophisticated, feature-rich software solutions that ensure optimal project planning and profitability. However, the free and personal application segments are also showing promising growth, catering to smaller-scale projects, educational institutions, and individual users exploring solar energy options. Geographic expansion into developing economies with high solar irradiance presents significant opportunities for market expansion. The continued growth trajectory is expected to be influenced by factors such as technological advancements leading to improved prediction accuracy and integration with other renewable energy modeling tools. Increased investment in research and development within the sector, coupled with the expanding adoption of cloud-based software solutions, will contribute to market expansion. However, challenges such as the need for accurate and reliable meteorological data, the complexity of software usage for non-experts, and the potential for market saturation in certain regions might impede growth to some degree. Nevertheless, the long-term outlook for the solar resource assessment software market remains positive, with a substantial increase in market value projected throughout the forecast period, driven by the relentless push towards global decarbonization and the escalating adoption of sustainable energy solutions.

High Voltage GIS Market Outlook

The global high voltage gas-insulated switchgear (GIS) market size is projected to witness significant growth from 2023 to 2032, with a strong compound annual growth rate (CAGR) of 8.5%. In 2023, the market size was valued at approximately USD 5.3 billion, and it is anticipated to reach around USD 10.8 billion by 2032. The growth in this market is primarily driven by the increasing demand for reliable and efficient power transmission and distribution systems, coupled with the burgeoning investments in renewable energy projects worldwide.

One of the key growth factors contributing to the high voltage GIS market is the rising need for compact and efficient power distribution systems, particularly in urban areas where space constraints are a significant challenge. High voltage GIS systems are favored for their compactness and reliability compared to traditional air-insulated switchgear (AIS) systems. This advantage is particularly relevant in densely populated regions where the space available for electrical infrastructure is limited. Furthermore, the integration of renewable energy sources such as wind and solar power into the power grid necessitates advanced and reliable switchgear solutions, further propelling the demand for high voltage GIS.

Another major driver for the market is the increasing investment in upgrading and expanding the power grid infrastructure globally. Governments and utility companies are investing heavily in modernizing their existing grid infrastructure to enhance efficiency, reduce transmission losses, and ensure stable power supply. High voltage GIS systems play a crucial role in these modernization efforts due to their high reliability, low maintenance requirements, and ability to operate under harsh environmental conditions. Additionally, the growing focus on smart grid technologies and digitalization in the power sector is expected to create substantial opportunities for the high voltage GIS market.

Environmental regulations and sustainability concerns are also driving the adoption of high voltage GIS. These systems have a lower environmental impact compared to AIS systems, primarily due to their reduced spatial footprint and enclosed design, which minimizes the risk of gas leaks and contamination. The implementation of stringent environmental regulations aimed at reducing greenhouse gas emissions and promoting energy efficiency is encouraging utilities and industrial players to adopt more environmentally friendly solutions like high voltage GIS. This trend is expected to further accelerate market growth during the forecast period.

From a regional perspective, the Asia Pacific region is expected to be the fastest-growing market for high voltage GIS during the forecast period. This growth can be attributed to the rapid industrialization, urbanization, and significant investments in renewable energy projects in countries like China, India, and Japan. Additionally, the ongoing efforts to upgrade aging power infrastructure and the increasing demand for reliable electricity supply are propelling the market growth in this region. Other regions, such as North America and Europe, are also expected to witness steady growth due to the modernization of grid infrastructure and the adoption of smart grid technologies.

High-Voltage Air-Insulated Switchgear, while often larger in physical size compared to their gas-insulated counterparts, offer distinct advantages in terms of cost and ease of maintenance. These systems are typically used in areas where space constraints are less of a concern, and where the environmental conditions allow for the installation of open-air systems. The design of air-insulated switchgear is relatively straightforward, which can lead to lower initial costs and simpler maintenance procedures. Despite the larger footprint, air-insulated switchgear remains a viable option for many utilities and industries, particularly in regions where the infrastructure supports such installations. The choice between air-insulated and gas-insulated systems often depends on specific project requirements, environmental considerations, and budgetary constraints.

Component Analysis

The high voltage GIS market is segmented by components, including circuit breakers, switches, busbars, surge arresters, and others. Each of these components plays a vital role in the efficient functioning of GIS systems, contributing to the overall reliability and performance of power distributio

This dataset contains information about the biomass resources generated by county in the United States. It includes the following feedstock categories: crop residues, forest residues, primary mill residues, secondary mill residues, and urban wood waste. The estimates are based on county-level statistics and/or point-source data gathered from the U.S. Department of Agriculture (USDA), USDA Forest Service, EPA and other organizations, which are further processed using relevant assumptions and conversions.

Annex 1 - plants powered by RES in the Lazio Region; Annex 2 - Electricity consumptions, RES electricity production and percentages of electricity consumption from local RES for each Lazio Municipality; Annex 3 - Additional PV power and PV surface for each Lazio Municipality