A machine readable collection of documented solar siting ordinances at the state and local (e.g., county, township) level throughout the United States. The data were compiled based on a locality-by-locality review zoning ordinances after completing an initial review of scholarly legal articles. The citations for each ordinance are included in the Solar Ordinances spreadsheet resource below.

This dataset represents solar energy setback requirements from roads based on county ordinances as of April 2022. A setback requirement is a minimum distance from a road that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a default setback of 30 meters was applied in counties without specific road setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where solar energy is prohibited or permitted across the contiguous United States.

For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

This dataset represents solar energy setback requirements from bodies of water based on county ordinances as of April 2022. A setback requirement is a minimum distance from water that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a default setback of 30 meters was applied in counties without specific water setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where solar energy is prohibited or permitted across the contiguous United States.

For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

This dataset represents solar energy setback requirements from transmission. A setback requirement is a minimum distance from transmission infrastructure that an energy project may be developed. As of April 2022, no ordinances were discovered for any counties. Such ordinances are likely to arise as regulations continue to expand. Therefore, this dataset applies a 30-meter setback, sourced from trends in other infrastructure. A TIF data file and a PNG map of the data are provided, showing areas where solar energy is prohibited or permitted across the contiguous United States.

For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

This dataset represents solar energy setback requirements from railroads. A setback requirement is a minimum distance from a railroad that an energy project may be developed. As of April 2022, no ordinances were discovered for any counties. Such ordinances are likely to arise as regulations continue to expand. Therefore, this dataset applies a 30-meter setback, sourced from trends in other infrastructure. A TIF data file and a PNG map of the data are provided, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

This dataset represents solar energy setback requirements from oil and gas pipelines. A setback requirement is a minimum distance from a pipeline that an energy project may be developed. As of April 2022, no ordinances were discovered for any counties. Such ordinances are likely to arise as regulations continue to expand. Therefore, this dataset applies a 30-meter setback, sourced from trends in other infrastructure. A TIF data file and a PNG map of the data are provided, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

Codes and Datasets for “A global assessment of the effects of solar farms on albedo, vegetation, and land surface temperature using remote sensing"Contacts: Zhengjie Xu (xuzj@mail.bnu.edu.cn); Yan Li* (yanli.geo@gmail.com)*Correspondance: Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.Abstract:This document describes the codes and datasets for“A global assessment of the effects of solar farms on albedo, vegetation, and land surface temperature using remote sensing”(Xu et al. 2023 Solar Energy). The data include MODIS Land Surface Temperature (LST), Enhanced Vegetation Index (EVI), albedo data, monthly air temperature and precipitation data from TERRACLIMATE, monthly solar radiation data from ERA5 and the Solar Farm(SF) database from Solar Wiki (wiki-solar.org). Please read this document for more details. The codes and datasets can be used freely under the CCY4.0 License. Users should cite the original paper of Xu et al. (2023) and the dataset (DOI: https://doi.org/10.6084/m9.figshare.24152766) when using it.ReferencesXu, Z., Li, Y., Qin, Y., & Bach, E. (2024). A global assessment of the effects of solar farms on albedo, vegetation, and land surface temperature using remote sensing. Solar Energy, 268, 112198. https://doi.org/10.1016/j.solener.2023.112198

These estimates are derived from the best available solar resource data available to NREL. Resources are organized by class and country. Resolution varies spatially from 1 km to 1 degree (approximately 100 km) depending on the data source. High spatial resolution datasets (1 km to 40 km cells) were modeled to support country or regional projects. Where high resolution datasets were not available, data from NASA’s Surface Meteorology and Solar Energy (SSE) version 6 database were used.

The data represent total potential solar energy per year as a function of land area per solar class (KWh/m²/day). Each solar class correlates to a specific 0.5 kWh/m²/day range. Energy is calculated by multiplying the productive land by the class, conversion efficiency and number of days per year. In this case, a standard calendar year of 365 days was used. The conversion efficiency rate applied was 10%.

E = Productive Land * kWh/m²/day * 365 days * 10% efficiency

The solar data has been derived from solar data measured or modeled between 1961 and 2008, depending on the dataset.

US Solar/Wind

The data comes from the Berkeley Lab. See the technical brief on the emp.lbl.gov site.

hatttip to Data is Plural

Berkeley Lab's "Utility-Scale Solar, 2021 Edition" presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developments in 2020, this report explores trends in deployment, technology, capital and operating costs, capacity factors, the levelized cost of solar energy (LCOE), power purchase agreement (PPA) prices, and wholesale market value.

Data Dictionary

capacity.csv

average_cost.csv

Average cost for each type of power in dollars/MWh

wind.csv

solar.csv

citation(tidytuesdayR)

Map by zip code of Green Energy Program Grants. These grants provide funding to Delmarva Power customers through the Green Grant Delaware program for purchase and installation of renewable energy sources such as photovoltaic (PV), geothermal, wind, and solar water heater systems.

Historical hourly time series of wind and solar generation profiles for every plant within the United States (US) that is part of the Energy Information Administration (EIA) 2020 dataset for the years 1980 through 2022. The data uses regional atmospheric climate model simulations and 2020 wind and solar power plant configurations across the entire contiguous US. This data is designed to be be aggregated to the Balancing Authority (BA) scale, or to other scales such as to the nodes of a production cost model which would allow the data to be used to perform reliability assessments and evaluations of technology innovation. There are ongoing efforts to extend this dataset for future climate projections, which additionally require the projection of future infrastructure under a wide range of uncertainties. This historical dataset is a benchmark for those projections and can be used to understand sensitivity to historical inter-annual variability, seasonality, and recent extreme events.

For more information please refer to the code repository at https://github.com/GODEEEP/tgw-gen.

The dataset consists of two components:

• Plant configuration files - The plant configuration files (eia_solar_configs.csv and eia_wind_configs.csv) contain all the plant data that is relevant to a generation model, derived from EIA 860 2020 data. Each row corresponds to a single logical plant. In some cases actual plants were split into two logical plants for modeling purposes.
• Generation data files - The generation data resides in the solar and wind directories and consists of one file per year. Each year contains an 8760 (hourly) profile of generation for every plant. The first column in each csv file is the datetime in Coordinated Universal Time (UTC), and the subsequent columns correspond to the plant_code_unique column in the configuration files. Data in these generation files is expressed as a capacity factor, which is generation divided by the plant capacity. To obtain the actual Megawatts (MW) generated, multiply the capacity factor for a plant by the value in the system_capacity column from the respective configuration file.

This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).

PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.

This dataset contains a subset of data from our Building Permit Application dataset. The data has been filtered to only include permit applications for solar (permit type = PVRS).To learn more about solar energy in Cary check out our Solar Energy webpage.This file is created from the Town of Cary permit application data. It has been created to conform to the BLDS open data specification for building permit data (permitdata.org). In the Town of Cary a permit application may result in the creation of several permits. Rows in this table represent applications for permits, not individual permits. Individual permits may be released as a separate dataset. With the exception of a few fields, we have applied all of the required and preferred fields of the required dataset for permits. This data is updated daily.Used as a part of the Solar, Cary, and You Dashboard

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

Purpose: Provide information on the solar resource potential for Alaska. 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 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. 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. 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.

52 Global export shipment records of Solar Module And HSN Code 8421 with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

271 Global import shipment records of Solar Inverter And HSN Code 85044029 with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

List of India Solar policy documents

Visit this link for solar atlas. Visit this link for policies details

Related KAPSARC Publication

357 Global import shipment records of Solar Power System And HSN Code 8537 with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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.

This dataset contains measured timeseries of renewable energy production and electricity consumption as well as exchange with neighboring countries/continents on hourly time resolution. The timeseries data has been divided into two xml files, one for each of the Danish price regions; DK1 (Western Denmark) and DK2 (Eastern Denmark). The data comes from the Danish TSO Energinet and was used in a flexibility study by Karen Olsen in 2018-19 leading to a paper that is to appear in the proceedings of the ICAE19 conference and is entitled: "Data-driven flexibility requirements for current and future scenarios with high penetration of renewables". A journal paper has also been submitted using the same data.The data has been extracted from a website run by Energinet at the following link where time series data is publicly available:https://www.energidataservice.dk/dataset/electricitybalanceThe present version was extracted in September 2019 and contains installation and production data from 2011 until and including the beginning of September 2019.The data is in the originally downloaded xml files, ready to be parsed by the python code written by Karen Olsen (see reference for Fanfare code).Data used for analysis:- offshore wind power generated (column: "Offshore Wind Power" in the xml file)- onshore wind power generated (column: "Onshore Wind Power" in the xml file)- solar power generated (column: "Solar Power Prod" in the xml file)- gross consumption (column: "Gross Con" in the xml file)Further information and code for analysis can be found under:https://kpolsen.github.io/FANFARE/Contains data used pursuant to 'Conditions for use of Danish public-sector data' from the Energi Data Service portal (www.energidataservice.dk).

This repository includes code, processed data, and plots that accompany the paper manuscript "Solar and battery can reduce energy costs and provide affordable outage backup for US households" in Nature Energy.

Some of the computing for this project was performed on the Sherlock cluster and we would like to thank Stanford University and the Stanford Research Computing Center for providing computational resources that contributed to these research results.