U.S. National GridThis feature layer, utilizing data from the Federal Geographic Data Committee (FGDC), displays the U.S. National Grid (USNG). The FGDC provides standards for a National Grid. Per the FGDC, "The objective of this standard is to create a more favorable environment for developing location-based services within the United States and to increase the interoperability of location services appliances with printed map products by establishing a nationally consistent grid reference system as the preferred grid for National Spatial Data Infrastructure (NSDI) applications. This standard defines the US National Grid. The U.S. National Grid is based on universally defined coordinate and grid systems and can, therefore, be easily extended for use world-wide as a universal grid reference system."Note: popups can be viewed for the USNG 1000m and USNG 100m layers.Note: the USNG 100m layer is only displayed for certain cities. To view those places, please select a row in the attribute table and then center (zoom) on selection.U.S. National Grid - Grid Zone DesignationsTop: 100,000-meter and 10,000-meter Square IdentificationsBottom: 1,000-meter and 100-meter Square IdentificationsData downloaded: October, 2011Data modifications: The Percent Complete field was removed from all layers. The following fields were added to the original data for layers:USNG 1000m - UTM ZoneUSNG 100m - Place; RegionFor more information:Standard for a U.S. National GridUnited States National GridHow to read a United States National Grid (USNG) spatial addressFor feedback, please contact: ArcGIScomNationalMaps@esri.comFederal Geographic Data Committee (FGDC)Per the FGDC, "The Federal Geographic Data Committee (FGDC) is an organized structure of Federal geospatial professionals and constituents that provide executive, managerial, and advisory direction and oversight for geospatial decisions and initiatives across the Federal government. In accordance with Office of Management and Budget (OMB) Circular A-16, the FGDC is chaired by the Secretary of the Interior with the Deputy Director for Management, OMB as Vice-Chair."

This feature layer, utilizing data from Homeland Infrastructure Foundation-Level Data (HIFLD), depicts electric power transmission lines in the United States. Per HIFLD, "Transmission Lines are the system of structures, wires, insulators and associated hardware that carry electric energy from one point to another in an electric power system. Lines are operated at relatively high voltages varying from 69 kV up to 765 kV, and are capable of transmitting large quantities of electricity over long distances. Underground transmission lines are included where sources were available."Data downloaded: 1/2/2023Data source: Transmission LinesData modification: noneFor more information: Electricity ExplainedSupport documentation: Transmission Lines

This feature class/shapefile represents electric power transmission lines. Transmission Lines are the system of structures, wires, insulators and associated hardware that carry electric energy from one point to another in an electric power system. Lines are operated at relatively high voltages varying from 69 kV up to 765 kV, and are capable of transmitting large quantities of electricity over long distances. Underground transmission lines are included where sources were available. The following updates have been made since the previous release: 1,166 features added.

Transmission lines metadata:Based on the HSIP Gold 2013 power transmission lines data. The HSIP data was clipped to California and then dissolved on the fields BUS_NAME and VOLT_CLASS. This information was provided by calema_gis on ArcGIS Online.Hydroelectric power plants metadata:Operable electric generating plants in the United States by energy source. This includes all plants that are operating, on standby, or short- or long-term out of service with a combined nameplate capacity of 1 MW or more. Only hydroelectric power plants where displayed by creating a definition query. The sources of this information include EIA-860, Annual Electric Generator Report, EIA-860M, Monthly Update to the Annual Electric Generator Report and EIA-923, Power Plant Operations Report. This data was provided by the U.S. Energy Information Administration. For more information on this data or the U.S. Energy Information Administration, please use the following link:https://www.eia.gov/maps/layer_info-m.phpThe Transmission Lines and Hydroelectric Power Plants web map is a feature service used in the Sierra Nevada Cascade story map; therefore, it should not be altered or deleted under any circumstances while the story map is in use.

U.S. border crossing points of electric transmission lines. A crossing point represents one or more electric lines.

Anomaly lists are presented documenting operational interference to electricity power grids and communication networks in the United States and Canada during magnetic storms. Four of the anomaly lists apply for magnetic storms that occurred in March 1989, August 1972, March 1940, and for various storms 1946-2000; yet another list consists of statistical values summarizing geomagnetically induced current data for 1969-1972. The lists are compiled from source published papers, technical documents, and research papers. These sources generally include brief descriptions of each anomaly and attribution to a particular magnetic storm. Other information, when given, includes utility company name, facility name, start date and time, end date and time. None of the sources include specific locations (latitude and longitude) of the anomalies. In the lists given here, the latitude and longitude of each anomaly are obtained either from a list of power-grid facilities available from the Department of Homeland Security, by estimating facility locations from digitized and georeferenced paper maps, or from internet-based maps.

This feature class/shapefile represents electric power balancing authorities. Balancing Authority Areas also known as Control Areas, are controlled by Balancing Authorities, who are responsible for monitoring and balancing the generation, load, and transmission of electric power within their region, often comprised of the retail service territories of numerous electric power utilities. Each control area is interconnected with neighboring ones to facilitate emergency support, coordinated operations, and power purchases and sales. These shapefiles are based on the Control Areas shapefiles published in the Homeland Infrastructure Foundation Level Database (HIFLD) as of April 3, 2022. Note that balancing authorities are electric entities and do not have well-defined geographical boundaries. As a result, the balancing authority shapefiles sometimes overlap each other and sometimes have gaps in geographic coverage. The geographic shapefiles provide a rough idea of the extent of coverage for each balancing authority; they are not meant to represent strict boundaries. Only balancing authorities in the Lower 48 states are included.

This polygon service represents easements acquired by Duke Energy and Piedmont Natural Gas, either directly or from legacy companies, for purposes of energy transmission throughout North Carolina. The layer is updated monthly. If you have questions or comments, please contact us at LandServicesGIS@duke-energy.com

description: 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.; abstract: 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.

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.

A table listing the deregulation status of electricity and natural gas markets across all 50 U.S. states and the District of Columbia as of March 17, 2025. Includes notes on partial deregulation and market specifics.

The Global Solar Power Tracker is a worldwide dataset of utility-scale solar PV facilities. It includes solar farm phases with capacities of 20 megawatts (MW) or more (10 MW or more in Arabic-speaking countries). A solar project phase is generally defined as a group of one or more solar units that are installed under one permit, one power purchase agreement, and typically come online at the same time. The Global Solar Power Tracker catalogs every solar farm phase at these capacity thresholds of any status, including operating, announced, under development, under construction, shelved, cancelled, mothballed, or retired. Each solar farm included in the tracker is linked to a wiki page on the GEM wiki.

Architecture

Global Energy Monitor’s Global Solar Power Tracker uses a two-level system for organizing information, consisting of both a database and wiki pages with further information. The database tracks individual solar farm phases and includes information such as project owner, status, and location. A wiki page for each solar farm is created within the Global Energy Monitor wiki. The database and wiki pages are updated annually.

Status Categories

• Announced: Proposed projects that have been described in corporate or government plans but have not yet taken concrete steps such as applying for permits.
• Development: Projects that are actively moving forward in seeking governmental approvals, land rights, or financing.
• Construction: Site preparation and equipment installation are underway.
• Operating: The project has been formally commissioned; commercial operation has begun.
• Shelved: Suspension of operation has been announced, or no progress has been observed for at least two years.
• Cancelled: A cancellation announcement has been made, or no progress has been observed for at least four years.
• Retired: The project has been decommissioned.
• Mothballed: The project is disused, but not dismantled.

Research Process

The Global Solar Power Tracker data set draws on various public data sources, including: - Government data on individual power solar farms (such as India Central Electricity Authority’s “Plant Wise Details of All India Renewable Energy Projects” and the U.S. EIA 860 Electric Generator Inventory), country energy and resource plans, and government websites tracking solar farm permits and applications; - Reports by power companies (both state-owned and private); - News and media reports; - Local non-governmental organizations tracking solar farms or permits.

Wiki Pages

For each solar farm, a wiki page is created on Global Energy Monitor’s wiki. Under standard wiki convention, all information is linked to a publicly-accessible published reference, such as a news article, company or government report, or a regulatory permit. In order to ensure data integrity in the open-access wiki environment, Global Energy Monitor researchers review all edits of project wiki pages.

Mapping

To allow easy public access to the results, Global Energy Monitor worked with GreenInfo Network to develop a map-based and table-based interface using the Leaflet Open-Source JavaScript library. In the case of exact coordinates, locations have been visually determined using Google Maps, Google Earth, Wikimapia, or OpenStreetMap. For proposed projects, exact locations, if available, are from permit applications, or company or government documentation. If the location of a solar farm or proposal is not known, Global Energy Monitor identifies the most accurate location possible based on available information.

This application is designed to allow stakeholders to understand renewable infrastructure of the electric sector by location and size.  It consolidates the previous Biomass, Geothermal, Hydroelectric, Wind, and Solar maps into one product that includes a map along with charts and tables.  This new tool provides stakeholders the ability to make selections and filter by state or renewable source.The dashboard data include: Power plants from EIA-860, EIA-860M, and EIA-923 that have primary source = biomass, geothermal, hydroelectric, pumped storage, solar or wind Electric transmission line data from Homeland Infrastructure Foundation-Level Data (HILFD) Wind turbines hosted by ArcGIS Living Atlas, based on data from U.S. Wind Turbine Database (USWTDB) by U.S. Geological Survey (USGS) Renewable resources from various National Renewable Energy Laboratory sources If you are interested in biofuel data, such as biodiesel and ethanol, you can find those data layers on the Petroleum and Biofuels map.

The NPMS Public Map Viewer allows the general public to view maps of transmission pipelines, LNG plants, and breakout tanks in one selected county. Distribution and Gathering systems are not included in NPMS. Users are permitted to print maps of the data, but the data is not downloadable.Always contact Dig Safely at 811 before digging. Visit Call Before You Dig for more information.

(Link to Metadata) Green Mountain Power (GMP) pole distribution model, linear distribution model, underground structure distribution model, and a pole-attachments table. The pole-attachments table is available by downloading a file-geodatabase bundle of the entire dataset (poles, lines, underground structures, and pole-attachments table) from https://s3.us-east-2.amazonaws.com/vtopendata-prd/Utilities/_Packaged_Zips/UtilityTransmit_GMPPOLES.zip.

Overview and Intended Use Cases

These scenarios establish a range of futures for U.S. buildings sector energy use and CO2 emissions to 2050 using Scout (scout.energy.gov), a reproducible and granular model of U.S. building energy use, emissions, and consumer costs developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office (BTO).

Scout benchmark scenario data are suitable for the following example use cases:

• setting high-level policy goals for the U.S. buildings sector to 2050 (e.g., X% building CO2 emissions reductions vs. 2005 levels by 2030, Y% reductions vs. 2005 levels by 2050);
• exploring the effects of key dynamics driving U.S. buildings sector energy and CO2 emissions to 2050 that could be affected by policy levers (e.g., raising minimum technology performance levels; accelerating electrification and/or retrofit rates; introducing breakthrough technologies to the market);
• determining priority segments (regions, building types, and end use/technology types) and sequencing of U.S. buildings sector energy and CO2 emissions reductions to 2050 under a given set of assumptions; and/or
• identifying the energy and emissions impacts or cost effectiveness of specific technologies or operational approaches of interest—in isolation or after considering competition with other measures in a scenario portfolio.

Scenario Summary

A total of 8 scenarios explore the effects of changes across both the demand- and supply-side of building energy use on annual U.S. building energy use and CO2 emissions from 2022–2050. Scenarios are organized into three groups representing low, moderate, and best-case potentials for building decarbonization, respectively. Individual scenarios are distinguished by four input dimensions:

• market-available technology performance range (EE): the energy performance levels of building technologies available for purchase by end use consumers, bounded by a minimum performance “floor” and maximum performance “ceiling”;
• load electrification (EL): the rate at which fossil-fired equipment is converted to electric service, and the efficiency level of the electric equipment;
• early retrofits (R): the fraction of consumers that choose to replace existing building equipment and/or envelope components before the end of their useful lifetimes; and
• power grid (P): the annual average CO2 emissions intensity of the electricity supplied to the buildings sector across the modeled time horizon (2022–2050), resolved by grid region.

Refer to the attached “Scenario_Guide" PDF for further scenario details and results; instructions for reproducing scenario results are available in “Scenario_Summary_Execution” XLSX.

Results data are reported as an annual time series (2022–2050) at both a national and regional (EMM grid region) spatial resolution. While not reflected in this dataset, annual time series data may be further translated to a sub-annual, hourly resolution for integration with grid modeling—please contact the authors for more information.

What's New in This Version

This set of benchmark scenarios carries forward elements of past versions of this dataset (previously titled “Scout Core Measures Scenario Analysis” and summarized in this paper) while also streamlining the scenario design and reflecting updated policy ambitions regarding deployment of building efficiency, flexibility, and electrification as well as power grid evolution. Three scenarios in the current dataset map back to past scenarios:

• Scenario 2.1: EE1.P1 -> Scenario 6: HR 1T-2T-3T
• Scenario 2.2: EE1.ELe1a.P1 -> Scenario 7: HR 1T-2T-3T FS0
• Scenario 2.3: EE1.ELe1b.P1 -> Scenario 8: HR 1T-2T-3T FS20

The following scenario features are new in this dataset:

• Measures in the “best available” tier are deployed with load flexibility features that are based on a previous study of the U.S. building-grid resource. Past versions reflected only efficiency and electrification measures.
• The effects of progressively raising the market-available technology performance “floor” are explored by including reference case technologies in the measure competition and assuming codes/standards remove these technologies from the market-available mix beginning in a certain year. Past versions only explored the effects of a higher technology “ceiling”.
• Increasing ambitions for the top “Prospective” tier of measure performance are reflected. Past versions mapped much of this measure tier to the 2016 BTO MYPP.
• Electrification is explored via both endogenous and exogenous model settings, where the former is based on Scout’s economic measure competition models and the latter is based on fuel switching scenarios developed by Guidehouse for the BTO E3 Initiative. Past versions only explored endogenous electrification.
• Inefficient electrification is explored (past versions did not explore inefficient electrification). In such cases, consumers switch fossil-based heating and water heating equipment to a mix of electric resistance and heat pump technologies, with the mix determined by AEO 2021 Reference Case sales share data for these technologies.
• The effects of early retrofitting behavior are isolated by running all but one scenario without early retrofits. Past versions assumed a 1% early retrofit rate.
• More aggressive grid scenarios are explored using Brattle’s GridSIM model. Two scenarios are included—an 80% decarbonized grid by 2050 and 100% decarbonized grid by 2035. Past versions used the AEO 2018 “$25 carbon allowance fee” side case, which reached ~73% carbon-free electricity generation (including nuclear) by 2050.

The presented dataset contains the following simulation-based monthly hydropower generation data for 110 facilities in British Columbia and Alberta, to support Western-US interconnect grid system studies:
1) Monthly hydropower generation estimates
2) Monthly hydropower flexibility metrics (minimum and maximum hourly generation and daily fluctuations)

The hydropower generation estimates are provided with reference to the facility list that contains the corresponding metadata for each facility.

For more details, please refer to Son et al. (2024). Monthly hydropower generation data for Western Canada to support Western-US interconnect power system studies [Manuscript submitted for publication].

Corresponding author(s): Youngjun Son (youngjun.son@pnnl.gov) and Nathalie Voisin (nathalie.voisin@pnnl.gov)

For data reproduction, please see the GitHub repository at https://github.com/GODEEEP/tgw-hydro-canada" target="_blank" rel="noopener">https://github.com/GODEEEP/tgw-hydro-canada.

Hydropower Facility List

The file, CAN_hydropower_facilities.csv, provides essential information on 146 hydropower facilities in British Columbia and Alberta, derived from https://www.eia.gov/trilateral/#!/maps" target="_blank" rel="noopener">Renewable Energy Power Plants, 1 MW or more, by Energy Source by North American Cooperation on Energy Information (NACEI). Additionally, the facility information has been updated with corresponding https://open.canada.ca/data/en/dataset/a4b190fe-e090-4e6d-881e-b87956c07977">National Hydrographic Network (NHN) Work Units, global reservoir and lake database (https://www.globaldamwatch.org/grand" target="_blank" rel="noopener">GRanD: Global Reservoirs and Dams Database and https://www.hydrosheds.org/products/hydrolakes" target="_blank" rel="noopener">HydroLAKES), diversion intake flow rates based on water license information (hydropower), and so on. Below are the descriptions for each column in the facility metadata:

• fid: Facility id according to NACEI data. New four-digit id starting with '9' are assigned for facilities with no fid in NACEI data
• Facility: Name of the facility
• X: Longitude of the facility's powerhouse
• Y: Latitude of the facility's powerhouse
• Province: Province where the facility is located
• Hydro_MW: Nameplate capacity of the facility
• NHN_Work_U: Associated NHN Work Units
• GRanD_ID: Associated reservoir id from the GRanD dataset
• HydroLAKES_ID: Associated lake id from the HydroLAKES dataset
• GINDEX: Grid id from the mosartwmpy Canada model
• GINDEX_CONUS: Grid id from the mosartwmpy CONUS model, used for facilities in the Columbia River Basin
• Basin_Note: Indicator for facilities located in the Columbia River Basin or outside of the TGW meteorological forcing domain
• WECC_ADS_2032: Indicator for facilities without the WECC ADS 2032 reference hydropower generation data
• Intake_Flow_Rate: Diversion intake flow rates based on hydropower water license information
• Type: Type of facility
• Water_License: Link to the source of water license information

Among the 146 hydropower facilities listed, only 110 facilities, which are within the TGW meteorological forcings domain and have reference hydropower generation data, are considered for monthly hydropower generation estimates.

Monthly Hydropower Generation Estimates and Flexibility Metrics

Each file contains a monthly timeseries dataset (rows: monthly timestamps) from 1981 to 2019 for 110 facilities (columns: Facility listed in CAN_hydropower_facilities.csv).

1. CAN_hydropower_monthly_generation_MWh.csv: monthly total hydropower generation in MWh
2. CAN_hydropower_monthly_p_min_MW.csv: monthly flexibility metric of minimum generation capacity in MW
3. CAN_hydropower_monthly_p_max_MW.csv: monthly flexibility metric of maximum generation capacity in MW
4. CAN_hydropower_monthly_p_ador_MW.csv: monthly flexibility metric of the daily operation range in MW

Funding Acknowledgements

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

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

Disclaimer

The presented dataset was prepared as an account of work sponsored by an agency of the U.S. Government. Neither the U.S. Government nor the U.S. Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, 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 trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. Government or any agency thereof, or Battelle Memorial Institute.

BRADD GIS staff digized the substation layer by following transmission lines. It is not intended to be an authoritative source of data as there are likely substations missing from the dataset.Electric Company offices were digitized by BRADD staff using existing address point information.The transmission line feature layer, utilizing data from Homeland Infrastructure Foundation-Level Data, depicts electric power transmission lines in the United States.DescriptionU.S. Electric Power Transmission LinesThis feature layer, utilizing data from Homeland Infrastructure Foundation-Level Data (HIFLD), depicts electric power transmission lines in the United States. Per HIFLD, "Transmission Lines are the system of structures, wires, insulators and associated hardware that carry electric energy from one point to another in an electric power system. Lines are operated at relatively high voltages varying from 69 kV up to 765 kV, and are capable of transmitting large quantities of electricity over long distances. Underground transmission lines are included where sources were available."138 Kilovolt Transmission LineData currency: This cached Esri service is checked weekly for updates from its federal source (Electric Power Transmission Lines)Data modification: noneFor more information: Electricity ExplainedFor feedback please contact: ArcGIScomNationalMaps@esri.comThe Homeland Infrastructure Foundation-Level DataPer HIFLD, "The Homeland Infrastructure Foundation-Level Data (HIFLD) Subcommittee was established…to address improvements in collection, processing, sharing, and protection of homeland infrastructure geospatial information across multiple levels of government, and to develop a common foundation of homeland infrastructure data to be used for visualization and analysis on all classification domains."

This data archive is a collection of GIS files and FGDC metadata prepared in 1995 for the Northampton County Planning Office by the Virginia Coast Reserve LTER project at the University of Virginia with support from the Virginia Department of Environmental Quality (DEQ) and the National Science Foundation (NSF). Original data sources include: 1:100,000-scale USGS digital line graph (DLG) hydrography and transportation data; 1:6,000-scale boundary, road, and railroad data for the town of Cape Charles from VDOT; 1:190,000-scale county-wide general soil map data and 1:15,540-scale detailed soil data for the Cape Charles area digitized from printed USDA soil survey maps; a land use and vegetation cover dataset (30 m. resolution) created by the VCRLTER derived from a 1993 Landsat Thematic Mapper satellite image; 1:20,000-scale plant association maps for 10 seaside barrier and marsh islands between Hog and Smith Islands, inclusive, prepared by Cheryl McCaffrey for TNC in 1975 and published in the Virginia Journal of Science in 1990; and 1993 colonial bird nesting site data collected by The Center for Conservation Biology (with partners The Nature Conservancy, College of William and Mary, University of Virginia, USFWS, VA-DCR, and VA-DGIF). Contents: HYDROGRAPHY Based on USGS 1:100,000 Digital Line Graph (DLG) data. Files: h100k_arc_u84 (streams, shorelines, etc.) and h100k_poly_u84 (marshes, mudflats, etc.). Note that the hydrographic data has been superseded by the more recent and more detailed USGS National Hydrography Dataset, available for the entire state of Virginia at "ftp://nhdftp.usgs.gov/DataSets/Staged/States/FileGDB/HighResolution/NHDH_VA_931v210.zip" (see http://nhd.usgs.gov/data.html for more information). A static 2013 version of the NHD data that includes shapefiles extracted from the original ESRI geodatabase format data and covering just the watersheds of the Eastern Shore of VA can also be found in the VCRLTER Data Catalog (dataset VCR14223). TRANSPORTATION Based on USGS 1:100,000 Digital Line Graph (DLG) data for the full county, and 1:6,000 VDOT data for the Cape Charles township. Files: 1:100k Transportation (lines) from USGS DLG data: rtf100k_arc_u84 (roads), rrf100k_arc_u84 (railroads), and mtf100k_arc_u84 (airports and utility transmission lines). Files: 1:6000 street, boundary, and rail line data for the town of Cape Charles, 1984, prepared by Virginia Department of Highways and Transportation Information Services (Division 1221 East Broad Street, Richmond, Virginia 23219). Streets correct through December 31,1983. Georeferencing corrected in 2014 for shapefiles only, using same methodology described for VCR14218 dataset. File : town_u84_adj (town_arc_u84old is the older unadjusted data). Note that the transportation data has been superseded by more recent and more detailed data contained in dataset VCR14222 of the VCRLTER Data Catalog. The VCR14222 data contains 2013 U.S. Census Bureau TIGER/Line road and airfield data supplemented by railroad and transmission lines digitized from high resolution VGIN-VBMP 2013 aerial imagery and additionally has boat launch locations not available here. SOILS General soil map for Northampton county (1:190k), and detailed soil map for Cape Charles and Cheriton areas (1:15,540) from published the USDA Soil Conservation Service's 1989 "Soil Survey of Northampton County, Virginia" digitized at UVA by Ray Dukes Smith: soilorig_poly_u84 (uses original shorelines from source maps), soil_poly_u84 (substitutes shorelines from 1993 landcover classification data), and cc_soil_poly_u84 (Cape Charles & Cheriton detailed data, map sheets 13 and 14). Note that the soil data has been superseded by more recent and more detailed SSURGO soil data from the USDA's Natural Resources Conservation Service (NRCS), which has seamless soil data from the 1:15,540 map series in tabular and GIS formats for the full county, as well as for all counties in VA and other states. A static 2013 version of the SSURGO data that contains merged data for Accomack and Northampton Counties can be found in the VCRLTER Data Catalog (dataset VCR14220). LANDUSE/LANDCOVER VCR Landuse and Vegetation Cover, 1993, created by Guofan Shao (VCRLTER) based on 30m resolution Landsat Thematic Mapper (TM) satellite imagery taken on July 28, 1993. Cropped to include just Northampton County. Landcover is divided into 5 classifications: (1) Forest or shrub, (2) Bare Land or Sand, (3) Planted Cropland, Grassland, or Upland Marsh, (4) Open Water, and (5) Low Salt Marsh. File = nhtm93s3_poly_u84. No spatial adjustments necessary. An outline of the county showing the shorelines based on the above 1993 TM classification is included as the shapefile:outline_poly_u84; however, no spatial adjustment has been applied. Note that a similar landuse/landcover classification based on the same 1993 Landsat TM image and spanning both Accomack and Northampton Counties, VA (plus portions of MD south of Snow Hill and Princess Ann), is also available in the VCRLTER Data Catalog (dataset VCR14221). PLANT ASSOCIATIONS Barrier Island Vegetation Maps (1:20,000) for islands of the Virginia Coast Reserve (TNC) that are within Northampton County, including Wreck Island (VA DCR) but excluding Fishermans Island (US FWS). By C.A. McCaffrey, based on air photos from 1974 and subsequent ground-truthing. Individual shapefiles for each major island: hog_u84_adj, cobb_u84_adj, wreck_u84_adj, shipshoal_u84_adj, myrtle_u84_adj, and smith_u84_adj. Note that in 2014 the georeferencing was fixed, as described in VCRLTER dataset VCR14218, but was only applied to the converted shapefiles, not the original e00 files. Note that the full original dataset, including vegetation maps for Parramore, Cedar, and Metompkin Islands in Accomack County, is available in the VCRLTER Data Catalog (dataset VCR14218). BIRD NESTING SITES Nesting sites for colonial waterbirds in Northampton County, VA, 1993. File: Birds_pt_u84. Spatial location generally marks a point in the center or at the edge of the colony. No spatial adjustments deemed necessary due to the error associated with the location measurement. Attribute information includes location; species; common name; number of adults, chicks, eggs and nests; land owner; and management area. Plovers and other endangered/threatened species included in the original database are not available in this version, nor are data for nesting sites in other counties or during other years. For more information concerning the original Virginia colonial waterbird survey data (1975-present), please see "http://www.ccbbirds.org/what-we-do/research/species-of-concern/species-of-concern-projects/va-colonial-waterbird-survey/" or contact The Center for Conservation Biology (http://www.ccbbirds.org). To view more recent bird colony locations, visit their online mapping application at "http://www.ccbbirds.org/maps/".