The CEC Power Plant geospatial data layer contains point features representing power generating facilities in California, and power plants with imported electricity from Nevada, Arizona, Utah and Mexico. The transmission line, substation and power plant mapping database were started in 1990 by the CEC GIS staffs. The final project was completed in October 2010. The enterprise GIS system on CEC's critical infrastructure database was leaded by GIS Unit in November 2014 and was implemented in May 2016. The data was derived from CEC's Quarterly Fuel and Energy Report (QFER), Energy Facility Licensing (Siting), Wind Performance Reporting System (WPRS), and Renewable Energy Action Team (REAT). The sources for the power plant point digitizing are including sub-meter resolution of Digital Globe, Bing, Google, ESRI and NAIP aerial imageries, with scale at least 1:10,000. Occasionally, USGS Topographic map, Google Street View and Bing Bird's Eye are used to verify the precise location of a facility.Although a power plant may have multiple generators, or units, the power plant layer represents all units at a plant as one feature. Detailed attribute information associated with the power plant layer includes CEC Plant ID, Plant Label, Plant Capacity (MW), General Fuel, Plant Status, CEC Project Status, CEC Docket ID, REAT ID, Plant County, Plant State, Renewable Energy, Wind Resource Area, Local Reliability Area, Sub Area, Electric Service Area, Service Area Category, California Balancing Authorities, California Air District, California Air Basin, Quad Name, Senate District, Assembly District, Congressional District, Power Project Web Link, CEC Link, Aerial, QRERGEN Comment, WPRS Comment, Geoscience Comment, Carto Comment, QFERGEN Excel Link, WPRS Excel Link, Schedule 3 Excel Link, and CEC Data Source. For power plant layer which is joined with QFer database, additional fields are displayed: CEC Plant Name (full name), Plant Alias, EIA Plant ID, Plant City, Initial Start Date, Online Year, Retire Date, Generator or Turbine Count, RPS Eligible, RPS Number, Operator Company Name, and Prime Mover ID. In general, utility and non-utility operated power plant spatial data with at least 1 MW of demonstrated capacity and operating status are distributed. Special request is required on power plant spatial data with all capacities and all stages of status, including Cold Standby, Indefinite Shutdown, Maintenance, Non-Operational, Proposed, Retired, Standby, Terminated, and Unknown.For question on power generation or others, please contact Michael Nyberg at (916) 654-5968.

A generating station is an industrial facility built and operated to generate electricity. The map shows the 916 generating stations (power plants) operating in 2007. There were 479 hydroelectric stations, 375 thermal plants (combustion, internal combustion and steam), 7 nuclear plants, 54 wind turbines and 1 tidal power plant.

This is the data set behind the Wind Generation Interactive Query Tool created by the CEC. The visualization tool interactively displays wind generation over different time intervals in three-dimensional space. The viewer can look across the state to understand generation patterns of regions with concentrations of wind power plants. The tool aids in understanding high and low periods of generation. Operation of the electric grid requires that generation and demand are balanced in each period. The height and color of columns at wind generation areas are scaled and shaded to represent capacity factors (CFs) of the areas in a specific time interval. Capacity factor is the ratio of the energy produced to the amount of energy that could ideally have been produced in the same period using the rated nameplate capacity. Due to natural variations in wind speeds, higher factors tend to be seen over short time periods, with lower factors over longer periods. The capacity used is the reported nameplate capacity from the Quarterly Fuel and Energy Report, CEC-1304A. CFs are based on wind plants in service in the wind generation areas.Renewable energy resources like wind facilities vary in size and geographic distribution within each state. Resource planning, land use constraints, climate zones, and weather patterns limit availability of these resources and where they can be developed. National, state, and local policies also set limits on energy generation and use. An example of resource planning in California is the Desert Renewable Energy Conservation Plan. By exploring the visualization, a viewer can gain a three-dimensional understanding of temporal variation in generation CFs, along with how the wind generation areas compare to one another. The viewer can observe that areas peak in generation in different periods. The large range in CFs is also visible.

The site suitability criteria included in the techno-economic land use screens are listed below. As this list is an update to previous cycles, tribal lands, prime farmland, and flood zones are not included as they are not technically infeasible for development. The techno-economic site suitability exclusion thresholds are presented in Table 1. Distances indicate the minimum distance from each feature for commercial scale solar development.Attributes:Steeply sloped areas: change in vertical elevation compared to horizontal distancePopulation density: the number of people living in a 1 km2 areaUrban areas: defined by the U.S. Census.8Water bodies: defined by the U.S. National Atlas Water Feature Areas, available from Argonne National Lab Energy Zone Mapping Tool9Railways: a comprehensive database of North America's railway system from the Federal Railroad Administration (FRA), available from Argonne National Lab Energy Zone Mapping ToolMajor highways: available from ESRI Living Atlas10Airports: The Airports dataset including other aviation facilities as of July 13, 2018 is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics' (BTS's) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product. Available from Argonne National Lab Energy Zone Mapping ToolActive mines: Active Mines and Mineral Processing Plants in the United States in 200311Military Lands: Land owned by the federal government that is part of a US military base, camp, post, station, yard, center or installation.Table 1 Solar Steeply sloped areas >10o Population density >100/km2 Capacity factor <20% Urban areas <500 m Water bodies <250 m Railways <30 m Major highways <125 m Airports <1000 m Active mines <1000 m Military Lands <1000m For more information about the processes and sources used to develop the screening criteria see sources 1-7 in the footnotes. Data updates occur as needed, corresponding to typical 3-year CPUC IRP planning cycles.Footnotes:[1] Lopez, A. et. al. “U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis,” 2012. https://www.nrel.gov/docs/fy12osti/51946.pdf[2] https://greeningthegrid.org/Renewable-Energy-Zones-Toolkit/topics/social-environmental-and-other-impacts#ReadingListAndCaseStudies[3] Multi-Criteria Analysis for Renewable Energy (MapRE), University of California Santa Barbara. https://mapre.es.ucsb.edu/[4] Larson, E. et. al. “Net-Zero America: Potential Pathways, Infrastructure, and Impacts, Interim Report.” Princeton University, 2020. https://environmenthalfcentury.princeton.edu/sites/g/files/toruqf331/files/2020-12/Princeton_NZA_Interim_Report_15_Dec_2020_FINAL.pdf.[5] Wu, G. et. al. “Low-Impact Land Use Pathways to Deep Decarbonization of Electricity.” Environmental Research Letters 15, no. 7 (July 10, 2020). https://doi.org/10.1088/1748-9326/ab87d1.[6] RETI Coordinating Committee, RETI Stakeholder Steering Committee. “Renewable Energy Transmission Initiative Phase 1B Final Report.” California Energy Commission, January 2009.[7] Pletka, Ryan, and Joshua Finn. “Western Renewable Energy Zones, Phase 1: QRA Identification Technical Report.” Black & Veatch and National Renewable Energy Laboratory, 2009. https://www.nrel.gov/docs/fy10osti/46877.pdf.[8] https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Urban+Areas[9] https://ezmt.anl.gov/[10] https://www.arcgis.com/home/item.html?id=fc870766a3994111bce4a083413988e4[11] https://mrdata.usgs.gov/mineplant/CreditsTitle: Techno-economic screening criteria for utility-scale solar photovoltaic energy installations for Integrated Resource PlanningPurpose for creation: These exclusion criteria are for use in electric system planning, capacity expansion modeling, and integrated resource planning.Keywords: solar, photovoltaic, resource potential, techno-economic, PV, IRPExtent: western states of the contiguous U.S.Use LimitationsThe geospatial data created by the use of these techno-economic screens inform high-level estimates of technical renewable resource potential for electric system planning and should not be used, on their own, to guide siting of generation projects nor assess project-level impacts. Confidentiality: PublicContactEmily Leslie Emily@MontaraMtEnergy.comSam Schreiber sam.schreiber@ethree.com Jared Ferguson Jared.Ferguson@cpuc.ca.gov Oluwafemi Sawyerr femi@ethree.com

Note: Find data at source. · This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and residential building stock. The End-Use Load Profiles project website includes links to data viewers for this new dataset. For documentation of dataset validation, model calibration, and uncertainty quantification, see Wilson et al. (2022).

These data were first created around 2012 as a byproduct of various analyses of solar photovoltaics and solar water heating (see references below for are two examples). This dataset contains several errors and limitations. It is recommended that users of this dataset transition to the updated version of the dataset posted in the resources. This dataset contains weather data, commercial load profile data, and residential load profile data.

Weather The Typical Meteorological Year 3 (TMY3) provides one year of hourly data for around 1,000 locations. The TMY weather represents 30-year normals, which are typical weather conditions over a 30-year period.

Commercial The commercial load profiles included are the 16 ASHRAE 90.1-2004 DOE Commercial Prototype Models simulated in all TMY3 locations, with building insulation levels changing based on ASHRAE 90.1-2004 requirements in each climate zone. The folder names within each resource represent the weather station location of the profiles, whereas the file names represent the building type and the representative city for the ASHRAE climate zone that was used to determine code compliance insulation levels. As indicated by the file names, all building models represent construction that complied with the ASHRAE 90.1-2004 building energy code requirements. No older or newer vintages of buildings are represented.

Residential The BASE residential load profiles are five EnergyPlus models (one per climate region) representing 2009 IECC construction single-family detached homes simulated in all TMY3 locations. No older or newer vintages of buildings are represented. Each of the five climate regions include only one heating fuel type; electric heating is only found in the Hot-Humid climate. Air conditioning is not found in the Marine climate region.

One major issue with the residential profiles is that for each of the five climate zones, certain location-specific algorithms from one city were applied to entire climate zones. For example, in the Hot-Humid files, the heating season calculated for Tampa, FL (December 1 - March 31) was unknowingly applied to all other locations in the Hot-Humid zone, which restricts heating operation outside of those days (for example, heating is disabled in Dallas, TX during cold weather in November). This causes the heating energy to be artificially low in colder parts of that climate zone, and conversely the cooling season restriction leads to artificially low cooling energy use in hotter parts of each climate zone. Additionally, the ground temperatures for the representative city were used across the entire climate zone. This affects water heating energy use (because inlet cold water temperature depends on ground temperature) and heating/cooling energy use (because of ground heat transfer through foundation walls and floors). Representative cities were Tampa, FL (Hot-Humid), El Paso, TX (Mixed-Dry/Hot-Dry), Memphis, TN (Mixed-Humid), Arcata, CA (Marine), and Billings, MT (Cold/Very-Cold).

The residential dataset includes a HIGH building load profile that was intended to provide a rough approximation of older home vintages, but it combines poor thermal insulation with larger house size, tighter thermostat setpoints, and less efficient HVAC equipment. Conversely, the LOW building combines excellent thermal insulation with smaller house size, wider thermostat setpoints, and more efficient HVAC equipment. However, it is not known how well these HIGH and LOW permutations represent the range of energy use in the housing stock.

Note that on July 2nd, 2013, the Residential High and Low load files were updated from 366 days in a year for leap years to the more general 365 days in a normal year. The archived residential load data is included from prior to this date.

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 May to 31 May 2014.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a map that shows the electrical generating network in Western Canada circa 1954. Generating plants are shown as either developed waterpower sites (in other words, hydro plants), or as fuel electric plants (thermal plants). The plants are portrayed on the basis of their capacity, measured in horsepower. The map also shows transmission lines of 60 000 volts or higher. Note that in a number of cases, cartographic considerations require that two or more side-by-side transmission lines be shown as a single line on the map. An interesting feature of the map is that it shows the location of potential hydro sites. Each of these is shown by a proportional circle measuring its potential capacity. There is an inset with two pie charts showing the distribution of net generating capability of facilities in 1954. Net Generating Capability estimates are based on actual operating experience assuming all equipment is available at the time of annual peak demand. These estimates make no allowance for the effect of unfavourable water or ice conditions. However, the estimates do allow for needs of the station and so deduct station services.