The databases contain all the technical, financial, and tariff data collected through the study "Making power affordable in Africa and viable for its utilities." The final study and background papers are available at http://www.worldbank.org/affordableviablepowerforafrica. The objective of making the database public is to make data collected through the study available to utility companies, regulators, and practitioners to provide benchmarks and help inform analysis. The databases will be updated from time to time to make corrections or updates for latest data available and therefore may differ from data that appears in the reports. This database is a publication of the African Renewable Energy Access Program (AFREA), a World Bank Trust Fund Grant Program funded by the Kingdom of the Netherlands through ESMAP. It was prepared by staff of the International Bank for Reconstruction and Development / The World Bank.

Data about power company service areas and their announcements about outages are critical for the effective coordination of resources after disasters, and also for building community and neighborhood resilience. As part of the 2015 White House Mapathon, the Department of Energy's Office of Electricity created a national geospatial database of power company service areas with pointers to public outage information (eg, through Twitter, web sites, and toll-free telephone numbers).

Mapathon participants researched public outage information state by state, and populated a lookup table so that disaster-impacted residents, tourists, first responders and relief volunteers can easily get to the information they need on scope and estimated restore times for power outages. This project benefited from participation of private and public sector folks who need this data for their work, and of third party app developers such as Red Cross and The Weather Channel who will incorporate this data into the information services they offer their users.

EIA previously collected sales and revenue data in a category called "Other." This category was defined as including activities such as public street highway lighting, other sales to public authorities, sales to railroads and railways, and interdepartmental sales. EIA has revised its survey to separate the transportation sales and reassign the other activities to the commercial and industrial sectors as appropriate.

<p class="Bodypara">This is an electric utility data file that includes

utility level retail sales of electricity and associated revenue by end-use sector, State, and reporting month. The data source is the survey: Form EIA-826, "Monthly Electric Utility Sales and Revenue Report with State Distributions." The Form EIA-826 is used to collect retail sales of electricity and associated revenue, each month, from a statistically chosen sample of electric utilities in the United States. The respondents to the Form EIA-826 are chosen from the Form EIA-861, "Annual Electric Utility Report." The data also include, for each State, a record (UTILITYID "000000") containing data values which represent the arithmetic differences between the "estimated" State totals and the sum of the retail sales and associated revenue data reported by the respondents to the Form EIA-826.

The data are compressed into a self-extracting (f826yyyy.exe) zip file. This self-extracting zip file expands into one DBF file (f826utilyyyy.dbf) that contains the yearly data and an ASCII text file (f826layoutyyyy.txt) that contains the file description and record layout for the data base structure. The current year's file will be a year-to-date file and is maintained in this monthly format until the data for the final month is finalized.

To expand the self-extracting zip file, type f826yyyy.exe from a DOS window, or double click on the file name from File Manager in Windows 3x or Windows Explorer in either Windows 95, Windows 98, Windows 2000, XP, or ME. Or, click Start, then Run, then select name of .EXE file to open, then "OK." (Requires approx. 600K space). Usually, the current year's file will be a "year-to-date" file until the data for the final month is finalized.

^*Note: Substitute the applicable year for "yyyy" in the file name.

File Size: 200 k

Methodology is based on the "Model-Based Sampling, Inference and Imputation."

Contact:

The Utility Rate Database (URDB) is a free storehouse of rate structure information from utilities in the United States. Here, you can search for your utilities and rates to find out exactly how you are charged for your electric energy usage. Understanding this information can help reduce your bill, for example, by running your appliances during off-peak hours (times during the day when electricity prices are less expensive) and help you make more informed decisions regarding your energy usage.

Rates are also extremely important to the energy analysis community for accurately determining the value and economics of distributed generation such as solar and wind power. In the past, collecting rates has been an effort duplicated across many institutions. Rate collection can be tedious and slow, however, with the introduction of the URDB, OpenEI aims to change how analysis of rates is performed. The URDB allows anyone to access these rates in a computer-readable format for use in their tools and models. OpenEI provides an API for software to automatically download the appropriate rates, thereby allowing detailed economic analysis to be done without ever having to directly handle complex rate structures. Essentially, rate collection and processing that used to take weeks or months can now be done in seconds!

NREL’s System Advisor Model (formerly Solar Advisor Model or SAM), currently has the ability to communicate with the OpenEI URDB over the internet. SAM can download any rate from the URDB directly into the program, thereby enabling users to conduct detailed studies on various power systems ranging in size from a small residential rooftop solar system to large utility scale installations. Other applications available at NREL, such as OpenPV and IMBY, will also utilize the URDB data.

Upcoming features include better support for entering net metering parameters, maps to summarize the data, geolocation capabilities, and hundreds of additional rates!

India Electricity: Consumption: Utilities: Industry data was reported at 645,000.000 GWh in 2024. This records an increase from the previous number of 593,895.000 GWh for 2023. India Electricity: Consumption: Utilities: Industry data is updated yearly, averaging 104,809.500 GWh from Mar 1971 (Median) to 2024, with 54 observations. The data reached an all-time high of 645,000.000 GWh in 2024 and a record low of 29,579.000 GWh in 1971. India Electricity: Consumption: Utilities: Industry data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under Global Database’s India – Table IN.RBE002: Electricity: Consumption: Utilities.

This dataset, compiled by NREL using data from ABB, the Velocity Suite (http://energymarketintel.com/) and the U.S. Energy Information Administration dataset 861 (http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not average rates per zip code), but not the detailed rate structure data found in the OpenEI U.S. Utility Rate Database (https://openei.org/apps/USURDB/).

Abstract

This dataset, compiled by NREL using data from ABB, the Velocity Suite and the U.S. Energy Information Administration dataset 861, provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not average rates per zip code), but not the detailed rate structure data found in the OpenEI U.S. Utility Rate Database.

The databases contain all the technical, financial, and tariff data collected through the study "Making power affordable in Africa and viable for its utilities." The WB study uses national household expenditure surveys conducted since 2008 in 22 countries; it makes use of tariff schedules in effect as of July 2014 in 39 countries, including all of the 22 countries with household surveys. The objective of making the database public is to make data collected through the study available to utility companies, regulators, and practitioners to provide benchmarks and help inform analysis. The databases will be updated from time to time to make corrections or updates for latest data available and therefore may differ from data that appears in the reports. This database is a publication of the African Renewable Energy Access Program (AFREA), a World Bank Trust Fund Grant Program funded by the Kingdom of the Netherlands through ESMAP. It was prepared by staff of the International Bank for Reconstruction and Development / The World Bank. The full report is available at https://openknowledge.worldbank.org/handle/10986/25091 Last Updated 26-Oct-2016 Citation: Trimble, Chris; Kojima, Masami; Perez Arroyo, Ines; Mohammadzadeh, Farah. 2016. Financial Viability of Electricity Sectors in Sub-Saharan Africa: Quasi-Fiscal Deficits and Hidden Costs. Policy Research Working Paper; No. 7788.

The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale utility-reported energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315. The order requires utilities under its regulation to develop and report community energy use data to the UER.This dataset includes electricity and natural gas usage data reported at the city, town, and village level collected under a data protocol in effect between 2016 and 2021. Other UER datasets include energy use data reported at the county and ZIP code level. Data collected after 2021 were collected according to a modified protocol. Those data may be found at https://data.ny.gov/Energy-Environment/Utility-Energy-Registry-Monthly-Community-Energy-U/4txm-py4p.Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld.

Investments in infrastructure have been on the development agenda of Latin American and Caribbean (LCR) countries as they move towards economic and social progress. Investing in infrastructure is investing in human welfare by providing access to and quality basic infrastructure services. Improving the performance of the electricity sector is one such major infrastructure initiative and the focus of this benchmarking data. A key initiative for both public and private owned distribution utilities has been to upgrade their efficiency as well as to increase the coverage and quality of service. In order to accomplish this goal, this initiative serves as a clearing house for information regarding the country and utility level performance of electricity distribution sector. This initiative allows countries and utilities to benchmark their performance in relation to other comparator utilities and countries. In doing so, this benchmarking data contributes to the improvement of the electricity sector by filling in knowledge gaps for the identification of the best performers (and practices) of the region.

This benchmarking database consists of detailed information of 25 countries and 249 utilities in the region. The data collected for this benchmarking project is representative of 88 percent of the electrification in the region. Through in-house and field data collection, consultants compiled data based on accomplishments in output, coverage, input, labor productivity, operating performance, the quality of service, prices, and ownership. By serving as a mirror of good performance, the report allows for a comparative analysis and the ranking of utilities and countries according to the indicators used to measure performance.

Although significant efforts have been made to ensure data comparability and consistency across time and utilities, the World Bank and the ESMAP do not guarantee the accuracy of the data included in this work.

Acknowledgement:
This benchmarking database was prepared by a core team consisting of Luis Alberto Andres (Co-Task Team Leader), Jose Luis Guasch (Co-Task Team Leader), Julio A. Gonzalez, Georgeta Dragoiu, and Natalie Giannelli. The team was benefited by data contributions from Jordan Z. Schwartz (Senior Infrastructure Specialist, LCSTR), Lucio Monari (Lead Energy Economist, LCSEG), Katharina B. Gassner (Senior Economist, FEU), and Martin Rossi (consultant).
Funding was provided by the Energy Sector Management Assistance Program (ESMAP) and the World Bank.

Comments and suggestion are welcome by contacting Luis Andres (landres@worldbank.org)

This data is usually updated quarterly by February 1st, May 1st, August 1st, and November 1st.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.California Energy Commission's Open Data Portal.

Electric utilities report a huge amount of information to the US government and other public agencies. This includes yearly, monthly, and even hourly data about fuel burned, electricity generated, operating expenses, power plant usage patterns and emissions. Unfortunately, much of this data is not released in well documented, ready-to-use, machine readable formats. Data from different agencies tends not to be standardized or easily used in tandem. Several commercial data services clean, package, and re-sell this this data, but at prices which are too high to be accessible to many smaller stakeholders.

The Public Utility Data Liberation (PUDL) project takes information that’s already publicly available, and makes it publicly usable, by cleaning, standardizing, and cross-linking utility data from different sources in a single database. Thus far our primary focus has been on fuel use, generation, operating costs, and operation history. It currently includes data from:

• EIA Form 860: 2001-2022
• EIA Form 860m: 2023-09
• EIA Form 861: 2001-2022
• EIA Form 923: 2001-2022
• EPA Continuous Emissions Monitoring System (CEMS): 1995-2022
• FERC Form 1: 1994-2021
• FERC Form 714: 2006-2020
• US Census Demographic Profile 1 Geodatabase: 2010

We archive snapshots of the raw inputs on Zenodo and all our data processing uses those snapshots as a starting place for reproducibility.

You can find the source code that generates this database in the PUDL repository on GitHub. The PUDL project is coordinated by Catalyst Cooperative.

The data is updated nightly by our automated nightly builds. When they are successful, new data is uploaded to the AWS Open Data Registry

We publish PUDL Data Dictionaries on Read the Docs which provide more descriptive information about the data.

Dataset header image courtesy of Gerry Machen via Flickr under a CC-BY-ND license

Electricity Consumption: Utilities: Meghalaya data was reported at 1,719.000 GWh in 2023. This records an increase from the previous number of 1,507.000 GWh for 2022. Electricity Consumption: Utilities: Meghalaya data is updated yearly, averaging 921.960 GWh from Mar 1996 (Median) to 2023, with 28 observations. The data reached an all-time high of 1,719.000 GWh in 2023 and a record low of 281.140 GWh in 1996. Electricity Consumption: Utilities: Meghalaya data remains active status in CEIC and is reported by Central Electricity Authority. The data is categorized under Global Database’s India – Table IN.RBE002: Electricity: Consumption: Utilities.

Abstract

Estache and Goicoechea present an infrastructure database that was assembled from multiple sources. Its main purposes are: (i) to provide a snapshot of the sector as of the end of 2004; and (ii) to facilitate quantitative analytical research on infrastructure sectors. The related working paper includes definitions, source information and the data available for 37 performance indicators that proxy access, affordability and quality of service (most recent data as of June 2005). Additionally, the database includes a snapshot of 15 reform indicators across infrastructure sectors.

This is a first attempt, since the effort made in the World Development Report 1994, at generating a database on infrastructure sectors and it needs to be recognized as such. This database is not a state of the art output—this is being worked on by sector experts on a different time table. The effort has however generated a significant amount of new information. The database already provides enough information to launch a much more quantitative debate on the state of infrastructure. But much more is needed and by circulating this information at this stage, we hope to be able to generate feedback and fill the major knowledge gaps and inconsistencies we have identified.

Geographic coverage

The database covers the following countries: - Afghanistan - Albania - Algeria - American Samoa - Andorra - Angola - Antigua and Barbuda - Argentina - Armenia - Aruba - Australia - Austria - Azerbaijan - Bahamas, The - Bahrain - Bangladesh - Barbados - Belarus - Belgium - Belize - Benin - Bermuda - Bhutan - Bolivia - Bosnia and Herzegovina - Botswana - Brazil - Brunei - Bulgaria - Burkina Faso - Burundi - Cambodia - Cameroon - Canada - Cape Verde - Cayman Islands - Central African Republic - Chad - Channel Islands - Chile - China - Colombia - Comoros - Congo, Dem. Rep. - Congo, Rep. - Costa Rica - Cote d'Ivoire - Croatia - Cuba - Cyprus - Czech Republic - Denmark - Djibouti - Dominica - Dominican Republic - Ecuador - Egypt, Arab Rep. - El Salvador - Equatorial Guinea - Eritrea - Estonia - Ethiopia - Faeroe Islands - Fiji - Finland - France - French Polynesia - Gabon - Gambia, The - Georgia - Germany - Ghana - Greece - Greenland - Grenada - Guam - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong Kong, China - Hungary - Iceland - India - Indonesia - Iran, Islamic Rep. - Iraq - Ireland - Isle of Man - Israel - Italy - Jamaica - Japan - Jordan - Kazakhstan - Kenya - Kiribati - Korea, Dem. Rep. - Korea, Rep. - Kuwait - Kyrgyz Republic - Lao PDR - Latvia - Lebanon - Lesotho - Liberia - Libya - Liechtenstein - Lithuania - Luxembourg - Macao, China - Macedonia, FYR - Madagascar - Malawi - Malaysia - Maldives - Mali - Malta - Marshall Islands - Mauritania - Mauritius - Mayotte - Mexico - Micronesia, Fed. Sts. - Moldova - Monaco - Mongolia - Morocco - Mozambique - Myanmar - Namibia - Nepal - Netherlands - Netherlands Antilles - New Caledonia - New Zealand - Nicaragua - Niger - Nigeria - Northern Mariana Islands - Norway - Oman - Pakistan - Palau - Panama - Papua New Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Puerto Rico - Qatar - Romania - Russian Federation - Rwanda - Samoa - San Marino - Sao Tome and Principe - Saudi Arabia - Senegal - Seychelles - Sierra Leone - Singapore - Slovak Republic - Slovenia - Solomon Islands - Somalia - South Africa - Spain - Sri Lanka - St. Kitts and Nevis - St. Lucia - St. Vincent and the Grenadines - Sudan - Suriname - Swaziland - Sweden - Switzerland - Syrian Arab Republic - Tajikistan - Tanzania - Thailand - Togo - Tonga - Trinidad and Tobago - Tunisia - Turkey - Turkmenistan - Uganda - Ukraine - United Arab Emirates - United Kingdom - United States - Uruguay - Uzbekistan - Vanuatu - Venezuela, RB - Vietnam - Virgin Islands (U.S.) - West Bank and Gaza - Yemen, Rep. - Yugoslavia, FR (Serbia/Montenegro) - Zambia - Zimbabwe

Kind of data

Aggregate data [agg]

Mode of data collection

Face-to-face [f2f]

Response rate

Sector Performance Indicators

Energy The energy sector is relatively well covered by the database, at least in terms of providing a relatively recent snapshot for the main policy areas. The best covered area is access where data are available for 2000 for about 61% of the 207 countries included in the database. The technical quality indicator is available for 60% of the countries, and at least one of the perceived quality indicators is available for 40% of the countries. Price information is available for about 41% of the countries, distinguishing between residential and non residential.

Water & Sanitation Because the sector is part of the Millennium Development Goals (MDGs), it enjoys a lot of effort on data generation in terms of the access rates. The WHO is the main engine behind this effort in collaboration with the multilateral and bilateral aid agencies. The coverage is actually quite high -some national, urban and rural information is available for 75 to 85% of the countries- but there are significant concerns among the research community about the fact that access rates have been measured without much consideration to the quality of access level. The data on technical quality are only available for 27% of the countries. There are data on perceived quality for roughly 39% of the countries but it cannot be used to qualify the information provided by the raw access rates (i.e. access 3 hours a day is not equivalent to access 24 hours a day).

Information and Communication Technology The ICT sector is probably the best covered among the infrastructure sub-sectors to a large extent thanks to the fact that the International Telecommunications Union (ITU) has taken on the responsibility to collect the data. ITU covers a wide spectrum of activity under the communications heading and its coverage ranges from 85 to 99% for all national access indicators. The information on prices needed to make assessments of affordability is also quite extensive since it covers roughly 85 to 95% of the 207 countries. With respect to quality, the coverage of technical indicators is over 88% while the information on perceived quality is only available for roughly 40% of the countries.

Transport The transport sector is possibly the least well covered in terms of the service orientation of infrastructure indicators. Regarding access, network density is the closest approximation to access to the service and is covered at a rate close to 90% for roads but only at a rate of 50% for rail. The relevant data on prices only cover about 30% of the sample for railways. Some type of technical quality information is available for 86% of the countries. Quality perception is only available for about 40% of the countries.

Institutional Reform Indicators

Electricity The data on electricity policy reform were collected from the following sources: ABS Electricity Deregulation Report (2004), AEI-Brookings telecommunications and electricity regulation database (2003), Bacon (1999), Estache and Gassner (2004), Estache, Trujillo, and Tovar de la Fe (2004), Global Regulatory Network Program (2004), Henisz et al. (2003), International Porwer Finance Review (2003-04), International Power and Utilities Finance Review (2004-05), Kikukawa (2004), Wallsten et al. (2004), World Bank Caribbean Infrastructure Assessment (2004), World Bank Global Energy Sector Reform in Developing Countries (1999), World Bank staff, and country regulators. The coverage for the three types of institutional indicators is quite good for the electricity sector. For regulatory institutions and private participation in generation and distribution, the coverage is about 80% of the 207 counties. It is somewhat lower on the market structure with only 58%.

Water & Sanitation The data on water policy reform were collected from the following sources: ABS Water and Waste Utilities of the World (2004), Asian Developing Bank (2000), Bayliss (2002), Benoit (2004), Budds and McGranahan (2003), Hall, Bayliss, and Lobina (2002), Hall and Lobina (2002), Hall, Lobina, and De La Mote (2002), Halpern (2002), Lobina (2001), World Bank Caribbean Infrastructure Assessment (2004), World Bank Sector Note on Water Supply and Sanitation for Infrastructure in EAP (2004), and World Bank staff. The coverage for institutional reforms in W&S is not as exhaustive as for the other utilities. Information on the regulatory institutions responsible for large utilities is available for about 67% of the countries. Ownership data are available for about 70% of the countries. There is no information on the market structure good enough to be reported here at this stage. In most countries small scale operators are important private actors but there is no systematic record of their existence. Most of the information available on their role and importance is only anecdotal.

Information and Communication Technology The report Trends in Telecommunications Reform from ITU (revised by World Bank staff) is the main source of information for this sector. The information on institutional reforms in the sector is however not as exhaustive as it is for its sector performance indicators. While the coverage on the regulatory institutions is 100%, it varies between 76 and 90% of the countries for more of the other indicators. Quite surprisingly also, in contrast to what is available for other sectors, it proved difficult to obtain data on the timing of reforms and of the creation of the regulatory agencies.

Transport Information on transport institutions and reforms is not systematically generated by any agency. Even though more data are needed to have a more comprenhensive picture of the transport sector, it was possible to collect data on railways policy reform from Janes World Railways (2003-04) and complement it with

The EIA-906, EIA-920, EIA-923 and predecessor forms provide monthly and annual data on generation and fuel consumption at the power plant and prime mover level. A subset of plants, steam-electric plants 10 MW and above, also provides boiler level and generator level data. Data for utility plants are available from 1970, and for non-utility plants from 1999. Beginning with January 2004 data collection, the EIA-920 was used to collect data from the combined heat and power plant (cogeneration) segment of the non-utility sector; also as of 2004, nonutilities filed the annual data for nonutility source and disposition of electricity. Beginning in 2007, environmental data was collected on Schedules 8A – 8F of the Form 923 and includes by-product disposition, financial information, NOX control operations, cooling system operations and FGP and FGD unit operations. Beginning in 2008, the EIA-923 superseded the EIA-906, EIA-920, FERC 423, and the EIA-423. Schedule 2 of the EIA-923 collects the plant level fuel receipts and cost data previously collected on the FERC and EIA Forms 423. Data for fuel receipts and costs prior to 2010 are published at /cneaf/electricity/page/eia423.html.

Power plant data prior to 2001 are published as database (.DBF) files, with separate files for utility and non-utility plants. For 2001 data and subsequent years, the data are in Excel spreadsheet files that include data for all plants and make other changes to the presentation of the data.

Note that beginning with January 2001, the data for combined heat and power plants (i.e., the plants that provide data on the EIA-920 form) will only be posted in the combined Excel file.

The links will allow you to download the current Excel files, and will take you to the locations from which you can download the DBF-format utility and non-utility files for 2000 and earlier. The "Database Notes from EIA" link will take you to information on changes to the data and other points of interest to users.

Historical database (.dbf) files for utility (1970-2000) and non-utility (1999-2000)

Utility Database	Legacy (.DBF) Format
Non-Utility Database	Legacy (.DBF) Format
Database Notes from EIA	Updated 4/21/10
Comments or Questions?	E-Mail EIA-923@eia.doe.gov

Additional Links:
Monthly Generation and Fuel Consumption by State
Electric Power Monthly
Form EIA-923, Power Plant Operations Report, form and instructions, (http://www.eia.doe.gov/oiaf/aeo/images/pdf.gif" alt="pdf file" height="16" width="16">) pdf format
Form EIA-923, Power Plant Operations Report, form and instructions, MS Word format

    <b>Contact:</b> <span class="bodypara"><div align="left"> Channele Wirman<br> Phone: 202-586-5356<br> Email: <a href="mailto:channele.wirman@eia.doe.gov">Channele Wirman</a></div></span>

NREL has assembled a list of U.S. retail electricity tariffs and their associated demand charge rates for the Commercial and Industrial sectors. The data was obtained from the Utility Rate Database. Keep the following information in mind when interpreting the data: (1) These data were interpreted and transcribed manually from utility tariff sheets, which are often complex. It is a certainty that these data contain errors, and therefore should only be used as a reference. Actual utility tariff sheets should be consulted if an action requires this type of data. (2) These data only contains tariffs that were entered into the Utility Rate Database. Since not all tariffs are designed in a format that can be entered into the Database, this list is incomplete - it does not contain all tariffs in the United States. (3) These data may have changed since this list was developed (4) Many of the underlying tariffs have additional restrictions or requirements that are not represented here. For example, they may only be available to the agricultural sector or closed to new customers. (5) If there are multiple demand charge elements in a given tariff, the maximum demand charge is the sum of each of the elements at any point in time. Where tiers were present, the highest rate tier was assumed. The value is a maximum for the year, and may be significantly different from demand charge rates at other times in the year. Utility Rate Database: https://openei.org/wiki/Utility_Rate_Database

Electricity: Gross Generation: Non Utilities: Industry: Uttar Pradesh data was reported at 13,957.850 GWh in 2022. This records a decrease from the previous number of 14,195.900 GWh for 2021. Electricity: Gross Generation: Non Utilities: Industry: Uttar Pradesh data is updated yearly, averaging 9,442.435 GWh from Mar 1996 (Median) to 2022, with 26 observations. The data reached an all-time high of 14,195.900 GWh in 2021 and a record low of 4,682.600 GWh in 1996. Electricity: Gross Generation: Non Utilities: Industry: Uttar Pradesh data remains active status in CEIC and is reported by Central Electricity Authority. The data is categorized under India Premium Database’s Energy Sector – Table IN.RBC027: Electricity: Gross Generation: Non Utilities: by States.

The US Federal Energy Regulatory Commission (FERC) Form 1 database, covering the years 1994-2018. Converted from a large collection of annual Microsoft Visual FoxPro databases into a single combined SQLite database. See the README.md file for details.

In this article, the 3 sets of Real European test feeders: industrial which is integrated medium and low voltage, rural low voltage, and urban low voltage networks are proposed by using real data of GIS and smart meter readings obtained from a distribution company. The authors provide the real mathematical OpenDSS model of the three standards as master files with their corresponding real smart meter readings and topological data in database.

Each of the three proposed networks has different network features. The industrial network is comprised of both medium and low voltage areas with 2888 nodes, 777 buses, and 556 lines. It addresses 165 low voltage and 26 medium voltage industrial customers using 22 distribution transformers. In the rural network, there are 18599 nodes, 4650 buses, and 4291 lines to supply 2731 end customers. While 26951 nodes, 6738 buses, and 5905 lines are found in the urban network that electrifies 35297 low voltage customers. For rural and urban networks 68 distribution transformers are used in each of the networks to address their customers with both single and three phase systems.

The movement of decarbonization leads to comprise several advanced and smart devices at electricity society and enhancing the application demand response systems. Mainly, deployment of different flexible devices such as EV, heat pump, distribution generation in the distribution system takes the existing system to higher level of complication. Hence, that drives distribution grid system to enter to revolutionary transition which is digitalization of the system, to enable real time management of distribution system as it is undergoing through huge complexity. Such systems requires real mathematical model of distribution network therefore this three different test cases are developed. Majority of the existing test systems are synthetic and not representing the real system of the European network. In addition to being limited quantitative wise and for a specific problem solving, their is a lack of integrated real European testcase which incorporates both the low voltage and medium voltage networks. To fill the gap authors develop the test feeders that address industrial, rural and urban areas which is significantly important for researchers. Here, the corresponding OpenDSS model and demand profiles extracted from smart meters of each standards archived in their 'Master' and 'PQ_csv' folders, respectively. In addition, their topological data is provided in their associated databases. The detail description about the data set and all the development are contained in a paper with the same title of the dataset that it is under review and will be linked to this dataset.