A comprehensive dataset of average residential, commercial, and combined electricity rates in cents per kWh for all 50 U.S. states and Washington D.C.

Graph and download economic data for Average Price: Electricity per Kilowatt-Hour in U.S. City Average (APU000072610) from Nov 1978 to Aug 2025 about electricity, energy, retail, price, and USA.

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/).

Ireland, Italy, and Germany had some of the highest household electricity prices worldwide, as of March 2025. At the time, Irish households were charged around 0.45 U.S. dollars per kilowatt-hour, while in Italy, the price stood at 0.43 U.S. dollars per kilowatt-hour. By comparison, in Russia, residents paid almost 10 times less. What is behind electricity prices? Electricity prices vary widely across the world and sometimes even within a country itself, depending on factors like infrastructure, geography, and politically determined taxes and levies. For example, in Denmark, Belgium, and Sweden, taxes constitute a significant portion of residential end-user electricity prices. Reliance on fossil fuel imports Meanwhile, thanks to their great crude oil and natural gas production output, countries like Iran, Qatar, and Russia enjoy some of the cheapest electricity prices in the world. Here, the average household pays less than 0.1 U.S. dollars per kilowatt-hour. In contrast, countries heavily reliant on fossil fuel imports for electricity generation are more vulnerable to market price fluctuations.

Hawaii is the state with the highest household electricity price in the United States. In February 2025, the average retail price of electricity for Hawaiian residences amounted to 41.11 U.S. cents per kilowatt-hour. California followed in second, with 32.41 U.S. cents per kilowatt-hour. Meanwhile, Utah registered the lowest price in the period, at around 12.41 U.S. cents per kilowatt-hour. Why is electricity so expensive in Hawaii? Fossil fuels, and specifically oil, account for approximately 80 percent of Hawaii’s electricity mix, so the electricity price in this state can be roughly brought down to the price of oil in the country. Oil was by far the most expensive fossil fuel used for electricity generation in the country. As Hawaii depends on oil imports, the cost of transportation and infrastructure must be added to the oil price. Electricity prices worldwide The U.S. retail price for electricity increased almost every year since 1990. In 2024, it stood at 13 U.S. cents per kilowatt-hour, almost double the charge put on electricity back in 1990. However, household electricity prices are around 25 U.S. dollar cents per kilowatt-hour lower in the U.S. when compared to European countries reliant on energy imports, such as Germany and Italy.

Gas peaking and nuclear power had the highest unsubsidized levelized costs of energy (LCOE) generation in the United States in 2025. Their LCOE averaged around *** and *** U.S. dollars per megawatt-hour, respectively. The high upfront capital costs for the construction of nuclear plants and the investment in gas infrastructure accounted for these figures. LCOE for solar PV Levelized cost of electricity or energy generation (LCOE) is a measure used to compare cost efficiency of different electricity-generating technologies. It describes the average expense of building and maintaining a power plant divided by its total power output over the facility’s lifetime. The global levelized cost of electricity for solar PV averaged **** U.S. dollars per kilowatt-hour in 2024. The economic viability of solar PV installations is dependent on factors largely centering around topography and the predominant weather pattern at the installation site. In regions with high sunshine duration, installing solar PV would come with lower LCOE’s as electricity production may be higher. As countries may stretch across highly variable topography and even across climate zones, solar PV LCOE may also vary greatly within a country. The U.S. has some of the lowest LCOE’s for utility-scale solar PV. Capital costs by energy technology In terms of capital costs – the one-time expense arising from the purchase of land, construction material, and building of the power plant for new power plants expected to come live in 2030, battery storage systems are the most expensive in the United States. They had an estimated levelized capital costs of roughly **** U.S. dollars per megawatt-hour as of April 2025. Nuclear energy and combustion turbines followed, while capital costs for solar PV are comparatively low.

State-level data on all energy sources. Data on production, consumption, reserves, stocks, prices, imports, and exports. Data are collated from state-specific data reported elsewhere on the EIA website and are the most recent values available. Data on U.S. territories also available.

NextEra Energy was the leading electric utility in the U.S. as of June 2024, based on market value, at almost *** billion U.S. dollars. Georgia-based electric utility Southern Company ranked second that same year, with a market value of ** billion dollars. A new age of energy company In addition to leading the ranking in the U.S., NextEra Energy was also the leading electric utility worldwide in terms of market value as of that month. Along with companies such as Iberdrola, Enel, Orsted, and EDF Energy, NextEra Energy is part of a new generation of energy giants nicknamed green supermajors. With large investments in renewable electricity capacity, these utilities have seen their market value soar recently, challenging established energy companies operating in the oil sector such as Chevron and Shell. Electric utilities in the United States Utilities provide the public with vital commodities or services such as power, natural gas, and water. In 2023, there were over ***** electricity providers operating in the U.S., of which cooperatives accounted for the largest share. While investor-owned utilities represented less than ** percent of electricity providers in the country that year, they usually serve the highest number of customers. In 2023, more than *** million customers were served by the U.S. electric industry.

Electricity prices in Europe are expected to remain volatile through 2025, with Italy projected to have some of the highest rates among major European economies. This trend reflects the ongoing challenges in the energy sector, including the transition to renewable sources and the impact of geopolitical events on supply chains. Despite efforts to stabilize the market, prices still have not returned to pre-pandemic levels, such as in countries like Italy, where prices are forecast to reach ****** euros per megawatt hour in August 2025. Natural gas futures shaping electricity costs The electricity market's future trajectory is closely tied to natural gas prices, a key component in power generation. Dutch TTF gas futures, a benchmark for European natural gas prices, are projected to be ***** euros per megawatt hour in July 2025. The reduced output from the Groningen gas field and increased reliance on imports further complicate the pricing landscape, potentially contributing to higher electricity costs in countries like Italy. Regional disparities and global market influences While European electricity prices remain high, significant regional differences persist. For instance, natural gas prices in the United States are expected to be roughly one-third of those in Europe by March 2025, at **** U.S. dollars per million British thermal units. This stark contrast highlights the impact of domestic production capabilities on global natural gas prices. Europe's greater reliance on imports, particularly in the aftermath of geopolitical tensions and the shift away from Russian gas, continues to keep prices elevated compared to more self-sufficient markets. As a result, countries like Italy may face sustained pressure on electricity prices due to their position within the broader European energy market. As of August 2025, electricity prices in Italy have decreased to ****** euros per megawatt hour, reflecting ongoing volatility in the market.

This repository includes python scripts and input/output data associated with the following publication:

[1] Brown, P.R.; O'Sullivan, F. "Shaping photovoltaic array output to align with changing wholesale electricity price profiles." Applied Energy 2019. https://doi.org/10.1016/j.apenergy.2019.113734

Please cite reference [1] for full documentation if the contents of this repository are used for subsequent work.

Some of the scripts and data are also used in the following working paper:

[2] Brown, P.R.; O'Sullivan, F. "Spatial and temporal variation in the value of solar power across United States electricity markets". Working Paper, MIT Center for Energy and Environmental Policy Research. 2019. http://ceepr.mit.edu/publications/working-papers/705

All code is in python 3 and relies on a number of dependencies that can be installed using pip or conda.

Contents

• pvvm.zip : Python module with functions for modeling PV generation, calculating PV revenues and capacity factors, and optimizing PV orientation.
• notebooks.zip : Jupyter notebooks, including:
  • pvvm-pvtos-data.ipynb: Example scripts used to download and clean input LMP data, determine LMP node locations, and reproduce some figures in reference [1]
  • pvvm-pvtos-analysis.ipynb: Example scripts used to perform the calculations and reproduce some figures in reference [1]
  • pvvm-pvtos-plots.ipynb: Scripts used to produce additional figures in reference [1]
  • pvvm-example-generation.ipynb: Example scripts demonstrating the usage of the PV generation model and orientation optimization
• html.zip : Static images of the above Jupyter notebooks for viewing without a python kernel
• data.zip : Day-ahead and real-time nodal locational marginal prices (LMPs) for CAISO, ERCOT, MISO, NYISO, and ISONE.
  • At the time of publication of this repository, permission had not been received from PJM to republish their LMP data. If permission is received in the future, a new version of this repository will linked here with the complete dataset.
• results.zip : Simulation results associated with reference [1] above, including modeled revenue, capacity factor, and optimized orientations for PV systems at all LMP nodes

Data terms and usage notes

• ISO LMP data are used with permission from the different ISOs. Adapting the MIT License (https://opensource.org/licenses/MIT), "The data are provided 'as is', without warranty of any kind, express or implied, including but not limited to the warranties of merchantibility, fitness for a particular purpose and noninfringement. In no event shall the authors or sources be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the data or other dealings with the data." Copyright and usage permissions for the LMP data are available on the ISO websites, linked below.
• ISO-specific notes:
  • CAISO data from http://oasis.caiso.com/mrioasis/logon.do are used pursuant to the terms at http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse.
  • ERCOT data are from http://www.ercot.com/mktinfo/prices.
  • MISO data are from https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/ and https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/.
  • PJM data were originally downloaded from https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx and https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx. At the time of this writing these data are currently hosted at https://dataminer2.pjm.com/feed/da_hrl_lmps and https://dataminer2.pjm.com/feed/rt_hrl_lmps.
  • NYISO data from http://mis.nyiso.com/public/ are used subject to the disclaimer at https://www.nyiso.com/legal-notice.
  • ISONE data are from https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly and https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final. The Material is provided on an "as is" basis. ISO New England Inc., to the fullest extent permitted by law, disclaims all warranties, either express or implied, statutory or otherwise, including but not limited to the implied warranties of merchantability, non-infringement of third parties' rights, and fitness for particular purpose. Without limiting the foregoing, ISO New England Inc. makes no representations or warranties about the accuracy, reliability, completeness, date, or timeliness of the Material. ISO New England Inc. shall have no liability to you, your employer or any other third party based on your use of or reliance on the Material.
• Data workup: LMP data were downloaded directly from the ISOs using scripts similar to the pvvm.data.download_lmps() function (see below for caveats), then repackaged into single-node single-year files using the pvvm.data.nodalize() function. These single-node single-year files were then combined into the dataframes included in this repository, using the procedure shown in the pvvm-pvtos-data.ipynb notebook for MISO. We provide these yearly dataframes, rather than the long-form data, to minimize file size and number. These dataframes can be unpacked into the single-node files used in the analysis using the pvvm.data.copylmps() function.

Code license and usage notes

• Code (*.py and *.ipynb files) is provided under the MIT License, as specified in the pvvm/LICENSE file.
• Updates to the code, if any, will be posted in the non-static repository at https://github.com/patrickbrown4/pvvm_pvtos. The code in the present repository has the following version-specific dependencies:
  • matplotlib: 3.0.3
  • numpy: 1.16.2
  • pandas: 0.24.2
  • pvlib: 0.6.1
  • scipy: 1.2.1
  • tqdm: 4.31.1
• To use the NSRDB download functions, modify the "settings.py" file to insert a valid NSRDB API key, which can be requested from https://developer.nrel.gov/signup/. Locations can be specified by passing latitude, longitude floats to pvvm.data.downloadNSRDBfile(), or by passing a string googlemaps query to pvvm.io.queryNSRDBfile(). To use the googlemaps functionality, request a googlemaps API key (https://developers.google.com/maps/documentation/javascript/get-api-key) and insert it in the "settings.py" file.
• Note that many of the ISO websites have changed in the time since the functions in the pvvm.data module were written and the LMP data used in the above papers were downloaded. As such, the pvvm.data.download_lmps() function no longer works for all ISOs and years. We provide this function to illustrate the general procedure used, and do not intend to maintain it or keep it up to date with the changing ISO websites. For up-to-date functions for accessing ISO data, the following repository (no connection to the present work) may be helpful: https://github.com/catalyst-cooperative/pudl.

Graph and download economic data for Global price of Energy index (PNRGINDEXM) from Jan 1992 to Jun 2025 about energy, World, indexes, and price.

This repository includes python scripts and input/output data associated with the following publication:

[1] Brown, P.R.; O'Sullivan, F. "Spatial and temporal variation in the value of solar power across United States Electricity Markets". Renewable & Sustainable Energy Reviews 2019. https://doi.org/10.1016/j.rser.2019.109594

Please cite reference [1] for full documentation if the contents of this repository are used for subsequent work.

Many of the scripts, data, and descriptive text in this repository are shared with the following publication:

[2] Brown, P.R.; O'Sullivan, F. "Shaping photovoltaic array output to align with changing wholesale electricity price profiles". Applied Energy 2019, 256, 113734. https://doi.org/10.1016/j.apenergy.2019.113734

All code is in python 3 and relies on a number of dependencies that can be installed using pip or conda.

Contents

pvvm/*.py : Python module with functions for modeling PV generation and calculating PV energy revenue, capacity value, and emissions offset.

notebooks/*.ipynb : Jupyter notebooks, including:

pvvm-vos-data.ipynb: Example scripts used to download and clean input LMP data, determine LMP node locations, assign nodes to capacity zones, download NSRDB input data, and reproduce some figures in [1]

pvvm-example-generation.ipynb: Example scripts demonstrating the use of the PV generation model and a sensitivity analysis of PV generator assumptions

pvvm-example-plots.ipynb: Example scripts demonstrating different plotting functions

validate-pv-monthly-eia.ipynb: Scripts and plots for comparing modeled PV generation with monthly generation reported in EIA forms 860 and 923, as discussed in SI Note 3 of [1]

validate-pv-hourly-pvdaq.ipynb: Scripts and plots for comparing modeled PV generation with hourly generation reported in NREL PVDAQ database, as discussed in SI Note 3 of [1]

pvvm-energyvalue.ipynb: Scripts for calculating the wholesale energy market revenues of PV and reproducing some figures in [1]

pvvm-capacityvalue.ipynb: Scripts for calculating the capacity credit and capacity revenues of PV and reproducing some figures in [1]

pvvm-emissionsvalue.ipynb: Scripts for calculating the emissions offset of PV and reproducing some figures in [1]

pvvm-breakeven.ipynb: Scripts for calculating the breakeven upfront cost and carbon price for PV and reproducing some figures in [1]

html/*.html : Static images of the above Jupyter notebooks for viewing without a python kernel

data/lmp/*.gz : Day-ahead nodal locational marginal prices (LMPs) and marginal costs of energy (MCE), congestion (MCC), and losses (MCL) for CAISO, ERCOT, MISO, NYISO, and ISONE.

At the time of publication of this repository, permission had not been received from PJM to republish their LMP data. If permission is received in the future, a new version of this repository will be linked here with the complete dataset.

results/*.csv.gz : Simulation results associated with [1], including modeled energy revenue, capacity credit and revenue, emissions offsets, and breakeven costs for PV systems at all LMP nodes

Data notes

ISO LMP data are used with permission from the different ISOs. Adapting the MIT License (https://opensource.org/licenses/MIT), "The data are provided 'as is', without warranty of any kind, express or implied, including but not limited to the warranties of merchantibility, fitness for a particular purpose and noninfringement. In no event shall the authors or sources be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the data or other dealings with the data." Copyright and usage permissions for the LMP data are available on the ISO websites, linked below.

ISO-specific notes on LMP data:

CAISO data from http://oasis.caiso.com/mrioasis/logon.do are used pursuant to the terms at http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse.

ERCOT data are from http://www.ercot.com/mktinfo/prices.

MISO data are from https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/ and https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/.

PJM data were originally downloaded from https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx and https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx. At the time of this writing these data are currently hosted at https://dataminer2.pjm.com/feed/da_hrl_lmps and https://dataminer2.pjm.com/feed/rt_hrl_lmps.

NYISO data from http://mis.nyiso.com/public/ are used subject to the disclaimer at https://www.nyiso.com/legal-notice.

ISONE data are from https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly and https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final. The Material is provided on an "as is" basis. ISO New England Inc., to the fullest extent permitted by law, disclaims all warranties, either express or implied, statutory or otherwise, including but not limited to the implied warranties of merchantability, non-infringement of third parties' rights, and fitness for particular purpose. Without limiting the foregoing, ISO New England Inc. makes no representations or warranties about the accuracy, reliability, completeness, date, or timeliness of the Material. ISO New England Inc. shall have no liability to you, your employer or any other third party based on your use of or reliance on the Material.

Data workup: LMP data were downloaded directly from the ISOs using scripts similar to the pvvm.data.download_lmps() function (see below for caveats), then repackaged into single-node single-year files using the pvvm.data.nodalize() function. These single-node single-year files were then combined into the dataframes included in this repository, using the procedure shown in the pvvm-vos-data.ipynb notebook for MISO. We provide these yearly dataframes, rather than the long-form data, to minimize file size and number. These dataframes can be unpacked into the single-node files used in the analysis using the pvvm.data.copylmps() function.

Usage notes

Code is provided under the MIT License, as specified in the pvvm/LICENSE file and at the top of each *.py file.

Updates to the code, if any, will be posted in the non-static repository at https://github.com/patrickbrown4/pvvm_vos. The code in the present repository has the following version-specific dependencies:

matplotlib: 3.0.3

numpy: 1.16.2

pandas: 0.24.2

pvlib: 0.6.1

scipy: 1.2.1

tqdm: 4.31.1

To use the NSRDB download functions, you will need to modify the "settings.py" file to insert a valid NSRDB API key, which can be requested from https://developer.nrel.gov/signup/. Locations can be specified by passing (latitude, longitude) floats to pvvm.data.downloadNSRDBfile(), or by passing a string googlemaps query to pvvm.io.queryNSRDBfile(). To use the googlemaps functionality, you will need to request a googlemaps API key (https://developers.google.com/maps/documentation/javascript/get-api-key) and insert it in the "settings.py" file.

Note that many of the ISO websites have changed in the time since the functions in the pvvm.data module were written and the LMP data used in the above papers were downloaded. As such, the pvvm.data.download_lmps() function no longer works for all ISOs and years. We provide this function to illustrate the general procedure used, and do not intend to maintain it or keep it up to date with the changing ISO websites. For up-to-date functions for accessing ISO data, the following repository (no connection to the present work) may be helpful: https://github.com/catalyst-cooperative/pudl.

The Office of Electricity (OE) provides national leadership to ensure that the Nation’s energy delivery system is secure, resilient and reliable. OE works to develop new technologies to improve the infrastructure that brings electricity into our homes, offices, and factories, and the federal and state electricity policies and programs that shape electricity system planning and market operations. OE also works to bolster the resiliency of the electric grid and assists with restoration when major energy supply interruptions occur.

This data packet contains supply curves, hourly generation profiles, and a composite siting exclusion TIFF for utility-scale PV across the contiguous United States. The supply curves offer comprehensive metrics such as capacity (MW), generation (MWh), levelized cost of energy (LCOE), levelized cost of transmission (LCOT), and more for each reV site (~60,000 sites). Hourly generation profiles are available for each reV site and can be matched to the available capacity in the supply curve (refer to the Jupyter Notebook). The composite exclusion TIFF is a single file that delineates areas where PV installations are permissible based on various siting assumptions. This data packet contains information for the Reference and Limited siting scenarios. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

This data packet contains supply curves, hourly generation profiles, and a composite siting exclusion TIFF for utility-scale PV across the contiguous United States. The supply curves offer comprehensive metrics such as capacity (MW), generation (MWh), levelized cost of energy (LCOE), levelized cost of transmission (LCOT), and more for each reV site (~60,000 sites). Hourly generation profiles are available for each reV site and can be matched to the available capacity in the supply curve (refer to the Jupyter Notebook). The composite exclusion TIFF is a single file that delineates areas where PV installations are permissible based on various siting assumptions. This data packet contains information for the Reference and Limited siting scenarios. The PV system cost and parameters were obtained from the Annual Technology Baseline (ATB) 2023. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Gabe Zuckerman, Pavlo Pinchuk, Michael Gleason, Marie Rivers, Owen Roberts, Travis Williams, Donna Heimiller, Sophie-Min Thomson, Trieu Mai, and Wesley Cole. 2025. Renewable Energy Technical Potential and Supply Curves for the Contiguous United States: 2024 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-91900.

Graph and download economic data for US Regular All Formulations Gas Price (GASREGW) from 1990-08-20 to 2025-10-13 about gas, commodities, and USA.

The City and County Energy Profiles lookup table provides modeled electricity and natural gas consumption and expenditures, on-road vehicle fuel consumption, vehicle miles traveled, and associated emissions for each U.S. city and county. Please note this data is modeled and more precise data may be available from regional, state, or other sources. The modeling approach for electricity and natural gas is described in Sector-Specific Methodologies for Subnational Energy Modeling: https://www.nrel.gov/docs/fy19osti/72748.pdf. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and complements the wealth of data, maps, and charts on the State and Local Planning for Energy (SLOPE) platform, available at the "Explore State and Local Energy Data on SLOPE" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

Monthly average hourly CO2, NOx, and SO2 emission factors for each U.S. eGRID subregion. This project utilized GridViewTM, an electric grid dispatch software package, to estimate hourly emission factors for all of the eGRID subregions in the continental United States. These factors took into account electricity imports and exports across the eGRID subregion boundary, and included estimated transmission and distribution (T) losses. Emission types accounted for included carbon dioxide (CO2), nitrogen oxides (NOx), and sulfur dioxide (SO2).Data reported as part of this project include hourly average, minimum, and maximum emission factors by month; that is, the average, minimum, and maximum emission factor for the same hour of each day in a month. Please note that the data are reported in lbs/MWh, where the MWh value reported is site electricity use (the actual electricity used at the building) and the pounds of emissions reported are the emissions created at the generator to meet the building load, including transmission and distribution losses. The demand profiles used to generate the data pertain to the following years: eastern interconnect - 2005; Electricity Reliability Council of Texas (ERCOT) - 2008; Western Electricity Coordinating Council (WECC) - 2008.

This dataset displays the price and expenditure figures on both coal and retail electricity use in the United States. This information is broken down to a state level. The figures on prices are in Nominal dollars per million BTU and the Expenditures being on a Million Nominal Dollars Scale. This information was made available by the EIA: Energy Information Administration. This information is for the year 2005.