This dataset presents a unified, cross-continental time-series day-ahead electricity prices compiled from major wholesale markets across Asia, Europe, North America, South America, and Oceania. The dataset offers a standardized format that supports time-series forecasting and enables robust comparative analysis across diverse global electricity markets.

Germany Electricity decreased 21.14 EUR/MWh or 18.27% since the beginning of 2025, according to the latest spot benchmarks offered by sellers to buyers priced in megawatt hour (MWh). This dataset includes a chart with historical data for Germany Electricity Price.

The Electricity Supply industry has developed considerably since its liberalisation in 1999. Following a period in which the Big Six suppliers dominated, energy regulator Ofgem endeavoured to introduce greater competition to the market as part of attempts to drive down energy bills. Major mergers and acquisitions effectively brought the dominance of the former Big Six suppliers to an end at the end of 2019-20. Along with weakening electricity consumption, swelling competition has applied further pressure on revenue in recent years. Electricity suppliers' revenue is slated to climb at a compound annual rate of 6.2% to reach £52.1 billion over the five years through 2025-26. With suppliers bound by the energy price cap, soaring wholesale prices led to widening operating losses in 2021-22, albeit with a modest revenue recovery from pandemic-induced lows. A renewed spike in wholesale prices led to a continued wave of insolvencies among energy suppliers going into 2022-23, with 31 suppliers falling victim to the energy crisis. Soaring non-domestic energy bills and significant hikes to the SVT price cap spurred significant revenue growth in 2022-23, while the transfer of customer accounts from failed suppliers reinstated the dominance of major suppliers. The introduction of the Energy Price Guarantee (EPG) and support for business energy customers prevented energy prices from spiralling out of control going into 2023-24. A faster-than-anticipated drop in wholesale electricity prices has since eased pressure on operating profit and caused revenue to come-down. However, wholesale prices and energy bills remain significantly above levels seen prior to the energy crisis. Revenue is forecast to decline by 4.5% in the current year. Revenue is forecast to creep up at a compound annual rate of 0.1% over the five years through 2030-31, reaching £52.3 billion. Prices will remain elevated in the medium term as concerns surrounding supplies of Russian fossil fuels into Europe inflate wholesale costs. Wholesale prices are set to stabilise in the long term, spurring tariff reductions. The continued drop in electricity consumption is also set to limit growth prospects in the coming years.

Day-ahead electricity price and demand for CAISO between 2016 to 2021. Also included is net generation from solar for California (region) hourly.

https://zenodo.org/records/8409943

Annual data on the average price of retail electricity to consumers. Data organized by U.S. state and by provider, i.e., total electric industry, full-service providers, restructured retail service providers, energy-only providers, and delivery-only service. Annual time series extend back to 1990. Based on Form EIA-861 data.

FR: Electricity Price: NH: Between 70000 & 149999 MwH: incl All Taxes & Levies data was reported at 0.094 EUR/MWh in Dec 2024. This records an increase from the previous number of 0.085 EUR/MWh for Jun 2024. FR: Electricity Price: NH: Between 70000 & 149999 MwH: incl All Taxes & Levies data is updated semiannually, averaging 0.070 EUR/MWh from Dec 2007 (Median) to Dec 2024, with 35 observations. The data reached an all-time high of 0.157 EUR/MWh in Dec 2022 and a record low of 0.061 EUR/MWh in Dec 2017. FR: Electricity Price: NH: Between 70000 & 149999 MwH: incl All Taxes & Levies data remains active status in CEIC and is reported by Eurostat. The data is categorized under Global Database’s France – Table FR.Eurostat: Electricity Price: Non-Household Consumers.

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.

France Electricity decreased 11.29 EUR/MWh or 16.16% since the beginning of 2025, according to the latest spot benchmarks offered by sellers to buyers priced in megawatt hour (MWh). This dataset includes a chart with historical data for France Electricity Price.

Italy Electricity decreased 17.54 EUR/MWh or 12.73% since the beginning of 2025, according to the latest spot benchmarks offered by sellers to buyers priced in megawatt hour (MWh). This dataset includes a chart with historical data for Italy Electricity Price.

This dataset provides values for ELECTRICITY PRICE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

HI Electricity Prices

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 dataset includes results from the MARKAL model. A technology that generates electricity from a combination of solar and natural gas energy, called ISCC, is evaluated. Results include electricity generation mixes and emissions projections. This dataset is associated with the following publication: Brown, K., and D. Loughlin. Market Sensitivity of Solar-Fossil Hybrid Electricity Generation to Price, Efficiency, Policy, and Fuel Projections. CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY. Springer-Verlag, New York, NY, USA, 21(3): 591-604, (2019).

Electricity prices components for household consumers -annual data (from 2007 onwards). Annual values taken from the Eurostat dataset with a national level resolution.

Electric power selling price index (EPSPI). Monthly data are available from January 1981. The table presents data for the most recent reference period and the last four periods. The base period for the index is (2014=100).

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.

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/).

This indicator presents electricity prices charged to final consumers. Electricity prices for non-household consumers are defined as follows: Average national price in Euro per kWh without taxes applicable for the first semester of each year for medium size industrial consumers (Consumption Band Ic with annual consumption between 500 and 2000 MWh). Electricity prices for household consumers are defined as follows: Average national price in Euro per kWh including taxes and levies applicable for the first semester of each year for medium size household consumers (Consumption Band Dc with annual consumption between 2500 and 5000 kWh).