Most US consumers are charged near-constant retail price for electricity, despite substantial hourly variation in the wholesale market price. This paper evaluates the first program to expose residential consumers to hourly real-time pricing (RTP). I find that enrolled households are statistically significantly price elastic and that consumers responded by conserving energy during peak hours, but remarkably did not increase average consumption during off-peak times. The program increased consumers surplus by $10 per household per year. While this is only one to two percent of electricity costs, it illustrates a potential additional benefit from investment in retail Smart Grid applications, including the advanced electricity meters required to observe a household's hourly consumption.

The retail price for electricity in the United States stood at an average of ***** U.S. dollar cents per kilowatt-hour in 2024. This is the highest figure reported in the indicated period. Nevertheless, the U.S. still has one of the lowest electricity prices worldwide. As a major producer of primary energy, energy prices are lower than in countries that are more reliant on imports or impose higher taxes. Regional variations and sector disparities The impact of rising electricity costs across U.S. states is not uniform. Hawaii stands out with the highest household electricity price, reaching a staggering ***** U.S. cents per kilowatt-hour in September 2024. This stark contrast is primarily due to Hawaii's heavy reliance on imported oil for power generation. On the other hand, states like Utah benefit from lower rates, with prices around **** U.S. cents per kilowatt-hour. Regarding U.S. prices by sector, residential customers have borne the brunt of price increases, paying an average of ***** U.S. cents per kilowatt-hour in 2023, significantly more than commercial and industrial sectors. Factors driving price increases Several factors contribute to the upward trend in electricity prices. The integration of renewable energy sources, investments in smart grid technologies, and rising peak demand all play a role. Additionally, the global energy crisis of 2022 and natural disasters affecting power infrastructure have put pressure on the electric utility industry. The close connection between U.S. electricity prices and natural gas markets also influences rates, as domestic prices are affected by higher-paying international markets. Looking ahead, projections suggest a continued increase in electricity prices, with residential rates expected to grow by *** percent in 2024, driven by factors such as increased demand and the ongoing effects of climate change.

Germany Electricity decreased 29.73 EUR/MWh or 25.69% 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.

An experiment was conducted to understand if and how households change their power consumption in response to variable hourly electricity prices. The data were collected from several Norwegian regions, and various price signals were tested over two winter periods from early 2020 to spring 2021. The dataset includes hourly consumption data of all participating households and answers to three surveys about household characteristics such as electric appliances, living conditions, socio-demographic variables, and willingness to be flexible. Temperature data are added to the dataset from public sources. This comprehensive dataset can be used for in-depth analysis of household flexibility potential. Furthermore, subgroups, such as low-income households or highly electrified households with electricity as a primary heating source, can be investigated to enhance the understanding of how these are affected by variable power prices.

The dataset is described in detail in an accompanying data article in Data in Brief: A rich dataset of hourly residential electricity consumption data and survey answers from the iFlex dynamic pricing experiment - ScienceDirect

Supplementary figures containing the survey results are available here: Supplementary result diagrams from household surveys on implicit demand response (zenodo.org)

Survey answers in Norwegian are available here: iFleks-prosjekt: Spørreundersøkelser med husholdninger og næringsliv om forbruksrespons på elektrisitetspriser

Daily data showing the System Price of electricity, and rolling seven-day average, in Great Britain. These are official statistics in development. Source: Elexon.

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

Real time pricing (RTP) is often promoted as a mechanism to improve the economic efficiency of the electricity system. However, many regulators have been hesitant to adopt RTP due to concerns about exposing customers to extreme price swings. To balance these concerns, this paper proposes a methodology for establishing price controls, based on the supply of demand-side flexibility in the system. As an illustrative example, we measure price responsiveness using an agent-based simulation model that is representative of the ERCOT market. The model is composed of a distribution feeder that has 250 customers with active agents controlling their HVAC systems in response to the historical ERCOT RTP with an artificially added high-price event. These agents are subjected to increasing electricity prices during the event, which we then use to create a supply curve for demand-side resources in our modeled scarcity event. We set potential price caps at points on the supply curve where customers’ have exhausted their flexible capacity. Using historical prices, we examine the systemic costs of these price caps, and present regulatory options for recouping them. Utilities and regulators interested in limiting consumer risk from dynamic pricing can utilize these methods to develop rate structures and encourage conservation. The attached data upload allows for the duplication or modification of the analysis performed in this study.

Access electric power futures products from 10 European electricity markets, sourced directly from ICE Endex, the leading energy exchange in continental Europe.

Our continuous contract symbology is a notation that maps to an actual, tradable instrument on any given date. The prices returned are real, unadjusted prices. We do not create a synthetic time series by adjusting the prices to remove jumps during rollovers.

UK Electricity decreased 27.05 GBP/MWh or 26.40% 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 the United Kingdom Electricity Price.

Real time pricing (RTP) is often promoted as a mechanism to improve the economic efficiency of the electricity system. However, many regulators have been hesitant to adopt RTP due to concerns about exposing customers to extreme price swings. To balance these concerns, this paper proposes a methodology for establishing price controls, based on the supply of demand-side flexibility in the system. As an illustrative example, we measure price responsiveness using an agent-based simulation model that is representative of the ERCOT market. The model is composed of a distribution feeder that has 250 customers with active agents controlling their HVAC systems in response to the historical ERCOT RTP with an artificially added high-price event. These agents are subjected to increasing electricity prices during the event, which we then use to create a supply curve for demand-side resources in our modeled scarcity event. We set potential price caps at points on the supply curve where customers’ have exhausted their flexible capacity. Using historical prices, we examine the systemic costs of these price caps, and present regulatory options for recouping them. Utilities and regulators interested in limiting consumer risk from dynamic pricing can utilize these methods to develop rate structures and encourage conservation.more » The attached data upload allows for the duplication or modification of the analysis performed in this study.« less

Hourly price data both day-ahead and real time for PJM and MISO electricity markets, going from 00:00 of 1st January 2014 to 23:00 of 9th March. The data has been collected, cleaned and provided by Invenia Technical Computing Corporation; when a price was not available, it has been replaced with a NaN value. The file contains a dataset in Matlab 2012 .mat file extension and a readme text file. The dataset contains day-ahead price data for the MCC, MEC and LMP time series for both markets. Each time series is in a matrix format - Node x Time - with variable names da_(mcc/mec/lmp)_(miso/pjm).

Under real-time pricing, a network operator sets the price level for a period according to a predefined scheme which depends on the state of demand and costs, and announces this price shortly before the period begins. The French state-owned electric utility experimented with a six-rate real-time tariff, which divides the year into three types of days and each day into two periods. The number of days of each type is known in advance to the consumer, but the type of any particular day is announced only at the end of the preceding day. In order to evaluate the responsiveness of customers to this pricing option, we estimate the Frisch demand functions for daily electricity consumption, derived from a simple dynamic model based on an additively separable intertemporal utility function. As the marginal utility of expenditure which enters the Frisch demands follows a known stochastic process, the econometric model has a state-space representation. We can then apply the Kalman filter to compute the log-likelihood function associated with each consumer's time series of electricity consumption. The main result of the analysis is that the real-time tariff improves the welfare of a majority of consumers participating in the experiment.

Electricity Trading Platform Market Outlook

The global electricity trading platform market is poised for substantial growth, with a market size of approximately USD 6.5 billion in 2023, projected to reach around USD 14.2 billion by 2032, reflecting a robust CAGR of 8.9% during the forecast period. This growth is fueled by various factors including the increasing penetration of renewable energy sources, advancements in smart grid technologies, and the rising need for energy efficiency and optimization.

One of the primary growth drivers for the electricity trading platform market is the increasing integration of renewable energy sources into the power grid. As countries worldwide strive to meet their sustainability goals and reduce carbon emissions, the adoption of renewable energy such as wind, solar, and hydroelectric power is accelerating. This shift necessitates sophisticated trading platforms to manage the intermittent and decentralized nature of renewable energy production, ensuring a balanced and efficient energy market.

Additionally, the advancements in smart grid technologies are playing a crucial role in the expansion of the electricity trading platform market. Smart grids leverage digital communication technology to detect and react to local changes in electricity usage, enhancing the efficiency and reliability of the power grid. These technologies enable real-time data exchange, advanced analytics, and automated control, all of which are essential for the effective functioning of electricity trading platforms. The integration of Internet of Things (IoT) devices and artificial intelligence (AI) further augments the capabilities of these platforms, facilitating better demand-response mechanisms and predictive maintenance.

Moreover, the growing demand for energy efficiency and optimization is driving the need for electricity trading platforms. With increasing energy costs and heightened awareness of environmental impacts, both consumers and businesses are seeking ways to optimize energy usage. Electricity trading platforms provide the tools and data analytics necessary to achieve this, enabling participants to buy and sell electricity based on real-time market conditions, thus maximizing efficiency and cost savings. This trend is particularly prominent in the industrial and commercial sectors, where energy consumption is substantial and the potential for optimization is significant.

Regionally, North America and Europe are leading the market due to their early adoption of renewable energy technologies and advanced grid infrastructures. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period. This is attributed to rapid industrialization, urbanization, and significant investments in smart grid projects across countries like China, India, and Japan. The Middle East & Africa and Latin America are also emerging markets, with increasing focus on renewable energy and infrastructural developments.

Type Analysis

The electricity trading platform market by type encompasses Day-Ahead Trading, Intraday Trading, Balancing Market, and Others. Day-Ahead Trading is one of the most prevalent types, where market participants commit to buy or sell quantities of electricity for the next day. This type of trading allows for better planning and scheduling of power generation and consumption, thereby enhancing grid stability. The increasing complexity of balancing supply and demand due to the integration of renewable energy sources has bolstered the need for efficient day-ahead trading mechanisms.

Intraday Trading, on the other hand, deals with the trading of electricity within the same day. This type of trading is gaining traction due to its ability to provide more flexibility and responsiveness to sudden changes in electricity demand or supply. With the rising penetration of variable renewable energy sources like solar and wind, intraday trading is becoming crucial for maintaining grid reliability and avoiding imbalances. The ability to make quick adjustments in response to real-time market signals makes it an essential component of modern electricity markets.

The Balancing Market is designed to ensure that the supply and demand of electricity are balanced in real-time. It plays a critical role in maintaining the stability and reliability of the power grid. Participants in the balancing market provide ancillary services such as frequency regulation and reserve power to mitigate short-term discrepancies between supply and demand. With the increasing penetration of intermittent renewa

Retail residential electricity prices in the United States have mostly risen over the last decades. In 2023, prices registered a year-over-year growth of 6.3 percent, the highest growth registered since the beginning of the century. Residential prices are projected to continue to grow by two percent in 2024. Drivers of electricity price growth The price of electricity is partially dependent on the various energy sources used for generation, such as coal, gas, oil, renewable energy, or nuclear. In the U.S., electricity prices are highly connected to natural gas prices. As the commodity is exposed to international markets that pay a higher rate, U.S. prices are also expected to rise, as it has been witnessed during the energy crisis in 2022. Electricity demand is also expected to increase, especially in regions that will likely require more heating or cooling as climate change impacts progress, driving up electricity prices. Which states pay the most for electricity? Electricity prices can vary greatly depending on both state and region. Hawaii has the highest electricity prices in the U.S., at roughly 43 U.S. cents per kilowatt-hour as of May 2023, due to the high costs of crude oil used to fuel the state’s electricity. In comparison, Idaho has one of the lowest retail rates. Much of the state’s energy is generated from hydroelectricity, which requires virtually no fuel. In addition, construction costs can be spread out over decades.

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

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Electricity Trading Market Size 2025-2029

The electricity trading market size is forecast to increase by USD 123.5 billion at a CAGR of 6.5% between 2024 and 2029.

The market is witnessing significant growth due to several key trends. The integration of renewable energy sources, such as solar panels and wind turbines, into the grid is a major driver. Energy storage systems are increasingly being adopted to ensure a stable power supply from these intermittent sources. Concurrently, the adoption of energy storage systems addresses key challenges like intermittency, enabling better integration of renewable sources, and bolstering grid resilience. Self-generation of electricity by consumers through microgrids is also gaining popularity, allowing them to sell excess power back to the grid. The entry of new players and collaborations among existing ones are further fueling market growth. These trends reflect the shift towards clean energy and the need for a more decentralized and efficient electricity system.

What will be the Size of the Electricity Trading Market During the Forecast Period?

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The market, a critical component of the global energy industry, functions as a dynamic interplay between wholesale energy markets and traditional financial markets. As a commodity, electricity is bought and sold through various trading mechanisms, including equities, bonds, and real-time auctions. The market's size and direction are influenced by numerous factors, such as power station generation data, system operator demands, and consumer usage patterns. Participants in the market include power station owners, system operators, consumers, and ancillary service providers. Ancillary services, like frequency regulation and spinning reserves, help maintain grid stability. Market design and news reports shape the market's evolution, with initiatives like the European Green Paper and the Lisbon Strategy influencing the industry's direction towards increased sustainability and competition.
Short-term trading, through power purchase agreements and power distribution contracts, plays a significant role in the market's real-time dynamics. Power generation and power distribution are intricately linked, with the former influencing the availability and price of electricity, and the latter affecting demand patterns. Overall, the market is a complex, ever-evolving system that requires a deep understanding of both energy market fundamentals and financial market dynamics.

How is this Electricity Trading Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Day-ahead trading
  Intraday trading


Application

  Industrial
  Commercial
  Residential


Source

  Non-renewable energy
  Renewable energy


Geography

  Europe

    Germany
    UK
    France
    Italy
    Spain


  APAC

    China
    India
    Japan
    South Korea


  North America

    US


  South America



  Middle East and Africa

By Type Insights

The day-ahead trading segment is estimated to witness significant growth during the forecast period.

Day-ahead trading refers to the voluntary, financially binding forward electricity trading that occurs in exchanges such as the European Power Exchange (EPEX Spot) and Energy Exchange Austria (EXAA), as well as through bilateral contracts. This process involves sellers and buyers agreeing on the required volume of electricity for the next day, resulting in a schedule for everyday intervals. However, this schedule is subject to network security constraints and adjustments for real-time conditions and actual electricity supply and demand. Market operators, including ISOs and RTOs, oversee these markets and ensure grid reliability through balancing and ancillary services. Traders, including utilities, energy providers, and professional and institutional traders, participate in these markets to manage price risk, hedge against price volatility, and optimize profitability.

Key factors influencing electricity prices include weather conditions, fuel prices, availability, construction costs, and physical factors. Renewable energy sources, such as wind and solar power, also play a growing role in these markets, with the use of Renewable Energy Certificates and net metering providing consumer protection and incentives for homeowners and sustainable homes. Electricity trading encompasses power generators, power suppliers, consumers, and system operators, with contracts, generation data, and power station dispatch governed by market rules and regulations.

Get a glance at the Electricity Trading Industry report of share of various segments Request Free Sample

The day-ahead tra

This report provides information on theprices of the balancing energy available in Belgium.The quarter-hourly volume is provided for each product category (if the product was actually used). This report contains data for the current day and is refreshed every 15min.This dataset contains data from 22/05/2024 (MARI local go-live) on.

Abstract: Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available.

Source: Georges Hebrail (georges.hebrail '@' edf.fr), Senior Researcher, EDF R&D, Clamart, France Alice Berard, TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France

Data Set Information: This archive contains 2075259 measurements gathered in a house located in Sceaux (7km of Paris, France) between December 2006 and November 2010 (47 months). Notes:

(global_active_power*1000/60 - sub_metering_1 - sub_metering_2 - sub_metering_3) represents the active energy consumed every minute (in watt hour) in the household by electrical equipment not measured in sub-meterings 1, 2 and 3. The dataset contains some missing values in the measurements (nearly 1,25% of the rows). All calendar timestamps are present in the dataset but for some timestamps, the measurement values are missing: a missing value is represented by the absence of value between two consecutive semi-colon attribute separators. For instance, the dataset shows missing values on April 28, 2007.

Attribute Information:

date: Date in format dd/mm/yyyy time: time in format hh:mm:ss global_active_power: household global minute-averaged active power (in kilowatt) global_reactive_power: household global minute-averaged reactive power (in kilowatt) voltage: minute-averaged voltage (in volt) global_intensity: household global minute-averaged current intensity (in ampere) sub_metering_1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered). sub_metering_2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light. sub_metering_3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.

Relevant Papers: N/A

Citation Request: This dataset is made available under the “Creative Commons Attribution 4.0 International (CC BY 4.0)” license

A table listing the average electricity rates (kWh) of all 50 U.S. states as of March 2025.