In 2022, the average end-use electricity price in the United States stood at around 12.2 U.S. cents per kilowatt-hour. This figure is projected to decrease in the coming three decades, to reach some 11 U.S. cents per kilowatt-hour by 2050.

US Electricity Prices and Sales by State, Sector, and Year

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8734253%2Fdba0dac3571f37e79f2891a6ffd80d5c%2Fus%20electric%20flag.png?generation=1712518711362350&alt=media" alt=""> This comprehensive dataset offers a detailed look at the United States electricity market, providing valuable insights into prices, sales, and revenue across various states, sectors, and years. With data spanning from 2001 onwards to 2024, this dataset is a powerful tool for analyzing the complex dynamics of the US electricity market and understanding how it has evolved over time.

The dataset includes eight key variables: | Column Name | Description | |-------|-------| | year | The year of the observation | | month | The month of the observation | | stateDescription | The name of the state | | sectorName | The sector of the electricity market (residential, commercial, industrial, other, or all sectors) | | customers | The number of customers (missing for some observations) | | price | The average price of electricity per kilowatt-hour (kWh) in cents | | revenue | The total revenue generated from electricity sales in millions of dollars | | sales | The total electricity sales in millions of kilowatt-hours (kWh) |

By providing such granular data, this dataset enables users to conduct in-depth analyses of electricity market trends, comparing prices and consumption patterns across different states and sectors, and examining the impact of seasonality on demand and prices.

One of the primary applications of this dataset is in forecasting future electricity prices and sales based on historical trends. By leveraging the extensive time series data available, researchers and analysts can develop sophisticated models to predict how prices and demand may change in the coming years, taking into account factors such as economic growth, population shifts, and policy changes. This predictive power is invaluable for policymakers, energy companies, and investors looking to make informed decisions in the rapidly evolving electricity market.

Another key use case for this dataset is in investigating the complex relationships between electricity prices, sales volumes, and revenue. By combining the price, sales, and revenue data, users can explore how changes in prices impact consumer behavior and utility company bottom lines. This analysis can shed light on important questions such as the price elasticity of electricity demand, the effectiveness of energy efficiency programs, and the potential impact of new technologies like renewable energy and energy storage on the market.

Beyond its immediate applications in the energy sector, this dataset also has broader implications for understanding the US economy and society as a whole. Electricity is a critical input for businesses and households across the country, and changes in electricity prices and consumption can have far-reaching effects on economic growth, competitiveness, and quality of life. By providing such a rich and detailed portrait of the US electricity market, this dataset opens up new avenues for research and insights that can inform public policy, business strategy, and academic inquiry.

I hope you all enjoy using this dataset and find it useful! 🤗

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 September 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.

Wholesale electricity prices in the United Kingdom hit a record-high in 2022, reaching **** British pence per kilowatt-hour that year. Projections indicate that prices are bound to decrease steadily in the next few years, falling under **** pence per kilowatt-hour by 2030.

The average wholesale electricity price in September 2025 in the United Kingdom is forecast to amount to*******British pounds per megawatt-hour, a decrease from the previous month. A record high was reached in August 2022 when day-ahead baseload contracts averaged ***** British pounds per megawatt-hour. Electricity price stabilization in Europe Electricity prices increased in 2024 compared to the previous year, when prices stabilized after the energy supply shortage. Price spikes were driven by the growing wholesale prices of natural gas and coal worldwide, which are among the main sources of power in the region.

… and in the United Kingdom? The United Kingdom was one of the countries with the highest electricity prices worldwide during the energy crisis. Since then, prices have been stabilizing, almost to pre-energy crisis levels. The use of nuclear, wind, and bioenergy for electricity generation has been increasing recently. The fuel types are an alternative to fossil fuels and are part of the country's power generation plans going into the future.

Indian Energy Stock Price Data: A Window into Future Energy Prices?

Stock price data for Indian energy companies can offer valuable insights, but predicting future energy prices in India solely based on this information is complex. Here's a breakdown:

Potential Uses of Stock Price Data:

Understanding Market Sentiment: Stock prices reflect investor confidence in a company's future performance. By analyzing trends in energy stock prices, we can gauge market sentiment towards the energy sector. Rising stock prices for renewable energy companies might indicate growing investor confidence in the transition to cleaner sources, potentially impacting future energy prices. Identifying Supply and Demand Shifts: Stock prices can react to anticipated changes in supply and demand for energy. For example, rising stock prices for coal companies could suggest a potential supply shortage, potentially pushing up future coal prices.

Limitations of Stock Price Data for Price Prediction:

Focus on Company Performance: Stock prices are primarily driven by a company's financial health and future prospects. While a company's performance might be linked to broader energy market trends, it's not the sole factor.

Multiple Influences on Energy Prices: Geopolitical events, government policies, technological advancements, and global energy market fluctuations all significantly impact energy prices in India. Stock price data alone cannot capture these complexities.

Conclusion:

Indian energy stock price data offers valuable insights, but it's just one piece of the puzzle. A comprehensive analysis that considers various factors like government regulations, global energy trends, and technological advancements is necessary for a more accurate prediction of future energy prices in India.

Additional Considerations:

Analyzing data from energy exchanges like the Indian Energy Exchange (IEX) can provide insights into short-term price movements.

Combining stock price data with other market indicators and expert analysis can lead to a more informed prediction.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8127972%2F3529e98c94fef7a1b9e722e4a0ba364d%2FStock.gif?generation=1719215077562928&alt=media" alt="">

Germany Electricity decreased 17.60 EUR/MWh or 15.21% 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.

UK Electricity decreased 23.24 GBP/MWh or 22.68% 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.

Corresponding paper: O. Schmidt, A. Hawkes, A. Gambhir & I. Staffell. The future cost of electrial energy storage based on experience rates. Nat. Energy 2, 17110 (2017).Link to the paper: http://dx.doi.org/10.1038/nenergy.2017.110This dataset compiles cumulative capacity and product price data for electrical energy storage technologies, including the respective regression parameters to construct experience curves. Please see the paper for a full discussion on experience curves for electrical energy storage technologies and associated analyses on future cost, cumulative investment requirements and economic competitiveness of storage.The dataset also presents the underlying data for Figures 1 to 5 and Supplementary Figures 2 and 3 of the paper.

Electricity prices in Germany are forecast to amount to ***** euros per megawatt-hour in November 2025. Electricity prices in the country have not yet recovered to pre-pandemic levels. Electricity price recovery German electricity prices began recovering back to pre-energy crisis levels in 2024, a period driven by a complex interplay of factors, including increased heating demand, reduced wind power generation, and water scarcity affecting hydropower production. Despite Germany's progress in renewable energy sources, with over ** percent of gross electricity generated from renewable sources in 2024, the country still relies heavily on fossil fuels. Coal and natural gas accounted for approximately ** percent of the energy mix, making Germany vulnerable to fluctuations in global fuel prices. Impact on consumers and future outlook The volatility in electricity prices has directly impacted German consumers. As of April 1, 2024, households with basic supplier contracts were paying around ** cents per kilowatt-hour, making it the most expensive option compared to other providers or special contracts. The breakdown of household electricity prices in 2023 showed that supply and margin, along with energy procurement, constituted the largest controllable components, amounting to **** and **** euro cents per kilowatt-hour, respectively. While prices have decreased since the 2022 peak, they remain higher than pre-crisis levels, underscoring the ongoing challenges in Germany's energy sector as it continues its transition towards renewable sources.

⚡ Energy Crisis and Stock Price Dataset: 2021-2024 📊

📋 About Dataset

This dataset provides a detailed view of how major energy companies' stock prices were influenced by the energy crises between 2021 and 2024. The data covers three prominent energy companies: ExxonMobil (XOM), Shell (SHEL), and BP (BP), with historical stock price information collected via the yfinance library. This dataset is particularly useful for those interested in financial analysis, market behavior, and the impact of global events on the energy sector. 🌍📉📈

📅 Date Range

• Start Date: January 1, 2021
• End Date: Present day (updated periodically)

🔍 Data Overview

The dataset contains the daily adjusted closing prices of the selected companies from January 2021 to the present. The data was gathered to analyze the impact of different energy crises, such as the fluctuations in oil and gas prices during 2021-2024, and to help provide insights into investor behavior during times of energy uncertainty.

The key columns available in each CSV file are:

💡 Potential Use Cases

This dataset can be used for various purposes including, but not limited to:

• Financial Time Series Analysis 📈: Explore trends and volatility in the stock market, particularly in the energy sector.
• Predictive Modeling 🤖: Develop models to predict future stock prices based on historical data.
• Energy Crisis Impact Studies ⚡: Assess the effect of energy crises on global markets, specifically the energy sector.
• Portfolio Analysis 💼: Evaluate the stability and performance of energy companies during different crisis periods.

📊 Data Files

Each CSV file includes the daily stock prices from January 1, 2021, to the present, with columns for open, high, low, close, adjusted close, and volume.

📂 Dataset Structure

• Directory: data/raw/
  • XOM_data.csv
  • SHEL_data.csv
  • BP_data.csv

🚀 Data Collection Process

The data for this dataset was collected using the yfinance Python library, which provides access to historical market data from Yahoo Finance. The collection script (data_collection.py) automates the download of stock data for the selected companies, saving each company's data in CSV format within the data/raw/ directory.

🔧 Tools Used

• Python 🐍: For scripting and data processing.
• yfinance 📈: To download historical stock data.
• pandas 🐼: For data manipulation and cleaning.

📜 License

The dataset is provided under the MIT License. You are free to use, modify, and distribute this dataset, provided that proper attribution is given.

🙌 Contributions

Contributions are welcome! If you have any suggestions or improvements, feel free to fork the repository and make a pull request. Let's make this dataset even more comprehensive and insightful together. 💪🌟

📧 Contact

For any questions or further information, feel free to reach out:

I hope this dataset helps you uncover new insights about the relationship between energy crises and stock prices! If you find it helpful, don't forget to give it a ⭐️ on Kaggle! 😊✨

Natural gas rose to 4.94 USD/MMBtu on December 3, 2025, up 2.04% from the previous day. Over the past month, Natural gas's price has risen 13.71%, and is up 62.29% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Natural gas - values, historical data, forecasts and news - updated on December of 2025.

Predicting The Energy Market's Day Ahead Prices

Analyses of Energy Systems and Prices

By [source]

About this dataset

This dataset offers an in-depth look at the dynamic European energy markets, with hourly updates on the power prices associated with each system. By offering a comprehensive view of electricity markets across Europe, this data can empower both academics and those in industry to draw implications from correlations between different energy systems, analyze how prices fluctuate across markets, and better understand the complex dynamics of these European energy systems. This comprehensive dataset provides invaluable insights into economic trends in this region and the future outlook for energy pricing

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides an excellent analysis of Europe’s energy systems and power prices on an hourly basis. It can be used in many ways to examine the electricity market of Europe, including correlations between different energy systems, implications for prices in specific markets, and much more.

Here is a guide for how to use this dataset: - First inspect the columns provided in this dataset; they include date/time information (fecha, hora), system (sistema), flag (bandera), price (precio), currency type (tipo_moneda), source of data(origen_dato) and date of last update(fecha_actualizacion). - Understand what each column represents as some columns may be more important than others depending on your particular analysis. For example, when examining energy system correlations you may want to focus primarily on the ‘system’ column while if price fluctuations are your focus you may want to pay most attention to the ‘price’ column. - Gather the data from all desired columns that you need for your analysis into a single table or format for better organization and readability. This will make it easier to visualize trends or patterns that you find interesting.
- Utilize tools such as Microsoft Excel functions or programming languages such as Python/R to create representations like line graphs which reveal correlations over time or region-specific market power price differences etc.
- Finally present your findings in written form such as a report or share visualized results like infographics!

Research Ideas

• Analyzing correlations between energy systems in Europe, price behavior and its implications across different markets.
• Analyzing historical trends in pricing behavior to predict future prices for energy markets in Europe.
• Recommending differentiated approaches for infrastructure investments that mitigate risk and optimize cost benefit analysis among utilities and businesses across Europe's electricity markets

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: da_market_data.csv | Column name | Description | |:------------------------|:--------------------------------------------------------------------------------------------------------------------------------| | fecha | Date of the power prices in DD/MM/YYYY format. (Date) | | hora | Hour that corresponds with each set of power prices listed by minute. (Time) | | sistema | Numeric code for system identifier for each set of reported price points for a specific hour across EU countries. (Numeric) | | bandera | Indicator of whether or not electricity is green (Y) or non-green/conventional electricity (N). (Boolean) | | precio | Cost per Megawatt Hour expressed in Euro €/MWh currency format. (Currency) | | tipo_moneda | Euros represented as Euros € EUROSCURSUSD ($ EURS = US Dollars $ USD) as well as other available foreign currencies. (Currency) | | origen_dato | D...

This dataset contains results estimating projections of change of annual capacity factors and levelized cost of energy for several turbine technologies in the 2024 Annual Technology Baseline (ATB). Projections of change are based on downscaled earth system model (ESM) data from Sup3rCC. There has been evidence of reductions in average wind speeds over land in North America since the 1980s, and several models project that average wind speeds will continue to decrease. Concurrently, the cost of wind energy systems in the United States has been decreasing since around 2010, a trend also projected to continue. There is considerable uncertainty in these future projections, with quantitative estimates of future wind resource and system costs varying widely. To study this, we run land-based wind energy models with a range of possible future system costs, turbine designs, and meteorological inputs from multiple downscaled earth system models over the contiguous United States to estimate critical system performance metrics such as annual energy production (AEP) and levelized cost of energy. Where multiple earth system models agree, changes in mean AEP from the time period 2000-2019 to 2040-2059 can be as high as +10% in South Texas or as low as -20% in Iowa. Several additional states in the Midwest that currently have considerable wind generation capacity show the possibility of substantial decreases in AEP by mid-century. Larger turbines and moderate reductions in system costs can offset even the largest projected decreases in wind resource, but much uncertainty remains in the extent to which wind resources will actually change into the future and to what extent wind energy systems can drive down future costs. An analysis of variance shows, in several states in the Midwest, the uncertainty in future wind resource can be almost as important for future changes in the cost of wind energy as the uncertainty in future system costs. Note: This data and manuscript will be finalized and assigned a DOI upon completion of peer review.

Corresponding paper: O. Schmidt, S. Melchior, A. Hawkes, I. Staffell. Projecting the future levelized cost of electricity storage technologies. Joule (2018).Link to the paper: https://cell.com/joule/fulltext/S2542-4351(18)30583-XThis dataset compiles levelized cost of storage data in energy terms (LCOS, US$/MWh) and power terms, i.e. annuitized capacity cost (ACC, US$/kW-yr), for 9 electricity storage technologies from 2015 projected to 2050. One spreadsheet provides the data for 12 applications as well as the probability for each of the 9 technologies to exhibit lowest LCOS or ACC in a distinct application. Figures 1 and 2 and Supplementary Figures 3 and 4 of the respective publication are based on this data.The remaining files contain LCOS and ACC results for various annual full equivalent cycle and discharge duration combinations, regardless of actual application requirements. Electricity price is fixed at 50 US$/MWh. Figures 3 and 4 and Supplementary Figures 5 and 6 of the respective publication are based on this data.Please see the paper for a full analysis and discussion of the results.

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.

About the ProjectKAPSARC is analyzing the shifting dynamics of the global gas markets. Global gas markets have turned upside down during the past five years: North America has emerged as a large potential future LNG exporter while gas demand growth has been slowing down as natural gas gets squeezed between coal and renewables. While the coming years will witness the fastest LNG export capacity expansion ever seen, many questions are raised on the next generation of LNG supply, the impact of low oil and gas prices on supply and demand patterns and how pricing and contractual structure may be affected by both the arrival of U.S. LNG on global gas markets and the desire of Asian buyers for cheaper gas.Key PointsIn the past year, global gas prices have dropped significantly, albeit at unequal paces depending on the region. All else being equal, economists would suggest that this should have generated a positive demand response. However, “all else” was not equal. Prices of other commodities also declined while economic growth forecasts were downgraded. Prices at benchmark points such as the U.K. National Balancing Point (NBP), U.S. Henry Hub (HH) and Japan/Korea Marker (JKM) slumped due to lower oil prices, liquefied natural gas (LNG) oversupply and unseasonal weather. Yet, the prices of natural gas in local currencies have increased in a number of developing countries in Africa, the Middle East, Latin America, former Soviet Union (FSU) and Asia. North America experienced demand growth while gas in Europe and Asia faced rising competition from cheaper coal, renewables and, in some instances, nuclear. Gains to European demand were mostly weather related while increases in Africa and Latin America were not significant. For LNG, Europe became the market of last resort as Asian consumption declined. Moreover, an anticipated surge in LNG supply, brought on by several new projects, may lead to a confrontation with Russian or other pipeline gas suppliers to Europe. At the same time, Asian buyers are seeking concessions on pricing and flexibility in their long-term contracts. Looking ahead, natural gas has to prove itself a credible and affordable alternative to coal, notably in Asia, if the world is to reach its climate change targets. The future of the gas industry will also depend on oil prices, evolution of Chinese energy demand and impact of COP21 on national energy policies. Current low prices mean there is likely to be a pause in final investment decisions (FIDs) on LNG projects in the coming years.

Peer-to-peer (P2P) energy sharing involves novel technologies and business models at the demand-side of power systems, which is able to manage the increasing connection of distributed energy resources (DERs). In P2P energy sharing, prosumers directly trade energy with each other to achieve a win-win outcome. A research paper titled "Evaluation of peer-to-peer energy sharing mechanisms based on a multiagent simulation framework" has been published on Applied Energy regarding this topic. In the paper, a general multiagent framework was established to simulate P2P energy sharing, with two original techniques proposed to facilitate simulation convergence. Furthermore, a systematic index system was established to evaluate P2P energy sharing mechanisms from both economic and technical perspectives.In case studies of the paper, two sets of cases were conducted to validate the proposed simulation and evaluation methods and to give some practical implications on applying P2P energy sharing in Great Britain (GB) at present and in the future. The household demand dataset and electric vehicle (EV) dataset used in the paper has been provided for researchers to reproduce the results in the paper or to conduct further related studies. Also, the original numerical data of the results in the case studies of the paper have been provided, for researchers to better understand the results or to use the results for other purposes.The whole dataset includes 9 excel files in total. The detailed description for them are presented as follows:1. “CREST_Demand_Model_v2.2 (Great Britain).xlsm” is a high-resolution stochastic integrated thermal-electrical domestic demand simulation tool developed by Centre for Renewable Energy Systems Technology (CREST) of Loughborough University (refering to http://www.lboro.ac.uk/research/crest/demand-model/). It contains a lot of sheets and VBA codes, which are used to generate “fake” demand curves of domestic customers sampled from statistical distributions that are based on real-life data. In the “Main Sheet”, input parameters like “day of month”, “month of year”, “latitude”, “longitude”, etc. can be entered, and then the “Run simulation” button can be clicked to start the simulation. After the simulation, daily curves like “occupancy and activity”, “total electrical demand”, “total gas demand”, etc. are generated and visualized, with very high time resolution.2. “Electric_Vehicle_Dataset (Great Britain).xlsx” is a dataset based on the research conducted jointly by Centre for Integrated Renewable Energy of Cardiff University and Key Laboratory of Smart Grid of Ministry of Education of Tianjin University (referring to https://doi.org/10.1016/j.apenergy.2015.10.159). It contains two sheets, which provide the parameters of 1000 typical electric vehicles of Great Britain respectively. For each electric vehicle, the parameters include: (1) “Time starting charging / returning home (hour)”, (2) “Time finishing charging / leaving home (hour)”, (3) “Battery capacity (kWh)”, (4) “Energy consumption due to travel (measured by SOC)”, (5) “Lowerlimit of SOC”, (6) “Upperlimit of SOC”, (7) “Maximum charging/discharging power”, (8) “Charging efficiency”, and (9) “Discharging efficiency”.3. “Numerical results and figures _ Case 1-1.xlsx” provides the numerical results of Case 1-1 of the paper. It contains three sheets, providing the data behind Fig. 6, Fig. 7 and Fig. 8 of the paper respectively. In the “Fig. 6” sheet, the “Total Net Consumption (kWh)” and “Total PV Generation (kWh)” under “SDR mechanism” and “conventional paradigm” are provided. In the “Fig. 7” sheet, the “Net energy cost under SDR mechanism (£)” and “Net energy cost under conventional paradigm (£)” of each prosumer are provided. In the “Fig. 8” sheet, the “Internal selling price (£/MWh)”, “Internal buying price (£/MWh)” and “Total Net Energy Cost (£)” of each iteration are provided.4. “Numerical results and figures _ Case 1-2.xlsx” provides the numerical results of Case 1-2 of the paper. It contains two sheets, providing the data behind Fig. 9, Fig. 10 and Fig. 11 of the paper. In the “Fig. 9 and 10” sheet, for Fig. 9, the “The iteration at which the simulation stopped” given different ramping rates are provided; for Fig. 10, the “Overall Performance Index” with different ramping rates given different demand profiles are provided. In the “Fig. 11” sheet, the “Total net energy cost (ramping rate = 0.3) (£)” and “Total Net Energy Cost (ramping rate = 0.6) (£)” at each iteration are provided.5. “Numerical results and figures _ Case 1-3.xlsx” provides the numerical results of Case 1-3 of the paper. It contains only one sheets, providing the data behind Fig. 12 of the paper. In the “Fig. 12” sheet, the “Overall Performance Index” with different learning rates given different demand profiles are provided.6. “Numerical results and figures _ Case 1-4.xlsx” provides the numerical results of Case 1-4 of the paper. It contains two sheets, providing the data behind Fig. 13 and Fig. 14 of the paper. In the “Fig. 13” sheet, the “Overall Performance Index” with different ramping rates given different initial values are provided. In the “Fig. 14” sheet, the “Overall Performance Index” with different learning rates given different initial values are provided.7. “Numerical results and figures _ Case 1-5.xlsx” provides the numerical results of Case 1-5 of the paper. It contains only one sheet, providing the data behind Fig. 15 and Fig. 16 of the paper. In the “Fig. 15 and 16” sheet, for Fig. 15, the number of iterations when the simulation stopped given different maximum number of iterations and ramping rates are provided; for Fig. 16, the overall performance given different maximum number of iterations and ramping rates are provided.8. “Numerical results and figures _ Case 2-2.xlsx” provides the numerical results of Case 2-2 of the paper. It contains only one sheet, providing the data behind Fig. 17 of the paper. In the “Fig. 17” sheet, the overall performance scores of the three mechanisms (SDR, MMR and BS) and conventional paradigm in scenarios with different PV and EV penetration levels are provided.

1. “Numerical results and figures _ Appendix B.xlsx” provides the numerical results of the cases in Appendix B of the paper. It contains two sheets, providing the data behind Fig. B1, Fig. B2, Fig. B3 and Fig. B4 of the paper. In the “Fig. B1 and B2” sheet, for Fig. B1, the EWH power consumption (kW) at t=1 and t=2 for each iteration without any techniques for convergence are provided; for Fig. B2, the Internal buying price (pence/kWh) at t=1 and t=2 without any techniques for convergence are provided. In the “Fig. B3 and B4” sheet, for Fig. B1, the EWH power consumption (kW) at t=1 and t=2 for each iteration with a limitation for its power change are provided; for Fig. B2, the Internal buying price (pence/kWh) at t=1 and t=2 with a limitation for its power change are provided.Research results based upon these data are published at https://doi.org/10.1016/j.apenergy.2018.02.089

This dataset provides comprehensive and up-to-date information on futures related to oil, gas, and other fuels. Futures are financial contracts obligating the buyer to purchase and the seller to sell a specified amount of a particular fuel at a predetermined price and future date.

Use Cases: 1. Trend Analysis: Scrutinize patterns and price fluctuations to anticipate future market directions in the energy sector. 2. Academic Research: Delve into the historical behavior of oil and gas prices and understand the influence of global events on these commodities. 3. Trading Strategies: Develop and test trading tactics based on the dynamics of oil, gas, and other fuel futures. 4. Risk Management: Utilize the dataset for hedging and risk management for corporations involved in the extraction, refining, or trading of fuels.

Dataset Image Source: Photo by Pixabay: https://www.pexels.com/photo/industrial-machine-during-golden-hour-162568/

Column Descriptions: 1. Date: The date when the data was documented. Format: YYYY-MM-DD. 2. Open: Market's opening price for the day. 3. High: Peak price during the trading window. 4. Low: Lowest traded price during the day. 5. Close: Price at which the market closed. 6. Volume: Number of contracts exchanged during the trading period. 7. Ticker: The unique market quotation symbol for the future. 8. Commodity: Specifies the type of fuel the future contract pertains to (e.g., crude oil, natural gas).

{"The objective of the plan4res project is to provide a well-structured and highly modular modelling framework to enable consistent insights into the different needs of future energy system. Three case studies will highlight the potentials of this framework by dealing with different aspects of a future energy systems. Case study 3 will focus on cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources. Ist overall objectives are to identify the Cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources will be the main focus of case study 3. The present dataset contains all the public data built for this case study. The related documentation is included in plan4res deliverable D4.5 https://doi.org/10.5281/zenodo.3785010"}