UK Gas fell to 72.60 GBp/thm on December 2, 2025, down 1.67% from the previous day. Over the past month, UK Gas's price has fallen 11.75%, and is down 40.33% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. UK Natural Gas - values, historical data, forecasts and news - updated on December of 2025.

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.

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.

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.

Context

This database About How diesel and petrol Prices Rising day By day in UK

Content

Diesel Price And Petrol Price

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Just For Learning

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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.

Energy Inflation in the United Kingdom decreased to 1.80 percent in October from 4.30 percent in September of 2025. This dataset includes a chart with historical data for the United Kingdom Energy Inflation.

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

Crude Oil fell to 59.17 USD/Bbl on December 2, 2025, down 0.25% from the previous day. Over the past month, Crude Oil's price has fallen 3.08%, and is down 15.40% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on December of 2025.

Spain Electricity decreased 65.44 EUR/MWh or 48.17% 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 Spain Electricity Price.

Abstract copyright UK Data Service and data collection copyright owner.

The English Housing Survey (EHS) Fuel Poverty Datasets are comprised of fuel poverty variables derived from the EHS, and a number of EHS variables commonly used in fuel poverty reporting. The EHS is a continuous national survey commissioned by the Ministry of Housing, Community and Local Government (MHCLG) that collects information about people's housing circumstances and the condition and energy efficiency of housing in England.

Safeguarded and Special Licence Versions
Similar to the main EHS, two versions of the Fuel Poverty dataset are available from 2014 onwards. The Special Licence version contains additional, more detailed, variables, and is therefore subject to more restrictive access conditions. Users should check the Safeguarded Licence (previously known as End User Licence (EUL)) version first to see whether it meets their needs, before making an application for the Special Licence version.

The English Housing Survey: Fuel Poverty Dataset, 2022: Special Licence is the outcome of analysis conducted to produce estimates of fuel poverty in England in 2022 undertaken by the Department for Energy Security and Net Zero (DESNZ).

Fuel poverty in England is measured using the Low Income Low Energy Efficiency (LILEE) indicator, which considers a household to be fuel poor if:

• it is living in a property with an energy efficiency rating of band D, E, F or G as determined by the most up-to-date Fuel Poverty Energy Efficiency Rating (FPEER) Methodology; and
• its disposable income (income after housing costs (AHC) and energy costs) would be below the poverty line. The poverty line (income poverty) is defined as an equivalised disposable income of less than 60 per cent of the national median in Section 2 of the ONS publication 'Persistent poverty in the UK and EU: 2017'.

The Low Income Low Energy Efficiency model is a dual indicator, which allows us to measure not only the extent of the problem (how many fuel poor households there are), but also the depth of the problem (how badly affected each fuel poor household is). The depth of fuel poverty is calculated using the fuel poverty gap. This is the reduction in fuel costs needed for a household to not be in fuel poverty. This is either the change in required fuel costs associated with increasing the energy efficiency of a fuel poor household to a Fuel Poverty Energy Efficiency Rating (FPEER) of band C or reducing the costs sufficiently to meet the income threshold.

The fuel poverty dataset is derived from the English Housing Survey, 2022 database created by the MHCLG. This database is constructed from fieldwork carried out between April 2021 and March 2023. The midpoint of this period is April 2022, which can be considered as the reference date for this dataset.

A brief summary of each of the variables included in the English Housing Survey: Fuel Poverty Dataset, 2022: Special Licence dataset is included in the study documentation. The variables can be grouped into the following categories:

• Low Income Low Energy Efficiency fuel poverty indicator variables
• income and fuel costs variables
• 10 per cent affordability indicator variables
• additional fuel poverty variables
• English Housing Survey variables
• policy eligibility flags
• income split variables
• energy cost variables
• energy use variables
• weights
• variables introduced in 2015 data release (first published 2017)

The 2025 annual OPEC basket price stood at ***** U.S. dollars per barrel as of August. This would be lower than the 2024 average, which amounted to ***** U.S. dollars. The abbreviation OPEC stands for Organization of the Petroleum Exporting Countries and includes Algeria, Angola, Congo, Equatorial Guinea, Gabon, Iraq, Iran, Kuwait, Libya, Nigeria, Saudi Arabia, Venezuela, and the United Arab Emirates. The aim of the OPEC is to coordinate the oil policies of its member states. It was founded in 1960 in Baghdad, Iraq. The OPEC Reference Basket The OPEC crude oil price is defined by the price of the so-called OPEC (Reference) basket. This basket is an average of prices of the various petroleum blends that are produced by the OPEC members. Some of these oil blends are, for example: Saharan Blend from Algeria, Basra Light from Iraq, Arab Light from Saudi Arabia, BCF 17 from Venezuela, et cetera. By increasing and decreasing its oil production, OPEC tries to keep the price between a given maxima and minima. Benchmark crude oil The OPEC basket is one of the most important benchmarks for crude oil prices worldwide. Other significant benchmarks are UK Brent, West Texas Intermediate (WTI), and Dubai Crude (Fateh). Because there are many types and grades of oil, such benchmarks are indispensable for referencing them on the global oil market. The 2025 fall in prices was the result of weakened demand outlooks exacerbated by extensive U.S. trade tariffs.

Coal fell to 108.35 USD/T on December 1, 2025, down 1.86% from the previous day. Over the past month, Coal's price has fallen 1.14%, and is down 20.33% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coal - values, historical data, forecasts and news - updated on December of 2025.

A Web Mapping Tile Service (WMTS) layer identifying optimal locations across Scotland for mine water geothermal development. Mine water geothermal energy describes the low-carbon practice of using water held in abandoned flooded mines to heat or cool surface thermal demands. The low temperatures (as low as 10°C) require heat pump technology to upgrade thermal energy to usable temperatures for heating homes or industrial applications. The intention of the mine water geothermal resource atlas is to highlight optimal areas to exploit MWG energy in Scotland. If the scale of Scotland’s mine water thermal resource, estimated at 12 GW, becomes better communicated we envisage that this atlas will prove influential for increasing the rate and success of MWG deployment. Ideally, the provision of feasible MWG sites will influence stakeholder decisions i.e., where to invest and develop land to make the best use of the low-carbon resource, resulting in MWG potential included as part of a standard appraisal for a residential or industrial development plan. Whilst it is acknowledged that focused expert input would be required to integrate surface heat demand and subsurface resources in detail, the atlas provides non-experts and decision makers with a first-pass high-level summary of the potential MWG resource located within their area of interest. The four criteria for site selection are summarised below: There are more than one (overlapping) mined seams. The mined seams are deeper than 30 m to minimise subsidence risk. The mine water head (i.e., mine “water table”) is not excessively deep (< 60 m below ground level) to avoid excessive pumping costs. The mined seams are shallower than 250 m below ground level to minimise drilling costs. As a result, this atlas has identified a total of 370.3 km2 across 19 local authority areas which are most suitable for MWG development. Symbology: The calculated depths for mine water head (Criterion 3) are mapped in 10 m increments with shallower values (0 m – 20 m BGL) shown in shades of pink and deeper values (20 m - 60 m BGL) shown in shades of blue. Where mine water head is shallow (0 m - 20 m BGL) there may be some risk that reinjection may cause mine water heads to approach the surface (depending on the transmissivity of the workings). “Open loop with discharge” may be more feasible for areas with very shallow mine water (0 m - 20 m BGL), but may require additional permitting (discharge consents) and available land for treatment depending on water chemistry. Areas shaded blue (20 m - 60 m BGL) correlate with deeper mine water heads which are most suited to the ‘open-loop with reinjection’ configuration. Both configurations are presented in Walls et al. (2022) - https://doi.org/10.3390/en14196215. Depths greater than 60 m BGL are not included since they indicate situations which would face excessive pumping costs. The Mine Water Geothermal Resource Atlas for Scotland (MiRAS) was the work of David Walls, a PhD researcher at the Universities of Strathclyde and Glasgow. David has been supervised by Dr Neil Burnside of the University of Strathclyde, David Banks of the University of Glasgow and Prof. Adrian Boyce of SUERC. The PhD studentship was funded by Engineering and Physical Sciences Research Council and some of the analysis was funded through the John Mather Trust Rising Star Award. Further contextual details of this work including the input data, specific processing and quality assurance can be found in David’s PhD (How can optimal sites for mine water geothermal energy systems be identified and where are they in Scotland?) and accompanying paper (to be shared when available). If you have any queries regarding the MiRAS, assistance can be provided via emails to david.walls@strath.ac.uk; or you can reach David at his new post with TownRock Energy at david.walls@townrock.com. NB. This dataset is not available as a Web Feature Service (WFS) due to the licencing restrictions of the source data. Contains data from © The Coal Authority. All rights reserved.

Brent fell to 63.05 USD/Bbl on December 2, 2025, down 0.19% from the previous day. Over the past month, Brent's price has fallen 2.84%, and is down 14.36% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Brent crude oil - values, historical data, forecasts and news - updated on December of 2025.

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