Dataset Description Title: Electricity Market Dataset for Long-Term Forecasting (2018–2024)

Overview: This dataset provides a comprehensive collection of electricity market data, focusing on long-term forecasting and strategic planning in the energy sector. The data is derived from real-world electricity market records and policy reports from Germany, specifically the Frankfurt region, a major European energy hub. It includes hourly observations spanning from January 1, 2018, to December 31, 2024, covering key economic, environmental, and operational factors that influence electricity market dynamics. This dataset is ideal for predictive modeling tasks such as electricity price forecasting, renewable energy integration planning, and market risk assessment.

Features Description Feature Name Description Type Timestamp The timestamp for each hourly observation. Datetime Historical_Electricity_Prices Hourly historical electricity prices in the Frankfurt market. Continuous (Float) Projected_Electricity_Prices Forecasted electricity prices (short, medium, long term). Continuous (Float) Inflation_Rates Hourly inflation rate trends impacting energy markets. Continuous (Float) GDP_Growth_Rate Hourly GDP growth rate trends for Germany. Continuous (Float) Energy_Market_Demand Hourly electricity demand across all sectors. Continuous (Float) Renewable_Investment_Costs Investment costs (capital and operational) for renewable energy projects. Continuous (Float) Fossil_Fuel_Costs Costs for fossil fuels like coal, oil, and natural gas. Continuous (Float) Electricity_Export_Prices Prices for electricity exports from Germany to neighboring regions. Continuous (Float) Market_Elasticity Sensitivity of electricity demand to price changes. Continuous (Float) Energy_Production_By_Solar Hourly solar energy production. Continuous (Float) Energy_Production_By_Wind Hourly wind energy production. Continuous (Float) Energy_Production_By_Coal Hourly coal-based energy production. Continuous (Float) Energy_Storage_Capacity Available storage capacity (e.g., batteries, pumped hydro). Continuous (Float) GHG_Emissions Hourly greenhouse gas emissions from energy production. Continuous (Float) Renewable_Penetration_Rate Percentage of renewable energy in total energy production. Continuous (Float) Regulatory_Policies Categorical representation of regulatory impact on electricity markets (e.g., Low, Medium, High). Categorical Energy_Access_Data Categorization of energy accessibility (Urban or Rural). Categorical LCOE Levelized Cost of Energy by source. Continuous (Float) ROI Return on investment for energy projects. Continuous (Float) Net_Present_Value Net present value of proposed energy projects. Continuous (Float) Population_Growth Population growth rate trends impacting energy demand. Continuous (Float) Optimal_Energy_Mix Suggested optimal mix of renewable, non-renewable, and nuclear energy. Continuous (Float) Electricity_Price_Forecast Predicted electricity prices based on various factors. Continuous (Float) Project_Risk_Analysis Categorical analysis of project risks (Low, Medium, High). Categorical Investment_Feasibility Indicator of the feasibility of energy investments. Continuous (Float) Use Cases Electricity Price Forecasting: Utilize historical and projected price trends to predict future electricity prices. Project Risk Classification: Categorize projects into risk levels for better decision-making. Optimal Energy Mix Analysis: Analyze the balance between renewable, non-renewable, and nuclear energy sources. Policy Impact Assessment: Study the effect of regulatory and market policies on energy planning. Long-Term Strategic Planning: Provide insights into investment feasibility, GHG emission reduction, and energy market dynamics. Acknowledgment This dataset is based on publicly available records and market data specific to the Frankfurt region, Germany. The dataset is designed for research and educational purposes in energy informatics, computational intelligence, and long-term forecasting.

Germany Electricity decreased 41.04 EUR/MWh or 35.46% 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 purpose of this paper is to show the sensitivity of COE to various economic parameters when the COE is calculated using the methodology given in EPRI's Technical Assessment Guide (TAG). The TAG methodology is a simplified methodology suitable for use in the assessment of advanced power generation systems. The use of TAG allows evaluation of power generation systems with different economic and operating characteristics. There are several accepted methods found in TAG used to report COE. Care must be taken when comparing COE calculations using different methods, particularly when comparing high capital/low fuel cost technologies with low capital/high fuel cost technologies. For example, plants with high capital/low fuel costs can be made to look better by reporting COE in the tenth-year constant dollars. Similarly, reporting COE in first-year current dollars would make the same plant look worse. There are other parameters that are very sensitive on COE. This paper has looked at capacity factor, operating and maintenance (O and M) cost, and fuel cost. Capacity factor has a significant effect on COE, particularly for high capital cost plants. As capacity is reduced, the COE rises quickly. O and M costs are not major factors in the plants assessed in this paper. The fuel cost can vary as much as 50 to 100% when reporting fuel cost in tenth-year current dollars compared to first-year fuel cost. 4 figs., 6 tabs.

EGPB - An Event-based Gold Price Benchmark Dataset

This benchmark dataset consists of 8030 rows and 36 variables sourced from multiple credible economic websites, covering a period from January 2001 to December 2022. This dataset can be utilized to predict gold prices specifically or to aid any economic field that is influenced by the variables in this dataset.

Key variables & Features include:

• Previous gold prices

• Future gold prices with predictions for one day, one week, and one month

• Oil prices

• Standard & Poor's 500 Index (S&P 500)

• Dow Jones Industrial (DJI)

• US dollar index

• US treasury

• Inflation rate

• Consumer price index (CPI)

• Federal funds rate

• Silver prices

• Copper prices

• Iron prices

• Platinum prices

• Palladium prices

Additionally, the dataset considers global events that may impact gold prices, which were categorized into groups and collected from three distinct sources: the Al-Jazeera website spanning from 2022 to 2019, the Investing website spanning from 2018 to 2016, and the Yahoo Finance website spanning from 2007 to 2001.

These events data were then divided into multiple groups:

• Economic data

• Politics

• logistics

• Oil

• OPEC

• Dollar currency

• Sterling pound currency

• Russian ruble currency

• Yen currency

• Euro currency

• US stocks

• Global stocks

• Inflation

• Job reports

• Unemployment rates

• CPI rate

• Interest rates

• Bonds

These events were encoded using a numeric value, where 0 represented no events, 1 represented low events, 2 represented high events, 3 represented stable events, 4 represented unstable events, and 5 represented events that were observed during the day but had no effect on the dataset.

Cite this dataset: Farah Mansour and Wael Etaiwi, "EGPBD: An Event-based Gold Price Benchmark Dataset," 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Tenerife, Canary Islands, Spain, 2023, pp. 1-7, doi: 10.1109/ICECCME57830.2023.10252987.

@INPROCEEDINGS{10252987, author={Mansour, Farah and Etaiwi, Wael}, booktitle={2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)}, title={EGPBD: An Event-based Gold Price Benchmark Dataset}, year={2023}, volume={}, number={}, pages={1-7}, doi={10.1109/ICECCME57830.2023.10252987}}

Italy Electricity decreased 27.65 EUR/MWh or 20.07% 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.

Explore the World Competitiveness Ranking dataset for 2016, including key indicators such as GDP per capita, fixed telephone tariffs, and pension funding. Discover insights on social cohesion, scientific research, and digital transformation in various countries.

Social cohesion, The image abroad of your country encourages business development, Scientific articles published by origin of author, International Telecommunication Union, World Telecommunication/ICT Indicators database, Data reproduced with the kind permission of ITU, National sources, Fixed telephone tariffs, GDP (PPP) per capita, Overall, Exports of goods - growth, Pension funding is adequately addressed for the future, Companies are very good at using big data and analytics to support decision-making, Gross fixed capital formation - real growth, Economic Performance, Scientific research legislation, Percentage of GDP, Health infrastructure meets the needs of society, Estimates based on preliminary data for the most recent year., Singapore: including re-exports., Value, Laws relating to scientific research do encourage innovation, % of GDP, Gross Domestic Product (GDP), Health Infrastructure, Digital transformation in companies is generally well understood, Industrial disputes, EE, Female / male ratio, State ownership of enterprises, Total expenditure on R&D (%), Score, Colombia, Estimates for the most recent year., Percentage change, based on US$ values, Number of listed domestic companies, Tax evasion is not a threat to your economy, Scientific articles, Tax evasion, % change, Use of big data and analytics, National sources, Disposable Income, Equal opportunity, Listed domestic companies, Government budget surplus/deficit (%), Pension funding, US$ per capita at purchasing power parity, Estimates; US$ per capita at purchasing power parity, Image abroad or branding, Equal opportunity legislation in your economy encourages economic development, Number, Article counts are from a selection of journals, books, and conference proceedings in S&E from Scopus. Articles are classified by their year of publication and are assigned to a region/country/economy on the basis of the institutional address(es) listed in the article. Articles are credited on a fractional-count basis. The sum of the countries/economies may not add to the world total because of rounding. Some publications have incomplete address information for coauthored publications in the Scopus database. The unassigned category count is the sum of fractional counts for publications that cannot be assigned to a country or economy. Hong Kong: research output items by the higher education institutions funded by the University Grants Committee only., State ownership of enterprises is not a threat to business activities, Protectionism does not impair the conduct of your business, Digital transformation in companies, Total final energy consumption per capita, Social cohesion is high, Rank, MTOE per capita, Percentage change, based on constant prices, US$ billions, National sources, World Trade Organization Statistics database, Rank, Score, Value, World Rankings

Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Latvia, Lithuania, Luxembourg, Malaysia, Mexico, Mongolia, Netherlands, New Zealand, Norway, Oman, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Singapore, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine, United Kingdom, Venezuela

Follow data.kapsarc.org for timely data to advance energy economics research.

This interactive webapp can be used to reproduce figures from an accompanying article by the same authors that studies the renewables pull and its impact on industrial relocation for future global green value chains of energy-intensive basic materials. Some of the main assumptions, i.e. the electricity prices and the transport cost can be changed here when generating the figures. We employ techno-economic assessments to compute the levelised cost of production for the studied green (i.e. low-carbon) value chains of steel, urea, and ethylene for cases of varying depth of relocation. The results show that substantial relocation savings for the levelised cost of production can be anticipated for full relocation of the studied value chains. Moreover, by studying cases of varying depth of relocation, we can demonstrate that a large share of the energy-cost savings is associated with relocating electrolysis to more renewable-favourable locations, yet the high transportation cost of shipping-based hydrogen imports result in only minor overall relocation savings. For more advanced changes and detailed information on the input data and methodology, we encourage users to inspect the article, its supplement, and the source code written in Python.

About the Project After decades of development and false starts, electric vehicles have now become commercial. However, they still rely on strong policy support for their further development and adoption. The project assesses the effectiveness of current electric vehicle policy in leading the technology toward self sustained market competitiveness. The multi-method approach chosen involves techno-economic, strategy and innovation systems analysis. In particular, we have developed a bottom-up electric vehicle fleet cost model in order to assess the economic implications of electric vehicle policy Key Points Passenger cars are responsible for a large and steadily growing share of global energy-related greenhouse gas (GHG) emissions. Electric vehicles (EVs) powered by renewable electricity have the potential to provide a substantial contribution to the decarbonization of passenger car transport. Unless carbon capture and storage technologies become cost competitive, EVs are likely to form a growing share of the personal mobility solution. But what is the lowest cost path to achieving high levels of EV penetration? Encouraged by the falling cost of batteries, EV policy today focuses on expediting electrification, paying comparatively little attention to the cost of the particular type of EVs and charging infrastructure being deployed. This paper argues that, due to its strong influence on EV innovation paths, EV policy could be better designed if it paid more attention to future cost and technology development risk. In particular, key findings include: EV policy with a strong bias toward long-range battery electric vehicles (BEVs) risks leading to a higher cost of electrification in the 2030 timeframe, possibly exceeding the ability of governments to sustain the necessary incentives until battery cost drops sufficiently. Plug-in hybrid electric vehicles (PHEVs) with long electric range could allow intermediate decarbonization targets to be met while being less sensitive to the rate of development of battery technology. The BEV option could be pursued in parallel by targeting specific segments where shorter ranges are acceptable to their users. Promoting a balanced mix of BEVs and PHEVs could set the electrification of passenger cars on a lower risk, lower cost, path that is more likely to become self-sustained before government support is withdrawn. Examining EV policy in the U.K. and in California, we find that it is generally not incompatible with achieving balanced mixes of BEVs and PHEVs. However, this may not be sufficient and some fine tuning would enable better balancing of medium-term risks and long-term goals.

The integration of renewable energy sources, such as wind and solar power, into the grid is essential for achieving carbon peaking and neutrality goals. However, the inherent variability and unpredictability of these energy sources pose significant challenges to power system stability. Advanced energy storage systems (ESS) are critical for mitigating these challenges, with gravity energy storage systems (GESS) emerging as a promising solution due to their scalability, economic viability, and environmental benefits. This paper proposes a multi-objective economic capacity optimization model for GESS within a novel power system framework, considering the impacts on power network stability, environmental factors, and economic performance. The model is solved using an enhanced Grasshopper Optimization Algorithm (W-GOA) incorporating a whale spiral motion strategy to improve convergence and solution accuracy. Simulations on the IEEE 30-node system demonstrate that GESS reduces peak-to-valley load differences by 36.1% and curtailment rates by 42.3% (wind) and 18.7% (PV), with a 15% lower levelized cost than CAES. The results indicate that GESS effectively mitigates peak load pressures, stabilizes the grid, and provides a cost-effective solution for integrating high shares of renewable energy. This study highlights the potential of GESS as a key component in future low-carbon power systems, offering both technical and economic advantages over traditional energy storage technologies.

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

The author presents a multi-impact economic valuation framework called the Social Cost of Atmospheric Release (SCAR) that extends the Social Cost of Carbon (SCC) used previously for carbon dioxide (CO2) to a broader range of pollutants and impacts. Values consistently incorporate health and agricultural impacts of air quality along with climate damages. The latter include damages associated with aerosol-induced hydrologic cycle changes that lead to net climate benefits when reducing cooling aerosols. Evaluating a 1% reduction in current global emissions, benefits with a high discount rate are greatest for reductions of sulfur dioxide (SO2), followed by co-emitted products of incomplete combustion (PIC) and then CO2 and methane. With a low discount rate, benefits are greatest for CO2 reductions, and are nearly equal to the total from SO2, PIC and methane. These results suggest that efforts to mitigate atmosphere-related environmental damages should target a broad set of emissions including CO2, methane and aerosols. Illustrative calculations indicate environmental damages are $150-510 billion yr-1 for current US electricity generation (~6-20¢ per kWh for coal, ~2-11¢ for gas) and $0.73±0.34 per gallon of gasoline ($1.20±0.70 per gallon for diesel). These results suggest that total atmosphere-related environmental damages plus generation costs are greater for coal-fired power than other sources, and damages associated with gasoline vehicles exceed those for electric vehicles.

This dataset complements the article "Frequency regulation with storage: On losses and profits" by Dirk Lauinger, François Vuille, and Daniel Kuhn, available at https://arxiv.org/abs/2306.02987.

The dataset contains the following files:

1. Case_study.ipynb, which relies on the datafiles delta_10s.h5, pa.h5, pb.h5, and pd.h5 to construct all figures in the article. The jupyter notebook is also available at https://github.com/lauinger/cost-of-frequency-regulation-through-electricity-storage.

2. delta_10s.h5, which contains normalized frequency deviations with a 10s resolution from 1 January 2015 through 31 December 2019. This dataset is constructed from the raw frequency measurement data in build_delta_10s.rar. The frequency measurements are taken from the website of the French transmission system operator RTE: https://www.services-rte.com/en/download-data-published-by-rte.html?category=public_transmission_system&type=network_frequencies (link live as of 7 June 2023).

3. pa.h5, which contains availability prices for delivering frequency regulation to RTE from 1 January 2015 through 31 December 2019. This dataset is constructed from the raw price data in build_price_data.rar. The availability price data are taken from the website of the French transmission system operator RTE:
https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&category=market&type=balancing_capacity&subType=procured_reserves (link live as of 7 June 2023).

4. pd.h5, which contains delivery prices for delivering frequency regulation to RTE from 1 January 2015 through 31 December 2019. This dataset is constructed from the raw price data in build_price_data.rar. The delivery price data are taken from the website of the French transmission system operator RTE:
https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&category=market&type=balancing_capacity&subType=actived_offers (link live as of 7 June 2023).

5. pb.h5, which contains utility prices for a subscribed apparent power of 9kVA from the state regulated "tarif bleu" of EDF, the largest electricity provider in France. This dataset is constructed from the raw price data in build_price_data.rar. Current price data (including taxes and transportation fees) is available at https://particulier.edf.fr/content/dam/2-Actifs/Documents/Offres/Grille_prix_Tarif_Bleu.pdf. The French Energy Department publishes the electricity prices in the official French Government journal. The corresponding legal texts are accessible under the following links:
01/11/2014 - 31/07/2015: https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000033172637
01/08/2015 - 31/07/2016: https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000030954456
01/08/2016 - 31/07/2017: https://www.edf.fr/sites/default/files/contrib/collectivite/electricite-et-gaz/CGV%2018avril/jo_du_29_juillet_2016_trv.pdf
01/08/2017 - 31/01/2018: https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000035297675
01/02/2018 - 31/07/2018: https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000036559814
01/08/2018 - 31/05/2019: https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000037262170
01/06/2019 - 01/08/2019: https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038528381
01/08/2019 - 31/12/2019: https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038850867
These prices exclude taxes and transportation fees. The "Option Base" offers a flat price throughout the day. The "Option Heures Creuses" has a higher price for peak (6:00-22:00) than off-peak (22:00-6:00) hours. EDF refers to peak hours as "Heures Pleines (HP)" and to off-peak hours as "Heures Creuses (HC)". The prices of these options only change, when EDF changes its electricity tariffs, which is up to three times per year. Conversely, the "Option Tempo" is a pricing scheme in which each day is either a high-price ("Rouge"), medium-price ("Blanc") or low-price ("Bleu") day. The price level of each day is announced by 10:30 am on the previous day. RTE, the French transmission system operator, keeps track of the daily price levels (https://www.services-rte.com/en/view-data-published-by-rte/schedule-of-Tempo-type-supply-offerings.html). The price data can be downloaded from RTE's eco2mix platform (https://www.rte-france.com/eco2mix/telecharger-les-indicateurs).

Economic Risk, Resources and Environment (ERRE) is a system dynamics model whose purpose is to analyse the financial pressures emerging from global economic growth while coping with natural limits in both energy and agricultural systems. A major feature of the model is to integrate in the same framework both the dynamic evolution of long term phenomena (e.g. energy transition, climate effects) and the short to medium term structures that are more relevant to decision making in the real world (e.g. extreme weather effects, irrational behaviours of markets). Building on the World3-03 Limits to Growth model, ERRE links the financial system with the energy, agriculture and climate systems through the real economy, by means of feedback loops, time lags and non-linear rationally bounded decision making. Prices and their interaction with growth, inflation and interest rates are assumed to be the main driver of economic failure while reaching planetary limits. Developed within the the CUSP System Dynamics theme, the model allows for the stress-testing of fat tail extreme risk scenarios, such as climate shocks, energy transition, monetary policies and carbon taxes. Risks are addressed via scenario analyses, compared to real available data, and assessed in terms of the economic theory that lies behind.We propose to establish a multi-disciplinary Centre for the Understanding of Sustainable Prosperity (CUSP). Led by the University of Surrey, CUSP will work with a range of academic and non-academic partners to establish a rich international network of collaborative research. The aim of this research will be to explore the economic, ecological, social and governance dimensions of sustainable prosperity and to make concrete recommendations to government, business and civil society in pursuit of it. Our guiding vision for sustainable prosperity is one in which people everywhere have the capability to flourish as human beings - within the ecological and resource constraints of a finite planet. Our work will explore not just the economic aspects of this challenge, but also its social, political and philosophical dimensions. We will address the implications of sustainable prosperity at the level of households and firms; and we will explore sector-level and macro-economic implications of different pathways to prosperity. We will pay particular attention to the pragmatic steps that need to be taken by enterprise, government and civil society in order to achieve a sustainable prosperity. The CUSP work programme is split into five themes (our MAPSS framework). Theme M explores the moral framing and contested meanings of prosperity itself. Taking a broadly philosophical approach we examine how people, enterprise and government negotiate the tensions between sustainability and prosperity. Theme A explores the role of the arts and of culture in our society. We will look not only at the role of the arts in communicating sustainability but at culture as a vital element in prosperity itself. Theme P addresses the politics of sustainable prosperity and explores the institutional shifts that will be needed to achieve it. We will work closely with both corporate and social enterprise to test new models of sustainability for business. Theme S1 explores the social and psychological dimensions of prosperity. We will work with households and individuals in order to understand how people negotiate their aspirations for the good life. As part of this theme we will engage with UNEP in a major study of young people's lifestyles across the world. Theme S2 examines the complex dynamics of social and economic systems on which sustainable prosperity depends. We will address in particular the challenge of achieving financial stability and high employment under conditions of constrained resource consumption. Alongside our MAPSS work programme, we will initiate a major international Sustainable Prosperity Dialogue (chaired by Dr Rowan Williams - former Archbishop of Canterbury and Master of Magdalene College Cambridge). We will also establish an international network of CUSP Fellows from both academic and non-academic institutions. Model development (software). A book, Resources, Financial Risk and Dynamics of Growth: Systems and Global Society by Roberto Pasqualino and Aled Jones, was published in 2020 by Routledge and describes the background to this model development. Here you will find the appendix to that book (Appendix_ERRE.pdf) which contains the detail equations and model structure alongside the Vensim ERRE model (ERRE_Model_10012020.vpm), a short guide (ERRE Vensim Reader Guide.pdf) and scenario runs and data (*.vdf files).

The decarbonisation of domestic heating is essential for the UK to achieve net zero carbon emissions, but requires significant changes in domestic infrastructure. Public participation plays a pivotal role in this transition, yet public attitudes towards decarbonised heating remain under-researched and poorly understood. We collected a nationally representative dataset via an online survey of 2,226 individuals in Great Britain, and an additional booster sample dataset of 1,378 individuals in Scotland and Wales specifically. The survey explored attitudes to three decarbonised heating technologies currently being trialled or entering the market: heat pumps, hydrogen heating, and district heating networks. A wide dataset of interrelated variables was collected, including heating system preference and usage, knowledge and support for decarbonised heating, environmental and energy security concerns, perceptions of trust and responsibility, financial considerations, and many others. Central to the study was an informed choice decision pathway element designed to investigate key factors underlying personal willingness to adopt each technology.The UK energy system is changing rapidly. Greenhouse gas emissions fell by 43% between 1990 and 2017, and renewables now account for 30% of electricity generation. Despite this progress, achieving emissions reductions has been difficult outside the electricity sector, and progress could stall without more effective policy action. The Paris Agreement means that the UK may have to go further than current targets, to achieve a net zero energy system. Reducing emissions is not the only important energy policy goal. Further, progress need to be made whilst minimising the costs to consumers and taxpayers; maintaining high levels of energy security; and maximising economic, environmental and social benefits. There is a clear need for research to understand the nature of the technical, economic, political, environmental and societal dynamics affecting the energy system - including the local, national and international components of these dynamics. This proposal sets out UKERC's plans for a 4th phase of research and engagement (2019-2024) that addresses this challenge. It includes a programme of interdisciplinary research on sustainable future energy systems. This is driven by real-world energy challenges whilst exploring new questions, methods and agendas. It also explains how UKERC's central activities will be developed further, including new capabilities to support energy researchers and decision-makers. The UKERC phase 4 research programme will focus on new challenges and opportunities for implementing the energy transition, and will be concerned with the three main questions: - How will global, national and local developments influence the shape and pace of the UK's transition towards a low carbon energy system? - What are the potential economic, political, social and environmental costs and benefits of energy system change, and how can they be distributed equitably? - Which actors could take the lead in implementing the next stage of the UK's energy transition, and what are the implications for policy and governance? To address these questions, the research programme includes seven interrelated research themes: UK energy in a global context; Local and regional energy systems; Energy, environment, and landscape; Energy infrastructure transitions; Energy for mobility; Energy systems for heat; and Industrial decarbonisation. The proposal sets out details of research within these themes, plans for associated PhD studentships and details of the flexible research fund that will be used to commission additional research projects, scoping studies and to support integration. A first integration project on energy and the economy will be undertaken at the start of UKERC phase 4. The research themes are complemented by four national capabilities that form part of the research programme: an expanded Technology and Policy Assessment (TPA) capability; a new Energy Modelling Hub; the UKERC Energy Data Centre; and a new Public Engagement Observatory. Research within TPA and the Observatory will align and integrate with the main research themes. These four capabilities will also enhance UKERC's ability to provide evidence, data and expertise for academic, policy, industry and other stakeholder communities. The UKERC headquarters (HQ) team will support the management and co-ordination of the research programme; and will also undertake a range of other functions to support the broader UK energy research community and its key stakeholders. These functions include promoting networking and engagement between stakeholders in academia, policy, industry and third sector (including through a networking fund), supporting career development and capacity building, and enhancing international collaboration (including through the UK's participation in the European Energy Research Alliance). The data was collected via a nationally representative online survey of the general public in Great Britain. The sample was selected via quota sampling, using nationally representative quotas for age, education, gender and region. The same procedure was applied for the booster samples which sampled from Scotland and Wales specifically, with the exception that only age, education and gender were used as the sampling quotas. All individuals sampled were over 18 and drawn from a panel provided by a third-party polling company.

France Electricity decreased 9.65 EUR/MWh or 13.82% 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.

The smart grid is on the basis of physical grid, introducing all kinds of advanced communications technology and form a new type of power grid. It can not only meet the demand of users and realize the optimal allocation of resources, but also improve the safety, economy and reliability of power supply, it has become a major trend in the future development of electric power industry. But on the other hand, the complex network architecture of smart grid and the application of various high-tech technologies have also greatly increased the probability of equipment failure and the difficulty of fault diagnosis, and timely discovery and diagnosis of problems in the operation of smart grid equipment has become a key measure to ensure the safety of power grid operation. From the current point of view, the existing smart grid equipment fault diagnosis technology has problems that the application program is more complex, and the fault diagnosis rate is generally not high, which greatly affects the efficiency of smart grid maintenance. Therefore, Based on this, this paper adopts the multimodal semantic model of deep learning and knowledge graph, and on the basis of the original target detection network YOLOv4 architecture, introduces knowledge graph to unify the characterization and storage of the input multimodal information, and innovatively combines the YOLOv4 target detection algorithm with the knowledge graph to establish a smart grid equipment fault diagnosis model. Experiments show that compared with the existing fault detection algorithms, the YOLOv4 algorithm constructed in this paper is more accurate, faster and easier to operate.

Spain Electricity decreased 39.18 EUR/MWh or 28.84% 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.

The enhancement of economic sustainability and the reduction of greenhouse gas (GHG) emissions are becoming more relevant in power system planning. Thus, renewable energy sources (RESs) have been widely used as clean energy for their lower generation costs and environmentally friendly characteristics. However, the strong random uncertainties from both the demand and generation sides make planning an economic, reliable, and ecological power system more complicated. Thus, this paper considers a variety of resources and technologies and presents a coordinated planning model including energy storage systems (ESSs) and grid network expansion, considering the trustworthiness of demand-side response (DR). First, the size of a single ESS was considered as its size has a close effect on maintenance costs and ultimately affects the total operating cost of the system. Second, it evaluates the influence of the trustworthiness of DR. Third, multiple resources and technologies were included in this high-penetration renewable energy integrated power system, such as ESSs, networks, DR technology, and GHG reduction technology. Finally, this model optimizes the decision variables such as the single size and location of ESSs and the operation parameters such as thermal generation costs, loss load costs, renewable energy curtailment costs, and GHG emission costs. Since the problem scale is very large not only due to the presence of various devices but also both binary and continuous variables considered simultaneously, we reformulate this model by decomposition. Then, we transform it into a master problem (MP) and a dual sub-problem (SP). Finally, the proposed method is applied to a modified IEEE 24-bus test system. The results show computational effectiveness and provide a helpful method in planning low-carbon electricity power systems.

UK Electricity decreased 27.65 GBP/MWh or 26.99% 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.

Energy Inflation in the United States decreased to -1.60 percent in July from -0.80 percent in June of 2025. This dataset includes a chart with historical data for the United States Energy Inflation.