Search Results: Energy Category: Other Power and Storage Technologies

The BMRS website provides near real time and historic data about the Balancing Mechanism which is used by the National Grid (System Operator) as a means of balancing power flows on to and off the electricity Transmission System in Great Britain. Data is added on a near real-time basis

The annual State of the Sector report is a dataset about the community energy sector. It has always covered England and Wales, and in 2021 include Scottish data for the first time. Reports detail the data from the community energy sector in the previous year.

The DOE Global Energy Storage Database provides free, up-to-date information on grid-connected energy storage projects around the world and relevant state and federal policies. There is a searchable database on projects and policies, with drill-down capability. In the Projects section, it is possible to drill-down to individual plants, detailing technology (e.g. Vanadium Redox Flow Battery), power, duration and status. This data can be downloaded and used freely subject to conditions of use. Data visualization is also available.

The mission of the US Department of Energy Hydrogen and Fuel Cells Program is to reduce petroleum use, greenhouse gas emissions, and air pollution and to contribute to a more diverse and efficient energy infrastructure by enabling the widespread commercialization of hydrogen and fuel cell technologies. The Program Records area contains supporting data for DOE publications, described as Program-related, Analysis, Fuel cells, Production/delivery, Safety codes, and standards and Storage The U.S. DOE Hydrogen and Fuel Cells Program works in partnership with industry, academia, national laboratories, federal and international agencies to:

• Overcome technical barriers through research and development of hydrogen production, delivery, and storage technologies, as well as fuel cell technologies for transportation, distributed stationary power, and portable power applications
• Address safety concerns and develop model codes and standards
• Validate and demonstrate hydrogen and fuel cell technologies in real-world conditions
• Educate key stakeholders whose acceptance of these technologies will determine their success in the marketplace.

The publications supported by these records can also be found on the website.

The database of European energy storage technologies and facilities includes: energy storage technologies and their characteristics; front of the meter energy storage facilities in the EU-28, operational or in project, that are connected to the generation and the transmission grid; behind the meter energy storage installed capacity per country. Database and report available.

The data is from tests performed at The University of Sheffield, on a 1MWh/2MW lithium-titanate battery energy storage (BESS) by Toshiba SCiB Technology, within the EPSRC funded Multi-scale ANalysis for Facilities for Energy STorage (Manifest) Project EP/N032888/1. The system is connected directly to the grid through an 11kV feed, located at Willenhall Primary Substation. Interfacing to grid is achieved through a 2.2MVA ABB PCS100 inverter, and a 2.1MVA dry-type transformer with 1:30.55 ratio. The main parameters are related to time-series of grid voltage and frequency, AC and DC voltages and currents, operating conditions (such as for example room temperature), etc.

The experimental background is explained in Experiment_Description_Readme.pdf

The data are time-stamped in the format MM/DD/YYYY HH:MM:SS UTC, while the parameters are given using the International System of Units.
This catalogue entry was updated on 2021-02-03 to link directly to the data from the calibration test carried out on 2017-03-01. Previously it linked to the top level of the Sheffield BESS test data at https://data.ukedc.rl.ac.uk/browse/edc/power/storage/MANIFEST/Sheffield. The title was updated to include the words "Calibration Test" to differentiate it from other tests carried out with the Sheffield BESS facility. The previous title was "EPSRC ManIfest Battery Energy Storage System (BESS) The University of Sheffield". No changes have been made to the dataset and no other changes have been made to this catalogue record.

The data was collected during instantaneous power loss and efficiency tests performed at The University of Manchester in 2018 and 2020 , on a 240kVA and 180kWh Siemens SieStorage lithium-ion battery energy storage system (BESS). The system is connected behind the meter to the low-voltage distribution network via a 260kVA, 400/433V TMC isolation transformer. This data was collected as part of an experiment within the MANIFEST project.
The EPSRC-funded Multi-scale ANalysis for Facilities for Energy Storage (MANIFEST) Project (EP/N032888/1) aimed to study and investigate the capabilities of energy storage facilities installed in UK Universities.
There are 47 data files in this dataset.

The data is from efficiency tests performed at The University of Sheffield, on a 1MWh/2MW lithium-titanate battery energy storage (BESS) by Toshiba SCiB Technology, within the EPSRC funded Multi-scale ANalysis for Facilities for Energy STorage (Manifest) Project EP/N032888/1. The system is connected directly to the grid through an 11kV feed, located at Willenhall Primary Substation. Interfacing to grid is achieved through a 2.2MVA ABB PCS100 inverter, and a 2.1MVA dry-type transformer with 1:30.55 ratio. This data was collected as part of an experiment within the MANIFEST project to study the efficiency of the Willenhall Energy Storage System, managed by The University of Sheffield.The EPSRC funded Multi-scale ANalysis for Facilities for Energy STorage (Manifest) Project (EP/N032888/1) aimed to study and investigate the capabilities of energy storage systems installed in UK Universities.The data presented here has contributed to the following publications:'Experimental Analysis of Efficiencies of a Large Scale Energy Storage System', S. Mori, M. J. Smith, D. T. Gladwin & D. A. Stone, 'Practical state estimation using Kalman filter methods for large-scale battery systems', Z. Wang, D. T. Gladwin, M. J. Smith, S. Haass (Under review)

The data is from tests performed at The University of Manchester on a commercial 240 kW 180 kWh lithium-ion battery electrical energy storage system (BESS) within the EPSRC funded Multi-scale ANalysis for Facilities for Energy STorage (Manifest) Project (EP/N032888/1). The system is connected to the LV network (400 V) and is located on campus within a mixed-use (offices, lecture theatres, and laboratories) university building (so behind the meter). The tests carried out in March 2017 are described in the PDF Experiment_Description_Readme.pdf

The experiments were conducted using an Energy Storage System (ESS) in the Smart Grid laboratory at Newcastle University that includes transformers and converters interfacing to the grid, and various energy storage assets. The aim was to demonstrate the control of power flows between the grid and a 90kW / 2kWh supercapacitor bank. Tests were performed to control real power steps (short duration / high power, and long duration / low power) and reactive power steps of the grid-coupled converter. The corresponding power time-series at the supercapacitor bank terminals and the State of Charge (SoC) were recorded and plotted in the accompanying report.

The data were collected as part of an experiment within the MANIFEST project to demonstrate control operations of an ESS using the Smart Grid Laboratory at Newcastle University, UK.

The EPSRC funded Multi-scale ANalysis for Facilities for Energy STorage (Manifest) Project (EP/N032888/1) aimed to study and investigate the capabilities of energy storage systems installed in UK Universities.

In this experiment the efficiency of an emulated Energy Storage System (ESS) is measured in the Smart Grid laboratory at Newcastle University. Real-time simulation is carried out using a TRIPHASE real-time simulator to model the ESS network, allowing the laboratory hardware to operate as though it is coupled to a real distribution-scale network. The ESS emulator was used to represent the characteristics of three Li-ion battery banks with nominal voltage, power and energy ratings as follows: (i) 200V 1kW 1kWh, (ii) 400V 5kW 5kWh, and (iii) 400V 10kW 10kWh.

The data were collected as part of an experiment within the MANIFEST project to study the efficiency of an emulated supercapacitor using the Smart Grid Laboratory at Newcastle University, UK.

The EPSRC funded Multi-scale ANalysis for Facilities for Energy STorage (Manifest) Project (EP/N032888/1) aimed to study and investigate the capabilities of energy storage systems installed in UK Universities.

In this experiment the response time of an Energy Storage System (ESS) was measured in the Smart Grid laboratory at Newcastle University. Real-time simulation was carried out using a TRIPHASE real-time simulator to model the ESS network, allowing the laboratory hardware to operate as though it is coupled to a real distribution-scale network. The ESS emulator was used to represent the characteristics of a Li-Ion battery with a single string of 50 series cells, each with a nominal voltage of 4 V and a capacity of 200 Ah; these parameters were selected in order to set the nominal voltage, power and energy ratings of the ESS to 200 V, 20 kW and 40 kWh respectively.

The data were collected as part of an experiment within the MANIFEST project to study the response time of an ESS using the Smart Grid Laboratory at Newcastle University, UK.

The EPSRC funded Multi-scale ANalysis for Facilities for Energy STorage (Manifest) Project (EP/N032888/1) aimed to study and investigate the capabilities of energy storage systems installed in UK Universities.

The experiments were conducted using an Energy Storage System (ESS) in the Smart Grid laboratory at Newcastle University, that includes transformers and converters interfacing to the grid, and various energy storage assets. They tested the efficiency of the system components, including the supercapacitor bank and associated power converters. Tests were performed to control the supercapacitor power steps, while measuring power at the terminals of each system component. Efficiencies of the system components for various initial values of supercapacitor state of charge (SoC) are presented in the data files and accompanying report.

The data were collected as part of an experiment within the MANIFEST project to demonstrate the efficiency of the supercapacitor energy storage system and associated power converters in the Smart Grid Laboratory at Newcastle University, UK.

The EPSRC funded Multi-scale ANalysis for Facilities for Energy STorage (Manifest) Project (EP/N032888/1) aimed to study and investigate the capabilities of energy storage systems installed in UK Universities.

High voltage electricity network data for Ireland, Northern Ireland and for all island of Ireland. Data include system demand and generation, wind generation, interconnection, balancing and prices, frequency, system non-synchronous penetration, carbon intensity and network maps. Can select Ireland, Northern Ireland or all island data.

Electricity North West is a distribution network operator. Data available includes network data, network assets, capacity and embedded generation. Electricity North West is a distribution network operator (DNO) covering the north-west of England.

Elexon data from the Balancing and Settlement Code (BCS) systems that are available include: Balancing Mechanism Reporting Service (BMRS) data; the Elexon Portal and operational reports. If you're a company who isnt a BSC Party but would like to receive specific BSC data, there are contact details to request standard packages of reports that are available to non-Parties on request, subject to set licensing arrangements and fees.

Central Data Collection Agent (CDCA) data : Inter Grid Supply Point Group Connection Period Data (IPD); Grid Supply Point Period Data (GP9); Aggregated Grid Supply Point Group Take Volumes (AGV); Balancing Mechanism Unit Aggregation Report (ABV). Settlement Administration Agent (SAA) data: Aggregate Party Day Charges (APC); Market Index Day Period Information (MD1); Settlement Period Information (SPI); Settlement Period Information (SSD); Trading Unit Period Information (TRA). Data descriptions are also available on this web page.

ELEXON Ltd is the Balancing and Settlement Code Company (BSCCo) defined and created by the Balancing and Settlement Code (BSC and also known as the Code). ELEXON Ltd procures, manages and operates services and systems, which enable imbalance settlement of the wholesale electricity market and retail competition in electricity supply. The Portal gives access to the archives of historic SSP, SBP and NIV data which are updated automatically every day, and historical BMRS data via the BMRA Data Archive on this site (formerly TIBCO Relay Service). The Elexon portal gives access to historical data from the Balancing Mechanism Reporting System (BMRS) (which only retains around 18 months of data online) along with reports and other publications, policy information and the contents of the actual BMRS code. There is a useful site map at the bottom of the home page.

ENERGYDATA.INFO is an open data platform providing access to datasets and data analytics that are relevant to the energy sector. Data on energy access, transmission and distribution, renewable energy and resource assessment. Tools and data sets are available.

Energy Networks Association databases including statutory data, safety statistics, engineering standards and terms, and an Energy Data Request Tool for requesting any type of network data. The Energy Data Request Tool can be used by anyone to request any type of network data.

Cost and performance metrics for individual energy storage technologies which track: cost to procure, install, and connect an energy storage system; associated operational and maintenance costs; and end-of life costs. Technologies are: lithium-ion (lithium iron phosphate and nickel manganese cobalt chemistries); vanadium redox flow; lead acid; pumped storage hydropower; compressed air energy storage; and hydrogen. 2020 data and 2030 estimates of costs and performance are available.

European electricity harmonisation and electrotechnical standards, based on a consensus, which reflects the economic and social interests of 33 CENELEC Member countries channelled through their National Electrotechnical Committees (NCs). Most standards are initiated by industry. Other standardization projects can come from consumers, Small and Medium-sized Enterprises (SMEs) or associations, or even European legislators. Besides European Standards, CENELEC produces other reference documents, which can be developed quickly and easily: Technical Specifications, Technical Reports and Workshop Agreements. CENELEC is a Non Profit International Association, forming the officially recognised European Standards Organisations (ESOs) together with CEN, the European Committee for Standardization and ETSI, the European Telecommunications Standards Institute. CENELEC acts as a platform for experts to develop European Standards (ENs), which facilitate world trade by removing barriers to trade, enhancing economic growth and leading to new markets.CENELEC concentrates most of its work on 2 major deliverables: The European Standard (EN) and the Harmonization Document (HD). These two documents are referred to commonly as "standards" and must be implemented in all CENELEC member countries, who must also withdraw any conflicting standard.

The European Environment Agency provides sound, independent information on the environment for those involved in developing, adopting, implementing and evaluating environmental policy, and also the general public. The site includes interactive data viewers for datasets. There are also visualizations on specific topics such as Progress on Energy Efficiency in Europe, and publications. A major information source for those involved in developing, adopting, implementing and evaluating environmental policy, and also the general public. Currently, the EEA has 33 member countries. The website is available in a wide range of languages. There is a Semantic Data Service, allowing access to datasets using APIs, but datasets can also be downloaded as zip files and INSPIRE-compliant metadata sets. No registration is needed, but on download, some usage feedback is requested.

Levelised unit cost data (£/MWh 2006) for electricity generation from various primary energy sources in various countries. Unit cost“relevance” rating. Plant types comprise: Coal, Nuclear, Gas, Wind, Coal (other), CHP, PV, Gas (other), Biomass & Energy Crops, Hydro, Waste, Wind (offshore), Geothermal, Solar Thermal, Wave & Tidal Stream, Fossil Fuel (other), Fuel cell, Nuclear (other). The database is in Heptonstall2007_LevelisedElectricityCostData.xls
Contains information for Australia, Austria, Belgium, Brazil, Canada, China, Czech Republic, Denmark, Finland, France, Germany, Greece, India, Indonesia, Italy, Japan, Mexico, Netherlands, New Zealand, Portugal, Spain, Romania, Slovakia, South Africa, South Korea, Sweden, Switzerland, Turkey, UK, USA
A full list of references with links to original documents is provided in "A Review of Electricity Unit Cost Estimates" (PDF 300 KB)

Data Portal for National Grid Electricity System Operators (ESO) in Great Britain. Data available include: ancillary services, balancing costs, carbon intensity, connection registers, constraint management, demand, generation, network charges, system-wide data, trading, plans, reports and Future Energy Scenarios. An API is available.

A forecast of electricity use in the UK in the next ten years, with different scenarios base on different assumptions about demand, supply and European interconnection. Published annually. The PDF report gives the overview and scenario forecasts. All the supporting data is downloadable to Excel.

Automated Demand Response trials within the New Thames Valley Vision (NTVV) project installed Building Management Systems within 27 commercial buildings within the Bracknell area. The project was then able to enact load shed events on the building with differing notice periods. Background information on the NTVV project and the ADR trial can be found in report SDRC_9.1c_Demand_Side_Response_Evidence_Report_1.0.pdf and at www.thamesvalleyvision.co.uk.
Use of these data is subject to terms and conditions.

The data set records EMMA thermal storage devices trialed in the New Thames Valley Vision (NTVV) project and includes PV and grid data for 73 households. The EMMA unit is a device which is fitted in conjunction with Solar PV installs in properties that have an immersion heater. The device diverts any excess generation energy, into the immersion heater to heat water, rather than send the excess electricity back onto the grid. The data set records EMMA, PV and grid data for 73 households.
Background information on the NTVV project and the EMMA trial can be found in report SDRC_9.4b_Evidence_Report_1.0.pdf and at www.thamesvalleyvision.co.uk.
Data format information is in EMMA_readme.txt.
Use of these data is subject to terms and conditions.

End point monitoring of household electricity demand and generation carried out within the New Thames Valley Vision (NTVV) project. End Point Monitoring in domestic households was carried out with two types of devices with 30 minute granularity. The data are described in EPM_readme.txt.
Background information on the NTVV project and end point monitoring with the EDMI units can be found in SDRC_9.2a_Evidence_Report_Rev_1.0_Final.pdf and at www.thamesvalleyvision.co.uk.
Use of these data is subject to terms and conditions.

Data from a trial of an Ice Bear cold thermal energy storage unit in one building during the New Thames Valley Vision (NTVV) project. This data records the trial of one Ice Bear unit in the New Thames Valley Vision (NTVV) project. The Ice Bear unit provides a block of ice, which is installed as part of an air conditioning system. During the day, the air conditioning unit will blow air through the ice, creating cool air. During the night, the Ice Bear will freeze the ice again, ready for use the next day, so deferring energy usage from the day time, to the night time.
The data format is described in Ice_Bear_readme.txt.
Background information on the NTVV project and the Ice Bear unit can be found in report SDRC_9.8a_4_Cold_Thermal_LV_Network_Energy_Storage.pdf and at www.thamesvalleyvision.co.uk.
Use of these data is subject to terms and conditions.

Monitoring data from 4 primary and 316 secondary substations in the New Thames Valley Vision (NTVV) project. Information about the substations data can be found in Substations_readme.txt.
Background information on the NTVV project and substation monitoring can be found in SDRC_9.2b_Evidence_Report_Rev_1.0.pdf and at www.thamesvalleyvision.co.uk.
Use of these data is subject to terms and conditions.

Northern Power Grid is a distribution network operator. Their network data includes Distribution Future Energy Scenarios, Auto Design connection tool, embedded generation capacity register, long-term development statement (including network information), generation and demand heat maps, and contracted capacity register. Northern Power Grid is a distribution network operator (DNO) covering north east England, Yorkshire and north Lincolnshire.

As well as linking to the OECD Data statistics, this includes statistics from other sources such as the IEA, Nuclear Energy Agency and EUROSTAT, indicator information, factbook and yearbook series and other analyses and syntheses. Differs from the OECD Data offering as it draws on more sources and covers a wider range of countries. Acts as a federated search rather than holding its own datasets. However, there are far more books, papers, factbooks and so on accessible from here.

Great Britain energy market data from the retail market, from the wholesale market, energy network indicators and customer service data. Material featured on the Ofgem website is subject to Crown copyright protection unless otherwise indicated. You may use Crown copyright information (not including logos) free of charge in any format or medium, under the terms of the Open Government Licence.

A free and open data platform for energy system modelling. OPSD collect, check, process, document, and publish data that are publicly available but currently inconvenient to use. Open access where possible but some data published here might be subject to copyright. Check specific terms by data set.
For further information see "Open Power System Data Frictionless data for electricity system modelling" in Applied Energy. https://doi.org/10.1016/j.apenergy.2018.11.097

Spatially disaggregate half-hourly data on GB electricity generation and consumption, including detailed per-generator half-hourly generation and balancing operations. The P114 data will be uploaded in batches currently the dataset contains folders for data released in June 2016, January 2018, January 2019, January 2020, January 2021, January 2022, January 2023 and April 2023. A data description document or read me file is available for each release. NB the number of records is very large - indicated by the value of 999M in the number of records field.

Energy systems are changing rapidly, bringing new types of risks, and new forms of potential disruption to energy supplies. Our growing dependence on energy, particularly electricity, means that more than ever we need to plan for disruptions and be prepared for them. What happens during the disruption is important: we need to understand how individuals, communities and organisations experience the event, and what measures can be taken to reduce the overall impacts. This study investigates how people and communities in the city of Glasgow (Scotland) might be expected to respond to a lengthy, widespread disruption to energy supplies. A novel three-stage diary-interview methodology was used to explore energy practices and expectations dependency, and to understand the ways in which peoples experience of disruptions may change in the coming decade. The results show that the most consistent determinant of participants perceived resilience, over and above socio-demographic factors, is their expectations and their degree of dependency on routine. In addition, the results suggest that common assumptions regarding peoples vulnerability may be misplaced, and are shifting rapidly as digital dependency grows, and are sometimes misplaced: in particular, determinants such as age and income should not be seen as straightforward proxies for vulnerability. A new set of indicators of vulnerability are identified. For longer outages, peoples ability to cope will likely decrease with duration in a non-linear step-change fashion, as interdependent infrastructures and services are affected. Community-level actions can improve resilience, and local scales may be more appropriate for identifying vulnerabilities than socio-demographic proxies, but this is only feasible if organisations and institutions are adequately resourced.Recent events have highlighted the potential impact of long, widespread energy supply interruptions, and the need for resilience is likely to create a requirement for greater flexibility from both the electricity and gas systems. This project will examine the engineering risks, and assess the need for new industry standards to drive appropriate action; and conduct a systematic assessment of the impacts of a serious energy disruption on consumers and critical services, such as heating, water, communications, health and transport. This dataset is held in the UKDS

UK Government has collected and compiled estimates of electricity consumption at regional (NUTS1) and local authority levels (LAU1 - formally NUTS 4) using aggregated Meter Point Reference Number (MPRN) readings throughout Great Britain as part of government's annual meter point electricity data exercise. In March 2008, this electricity consumption dataset gained National Statistics status. This status applies to all data from 2005 onwards. The 2003 and 2004 data are still classed as experimental.

The Renewable Energy Planning Database (REPD) and the Heat Networks Planning Database are updated and published every quarter. The Renewable Energy Planning Database tracks the progress of renewable electricity and heat networks projects. The Heat Networks Planning Database covers district as well as communal heat network projects. The Renewable Energy Planning Database tracks the progress of renewable electricity and heat networks projects. It provides as accurate and comprehensive a snapshot as possible of projects in both areas, and of progress across the technology sectors.The Heat Networks Planning Database covers district as well as communal heat network projects.It aims to provide a more complete picture of heat network deployment across the UK.
The databases are: updated during the month following the end of each quarter; and are provided in 2 separate CSV files, and on separate worksheets within a single XLSX file.

Flexible Networks for a Low Carbon Future was a Low Carbon Networks Fund innovation trial project, led by SP Energy Networks. As part of the project, network monitoring equipment was installed in eight primary (33/11kV) and over 150 secondary (11kV/415V) substations in three test areas

SP Energy Networks are pleased to provide access to the complete data set (which is hosted by the University of Strathclyde) in response to requests.

SP Energy Networks is a distribution and transmission network operator. The data hub includes mapping data, strategic documentation, asset data and stakeholder data. SP Energy Networks is the distribution network operator for northern Wales, southern Scotland and part of north-west England. It is also the transmission operator for southern Scotland.

Scottish and Southern Electricity Networks is a distribution and transmission network operator. The embedded capacity register includes generation and storage resources (1MW) that are connected, or accepted to connect. It includes information on the flexibility services that are being provided by connected resources. Scottish and Southern Electricity Networks is the distribution network operator (DNO) for central southern England and northern Scotland. It is the transmission operator for southern Scotland.
The embedded capacity register provides information on generation and storage resources (1MW) that are connected, or accepted to connect, to the electricity distribution networks owned and operated by us and it will be updated on a monthly basis. The register also includes information on the flexibility services that are being provided by connected resources, assisting to control or schedule demand and/or generation to reduce network constraints. As of July 2020, the register has been updated to include additional data fields and information including the network reinforcement needed to provide connections.

Scottish and Southern Electricity Networks is a distribution and transmission network operator. Data available include network assets maps, generation availability map, demand heat map, loading and rating information and high voltage schematics, long-term development statement and technical specifications. Scottish and Southern Electricity Networks is the distribution network operator (DNO) for central southern England and northern Scotland. It is the transmission network operator for northern Scotland.

The Electricity Pool operated from 1st April 1990 to 26th March 2001, controlling the trading of electricity within England and Wales. The Electricity Pool data contains system demand and transmission losses plus the price information (/MWh) within the trading pool, comprising the three main prices: Pool purchase price (PPP), the basis of payments to generators; Pool selling price (PSP), the basis of payments by suppliers; and system marginal price (SMP), the highest offered price for any generating set scheduled by the Pool to run before system constraints are taken into account. The Electricity Pool system was replaced by the New Electricity Trading Arrangements (NETA/BETTA) from 27 March 2001. (Note : It is mandatory to acknowledge the UKERC EDC and the data owner Poolit Ltd when any use of this data is made in publications) Explanation of parameters and pool operation
PSP : Pool Selling Price, is the price paid mostly by RECs purchasing electricity from the Pool to sell to their final commercial, industrial and residential customers; this value is determined for two distinct price-rule regimes referred to as Table A and Table B periods, such that: PSP = SMP + CC + UPLIFT = PPP + UPLIFT
SMP : System Marginal Price is the highest offered price for any generating set scheduled by the Pool before system constraints are taken into account
LOLP : Loss Of Load Probability, a decreasing function of the expected amount of excess capacity available during each half-hour period, determined for each half-hour as the probability of a supply interruption due to generation capacity being insufficient to meet demand (the greater the amount of capacity available relative to expected demand in any half-hour, the lower the LOLP and therefore the lower the capacity charge per KWH paid to generators)
VOLL : Value Of Lost Load, representing the per kWh willingness of customers to pay to avoid supply interruptions; it was set by the Director General of the Office of Electricity Regulation (OFFER) at 2,000 per megawatt-hour (MWH) for 1990/91 and was then increased annually by the growth in the RPI
PPP : Pool Purchase Price, is the price paid to generators for electricity purchased into the Pool
CC : Capacity Charge: CC = LOLP * (VOLL - SMP) is a signal to generators of the necessity for additional generation capacity and to consumers that their consumption has a significant probability of requiring the maximum amount of generating capacity available in that load period
UPLIFT : a charge to compensate generators for reserve, plant available but not actually used to meet demand, and startup costs, known only ex post and therefore the only price uncertainty from the day ahead perspective; it is collected over at least 28 Table A pricing periods each day (UPLIFT is zero for Table B pricing periods) according to the formula: Availability Payment/MWh = LOLP*(VOLL - max(SMP, bid price))

UKESTO showcases national energy storage innovation, describing energy storage facilities in the UK and providing data from test beds.

In 2012 the Engineering and Physical Sciences Research Council (EPSRC) funded the Energy Storage Capital Grants call, where fifteen institutions received 30m pounds of funding across five consortia for the development and testing of energy storage technologies that span application areas. The consortia leads were the University of Birmingham, Imperial College, Loughborough University, the University of Manchester, and the University of Sheffield.

In 2016 these institutions secured a 4m pound investment from EPSRC to deliver the Multi-scale Analysis for Facilities for Energy Storage (MANIFEST) project, where the UK Energy Storage Observatory is a major deliverable.

Annual, quarterly and monthly electricity statistics available to download. This page brings together all the UK Government data sources that focus on electricity including historic data (1920-2012) and imports, exports and transfers of electricity Brings together electricity data from DUKES (annual data), energy trends (articles, quarterly and monthly data) and historic data. These datasets form part of other dataset collections in the UK Government statistics.

UK Power Networks distribution network operators open data include data about their networks, operations, embedded generation and market intelligence data. UK Power Networks are the distribution network operator (DNO) for parts of eastern and south eastern England and London.

This dataset relates to the modelling component of the UKERC Phase 4 Project '4.2a Investment in infrastructure'. The aim is to assess the scale of investment risks in the electricity generation sector associated with uncertainty over the pathway towards net zero. The model comprises three components:1. Input data. Based in Excel, contains the scenario assumptions on the electricity system including generation technologies, wind and solar power availability, demand profiles, interconnector and storage capacities etc.2. Modelling files based on the open-source Antares modelling framework. This requires additional free software to be able to run the model. The model calculates the hourly dispatch of all types of plant included in the system.3. Excel-based financial model, based on post-processing the output of the Antares model.Additional 1_Metadata.xls, Input_ReadMe and Outputs_ReadMe files explain the method for running each step of the modelling process. Software available at https://antares-simulator.org/. Version used 8.0.2

Data collected at the UKGEOS (UK Geoenergy Observatories) facilities including drilling data packs, ongoing monitoring data and experiment results. The data from UKGEOS will apply to geothermal energy, hydrogen, carbon capture and storage, and storage solutions for wind, solar and tidal energy can reduce our carbon emissions.

UNSD maintains the Energy Statistics Database, which provides annual statistics on production, trade, transformation and consumption (end-use) for solid, liquid, and gaseous fuels, electricity, and heat. It contains basic statistics for more than 230 countries/territories from 1950 year onwards and is updated annually. The database contains data in their original units (e.g. metric tonnes, GWh) as well as calorific values to allow interfuel comparison in a common energy unit (terajoules).

Western Power distribution network operators Connected Data Portal includes data about their networks, operations, generation included embedded generation, flexibility and trial projects. Western Power Distribution is the electricity distribution network operator (DNO) for the Midlands and South West of England and for South Wales.