Results: Search for Energy System Analysis

Climate data explorer, provides free access to climate related data and visualization tools. Climate Watch, a new online climate data platform aimed at policymakers, researchers, media and other stakeholders. Climate Watch holds climate data, visualizations and resources on national and global progress on climate change.

A set of maps, graphs and other figures showing countries power consumptions, population densities, and areas, and comparing power consumptions per unit area with the power production per unit area of various renewables. Created Sir David John Cameron MacKay, FRS, FInstP, FICE, Regius Professor of Engineering in the Department of Engineering at the University of Cambridge. From 2009 to 2014 he was Chief Scientific Adviser to the UK Department of Energy and Climate Change (DECC). Author of the book Sustainable Energy - Without the Hot AirVarious versions of each map, chart or figure are available depending on the intended use, such as higher resolution for printing and as sequences for presentations.

The U.S. Energy Information Administration (EIA) collects, analyzes, and disseminates independent and impartial energy information. The International Energy Statistics provides an overview with visualizations for different energy sources, an interactive graphical tool, and various analyses at country and topic level.

The U.S. Energy Information Administration is committed to enhancing the value of its free and open data by making it available through an Application Programming Interface (API) and open data tools to better serve our customers. The data in the API is also available in bulk file, in Excel via the add-in, and via widgets that embed interactive data visualizations of EIA data on any website. By making EIA data available in machine-readable formats, the creativity in the private, the non-profit, and the public sectors can be harnessed to find new ways to innovate and create value-added services powered by public data. https://www.eia.gov/opendata/

The Greenhouse Gas Reporting Program (GHGRP) collects Greenhouse Gas (GHG) data from large emitting facilities, suppliers of fossil fuels and industrial gases that result in GHG emissions when used, and facilities that inject carbon dioxide underground. Datasets available include high level summaries (maps, charts etc), an interactive tool for facility-level access, multi-year data summary spreadsheets and searchable, downloadable facility-level datasets by 32 industry types. The data is not confined to CO₂ There are other datasets available on the site from the Greenhouse Gas Reporting Program (GHGRP) and the U.S. Inventory of Greenhouse Gas Emissions and Sinks. The Inventory goes back to 1990, but is not mappable to location as it works on aggregate national figures. The GHGRP is a more recent program, developed in response to Congressional mandate from 2008, so there are fewer years of data available. It works on reports from facilities, so can be mapped by location. Annual GHG data for covered facilities in 29 industry types is available starting in 2010. Annual GHG data for covered facilities in an additional 12 industry types is available starting in 2011.

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.

Energy Systems Catapult was set up to accelerate the transformation of the UK’s energy system and ensure UK businesses and consumers capture the opportunities of clean growth. The Catapult is an independent, not-for-profit centre of excellence that bridges the gap between industry, government, academia and research. This links to the searchable interface for the public datasets.

A list of UK energy infrastructure development applications currently under consideration, it is regularly updated. This document lists current applications under consideration. It includes:

• recommendations from the Planning Inspectorate to the Secretary of State at the Department for Business, Energy and Industrial Strategy for decisions on energy infrastructure applications under the Planning Act 2008
• applications for changes to Development Consent Orders
• applications under section 36 under the Electricity Act 1989
• applications for variations to existing S36 consents
• applications for Compulsory Purchase Orders under the Electricity Act 1989 and Acquisition of Land Act 1981
• requests to the Secretary of State for Direction under section 35 of Planning Act 2008
• applications under the Transport and Works Act 1992

Using heat demand data for domestic buildings sourced from the UKERC Energy Data Centre's "Spatio-temporal heat demand for LSOAs in England and Wales", we have identified Lower Layer Super Output Areas (LSOAs) exhibiting a linear heat density surpassing 2900 kWh/m indicating their potential suitability for a heat network. We have integrated borehole information from the British Geological Survey (BGS) Single Onshore Borehole Index (SOBI), the average thermal conductivity of general soils and rocks, SAP 10.2 annual average temperature on the ground surface, and the average geothermal gradient for council areas in Great Britain. Employing the specific heat extraction rate methodology, and referencing MCS 022 Borehole Heat Exchanger look-up tables, we have generated comprehensive heat supply potential data from boreholes.

View an interactive map using ArcGIS map viewer

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.

National Grid gas transmission data for Great Britain. A wide range of data available on balancing, calorific value, demand, flows, linepack, LNG, price, storage, supplies and trading. An API is available for data access.

Summary of analyses of options for a low carbon economy (includes MARKAL model results by Future Energy Solutions). This report contains the results of a study, undertaken on behalf of DTI, DEFRA and the PIU, to develop a range of bottom-up estimates of carbon dioxide emissions from the UK energy sector up to 2050, and to identify the technical possibilities and costs for the abatement of these emissions. Three levels of abatement by 2050 have been considered: a 60% reduction relative to emissions in 2000 - approximating to the level considered by the Royal Commission on Environmental Pollution (RCEP 2000) - plus 45% and 70% reductions relative to 2000 levels. Marsh, G, Taylor, P, Haydock, H, Anderson, D, Leach, M (2003). Options for a Low Carbon Future. DTI Economics Paper No 4, Department of Trade and Industry.

This report summarises the modelling work undertaken for the 2003 Energy White Paper using the MARKAL energy model. It describes the scenarios andsensitivities used in the modelling work. It concludes that there is a variety of carbon abatement options across the energy supply sector, in improving energy efficiency and across the transport sector. Total abatement costs are uncertain and vary considerably by scenario andsensitivity but are unlikely to have a significant impact on GDP growth.Innovation and technical progress are key elements in reducing the costs ofcarbon abatement. The report compares the costs from the MARKAL model with those from a range of other models. Most of the studies suggest GDP losses of under 3% in 2050. It looks at the different types of economic models used to analyse abatement costs and the reasons why they might produce varying cost estimates This is now archived.Leach, M, Anderson, D, Taylor, P, Marsh, G (2005). Options for a Low Carbon Future: Review of Modelling Activities and an Update. DTI Occasional Paper No 1, Department of Trade and Industry.

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

The datasets available for download include:1. The thermal characteristics for 16 dwelling categories for all the LSOAs in England and Wales before and after considering energy efficiency measures.2. The magnitude and duration of positive and negative flexibility services that can be provided by air source heat pumps in dwellings at local authority level. Dates refer to the input data to the model.

Annual residential heat demand data for Scotland at Lower Layer Super Output Area (LSOA) level, before and after energy efficiency measures

This data set presents the underlying data used to produce the results for the UKERC working paper: Sensitivity Analysis of Net Zero Pathways for UK Industry or by searching the UKERC Publications section of this website. These data are the outputs of a how the The Net-Zero Industry Pathways (N-ZIP) model results are affected by changing a wider range of inputs than have been previously studied. The Net-Zero Industry Pathways (N-ZIP) model, developed by Element Energy, has been used by both the Climate Change Committee (CCC) and the Government to explore how industry can be decarbonised in way that is consistent with the UKs netzero greenhouse gas (GHG) target. This data set presents the underlying data used to produce the results for the UKERC working paper: Sensitivity Analysis of Net Zero Pathways for UK Industry or by searching the UKERC Publications section of this website.

Annual heat demand data for England and Wales at Lower Layer Super Output Area (LSOA) level, before and after energy efficiency measures. Normalised half-hourly profiles for heat production and energy consumption of different heating technologies. Costs for energy efficiency measures by local authority. The three datasets available for download include:

1. Annual heat demand data before and after adopting energy efficiency measures for 16 dwelling categories for all the LSOAs in England and Wales.
2. Normalised half-hourly profiles for the heat production and energy consumption of air-source heat pumps, ground source heat pumps, gas boilers and resistance heaters based on the UK average daily outside air temperature profile from 2013
3. The estimated costs to implement the energy efficiency measures to reach the annual heat demand displayed in the annual heat demand dataset, for each local authority in England and Wales.

This data set was updated on 14/10/2021. The top-level folder holds the latest version and a README file detailing the changes. The original version is retained in a sub-folder.

The numbers behind the Climate Change Committee's (CCC) sixth carbon budget. The sixth carbon budget provides UK ministers with advice on the volume of greenhouse gases that the UK can emit between 2033 and 2037 and has now been adopted in UK law. Interactive charts are available to explore the data.

The UK Data Service provides a single point of access to a wide range of social science secondary data, including international macrodata, and UK Census data. along with supporting documentation, guides and specialist support. The Economic and Social Data Service (ESDS) is now part of the UK Data Service, along with the former Secure Data Service, Survey Question Bank and elements of the ESRC Census Programme. The catalogue can be explored using the Discover tool. There is also a variable and question bank. Much of the data is open access, but registration is required for controlled and safeguarded data, and there is a charge for commercial use. Keyword searches in Discover can be refined by a wide range of facets such as case studies, series, ESRC outputs, Subject, data, spatial unit and so on.

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))

Model output from UK MARKAL Elastic Demand for all UKERC Energy 2050 project revised scenarios.
This consists of datasets as .xls files plus graphics summaries as PDFs Please acknowledge the UKERC Energy Systems Theme when making use of this data.
Reference this data source as: Keppo I Skea J. and Ekins P. 2013 UK Energy 2050 Revised Scenario Set Data UKERC Energy Data Centre http://ukedc.rl.ac.uk
The revisions are described in UKERC_Energy_2050_RevisedScenarios.pdf
There is further information at ReadMe_2050Scenarios.htmll
The .xls files are in the Data subdirectory

Annual energy projections, analyzing and projecting future energy use and greenhouse gas emissions in the UK to assist in monitoring progress towards meeting carbon budgets for the UK and to inform energy policy and associated analytical work across government departments. The projections are based on assumptions of future economic growth, fossil fuel prices, electricity generation costs, UK population and other key variables regularly updated. They also give an indication of the impact of the uncertainty around some of these input assumptions.
Each year, a PDF report is produced, along with a number of Annexes which are spreadsheets of the tables in the reports.

Technology variants, capital & O&M costs, efficiency, plant life-time characteristics for electricity generating plant as input to the UKERC Markal Energy Model (2007) sub-divided as CHP, Renewables, Nuclear, CO2 Pipeline & Storage, Gas-fired Plant, Coal, Electricity data for MARKAL The MARKAL_Electricity_cost_data_version_EDC_2007.xls spreadsheet contains cost information in the electricity generation sector drawn from a variety of sources. These references are listed in the worksheet entitled Sources.
The workbook is divided into labelled worksheets, according to different generic types of generating technology: CHP, Renewables, Nuclear, CO₂ pipeline and storage, Gas-fired plant, Coal. Each worksheet contains the data as input to the MARKAL model, showing a range of aspects of different generation technologies. The full set of MARKAL data covering all technologies is included in worksheet 7, entitled 7 Electricity data for MARKAL.
In worksheets 1-6, beneath the MARKAL data, other data has been added from the sources reviewed to illustrate the range of data which the models assumptions could plausibly have covered. The data is provided in two forms. In the first worksheet for each technology, the data is included in the form in which it is presented in the original sources. Thus, the units and currencies are not always those of the MARKAL model. Notes have been inserted to indicate the currency or unit of measurement. The information has been provided in this way so as to maintain transparency. In the second worksheet for each technology (marked A - for Adjusted), the data has been adjusted to match the currencies and units used in MARKAL. The numbers which have been subject to adjustment have been highlighted in yellow. A separate sheet showing the conversion factors used has been included (Conversion).
The information from other sources covers only a subset of the information within MARKAL. The definitions of key terms (such as operational and maintenance cost), is rarely consistent from one source to the next. In two places therefore, new columns have been inserted to the original structure. Information on construction periods (in years) has been included adjacent to the existing column showing first year available. The lack of distinction in the sources between fixed and variable O&M costs has also resulted in the creation of a single O&M column so that information can still be included.
The workbook should be used in conjunction with the report: Derek Smith, Comparative Cost Information: Supporting the development of the MARKAL MACRO model of the UK Energy Research Centre (UKERC), November 2006 which summarises the cost comparisons. These summaries include additional information to provide the context from which the data in this workbook have been drawn.

Model output (from UK MARKAL Elastic Demand) for all UKERC Energy 2050 project scenarios. Please acknowledge the UKERC Energy Systems Theme when making use of this data.
Reference this data source as: Strachan N. Anandarajah G. Hughes N. and Ekins P. 2010 UKERC Energy 2050 energy systems scenario data UKERC Energy Data Centre http://ukedc.rl.ac.uk
More information is available in UKERC_Energy_2050_Scenario_Data.pdf and ReadMe_2050Scenarios.html

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