This Data Package contains a list of renewable energy power plants in lists of renewable energy-based power plants of Czechia, Denmark, France, Germany, Poland, Sweden, Switzerland and United Kingdom. Czechia: Renewable-energy power plants in Czech Republic. Denmark: Wind and phovoltaic power plants with a high level of detail. France: Renewable-energy power plants of various types (solar, hydro, wind, bioenergy marine, geothermal) in France. Germany: Individual power plants, all renewable energy plants supported by the German Renewable Energy Law (EEG). Poland: Summed capacity and number of installations per energy source per municipality (Powiat). Sweden: Wind power plants in Sweden. Switzerland: All renewable-energy power plants supported by the feed-in-tariff KEV (Kostendeckende Einspeisevergütung). United Kingdom: Renewable-energy power plants in the United Kingdom. Due to different data availability, the power plant lists are of different accurancy and partly provide different power plant parameter. Due to that, the lists are provided as seperate csv-files per country. Suspect data or entries with high probability of duplication are marked in the column 'comment'. Theses validation markers are explained in the file validation_marker.csv. Additionally, the Data Package includes daily time series of cumulated installed capacity per energy source type for Germany, Denmark, Switzerland, the United Kingdom and Sweden.

Source https://data.open-power-system-data.org/renewable_power_plants/2020-08-25

This statistic shows the United Kingdom (UK) public opinion on the 2020 European Union (EU) objective to increase the share of renewable energy in the EU by 20 percent, as of May 2017. This was considered too ambitious by 19 percent of respondents.

The installed capacity of the *** operational landfill gas sites in the United Kingdom amounted to ***** megawatts as of September 2020. Rainham Phase II had an installed capacity of ** megawatts, which was the largest capacity of all operational landfill gas sites. Following Rainham Phase II, Pitsea Tipp and Dunbar Power Plant were the second and third largest installed capacity, with ** megawatts each. In 2019, electricity generated from landfill gas in the UK amounted to ***** gigawatt hours.

In 2020, gross electricity production from renewables in the United Kingdom reached nearly ***** terawatt-hours. Renewable electricity generation in the United Kingdom has shown an increase over the years. Renewables produce more than ** percent of the electricity in the United Kingdom.

The United Kingdom Renewable Energy Market Report is Segmented by Technology (Solar Energy, Wind Energy, Hydropower, Bioenergy, Geothermal, and Ocean Energy) and End-User (Utility, Commercial and Industrial, and Residential). The Market Sizes and Forecasts are Provided in Terms of Installed Capacity (GW).

The Llyn Celyn small hydro plant, located in Wales, had an installed capacity of *** megawatts, which was the highest installed capacity among all ** operational small hydro plants as September 2020. Combined, these ** operational small hydro plants, had an installed capacity of ***** megawatts as of that time. The leading 20 sites installed capacity amounted to **** megawatts. In comparison, the large hydro plant Glendoe Hydro had an installed capacity of 100 megawatts.

There have not been any large increases in installed hydropower capacity during the last decade in the United Kingdom, with a ***** megawatts capacity in 2023, an increase of five megawatts from 2020.

Electricity generated by renewables as a percentage of gross consumption 2000 - 2020

The United Kingdom generated **** terawatt hours worth of electricity and heat through wind power in 2024. Onshore wind farms produced **** terawatt hours of power, which was less than the amount generated by farms situated offshore. Wind power capacities have steadily increased in the past year, with renewable energies taking up a greater share of the UK's energy mix, following the phase-out of coal.

Ethiopia faces a critical challenge in delivering affordable, reliable, and sustainable energy to its predominantly rural population. While renewable energy offers a promising solution to reduce carbon emissions and improve energy access, progress has been slow due to structural, social, and infrastructural barriers. More than 80% of Ethiopians live in rural areas where national grid expansion is economically and logistically unfeasible, underscoring the need for decentralized, off-grid alternatives. Community energy systems, off-grid projects in which local communities actively participate in the development and management of energy infrastructure, represent a promising but underutilized pathway to bridging Ethiopia’s energy access gap. Despite their potential to promote local ownership, resilience, and social inclusion, these systems remain poorly developed and understood in the Ethiopian context. This study aims to explore the opportunities and barriers associated with community energy systems in Ethiopia and their potential role in advancing energy transitions. Using an experimental and interpretive lens, the research draws on a comparative analysis of three in- depth, multi-method qualitative case studies. It investigates how community energy projects are initiated, managed, and experienced in practice. Findings reveal that community involvement is central to the sustainability of these systems, with communities often assuming full operational responsibilities post-commissioning. However, projects face persistent challenges related to accessing capital, managing fragmented supply chains, and building local capacity: particularly around governance, technical maintenance, and understanding viable business models. The study highlights the need for enabling policy frameworks and capacity-building interventions to unlock the full potential of community energy in Ethiopia’s clean energy transition. Data collection method This study explores three community energy projects in Ethiopia: The Gira Tsatse solar mini-grid in Tigray, the Bura micro-hydro plant, and the Mesino Tebita solar irrigation system. Serving 65 to 300 households each, the projects were initially supported by regional governments, GIZ, and Bahir Dar University, with ownership later transferred to the communities. They vary in application, from basic electricity needs like lighting and charging to productive uses such as milling, irrigation, and small businesses, reflecting diverse local contexts and energy needs. Primary data was gathered from 151 households (26.7% of 565 total beneficiaries) through structured surveys, with attention to gender and cultural diversity. Local multilingual data collectors conducted and translated the interviews. Purposive sampling was used to ensure geographic and cultural representation. Additionally, six experts and local administrators provided insights through interviews and written responses, conducted in Tigrigna, Amharic, and English. This mixed-method, multilingual approach yielded a comprehensive understanding of community energy systems, benefits, sustainability, and implementation challenges.

The 2019 Energy Progress Report shows the need to step up efforts to link on-grid and off-grid strategies to facilitate access to electricity (EIA et al, 2019). According to the report, eight of the twenty countries with the largest deficits in access to electricity are in East Africa, including Ethiopia, Malawi, and Mozambique. In countries facing such significant gaps in energy access, the rapid adoption of renewable energy may help to deliver access to energy sustainably. The growing availability of renewable technologies in East Africa's countries suggests that such a transition is possible. However, technology alone will not solve the challenge of energy access.

A transition to sustainable energy needs to prioritise the social needs of excluded and disadvantaged groups. Responding to people's energy needs requires institutional, organisational, and financial models of energy delivery that prioritise social benefits over profits.

New models of energy delivery have been developed to involve communities in the design and management of off-grid systems. While the size and technologies used vary, all Community Energy Systems (henceforth CESs) incorporate the perspectives of beneficiaries on electricity generation and distribution through collaborative mechanisms for decision-making. CESs can provide additional capacity to existing grids, provide off-grid services where the grid is absent, and bridge on-grid and off-grid systems.

The project CESET brings together researchers from political science, human geography, engineering and technology providers to understand the role of CESs in advancing a just sustainable energy transition that will bridge the energy access gap in East Africa.

Our focus is in Ethiopia, Malawi, and Mozambique, three countries where there is considerable local enthusiasm about CESs. Proponents of CESs argue that they can foster deep structural transformations in countries facing large electricity deficits. First, by giving ownership to communities, CESs challenge the political economy of energy and reveal energy-related inequalities. Second, by demonstrating new modes of service provision, CESs can diversify the institutional landscape of energy delivery. Third, by incorporating the concerns of the more disadvantaged populations in the design and management of energy services, CESs can respond to their needs directly and generate innovations tailored to those needs.

There is little evidence of how CESs work in practice and their impacts in East Africa because of the shortage of data on CESs, and energy systems more generally. There is a need to renew policy and practice. Research and interventions often rely on technological blueprints that do not fit the institutional and material conditions in which CESs operate. Moreover, conceptualisations of communities as harmonious, homogenous units obscure the multiple forms of exclusion that influence energy access and infrastructure management. There is already an international consensus about the need for disaggregated data to understand the gender gap in energy access. CESET advocates going beyond by considering the intersection of gender with multiple social characteristics that may also lead to exclusion from energy services (such as age, sexual orientation, ethnicity, place of origin).

CESET will produce three outcomes to address this challenge. CESET's theoretical framework will recognise the variety of CESs models and how they interact with multiple variables of community diversity. CESET will also characterise the landscape of operation of CESs in East Africa at three scales: local, national, and regional. Further learning will happen with the activation of a Community Energy Lab in Mozambique to compile evidence of what works in practice. CESET's efforts will lead to the creation of a Regional Energy Learning Alliance to deliver a long-term research programme and support trans-sectorial learning on CESs in East Africa.

National Grid ESO is the electricity system operator for Great Britain. They have gathered information of the electricity demand in Great Britain from 2009. The is updated twice an hour, which means 48 entries per day. This makes this dataset ideal for time series forecasting.

File information

The dataset consists of three type of files: - Historic_demand_year_20xx.csv: electricity demand in that year - Historic_demand_year_2009_2024.csv: all the yearly datasets merged in one - Historic_demand_year_2009_2024_noNaN.csv: same as above, but NaN values have been removed and the date includes the hour as opposed to only the day

Columns

The columns in the dataset are: * SETTLEMET_DATA: date in format dd/mm/yyyy * SETTLEMENT_PERIOD: half hourly period for the historic outtunr occurred * ND (National Demand). National Demand is the sum of metered generation, but excludes generation required to meet station load, pump storage pumping and interconnector exports. National Demand is calculated as a sum of generation based on National Grid ESO operational generation metering. Measured in MW. * TSD (Transmission System Demand). Transmission System Demand is equal to the ND plus the additional generation required to meet station load, pump storage pumping and interconnector exports. Measured in MW. * ENGLAND_WALES_DEMAND. England and Wales Demand, as ND above but on an England and Wales basis. Measured in MW. * EMBEDDED_WIND_GENERATION. This is an estimate of the GB wind generation from wind farms which do not have Transmission System metering installed. These wind farms are embedded in the distribution network and invisible to National Grid ESO. Their effect is to suppress the electricity demand during periods of high wind. The true output of these generators is not known so an estimate is provided based on National Grid ESO’s best model. Measured in MW. * EMBEDDED_WIND_CAPACITY. This is National Grid ESO’s best view of the installed embedded wind capacity in GB. This is based on publicly available information compiled from a variety of sources and is not the definitive view. It is consistent with the generation estimate provided above. Measured in MW * EMBEDDED_SOLAR_GENERATION. This is an estimate of the GB solar generation from PV panels. These are embedded in the distribution network and invisible to National Grid ESO. Their effect is to suppress the electricity demand during periods of high radiation. The true output of these generators is not known so an estimate is provided based on National Grid ESO’s best model. Measured in MW. * EMBEDDED_SOLAR_CAPACITY. As embedded wind capacity above, but for solar generation. Measured in MW. * NON_BM_STOR (Non-Balancing Mechanism SHort-Term Operating Reserve). For units that are not included in the ND generator definition. This can be in the form of generation or demand reduction. Measured in MW. * PUMP_STORAGE_PUMPING. The demand due to pumping at hydro pump storage units; the -ve signifies pumping load. * IFA_FLOW (IFA Interconnector Flow). The flow on on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * IFA2_FLOW (IFA Interconnector Flow). The flow on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * MOYLE_FLOW (Moyle Interconnector FLow). The flow on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * EAST_WEST_FLOW (East West Innterconnector FLow). The flow on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * NEMO_FLOW (Nemo Interconnector FLow). The flow on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * NSL_FLOW (North Sea Link Interconnector Flow). The flow on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * ELCLINK_FLOW. Blank

To improve energy security, reduce CO2 emissions and attain corporate sustainability goals, the global power sector has witnessed a shift in capacity additions from conventional power sources to renewable sources. With a focus on decarbonizing electricity supply, several governments and utilities are focusing on increasing the share of renewables in the overall energy mix. They have provided support measures that include incentives for renewable power development and to offer a level playing field against conventional sources.
The changing geopolitical situation in the oil and gas supply markets in the Middle East is also expected to lead the demand for renewable energy across the globe. Over the past decade, the growth of renewable power has gained momentum in many countries. With the industry maturing and costs falling significantly to make renewable power economically viable with little or no subsidies, we expect renewable energy adoption to continue its upward trend. In 2019, generators in several countries included large renewable power capacities for the first time. In 2020, more countries are expected to enter the league of large scale renewable power installations. Read More

The location of existing and planned sources of energy, both electricity and heat, is provided as part of the Scotland Heat Map. Alongside data on heat demand, this is used to identify opportunities to reduce carbon emissions from heat in buildings, either by connecting supply and demand in a more efficient manner or by using lower carbon alternatives to existing supply. Data on each energy supply point includes, where available, capacity size category, main technology used (e.g., ‘wind’, ‘biomass’) and planning status (e.g., ‘operational’, ‘in development’). This dataset is new for the Scotland Heat Map 2022 (which was released to local authorities in November 2023). It replaces the data on existing and planned energy supply in earlier versions of the heat map. The Scotland Heat Map is produced by the Scottish Government. Data on existing and planned energy supply comes three sources. Two are UK Government sources: the Renewable Energy Planning Database (REPD) and the Major Power Producers (MPP) dataset. The third is the Energy Saving Trust’s (EST’s) Renewable Heat Database (RHD). Records from the MPP dataset have only been included where they have a fuel type of fossil fuel or nuclear, or where they have a renewable fuel type but their installed capacity is less than 1 MW. This is to avoid overlap with the REPD as much as possible. Records from the RHD have only been included where they output heat only, their installed capacity is 1 MW or higher and they can be shared. The 2020 quarter 4 extract of REPD has been used. MPP data was provided by the UK Government in late 2020. The RHD provides installation information as at end December 2021. More information can be found in the documentation available on the Scottish Government website: https://www.gov.scot/publications/scotland-heat-map-documents/

GlobalData's "Taiwan Power Market Outlook to 2020", report gives detailed information on the Taiwan power market and provides historical and forecast numbers for generation, capacity and consumption up to 2020. The research analyzes upcoming power projects, key import and export trends, regulatory frameworks and infrastructure for the market. This coupled with elaborate profiles of key market participants provides a comprehensive understanding of the market’s competitive scenario. The economy of Taiwan has witnessed a robust growth, at an average of 8% during the past three decades. Industrial growth and foreign trade have been major contributors to the high economic growth of the country.Taiwan does not have sufficient natural resources and hence, imports thermal fuels for its domestic needs. Energy consumption has increased 4.8% during the past two decades. It imports large volumes of both coal and natural gas for usage in thermal power plants for generation of electricity. Taiwan’s cumulative installed capacity is dominated by thermal fuel sources – coal, oil and gas – which contributed 77.2% to the total in 2009. Majority of thermal power plants use coal for generation and the remaining use gas and oil. Dependence on fossil fuel imports is adding to the expenditure of the country and thermal generation is also leading to greenhouse gas emissions and climate change. The government has made development of clean energy sources its top priority. It aims to develop clean, sustainable, and independent energy in order to diversify the energy mix, enhance environmental protection, and reduce greenhouse gas emissions. It is constructing various nuclear, hydro, wind and solar power projects for this purpose. The government is contemplating extending the lifetime of its existing nuclear power plants. Further, it is also aiming to approve the Renewable Energy Development Bill and contribute 15% of renewable energy installed capacity to the total energy mix by 2025. Read More

Solar has become the world’s favorite new type of electricity generation, with more solar photovoltaic (PV) capacity being installed than any other generation technology. Worldwide, approximately 72 gigawatts (GW) of new solar PV capacity was installed in 2016. Wind energy was in second place with 53 GW, followed by coal with 52 GW, gas with 41 GW, and hydro with 31 GW.
China and India occupy the top two spots in the overall power market attractiveness index as the most lucrative markets in the short term. These are followed by the US, Turkey, Germany, and Brazil.
China rolled out its latest five-year energy development plan, detailing the country’s aim of investing about CNY2.5 trillion (more than $363 billion) through 2020 in the development of renewable energy resources. If the planned energy development plan is followed, solar, hydro, and wind power would be the biggest benefactors.
The US lost its gleam, due to a shift in its energy policy under President Donald Trump. The Trump administration has issued orders to roll back many of the previous administration's climate change policies, revive the US coal industry, and review the Clean Power Plan, which requires states to cut carbon emissions from power plants.
The UK's market lost attractiveness post-Brexit with uncertainty over the impacts of the country’s decision to leave the European Union (EU).
A number of Southeast Asian markets show high market attractiveness with strong growth fundamentals and all-round capacity addition. Read More

Introduction The dataset captures yearly generation profiles for different generation technology types, used by UK Power Networks to run export curtailment assessment studies.

UK Power Networks has been running curtailment studies since 2014 in the three licence areas and have been using standards technology specific profiles to model the accepted not yet connected generation capacity.

Generation specific profile include the following generation types: solar photovoltaic, wind, battery and non-variable generation.

The profiles have been developed using actual generation data from connected sites within UK Power Networks licence areas falling into each of the generation categories. The output is a yearly profile with half hourly granularity.

The values are expressed as load factors (percentages) i.e., at each half hour the value can range from 0% to 100% of the maximum export capacity.

The profiles are revised on a regular basis to ensure that they represent as closely as possible the operational behaviour of unconnected sites. The following change have taken place since UK Power Networks has started issuing curtailment reports:

The solar profile was updated in 2019/2020 and capacity factor was increased from 13.2% to 14.2%; The electricity storage profiles were updated in April 2024. new profiles available:“Storage_export_enhanced” and “Storage_import_enhanced”; Gas profiles were updated in September 2024 differentiating between small and large gas generators.

Methodological Approach This section outlines the methodology for generating annual half-hourly demand profiles.

Connected metered generators falling in each of the categories have been used to create the representative profiles. Historical data from each of these connected generation sites are retrieved from UK Power Networks’ Remote Terminal Unit (RTU) and consist of annual half-hourly meter readings. The profiles are averages of power/capacity at every time period i.e.:

Paverage,t = average(P1,tc1 + P2,tc2 + … + Pn,tcn)

where

t is time, 30 minutes resolution for one year; P is the export in MW from sites 1, 2, ..., n; and c is capacity of site 1, 2, ..., n.

If there was bad/missing data then PVSYST and local meteorological data were used.

Electricity Storage profiles

For export/generation studies, storage is modelled as constantly exporting with a varying profile throughout the day, whereas for demand/import studies, storage is modelled as constantly importing with a varying import profile throughout the day. This represent a conservative view, which is used due to the unpredictable pattern of Electricity Storage sites

The solar and storage combined profile is calculated as the maximum of the storage and solar profiles during each 30-minute timestamp.

Storage profiles are detailed in our design standard Engineering Design Standard (EDS 08-5010).

Storage profiles were updated in April 2024 based following a piece of work delivered by Regen to UK Power Networks on the operational behaviour of battery storage, with the purpose to model reflect battery storage more realistically in curtailment studies . the revised profile are “Storage_export_enhanced” and “Storage_import_enhanced”. Curtailment reports issued prior to 22 April 2024 were produced using the legacy storage profiles (“Storage”).

Gas Profiles Gas profiles were updated in September 2024 to provide a more representative view on how gas generator operate and to enable more representative curtailment results. The legacy profile used to model unconnected gas generators until September 2024 was the “non-variable” profiles. From September 2024, unconnected gas generators are modelled using new “Gas_large” and “Gas_small” profile. The two profiles are meant to capture the differences between smaller genset, quickly ramping up from 0 to maximum capacity, and larger gas generators that rather have a more stable behaviour. The profile have been created with the same equation described above, taking a percentile between 95 and 98, rather than the average.

Quality Control Statement

Quality Control Measures include: Manual review and correct of data inconsistencies Use of additional verification steps to ensure accuracy in the methodology

Assurance Statement The Open Data Team and DSO Data Science worked together to ensure data accuracy and consistency.

Other Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/