Find research datasets worth reusing

opencc-by-4.0Nov 2020View details →

Case study result data set for Energy Economics article "Demystifying market clearing and price setting effects in low-carbon energy systems"

The data set contains country-specific power generation and consumption time series data for the European energy system, including both traditional and new market participants due to cross-sectoral integration. Country codes: ALPHA-3 Unit: Megawatt (electric) (interval average values, i.e. MWh/h) Generation technology types <ul> <li>batteryStorage (Li-Ion)</li> <li>conventionalHydro (aggregated for different equivalent hydropower systems)</li> <li>natural_gas_CC_COND (Combined Cycle Gas Turbine)</li> <li>natural_gas_CC_EXCOND (Combined Cycle Gas Turbine as extraction condensing CHP plant for district heating)</li> <li>natural_gas_GT_COND (Open-Cycle Gas Turbine)</li> <li>natural_gas_GT_EXCOND (Open-Cycle Gas Turbine as extraction condensing CHP plant for industry)</li> <li>offshoreWind (aggregated for different LCOE and IEC wind turbine classes)</li> <li>offshoreWindExplicit (offshore wind generation considered for offshore grid investments in the North Seas area, aggregated for different LCOE classes)</li> <li>onshoreWind (solar PV, aggregated for different LCOE classes)</li> <li>other (geothermal, waste)</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> <li>solar (solar PV, aggregated for different LCOE classes)</li> <li>uran_ST_COND (steam turbine condensing power plant)</li> </ul> Consumption technology types <ul> <li>BEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>PHEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>airConditioning</li> <li>batteryStorage (Li-Ion)</li> <li>conventionalLoad</li> <li>heatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridHeatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridTruck (Hybrid Overhead-Line truck)</li> <li>largeScaleDirectResistiveHeating (Centralised CHP systems)</li> <li>natural_gas_CC_EXCOND_electrodeHeater</li> <li>natural_gas_CC_EXCOND_heatpumpHeater</li> <li>natural_gas_GT_EXCOND_electrodeHeater</li> <li>natural_gas_GT_EXCOND_heatpumpHeater</li> <li>powerToGas</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> </ul>

opencc-by-4.0May 2024View details →

Result data related to Tröndle et al (2024): Rebuilding Ukraine's energy supply in a secure, economic, and decarbonised way

This dataset contains the result data of all the scenarios ran in the scientific article "Rebuilding Ukraine’s energy supply in a secure, economic, and decarbonised way". The results of the main five scenarios of the study are available as PyPSA result files: <ul> <li>nuclear-and-renewables-high.nc: A scenario with nuclear in the mix and high economic growth assumption.</li> <li>nuclear-and-renewables-low.nc: A scenario with nuclear in the mix and low economic growth assumption.</li> <li>only-renewables-high-low-bio.nc: A scenario with only renewables, high economic growth assumption, and only 10% of assumed biomass potential.</li> <li>only-renewables-high.nc: A scenario with only renewables and high economic growth assumption.</li> <li>only-renewables-low.nc: A scenario with only renewables and low economic growth assumption.</li> </ul> See the PyPSA documentation for more information: <a href="https://pypsa.readthedocs.io" target="_blank" rel="noopener">https://pypsa.readthedocs.io</a>. The results of the 330 global sensitivity analysis runs are available as summary files in CSV format: <ul> <li>gsa-capacities-energy-gwh.csv: The installed energy storage capacities for each scenario.</li> <li>gsa-capacities-power-gw.csv: The installed generation capacities for each scenario.</li> <li>gsa-lcoe.csv: The levelised cost of electricity for each scenario.</li> </ul>

opencc-by-4.0Mar 2019View details →

(PRE) Socio-economic and cultural dataset in relation to Persuasive Strategies to boost Energy Efficiency and in the UK, Spain, Greece and Austria

The dataset has been created from obtaining answers from 303 participants of four different countries in the EU (the questionnaire can be studied in GreenSoul_Questionnaire.pdf). It is composed by several factors which are explained in different TXT files. All these factors are contained in a "all_code_final_zenodo.xlsx" along with their answers by participants. In the following a short descrition of each TXT which explian the dataset is provided.:     * socio-economic_description_not_dependent_of_work         - Contains all the information from participants which is irrespective of their current workplace. This file contains typical socio-demographic and cultural attributes from respondents.     * socio-economic_description_dependent_of_work         - Contains socio-economic and cultural information from participants which is relevant to the workplace in relation to energy efficient practices in such environment.     * actions-at-work         - Are a set of attributes which describe certain practices of employees in relation to energy efficiency.     * persuasive_strategies         - Explain the ratings from 1 to 5 that participants attributed to a set of persuasive strategies. These strategies are framed within Phychological Persuasive principles which are also explained in the file.     * all_attributes_together         - All the variables together without distinction of the environment where they are applicable. Finally, plots from every construct or attribute are provided in a zip file (plots_descriptive_analysis_per_city.zip) which contains the plots uploaded in "PNG" extension

opencc-by-4.0Jul 2024View details →

Open and Lite Techno-economic Dataset for Long-term Energy Systems Modelling in the Republic of South Africa

An open-source lite techno-economic dataset for long term energy systems modelling in the Republic of South Africa. Includes data on electricity generation and demand, electricity imports and exports, power transmission and distribution, residual capacity, capacity factor, operational lifetime, and fixed, variable and capital costs of electricity generation technologies. It also contains estimates for renewable potential and fossil fuel reserves in South Africa.

embargoedcc-by-4.0Nov 2024View details →

Dataset for "Sustainable Disposal and End-of-Life Treatment of Battery Energy Storage Systems: An Environmental and Economic Case Study"

This study investigates the environmental and economic impacts of end-of-life (EOL) treatment for a 2.8 MWh/2.5 MW battery energy storage system (BESS) based on lithium-ion batteries (LIBs). It focuses on recycling pre-treatment processes for battery systems and recycling procedures for components like cooling systems, fire extinguishing systems, inverters, and the reuse of BESS containers and substations. A life cycle assessment (LCA) was employed to evaluate key environmental impacts, including climate change, eutrophication, and resource use. The study reveals substantial environmental benefits, particularly from recovering secondary materials like aluminium and copper, with recycling pre-treatment contributing significantly to overall benefits. Additionally, the economic analysis projects profits, emphasizing the advantages of locally sourcing critical raw materials. The research highlights the need for more sustainable recycling practices and provides insights for improving environmental and economic strategies in BESS management, offering guidance for future research and policy development in battery waste processing.

opencc-by-4.0Mar 2021View details →

Case study result data set for Energy Economics (submitted) article "On Wholesale Electricity Prices and Market Values in a Carbon-Neutral Energy System"

The data set contains wholesale power price time series data for Germany and France focussing on price setting effects in a long term low carbon European energy system context (scenario year 2050) generated with the model SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. The single time series are focussing on the price setting effects of different flexible technologies including both traditional and new market participants due to cross-sectoral integration. Unit: Euro/Megawatthour Abbreviations: <ul> <li>BEV - Battery Electric Vehicles</li> <li>GER - Germany</li> <li>FRA - France</li> <li>OCGT - Open Cycle Gas Turbine</li> <li>PHEV - Plug-In Hybrid Vehicles</li> <li>RES - Renewable energy sources (here: wind and solar power)</li> <li>th. - thermal</li> </ul>

opencc-by-4.0Feb 2023View details →

Supplying renewable energy to Central European research facilities: A techno-economic comparison of electricity and hydrogen (Dataset)

This dataset contains central input assumptions and results related to the publication "Supplying renewable energy to Central European research facilities: A techno-economic comparison of electricity and hydrogen". Result files are contained in the results.zip archive file. The file contains for each scenario, as indicated by the folder structure, the following files: <ul> <li>results.csv: Central scenario results exported as character separated value (csv) file, with a semicolon (;) as field separator. All fields are quoted using double quotation marks "...". Can be explored using standard office software like Microsoft Excel/Libre Office or other tools.</li> <li>network.nc: PyPSA network file containing the optimized scenario with all input and unprocessed outputs (results). Can be explored using the <a href="https://pypsa.readthedocs.io">PyPSA software package</a>.</li> <li>lcoes.csv: Levelised Cost of Electricity used to construct the renewable energy source (RES) based supply curve for each scenario.</li> </ul> The dataset further contains the following files which represent central input assumptions to the model and scenarios, both as CSV files: <ul> <li>efficiencies.csv: Technology process and conversion efficiencies including more details on the assumptions and information on which references the assumptions are based.</li> <li>costs_2030.csv: Technology cost assumptions for 2030 including more details on the assumptions and information on which references the assumptions are based. This data is based on this <a href="https://github.com/pypsa/technology-data">Technology Data repository</a> on GitHub.</li> </ul>

opencc-by-4.0Dec 2019View details →

(POST) Socio-economic and cultural dataset in relation to Persuasive Strategies to boost Energy Efficiency and in the UK, Spain, Greece and Austria

The dataset has been created from obtaining post-pilot answers from 106 participants of four different countries in the EU (the questionnaire can be studied in GreenSoul_Validation_Questionnaire-POST.pdf). It is composed by several factors which are explained in POST-coding.ods file. All these factors are contained in: "POST-results-socio-economic-model.ods" and "POST-results-treatments-evaluation.ods" along with their answers by participants. Finally, we provided a cleaned version of the dataset to study how can a researcher is able to forecast the ranking that a user will give to different persuasion strategies according to user profiles: "POST-results-ranking-model.ods"

opencc-by-4.0Apr 2024View details →

Socio- and Techno-Economic Dataset for Energy Modelling in Sierra Leone

This repositary contains a Reference Energy Syatem (RES) and dataset containing the raw data used in the Sierra Leone energy models created by CCG and the Ministry of Energy in Sierra Leone including scenario-specific constraints used in the modelling. The models used were MAED and OSeMOSYS. Full information regarding data sources and assumptions used can be found in the corresponding Data in Brief. This work was supported by the Climate Compatible Growth Programme (#CCG) of the UK's Foreign Development and Commonwealth Office (FCDO). The views expressed in this paper do not necessarily reflect the UK government's official policies.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for hydro power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for hydroelectric generation from the open literature. Hydroelectric power is the largest source of renewable energy, supplying 15% of global electricity. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 1011 datapoints from 11 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time.  Technoeconomic data on utility-scale hydroelectric power was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for battery storage in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for battery energy storage systems from the open literature. Battery energy storage is the fastest growing form of power system flexibility, and will be critical to integrating large shares of variable renewable energy. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 671 datapoints from 18 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale batteries was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for wind power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for wind power generation from the open literature. Wind energy supplies 7% of global electricity, and production has grown three-fold in the decade to 2022. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 1506 datapoints from 28 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale wind energy was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for gas power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for gas-fired power generation from the open literature. Natural gas supplies 23% of global electricity, but must be rapidly phased down to meet global decarbonisation objectives. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 620 datapoints from 14 sources.   The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time.Technoeconomic data on new-build gas-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for coal power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for coal-fired power generation from the open literature. Coal supplies 35% of global electricity, but must be rapidly phased down to meet global decarbonisation objectives. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 345 datapoints from 12 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on new-build coal-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for solar power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for solar power generation from the open literature. Solar energy supplies 5% of global electricity, and production has grown ten-fold in the decade to 2022. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 753 datapoints from 31 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale solar PV was collected from websites, reports, academic articles and databases of national and international organisations.

opencc-by-4.0Apr 2022View details →

CROSSBOW HLU2-UC6-TC1 Economical benefit of mFRR market participation with 5% of energy saved for upward regulation

The energy market participation algorithm of RES-CC allows for participating in DA/ID markets and balancing markets. The process uses the energy generation forecasting of the plants to generate the energy bids for the DA/ID market. With regards to the mFRR market, the whole amount of curtailable energy is offered for downward regulation. Additionally, the process is configurable so that it can save a given amount of the potential generation, do not offering this energy in the DA/ID market but offering it for upward regulation in the mFRR. This upward regulation can only be done with Renewables by means of not selling part of the forecasted energy. This is a risk, as the upward regulation energy might not be requested by the operator, and thus the profit for the generation will be lost. This dataset contains the results of the market participation algorithm running for a week, configured for offering 5% of the forecasted energy for upward regulation. Dataset contains the results for the CROSSBOW portfolios of Croatia, Bulgaria, Romania and Greece. For each country, hour by hour, the following information is provided: <ul> <li>Energy sold in IDM</li> <li>Forecasted generation</li> <li>Energy sold mFRR down</li> <li>Energy sold mFRR up</li> <li>Energy price mFRR down</li> <li>Energy price mFRR up</li> <li>Energy price in IDM</li> <li>Energy looses (saved and not sold in mFRR up)</li> </ul>