Find research datasets worth reusing

opencc-zeroFeb 2020View details →

EPA Integrated Planning Model (IPM) National Electric Energy Data System (NEEDS) database

EPA is making the latest power sector modeling platform available, including the associated input data and modeling assumptions, outputs, and documentation.

opencc-by-4.0Apr 2020View details →

plan4res public dataset for case study 3 "Cost of RES integration and impact of climate change for the European Electricity System in a future world with high shares of renewable energy sources"

The objective of the plan4res project is to provide a well-structured and highly modular modelling framework to enable consistent insights into the different needs of future energy system. Three case studies will highlight the potentials of this framework by dealing with different aspects of a future energy systems. Case study 3 will focus on cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources. Ist overall objectives are to identify the Cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources will be the main focus of case study 3. The present dataset contains all the public data built for this case study. The related documentation is included in plan4res deliverable D4.5  <pre>https://doi.org/10.5281/zenodo.3785010</pre>

openother-openDec 2017View details →

Reference Data Set: Electricity, Heat, and Gas Sector Data for Modeling the German System

This reference data set representing the status quo of the German electricity, heat, and natural gas sectors was compiled within the research project ‘LKD-EU’ (Long-term planning and short-term optimization of the German electricity system within the European framework: Further development of methods and models to analyze the electricity system including the heat and gas sector). While the focus is on the electricity sector, the heat and natural gas sectors are covered as well. With this reference data set, we aim to increase the transparency of energy infrastructure data in Germany. Where not otherwise stated, the data included in this report is given with reference to the year 2015 for Germany. The data set is documented in DIW Data Documentation 92 (see references). The project is a joined effort by the German Institute for Economic Research (DIW Berlin), the Workgroup for Infrastructure Policy (WIP) at Technische Universität Berlin (TUB), the Chair of Energy Economics (EE2) at Technische Universität Dresden (TUD), and the House of Energy Markets & Finance at University of Duisburg-Essen. The project was funded by the German Federal Ministry for Economic Affairs and Energy through the grant ‘LKD-EU’, FKZ 03ET4028A-D.

opencc-by-4.0Jun 2024View details →

Data for Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty

Data from the paper "Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty"

opencc-by-4.0Jun 2019View details →

Effects of a Delayed Expansion of Interconnector Capacities in a High RES-E European Electricity System - Dataset

Input and output data of the modelling work for the paper Effects of a Delayed Expansion of Interconnector Capacities in a High RES-E European Electricity System <ul> <li>Considered scenario years: 2030, 2040 and 2050</li> <li>The profiles are based on the historical year 2016.</li> <li>Two scenarios are included: lower connectivity and high connectivity</li> <li>The data cover the ENTSO-E member countries except Iceland and Cyprus and is given in country-specific resolution.</li> </ul> Input: <ul> <li>Demand as hourly profile in MWh</li> <li>Variable RES-E as hourly profile in MWh</li> <li>Power plant fleet as capacities in MW</li> <li>NTCs as capacities in MW</li> </ul> Output: <ul> <li>CO2 emissions as annual data in Mt</li> <li>Variable electricity generation costs as annual data in MEur</li> <li>Variable electricity generation costs per generation as annual data in Euro/MWh</li> <li>Electricity generation as annual data in TWh</li> <li>Electricity export as annual data in TWh</li> <li>Electricity import as annual data in TWh</li> <li>Transit flows as annual data in TWh</li> </ul> The sources are described in the corresponding paper under the following link: <a href="https://www.mdpi.com/1996-1073/12/16/3098">https://www.mdpi.com/1996-1073/12/16/3098</a>

opencc-zeroOct 2024View details →

Weekly plots of Great Britain's half-hourly electrical system weather dependent generation, net imports and overall demand from 2008-11-10

Plots that show the electrical system transition of Great Britain, they were created to form the individual frames for a video of the transition.

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-4.0Dec 2019View details →

20% of US electricity from wind will have limited impacts on system efficiency and regional climate

Simulations of wind turbine wakes conducted with WRF for current and possible future installed capacities upto 20% US electricity from wind.

opencc-by-4.0Jan 2017View details →

Data and results for manuscript "Multi-frequency electrical impedance tomography as a non-invasive tool to characterize and monitor crop root systems "

Root systems are essential in nutrient uptake and translocation, but are difficult to characterize non-invasively with existing methods. We propose electrical impedance tomography (EIT) as a new tool for the imaging and monitoring of crop root systems. In a laboratory experiment we demonstrate the capability of the method to capture physiological responses of root systems with high spatial and temporal resolution. We conclude that EIT is a promising functional imaging technique for crop roots. This package contains measured raw EIT data, electrical imaging results, spectral results from the Debye decomposition, and the Python scripts used to generate the plots in the manuscript.

opencc-by-4.0Sep 2023View details →

Optimal planning of autonomous electric vehicles charging stations with photovoltaic generations and energy storage systems

This database contains technical information on the 69-bus electrical distribution system. This system was tested in a mixed integer linear programming model for allocating autonomous electric vehicle charging stations equipped with photovoltaic generation and energy storage systems. Additionally, this document contains data related to charging stations, energy storage systems, and operational scenarios applied to the case studies.

Impedance data for various electrochemical and electrical systems

Impedance dataset for different electrochemical and electrical systems: <table> <tbody> <tr> <td> </td> <td> Lithium-Ion battery LFP (A123 26650) </td> <td> Lithium-Ion battery NCR (Panasonic 18650) </td> <td>Vanadium redox-flow Battery</td> <td>Polymer electrolyte membrane fuel cell</td> <td> High-temperature PEM (Custom made) </td> <td> Double-layer capacitor 3.4 kF (Maxwell) </td> <td> Analogue RLC circuit (Custom made) </td> </tr> <tr> <td>Details</td> <td>A123 Systems,  26650 lithium iron phosphate | graphite, 2,5 Ah</td> <td>Panasonic NCR-18650B, 18650 nickel-manganese-cobalt-oxid | graphite, 2.5 Ah</td> <td>Micro Flow Cell  (Electrocell A/S, Tarm, DK), surface 10 cm2 </td> <td>ElringKlinger (EK) single PEM fuel cell, active surface area 50 cm2 </td> <td>Custom made fuel cell assembly, metallic bipolar plates with single serpentine flow-fields, electrode surface area of 4 cm², polybenzimidazole (PBI) membranes (Dapazol1, Danish Power Systems-DPS1) </td> <td>Maxwell, capacitance 3400 F, 2.85 V</td> <td>Custom made circuit with a parallel connection of a 2.2 μF foil capacitor and a lossy inductor with 9.7 mΩ  and 5 mH, factory values may vary ± 10 %</td> </tr> <tr> <td>Operating point</td> <td>50% state of charge, 3.27 V, 20 °C</td> <td>50 % state of charge, 3.27 V, 20 °C</td> <td>50% state of charge, 1:4 V </td> <td>1 A, RH of 83.4%, stoichiometry of 4.0/2.0</td> <td>H2 / air stoichiometry 1.8 / 2.5, 160 °C, 800 mA/cm²</td> <td>voltage 603.5 mV, temperature 30 °C</td> <td>-</td> </tr> <tr> <td>Device</td> <td>Zahner Zennium Pro workstation</td> <td>Zahner Zennium Pro workstation</td> <td>Scribner 857, Gamry Reference 3000</td> <td>Scribner Associates fuel cell test station with 885 Fuel Cell Potentiostat</td> <td>Zahner Zennium Pro workstation</td> <td>Zahner Zennium Pro workstation</td> <td>Biologic VMP300</td> </tr> <tr> <td>EIS mode</td> <td>galvanostatic</td> <td>galvanostatic</td> <td>potentiostatic</td> <td>galvanostatic</td> <td>galvanostatic</td> <td>potentiostatic</td> <td>galvanostatic</td> </tr> <tr> <td>Amplitude</td> <td>30 mA</td> <td>250 mA</td> <td>10 mV</td> <td>100 mA</td> <td>100 mA</td> <td>10 mV</td> <td>30 mA</td> </tr> <tr> <td>Frequency</td> <td>52 mHz - 17 kHz</td> <td>10 mHz - 10 kHz</td> <td>100 mHz - 10 kHz</td> <td>10 mHz - 3.98 kHz</td> <td>100 mHz - 100 kHz</td> <td>100 mHz - 1 kHz</td> <td>100 Hz - 10 kHz</td> </tr> <tr> <td>Reference</td> <td>Danzer, M.A. (2019). Generalized Distribution of Relaxation Times Analysis for the Characterization of Impedance Spectra.</td> <td>Rüther, T., Schamel, M., Plank, C., Schomburg, F., Röder, F., & Danzer, M. A. (2023). Cell-to-Cell-Variations of a Panasonic NCR18650B. Zenodo. https://doi.org/10.5281/zenodo.8369275</td> <td> Danzer, M.A. (2019). Generalized Distribution of Relaxation Times Analysis for the Characterization of Impedance Spectra. Schneider, J., Tichter, T., Khadke, P., Zeis, R., and Roth, C. (2020). Deconvolution of electrochemical impedance data for the monitoring of electrode degradation in VRFB. Electrochimica Acta 336, 135510.   </td> <td>Ivan Pivac, Ivar J. Halvorsen, Dario Bezmalinovic, Frano Barbir, & Federico Zenith. (2020). Low-frequency EIS intercept as a diagnostic tool for PEM fuel cells degradation. European Fuel Cell Technology & Applications Conference - Piero Lunghi Conference (EFC17), Naples, Italy. Zenodo. https://doi.org/10.5281/zenodo.3631156</td> <td>Weiß, A., Schindler, S., Galbiati, S., Danzer, M.A., and Zeis, R. (2017). Distribution of Relaxation Times Analysis of High-Temperature PEM Fuel Cell Impedance Spectra. Electrochimica Acta 230, 391–398.</td> <td>Danzer, M.A. (2019). Generalized Distribution of Relaxation Times Analysis for the Characterization of Impedance Spectra.</td> <td>-</td> </tr> </tbody> </table>

Impedance analysis with ELSA and DRT for various electrochemical and electrical systems

Dataset of the analysis of various electrochemical and electrical systems with Electrochemical System Analysis (ELSA) and Distribution of Relaxation Times method (DRT). For full explanation see publication: [[ Electrochemical System Analysis – from impedance data to system identification ]] The impedance dataset used is available at Zenodo for download: doi:10.5281/zenodo.10794584. A detailed information about the systems and the properties of the spectrum can be found there. The following systems were analyzed: <table> <tbody> <tr> <td> </td> <td> Lithium-Ion battery LFP (A123 26650) </td> <td> Lithium-Ion battery NCR (Panasonic 18650) </td> <td>Vanadium redox-flow Battery</td> <td>Polymer electrolyte membrane fuel cell</td> <td> High-temperature PEM (Custom made) </td> <td> Double-layer capacitor 3.4 kF (Maxwell) </td> <td> Analogue RLC circuit (Custom made) </td> </tr> <tr> <td>Details</td> <td>A123 Systems,  26650 lithium iron phosphate | graphite, 2,5 Ah</td> <td>Panasonic NCR-18650B, 18650 nickel-manganese-cobalt-oxid | graphite, 2.5 Ah</td> <td>Micro Flow Cell  (Electrocell A/S, Tarm, DK), surface 10 cm2 </td> <td>ElringKlinger (EK) single PEM fuel cell, active surface area 50 cm2 </td> <td>Custom made fuel cell assembly, metallic bipolar plates with single serpentine flow-fields, electrode surface area of 4 cm², polybenzimidazole (PBI) membranes (Dapazol1, Danish Power Systems-DPS1) </td> <td>Maxwell, capacitance 3400 F, 2.85 V</td> <td>Custom made circuit with a parallel connection of a 2.2 μF foil capacitor and a lossy inductor with 9.7 mΩ  and 5 mH, factory values may vary ± 10 %</td> </tr> </tbody> </table> The data is available as .json and as .mat to allow easy import in different programming languages. Please note, that complex data in the .json files are converted to strings and thus need to be converted after importing. Naming convention: [[name of system]]_[[rxx (xx = model order for special cases)]]_[[method]] method: drt, elsa, elsa_simulation (simulated impedance data) Data structure: _meta: metadata for software package used _class: classname for class used config: configuration for the analysis data: input data used for the analysis Specific to ELSA: - system: Here, all calculated systems (including zeros, poles, gain, partial fraction decomposition, ...) for every step are saved - impedance: Simulated impedance and residuals for all systems Specific to DRT: - drt: Result of DRT calcualtion (including h, h_rc, h_rl, r_0, l_0, c_0, lambda, l_curve, ...) - optimization: Matrices of the linear optimization problem - simulation: Simulated impedance and residuals for calculated DRT

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.