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858 results for “Energy Data”
Additional Data and Code from "Increased energy use for adaptation significantly impacts mitigation pathways".
<p>Additional Data and Code from: "Increased energy use for adaptation significantly impacts mitigation pathways".</p> <p>Authors: Francesco Pietro Colelli, Johannes Emmerling, Giacomo Marangoni, Malcolm Mistry, Enrica De Cian</p> <p>This repository contains:</p> <p>- The output data and R-scripts to produce the figures of the manuscript.</p> <p>- The code building the additional WITCH modules and input data generated. The results obtained in the study can be replicated by running the additional WITCH modules and input data in combination with the open source WITCH available in the repository: https://github.com/witch-team/witchmodel</p> <p> </p>
Data and analysis for the paper "Nonlocal measurement of quasiparticle charge and energy relaxation in proximitized semiconductor nanowires using quantum dots"
<p>This repository contains the raw data and analysis code used to generate the figures in the manuscript <em>Nonlocal measurement of quasiparticle charge and energy relaxation in proximitized semiconductor nanowires using quantum dots</em>. </p> <p><a href="https://journals.aps.org/prb/abstract/10.1103/PhysRevB.106.064503">Link to publication</a></p> <p><a href="https://arxiv.org/abs/2110.05373">Link to arXiv</a></p>
Original data for publications: Direct solution–based synthesis of the Na4(B12H12)(B10H10) solid electrolyte, Experimental investigation of Mg(B3H8)2 dimensionality, materials for energy storage applications, Thermal Conversion of Unsolvated Mg(B3H8)2 to BH4– in the Presence of MgH2
<p>Original data for the following papers (Only part of University of Geneva, synthesis part):</p> <p>Direct solution–based synthesis of the Na4(B12H12)(B10H10) solid electrolyte<br> Gigante, A.; Duchêne, L.; Moury, R.; Pupier, M.; Remhof, A.; Hagemann, H.<br> ChemSusChem 2019, 12, 4832-4837</p> <p>Experimental investigation of Mg(B3H8)2 dimensionality, materials for energy storage applications<br> Romain Moury, Angelina Gigante, Arndt Remhof, Elsa Roedern and Hans Hagemann<br> Dalton Trans., 2020,49, 12168-12173, https://doi.org/10.1039/D0DT02170A</p> <p>Thermal Conversion of Unsolvated Mg(B3H8)2 to BH4– in the Presence of MgH2<br> Angelina Gigante, Noemi Leick, Andrew S. Lipton, Ba Tran, Nicholas A. Strange, Mark Bowden, Madison B. Martinez, Romain Moury, Thomas Gennett, Hans Hagemann, and Tom S. Autrey<br> ACS Appl. Energy Mater. 2021, 4,3737-3747. https://doi.org/10.1021/acsaem.1c00159</p> <p> </p>
Data release for "Measurements of muon-antineutrino and muon-neutrino+muon-antineutrino charged-current cross-sections without detected pions nor protons on water and hydrocarbon at mean antineutrino energy of 0.86 GeV"
<p>This data release is associated with the publication "Measurement of charged-current cross-sections on water and hydrocarbon without detected pions nor protons using the T2K anti-neutrino beam at an off-axis angle 1.5 degrees". It is available in <a href="https://doi.org/10.1093/ptep/ptab014">Progress of Theoretical and Experimental Physics</a> and <a href="https://arxiv.org/abs/2004.13989">arXiv:2004.13989 [hep-ex]</a>.<br><br>The data release contains:</p> <ul> <li>The "histograms.root" file contains several histograms related to the cross-sections. <ul> <li>flux_numubar_* -> 1D histogram with the flux prediction at the WAGASCI module or the Proton Module of the T2K experiment.</li> <li>flux_numu_* -> 1D histogram with the flux prediction at the WAGASCI module or the Proton Module of the T2K experiment.</li> <li>Err_numubar_* -> 1D TGraphAsymmErrors with the measured flux-integrated numubar cross-sections and their uncertainties.</li> <li>Err_numu_numubar_* -> 1D TGraphAsymmErrors with the measured flux-integrated numu+numubar cross-sections and their uncertainties.</li> <li>xsec_numubar_* -> 1D histogram with the predicted flux-integrated numubar cross-sections by NEUT (5.3.3).</li> <li>xsec_numu_numubar_* -> 1D histogram with the predicted flux-integrated numu+numubar cross-sections by NEUT (5.3.3).</li> </ul> </li> <li>The "Covariance_Matrix_Numubar.root" file contains the covariance matrix for the flux-integrated numubar cross-sections, considering all the uncertainties.</li> <li>The "Covariance_Matrix_Numu+Numubar.root" file contains the covariance matrix for the flux-integrated numu+numubar cross-sections, considering all the uncertainties.</li> <li>The "flux" file contains the (anti-)muon neutrino flux prediction at the WAGASCI module or the Proton Module of the T2K experiment</li> </ul>
Data for: Industrial energy development decouples ungulate migration from the green wave
<p>The ability to freely move across the landscape to track the emergence of nutritious spring green-up (termed "green-wave surfing"), is key to the foraging strategy of migratory ungulates. Across the vast landscapes traversed by many migratory herds, habitats are being altered by development with unknown consequences for surfing. Using a unique long-term tracking dataset, we found that when energy development occurs within mule deer (<em>Odocoileus hemionus</em>) migration corridors, migrating animals become decoupled from the green wave. During the early phases of a coalbed natural gas development, deer synchronized their movements with peak green-up. But faced with increasing disturbance as development expanded, deer altered their movements by holding up at the edge of the gas field and letting the green wave pass them by. Development often modified only a small portion of the migration corridor but had far-reaching effects on behavior before and after migrating deer encountered it, thus reducing surfing along the entire route by 38.65% over the 14-year study period. Our study suggests that industrial development within migratory corridors can change the behavior of migrating ungulates and diminish the benefits of migration. Such disruptions to migratory behavior present a common mechanism whereby corridors become unprofitable and could ultimately be lost on highly developed landscapes.</p>
Supplemental data for the report "Optimisation of lattice simulations energy efficiency"
<p>Supplemental data for the report <a href="http://doi.org/10.5281/zenodo.7057319">"Optimisation of lattice simulations energy efficiency"</a>. Also available as a <a href="https://git.dev.dirac.ed.ac.uk/portelli/tursa-energy-efficiency">git repository</a>.</p> <p>It contains:</p> <ul> <li>Full copy of benchmark run directories</li> <li>Power monitoring scripts</li> <li>Power monitoring raw measurements</li> <li>Power monitoring data analysis and results used in the report</li> </ul> <p>For a more complete description, please see the README.md file.</p>
Supplementary Data: Code, Input Data and Result Summaries: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the scripts to build the model, input data and result summaries for the model PyPSA-Eur-Sec-30 described in the above publication.</p> <p>The full results files (which include the post-processed input data) can be found in a <a href="https://zenodo.org/record/1146649">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p><strong>WARNING:</strong> A newer, improved version of this model, <a href="https://github.com/PyPSA/pypsa-eur-sec">PyPSA-Eur-Sec</a>, is under construction on GitHub.</p> <p><strong>Scripts</strong></p> <p>To use the scripts, you need the following free software Python libraries:</p> <ul> <li><a href="https://github.com/PyPSA/PyPSA">PyPSA</a> for the modelling framework</li> <li><a href="https://github.com/FRESNA/vresutils">vresutils</a> for various helper functions to build the model instance</li> <li><a href="https://github.com/FRESNA/atlite">atlite</a> to process weather data into power system data</li> <li><a href="https://snakemake.readthedocs.io/en/latest/">snakemake</a> to organise the execution of the software</li> </ul> <p>and other standard libraries from the <a href="https://pypi.python.org/pypi">Python Package Index</a> (PyPI), such as pandas, pyomo, countrycode, etc.</p> <p>snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work. If you insist on using the latest versions, please be aware that you'll need to make at least the following changes:</p> <p>i) To accommodate changes in pandas versions 0.22 and higher, in scripts/prepare_network.py change "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby("technology").sum()" to "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby(level="technology").sum(min_count=1)".</p> <p>ii) In later versions of PyPSA the component groups like "pypsa.components.one_port_components" have become network-specific and are stored instead at "network.one_port_components".</p> <p>To solve the optimisation problem the scripts are coded to use the commercial solver <a href="http://www.gurobi.com/">Gurobi</a>. To solve the problems in a reasonable time, you will need <a href="http://www.gurobi.com/">Gurobi</a> or an equivalently fast solver such as <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a>. <a href="http://www.gurobi.com/">Gurobi</a> and <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a> both have cost-free licences for academic users.</p> <p>You will also need a computer with at least 64 GB of RAM, since pyomo and the solver are memory intensive.</p> <p>The Python scripts in this repository (in the directory scripts/) are released under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GNU General Public Licence Version 3.0</a> (GPL 3.0).</p> <p>The scripts build_*.py process all raw input data into a form where it can be used in the model.</p> <p>make_options.py prepares the options.yml file for each model run.</p> <p>prepare_network.py populates the PyPSA network for each model run with the input data.</p> <p>solve_network.py solves the optimisation problem with <a href="http://www.gurobi.com/">Gurobi</a> or the solver of your choice (this step takes several hours).</p> <p>make_summary.py aggregates the results into CSV files in the directory results/ (also provided in this repository).</p> <p>The scripts plot_*.py and paper_graphics*.py prepare graphical output.</p> <p>All scripts are managed with the <a href="http://snakemake.readthedocs.io/en/latest/">snakemake</a> workflow management tool.</p> <p>To run the scripts, adjust the parameters in config.yaml and cluster.yaml to your local configuration. Then simply execute</p> <pre><code>snakemake</code></pre> <p>for the rule you want to run.</p> <p>Since the jobs are computationally intensive you may want to run them on a cluster. To run the jobs on a cluster with <a href="https://slurm.schedmd.com/">Slurm</a>, then execute e.g.</p> <pre><code>./snakemake_cluster --jobs 6</code></pre> <p>The cluster is configured in cluster.yaml. You will need to create the directory for the logs, i.e. logs/cluster/, before running the script.</p> <p><strong>Data</strong></p> <p>All input data (in the directory scripts/) and results summaries (in the directory results/) are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0), except those where explicit sources and licences are mentioned in the data folders.</p> <p>The input data include:</p> <ul> <li>Electricity sector data, which largely follows the <a href="https://doi.org/10.5281/zenodo.804337">Zenodo repository</a> for <strong><a href="https://doi.org/10.1016/j.energy.2017.06.004">The Benefits of Cooperation in a Highly Renewable European Electricity Network</a></strong>, except the current repository uses the <a href="https://data.open-power-system-data.org/time_series/2017-07-09/">Open Power System Data Time Series Data Package</a> for load data and <a href="http://renewables.ninja/">Renewables.ninja</a> for solar time series.</li> <li>Heating time series based on the degree-day approximation, constructed with the library <a href="https://github.com/FRESNA/atlite">atlite</a>.</li> <li>Hourly traffic statistics for a week from the German Federal Highway Research Institute (BASt).</li> <li>Yearly energy per country per sector from the <a href="http://www.indicators.odyssee-mure.eu/energy-efficiency-database.html">Odyssee database</a> and <a href="http://ec.europa.eu/eurostat/web/energy/data/energy-balances">Eurostat</a>.</li> <li>A cost database with literature sources.</li> </ul>
Data from: seasonal patterns and processes of migration in a long-distance migratory bird: energy or time minimization?
<p>Optimal migration theory prescribes adaptive strategies of energy, time or mortality minimization. To test alternative hypotheses of energy and time minimization migration we used multisensory data loggers recording time-resolved flight activity and light for positioning by geolocation in a long-distance migratory shorebird, little ringed plover Charadrius dubius. We could reject the hypothesis of energy minimization based on a relationship between stopover duration and subsequent flight time as predicted for a time minimizer. We found seasonally diverging slopes between stopover and flight durations in relation to the progress (time) of migration, which follows for a time minimizing policy if resource gradients increase and decrease, respectively. Total flight duration did not differ significantly between autumn and spring migration, although spring migration was 6% shorter. Overall duration of autumn migration was longer than that in spring, mainly due to a mid-migration stop in most birds, when they likely initiated moult. Overall migration speed was not significantly different between autumn and spring. Migratory flights often occurred as runs of 2-7 nocturnal flights on adjacent days, which may be countering a time minimization strategy. Other factors may influence a preference for nocturnal migration, such as avoiding flight in turbulent conditions, heat stress, and diurnal predators.</p>
Data files for "Curvature in the very-high energy gamma-ray spectrum of M87"
<h1>Summary</h1> <p>In this repository, we provide some auxiliary material in connection to our paper “Curvature in the very-high energy gamma-ray spectrum of M87" accepted for publication in the Astronomy and Astrophysics (A&A) Journal and available on Arxiv through the ID <a href="https://arxiv.org/abs/2402.13330" target="_blank" rel="noopener">arXiv:2402.13330</a>. For the full list of authors, please refer to the paper.</p> <p>In the publication, we study the very-high energy gamma-ray spectrum of a stacked high emission state of M87 using H.E.S.S. observations. We detect a curvature in the spectrum that is not related to the EBL absorption. In addition to that, we show that the gamma-gamma absorption by star light from the galaxy is weak to explain the measured curvature and that it is unlikely that different high states with similar spectral distribution could be able to explain the same curvature.</p> <h1>Data and example code</h1> <h2>ECSV tables</h2> <p>The ecsv tables provide the means to reproduce the figures found in the paper. They can be opened with `astropy.QTable` as demonstrated below.</p> <pre><code>from astropy.table import QTable table = QTable.read('Fig1_lightcurve_table.ecsv') print(table)</code></pre> <p>The following example shows how to reproduce Fig. A2 from the paper (the modules imported are needed in the loaded enviroment):</p> <pre><code>from astropy.table import QTable from scipy.stats import gmean %matplotlib inline import matplotlib.pyplot as plt import numpy as np import seaborn as sns from ebltable.ebl_from_model import EBL cmap = sns.color_palette("colorblind", as_cmap=True) colors = sns.color_palette("colorblind", 6) ebl = {} for m in ["finke2022", "kneiske", "dominguez-upper"]: ebl[m] = EBL.readmodel(m) lmu = np.logspace(-1,3.,100) z = 0.0042 nuInu = {} for m, e in ebl.items(): nuInu[m] = e.ebl_array(z,lmu) nuInu table = QTable.read('FigA2_EBL_ULs.ecsv') wavelengths = table["Wavelength"].value wavelengths = wavelengths counter = 0 for m in ["finke2022", "kneiske", "dominguez-upper"]: plt.loglog(lmu,nuInu[m], lw = 2.,label=f"{m} UL", color=colors[counter] ) ULs = table[f"{m} UL"][(wavelengths>12.4)*(wavelengths<40)].value plt.loglog(wavelengths[(wavelengths>12.4)*(wavelengths<40)],ULs, lw = 2., label = f"UL (this work)", ls='dashed', color=colors[counter]) plt.arrow(gmean(wavelengths[(wavelengths>12.4)*(wavelengths<40)]), np.median(ULs), 0, -0.2*np.median(ULs), head_width=5 ,head_length=0.1*np.median(ULs), alpha=0.5, color=colors[counter]) counter+=1 plt.gca().set_xlabel('Wavelength ($\mu$m)',size = 'x-large') plt.gca().set_ylabel(r'$\nu I_\nu (\mathrm{nW}\,\mathrm{sr}^{-1}\mathrm{m}^{-2})$',size = 'x-large') plt.legend(loc = 'lower center', ncol = 2) plt.tight_layout() plt.show()</code></pre> <h2>Text files</h2> <p>The text files provide the gammapy fit results for the various analyses in the main text of the paper. For a complete definition of the models, we refer the reader to the paper. The name of the file is given by <em>fit_stacked_M87_<strong>MODEL</strong>_flux_90perc_<strong>ENERGYRANGE</strong>_90perc.txt</em>, where <strong>MODEL</strong> is the spectral model fitted (e.g., <em>PLxEBLfinke2022</em> or <em>PLxEBLfinke2022-free</em> in case the EBL intensity alpha_norm is a free parameter) and <strong>ENERGYRANGE</strong> is the energy range of the reduced dataset (e.g., <em>0.3_31.6TeV</em>).</p> <p> </p>
Data for "Low-Energy Electronic Structure in the Unconventional Charge-Ordered State of ScV6Sn6"
<p>The files contain the data for the paper "Low-Energy Electronic Structure in the Unconventional Charge-Ordered State of ScV6Sn6". For data Fig1c+d_topography.gwy, the data has been well plotted in a free and commonly used software Gwyddion. The raw data can also be found in Fig1_c_topography.txt and Fig_1c_FFT.txt. For the 2D data, the scales for each axes can be obtained either normalized to Bragg peaks or can be found in the published manuscript online. Any question on the data, please contact the authors.</p>
Data: Green ammonia imports could supplement long-duration energy storage in the UK
<div> <p>Data used for the analysis presented in 'Green ammonia imports could supplement long-duration energy storage in the UK'. </p> <p> </p> </div>
Case study input data set for article "Stochastic planning of energy system transformation pathways under uncertain industry demands"
<p>The data set contains input data for the model EMPRISE of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. </p>
Data from: Effect of parasite infection and invasion history on feeding, growth and energy allocation of cane toads
<p>The energy allocation decisions that organisms make can differ between sexes and populations and be influenced by factors such as age and parasite infection. We conducted experimental parasite infections on common-garden reared cane toads originating from sites across the species' invasive range in Australia to assess how sex, parasite infection and invasion history affected the toad's food intake, growth rate and organ weights. Female toads had larger fat stores, larger livers and larger gonads than did males, reflecting increased investment into gametes. Growth rate did not differ between the sexes. Lungworm infection increased feeding by male but not female toads and increased fat storage in all toads. Fat body, liver, gonad sizes and feeding rates all differed among toads from different locations within the toad's invasion transect across Australia, even though our measurements were made under standardized conditions on captive animals. Toads from populations close to the invasion front ate more and had heavier fatbodies, and livers than did toads from long-colonised areas, but they had smaller gonads. This pattern reflects the evolution of a more dispersive phenotype among invasive populations, whereby the rate of dispersal is enhanced by increased energy intake and storage, and delayed reproduction.</p>
Data archive for paper "Energy and environmental impacts of air-to-air heat pumps in a mid-latitude city"
<p><strong>Overview</strong></p> <p>This is the data archive for paper "<a href="https://www.nature.com/articles/s41467-024-49836-3" target="_blank" rel="noopener">Energy and environmental impacts of air-to-air heat pumps in a mid-latitude city</a>". It contains the paper's data archive with model outputs (see <code>notebooks</code> folder) and the Singularity image for (optionally) re-running experiments.</p> <p>For the standalone models to model air conditioners and heat pumps please refer to <a href="https://github.com/dmey/minimal-dx">MinimalDX</a>.</p> <p><strong>Prerequisites</strong></p> <ul> <li>Linux with Bash shell.</li> <li><a>Git</a> version >= 2.</li> <li><a href="https://sylabs.io/">Singularity</a> version >= 3.</li> <li><a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System</a>*.</li> <li><a href="https://en.wikipedia.org/wiki/Intel_Fortran_Compiler">Intel Fortran Compiler</a> [<em>Required for MesoNH simulations</em>].</li> <li>A compatible version of the MPI library implementation version 3 [<em>Required for MesoNH simulations</em>].</li> </ul> <p>Please note that most steps require <a href="https://sylabs.io/">Singularity</a>. If you are looking for information on how to install or use Singularity, please refer to the <a href="https://sylabs.io/docs">Singularity documentation</a>. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the hpc directory.</p> <p><strong>Simulations</strong></p> <p><em><strong>Offline</strong></em></p> <ol> <li>Build Surfex with <code>qsub hpc/surfex_build.pbs</code>.</li> <li>Run scenarios with the following commands: <pre><code> qsub -v case_name=fincap hpc/surfex_run.pbs qsub -v case_name=fincap_extended_autosize hpc/surfex_run.pbs qsub -v case_name=minidx_cop=2.5 hpc/surfex_run.pbs qsub -v case_name=minidx_cop=3.0 hpc/surfex_run.pbs qsub -v case_name=minidx_cop=3.5 hpc/surfex_run.pbs qsub -v case_name=minidx_cop=4.0 hpc/surfex_run.pbs<br> qsub -v case_name=minidx_cop=4.5 hpc/surfex_run.pbs </code></pre> </li> </ol> <p><em><strong>Online</strong></em></p> <p>To run MesoNH simulations, follow the three steps outlined below in the same order. Note: Depending on your system and configuration, submit scripts may require change.</p> <ol> <li>Build MesoNH with <code>qsub hpc/build_mnh_intel.pbs</code>.</li> <li>Run the preprocessing step with <code>qsub hpc/submit_mnh_prep.pbs</code>.</li> <li>Finally run MesoNH cases with the following commands for <code>fincap</code> and <code>minidx</code> simulations respectively: <pre><code> hpc/submit_mnh.sh fincap 20050120 12 1 toulouse hpc/submit_mnh.sh minidx 20050120 12 1 toulouse </code></pre> </li> <li>Post-process the results with <code>qsub hpc/submit_mnh_post.pbs</code></li> </ol> <p><strong>Analyses</strong></p> <pre><code>qsub hpc/postprocess_results.pbs # Plots in notebooks/ </code></pre>
Data for the Article "Maturity Assessment of Grid-Scale Flexibility and Energy Storage Services Towards a Decarbonized Europe"
<p>This dataset includes the data collected form various stakeholders regarding grid-scale flexibility and energy services as part of the SINNOGENES Project.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Cost and performance data for electricity generation and storage technologies
<p>Here, we present a database which collates historical, current, and future cost and performance data and assumptions for the six most prominent electricity generation technologies; coal, gas, hydroelectric, nuclear, solar photovoltaic (PV) and wind power, which together accounted for over 92% of installed generation capacity in 2022. In addition, we provide the same data for utility-scale battery energy storage systems (BESS), regarded as critical to the integration of variable renewables such as wind and solar PV.</p> <p>The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 5510 datapoints from 56 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 in future.</p>
Material Intensity data for Renewable Energy Technologies
<p>The file contains detailed data on material intensities (measured in tons per gigawatt) collected from various literature sources. This data encompasses the material intensity of 30 metals through a range of technologies, including offshore and onshore wind power, solar energy, nuclear power, heat pumps, hydroelectric power, hydrogen production via electrolyzers, biomass energy, and biomass with carbon capture and storage (CCS).</p> <p>The variables are:</p> <ul> <li><strong>tech:</strong> Technology (<em>offshore_wind, onshore_wind, Solar, heat_pumps, hydrogen, biomass, biomassccs, hydroelectric, nuclear</em>).</li> <li><strong>material: </strong>Metal the material intensity refers to (<em>Aluminium, Arsenic, Bismuth, Boron, Cadmium, Chromium, Copper, Gallium, Germanium, Indium, Iron, Lead, Molybdenum, Nickel, Selenium, Silicon, Silver, Tellurium, Tin, Vanadium, Zinc, Dysprosium, Manganese, Neodymium, Niobium, Praseodymium, Terbium, Titanium, Iridium, Platinum</em>).</li> <li><strong>type: </strong>for Solar defines whether it is <em>roof_mounted</em> or <em>open_field.</em></li> <li><strong>m_int:</strong> The value of material intensity for the corresponding metal in the corresponding technology.</li> <li><strong>classification: </strong>specific classification for Solar, Wind and Heat pumps' technologies: e.g. for solar, defines the type of photovoltaic cell.</li> <li><strong>year: </strong>year in which it is assumed the data was collected, usually defined as the year of publication of the source paper.</li> </ul> <p>This dataset is part of the Master Thesis <em><span>Mining Industry and Energy Transition </span><span>Scenarios for Europe: Promoting a </span><span>Pluriversal Approach, </span></em><span>developed in the TISE (Transition, Innovation, and Sustainability Environments) program with collaboration with the Complexity Science Hub.</span></p>
2023 Portuguese Energy Tariffs: Retail and Feed-In Tariff Data
<p><strong>Introduction</strong></p> <p>This dataset contains historic data for 20 electric grid consumption and energy injection tariffs in Portugal, during 2023. The data includes both retail tariffs for grid consumption energy and feed-in income tariffs for energy injected back into the grid.</p> <p> </p> <p><strong>Data Overview</strong></p> <p>The dataset comprises the following key elements:</p> <p> 1. Grid Consumption (Retail Tariffs):</p> <ul> <li>Retail tariffs refer to the cost per unit of electricity consumed from the grid.</li> <li>Tariff values are given in euro (€) per kWh and vary across different retailers and hour schemes.</li> </ul> <p> 2. Energy Injection (Feed-in Tariffs):</p> <ul> <li>Feed-in tariffs represent the compensation per unit of electricity generated by solar systems and fed back into the grid.</li> <li>Tariff values are presented in € per kWh and vary across different retailers and conditions.</li> </ul> <p><br><strong>Assumptions</strong></p> <p><br>The retail tariffs in this dataset refer to the consumed energy only and do not include taxes, contracted power costs, and other fees.</p> <p>The dataset starts on Jan 1st, 2023, and ends on Dec 31st, 2023.</p> <p>The dataset uses a 15min time step for each line. The price per hour is applied to each of the four 15 minutes steps.</p> <p><br><strong>Reference data used to create this dataset:</strong></p> <ul> <li>ERSE Regulatory data simulator - https://simulador.precos.erse.pt/eletricidade/</li> <li>2023 Energy Cost Description - https://www.erse.pt/en/activities/market-regulation/tariffs-and-prices-electricity</li> <li>GALP - https://casa.galp.pt/planos-eletricidade-e-gas</li> <li>Eni Plenitude - https://eniplenitude.pt/eletricidade</li> <li>Repsol - https://www.repsol.pt/particulares/casa/eletricidade-gas/</li> <li>EDP - https://www.edp.pt/empresas/energia/tarifarios/</li> </ul> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications.</p>
Research data for: Patchy energy landscapes promote stability of small groups of active particles
<p>Research data supporting the pubblication "Patchy energy landscapes promote stability of small groups of active particles"</p>
eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies (data)
<p>Dataset and results used for the simulations in following publication:</p> <p>Carsten Wegkamp, Henrik Wagner, Eike Niehs, Julien Essers, Marcel Lüdecke, Mattias Hadlak, Bernd Engel:<br>"<strong>eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies</strong>",<br>Open Source Modelling and Simulation of Energy Systems (OSMSES) 2024, Vienna, Austria, 2024</p> <p> </p> <p>This contains the input (scenario) files for the building & grid scenario and the results of the two simulations.<br>It uses the elenia Energy Library (eELib) with release version 1.0.0: https://gitlab.com/elenia1/elenia-energy-library</p>
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