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99 results for “Renewable energy”
Impact of Clean Energy on CO2 Emissions and Economic Growth within the Phases of Renewables Diffusion in Selected European Countries
<p>This study explores the impact of clean energy and non-renewable energy consumption on CO<sub>2</sub> emissions and economic growth within two phases (formative and expansion) of renewable energy diffusion for three selected countries (France, Spain, and Sweden). The vector autoregression (VAR) model is estimated on the basis of annual data disaggregated into quarterly data. The Granger causality results reveal distinctive differences in the causality patterns across countries and two phases of renewables diffusion. Clean energy consumption contributes to a decline of emissions more clearly in the expansion phase in France and Spain. However, this effect seems to be counteracted by the increases in emissions due to economic growth and non-renewable energy consumption. Therefore, clean energy consumption has not yet led to a decoupling of economic growth from emissions in France and Spain; in contrast, the findings for Sweden evidence such a decoupling due to the neutrality between economic growth and emissions. Generally, the findings show that despite the enormous growth of renewables and active mitigation policies, CO<sub>2</sub> emissions have not substantially decreased in selected countries or globally. Focused and coordinated policy action, not only at the EU level but also globally, is urgently needed to overhaul existing fossil-fuel economies into low-carbon economies and ultimately meet the relevant climate targets.</p>
Scripts and data for "The adequacy of time-series reduction for renewable energy systems"
<p>This upload provides the scripts and data used for the computations in the aforementioned working paper. To run these files, you will need to adjust the directory in the files 'testTimeSeries.jl' and 'calli.bat' to your local directory.</p> <p>The subfolder 'reduceTimeSeries' contains all data and the script 'reduceTimeSeries.jl' to reduce the full time-series. Reduction using the 'Gerbaulet' method unfortunately requires a GAMS installation. The results of the reduction are already provided in the folder 'output'.</p> <p>The subfolder 'testTimeSeries' contains all data and the script 'testTimeSeries.jl' to test the reduced time-series with a capacity expansion model. The 'comment' and ‘source’ columns in the AnyMOD.jl input files provide further documentation on the used input parameters. The labels 'lowDem' and 'newDem' relate to what was referred to conventional demand and demand with sector integration in the paper, respectively.</p>
Dataset for "Short-term integration costs of variable renewable energy"
<p>This spreadsheet provides the data used to produce the figures in "Short-term integration costs of variable renewable energy: Wind curtailment and balancing in Britain and Germany" by Michael Joos and Iain Staffell.</p> <div class="itanywhere-activator bounceIn"> </div>
Aligning renewable energy expansion with climate-driven range shifts
<p>Fossil fuel dependence can be reduced, in part, by renewable energy (RE) expansion. Increasingly, RE siting seeks to avoid significant impacts on biodiversity but rarely considers how species ranges will shift under climate change. Here, we undertake a systematic literature review on the topic and overlay future RE siting maps with the ranges of two threatened species under future climate scenarios to highlight this potential conflict.</p>
Dataset for "Assessing the availability and feasibility of renewable energy on the Great Barrier Reef-Australia"
<p>This repository contains all the dataset used in "Assessing the availability and feasbility of renewable energy on the Great Barrier Reef-Australia"</p>
Impact of declining renewable energy costs on electrification in low emission scenarios - Scenario Data
<p>This data archive contains model runs and data analysis files to the research article</p> <p><strong>Impact of declining renewable energy costs on electrification in low emission scenarios</strong></p> <p>by<br> <em>Gunnar Luderer, Silvia Madeddu, Leon Merfort, Falko Ueckerdt, Michaja Pehl, Robert Pietzcker, Marianna Rottoli, Felix Schreyer, Nico Bauer, Lavinia Baumstark, Christoph Bertram, Alois Dirnaichner, Florian Humpenöder, Antoine Levesque, Alexander Popp, Renato Rodrigues, Jessica Strefler, Elmar Kriegler</em></p> <p>forthcoming in <em>Nature Energy (2021).</em></p> <p> </p> <p><em><strong>ModelRuns </strong></em>(directory) contains all model runs of the scenarios underlying the paper.</p> <p><em><strong>DataAnalysis </strong></em>(directory) contains all RMarkDown-Notebooks that were used for the data analysis and the generation of the figures of the paper.</p> <p><em><strong>ScenarioNames.pdf</strong></em> contains the mapping from scenario names used in the paper to the model experiment names (in the directory ModelRuns).<br> <br> <em><strong>ScenarioData_IAMC_Format.xlsx </strong></em>contains the scenario output date in the generic IAMC-format (https://data.ene.iiasa.ac.at/database/) as submitted to the IPCC-AR6-database (https://iiasa.ac.at/web/home/research/researchPrograms/Energy/200513_IPCCwebinar.html)</p>
Optimal Control of Renewable Energy Communities with Controllable Assets: consumption and production profiles
<p>consumption and production profiles for cases I and II used for computing simulations in Optimal Control of Renewable Energy Communities with Controllable Assets</p>
Greening the financial regulation by optimizing credit limits for renewable energy
<p>Numerical Data used in the empirical part of the paper</p>
Business load profiles used in "Maximising the benefits of renewable energy infrastructure in displacement settings: Optimising the operation of a solar-hybrid mini-grid for institutional and business users in Mahama Refugee Camp, Rwanda"
<p>Version used in the submission of "Maximising the benefits of renewable energy infrastructure in displacement settings: Optimising the operation of a solar-hybrid mini-grid for institutional and business users in Mahama Refugee Camp, Rwanda" by Hamish Beath, Javier Baranda Alonso, Richard Mori, Ajay Gambhir, Jenny Nelson and Philip Sandwell.</p>
Datasets for the "Qualitative and Quantitative Analyses of the Research on Renewable Energy based on Hydrogen" paper
<p>This dataset is used in preparing the "Qualitative and Quantitative Analyses of the Research on Renewable Energy based on Hydrogen" bibliometric study. Please cite the original paper whenever using this dataset. The data were collected from the SCOPUS database. The data were collected on February 5, 2022. The data collection procedures were completed in a single day, to minimize deviations caused by frequent database updates.</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>
Challenges to Profitability for Energy Storage in High Renewable Energy Systems
<p>This figure illustrates the impact of renewable energy source (RES) penetration on electricity price spreads and the profitability of energy storage systems.</p> <p>In scenarios with a low share of RES, the price spread is significant. During low-demand periods, electricity is predominantly supplied by cost-effective RES, leading to lower prices. Conversely, during high-demand hours, traditional power plants with higher operational costs are required, resulting in elevated prices. Energy storage systems can capitalize on this large price spread by charging during low-price periods and discharging during high-price periods, thereby maximizing their profits.</p> <p>In contrast, with a high share of RES, both low and high-demand periods are largely covered by renewable sources. This extensive reliance on RES minimizes the price differential between these periods, resulting in a much smaller price spread. Consequently, the potential for storage systems to profit from price arbitrage is reduced, as the opportunities to buy low and sell high diminish.</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>
Modeling and simulation of a new Urban Lightweight Electric Vehicle concept based on the optimized use of renewable energies and the reduction of CO2 emissions
<p>This work has produced a series of scientifc contributions. This library develops different mathematical expressions and assumptions for the dynamic modelling of an smart-grid located within a solar-powered ULEV are derived. The code was developed using Dymola</p>
Raw data on Renewable Energies
<p>Raw data corresponding to the paper "Worldwide trends in research, funding and international collaboration on renewable energies"</p>
Examining the oil price and renewable energy price nexus: Comparative wavelet analysis for the aftermath of 2008 financial crisis, shale oil crisis and COVID-19 pandemic.
<p><span>In this study, we conducted a wavelet analysis on the dependence between renewable energy indices and Brent oil index (Brent) for the period of 21st November, 2003 till 24th May, 2024. The objective of the paper includes comparing the co-movement of renewable energy stock prices and oil prices during three different crises including the global financial crisis, shale oil crisis and the covid-19 pandemic. We found that the dependence is similar for both Europe and on a global scale during the pre-crises time, where renewable energy prices lead Brent oil prices in the short and medium term. Furthermore, results confirm that there is substitutability between oil prices and renewable energy prices before all crises which shows a positive correlation. The results further show that the short and medium term dependence disappears after the oil crisis and financial crisis which is supported by the sudden loss in demand for oil. These findings show that co-movement changes between the three crises where there is no dependence between the indices after the financial and oil crisis while there is a negative correlation after the covid-19 pandemic. These findings could have significant ramifications for investors seeking to mitigate risks and for policymakers making decisions about supporting the advancement of renewable energy while understanding the change of behaviour between the two crises.</span></p>
Rapidly falling costs of renewables - Are energy scenarios lagging behind? (dataset)
<p>Raw data for the working paper "Rapidly falling costs of renewables - Are energy scenarios lagging behind?"</p>
Supporting Data and Guidance: Modeling policy pathways to maximize renewable energy growth and investment in Democratic Republic of the Congo using OSeMOSYS
<p>This repository contains data files and guidance documents that are supplementary materials to accompany the policy paper "Modeling policy pathways to maximize renewable energy growth and investment in Democratic Republic of the Congo using OSeMOSYS" available on Research Square here: <a href="https://www.researchsquare.com/article/rs-2702275/v1">https://www.researchsquare.com/article/rs-2702275/v1</a></p>
Data from: Integrated survey methodologies provide process-driven framework for marine renewable energy environmental impacts
<p>Data from: Integrated survey methodologies provide process-driven framework for marine renewable energy environmental impacts</p>
German Renewable Energy Unit Register
<p>The UnitRegister dataset is a comprehensive compilation of all renewable energy units connected to the grid in Germany. It combines data from two primary sources: the <a href="https://www.marktstammdatenregister.de/MaStR/Datendownload">Marktstammdatenregister</a> published by Germany's Federal Network Agency (BNetzA), and individual publications from the four German Transmission System Operators (TSOs) on their conjoint reporting <a href="https://www.netztransparenz.de/EEG/Anlagenstammdaten">webpage</a>.<br> The dataset encompasses active, decommissioned, and planned units, resulting from the merging and cleaning of these two unit registers. The Marktstammdatenregister dataset is regularly updated and was last downloaded in December 2022. The TSOs' unit register is updated annually around August, with a one-year lag, and the last data extraction took place in 2022, providing information until 2021.<br> While both datasets are similar, the main difference lies in the reporting format. The TSOs report units as bundles of installations under a common payment code (Unit_ID), whereas BNetzA publishes each installation as a separate unit with an individual identification code (BNetzA_Unit_ID). For example, a wind park with multiple turbines is reported as a single unit by the TSOs, whereas BNetzA provides information on each turbine separately.<br> The main dataset, "UnitRegister.csv," is located in the "3Output" folder. It comprises more than 2.4 million units categorized into ten technologies, including Geothermal, Hydropower, Gas_landfill, Gas_mine, Gas_sewage, Biomass, Wind_on, Wind_off, Solar_GM, and Solar_RM. This dataset consists of 13 columns/variables, including "Unit_ID," "BNetzA_EEG_ID," "BNetzA_Unit_ID," "Technology," "Capacity_kW," "Commissioning," "Decommissioning," "State," "PLZ," "Source," "RefYield," "Depth," and "Coast_dist."<br> In addition to the main dataset, this publication provides separate files for different stages of data processing. For example, "UnitRegister_TSOs.csv" and "UnitRegister_BNetzA.csv" offer clean datasets for the TSOs and BNetzA sources individually. The file "UnitRegister_BNetzA_Raw.csv" contains the compiled BNetzA register in its raw form, comprising 82 columns/variables. Detailed information on these variables can be found in the BNetzA user manual, which is included in the "Dokumentation MaStR Gesamtdatenexport" folder within the BNetzA directory.</p> <p>Notes:</p> <p>The dataset has undergone merging and cleaning processes aligned with the research objectives of the Chair of Energy Economics at BTU. It is important to note that these objectives may differ from other research purposes. Specifically, our research focused on analyzing payments associated with renewable energy projects, giving priority to the TSO data. The BNetzA data set was used to supplement the unit stock and fill in data gaps for TSO units whenever possible.<br> Users should exercise caution when selecting the data source that best aligns with their research goals. To assist in this decision-making process, we have included the R-code used for data processing. By downloading the data package and modifying the top-level directory (line 22), users can run the code. However, it is crucial to verify that any updated datasets maintain consistency with the previous file structure (which has not always been the case in the past) and update the file reading sections in the code accordingly.</p> <p>This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, a <a href="https://zenodo.org/record/8013071">TariffRegister</a> and the combination of this information to analyze individual bids in renewable auctions. To gain a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications:<br> • Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. <a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a> | <a href="https://ssrn.com/abstract=4130232">Preprint</a></p> <p>• Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. <a href="https://doi.org/10.1016/j.enpol.2021.112618">https://doi.org/10.1016/j.enpol.2021.112618</a> | <a href="http://arxiv.org/abs/2104.07536">Preprint</a></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.