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99 results for “Renewable energy”
Data from: Conservation planning for offsetting the impacts of development: a case study of biodiversity and renewable energy in the Mojave Desert
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Renewable energies and biodiversity: impact of ground-mounted solar photovoltaic sites on bat activity
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Code and data used in "Health co-benefits of sub-national renewable energy policy in the US"
<p>Archive of modeling, inputs, and results.</p>
Development and demonstration of the next generation of renewable energy-driven technologies for buildings and industrial processes heating and cooling - João Soares 6 year plan
<p>This figure illustrates the six years research plan and methods of João Soares, with the main goal to develop, evaluate and demonstrate the next generation of RES (solar, biomass or hybrid) driven technologies for heating and cooling (H/C) in buildings and industrial processes. </p>
The effect of renewable and nuclear energy consumption on decoupling economic growth from CO2 emissions in Spain
<p>This study examines the relationship between renewable and nuclear energy consumption, carbon dioxide emissions and economic growth by using the Granger causality and non-linear impulse response function in a business cycle in Spain. We estimate the threshold vector autoregression (TVAR) model on the basis of annual data from the period 1970‒2018, which are disaggregated into quarterly data. Our analysis reveals that economic growth and CO<sub>2</sub> emissions are positively correlated during expansions but not during recessions. Moreover, we find that rising nuclear energy consumption leads to decreased CO<sub>2</sub> emissions during expansions, while the impact of increasing renewable energy consumption on emissions is negative but insignificant. In addition, there is a positive feedback between nuclear energy consumption and economic growth, but unidirectional positive causality running from renewable energy consumption to economic growth in upturns. Our findings do indicate that both nuclear and renewable energy consumption contribute to a reduction in emissions; however, the rise in economic activity, leading to a greater increase in emissions, offsets this positive impact of green energy. Therefore, a decoupling of economic growth from CO<sub>2</sub> emissions is not observed. These results demand some crucial changes in legislation targeted at reducing emissions, as green energy alone is insufficient to reach this goal.</p>
Data for Assessing the renewable energy policy paradox: a scenario analysis for the Italian electricity market
<p>This page contains the datasets and codes used to generate the figures for the article Assessing the renewable energy policy paradox: a scenario analysis for the Italian electricity market.</p> <p>Below you will find two datasets (.dta) and four codes (.do) files. Please note that the .do files contain the original paths to where the datasets were saved, you should change them to where they are saved in your computer. </p> <ol> <li>The file <em>clustering_prer_ok.do</em> uses dataset <em>yearlyvars_raw.dta</em> to generate three additional datasets <em>clusters.dta</em>, <em>yearly_scenarios.dta</em> and <em>xwalk.dta</em> <ul> <li><em>clusters.dta</em> is used in <em>Figs_2-5-6_ok.do</em></li> <li><em>yearly_scenarios.dta</em> is used in <em>Figs_3_ok.do</em></li> </ul> </li> <li>Dataset <em>hprice_raw.dta</em> is used together with the generated <em>xwalk.dta</em> in <em>Fig_4_ok.do</em>.</li> </ol>
Nodal LMP and Renewable Curtailment Data in Support of Unlocking the Potential of Renewable Energy Through Curtailment Prediction
<p>This data was collected to support research projects like the ones proposed in our presentation at the Climate Change Workshop at NeurIPS 2023, "Unlocking the Potential of Renewable Energy Through Curtailment Prediction"</p><p>Location Marginal Price (LMP) and renewable curtailment data are provided for two major ISOs in the US: SPP and ERCOT. Data covers the dates 2021-01-01 until 2023-09-30. All timestamps are provided in UTC, pricing information is in US Dollars and curtailment is provided in megawatts. This data was retrieved from the respective ISOs data portals and processed into a concise and collated format. Data provided here is copyright of the publishing ISO, and subject to their terms of use agreements. ERCOT's terms of use can be read <a href="https://www.ercot.com/help/terms">here</a>, and SPP's terms of use are available <a href="https://www.spp.org/terms-conditions/">here</a>. Both ISOs permit redistribution of their materials, but SPP requires express written authorization for commercial use.</p><p>We are not able to publish data for additional ISOs until we receive their permission to do so. However, we have compiled a table of locations where the ISOs publish the data in the document <a href="https://zenodo.org/api/records/10235806/draft/files/ISO_curtailment_LMP_data_availability.ods/content">ISO_curtailment_LMP_data_availability.ods</a></p>
Dataset for the publication "Land conservation and renewable energy transition are simultaneously possible in Brazil"
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Step Change -CSI 5 – Off-Grid Renewable Energy in Agriculture in Uganda – Infographic
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A new low-carbon project scheduling problem with renewable and traditional energy A comprehensive analysis and its solution
<p>The data and related experimental results used in the article</p>
Quantifying, Activating and Rewarding Flexibility for Renewable Energy Communities
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Analysis and Optimizing Solar Panels for Offshore Remote Wellhead Platforms as a Sustainable and Renewable Energy Source
<p>Data set</p>
Utilizing Highway Rest Areas for Electric Vehicle Charging: Economics and Impacts of Renewable Energy Penetration in California
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Figure 6 in Assessment of the Renewable Energy Potential in the Republic of Adygeya
Figure 6. Distribution of thermal water in the Republic of Adygeya according to ROSNIPITERMNEFT.
Feasibility and Preliminary Effects of the Resilience-based, Energy Management to Enhance Wellbeing in Systemic Sclerosis (RENEW) Intervention
ClinicalTrials.gov study NCT04588714. IPD Sharing: NO. Countries: 1. Publications: 0.
California grid electrical energy storage requirements for select renewables integration and fleet electrification scenarios
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Global assessment of the merit order effect and revenue cannibalisation for variable renewable energy
<p>This is data set for the paper "Global assessment of the merit‑order effect and revenue cannibalisation for variable renewable energy". All Figures shown in the paper are produced from this data set.</p> <p>Abstract:</p> <p>The rapid growth of wind and solar power has been a major driver for decarbonisation worldwide. They tend to reduce wholesale electricity prices, both the time-weighted average (the merit‑order effect) and their own output-weighted average (price cannibalisation). Whilst these effects have been widely observed, most previous studies focus on single countries. Here, we compare 37 electricity markets across Europe, North America, Australia and Japan and explore variations between them.</p> <p>Merit-order and cannibalisation effects are observed in nearly all countries studied. However, only in Germany, Spain, Poland, Portugal, Denmark and California can renewable output explain more than 10% of variation in wholesale electricity prices. The global average merit‑order effect is €0.68±€0.54 /MWh per percentage point increase in variable renewable energy penetration, and this falls with higher penetration. Revenues captured by wind farms decrease by 0.23% (€0.16 /MWh) for each percentage point increase of wind penetration and by 1.94% (€0.90 /MWh) for solar PV.</p>
Geo-locations and System Data of Renewable Energy Installations in Germany
<h2>Content</h2> <p>The <strong>dataset contains </strong>geolocation and system data on <strong>renewable energy installations in Germany</strong>. It is provided as <strong>GeoPackage (.gpkg) files </strong>with point geometries for onshore and offshore wind turbines, ground-mounted photovoltaic systems, bioenergy systems and their associated combined heat and power plants, hydropower plants, renewable gas production sites, and energy storage systems. In addition, it includes a file with polygon geometries for photovoltaic field systems and the same data in <strong>CSV format</strong>.</p> <p>The dataset is a <strong>site-verified extract</strong> from the <a href="https://www.marktstammdatenregister.de/MaStR/Datendownload" target="_blank" rel="noopener"><strong>Marktstammdatenregister</strong> </a>(Core Energy Market Data Register, <strong>MaStR</strong>) maintained by the Bundesnetzagentur (Federal Network Agency, BNetzA) as of <strong>January 1, 2025</strong>. System-relevant information was further checked for plausibility and, where necessary, corrected through individual research.</p> <p>The <strong>data processing steps</strong> are documented in the <strong>Git repository</strong>:<strong> <a href="https://git.ufz.de/manske/regeoloc" target="_blank" rel="noopener">https://git.ufz.de/manske/regeoloc</a></strong>.</p> <p>A <strong>detailed description</strong> of the processing <strong>workflow </strong>is provided in the related article: <strong><a href="https://doi.org/10.3390/data7090128" target="_blank" rel="noopener">https://doi.org/10.3390/data7090128</a></strong>.<strong> </strong>The article outlines the guiding principles, while the methodology is continuously being refined.</p> <h2>Metrics</h2> <p>The table presents a executive summary of the compiled dataset compared to the MaStR data extract at the specified cut-off date.</p> <table> <tbody> <tr> <td><strong>Dataset (V20250101)</strong></td> <td><strong>n records</strong></td> <td><strong>sum capacity</strong></td> <td><strong>n location corrected*</strong></td> <td><strong>Records not included from MaStR extract**</strong><br><strong>(n records / sum capacity)</strong></td> </tr> <tr> <td>Bioenergy</td> <td>23,012</td> <td>9144 MW</td> <td>11,377</td> <td>4 / 0.1</td> </tr> <tr> <td>Cogeneration_Units</td> <td>13,110</td> <td>29,992 MW</td> <td>not applicable</td> <td>not applicable</td> </tr> <tr> <td>Energy_Storage</td> <td>912</td> <td>11,864 MW</td> <td>134</td> <td>0 / 0</td> </tr> <tr> <td>Gas_Production</td> <td>274</td> <td>85,956 MWh / h</td> <td>22</td> <td>54 / not applicable</td> </tr> <tr> <td>Hydropower</td> <td>8686</td> <td>5405 MW</td> <td>2078</td> <td>4 / 0.3</td> </tr> <tr> <td>Solar_Energy_Polygons</td> <td>9770</td> <td>35,056 ha</td> <td>not applicable</td> <td>not applicable</td> </tr> <tr> <td>Solar_Energy</td> <td>12,237</td> <td>27,158 MW</td> <td>11,594</td> <td>54855 / 1067</td> </tr> <tr> <td>Wind_Energy</td> <td>32,317</td> <td>74,793 MW</td> <td>4132</td> <td>1054 / 55</td> </tr> </tbody> </table> <p>*Number of records whose location was adjusted by at least 10 meters.<br>**There are several reasons why certain records were not included. In most cases, they could not be assigned to a specific location due to incorrect or incomplete location information in MaStR.</p> <h2>Version History</h2> <table style="width: 100.059%; height: 141.067px;"> <tbody> <tr style="height: 17.6333px;"> <td style="width: 9.54374%; height: 17.6333px; text-align: left;"><strong>Version</strong></td> <td style="width: 11.4269%; text-align: left;"><strong>Cut-off Date</strong></td> <td style="width: 78.9876%; height: 17.6333px; text-align: left;"><strong>Documentation / Executive Summary </strong></td> </tr> <tr style="height: 35.2667px;"> <td style="width: 9.54374%; height: 35.2667px; text-align: left;"><a href="https://doi.org/10.5281/zenodo.16942382">V20250101</a></td> <td style="width: 11.4269%; text-align: left;">2025/01/01</td> <td style="width: 78.9876%; height: 35.2667px; text-align: left;">- Photovoltaic installations in MaStR previously classified as 'Building (Others)' have been reclassified as 'Ground-mounted' where applicable for solar energy systems.<br>- Category 'Big parking lot' has been introduced for solar energy systems.<br>- Additional locations for hydro power plants have been identified.<br>- All wind turbine locations were re-examined.</td> </tr> <tr style="height: 35.2667px;"> <td style="width: 9.54374%; height: 35.2667px; text-align: left;"><a href="https://doi.org/10.5281/zenodo.14627853">V20240204</a></td> <td style="width: 11.4269%; text-align: left;">2024/01/04</td> <td style="width: 78.9876%; height: 35.2667px; text-align: left;">- Category 'Agrivoltaics' has been introduced for solar energy systems.<br>- Related cogeneration units have been additionally added to bioenergy systems.<br>- Energy storage systems have been added to the dataset.<br>- Gas production sites have been added to the dataset.</td> </tr> <tr style="height: 35.2667px;"> <td style="width: 9.54374%; height: 35.2667px; text-align: left;"><a href="https://doi.org/10.5281/zenodo.8188601">V20230420</a></td> <td style="width: 11.4269%; text-align: left;">2023/04/20</td> <td style="width: 78.9876%; height: 35.2667px; text-align: left;">- Category 'Floating-mounted' has been introduced for solar energy systems.</td> </tr> <tr style="height: 17.6333px;"> <td style="width: 9.54374%; height: 17.6333px; text-align: left;"><a href="https://doi.org/10.5281/zenodo.6922043">V20210507</a></td> <td style="width: 11.4269%; text-align: left;">2021/05/07</td> <td style="width: 78.9876%; height: 17.6333px; text-align: left;">- Detailed documentation can be found in the related article: <a href="https://doi.org/10.3390/data7090128" target="_blank" rel="noopener">https://doi.org/10.3390/data7090128</a></td> </tr> </tbody> </table> <p> </p>
Redox flow batteries for renewable energy storage (Video)
<p>Lecture: <strong>Redox flow batteries for renewable energy storage</strong></p> <p><em>International Summer School on Energy Storage Systems: New Developments and Directions</em></p> <p>Zaragoza, 18-20 July 2022</p> <p><a href="https://www.youtube.com/watch?v=gzY5RrliPLY">https://www.youtube.com/watch?v=gzY5RrliPLY</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.