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11 results for “Solar PV”
Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America
<p>This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country. </p> <p>The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023). </p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 3, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Argentina<br>Belize<br>Bolivia<br>Brazil<br>Chile<br>Colombia<br>Costa Rica<br>Cuba<br>Dominican Republic<br>Ecuador<br>El Salvador<br>French Guiana<br>Guatemala<br>Guyana<br>Haiti<br>Honduras<br>Jamaica<br>Nicaragua<br>Panama<br>Paraguay<br>Peru<br>Suriname<br>Uruguay<br>Venezuela</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></p> <p>Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14870967">https://doi.org/10.5281/zenodo.14870967</a></p>
Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power »
<p>This dataset contains data provided alongside the paper "An all-Africa dataset of energy model “supply regions” for solar PV and wind power" by Sterl et al. (2022).</p> <p>It concerns a novel representative subset of attractive sites for solar PV and onshore wind power for the entire African continent. We refer to these sites as “Model Supply Regions” (MSRs). This MSR dataset was created from an in-depth analysis of various existing datasets on resource potential, grid infrastructure, land use, topography and others (see Methods), and achieves hourly temporal resolution and kilometre-scale spatial resolution. This dataset fills an important research need by closing the gap between comprehensive datasets on African VRE potential (such as the Global Solar Atlas and Global Wind Atlas) on the one hand, and the input needed to run cost-optimisation models on the other. It also allows a detailed analysis of the trade-offs involved in exploiting excellent, but far-from-grid resources as compared to mediocre but more accessible resources, which is a crucial component of power systems planning to be elaborated for many African countries.</p> <p>Five separate datasets are included:</p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 2, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Algeria<br>Angola<br>Benin<br>Botswana<br>Burkina Faso<br>Burundi<br>Cameroon<br>Central African Republic<br>Chad<br>Congo Republic<br>Democratic Republic of the Congo<br>Djibouti<br>Egypt<br>Equatorial Guinea<br>Eritrea<br>Eswatini<br>Ethiopia<br>Gabon<br>The Gambia<br>Ghana<br>Guinea<br>Guiné-Bissau<br>Côte d'Ivoire<br>Kenya<br>Lesotho<br>Liberia<br>Libya<br>Madagascar<br>Malawi<br>Mali<br>Mauritania<br>Morocco<br>Mozambique<br>Namibia<br>Niger<br>Nigeria<br>Rwanda<br>Senegal<br>Sierra Leone<br>Somalia<br>South Africa<br>South Sudan<br>Sudan<br>Togo<br>Tunisia<br>Uganda<br>Tanzania<br>Zambia<br>Zimbabwe</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <span><a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></span></p> <p><strong>See also</strong></p> <p>Sterl, S. (2024). Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10650822" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10650822</a></p>
Dataset of paper "Comparing the efficiency of solar water treatment: CPC vs PV-LED photoreactors"
<p>Dataset of paper "Improving the efficiency of solar water treatment: CPC vs PV-LED photoreactors":</p> <ul> <li>Relative spectral power of UVC, UVA LED and solar spectrum.</li> <li>Incident radiation measured by ferrioxalate actinometry for the UVC LED and UVA LED sources.</li> <li>Solar UV cumulative incident radiation measured by ferrioxalate actinometry in the CPC reactor versus cumulative incident radiation measured by a radiometer.</li> <li>Instant power generated by the solar PV panel as a function of the UV instant incident radiation measured in the radiometer.</li> <li>Zero-order kinetic constant for methanol oxidation by the studied solar driven photochemical processes calculated as a function of (a) time (b) photons (c) energy consumption (d) solar UV radiation and (e) equipment cost.</li> <li>First-order kinetic constant for <em>E.coli</em> inactivation by the studied solar driven photochemical processes calculated as a function of (a) time (b) photons (c) energy consumption (d) solar UV radiation and (e) equipment cost.</li> </ul>
Open database of small-scale solar PV installations: a Citizen Science initiative
<p>Presentation given at the European PV Solar Energy Conference 2020 about the development of a Citizen Science initiative related to PV installations, Generation Solar.</p>
Large-scale green grabbing for wind and solar PV development in Brazil
<h2>Large-scale green grabbing for wind and solar PV development in Brazil</h2><p>This repository contains the R code and parts of the data used for the analysis in the paper "Large-scale green grabbing for wind and solar PV development in Brazil" by Michael Klingler, Nadia Amelie, Jamie Rickman, and Johannes Schmidt, available as <a href="https://eartharxiv.org/repository/view/5824/">pre-print</a>.</p><p>Due to data sharing limitations, we cannot provide all data in the repository. Partly this data is not available publically at all (i.e. Bloomberg data, data by the instituto socio ambiental), partly the data has to be downloaded manually (CAR).</p><p>We still provide a repository which at least allows to understand the procedures we used during the analysis.</p><h3>Land tenure data set</h3><p>The procedures used to form our final land tenure data set can be found in land-tenure-data/processing.txt It is a mix of analyses in Python and in QGis.</p><h3>Analysis of land tenure data and park ownership/investment information</h3><p>The R-code to analyze the owernship relationships between windpark areas and investors/owners can be found in src/. All required libraries will install automatically.</p><p>The first two scripts cannot be executed due to data limitations. They create the sankey diagrams linking park areas to onwers and investors:</p><p>- 1.1-figures-results-1-wind.R</p><p>- 1.2-figures-results-1-solar.R</p><p>These three scripts are used to analyze the land tenure types prevailing on parks and comparing them to random areas. They should run with the provided data sets:</p><p>- 2-random-sampling-areas.R</p><p>- 3-intersection-parks-land-tenure.R</p><p>- 4-figures-land-tenure.R</p><p>This script validates our data against an independent data source. However, it cannot be run as it needs the proprietary Bloomberg database:</p><p>- 5-validation.R</p>
Survey data on norwegian household energy use, with focus on solar PV, flexible energy use, and retrofitting in 2023 (variables dictionary)
<p>A variable dictionary (both in Norwegian and English) is added. Please note that Norwegian characters like <strong>ø</strong> may not display correctly in the webpage preview.<br>To view them properly, download the CSV file—all characters will appear correctly there.</p>
Supporting data for 'Sustainability trade-offs of solar PV tracking technologies'
<p>The dataset contains the data and code for the research 'High land costs favor fixed-tilt solar power'.</p>
PV Solar Cell Circuit Model
<p>PV Solar Cell Circuit Model</p>
Monitoring Solar Irradiance and PV Module Performance in Mobile Applications
<p>Video footage from the car in the PV in motion experiment.</p>
JRC - Raw materials demand for wind and solar PV technologies in the transition towards a decarbonised energy system - Materials demand database
<p>This dataset contains the results of the materials demand scenarios for wind and solar PV technologies developed in the following report by the European Commission's Joint Research Centre (JRC):</p> <p>Carrara S., Alves Dias P., Plazzotta B. and Pavel C., Raw materials demand for wind and solar PV technologies in the transition towards a decarbonised energy system, EUR 30095 EN, Publication Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-16225-4, doi:10.2760/160859, JRC119941</p> <p>The report can be found at the following link:</p> <p><a href="https://ec.europa.eu/jrc/en/publication/raw-materials-demand-wind-and-solar-pv-technologies-transition-towards-decarbonised-energy-system">https://ec.europa.eu/jrc/en/publication/raw-materials-demand-wind-and-solar-pv-technologies-transition-towards-decarbonised-energy-system</a></p>
Solar PV competitiveness in mature markets without subsidy - Supplementary Data
<p>This is the data set named "Supplementary Data 1" for the research paper "Solar PV competitiveness in mature markets without subsidy". This data set also contains the raw data for reproducing Figure 1 through to Figure 4. The paper is currently under review and access is for peer-review purposes only.</p>
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