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101 results for “Solar Energy”
Code for data and figures published in "Solar energy as an early just transition opportunity for coal-bearing states in India"
<p>The following code and data were used to generate the figures in the article "Solar energy as an early just transition opportunity for coal-bearing states in India". The article was published in Environmental Research Letters (<a href="https://iopscience.iop.org/article/10.1088/1748-9326/ac5194">https://iopscience.iop.org/article/10.1088/1748-9326/ac5194</a>)</p> <p>The code is written in R. Before running the Rmd file, create a folder called "Data" and store all the files there, except the Rmd file.</p>
Will it float? Exploring the social feasibility of floating solar energy infrastructure in the Netherlands
<p>Floating photovoltaic (FPV) is emerging as a promising renewable energy concept in which solar panels are installed on floating infrastructure to enable the production of renewable energy on water. While the body of knowledge on technical, financial and environmental aspects is expanding steadily, so far the societal implications of FPV remain largely unstudied. Here, we investigate public attitudes to a FPV pilot project at the Oostvoornse lake, the Netherlands. We conducted interviews with stakeholders to explore how the local community with high interest and involvement in the lake perceives the pilot project. Thereupon, we conducted a field survey with recreational users of the lake and carried out a random forest regression analysis to examine what factors shape recreationists’ support or opposition. Interview results show that the diversity of stakeholders and their diverging use of the Oostvoornse lake leads to a broad variety of concerns about how the pilot project could affect their activities and interests. Particularly the uncertainty on possible impacts due to the newness of FPV was a reason for stakeholders to take a reluctant stance towards the pilot. In contrast, our quantitative results show that recreationists were highly supportive of the project, mainly due to their positive attitudes towards local authorities and the broader societal benefits the pilot project is perceived to generate. Landscape alteration was identified to be by far the most important objection, which indicates that negative implications from a recreation perspective could be largely accommodated through appropriate siting decisions or other measures that mitigate visibility.</p>
Solar Energy
<p>This data set is composed of three parts each having its proper origins, formats and rights. This data set was used for a local contest where students were asked to propose new applications for helping decision makers to decide on where to implant solar systems.</p>
Analysis of a solar-based energy transition in Greece
<p>These datasets contain the underlying data for the following publication: <strong>Barriers to and consequences of a solar-based energy transition in Greece, Environmental Innovation and Societal Transitions, https://doi.org/10.1016/j.eist.2018.12.004.</strong></p>
Video: Turkish National Advisory Group Meeting on Horizon Europe Solar Energy Call topics, 18 Nov. 2022
<p>Video of a 1 day open meeting to catalyze and support Turkish participation in a cluster of upcoming Horizon Europe Calls on Solar Energy.</p>
Supplementary material for the publication: J. D. Nixon, K. Bhargava and E. Gaura, Analysis of standalone solar streetlights for improved energy access in displaced settlements, 2020.
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people.</p> <p>As part of the project, we deployed 11 advanced solar streetlights, with additional energy access provided by ground-level AC sockets, at two project sites: Gihembe refugee camp, Rwanda (4 lights) and Uttargaya settlement, Nepal (7 lights), in July 2019. The aim of this study was to (a) identify best practices in the construction, location and security measures for long-lived street lighting (b) understand how communities would use a shared energy resource when available through energy sockets, auxiliary to the main function of the streetlights.</p> <p>The system data used for the performance analysis for this study (July 2019 and March 2020) is deposited here along with the metadata. The results from analysis are presented in a paper titled '<strong>Analysis of standalone solar streetlights for improved energy access in displaced settlements</strong>' (currently under review). The scripts for analysis can be found at our Github account <a href="https://github.com/cogent-computing">Cogent Labs</a> under HEED_Nepal_SL and HEED_Rwanda_SL repositories.</p>
Data and results related to "Fattori et al. 2017 - High Solar Photovoltaic Penetration in the Absence of Substantial Wind Capacity: Storage Requirements and Effects on Capacity Adequacy - Energy"
<p>The file includes data used for the analysis and results coming from the study (which was focused on the Italian "Nord" bidding zone). In particular:</p> <p>(i) Series of hourly load data [MW], from 01.01.2006 to 31.12.2015. The data come from elaborations based on ENTSO-E (https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package) and Terna S.p.A. (http://www.terna.it/en-gb/sistemaelettrico/transparencyreport/load/actualload.aspx). All the elaborations are described in details on the paper.</p> <p>(ii) Data related to the penetration of PV. Installed capacity of PV is assumed to increase from zero up to the capacity needed so that the average annual PV generation (based on the years 1986-2015) potentially equals the average annual demand (based on the years 2006-2015).</p> <p>(iii) Synthesis of the results about: residual load (with and w/o storage), ramps (with and w/o storage), excess energy (with and w/o storage), storage requirements</p>
Of Mojave milkweed and mirrors: The population genomic structure of a species impacted by solar energy development
<p>A rapid renewable energy transition has facilitated the development of large, ground‐mounted solar energy facilities worldwide. Deserts, and other sensitive aridland ecosystems, are the second most common land‐cover type for solar energy development globally. Thus, it is necessary to understand existing diversity within environmentally sensitive desert plant populations to understand spatiotemporal effects of solar energy siting and design. Overall, few population genomic studies of desert plants exist, and much of their biology is unknown. To help fill this knowledge gap, we sampled Mojave milkweed (<em>Asclepias</em> <em>nyctaginifolia</em>) in and around the Ivanpah Solar Electric Generating Station (ISEGS) in the Mojave Desert of California to understand the species' population structure, standing genetic variation, and how that intersects with solar development. We performed Restriction‐site Associated Sequencing (RADseq) and discovered 9942 single nucleotide polymorphisms (SNPs). Using these data, we found clear population structure over small spatial scales, suggesting each site sampled comprised a genetically distinct population of Mojave milkweed. While mowing, in lieu of blading, the vegetation across the solar energy facility's footprint prevented the immediate loss of the ISEGS Mojave milkweed population, we show that the effects of land‐cover change, especially those impacting desert washes, may impact long‐term genetic diversity and persistence. Potential implications of this include a risk of overall loss of genetic diversity, or even hastened extirpation. These findings highlight the need to consider the genetic diversity of impacted species when predicting the impact and necessary conservation measures of large‐scale land‐cover changes on species with small population sizes.</p>
Supplement of "Algorithm for continual monitoring of fog life cycles based on geostationary satellite imagery as a basis for solar energy forecasting"
<p>The file uploaded here is an animation that visually illustrates the outputs of the a newly developed machine learning based FLS (<strong>F</strong>og and <strong>L</strong>ow <strong>S</strong>tratus) detection algorithm for the SEVIRI (<strong>S</strong>pinning <strong>E</strong>nhanced <strong>V</strong>isible and <strong>I</strong>nfra<strong>R</strong>ed <strong>I</strong>mager) instrument onboard the MSG (<strong>M</strong>eteosat <strong>S</strong>econd <strong>G</strong>eneration) geo-stationary satellites over the 24hr cycle of the day for the day of <strong>02/March/2021</strong> and compares them with the corresponding raw channel values observed by SEVIRI. The proposed algorithm classifies each SEVIRI pixel as "clear-sky", "FLS", or "non-FLS-cloud" (identified with Khaki, Red, and Blue in the animation) based on the SEVIRI pixel values of BT12.0, BT8.7 - BT12.0, BT10.8 - BT12.0, and BT12.0 - BT13.4 plus the standard deviation of each of these variables in a spatial window sized 3x3 pixels with the central pixel being the target pixel. </p><p><br>In this animation, the left-hand panel shows a false-color RGB image constructed based on the SEVIRI raw channel data with the red, green, and blue channels being BT12.0- BT13.4, BT8.7 - BT12.0, and BT10.8 - BT12.0, respectively. In this panel, the green color represents the high clouds, and the light and dark red colors represent the clear-sky and FLS, respectively. The right-hand panel of this animation also shows the outputs of the ML FLS detection algorithm developed in the present study.</p>
Data from: Solar energy-driven land cover change could alter landscapes critical to animal movement in the continental United States
<p>The United States may produce as much as 45% of its electricity using solar energy technology by 2050, which could require more than 40,000 km<sup>2 </sup>of land to be converted to large-scale solar energy production facilities. Little is known about how such development may impact animal movement. Here, we use five spatially-explicit projections of solar energy development through 2050 to assess the extent to which ground-mounted photovoltaic solar energy expansion in the continental United States may impact land cover and alter areas important for animal movement. Our results suggest that there could be a substantial overlap between solar energy development and land important for animal movement: across projections, 7-17% of total development is expected to occur on land with high value for movement between large protected areas, while 27-33% of total development is expected to occur on land with high value for climate-change-induced migration. We also found substantial variation in the potential overlap of development and land important for movement at the state level. Solar energy development, and the policies that shape it, may align goals for biodiversity and climate change by incorporating the preservation of animal movement as a consideration in the planning process.</p>
India Onshore Wind Energy Atlas Accounting for Altitude and Land Use Restrictions and Co-Located Solar
<p>India faces the simultaneous challenges of meeting rising energy demand and reducing carbon emissions. To address these, India must transition to renewable energy sources. These high-resolution maps are used to quantify available areas for wind farms, after accounting for restrictions, including airports, buildings, protected land use, military zones, railways, roads, water bodies, waterways, wildlife and nature, high elevation and slope, and existing solar farms, to which policy-informed setback distances are applied. This study finds the wind and solar potential within available areas considering three altitudes (100 m, 150 m, 200 m) and four wind speed thresholds (5-8 m/s), and modern wind turbine and solar array dimensions. The raster files included here indicate available areas after aggregating restrictions for different combinations of altitude and wind speed threshold. Availability is indicated with a binary system in which available land is designated with a value of zero and restricted land is designated with a value of one.</p>
Surface science and liquid phase investigations of oxanorbornadiene/oxaquadricyclane ester derivatives as molecular solar thermal energy storage systems on Pt(111) [doi: 10.1063/5.0158124]
<p>Primary data, meta data, and corresponding lists of figures & tables are included. [doi: 10.1063/5.0158124]</p>
Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations
<p>Data for article "Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations"</p>
Data of manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" submitted to Solar Energy
<p>This is the data corresponding to manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" by Kreuwel et al., 2022, submitted to Solar Energy.</p> <p> </p> <p>The file `basic_stats.tar.gz` contains a broad set of standard statistics of surface meteorology and vertical profiles. The file `sw_flux_dn_xy.tar.gz` contains spatial cross sections of downwelling shortwave radiation.</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>
Solar and Wind Energy Drought Data for 15 BAs in the CONUS
<p><strong>Solar and wind energy drought data for 15 BAs in the CONUS</strong></p> <p>This dataset has 2 components, (1) physically consistent wind, solar and load data for 15 Balancing Authorities (BAs) in the CONUS and (2) pre-computed BA-level energy droughts for a variety of time scales from 1 hour to 5 days. The generation and load data is aggregated from plant level data based on EIA-860 2020 infrastructure. </p> <p>For more information please refer to Bracken et al. 2023, Standardized Benchmark of Historical Compound Wind and Solar Energy Droughts Across the Continental United States, in prep, or refer to the Github repository https://github.com/GODEEEP/energy-droughts</p> <p><strong>Wind, solar and load data</strong></p> <p>The data is broken up with one csv file per time scale, the available time scales are 1-hour, 4-hour, 12-hour, 1-day, 2-day, 3-day, and 5-day. Each file has the following columns</p> <ul> <li>ba - Abbreviated name for the BA </li> <li>year - The current year as an integer</li> <li>period - A unique integer for the current time step</li> <li>solar_gen_mwh - Aggregated solar generation in units of MWh</li> <li>solar_capacity_mwh - Aggregated solar plant capacity expresed as MWh </li> <li>wind_gen_mwh - Aggregated wind generation in units of MWh</li> <li>wind_capacity_mwh - Aggregated wind plant capacity expresed as MWh </li> <li>load_mwh - BA load in MWh</li> <li>load_max_mwh - The maximum BA load over the entire historical period</li> <li>datetime_utc - Time stamp for the current time step, in UTC, All time stamps are beginning of period. </li> <li>timezone - The predominant time zone for the BA</li> <li>wind_cf - Wind capacity factor, wind_gen_mwh/wind_capacity_mwh</li> <li>solar_cf - Solar capacity factor, wind_gen_mwh/wind_capacity_mwh </li> <li>load_cf - Load "capacity factor", expresed as a fraction of the maximum BA load, load_mwh/load_max_mwh </li> </ul> <p><strong>Energy drought data</strong></p> <p>Several kinds of energy droughts are available</p> <ul> <li><strong>lws</strong> - Load, wind, and solar compound droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>rl</strong> - Residual load (load minus wind and solar gen) droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>solar</strong> - Solar only droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>solar_fixed</strong> - Solar only droughts defined using a single 10th percentile threshold </li> <li><strong>wind</strong> - Wind only droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>wind_fixed</strong> - Wind only droughts defined using a single 10th percentile threshold </li> <li><strong>ws</strong> - Wind only droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>ws_fixed</strong> - Wind and solar droughts defined using a single 10th percentile threshold </li> </ul> <p>Each drought type and time scale is in a csv file with the following columns (not all columns are available for every drought type)</p> <ul> <li>ba - Abbreviated name for the BA </li> <li>run_id - unique id for each drought event</li> <li>datetime_utc - Date stamp for the start of the drought, in UTC</li> <li>timezone - The predominant time zone for the BA</li> <li>run_length - The length of a drought in time steps</li> <li>run_length_days - The length of the drought in days</li> <li>severity_ws - Drought severity for wind and solar droughts, computed using the compound drought magnitude metric</li> <li>severity_lws - Drought severity load, wind, and solar droughts, computed using the compound drought magnitude metric</li> <li>severity_mwh - Drought severity expressed as MWh</li> <li>zero_prob - For solar, this value indicates if the timestep has zero probability of solar production, i.e. night time</li> <li>year - The year of the timestep</li> <li>month - The month of the timestep </li> <li>hour - The hour of the timestep </li> <li>wind_cf - Wind capacity factor for the drought</li> <li>solar_cf - Solar capacity factor for the drought </li> <li>srepi_solar - Standardized renewable energy production index for solar</li> <li>srepi_wind - Standardized renewable energy production index for wind</li> </ul> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p> </p>
Dataset of avian samples collected and analyzed in: Genetic identification of avian samples recovered from solar energy installations
<p class="MsoNormal">Renewable energy production and development will drastically affect how we meet global energy demands, while simultaneously reducing the impact of climate change. Although the possible effects of renewable energy production (mainly from solar- and wind-energy facilities) on wildlife have been explored, knowledge gaps still remain, and collecting data from wildlife remains (when negative interactions occur) at energy installations can act as a first step regarding the study of species and communities interacting with facilities. In the case of avian species, samples can be collected relatively easily (as compared to other sampling methods), but may only be able to be identified when morphological characteristics are diagnostic for a species. Therefore, many samples that appear as partial remains, or "feather spots" – known to be of avian origin but not readily assignable to species via morphology – may remain unidentified, reducing the efficiency of sample collection and the accuracy of patterns observed. To obtain data from these samples and ensure their identification and inclusion in subsequent analyses, we applied, for the first time, a DNA barcoding approach that uses mitochondrial genetic data to identify unknown avian samples collected at solar facilities to species. We also verified and compared identifications obtained by our genetic method to traditional morphological identifications using a blind test, and discuss discrepancies observed. Our results suggest that this genetic tool can be used to verify, correct, and supplement identifications made in the field and can produce data that allow accurate comparisons of avian interactions across facilities, locations, or technology types. We recommend implementing this genetic approach to ensure that unknown samples collected are efficiently identified and contribute to a better understanding of wildlife impacts at renewable energy projects.</p>
Photometry of outer Solar System objects from the Dark Energy Survey I: photometric methods, light curve distributions and trans-Neptunian binaries - data release
<p>This repository contains the full data release for the 814 outer Solar System objects found in the Dark Energy Survey.</p> <p>A full description of the object search is described in <a href="http://(https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022)</a>, and a full description of the photometric processing is described in<a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230403017B/abstract"> Bernardinelli et al (2023)</a>. If you use these files, we ask you to cite the corresponding papers.</p> <p>The FITS table `y6_des_tnos_color.fits` contains both the orbital elements and the colors for each object. The full description of the orbital element information is given in Table 3 of <a href="https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022</a>). In addition to these, the table also includes the mean absolute magnitudes in each band, as well as mean <span class="math-tex">\((g-r, r-i, r-z)\)</span> colors and their corresponding covariance matrix, and the 68% confidence interval for the lightcurve amplitude.</p> <p>Inside the `fluxes` directory, the complete photometric record for each object is included in a `hdf5` file (for each object), and the MCMC chains for their fluxes and LCAs. The three Jupyter Notebooks included in the `notebooks` directory explains the columns and how to use these files to reproduce the results of the paper. Inside this directory there are also additional files needed to reproduce the code.</p> <p>The `binary` directory has the MCMC chains for their mutual orbits (in `.npy` files), as well as the astrometric and photometric record for the binary measurements. Another `README` file is included in that directory with a detailed explanation.</p>
Data for: Bibliographic synthesis of biodiversity-relevant criteria for solar energy siting
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Of Mojave milkweed and mirrors: The population genomic structure of a species impacted by solar energy development
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International Brain Laboratory public data
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OpenNeuro
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