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89 results for “power plants”
Dataset for Accuracy of Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and CNN-LSTM Model
<p>This research paper introduces a deep learning hybrid model employing Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting.The proposed method integrates the Variational Mode Decomposition (VMD) algo-rithm with the CNN-LSTM model to predict PV power generation from a solar farm in Boussada, Algeria, from January 1, 2019, to December 31, 2020. The performance of the developed model is benchmarked against other deep learning models (VMD-CNN, VMD-LSTM, CNN-LSTM) across various time horizons (15, 30, and 60 minutes) to provide a comprehensive evaluation. Our findings exhibit greater performance of the developed model compared to other architectures, showcasing promising results in solar power forecasting. This research contributes to the main goal of enhancing EMS by providing accurate solar energy forecasts.</p>
Wind power plants layouts according to arbitrary reference points, Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array
<p><strong>If this dataset helps your research, please cite it and the papers further below (according to which OWPP you study).</strong></p> <p>This dataset contains the layouts of the Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array offshore wind power plants (OWPPs), which can be utilized in a variety of studies. </p> <p>The X and Y coordinates, in kilometers, were written according to arbitrary reference points. The positions of wind turbine generators (WTGs) and substations (SS) for the OWPPs came from the sources below.</p> <p><strong>Thanet</strong>: WTGs from [1] (page 7), SS based on [3] (page 9).<br><strong>West of Duddon Sands</strong>: WTGs from [2] (page 5), SS based on [3] (page 9).<br><strong>Ormonde</strong>: WTGs and SS from [4] (page 2).<br><strong>Westermost Rough</strong>: WTGs and SS from [5] (page 4).<br><strong>Horns Rev 1</strong>: WTGs and SS from [6] (page 6).<br><strong>Horns Rev 2</strong>: WTGs and SS from [7] (page 5).<br><strong>Anholt</strong>: WTGs and SS from [8] (page 3).<br><strong>London Array</strong>: WTGs from [9] (page 15), SS based on [10] (page 2).</p> <p>From the OWPPs' layout figures [1-9], I used Graph Grabber 2.0.2* to extract the data points. Then, based on visual inspection of layouts in [1-9], I utilized 2D projections to align WTGs that should be aligned. References [1], [2], and [9] do not provide the SS positions. Thus, I carefully overlapped the layouts with other layouts from [3] and [10] to approximate the SS locations.</p> <p>Regarding the arbitrary reference points for the coordinates, although the values in the X and Y axes differ from [1-9], note that the distances among the WTGs are the same from [1-9]. Furthermore, there are no axes in [1,5]. Instead, distances are given, which are enough to obtain the layout. Values in meters were converted to kilometers.</p> <p>As seen in the attachments, the coordinates can be obtained via either "h5" or "csv" files, which can be easily read by Matlab, Julia, and Python, among others. <strong>The name of the datasets in the "h5" file are</strong>: "Thanet", "WDS", "Ormonde", "WMR", "HornsRev1", "HornsRev2", "Anholt", and "LondonArray".</p> <p><strong>Except for the London Array OWPP, the first row in all data matrices represents the SS coordinates, whereas the subsequent rows represent the WTGs. London Array has 2 substations, thus the first and second rows represent their coordinates. In all matrices, the first and second columns are the X and Y coordinates, respectively.</strong></p> <p>*<a href="https://www.quintessa.org/software/downloads-and-demos/graph-grabber-2.0.2">Graph Grabber 2.0.2 | Downloads And Demos | Software | Quintessa Limited | Scientific and Mathematical Consultancy</a></p>
Voltage and current data for IEC 62600-30 power quality monitoring from the Mutriku Wave Power Plant and Lir National Ocean Test Facility electrical laboratory
<p>This Technical Note describes the electrical data collected from the Mutriku Wave Power Plant (MWPP) and the Lir National Ocean Test Facility (NOTF) electrical laboratory at the MaREI Centre in the Environmental Research Institute, at University College Cork.</p> <p>In summary, the electrical data collect is for the purpose of analysing the power quality output of a Wave Energy Converter (WEC). The data includes voltage and current signals from the output of a WEC sampled at 15 kHz from the MWPP and a WEC emulator sampled at 20 kHz from the Lir NOTF electrical laboratory. There are 24 datasets from the MWPP taken at various sea state conditions, and there are 56 datasets from the Lir NOTF which are taken with at various sea state conditions, with different control laws, and grid connections.</p> <p>This data is published for purpose of power quality analysis and comparison for future tests. For OPERA, power quality analysis was performed as part of WP5 T5.2 and T5.5, and presented in depth in Deliverables D5.2 and D5.4.</p> <p>See accompanying technical note for more Information.</p>
Water availability and temperature scenarios for water-dependent power plants in the Danube river basin and the Iberian Peninsula
<p>The dataset is composed by 12 files reporting the water availability and temperature scenarios for 167 water-dependent power plants in the Danube river basin and the Iberian Peninsula.</p> <p>The dataset is split into multiple files by region (Danube river basin (Danube) or Iberian Peninsula (IP)), variable (discharge or river temperature) and scenario (baseline, RCP26 or RCP85) considered.</p> <p>The title of each file is composed by the variable reported (discharge or river temperature) and the scenario considered (baseline: 1951-2004, RCP26: 2006-2100, RCP85: 2006-2100). The first row is used to report the fields considered: the first three columns report the day, the month and the year. The remaining columns report the name of the power plant considered in each region (57 for the Daube river basin and 110 for the Iberian Peninsula). In each row day, month, year and streamflow or river temperature values are reported for every water-dependent power plant examined in the study.</p> <p>Temperature is reported as daily average temperature in degrees Celsius (°C) while water availability is reported as daily average streamflow in cubic meters per second (m^3/s).</p> <p>For a description on how these files were obtained, please refer to <a href="https://doi.org/10.2777/135510">https://doi.org/10.2777/135510</a>.</p>
Modeled tritium in precipitation from Fukushima Daiichi Nuclear Power Plant accident simulations with MIROC5-iso
<p>This data set contains modeled tritium in precipitation values from different simulations of Fukushima Daiichi Nuclear Power Plant (FDNPP) accident produced with MIROC5-iso. The simulations are for the period 2011-20121 and were with different anthropogenic tritium source functions. A complete description can be found in Cauquoin, A., Gusyev, M., Bong, H., Okazaki, A., and Yoshimura, K.: Modeling tritium release to the atmosphere during the Fukushima Daiichi Nuclear Power Plant accident and application to estimating post-accident water system transit times, <em>Environ. Sci. Pollut. Res.</em>, <a href="https://doi.org/10.1007/s11356-025-35919-1" target="_blank" rel="noopener">https://doi.org/10.1007/s11356-025-35919-1</a>, 2025. </p> <p>The simulations are named fukushima_accident_{jra55, era5}_total_gas_{div100, div200, div500, div1000}, with {jra55, era5} describing a nudging to JRA-55 or ERA5 reanalyses, and with {div100, div200, div500, div1000} describing the anthropogenic tritium input function used in DatasetS1_table_tritium_release_atm_fukushima_input.csv.</p> <p>The modeled values of tritium in Hiso river water, Minamisoma spring and artesian groundwater, calculated using MIROC5-iso tritium in monthly precipitation in Fukushima, scaled Tokyo GNIP data, and tritium measurements in preciptation at Fukushima as input of the TracerLPM model, are included too. </p> <p>The model data can be downloaded as netcdf, csv or xlsx files:</p> <ul> <li>*_daymean.prcpTU.nc: daily mean tritium in precipitation over the period 2011-2021, expressed in TU;</li> <li>*_monmean.prcpTU.nc: monthly mean tritium in precipitation over the period 2011-2021, expressed in TU;</li> <li>*_daymean.prcp.nc: daily precipitation over the period 2011-2021, expressed in mm/day;</li> <li>*_monmean.prcp.nc: monthly precipitation over the period 2011-2021, expressed in mm/month;</li> <li>*_prcp_daymean.remapnn.csv: daily precitation at nearest grid cells of Tsukuba, Kashiwa, Hongo, Yokosuka, Konan, and Misasa over the period 2011-2012, expressed in mm/day;</li> <li>*_prcp_monmean.remapnn.csv: montly mean precitation at nearest grid cells of Chiba, Niigata, and Fukushima over the period 2011-2021, expressed in mm/month;</li> <li>*_prcpTU_daymean.remapnn.csv: tritium in daily precitation at nearest grid cells of Tsukuba, Kashiwa, Hongo, Yokosuka, Konan, and Misasa over the period 2011-2012, expressed in TU;</li> <li>*_prcpTU_monmean.remapnn.csv: tritium in montly precitation at nearest grid cells of Chiba, Niigata, and Fukushima over the period 2011-2021, expressed in TU;</li> <li>DatasetS1_table_tritium_release_atm_fukushima_input.csv: Table of anthropogenic tritium daily release, based on reconstructed iodine-131 total gas emissions from <a href="https://doi.org/10.5194/acp-15-1029-2015" target="_blank" rel="noopener">Katata et al. (2015)</a>, used as inputs for MIROC5-iso.</li> <li>TracerLPM_fukushima_with_peak_jra55.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation div100 nudged to JRA-55 was used for constructing Cin(t).</li> <li>TracerLPM_fukushima_without_peak_jra55.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation ctrl nudged to JRA-55 (without FDNPP peak) was used for constructing Cin(t).</li> <li>TracerLPM_fukushima_with_peak_era5.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation div100 nudged to ERA5 was used for constructing Cin(t).</li> </ul>
Concentrating solar power (CSP) plants AI-training dataset for flux density measurements.
<p>In this dataset, the tools required for the training of a neural net in the context of flux density measurements in concentrating solar power (CSP) plants are included. An Excel file with 931 meteorological conditions and the positions of the power plant and the receiver is included, as well as 15928 pairs of images resulting from ray-tracing in Solarturm Juelich (STJ) each of these conditions with 17 different combinations of heliostats. <br> <br>This dataset is part of the WP1 of TOPCSP european project (funded by HORIZON MSCA Doctoral Network, Project number 101072537).</p>
The dataset of photovoltaic power plant distribution in China by 2020
<p>Photovoltaic (PV) technology, an efficient solution for mitigating the impacts of climate change, has been increasingly used across the world to replace fossil-fuel power to minimize greenhouse gas emissions. With the world's highest cumulative and fastest built PV capacity, China needs to assess the environmental and social impacts of these established photovoltaic (PV) power plants. However, a comprehensive map regarding the PV power plants' locations and extent remain scarce on the country scale. This study developed a workflow combining machine learning and visual interpretation methods with big satellite data to map PV power plants across China. We applied a pixel-based Random Forest (RF) model to classify the PV power plants from composite images in 2020 with 30-meter spatial resolution on Google Earth Engine (GEE). The result classification map was further improved by a visual interpretation approach. Eventually, we established a map of PV power plants in China by 2020, covering a total area of 2917 km<sup>2</sup>. We found that most PV power plants were sited on cropland, followed by barren land and grassland based on the derived national PV map. In addition, the installation of PV power plants has generally decreased the vegetation cover. This new dataset is expected to be conducive to policy management, environmental assessment, and further classification of PV power plants.</p>
Supplementary Data from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>These data are used to conduct the analysis in, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied</em> Statistics. This is purely for archival purposes to facilitate access to and replication of the aforementioned analysis. Data were obtained from the following sources:</p> <ol> <li> U.S. Emissions Data [<a href="https://ampd.epa.gov/ampd">U.S. EPA, Air markets program data (AMPD)</a>] <ul> <li>AMPD_Unit_with_Sulfur_Content_and_Regulations_with_Facility_Attributes.csv</li> </ul> </li> <li> US Census 2016 American Community Survey [<a href="https://www.census.gov/programs-surveys/acs">US Census Bureau ACS</a>] <ul> <li>Census_2016_TxZCTA.RDS</li> <li><em>Note: data were obtained using the r package ‘<a href="https://walker-data.com/tidycensus/">tidycensus</a>’.</em></li> </ul> </li> <li> Daymet Annual Climate Summaries [<a href="https://daac.ornl.gov/DAYMET/guides/Daymet_V4_Annual_Climatology.html">Daymet Version 4</a>] <ul> <li>daymet_v4_prcp_annttl_na_2016.nc</li> <li>daymet_v4_tmax_annavg_na_2016.nc</li> <li>daymet_v4_tmin_annavg_na_2016.nc</li> <li>daymet_v4_vp_annavg_na_2016.nc</li> </ul> </li> <li> SO<sub>4</sub> and Black Carbon Concentrations [<a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03">Randall Martin Atmospheric Composition Analysis Group, North American Regional Estimates, version V4.NA.02</a>] <ul> <li>GWRwSPEC_BC_NA_201601_201612.nc</li> <li>GWRwSPEC_SO4_NA_201601_201612.nc</li> </ul> </li> <li> HyADS Coal-Attributed PM2.5 Concentrations [<a href="https://doi.org/10.1097/EDE.0000000000001024">Henneman et al. (2019)</a>] <ul> <li>HyADS_grids_pm25_byunit_2016.fst</li> <li>HyADS_grids_pm25_total_2016.fst</li> </ul> </li> <li> Mexico Emissions Data [<a href="https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms">National Emissions Inventory Collaborative, 2016v1 emissions modeling platform</a>] <ul> <li>Mexico_2016_point_interpolated_02mar2018_v0.csv</li> </ul> </li> <li> North American Regional Reanalysis Meteorological Data [<a href="https://psl.noaa.gov/data/gridded/data.narr.monolevel.html">NOAA</a>] <ul> <li>rhum.2m.mon.mean.nc</li> <li>uwnd.10m.mon.mean.nc</li> <li>vwnd.10m.mon.mean.nc</li> </ul> </li> <li> Cigarette smoking data [<a href="https://doi.org/10.1186/1478-7954-12-5">Dwyer-Lindgren et al. (2014)</a>] <ul> <li>smokedatwithfips_1996-2012.csv</li> </ul> </li> <li> Synthetic pediatric asthma data [<em>Note:<strong> synthetic data!</strong> Simulated to match the format, but not the observations, from the <a href="https://www.dshs.texas.gov/texas-health-care-information-collection">Texas Health Care Information Collection (THCIC), Texas DSHS</a></em>] <ul> <li>synth-ped-asthma-data.csv</li> </ul> </li> <li> Texas state shape file [<a href="https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html">US Census</a>] <ul> <li>texas-state-sf.RDS</li> </ul> </li> <li> US ZIPcode-to-county data crosswalk [<a href="https://mcdc.missouri.edu/applications/geocorr2014.html">Missouri Census Data Center</a>] <ul> <li>tx-zip-to-county.csv</li> </ul> </li> </ol> <p>Code and supplementary material from this analysis, as well as more detailed data descriptions, are available at: <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a></p>
Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework
<p>Dataset for "Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework"</p>
Data for Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications
<p>These data were generated for the Open-Acces Article :</p> <p>Kamerling, S.; Vuillerme, V.; Rodat, S. Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3731. https://doi.org/10.3390/en14133731</p> <p>In these dataset, the data for the Case Study and the Sensitivity Analysis are available. Jupyter Notebooks for further process of these data are also available. The NoteBooks AnalyseHourlyValues, AnalyseDailyValues and AnalyseMonthlyValues allow for easy change of variable, whereas CaseStudyAnalysis is for one specific set of data. The AnalyseSets were created in order to analyse the influence of the optimization on the solar fraction of the different datasets.</p>
High-resolution analysis of power plant land requirements for GODEEEP
<p>This dataset contains data associated with Mongird et al. (under review). Files include output from the following three analyses found in the paper: (1) Projected power plant siting intersections with US Disadvantaged Communities (DACs), important farmland, and natural areas; (2) onshore wind and solar photovoltaic capacity factor availability under 27 different siting restriction cases, and (3) output from an analysis that determines how many DACs are projected to see both fossil fuel generation retirement and new renewable power plant development. Each of the files associated with these components are described below. </p> <p>For more detailed information please refer to Mongird et al. (under review), "High-resolution analysis of power plant land requirements for the evolving Western United States power grid indicates coordinated land use policies will be essential"</p> <p>Outputs included in this dataset are associated with two different scenarios. Summaries of each of the two scenarios included are provided below. For additional information, see <a href="https://doi.org/10.1016/j.egycc.2023.100117">Ou et al. 2023.</a></p> <h2>Scenario Descriptions</h2> <ul> <li><strong>business-as-usual</strong>: <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does include the US Inflation Reduction Act (IRA) incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> <li><strong>high renewables</strong>: <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does include US IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> </ul> <h2>Data Descriptions</h2> <h3>1. Projected power plant siting intersections</h3> <p><strong>Description</strong></p> <p>These files identify the intersection of projected power plant locations with three types of land: federall identified disadvantaged communities (DACs), important farmland, and land in close proximity to natural areas.</p> <p><strong>Scenario Files:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>bau_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with federally identified US DACs by technology type and Western US state</td> </tr> <tr> <td>bau_env_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state</td> </tr> <tr> <td>bau_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with important farmland by technology type and Western US state</td> </tr> <tr> <td>hr_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with federally identified US DACs by technology type and Western US state</td> </tr> <tr> <td>hr_env_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state</td> </tr> <tr> <td>hr_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with important farmland by technology type and Western US state</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> <td><strong>Units</strong></td> </tr> <tr> <td>state</td> <td>Name of US state</td> <td>N/A</td> </tr> <tr> <td>technology</td> <td>Power plant technology type inclusive of turbine type, presence of CCS, and cooling type (as applicable)</td> <td>N/A</td> </tr> <tr> <td>technology_simple</td> <td>Power plant technology type excluding turbine type, presence of CCS, and cooling type (as applicable)</td> <td>N/A</td> </tr> <tr> <td>layer_name</td> <td>Descriptive name of geospatial raster layer used for intersection analysis</td> <td>N/A</td> </tr> <tr> <td>layer</td> <td>Name of geospatial raster layer used for intersection analysis</td> <td>N/A</td> </tr> <tr> <td>total_plants</td> <td>Number of projected power plants of specified technology in specified state under given scenario</td> <td>#</td> </tr> <tr> <td>intersection</td> <td>Number of projected power plant intersections of specified technology in specified state with given layer under given scenario </td> <td>#</td> </tr> <tr> <td>fraction</td> <td>ratio of intersection and total_plants</td> <td>fraction</td> </tr> </tbody> </table> <p> </p> <p><strong>Scenario Difference Analysis Files:</strong></p> <table> <tbody> <tr> <td>difference_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with federally identified US DACs by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> <tr> <td>difference_env_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> <tr> <td>difference_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with important farmland by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Dictionary:</strong></p> <table style="width: 85.255198%; height: 152px;"> <tbody> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;"><strong>Column</strong></td> <td style="width: 82.845413%; height: 19px;"><strong>Description</strong></td> <td style="width: 4.029241%; height: 19px;"><strong>Units</strong></td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">state</td> <td style="width: 82.845413%; height: 19px;">Name of US state</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">technology</td> <td style="width: 82.845413%; height: 19px;">Power plant technology type</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">layer</td> <td style="width: 82.845413%; height: 19px;">Name of geospatial raster layer used for intersection analysis</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">hr</td> <td style="width: 82.845413%; height: 19px;">Number of projected power plant intersections with given layer under the high renewables scenario</td> <td style="width: 4.029241%; height: 19px;">#</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">bau</td> <td style="width: 82.845413%; height: 19px;">Number of projected power plant intersections with given layer under the business-as-usual scenario</td> <td style="width: 4.029241%; height: 19px;">#</td> </tr> <tr style="height: 38px;"> <td style="width: 8.458634%; height: 38px;">intersection</td> <td style="width: 82.845413%; height: 38px;">Difference in projected power plant intersections between the high renewables scenario and the business-as-usual scenario</td> <td style="width: 4.029241%; height: 38px;">#</td> </tr> </tbody> </table> <h3> </h3> <h3>2. Projected onshore wind and solar photovoltaic capacity factor availability under 27 siting restriction cases</h3> <p>Description:</p> <p>This file contains results from an analysis on the capability of reaching high renewables scenario solar and wind generation in 2050 under 27 different siting restriction cases.</p> <p><strong>Relevant File:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>capacity_factor_analysis_2050.csv</td> <td>Amount of solar PV or onshore wind generation projected to be available in a given state under a specified siting restriction case </td> </tr> </tbody> </table> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> <td><strong>Units</strong></td> </tr> <tr> <td>region_name</td> <td>Name of US state</td> <td>N/A</td> </tr> <tr> <td>technology</td> <td>Power plant technology type (either solar PV or Wind)</td> <td>N/A</td> </tr> <tr> <td>capacity_density_mw</td> <td>Assumed MW per square-km</td> <td>MW</td> </tr> <tr> <td>case</td> <td>Name of siting exclusion case</td> <td>N/A</td> </tr> <tr> <td>total_generation_mwh</td> <td>Projected total generation available given remaining available land after exclusions</td> <td>MWh</td> </tr> <tr> <td>target_generation_mwh</td> <td>Projected target annual generation in 2050 for technology type under high renewables scenario</td> <td>MWh</td> </tr> <tr> <td>gcam_trading_region</td> <td>Name of zonal representation of electricity trading regions as defined in the capacity expansion model</td> <td>N/A</td> </tr> </tbody> </table> <h3> </h3> <h3>3. US DACs that see both fossil fuel generation retirement and new renewable power plant development by 2050</h3> <p><strong>Description:</strong></p> <p>This data contains US census tract GEOIDs that see both new renewable sitings and the retirement of fossil generating resources.</p> <p><strong>Relevant File:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>dac_fossil_retire_analysis_2050.csv</td> <td>List of US census tracts that see both fossil fuel generation retirement and new renewable generation siting by 2050 </td> </tr> </tbody> </table> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td> census_tract</td> <td> US census tract GEOID</td> </tr> <tr> <td>state_name</td> <td>Name of US state</td> </tr> <tr> <td>county_name</td> <td>Name of US county</td> </tr> <tr> <td>scenario</td> <td>scenario name</td> </tr> </tbody> </table> <p> </p> <h2>Funding statement</h2> <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> <h2>Changelog</h2> <p>v1.1</p> <p> The following updates were made following manuscript revision:</p> <ul> <li>"power_density_mw" variable name in `capacity_factor_analysis_2050.csv` file changed to "capacity_density_mw"</li> <li>More estimates are provided in `capacity_factor_analysis_2050.csv` reflecting additional capacity density and turbine hub height assumptions.</li> <li>Scenario naming adjusted to align with manuscript naming</li> </ul>
Fig. 2 in Distribution of Siluriformes in a river under the influence of a small hydroelectric power plant of the Paraná River Basin, Brazil
Fig. 2. Mean total abundance of Siluriformes from the Jaguariaiva River, Upper Parana River basin by site [capture-per-unit-effort (CPUE); unit: number of individuals/1,000 m² of nets/16 h]. Vertical bars = standard error.
Fig. 4 in Distribution of Siluriformes in a river under the influence of a small hydroelectric power plant of the Paraná River Basin, Brazil
Fig. 4. Variations in composition of Siluriformes in the three distinct zones in the Nova Jaguaraiva River under the effects of damming. Species richness (beta diversity) was assessed as species dispersion within the three zones using permutational analysis of multivariate dispersions (PERMDISP; e.g., a greater distance to the spatial median indicates a larger dispersion and, therefore, broader beta diversity). The upper and lower hinges correspond to the 25th and 75th quartiles, respectively.
Fig. 1 in Distribution of Siluriformes in a river under the influence of a small hydroelectric power plant of the Paraná River Basin, Brazil
Fig. 1. Location of sampling sites in the Jaguariaiva River, Upper Parana River basin, Brazil: PCH Nova Jaguariaíva (bar); upstream (red circle); reservoir (black circle); downstream (yellow circle).
Fig. 3 in Distribution of Siluriformes in a river under the influence of a small hydroelectric power plant of the Paraná River Basin, Brazil
Fig. 3. Mean abundance of the Siluriformes species from the Jaguariaiva River, Upper Parana River basin by site [capture-per-unit-effort (CPUE); unit: number of individuals/1,000 m² of nets/16 h]. Vertical bars = standard error (Cher, Corydoras ehrhardti; Halb, Hypostomus albopunctatus; Hanc, Hypostomus ancistroides; Hher, Hypostomus hermanni; Hpau, Hypostomus paulinus; Hstr, Hypostomus strigaticeps; Nsel, Neoplecostomus selenae; Rque, Rhamdia quelen; Tcan, Trichomycterus candidus; Cdia, Cambeva diabola).
Shapefiles with the outline of maximum water spread resulting from the catastrophic release of the Kakhovka Reservoir after the destruction of the Kakhovka Hydroelectric Power Plant by Russian occupying forces
<p>The map is based on remote sensing data from Sentinel-2A (Processing Level L2A), dated June 8, June 13, and June 18, 2023, and Landsat-9 (Collection 2 Level-1), dated June 9, 2023. </p> <p>The following Sentinel-2 remote sensing data granules were used:<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TUS_20230608T132103.SAFE S2A_MSIL2A_20230608T084601_N0509_R107_T36TVS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TWS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TVT_20230608T132103.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TUS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TVS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TWS_20230613T102806.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TUS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TVS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TWS_20230618T151602.SAFE</p> <p>The following remote sensing data scenes from Landsat-9 were used:<br>LC09_L1TP_179028_20230609_20230610_02_T1<br>LC09_L1TP_179027_20230609_20230610_02_T1</p> <p>The contour of the maximum water spread was constructed using a method of manual visual interpretation of remote sensing data, relying on knowledge of the local terrain. We consciously chose not to use automated methods with water indices such as the Normalized Difference Water Index (NDWI) or the Modified Normalized Difference Water Index (MNDWI), as these do not effectively distinguish water surfaces in areas covered with forest or dense reed thickets. Similarly, we did not use the SRTM digital elevation model due to significant artifacts in the study area, where the model shows the height of the forest canopy instead of the ground surface in forested areas.</p> <p>For visual interpretation of Sentinel-2A remote sensing data, we used combinations of spectral bands NIR-Red-Green (8-4-3) and SWIR2-NIR-Green (12-8-3). For the visual interpretation of Landsat-9 remote sensing data, we used combinations of bands SWIR1-NIR-Red (6-5-4) and NIR-Red-Green (5-4-3). To better align the resolution of Sentinel-2A remote sensing data (10 m/pixel) with that of Landsat-9 (30 m/pixel), the latter's data was enhanced using the panchromatic channel (Band 8) through IHS-based pansharpening to 15 m/pixel. The pansharpening was performed using a custom bash script, utilizing command-line tools and utilities such as ImageMagick (<a href="https://imagemagick.org" rel="nofollow">https://imagemagick.org</a>), listgeo, and geotifcp (<a href="https://github.com/OSGeo/libgeotiff">https://github.com/OSGeo/libgeotiff</a>). To expedite the pansharpening process, both Landsat scenes were cropped to the study region and merged by bands using the gdal_translate and gdal_merge.py utilities from the GDAL library (<a href="https://gdal.org/" rel="nofollow">https://gdal.org/</a>). For convenience, the Sentinel-2A data tiles T36TUS, T36TVS, and T36TWS were also cropped and merged by bands using custom scripts available at <a href="https://doi.org/10.5281/zenodo.13205058" rel="nofollow">https://doi.org/10.5281/zenodo.13205058</a>.</p> <p>During visual interpretation, the above-mentioned remote sensing data were compared with satellite images acquired before the destruction of the Kakhovka Hydroelectric Power Plant. In particular, Landsat-9 remote sensing data were compared with Landsat-8 data from June 1, 2023, and Sentinel-2A data were compared with Sentinel-2B data from June 3, 2023.</p> <p>Repository files:<br>floodMax_UTM36N.zip — contains the shapefile in UTM36N projection (EPSG:32636);<br>floodMax_WGS84.zip — contains the shapefile in geographic coordinates in WGS84 (EPSG:4326);<br>floodMax_WGS84.geojson.zip — contains a GeoJSON file in WGS84 coordinates (EPSG:4326).</p> <div> <h1>Web version of the map</h1> </div> <p>The web version of the maximum water spread map is available at:<br><a href="https://yumoskalenko.github.io/floodmap_Kakhovka2023/" rel="nofollow">https://yumoskalenko.github.io/floodmap_Kakhovka2023/</a></p> <p> </p> <p>Embed code for the map on a webpage:</p> <div> <pre><code><iframe style="border: 1px solid black" src="https://yumoskalenko.github.io/floodmap_Kakhovka2023/index.html" marginwidth="0" marginheight="0" scrolling="no" width="100%" height="360" frameborder="0"></iframe> </code></pre> <div> </div> </div> <p><em><strong>This scientific and technical product was created by the scientists of the Black Sea Biosphere Reserve of the National Academy of Sciences of Ukraine during the implementation of research on the topic "Monitoring the condition of natural complexes of the Black Sea Biosphere Reserve (‘Chronicle of Nature’)" (state registration number 0121U109174).</strong></em></p>
Model Inputs and Results - The role of coal plant retrofitting strategies in decarbonizing India's power system
<p>These files are the model inputs and results for the submission based on GenX version v0.3.6 - The role of coal plant retrofitting strategies in decarbonizing India’s power system</p>
Data from: Pitcher geometry facilitates extrinsically powered 'springboard trapping' in carnivorous Nepenthes gracilis pitcher plants
<div> <p>Carnivorous pitcher plants capture insects in cup-shaped leaves that function as motionless pitfall traps. <em>Nepenthes gracilis</em>, evolved a unique 'springboard' trapping mechanism that exploits the impact energy of falling raindrops to actuate a fast pivoting motion of the canopy-like pitcher lid. We superimposed multiple computerized micro-tomography images of the same pitcher to reveal distinct deformation patterns in lid-trapping <em>N. gracilis</em> and closely related pitfall-trapping <em>N. rafflesiana</em>. We found prominent differences between downward and upward lid displacement in <em>N. gracilis </em>only. Downward displacement was characterised by bending in two distinct deformation zones while upward displacement was accomplished by evenly distributed straightening of the entire upper rear section of the pitcher. This suggests an anisotropic impact response, which may help to maximize initial jerk forces for prey capture, as well as the subsequent damping of the oscillation. Our results point to a key role of pitcher geometry for effective 'springboard' trapping in <em>N. gracilis</em>.</p> </div>
Mechanisms of unbalanced sediment transport linked to anthropogenic disturbance in world's largest tidal power plant
<p>Analysis_code.zip and Mooring_toolbox.zip contain matlab m-files. The code was designed to analysis the changes from the mooring data.</p> <p>Data.zip contains a mooring observation data in Lake Sihwa. The velocity profiles and backscatter intensities were measured with a downward-looking acoustic Doppler current profiler (ADCP) (RDI, 600 kHz WorkHorse Sentinel). The mooring system was designed to measure the near-bed temperature, salinity, and turbidity using conductivity-temperature-depth (CTD) sensors (RBR, XR-420, and Concerto) and an optical backscatter sensor (OBS) (Seapoint Turbidity Meter).</p> <p>Waterlevel.zip has water level data in the ocean and Lake Sihwa.</p> <p> </p> <p> </p>
Power Plant Siting Results for GODEEEP
<h3>Introduction</h3> <p>This dataset contains power plant infrastructure siting information modeled by the Capacity Expansion Regional Feasibility (<a href="https://github.com/IMMM-SFA/cerf" target="_blank" rel="noopener">CERF</a>) model for the <a href="https://godeeep.pnnl.gov/">GODEEEP</a> project for the purpose of studying power plant landscape evolution under alternative capacity expansion scenarios. </p> <p>CERF is an open-source geospatial Python package for evaluating and analyzing future electricity technology capacity expansion feasibility.</p> <p>Summaries of each of the two siting scenarios included are provided below. For additional information, see <a href="https://doi.org/10.1016/j.egycc.2023.100117">Ou et al. 2023</a>and the <a href="https://godeeep.pnnl.gov/">GODEEEP website</a>.</p> <h3>Scenario Descriptions</h3> <ul> <li><strong>business_as_usual_ira_ccs_climat</strong>e : <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does include the US Inflation Reduction Act (IRA) incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> <li><strong>net_zero_ira_ccs_climate</strong>: <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does include US IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> </ul> <h3>Climate and Socioeconomic Assumptions</h3> <ul> <li><strong>Climate:</strong> These scenarios include the dynamic effects of a climate pathway (<a href="https://www.nature.com/articles/s41597-023-02485-5">RCP8.5</a>) on heating and cooling degree days (HDD/CDD) in the period 2020-2050. </li> <li><strong>Socioeconomic</strong>: <a href="https://doi.org/10.1016/j.gloenvcha.2015.01.004">Shared Socioeconomic Pathway 2</a>. A "middle of the road" socioeconomic pathway where population and economic growth trends follow historical patterns.</li> </ul> <h3>Data Information</h3> <ul> <li>Coordinates provided in each of the data files follows the NAD 1983 Albers contiguous USA Coordinate Reference System (ESRI:102003).</li> <li>New infrastructure sitings are provided for the 11 states in the Western Interconnection including: Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Oregon, Washington, and Wyoming.</li> <li>The file type represents a specific spatial resolution of infrastructure sitings: <ul> <li> 1 km level data: <br> <ul> <li>Includes new power plant sitings by year at a 1 km-squared resolution.</li> <li>For technologies where a representative power plant facility can be sited within the area of a single square km (e.g., natural gas, biomass), coordinates provided at this resolution are representative of the entire plant location where the facility is sited. </li> <li>For Solar PV, Solar CSP, Onshore Wind, and Offshore Wind technologies, representative power plants (i.e., farms) are typically larger than a single square km. Therefore, coordinates provided at the 1 km-squared resolution in this file represent the total capacity (MW) of these technologies that was sited within a 1 km x 1km grid cell and does not represent a standalone wind or solar farm facility or an individual wind turbine.</li> </ul> </li> <li>Plant-level data <ul> <li>This file type includes all power plant locations at the plant level including both new additions and pre-existing facilities.</li> <li>Coordinates in this file represent the center of a power plant facility.</li> <li>For CERF-sited technologies where a representative power plant facility can be sited within the area of a single square km (e.g., natural gas, biomass), coordinates will match those provided in the CERF siting output file.</li> <li>For coodinates associated with CERF-sited Solar PV, Solar CSP, Onshore Wind, and Offshore Wind farms, the center coordinates of the farm are provided. Farms in this file are spatially-clustered aggregates of individual 1 km-squared solar and wind sitings from the CERF-siting output.</li> </ul> </li> </ul> </li> </ul> <h3>File Descriptions</h3> <table> <tbody> <tr> <td><strong>File Name</strong></td> <td><strong>File Type</strong></td> <td><strong>Scenario</strong></td> </tr> <tr> <td>power_plant_additions_1km_bau.csv</td> <td>1 km data</td> <td> <p>business_as_usual_ira_ccs_climate</p> </td> </tr> <tr> <td>power_plants_bau.csv</td> <td>Plant-level data</td> <td>business_as_usual_ira_ccs_climate</td> </tr> <tr> <td>power_plant_additions_1km_net_zero.csv</td> <td>1 km data</td> <td>net_zero_ira_ccs_climate</td> </tr> <tr> <td>power_plants_net_zero.csv</td> <td>Plant-level data</td> <td>net_zero_ira_ccs_climate</td> </tr> </tbody> </table> <h3>Data Descriptions</h3> <table> <tbody> <tr> <td><strong>Column Name</strong></td> <td><strong>Data Description</strong></td> <td><strong>Units</strong></td> </tr> <tr> <td>scenario</td> <td>Name of scenario</td> <td>N/A</td> </tr> <tr> <td>region_name</td> <td>US state name</td> <td>N/A</td> </tr> <tr> <td>tech_id</td> <td>Unique technology identifier</td> <td>N/A</td> </tr> <tr> <td>technology</td> <td>Type of generation technology with detailed information on cooling type, turbine type, hub-height, and carbon capture (as applicable)</td> <td>N/A</td> </tr> <tr> <td>technology_simple</td> <td>Type of generation technology with no additional detailed information</td> <td>N/A</td> </tr> <tr> <td>unit_size_mw</td> <td>Rated power plant capacity </td> <td>Megawatts (MW)</td> </tr> <tr> <td>xcoord</td> <td>x coordinate of siting location</td> <td>N/A</td> </tr> <tr> <td>ycoord</td> <td>y coordinate of siting location</td> <td>N/A</td> </tr> <tr> <td>buffer_in_km</td> <td>Buffer applied to siting location in CERF</td> <td>kilometers (km)</td> </tr> <tr> <td>sited_year</td> <td>Year power plant was sited</td> <td>N/A</td> </tr> <tr> <td>retirement_year</td> <td>Year power plant was retired</td> <td>N/A</td> </tr> <tr> <td>lifetime_yrs</td> <td>Technology lifetime</td> <td>Years</td> </tr> <tr> <td>operational_life_yrs</td> <td>Years technology operated for</td> <td>Years</td> </tr> <tr> <td>locational_marginal_price_usd_per_mwh</td> <td>Locational marginal price of energy</td> <td>USD/Megawatt-hour (MWh)</td> </tr> <tr> <td>generation_mwh_per_year</td> <td>Annual generation </td> <td>MWh</td> </tr> <tr> <td>capacity_factor_fraction</td> <td>Capacity factor</td> <td>Fraction</td> </tr> <tr> <td>carbon_capture_rate_fraction</td> <td>Rate of carbon capture </td> <td>Fraction</td> </tr> <tr> <td>fuel_price_usd_per_mmbtu</td> <td>Fuel price</td> <td>USD/million British thermal units</td> </tr> <tr> <td>heat_rate_btu_per_kWh</td> <td>Heat rate</td> <td>British thermal units/kilowatt-hour</td> </tr> <tr> <td>variable_om_usd_per_mwh</td> <td>Variable operations and maintenance cost</td> <td>USD/MWh</td> </tr> <tr> <td>cerf_sited</td> <td>Binary variable indicating whether the power plant was sited by CERF (value=1) or a pre-existing power plant (value=0)</td> <td>N/A</td> </tr> </tbody> </table> <h3> </h3> <h3>Acknowledgement</h3> <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>
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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)
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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.