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13 results for “capacity expansion”
Effects of a Delayed Expansion of Interconnector Capacities in a High RES-E European Electricity System - Dataset
<p>Input and output data of the modelling work for the paper Effects of a Delayed Expansion of Interconnector Capacities in a High RES-E European Electricity System</p> <ul> <li>Considered scenario years: 2030, 2040 and 2050</li> <li>The profiles are based on the historical year 2016.</li> <li>Two scenarios are included: lower connectivity and high connectivity</li> <li>The data cover the ENTSO-E member countries except Iceland and Cyprus and is given in country-specific resolution.</li> </ul> <p><strong>Input:</strong></p> <ul> <li>Demand as hourly profile in MWh</li> <li>Variable RES-E as hourly profile in MWh</li> <li>Power plant fleet as capacities in MW</li> <li>NTCs as capacities in MW</li> </ul> <p><strong>Output:</strong></p> <ul> <li>CO2 emissions as annual data in Mt</li> <li>Variable electricity generation costs as annual data in MEur</li> <li>Variable electricity generation costs per generation as annual data in Euro/MWh</li> <li>Electricity generation as annual data in TWh</li> <li>Electricity export as annual data in TWh</li> <li>Electricity import as annual data in TWh</li> <li>Transit flows as annual data in TWh</li> </ul> <p>The sources are described in the corresponding paper under the following link: <a href="https://www.mdpi.com/1996-1073/12/16/3098">https://www.mdpi.com/1996-1073/12/16/3098</a></p>
GRIDCERF: Geospatial Raster Input Datasets for Capacity Expansion Regional Feasibility
<p>Geospatial Raster Input Datasets for Capacity Expansion Regional Feasibility (GRIDCERF) is a data package containing all the necessary input layers for the <a href="https://github.com/IMMM-SFA/cerf">Capacity Expansion Regional Feasibility (CERF) model</a>. The CERF model uses these layers to find feasible power plant siting locations at a 1 kilometer scale across the conterminous United States for renewable and non-renewable electricity production technologies. This package encompasses a wide variety of geospatial layers pertaining to restrictions, regulations, and challenges that come with siting new electricity production facilities.</p>
GRIDCERF: Geospatial Raster Input Data for Capacity Expansion Regional Feasibility
<p><strong>Abstract:</strong></p> <p>Climate change, energy system transitions, and socioeconomic change are compounding influences affecting the growth of electricity demand. While energy efficiency initiatives and distributed resources can address a significant amount of this demand, the United States will likely still need new utility-scale generation resources. The energy sector uses capacity expansion planning models to determine the aggregate need for new generation, but these models are typically at the state or regional scale and are not equipped to address the wide range of location- and technology-specific issues that are increasingly a factor in power plant siting. To help address these challenges, we have developed the Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package, a high-resolution product to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. GRIDCERF offers 265 suitability layers for use with 56 power plant technology configurations in a harmonized format that can be easily ingested by geospatially-enabled modeling software. It also provides pre-compiled technology-specific suitability layers and allows for user customization to robustly address science objectives when evaluating varying future conditions.</p> <p><strong>Accompanying GitHub repository:</strong></p> <p>The following GitHub repository contains the code used to generate the data in this archive: https://github.com/IMMM-SFA/vernon-etal_2023_scidata</p> <p><strong>Contents:</strong></p> <p><strong>Note:</strong></p> <p>GRIDCERF does not provide the source data directly due to some license restrictions related for direct redistribution of the unaltered source data. However, the included file "gridcerf_source_data_description.csv" details the provenance associated with each source dataset and notes their individual licenses/disclaimers.</p> <p><strong>Common Rasters:</strong></p> <p><strong>Suitability Layer Type and Source</strong></p> <p><strong>GRIDCERF Raster Name</strong></p> <p><strong>Bureau of Land Management (BLM) Surface Management Agency Areas</strong><strong><sup>33</sup></strong></p> <p>gridcerf_blm_surface_management_agency_areas.tif</p> <p><strong>BLM National Landscape Conservation System (NLCS) - National Monuments</strong><strong><sup>34</sup></strong></p> <p>gridcerf_blm_nlcs_national_monument_conus.tif</p> <p><strong>BLM NLCS - Outstanding Natural Areas</strong><strong><sup>35</sup></strong></p> <p>gridcerf_blm_nlcs_outstanding_natural_areas_conus.tif</p> <p><strong>BLM NLCS - Wilderness</strong><strong><sup>36</sup></strong></p> <p>gridcerf_blm_nlcs_wilderness_conus.tif</p> <p><strong>BLM NLCS - Wilderness Study Areas</strong><strong><sup>37</sup></strong></p> <p>gridcerf_blm_nlcs_wilderness_study_areas_conus.tif</p> <p><strong>National Park Service (NPS) Class 1 airsheds</strong><strong><sup>38</sup></strong></p> <p>gridcerf_class1_airsheds_conus.tif</p> <p><strong>NPS Administrative Boundaries</strong><strong><sup>39</sup></strong></p> <p>gridcerf_nps_administrative_boundaries_conus.tif</p> <p><strong>NPS Historic Trails</strong><strong><sup>40</sup></strong></p> <p>gridcerf_nps_historic_trails_conus.tif</p> <p><strong>NPS Scenic Trails</strong><strong><sup>41</sup></strong></p> <p>gridcerf_nps_scenic_trails_conus.tif</p> <p><strong>U.S. Fish and Wildlife Service (USFWS) - Critical Habitat</strong><strong><sup>42</sup></strong></p> <p>gridcerf_usfws_critical_habitat_conus.tif</p> <p><strong>USFWS - Special Designation</strong><strong><sup>43</sup></strong></p> <p>gridcerf_usfws_special_designation_conus.tif</p> <p><strong>USFWS - Wild and Scenic River System</strong><strong><sup>44</sup></strong></p> <p>gridcerf_usfws_national_wild_scenic_river_system_conus.tif</p> <p><strong>USFWS - National Realty Tracts</strong><strong><sup>45</sup></strong></p> <p>gridcerf_usfws_national_realty_tracts_conus.tif</p> <p><strong>National Land Cover Dataset (NLCD) Wetlands</strong><strong><sup>46</sup></strong></p> <p>gridcerf_nlcd_wetlands_conus.tif</p> <p><strong>U.S. Forest Service (USFS) Administrative Boundaries</strong><strong><sup>47</sup></strong></p> <p>gridcerf_usfs_administrative_boundaries_conus.tif</p> <p><strong>USFS Wilderness Areas</strong><strong><sup>48</sup></strong></p> <p>gridcerf_usfs_wilderness_areas_conus.tif</p> <p><strong>U.S. Geological Survey (USGS) National Wilderness Lands</strong><strong><sup>49</sup></strong></p> <p>gridcerf_usgs_wilderness_areas_conus.tif</p> <p><strong>USGS Protected Areas of the U.S - Class 1&2</strong><strong><sup>50</sup></strong></p> <p>gridcerf_usgs_padus_class_1_to_2_conus.tif</p> <p><strong>U.S. State Protected Lands</strong><strong><sup>51</sup></strong></p> <p>gridcerf_wdpa_state_protected_lands_conus.tif</p> <p><strong>Nature Conservancy lands</strong><strong><sup>52</sup></strong></p> <p>gridcerf_wdpa_tnc_managed_lands_conus.tif</p> <p> </p> <p><strong>Technology-specific Rasters:</strong></p> <p><strong>Suitability Layer Type and Source</strong></p> <p><strong>GRIDCERF Raster Name</strong></p> <p><strong>Bureau of Indian Affairs (BIA) Land Area Representations Dataset</strong><strong><sup>53</sup></strong></p> <p>gridcerf_bia_land_area_representations_conus.tif</p> <p><strong>Slope 5% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_5pct_or_less.tif</p> <p><strong>Slope 10% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_10pct_or_less.tif</p> <p><strong>Slope 12% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_12pct_or_less.tif</p> <p><strong>Slope 20% or less suitable</strong><strong><sup>20</sup></strong></p> <p>gridcerf_srtm_slope_20pct_or_less.tif</p> <p><strong>Airports (10-mile buffer)</strong><strong><sup>54</sup></strong></p> <p>gridcerf_airports_10mi_buffer_conus.tif</p> <p><strong>Airports (3-mile buffer)</strong><strong><sup>54</sup></strong></p> <p>gridcerf_airports_3mi_buffer_conus.tif</p> <p><strong>Proximity to Railroad and Navigable Waters (< 5 km)</strong> <strong><sup>55,56</sup></strong></p> <p>gridcerf_usdot_railnodes_navwaters_within5km.tif</p> <p><strong>Coal Supply</strong><strong><sup>55–57</sup></strong></p> <p>gridcerf_coalmines20km_railnodes5km_navwaters5km_conus.tif</p> <p><strong>United States Environmental Protection Agency (EPA) CO Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_co_conus.tif</p> <p><strong>EPA NOx Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_no2_conus.tif</p> <p><strong>EPA Ozone Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_ozone_conus.tif</p> <p><strong>EPA Lead Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_lead_conus.tif</p> <p><strong>EPA PM10 Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_pm10_conus.tif</p> <p><strong>EPA PM2.5 Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_pm2p5_conus.tif</p> <p><strong>EPA SOx Non-attainment Areas</strong><strong><sup>58</sup></strong></p> <p>gridcerf_epa_nonattainment_so2_conus.tif</p> <p><strong>Earthquake Potential</strong><strong><sup>59</sup></strong></p> <p>gridcerf_usgs_earthquake_pga_0.3_at_2pct_in_50yrs_conus.tif</p> <p><strong>Densely population areas</strong><strong><sup>11</sup></strong></p> <p>gridcerf_densely_populated_ssp[2,3,5]_[year].tif</p> <p><strong>Densely population areas buffered by 25 miles</strong><strong><sup>11</sup></strong></p> <p>gridcerf_densely_populated_ssp[2,3,5]_[year]_buff25mi.tif</p> <p><strong>Densely population areas – nuclear</strong><strong><sup>11</sup></strong></p> <p>gridcerf_densely_populated_ssp[2,3,5]_[year]_nuclear.tif</p> <p><strong>National Hydrography Dataset (version 2; NHDv2)</strong><strong><sup>32</sup></strong></p> <p>gridcerf_nhd2plus_surfaceflow_greaterthan[bin]mgd_buffer20km.tif</p> <p><strong>National Renewable Energy Laboratory (NREL) concentrating solar direct normal potential</strong><strong><sup>26</sup></strong></p> <p>gridcerf_nrel_solar_csp_centralized_potential.tif</p> <p><strong>NREL photovoltaic potential</strong><strong><sup>26</sup></strong></p> <p>gridcerf_nrel_solar_pv_centralized_potential.tif</p> <p><strong>NREL Wind Integration National Dataset (WIND) toolkit</strong><strong><sup>22</sup></strong></p> <p>gridcerf_nrel_wind_development_potential_hubheight[080,110,140]_cf35.tif</p> <p> </p> <p><strong>Compiled Technology Rasters:</strong></p> <p>The list of layers that make up each compiled technology raster can be found in the "reference/compiled_layer_configuration.txt" file in this data archive.</p> <p>The following technology raster file names are self-descriptive in the format "gridcerf_<technology>_<subtype>_<carbon_capture>_<cooling_type>.tif". Some technologies do not have a carbon capture or cooling type designation and will simply have technology specific considerations listed.</p> <pre>gridcerf_biomass_conventional_ccs_dry.tif gridcerf_biomass_conventional_ccs_oncethrough.tif gridcerf_biomass_conventional_ccs_recirculating.tif gridcerf_biomass_conventional_no-ccs_dry.tif gridcerf_biomass_conventional_no-ccs_oncethrough.tif gridcerf_biomass_conventional_no-ccs_pond.tif gridcerf_biomass_conventional_no-ccs_recirculating.tif gridcerf_biomass_igcc_no-ccs_dry.tif gridcerf_biomass_igcc_no-ccs_oncethrough.tif gridcerf_biomass_igcc_no-ccs_recirculating.tif gridcerf_biomass_igcc_with-ccs_dry.tif gridcerf_biomass_igcc_with-ccs_oncethrough.tif gridcerf_biomass_igcc_with-ccs_recirculating.tif gridcerf_coal_conventional_ccs_dry.tif gridcerf_coal_conventional_ccs_oncethrough.tif gridcerf_coal_conventional_ccs_recirculating.tif gridcerf_coal_conventional_no-ccs_dry.tif gridcerf_coal_conventional_no-ccs_oncethrough.tif gridcerf_coal_conventional_no-ccs_pond.tif gridcerf_coal_conventional_no-ccs_recirculating.tif gridcerf_coal_igcc_no-ccs_dry.tif gridcerf_coal_igcc_no-ccs_oncethrough.tif gridcerf_coal_igcc_no-ccs_recirculating.tif gridcerf_coal_igcc_with-ccs_dry.tif gridcerf_coal_igcc_with-ccs_oncethrough.tif gridcerf_coal_igcc_with-ccs_recirculating.tif gridcerf_gas_cc_ccs_dry.tif gridcerf_gas_cc_ccs_oncethrough.tif gridcerf_gas_cc_ccs_recirculating.tif gridcerf_gas_cc_no-ccs_dry.tif gridcerf_gas_cc_no-ccs_oncethrough.tif gridcerf_gas_cc_no-ccs_pond.tif gridcerf_gas_cc_no-ccs_recirculating.tif gridcerf_gas_turbine_dry.tif gridcerf_gas_turbine_oncethrough.tif gridcerf_gas_turbine_pond.tif gridcerf_gas_turbine_recirculating.tif gridcerf_nuclear_gen3_oncethrough.tif gridcerf_nuclear_gen3_pond.tif gridcerf_nuclear_gen3_recirculating.tif gridcerf_refinedliquids_cc_ccs_dry.tif gridcerf_refinedliquids_cc_ccs_oncethrough.tif gridcerf_refinedliquids_cc_ccs_recirculating.tif gridcerf_refinedliquids_cc_no-ccs_dry.tif gridcerf_refinedliquids_cc_no-ccs_oncethrough.tif gridcerf_refinedliquids_cc_no-ccs_recirculating.tif gridcerf_refinedliquids_ct_dry.tif gridcerf_refinedliquids_ct_oncethrough.tif gridcerf_refinedliquids_ct_pond.tif gridcerf_refinedliquids_ct_recirculating.tif gridcerf_solar_csp_centralized_dry-hybrid.tif gridcerf_solar_csp_centralized_recirculating.tif gridcerf_solar_pv_centralized.tif gridcerf_wind_onshore_hubheight080m.tif gridcerf_wind_onshore_hubheight110m.tif gridcerf_wind_onshore_hubheight140m.tif </pre> <p><strong>Reference Data: </strong>Contains land mask and other useful boundary data. Also contains additional literature review resource and the layers used to build the compiled suitability.</p> <p><strong>References:</strong></p> <p>1. Bureau of Land Management. BLM National Surface Management Agency Area Polygons - National Geospatial Data Asset (NGDA). <em>Landscape Approach Data Portal</em> https://gbp-blm-egis.hub.arcgis.com/datasets/blm-national-sma-surface-management-agency-area-polygons/about (2023).</p> <p>2. Bureau of Land Management. BLM National NLCS National Monuments, National Conservation Areas and Similar Designations Polygons. <em>U.S.Department of Interior Bureau of Land Management Geospatial Business Plaform</em> https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-natl-nlcs-national-monuments-national-conservation-areas-polygons/about (2023).</p> <p>3. Hartger, P. NLCS Outstanding Natural Areas. <em>ArcGIS Hub</em> https://hub.arcgis.com/datasets/Wilderness::nlcs-outstanding-natural-areas/about (2017).</p> <p>4. Bureau of Land Management. BLM National NLCS Wilderness Areas Polygons. <em>U.S.Department of Interior Bureau of Land Management Geospatial Business Plaform</em> https://arcg.is/a01uC (2023).</p> <p>5. Bureau of Land Management. BLM National NLCS Wilderness Study Areas Polygons. <em>U.S.Department of Interior</em> https://arcg.is/14XPiC (2023).</p> <p>6. United States Environmental Protection Agency. Mandatory Class 1 Federal Areas Web Service. <em>Mandatory Class 1 Federal Areas Web Service</em> https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7BF234E37B-E7CE-4A47-89D6-68399D540576%7D (2015).</p> <p>7. National Park Service. National Park Service Boundary. <em>National Park Service</em> https://public-nps.opendata.arcgis.com/datasets/nps::nps-boundary-4/about (2023).</p> <p>8. National Park Service. National Historic Trails. <em>Geospatial Energy Mapper</em> https://gem.anl.gov/tool (2019).</p> <p>9. National Park Service. National Scenic Trails. <em>Geospatial Energy Mapper</em> https://gem.anl.gov/tool (2019).</p> <p>10. U.S. Fish and Wildlife Service. USFWS Threatened & Endangered Species Active Critical Habitat Report. <em>ECOS Environmental Conservation Online System</em> https://ecos.fws.gov/ecp/report/table/critical-habitat.html (2023).</p> <p>11. U.S. Fish and Wildlife. U.S. Fish and Wildlife Special Designation. <em>U.S. Fish and Wildlife</em> https://gis-fws.opendata.arcgis.com/datasets/fws::fws-special-designation/about (2023).</p> <p>12. U.S. Fish and Wildlife Service. National Wild and Scenic Rivers System. <em>Geospatial Energy Mapper</em> https://data.fs.usda.gov/geodata/edw/edw_resources/shp/S_USA.WildScenicRiver_LN.zip (2014).</p> <p>13. U.S. Fish and WIldlife Service. USFWS National Realty Tracts. <em>ServCat Fish and Wildlife Service Catalog</em> https://ecos.fws.gov/ServCat/Reference/Profile/154057 (2023).</p> <p>14. Dewitz, J. & U.S. Geological Survey. National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021). <em>Multi-Resoltuion Land Characteristics Consortium</em> https://www.mrlc.gov/data/nlcd-2019-land-cover-conus (2021).</p> <p>15. USDA Forest Service. Administrative Forest Boundaries. <em>Download National Datasets</em> https://data.fs.usda.gov/geodata/edw/datasets.php?dsetCategory=boundaries (2015).</p> <p>16. U.S. Forest Service. National Wilderness Areas. <em>Download National Datasets</em> https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=Wilderness+areas (2023).</p> <p>17. U.S. Geological Survey. Wilderness Areas in the United States. <em>ScienceBase-Catalog</em> https://www.sciencebase.gov/catalog/item/4fc8f0e4e4b0bffa8ab259e7 (2000).</p> <p>18. U.S. Geological Survey Gap Analysis Project. Protected Areas Database of the United States (PAD-US) 3.0 (ver. 2.0, March 2023). <em>ScienceBase-Catalog</em> https://doi.org/10.5066/P9Q9LQ4B (2023).</p> <p>19. Protected Planet. Protected Areas (WDPA). <em>Protected Areas (WDPA)</em> https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA (2023).</p> <p>20. The Nature Conservancy. The Nature Conservancy lands. <em>The Nature Conservancy (TNC) Lands</em> https://geospatial.tnc.org/datasets/TNC::tnc-lands-north-america/about?layer=0 (2022).</p> <p>21. Bureau of Indian Affairs. American Indian and Alaskan Native Land Area Representations (LAR). <em>U.S. Department of the Interior Indian Affairs</em> https://biamaps.doi.gov/bogs/datadownload.html (2019).</p> <p>22. Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-filled SRTM for the globe Version 4. <em>CGIAR Consortium for Spatial Information</em> https://research.utwente.nl/en/publications/hole-filled-srtm-for-the-globe-version-4-data-grid (2008).</p> <p>23. Bureau of Transportation Statistics. Aviation Facilities. <em>Transportation.gov U.S. Department of Transportation</em> https://geodata.bts.gov/maps/usdot::aviation-facilities (2023).</p> <p>24. U.S. Department of Transportation. North American Rail Network Nodes. <em>U.S. Department of Transportation</em> https://data-usdot.opendata.arcgis.com/datasets/usdot::north-american-rail-network-nodes/explore?location=34.085743%2C-108.310932%2C3.96 (2023).</p> <p>25. U.S. Department of Transportation. Navigable Waterway Network Lines. <em>Bureau of Transportation Statistics</em> https://geodata.bts.gov/datasets/usdot::navigable-waterway-network-lines/about (2023).</p> <p>26. Energy Information Association (EIA). Energy Information Association U.S. Coal Mining Locations. <em>ScienceBase-Catalog</em> https://www.sciencebase.gov/catalog/item/5748a4cbe4b07e28b664dd78 (2017).</p> <p>27. Environmental Protection Agency. Green Book GIS Download. https://www.epa.gov/green-book/green-book-gis-download (2023).</p> <p>28. Shumway, A. <em>Data Release for the 2014 National Seismic Hazard Model for the Conterminous U.S</em>. https://doi.org/10.5066/P9P77LGZ (2019).</p> <p>29. Zoraghein, H. & O’Neill, B. C. U.S. State-level Projections of the Spatial Distribution of Population Consistent with Shared Socioeconomic Pathways. <em>Sustainability</em> <strong>12</strong>, (2020).</p> <p>30. Moore, R. B. <em>et al.</em> <em>User’s guide for the national hydrography dataset plus (NHDPlus) high resolution: U.S. Geological Survey Open-File Report 2019–1096</em>. https://pubs.er.usgs.gov/publication/ofr20191096 (2019).</p> <p>31. Perez, R. <em>et al.</em> A new operational model for satellite-derived irradiances: description and validation. <em>Solar Energy</em> <strong>73</strong>, 307–317 (2002).</p> <p>32. Draxl, C., Clifton, A., Hodge, B.-M. & McCaa, J. The Wind Integration National Dataset (WIND) Toolkit. <em>Applied Energy</em> <strong>151</strong>, 355–366 (2015).</p> <p> </p> <p> </p>
Computing Necessary Conditions for Near-Optimality in Capacity Expansion Planning Problems - Dataset
<p>This dataset contains the inputs used to compute the results presented in the submission titled "Computing Necessary Conditions for Near-Optimalityin Capacity Expansion Planning Problems".</p> <p>The dataset contains a first folder with the inputs of the model, a second folder with the outputs of the model and a file describing the content of the input folder as well as the techno-economic assumptions and sources used for the submission.</p>
The code used to simulate range expansion in "The effect of the recombination rate between adaptive loci on the capacity of a population to expand its range"
Open the record for dataset details and reuse information.
Reducing transmission expansion by co-optimizing sizing of wind, solar, storage, and grid connection capacity: Raw Data
<p>This dataset contains all GenX model input and results data relevant to the working paper ‘Reducing transmission expansion by co-optimizing sizing of wind, solar, storage, and grid connection capacity.’ Data for each modeled scenario is contained within a folder in the main directory ('Final_Outputs'), using the naming convention p1_2030_case[number]. Scenarios correspond to the following table:</p> <table> <tbody> <tr> <td><strong>Case Number</strong></td> <td><strong>Scenario</strong></td> <td><strong>VRE Cost</strong></td> <td><strong>Forced Battery Capacity (GW)</strong></td> </tr> <tr> <td>1</td> <td>Fixed Interconnection</td> <td>Low</td> <td>3.75</td> </tr> <tr> <td>2</td> <td>Fixed Interconnection</td> <td>Low</td> <td>5</td> </tr> <tr> <td>3</td> <td>Fixed Interconnection</td> <td>Low</td> <td>7.5</td> </tr> <tr> <td>4</td> <td>Fixed Interconnection</td> <td>Low</td> <td>15</td> </tr> <tr> <td>5</td> <td>Optimized Interconnection</td> <td>Low</td> <td>3.75</td> </tr> <tr> <td>6</td> <td>Optimized Interconnection</td> <td>Low</td> <td>5</td> </tr> <tr> <td>7</td> <td>Optimized Interconnection</td> <td>Low</td> <td>7.5</td> </tr> <tr> <td>8</td> <td>Optimized Interconnection</td> <td>Low</td> <td>15</td> </tr> <tr> <td>9</td> <td>Co-Located Storage</td> <td>Low</td> <td>3.75</td> </tr> <tr> <td>10</td> <td>Co-Located Storage</td> <td>Low</td> <td>5</td> </tr> <tr> <td>11</td> <td>Co-Located Storage</td> <td>Low</td> <td>7.5</td> </tr> <tr> <td>12</td> <td>Co-Located Storage</td> <td>Low</td> <td>15</td> </tr> <tr> <td>13</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>3.75</td> </tr> <tr> <td>14</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>5</td> </tr> <tr> <td>15</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>7.5</td> </tr> <tr> <td>16</td> <td>Fixed Interconnection</td> <td>Mid</td> <td>15</td> </tr> <tr> <td>17</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>3.75</td> </tr> <tr> <td>18</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>5</td> </tr> <tr> <td>19</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>7.5</td> </tr> <tr> <td>20</td> <td>Optimized Interconnection</td> <td>Mid</td> <td>15</td> </tr> <tr> <td>21</td> <td>Co-Located Storage</td> <td>Mid</td> <td>3.75</td> </tr> <tr> <td>22</td> <td>Co-Located Storage</td> <td>Mid</td> <td>5</td> </tr> <tr> <td>23</td> <td>Co-Located Storage</td> <td>Mid</td> <td>7.5</td> </tr> <tr> <td>24</td> <td>Co-Located Storage</td> <td>Mid</td> <td>15</td> </tr> </tbody> </table> <p>The 'fixed interconnection' scenario describes the scenario where the capacity of interconnection for each solar photovoltaic (PV) or wind site is fixed to assumed values. The 'optimized interconnection' scenario enables the model to independently size the renewable energy to interconnection and grid connection capacity. The 'co-located storage' scenario enables any solar PV or wind resource and storage resource to be sited behind a grid connection point while optimizing the interconnection buildout for each site. These scenarios are further explained in the respective working paper. Renewable energy cost sensitivity tags include ‘low’ for assumed low projected VRE and battery costs in 2030 and ‘mid’ for assumed mid projected VRE and battery costs in 2030. Various storage discharge capacities are forced into the system as a percentage of peak demand and range from 3.75-15 GW. Within each case folder, all of the input files (.csv), result files directly outputted by the model (in the 'Results' folder), and setting files (GenX and solver settings in the 'Settings' folder) can be found. All model outputs are described in detail in the GenX documentation. The code can be found on the GenX GitHub repository: https://github.com/GenXProject/GenX.jl. This work has not yet been peer-reviewed.</p>
Colorectal Cancer Awareness, Research and Education and Screening - Rural Expansion, Access and Capacity for Health
ClinicalTrials.gov study NCT04464668. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Crossing the Brownian Bridge: A Discussion of Infrastructure Capacity Expansion Policies
<p>This spreadsheet supports the model developed in the corresponding paper</p>
Regularized Benders Decomposition for High Performance Capacity Expansion Models: Data and Code
<p>This dataset contains all code, input and results data relevant to the working paper <a href="https://arxiv.org/abs/2403.02559">‘Regularized Benders Decomposition for High Performance Capacity Expansion Models’</a>, in IEEE Transactions on Power Systems DOI 10.1109/TPWRS.2025.3526413.</p> <p>The dataset is comprised of three folders:<br><br><code><em>GenX-Benders.zip</em></code> Contains the source code used in this study, which extends the open-source electricity capacity expansion model GenX (<a href="https://github.com/GenXProject/GenX.jl/releases/tag/v0.3.5">@v0.3.5</a>), implementing different regularized Benders decomposition algorithms as solving routines.</p> <p><em><code>Test_Systems.zip</code> </em>Includes the input files for generating the GenX model for the five considered test systems. Note that GenX-Brazil has been derived from the <a href="https://gitlab.com/dlr-ve/esy/open-brazil-energy-data">Open Brazil Energy Data</a> as detailed in the manuscript.<br><br><em><code>Results.zip</code> </em>Includes all result files for the computaitonal experiments reported in the manuscript.</p>
Source code and data for Ou et al. 2021 (US state-level capacity expansion pathways with improved modeling of the power sector dynamics within a multisector model)
<p>For details, please check the "readme" file.</p>
Core Stabilization Training on Chest Expansion, Functional Capacity, Trunk Muscle Endurance in Hearing-Impaired Children
ClinicalTrials.gov study NCT06354400. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Isolation and long-term expansion of adult SOX9+ renal epithelial cells with efficient kidney regeneration capacity
GEO Series GSE143154. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Reducing transmission expansion by co-optimizing sizing of wind, solar, storage, and grid connection capacity: Raw Data
<p>This dataset contained an older and out-of-date version of all GenX model input and results data relevant to the working paper ‘Reducing transmission expansion by co-optimizing sizing of wind, solar, storage, and grid connection capacity.’ The data for the more recent version of the paper can be found here: https://zenodo.org/records/13340214. </p>
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