Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6
datasets available to search
ShareScore release 0.9.0
Dataset results
6 results for “balancing authority”
CONUS-wide Balancing Authority Scale Hydropower Projections derived from 9505 Third Assessment
<p>This dataset provides historical and climate projection monthly hydropower generation timeseries for balancing authorities within the contiguous U.S. (CONUS). These data were developed as an extension to the Department of Energy Water Power Technologies Office's SECURE Water Act Section 9505 Third Assessment (9505) and include both federal and non-federal hydropower facilities. Additional modeling detail can be found in <a href="https://iopscience.iop.org/article/10.1088/1748-9326/ad6ceb" target="_blank" rel="noopener">Broman et al., 2024</a> and in the article's <a href="https://github.com/9505-PNNL/broman-etal_2024_erl">metarepository</a>. </p> <p>The dataset is provided in three separate formats to facilitate ease of use:</p> <p>1) Machine-readable csv in 'tidy' data format:</p> <table> <tbody> <tr> <td><strong>Short Name</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>class</td> <td>N/A</td> <td>simulation type; control: historical, cc: climate scenario</td> </tr> <tr> <td>forcing</td> <td>N/A</td> <td>meteorological forcing used to drive hydrology model</td> </tr> <tr> <td>model</td> <td>N/A</td> <td>hydrology model</td> </tr> <tr> <td>hp</td> <td>N/A</td> <td>hydropower model</td> </tr> <tr> <td>gcm*</td> <td>N/A</td> <td>global climate model name</td> </tr> <tr> <td>ds*</td> <td>N/A</td> <td>downscaling method; DBCCA (statistical), RegCM (dynamical)</td> </tr> <tr> <td>balancing_authority</td> <td>N/A</td> <td>balancing authority code</td> </tr> <tr> <td>year</td> <td>N/A</td> <td>year</td> </tr> <tr> <td>month</td> <td>N/A</td> <td>month</td> </tr> <tr> <td>modeled_generation_MWh</td> <td>MWh per month</td> <td>simulated generation</td> </tr> </tbody> </table> <p>* only present in the climate projection (cc) files</p> <p>2) xlsx with balancing authority data by tab</p> <p>3) csv by balancing authority:</p> <p>for historical data: year, <em>month</em>, and <em>HUC4_group</em> columns are the same as above. Data column headers are <em>class</em>_<em>forcing</em>_<em>model</em>_<em>hp</em> and with the units <em>MWh per month</em>.</p> <p>for climate projection (cc) data: year, <em>month</em>, and <em>HUC4_group</em> columns are the same as above. Data column headers are <em>class</em>_<em>forcing</em>_<em>model</em>_<em>hp_gcm_ds</em> and with the units <em>MWh per month</em>.</p>
Total Load Profiles by Balancing Authority in the Western United States for GODEEEP
<p>This dataset contains time-series of total load profiles across Balancing Authorities (BAs) in the western United States (U.S.) electricity grid interconnection for the years 2025, 2030, 2035, 2040, 2045, and 2050. The data is provided for two different socioeconomic pathways and one climate scenario. The socioeconomic pathways -- Business-As-Usual (BAU_Climate) and Net-Zero without CCS (NetZeroNoCCS_Climate) -- are described by <a href="https://doi.org/10.5281/zenodo.7838871">https://doi.org/10.5281/zenodo.7838871</a>. The climate scenario -- Representative Concentration Pathway 8.5 hotter (rcp85hotter) -- is described by <a href="https://doi.org/10.57931/1885756">https://doi.org/10.57931/1885756</a>. Transportation loads in this dataset are derived from <a href="https://doi.org/10.5281/zenodo.8065137">https://doi.org/10.5281/zenodo.8065137</a>. Non-transportation loads are derived using the Total Electricity Loads (TELL) model which is available <a href="https://github.com/IMMM-SFA/tell">here</a>. The data was post-processed using a Jupyter notebook available <a href="https://github.com/GODEEEP/load_analysis/blob/main/notebooks/process_load_data.ipynb">here</a>.</p> <p>A brief summary of the files and directories in this data package is provided below. Each file in the "<em>total_loads</em>" subdirectory is a comma-separated-value (CSV) format with the following columns:</p> <ul> <li><strong>BA</strong> - Acronym of the balancing authority (BA) for this data point</li> <li><strong>Time_UTC</strong> - Timestamp of the hourly data in UTC format</li> <li><strong>Non-Transportation_Load_MWh</strong> - Total hourly load in the BA from non-transportation sources in megawatt hours</li> <li><strong>Transportation_Load_MWh</strong> - Total hourly load in the BA from transportation sources in megawatt hours</li> <li><strong>Total_Load_MWh</strong> - Sum of the load from non-transportation and transportation sources in megawatt hours</li> </ul> <p>Data in the "<em>gridview_ready_total_loads</em>" subdirectory contains the hourly total loads by BA in a format that is ready for ingestion into the GridView production cost model.</p> <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>
Transportation Electrification Load Profiles by Balancing Authority and State-Level Electrification Rates in the Western United States for GODEEEP
<p>Time-series hourly electric charging load profiles for the transportation sector across Balancing Authorities (BAs) in the Western Electricity Coordinating Council (WECC) interconnect, annual fleet sizes by state and vehicle type, annual transportation sector energy usage by state and fuel, and annual transportation fuel usage by state. The data is provided for three different socioeconomic pathways and two different climate pathways, resulting in four total scenarios. The socioeconomic pathways--Net-Zero (<code>nz_climate</code>), Net-Zero allowing for Carbon Capture Sequestration (CCS) technology (<code>nz_ccs_climate</code>), and Net-Zero allowing for CCS with Inflation Reduction Act (IRA) policies (<code>nz_ira_ccs_climate</code>)--are described by <a href="https://doi.org/10.5281/zenodo.10642507">https://doi.org/10.5281/zenodo.10642507</a>. The climate pathways--Representative Concentration Pathway (RCP) 4.5 cooler (<code>rcp45cooler</code>) and RCP 8.5 hotter (<code>rcp85hotter</code>)--are described by <a href="https://doi.org/10.57931/1885756">https://doi.org/10.57931/1885756</a>. The climate influence is only considered for Light Duty Vehicles (LDVs).</p> <p>For additional details please consult the paper Acharya et al 2024, Impact of the Inflation Reduction Act and Carbon Capture on Transportation Electrification for a Net-Zero Western U.S. Grid, submitted, and the code repository <a href="https://github.com/GODEEEP/transportation_electrification">https://github.com/GODEEEP/transportation_electrification</a>.</p> <p>A brief summary of the files and directories in this data package is provided below. Text within chevrons implies a multiplicity of files, one for each actual value.</p> <ul> <li>nz_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ira_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>WECC_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> <li>EV_electric_and_total_energy.csv</li> <li>LDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>MDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>HDV_fleet_size_all_fuel_types_state_wise.csv</li> </ul> <p> </p> <p><strong>Hourly transportation load:</strong></p> <ul> <li><code>time</code> - ISO 8601 timestamp representing the end of the hourly timestep; values are reported as the summation over the preceding hour</li> <li><code>balancing_authority</code> - Acronym of the balancing authority for this data point</li> <li><code>LDV_load_MWh</code> - Energy consumed by the charging of Light Duty Vehicles (LDVs) during the previous hour in Megawatt hours</li> <li><code>MDV_load_MWh</code> - Energy consumed by the charging of Medium Duty Vehicles (MDVs) during the previous hour in Megawatt hours</li> <li><code>HDV_load_MWh</code> - Energy consumed by the charging of Heavy Duty Vehicles (HDVs) during the previous hour in Megawatt hours</li> <li><code>passenger_rail_load_MWh</code> - Energy consumed by the charging of passenger rail vehicles during the previous hour in Megawatt hours</li> <li><code>freight_rail_load_MWh</code> - Energy consumed by the charging of freight rail vehicles during the previous hour in Megawatt hours</li> <li><code>aviation_load_MWh</code> - Energy consumed by the charging of aviation vehicles during the previous hour in Megawatt hours</li> <li><code>ship_load_MWh</code> - Energy consumed by the charging of ships during the previous hour in Megawatt hours</li> <li><code>transportation_load_MWh</code> - Total energy consumed by the charging of vehicles during the previous hour in Megawatt hours (summation of the other columns)</li> </ul> <p>The WECC files provide summations of all BAs for each scenario, with the same columns as above excepting <code>balancing_authority</code></p> <p><strong>State-wise fleet sizes by vehicle type:</strong></p> <ul> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>technology</code> - fuel type such as BEV (battery electric vehicle), FCEV (fuel cell electric vehicle), hybrid liquids and liquids (refined liquids)</li> <li><code>veh_type</code> - one of LDV, MDV, or HDV (Light, Medium, or Heavy Duty Vehicle)</li> <li><code>fleet_size</code> - the number of vehicles</li> </ul> <p>To calculate an electrification rate in terms of fleet size for a given scenario, state, year, and veh<em>type, we divide the fleet</em>size for BEV technology by the summation of fleet_size for all technologies.</p> <p><strong>State-wise electric and total energy for LDVs, MDVs, and HDVs:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hdv_total</code> - energy in ExaJoules consumed by all HDVs irrespective of fuel type</li> <li><code>ldv_total</code> - energy in ExaJoules consumed by all LDVs irrespective of fuel type</li> <li><code>mdv_total</code> - energy in ExaJoules consumed by all MDVs irrespective of fuel type</li> <li><code>hdv_electric</code> - electric energy in ExaJoules consumed by HDVs</li> <li><code>ldv_electric</code> - electric energy in ExaJoules consumed by LDVs</li> <li><code>mdv_electric</code> - electric energy in ExaJoules consumed by MDVs</li> </ul> <p>To calculate the electrification rate in terms of EV energy for a given scenario, state, year, and veh_type, we divide electric energy by the total energy.</p> <p><strong>State-wise transportation fuel mix:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hydrogen</code> - hydrogen energy in ExaJoules consumed by the transportation sector</li> <li><code>electricity</code> - electric energy in ExaJoules consumed by the transportation sector</li> <li><code>refined liquids</code> - refined liquid energy in ExaJoules consumed by the transportation sector</li> </ul> <p><br><br></p> <p><strong>Changelog:</strong></p> <ul> <li>v2.0.0 - new set of scenarios; fuel mix data added</li> </ul> <p> </p> <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>
Balancing Authority Hourly Generation Of Installed Plant Capacities in CONUS
<p>This dataset contains the hourly generation time series for each Balancing Authority representing the installed capacity in each month from 2007 through 2020. The time series is aggregated from plant-level generation of plants that exist in the Energy Information Administration (EIA) database as of 2020. The time series are simulated <strong>actual generation</strong> meaning that the installed capacities have been applied to the hourly capacity factor profiles in <a href="https://doi.org/10.5281/zenodo.7901614">Bracken et al. 2023</a>. These historical generation time series reflect the actual monthly installed capacity at each plant; the EIA 860 and EIA 860m databases were mined to identify months of first operation, retirement, and extended periods of maintenance or non-operation. These databases were also used to create time series of Balancing Authority (BA) level hourly generation to reflect the actual monthly installed capacity. The BA-level time series tracks the BA membership of each power plant in each month; the time series reflects the monthly inventory and hourly production in each BA.</p> <p>For more information please refer to Campbell et al. 2023, Dynamically Downscaled Power Production for All EIA Wind and Solar Power Plants, in prep, and to the code repository at <a href="https://github.com/GODEEEP/godeeep-eia-power">https://github.com/GODEEEP/godeeep-eia-power</a>.</p> <p>The dataset contains three types of files:</p> <p>Monthly Plant-Level Inventory - The monthly plant-level inventory (<code>all_years_860m.csv</code>) contains the identification codes for each generator (including the <code>plant_code_unique</code> identifier to link with the solar and wind profiles in Bracken et al. 2023), the capacity in that month, the balancing authority that plant operated for in that month, the resource type (<code>solar</code> or <code>wind</code>), the month and year, and whether the nameplate capacity in that month needs to be scale to account for aggregation of very large wind power plants (i.e., more than 300 turbines).</p> <p>BA-level Hourly Generation - The BA-level hourly generation files (<code>solar_BA_generation.csv</code> and <code>wind_BA_generation.csv</code>) contain hourly generation at the BA level for each BA in the 2020 EIA 860 database. The first column contains a time stamp and each column header is the name of the BA as it exists in the EIA 860 database (e.g., ISO New England is ISNE, CAISO is CISO). The time series spans 2007 through 2020. The time series begin in 2007, as this aligns with the first year of publication of the EIA 923 monthly plant-level generation dataset and with the first year of available BA-level self-reported generation. The purpose of the temporal baseline alignment is for validation of the time series, discussed in Campbell et al. 2023. Validation metrics in this paper are provided at the BA-level.</p> <p>Plant-level Hourly Generation - The plant-level hourly generation files (<code>solar_plant_generation.csv</code> and <code>wind_plant_generation.csv</code>) contain hourly generation at the generator level for each power plant that exists in the EIA 860 database in 2020. The files are organized with an hourly timestamp for each row and a unique generator id for each column. The generator id is a concatenation of the EIA Plant ID and the EIA Generator ID with an underscore separating the strings. <strong>The plant-level hourly generation time series are intended to be aggregated to the BA-level.</strong> These plant-level time series are provided to the user to allow for re-aggregation for bespoke regional analyses.</p> <p> </p> <p><strong>Known Issues</strong></p> <ul> <li>The following wind power plants (identifier <code>plant_code_unique</code>) have a cf greater than 1 and were scaled to 0.885 <ul> <li>['2024', '2024_1', '2024_3', '2024_4', '7855', '7855_1', '7927', '7927_1', '7927_2', '7965', '7965_1', '7974', '7974_1', '52162', '52163', '54300', '54793', '54793_2', '55741', '55944', '55995_1', '56577', '57214', '57257', '57258', '57258_1', '57594', '57721', '57721_1', '58105', '58112', '58113', '58113_1', '59328', '59329', '59330', '59331', '61677', '61677_1', '61677_2', '62442', '64130']</li> </ul> </li> </ul> <p> </p> <p><strong>Changelog</strong></p> <ul> <li>v1.1.0 - Updates the basis for plant-level inventory from the EIA860 monthly reports (considered preliminary) to the EIA860 annual reports (considered complete and final).</li> </ul> <p> </p> <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>
Hourly Balancing Authority Transfers, Streamflows, and Climate Data for Carolinas Region
<p>The data contain the electricity transfers and relevant indicators associated with nine exchanges between balancing authorities in the Carolinas region. The information comes from the following sources:</p> <p>Hydrology Dataset from USGS:</p> <p>CPLE<span> </span>02084557- Van Swamp near Hoke, NC</p> <p><span> </span>02089000- Neuse River near Goldsboro, NC</p> <p><span> </span>02087324-Crabtree Creek at US 1 at Raleigh, NC</p> <p>DUK<span> </span>0212427947- Reedy Creek at SR2803 near Charlotte, NC</p> <p><span> </span>0212430653- McKee Creek at SR2804 near Wilgrove, NC</p> <p><span> </span>02124080- Clarke Creek near Harrisburg, NC</p> <p>SC<span> </span>02171500- Santee River near Pineville, SC</p> <p><span> </span>02131010- Pee Dee River below Pee Dee, SC</p> <p><span> </span>02130980- Black Creek near Quinby, SC</p> <p>SCEG<span> </span>02175500- Salkehatchie River near Miley, SC</p> <p><span> </span>02176500- Coosawhatchie River near Hampton, SC</p> <p>YAD<span> </span>02121500- Abbotts Creek at Lexington, NC</p> <p>CPLW<span> </span>02140991- Johns River at Arneys Store, NC</p> <p>*CPLE = Duke Energy Progress East; DUK = Duke Energy Carolina; SC = Santee Cooper; SCEG = South Carolina Electric & Gas Company; YAD = Yadkin, Inc.; CPLW = Duke Energy Progress West</p> <p> </p> <p>Balancing Authority Data</p> <p><span>Nugent J, Chini C M, Peer R A M and Stillwell A S 2023 Monthly virtual water transfers on the U.S. electric grid Environ. Res. Infrastruct. Sustain. 3 035006</span></p> <p><span>Balancing Authority Climate Data</span></p> <p>Burleyson C, Thurber T and Vernon C 2023 Projections of hourly meteorology by balancing authority based on the IM3/HyperFACETS thermodynamic global warming (TGW) simulations (v1.0.0) [Data set] MSD-LIVE Data Repos.</p> <p>NOAA National Centers for Environmental Information 2024 U.S. Air Force 14th weather squadron (2013): United States Air Force 14th weather squadron surface weather observations (restricted). NCEI DSI 9966</p> <p> </p>
PUDL Raw EIA Form 930 -- Hourly and Daily Balancing Authority Operations Report
<p>The EIA Form 930 provides hourly demand and generation statistics by balancing area (or sub-balancing area in the case of some larger areas). These statistics include a breakdown by energy source (coal, gas, hydro, wind, solar, etc.) as well as interchange between the balancing areas, including international exchanges with Canada and Mexico. Archived from <a href="https://www.eia.gov/electricity/gridmonitor/">https://www.eia.gov/electricity/gridmonitor/</a></p> <p>This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by <a href="https://catalyst.coop">Catalyst Cooperative</a>. It is organized into <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Packages</a>. For additional information about this data and PUDL, see the following resources: </p><ul> <li><a href="https://github.com/catalyst-cooperative/pudl">The PUDL Repository on GitHub</a></li> <li><a href="https://catalystcoop-pudl.readthedocs.io">PUDL Documentation</a></li> <li><a href="https://zenodo.org/communities/catalyst-cooperative/">Other Catalyst Cooperative data archives</a></li> </ul> <p></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.