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60 results for “load profiles”
5359 industrial VEA load profiles
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Net load profile for a 10 MWh hydrogen storage system
<p><i>Subject: <strong>Energy</strong></i></p><p><i>Specific subject area: <strong>Hydrogen Storage Modelling</strong></i></p><p><i>Data format: <strong>Raw</strong></i></p><p><i>Data source: <strong>Simulation </strong></i></p><p><i>Data format: <strong>Table</strong></i></p><p><i>Type of data: <strong>Timeseries </strong></i></p><p><i>Date format:<strong> YYYY-MM-GG hh:mm:ss</strong></i></p><p><i>Resolution: <strong>1 hour</strong></i></p><p><i>Units: <strong>Watt [W]</strong></i></p><p> </p><p><strong>Description</strong></p><p>This dataset encompasses 102 net-load, each serving as a guide for either charging or discharging a hydrogen energy storage system with a 0.4 roundtrip efficiency. These profiles correspond to a variety of power energy services.</p><p>Two primary characteristics distinguish each power energy service in the dataset:</p><ul><li>The length of the discharge period refers to the duration over which the stored energy is released.</li><li>The number of charge/discharge cycles within a year indicates how often the service is delivered.</li></ul><p>Although these characteristics vary among the different services, the energy fed to the storage remains constant at 10 MWh per cycle. The unique aspect is the rate at which this stored energy is delivered to fulfill varying power demands, which differs according to each service's specific profile.</p><p>In terms of interpreting the net-load profiles:</p><ul><li>A positive value in a profile signifies a phase of charging, indicating that excess generation is being stored.</li><li>A negative value, on the other hand, denotes a discharge phase, where the storage system delivers a power energy service.</li></ul>
Renewable Generation Profiles and Weather Correlated Loads Used in CEC EPC-19-056 Final Report
<p>This repository contains 8760 hour time series data for load and renewable generation utilized in the <br> CEC EPC-19-056 Final Report: Assessing the Value of LDES in California. The details of how these profiles were constructed can be found in Appendix B of the report.</p> <p>The load balancing authorities and renewable resources modeled in this study are consistent with those found in the 2019-20 CPUC IRP Inputs and Assumptions, which can be found here: https://www.cpuc.ca.gov/-/media/cpuc-website/divisions/energy-division/documents/integrated-resource-plan-and-long-term-procurement-plan-irp-ltpp/2019-2020-irp-events-and-materials/inputs--assumptions-2019-2020-cpuc-irp_20191106.pdf</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>
Power profiles of loads, DERs and MGs' internal units
<p>Dataset of power profiles used in the Simulation section of the paper </p> <blockquote> <p>Bonassi, F., La Bella, A., Lazzari, R., Sandroni, C., & Scattolini, R. (2021). Supervised control of hybrid AC-DC grids for power balance restoration. <em>Electric Power Systems Research</em>, <em>196</em>, 107107.</p> </blockquote> <p><br> Please cite the paper if you use this data.</p> <p><strong>The dataset is made of the following files:</strong></p> <ul> <li>'cluster_references.xls': Table of the reference power programs of the four clusters. <ul> <li>Column 'Cluster_i_ref': Power program of the i-th cluster [kW]<br> </li> </ul> </li> <li>'converter_schedules.xls': Table of power converters' scheduled power profiles. <ul> <li>Column 'Converter_i_P': Active power program of the i-th power converter [kW]</li> <li>Column 'Converter_i_Q': Reactive power program of the i-th power converter [kVAR]<br> </li> </ul> </li> <li>'load_actual.xls': Table of the actual power profiles of the loads connected to the grid. <ul> <li>Column 'Load_i_P': Active power absorbed by the i-th load [kW]</li> <li>Column 'Load_i_Q': Reactive power absorbed by the i-th load [kVAR]<br> </li> </ul> </li> <li>'load_forecasts.xls': Table of the power profiles' forecasts of the loads connected to the grid. <ul> <li>Column 'Load_i_P': Active power forecast of the i-th load [kW]</li> <li>Column 'Load_i_Q': Reactive power forecast of the i-th load [kVAR]<br> </li> </ul> </li> <li>'MG_schedules.xls': Table of MGs' schedules for dispatchable units. <ul> <li>Column 'MG_i_Gen_P': Active power schedule for the dispatchable generator of the i-th MG [kW] </li> <li>Column 'MG_i_Gen_Q': Reactive power schedule for the dispatchable generator of the i-th MG [kVAR]</li> <li>Column 'MG_i_Batt_P': Active power schedule for the battery of MG_i [kW] </li> <li>Column 'MG_i_Batt_Q': Reactive power schedule for the battery of MG_i [kVAR]<br> </li> </ul> </li> <li>'pv_actual.xls': Table of the actual power profiles of the PV panels. <ul> <li>Column 'MG_i_PV_P': Active power injected by the PV panel of MG_i [kW] <br> </li> </ul> </li> <li>'pv_forecasts.xls': Table of the power profiles' forecasts of the PV panels. <ul> <li>Column 'MG_i_PV_P': Active power forecast of the PV panel of MG_i [kW]<br> </li> </ul> </li> </ul> <ul> <li>'units_network_data.xls': Table of units' and network data. <ul> <li>Sheet 'AC nodes': Data concerning the AC buses.</li> <li>Sheet 'AC branches': Data concerning the AC branches connecting the AC nodes.</li> <li>Sheet 'AC-DC interfaces': Data of the AC-DC interfacing power converters.</li> <li>Sheet 'DC links': Data concerning the DC links.</li> <li>Sheet 'MGs Generators': Nominal data of MGs' internal generators.</li> <li>Sheet 'MGs Batteries': Nominal data of MGs' internal batteries.</li> </ul> </li> </ul>
Standrad Household Load Profile
<p>BDEW standard load profile generated using demandlib python library with an annual consumption of 4.7 MWh/a. </p>
Business load profiles used in "Maximising the benefits of renewable energy infrastructure in displacement settings: Optimising the operation of a solar-hybrid mini-grid for institutional and business users in Mahama Refugee Camp, Rwanda"
<p>Version used in the submission of "Maximising the benefits of renewable energy infrastructure in displacement settings: Optimising the operation of a solar-hybrid mini-grid for institutional and business users in Mahama Refugee Camp, Rwanda" by Hamish Beath, Javier Baranda Alonso, Richard Mori, Ajay Gambhir, Jenny Nelson and Philip Sandwell.</p>
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>
Impact of Ticagrelor Re-load on Pharmacodynamic Profiles
ClinicalTrials.gov study NCT01731041. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Battery discharge characteristics for IEEE 802.15.4 based radio load profile
Open the record for dataset details and reuse information.
Load profile data of 50 industrial plants in Germany for one year
<p>This dataset holds the electric load profiles of 50 small and mid-size enterprises in Germany. The load profiles are in 15-minute time resolution for one year. The load is shown in kW as an average over 15 minutes.</p> <p>The dataset is divided into two:</p> <ul> <li>LoadProfile_20IPs_2016 shows load profiles of 20 industrial plants (IP) for the year 2016.</li> <li>LoadProfile_30IPs_2017 shows load profiles of 30 industrial plants (IP) for the year 2017.</li> </ul> <p>The IPs from the dataset for 2016 do not reappear in the dataset for 2017.</p> <p> </p> <p>The dataset LoadProfile_20IPs_2016 is evaluated in the following publication:</p> <ul> <li>Covic, N., Braeuer, F., McKenna, R., Pandzic, H., Optimizing Industrial Facilities’ Active Participation<br> in Electricity Markets under Uncertainty, 2020.</li> </ul> <p>Both datasets together are evaluated in multiple publications:</p> <ul> <li>Braeuer, F., Finck, R., McKenna, R., Comparing empirical and model-based approaches for calculating dynamic grid emission factors: An application to CO2-minimizing storage dispatch in Germany, Journal of Cleaner Production, Volume 266, 2020, 121588, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2020.121588.</li> <li>Braeuer, F., Rominger, J., McKenna, R.,Fichtner, W., Battery storage systems: An economic model-based analysis of parallel revenue streams and general implications for industry, Applied Energy, Volume 239, 2019, Pages 1424-1440, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2019.01.050.</li> </ul> <p>Enjoy.</p>
500 Hourly Synthetic Single-Family Household Heat Pump Load Profiles for Karlsruhe, Germany (2021)
<p>We created a synthetic dataset of 500 hourly single-family household water-to-water heat pump load profiles based on the weather profile of Karlsruhe, Germany in 2021. We have applied the open-source methodology published in [1], which applies a k-means clustering process to match daily weather profiles with randomly drawn empirical observations from the high-quality heat pump load profile dataset published in [2]. We have selected a number of 5 clusters, for a good balance between variance of profiles and accuracy, as discussed in [1]. The dataset can be used for modeling large numbers of heat pumps in grid sections or energy communities. </p> <p>The unit of the measurement is Wh. Through the "SFH" identifier, the underlying, randomly drawn households from [2] can be identified. </p> <p>[1] Semmelmann, L., Jaquart, P., & Weinhardt, C. (2023). Generating synthetic load profiles of residential heat pumps: a k-means clustering approach. <em>Energy Informatics</em>, <em>6</em>(Suppl 1), 37.</p> <p>[2] Schlemminger, M., Ohrdes, T., Schneider, E., & Knoop, M. (2022). Dataset on electrical single-family house and heat pump load profiles in Germany. <em>Scientific data</em>, <em>9</em>(1), 56.</p>
Daily profiles (2020) of load of a rural synthetic electricity distribution network from UK – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a synthetic UK Distribution network located in a rural area (Exchange St.) as in 2020. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 6.6 kV, has 66 nodes and 66 branches. The auxiliary load data comprises 1 typical winter day gathering the active and reactive consumption at each node of the network in intervals of 1 hour.</p>
Daily profiles (2020) of load of a electricity distribution network (40 nodes) from Croatia – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Distribution network, totally anonymized, located in Croatia as in 2020. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 400 V, 35 kV, 20 kV and 10 kV, has 40 nodes, 59 branches and 14 generators modelled as negative loads. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at specific feeders of the network in intervals of 15 minutes. Besides, a single line diagram of the network is also provided.</p>
Daily profiles (2020) of load of a electricity distribution network (25 nodes) from Croatia – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Distribution network, totally anonymized, located in Croatia as in 2020. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 110 kV, 35 kV, 20 kV and 10 kV, has 25 nodes, 34 branches and 107 generators modelled as negative loads. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at specific feeders of the network in intervals of 15 minutes. Besides, a single line diagram of the network is also provided.</p>
Daily profiles (2020) of load of a electricity distribution network (24 nodes) from Croatia – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Distribution network, totally anonymized, located in Croatia as in 2020. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 110 kV, 35 kV, 10 kV and 400 V, has 24 nodes, 32 branches and 2 generators modelled as negative loads. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at specific feeders of the network in intervals of 15 minutes. Besides, a single line diagram of the network is also provided.</p>
Daily profiles (2020) of load of a electricity distribution network (26 nodes) from Croatia – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Distribution network, totally anonymized, located in Croatia as in 2020. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 220 kV, 110 kV, 35 kV, 10 kV and 400 V, has 26 nodes, 32 branches and 97 generators modelled as negative loads. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at specific feeders of the network in intervals of 15 minutes. Besides, a single line diagram of the network is also provided.</p>
Daily profiles (2020) of load of a electricity distribution network (86 nodes) from Croatia
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Distribution network, totally anonymized, located in Croatia as in 2020. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 10 kV, has 86 nodes, 87 branches and 4 generators modelled as negative loads. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at specific feeders of the network in intervals of 15 minutes. Besides, a single line diagram of the network is also provided.</p>
Daily profiles (2020) of load of an urban synthetic electricity distribution network from UK – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a synthetic UK Distribution network located in an urban area (Green Lane – Altrincham) as in 2020. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 11 kV, has 30 nodes and 30 branches. The auxiliary load data comprises 1 typical winter day gathering the active and reactive consumption at each node of the network in intervals of 1 hour.</p>
Daily profiles (2020) of load and flexibility of a semi-urban electricity distribution network from Portugal – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Portuguese Distribution test network, totally anonymized, located in a semi-urban area as in 2020. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 30 kV and 60 kV, has 191 nodes (100 with consumption) and 190 branches. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at each node of the network in intervals of 15 minutes. The auxiliary flexibility files comprises 4 typical days (business day of summer, Sunday of summer, business day of winter and Sunday of winter) gathering the upward and downward active power of flexibility at each node of the network in intervals of 15 minutes. In addition, a “read me” file called “Manual” includes technical detailed information about how to read the data properly. For the sake of coherence, each flexibility file should be used together with the respective load data file.</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
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International Brain Laboratory public data
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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.