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47 results for “Electrical profiling”
Auxiliary Euro-Calliope datasets: Spatio-temporal data representing national cooking demand and electric vehicle characteristic profiles in Europe
<p>Output generated by the <a href="https://github.com/RAMP-project/">RAMP engine</a> for use in the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope model</a>. The three datasets in this repository are described briefly here and in more detail in the accompanying README files. Each dataset has an hourly temporal resolution spanning the years 2000 - 2018 (inclusive) and a national spatial resolution spanning 26* - 28** countries in Europe. All datasets are dimensionless; only the profile shapes are used in Euro-Calliope.</p> <ul> <li>Cooking energy demand profiles (<em>ramp-cooking-profiles</em>): Profiles of heat energy demand for cooking in buildings in Europe, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP">RAMP model</a> [1]. These profiles are used to distribute annual cooking energy demand in the Euro-Calliope workflow. This dataset covers 28 European countries**.</li> <li>Electric vehicle plug-in profiles (<em>ramp-ev-plugin-profiles</em>): Profiles of the percentage of parked electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are used in Euro-Calliope to define the maximum number of electric vehicles that could be plugged in and therefore available to be charged at any given time, assuming controlled (or "smart") charging. This dataset covers 26 European countries*.</li> <li>Electric vehicle energy consumption profiles (<em>ramp-ev-consumption-profiles</em>): Profiles of the electricity consumption of electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are aggregated in Euro-Calliope to provide a required percentage of total vehicle electricity demand that must be met in each month. This dataset covers 26 European countries*.</li> </ul> <p>* AUT, BEL, CHE, CZE, DEU, DNK, ESP, EST, FIN, FRA, GBR, HRV, HUN, IRL, ITA, LTU, LUX, LVA, NLD, NOR, POL, PRT, ROU, SVK, SVN, SWE</p> <p>** (*) + BGR, SRB</p> <p>*** ALB, MKD, GRC, CYP, BIH, MNE, ISL</p> <p>[1] Lombardi, Francesco, Sergio Balderrama, Sylvain Quoilin, and Emanuela Colombo. 2019. ‘Generating High-Resolution Multi-Energy Load Profiles for Remote Areas with an Open-Source Stochastic Model’. <em>Energy</em> 177 (June): 433–44. https://doi.org/10.1016/j.energy.2019.04.097.</p> <p>[2] Mangipinto, Andrea, Francesco Lombardi, Francesco Davide Sanvito, Matija Pavičević, Sylvain Quoilin, and Emanuela Colombo. 2022. ‘Impact of Mass-Scale Deployment of Electric Vehicles and Benefits of Smart Charging across All European Countries’. <em>Applied Energy</em> 312 (April): 118676. https://doi.org/10.1016/j.apenergy.2022.118676.</p>
Bidirectional and Unidirectional Charging Profiles of Electric Vehicles
<p>This dataset contains bidirectional and unidirectional charging profiles of Electric Vehicles (EVs) measured in laboratory environment at the Smart Grid Technology Lab of ie³ institute at TU Dortmund University. The dataset not only considers charging power and current but also harmonics/interharmonics emission of EV charging in both static and dynamic scenarios. Thus, it provides a solid foundation for the development of advanced EV charging algorithms and model validation. Raw data are available in csv format from the file <em>dataset_raw.zip</em> and a selection of merged measurements is provided in the file <em>dataset_merged.zip</em>.</p> <p>The following commercially available EV models are considered:</p> <ul> <li>Opel Corsa-e (2020)</li> <li>Fiat 500e (2022)</li> <li>Honda-e Advance (bidirectional, 2020)</li> <li>Nissan Leaf (bidirectional, 2020)</li> <li>VW ID.4 (2020)</li> <li>Hyundai Ioniq 5 (2021)</li> <li>Mitsubishi Eclipse Cross PHEV (bidirectional, 2022)</li> <li>Tesla Model Y SR (2022)</li> </ul> <p>The dataset is part of the deliverable D8.1 of DriVe2X project and is accompanied by a report including a description about data acquisition and measurement setup. The report is available from the project website's resources section. A more in-depth description of the tests and exemplary analysis is currently being prepared for publication.</p> <p><strong>References</strong></p> <ul> <li>DriVe2X project website: <a href="https://drive2x.eu/">Link</a></li> <li>CORDIS website: <a href="https://cordis.europa.eu/project/id/101056934">Link</a></li> <li>ie³ institute: <a href="https://ie3.etit.tu-dortmund.de/">Link</a></li> <li>Smart Grid Technology Lab: <a href="http://sgtl.et.tu-dortmund.de/">Link</a></li> </ul>
Supplementary materials for: Imaging the Devene fault system beneath the Iskar floodplain in Bulgaria through shallow electrical resistivity profiling
<p>Supplementary materials for the paper Imaging the Devene fault system beneath the Iskar floodplain in Bulgaria, submitted to Review of the Bulgarian Geological Society </p> <p>We used shallow electrical resistivity profiling to image the Nivyanin fault zone from the Devene fault system in NW Bulgaria. We aimed to verify whether a portion of<br>the Devene fault system has affected Quaternary fluvial deposits. The Supplementary materials contain the coordinates (WGS84) of measuring sensors and resistivity data in Boundless Electrical Resistivity Tomography (BERT) file format. The file bert.cfg.txt is the configuration file for running BERT software to obtain the resistivity model in figure 1c in paper.</p>
Supporting data to the paper "Modelling charge profiles of electric vehicles based on charges data"
<p>This dataset contains the<em> underling data</em> and the <em>extended data</em> for the paper Modelling charge profiles of electric vehicles based on charges data”, submitted for consideration and open review in Open Research Europe.</p> <p>In the follow the description of the files is reported:</p> <p>HISTORIC DATA 2019 ELECTROLINERES AMB.csv: contains information on the charge events at the public charging points managed by the municipality in the metropolitan area of Barcelona in 2019. Fields are: charging point name; connector typology and number; charge start time; charge stop time; charge duration in minutes, energy delivered in kWh; vehicle manufacturer (optional); vehicle model (optional).</p> <p>STATIC INFORMATION CHARGING POINTS AMB 29042020.csv: contains the information about the public charging points of the metropolitan area of Barcelona. Fields are: charger typology (Quick/Normal); Charging point name and address; OCCP version; charger location; longitude; latitude; 7 flag fields for the connector type; observations; charging point maker.</p> <p>Lataustapahtumat, julkiset latauslaitteet 2019.csv: contains the information about the Turku Energia charge events for the city of Turku in 2019. Fields are: date of record creation, Station ID, Station name, charge start time, charge stop time, charge duration in minutes, energy delivered in Wh, Plug type (AC 22 kW/DC 50 kW), Cumulative energy delivered in the year (Wh), Average charge power (W)</p> <p>EV.csv: containes data on battery size retrived from vehicle datasheet or manufacturer website. Fields are: record ID, vehicle manufacturer ; vehicle model; battery size in kWh.</p> <p>Charge2019_EV_AMB.csv: contains the data on charge requests ( HISTORIC DATA 2019 ELECTROLINERES AMB.csv ) combined with the information on vehicle battery (EV.csv).</p> <p> </p>
Shaping photovoltaic array output to align with changing wholesale electricity price profiles
<p>This repository includes python scripts and input/output data associated with the following publication:</p> <p>[1] Brown, P.R.; O'Sullivan, F. "Shaping photovoltaic array output to align with changing wholesale electricity price profiles." Applied Energy 2019. <a href="http://doi.org/10.1016/j.apenergy.2019.113734">https://doi.org/10.1016/j.apenergy.2019.113734</a></p> <p>Please cite reference [1] for full documentation if the contents of this repository are used for subsequent work.</p> <p>Some of the scripts and data are also used in the following working paper:</p> <p>[2] Brown, P.R.; O'Sullivan, F. "Spatial and temporal variation in the value of solar power across United States electricity markets". Working Paper, MIT Center for Energy and Environmental Policy Research. 2019. <a href="http://ceepr.mit.edu/publications/working-papers/705">http://ceepr.mit.edu/publications/working-papers/705</a></p> <p>All code is in python 3 and relies on a number of dependencies that can be installed using pip or conda.</p> <p><strong>Contents</strong></p> <ul> <li>pvvm.zip : Python module with functions for modeling PV generation, calculating PV revenues and capacity factors, and optimizing PV orientation.</li> <li>notebooks.zip : Jupyter notebooks, including: <ul> <li>pvvm-pvtos-data.ipynb: Example scripts used to download and clean input LMP data, determine LMP node locations, and reproduce some figures in reference [1]</li> <li>pvvm-pvtos-analysis.ipynb: Example scripts used to perform the calculations and reproduce some figures in reference [1]</li> <li>pvvm-pvtos-plots.ipynb: Scripts used to produce additional figures in reference [1]</li> <li>pvvm-example-generation.ipynb: Example scripts demonstrating the usage of the PV generation model and orientation optimization</li> </ul> </li> <li>html.zip : Static images of the above Jupyter notebooks for viewing without a python kernel</li> <li>data.zip : Day-ahead and real-time nodal locational marginal prices (LMPs) for CAISO, ERCOT, MISO, NYISO, and ISONE. <ul> <li>At the time of publication of this repository, permission had not been received from PJM to republish their LMP data. If permission is received in the future, a new version of this repository will linked here with the complete dataset.</li> </ul> </li> <li>results.zip : Simulation results associated with reference [1] above, including modeled revenue, capacity factor, and optimized orientations for PV systems at all LMP nodes</li> </ul> <p><strong>Data terms and usage notes</strong></p> <ul> <li>ISO LMP data are used with permission from the different ISOs. Adapting the MIT License (<a href="http://opensource.org/licenses/MIT">https://opensource.org/licenses/MIT</a>), "The data are provided 'as is', without warranty of any kind, express or implied, including but not limited to the warranties of merchantibility, fitness for a particular purpose and noninfringement. In no event shall the authors or sources be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the data or other dealings with the data." Copyright and usage permissions for the LMP data are available on the ISO websites, linked below.</li> <li>ISO-specific notes: <ul> <li>CAISO data from <a href="http://oasis.caiso.com/mrioasis/logon.do">http://oasis.caiso.com/mrioasis/logon.do</a> are used pursuant to the terms at <a href="http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse">http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse</a>.</li> <li>ERCOT data are from <a href="http://www.ercot.com/mktinfo/prices">http://www.ercot.com/mktinfo/prices</a>.</li> <li>MISO data are from <a href="http://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/</a> and <a href="https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/</a>.</li> <li>PJM data were originally downloaded from <a href="https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx">https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx</a> and <a href="https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx">https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx</a>. At the time of this writing these data are currently hosted at <a href="https://dataminer2.pjm.com/feed/da_hrl_lmps">https://dataminer2.pjm.com/feed/da_hrl_lmps</a> and <a href="https://dataminer2.pjm.com/feed/rt_hrl_lmps">https://dataminer2.pjm.com/feed/rt_hrl_lmps</a>.</li> <li>NYISO data from <a href="http://mis.nyiso.com/public/">http://mis.nyiso.com/public/</a> are used subject to the disclaimer at <a href="https://www.nyiso.com/legal-notice">https://www.nyiso.com/legal-notice</a>.</li> <li>ISONE data are from <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly</a> and <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final</a>. The Material is provided on an "as is" basis. ISO New England Inc., to the fullest extent permitted by law, disclaims all warranties, either express or implied, statutory or otherwise, including but not limited to the implied warranties of merchantability, non-infringement of third parties' rights, and fitness for particular purpose. Without limiting the foregoing, ISO New England Inc. makes no representations or warranties about the accuracy, reliability, completeness, date, or timeliness of the Material. ISO New England Inc. shall have no liability to you, your employer or any other third party based on your use of or reliance on the Material.</li> </ul> </li> <li>Data workup: LMP data were downloaded directly from the ISOs using scripts similar to the pvvm.data.download_lmps() function (see below for caveats), then repackaged into single-node single-year files using the pvvm.data.nodalize() function. These single-node single-year files were then combined into the dataframes included in this repository, using the procedure shown in the pvvm-pvtos-data.ipynb notebook for MISO. We provide these yearly dataframes, rather than the long-form data, to minimize file size and number. These dataframes can be unpacked into the single-node files used in the analysis using the pvvm.data.copylmps() function.</li> </ul> <p><strong>Code license and usage notes</strong></p> <ul> <li>Code (*.py and *.ipynb files) is provided under the <a href="https://opensource.org/licenses/MIT">MIT License</a>, as specified in the pvvm/LICENSE file.</li> <li>Updates to the code, if any, will be posted in the non-static repository at <a href="https://github.com/patrickbrown4/pvvm_pvtos">https://github.com/patrickbrown4/pvvm_pvtos</a>. The code in the present repository has the following version-specific dependencies: <ul> <li>matplotlib: 3.0.3</li> <li>numpy: 1.16.2</li> <li>pandas: 0.24.2</li> <li>pvlib: 0.6.1</li> <li>scipy: 1.2.1</li> <li>tqdm: 4.31.1</li> </ul> </li> <li>To use the NSRDB download functions, modify the "settings.py" file to insert a valid NSRDB API key, which can be requested from <a href="https://developer.nrel.gov/signup/">https://developer.nrel.gov/signup/</a>. Locations can be specified by passing latitude, longitude floats to pvvm.data.downloadNSRDBfile(), or by passing a string googlemaps query to pvvm.io.queryNSRDBfile(). To use the googlemaps functionality, request a googlemaps API key (<a href="https://developers.google.com/maps/documentation/javascript/get-api-key">https://developers.google.com/maps/documentation/javascript/get-api-key</a>) and insert it in the "settings.py" file.</li> <li>Note that many of the ISO websites have changed in the time since the functions in the pvvm.data module were written and the LMP data used in the above papers were downloaded. As such, the pvvm.data.download_lmps() function no longer works for all ISOs and years. We provide this function to illustrate the general procedure used, and do not intend to maintain it or keep it up to date with the changing ISO websites. For up-to-date functions for accessing ISO data, the following repository (no connection to the present work) may be helpful: <a href="https://github.com/catalyst-cooperative/pudl">https://github.com/catalyst-cooperative/pudl</a>.</li> </ul>
Bagoue dataset-Cote d'Ivoire: Electrical profiling, electrical sounding and boreholes data
<p>Bagoue region lies between longitudes 6° and 7° W and latitudes 9° and 11° N in the north of Cote d’Ivoire. The geophysical and boreholes data were collected from National Office of Drinking Water (ONEP) and West-Africa International Drilling Company (FORACO-CI) during the Presidential Emergency Program (PPU) in 2012-2013 and the National Drinking Water Supply Program (PNAEP) in 2014. During the progress of both projects, the electrical methods is the most used especially the resistivity profiling and the electrical sounding methods. Originally, data were used for Groundwater Flow Rate (GFR) prediction using a Support Vector Machines(SVMs).</p>
Vertical profiling of the electrical properties of charged desert dust during the pre-ASKOS campaign: Dataset
<p>The zipped files contain the datasets used to produce Figure 1 of the following conference proceedings paper:</p> <p>Vasiliki Daskalopoulou, George Hloupis, Sotirios A. Mallios, Ilias Makrakis, Evangelos Skoubris, Maria Kezoudi, Zbigniew Ulanowski, & Vassilis Amiridis. (2021, July 6). <em>Vertical profiling of the electrical properties of charged desert dust during the pre-ASKOS campaign</em>. 15th International Conference on Meteorology, Climatology and Atmospheric Physics (COMECAP 2021), Ioannina, Greece. https://doi.org/10.5281/zenodo.5076042</p> <p>The repository contains overall:</p> <ol> <li>the ground-based JCI 131 Fieldmill Electrometer data that were acquired during the campaign (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Fieldmill_Ion_counter_Cyprus_campaign.rar">Fieldmill_Ion_counter_Cyprus_campaign.rar</a>)</li> <li>Data from an Alphalab Air Ion counter co-located with the fieldmill (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Fieldmill_Ion_counter_Cyprus_campaign.rar">Fieldmill_Ion_counter_Cyprus_campaign.rar</a>)</li> <li>Data from the five MiniMill electrometers that were launched (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/MiniMills_Cyprus_campaign.rar">MiniMills_Cyprus_campaign.rar</a>)</li> <li>Data from the two of the charge sensors that were launched (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Charge_sensors_Cyprus_campaign.rar">Charge_sensors_Cyprus_campaign.rar</a>)</li> <li>Data from the eleven ion counters that were launched, tethered together with the MiniMills or the charge sensors (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Ion_counters_Cyprus_campaign.rar">Ion_counters_Cyprus_campaign.rar</a>)</li> <li>A campaign calendar with the launches schedule (<a href="https://zenodo.org/api/files/b9a97eac-3bc2-4468-a43d-efa270ce8d95/Cyprus_campaign_November2019_calendar.pdf">Cyprus_campaign_November2019_calendar.pdf</a>).</li> </ol>
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>
Daily profiles (2040) 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 2040. 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>
Daily profiles (2040) of load of an 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 an urban area as in 2040. 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 and 60 kV, has 207 nodes (99 with consumption) and 206 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. In addition, a “read me” file called “Manual” includes technical detailed information about how to read the data properly.</p>
Daily profiles (2040) of load and flexibility of an 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 an urban area as in 2040. 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 and 60 kV, has 221 nodes (104 with consumption) and 220 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 comprise 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>
Daily profiles (2050) 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 2050. 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>
Daily profiles (2050) of load and generation 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 2050. 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 15 kV, 30 kV and 60 kV, has 229 nodes (118 with consumption) and 229 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. 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 generation file should be used together with the respective load data file.</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.