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10 results for “Transmission grid”
Dataset of "Smart Grids Transmission Network Testbed: Design, Deployment, and Beyond"
<p>Our test environment incorporates a unique blend of physical, emulated, and virtualized<br>components, spanning from electrical substations to SCADA systems,<br>thereby offering a versatile platform for testing against cyber threats, facilitating<br>educational programs, and supporting advanced traffic simulation. Key findings<br>from our deployment highlight the testbed’s effectiveness in identifying vulnerabilities,<br>enhancing cybersecurity measures, and providing valuable hands-on<br>learning experiences. The integration of such diverse components not only exemplifies<br>a significant step forward in testbed design but also showcases its potential<br>in fostering innovation and security in the power sector. Through detailed comparisons<br>with existing testbeds, we underscore our testbed’s distinct features<br>and its contribution to bridging the gap in current methodologies, setting a new<br>benchmark for future developments in smart grid testing and education.</p>
Thermally switchable, bifunctional, scalable, mid-infrared metasurfaces with VO2 grids capable of versatile polarization manipulation and asymmetric transmission
<p>The data generated by CST Studio Suite that are used to plot a part of the figures, and sample CST scripts. </p> <p>Research supported by Narodowe Centrum Nauki, project no UMO-2020/39/I/ST3/02413. </p>
PanTaGruEl - a pan-European transmission grid and electricity generation model
<p>If you have any questions or comments, please write to <a href="mailto:laurent.vincent.pagnier@gmail.com">laurent.vincent.pagnier@gmail.com</a>.</p> <p>When publishing results based on this data set, please cite:</p> <p>L. Pagnier, P. Jacquod, “Inertia location and slow network modes determine disturbance propagation in large-scale power grids”, PLOS ONE 14(3): e0213550, 2019. <a href="https://doi.org/10.1371/journal.pone.0213550">PLOS ONE 14(3): e0213550</a>, 2019.</p> <p>and</p> <p>M. Tyloo, L. Pagnier, P. Jacquod, “The Key Player Problem in Complex Oscillator Networks and Electric Power Grids: Resistance Centralities Identify Local Vulnerabilities”, <a href="https://doi.org/10.1126/sciadv.aaw8359">Science Advances 5(11): eaaw8359</a>, 2019.</p> <p><strong>Description:</strong></p> <p>PanTaGruEl is a dynamical grid model designed to investigate the propagation of disturbances in the continental European transmission grid.</p> <p>The construction of the model is detailed <a href="https://doi.org/10.1371/journal.pone.0213550.s002">here</a>.</p> <p><strong>Features</strong>:</p> <ul> <li>Precise distribution of national demands to network buses.</li> <li>Realistic electrical parameters of transmission lines.</li> <li>Merit-Order based economic dispatch of generators.</li> <li>Dynamical parameters of generators and loads for transient stability investigations.</li> </ul> <p><strong>Files:</strong></p> <p>Data files:</p> <p>Our model is provided in an extended Matpower format and as csv raw data. For more information on Matpower format, see Appendix B of its <a href="https://matpower.org/docs/MATPOWER-manual.pdf">manual</a>.</p> <p>Script files:</p> <p><em>opf_ex.m </em>performs optimal power flow computations for two load configurations.<br> <em>spectral_ex.m</em> presents a basic spectral analysis.<br> <em>dynamics</em><em>_ex.m</em> give a minimal example of dynamical simulations.</p> <p><strong>Requirements:</strong></p> <p>Our model has been developed for use with <a href="https://matpower.org/">Matpower</a>. If you are interested in a port to another language, please <a href="mailto:laurent.vincent.pagnier@gmail.com?subject=Info%20on%20PanTaGruEl">contact us</a>.</p> <p><strong>Acknowledgement:</strong></p> <p>The authors thank M. Tyloo and K. Van Walstijn for their useful comments and remarks on the model.</p> <p><strong>Sources</strong>:</p> <p>B. Wiegmans, <a href="https://doi.org/10.5281/zenodo.55853">“GridKit extract of ENTSO-E interactive map”</a><br> Global Energy Observatory, <a href="http://globalenergyobservatory.org">“GEO Power plants database”</a><br> Siemens, <a href="http://siemens.com/power-engineering-guide">“Power Engineering Guide”</a></p>
Supplementary data: "Influence of flexibility options on the German transmission grid — A sector-coupled mid-term scenario"
<p>This repository contains result data for the paper <i> "Influence of flexibility options on the German transmission grid — A sector-coupled mid-term scenario"</i>.</p><p>The published data includes optimization results of the three main scenarios in the mentioned publication. </p><p>The data for each scenario is stored as csv-files, which allows analysing it with many different tools. In addition, the data can be imported in Python as a network object of the open-source tool PyPSA by using the function <a href="https://pypsa.readthedocs.io/en/latest/api_reference.html#pypsa.Network.import_from_csv_folder">import from csv folder </a>. </p><p> </p><p>The authors thank the Federal Ministry for Economic Affairs and Climate Action for funding the research project eGon (funding code: 03EI1002).</p>
Western Florida Panhandle Electric Transmission Grid Substations, Lines, and Towers
<p>The upload consists of 5 different datasets pertaining to the electric transmission grid in the nine counties of the western Florida Panhandle. The area largely coincides with the former operation area of the Gulf Power Company (GPCO) but is not limited to this utility. The five datasets describe the substations, lines, and transmission towers of the grid. The data were created and validated through a variety of datasets from the utility, national data, and state-level information. In total, 195 substations, 1800 miles of transmission lines, and over 18,000 transmission towers were cataloged and described spatially in the data. The spatial files are uploaded as feature classes within a ArcGIS geodatabase. Three metadata files are provided, one each for substations, transmission lines, and transmission towers, which describe the process and sources for creating each data as well as a detailed list of all fields in the files.</p>
HIGGS_Inventory of the European transmission gas grid
<p> This file contains an assesment of the quantitative structure of the existing gas infrastructure focussing on asset elements that are known to be sensitive over hydrogen additions and that in case of modifications or renewal introduce significant cost.<br> </p>
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>
A large synthetic dataset for machine learning applications in power transmission grids
<p>With the ongoing energy transition, power grids are evolving fast. They operate more and more often close to their technical limit, under more and more volatile conditions. Fast, essentially real-time computational approaches to evaluate their operational safety, stability and reliability are therefore highly desirable. Machine Learning methods have been advocated to solve this challenge, however they are heavy consumers of training and testing data, while historical operational data for real-world power grids are hard if not impossible to access. </p> <p>This dataset contains long time series for production, consumption, and line flows, amounting to 20 years of data with a time resolution of one hour, for several thousands of loads and several hundreds of generators of various types representing the ultra-high-voltage transmission grid of continental Europe. The synthetic time series have been statistically validated agains real-world data.</p> <h2>Data generation algorithm</h2> <p>The algorithm is described in a <a href="https://doi.org/10.1038/s41597-025-04479-x">Nature Scientific Data paper</a>. It relies on <a href="https://zenodo.org/records/2642175" target="_blank" rel="noopener">the PanTaGruEl model of the European transmission network</a> -- the admittance of its lines as well as the location, type and capacity of its power generators -- and aggregated data gathered from <a href="https://transparency.entsoe.eu/" target="_blank" rel="noopener">the ENTSO-E transparency platform</a>, such as power consumption aggregated at the national level.</p> <h2>Network</h2> <p>The network information is encoded in the file <a href="https://zenodo.org/records/13378476/files/europe_network.json">europe_network.json</a>. It is given in <a href="https://lanl-ansi.github.io/PowerModels.jl/stable/" target="_blank" rel="noopener">PowerModels format</a>, which it itself derived from <a href="https://matpower.org/" target="_blank" rel="noopener">MatPower</a> and compatible with <a href="https://www.pandapower.org/" target="_blank" rel="noopener">PandaPower</a>. The network features 7822 power lines and 553 transformers connecting 4097 buses, to which are attached 815 generators of various types.</p> <h2>Time series</h2> <p>The time series forming the core of this dataset are given in CSV format. Each CSV file is a table with 8736 rows, one for each hourly time step of a 364-day year. All years are truncated to exactly 52 weeks of 7 days, and start on a Monday (the load profiles are typically different during weekdays and weekends). The number of columns depends on the type of table: there are 4097 columns in load files, 815 for generators, and 8375 for lines (including transformers). Each column is described by a header corresponding to the element identifier in the network file. All values are given in per-unit, both in the model file and in the tables, i.e. they are multiples of a base unit taken to be 100 MW.</p> <p>There are 20 tables of each type, labeled with a reference year (2016 to 2020) and an index (1 to 4), zipped into archive files arranged by year. This amount to a total of 20 years of synthetic data. When using loads, generators, and lines profiles together, it is important to use the same label: for instance, the files <em>loads_2020_1.csv</em>, <em>gens_2020_1.csv</em>, and <em>lines_2020_1.csv</em> represent a same year of the dataset, whereas <em>gens_2020_2.csv</em> is unrelated (it actually shares some features, such as nuclear profiles, but it is based on a dispatch with distinct loads).</p> <h2>Usage</h2> <p>The time series can be used without a reference to the network file, simply using all or a selection of columns of the CSV files, depending on the needs. We show below how to select series from a particular country, or how to aggregate hourly time steps into days or weeks. These examples use Python and the data analyis library <em>pandas</em>, but other frameworks can be used as well (Matlab, Julia). Since all the yearly time series are periodic, it is always possible to define a coherent time window modulo the length of the series.</p> <h3>Selecting a particular country</h3> <p>This example illustrates how to select generation data for Switzerland in Python. This can be done without parsing the network file, but using instead <a href="https://zenodo.org/records/13378476/files/gens_by_country.csv">gens_by_country.csv</a>, which contains a list of all generators for any country in the network. We start by importing the <em>pandas</em> library, and read the column of the file corresponding to Switzerland (country code CH):</p> <pre><code>import pandas as pd CH_gens = pd.read_csv('gens_by_country.csv', usecols=['CH'], dtype=str)</code></pre> <p>The object created in this way is Dataframe with some null values (not all countries have the same number of generators). It can be turned into a list with:</p> <pre><code>CH_gens_list = CH_gens.dropna().squeeze().to_list()</code></pre> <p>Finally, we can import all the time series of Swiss generators from a given data table with</p> <pre><code>pd.read_csv('gens_2016_1.csv', usecols=CH_gens_list)</code></pre> <p>The same procedure can be applied to loads using the list contained in the file <a href="https://zenodo.org/records/13378476/files/loads_by_country.csv">loads_by_country.csv</a>.</p> <h3>Averaging over time</h3> <p>This second example shows how to change the time resolution of the series. Suppose that we are interested in all the loads from a given table, which are given by default with a one-hour resolution:</p> <pre><code>hourly_loads = pd.read_csv('loads_2018_3.csv')</code></pre> <p>To get a daily average of the loads, we can use: </p> <pre><code>daily_loads = hourly_loads.groupby([t // 24 for t in range(24 * 364)]).mean()</code></pre> <p>This results in series of length 364. To average further over entire weeks and get series of length 52, we use: </p> <pre><code>weekly_loads = hourly_loads.groupby([t // (24 * 7) for t in range(24 * 364)]).mean()</code></pre> <h2>Source code</h2> <p>The code used to generate the dataset is freely available at <a href="https://github.com/GeeeHesso/PowerData" target="_blank" rel="noopener">https://github.com/GeeeHesso/PowerData</a>. It consists in two packages and several documentation notebooks. The first package, written in Python, provides functions to handle the data and to generate synthetic series based on historical data. The second package, written in Julia, is used to perform the optimal power flow. The documentation in the form of Jupyter notebooks contains numerous examples on how to use both packages. The entire workflow used to create this dataset is also provided, starting from raw ENTSO-E data files and ending with the synthetic dataset given in the repository.</p> <h2>Funding</h2> <p>This work was supported by the <a href="https://www.cydcampus.admin.ch">Cyber-Defence Campus of armasuisse</a> and by an internal research grant of the Engineering and Architecture domain of <a href="https://www.hes-so.ch">HES-SO</a>.</p>
Sector-coupled input data for optimizing the German transmission grid
<p>This repository contains input data for the open-source python tool <a href="https://github.com/openego/eTraGo">eTraGo</a> (<strong>e</strong>lectricity <strong>Tra</strong>nsmission <strong>G</strong>rid <strong>o</strong>ptimization) version 0.9.0.<br> This data will be uploaded to the <a href="https://openenergy-platform.org/">OpenEnergy Platform</a> which can be accessed by eTraGo. This dataset is an intermediate solution until the data is uploaded.</p> <p>The published data includes the sector-coupled transmission grid data for two scenarios (eGon2035 and eGon2035_lowlfex). It was created with the open-source tool <a href="https://github.com/openego/eGon-data/">eGon-data</a> within the research project <a href="https://ego-n.org">eGon</a>. All input data sets as well as the code are available under open source licenses.</p> <p>The data is stored as a PostgreSQL database in the attached backup file. First, the required schemas and extensions have to be created within the database by running the following SQL statements:</p> <p><code>CREATE SCHEMA grid;<br> CREATE SCHEMA boundaries;<br> CREATE EXTENSION postgis;</code></p> <p>Afterwards the data can be restored by using e.g. pgAdmin or via PostgreSQL's <a href="https://www.postgresql.org/docs/current/app-pgrestore.html">pg_restore</a> command (replace <code>HOST</code>, DATABASE_<code>NAME, PORT</code> and <code>USER</code> by your settings):</p> <p><code>pg_restore --host HOST --port PORT --username USER --no-password --dbname </code>DATABASE_<code>NAME --no-owner --no-privileges --verbose "etrago_data_egon2035.backup"</code></p>
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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