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15 results for “Electric Grid”
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>
Time series of electricity output for large grid connected photovoltaic installations in Chile
<p>These data sets accompany the paper "Simulation of multi-annual time series of solar photovoltaic power: is the ERA5-land reanalysis the next big step?". They include capacity factors (values 0 to 1) in hourly temporal resolution of 103 large PV installations in Chile derived from official sources as well as simulated capacity factors using PV_LIB with ERA5-land and MERRA-2 reanalysis data. The data covers the period 2014-2018 and simulations were performed assuming either a "fixed" system with orientation towards north and inclination equal to the latitude (i.e. optimal inclination) or a horizontal single axis “tracking” system with backtracking. Furthermore, accuracy indicators (Pearson’s correlation, mean bias error and root mean square error) are provided, comparing the simulated time series with capacity factors derived from official sources. Capacity factors were calculated for the 103 installations and both alternative configurations (“fixed” and “tracking”) but only a subset of 23 installations has reference data of sufficient quality to allow for a validation (for a more detailed description, see the paper). Indicators were also calculated for all installations and configurations but should be only compared for installations inside a particular configuration: there are 14 systems classified as “tracking” and 9 as “fixed”. Further details are available in the paper and the entire Python and R code is available on github at <a href="https://github.com/inwe-boku/PV_from_era5">https://github.com/inwe-boku/PV_from_era5</a>. </p>
DATABASE: Electric Vehicle, Battery and Smart Grid patent citation networks and main paths.
<p>This dataset comprises the original patent citation networks that were created to calculate the main citation paths for the technologies of Electric Vehicle, Battery and Smart Grid.</p> <p>For each technology (1- Electric Vehicle, 2- Battery, 3- Smart Grid), four outputs are provided:</p> <p>a- Patent extraction: USPTO patents filtered by IPC or CPC and found in the Triadic Patent Families database (OECD, 2021) </p> <p>b- Full nodes and links reconstructed by following patent citations through a snowball method (until no further patents found)</p> <p>c- Filtered nodes and links according to keywords</p> <p>d- Main path nodes and links (with citation weights).</p> <p>For a detailed explanation of the methodology please refer to the submitted paper:</p> <p><strong>Transitions as a coevolutionary process: the urban emergence of electric vehicle inventions</strong></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>
U.S. building energy efficiency and flexibility as an electric grid resource (Data and Code)
<p><strong>* New in Version 2.1 *</strong></p> <ul> <li> <p>All residential measure savings shapes data (<strong>Latest_Res_Shapes.zip</strong> and residential measures in <strong>Latest_BM_Shapes.zip</strong>) were updated to correct post-processing errors present in version 2.</p> </li> <li> <p>The raw baseline-case data that are used in Scout to estimate sector-level baseline hourly loads (file <a href="https://github.com/trynthink/scout/blob/master/supporting_data/tsv_data/tsv_load.gz">tsv_load</a>) are now included in this data resource (see files <strong>Latest_Res_Baselines.zip</strong> and <strong>Latest_Com_Baselines.zip</strong>).</p> </li> <li> <p>Additional residential measure run documentation is available (<a href="https://github.com/NREL/resstock/blob/e2a98b7345d5c453ba35341b70af2f8859dd22fe/GEB_Potential.yml">here</a> for all except water heating efficiency plus flexibility (EE+DF) measure and <a href="https://github.com/NREL/resstock/blob/9611d92388e1e23466c9dc451e115c21321b4012/GEB_Potential_v2.5.0_appl_ee_dr.yml">here</a> for the water heating EE+DF measure).</p> </li> <li>A guide to reading and/or preparing savings shapes CSVs is available <a href="https://scout-bto.readthedocs.io/_/downloads/en/latest/pdf/">in the Scout documentation</a>, p. 36. The documentation also summarizes the net system load conditions that measures with flexibility (DF) characteristics respond to (Table 1, p. 37).</li> </ul> <p><strong>* New in Version 2 *</strong></p> <p>All hourly savings shapes CSV files that support the original <a href="https://doi.org/10.1016/j.joule.2021.06.002">analysis</a> have been updated to reflect the following improvements:</p> <ul> <li> <p>Generate residential data using ResStock v2.5.0 and commercial data using DOE Commercial Prototypes generated with OpenStudio v3.3.0.</p> </li> <li> <p>Residential and commercial measures with flexibility (DF) features respond to updated grid conditions (net peak/low load periods) that are consistent with projections from the EIA 2022 Annual Energy Outlook (AEO) “Low renewables cost” <a href="https://www.eia.gov/outlooks/aeo/tables_side_xls.php">side case</a>.</p> </li> <li> <p>Residential baseline loads and load savings are now distinguished by three building types (single family, multi family, and mobile homes).</p> </li> </ul> <p>Updated savings shape CSVs are organized into three ZIP files that may be separately downloaded depending on user interests:</p> <p><strong>Latest_BM_Shapes.zip</strong> includes only the subset of savings shape CSVs needed to execute the <a href="https://doi.org/10.5281/zenodo.3158929">Scout Benchmark Scenarios</a>.</p> <p><strong>Latest_Res_Shapes.zip</strong> includes all residential savings shape CSVs.</p> <p><strong>Latest_Com_Shapes.zip</strong> includes all commercial savings shape CSVs.</p> <p>Baseline load shapes in Scout have also been updated based on the same versions of ResStock and the DOE Commercial Prototypes, and peak/take period impact calculations have been updated to reflect the 2022 AEO system conditions. These updated data are contained in <a href="https://github.com/trynthink/scout/releases/tag/v0.8">Scout v0.8</a> (see ./supporting_data/tsv_data).</p> <p><br> <strong>Summary of Original Data Files</strong></p> <p>These data underpin an analysis of the near- and long-term technical potential bulk power grid resource offered by best available U.S. building efficiency and flexibility measures. Using multiple openly-available modeling frameworks supported by the U.S. Department of Energy, including <a href="https://scout.energy.gov/">Scout</a>, <a href="https://resstock.nrel.gov/">ResStock</a>, and the <a href="https://www.energycodes.gov/development/commercial/prototype_models">Commercial Building Prototype Models</a>, we pair bottom-up simulations of measures' building-level impacts with regional representations of the building stock and its projected electricity use to estimate the impacts of multiple building efficiency and flexibility scenarios on hourly regional system loads across the contiguous U.S. in 2030 and 2050. We find that demand-side management via building efficiency and flexibility could avoid up to nearly ⅓ of annual fossil-fired generation and ½ of fossil-fired capacity additions after 2020.<strong> </strong>Results are reported at both the national and regional scales and are disaggregated by building type and end use, facilitating a quantitative understanding of the role that buildings as a whole and specific building technologies or operational approaches can play in the future evolution of the U.S. electricity system.</p> <p>The four ZIP files that make up this data record are interpreted as follows:</p> <p><strong>Measure_Data.zip: </strong>Includes the Scout energy conservation measure (ECM) JSON definitions that were used to generate the main baseline and efficient/flexible scenario results ("Baseline_Measures" and "Efficiency_Flexibility_Measures", respectively), as well as side cases that assess the sensitivity of results to higher levels of variable renewable penetration ("High_RE_Sensitivity_Analysis") and a high degree of building load electrification ("High_Electrification_Measures"). Each measure set includes supporting 8760 load savings shapes in the sub-folder "Savings_Shapes". Additional details about defining and interpreting Scout measures with time-sensitive analysis features are available <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#time-sensitive-valuation">here</a>.</p> <p><strong>Results_Data.zip: </strong>Includes the main and side case results data. Baseline-case outcomes, which are consistent with the <a href="https://www.eia.gov/outlooks/archive/aeo19/">EIA 2019 Annual Energy Outlook</a>, are stored in "Baseline_Loads". Efficient/flexible scenario results are stored in "Efficiency_Flexibility_Measure_Impacts_Individual" and "Efficiency_Flexibility_Measure_Impacts_Portfolio," respectively, where the former includes results for individual measures in our analysis without considering any interactions across measures, and the latter includes results for aggregations of energy efficiency (EE), demand flexibility (DF), and efficiency and flexibility (EE+DF) portfolios that do consider interactions across measures in each portfolio. Results for the high electrification side case are stored in the "High_Electrification" sub-folder in the EE+DF case only. Results for the high renewable sensitivity analysis are stored in "High_RE_Sensitivity_Analysis", and residential and commercial 8760 savings shape outcomes for each of the EE, DF, and EE+DF measure portfolios and five of the 2019 EIA Electricity Market Module (EMM) <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">regions</a> (p.6) of focus are stored in "Sector_Level_8760s".</p> <p><strong>Source_Code.zip: </strong>Includes the source code needed to translate the measure inputs provided in "Measures_Data.zip" into the outputs provided in "Results_Data.zip". The core set of files required to execute the main analysis results is stored in "Base_Code_Package", while variants to certain files in the core package needed to execute the high renewable sensitivity and high electrification side cases are stored in "Code_Variants". In general, the process of running an analysis is as described in the Scout <a href="https://scout-bto.readthedocs.io/en/latest/quick_start_guide.html">Quick Start Guide</a>; however, the file "ecm_prep_batch.py" should be substituted for "ecm_prep.py" and the file "run_batch.py" should be substituted for "run.py". These batch files execute multiple versions of "ecm_prep.py" and "run.py" that are tailored to generate individual measure and whole portfolio results for annual, net peak summer and winter, and net off-peak summer and winter metrics (individual measures: "ecm_prep.json," "ecm_prep_spa," "ecm_prep_wpa," "ecm_prep_sta," "ecm_prep_wta"; whole portfolio: "ecm_results.json," "ecm_results_spa.json," "ecm_results_wpa.json," and "ecm_results_sta.json," and "ecm_results_wta.json"). Results for the side cases are generated by replacing the versions of the "ecm_prep" and "run" files included in the "Base_Code_Package" folder with those in the "Code_Variants" folder. Sector-level 8760 shapes are generated using the "--sect_shapes" command line option as described <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#sector-level-hourly-energy-loads">here</a>. See Scout's <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#local-execution-tutorials">Local Execution Tutorials</a> for more details on how to develop Scout inputs and outputs.</p> <p><strong>Supporting_Data.zip: </strong>Includes supplemental data files provided by EIA that describe key inputs and outputs to the <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">Electricity Market Module</a> in the AEO 2019 run of the National Energy Modeling System ("EIA EMM Data (AEO 2019)"), as well as raw EnergyPlus outputs that were used to develop the baseline Scout hourly load shape file found in "./Source_Code/Base_Code_Package/supporting_data/tsv_data/tsv_load.json". </p>
2009 modeled electricity prices from HiGRID for the California grid
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Climate Energy Dataset For Off Grid Electricity Infrastructure
<p>The dataset comprises real-time electrical measurements—including voltages, currents, and power factors—for three-phase and single-phase systems across generation, distribution, and consumption stages showcasing the energy generation and demand within an off-grid electricity infrastructure located in the Kalam Region, a specific climate zone in Pakistan. Collected every five minutes from March 6, 2023, to October 24, 2024, it includes over 45 million instances covering data from four micro-hydropower generators, 26 transformers, and 585 end-users. Additionally, the dataset incorporates climate data—such as temperature, dew point, wind components, precipitation, snowfall, and snow cover—from the ERA5 dataset. This comprehensive collection enhances its utility for research in energy systems analysis, climate change studies, electrical engineering, and artificial intelligence applications.</p> <p>This dataset was originally created with support from Lacuna Fund, the world’s first collaborative effort to provide data scientists, researchers, and social entrepreneurs in low- and middle-income contexts globally with the resources they need to produce labeled datasets that address urgent problems in their communities. Lacuna Fund is a funder collaborative that includes The Rockefeller Foundation, Google.org, Canada’s International Development Research Centre, the German Federal Ministry for Economic Cooperation and Development (BMZ) with GIZ as implementing agency, Wellcome Trust, Gordon and Betty Moore Foundation, Patrick J. McGovern Foundation, and The Robert Wood Johnson Foundation. See https://lacunafund.org/about/ for more information.</p>
Assessing Grid Affordability for Universal Electricity Access in Sub-Saharan Africa
<p>This repository contains all the necessary original data for the publication <em>"Assessing Grid Affordability for Universal Electricity Access in Sub-Saharan Africa."</em> It includes the original documents detailing the tariff structures of 48 SSA countries, as well as population data, the Gini index, and gross national income per capita, which are used to simulate income distribution. Additionally, it provides calculator tools for estimating electricity bills based on the Multi-Tier Framework and assessing grid electricity affordability by comparing simulated income with electricity costs.</p> <p>The related code and result datasets supporting this publication are available through the GitHub public repository: <a href="https://rb.gy/f68tx5" target="_new" rel="noopener">https://rb.gy/f68tx5</a>.</p>
Grid-Free Evaluation of Phonon-Limited Relaxation Times and Electrical Transport Properties
<p>Data, source codes and figures for the manuscript:</p> <p>"Grid-Free Evaluation of Phonon-Limited Relaxation Times and Electrical Transport Properties"</p>
Vehicle-to-grid Response on 13 February 2024 in Australian National Electricity Market
<p>This data captures the response of 16 Nissan LEAF electric vehicles to a frequency contingency in the Australian National Electricity Market on the 13th of February 2024, which led to widespread blackouts in Melbourne. The data comes from the bidirectional Wallbox Quasar chargers, as well as six high speed power meters located at the grid connection of each of the properties in which the vehicles were charging.</p>
Codes for the article: Biodiversity Impacts of Norway's Renewable Electricity Grid
<p>This repository contains the codes required to run the LCIA models and reproduce the results. The models are described in the paper "Biodiversity Impacts of Norway’s Renewable Electricity Grid" (https://doi.org/10.1016/j.jclepro.2024.143096).</p>
Comparison table of betweenness centrality and electrical grid centrality values for Ural main power lines
<p>Comparison table of betweenness centrality and electrical grid centrality values for Ural united power system (lines with voltage 220-500 kV), calculated with ArcGIS and Networkx tools.</p>
Online companion -Technical Impacts of the Deployment of Renewable Energy Community on Electricity Distribution Grids
<p>Online companion for the article "Technical Impacts of the Deployment of Renewable Energy Community on Electricity Distribution Grids".</p>
Nordic44 - 2015 Powerflow Data: An Open Data Repository of an Equivalent Nordic Grid Model Matched to Historical Electricity Market Data for 2015
<p>This repository is used to provide documentation related to the model and data development process, provide source (raw) data for the model in different forms (i.e. Modelica, CIM 14, and PSS/E) for an equivalent Nordic grid model that has been matched to historical power flow data.</p> <p>The repository is documented in the paper below, see [Ref00].</p> <p><strong>Using this model, data or related software = cite our publications!</strong></p> <p>We are happy to contribute with this dataset, however, if you use any of the data or software provided, we will appreciate if you cite the following publications, as follows:</p> <p>A) Cite that "the raw and processed data files corresponding to the model are available as an open data set and documented in [Ref00]."</p> <p>B) Cite that the first appearance of the model, i.e. "the model is first presented in [Ref01]"</p> <p>[Ref00] L. Vanfretti, S.H. Olsen, V. S. Narasimham Arava, G. Laera, A. Bibadafar, T. Rabuzin, H. Jackobsen, J. Lavenius, and M. Baudette, "An Open Data Repository and a Data Processing Software Toolset of an Equivalent Nordic Grid Model Matched to Historical Electricity Market Data," submitted for publication, Data in Brief, 2016.</p> <p>[Ref01] L. Vanfretti, T. Rabuzin, M. Baudette, M. Murad, iTesla Power Systems Library (iPSL): A Modelica library for phasor time-domain simulations, SoftwareX, Available online 18 May 2016, ISSN 2352-7110, http://dx.doi.org/10.1016/j.softx.2016.05.001.</p> <p><strong>Acknowledgment:</strong></p> <p>This model was originally developed in the context of the FP7 iTesla project, and further extended within the ITEA3 openCPSproject.</p> <p>Structure of the repository:</p> <p><strong>01_PSSE_Resources</strong>:</p> <ol> <li> <p><strong>Models</strong> :</p> <ul> <li> <p>A folder with PSS/E files of the base case</p> </li> <li> <p>A folder with a 7zip archive containing files of the original N44 system that has been modified to have the PSS/E base case</p> </li> </ul> </li> <li> <p><strong>Snapshots</strong> :</p> <ul> <li> <p><strong>N44_2015xxxx</strong> are folders named according to the day they refer to (for example <em>N44_20150401</em> refers to the 1st of April 2015). In each folder there are Excel files (<em>Consumption_xx.xlsx</em>, <em>Exchange_xx.xlsx</em>, <em>Production_xx.xlsx</em>) with data downloaded from Nord Pool website, an Excel file (<em>PSSE_in_out.xlsx</em>) summarizing the results from the Python script <em>Nordic44.py</em> in the folder <strong>04_Python_Resources</strong>, PSS/E snapshots for each hour before solving the power flow (<em>hx_before_PF.raw</em>) and after solving the power flow (<em>hx_after_PF.raw</em>)</p> </li> <li> <p><em>N44_BC.sav</em> is the PSS/E solved base case that Python script <em>Nordic44.py</em> (put the reference)</p> </li> </ul> </li> </ol> <p><strong>02_CIM14_Snapshots</strong>:</p> <ul> <li> <p><strong>N44_2015xxxx</strong> are folders named according to the day they refer to (e.g. <strong>N44_20150401</strong> refers to the 1st of April 2015). In each folder there are CIM files for each hour (<em>N44_hx_EQ.xml</em>, <em>N44_hx_SV.xml_, _N44_hx_TP.xml</em>)</p> </li> <li> <p><strong>N44_noOL_RDFIDMAP.xml</strong> is the file with IDs mapping of those cases (<em>N44_hx_noOL_EQ.xml</em>, <em>N44_hx_noOL_SV.xml</em>, <em>N44_hx_noOL_TP.xml</em>) with fixed overloading problems.</p> </li> <li> <p><strong>N44_RDFIDMAP_2015-1.xml</strong> and <strong>N44_RDFIDMAP_2015-2.xml</strong> are the files with IDs mapping of the remaining snapshots from 2015</p> </li> </ul> <p><strong>03_Modelica</strong>:</p> <ol> <li> <p><strong>iTesla_Platform</strong></p> <ul> <li> <p><strong>iPSL</strong> folder contains the version of the library which can be used to simulate snapshots generated from the iTesla Platform</p> </li> <li> <p><strong>Modelica_snapshots</strong> Modelica models generated from the snapshots by iTesla Platform</p> </li> </ul> </li> <li> <p><strong>SmarTSLab</strong></p> <ul> <li> <p><strong>OpenIPSL</strong> folder contains the version of the forked iPSL library which can be used to simulate the manually generated Modelica model of N44 with the record structures corresponding to the snapshots</p> </li> <li> <p><strong>Snapshots</strong> folder contains Modelica records automatically generated from the PSS/E records</p> </li> <li> <p><em>N44_Base_Case.mo</em> is the handmade N44 model with the loaded record of the power flow results from the PSS/E base case. It can be used to load other PF results from the folder <strong>03_Modelica/Snapshots</strong></p> </li> </ul> </li> </ol>
California grid electrical energy storage requirements for select renewables integration and fleet electrification scenarios
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