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opencc-by-4.0Jan 2022View details →

Profitability and investment risk of Texan power system winterization

Profitability and investment risk of Texan power system winterization This data repository contains interim and final results of the <a href="https://www.nature.com/articles/s41560-022-00994-y">paper </a>“Profitability and investment risk of Texan power system winterization” published in Nature Energy. Code used to generate these results can be found at <a href="https://github.com/inwe-boku/texas-power-outages">github</a> Abstract A lack of winterization of power system infrastructure resulted in significant rolling blackouts in Texas in 2021 though debate about the cost of winterization continues. Here, we assess if incentives for winterization on the energy only market are sufficient. We combine power demand estimates with estimates of power plant outages to derive power deficits and scarcity prices. Expected profits from winterization of a large share of existing capacity are positive. However, investment risk is high due to the low frequency of freeze events, potentially explaining under-investment, as do high discount rates and uncertainty about power generation failure under cold temperatures. As the social cost of power deficits is one to two orders of magnitude higher than winterization cost, regulatory enforcement of winterization is welfare enhancing. Current legislation can be improved by emphasizing winterization of gas power plants and infrastructure. Date and time format Please observe that we omit the date column from the description of columns below for all datasets. The ERA5 data in input/ is in UTC, all other input datasets are in local Texas time (GMT-6). In interim, temperatures/temppop/, temperatures/temp_gas_powerplant.csv, temperatures/temp_gas_outages.csv, temperatures/temp_coal_powerplant.csv, temperatures/temp_coal_outages.csv and the wind power simulation output (windpower/) is in UTC. All other datasets are in local Texas time. Data cache/ Data cache used by the scripts analyzing the extreme events: extreme temperatures, loss of load, their return periods, durations, maxima/minima (the cached files are not included, but can be generated with scripts/R/events.R) figures/ Figures shown in the manuscript <ul> <li>raw_data: includes raw data for reproducing the figures in the main part of the manuscript</li> <li>outage_model: figures representing the outage function as derived with our model</li> </ul> input/ Input data from external sources (with exception of orcd not included due to licensing issues) <ul> <li>ERA5_windspeeds_USA: available from the <a href="https://cds.climate.copernicus.eu/#!/home">CDS</a>. Download with scripts/download_era5_USA.py</li> <li>gas_production: available from the Texas Railroad Commission in PDF format <a href="https://www.rrc.state.tx.us/media/qcpp3bau/2020-12-monthly-production-county-gas.pdf">here</a>. We extracted the data manually.</li> <li>Load: available from ERCOT <a href="https://www.ercot.com/gridinfo/load">here</a></li> <li>orcd: Scarcity prices as regulated by ERCOT. Manually extracted from <a href="https://doi.org/10.1016/j.enpol.2019.111143.334">J. Zarnikau et al.</a></li> <li>outages: Outage Events from ERCOT with geo locations provided by Edgar Virguez <a href="https://bit.ly/EGOVADatabase">here</a> resulting from unit outage data provided by <a href="http://www.ercot.com/content/wcm/lists/226521/Unit_Outage_Data_20210312.xlsx">Ercot</a></li> <li>population: population density data provided by arcgis <a href="https://www.arcgis.com/home/item.html?id=28bcaee42e2c4ace9fcb7c8b9ca524e7">here</a></li> <li>powerplants: locations of power plants in Texas provided by the Energy Information Administration <a href="https://www.eia.gov/maps/layer_info-m.php">here</a></li> <li>shp: shapefile of Texas state boundaries provided by arcgis <a href="https://gis-txdot.opendata.arcgis.com/datasets/texas-state-boundary-detailed">here</a></li> <li>temperatures: available from the CDS <a href="https://cds.climate.copernicus.eu/#!/home">here</a>. Can be downloaded with script scripts/download_era5_TX_temp.py</li> <li>USWTDB: US wind turbine data base provided by the US Geological Service <a href="https://eerscmap.usgs.gov/uswtdb/">here</a>. We used version: uswtdb_v3_3_20210114</li> <li>GWA2: Global Wind Atlas Version 2.1 accessible <a href="https://silo1.sciencedata.dk/shared/cf5a3255eb87ca25b79aedd8afcaf570?path=%2FGWA2.1">here</a></li> </ul> interim/ Intermediary files from the analysis <ul> <li>bootstrap_year.csv: 30 randomly selected years between 1950 and 2021, 10,000 times used for bootstrapping Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb</li> <li>bootstrap_year2020.csv: 30 randomly selected years between 1950 and 2020, 10,000 times used for bootstrapping without 2021 event Generated by outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb</li> <li>turbine_data.csv: turbine data for Texan wind turbines Generated by scripts/prepare_TX_turbines.py Columns: <ul> <li>capacity: turbine capacity (kW)</li> <li>height: turbine height (m)</li> <li>lon: longitude coordinate (°)</li> <li>lat: latitude coordinate (°)</li> <li>sp: specific power (W/m²)</li> <li>ind: running index</li> </ul> </li> </ul> interim/load/ Temperature dependent estimates of electricity load for Texas. <ul> <li>load_est70_LR24_temptrend_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns: <ul> <li>load_est: load estimated for the period 1950-2021 assuming an average load level as in 2021 (MWh)</li> <li>temp: population weighted temperature (°C)</li> </ul> </li> <li>load_est10_LR24_temptrend_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns: <ul> <li>load: observed load in period 2012-2021 as published by ERCOT (MWh)</li> <li>load_est: load estimated for period 2012-2021 considering time trend, i.e. this is a replication of the observed load without outages with our model for validation purposes (MWh)</li> </ul> </li> <li>load_est9_LR24temptrend2021_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns: <ul> <li>load: observed load in period 2004-2021/01 and load forecast 2021/02 as published by ERCOT (MWh)</li> <li>load_est: load estimated for the years 2012-2020 for cross validation of load model. For training, the years 2012-2021 (2021/02 forecast) were used, except the predicted year, i.e. this is a replication of the observed load with our model for validation purposes. (MWh)</li> </ul> </li> <li>load_est17_crossvalidation_LR24temptrend_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns: <ul> <li>load: observed load 2004 - 2021/01 and load forecast 2021/02 as published by ERCOT (MWh)</li> <li>load_est: load estimated for cross validation for years 2004-2021, training years 2012-2020, trained with each year in traning period except modelled year with variable load level, i.e. this is a replication of the observed load with our model for validation purposes(MWh)</li> </ul> </li> <li>load_est_LR24temptrend_Hook-8.csv Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb Columns: <ul> <li>load: observed load 2004 - 2021 as published by ERCOT (MWh)</li> <li>load_est: years 2004-2021 predicted with a model which was trained for the years 2012-2020 considering time trend, i.e. this is a replication of the observed load without outages with our temperature dependent model with our model for validation purposes (MWh)</li> </ul> </li> </ul> interim/outages <ul> <li>outages.feather Outage by minute of all generation units in Texas in February 2021. Created by scripts/R/create-ercot-outage-timeseries.R In Texas local time. Columns: <ul> <li>station: name of power plant</li> <li>unit: name of generation unit</li> <li>fullname: concatenated string of station and name</li> <li>dataset: ercot or edgar. ercot refers to the raw dataset provided by ERCOT, Edgar to the dataset provided by Edgar Virguez (for details see above in section input/)</li> <li>Longitude: Longitude of location of power plant</li> <li>Latitude: Latitude of location of power plant</li> <li>reduction: hourly reduction of capacity due to outage in this minute (MW)</li> <li>cap_available: available capacity in this minute (MW)</li> <li>cap_max: maximum capacity of unit (MW)</li> </ul> </li> <li>outages-hourly.feather Hourly outages at all generation units in Texas in February 2021. Created by scripts/R/create-ercot-outage-timeseries.R In Texas local time. Columns: <ul> <li>station: name of power plant</li> <li>unit: name of generation unit</li> <li>fullname: concatenated string of station and name</li> <li>dataset: ercot or edgar. ercot refers to the raw dataset provided by ERCOT, Edgar to the dataset provided by Edgar Virguez (for details see above in section input/)</li> <li>Longitude: Longitude of location of power plant</li> <li>Latitude: Latitude of location of power plant</li> <li>reduction: hourly reduction of capacity due to outage in this time step (MW)</li> <li>cap_available: hourly available capacity in this minute (MW)</li> <li>cap_max: maximum capacity of unit (MW)</li> </ul> </li> <li>outages_reduction.csv Hourly outages per fuel (MW). We use these outages for COAL and GAS only in the analysis. Generated by notebooks/prepare_outages_NSsplit.ipynb Columns: <ul> <li>NG: natural gas power plants</li> <li>WIND: wind power plants</li> <li>SOLAR: solar power plants</li> <li>ESR: energy storage resource</li> <li>HYDRO: hydropower plants</li> <li>NUCLEAR: nuclear power plants</li> </ul> </li> <li>outages_reductionNorth.csv Hourly outages for the Northern part of Texas (latitude > 30) (MW). We use these outages for WIND only in the analysis. Generated by notebooks/prepare_outages_NSsplit.ipynb Columns as above.</li> <li>outages_reductionSouth.csv Hourly outages for the Southern part area of Texas (latitude <= 30) (MW). We use these outages for WIND only in the analysis. Generated by notebooks/prepare_outages_NSsplit.ipynb Columns as above.</li> </ul> interim/temperatures <ul> <li>temppop Generated by scripts/calc_temppopC.py <ul> <li>contains population weighted temperatures for Texas, one file for each year (°C).</li> </ul> </li> <li>temp_coal_outage.csv Generated by notebooks/temperatures_NSsplit.ipynb Columns: <ul> <li>t2m: temperature weighted by coal power plants experiencing outages in February 2021 (°C)</li> </ul> </li> <li>temp_coal_powerplant.csv Generated by notebooks/temperatures_NSsplit.ipynb Columns: <ul> <li>t2m: temperature weighted by all coal power plants (°C)</li> </ul> </li> <li>temp_gas_outage.csv Generated by notebooks/temperatures_NSsplit.ipynb Columns: <ul> <li>t2m: temperature weighted by gaspower plants experiencing outages in February 2021 (°C)</li> </ul> </li> <li>temp_gas_powerplant.csv Generated by notebooks/temperatures_NSsplit.ipynb Columns: <ul> <li>t2m: temperature weighted by all gas power plants (°C)</li> </ul> </li> <li>temp_gasfields.csv Generated by notebooks/temperatures_NSsplit.ipynb Columns: <ul> <li>t2m: temperature weighted by all gasfields (°C)</li> </ul> </li> <li>tempWP_NSsplit.csv Generated by notebooks/wp_temp_NSsplit.ipynb Columns: <ul> <li>t2mSouth: temperatures weighted by all wind power plants in the South (°C)</li> <li>t2mNorth: temperatures weighted by all wind power plants in the North (°C)</li> </ul> </li> </ul> interim/thresholds <ul> <li>thresh_total63.5GW.csv Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>total available capacity of gas, coal and wind considering outages, assuming gasfield temperatures for gas outages (GW)</li> </ul> </li> <li>thresh_totalPP63.5GW.csv Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>total available capacity of gas, coal and wind, considering outages, assuming gas power plant temperatures for gas outages (GW)</li> </ul> </li> <li>threshold_coal.csv Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>outages of coal power plants based on coal power plant temperatures (GW)</li> </ul> </li> <li>threshold_gas.csv Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>outages of gas power plants based on gasfield temperatures (GW)</li> </ul> </li> <li>threshold_gasPP.csv Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>outages of gas power plants based on gas power plant temperatures (GW)</li> </ul> </li> <li>threshold_gas_coal63.5GW.csv Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>available capacity of gas and coal, considering outages, assuming gasfield temperatures for gas outages (GW)</li> </ul> </li> <li>threshold_gas_coalPP63.5GW.csv Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>available capacity of gas and coal, considering outages, assuming gas power plant temperatures for gas outages (GW)</li> </ul> </li> </ul> interim/windpower <ul> <li>cfTXh.csv Generated by notebooks/windpower_ERA5_GWA2_const_cap.ipynb Columns: <ul> <li>Capacity factors of simulated Texan wind power (dimensionless)</li> </ul> </li> <li>wpTXh.csv Generated by notebooks/windpower_ERA5_GWA2_const_cap.ipynb Columns:</li> <li>simulated Texan wind power generation (kWh)</li> </ul> output/ <ul> <li>marginal_revenue_coal_10_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization for coal (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_coal_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of coal (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_coal2020_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of coal (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li>marginal_revenue_gas_10_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of gas (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_gas_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of gas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_gas2020_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of gas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li>marginal_revenue_wind_north_10_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of Northern Wind in Texas (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_wind_north_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of Northern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_wind_north2020_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of Northern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li>marginal_revenue_wind_south_10_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of Southern Wind in Texas (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_wind_south_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of Southern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li>marginal_revenue_wind_south2020_LR24temptrend_Hook-8.csv Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>Marginal revenues from winterization of Southern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li>sensitivity_analysis_temperature_thresholds_all_LR24temptrend_Hook-8.csv Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>delta_thresh_temp: change in outage temperature thresholds for all technologies (°C)</li> <li>delta_rec_temp: change in recovery temperature thresholds for all technologies (°C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li>sensitivity_analysis_temperature_thresholds_coal_LR24temptrend_Hook-8.csv Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>thresh_coal_temp: outage temperature thresholds for coal (°C)</li> <li>rec_coal_temp: recovery temperature thresholds for coal (°C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li>sensitivity_analysis_temperature_thresholds_gas_LR24temptrend_Hook-8.csv Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>thresh_gas_temp: outage temperature thresholds for gas (°C)</li> <li>rec_gas_temp: recovery temperature thresholds for gas (°C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li>sensitivity_analysis_temperature_thresholds_wind_north_LR24temptrend_Hook-8.csv Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>thresh_windn_temp: outage temperature thresholds for wind north (°C)</li> <li>rec_windn_temp:recovery temperature thresholds for wind north (°C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li>sensitivity_analysis_temperature_thresholds_wind_south_LR24temptrend_Hook-8.csv Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb Columns: <ul> <li>thresh_winds_temp: outage temperature thresholds for wind south (°C)</li> <li>rec_winds_temp:recovery temperature thresholds for wind south (°C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> </ul>

opencc-by-4.0Jun 2024View details →

Data for Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty

Data from the paper "Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty"

opencc-by-4.0Oct 2022View details →

Reference data set for a Norwegian medium voltage power distribution system

This reference data set describes a representative Norwegian radial, medium voltage (MV) electric power distribution system operated at 22 kV. The data set is developed in the Norwegian research centre CINELDI and will in brief be referred to as the CINELDI MV reference system. Data for a real Norwegian distribution system were provided by a distribution grid company. The data have been anonymized and processed to obtain a simplified but still realistic grid model with 124 nodes. The data set consists of the following three parts: 1. Grid data files: describe the base version of the reference system that represents the present-day state of the grid, including information about topology, electrical parameters, and existing load points. 2. Load data files: comprise load demand time series for a year with hourly resolution and scenarios for the possible long-term development of peak load. These data describe an extended version of the reference system with information about possible new load points being added to the system in the future. 3. Reliability data files: contain data necessary for carrying out reliability of supply analyses for the system. The data set is described in detail in the following data article: I. B. Sperstad, O. B. Fosso, S. H. Jakobsen, A. O. Eggen, J. H. Evenstuen, and G. Kjølle, “Reference data set for a Norwegian medium voltage power distribution system,” Data in Brief, 109025, 2023, doi: 10.1016/j.dib.2023.109025.

opencc-by-4.0Jul 2023View details →

Cipher System T310/50, power supply unit (Inv. 1997-15T2)

This dataset represents the computed tomography image acquisition of a historical cipher machine from the collection of the Deutsches Museum. It is composed of CT reconstructed image stacks in the DICOM (.dcm) format. It can be imported in any free or propietary CT-Viewer that supports the DICOM standard to generated 2D and 3D imaging. If segmentations/ROIs are availabe, they are uploaded in a seperate image stacks and are a subset of the scanned cipher machine. object details: name: Cipher System T310/50, power supply unit Inv.-No. of the Deutsches Museum: 1997-15T2 file object details: file format: image/dcm pixel spacing unit: mm X pixel spacing: 0.668 Y pixel spacing: 0.669 Z pixel spacing: 0.500 grid size x: 1715 grid size y: 1626 grid size z: 1117 color depth: 16Bit ownership & image acquisition: project: <a href="https://digital.deutsches-museum.de/en/projects/3d-cipher">3D-Cipher</a> collection: <a href="https://digital.deutsches-museum.de">Deutsches Museum</a> IP holder: Deutsches Museum license: <a href="https://creativecommons.org/licenses/by/4.0/deed.en">Creative Commons BY-SA 4.0</a> scanning facility: <a href="https://www.iis.fraunhofer.de/en/ff/zfp.html">Fraunhofer Development Center X-ray Technology EZRT/Fraunhofer IIS</a> scanning device: High Energy CT XXL-CT Funding attribution: <a href="https://www.bmbf.de/bmbf/en">German Federal Ministry of Education and Research</a> acknowledgement: <a href="https://www.cryptomuseum.com">CryptoMuseum</a> (as main source for informations about the cipher machines)

opencc-by-4.0Jul 2023View details →

Dataset for "A new method for identifying weather-induced power system stress using shadow prices"

These are data accompanying "A new method for identifying weather-induced power system stress using shadow prices". They consist of <ul> <li>solved network files (generated with <a href="https://github.com/PyPSA/pypsa-eur/">PyPSA-Eur</a>, here v0.6.1), used for the analysis,</li> <li>necessary data to reproduce the figures in the paper and supplementary material.</li> </ul> The optimised network files are of the form `workflow_data/results/stressful-weather/optimum/{weather_year}_181_90m_c1.25_Co2L0.0-1H.nc` (for weather_years in {1980,...,2019}). Unsolved ones can be found in `workflow_data/networks/...`. The filenames in `plot_data/` indicate which figure the data are associated to (e.g. `plot_data/fig_1_hourly_costs.csv` contains the hourly electricity costs during the winter of all networks and is necessary for Figure 1). We also added weather data for all system-defining events (mean surface level pressure, 10m wind speed anomaly, 2m temperature anomaly) in .nc files. Find more information about how to use these data and how they were generated in the README of the GitHub repository: <a href="https://github.com/koen-vg/stressful-weather/tree/v0">https://github.com/koen-vg/stressful-weather/tree/v0</a>.

opencc-by-4.0Apr 2020View details →

Affine Policies for Flexibility Provision by Natural Gas Networks to Power Systems

Online appendix for the paper - "Affine Policies for Flexibility Provision by Natural Gas Networks to Power Systems", containing the power generators, gas producers and demand data as well as physical characteristics of the electrical transmission lines and gas pipelines. The empirically estimated forecast error covariance matrix used in the model is provided as well. The power systems and natural gas systems data is adapted from: C. Ordoudis, P. Pinson and J. M. González, "An integrated market for electricity and natural gas systems with stochastic power producers", European Journal of Operational Research, vol. 272, no. 2, pp. 642-654, 2019.

opencc-zeroJun 2016View details →

Modified pool system based on the IEEE RTS-96 system incl. 39 wind power producers

This is the data-set associated  with the numerical simulations in the paper "A. Papakonstantinou, P. Pinson, Population Dynamics for Renewables in Electricity Markets: A Minority Game View". The paper will be presented in 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) in Oct. 16-20, 2016 in Beijing, China. We modify the original data-set [1] by adding the marginal costs for conventional generation introduced by [2] and flexible generators capable of providing up and down regulation following [3].  The cost of up-regulation is assumed to be 10% higher than the day-ahead cost and the cost of down-regulation 9% less than the day-ahead ahead costs. Furthermore, regarding stochastic generation, we assume zero marginal and cost free spilling action, while load shedding induces a cost of 1000 EUR/MWh. Finally, we assume that the total demand is at 80% of the conventional generation [3], while the total capacity of the 39 stochastic producers is at 30% of the demand. Within the data file the specific data used for the analysis in the paper are under pes_input(). [1]  IEEE RTS Task Force of APM Subcommittee, “The ieee reliability test system-1996.” IEEE Transactions on Power Systems, vol. 14, no. 3, pp. 1010–1020, 1999. [2]  D. Kirschen. Unit commitment data for modernized ieee rts-96. Accessed: 10-03-2016. [Online]. Available: http://www.ee.washington.edu/research/real/library.html [3] A. J. Conejo, M. Carri ́on, and J. M. Morales, Decision Making Under Uncertainty in Electricity Markets. Springer, 2010.

opencc-by-4.0May 2017View details →

Dataset supplementing B. Ojha, N. Illyaskutty, J. Knoblauch, H. Kohler (2017): High temperature CO/HC gas sensors to optimize firewood combustion in low power fireplaces, Journal of Sensors and Sensor Systems (JSSS), 6, 237–246, 2017 (doi:10.5194/jsss-6-237-2017)

Dataset presented in B. Ojha, N. Illyaskutty, J. Knoblauch, H. Kohler (2017): High temperature CO/HC gas sensors to optimize firewood combustion in low power fireplaces, Journal of Sensors and Sensor Systems (JSSS), 6, 237–246, 2017 (doi:10.5194/jsss-6-237-2017)

opencc-by-4.0Jun 2017View details →

Hydro Energy Inflow for Power System Studies

Energy inflow time series for hydro power on the European country level. Inflow was derived from reanalysis data using a potential energy approach. Dataset includes ten years (2003-2012) of data with daily resolution for 30 European countries. The dataset is described in more detail in A Kies, K Chattopadhyay, L von Bremen, E Lorenz, D Heinemann ,Simulation of renewable feed-in for power system studies, RESTORE 2050 project report

opencc-by-4.0Sep 2021View details →

Intelligent Energy Systems Ontology: Local flexibility market and power system co-simulation demonstration

The Intelligent Energy Systems Ontology (IESO) provides semantic interoperability within a society of multi-agent systems (MAS) developed in the scope of power and energy systems (PES). It leverages the knowledge from existing and publicly available semantic models developed for specific PES subdomains to accomplish a shared vocabulary among the agents of the MAS community, overcoming heterogeneity among the reused ontologies. IESO provides agents with semantic reasoning, constraints validation, and data uniformization. The use of IESO is demonstrated through the simulation of the management of a rural distribution network, considering the validation of the grid’s technical constraints. This dataset publishes files demonstrating: i) a snapshot of the initial semantic knowledge base (KB); ii) queries to the KB to get services inputs; iii) conversions between syntactic and semantic models;  iv) constraints validations; v) automatic conversion of units of measure.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for hydro power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for hydroelectric generation from the open literature. Hydroelectric power is the largest source of renewable energy, supplying 15% of global electricity. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 1011 datapoints from 11 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time.  Technoeconomic data on utility-scale hydroelectric power was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for wind power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for wind power generation from the open literature. Wind energy supplies 7% of global electricity, and production has grown three-fold in the decade to 2022. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 1506 datapoints from 28 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale wind energy was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for gas power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for gas-fired power generation from the open literature. Natural gas supplies 23% of global electricity, but must be rapidly phased down to meet global decarbonisation objectives. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 620 datapoints from 14 sources.   The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time.Technoeconomic data on new-build gas-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for coal power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for coal-fired power generation from the open literature. Coal supplies 35% of global electricity, but must be rapidly phased down to meet global decarbonisation objectives. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 345 datapoints from 12 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on new-build coal-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for solar power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for solar power generation from the open literature. Solar energy supplies 5% of global electricity, and production has grown ten-fold in the decade to 2022. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 753 datapoints from 31 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale solar PV was collected from websites, reports, academic articles and databases of national and international organisations.

opencc-by-4.0Nov 2024View details →

Cyber-Physical System power Consumption

<h1>Files</h1> This dataset is comprised of 5 CSV files contained in the data.zip archive. Each one represents a production machine from which various sensor data has been collected. The average cadence for collection was 5 measurements per second. The monitored devices where used for hydroforming. The collection period covered the period from 2023-06-01 until 2023-08-05. <h2>Data</h2> These files represent a complete data dump from the data available in the time-series database, InfluxDB, used for collection. Because of this some columns have no semantic value for detecting production cycles or any other analytics. Each file contains a total of 14 columns. Some of the columns are artefacts of the query used to extract the data from InfluxDB and can be discarded. These columns are: results, table _start, _stop <ul> <li>results - An artefact of the InfluxDB query, signifies postprocessing of results in this dataset. It is "mean".</li> <li>table - An artefact of the InfluxDB query, can be discarded.</li> <li>_start and _stop - Refers to ingestion related data, used in monitoring ingestion. </li> <li>_field - An artefact of the InfluxDB query, specifying what field to use for the query.</li> <li>_measurement - An artefact of the InfluxDB query, specifying what measurement to use for the query. Contains the same information as device_id.</li> <li>host - An artefact of the InfluxDB query, the unique name of the host used for the InfluxDB sink in Kubernetes.</li> <li>kafka_topic - Name of the Kafka topic used for collection.</li> </ul>   Pertinent columns are: <ul> <li>_time - Denotes the time at which a particular event has been measured, it is used as index when creating a dataframe.</li> <li>_time.1 - Duplicate of _time for sanity check and ease of analysis when _time is set as index</li> <li>_value - Represents the value measured by each sensor type.</li> <li>device_id - Unique identifier of the manufacturing device, should be the same as the file name, i.e. B827EB8D8E0C.</li> <li>ingestion_time - Timestamp when the data has been collected and ingested by influxDB.</li> <li>sid - Unique sensor ID; the power measurements can be found at sid 1.</li> </ul>   <h1>Annotations</h1> There are two additional files which contain annotation data:  <ul> <li>scamp_devices.csv - Contains mapping information between the dataset device ID (defined in column "DeviceIDMonitoring") and the ground truth file ID (defined in column "DeviceID")</li> <li>scamp_report_3m.csv - Contains the ground truth, which can be used for validation of cycle detection and analysis methods. The columns are as follows: <ul> <li>ReportID - Internal unique ID created during data collection. It can be discarded.</li> <li>JobID - Internal Scheduling Job unique ID.</li> <li>DeviceID - The unique ID of the devices used for manufacturing needs to be mapped using the scamp_device.csv data.</li> <li>StartTime - Start time of operations</li> <li>EndTime - End time of operations</li> <li>ProductID - Unique identifier of the product being manufactured.</li> <li>CycleTime - Average length of cycle in seconds, added manually by operators. It can be unreliable.</li> <li>QuantityProduced - Number of products manufactured during the timeframe given by StartTime and EndTime.</li> <li>QuantityScrap - Number of scraped/malformed products in the given timeframe. These are part of the QuantityProduced, not in addition to it.</li> <li>IntreruptionMinuted - Minutes of production halt.</li> </ul> </li> <li>scamp_patterns.csv - Contains the start and end timestamp for selected example production cycles. These where chosen based on expert users.</li> </ul> <h1>Jupyter Notebook</h1> We have provided a sample Jupyter notebook (verify_data.ipynb), which gives examples of how the dataset can be loaded and visualised as well as examples of how the sample patterns and ground truth can be addressed and visualised. <h2>Note</h2> The Jupyter Notebook contains an example of how the data can be loaded and visualised. Please note that both data should be filtered based on sid; the power measurements are collected by sid 1. See Notebook for example.

opencc-by-4.0Jul 2024View details →

Model Inputs and Results - The role of coal plant retrofitting strategies in decarbonizing India's power system

These files are the model inputs and results for the submission based on GenX version v0.3.6 - The role of coal plant retrofitting strategies in decarbonizing India’s power system