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35 results for “electrification”
Data Repository - Distinct Roles of Direct and Indirect Electrification in Pathways to a Renewables-dominated European Energy System
<p>This is the data repository to reproduce the scenario analysis of the paper "<a href="https://www.cell.com/one-earth/fulltext/S2590-3322(24)00037-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS259033222400037X%3Fshowall%3Dtrue">Distinct roles of direct and indirect electrification in pathways to a renewables-dominated European energy system</a>".</p> <p>The source code for the REMIND version used in this study is available at <a href="https://github.com/fschreyer/remind/tree/ElecH2_prod">https://github.com/fschreyer/remind/tree/ElecH2_prod</a>. The scenario config file that was used to start the specific model runs of the paper and that inlucdes all scenario-specific model settings can be found in the repository under <a href="https://github.com/fschreyer/remind/blob/ElecH2_prod/config/21_regions_EU11/scenario_config_ElecH2.csv">./config/21_regions_EU11/scenario_config_ElecH2.csv</a>. The repository is a fork with slight changes relative to the main release version available at <a href="https://github.com/remindmodel/remind/tree/v3.2.1">https://github.com/remindmodel/remind/tree/v3.2.1</a> and <a href="https://doi.org/10.5281/zenodo.7852740">https://doi.org/10.5281/zenodo.7852740</a>. The model documentation can be found at <a href="https://rse.pik-potsdam.de/doc/remind/3.2.0">https://rse.pik-potsdam.de/doc/remind/3.2.0</a>. </p> <p>Model output data as well as other data that were used in the study are stored in data.zip. Moreover, we added a PlotsData.zip file, which contains the data shown in the figures of the paper. The R script to produce the figures and analysis of the paper can be found in ElecH2paper_Plots.Rmd. We publish a comprehensive dataset of our model output which includes more data than what is needed to reproduce the figures of the paper. Those data can be helpful to compare and contextualize our scenarios or use them for further analyses. However, due to the scope and complexity of our modeling framework, these data need to be used with care. The data used for the analysis of this study have been thoroughly validated. However, we cannot always perform such validation for the whole dataset and data need to treated with caution in particular at high regional or sectoral resolution and with respect to aspects that were not in the focus of the study as there maybe artefacts or limitations of our modeling approach. Please contact us in case you would like to use our scenarios for further analyses. We welcome open and constructive exchange on our data. </p> <p> </p> <p>Contact:<br>Felix Schreyer<br>Potsdam Institute for Climate Impact Research<br>felix.schreyer@pik-potsdam.de</p>
Codes and Data for 'Cost-effective Planning of Decarbonized Power-Gas Infrastructure to Meet the Challenges of Heating Electrification'
<p>The codes and data used in the followng paper</p> <p>''Khorramfar, R., Santoni-Calvin, M., Mallapragada, D., Amin, S., Botterud, A.,<br>Norfork L., (2025) Cost-effective Planning of Power-Gas Infrastructure to Meet the Challenges<br>of Heating Electrification, Cell Reports Sustainability</p> <p> </p> <p>Link (open source): https://www.cell.com/cell-reports-sustainability/fulltext/S2949-7906(25)00003-5</p> <p> </p>
Role of residential air circulation and cooling for universal household electrification
<p>This repository contains the data to replicate the analysis of the paper 'Role of residential air circulation and cooling for universal household electrification' by Giacomo Falchetta. </p> <p>Replication code and instruction are hosted at <a href="https://github.com/giacfalk/cooling_electrification">https://github.com/giacfalk/cooling_electrification</a></p> <p>To replicate the analysis:</p> <ol> <li>Clone the Github repository on your machine</li> <li>Download the Zenodo archive and extract it in the home folder of the cloned Github repository</li> <li>Ensure Python (Anaconda 3) and R 3.5.1+ are installed on your local machine</li> <li>Run the 'wrapper.R' file.</li> </ol> <p> </p>
Datasets used for "Heat Pump - Heating Electrification and Climate Change - Grid Impact Studies"
<h2> Summary</h2> <p> </p> <p>In this work, we explore long term patterns in electricity demand driven by the dual effects of space heating electrification and climate change. We use an open source nodal power system model of the Electric Reliability Council of Texas (ERCOT) system to investigate a wide range of future climate and technology scenarios that evolve over time, and report results in terms of market prices, reliability and corresponding relative capacity requirements </p> <h2> About </h2> <p>The technical analysis aimed to:</p> <h3>1) Understand the Long-Term Patterns:</h3> <p>We aim to analyze patterns in peak load, total load, loss of load, and the seasonality of these phenomena, driven by widespread heat pump adoption alongside climate change.</p> <h3>2) Use Extensive Scenario Analysis:</h3> <p>Explore a wide range of future scenarios, including variations in climate pathways, to capture the uncertainty associated with these long-term changes. In total, 1280 simulation years.</p> <h3>3) Use a validated open source DC OPF model(reproducibility)</h3> <p>Use an open-source nodal power system model of the ERCOT system to simulate and understand the potential impacts on market prices, reliability, and relative capacity requirements. Similar models are available for all interconnections of the conterminous US.</p> <h3>4) Assess Grid Vulnerability:</h3> <p>Assess the vulnerability of the grid to these simultaneous changes, identify potential vulnerability.</p> <h3>5) Provide Insights for System Planners:</h3> <p>Offer results that can assist long-term system planners in anticipating and preparing for potential shifts in grid reliability.</p>
The case of 100% electrification of domestic heat in Great Britain
<p>Input data set for OPHELIA optimisation model investigating optimal heat decarbonization pathways through electrification for net zero economy in Great Britain. </p>
Data for "How flexible electrification can integrate fluctuating renewables"
<p>This upload provides the files used to compile and compute the input data used in the aforementioned working paper.</p>
Railbelt 2050 Load, Electrification, and Behind-the-Meter Solar Hourly Load Demand for Aggressive and Moderate Electrification Forecasts
<p>The data in this file is comprised of hourly load demand data for the year of 2050 for Alaska's Railbelt transmission system from the aggressive and moderate load, electrification adoption, and behind-the-meter solar forcasts generated by the ACEP Railbelt Decarbonization Study. </p>
The changing impact of rural electrification on Indian agriculture
<p>Repository of datasets and code for publication titled "The changing impact of rural electrification on Indian agriculture." <i>https://doi.org/10.1038/s41467-023-42533-7</i></p>
Electrification shares of trucks
<p>XX_Tno',' daily truck number for XX scenario</p> <p>XX_TnoF',' daily truck number for XX scenario using fast chargers</p> <p>XX_TnoS', daily truck number for XX scenario using slow chargers</p> <p>XX_FC, Fast chargers number for XX scenario</p> <p>XX_SC, Slow chargers number for XX scenario</p>
Impact of declining renewable energy costs on electrification in low emission scenarios - Scenario Data
<p>This data archive contains model runs and data analysis files to the research article</p> <p><strong>Impact of declining renewable energy costs on electrification in low emission scenarios</strong></p> <p>by<br> <em>Gunnar Luderer, Silvia Madeddu, Leon Merfort, Falko Ueckerdt, Michaja Pehl, Robert Pietzcker, Marianna Rottoli, Felix Schreyer, Nico Bauer, Lavinia Baumstark, Christoph Bertram, Alois Dirnaichner, Florian Humpenöder, Antoine Levesque, Alexander Popp, Renato Rodrigues, Jessica Strefler, Elmar Kriegler</em></p> <p>forthcoming in <em>Nature Energy (2021).</em></p> <p> </p> <p><em><strong>ModelRuns </strong></em>(directory) contains all model runs of the scenarios underlying the paper.</p> <p><em><strong>DataAnalysis </strong></em>(directory) contains all RMarkDown-Notebooks that were used for the data analysis and the generation of the figures of the paper.</p> <p><em><strong>ScenarioNames.pdf</strong></em> contains the mapping from scenario names used in the paper to the model experiment names (in the directory ModelRuns).<br> <br> <em><strong>ScenarioData_IAMC_Format.xlsx </strong></em>contains the scenario output date in the generic IAMC-format (https://data.ene.iiasa.ac.at/database/) as submitted to the IPCC-AR6-database (https://iiasa.ac.at/web/home/research/researchPrograms/Energy/200513_IPCCwebinar.html)</p>
Electrification of Transportation Means a Lot More Than a Lot More Electric Vehicles
<p>Energy use in 2050 from the Annual Energy Outlook 2021 published by the U.S. Department of Energy's Energy Information Agency.</p>
Transportation Electrification Load Profiles by Balancing Authority and State-Level Electrification Rates in the Western United States for GODEEEP
<p>Time-series hourly electric charging load profiles for the transportation sector across Balancing Authorities (BAs) in the Western Electricity Coordinating Council (WECC) interconnect, annual fleet sizes by state and vehicle type, annual transportation sector energy usage by state and fuel, and annual transportation fuel usage by state. The data is provided for three different socioeconomic pathways and two different climate pathways, resulting in four total scenarios. The socioeconomic pathways--Net-Zero (<code>nz_climate</code>), Net-Zero allowing for Carbon Capture Sequestration (CCS) technology (<code>nz_ccs_climate</code>), and Net-Zero allowing for CCS with Inflation Reduction Act (IRA) policies (<code>nz_ira_ccs_climate</code>)--are described by <a href="https://doi.org/10.5281/zenodo.10642507">https://doi.org/10.5281/zenodo.10642507</a>. The climate pathways--Representative Concentration Pathway (RCP) 4.5 cooler (<code>rcp45cooler</code>) and RCP 8.5 hotter (<code>rcp85hotter</code>)--are described by <a href="https://doi.org/10.57931/1885756">https://doi.org/10.57931/1885756</a>. The climate influence is only considered for Light Duty Vehicles (LDVs).</p> <p>For additional details please consult the paper Acharya et al 2024, Impact of the Inflation Reduction Act and Carbon Capture on Transportation Electrification for a Net-Zero Western U.S. Grid, submitted, and the code repository <a href="https://github.com/GODEEEP/transportation_electrification">https://github.com/GODEEEP/transportation_electrification</a>.</p> <p>A brief summary of the files and directories in this data package is provided below. Text within chevrons implies a multiplicity of files, one for each actual value.</p> <ul> <li>nz_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ira_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>WECC_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> <li>EV_electric_and_total_energy.csv</li> <li>LDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>MDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>HDV_fleet_size_all_fuel_types_state_wise.csv</li> </ul> <p> </p> <p><strong>Hourly transportation load:</strong></p> <ul> <li><code>time</code> - ISO 8601 timestamp representing the end of the hourly timestep; values are reported as the summation over the preceding hour</li> <li><code>balancing_authority</code> - Acronym of the balancing authority for this data point</li> <li><code>LDV_load_MWh</code> - Energy consumed by the charging of Light Duty Vehicles (LDVs) during the previous hour in Megawatt hours</li> <li><code>MDV_load_MWh</code> - Energy consumed by the charging of Medium Duty Vehicles (MDVs) during the previous hour in Megawatt hours</li> <li><code>HDV_load_MWh</code> - Energy consumed by the charging of Heavy Duty Vehicles (HDVs) during the previous hour in Megawatt hours</li> <li><code>passenger_rail_load_MWh</code> - Energy consumed by the charging of passenger rail vehicles during the previous hour in Megawatt hours</li> <li><code>freight_rail_load_MWh</code> - Energy consumed by the charging of freight rail vehicles during the previous hour in Megawatt hours</li> <li><code>aviation_load_MWh</code> - Energy consumed by the charging of aviation vehicles during the previous hour in Megawatt hours</li> <li><code>ship_load_MWh</code> - Energy consumed by the charging of ships during the previous hour in Megawatt hours</li> <li><code>transportation_load_MWh</code> - Total energy consumed by the charging of vehicles during the previous hour in Megawatt hours (summation of the other columns)</li> </ul> <p>The WECC files provide summations of all BAs for each scenario, with the same columns as above excepting <code>balancing_authority</code></p> <p><strong>State-wise fleet sizes by vehicle type:</strong></p> <ul> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>technology</code> - fuel type such as BEV (battery electric vehicle), FCEV (fuel cell electric vehicle), hybrid liquids and liquids (refined liquids)</li> <li><code>veh_type</code> - one of LDV, MDV, or HDV (Light, Medium, or Heavy Duty Vehicle)</li> <li><code>fleet_size</code> - the number of vehicles</li> </ul> <p>To calculate an electrification rate in terms of fleet size for a given scenario, state, year, and veh<em>type, we divide the fleet</em>size for BEV technology by the summation of fleet_size for all technologies.</p> <p><strong>State-wise electric and total energy for LDVs, MDVs, and HDVs:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hdv_total</code> - energy in ExaJoules consumed by all HDVs irrespective of fuel type</li> <li><code>ldv_total</code> - energy in ExaJoules consumed by all LDVs irrespective of fuel type</li> <li><code>mdv_total</code> - energy in ExaJoules consumed by all MDVs irrespective of fuel type</li> <li><code>hdv_electric</code> - electric energy in ExaJoules consumed by HDVs</li> <li><code>ldv_electric</code> - electric energy in ExaJoules consumed by LDVs</li> <li><code>mdv_electric</code> - electric energy in ExaJoules consumed by MDVs</li> </ul> <p>To calculate the electrification rate in terms of EV energy for a given scenario, state, year, and veh_type, we divide electric energy by the total energy.</p> <p><strong>State-wise transportation fuel mix:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hydrogen</code> - hydrogen energy in ExaJoules consumed by the transportation sector</li> <li><code>electricity</code> - electric energy in ExaJoules consumed by the transportation sector</li> <li><code>refined liquids</code> - refined liquid energy in ExaJoules consumed by the transportation sector</li> </ul> <p><br><br></p> <p><strong>Changelog:</strong></p> <ul> <li>v2.0.0 - new set of scenarios; fuel mix data added</li> </ul> <p> </p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Truck and grid code from: Impact of truck electrification on air pollution disparities in the U.S
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Developing an efficient dispatching strategy to support commercial fleet electrification
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Satellite observations reveal inequalities in the progress and effectiveness of recent electrification in sub-Saharan Africa
<p>Replication code and data for the paper "Satellite observations reveal inequalities in the progress and effectiveness of recent electrification in sub-Saharan Africa" published in One Earth (DOI: 10.1016/j.oneear.2020.03.007)</p> <p>________________________________</p> <p>This repository hosts:</p> <ul> <li> <p>A JavaScript file to process remotely-sensed data into Google Earth Engine (step 1)</p> </li> <li> <p>A R script, to be run after the successful completion of the Google Earth Engine processing (step 2)</p> </li> <li> <p>Supporting files to run the analysis (e.g. a shapefile of provinces, validation data, etc.)</p> </li> <li> <p>Create a Google account, if you do not have one, and require access to Earth Engine <a href="https://signup.earthengine.google.com/">https://signup.earthengine.google.com</a>.</p> </li> <li> <p>Make sure your Google Drive has enough cloud storage space available.</p> </li> <li> <p>Clone the repository.</p> </li> <li> <p>Run the JavaScript file in Google Earth Engine and wait that the data processing is complete <strong>(can take >24 hours)</strong></p> </li> <li> <p>Run the R script, which will reproduce the analysis and the figures contained in the paper.</p> </li> <li> <p>Open the QGIS project files to replicate maps with the appropriate layout.</p> </li> </ul> <p>Source code-related issues should be opened directly on GitHub. Broader questions of the methods should be addressed to <a href="mailto:giacomo.falchetta@feem.it">giacomo.falchetta@feem.it</a></p>
Robust CO2-abatement from early end-use electrification under uncertain power transition speed in China's netzero transition - Data and Plotting script
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Raw Economic Census data for full replication of Burlig and Preonas, "Out of the Darkness and Into the Light? Development Effects of Rural Electrification"
<p>These raw data are intended to be copy-pasted into the folder "data/Economic Census" in Burlig and Preonas's replication archive</p>
Raw SECC data (part 2) for full replication of Burlig and Preonas, "Out of the Darkness and Into the Light? Development Effects of Rural Electrification"
<p>These raw data are intended to be coyp-pasted into the folder "data/secc data/secc_raw" in Burlig and Preonas's replication archive. This is 2 of 2 Zenodo uploads, each with 3 zipped folders. Together, all six files contain 58.9 GB of zipped raw data.</p>
Raw SECC data (part 1) for full replication of Burlig and Preonas, "Out of the Darkness and Into the Light? Development Effects of Rural Electrification"
<p>These raw data are intended to be copy-pasted into the folder “data/secc data/secc_raw” in Burlig and Preonas's replication archive. This is 1 of 2 Zenodo uploads, each with 3 zipped folders. Together, all six files contain 58.9 GB of zipped raw data.</p>
Raw NSS data for full replication of Burlig and Preonas, "Out of the Darkness and Into the Light? Development Effects of Rural Electrification"
<p>These raw data are intended to be copy-pasted into the folder "data/nss/raw" in Burlig and Preonas's replication archive</p>
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