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239 results for “Energy system”
Dataset related to "Material recycling in energy system modeling: a review and showcase"
<p>This dataset has been generated and used for the publication:</p> <blockquote> <p>Zwickl-Bernhard, S., 2024. Material recycling in energy system modeling: a review and showcase. ... DOI: ...</p> </blockquote> <p>The corresponding code is published on *Github. #add link after publication</p> <p>The dataset includes:</p> <ol> <li>Compiled data for manufacturing costs of Solar Modules and Wind Turbines in the EU (manufacturing costs in the EU.xlsx)</li> <li>Compiled data for modified prices (modified prices.xlsx)</li> <li>Additional Data (scalars.xlsx)</li> <li>Sources (sources.txt)</li> <li>Compiled data for yearly capacity of Solar Modules and Wind Turbines (vectors_capacity.xlsx)</li> <li>Compiled data for yearly costs of Solar Modules and Wind Turbines (vectors_costs.xlsx)</li> <li>Compiled data for yearly manufacturing costs of Solar Modules and Wind Turbines (vectors_manufacturing.xlsx)</li> </ol>
Data for Paper: A rigorous optimization method for long-term multi-stage investment planning: Integration of hydrogen into a decentralized multi-energy system
<p>Data containing the results and figures presented in the paper "A rigorous optimization method for long-term multi-stage investment planning: Integration of hydrogen into a decentralized multi-energy system" by Luka Bornemann and Jelto Lange and Martin Kaltschmitt, submitted to the Journal Energy Reports.</p>
Code and data for publication "pyGRETA, pyCLARA, pyPRIMA: A pre-processing suite to generate flexible model regions for energy system models"
<p>This dataset contains the code of the three pre-processing tools <a href="https://github.com/tum-ens/pyGRETA">pyGRETA</a>, <a href="https://github.com/tum-ens/pyPRIMA">pyPRIMA</a> and <a href="https://github.com/tum-ens/pyCLARA">pyCLARA</a> and an examplary database for the scope of Austria.</p> <p>To run the code with full functionality additional data is needed. Check the documentation of the tools for further information.</p> <p> </p> <p>Sources for data can be found here: </p> <p>pyGRETA: https://pygreta.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyPRIMA: https://pyprima.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyCLARA: https://pyclara.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p>
An Assessment of Nonhydrostatic and Hydrostatic Dynamical Cores at Seasonal Time Scales in the Energy Exascale Earth System Model (E3SMv1)
<p>This is the companion data for the manuscript of the same title, submitted to Journal of Advances in Modeling Earth Systems on 09/03/2021. </p> <p><strong>summer: </strong>this folder contains part of the model outputs I ran on NERSC Cori in 2020-2021 corresponding to the summer simulations in the manuscript.</p> <p><strong>winter: </strong>this folder contains part of the outputs I ran on NERSC Cori in 2020-2021 corresponding to the winter simulations in the manuscript.</p> <p><strong>ne256: </strong>this folder contains part of the outputs I ran on NERSC Cori in 2021 corresponding to the ne256 simulations in the manuscript.</p> <p><strong>bubble</strong>: this folder includes the namelists used in the rising bubble experiments.</p> <p><strong>script: </strong>this folder includes an example of a script to generate the realistic SCREAM simulation </p> <p> </p> <p>Unfortunately, model outputs are too large (~ 30 TB). Therefore, I only provide mean data, used directly to generate figures, in this repository. All model output are archived on tape at NERSC. </p> <p>For more details, refer to the manuscript, or contact me (wrliu@ucdavis.edu). </p>
Tracking wildlife energy dynamics with unoccupied aircraft systems and 3-dimensional photogrammetry
<p>We present a novel application using unoccupied aircraft systems (UAS; drones) for structure-from-motion three-dimensional (3-D) photogrammetry of multiple, free-ranging animals simultaneously. Pinnipeds reliably haul-out on shore for pupping and breeding each year, accompanied by dramatic female-to-pup mass transfer over a short lactation period and males lose mass while defending mating territories. This provides a tractable study system for validating the use of UAS as a non-invasive tool for tracking energy dynamics in wild populations.</p> <p>UAS imagery of grey seals (<i>Halichoerus grypus</i>) was collected at Saddle Island, Nova Scotia. A multirotor UAS was piloted in 360-degree orbits around relatively dense animal aggregations and georeferenced images were used for construction of a 3-D point cloud, orthomosaic, and Digital Surface Model for animal volumetric measurements. Directly following UAS survey, a subset of adult females were hand-measured (morphometrics, blubber depth, n=21 handlings [15 were unique animals]) and female-pup pairs were weighed (adult females: n=32 [24]; pups: n=33 [23]) to validate that UAS 3-D photogrammetric models provided accurate animal volume and mass estimates.</p> <p>UAS two-dimensional body length measurements were sensitive to animal recumbency and posture. The new UAS 3-D photogrammetric method overcame these constraints, and aerial-derived body volume measurements were equivalent to those collected from the ground. UAS body volume measurements precisely predicted 'true' body mass (mean-absolute-error, adult female: 8 kg, 2.1% body mass; pup: 4.1 kg, 9.8%), and exhibited a stronger relationship with total body mass than with blubber volume.</p> <p>The method was applied to 673 free-ranging animals to characterize volume and mass dynamics across lactation and breeding for a much larger sample size than would be possible using traditional ground methods. Indeed, 1-46 animals (mean±SE: 9.2±1.2) were modeled concurrently within the focal area of a UAS flight. Application of the method also captured significant inter-annual variation in body volume/mass dynamics, and female-to-pup energy transfer efficiencies were lower when there was low sea-ice extent. The UAS 3-D photogrammetric method presented in this study is likely to be broadly applicable to other species, and the ability to measure whole groups of free-ranging animals at once makes strides towards 'weighing populations'.</p>
Data for the Eastern African power pool's energy systems model, developed in OSeMOSYS
<p>This repository consists of the following datasets</p> <p>1. EAPP_reference scenario_datafile.DD- This dataset is a model file that needs to be used with the code available in this <a href="https://github.com/KTH-dESA/OSeMOSYS/blob/master/OSeMOSYS_GNU_MathProg/osemosys_short.txt">GitHub</a> link. This data file (in concurrence with the OSeMOSYS code) can be used to create a linear programming file (LP file) to be solved using any mathematical optimisation solver like GLPSOL/C-PLEX/GUROBI/CBC.</p> <p>2. Main article_EAPP_data for figures.xlsx- This excel file contains the base data used to illustrate the figures in the main article.</p> <p>3. Supplementary article_EAPP_data for figures.xlsx- This excel file contains the base data used to illustrate the figures in the supplementary article.</p>
Dataset supporting publication: 'GEOFIT: Ground source heat pump systems for energy efficient building retrofitting'
<p>Dataset supporting publication: Presentation of GEOFIT at the <a href="https://www.hp-summit.de/en">European Heat Pump Summit</a> (2019) </p> <p>Available for download: <a href="https://zenodo.org/record/3901527">GEOFIT Zenodo</a></p> <p>The integration of geothermal systems for heating / cooling solutions in conjunction with heat pump technology is a major challenge, particularly in the case of renovation. In order to find the best solution for the increased flow temperatures compared to a new building for the renovation, various heat pump configurations are evaluated according to energy and economic criteria. A refrigerant with low greenhouse gas potential (GWP) is used as the working medium, e.g. R1234ze (E)) less than 10 used. A favorable refrigeration circuit configuration for high flow temperatures is a twin-circuit system, which essentially consists of two heat pumps with different condensing temperatures and the same evaporation temperatures. An alternative to this is a single-stage configuration with a significantly larger condenser for improved supercooling. Both systems should enable efficient operation when renovating buildings.</p>
Influence of Adaptive Coupling Points on Coalition Formation in Multi-Energy Systems: Simulation Result Tables
<p>The dataset contains the result tables used for the paper "Influence of Adaptive Coupling Points on Coalition Formation in Multi-Energy Systems". </p> <p>Short description of the tables:</p> <ul> <li>all_in_one_tab.csv: contains all calculated metrics and attributes of the graph over time and adaptation rate</li> <li>event_tab.csv: contains all toggle events of the coupling points</li> <li>impact_dict.csv: contains the calculated impact values for every coupling point</li> <li>node_region.csv: contains region attributes for every node over adaptation rate</li> <li>static_component_properties.csv: contains static attributes </li> <li>static_node_component_properties.csv: contains static node attributes</li> </ul>
Output data: China's energy-water-land system co-evolution under carbon neutrality goal and climate impacts
<p>Outputs for Wang, J., Duan, Y., Wang, C., 2023. China’s energy-water-land system co-evolution under carbon neutrality goal and climate impacts. (In progress)</p> <p>Folder demeter contains the spatially downscaled land use/land cover datasets.<br> Folder tethys contains the spatially downscaled water withdrawal datasets.</p> <p>The sub-folder names correspond to the scenarios described in the paper.</p> <p>For landcover datasets, land type ratios in each grid are presented. Land types include water, forest, shrub, grass, urban, snow, sparse and crops.</p> <p>For water withdrawal datasets, “wd” = “water withdrawal spatially downscaled”, “twd” = “water withdrawal spatially and temporally downscaled”, “dom”=”domestic/municipal sector”, “elec”=”electricity sector”, ”irr”=”irrigation sector”, “liv”=”livestock sector”, “mfg”=”manufacturing/industry sector”, “min”=”mining/primary energy sector”, “nonag”=”non-agricultural sector”, “total”=”all sectors”.<br> </p>
Case study result data set for Energy Systems (submitted) article "Influence of hydrogen import prices on hydropower systems in climate-neutral Europe"
<p>The data set contains result data for the European system in a long term climate-neutral European energy system (scenario year 2050) as described in the publication "Influence of hydrogen import prices on hydropower systems in climate-neutral Europe". The results have been generated with the model SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. </p> <p><strong>Abbreviations:</strong></p> <ul> <li>BEV - Battery Electric Vehicles</li> <li>CCGT - Combined Cycle Gas Turbine</li> <li>CHP - Combined heat and power</li> <li>con - consumption</li> <li>gen - generation</li> <li>HighCLEQ - High import prices / clustered-equivalent hydropower units</li> <li>HighEQ - High import prices / equivalent hydropower units</li> <li>LowCLEQ - Low import prices / clustered-equivalent hydropower units</li> <li>LowEQ - Low import prices / equivalent hydropower units</li> <li>MedCLEQ - Medium import prices / clustered-equivalent hydropower units</li> <li>MedEQ - Medium import prices / equivalent hydropower units</li> <li>OCGT - Open Cycle Gas Turbine</li> <li>PHEV - Plug-In Hybrid Vehicles</li> <li>PS - Pumped Storage</li> <li>w/ - with</li> <li>w/o - without</li> <li>yr - year</li> </ul>
Data for Process Design and Energy Assessment of an Onboard Carbon Capture System with Boilers or Heat Pumps for Additional Steam Generation
<p>1. Supporting file includes main stream information used in Aspen HYSYS model, process simulations of boiler and heat pump for model construction.<br> 2. Supporting file also includes main information used in ProMAX model, process simulations of carbon capture process for model construction.</p>
CCG: Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System)
<p>Seven clicSAND scenario files for <strong>Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System).</strong> </p> <p><strong>How to Visualise Results Online and Offline</strong> outline the steps required to re-run the scenarios on OSeMOSYS Cloud</p> <p><strong>Scenario Short Note</strong> outlines the steps to replicate the analysis and rebuild the scenarios</p> <p><strong>Annex - Input Data and Assumptions</strong> listing the data sources and assumptions in the scenarios</p>
An approach using performance models for supporting energy analysis of software systems
<p>Replication package of the paper titled "An approach using performance models for supporting energy analysis of software systems". Usage instructions are contained in the README.md file.</p>
Deep decarbonisation pathways of the energy system in times of unprecedented uncertainty in the energy sector. Energy Policy (2023). Supplementary Information on Assumptions and Results
<p>This dataset supplements the article with the title "Deep decarbonisation pathways of the energy system in times of unprecedented uncertainty in the energy sector", published in Energy Policy. </p> <p>The dataset contains the following:</p> <ul> <li>The Latin Hypercube Sample of the multipliers that are applied to the key input parameters of ETSAP-TIAM in order to generate 1000 different states of the world regarding economic and demographic growth, energy resources potentials, energy technology costs, climate sensitivity and radiative forcing, LULUCF CO<sub>2</sub> sink potential, CO<sub>2</sub> sequestration potential, and decoupling between energy consumption and economic development. The multipliers are sampled from the underlying probability distributions described in the article. </li> <li>The results (at the global scale) from four scenario families for each one of the 1000 wofld states. These scenario families are: <ul> <li>BASE_SSP2: Describes the development of the global energy system consistent with recent trends and policies.</li> <li>2C_SSP2: Introduces to the BASE_SSP2 scenario a global constraint of 2 °C as the maximum post-industrial temperature change from 2020 to 2100.</li> <li>2C_SSP2_DA30: Delayed climate action. The climate change mitigation policies of 2C_SSP2 start in 2030. </li> <li>1p5c_OS_SSP2: Introduces to the BASE_SSP2 scenario a global constraint of 1.5 °C as the maximum post-industrial temperature change from 2020 onwards to 2100</li> </ul> </li> </ul> <p>Key results included in the dataset are: Temperature change, Radiative Forcing, GHG concentrations, CO2 emissions, Marginal abatement cost, Electricity Supply Primary Energy Consumption, and Annual Total Global Energy System Cost.</p> <p>The dataset also includes sectoral results regarding energy consumption and use, such as shares of different electric uses, shares of hydrogen consumption in end-use sectors, hydrogen supply, Demand electrification by sector, Renewable energy consumption by sector, Alternative fuels consumption in transport, and Total final energy consumption by sector. </p>
Data set for Dynamic Service Restoration of Distribution Networks with Volt-Var Devices, Distributed Energy Resources, and Energy Storage Systems
<p>Two power distribution systems are presented. The first system consists of 53 nodes and 61 branches, while the second system consists of 404 buses and 430 branches. Both distribution systems offer extensive applications in problems related to multi-time service restoration, Volt/Var devices, and distributed energy resource operation.</p>
Photometry of outer Solar System objects from the Dark Energy Survey I: photometric methods, light curve distributions and trans-Neptunian binaries - data release
<p>This repository contains the full data release for the 814 outer Solar System objects found in the Dark Energy Survey.</p> <p>A full description of the object search is described in <a href="http://(https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022)</a>, and a full description of the photometric processing is described in<a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230403017B/abstract"> Bernardinelli et al (2023)</a>. If you use these files, we ask you to cite the corresponding papers.</p> <p>The FITS table `y6_des_tnos_color.fits` contains both the orbital elements and the colors for each object. The full description of the orbital element information is given in Table 3 of <a href="https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022</a>). In addition to these, the table also includes the mean absolute magnitudes in each band, as well as mean <span class="math-tex">\((g-r, r-i, r-z)\)</span> colors and their corresponding covariance matrix, and the 68% confidence interval for the lightcurve amplitude.</p> <p>Inside the `fluxes` directory, the complete photometric record for each object is included in a `hdf5` file (for each object), and the MCMC chains for their fluxes and LCAs. The three Jupyter Notebooks included in the `notebooks` directory explains the columns and how to use these files to reproduce the results of the paper. Inside this directory there are also additional files needed to reproduce the code.</p> <p>The `binary` directory has the MCMC chains for their mutual orbits (in `.npy` files), as well as the astrometric and photometric record for the binary measurements. Another `README` file is included in that directory with a detailed explanation.</p>
Data Set Accompanying "Free Energy Decompositions Illuminate Synergistic Effects in Interfacial Binding Thermodynamics of Mixed Surfactant Systems"
<p>This data set accompanies "Free Energy Decompositions Illuminate Synergistic Effects in Interfacial Binding<br> Thermodynamics of Mixed Surfactant Systems" by Colin K. Egan and Ali Hassanali. It includes example GROMACS<br> input files for all simulations analyzed in the paper, as well as example data sets and analysis scripts. See<br> https://doi.org/10.26434/chemrxiv-2023-h11k5 for the preprint manuscript.</p>
Code: Decarbonization Employment and Energy Systems (DEERS) Model
<p>The Decarbonization Employment and Energy Systems (DEERS) model is a data-driven framework for estimating labor market pathways of large-scale, low-carbon energy-supply infrastructure development. The DEERS model is designed as a tool to inform regional and national workforce and infrastructure planning and policy-making in the U.S. The model simulates the distribution of labor effects over time and across economic sectors, resource sectors, occupations, and geography for multi-decadal energy-supply system transition scenarios. The model is used to estimate employment demand and wages, as well as experience, education, and training requirements, across domestic energy supply chains. We also incorporate time-variant factors, such as labor productivity and wage inflation, which are especially important in the context of emerging labor markets and long-term transitions. The DEERS model is adaptable to different energy system contexts and readily coupled with regional and downscaled macro-energy system modeling outputs. It can also be used to explore modifiable workforce and infrastructure planning and policy decisions, such as high road labor policies, siting domestic manufacturing facilities, creating just transition funds, and changing fossil fuel exports over time.</p>
Dataset and description of an EnergyPLAN model of the Italian energy system in 2021
<p>This document describes an EnergyPLAN model of the Italian energy systems for the year 2021.</p> <p>The dataset, consisting of the EnergyPLAN input files necessary to run the simulation, is also provided.</p> <p>See the EnergyPlan website (<a href="https://www.energyplan.eu/">https://www.energyplan.eu/</a>) for instructions.</p>
Systems-level analyses dissociate genetic regulators of reactive oxygen species and energy production
<p>Dataset associated with publication "Systems-level analyses dissociate genetic regulators of reactive oxygen species and energy production"</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.