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130 results for “Power Systems”
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>
Analyzing defense-in-depth properties of nuclear power plant instrumentation and control system architectures using ontologies
<p>A proof-of-concept OWL ontology for representing knowledge over nuclear overall instrumentation & control (I&C) system architectures, and two case studies built around a proposed US variant of the European Pressurized Water Reactor and the NuScale Small Modular Reactor.</p>
Dataset supplementing " X. Zhang, B. Ojha, H. Bichlmaier, I. Hartmann and H. Kohler; Extensive Gaseous Emissions Reduction of Firewood-Fueled Low Power Fireplaces by a Gas Sensor-Based Advanced Combustion Airflow Control System and Catalytic Post-Oxidation, Sensors, 2023"
<p>Dataset supplementing " X. Zhang, B. Ojha, H. Bichlmaier, I. Hartmann and H. Kohler; Extensive Gaseous Emissions Reduction of Firewood-Fueled Low Power Fireplaces by a Gas Sensor-Based Advanced Combustion Airflow Control System and Catalytic Post-Oxidation, Sensors, 2023"</p>
PyPSA-PL: Limited flexibility of the Polish power system
<p>This record contains all the scripts and data from the PyPSA-PL modelling exercise that supported the report:</p> <ul> <li>Kubiczek P. (2023). Praca w podstawie. Modelowanie kosztów niskiej elastyczności polskiego systemu<br> elektroenergetycznego. Instrat Policy Paper 04/2023. <a href="https://www.instrat.pl/praca-w-podstawie">https://www.instrat.pl/praca-w-podstawie</a></li> </ul> <p>The record structure is based on the PyPSA-PL repository <a href="https://github.com/instrat-pl/pypsa-pl">https://github.com/instrat-pl/pypsa-pl</a> (v2.0 with changes).</p>
Power Morcellation Systems for Laparoscopic Hysterectomy and Myomectomy
ClinicalTrials.gov study NCT02777203. IPD Sharing: NO. Countries: 1. Publications: 5.
Behaviour of dissolved inorganic salts in the cooling water of a nuclear power plant open recirculation system and formation of water discharge
Open the record for dataset details and reuse information.
Plasticity of the gastrocnemius elastic system in response to decreased work and power demand during growth
Open the record for dataset details and reuse information.
Data from: Predictive mapping of the global power system using open data
<p>Three primary global data outputs from the research:</p> <ul> <li><strong>grid.gpkg:</strong> Vectorized predicted distribution and transmission line network, with existing OpenStreetMap lines tagged in the 'source' column</li> <li><strong>targets.tif:</strong> Binary raster showing locations predicted to be connected to distribution grid. </li> <li><strong>lv.tif:</strong> Raster of predicted low-voltage infrastructure in kilometres per cell.</li> </ul> <p>This data was created with code in the following three repositories:</p> <ul> <li>https://github.com/carderne/gridfinder</li> <li>https://github.com/carderne/predictive-mapping-global-power</li> <li>https://github.com/carderne/access-estimator</li> </ul> <p>Full steps to reproduce are contained in this file:</p> <ul> <li>https://github.com/carderne/predictive-mapping-global-power/blob/master/README.md</li> </ul> <p>The data can be visualized at the following location:</p> <ul> <li>https://gridfinder.org</li> </ul>
Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling
<p>This supplementary material includes data and code for the research described in the paper "Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling". The code containts an interface between the output files of the agent-based simulation model CURRENT and the energy system optimization model REMix as well as some scripts for analyzing REMix results. The data folder contains input data for REMix, the complete list of all model runs analyzed in the paper in the GAMS format .gdx as well as Excel files containing annual results of the sensitivity runs and respective pivot tables and figures for respective analysis.</p>
Direct Air Capture Integration with Low-Carbon Heat: Process Engineering and Power System Analysis
<p>Supporting data for our manuscript Direct Air Capture Integration with Low-Carbon Heat: Process Engineering and Power System Analysis</p><p> </p>
PM100: A Job Power Consumption Dataset of a Large-Scale HPC System
<p>The dataset is a collection of jobs extracted from the job_table data of M100 (<a href="https://doi.org/10.5281/zenodo.7588815">https://doi.org/10.5281/zenodo.7588815</a>), a collection of data extracted from a Tier-0 supercomputer hosted at CINECA (Marconi100, <a href="https://www.hpc.cineca.it/hardware/marconi100">https://www.hpc.cineca.it/hardware/marconi100</a>). The original job data present in M100 are filtered out by considering only the jobs running exclusively on the resources. Each job entry included in PM100 contains the power consumption of the job recorded at Node level, CPU level and Memory level. The final dataset contains 231116 jobs, executed on Marconi100 between May and October 2020. </p><p>The dataset is stored as a parquet file, where each entry contains the information on a job execution. </p><p>The structure of the data, as well as the code to generate them, is contained in the official GitHub repository of the project: <a href="https://github.com/francescoantici/PM100-data/">https://github.com/francescoantici/PM100-data/</a>.</p>
Data set for A Novel VNS-based Algorithm for SVC Allocation in the Brazilian Interconnected Power System
<p>This release includes the 107-bus version of the Brazilian Interconnected Power System (available <a href="https://www.sistemas-teste.com.br/">here</a>). The system consists of 107 buses, 104 lines, and 67 transformers distributed across three areas: South, Southeast, and Mato Grosso. This test system provides extensive applications for problems related to steady-state analysis.</p>
A Data Set for State and Parameter Estimation in Power Systems
<p>This data set consists of data from three power system models of different scales (IEEE 14, IEEE 118 and <a href="https://doi.org/10.5281/zenodo.2642175">PanTaGruEl</a>). For each of these systems, 5 different cases are provided, they are sorted from the least to the most "advanced" system operations.</p> <p>Data are stored in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5</a> format (as H5T_NATIVE_FLOAT) which can be read by (mostly) any language (e.g. Python, Matlab or Julia).</p> <p><strong>Description of the different cases:</strong></p> <ul> <li><em>Case 1#</em> consists of 2000 samples. Each sample is obtained by: firstly, defining total active and reactive loads in the system which are then distributing to the buses and, secondly, dispatching generation (this is performed by running an OPF (Optimal Power Flow) with <a href="https://matpower.org/">Matpower</a>). The same distribution factors were used for every samples.</li> <li><em>Case 2#</em> is similar to <em>case 1#</em> with the addition of independent white noises to each bus load.</li> <li><em>Case 3#</em> differs from <em>case 1#</em> in that independent active and reactive bus loads are randomly drawn.</li> <li><em>Case 4# </em>is similar to <em>case 3#</em>, but some generators are randomly drawn to be in maintenance. This set of generators is independently generated for each sample.</li> <li><em>Case 5#</em> is similar to <em>case 4#</em>, plus the generation cost of each generator is randomly drawn from a predefined range. Costs are independently generated for each sample.</li> </ul> <p><strong>General Description:</strong></p> <p>Each data set case file contains the following elements:</p> <ul> <li>V (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): Voltage magnitudes,</li> <li>theta (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): Voltage phases,</li> <li>P (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): <a href="https://en.wikipedia.org/wiki/AC_power">Active</a> power injections (i.e. = generation - load),</li> <li>Q (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): <a href="https://en.wikipedia.org/wiki/AC_power">Reactive</a> power injections,</li> <li>idgen (<span class="math-tex">\(N_{\rm gen}\)</span> vector): index of generator buses,</li> <li>id_slack: index of the bus used as <a href="https://en.wikipedia.org/wiki/Slack_bus">slack bus</a>,</li> <li>epsilon (<span class="math-tex">\(N_{\rm line} \times 2\)</span> matrix): list of the lines in the system (Each row corresponds to a line. Entries are buses’ indices.),</li> <li>b (<span class="math-tex">\(N_{\rm line}\)</span> vector): line <a href="https://en.wikipedia.org/wiki/Admittance">susceptances</a>,</li> <li>g (<span class="math-tex">\(N_{\rm line}\)</span> vector): line <a href="https://en.wikipedia.org/wiki/Admittance">conductances</a>,</li> <li>bsh (<span class="math-tex">\(N_{\rm bus}\)</span> vector): shunt susceptances,</li> <li>gsh (<span class="math-tex">\(N_{\rm bus}\)</span> vector): shunt conductances.</li> </ul> <p><strong>Visualization:</strong></p> <p>The data set also includes bus coordinates.</p> <p><strong>Some theory:</strong></p> <p>The <a href="https://en.wikipedia.org/wiki/Incidence_matrix">incidence matrix</a> B is defined as</p> <p><span class="math-tex">\(B_{ij} = \left\{\begin{array}{l}-1,\; \text{if line $j$ starts at bus $i$,}\\1,\; \text{if line $j$ ends at bus $i$,}\\ 0,\; \text{otherwise.} \end{array}\right.\)</span></p> <p>(“Ends” and “starts” are purely conventional, but they have to be assigned to account for the direction power flows in the system. We use the first column of epsilon as "starts" and the second one as "ends".)</p> <p>The <a href="https://en.wikipedia.org/wiki/Nodal_admittance_matrix">admittance matrix</a> Y is obtained by</p> <p><span class="math-tex">\(y = g + ib,\\ y_{\rm sh} = g_{\rm sh} + ib_{\rm sh},\\ Y = B\,{\rm diag}(y)\,B^\top + {\rm diag}(y_{\rm sh}) .\)</span></p> <p>Defining the <a href="https://en.wikipedia.org/wiki/AC_power">complex</a> power injections and voltages, respectively, as</p> <p><span class="math-tex">\(S = P + iQ,\\ \underline{V} = V \cdot e^{i \theta}, \)</span></p> <p>where <span class="math-tex">\(\cdot\)</span> denotes the element-wise product. One has the following relation</p> <p><span class="math-tex">\(S = \underline{V} \cdot {\rm conj}(Y\, \underline{V}).\)</span></p> <p>This relation is equivalent to the <a href="https://en.wikipedia.org/wiki/Power-flow_study">power flow equations</a>.</p> <ul> </ul> <p> </p>
European power system infrastructure in the open energy system model PyPSA-Eur
<p>The image is created using the data and scripts in the European open energy system model <a href="https://github.com/PyPSA/pypsa-eur">PyPSA-Eur.</a></p>
Extreme power shortage events of wind-solar supply systems for individual countries
<p><span>Raw data of extreme power shortage events in wind-solar supply system in the paper entitled “Climate change impacts on the power shortage events of wind-solar supply systems worldwide during 1980–2022” on Nature Communications.</span></p>
Equipment Design for a Small Binary System Power Plant Using Geothermal Heating Fluid A Case Study
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
OpenFoam model output for "A Fuel Cell Power Supply System Equipped with Artificial Gill Membranes for Underwater Applications"
<p>Dataset of numerical experiments carried out with OpenFOAM v 10 as used in the manuscript "A Fuel Cell Power Supply System Equipped with Artificial Gill Membranes for Underwater Applications" by Lucas Merckelbach and Prokopios Georgopanos.</p> <p> </p>
SimBench - Electrical Power System Benchmark Models
<p>SimBench (<a href="https://www.simbench.net">www.simbench.net</a>) is a research project to create a "simulation database for uniform comparison of innovative solutions in the field of network analysis, network planning and operation", which was conducted for three and a half years from 1.11.2015 to 30.04.2019. It was part of the German Federal Government's 6th Energy Research Program "Research for an Environmentally Friendly, Reliable and Affordable Energy Supply". The project was carried out by the University of Kassel, the Fraunhofer IEE, the RWTH Aachen University and the Technical University of Dortmund in accordance with the authors mentioned above. The project, coordinated by the University of Kassel, was supported by the professional advisory from six German distribution network operators: DREWAG NETZ GmbH, Energie Netz Mitte GmbH, ENSO NETZ GmbH, Netze BW GmbH, Syna GmbH and Westnetz GmbH.</p> <p>The objective of the research project SimBench is the development of a benchmark data set to support research in grid planning and operation. SimBench Grid differs from other benchmark grids under the following key points:</p> <ul> <li>Consideration of a wide range of use cases during the development of data sets</li> <li>Provision of grid data for low voltage (LV), medium voltage (MV), high voltage (HV), extra high voltage (EHV) as well as design of data sets for a suitable interconnection of a grid among different voltage levels for cross-level simulations</li> <li>Ensuring highreproducibility and comparability by providing clearly assigned load and generation time series</li> <li>Validation of the suitability of the data sets with simulation, deliberately determined grid states including suitable dimensioning of grid assets</li> </ul> <p>In total SimBench provides 13 unique electrical power system grids (EHV: 1, HV: 2, MV: 4, LV: 6). Since SimBench is enables multi-voltage simulations, this dataset includes not only 13 folder but many more. Each excerpt of the complete dataset, composed to a folder, is distinctively named by the SimBench code.</p> <p>For further information, please visit <a href="https://www.simbench.net">www.simbench.net</a> and the documentation published there.</p>
Data for "Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: A case study on the Italian grid"
<p>Data employed for the paper "Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: A case study on the Italian grid"<br><br>Abstract<br>This study explores compounding impacts of climate change on power system's load and generation, emphasising the need to integrate adaptation and mitigation strategies into investment planning. We combine existing and novel empirical evidence to model impacts on: i) air-conditioning demand; ii) thermal power outages; iii) hydro-power generation shortages. Using a power dispatch and capacity expansion model, we analyse the Italian power system's response to these climate impacts in 2030, integrating mitigation targets and optimising for cost-efficiency at an hourly resolution. We outline different meteorological scenarios to explore the impacts of both average climatic changes and the intensification of extreme weather events. We find that addressing extreme weather in power system planning will require an extra 5-8 GW of photovoltaic (PV) capacity, on top of the 50 GW of the additional solar PV capacity required by the mitigation target alone. Despite the higher initial investments, we find that the adoption of renewable technologies, especially PV, alleviates the power system's vulnerability to climate change and extreme weather events. In fact, renewable energy sources are generally less vulnerable to the impacts of climate change, such as rising temperatures and shifting precipitation patterns, compared to thermal power and hydropower generation. Furthermore, enhancing short-term storage with lithium-ion batteries is crucial to counterbalance the reduced availability of dispatchable hydro generation.</p>
Viet Nam Technology catalogue for power generation and storage. Input for power system modelling
<p>The first Viet Nam Technology Catalogue was published in 2019. This new version includes all the technologies from the 2019 version that have been reviewed and updated where necessary. A main focus of the update has been to add new subcategories of technologies (roof-top solar PV, floating offshore wind, low wind speed turbines, improved flexibility of coal fired plants and pollution prevention technologies for coal power) as well as completely new technology descriptions and data sheets (tidal power, wave power, carbon capture and storage, coal CFB boilers and industrial cogeneration).</p> <p>This publication is developed under the Danish-Vietnamese Energy Partnership.</p> <p>The technologies described in this catalogue cover both very mature technologies and emerging technologies, which<br> are expected to improve significantly over the coming decades, both with respect to performance and cost. This<br> implies that the cost and performance of some technologies may be estimated with a rather high level of certainty<br> whereas, in the case of other technologies, both cost and performance today and in the future is associated with a<br> high level of uncertainty. All technologies have been grouped within one of four categories of technological<br> development described in the section on research and development indicating their technological progress, their<br> future development perspectives and the uncertainty related to the projection of cost and performance data.</p> <p>The technologies in the catalogue include the power production unit and the connection to the grid. This means<br> that the boundary for both cost and performance data are the generation assets plus the infrastructure required to<br> deliver the energy to the main grid. For electricity, this is the nearest substation of the transmission grid. This<br> implies that a MW of electricity represents the net electricity delivered, i.e. the gross generation minus the auxiliary<br> electricity consumed at the plant. Hence, efficiencies are also net efficiencies.</p> <p>The text and data have been edited based on Vietnamese cases to represent local conditions. For the mid- and long-<br> term future (2030 and 2050) international references have been relied upon for most technologies since Vietnamese<br> data is expected to converge to these international values. In the short run differences may exist, especially for the<br> emerging technologies. Differences in the short run can be caused by e.g. current rules and regulations and level of<br> market maturity of the technology. Differences in both the short and long run can be caused by local physical<br> conditions, e.g. seabed material and offshore conditions can affect costs of offshore wind farms and wind speed can<br> affect the dimensioning of rotor vs. generator which can influence the cost, or domestic coal quality can affect<br> efficiency and variable cost of coal-fired plants as well.</p> <p>Land use is assessed but the cost of land is not included in the total cost assessment since this depends on local<br> conditions.</p>
ScienceDex guides
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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.