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42 results for “power system data”
Viet Nam Technology Catalogue - Technology data input for power system modelling in Viet Nam
<p>Today, innovations and technology improvements within renewable energy are taking place at a very rapid pace. Long-term energy planning is very dependent on cost and performance of future energy producing technologies.<br> This technology catalogue provides estimates of costs and performance for a wide range of power producing technologies, thereby building one of the key inputs to good energy planning in Vietnam.<br> Due to the multi-stakeholder involvement in the data collection process, the technology catalogue contains data that have been scrutinised and discussed by a broad range of relevant stakeholders including the Ministry of Industry and Trade – MOIT, Vietnam Electricity – EVN, independent power producers, local and international consultants, organizations, associations and universities. This is essential because a main objective is to produce a technology catalogue which is well anchored amongst all stakeholders.<br> The technology catalogue will assist the long-term energy modelling in Vietnam and support government institutions, private energy companies, think tanks and others with a common and broadly recognized set of data for electricity producing technologies in Vietnam in the future.</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>
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>
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>
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>
Output data for the article "Climate variability in 2030 European power systems"
<p>Model outputs for the paper "Climate variability on Fit for 55 European power systems"</p>
Raw data for the article 'Revisiting power-law distributions in empirical outage data of power systems'
<p>Raw data for the article 'Revisiting power-law distributions in empirical outage data of power systems' (see the manuscript preprint https://arxiv.org/abs/2303.12714 for more details).</p>
The Role of Flexible Geothermal Power in Decarbonized Electricity Systems: Supplementary Data
<p>This repository contains raw data related to the paper "The Role of Flexible Geothermal Power in Decarbonized Electricity Systems." 'EGS_Costing.xlsx' is an excel model used to determine the cost and performance of EGS resources based on input characteristics such as resource depth, temperature, and location. 'Reservoir_Simulations.zip' contains outputs from the ResFrac reservoir simulations used to calibrate the EGS optimization model. The 'PowerGenome.zip' contains the PowerGenome input and output files used to create non-EGS inputs for the GenX power systems model. 'GenX_Source_Code.zip' contains the source code for the modified version of GenX used in this study. Other compressed folders contain the input and output files for all GenX runs referenced in this study. </p>
Data from: Solar-powered flow-through system for aquatic field studies
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Data from: Modularity and rates of evolutionary change in a power-amplified prey capture system
The dynamic interplay among structure, function and phylogeny form a classic triad of influences on the patterns and processes of biological diversification. While these dynamics are widely recognized as important, quantitative analyses of their interactions have infrequently been applied to biomechanical systems. Here we analyze these factors using a fundamental biomechanical mechanism: power amplification. Power-amplified systems use springs and latches to generate extremely fast and powerful movements. This study focuses specifically on the power amplification mechanism in the fast raptorial appendages of mantis shrimp (Crustacea: Stomatopoda). Using geometric morphometric and phylogenetic comparative analyses, we measured evolutionary modularity and rates of morphological evolution of the raptorial appendage's biomechanical components. We found that "smashers" (hammer-shaped raptorial appendages) exhibit lower modularity and 10-fold slower rates of morphological change when compared to non-smashers (spear-shaped or undifferentiated appendages). The morphological and biomechanical integration of this system at a macro-evolutionary scale and the presence of variable rates of evolution reveal a balance between structural constraints, functional variation, and the developmental and genetic roles in evolutionary diversification.
Data from: Muscle tradeoffs in a power-amplified prey capture system
Should animals operating at great speeds and accelerations use fast or slow muscles? The answer hinges on a fundamental tradeoff: muscles can be maximally fast or forceful, but not both. Direct lever systems offer a straightforward manifestation of this tradeoff, yet the fastest organisms use power amplification, not direct lever action. Power-amplified systems typically use slow, forceful muscles to pre-load springs which then rapidly release elastic potential energy to generate high speeds and accelerations. However, a fast response to a stimulus may necessitate fast spring-loading. Across 22 mantis shrimp species (Stomatopoda), this study examined how muscle anatomy correlates with spring mechanics and appendage type. We found that muscle force is maximized through physiological cross-sectional area, but not through sarcomere length. Sit-and-wait predators had the shortest sarcomere lengths (fastest contractions) and the slowest strike speeds. The species that crush shells ("smashers") had the fastest speeds, most forceful springs and longest sarcomeres. The origin of the smasher clade yielded dazzlingly high accelerations, perhaps due to the release from fast spring-loading for evasive prey capture. This study offers a new window into the dynamics of force-speed tradeoffs in muscles in the biomechanical, comparative evolutionary framework of power-amplified systems.
Data from: Levers and linkages: Mechanical trade-offs in a power-amplified system
Mechanical redundancy within a biomechanical system (e.g., many-to-one mapping) allows morphologically divergent organisms to maintain equivalent mechanical outputs. However, most organisms depend on the integration of more than one biomechanical system. Here we test whether coupled mechanical systems follow a pattern of amplification (mechanical changes are congruent and evolve towards the same functional extreme) or independence (mechanisms evolve independently). We examined the correlated evolution and evolutionary pathways of the coupled 4-bar linkage and lever systems in mantis shrimp (Stomatopoda) ultrafast raptorial appendages. We examined models of character evolution in the framework of two divergent groups of stomatopods – "smashers" (hammer-shaped appendages) and "spearers" (bladed appendages). Smashers tended to evolve towards force amplification, while spearers evolved towards displacement amplification. These findings show that coupled biomechanical systems can evolve synergistically, thereby resulting in functional amplification rather than mechanical redundancy.
Power System Simulation Data Output from January 2024
<p>Data for the paper "Electricity Island Decarbonization Challenges: Evaluating the Environmental and Energy Security Tradeoffs Across Solar PV, Natural Gas, and Nuclear-Reliant Generation Portfolios" by Michael Buchdahl Roth and Yael Parag</p>
Data from: Modularity and rates of evolutionary change in a power-amplified prey capture system
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Data from: Levers and linkages: Mechanical trade-offs in a power-amplified system
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Data from: Muscle tradeoffs in a power-amplified prey capture system
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Supporting data for Loik et al. 2017 Wavelength-Selective Solar Photovoltaic Systems: Powering greenhouses for plant growth at the food-energy-water nexus. Earth's Future
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Data on IEEE and Synthetic Test Power Systems for Oriol Cartiel's PhD
<p>The data used in my PhD thesis comes from internet repositories (see within files). It is based on detailed information from both IEEE n-bus test power systems and synthetic power grid test cases developed by the scientific community. The dataset, stored in spreadsheet format (.xlsx), includes comprehensive technical details such as line impedances, equivalent internal impedances, locations of generators, load demands and their locations, and shunt element specifications. All values are normalized to per-unit (pu), assuming a base power of 100 MVA. Additionally, the reference to the internet repository is available in the same file for potential further consultation.</p>
Data for "Resolving Structures of Paramagnetic Systems in Chemistry and Materials Science by Solid-State NMR: the Revolving Power of Ultra-Fast MAS"
<p>Raw NMR data</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.