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130 results for “Power Systems”
GODEEEP-hydro - Historical and projected power system ready hydropower data for the United States
<p>This dataset contains monthly and weekly hydropower generation and generation constraints (min, max, daily range) for over 1,400 hydropower plants in the conterminous United States. The dataset includes a historical period (1982-2019) and a future period (2020-2099) with 4 future warming scenarios.</p> <p>For more information please refer to Bracken et al. 2024, godeeep_hydro: Historical and projected power system ready hydropower data for the United States, in prep, or refer to the Github repository https://github.com/GODEEEP/tgw-hydro</p> <h3>Data description</h3> <p>The dataset contains 10 data files with the naming convention <code><scenario>_<monthly/weekly>.csv</code> where scenario can be either "historical", "rcp45cooler", "rcp45hotter", "rcp85cooler", or "rcp85hotter". "monthly" or "weekly" refers to the timestep of the data.</p> <ul> <li>datetime - The datetime stamp of the current timestep</li> <li>eia_id - An integer value with the EIA plant code that represents the facility</li> <li>plant - The name of the facility according to the EIA</li> <li>power_predicted_mwh - The total energy gnerated over the period in MWh, aka the energy target</li> <li>n_hours - The number of hours in the period, useful for converting between power and energy</li> <li>p_avg - Average power generation for the period</li> <li>p_max - Maximum allowable power generation for the period</li> <li>p_min - Minimum allowable power generation for the period</li> <li>ador - Average daily operational range for any given day in the period</li> <li>scenario - The name of the scenario, either "historical", "rcp45cooler", "rcp45hotter", "rcp85cooler", or "rcp85hotter"</li> </ul> <p>Also included is the metadata file <code>godeeep_hydro_plants.csv</code> which contains metadata for each hydropower plant that is included in the dataset. Each row in this file refers to one hydropower facility. This file has the following columns:</p> <ul> <li>eia_id - An integer value with the EIA plant code that represents the facility</li> <li>plant - The name of the facility according to the EIA</li> <li>mode - Either "Storage" or "RoR" indicating if the plant is primarily operated as a storage or ron-of-river facility</li> <li>state - Two letter U.S. state name</li> <li>lat - Latitude of the facility</li> <li>lon - Longitude of the facility</li> <li>nameplate_capacity - The total nameplate capacity of the facility according to the EIA</li> <li>nerc_region - Four letter code for the NERC region of the facility</li> <li>ba - Balacing authority of the facility</li> <li>max_param - Value of the a_{max} parameter used to derive p_max</li> <li>min_param - Value of the a_{min} parameter used to derive p_min</li> <li>ador_param - Value of the a_{ador} parameter used to derive ador</li> <li>huc2 - Two digit hydrologic unit code (HUC) which contains the facility</li> </ul> <h3>Funding</h3> <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> <p>Corresponding author:</p> <p>Cameron Bracken, cameron.bracken@pnnl.gov</p> <p>v1.1.0 - Update file naming convention</p>
Power, performance and system measures of HPC benchmarks on multiple hardware
Open the record for dataset details and reuse information.
Co-Optimization of Reservoir and Power Systems (COREGS) for Seasonal Planning and Operation
<p>Input and output data associated with the paper Co-Optimization of Reservoir and Power Systems (COREGS) for Seasonal Planning and Operation. Can be used with the COREGS model at https://github.com/lcford2/coregs.</p>
Hourly average and marginal electricity mixes for Spain for assessing systems with variable load or power
<p>This upload contains the Supplementary Information file and the underlying data as Excel-file for the referenced Journal article ("Hourly marginal electricity mixes and their relevance for assessing the environmental performance of installations with variable load or power")</p> <p>More specifically, it provides</p> <ul> <li>time series of the Spanish electricity generation mix for the year 2021 for energy system analysis (as used as basis for the underlying publication), including imports and environmental impacts of the hourly generation mix for all EF3.0 impact categories (<em>Marginal_Electricity_Results_Analysis_2021_V17.xls)</em></li> <li>a spreadshhet calculator for determining the average and marginal hourly benefits of a generator with variable load (a PV installation, but the generation profile can be substituted by any other) <em>Load-Gen_Balancing__V15.xlsm</em></li> <li>life cycle inventory data for import into openLCA and re-use in combination with the ecoinvent databse (Version 3.7.1).</li> </ul> <p>Further details are available on request to the main author.</p>
Pre-generated network files for "Intersecting near-optimal spaces: European power systems with more resilience to weather variability"
<p>These are network files that can be used to investigate the impacts of weather variability on the European power system using PyPSA-Eur as in <a href="https://github.com/aleks-g/intersecting-near-opt-spaces/tree/v1.0">https://github.com/aleks-g/intersecting-near-opt-spaces/tree/v1.0</a>. Find more information about the approach in the README of that repository.</p> <p>These network files are a shortcut to reproduce the results and use a fixed configuration ("v1.0"). For other configurations, it may be necessary to download ERA5 reanalysis cutouts (more on this in the git repository).</p> <p>Instructions can be found in the git repository.</p>
Data set from 'Sequential Feature Selection for Power System Event Classification Utilizing Wide-Area PMU Data'
<p>The increasing penetration of intermittent, nonsynchronous<br> generation has led to a reduction in total power<br> system inertia. Low inertia systems are more sensitive to sudden<br> changes, and more susceptible to secondary issues that can result<br> in large scale events. Due to the short time frames involved,<br> automatic methods for power system event detection and diagnosis<br> are required. Wide-area monitoring systems can provide<br> the data required to detect and diagnose events; however due to<br> the increasing quantity of data it is next to impossible for power<br> system operators to manually process raw data. The important<br> information is required to be extracted and presented to system<br> operators for real/near-time decision making and control. This<br> paper demonstrates an approach for the wide-area classification<br> of a number of power system events. A mixture of sequential<br> feature selection and linear discriminant analysis is adopted<br> to reduce the dimensionality of PMU data. Successful event<br> classification is obtained by employing quadratic discriminant<br> analysis on wide-area synchronized frequency, phase angle and<br> voltage measurements. The reliability of the proposed method is<br> evaluated using simulated case studies and benchmarked against<br> other classification methods.</p>
Impact of weather and hydro variability on the operation of the Bolivian power system.
<p>This DataSet is the support for the study of the Impact of climate variability on the hydroelectric generation of the Bolivian electrical system, for which a hydrological model (WEAP) and unit commitment and optimal dispatch model (DispaSet) have been used. The study provides information about the flexibility of the electrical system, by the variation of the hydropower generation through the time series of historical inflows, in terms of power generation, total system costs, consumption, and carbon dioxide emissions.</p>
Importance of storage in renewable power systems
<p>This figure illustrates the renewable energy production from run-of-river hydro, wind, and photovoltaic (PV) sources, alongside the daily electricity demand curve. It highlights that during midday, the combined renewable energy production exceeds the demand, creating an opportunity to charge energy storage systems. Conversely, during the night, morning, and evening hours, renewable production falls short of meeting the demand. During these periods, the stored energy should be discharged to ensure a stable and reliable power supply. This emphasizes the critical role of energy storage in balancing supply and demand in renewable power systems.</p>
Рис. 1. Схема водоЗаборного ковШа и насосной станции (по: ЗвЯгинцев [2005], с иЗменениЯми). in Opisthobranch mollusks (Gastropoda: Opisthobranchia) of cooling system of the Vladivostok Thermal Power Station 2
Рис. 1. Схема водоЗаборного ковШа и насосной станции (по: ЗвЯгинцев [2005], с иЗменениЯми).
Behaviour of dissolved inorganic salts in the cooling water of a nuclear power plant open recirculation system and formation of water discharge
<p>The main problem in the operation of nuclear power plants is the scale formation of mineral impurities in an open recirculating system. However, water discharge from an open recirculating system into water bodies can lead to changes in the chemical equilibrium of wastewater components and requires constant monitoring. The purpose of this study was to analyse the behaviour of dissolved inorganic salts in water in an open recirculating system during water treatment using the example of the Rivne Nuclear Power Plant. Moreover, the analysis impact of their discharge with return water in the Styr River. The dissolved inorganic salt concentration has a significant impact on the efficiency of the system and the environment of an open recirculating system power plant. Altogether, each dissolved inorganic salt component was analysed separately using standard measurement methods, using statistical methods of data processing, and correlation analysis. In addition, the annual discharge of the dissolved inorganic salts components was calculated and the amount of discharge was assessed for compliance with the maximum discharge limit. Thus, the influence of the formation of the dissolved inorganic salts and changes in their concentration value during the discharge of returned water into a natural water body was analysed.</p>
"Power system investment optimization to identify carbon neutrality scenarios for Italy", scripts and data
<p>Script and data to reproduce the main results of "Power system investment optimization to identify carbon neutrality scenarios for Italy"</p>
eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies (data)
<p>Dataset and results used for the simulations in following publication:</p> <p>Carsten Wegkamp, Henrik Wagner, Eike Niehs, Julien Essers, Marcel Lüdecke, Mattias Hadlak, Bernd Engel:<br>"<strong>eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies</strong>",<br>Open Source Modelling and Simulation of Energy Systems (OSMSES) 2024, Vienna, Austria, 2024</p> <p> </p> <p>This contains the input (scenario) files for the building & grid scenario and the results of the two simulations.<br>It uses the elenia Energy Library (eELib) with release version 1.0.0: https://gitlab.com/elenia1/elenia-energy-library</p>
Dataset for the paper "Ocean wave energy harvesting with high energy density and self-powered monitoring system"
<p>Dataset for the paper "Ocean wave energy harvesting with high energy density and self-powered monitoring system“.</p>
Exploring Market Properties of Policy-based Reserve Procurement for Power Systems
<p>Online appendix for the paper - "Exploring Market Properties of Policy-based Reserve Procurement for Power Systems", containing the generators and demand data as well as 1000 scenarios of wind forecast errors used in the case study. The generators and demand data is adapted from a modified IEEE RTS 24-Bus System (with large share of installed wind power production capacity), originally published as: </p> <p>C. Ordoudis, P. Pinson, J. Morales González, and M. Zugno, An Updated Version of the IEEE RTS 24-Bus System for Electricity Market and Power System Operation Studies. Technical University of Denmark (DTU), 2016.</p>
Supplementary data for "An initial assessment of the value of Allam Cycle power plants with liquid oxygen storage in future GB electricity system"
<p>The code for the Unit Commitment & Economic Dispatch model that was used in this work is available at: https://gist.github.com/vitali87/20688c161d7b5ad598b5d52b524f4585</p> <p>Sample output data can be found in the "Example Outputs.zip" file. This corresponds to the case outlined in the article that simulates a system with 5 Allam Cycle plants without Liquid Oxygen Storage, for the winter test week.</p> <p>To run the UCED model:</p> <ul> <li>Download "UC AIMMS Allam Cycle Model" code from the github and save as an AIMMS project file.</li> <li>Save the file in a folder that contains all the necessary input datasets, found in the "Universal Inputs for UCED Model.zip" file, and the example outputs, found in the "Example Outputs.zip" file, which are to be overwritten. Do not change the name of the input or output files.</li> <li>Open the project and execute the following procedures: <ul> <li>"Main Initialisation" - to initialise the problem</li> <li>"Read from Excell" - to read data from the input files</li> <li>"Main Execution" - to begin running the problem</li> </ul> </li> <li>Once the run is complete, execute "Run External Procedure" to overwrite the output files with the new data.</li> </ul> <p>To change the test week:</p> <ul> <li>Open "Demand Profiles" in 'sets' and change the set definition. Enter "C1" for the winter week and "C21" for the summer week. Another week can alternatively be selected. For example, entering "C45" would allow the model to run with the weather and demand data from the 45th week in the year 2010. </li> <li>Save and close the set.</li> </ul> <p>To change the number of plants in the system:</p> <ul> <li>Open "PCCSGenerators" in 'sets' and change the set definition. To run with 5 Post Combustion Capture plants, end the list of generators after plant number 5 by commenting the remaining plants. This is done by using "!" after the 5th plant name in the string. Then save and close the set.</li> <li>Repeat the above step for the "ACGenerators" and "AirSeparationUnits" sets, to change the number of Allam Cycle plants in the system.</li> </ul> <p>To add or remove oxygen storage capability from the Allam Cycle plants:</p> <ul> <li>Open the "Main Initialisation" procedure.</li> <li>To run the model without oxygen storage: <ul> <li>make sure the following command is stated: "AC_ASU_coupled := 0;"</li> <li>save and close the procedure</li> </ul> </li> <li>To run the model with oxygen storage: <ul> <li>make sure the following is command is stated: "AC_ASU_coupled := 1;"</li> <li>make sure that the number, 'X', of "map_AC_to_ASU('Gas_CCS_AC_X') := 'ASU_X';" commands that are active matches the number of active Allam Cycle plants in the model</li> <li>save and close the procedure.</li> </ul> </li> </ul>
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>
Development of a Self-Powered Structural Health Monitoring System for Transportation Infrastructure
<p>Corresponding data set for Tran-SET Project No. 17PTAM03. Abstract of the final report is stated below for reference:</p> <p>"Roadways and bridges play an important role in the economic and social health of society by connecting commerce and people. Economic growth and population expansion pose considerable burden on the aging infrastructure (i.e., pavements and bridges). There is a pressing need to develop structural health monitoring (SHM) technologies capable of collecting infrastructure utilization data. Doing so inexpensively with self-powered systems will revolutionize infrastructure monitoring technology, and will improve decision making enabling roadway and bridge preservation. In this study, a self-powered battery-less structural health monitoring (SHM) system was developed. It is powered by a thermal energy harvester equipped with thermoelectric generators (TEGs) driven by temperature differentials between the top of asphalt pavements and their lower layers. An innovative 2-tier TEG harvester was designed to limit the downtime of the SHM system when the temperature differentials are insufficient to power a single unit. The 2-tier system requires a minimum of 2.1⁰C in temperature differential to generate the minimum of 40 mV needed to power the SHM system. The SHM system consists of a DC-DC booster to increase the voltage generated by the harvester, a buck controller to bring this voltage down to the 3.3 Volts required for powering the microcontroller, a microcontroller and a wireless transceiver for transmitting the data. Another transceiver carried on-board a pilot vehicle is needed to retrieve the data. Software was developed in this project to allow data communication between the two transceivers. The SHM system developed accepts analogue voltage input from any sensor that generates analogue voltage, (e.g., piezoelectric axle load sensors, strain gauges, temperature gauges and so on). A prototype of this SHM system was constructed and tested in the lab and it is ready for field implementation."</p>
Analysis of the water-power nexus of the Balkan Peninsula power system - results
<p>Power generation sector worldwide accounts for high water withdrawal and consumption due to the hydropower generation and cooling of thermal power plants. Hence, the operation of the power generation sector is constrained by the availability of the water resources, as well as the addition of constrains on water resources used for other purposes, such as irrigation, flood control, water supply, agriculture, etc. The optimal utilization of water resources between the water and energy sector is defined under the term water-energy (or water-power) nexus. This study describes the implementation of hydrological LISFLOOD, Medium-Term Hydrothermal Coordination (DispaSET-MTHC) and Unit Commitment and Dispatch (DispaSET UCD) models for detailed analysis of impacts on the SEE regional power system for three different hydrological years. Results were validated based on the available ENTSO-E data for the average hydrological (2015) year. Moreover, calculations on water withdrawal and consumption for cooling of thermal power plants were added. Addition of water stress index (WSI) calculations can determine locations that could experience water scarcity.</p> <p>This dataset is composed of results of the latter described study.</p> <p>Results are divided into three sets, as results from the first model DispaSET MTHC (MTHC_results_Balkan), second model DispaSET UCD (UCD_results_Balkan) and additional calculations on water consumption and withdrawal for thermal power plant cooling (Water_relazed_results_Balkan).</p>
Comparison of Reference Setups for Calibrating Power Transformer Loss Measurement Systems
<p>Data set belonging to the IEEE Trans. Instr. Meas. paper with DOI: <a href="https://doi.org/10.1109/TIM.2018.2879171">10.1109/TIM.2018.2879171</a></p> <p>G. Rietveld, E. Mohns, E. Houtzager, H. Badura, and D. Hoogenboom,<br> <em>Comparison of Reference Setups for Calibrating Power Transformer Loss Measurement Systems</em></p> <p>This project has received funding from the European Metrology Programme for Innovation and Research co-financed by the Participating States and in part by the European Union’s Horizon 2020 Research and Innovation Programme.</p>
Plasticity of the gastrocnemius elastic system in response to decreased work and power demand during growth
<p class="MsoBodyText">Elastic energy storage and release can enhance performance that would otherwise be limited by the force-velocity constraints of muscle. While functional influence of a biological spring depends on tuning between components of an elastic system (the muscle, spring, driven mass, and lever system), we do not know whether elastic systems systematically adapt to functional demand. To test whether altering work and power generation during maturation alters the morphology of an elastic system, we prevented growing guinea fowl (<i>Numida Meleagris</i>) from jumping. At maturity, we compared the jump performance of our treatment group to that of controls and measured the morphology of the gastrocnemius elastic system. We found that restricted birds jumped with lower jump power and work, yet there were no significant between-group differences in the components of the elastic system. Further, subject-specific models revealed no difference in energy storage capacity between groups, though energy storage was most sensitive to variations in muscle properties (most significantly operating length and least dependent on tendon stiffness). We conclude that the gastrocnemius elastic system in the guinea fowl displays little to no plastic response to decreased demand during growth and hypothesize that neural plasticity may explain performance variation.</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.