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87 results for “Energy Storage”
Passive Perching with Energy Storage for Winged Aerial Robots Dataset
<p>This dataset corresponds to the publication:</p> <p>"Passive Perching with Energy Storage for Winged Aerial Robots" W. Stewart, L. Guarino, Y. Piskarev, and D. Floreano. Advanced Intelligent Systems, <a href="http://doi.org/10.1002/aisy.202100150">http://doi.org/10.1002/aisy.202100150</a></p>
Dataset of multi-objective optimization results for a new latent energy storage approach in buildings based on several phase change materials with different melting temperatures
<p>This dataset comprises the multi-objective optimization results obtained for a new latent energy storage approach based on several phase change materials (PCMs) with different melting temperatures in buildings. The results were obtained for a small office building in eight climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe).</p> <p>The dataset contains:</p> <p>- The EnergyPlus baseline models employed as a case study for each climate.</p> <p>- The Pareto fronts obtained after the multi-objective optimization in each climate.</p> <p>- The EnergyPlus models for the best designs achieved on the Pareto fronts in terms of annual total load reductions.</p>
Historical Annual Revenue of Energy Storage on European Electricity Markets
<p>This dataset provides the optimized annual revenue for 96 generic storage technologies (12 efficiency x 8 discharge duration). It covers 17 European electricity markets for up to 16 years (Austria, AT; Belgium, BE; Switzerland, CH; Czech Republic, CZ; German, DE; Spain, ES; France, FR; Italy, IT; Ireland, IR; Netherlands, NL; Nordpool which includes Denmark, Estonia, Finland, Latvia, Lithuania, Norway, Sweden, NP; Poland, PL; Portugal, PT; Romania, RO; Slovakia, SK; United Kingdom, UK). The optimization is an adaptation of the model presented in Gaudard et al. [2013]. It assumes perfect foresight assumption and a stochastic algorithm. Therefore, the results are an approximation of the maximum rather than the absolute optimum. Further information is provided in "Gaudard L. and Madani K., Energy storage race: Has the monopoly of pumped-storage in Europe come to an end?, forthcoming". </p> <p>The following information is provided:</p> <p>Country: Code of the specific market (also the filename)</p> <p>Currency: The currency in which the results are expressed</p> <p>Discharge duration [hours]: The time required to empty at full nominal power a device that is fully charged. </p> <p>Efficiency: Ratio between the amount of discharged and charged energy during a full cycle.</p> <p>Year: From January 1st to December 31st.</p> <p>The given numbers are in euros or GBP per year and normalized to 1kWh of energy storage. This means that for a specific device, the given figures must be multiplied by the volume of energy storage (in terms of kWh). As an example, for an energy device with the efficiency of 0.95, discharge duration of 6h and volume of energy storage of 2000kWh, the revenues in 2003 in Austria would be 9.26 x 2000=18520 euros. </p> <p>For any questions or further requirements, please feel free to get in touch with the authors.</p>
Dataset for "Sustainable Disposal and End-of-Life Treatment of Battery Energy Storage Systems: An Environmental and Economic Case Study"
This study investigates the environmental and economic impacts of end-of-life (EOL) treatment for a 2.8 MWh/2.5 MW battery energy storage system (BESS) based on lithium-ion batteries (LIBs). It focuses on recycling pre-treatment processes for battery systems and recycling procedures for components like cooling systems, fire extinguishing systems, inverters, and the reuse of BESS containers and substations. A life cycle assessment (LCA) was employed to evaluate key environmental impacts, including climate change, eutrophication, and resource use. The study reveals substantial environmental benefits, particularly from recovering secondary materials like aluminium and copper, with recycling pre-treatment contributing significantly to overall benefits. Additionally, the economic analysis projects profits, emphasizing the advantages of locally sourcing critical raw materials. The research highlights the need for more sustainable recycling practices and provides insights for improving environmental and economic strategies in BESS management, offering guidance for future research and policy development in battery waste processing.
Dataset of 20 energy prosumers with flexibility data, distributed generation and energy storage
<p>The dataset has 20 prosumers, each with three appliances to provide flexibility for DR events, two PV generation resources, and an energy storage system. The values represent a day using 15 minutes reading periods. All the values are expressed in W, and the matrixes were created as [ time_period x info].</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. Data Set for Final Data Report
<p>The 43 txt-files included in this dataset relate to the report: Ryhl-Svendsen, Jensen, Bøhm, and Klenz Larsen (2012): <em>Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. UMTS Research Project 2007</em>–<em>2011: Final Data Report</em>, Kgs. Lyngby: National Museum of Denmark, 122 pp.</p> <p>The document <a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/00_List-of-data-files.pdf?versionId=a3e9691f-6e73-4a7b-aaab-c8ccee7c419b">00_List-of-data-files.pdf</a> contain a full list of the data files with a description of their structure and content, and is the key to how the individual data files relate to the report. </p> <p>The research project focussed on four modern museum storage facilities in Denmark, for which the indoor climate, air quality, and the energy consumption of the climate control systems was measured at several locations, typically for a period of between two and four years. The storage facilities were Museum of Southwest Jutland’s storage building in Ribe (‘Ribe’), The Shared Storage Facility at The Centre for Preservation of Cultural Heritage in Vejle (‘Vejle’), The Joint Storage Facility for museums in East Jutland/ Museum Østjylland (‘Randers’), and from The National Museum of Denmark the storage building Hall P at the Ørholm Storage Facility (‘Ørholm’). For description of the sites, monitoring campaigns, and graphed data, the report should be consulted.</p> <p>For completeness, the report is included with the dataset (<a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/Report_low-energy-museum-storage-buildings.pdf?versionId=44097d39-775b-4031-9e07-6978c68912a9">Report_low-energy-museum-storage-buildings.pdf</a>).</p>
Beyond cost reduction: Improving the value of energy storage.
<p>The here provided ".nc" files are data files from the paper "Beyond cost reduction: Improving the value of energy storage". The data files represent 3 scenarios from a European energy system model PyPSA-Eur. It can be used as input to the Jupyter notebook analysis and plotting scripts provided in <a href="https://github.com/pz-max/Beyond-cost-reduction-Improving-the-value-of-energy-storage">GitHub.</a></p>
Optimal planning of autonomous electric vehicles charging stations with photovoltaic generations and energy storage systems
<p>This database contains technical information on the 69-bus electrical distribution system. This system was tested in a mixed integer linear programming model for allocating autonomous electric vehicle charging stations equipped with photovoltaic generation and energy storage systems. Additionally, this document contains data related to charging stations, energy storage systems, and operational scenarios applied to the case studies.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for battery storage in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>battery energy storag</span><span>e </span><span>systems</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Battery energy storage is the fastest growing form of power system </span><span>flexibility, and</span><span> will be critical to integrating large shares of variable renewable energy.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>671</span></span><span><span> datapoints from </span></span><span><span>18</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the </span><span>literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span> <span>It is designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span><span> </span>Technoeconomic data on utility-scale batteries was collected from websites, reports, academic articles and databases of national and international organisations.</p>
CROSSBOW HLU3-UC1-TC1 Energy arbitrage revenues for storage asset
<p>In the process of energy arbitrage described in HLU3_UC3, storage assets buy energy from RES assets in valley price periods and later on sell this energy in peak price periods, thus earning some money for the shifting of the energy consumption to the periods were it more demanded (higher prices). The total benefits for the storage asset have some factors into account:</p> <ul> <li>The energy acquisition (charge) is only happening is there is possibility to sold (discharge) the energy in a near future with benefits (higher price)</li> <li>The energy is paid in the storage market at a price that compensate the RES owners for the low prices in the DA/ID markets. The energy is bought at the DA/ID market at the existing price</li> <li>Energy stored is sold in the most beneficial periods</li> </ul> <p>The dataset contains the revenues theoretically obtained in the DA/ID markets and the real revenues obtained considering the bilateral agreement with the storage asset. Fields:</p> <ul> <li>Time</li> <li>Payments: cost of the energy bought (including both markets)</li> <li>Revenues: earning for the energy sold</li> <li>Balance: revenues - payments</li> </ul>
Modelling assumptions and input dataset for the case study of the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study"
<p>This data package includes the modelling assumptions and input data to replicate the results of the case study included in the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study". This paper is part of the 18th International Conference on the European Energy Market (EEM22).</p> <p>The case study models the Belgian day-ahead electricity market, in which the existing storage is considered, in addition to large-scale battery energy storage systems of different sizes for varying renewable energy shares. A detailed description of the case study is provided in the readme file. </p> <p>This supplementary data package includes the following files: </p> <p> --Belgium Model Input Data.xlsx: Dataset used as input in the case study of the mentioned paper<br> --Modelling Assumptions.pdf: Modelling assumptions considered in the case study<br> --readme.txt (this file): Includes a detailed description of the data package</p> <p> </p> <p>The data included in this dataset was collected from public open sources [1]-[2]. Please notice that this dataset does not replace the original open access information. For accessing the data, please visit the following websites:</p> <p>[1] “ENTSO-E Transparency Platform.” [Online]. Available: https://transparency.entsoe.eu/dashboard/show. [Accessed: 06-Jul-2022].<br> [2] “Grid data.” [Online]. Available: https://www.elia.be/en/grid-data. [Accessed: 06-Jul-2022].</p> <p><br> </p> <p> </p>
Two-dimensional vanadium sulfide flexible graphite/polymer films for near-infrared photoelectrocatalysis and electrochemical energy storage
<p>Raw data of published article "Two-dimensional vanadium sulfide flexible graphite/polymer films for near-infrared photoelectrocatalysis and electrochemical energy storage", DOI: 10.1016/j.cej.2022.135131</p>
2D MoS2/carbon/polylactic acid filament for 3D printing: Photo and electrochemical energy conversion and storage
<p>Raw data of published journal article "2D MoS2/carbon/polylactic acid filament for 3D printing: Photo and electrochemical energy conversion and storage", DOI: 10.1016/j.apmt.2021.101301</p>
Dataset: iShares Energy Storage & Materials ETF (IBAT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
SparBOFWEC Spar Buoy for Offshore Floating Wind Energy Conversion - Data Storage Report
<p>The present work describes the experiences gained from the design methodology and operation of a 3D physical model experiment aimed to investigate the dynamic behaviour of a spar buoy (SB) off-shore floating wind turbine (WT) under different wind and wave conditions. The physical model tests have been performed at Danish Hydraulic Institute (DHI) off-shore wave basin within the European Union-Hydralab+ Initiative, in April 2019. The floating WT model has been subjected to a combination of regular and irregular wave attacks and wind loads.</p>
Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses - Supporting Dataset
<p>This dataset contains the files used to substantiate the outcomes of the publication <em>"Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses"</em>. </p> <p>It includes the output of 250 random model realizations of an aquifer thermal energy storage system in a thick productive aquifer (Case 1). It also includes the output of 500 random model realizations of an aquifer thermal energy storage system in a shallow alluvial aquifer (Case 2 part 1 and part 2).</p> <p>If there is interest in generating new output, the datset also includes the model input files for both cases.</p> <p>(Scripts to process the output data or to generate new output data can be found in the corresponding GitHub repository: https://github.com/lukatas/ATES_SensitivityAnalyses.git )</p>
Dataset for "Fine-Tuning A Robust Metal–Organic Framework Towards Enhanced Clean Energy Gas Storage"
<p>Dataset covering the DFT simulations performed for the journal article "Fine-Tuning A Robust Metal–Organic Framework Towards Enhanced Clean Energy Gas Storage"</p>
Dataset for Service Restoration of Distribution Networks Considering Energy Storage System
<p>The power distribution system presented is composed with 53 node and 61 branches and can be employed in multi-time service restoration problem, islading operation and energy storage system optimal operation. The system was designed based on a 53 node system (available <a href="https://ieee-dataport.org/documents/optimal-service-restoration-active-distribution-networks-considering-microgrid-formation">here</a>). The dataset was modified to include 6 photovoltaic generation, 3 energy storage system and time-changing demand load nodes.</p>
Optimal Dynamic Service Restoration of Distribution Networks Considering Energy Storage System Data
<p>The power distribution system presented is composed with 53 node and 61 branches and can be employed in multi-time service restoration problem, islading operation and energy storage system optimal operation. The system was designed based on a 53 node system (available <a href="https://ieee-dataport.org/documents/optimal-service-restoration-active-distribution-networks-considering-microgrid-formation">here</a>). The dataset was modified to include 6 photovoltaic generation, 3 energy storage system and time-changing demand load nodes.</p>
Core-Hole Spectroscopy of Energy Conversion and Storage Related-Phosphorus Compounds Using Soft and Hard X rays
<p>Dear reader,</p> <p> </p> <p>Please find attached the input files (.xml) and their corresponding outputs for the Exciting calculations of the imaginary component of the dielectric tensor ("XAS".dat) for: InP, GaP, red P and InPO4 which have been used in our publication:"Core-Hole Spectroscopy of Energy Conversion and Storage Related-Phosphorus Compounds Using Soft and Hard X rays". </p>
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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)
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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.