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89 results for “power plants”
Generation and emission data for main power plants in Germany (2015 - 2021)
<p>This dataset contains generation and emission data for German main power plants (2015 - 2021). The underlying code with additional analysis is available on <a href="https://github.com/INATECH-CIG/CO2_emissions_factors_DE">Github</a> (still under development).</p> <p><strong>"blocks.xlsx":</strong></p> <p>This file collects power plant information per unit from different data sources. The <a href="https://www.entsoe.eu/data/energy-identification-codes-eic/">EIC </a>is the identifier used by ENTSO-E for the per unit generation time series [1]. This identifier is matched with BNA-ID and MaStR-Nr used in the official power plant list published by the <a href="https://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/Sachgebiete/Energie/Unternehmen_Institutionen/Versorgungssicherheit/Erzeugungskapazitaeten/Kraftwerksliste/Kraftwerksliste_CSV.html?nn=266908">Bundesnetzagentur</a> [2]. Information about the plant (location, name, block name, electrical and heat power capacity, fuel type) has been collected from the power plant lists from <a href="https://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/Sachgebiete/Energie/Unternehmen_Institutionen/Versorgungssicherheit/Erzeugungskapazitaeten/Kraftwerksliste/Kraftwerksliste_CSV.html?nn=266908">Bundesnetzagentur </a>[2] and <a href="https://www.umweltbundesamt.de/dokument/datenbank-kraftwerke-in-deutschland">Umweltbundesamt</a> [3]. Fuel-specific emission factors have been assigned from <a href="https://www.umweltbundesamt.de/publikationen/co2-emissionsfaktoren-fuer-fossile-brennstoffe-0">data published by Umweltbundesamt</a> [4] according to plant type and location. The ETS ID is the identifier of the installation in the <a href="https://climate.ec.europa.eu/eu-action/eu-emissions-trading-system-eu-ets/union-registry_en">EU emissions trading system</a>. Note that EIC, BNA-ID and MaStR-Nr differ for each generation unit, whereas the ETS ID relates to a stationary installation which might contain several generation units.</p> <p><strong>"block-generation.xslx"</strong><br> This file contains yearly generation for each generation unit calculated from the per unit generation time series published by ENTSO-E. Generation units are identified by the EIC and the BNA-ID.</p> <p><strong>"installation-results-yearly"</strong></p> <p>This file contains yearly results for generation and emissions for each installation identified by the ETS-ID [5]. Each installation might contain several generation units. Power generation has been calculated from per unit generation time series (ENTSO-E) for the units associated with the installation. Total emissions represent verified emissions as published as part of the EU emission trading system. These total emissions have been split up into emissions associated with power generation and heat provisioning based on the free allocations granted to the installation, <span class="math-tex">\(E_i=E_i^{el}+E_i^{heat}\)</span>. From emissions associated with power generation and total generation, installation-specific emission factors have been calculated, whereas fuel-specific emission factors are used to estimate the utilization factor of the installation.</p> <p>Emissions associated with heat are calculated by <span class="math-tex">\(E_{i}^{heat}=H_i\cdot hb\)</span>, where <span class="math-tex">\(H_i\)</span> is the heat provided by the installation and <span class="math-tex">\(hb=62.3 tCO_2/TJ\)</span> is the heat benchmark used in the EU ETS as part of the process for determining <a href="https://climate.ec.europa.eu/system/files/2016-11/gd1_general_guidance_en.pdf">free allocations</a> [6,7,8]. We assume that free allocations are calculated from the heat as follows:</p> <p><span class="math-tex">\(A_i(k) = H_i\cdot hb\cdot\beta(k)\cdot[\sigma + (1-\sigma)\gamma(k)]\)</span></p> <p>Here <span class="math-tex">\(A_i(k)\)</span> denotes the free allocations in year <span class="math-tex">\(2013+k\)</span> and <span class="math-tex">\(\beta(k)=(1-k\cdot 0,0174)\)</span> is the linear reduction factor. The term <span class="math-tex">\(\gamma(k)=(0.8-(0.5/7)\cdot k)\)</span> represents the carbon leakage exposure factor for provisioning to installations exposed to no significant carbon leakage risk. This factor is <span class="math-tex">\(1\)</span> for heat provisioning to installations which show a significant risk for carbon leakage. The parameter <span class="math-tex">\(\sigma\)</span> denotes the share of heat provided to the latter sector (with risk of carbon leakage), whereas <span class="math-tex">\((1-\sigma)\)</span> gives the remaining share of heat provided to the sector without significant risk of carbon leakage. The parameter <span class="math-tex">\(\sigma\)</span> has been calculated by comparing the given data for free allocations for consecutive years <span class="math-tex">\(A_i(k+1)/A_i(k)\)</span> under the assumption that <span class="math-tex">\(H_i(k+1)=H_i(k)\)</span>. Values for <span class="math-tex">\(\sigma\)</span> are given in the file "<strong>sigma.xlsx</strong>". Note that no free allocations are given for heat provided to installations taking part in the EU ETS, so these emissions are not covered by this approach. Not for all installation consistent information for free allocations was available, in these cases no entry in the file "installation-results-yearly.xslx" is given, respectively.</p> <p>The utilization factor is calculated by <span class="math-tex">\(G_i/((E_i^{el}/e(f_i))\)</span>, where <span class="math-tex">\(G_i\)</span> denotes the power generation at location <span class="math-tex">\(i\)</span>, <span class="math-tex">\(E_i^{el}\)</span> the emissions associated with power generation, and <span class="math-tex">\(e(f_i)\)</span> the associated fuel-specific emission factor for the installation. The installation-specific emission factor is calculated by <span class="math-tex">\(E_i^{el}/G_i\)</span>.</p> <p>Sources:</p> <p>[1] ENTSO-E Transparency Platform, <a href="https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show">Actual Generation per Generation Unit</a>.<br> [2] Bundesnetzagentur, <a href="https://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/Sachgebiete/Energie/Unternehmen_Institutionen/Versorgungssicherheit/Erzeugungskapazitaeten/Kraftwerksliste/Kraftwerksliste_CSV.html?nn=266908">Kraftwerksliste</a>.<br> [3] Umweltbundesamt, <a href="https://www.umweltbundesamt.de/dokument/datenbank-kraftwerke-in-deutschland">Datenbank Kraftwerke in Deutschland</a>.<br> [4] Umweltbundesamt, <a href="https://www.umweltbundesamt.de/publikationen/co2-emissionsfaktoren-fuer-fossile-brennstoffe-0">CO2-Emissionsfaktoren für fossile Brennstoffe</a>, Aktualisierung 2022.<br> [5] EU Union Registry, <a href="https://climate.ec.europa.eu/eu-action/eu-emissions-trading-system-eu-ets/union-registry_en#european-union-transaction-log">Verified Emissions 2021</a>.<br> [6] European Commission, <a href="https://climate.ec.europa.eu/system/files/2016-11/gd1_general_guidance_en.pdf"> Guidance Document n°1 on the harmonized free allocation methodology for the EU-ETS<br> post 2012</a>.<br> [7] Agora Energiewende, <a href="https://www.agora-energiewende.de/veroeffentlichungen/die-deutsche-braunkohlenwirtschaft-1/">Die deutsche Braunkohlenwirtschaft</a> (2017)<br> [8] Jan Frederick Unnewehr, Anke Weidlich, Leonhard Gfüllner, Mirko Schäfer, <a href="https://www.sciencedirect.com/science/article/pii/S2772783122000176">Open-data based carbon emission intensity signals for electricity generation in European countries – top down vs. bottom up approach</a>, Cleaner Energy Systems 3, 100018 (2022).</p> <p><br> </p> <p><br> </p>
Design Strategies for Oxy-Combustion Power Plant Captured CO2 Purification
<p>This submission contains Aspen Plus files for the design and systems performance analysis of oxy-combustion power plant captured CO2 purification using different techniques.</p> <p>This data is taken from https://psecommunity.org/LAPSE:2021.0570</p>
CO2 retrievals at global power plants using PRISMA satellite data
<p>PRISMA (prisma.asi.it) data for a set of global power plants tasked between 2021-2022. Each scene includes a NETCDF file containing raw radiance data (1e-4 * W/(str µm m<sup>2</sup>)), retrieved XCO2 (using an IMAP-DOAS algorithm), and retrieval precision. Also included is a spreadsheet "PRISMA tracking-2023-06-23.xlsx" that details the result of an analyst's QC of each scene regarding retrieval quality and CO2 plume detection. Another tracking sheet "PRISMA emissions-2023-06-23.xlsx" lists derived emission rates (via Integrated Mass Enhancement approach) with uncertainties and ERA5 wind speeds.</p> <p>Also included for each scene is an RGB and XCO2 PNG file that allows the user to quickly scan retrieval results. </p>
Data from: Edge effects on plant communities along power line clearings
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Data from: Resolving recent plant radiations: power and robustness of genotyping-by-sequencing
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Data from: Fish population genetic structure shaped by hydroelectric power plants in the upper Rhine catchment
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Data from: Symbiont community and host genetic structure of the brain coral Platygyra verweyi, at the outlet of a nuclear power plant and adjacent areas
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ANALYSIS OF THE PROBLEMS OF THE DEVELOPMENT OF PHOTOVOLTAIC SOLAR POWER PLANTS IN UZBEKISTAN
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Supplementary data for Life-cycle assessment shows that retrofitting coal-fired power plants with fuel cells will substantially reduce greenhouse gas emissions
<p>This dataset contains supplementary data for "Life-cycle assessment shows that retrofitting coal-fired power plants with fuel cells will substantially reduce greenhouse gas emissions" DOI: <strong>10.1016/j.oneear.2022.03.009</strong>.</p> <p>S1-S10 contains life-cycle inventories of solid oxide fuel cells, molten carbonate fuel cells, phosphoric acid fuel cells, and proton exchange membrane fuel cells with either natural gas or wind-electrolysis hydrogen as a feedstock.</p> <p>S11-S13 contains technological information of coal-fired power plants in China</p>
Supplementary material 1 from: Connolly BM, Powers J, Mack RN (2017) Biotic constraints on the establishment and performance of native, naturalized, and invasive plants in Pacific Northwest (USA) steppe and forest. NeoBiota 34: 21-40. https://doi.org/10.3897/neobiota.34.10820
Table S1 : Explanation note: Site sites with UTM coordinates and elevation a.s.l.
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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Whole genome sequencing and assembly of Eukaryotic microbes isolated from ISS environmental surface Kirovograd region soil Chernobyl Nuclear Power Plant and Chernobyl Exclusion Zone
The whole-genome sequences of eight fungal strains that were selected for exposure to microgravity at the International Space Station are presented here. These baseline sequences will help to understand the observed production of novel bioactive compounds.
Data from: Association mapping for phenology and plant architecture in maize shows higher power for developmental traits compared with growth influenced traits
Plant architecture, phenology and yield components of cultivated plants have repeatedly been shaped by selection to meet human needs and adaptation to different environments. Here we assessed the genetic architecture of 24 correlated maize traits that interact during plant cycle. Overall, 336 lines were phenotyped in a network of 9 trials and genotyped with 50K single-nucleotide polymorphisms. Phenology was the main factor of differentiation between genetic groups. Then yield components distinguished dents from lower yielding genetic groups. However, most of trait variation occurred within group and we observed similar overall and within group correlations, suggesting a major effect of pleiotropy and/or linkage. We found 34 quantitative trait loci (QTLs) for individual traits and six for trait combinations corresponding to PCA coordinates. Among them, only five were pleiotropic. We found a cluster of QTLs in a 5 Mb region around Tb1 associated with tiller number, ear row number and the first PCA axis, the latter being positively correlated to flowering time and negatively correlated to yield. Kn1 and ZmNIP1 were candidate genes for tillering, ZCN8 for leaf number and Rubisco Activase 1 for kernel weight. Experimental repeatabilities, numbers of QTLs and proportion of explained variation were higher for traits related to plant development such as tillering, leaf number and flowering time, than for traits affected by growth such as yield components. This suggests a simpler genetic determinism with larger individual QTL effects for the first category.
Main code and data for manuscript - Global Coal Power Plants Efficiency Analysis
<p>This repository contains the datasets and MATLAB code utilized in the study Revisiting Global Coal Power Plants Efficiency Gains in Delivering Climate Goals. The research integrates artificial neural networks (ANN), optimization models, and Monte Carlo-based sensitivity analyses to assess efficiency improvements in global coal-fired power plants and their potential contributions to climate objectives.</p>
PLANT POWER FOR HEALTH: PHYTO-PREPARATIONS AGAINST INFLAMMATORY DISEASES USES OF PLANTS AS A MEDICINE
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Data from: Long-term monitoring dataset of fish assemblages impinged at nuclear power plants in northern Taiwan
The long-term species diversity patterns in marine fish communities are garnering increasing attention from ecologists and conservation biologists. However, current databases on quantitative abundance information lack consistent long-term time series, which are particularly important in exploring the possible underlying mechanism of community changes and evaluating the effectiveness of biodiversity conservation measures. Here we describe an impinged fish assemblage dataset containing 1, 283, 707 individuals from 439 taxa. Once a month over 19 years (1987–1990 and 2000–2014), we systematically collected the fish killed by impingement upon cooling water intake screens at two nuclear power plants on the northern coast of Taiwan. Because impingement surveys have low sampling errors and can be carried out over many years, they serve as an ideal sampling tool for monitoring how fish diversity and community structure vary over an extended period of time.
Ruin of a power plant on the coast of Svalbard
The power station of a British coal mine was built at the beginning of the nineteenth century and was only in operation for a few years. The power station was extremely innovative for its time and probably not very mature, so that the plant was left behind at the mine site and was not used elsewhere. Even today there are still crates of packed spare parts on the beach. The increasing thawing of the ground due to the particularly drastic climatic change on Svalbard will cause the power plant to disappear completely in the coming years. Source: Objaverse 1.0 / Sketchfab
Data from: Association mapping for phenology and plant architecture in maize shows higher power for developmental traits compared with growth influenced traits
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Data from: Time series dataset of fish assemblages near thermal discharges at nuclear power plants in northern Taiwan
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Data from: Long-term monitoring dataset of fish assemblages impinged at nuclear power plants in northern Taiwan
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