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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>

Ultra-low-power Edge Detection Based on Ferroelectric Field Effect Transistor (data and code)

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Input data and code related to "Utilizing curtailed wind and solar power to scale up electrolytic hydrogen production in Europe"

<p>Datasets and code for the submitted article: "Utilizing curtailed wind and solar power to scale up electrolytic hydrogen production in Europe" </p> <p>by Alissa Ganter<sup>1,2</sup>, Tyler H. Ruggles<sup>2</sup>, Paolo Gabrielli<sup>1</sup>, Giovanni Sansavini<sup>1,*</sup>, Ken Caldeira<sup>2</sup></p> <p><sup>1</sup> Institute of Energy and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland</p> <p><sup>2</sup> Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA</p> <p><sup>*</sup> Corresponding author: email - sansavig@ethz.ch</p> <p>All rights lie with the authors. Refer to the README.docx for a description of the datasets and their usage in the article.</p>

[Supplementary Data] PowerModel-AI: A First On-the-fly Machine-Learning Predictor for AC Power Flow Solutions.

<h1><strong>Abstract</strong></h1> <p>The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines.  These algorithms enable machines to autonomously build models while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce <em>PowerModel-AI</em>, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures the load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model's performance is sub-optimal, based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems.  The training and test datasets were generated using <em>PowerModel.jl</em>, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.</p> <div> <h1><strong>Overview</strong></h1> <p>This dataset, provided as supplementary material for the above-referenced study, includes a comprehensive collection of images (plots) from the study’s analyses, along with Jupyter notebooks containing Python scripts used for the training, validation, and testing phases of PowerModel-AI. Additionally, it includes all training and external test data used in this work, generated via LANL-based open-source power flow solver, PowerModels.jl.</p> <p>The primary objective of this dataset is to ensure full reproducibility of the study’s analyses and facilitate critical examination by the scientific community, thereby maximizing the overall impact of the work.</p> <h1>Directory Structure</h1> <p>The hierarchy of folders and file organization of the dataset is illustrated in the chart below. The directory contains a README.md file which contains the information provided here and a requirements.txt that contains all libraries necessary to run the python scripts or Jupyter notebooks in this directory.  A brief description of the folders and what they contain are provided below:    </p> </div> <div> <strong>.</strong></div> <div>├── <strong>Models/</strong></div> <div>│   ├── _PM_AI_Models/</div> <div>│   │    ├── Type1/</div> <div>│   │    ├── Type2/</div> <div>│   │    └── Type3/</div> <div>│   │</div> <div>│   ├── _PM_AI_Module/</div> <div>│   │    ├── __init__.py</div> <div>│   │    └── PM_Methods.py</div> <div>│   │</div> <div>│   ├── _PM_JL_Data/</div> <div>│   ├── C1_Model/</div> <div>│   ├── C2_Model/</div> <div>│   ├── C3_Model/</div> <div>│   ├── M1_Model/</div> <div>│   ├── M2_Model/  </div> <div>│   └── M3_Model/</div> <div>│</div> <div>├── <strong>NodeSensitivityAnalysis/</strong></div> <div>│   ├── Sensitivity_Plots/</div> <div>│   ├── Sensitivity_PM_JL_Data/</div> <div>│   ├── get_sensitivity_PMJL_data.py</div> <div>│   └── NodeSensitivityAnalysis.ipynb</div> <div>│</div> <div>├── <strong>PlotsForOnTheFlyAnalysis/</strong></div> <div>│   ├── BaseModel_A/</div> <div>│   ├── BaseModel_B/  </div> <div>│   └── BaseModel_C/</div> <div>│  </div> <div>├── <strong>README.md</strong></div> <div>└── <strong>requirements.txt</strong></div> <p> </p> <h2>Files Description </h2> <h3><strong>1.      </strong><strong>Models/</strong></h3> <p><strong><em>_PM_AI_Models/</em></strong> contains the ML models discussed in the manuscript. Type1, Type2, and Type3 refers to the C and M models with numbers "1", "2," and "3". Each Type folder contains individual subfolders for the synthetic grids discussed.</p> <p><strong> <em>_PM_AI_Module/</em></strong> contains a python script that has all the functions used in model training and analysis. It is imported in the Jupyter notebooks in the C and M subfolders in this directory.</p> <p><strong><em>_PM_JL_Data/</em> </strong>contains the following subfolders:</p> <p>a)  <strong> </strong><em>_</em><strong><em><strong>G</strong>eneratePowerModelData/</em></strong> has a python script (<u>get_PowerModelJLData.py)</u> that is used to parse <u>PowerModels.jl</u> to compute AC power flow solutions for different power demand configurations. It also contains a subfolder, <strong><em>BusData_MATLAB/</em></strong>, that has all the synthetic grids used in the study and in MATLAB format.</p> <p>b)   It also contains other subfolders (not shown in the chart above) that contains AC power flow solutions generated using LANL’s PowerModels.jl, for each synthetic grids and other grid-related data.</p> <p>The folders starting with C and M are the control and candidate models (more details in manuscript). Each folder contains Jupyter notebooks (for each power grid) that has python algorithms used in model training and testing, as well as functions to analyze and plot the prediction performance of the models. It accesses (if already trained) or stores (if newly trained) the models in the <strong><em>_PM_AI_Models/</em></strong> directory. Additionally, they contain 2 subfolders (not shown in the chart above): <strong>AbsoluteErrorPlots/</strong> and <strong>PredictionPlots/</strong> were the results (plots) from the analyses are stored. Some of these results are shown in the manuscript (<em>Figures 4</em>,<em> 5</em>, <em>6</em>, and <em>7</em>).</p> <h3><strong>2.      </strong><strong>NodeSensitivityAnalysis/</strong></h3> <p>This folder contains the tools used for the node dependency analysis in Section 3.1.1 of the manuscript.</p> <p>It contains a python script called get_sensitivity_PMJL_data.py, which has similar operation like the <u>get_PowerModelJLData.py</u> script but only compute changes for one bus at a time (see details in manuscript). There is also a Jupyter notebook called <u>NodeSensitivityAnalysis.ipynb </u>that analyzes the bus node dependencies and produces the plots that are shown in Figure 3 in the manuscript. It contains 2 additional sub-folders:</p> <p>a)      <strong> <em>Sensitivity_PM_JL_Data/</em></strong> where PowerModels.jl generated AC power flow solution data are stored, and</p> <p>b)       <em><strong>Sensitivity_Plots/</strong> </em>where the results from <u>NodeSensitivityAnalysis.ipynb</u> are stored.</p> <h3><strong>3.      </strong><strong>PlotsForOnTheFlyAnalysis/</strong></h3> <p>This directory contains only the results for the discussion in Section 3.2 in the manuscript, which is the on-the-fly implementation of PowerModel-AI. The on-the-fly algorithm will be provided and distributed separately in the PowerModel-AI package, which will be publicly available through LANL’s <a href="https://github.com/lanl-ansi" target="_blank" rel="noopener">The Advanced Network Science Initiative</a> (Github). It contains 3 subfolders with similar names but ends with "A", "B," and "C" which correspond to the designations shown and discussed in <em>Figure 8</em> of the manuscript.</p> <h1>Summary</h1> <h3><strong> </strong><strong>Models/</strong></h3> <p><strong>1. _PM_AI_Models/:  </strong></p> <p>Contains the machine learning models discussed in the manuscript. The Type1, Type2, and Type3 folders refer to C and M models labeled "1", "2," and "3". Each type folder includes subfolders for the corresponding synthetic grids analyzed.</p> <div><strong>2. _PM_AI_Module/: </strong> </div> <div>Contains Python scripts for model training and analysis.  PM_Methods.py is the script that defines functions for model training and analysis, which are imported in the Jupyter notebooks in the C and M subfolders.  </div> <div>   </div> <div><strong>3. _PM_JL_Data/:  </strong></div> <div>-_GeneratePowerModelData/: Contains a Python script (get_PowerModelJLData.py) used to parse PowerModels.jl and compute AC power flow solutions for various power demand configurations. This folder also includes BusData_MATLAB/, which holds the synthetic grid data in MATLAB format.  </div> <div>- Other Subfolders: Contain PowerModels.jl AC power flow solutions for each synthetic grid, as well as related data.</div> <div>   </div> <div><strong>4. C1_Model/, C2_Model/, C3_Model/, M1_Model/, M2_Model/, M3_Model/:  </strong></div> <div>These folders contain the control and candidate models (refer to manuscript details). Each folder includes pre-run Jupyter notebooks for model training and analysis, as well as two subfolders:  </div> <div>   - <em>AbsoluteErrorPlots/</em>: Contains saved analysis results for each grid.</div> <div> <strong>  </strong>- <em>PredictionPlots/</em>: Stores model prediction results.  </div> <div>Some results are shown in <em>Figures 4, 5, 6,</em> and <em>7</em> of the manuscript and can be reproduced using the notebooks.</div> <h3>NodeSensitivityAnalysis/</h3> <div><strong>1. Node Dependency Analysis Tools: </strong> </div> <div>   This folder contains scripts and data used for the node dependency analysis in Section 3.1.1 of the manuscript.</div> <div>   </div> <div><strong>2. Scripts:  </strong></div> <div>   - get_sensitivity_PMJL_data.py: Computes power flow changes for individual buses (refer to the manuscript for details).  </div> <div>   - NodeSensitivityAnalysis.ipynb: Analyzes dependencies and generates plots for Figure 3 in the manuscript.  </div> <div>   </div> <div><strong>3. Subfolders:</strong>  </div> <div>   - Sensitivity_PM_JL_Data/: Stores PowerModels.jl data for sensitivity analysis.  </div> <div>   - Sensitivity_Plots/: Contains the generated results from the Jupyter notebook.</div> <h3>PlotsForOnTheFlyAnalysis/</h3> <div><strong>On-the-Fly Learning Results:  </strong>Contains the results for the on-the-fly learning analysis discussed in Section 3.2 of the manuscript.  The subfolders BaseModel_A/, BaseModel_B/, and BaseModel_C/ correspond to the designations in <em>Figure 8</em> of the manuscript. These subfolders contain plots generated during analysis.</div> <h3>Additional Files</h3> <div>- README.md: This file.</div> <div>- requirements.txt: Contains a list of necessary Python libraries required to run the scripts and Jupyter notebooks.</div> <h3>Notes:</h3> <div>- The PowerModel-AI code is publicly available on GitHub.</div> <div>- The supplementary materials include additional Jupyter notebooks and results for each power grid analysis.  </div> <div>- The generated results and predictions in this repository are consistent with those discussed in the manuscript.</div>

opencc-zeroDec 2014View details →

Data from: Edge effects on plant communities along power line clearings

1. Power line clearings are edge-creating disturbances in landscapes world-wide, but there have been few studies on their bordering vegetation. Our aim was to quantify edge effects on plant communities along such clearings in Norway and to identify factors that influence these edge effects. 2. We surveyed understorey plant communities on either side of the power line clearing–forest edge at 51 sites, along four parallel transects at each site. Each transect had four plots located, respectively, in the clearing centre, clearing edge, forest edge and forest. We quantified the magnitude of edge effects (MEE) on either side by comparing edges with their corresponding 'non-edge' reference habitats. We also measured differences in species composition across the edge (clearing edge vs. forest edge). Habitat characteristics were sampled at plot and site level and from digital maps. 3. Differences in species composition were greater between clearing centres and clearing edges than between forests and forest edges. Differences in species composition across the edge increased with edge contrast and forest productivity. Edge effects on species composition into the forest were smallest along north-facing edges, whereas those in the clearings increased with power line age. 4. Species richness increased slightly towards the edge in forests but decreased considerably towards the edge in clearings. The direction and MEE on either side differed among functional groups. Edge contrast and edge aspect were the prime factors influencing the MEE into forests, whereas in clearings, these were influenced principally by tree regrowth in the clearings and by forest productivity. 5. Synthesis and applications. Edge effects on plant communities bordering power line clearings were determined by factors that can be influenced by planners and managers. For existing power lines, management plans should differentiate between the following: (i) clearings through high conservation value forests, where edge effects into the adjacent forest should be limited; (ii) clearings that can act as replacement habitat for cultural landscape species, where maintaining open-canopy habitats should be prioritized; and (iii) 'business-as-usual' clearings, where continuing the current practice of cutting every 5–10 years is recommended.

opencc-zeroDec 2012View details →

Data from: Admixture mapping of quantitative traits in Populus hybrid zones: power and limitations

Uncovering the genetic architecture of species differences is of central importance for understanding the origin and maintenance of biological diversity. Admixture mapping can be used to identify the number and effect sizes of genes that contribute to the divergence of ecologically important traits, even in taxa that are not amenable to laboratory crosses due to their long generation time or other limitations. Here, we apply admixture mapping to naturally occurring hybrids between two ecologically divergent Populus species. We map quantitative trait loci (QTL) for eight leaf morphological traits using 77 mapped microsatellite markers from all 19 chromosomes of Populus. We apply multivariate linear regression analysis allowing the modeling of additive and non-additive gene action and identify several candidate genomic regions associated with leaf morphology using an information-theoretic approach. We perform simulation studies to assess the power and limitations of admixture mapping of quantitative traits in natural hybrid populations for a variety of genetic architectures and modes of gene action. Our results indicate that (1) admixture mapping has considerable power to identify the genetic architecture of species differences if sample sizes and marker densities are sufficiently high, (2) modeling of non-additive gene action can help to elucidate the discrepancy between genotype and phenotype sometimes seen in interspecific hybrids, and (3) the genetic architecture of leaf morphological traits in the studied Populus species involves complementary and overdominant gene action, providing the basis for rapid adaptation of these ecologically important forest trees.

opencc-zeroDec 2017View details →

Data from: Extremely fast feeding strikes are powered by elastic recoil in a seahorse relative, the snipefish, Macroramphosus scolopax

Among over 30,000 species of ray-finned fishes, seahorses and pipefishes have a unique feeding mechanism whereby the elastic recoil of tendons allows them to rotate their long snouts extremely rapidly in order to capture small elusive prey. To understand the evolutionary origins of this feeding mechanism, its phylogenetic distribution among closely related lineages must be assessed. We present evidence for elastic recoil powered feeding in the snipefish (Macroramphosus scolopax) from kinematics, dynamics, and morphology. High-speed videos of strikes show they achieve extremely fast head and hyoid rotational velocities, resulting in rapid prey capture in as short at 2 ms. The maximum instantaneous muscle-mass-specific power requirement for head rotation in snipefish was above the known vertebrate maximum, which is evidence that strikes are not the result of direct muscle power. Finally, we show that the over-center conformation of the four-bar linkage mechanism coupling head elevation to hyoid rotation in snipefish can function as a torque reversal latch, preventing the head from rotating and providing the opportunity for elastic energy storage. The presence of elastic recoil feeding in snipefish means that this high-performance mechanism is not restricted to the Syngnathidae (seahorses and pipefish) and may have evolved in parallel.

opencc-zeroJun 2021View details →

Data: Similar neural and perceptual masking effects of low-power optogenetic stimulation in primate V1

Can direct stimulation of primate V1 substitute for a visual stimulus and mimic its perceptual effect? To address this question, we developed an optical-genetic toolkit to "read" neural population responses using widefield calcium imaging, while simultaneously using optogenetics to "write" neural responses into V1 of behaving macaques. We focused on the phenomenon of visual masking, where detection of a dim target is significantly reduced by a co-localized medium-brightness pedestal. Using our toolkit, we tested whether V1 optogenetic stimulation can recapitulate the perceptual masking effect of a visual pedestal. We find that, similar to a visual pedestal, low-power optostimulation can significantly reduce visual detection sensitivity, that a sublinear interaction between visual and optogenetic evoked V1 responses could account for this perceptual effect, and that these neural and behavioral effects are spatially selective. Our toolkit and results open the door for further exploration of perceptual substitutions by direct stimulation of sensory cortex.

opennotspecifiedJul 2021View details →

Supplementary data: "Revealing drivers and risks for power grid frequency stability with explainable AI"

<p>This repository contains processed data and result files for the paper <a href="https://arxiv.org/abs/2106.04341">Revealing drivers and risks for power grid frequency stability with explainable AI</a>.  The code for producing the processed data and the results is <a href="https://github.com/johkruse/XAI-for-grid-frequency-stability">available at github</a>.</p> <p><strong>Data</strong></p> <p>For each area, the data folder contains the feature and target data used to train the ML model.</p> <ul> <li><em>raw_input_data.h5</em><strong> </strong>:<strong> </strong>The aggregated external features without additional engineered features.</li> <li><em>input_forecast.h5 </em>and<em> input_actual.h5: </em>The day-ahead available (forecast) and ex-post available (actual) data of external features including the engineered features.</li> <li><em>outputs.h5 </em>:  The grid frequency stability indicators.</li> <li><em>version_2021-07-01</em>: Folder containing the training and test sets used for the results.</li> <li><em>documentation_of_data_download</em><strong>: </strong>Plots and information files concerning the ENTSO-E raw data and its aggregation.</li> </ul> <p><strong>Data sources</strong></p> <p>The data for input features (<em>raw_input_data.h5, input_forecast.h5 </em>and<em> input_actual.h5</em>) is derived from ENTSO-E Transparency Platform data [1]. The target data (<em>outputs.h5</em>) is based on power grid frequency recordings from the German Transmission System Operator TransnetBW [2].</p> <p><strong>Results</strong></p> <p>For each area and each target, the result folder comprises the results of hyper-parameter optimization, model prediction and interpretation via SHAP. The results refer to the full model and the restricted model (containing only day-ahead features).</p> <ul> <li><em>cv_results_gtb_full.csv</em> and <em>cv_results_gtb_day_ahead.csv</em> : Performance results for each combination in the hyper-parameter grid search.</li> <li><em>cv_best_params_gtb_full.csv</em> and <em>cv_best_params_gtb_day_ahead.csv</em> : Hyper-parameters used in the final (optimized) model.</li> <li><em>shap_values_gtb_full.npy</em> and <em>shap_interaction_values_gtb_full.npy </em>: First-order SHAP values and second-order SHAP interaction values for the full model.</li> <li><em>y_pred.h5</em> : Predictions of daily profile predictor, full model and day-ahead model.</li> </ul> <p><strong>Disclaimer</strong></p> <p>The data might be subject to copyright or related rights. Please consult the primary data owner.</p>

opencc-zeroSep 2021View details →

Rainwater-driven microbial fuel cells for power generation in the remote areas' raw data

<p>The possibility of utilizing rainwater as a sustainable anolyte in an air-cathode microbial fuel cell is investigated in this study. The results indicate that the proposed microbial fuel cell can work within a wide temperature range (from 0 to 30 oC), and under aerobic or anaerobic conditions. However, the rainwater season has a distinct impact. Under anaerobic conditions, the summer rainwater achieves a promised open circuit potential of 553±2 mV without addition of nutrients at the ambient temperature, while addition of nutrients leads to increase the cell voltage to 763±3 and 588±2 mV at 30 oC and ambient temperature, respectively. The maximum open circuit potential for the winter rainwater (492±1.5 mV) is obtained when the reactor is exposed to the air (aerobic conditions) at ambient temperature. Furthermore, the winter rainwater microbial fuel cell generates a maximum power output of 7±0.1 mWm-2 at a corresponding current density value of 44±0.7 mAm-2 at 30 oC. While, at the ambient temperature, the maximum output power is obtained with the summer rainwater (7.2±0.1 mWm-2 at 26±0.5 mAm-2). Moreover, investigation of the bacterial diversity indicates that lactobacillus sp. is the dominant electroactive genus in the summer rainwater, while in the winter rainwater, Staphylococcus sp. is the main electroactive bacteria. The cyclic voltammetry analysis confirms that the electrons are delivered directly from the bacterial biofilm to the anode surface and without mediators. Overall, the study opens a new avenue for utilizing a novel sustainable type of microbial fuel cell derived by rainwater.</p>

opencc-by-4.0Nov 2022View details →

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>

opencc-by-4.0Dec 2022View details →

Lacor Hospital's energy community - Power data

<p>The dataset contains power measurements of the St. Mary's hospital community, located in Gulu, Uganda. The variables available are: PV production, the total load, the back-up diesel generator production and the consumption of the sterilization machine (which are part of the total load). Measurements starts from 01/05/2022 until 25/10/2022 with a 5-minutes time-resolution.</p>

opencc-by-4.0Feb 2023View details →

Meridional wave power distribution data for article "Latitudinal Dependence of Ground VLF Transmitter Wave Power in the Inner Magnetosphere"

<p>The data are the meridional wave power distribution for the four VLF transmitters: NWC, NAA, NML, and NLK in the inner magnetosphere. Each transmitter has a .mat file, and in each data file, we provide the meridional wave power distribution for both ray tracing simulation (parameter "rt") and ERG observation (parameter "erg"). In each distribution data, the variables "x2d" and "z2d" are the cartesian coordinates in the meridional plane in the unit of Re (the Eearth's radius). x2d=r*cos(theta) and z2d=r*sin*(theta), where r is the radius from the Earth's center, and theta is the magnetic latitude. The variable "power" is the corresponding transmitter wave power in the unit of V^2/m^2.</p>

opencc-by-4.0Mar 2023View details →

Data of optimal IBO for battery-powered OFDM transmitter

<p>Data generated by numeric calculations.</p>

opencc-by-4.0May 2023View details →

Automated patent extraction powers generative modeling in focused chemical spaces: Training data and model checkpoints release

<p>Training data and model checkpoints accompanying paper on "Automated patent extraction powers generative modeling in focused chemical spaces". If you use this data, please cite the following manuscript:</p> <pre>@article{subramanian2023automated, title={Automated patent extraction powers generative modeling in focused chemical spaces}, author={Subramanian, Akshay and Greenman, Kevin P and Gervaix, Alexis and Yang, Tzuhsiung and G{\'o}mez-Bombarelli, Rafael}, journal={Digital Discovery}, year={2023}, publisher={Royal Society of Chemistry} }</pre>

opencc-by-4.0May 2023View details →

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>

opencc-by-4.0Apr 2023View details →

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>

opencc-by-4.0Jun 2023View details →

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>

opencc-by-4.0Jul 2023View details →

Data for "Tracking physical delivery of electricity from generators to loads with power flow tracing"

<p>Data and analysis code for "Tracking physical delivery of electricity from generators to loads with power flow tracing".</p> <p>Data includes the following folders:</p> <ul> <li>validation: Ground truth generation data for 2020</li> <li>tracing: Power flow tracing results for one day</li> <li>secondary_analysis: Node-level power flow statistics</li> <li>networks: Solved networks, including Eastern U.S., Europe, Texas at 4 levels of clustering, and Europe with altered dispatch</li> <li>meta: between-node straight line distance for Eastern U.S. and Europe</li> <li>boundaries: shapefiles and per-node mappings for existing boundaries (Eastern U.S. and Europe) and example boundaries (Texas)</li> <li>analysis: physical delivery and deliverability metrics for all networks over all modeled days</li> </ul> <p>Code (`flow-delivery-analysis-main`) includes notebooks and helper functions for all visualizations and statistics in "Tracking physical delivery of electricity from generators to loads with power flow tracing". See the README for more information.</p>