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30 results for “power grid”
case60nordic random topology power grid dataset
<p>Dataset of randomly generated power grids derived from the case60nordic (also known as nordic32).</p> <p>Random object disconnections.</p> <p>Data generation script available at : <a href="https://github.com/bdonon/powerdatagen">https://github.com/bdonon/powerdatagen</a></p>
Data for "Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: A case study on the Italian grid"
<p>Data employed for the paper "Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: A case study on the Italian grid"<br><br>Abstract<br>This study explores compounding impacts of climate change on power system's load and generation, emphasising the need to integrate adaptation and mitigation strategies into investment planning. We combine existing and novel empirical evidence to model impacts on: i) air-conditioning demand; ii) thermal power outages; iii) hydro-power generation shortages. Using a power dispatch and capacity expansion model, we analyse the Italian power system's response to these climate impacts in 2030, integrating mitigation targets and optimising for cost-efficiency at an hourly resolution. We outline different meteorological scenarios to explore the impacts of both average climatic changes and the intensification of extreme weather events. We find that addressing extreme weather in power system planning will require an extra 5-8 GW of photovoltaic (PV) capacity, on top of the 50 GW of the additional solar PV capacity required by the mitigation target alone. Despite the higher initial investments, we find that the adoption of renewable technologies, especially PV, alleviates the power system's vulnerability to climate change and extreme weather events. In fact, renewable energy sources are generally less vulnerable to the impacts of climate change, such as rising temperatures and shifting precipitation patterns, compared to thermal power and hydropower generation. Furthermore, enhancing short-term storage with lithium-ion batteries is crucial to counterbalance the reduced availability of dispatchable hydro generation.</p>
Power grid attack detection and state estimation with machine learning
<p>Detecting attacks and estimating states of power grids from partial observations with machine learning. A manuscript submitted to PRX Energy.</p>
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>
Data from: A gateway for phylogenetic analysis powered by grid computing featuring GARLI 2.0
We introduce molecularevolution.org, a publicly available gateway for high-throughput, maximum likelihood phylogenetic analysis powered by grid computing. The gateway features a garli 2.0 web service that enables a user to quickly and easily submit thousands of maximum likelihood tree searches or bootstrap searches that are executed in parallel on distributed computing resources. The garli web service allows one to easily specify partitioned substitution models using a graphical interface, and it performs sophisticated post-processing of phylogenetic results. Although the garli web service has been used by the research community for over three years, here we formally announce the availability of the service, describe its capabilities, highlight new features and recent improvements, and provide details about how the grid system efficiently delivers high-quality phylogenetic results.
Power Quality State Estimation for Distribution Grids based on Physics-Aware Neural Networks - Harmonic State Estimation
<p>Data set for the paper "Power Quality State Estimation for Distribution Grids based on Physics-Aware Neural Networks - Harmonic State Estimation"</p> <p>This upload contains</p> <ul> <li>Training set</li> <li>Validation set</li> <li>Test set</li> <li>Admittance matrices per frequency</li> </ul> <p>used for the paper as pickle files and weights of trained models as zip files.</p> <p>Weights represent the model with the best validation loss recorded within the first 3000 Epochs of training.</p> <p>Code for reading in the data sets, preprocessing and state estimation is available in the linked repository.</p> <p>To replicate the results of the paper follow these steps:</p> <ol> <li>clone the linked repository</li> <li>save the provided pickle files in the data folder of the linked repository</li> <li>optional: unzip weights and save them in the data folder, otherwise train a model yourself instead</li> </ol> <p><strong>Version 1.1:</strong></p> <p>Added data and model weights for the IEEE33 grid to improve comparability.</p> <p>For the IEEE33 grid, all data (train, test, validation) is saved in one pickle file; see the release tag 1.1.0 in the accompanying GitHub repository for details on the data format. Moreover, the training set size of the new grid was increased from 35040 to 131400 samples to incorporate simulation results that capture a broader range of system states. </p> <p>The code was slightly updated to account for inclusion of the IEEE33 grid. Therefore, model weights and input data are now expected in either `cigrelv` or `ieee33` subfolder.</p> <p>Added Transformer and CNN model weights for IEEE33 and CNN weights for the CIGRE grid. The Transformer model is trained with a smaller batch size since the model did not fit into GPU memory using the same batch size as in other models. This change results in more gradient updates and significantly longer training times, thus the amount of epochs was reduced to achieve a fairer comparison (batch sized reduced from 16384 to 1024, epochs reduced from 3000 to 375, total amount of gradient updates increased from 27000<em> </em>to 48375). The training of the PANN model over 3000 Epochs is significantly faster than that of the Transformer model trained over 375 epochs (approximately 2.5 hours vs 12.5 hours).</p>
Supplementary data: "Physics-informed machine learning for power grid frequency modelling"
<p>This repository contains result files for the paper "Physics-informed machine learning for power grid frequency modelling" <a href="https://doi.org/10.48550/arXiv.2211.01481">(Preprint)</a>. The code for producing the processed data and the results is <a href="https://github.com/johkruse/PIML-for-grid-frequency-modelling">available at github</a>.</p> <p><strong>Results</strong></p> <p>The result folder comprises the results of hyper-parameter optimisation, scaling variation and interpretation via SHAP. In particular, it contains these sub-folders and files:</p> <ul> <li><em>tuning </em>: Results of hyper-parameter tuning.</li> <li><em>best_model </em>: Weights of the trained model with best hyper-parameters.</li> <li><em>best_model_<scaling-variation> </em>: Weights of the trained models with best hyper-parameters but with a variation of the parameter scaling.</li> <li><em>fixed_model_hps.pkl </em>: Hyper-parameters that are not optimised.</li> <li><em>shap_values_<parameter>_long.h5</em> : SHAP values for the prediction of the system parameters.</li> </ul>
Data from: A gateway for phylogenetic analysis powered by grid computing featuring GARLI 2.0
Open the record for dataset details and reuse information.
A large synthetic dataset for machine learning applications in power transmission grids
<p>With the ongoing energy transition, power grids are evolving fast. They operate more and more often close to their technical limit, under more and more volatile conditions. Fast, essentially real-time computational approaches to evaluate their operational safety, stability and reliability are therefore highly desirable. Machine Learning methods have been advocated to solve this challenge, however they are heavy consumers of training and testing data, while historical operational data for real-world power grids are hard if not impossible to access. </p> <p>This dataset contains long time series for production, consumption, and line flows, amounting to 20 years of data with a time resolution of one hour, for several thousands of loads and several hundreds of generators of various types representing the ultra-high-voltage transmission grid of continental Europe. The synthetic time series have been statistically validated agains real-world data.</p> <h2>Data generation algorithm</h2> <p>The algorithm is described in a <a href="https://doi.org/10.1038/s41597-025-04479-x">Nature Scientific Data paper</a>. It relies on <a href="https://zenodo.org/records/2642175" target="_blank" rel="noopener">the PanTaGruEl model of the European transmission network</a> -- the admittance of its lines as well as the location, type and capacity of its power generators -- and aggregated data gathered from <a href="https://transparency.entsoe.eu/" target="_blank" rel="noopener">the ENTSO-E transparency platform</a>, such as power consumption aggregated at the national level.</p> <h2>Network</h2> <p>The network information is encoded in the file <a href="https://zenodo.org/records/13378476/files/europe_network.json">europe_network.json</a>. It is given in <a href="https://lanl-ansi.github.io/PowerModels.jl/stable/" target="_blank" rel="noopener">PowerModels format</a>, which it itself derived from <a href="https://matpower.org/" target="_blank" rel="noopener">MatPower</a> and compatible with <a href="https://www.pandapower.org/" target="_blank" rel="noopener">PandaPower</a>. The network features 7822 power lines and 553 transformers connecting 4097 buses, to which are attached 815 generators of various types.</p> <h2>Time series</h2> <p>The time series forming the core of this dataset are given in CSV format. Each CSV file is a table with 8736 rows, one for each hourly time step of a 364-day year. All years are truncated to exactly 52 weeks of 7 days, and start on a Monday (the load profiles are typically different during weekdays and weekends). The number of columns depends on the type of table: there are 4097 columns in load files, 815 for generators, and 8375 for lines (including transformers). Each column is described by a header corresponding to the element identifier in the network file. All values are given in per-unit, both in the model file and in the tables, i.e. they are multiples of a base unit taken to be 100 MW.</p> <p>There are 20 tables of each type, labeled with a reference year (2016 to 2020) and an index (1 to 4), zipped into archive files arranged by year. This amount to a total of 20 years of synthetic data. When using loads, generators, and lines profiles together, it is important to use the same label: for instance, the files <em>loads_2020_1.csv</em>, <em>gens_2020_1.csv</em>, and <em>lines_2020_1.csv</em> represent a same year of the dataset, whereas <em>gens_2020_2.csv</em> is unrelated (it actually shares some features, such as nuclear profiles, but it is based on a dispatch with distinct loads).</p> <h2>Usage</h2> <p>The time series can be used without a reference to the network file, simply using all or a selection of columns of the CSV files, depending on the needs. We show below how to select series from a particular country, or how to aggregate hourly time steps into days or weeks. These examples use Python and the data analyis library <em>pandas</em>, but other frameworks can be used as well (Matlab, Julia). Since all the yearly time series are periodic, it is always possible to define a coherent time window modulo the length of the series.</p> <h3>Selecting a particular country</h3> <p>This example illustrates how to select generation data for Switzerland in Python. This can be done without parsing the network file, but using instead <a href="https://zenodo.org/records/13378476/files/gens_by_country.csv">gens_by_country.csv</a>, which contains a list of all generators for any country in the network. We start by importing the <em>pandas</em> library, and read the column of the file corresponding to Switzerland (country code CH):</p> <pre><code>import pandas as pd CH_gens = pd.read_csv('gens_by_country.csv', usecols=['CH'], dtype=str)</code></pre> <p>The object created in this way is Dataframe with some null values (not all countries have the same number of generators). It can be turned into a list with:</p> <pre><code>CH_gens_list = CH_gens.dropna().squeeze().to_list()</code></pre> <p>Finally, we can import all the time series of Swiss generators from a given data table with</p> <pre><code>pd.read_csv('gens_2016_1.csv', usecols=CH_gens_list)</code></pre> <p>The same procedure can be applied to loads using the list contained in the file <a href="https://zenodo.org/records/13378476/files/loads_by_country.csv">loads_by_country.csv</a>.</p> <h3>Averaging over time</h3> <p>This second example shows how to change the time resolution of the series. Suppose that we are interested in all the loads from a given table, which are given by default with a one-hour resolution:</p> <pre><code>hourly_loads = pd.read_csv('loads_2018_3.csv')</code></pre> <p>To get a daily average of the loads, we can use: </p> <pre><code>daily_loads = hourly_loads.groupby([t // 24 for t in range(24 * 364)]).mean()</code></pre> <p>This results in series of length 364. To average further over entire weeks and get series of length 52, we use: </p> <pre><code>weekly_loads = hourly_loads.groupby([t // (24 * 7) for t in range(24 * 364)]).mean()</code></pre> <h2>Source code</h2> <p>The code used to generate the dataset is freely available at <a href="https://github.com/GeeeHesso/PowerData" target="_blank" rel="noopener">https://github.com/GeeeHesso/PowerData</a>. It consists in two packages and several documentation notebooks. The first package, written in Python, provides functions to handle the data and to generate synthetic series based on historical data. The second package, written in Julia, is used to perform the optimal power flow. The documentation in the form of Jupyter notebooks contains numerous examples on how to use both packages. The entire workflow used to create this dataset is also provided, starting from raw ENTSO-E data files and ending with the synthetic dataset given in the repository.</p> <h2>Funding</h2> <p>This work was supported by the <a href="https://www.cydcampus.admin.ch">Cyber-Defence Campus of armasuisse</a> and by an internal research grant of the Engineering and Architecture domain of <a href="https://www.hes-so.ch">HES-SO</a>.</p>
H2020 Platone German Demonstrator - Active Power Exchange at Grid Connection Point (Medium Voltage/Low Voltage)
<p>The given data are measured values of active power , measured at the low voltage busbar of the medium voltage (MV)/low voltage (LW) grid connection point (secondary substation). The data are provided as 15-minutes mean values in kilowatt.</p> <p><strong>Data Description</strong></p> <ul> <li>p_tei_mean = arithmetic mean of p_tei measured in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</li> <li>p_tei_min = the minimum value (1-minute mean) measured within the period of p_tei_mean (15-minutes)</li> <li>p_tei_max = the maximum value (1-minute mean) measured within the period of p_tei_mean period (15-minutes)</li> <li>p_tei_count = the number a 1-minute mean values available for each15-minute interval.</li> </ul> <p><strong>Field Test Setup</strong></p> <p>The substation includes a transformer feeding a low voltage grid, consisting of 89 households, 450kW of installed PV generation capacity, a large scale battery with 300 kW and 850 kWh. </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300</p>
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