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10 results for “electricity system model”
EPA Integrated Planning Model (IPM) National Electric Energy Data System (NEEDS) database
EPA is making the latest power sector modeling platform available, including the associated input data and modeling assumptions, outputs, and documentation.
Reference Data Set: Electricity, Heat, and Gas Sector Data for Modeling the German System
<p>This reference data set representing the status quo of the German electricity, heat, and natural gas sectors was compiled within the research project ‘LKD-EU’ (Long-term planning and short-term optimization of the German electricity system within the European framework: Further development of methods and models to analyze the electricity system including the heat and gas sector).</p> <p>While the focus is on the electricity sector, the heat and natural gas sectors are covered as well. With this reference data set, we aim to increase the transparency of energy infrastructure data in Germany. Where not otherwise stated, the data included in this report is given with reference to the year 2015 for Germany. The data set is documented in DIW Data Documentation 92 (see references).</p> <p>The project is a joined effort by the German Institute for Economic Research (DIW Berlin), the Workgroup for Infrastructure Policy (WIP) at Technische Universität Berlin (TUB), the Chair of Energy Economics (EE2) at Technische Universität Dresden (TUD), and the House of Energy Markets & Finance at University of Duisburg-Essen. The project was funded by the German Federal Ministry for Economic Affairs and Energy through the grant ‘LKD-EU’, FKZ 03ET4028A-D.</p>
Historic data of the national electricity system transitions in Europe in 1990–2019 for retrospective evaluation of models [dataset]
<p>This data package supports empirical analysis of national electricity system transitions and retrospective evaluation of electricity system models in 1990–2019 in 31 European countries, including the EU27, Switzerland, Iceland, Norway, and the United Kingdom. The data package covers two types of content. Firstly, we provide an annotated list of 528 original data sources and references relevant for retrospective electricity system modeling with emphasis on open-access sources. Secondly, we provide a total of 1359 processed and harmonized data files in a format that is suitable as inputs to electricity system models. Four types of data files are included for each country: (i) a country file documenting national demand and economic data, (ii) technology files describing techno-economic data for each major generation technology in the country's electricity mix, (iii) resource files describing fuel prices and CO2 emissions for each fuel, and (iv) load profiles describing 24-hour national load curves for each available year. We provide these data files as comma-separated files to enable their wider reuse for retrospective evaluation of models as well as for empirical analyses of the European electricity system transitions. </p>
Models and output datasets for OSeMOSYS Bolivia electricity system paper
<p>These are files with the outputs for each scenario in the paper "Analyzing Carbon Emissions Policies for the Bolivian Electric Sector" submitted to the journal Renewable and Sustainable Energy Transition.</p>
Supplemental data files for: Evidence for the superposition of tectonic systems in the northern Songliao Block, NE China, revealed by a 3-D electrical resistivity model
<p>Data files for a 3-D electrical resistivity model in the northern Songliao Block, NE China, including the MT data observed there (note the data format is for 3-D inversion using ModEM), and the preferred resistivity model.</p> <p>The software EMdesk from Jilin Kingti Geoexploration Tech, Ltd (Changchun, China) can be used for data analysis and modeling (http://www.kingti.net).</p>
CCG: Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System)
<p>Seven clicSAND scenario files for <strong>Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System).</strong> </p> <p><strong>How to Visualise Results Online and Offline</strong> outline the steps required to re-run the scenarios on OSeMOSYS Cloud</p> <p><strong>Scenario Short Note</strong> outlines the steps to replicate the analysis and rebuild the scenarios</p> <p><strong>Annex - Input Data and Assumptions</strong> listing the data sources and assumptions in the scenarios</p>
Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling
<p>This supplementary material includes data and code for the research described in the paper "Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling". The code containts an interface between the output files of the agent-based simulation model CURRENT and the energy system optimization model REMix as well as some scripts for analyzing REMix results. The data folder contains input data for REMix, the complete list of all model runs analyzed in the paper in the GAMS format .gdx as well as Excel files containing annual results of the sensitivity runs and respective pivot tables and figures for respective analysis.</p>
SimBench - Electrical Power System Benchmark Models
<p>SimBench (<a href="https://www.simbench.net">www.simbench.net</a>) is a research project to create a "simulation database for uniform comparison of innovative solutions in the field of network analysis, network planning and operation", which was conducted for three and a half years from 1.11.2015 to 30.04.2019. It was part of the German Federal Government's 6th Energy Research Program "Research for an Environmentally Friendly, Reliable and Affordable Energy Supply". The project was carried out by the University of Kassel, the Fraunhofer IEE, the RWTH Aachen University and the Technical University of Dortmund in accordance with the authors mentioned above. The project, coordinated by the University of Kassel, was supported by the professional advisory from six German distribution network operators: DREWAG NETZ GmbH, Energie Netz Mitte GmbH, ENSO NETZ GmbH, Netze BW GmbH, Syna GmbH and Westnetz GmbH.</p> <p>The objective of the research project SimBench is the development of a benchmark data set to support research in grid planning and operation. SimBench Grid differs from other benchmark grids under the following key points:</p> <ul> <li>Consideration of a wide range of use cases during the development of data sets</li> <li>Provision of grid data for low voltage (LV), medium voltage (MV), high voltage (HV), extra high voltage (EHV) as well as design of data sets for a suitable interconnection of a grid among different voltage levels for cross-level simulations</li> <li>Ensuring highreproducibility and comparability by providing clearly assigned load and generation time series</li> <li>Validation of the suitability of the data sets with simulation, deliberately determined grid states including suitable dimensioning of grid assets</li> </ul> <p>In total SimBench provides 13 unique electrical power system grids (EHV: 1, HV: 2, MV: 4, LV: 6). Since SimBench is enables multi-voltage simulations, this dataset includes not only 13 folder but many more. Each excerpt of the complete dataset, composed to a folder, is distinctively named by the SimBench code.</p> <p>For further information, please visit <a href="https://www.simbench.net">www.simbench.net</a> and the documentation published there.</p>
Probabilistic Model-Based Diagnosis for Electrical Power Systems
We present in this article a case study of the probabilistic approach to model-based diagnosis. Here, the diagnosed system is a real-world electrical power system, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. Our probabilistic approach is formally well-founded, and based on Bayesian networks and arithmetic circuits. We pay special attention to meeting two of the main challenges model development and real-time reasoning often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-touse specication language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In experiments with the ADAPT Bayesian network, which contains 503 discrete nodes and 579 edges and produces accurate results, the time taken to compute the most probable explanation using arithmetic circuits has a mean of 0.2625 milliseconds and a standard deviation of 0.2028 milliseconds. In comparative experiments, we found that while the variable elimination and join tree propagation algorithms also perform very well in the ADAPT setting, arithmetic circuit evaluation was an order of magnitude or more faster. **Reference:** O. J. Mengshoel, M. Chavira, K. Cascio, S. Poll, A. Darwiche, and S. Uckun. "Probabilistic Model-Based Diagnosis: An Electrical Power System Case Study”. Accepted to IEEE Transactions on Systems, Man, and Cybernetics, Part A, 2009.
Italy's energy system model (Electricity, Heat and Hydrogen), with 6 region resolution simulation under PNIEC2019 scenario. (April 2024)
<p>Parameters and Sets files for an updated model built for Italy's energy system model (Electricity, Heat and Hydrogen), with 6 region resolution simulation under PNIEC2019 scenario. (April 2024)</p> <p>* Data are designed as an input for Hypatia Modelling Framework</p> <p>** Model was develeped for master thesis study " Investigating the regional contributions to the Italian decarbonization: an Energy Modelling multi-regional approach." C. Lo Guidice, F. Cruz, K. Gad, E. Colombo</p>
ScienceDex guides
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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.