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4 results for “fuel cost”
The monthly operating costs (vehicle, fuel, and maintenance) of each compared vehicle, Tesla 3 (283 HP), and Infiniti Q50 (300 HP).
<p>We compared two vehicles with similar horsepower, Tesla 3 (283 HP), and Infiniti Q50 (300 HP). The monthly operating costs of each compared vehicle were:</p> <ul> <li> <p>for the model, Tesla 3 electric vehicle was US$426.10/month, including purchase and depreciation US$333/month, fuel (electricity) US$27.8/month, maintenance US$65.3/month.</p> </li> <li> <p>for the model, Infiniti Q50, the internal combustion engine car was US$583.7/month, including purchase and depreciation US$321/month, fuel US$166/month, maintenance US$96.7/month.</p> </li> </ul>
Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use - Supplementary Materials
<p><strong>This repository contains supporting data for: "Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use"</strong></p> <ol> <li> <p><strong>Input Data</strong>: This file includes the model input data.</p> </li> <li> <p><strong>Payoff Matrix Solution</strong>: This file contains all decision variables of payoff matrix solutions (Cost optimal, Land-use optimal, and Water-use optimal solution).</p> </li> <li><strong>Compromise solution</strong>: This file contains all decision variables of the compromise solution.</li> <li> <p><strong>Pareto Curve</strong>: This file presents the Pareto curve solution. </p> </li> <li><strong>Pareto Curve Solution</strong>: This file contains all decision variables in each of the Pareto curve solutions. </li> </ol>
Results Data for "Price Formation Without Fuel Costs: The Interaction of Elastic Demand with Storage Bidding"
<h2>Abstract</h2> <p>Studies on electricity market design for high shares of wind and solar often predict the breakdown of energy-only markets, citing a lack of fuel costs to set prices. Issues include prolonged zero prices, politically unacceptable scarcity prices, price collapses from minor capacity changes, low market value cost recovery, revenue variability across different weather years, and challenges in long-term storage operation. These issues arise from modeling with perfectly inelastic demand. Introducing even a small amount of short-term elasticity (-5%) significantly mitigates these problems. A simplified model with wind, solar, batteries, and hydrogen storage shows that demand elasticity and storage opportunity costs stabilize pricing, smoothing the price duration curve, reducing zero-price hours from 90% to 30%, and ensuring price stability across capacities and weather years. Green hydrogen-derived fuels replace fossil fuels as backup. The long-term model matches the short-term model prices with identical capacities, guiding storage bidding strategies for short-term operations. A model trained on 35 years of weather data and tested on another 35 years demonstrates the energy-only market's potential in future dispatch and investment coordination.</p> <h2>Data Sources</h2> <p>- <strong>Solar and Wind Time Series (1950-2020):</strong> <a href="https://doi.org/10.17864/1947.000321">Bloomfield and Brayshaw (2021)</a><br>- <strong>Techno-Economic Assumptions:</strong><a href="https://github.com/PyPSA/technology-data/tree/v0.8.1"> technology-data (v0.8.1)</a>, <a href="https://ens.dk/en/our-services/technology-catalogues">Danish Energy Agency</a><br>- <strong>Demand Elasticity Assumptions:</strong> <a href="https://doi.org/10.1016/j.eneco.2024.107652">Hirth et al. (2024)</a>, <a href="https://www.ewi.uni-koeln.de/en/publications/on-the-functional-form-of-short-term-electricity-demand-response-insights-from-high-price-years-in-germany-2/">Arnold (2023)</a></p> <h2>Installation</h2> <p>Use <code>conda</code> environment manager:</p> <p><br><code>conda update conda</code><br><code>conda env create -f workflow/envs/environment.fixed.yaml</code><br><code>conda activate price-formation</code></p> <p><strong>Main Dependencies:</strong></p> <ul> <li>pypsa (v0.27.1)</li> <li>linopy (v0.3.8)</li> <li>snakemake (v8.5)</li> <li>gurobi (v11.0.2)</li> </ul> <h2>Run</h2> <p>From the root of the repository:</p> <p><br><code>snakemake -call --use-conda --conda-frontend conda</code></p> <p>Or with a specific scenario configuration file:</p> <p><br><code>snakemake -call --use-conda --conda-frontend conda --configfile config/config.foo.yaml</code></p> <h2>Cluster</h2> <p>On an HPC cluster, run:</p> <p><br><code>snakemake -call --profile slurm --use-conda --conda-frontend conda</code></p> <h2>Compress Results</h2> <p>Use <code>tar</code> to compress results (excluding the report directory):</p> <p><br><code>tar -cJf price-formation-results.tar.xz \</code><br><code> config data figures results resources workflow \</code><br><code> .gitignore .pre-commit-config.yaml .syncignore-receive \</code><br><code> .syncignore-receive CITATION.cff LICENSE matplotlibrc README.md</code></p> <h2>Licenses</h2> <p>The code in this repository is MIT licensed.</p> <p>The data in this repository is CC-BY-4.0 licensed.</p> <h2>Amendments</h2> <p>In <code>v0.2.0</code>, we added the file <code>revision-1-amendments.tar.xz</code>, which includes additional results for sensitivity runs with cross-elastic terms.</p>
Data Set: Renewable hydrogen fuels versus fossil fuels for trucking, shipping and aviation: A holistic cost model
<p>Data Set: Renewable hydrogen fuels versus fossil fuels for trucking, shipping and aviation: A holistic cost model</p>
ScienceDex guides
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Allen Brain Atlas
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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.