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6 results for “Fuel prices”
Spain fuel prices
<p>This dataset provides insight into:</p> <ul> <li>All fuel types available at each petrol station</li> <li>Covers all petrol stations of Spain</li> <li>Informs fuel price evolution along wk46, 2022</li> </ul>
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>
Fuel tax loss in a world of electric mobility: A window of opportunity for congestion pricing
<p>The excel file "Data_Documentation" provides estimations of the passenger car stocks in Germany and the two states Berlin-Brandenburg until 2030 under a scenario of a dynamic diffusion of electric vehicles to the market. From the car stocks, the energy tax revenues are also estimated. The Tableau package book provides data and visualizations of congestion pricing simulation results. This is an updated version of its previous one. The corrections are only the names of the sheets in the files. The data itselft stays the same.</p>
Input Data for paper "Energy Storage Profit Risk under Stochastic Fuel Prices"
<p>This is a supplementary information accompanying "Energy Storage Profit Risk under Stochastic Fuel Prices" paper submitted to <a href="https://www.journals.elsevier.com/energy-economics/">Energy Economics</a>.</p>
Correlation analysis for paper "Energy Storage Profit Risk under Stochastic Fuel Prices"
<p>This is a supplementary data for reproducibility.</p>
Data for cluster generation for paper "Energy Storage Profit Risk under Stochastic Fuel Prices"
<p>This is a supplementary data for reproducibility. </p>
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