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# PyPSA-DE - Hochaufgelöstes, sektorengekoppeltes Modell des deutschen Energiesystems
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# nPyPSA-DE - Hochaufgelöstes, sektorengekoppeltes Modell des deutschen Energiesystems
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PyPSA-DE ist ein sektorengekoppeltes Energiesystem-Modell auf Basis der Toolbox [PyPSA](https://github.com/PyPSA/pypsa) und des europäischen Modells [PyPSA-Eur](https://github.com/PyPSA/pypsa-eur). Der PyPSA-DE Workflow modelliert das deutsche Energiesystem mit deutschlandspezifischen Datensätzen (MaStR, Netzentwicklungsplan,...) im Verbund mit den direkten Stromnachbarn sowie Spanien und Italien. Der Ausbau und der Betrieb von Kraftwerken, des Strom- und Wasserstoffübertragunsnetzes und die Energieversorgung aller Sektoren werden dann in einem linearen Optimierungsproblem gelöst, mit hoher zeitlicher und räumlicher Auflösung. PyPSA-DE wurde im Rahmen des Kopernikus-Projekts [Ariadne](https://ariadneprojekt.de/) entwickelt in dem Szenarien für ein klimaneutrales Deutschland untersucht werden, und spielt eine zentrale Rolle im [Ariadne Szenarienreport](https://ariadneprojekt.de/publikation/report-szenarien-zur-klimaneutralitat-2045/), als Leitmodell für den [Sektor Energiewirtschaft und Infrastruktur](https://ariadneprojekt.de/publikation/report-szenarien-zur-klimaneutralitat-2045/#6-sektorale-perspektive-energiewirtschaft) und als eines von drei Gesamtsystemmodellen. Die Ergebnisse aus der Modellierung mit PyPSA-DE werden auch im [Ariadne-Webinar zu den Kernaussagen des Berichts](https://youtu.be/UL3KAH7e0zs) ([Folien](https://ariadneprojekt.de/media/2025/03/Ariadne_Szen2025_Webinar_Folien_Kernaussagen.pdf)) und im [Ariadne-Webinar zur Energiewirtschaft](https://youtu.be/FcmHBL1MKQA) ([Folien](https://ariadneprojekt.de/media/2025/03/Ariadne_Szen2025_Webinar_Folien_Energiewirtschaft.pdf)) vorgestellt
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This repository contains the entire scientific project, including data sources and code. The philosophy behind this repository is that no intermediary results are included, but all results are computed from raw data and code.
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## Run the analysis
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Before running any analysis with scenarios, the rule `build_scenarios` must be executed. This will create the file `config/scenarios.automated.yaml` which includes input data and CO2 targets from the IIASA Ariadne database as well as the specifications from the manual scenario file. [This file is specified in the default config.yaml via they key `run:scenarios:manual_file`(by default located at `config/scenarios.manual.yaml`)].
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Before running any analysis with scenarios, the rule `build_scenarios` must be executed. This will create the file `config/scenarios.automated.yaml` which includes input data and CO2 targets from the IIASA Ariadne database as well as the specifications from the manual scenario file. [This file is specified in the config.de.yaml via they key `run:scenarios:manual_file`and located at `config/scenarios.manual.yaml` by default].
Note that the hierarchy of scenario files is the following: `scenarios.automated.yaml` > (any `explicitly specified --configfiles`) > `config.yaml `> `config.default.yaml `Changes in the file `scenarios.manual.yaml `are only taken into account if the rule `build_scenarios` is executed.
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Note that the hierarchy of scenario files is the following: `scenarios.automated.yaml` > (any `explicitly specified --configfiles`) > `config.de.yaml `> `config.default.yaml `Changes in the file `scenarios.manual.yaml `are only taken into account if the rule `build_scenarios` is executed.
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To run the analysis use
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The code in this repo is MIT licensed, see `./LICENSE.md`.
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> [!NOTE]
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> [!NOTE]
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> Find below the original `README.md` from [PyPSA-Eur](https://github.com/PyPSA/PyPSA-Eur)
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<!--
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The continental scope and highly resolved spatial scale enables a proper description of the long-range
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smoothing effects for renewable power generation and their varying resource availability.
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The model is described in the [documentation](https://pypsa-eur.readthedocs.io)
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and in the paper
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[PyPSA-Eur: An Open Optimisation Model of the European Transmission
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hundred nodes to remove these local inconsistencies. See the discussion in
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Section 3.4 "Model validation" of the paper.
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The dataset consists of:
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We strongly welcome anyone interested in contributing to this project. If you have any ideas, suggestions or encounter problems, feel invited to file issues or make pull requests on GitHub.
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- To **discuss** with other PyPSA users, organise projects, share news, and get in touch with the community you can use the [Discord server](https://discord.gg/AnuJBk23FU).
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- For **bugs and feature requests**, please use the [PyPSA-Eur Github Issues page](https://github.com/PyPSA/pypsa-eur/issues).
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- To **discuss** with other PyPSA users, organise projects, share news, and get in touch with the community you can use the [Discord server](https://discord.gg/AnuJBk23FU).
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- For **bugs and feature requests**, please use the [PyPSA-Eur Github Issues page](https://github.com/PyPSA/pypsa-eur/issues).
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