Automated Author ProfileHofmann, Fabian
Frankfurt Institute for Advanced Studies
Hofmann, Fabian
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 5.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Supplementary Data (preliminary version)PyPSA-Eur: An Open Optimisation Model of the European Transmission SystemAuthors: J. Hörsch, F. Hofmann, D. Schlachtberger, T. BrownandThe role of spatial scale in joint optimisations of generation and transmission for European highly renewable scenariosAuthors: J. Hörsch, T. BrownThe files in this record contain the scripts to build a PyPSA model of the European Electricity System including renewable feed-in from wind, solar and hydro installations derived from reanalysis weather data satellite irradiation. The model PyPSA-Eur is described in the above publication.ScriptsTo use the scripts, you need the following free software Python libraries:PyPSA for the modelling frameworkvresutils for various helper functions to build the model instanceatlite to process weather data into power system datasnakemake to organise the execution of the softwareand other standard libraries from the Python Package Index (PyPI), such as pandas, pyomo, countrycode, etc.snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work.The Python scripts in this repository (in the directory scripts/) are released under the GNU General Public Licence Version 3.0 (GPL 3.0).The scripts build_*.py process all raw input data into a form where it can be used in the model.base_network.py creates the initial PyPSA network topology.add_electricity.py adds generators and storage units to the models, it generates the detailed resolved model described in the PyPSA-Eur paper.simplify_network.py removes stub ac-buses from network topology and simplifies long dc lines.cluster_network.py creates clustered representations of the electricity network for a given number of buses following the topology described in the "spatial scale" paper.prepare_network.py adds parameters like the CO2 limit and the transmission expansion volume relevant for the optimization to the model.All scripts are managed with the snakemake workflow management tool.To run the scripts, adjust the parameters in config.yaml and cluster.yaml to your local configuration. Then simply execute
snakemakefor the rule you want to run.DataThe input data include:Electricity sector dataTopology derived from the analysis of an extract of the ENTSO-E online map using GridKit .A cost database with literature sources.
Authors
- Hörsch, Jonas ;
- Hofmann, Fabian ;
- Schlachtberger, David ;
- Brown, Tom
Supplementary Data (preliminary version)PyPSA-Eur: An Open Optimisation Model of the European Transmission SystemAuthors: J. Hörsch, F. Hofmann, D. Schlachtberger, T. BrownandThe role of spatial scale in joint optimisations of generation and transmission for European highly renewable scenariosAuthors: J. Hörsch, T. BrownThe files in this record contain the scripts to build a PyPSA model of the European Electricity System including renewable feed-in from wind, solar and hydro installations derived from reanalysis weather data satellite irradiation. The model PyPSA-Eur is described in the above publication.ScriptsTo use the scripts, you need the following free software Python libraries:PyPSA for the modelling frameworkvresutils for various helper functions to build the model instanceatlite to process weather data into power system datasnakemake to organise the execution of the softwareand other standard libraries from the Python Package Index (PyPI), such as pandas, pyomo, countrycode, etc.snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work.The Python scripts in this repository (in the directory scripts/) are released under the GNU General Public Licence Version 3.0 (GPL 3.0).The scripts build_*.py process all raw input data into a form where it can be used in the model.base_network.py creates the initial PyPSA network topology.add_electricity.py adds generators and storage units to the models, it generates the detailed resolved model described in the PyPSA-Eur paper.simplify_network.py removes stub ac-buses from network topology and simplifies long dc lines.cluster_network.py creates clustered representations of the electricity network for a given number of buses following the topology described in the "spatial scale" paper.prepare_network.py adds parameters like the CO2 limit and the transmission expansion volume relevant for the optimization to the model.All scripts are managed with the snakemake workflow management tool.To run the scripts, adjust the parameters in config.yaml and cluster.yaml to your local configuration. Then simply execute
snakemakefor the rule you want to run.DataThe input data include:Electricity sector dataTopology derived from the analysis of an extract of the ENTSO-E online map using GridKit .A cost database with literature sources.
Authors
- Hörsch, Jonas ;
- Hofmann, Fabian ;
- Schlachtberger, David ;
- Brown, Tom