How to use eTraGo

Once the eTraGo application has been installed, optimization runs can be initiated by executing the appl.py script, which is located in the ./eTrago/etrago/ directory. This may be done, for example, by entering the following command:

$ python3 appl.py

The etrago.appl module presents the user interface and may be edited with the preferred Python editor. Within this file, the scenario settings, parameters and calculation methods are defined within a Python dictionary, referred to as args. It is important to comprehend the parameters in order to execute the desired calculation. It should be noted that some parameters are mutually exclusive, and thus, their usage must be carefully considered. Further information can be found in the subsequent section (Section Functionalities) or in the documentation of all defined parameters from the args dictionary, accessible in the method etrago.appl.run_etrago().

As an alternative approach, the args dictionary can be modified through the use of a JSON-file. The path to the JSON-file must be specified during the initialization of the Etrago object. Once a path is provided, the args dictionary in the appl.py is disregarded and substituted with the dictionary from the JSON-file.

The etrago.appl contains the function etrago.appl.run_etrago() which uses the defined args dictionary to start the desired calculation.

In order to enhance the efficiency of the optimization process using the selected solver, it may be beneficial to consider the utilisation of solver options (which form part of the args dictionary). For Gurobi, the some preferrable settings are outlined in Section Functionalities.

For more specific or extensive changes you are kindly invited to write code and add new functionalities. Please see Section Contributing.

Once the calculation has finished, the Etrago object will contain all of the resulting data. Some principal results (e.g. annual system costs) are calculated by the method etrago.analyze.calc_results(). Additionally, several plotting functions are available within the module etrago.analyze.plot. To save the results, you can write them to csv-files. You can specify this export within the args.

Examples and Tutorial Notebooks

On the Github-Repository you can find example workflows demonstrating how to use eTraGo:

• minimal_example provides a basic introduction to the core handling and functionalities of eTraGo.

• analysis_example uses publicly available results from a published study to illustrate how to analyse eTraGo outputs.

Troubleshooting

Installation with cartopy

If you run into problems when using cartopy, try to install shapely without binaries:

$ pip3 install shapely --no-binary shapely