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Contributions to improve and further develop the models and comments and feedback are always welcome!

Get in contact with our institute management or directly file a pull request on GitHub.

FINE

FINE

The FINE python package provides a framework for modeling, optimizing and assessing energy systems with multiple regions, commodities and time steps. Target of the optimization is the minimization of the total annual cost while considering technical and enviromental constraints. Besides using the full temporal resolution, an interconnected typical period storage formulation can be applied, that reduces the complexity and computational time of the model.

GLAES

GLAES

GLAES is a framework for conducting land eligibility analyses and is designed to easily incorporate disparate geospatial information from a variety of sources into a unified solution. The main purpose of GLAES is performing land eligibility analyses which are used to determine which areas within a region are deemed eligible for distributed renewable energy ressources, such as onshore wind and open-field solar parks.

tsam

tsam

tsam is a python package which uses different machine learning algorithms for the aggregation of typical periods. It is applicable for all type of time series, either weather data, load data or both simultaneously. The module is able to significantly reduce input time series for energy system models, and therefore the model's complexity and computational time.


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