The FINE python package provides a framework for modeling, optimizing and assessing energy systems. With the provided framework, systems with multiple regions, commodities and time steps can be modeled. 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.
If you want to use FINE in a published work, please kindly cite following publication which gives a description of the first stages of the framework. The python package which provides the time series aggregation module and its corresponding literatur can be found here.
A "Read the Docs" documentation of FINE can be found here.
You can directly install FINE via pip as follows
pip install FINE
However in this case, the connection to this GitHub repository is not preserved. If you want to preserve it, you can clone a local copy of the repository to your computer
git clone https://github.com/FZJ-IEK3-VSA/FINE.git
Then install FINE via pip as follow
cd FINE pip install .
Or install directly via python as
python setup.py install
A number of examples shows the capabilities of FINE.
Copyright (C) 2016-2020 Lara Welder, Theresa Groß, Leander Kotzur, Robin Beer, Henrik Büsing, Dilara Caglayan, Thomas Grube, Heidi Heinrichs, Maximilian Hoffmann, Timo Kannengießer, Kevin Knosala, Felix Kullmann, Stefan Kraus, Jochen Linßen, Peter Markewitz, Lars Nolting, Jan Priesmann, Bismark Singh, Andreas Smolenko, Peter Stenzel, Chloi Syranidou, Johannes Thürauf, Michael Zier, Martin Robinius, Detlef Stolten
You should have received a copy of the MIT License along with this program. If not, see https://opensource.org/licenses/MIT
We are the Techno-Economic Energy Systems Analysis department at the Institute of Energy and Climate Research: Electrochemical Process Engineering (IEK-3) belonging to the Forschungszentrum Jülich. Our interdisciplinary department's research is focusing on energy-related process and systems analyses. Data searches and system simulations are used to determine energy and mass balances, as well as to evaluate performance, emissions and costs of energy systems. The results are used for performing comparative assessment studies between the various systems. Our current priorities include the development of energy strategies, in accordance with the German Federal Government’s greenhouse gas reduction targets, by designing new infrastructures for sustainable and secure energy supply chains and by conducting cost analysis studies for integrating new technologies into future energy market frameworks.
Within the BMWi funded project METIS we develop together with the RWTH-Aachen (Prof. Aaron Praktiknjo), the EDOM Team at FAU (PD Lars Schewe) and the Jülich Supercomputing Centre new methods and models within FINE.
Dr. Martin Robinius is teaching a course at TU Berlin in which he is introducing FINE to students.
This work was supported by the Helmholtz Association under the Joint Initiative "Energy System 2050 A Contribution of the Research Field Energy".