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FINE - Framework for Integrated Energy System Assessment

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.

Features

  • representation of an energy system by multiple locations, commodities and time steps
  • complexity reducing storage formulation based on typical periods

Documentation

A "Read the Docs" documentation of FINE can be found here.

Requirements

Framework

The FINE Framework itself requires the following components:

  • FINE sourcecode
  • Python dependencies
  • A Mixed Integer Linear Programming (MILP) solver like Gurobi or GLPK

Installation

The installation proceedure requires:

  • Git
  • Anaconda

Installation of framework and dependencies

Installation requirements

  1. Install anaconda by choosing your operating system here. If you are a Windows 10 user, remember to tick "Add Anaconda to my PATH environment variable" during installation under "Advanced installations options".
  2. Install git from https://git-scm.com/downloads

Prepare folder

  1. Open a prompt e.g. "anaconda prompt" or "cmd" from the windows start menu
  2. Make a folder where you want to work, for example C:\Users<your username>\work with "mkdir C:\Users<your username>\work"
  3. Go to that directory with "cd C:\Users<your username>\work" at the command line

Get source code via GIT

Clone public repository or repository of your choice first

git clone https://github.com/FZJ-IEK3-VSA/FINE.git 

Move into the FINE folder with

cd fine

Installation for users

It is recommended to create a clean environment with conda to use FINE because it requires many dependencies.

conda env create -f requirements.yml

This directly installs FINE and its dependencies in the FINE conda environment. Activate the created environment with:

activate FINE

Installation for developers

Create a development environment if you want to modify it. Install the requirements in a clean conda environment:

conda env create -f requirements_dev.yml
activate FINE_dev

This installs FINE and its requirements for development (testing, formatting). Further changes in the current folder are reflected in package installation through the installation with pip -e.

Run the test suite with:

pytest --cov=FINE test/

Installation of an optimization solver

FINE requires an MILP solver which can be accessed using PYOMO. There are three standard solvers defined:

  • GUROBI
    • Recommended due to better performance but requires license (free academic version available)
    • Set as standard solver
  • GLPK
    • Free version available
  • CBC
    • Free version available

Gurobi installation

The installation requires the following three components:

  • Gurobi Optimizer
    • In order to download the software you need to create an account and obtain a license.
  • Gurobi license
    • The license needs to be installed according to the instructions in the registration process.
  • Gurobi python api

GLPK installation

A complete installation instruction for Windows can be found here.

CBC

Installation procedure can be found here.

Examples

A number of examples shows the capabilities of FINE.

License

MIT License

Copyright (C) 2016-2022 FZJ-IEK-3

Active Developers: Theresa Groß, Kevin Knosala, Noah Pflugradt, Johannes Behrens, Julian Belina, Arne Burdack, Toni Busch, Philipp Dunkel, Patrick Freitag, Thomas Grube, Heidi Heinrichs, Maximilian Hoffmann, Shitab Ishmam, Stefan Kraus, Felix Kullmann, Jochen Linßen, Rachel Maier, Peter Markewitz, Lars Nolting, Shruthi Patil, Jan Priesmann, Stanley Risch, Julian Schönau, Bismark Singh, Maximilian Stargardt, Christoph Winkler, Michael Zier, Detlef Stolten

Alumni: Robin Beer, Henrik Büsing, Dilara Caglayan, Timo Kannengießer, Leander Kotzur, Martin Robinius, Andreas Smolenko, Peter Stenzel, Chloi Syranidou, Johannes Thürauf, Lara Welder

You should have received a copy of the MIT License along with this program. If not, see https://opensource.org/licenses/MIT

About Us

Institut TSA

We are the Institute of Energy and Climate Research - Techno-economic Systems Analysis (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.

Contributions and Users

Within the BMWi funded project METIS we develop together with the RWTH-Aachen (Prof. Aaron Praktiknjo), the EDOM Team at FAU (PD Bismark Singh) and the Jülich Supercomputing Centre new methods and models within FINE.

METIS Team

           

Acknowledgement

This work was supported by the Helmholtz Association under the Joint Initiative "Energy System 2050 A Contribution of the Research Field Energy".

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