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README.md

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MIT License
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Copyright (C) 2016-2024 FZJ-ICE-2
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Copyright (C) 2016-2025 FZJ-ICE-2
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Active Developers: Theresa Groß, Kevin Knosala, Noah Pflugradt, Johannes Behrens, Julian Belina, Arne Burdack, Toni Busch, Philipp Dunkel, David Franzmann, Patrick Freitag, Thomas Grube, Heidi Heinrichs, Maximilian Hoffmann, Jason Hu, Shitab Ishmam, Sebastian Kebrich, Felix Kullmann, Jochen Linßen, Rachel Maier, Shruthi Patil, Jan Priesmann, Julian Schönau, Maximilian Stargardt, Lovindu Wijesinghe, Christoph Winkler, Detlef Stolten
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Active Developers: Johannes Behrens, Theresa Klütz, Noah Pflugradt, Julian Belina, Arne Burdack, Toni Busch, Philipp Dunkel, David Franzmann, Maike Gnirß, Thomas Grube, Lars Hadidi, Heidi Heinrichs, Shitab Ishmam, Sebastian Kebrich, Jochen Linßen, Nils Ludwig, Lilly Madeisky, Drin Marmullaku, Gian Müller, Kenneth Okosun, Olalekan Omoyele, Shruthi Patil, Kai Schulze, Julian Schönau, Maximilian Stargardt, Lana Söltzer, Henrik Wenzel, Bernhard Wortmann, Lovindu Wijesinghe, Christoph Winkler, Detlef Stolten
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Alumni: Robin Beer, Henrik Büsing, Dilara Caglayan, Timo Kannengießer, Leander Kotzur, Stefan Kraus, Peter Markewitz, Lars Nolting,Stanley Risch, Martin Robinius, Bismark Singh, Andreas Smolenko, Peter Stenzel, Chloi Syranidou, Johannes Thürauf, Lara Welder, Michael Zier
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Alumni: Robin Beer, Henrik Büsing, Dilara Caglayan, Patrick Freitag, Maximilian Hoffmann, Jason Hu, Timo Kannengießer, Kevin Knosala, Leander Kotzur, Felix Kullmann, Stefan Kraus, Rachel Maier, Peter Markewitz, Lars Nolting, Jan Priesmann, Stanley Risch, Martin Robinius, Bismark Singh, Andreas Smolenko, Peter Stenzel, Chloi Syranidou, Johannes Thürauf, Lara Welder, Michael Zier
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You should have received a copy of the MIT License along with this program.
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If not, see https://opensource.org/licenses/MIT

docs/source/componentsIntroDoc.rst

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**********************
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Energy System Modeling
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**********************
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######################
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For creating your own energy system model, you start adding different components such as sources (e.g.,
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photovoltaic panels), storage units (e.g., batteries), conversion units (e.g., heat pumps), transmission units

docs/source/furtherReadingDoc.rst

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* Caglayan et al. (2019): `The techno-economic potential of offshore wind energy with optimized future turbine designs in Europe <https://www.sciencedirect.com/science/article/abs/pii/S0306261919314813?via%3Dihub>`_ .
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* Kannengießer et al. (2020): `Reducing Computational Load for Mixed Integer Linear Programming: An Example for a District and an Island Energy System <https://www.mdpi.com/1996-1073/12/14/2825>`_ .
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* Kannengießer et al. (2020): `Reducing Computational Load for Mixed Integer Linear Programming: An Example for a District and an Island Energy System <https://doi.org/10.3390/en12142825>`_ .
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* Knosala et al. (2021): `Hybrid Hydrogen Home Storage for Decentralized Energy Autonomy <https://www.sciencedirect.com/science/article/pii/S0360319921013409>`_ .
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* Knosala et al. (2022): `The role of hydrogen in German residential buildings <https://doi.org/10.1016/j.enbuild.2022.112480>`_.
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* Spiller et al. (2022): `Reducing Carbon Emissions from the Tourist Accommodation Sector on Non-Interconnected Islands: A Case Study of a Medium-Sized Hotel in Rhodes, Greece <https://doi.org/10.3390/en15103801>`_.
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* Patil et al. (2022): `Advanced Spatial and Technological Aggregation Scheme for Energy System Models <https://doi.org/10.3390/en15249517>`_.
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* Singh et al. (2022): `Budget-cut: introduction to a budget based cutting-plane algorithm for capacity expansion models. <https://doi.org/10.1007/s11590-021-01826-w>`_.
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* Busch et al. (2023): `The role of liquid hydrogen in integrated energy systems–A case study for Germany <https://doi.org/10.1016/j.ijhydene.2023.05.308>`_.
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* Weinand et al. (2023): `Low-carbon lithium extraction makes deep geothermal plants cost-competitive in future energy systems <https://doi.org/10.1016/j.adapen.2023.100148>`_.
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* Franzmann et al. (2023): `Green hydrogen cost-potentials for global trade <https://doi.org/10.1016/j.ijhydene.2023.05.012>`_.
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* Jacob et al. (2023): `The future role of thermal energy storage in 100% renewable electricity systems <https://doi.org/10.1016/j.rset.2023.100059>`_:

docs/source/index.rst

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:caption: Contents:
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Home <self>
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purposeAndVisionDoc
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purposeDoc
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installationDoc
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usersGuideDoc
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newsDoc

docs/source/installationDoc.rst

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############
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Installation
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############
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*************************
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Mathematical Descriptions
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#########################
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The underlying mathematical structure of FINE leads to big linear optimization problems
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.. math::
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& \min{c^Tx} \\
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s.t. \> & Ax=b \\
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& Cx \leq d
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mixed-integer linear optimization problems
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.. math::
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& \min{c^Tx} \\
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s.t. \> & Ax = b \\
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& Cx \leq d \\
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& x_i \in \mathbb Z \qquad \forall i \in \mathfrak{I}
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or mixed-integer quadratic optimization problems
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.. math::
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& \min{\frac{1}{2}x^TQx+c^Tx} \\
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s.t. \> & Ax=b \\
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& Cx \leq d \\
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& x_i \in \mathbb Z \qquad \forall i \in \mathfrak{I}
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The objective function describes for the case of FINE the total annual cost of the system (which is to be minimized).
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The underlying mathematical structure of FINE leads to big linear optimization problems,
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mixed-integer linear optimization problems, or mixed-integer quadratic optimization problems.
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The objective function describes for the case of FINE the net present value of the system which is to be minimized.
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The constraints enforce that the operation and design of the system is within eligible technical and ecological boundaries.
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Variables represent are for example the capacity of a component or its operation in each region and at each time step.
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Variables are for example the capacity of a component or its operation in each region and at each time step.
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The structure allows to consider several investment periods.
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The following applies: The net present value equals the total annual costs of the system if the modeled time horizon is set up with only one investment period.
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The mathematical description is based on the description in `Welder (2022) <https://publications.rwth-aachen.de/record/861215/files/861215.pdf>`_
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and has been updated with the changes of the latest ETHOS.FINE version.
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.. toctree::
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:maxdepth: 2
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mathematicalDocumentation/parametersAndSetsDoc
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mathematicalDocumentation/basicComponentDoc
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mathematicalDocumentation/sourceSinkDoc
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mathematicalDocumentation/conversionDoc
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mathematicalDocumentation/storageDoc
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mathematicalDocumentation/transmissionDoc
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mathematicalDocumentation/interComponentDoc
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mathematicalDocumentation/objectiveFunctionDoc
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A more detailed description of the underlying mathematical optimization problem will be provided in a future release.
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DC power flow extension
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#######################
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A basic *Transmission* component is modeled with a simple commodity
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exchange based on balance equations and a linear loss factor. However,
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the transmission of a commodity is generally subject to far more complex
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physics. The incorporation of a higher modeling detail of these physics
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into the optimization program has to be seen in the context of
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increasing computation times. With respect to this topic, `Syranidis et
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al. (2018) <https://doi.org/10.1016/j.rser.2017.10.110>`_ reviewed the modeling of
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electrical power flow across transmission networks. They discuss the
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general formulation of an AC power flow with a set of non-linear
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equations for which direct, analytical solutions are rarely feasible and
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which are therefore often solved with iterative methods. Based on the
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premise that the optimization program provided by FINE should stay a
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mixed integer linear program, these equations cannot be incorporated in
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the framework. A linearization of these equations, as provided by the DC
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power flow method, is however suitable for incorporation. The
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linearized equations result in an acceptable increase in computation
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time while increasing the electrical power flow modeling detail to a
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more sophisticated level.
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In the following, the constraints constituting the DC power flow are
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presented, based on the detailed description by `Van den Bergh et
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al. (2014) <https://www.mech.kuleuven.be/en/tme/research/energy_environment/Pdf/wpen2014-12.pdf>`_. The constraints thereby extend
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the *Transmission* component model. In the following, let
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:math:`\mathcal{C}^\text{trans,LPF}\subseteq\mathcal{C}^\text{trans}\subset\mathcal{C}`
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be the set of *Transmission* components that are modeled with a DC power
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flow.
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The constraints that enforce the linear power flow are implemented for
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each component :math:`\text{c}\in\mathcal{C}^\text{trans,LPF}`, for
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all :math:`\text{l}\in\mathcal{L}^\text{c}`, and for all
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:math:`\theta \in \Theta` as
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.. math::
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\begin{aligned}
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o_\text{$\omega$,a,$\theta$}-o_{\omega,\hat{\text{a}},\theta}=\left(\phi^\text{c,l$_1$,$\theta$}-\phi_\text{c,l$_2$,$\theta$}\right) / \text{x}_\text{c,a}~.
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\end{aligned}
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Here, :math:`\phi_\text{c,l,p,t}\in\mathbb{R}` is the
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variable which models the phase
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angle. :math:`\text{x}_\text{c,a}`
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represents the electric reactance of the line between locations
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l\ :math:`_1` and l\ :math:`_2` (:math:`\text{a} \in \mathcal{A}_\text{c}`). These equations
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leave one degree of freedom for the phase angle variables at each time
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step. To obtain a unique solution, an additional set of constraints is
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given by
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.. math::
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\begin{aligned}
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\phi_\text{c,l$_\text{ref}$,$\theta$}=0
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\end{aligned}
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for each component :math:`\text{c}\in\mathcal{C}^\text{trans,LPF}`
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and for all :math:`\theta \in \Theta` which
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sets the phase angle for one location :math:`\text{l}_\text{ref}` to
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zero.
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At this point, it should be remarked that the reactance parameter is in
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practice a function of the capacity of the line. The capacity expansion of transmission lines modeled with a *DC power flow* is not implemented to reduce model complexity,
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i.e., AC line capacities, which are modeled with a *DC power flow*, are kept at a fixed value and thus their reactance parameters remain constant.

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