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abstract: "pommesdispatch. A bottom-up fundamental power market model for the German electricity sector. https://github.com/pommes-public/pommesdispatch, accessed YYYY-MM-DD."
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authors:
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- family-names: Kochems
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given-names: Johannes
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orcid: "https://orcid.org/0000-0002-3461-3679"
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- family-names: Werner
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given-names: Yannick
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orcid: "https://orcid.org/0000-0002-6674-805X"
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- family-names: Giehl
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given-names: Johannes
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orcid: "https://orcid.org/0000-0002-1769-1907"
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- family-names: Grosse
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given-names: Benjamin
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orcid: "https://orcid.org/0000-0002-3323-9734"
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- family-names: Westphal
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given-names: Sophie
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- family-names: "Mikulicz-Radecki"
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given-names: Flora
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- family-names: Spiller
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given-names: Carla
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- family-names: "Büllesbach"
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given-names: Fabian
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- family-names: Ghosh
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given-names: Timona
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- family-names: Verwiebe
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given-names: Paul
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orcid: "https://orcid.org/0000-0002-6877-2846"
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- family-names: "Encinas-Rosa"
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given-names: Leticia
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- family-names: "Müller-Kirchenbauer"
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given-names: Joachim
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cff-version: "1.1.0"
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date-released: 2021-10-15
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doi: https://doi.org/
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keywords:
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- "power market"
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- "fundamental model"
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- "dispatch"
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- "power price"
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- "oemof.solph"
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license: "MIT license"
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message: "If you use this software, please cite as follows."
**A bottom-up fundamental power market model for the German electricity sector**
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This is the **dispatch variant** of the fundamental power market model *POMMES* (**PO**wer **M**arket **M**odel of **E**nergy and re**S**ources).<br>
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This is the **dispatch variant** of the fundamental power market model *POMMES* (**PO**wer **M**arket **M**odel of **E**nergy and re**S**ources).
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Please navigate to the section of interest to find out more.
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## Contents
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*[Introduction](#introduction)
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*[Documentation](#documentation)
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*[Installation](#installation)
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*[Setting up the environment](#setting-up-the-environment)
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*[Setting up pommesdispatch](#setting-up-pommesdispatch)
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*[Installing a solver](#installing-a-solver)
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*[Contributing](#contributing)
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*[Citing](#citing)
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*POMMES* itself is a cosmos consisting of a **dispatch model** (stored in this repository and described here), a **data preparation routine** and an **investment model** for the German wholesale power market. The model was originally developed by a group of researchers and students at the [chair of Energy and Resources Management of TU Berlin](https://www.er.tu-berlin.de/menue/home/) and is now maintained by a group of alumni and open for other contributions.
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If you are interested in the data preparation routines used or investment modeling, please find more information here:
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-[pommes-data](https://github.com/pommes-public/pommes-data): A full-featured transparent data preparation routine from raw data to POMMES model inputs
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-pommes-invest: A multi-period integrated investment and dispatch model for the German power sector (upcoming).
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-[pommesdata](https://github.com/pommes-public/pommesdata): A full-featured transparent data preparation routine from raw data to POMMES model inputs
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-pommesinvest: A multi-period integrated investment and dispatch model for the German power sector (upcoming).
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### Purpose and model characterization
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The **dispatch variant** of the power market model *POMMES*`pommes-dispatch` enables the user to simulate the **dispatch of backup power plants, storages as well as demand response units for the Federal Republic of Germany** for an arbitrary year or timeframe between 2017 and 2030. The dispatch of renewable power plants is exogeneously determined by normalized infeed time series and capacity values. The models' overall goal is to minimize power system costs occuring from wholesale markets whereby no network constraints are considered except for the existing bidding zone configuration used for modeling electricity exchange. Thus, the model purpose is to simulate **dispatch decisions** and the resulting **day-ahed market prices**. A brief categorization of the model is given in the following table. An extensive categorization can be found in the [model documentation]().
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The **dispatch variant** of the power market model *POMMES*`pommesdispatch` enables the user to simulate the **dispatch of backup power plants, storages as well as demand response units for the Federal Republic of Germany** for an arbitrary year or timeframe between 2017 and 2030. The dispatch of renewable power plants is exogeneously determined by normalized infeed time series and capacity values. The models' overall goal is to minimize power system costs occuring from wholesale markets whereby no network constraints are considered except for the existing bidding zone configuration used for modeling electricity exchange. Thus, the model purpose is to simulate **dispatch decisions** and the resulting **day-ahed market prices**. A brief categorization of the model is given in the following table. An extensive categorization can be found in the [model documentation]().
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|**criterion**|**manifestation**|
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| ---- | ---- |
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| Purpose | - simulation of power plant dispatch and day-ahead prices for DE (scenario analysis) |
| Time horizon | - usually 1 year in hourly resolution |
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| Technologies | - conventional power plants, storages, demand response (optimized)<br> - renewable generators (fixed)<br> - demand: exogenous time series |
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| Data sources | - OPSD, BNetzA, ENTSO-E, others (see [pommes-data](https://github.com/pommes-public/pommes-data))|
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| Implementation | - graph representation & linear optimization: oemof.solph / pyomo<br> - data management: python / .csv |
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| Data sources | - input data not shipped out, but can be obtained from [pommesdata](https://github.com/pommes-public/pommesdata); OPSD, BNetzA, ENTSO-E, others|
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| Implementation | - graph representation & linear optimization: [oemof.solph](https://github.com/oemof/oemof-solph) / [pyomo](https://github.com/Pyomo/pyomo)<br> - data management: python / .csv |
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### Mathematical and technical implementation
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The models' underlying mathematical method is a **linear programming** approach, seeking to minimize overall power system costs under constraints such as satisfying power demand at all times and not violating power generation capacity or storage limits. Thus, binary variables such as units' status, startups and shutdowns are not accounted for.
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The models' underlying mathematical method is a **linear programming** approach, seeking to minimize overall
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power system costs under constraints such as satisfying power demand at all times and not violating power generation
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capacity or storage limits. Thus, binary variables such as units' status, startups and shutdowns are not accounted for.
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The model builds on the framework **[oemof.solph](https://github.com/oemof/oemof-solph)** which allows to model energy systems in a graph-based representation with the underlying mathematical constraints and objective function terms implemented in **[pyomo](https://pyomo.readthedocs.io/en/stable/)**. Some of the required oemof.solph features - such as demand response modeling - have been provided by the *POMMES* core developers which are also active in the oemof community. Users not familiar with oemof.solph may find further information in the [oemof.solph documentation](https://oemof-solph.readthedocs.io/en/latest/readme.html).
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The model builds on the framework **[oemof.solph](https://github.com/oemof/oemof-solph)** which allows modeling
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energy systems in a graph-based representation with the underlying mathematical constraints and objective function
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terms implemented in **[pyomo](https://pyomo.readthedocs.io/en/stable/)**. Some of the required oemof.solph featuresm - such as demand response modeling - have been provided by the *POMMES* main developers which are also active in the
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oemof community. Users not familiar with oemof.solph may find further information in the
An extensive **[documentation of pommes-dispatch]()** can be found on readthedocs. It contains a model categorization, some energy economic and technical background information as well as documentation of the model functions and classes.
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An extensive **[documentation of pommesdispatch](https://pommesdispatch.readthedocs.io/)** can be found on readthedocs. It contains a user's guide, a model categorization, some energy economic and technical background information, a complete model formulation as well as documentation of the model functions and classes.
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## Installation
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To set up `pommes-dispatch`, you have to set up a virtual environment (e.g. using conda) or add the required packages to your python installation. Additionally, you have to install a solver in order to solve the mathematical optimization problem.
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To set up `pommesdispatch`, set up a virtual environment (e.g. using conda) or add the required packages to your python installation. Additionally, you have to install a solver in order to solve the mathematical optimization problem.
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### Setting up the environment
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`pommes-dispatch` is (to be) hosted on [PyPI](). To install it, please use the following command
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### Setting up pommesdispatch
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`pommesdispatch` is hosted on [PyPI](https://pypi.org/project/pommesdispatch/).
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To install it, please use the following command
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```
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pip install pommes-dispatch
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pip install pommesdispatch
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```
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For now, you still have to clone the environment and copy the files locally by typing
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If you want to contribute as a developer, you fist have to
After cloning the repository, you have to install the required dependencies. Make sure you have conda installed as a package manager. If not, you can download it [here](https://www.anaconda.com/). Open a command shell and navigate to the folder where you copied the environment to. Use the following command to install dependencies
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After cloning the repository, you have to install the required dependencies.
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Make sure you have conda installed as a package manager.
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If not, you can download it [here](https://www.anaconda.com/).
In order to solve a `pommes-dispatch` model instance, you need a solver installed. Please see [oemof.solph's information on solvers](https://github.com/oemof/oemof-solph#installing-a-solver). As a default, gurobi is used for `pommes-dispatch` models. It is a commercial solver, but provides academic licenses, though, if this applies to you. Elsewhise, we recommend to use CBC as the solver oemof recommends. To test your solver and oemof.solph installation, again see information from [oemof.solph](https://github.com/oemof/oemof-solph#installation-test).
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In order to solve a `pommesdispatch` model instance, you need a solver installed. Please see [oemof.solph's information on solvers](https://github.com/oemof/oemof-solph#installing-a-solver). As a default, gurobi is used for `pommesdispatch` models. It is a commercial solver, but provides academic licenses, though, if this applies to you. Elsewhise, we recommend to use CBC as the solver oemof recommends. To test your solver and oemof.solph installation, again see information from [oemof.solph](https://github.com/oemof/oemof-solph#installation-test).
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## Contributing
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Every kind of contribution or feedback is warmly welcome.<br>
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We use the GitHub issue management as well as pull requests for collaboration. We try to stick to the PEP8 coding standards.
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We use the [GitHub issue management](https://github.com/pommes-public/pommesdispatch/issues) as well as
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[pull requests](https://github.com/pommes-public/pommesdispatch/pulls) for collaboration. We try to stick to the PEP8 coding standards.
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The following people have contributed in the following manner to `pommesdispatch`:
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| Name | Contribution | Status |
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| ---- | ---- | ---- |
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| Johannes Kochems | major development & conceptualization<br>conceptualization, core functionality (esp. dispatch, power prices, demand response, rolling horizon modeling), architecture, publishing process | coordinator & maintainer,<br>developer & corresponding author |
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| Yannick Werner | major development & conceptualization<br>conceptualization, core functionality (esp. exchange, RES, CHP modeling), interface to pommesdata | developer & corresponding author |
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| Johannes Giehl | development<br>early-stage core functionality | developer |
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| Benjamin Grosse | development<br>support for conceptualization, early-stage contributions at the interface to pommesdata | developer |
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| Sophie Westphal | development<br>early-stage contributions at the interface to pommesdata | former developer (student assistant) |
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| Flora von Mikulicz-Radecki | testing<br>early-stage comprehensive testing | former tester (student assistant) |
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| Carla Spiller | development<br>early-stage rolling horizon and cross-border exchange integration | former developer (student assistant) |
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| Fabian Büllesbach | development<br>early-stage rolling horizon implementation | former developer (master's student) |
| Paul Verwiebe | support<br>support of early-stage core functionality development | former supporter (research associate) |
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| Leticia Encinas Rosa | support<br>support of early-stage core functionality development | former supporter (research associate) |
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| Joachim Müller-Kirchenbauer | support & conceptualization<br>early-stage conceptualization, funding | supporter (university professor) |
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*Note: Not every single contribution is reflected in the current version of
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`pommesdispatch`. This is especially true for those marked as early-stage
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contributions that may have been extended, altered or sometimes discarded.
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Nonetheless, all people listed have made valuable contributions. The ones
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discarded might be re-integrated at some point in time.
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Dedicated contributions to `pommesdata` and `pommesinvest` are not included
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in the list, but listed individually for these projects.*
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## Citing
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A publication using and introducing `pommes-dispatch` is currently in preparation.
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A publication using and introducing `pommesdispatch` is currently in preparation.
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If you are using `pommesdispatch` for your own analyses, we recommend citing as:<br>
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*Kochems, J.; Werner, Y.; Giehl, J.; Grosse, B. et al. (2021): pommesdispatch. A bottom-up fundamental power market model for the German electricity sector. https://github.com/pommes-public/pommesdispatch, accessed YYYY-MM-DD.*
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If you are using `pommes-dispatch` for your own analyses, please cite as:<br>
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*Kochems, J.; Werner, Y.; Giehl, J.; Grosse, B. et al. (2021): pommes-dispatch. A bottom-up fundamental power market model for the German electricity sector. https://github.com/pommes-public/pommes-dispatch, accessed YYYY-MM-DD.*
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We furthermore recommend naming the version tag or the commit hash used for the sake of transparency and reproducibility.
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We furthermore recommend to name the version tag or the commit hash used for the sake of transparancy and reproducibility.
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Also see the *CITATION.cff* file for citation information.
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