pommesdispatch

A bottom-up fundamental power market model for the German electricity sector

This is the dispatch variant of the fundamental power market model POMMES (POwer Market Model of Energy and reSources).
Please navigate to the section of interest to find out more.

Contents

• Introduction
• Documentation
• Installation
  • Setting up pommesdispatch
  • Installing a solver
• Contributing
• Citing
• License

Introduction

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 and is now maintained by a group of alumni and open for other contributions.

If you are interested in the data preparation routines used or investment modeling, please find more information here:

• pommesdata: A full-featured transparent data preparation routine from raw data to POMMES model inputs
• pommesinvest: A multi-period integrated investment and dispatch model for the German power sector (upcoming).

Purpose and model characterization

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.

Mathematical and technical implementation

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.

The model builds on the framework oemof.solph which allows modeling
energy systems in a graph-based representation with the underlying mathematical constraints and objective function
terms implemented in pyomo. 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
oemof community. Users not familiar with oemof.solph may find further information in the
oemof.solph documentation.

Documentation

An extensive documentation of pommesdispatch 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.

Installation

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.

Setting up pommesdispatch

pommesdispatch is hosted on PyPI.
To install it, please use the following command

pip install pommesdispatch

If you want to contribute as a developer, you fist have to
fork
it and then clone the repository, in order to copy the files locally by typing

git clone https://github.com/your-github-username/pommesdispatch.git

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.
Open a command shell and navigate to the folder
where you copied the environment to.

Use the following command to install dependencies

conda env create -f environment.yml

Activate your environment by typing

conda activate pommes_dispatch

Installing a solver

In order to solve a pommesdispatch model instance, you need a solver installed. Please see oemof.solph's information on solvers. 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.

Contributing

Every kind of contribution or feedback is warmly welcome.
We use the GitHub issue management as well as
pull requests for collaboration. We try to stick to the PEP8 coding standards.

Authors

• Authors of pommesinvest are Johannes Kochems and Yannick Werner. It is maintained by Johannes Kochems.
• All people mentioned below contributed to early-stage versions or predecessors of POMMES or ideally supported it.

List of contributors to POMMES

The following people have contributed to POMMES.
Most of these contributions belong to early-stage versions and are not part
of the actual source code. Nonetheless, all contributions shall be acknowledged and the full list is provided for transparency reasons.

The main contributors are stated on top, the remainder
is listed in alphabetical order.

Citing

A publication using and introducing pommesdispatch is currently in preparation.

If you are using pommesdispatch for your own analyses, we recommend citing as:
Kochems, J. and Werner, Y. (2024): pommesdispatch. A bottom-up fundamental power market model for the German electricity sector. https://github.com/pommes-public/pommesdispatch, accessed YYYY-MM-DD.

We furthermore recommend naming the version tag or the commit hash used for the sake of transparency and reproducibility.

Also see the CITATION.cff file for citation information.

License

This software is licensed under MIT License.

Copyright 2024 pommes developer group

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.