pommesdispatch 0.1.0

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

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.

criterion	manifestation
Purpose	- simulation of power plant dispatch and day-ahead prices for DE (scenario analysis)
Spatial coverage	- Germany (DE-LU) + electrical neighbours (NTC approach)
Time horizon	- usually 1 year in hourly resolution
Technologies	- conventional power plants, storages, demand response (optimized) - renewable generators (fixed) - demand: exogenous time series
Data sources	- input data not shipped out, but can be obtained from pommesdata; OPSD, BNetzA, ENTSO-E, others
Implementation	- graph representation & linear optimization: oemof.solph / pyomo - data management: python / .csv

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.

The following people have contributed in the following manner to pommesdispatch:

Name	Contribution	Status
Johannes Kochems	major development & conceptualization conceptualization, core functionality (esp. dispatch, power prices, demand response, rolling horizon modeling), architecture, publishing process	coordinator & maintainer, developer & corresponding author
Yannick Werner	major development & conceptualization conceptualization, core functionality (esp. exchange, RES, CHP modeling), interface to pommesdata	developer & corresponding author
Johannes Giehl	development early-stage core functionality	developer
Benjamin Grosse	development support for conceptualization, early-stage contributions at the interface to pommesdata	developer
Sophie Westphal	development early-stage contributions at the interface to pommesdata	former developer (student assistant)
Flora von Mikulicz-Radecki	testing early-stage comprehensive testing	former tester (student assistant)
Carla Spiller	development early-stage rolling horizon and cross-border exchange integration	former developer (student assistant)
Fabian Büllesbach	development early-stage rolling horizon implementation	former developer (master's student)
Timona Ghosh	development early-stage cross-border exchange implementation	former developer (master's student)
Paul Verwiebe	support support of early-stage core functionality development	former supporter (research associate)
Leticia Encinas Rosa	support support of early-stage core functionality development	former supporter (research associate)
Joachim Müller-Kirchenbauer	support & conceptualization early-stage conceptualization, funding	supporter (university professor)

Note: Not every single contribution is reflected in the current version of pommesdispatch. This is especially true for those marked as early-stage contributions that may have been extended, altered or sometimes discarded. Nonetheless, all people listed have made valuable contributions. The ones discarded might be re-integrated at some point in time. Dedicated contributions to pommesdata and pommesinvest are not included in the list, but listed individually for these projects.

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.; 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.

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 2021 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.