POMATO - Power Market Tool

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Overview

POMATO stands for (POwer MArket TOol) and is an easy to use tool for the comprehensive analysis of the modern electricity market. It comprises the necessary power engineering framework to account for power flow physics, thermal transport constraints and security policies of the underlying transmission infrastructure, depending on the requirements defined by the user. POMATO was specifically designed to realistically model Flow-Based Market-Coupling (FBMC) and is therefore equipped with a fast security constrained optimal power flow algorithm and allows zonal market clearing with endogenously generated flow-based parameters, and redispatch.

Documentation

Comprehensive documentation is available at pomato.readthedocs.io.

Installation

POMATO is written in python and julia. Python takes care of the data processing and julia runs the economic dispatch and N-1 redundancy removal algorithm.

The recommended way to install POMATO with python and pip:

• Install python for your operating system. On linux based operating systems python is often already installed and available under the python3 command. For Windows install python into a folder of your choice. POMATO is written and tested in python 3.10.

• Install julia for your operating system. POMATO is written and tested with 1.10.

• Add python and julia to the system Path, this allows you to start python and julia directly for the command line without typing out the full path of the installation. PLattform specific instructions on how to do this are part of the julia installation instructions and work analogous for the python .

• Install POMATO through pip in python. It is recommended to create a virtual environment and install pomato into it, but not necessary:

  python -m venv pomato
  ./pomato/Scripts/activate
  pip install git+https://github.com/richard-weinhold/pomato.git
  

This will not only clone the master branch of this repository into the local python environment, but also pull the master branch of the MarketModel and RedundancyRemoval julia packages which are required to run POMATO. This process can take a few minutes to complete.

After this is completed pomato can be imported in python:

from pomato import POMATO

See the POMATO Documentation for further information on the installation process.

Examples

This release includes two examples in the examples folder. Including the contents of this folder into a pomato working directory will allow their execution:

• The IEEE 118 bus network, which contains a singular timestep. The data is available under open license at https://power-grid-lib.github.io/ and re-hosted in this repository.

  $ python /run_pomato_ieee.py
  

• The DE case study, based on data from openly available data sources. The file can be run via

  $ python /run_pomato_de.py
  

See more in depth descriptions of this two case studies along with a FBMC and chance constrained examples, part of the POMATO Documentation.

The examples folder also contains the two examples as Jupyter notebooks. Another possibility to access the functionality of POMATO with an online REPL/Console when running POMATO inside a IDE with an interactive IPython Console (e.g. Spyder) to access POMATO objects and variables.

Release Status

POMATO was part of my PhD that has concluded in 2021. The dissertation is available from TU-Berlin and the slides of the defence here in this repository Link.

This means the model is no longer actively developed, although I try to have an eye on things. So please feel free to contact me, but also know that POMATO is now a hobby that exists beside work.

I have tried to make everything robust by including a documentation and testing, but I am not a software engineer, thus the "program" is not written with robustness in mind and our experience is limited when it comes to common best practices. Expect errors, bug, funky behavior and code structures from the minds of two engineering economists.

Related Publications

• Weinhold and Mieth (2023), Uncertainty-Aware Capacity Allocation in Flow-Based Market Coupling
• Weinhold (2022), Dissertation, Open-source modeling of flow based market coupling (Dissertation - Available from TU - Berlin).
• Weinhold (2021), Dissertation, Open-source modeling of flow based market coupling (Defence Slides).
• Weinhold (2020), Evaluating Policy Implications on the Restrictiveness of Flow-based Market Coupling with High Shares of Intermittent Generation: A Case Study for Central Western Europe
• Weinhold and Mieth (2021), Power Market Tool (POMATO) for the Analysis of Zonal Electricity Markets
• Weinhold and Mieth (2020), Fast Security-Constrained Optimal Power Flow through Low-Impact and Redundancy Screening
• Schönheit, Weinhold, Dierstein (2020), The impact of different strategies for generation shift keys (GSKs) on the flow-based market coupling domain: A model-based analysis of Central Western Europe

POMATO was also presented as part of the 2021 JuliaCon JuMP-dev train. The talk is available on JuliaCon2021 youtube channel.

Acknowledgments

Richard and Robert would like to acknowledge the support of Reiner Lemoine-Foundation, the Danish Energy Agency and Federal Ministry for Economic Affairs and Energy (BMWi). Robert Mieth was funded by the Reiner Lemoine-Foundation scholarship. Richard Weinhold was funded by the Danish Energy Agency through parts of the project. The development of POMATO and its applications was also funded by BMWi in the project “Long-term Planning and Short-term Optimization of the German Electricity System Within the European Context” (LKD-EU, 03ET4028A) and the project “Modellierung (De-)Zentraler Energiewenden: Wechselwirkungen, Koordination und Lösungsansätze aus systemorientierter Perspektive” (MODEZEEN, 03EI1019B).