ZEN-garden is an optimization model of energy systems and value chains.

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ZEN-garden

Python Version from PEP 621 TOML

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Welcome to the ZEN-garden! ZEN-garden is an optimization framework for energy transition pathways. It is currently used to model the electricity system, hydrogen value chains, and carbon capture, storage and utilization (CCUS) value chains. However, it is designed to be modular and flexible, and can be extended to model other types of energy systems, value chains or other network-based systems.

ZEN-garden is developed by the Reliability and Risk Engineering Laboratory at ETH Zurich.

Quick Start

To get started with ZEN-garden, you can follow the instructions in the installation guide.

If you want to use ZEN-garden without working on the codebase, run the following command:

pip install zen-garden

If you want to work on the codebase, fork and clone the repository and install the package in editable mode. More information on how to install the package in editable mode can be found in the installation guide.

Documentation

Please refer to the documentation of the ZEN-garden framework on Read-the-Docs. Additionally, example datasets are available in the dataset_examples folder and described in the documentation.

News

Review recent modifications outlined in the changelog.

Citing ZEN-garden

If you use ZEN-garden for research, please cite

Jacob Mannhardt, Alissa Ganter, Johannes Burger, Francesco De Marco, Lukas Kunz, Lukas Schmidt-Engelbertz, Paolo Gabrielli, Giovanni Sansavini (2025). ZEN-garden: Optimizing energy transition pathways with user-oriented data handling. https://www.sciencedirect.com/science/article/pii/S2352711025000263

and use the following BibTeX:

@article{ZENgarden2025,
title = {ZEN-garden: Optimizing Energy Transition Pathways with User-Oriented Data Handling},
author = {Mannhardt, Jacob and Ganter, Alissa and Burger, Johannes and De Marco, Francesco and Kunz, Lukas and {Schmidt-Engelbertz}, Lukas and Gabrielli, Paolo and Sansavini, Giovanni},
year = {2025},
journal = {SoftwareX},
volume = {29},
pages = {102059},
issn = {2352-7110},
doi = {10.1016/j.softx.2025.102059},
}

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