ZEN-garden

Welcome to the ZEN-garden! ZEN-garden is an optimization model of energy systems and value chains. 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.

In the file documentation/how_to_ZEN-garden.md, you can find additional information on how to use the framework. The documentation/dataset_creation_tutorial.md file contains a tutorial on how to create a simple dataset for the framework. Additionally, example datasets are available in the dataset_examples folder.

More in-depth manuals are available in the discussions forum of our repo.

News

Review recent modifications outlined in the changelog.

Citing ZEN-garden

If you use ZEN-garden for research please cite

Ganter Alissa, Gabrielli Paolo, Sansavini, Giovanni (2024). Near-term infrastructure rollout and investment strategies for net-zero hydrogen supply chains 2024. https://doi.org/10.1016/j.rser.2024.114314

and use the following BibTeX:

@article{GANTER2024114314,
author = {Alissa Ganter and Paolo Gabrielli and Giovanni Sansavini}
title = {Near-term infrastructure rollout and investment strategies for net-zero hydrogen supply chains},
journal = {Renewable and Sustainable Energy Reviews},
volume = {194},
pages = {114314},
year = {2024},
issn = {1364-0321},
doi = {https://doi.org/10.1016/j.rser.2024.114314},
url = {https://www.sciencedirect.com/science/article/pii/S1364032124000376},
}