ZEN-garden

Optimization model for energy systems and value chains

Contact: Jacob Mannhardt

Overview of the ZEN-garden
Figure 1: Overview of ZEN-garden's code execution (panel a) and input data structure (panels b and c), which are strictly separated. The folder tree in panel b) shows the structure of the input datasets, corresponding to the energy system in panel c).

Energy transition models are essential to outline possible pathways from today's carbon-intensive system to a future decarbonized system. With the focus on transition pathways of sector-coupled energy systems, models have become increasingly diverse in terms of technological, spatial, and temporal detail. This increase in complexity and diversity complicates the handling of input data: Adapting or creating new input datasets is time-consuming, frustrating, and error-prone. Input-data handling is further complicated when investigating variations of the same dataset in scenarios and sensitivity analyses.

ZEN-garden (Zero-emission Energy Networks) is an open-source optimization software designed to navigate the increasing complexity of both the optimization problem and the input data handling. ZEN-garden optimizes long-term transition pathways of sector-coupled energy systems. The software supports the possibility of creating a multitude of models that together form a garden of widely different technological, temporal, and spatial scopes. As an energy system optimization model, ZEN-garden supplies the exogenous demands of various energy carriers at the lowest cost or emission by installing and operating conversion, storage, and transport technologies.

To support research focused on the transition of sector-coupled energy systems toward net-zero emissions, ZEN-garden is built upon two principles: Optimizing highly complex sector-coupled energy transition pathways and supporting user-friendly data handling through small, flexible, and robust input datasets. ZEN-garden separates the codebase from the input data to allow for very diverse case studies. Lightweight and intuitive input datasets and unit consistency checks reduce user errors and facilitate using ZEN-garden for both novice and experienced energy system modelers.

Implemented in Python, the codebase is strictly separated from the input data. Thus, using ZEN-garden as such does not require prior Python knowledge beyond simple package execution, e.g., in a command line or Jupyter Notebook, which lowers the entry barriers for new users. ZEN-garden is used in teaching the introductory optimization course iMOSES at ETH Zurich.

Find below the published results of two energy system optimization models built in ZEN-garden.

Evolution of the hydrogen production and transport infrastructure
Figure 2: Evolution of the hydrogen production and transport infrastructure in the regional model for net-zero hydrogen supply chains, from Ganter et al. (https://doi.org/10.1016/j.rser.2024.114314)
Myopic foresight
Figure 3: European electricity generation capacities from 2022 until 2050 under perfect foresight and instantaneous technology deployment (left bars) and under myopic foresight and constrained technology deployment (right bars), from Mannhardt et al. (https://doi.org/10.1016/j.isci.2024.111369)