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PyPSA-Xplore

PyPSA-Xplore is a collection of Jupyter notebooks for exploring and visualizing results from PyPSA-X models (e.g., PyPSA-Spain, PyPSA-Eur).

Installation

Clone the repository:

git clone https://github.com/cristobal-GC/pypsa-Xplore

or download the ZIP file and extract it locally.

Usage

  1. Open params.yaml and set the path to your PyPSA-X model:

    rootpath: /path/to/your/PyPSA-X/model/
  2. Open any notebook and run it. Using the same Python environment as your PyPSA-X model is recommended.

Repository Structure

pypsa-Xplore/
│
├── Xplore_rules/              # Notebooks for main workflow rules (network building process)
├── Xplore_rules_heating/      # Notebooks for heating sector rules
├── Xplore_rules_sectors/      # Notebooks for multi-sector rules
├── Xplore_rules_transport/    # Notebooks for transport sector rules
├── Xplore_rules_shipping/     # Notebooks for shipping sector rules
├── Xplore_rules_biomass/      # Notebooks for biomass sector rules
├── Xplore_rules_industry/     # Notebooks for industry sector rules
├── Xplore_data/               # Notebooks for data from PyPSA-Eur
├── Xplore_data_ES/            # Notebooks for data from PyPSA-Spain
│
├── functions/                 # Helper functions for data processing and visualization
│
└── params.yaml                # General parameters for PyPSA-Xplore

Each notebook in Xplore_rules*/ corresponds to a rule from the model workflow. Fill in the parameters section with the values from your PyPSA-X configuration file.

Contributing

Contributions are welcome! If you’d like to improve or extend PyPSA-Xplore, feel free to:

  • Open an issue to report a bug or suggest an enhancement.
  • Submit a pull request with your proposed changes.

License

This project is licensed under the MIT License — feel free to use and modify it for your own research or projects.

About

PyPSA-Xplore provides a collection of Jupyter notebooks for step-by-step analysis of results obtained with PyPSA-X models (e.g., PyPSA-Spain, PyPSA-Eur, etc.). It is designed to help users explore, visualize, and understand model outputs in a structured and reproducible way.

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