Open-TYNDP: Open-Sourcing the EU's Ten-Year Network Development Planning

[!WARNING] Development Status: Open-TYNDP is currently under active development and is fully operational not yet feature-complete. Users must be aware of current limitations and the development stage before utilizing the model.

Project Overview

The Open-TYNDP initiative is a research and innovation collaboration between the European Network of Transmission System Operators for Electricity (ENTSO-E) and Open Energy Transition (OET).

The primary objective is to investigate the integration of PyPSA into the Ten-Year Network Development Plan (TYNDP). By leveraging a workflow based on PyPSA-Eur, the project aims to augment existing TYNDP tools, specifically focusing on:

1. Scenario Building (SB)
2. Cost-Benefit Analysis (CBA)

Strategic Goals

The project is driven by a desire to increase transparency and reduce the barriers that prevent stakeholders from participating in European energy planning. Furthermore, it serves as a global pilot to prove that open-source (OS) frameworks are viable for large-scale energy planning.

Implementation Roadmap

To establish reproducibility and trust, the project follows a phased approach:

• Phase 1: Replicate key data and figures from the 2024 TYNDP cycle.
• Phase 2: Align the toolchain with the 2026 TYNDP cycle.
• Phase 3: Develop new OS features to fill existing functional gaps.
• Phase 4: Integrate dynamic visualization tools and data interoperability standards.
• Phase 5: Publish best practices and conduct interactive stakeholder workshops.

In the context of the Cost-Benefit Analysis (CBA), the project evaluates the economic viability of network expansions, which can be conceptually represented as: $\text{Net Benefit} = \sum_{t=1}^{n} \frac{\text{Benefits}_t - \text{Costs}_t}{(1+r)^t}$ (Where $r$ is the discount rate and $t$ is the time horizon).

Technical Architecture & Philosophy

This repository functions as a soft-fork of OET/PyPSA-Eur.

The "No Intermediaries" Philosophy

The project adheres to a strict data philosophy: no intermediate results are stored in the repository. Every output must be computable directly from the raw data and the provided source code.

Soft-Fork Strategy

OET employs a specific strategy to balance customization with community contribution:

Repository Structure

The project utilizes Snakemake for workflow management. Below is the breakdown of the directory layout:

• benchmarks: Storage for snakemake benchmarks (initially empty).
• config: Study-specific configuration files.
• cutouts: Raw weather data from atlite (initially empty).
• data: Input data not generated by snakemake.
• doc: Documentation source files for ReadTheDocs.
• envs: Backup conda environments (fallback for pixi).
• logs: Execution log files (initially empty).
• notebooks: Ad-hoc analysis via Jupyter.
• report: Files for report generation (plots/results are automated).
• rules: Snakemake rules loaded by the Snakefile.
• resources: Intermediate workflow results (initially empty).
• results: Solved network data, summary files, and plots (initially empty).
• scripts: Python scripts for model execution.

Example Execution

To run the workflow, users typically interact with the Snakefile via the command line:

# Example command to execute the snakemake workflow
snakemake --cores 4 --use-conda

Current Status & Support

The model currently incorporates partial data from the 2024 TYNDP cycle, and validation is still in progress. Because of the soft-fork nature, any issues related to the core engine should be reported in the PyPSA-Eur repository.

For project-specific issues, suggestions, or help, please utilize the GitHub Issues tab.