munacco: Model for Uncertainty-Aware Capacity Calculation and Congestion Management

This repository contains the source code and analysis scripts for the munacco model, developed as part of the Master's thesis "Uncertainty-Aware Capacity Calculation and Congestion Management".

The model simulates the sequential processes of capacity calculation, validation, market coupling, and redispatch, following the structure of the European Core Capacity Calculation Region (Core CCR). It includes support for both deterministic and uncertainty-aware formulations, including robust validation methods using chance constraints.

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Repository Structure

munacco/
├── munacco/                # Core Python model (modularized)
│   ├── analysis/           # KPI analysis and visualization
│   ├── input/              # Data loading from CSV/PyPSA
│   ├── model/              # Model logic (capacity calc, validation, MC, redispatch)
│   ├── scenario/           # Scenario generation and uncertainty handling
│   └── tools.py            # Shared utilities
│
├── example/                # Minimal example on a small 4-node test system
│   ├── 00_example_usage.py
│   └── base_model/         # CSV input files for the 4-node test case
│
├── thesis-analysis/        # Reproduction scripts for thesis experiments
│   ├── stylised-4node/     # Scripts for 4-node case study
│   └── pypsa-eur-50node/   # Scripts for 50-node PyPSA-Eur analysis
│
├── .gitignore
├── README.md
└── requirements.txt

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Installation

Clone the repository and install the dependencies via pip:

git clone https://github.com/mvoitl/munacco.git
cd munacco
pip install -r requirements.txt

Data

The required input and result data is too large to be included in this repository. All datasets used in the thesis (including the PyPSA-Eur-50 network, forecast uncertainty profiles, and results) are available via Zenodo.

Download the data archive here:

Zenodo DOI: https://doi.org/10.5281/zenodo.17202843 File: thesis-data.zip

After downloading, extract the archive to recreate the expected folder structure:

unzip thesis-data.zip

This will create the following directory:

thesis-data/
├── stylised-4node/         # Input and results for the 4-node test case
├── pypsa-eur-50node/       # Data and results for the 50-node analysis
└── ...

All scripts in thesis-analysis/ assume this structure.

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Getting Started

Run example with 4-node system

python example/00_example_usage.py

This demonstrates a single capacity calculation and market coupling run using CSV input files.

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Reproduce stylised 4-node analysis

Navigate to the analysis folder:

cd thesis-analysis/stylised-4node

Run one of the scripts, for example:

python 02_Single_scenario_analysis.py

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Reproduce 50-node PyPSA-Eur case study

Run the full capacity calculation and validation across multiple hourly snapshots:

cd thesis-analysis/pypsa-eur-50node
python 03_pypsa_run_snapshots_w_uncertainty_high_low_res.py

This will process multiple hourly snapshots (with and without uncertainty), and save results to thesis-data/pypsa-eur-50node/result_data.

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