Data for 'Uncertainty-Aware Capacity Calculation and Congestion Management'

This dataset accompanies the thesis "Uncertainty-Aware Capacity Calculation and Congestion Management". It includes all input data, forecast scenarios, and simulation results for two case studies modeled using the open-source framework `munacco`. All result files are provided in .pkl (Python pickle) format for efficient loading in Python environments. Dataset Structure thesis-data/├── stylised-4node/│ ├── base_model/ # Input data (CSV) for the stylised 4-zone test system│ ├── 01_analyzer_data_*.pkl # Results from Uncertainty Impact on capacity calculation and validation (Section 4.1.1) │ ├── 02_meshpoints_*.pkl # Domain meshpoints for plotting│ ├── 03_multiple_scenarios.pkl # Results from multiple scenario experiment (Section 4.1.3)│ └── 04_results_sensitivity_*.pkl# Risk sensitivity experiments (Section 4.1.4)│└── pypsa-eur-50node/ ├── cwe_network_data/ │ ├── config/ # Model configuration files (.yaml) │ ├── input_data/ # PyPSA-Eur derived data for the CWE region │ │ ├── RES availability (NetCDF) │ │ ├── Load profiles │ │ ├── Bus and line mappings │ │ ├── Power plant data │ │ └── Regional shapes (GeoJSON) │ ├── networks/ # PyPSA networks (.nc) used as the base for simulations │ └── solved_network/ # Pre-solved network instance │ └── result_data/ # Results for 50 node case stud (Section 4.3.3) ├── all_data_det_*.pkl # All scenario-level KPIs for deterministic validation ├── all_data_robust_*.pkl # All scenario-level KPIs for robust (chance-constrained) validation ├── snapshot_overview_det_*.pkl # Aggregated KPIs per snapshot (deterministic) └── snapshot_overview_robust_*.pkl # Aggregated KPIs per snapshot (robust) Notes All simulations were performed using a modified version of `PyPSA-Eur` (for the 50-node case) and CSV-based models (for the 4-node case). Forecast scenarios were generated with stochastic RES models, including configurable forecast error levels and timing. The data enables full reproduction of the model experiments discussed in the thesis and supports further analysis of uncertainty-aware zonal capacity calculation. Some experiments use random draws without fixed seeds. Exact results may differ slightly. For the simulation code, see the accompanying repository: https://github.com/mvoitl/munacco