NEP Automation Toolkit

Reduces connection assessment time by 85% - Automated N-1 contingency analysis for German TSO grid planning

Project overview

This repository contains a Digital Twin Simulation Framework designed to demonstrate automated grid planning workflows relevant to the 50Hertz "From 60 to 100 by 2032" strategic mission.
It serves as a technical proof-of-concept for the Network Planner role, showcasing how Python automation can streamline the massive computational requirements of the Netzentwicklungsplan (NEP).

Strategic alignment

• Hybrid AC/DC grid modeling: Explicit modeling of the SuedOstLink HVDC corridor alongside parallel 380 kV AC lines.
• Automated N-1 security: Automated contingency loops (N-1) to handle thousands of NEP scenarios.
• Data integrity validation: Automated sanity checks on input data (CIM/CGMES) to prevent simulation divergence.

Key features

• Mock grid generation for a simplified North–South corridor model (Wind North → Industry South).
• Pandapower integration as the power flow engine.
• Resilience and error handling for solver divergence.
• Management reporting with summary tables of thermal violations and system status.

Installation

Prerequisites

• Python 3.8+
• pip

Setup

git clone https://github.com/omari91/NEP_Automation_Toolkit.git
cd NEP_Automation_Toolkit
pip install -r requirements.txt

Usage

python grid_simulation_toolkit.py

The script will:

• Initialize the mock grid (including SuedOstLink).
• Perform data integrity validation (voltage levels, impedance checks).
• Run the N-1 contingency loop.
• Output a pandas DataFrame summarizing the max_loading_percent for each contingency.

Project structure

.
├── grid_simulation_toolkit.py  # Main simulation logic (Digital Twin class)
├── requirements.txt            # Python dependencies
├── README.md                   # Project documentation
└── .gitignore                  # Git exclusion rules

License

This project is open-source and available under the MIT License.