A Seamless Workflow from Open-Source Planning Models to Industry-Standard Stability Simulations

Abstract

The ongoing transition toward decentralized generation and inverter-based resources has heightened the need for robust simulation methodologies capable of evaluating system stability under a wide range of operating conditions. Nevertheless, restricted access to real world grid data and prevailing confidentiality constraints hinder the dissemination of detailed models, thereby limiting collaborative research and reproducibility. Although open frameworks such as Python for Power System Analysis (PyPSA) offer transparent and flexible tools for system optimization and scenario development, they currently lack advanced analytical capabilities, particularly for dynamic simulation. To address this deficiency, we present pypsa2pf, a Python-based converter that translates PyPSA network representations into DIgSILENT PowerFactory projects. The converter automatically maps network topology, equipment specifications, control schemes, and time-series data to PowerFactory, requiring minimal subsequent manual intervention. The utility of pypsa2pf is illustrated through its application to a multi-sectoral 2045 reference network for Northwest Germany. Validation of the conversion is performed by comparing direct-current (DC) load-flow results of the PowerFactory model with those obtained from the original PyPSA model. The resulting model reproduces the original network parameters and operating set points with high fidelity, requiring only minimal adjustments to enable AC load-flow and dynamic power-system analyses. pypsa2pf markedly streamlines the workflow from open-source scenario development with PyPSA to industry-grade stability assessment in PowerFactory, facilitating more efficient and collaborative power system studies.