Feature- and structure-preserving network reduction for large-scale transmission grids

Many countries are currently challenged with the extensive integration of renewable energy sources, which necessitates vast capacity expansion measures. These measures in turn require comprehensive power flow studies, which are often computationally highly demanding. In this work a reduction strategy for large-scale grid models is introduced which not only reduces the model complexity but also preserves the structure and designated grid features. The objective is to ensure that areas crucial to the behavior and the relation of all elements to their physical counterparts remain unchanged. This is accomplished through a specifically designed reduction method for suitable areas identified through topological, electrical and market-based approaches for which we provide an open-source implementation. We show that the proposed strategy adapts to various models and accomplishes a strong reduction of buses and branches while retaining a low dispatch and branch flow deviation. Furthermore, the accuracy of the reduction generalizes well to other scenarios.