2.1 Comparative Analysis of Transmission Grid Reduction and Spatial Clustering Techniques
The methodological architecture of this dissertation assesses transmission grid bottlenecks through an integrated network modeling and policy analysis framework. To capture the spatial dynamics of the German energy transition under the Renewable Energy Sources Act (EEG), which establishes grid prioritization mechanisms to support statutory renewable expansion targets (Legislative Frameworks Analysis, 2025), this study adopts a calibrated power system modeling strategy. Evaluating transmission constraints requires addressing the computational challenges and parameter uncertainties inherent in spatial demand and generation allocations. Following the open-source PyPSA-Eur framework, the empirical network topology is structured across distinct scenario levels that compare high-voltage and extra-high-voltage representations alongside spatial clustering algorithms (Congestion Modeling Study, 2026). Incorporating high-voltage network reduction combined with spatial clustering is methodologically essential because unreduced, full-resolution configurations produce implausibly elevated redispatch estimates (Congestion Modeling Study, 2026). The modeling pipeline calibrates grid configurations against historical transmission operations from 2019, validating redispatch volumes and regional distributions through topological indicators while accounting for the systematic tendency of stylized representations to overestimate baseline redispatch levels by a factor of 2.26 under specific benchmark conditions (Congestion Modeling Study, 2026). This network modeling approach is coupled with sectoral sensitivity metrics to determine how locational congestion management expenditures translate into industrial network charges. By linking spatial transmission flow reductions to industrial tariff structures, the framework provides a rigorous foundation for evaluating regulatory proposals, bidding zone configurations, and infrastructure planning pathways across energy-intensive manufacturing clusters.