Impact of model parametrization and formulation on the explorative power of electricity network congestion management models

Integrating increasing shares of weather-dependent renewable energies into energy systems while maintaining high levels of security of supply constitutes a challenge for network utilities. Obtaining the goal of large shares of renewable-based generation sources on electricity supply requires an effective operation of electricity grids and efficient coordination among grid operators. Therefore, detailed modelling of grid operation has increasingly become important in recent years. Methods for modelling the operation of (extra) high-voltage grids are undergoing persistent enhancements in academia and energy industries. Existing approaches vary in data granularity and computational methods. Moreover, assumptions on technical details in grid models vary. Differences in input data and modelling methods likely have an impact on simulation results. This paper aims to identify the most relevant differences present in grid simulation models and methods for studying congestion management in a European context. Differences are studied based on a comparison of grid simulation models from eight German energy modelling institutions. The effects of model parameterization and formulation on congestion management results are further investigated with three different case studies focusing on outage simulation, line-constraint relaxation and the modelling of cross-border measures applying selected grid simulation models. Results indicate that data parametrization can have large impacts on model results about congestion management volumes and geographic distribution of necessary measures. Model key parameters must be calibrated thoroughly. The findings of this research will assist future grid modelers and power system planners in efficiently simulating congestion management and increases the validity and explorative power of grid simulation models.