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Integrating increasing shares of weather-dependent renewable energies into energy systems while maintaining high levels of security of supply constitutes a challenge for system operators. The system integration of large shares of variable renewable energy generation requires an effective operation of electricity grids and efficient coordination among grid operators. Hence, the 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 industry. Existing approaches vary in data granularity and computational methods. Moreover, assumptions on technical details in grid models vary. The results are thus subject to differences in input data and modelling methods. The paper at hand aims to identify the most relevant differences that exist amongst 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 investigated based on three different case studies focusing on outage simulation, line constraint relaxation and the modelling of cross-border measures. Results indicate that data parametrization can have large impacts on model results with respect to congestion management volumes and the geographic distribution of the respective measures. The analysis also highlights the need to thoroughly calibrate key model parameters. The research findings will assist the grid modelling community and power system planners in simulating congestion management and increase the validity and explorative power of grid simulation models. • Various differences in model formulations for grid operation exist in academia. • Soft-constraints are widely used to detect data inconsistencies and infeasibilities. • Simplified outage simulations particularly impact congestion results in meshed grids. • Parametrization of cross-border measures largely affects congestion volumes. [ABSTRACT FROM AUTHOR]

• Integrated optimization of multi-stage expansion planning and grid operation. • Selection of expansion candidates via linear programming relaxations. • Optimizing the grid operation reduces the need for network expansion. • Need for new lines is substituted by the placement of phase shifting transformers. • Minimization of expansion costs by dynamic multi-stage expansion planning. The ongoing transformation of the power system characterized by an increased share of renewable energy sources requires additional transmission capacities. Nevertheless, the need for new transmission capacities can be reduced by optimizing grid operation. This contribution presents a novel approach to the co-optimization of multi-stage transmission expansion planning and grid operation within a mixed-integer linear programming formulation. The expansion via AC systems and phase shifting transformers (PSTs) is combined with the control of PSTs and the determination of congestion management interventions such as redispatch of power plants or curtailment of renewable energies. The number of potential expansion candidates is reduced by linear programming relaxations. Exemplary results based on a synthetic test system containing 120 buses evidence the impact of the proposed approach on overall system costs. The proposed co-optimization reduces the total costs by 16.5 % when applied instead of the sole expansion via AC technology. [ABSTRACT FROM AUTHOR]