Uncertainty-aware capacity calculation and congestion management

The growing share of renewable energy in Europe’s electricity system introduces forecast uncertainty that challenges the coordinated capacity calculation and congestion manage- ment process. Current deterministic methods treat forecasts as fixed inputs and address uncertainty only indirectly, risking infeasible market outcomes that can compromise operational security. This thesis develops a modelling framework that explicitly represents the subprocesses of the flow-based process—capacity calculation, validation, market coupling, and redispatch— under both deterministic and probabilistic formulations. Two case studies are analysed: a stylised 4-node system to isolate key mechanisms, and a 50-node PyPSA-Eur network to assess results under more complex conditions. Across both cases, the validation step emerged as the critical stage where robustness against forecast uncertainty can be introduced most effectively. Deterministic validation reduced but did not eliminate operational infeasibilities, while probabilistic validation substantially improved robustness. Higher day-ahead market costs were offset by lower redispatch needs, reducing the impact on total system costs. These results highlight the potential of uncertainty-aware methods to enhance operational security in systems dominated by renewable energy.