With increasing intermittent distributed power generation, emergency situations in the power system are more likely to occur. Current emergency operation in power systems is mostly based on manual remedial actions. The growing number of distributed generation devices add complexity to emergency operation and make decisions on remedial actions harder. In this thesis, a feedback optimization (FO) controller for emergency power system operation is implemented. To reflect the fast timescales and model inaccuracies in emergency operation, FO is pushed beyond the limit of current stability and robustness methods. As a benchmark for the FO controller, a controller based on the optimal power flow (OPF) approach is used. Both controllers are tested using the dynamic power system simulator DynPSSimPy. In contrast to the OPF controller, the FO controller meets all the posed operational constraints, even in the presence of imperfect model information. In terms of optimality, imperfect model information can cause both controllers to converge to a suboptimal operating point. Based on timescale separation, two methods of estimating the step size limit and thus guarantee convergence of the FO controller are applied. No practical relevant certificates for the stability and robustness of the FO controller can be made, since the step size limit estimations yield either too high, or too conservative limits. However, the simulations show evidence, that FO is applicable to emergency power system operation by controlling the system stably even in the presence of imperfect model information. With that, FO is capable of supporting power system operators in handling complex emergency situations
