Real-time Curative Actions for Power Systems via Online Feedback Optimization

Curative or remedial actions are the set of immediate actions intended to bring the power grid to a safe operating point after a contingency. The effectiveness of these actions is essential to guarantee curative N-1 security. Nowadays, curative actions are derived ahead of time, based on the anticipated future grid state. Due to the shift from steady to volatile energy resources, the grid state will frequently change and the curative actions would need to be pre-planned increasingly often. Furthermore, with the shift from large bulk production to many small decentralized energy sources more devices need to be actuated simultaneously to achieve the same outcome. Instead of pre-planning, we propose to calculate these complex curative actions in real-time after the occurrence of a contingency. We show how the method of Online Feedback Optimization (OFO) is well suited for this task. As a preliminary demonstration of these capabilities, we use an (OFO) controller, that after a fault, reduces the voltage difference over a breaker to enable the operators to reclose it. This test case is inspired by the 2003 Swiss-Italian blackout, which was caused by a relatively minor incident followed by ineffective curative actions. Finally, we identify and discuss some open questions, including closed-loop stability and robustness to model mismatch

Online Feedback Optimization for Emergency Power System Operation

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