Model Predictive Control employing trajectory sensitivities for power systems applications

Abstract

Model Predictive Control (MPC) is a widely used method in process industry for control of multi-input, multi-output systems. It possesses some features, which make it attractive for applications in power systems. Power systems exhibit several features of complex systems, such as hybrid nature (mixed continuous and discrete dynamics), nonlinear dynamics and very large size. Since MPC involves optimization computations, it represents a big challenge to handle above listed properties in a reasonable time for large power systems. Therefore the reduction of the computational burden associated with MPC is a crucial factor. We describe in this paper a formulation of MPC for power systems based on trajectory sensitivities. Trajectory sensitivities are time varying sensitivities derived along the predicted nominal trajectory of the system. They refer to possible changes of initial conditions from which the nominal trajectory is predicted, or to a modification of system parameters including changes of discrete valued quantities, e. g. positions of transformers taps. Trajectory sensitivities allow an accurate reproduction of the nonlinear system behavior using a considerable reduced computational burden as compared with the full non-linear integration of the system trajectories.