Model Predictive Real-Time Control of Electric Power Systems Under Emergency Conditions

Abstract

Model Predictive Control (MPC) is a widely used method in process industry for the control of multi-input and multi-output systems. It possesses features that make it attractive for power system applications. Power systems exhibit complex characteristics such as hybrid nature (mixed continuous and discrete dynamics), nonlinear dynamics, and very large size. The optimization computations involved in MPC further increase the challenge of handling such features in a reasonable time. Therefore, the reduction of the computational burden associated with MPC is a crucial factor for real-time applications. In this chapter, we describe 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, which allow an accurate reproduction of the nonlinear system behavior using a considerable reduced computational burden as compared with the full nonlinear integration of the system trajectories. Therefore, their deployment opens application possibilities for MPC in new, previously restricted, areas