Salp swarm algorithm-based model predictive controller for frequency regulation of solar integrated power system

Abstract

Global analysis of power system markets project frequency regulation as one of the most profitable ancillary services. It is associated with second-to-second balance of load and frequency within a control area and acquires a principal role in enabling power interchanges while offering better conditions for electricity exchange. In the light of the above, a novel control strategy, namely salp swarm algorithm (SSA)-based model predictive controller, is proposed for frequency regulation of an unequal two-area realistic power system, incorporating solar thermal power plant and conventional thermal plant. Governor dead band, generation rate constraint and transport delay are considered in each control area. Over the past few years, model predictive controller (MPC) has come forward as a prediction-based control strategy for stabilizing dynamical systems while considering non-linearities, system uncertainties and constraints. The MPC parameters are optimized using SSA. The performance of the proposed approach is validated by comparing the dynamic time responses of SSA-optimized MPC with the other SSA-optimized conventional controllers, namely PID, FOPID and cascade PIDN-FOPID controller. The simulation result analysis shows that the proposed optimal MPC outpaces the conventional controllers with respect to peak overshoot, undershoot and settling time of the time responses. A comparative study of various objective functions indicates that, as compared to other indices, integral square error is better for the considered test system. Further, sensitivity analysis reveals the robustiousness of MPC parameters obtained at nominal values and hence is not required to be retuned, against variations in system loading and inertia constant.