An efficient coordinated strategy for frequency stability in hybrid power systems with renewables considering interline power flow controller and redox flow battery

Abstract

This paper proposes a new load frequency control (LFC) structure based on a tilt fraction-order integral (TIλ) controller (i.e., as a feed-forward controller) and a proportional fraction-order derivative controller with a filter (PDμN) (i.e., as a feedback controller), referred to as TIλ − PDμN controller. This proposed controller is optimally designed using a recent optimization algorithm, artificial gorilla troops optimizer (GTO). Where the proficiency of the new optimization algorithm (GTO) is verified over other optimization algorithms used in the literature (e.g., differential evolution and firefly algorithms) based on statistical tests. Also, the superiority of the proposed TIλ − PDμN controller's performance is validated by comparing it with another proposed PDμN − TIλ controller beside other controllers from the literature (e.g., PIλDμ and ITD controllers). The power system under investigation in this work is a two-area hybrid power system in which each area incorporates a thermal power plant with a reheat turbine, a hydropower plant, and a generation unit by gas. Additionally, the considered power system with the proposed controller is also examined under the influence of the high penetration of renewables. Furthermore, this paper proposes new efficient coordination between the proposed TIλ − PDμN controller based LFC, an interline power flow controller, and a redox flow battery, to ensure robust performance and further frequency stability for the considered hybrid power system against loads/renewables fluctuations, system uncertainties, and communication time delays. The simulation results carried out by MATLAB prove that the proposed coordination scheme based on the proposed GTO algorithm enhances system frequency stability significantly under a variety of load perturbations, renewable power fluctuations, communication time delays, system uncertainties, and physical constraints.