Design of Optimal Model Predictive Controller for LFC of Nonlinear Multi-area Power System with Energy Storage Devices

Abstract

This paper proposes the bat-inspired algorithm (BIA) and gravitational search algorithm (GSA) as a new two artificial intelligence (AI) technique to design the model predictive controllers (MPC) with superconducting magnetic energy storage (SMES) and capacitive energy storage (CES) for load frequency control (LFC). In recent years, the designer used the trial, error technique and their expertise to set the parameters of the MPC with SMES and CES units. Herein, the BIA and GSA are used to tune the parameters of MPC with SMES and CES units simultaneously to overcome this problem. The proposed methods are applied to nonlinear interconnected three area power system to minimize the deviations of frequency and tie line powers against load perturbations. The superiority of the proposed methods is emphasized by comparing them with the BIA-based MPC without SMES and CES units and the conventional proportional-integral (PI) controller based on Ziegler-Nichols (ZN) technique.