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SummaryThis paper proposes the optimal design of model predictive control (MPC) with energy storage devices by the bat-inspired algorithm (BIA) as a new artificial intelligence technique. Bat-inspired algorithm-based coordinated design of MPCs with superconducting magnetic energy storage (SMES) and capacitive energy storage (CES) is proposed for load frequency control. Three-area hydrothermal interconnected power system installed with MPC and SMES is considered to carry out this study. The proposed design procedure can account for generation rate constraints and governor dead bands. Transport time delays imposed by governors, thermodynamic processes, and communication telemetry can be captured as well. In recent papers, the parameters of MPC with SMES and CES units are typically set by trial and error or by the designer's expertise. This problem is solved here by applying BIA to tune the parameters of MPC with SMES and CES units simultaneously to minimize the deviations of frequency and tie line powers against load perturbations.Simulation results are carried out to emphasize the superiority of the proposed coordinated design as compared with conventional proportional-integral controller and with BIA-based MPC without SMES and CES units. , unmeasured and measured disturbances resp; x n , y n , noise model state and output vectors resp; A n , B n , C n , D n , state-space realization of the noise measurement model; Ψ d , Ψ n , dimensionless white noise input to disturbance and noise models resp; T s , sampling period; P, M, integer prediction and control horizons resp; Q, R, Two scalars to weight both the input and error signal; N, number of the control inputs; T sim , simulation time (s)

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