Abstract
This paper introduces a novel metaheuristic approach of sooty terns optimization algorithm (STOA) to determine the optimum parameters of model predictive control (MPC)-based deregulated load frequency control (LFC). The system structure consists of three interconnected plants with nonlinear multisources comprising wind turbine, photovoltaic model with maximum power point tracker, and superconducting magnetic energy storage under deregulated environment. The proposed objective function is the integral time absolute error (ITAE) of the deviations in frequencies and powers in tie-lines. The analysis aims at determining the optimum parameters of MPC via STOA such that ITAE is minimized. Moreover, the proposed STOA-MPC is examined under variation of the system parameters and random load disturbance. The time responses and performance specifications of the proposed STOA-MPC are compared to those obtained with MPC optimized via differential evolution, intelligent water drops algorithm, stain bower braid algorithm, and firefly algorithm. Furthermore, a practical case study of interconnected system comprising the Kuraymat solar thermal power station is analyzed based on actual recorded solar radiation. The obtained results via the proposed STOA-MPC-based deregulated LFC confirmed the competence and robustness of the designed controller compared to the other algorithms.
Highlights
In the interconnected system, the frequency stabilization is very significant to keep the stability of the power system which is achieved by load frequency control (LFC)
Deregulated LFC has been presented with incorporating dish-Stirling solar thermal system (DSTS), geothermal power plant (GTPP), and high-voltage direct current (HVDC)-based cascade FOPI-FOPID optimized via sin cosine algorithm (SCA) [10]
The obtained results via the proposed approach are compared to those obtained by model predictive control (MPC) optimized via differential evolution (DE), stain bower braid algorithm (SBO), firefly algorithm (FA), and intelligent water drops algorithm (IWD)
Summary
Hossam Hassan Ali 1 , Ahmed Fathy 2 , Abdullah M.
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