A Novel Sooty Terns Algorithm for Deregulated MPC-LFC Installed in Multi-Interconnected System with Renewable Energy Plants

Hossam Hassan Ali, Ahmed Fathy, Abdullah M. Al-Shaalan, Ahmed M. Kassem, Hassan M. H. Farh, Abdullrahman A. Al-Shamma’a and Hossam A. Gabbar
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Hossam Hassan Ali: Electrical Department, Faculty of Technology and Education, Sohag University, Sohag 82524, Egypt
Ahmed Fathy: Electrical Power & Machine Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt
Abdullah M. Al-Shaalan: Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Ahmed M. Kassem: Electrical Engineering Department, Faculty of Engineering, Sohag University, Sohag 82524, Egypt
Hassan M. H. Farh: Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Abdullrahman A. Al-Shamma’a: Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Hossam A. Gabbar: Faculty of Energy Systems and Nuclear Science, Ontario Tech University (UOIT), 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada

Energies, 2021, vol. 14, issue 17, 1-27

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.

Keywords: deregulated LFC; renewable energy; model predictive control; sooty terns optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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