This paper presents a recent metaheuristic optimization approach of multi-verse optimizer (MVO) to design load frequency control (LFC) based model predictive control (MPC) incorporated in large multi-interconnected system. The constructed system comprises six plants with renewable energy sources (RESs). MVO is employed to determine the optimal parameters of MPC-LFC to achieve the desired output of the interconnected system in case of load disturbances. The presented system comprises reheat thermal, hydro, photovoltaic (PV) model with maximum power point tracker (MPPT), wind turbine (WT), diesel generation (DG), and superconducting magnetic energy storage (SMES). The integral time absolute error (ITAE) of the frequencies and tie-line powers deviations is proposed as objective function. The effects of governor dead zone and generation rate constraint (GRC) of thermal plants are considered. The performance of the proposed MPC optimized via MVO is compared with the other designed via intelligent water drops (IWD) and genetic algorithm (GA). Additionally, the robustness of the proposed MPC-LFC based MVO with variation of the system parameters is presented. The obtained results confirmed the superiority and reliability of the proposed controller compared to the others.

Modeling the photovoltaic (PV) generating unit is one of the most important and crucial tasks when assessing the accurate performance of the PV system in power systems. The modeling of the PV system refers to the assigning of the optimal parameters of the PV’s equivalent circuit. Identifying these parameters is considered to be a complex optimization problem, especially with the deviation of the solar irradiance and the ambient temperature. In this regard, this paper proposes a novel hybrid multi-population gorilla troops optimizer and beluga whale optimization (HMGTO-BWO) model to evaluate the optimal parameters of the PV cell/panel; it is based on a multi-population strategy to improve its diversity and to avoid the stagnation of the conventional GTO. The BWO explorative and exploitative powers, which are based on synchronized motion and Lévy flight, are used. The suggested HGTO-BWO is implemented to minimize the root mean square error (RMSE) between the simulated and measured data for each cell/panel represented by a double diode model (DDM) and triple diode model (TDM). The proposed HGTO-BWO is investigated according to the standard and CEC-2019 benchmark functions, and the obtained results are compared with seven other optimization techniques in terms of statistical analysis, convergence characteristics, boxplots, and the Wilcoxon rank sum test. The minimum obtained RMSE values of the PVW 752 cell were 2.0886 × 10⁻⁴ and 1.527 × 10⁻⁴ for the DDM and TDM, respectively. Furthermore, the minimum fetched fitness value for the STM6-40/36 modules was 1.8032 × 10⁻³. The obtained results proved the effectiveness and preference of the suggested HGTO-BWO in estimating the parameters of the PV modules.

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.

This paper presents a novel strategy to achieve adjustable frequency stability in hybrid interconnected power systems with high penetration of renewable energy sources (RESs). The considered system incorporates real-world RESs data to emulate practical grid operation, addressing the challenges posed by RESs variability and intermittency more realistically than previous works. The proposed approach integrates a hybrid energy storage systems (HESSs) with load frequency control (LFC) based on a proportional derivative–proportional integral (PD-PI) controller. The HESSs leverage the complementary strengths of plug-in electric vehicles (PEVs) and superconducting magnetic energy storage (SMES) units, with PEVs providing long-term energy balancing and SMES ensuring rapid transient response. The PD-PI controller is applied to simultaneously manage both LFC and HESSs operation, with its parameters optimally tuned using the electric eel foraging optimizer (EEFO) to ensure precise and effective controller. Comparative analyses demonstrate that the PD-PI controller significantly outperforms traditional proportional integral derivative (PID) controllers in maintaining frequency stability under high-RESs penetration and load disturbances. Specifically, the proposed strategy improves system performance by 55% compared to SMES-based PD-PI controllers and by 45% compared to PEVs-based PD-PI controllers. While the approach is most effective in hybrid systems with available PEVs infrastructure and SMES units, its applicability may be limited in power systems lacking such resources or facing large-scale, long-duration disturbances. Furthermore, the effectiveness of the proposed strategy has been validated under cyber attack conditions ,and system parameters variations. Overall, the findings confirm the critical role of the proposed strategy in mitigating frequency fluctuations during periods of high renewable energy penetration, thereby offering a robust solution for the challenges faced by modern power systems.