Wind energy (WE) as an environment‐friendly resource is green, clean, and innovative solution for the globe energy dilemma. For the blade pitch control (BPC) of the WE, the gain scheduling of the BPC becomes important to cope with the wind intermittency. The innovative idea of this study is to employ the novel advanced MOTs for enhancing the dynamic behaviour of the wind power plant. For this purpose, the genetic algorithm (GA), artificial bee colony (ABC) algorithm and grey Wolf optimiser (GWO) are used for the optimal tuning of the BPC system parameters. A comprehensive comparative study is presented to verify the effectiveness of the proposed MOTs over different conventional methods such as the conventional Zeigler–Nichols, and the simplex algorithm). This comparative analysis is based on the time response specifications such as maximum overshoot, settling time and steady‐state error. The proportional‐integral‐differential (PID) controller is applied to the multivariable BPC for a wind turbine generating system connected to a large power system. The simulation results show that the GWO‐PID is more effective than the ABC, GA and the conventional methods. Moreover,the GWO‐PID controller robustness is verified in the presence of system parameter variations, an abrupt change in the mechanical torque and the wind speed variation.

Two modern methods of the energy management system (EMS) based on a modified cost function are addressed in this paper. Fuzzy logic (FL) and Harris Hawks Optimization (HHO) is implemented to achieve the optimal performance of seawater desalination plants (SWDP) within the minimum feed-in tariff (FiT). The technical difficulties involved in the variation of energy price from one time to another and the system parameters uncertainties. For example, the price of energy is higher at the peak time and the price is lower at normal times. Also, the peak time can change from one day to another day. The proposed management system can deal with these variations and uncertainty cases. The suggested EMS is achieved through a bidirectional electrical energy interchange approach ( $\pi $ -EEIA). The main concept behind the proposed $\pi $ -EEIA is how and when to inject the excess generated energy of renewable energy into the utility grid or charge the battery depending on the minimum dynamic cost criterion and vice versa. To accomplish this study a 700 m ³ /day SWDP located in Egypt fed on solar energy and a utility network has been constructed and analyzed. The system includes SWDP fed from a photovoltaic (PV) array as well as the utility grid in addition to a battery energy storage system (BESS). The main objective of this study is the management and coordination between the energy exchange process from the solar energy, the utility network, and BESS to provide sufficient electrical energy for SWDP within the minimum FiT. The system is constructed and validated using the MATLAB/SIMULINK™ software package. The proposed FL and HHO-based EMSs are investigated in the presence of the system uncertainties such as the change in the energy (excess or shortage) as well as the change in the energy price in the utility network (high or low) with (normal or peak) time. The attained results demonstrate that the proposed FL and HHO-based EMSs provide high dynamic performance and accurate coordination between various energy resources and BESS. The results show that FL-based EMS achieves a profit of 10.28 $\$ $ but the HHO-based EMS achieves a profit of 10.11 $\$ $ in the same period.

This paper suggests an optimal maximum power point tracking (MPPT) control scheme for a grid-connected photovoltaic (PV) system using the arithmetic optimization algorithm (AOA). The parameters of the proportional-integral (PI) controller-based incremental conductance (IC) MPPT are optimally selected using AOA. To accomplish this study, a 100-kW benchmark PV system connected to a medium distribution utility is constructed and analyzed employing MATLAB/SIMULINK. The optimization framework seeks to minimize four standard benchmark performance indices, then select the best of the best among them. To verify the efficacy of the recommended methodology, a comprehensive comparison is conducted between AOA-based PI-IC-MPPT, modified incremental conductance MPPT (MIC), grey wolf optimization (GWO), genetic algorithm (GA), and particle swarm optimization (PSO)-based MPPT. The proposed control approach has achieved a reduction of 61, 3, 4.5, and 26.9% in the rise time and a decrease of 94, 84.7, 86.6, and 79.3% in the settling time compared with MIC, GWO, GA, and PSO in extracting MPPT of the proposed system, respectively.

This article presents frequency regulation of an interconnected three-area power system (Thermal + Wind + Hydro). Fractional Order PID (FOPID) and Proportional-Integral-Derivative (PID) controllers are applied as subsidiary regulators to control the electrical power interconnected system at the time of sudden load variation. To accomplish this study, Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Sine Cosine Inspired Algorithm (SCIA) and Atom Search Inspired Algorithm (ASIA) are implemented to optimize the secondary regulators' gains (PID and FOPID) by considering various cost functions such as Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), Integral Square Error (ISE), and Integral Time Square Error (ITSE). Performance analysis in this work is conducted using various cost functions based on GA, GWO, SCIA and ASIA. The comparative analysis of the attained results reveals that GWO-PID and ASIA–PID settle at (83.83 s) and (30.31 s), respectively and ASIA-FOPID at (25.12 s). The controllers based on ITSE as a cost function outperform the comptrollers with other cost functions (ISE, IAE and ITAE). In addition, the ISE-based GA–PID and SCIA–PID settle at (113.92 s) and (35.1 s), respectively and SCIA-FOPID at (24.78 s). The ISE-based regulators yield improved response equated to other cost functions (ITSE, IAE and ITAE) optimized controllers. The robustness test also is carried out to validate the effectiveness of the proposed optimization techniques by changing the system parameters within ± 25% and ± 50% from their nominal values as well as changing the load pattern.