Automatic voltage regulator (AVR) system is an important device to regulate synchronous generator output voltage. Controllers are used to provide better stability and faster response for AVR system and this article shows two types of controllers: proportional-integral-derivative (PID) and proportional-integral-derivative-acceleration (PIDA) controllers. Harmony search algorithm (HSA), local unimodal sampling (LUS), and teaching learned based optimization (TLBO) are the optimization techniques used to tune each controller parameters. Integrated square error (ISE) is used as an objective function to minimize error in voltage for better stability and response. Transient response, root locus, and bode diagram are calculated for each controller using the three optimization techniques. The results are compared with other optimization techniques to find out the best performance and stability for an AVR system. The results show also which controller is better. Robustness analysis is made on the best system to check terminal voltage response with respect to load variation to ensure that this system can be applied for the AVR of synchronous generator in power system.

The frequency of power systems is very sensitive to load variations. Additionally, with the increased penetration of renewable energy sources in electrical grids, stabilizing the system frequency becomes more challenging. Therefore, Load Frequency Control (LFC) is used to keep the frequency within its acceptable limits. In this paper, an adaptive controller is proposed to enhance the system performance under load variations. Moreover, the proposed controller overcomes the disturbances resulting from the natural operation of the renewable energy sources such as Wave Energy Conversion System (WECS) and Photovoltaic (PV) system. The superiority of the proposed controller compared to the classical LFC schemes is that it has auto tuned parameters. The validation of the proposed controller is carried out through four case studies. The first case study is dedicated to a two-area LFC system under load variations. The WECS is considered as a disturbance for the second case study. Moreover, to demonstrate the superiority of the proposed controller, the dynamic performance is compared with previous work based on an optimized controller in the third case study. Finally in the fourth case study, a sensitivity analysis is carried out through parameters variations in the nonlinear PV-thermal hybrid system. The novel application of the adaptive controller into the LFC leads to enhance the system performance under disturbance of different sources of renewable energy. Moreover, a robustness test is presented to validate the reliability of the proposed controller.

This paper proposes a relatively new optimization algorithm namely the Turbulent Flow Water-Based Optimization (TFWO) to find the optimal size of a hybrid isolated microgrid generation. Moreover, validation of the proposed algorithm is proved through a comprehensive comparison with three robust performance and fast convergence algorithms which are the Harris Hawks Optimization (HHO), Whale Optimization Algorithm (WOA) and Jellyfish Search Optimizer (JSO). Two topologies with different renewable sources were considered in studying which are based on the meteorological data of the Zafarana area, a site located on the eastern coast of Egypt. The study minimizes the annual system cost (ASC) and CO ₂ emissions of the proposed hybrid system while considering the following constraints: Loss of Power Supply Probability (LPSP), Fraction Renewable (FR) and System Excess Energy Ratio (EER). Violation of constraints is penalized by including a penalty factor into the objective function that varies according to the amount of the violation. Moreover, a sensitivity study is presented at the end of the paper through Load variation, irradiance variation, wind speed variation, and diesel generator efficiency decreasing. Results show not only the robustness and the fast convergence of the TFWO algorithm but also its ability to minimize the annual system cost and emission costs to values better than the aforementioned optimization techniques.

Wind energy is particularly significant in the power system today since it is a cheap and clean power source. The unpredictability of wind speed leads to uncertainty in devolved power which increases the difficulty in wind energy system operation. This paper presents a stochastic optimal power flow (SCOPF) for obtaining the best scheduled power from wind farms while lowering total operational costs. A novel metaheuristics method called Aquila Optimizer (AO) is used to address the SCOPF problem due to its highly nonconvex and nonlinear nature. Wind speed is represented by the Weibull probability distribution function (PDF), which is used to anticipate the cost of wind-generated power from a wind farm based on scheduled power. Weibull parameters that provide the best match to wind data are estimated using the AO approach. The suggested wind generation cost model includes the opportunity costs of wind power underestimation and overestimation. Three IEEE systems (30, 57, and 118) are utilized to solve optimal power flow (OPF) using the AO method to prove the accuracy of this method, and results are compared with other metaheuristic methods. With six scenarios for the penalty and reverse cost coefficients, SCOPF is applied to a modified IEEE-30 bus system with two wind farms to obtain the optimal scheduled power from the two wind farms which reduces total operation cost.

This article presents a load frequency control in a two-area interconnected system under wave energy disturbance. At steady state, the system frequency is maintained at the nominal value so there is no frequency deviation. Also, there is no deviation in the tie line power from the value at steady state. But increasing and decreasing of load in any of the two areas may affect the system frequency. Renewable energy variation also can affect the frequency deviation. In this article, first two controllers are used to enhance the system stability during the load change. These controllers are proportional-integral-derivative (PID) and proportional-integral-derivative and acceleration (PIDA). Three optimization techniques are presented in this article to tune the controllers’ parameters. The optimization techniques are teaching-learning-based-optimization (TLBO), harmony search algorithm (HS) and sine-cosine algorithm (SCA). PIDA and TLBO have been proved the best performance during the transient period. The system is then subjected to a disturbance in the form of wave energy conversion system (WECS) when it is equipped with PIDA and using TLBO technique.