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.

During the day, photovoltaic (PV) systems are exposed to different sunlight conditions in addition to partial shading (PS). Accordingly, maximum power point tracking (MPPT) techniques have become essential for PV systems to secure harvesting the maximum possible power from the PV modules. In this paper, optimized control is performed through the application of relatively newly developed optimization algorithms to PV systems under Partial Shading (PS) conditions. The initial value of the duty cycle of the boost converter is optimized for maximizing the amount of power extracted from the PV arrays. The emperor penguin optimizer (EPO) is proposed not only to optimize the initial setting of duty cycle but to tune the gains of controllers used for the boost converter and the grid-connected inverter of the PV system. In addition, the performance of the proposed system based on the EPO algorithm is compared with another newly developed optimization technique based on the cuttlefish algorithm (CFA). Moreover, particle swarm optimization (PSO) algorithm is used as a reference algorithm to compare results with both EPO and CFA. PSO is chosen since it is an old, well-tested, and effective algorithm. For the evaluation of performance of the proposed PV system using the proposed algorithms under different PS conditions, results are recorded and introduced.

Improving the control of Photovoltaic (PV) power plants is an increasing interest worldwide. This improvement would hopefully help in reaching the maximum benefit of PV performance all the time. A lot of challenges are facing the control of PV systems such as Maximum Power Point Tracking (MPPT) of PV. Partial shading has become recently one of the most important challenges facing MPPT. This calls for improving the control strategy of PV power plants to cope with it. The work in this paper utilizes a relatively new optimization method which is the Cuttlefish Algorithm (CFA) to tune a Second Order Amplifier (SOA) for enhancing the PV system performance. The CFA has proved to be a very effective and fast optimization technique that can reach an accurate optimum solution with minimum effort and time. In addition, the SOA also has been found capable of enhancing the PV system performance at any condition if well-tuned. The main objective of this paper is to enhance the performance of a PV system under partial shading conditions through the use of a well selected combination of both the CFA and SOA. The needed mathematical models along with the required computer simulations are developed and the obtained results are analyzed. The reached at conclusions prove that the proposed control system and strategy are successful in achieving the declared objectives of the paper.

Worldwide, due to the abrupt growth of population, the load demand has been rising dramatically in the last few years. This led to an increase in branch overloads, voltage deviations, and power losses. These problems may result in line outages or the occurrence of blackouts. Flexible AC transmission system (FACTS) devices can be installed in the power system to ensure increased power flow capability and flexible voltage control to address these issues. In this paper, one of the most used FACTS is utilised. It is called Static VAR Compensator (SVC). This controller is one of the most commonly used shunt FACTS controllers due to its low cost in comparison to others, ease of operation, and integration into the power grid. Two Optimization algorithms are combined to form a hybrid optimization approach: Cuckoo Search (CS) and Antlion Optimization (ALO). This hybrid approach employs the exploration of ALO to adjust the optimum allocation and size for SVCs in the power system. This study proposes the IEEE 57 bus scheme as a fairly large structure, with the 50 and 41 branch outages considered the worst-case scenarios for line outages in this system. The simulation results show that the proposed methodology balances exploring the research space and exploiting the best existing solutions compared to some of the other introduced approaches in the literature.

In general, optimization control is a relatively new trend where an optimization technique is used to tune the controller parameters for a given system under study. The optimization control is proved to be better than traditional adaptive control in many ways as discussed in this paper. This research work presents optimization control schemes for a Photovoltaic (PV) system. Two optimization techniques are proposed to find the optimum gain values of the controller in a hybrid approach. These techniques are Particle Swarm Optimization (PSO) and Cuttlefish Algorithm (CFA). In addition, two different controllers are considered which are the Proportional-Integral (PI) and the Proportional-Integral Acceleration (PIA) controllers. Different results are presented and analysed to evaluate the dynamic performance of the proposed control schemes. Finally, robustness tests are carried out to prove the stability of the proposed controllers.