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The efficiency of grid-connected photovoltaic (PV) systems can be improved using a suitable maximum power point tracking (MPPT) control. Many MPPT techniques have been developed. However, when atmospheric conditions change rapidly, the PV modules respond nonlinearly and the performance of these methods especially the conventional ones is significantly reduced. Therefore, to successfully pursue the maximum power point (MPP) in PV systems, a robust MPPT integrated with sliding mode control (SMC) is employed. This paper proposes a circle search algorithm based super twisting SMC (CSA-STSMC) for tracking the MPP for 100-kW of different commercial grid-connected PV modules coupled with an energy storage system that used to smooth their output fluctuations. Moreover, the STSMC and PI controllers’ gains are optimized using the CSA and Grey Wolf Optimizer (GWO) methods and the performance of proposed method is demonstrated through comparisons with CSA based Proportional-Integral controller (CSA-PI), GWO-STSMC, and Incremental Conductance based PI controller (InC-PI) methods. The optimization and simulation analysis are executed in A MATLAB/Simulink environment. It is observed that the proposed method outperforms the considered methods in terms of robustness, tracking speed, best convergence to the minimum error’s value and better tracking efficiency in the maximizing the extracted power from the introduced PV systems.

Abstract This article describes a technique for improving the performance of an offshore wind farm (OWF), which includes a permanent magnet synchronous generator (PMSG) driven by a variable‐speed wind turbine (VSWT) and connected to the grid through a high‐voltage direct current transmission system (HVDC). Under various network disturbances, the gorilla tropical optimization (GTO) metaheuristic method is used to fine‐tune the proportional‐integral (PI) controller gains for VSC‐based PMSG‐VSWT and VSC‐based HVDC transmission systems. This study compares several strategies for extracting the maximum power from a system while maintaining stability under symmetrical and unsymmetrical fault circumstances. Compared to different algorithms, GTO provides better results and improves system recovery and stability after disturbances. As a consequence, dynamic and transient stability may be enhanced. MATLAB/Simulink is used to implement the control mechanism.

Three-diode model (TDM) of photovoltaic (PV) cells is a significantly precise model that addresses the electrical and optical losses in such PVs. Due to its nonlinearity and multivariable characteristics, the TDM is a complex and debatable PV model. This article proposes a novel hybrid African vultures–grey wolf optimizer (AV–GWO) approach to precisely estimate the electrical parameters of such TDM. The AVO is a novel meta-heuristic approach inspired by African vultures’ behavior in nature. In the hybrid approach offered, the vultures’ updating position formula of the AVO is applied to update the key-group parameters in GWO, resulting in an enhanced GWO approach. A new objective function that depends on the current error is proposed in this study, which the AV–GWO minimizes to precisely estimate the optimal nine parameters of such TDM. The nine electrical parameters attained through the hybrid AV–GWO approach are compared with that obtained via other meta-heuristic methods. In that regard, the AV–GWO approach has achieved superior and outstanding outcomes. For more realistic studies, the offered AV–GWO is efficiently utilized to design the optimal parameters of TDM for two industrial KC200GT and MSX-60 PV cells. In the optimization process, the hybrid AV–GWO has recorded the lowest optimal fitness values of 8.475e−13 and 7.412e−12 for KC200GT and MSX-60, respectively. Additionally, the AV–GWO has recorded the shortest computing time in 0.43412 (s) and 0.3142 (s) for KC200GT and MSX-60, respectively, which reflects its rapid convergence, supremacy, and stability, among other approaches. Those PV cells’ modeled I-V and P-V curves closely coincide with the real data measured under various climatic conditions. The error between these results is less than 0.4%. The high performance of the hybrid AV–GWO​ approach-based TDM is verified by examining its absolute current error with that realized from other PV models. Consequently, the outcomes have depicted that the offered AV–GWO approach is superior and can be used to generate a precise PV model of any industrial PV cell, which is a unique addition to the PVs market.

Harmonic pollution is one of the challenging problems facing power networks recently due to the widespread non-linear loads and inverter-based renewables. In this regard, this work presents the optimal design of damped and undamped passive filters using a solver called Mixed-Integer Distributed Ant Colony Optimisation (MIDACO). This solver is employed to obtain an optimal design strategy for single-tuned passive power filters by investigating three primary criteria – minimisation of active power losses of the Thevenin’s resistor, maximisation of the true power factor, and maximisation of the transmission efficiency. Several constrictions associated with the designed filters have been considered, in which the global maximum or minimum criterion was attained by retaining the quality factors of the designed filters within a particular range, damping harmonic resonance, achieving a permissible range of the power factor, limiting voltage harmonic distortion by complying with IEEE Std. 519–2014 restrictions. Besides, the performance limits of capacitors operating in distorted systems have been met while complying with IEEE Std. 18–2012. Further, the results obtained using the MIDACO solver in four different case studies are compared to those obtained using particle swarm optimisation and genetic algorithm. In addition, this work depicts the damping resistor of the inductance in the single-tuned filters. The benefits and drawbacks of damping over an undamped filter are discussed. Finally, the results validate the effectiveness of the MIDACO solver employed in this paper.

This research paper introduces the Generalized Continuous Mixed P-Norm Sub-Band Adaptive Filtering (GCMPNSAF) algorithm, designed for efficient online control of Superconducting Magnetic Energy Storage Devices (SMESDs) in Wind Energy Systems (WESs). The primary objective of this algorithm is for minimizing power ripples in WESs. The Wind Energy System (WES) under consideration is tied to the IEEE 39 bus system, with the Superconducting Magnetic Energy Storage Device (SMESD) integrated at the point of common coupling. The GCMPNSAF algorithm is applied to update or adapt proportional-integral (PI) controller gains of SMESD interface circuits. The proposed algorithm is an enhanced version of the CMPN by adding the sub-band filtering algorithm effect. It depends mainly on the actuating error signal, and it has a variable step size of the CMPN. The detailed modeling of the whole system is presented, including measured wind speed data, detailed switching techniques, a drive train model of the turbine, and real SMESD. The efficacy of the proposed SMESD has been validated through a comparative analysis with the Least Mean Square-SMESD approach, under conditions of varying and unpredictable wind speeds. The simulation results produced by the PSCAD software are used to evaluate the study's validity. The utilization of controlled SMESDs has the potential to significantly enhance the power quality of WESs.

Abstract In the active distribution networks (ADNs), the emerging trend of high penetration of sustainable energy sources such as wind turbines, photovoltaics, and the forthcoming integration of energy storage devices and additional dynamic loads like plug‐in‐electric vehicles (PEVs) influences to change the nature of electric distribution networks. Consequently, it deteriorates power quality and reliability in terms of unacceptable voltage rise‐drop, harmonic distortion, voltage unbalance, and excessive power loss. In this context of transition into the active distribution network, the probabilistic optimal power flow (P‐OPF) in the ADN is considered an important tool that helps the distribution grid operators for defining the optimal settings of the system's control variables. However, the optimal solution of P‐OPF becomes highly complex while considering the multiple variables, time‐varying characteristics and operational uncertainties associated with sustainable resources, consumer behaviour and electricity market prices. This review aims at promoting the research and discussion of P‐OPF studies in the context of ADNs characteristics and challenges. First, we perform a data‐driven and visualized scientometric analysis of 1988 quality documents retrieved from the Scopus during the last ten years using VOSviewer software. Second, an in‐depth investigation of the recent and emerging techniques for solving the P‐OPF problem is presented. Third, a set of recommendations and highlights of future challenges for P‐OPF in ADNs are offered. The review findings showed high interest among the scientific community in sustainable energy source integration, given the recent rise in publications and citations. Also, there is still a lack of innovative optimization techniques that can adequately handle the current issues associated with P‐OPF and objectives with fair computational efficiency and accuracy. Finally, this paper offers a guide for P‐OPF researchers to navigate the recent studies considering the sustainable sources, spot the new research frontiers, and identify the most impactful countries and publishers.

The broad spread of renewable energy sources (RESs) and storage systems increases modern power systems’ challenges and may conflict with system operation requirements. Determination of the maximum hosting capacity (HC) is crucial for utilities to estimate the maximum capacity of RESs and storage units that a network can accommodate efficiently. Several models were developed in the literature that lacks studying the impact of reliability requirements and the size of other network components on the HC levels. This paper proposes a framework to effectively maximize the hosting of RESs and storage systems in power systems. An AC optimal power flow is used to formulate the problem and consider the networks’ real operation. The objective function is formulated to expand transmission lines, allocate fault current limiters (FCLs), and ensure HC enhancement. The AC models are always complex and non-convex, and finding optimal solutions is critical. A hybrid artificial rabbits sine–cosine algorithm is developed to solve the problem, and its performance is compared to some well-known metaheuristics. The numerical studies on the Garver network and the IEE 24-bus system demonstrated that the size of different power technologies directly impacted the amount of RESs deployed. The penetration of RESs and storage systems was maximized by increasing the number of candidate circuits. The results revealed a 22 % increase in the size of RESs installed in the Garver network. Also, a 25.7 % increase in the size of RESs for the 24-bus system was obtained due to the increased candidate transmission lines. Further, an increase of 49 % was noticed in the size of storage systems built for the 24-bus system. The use of FCLs was necessary to maintain the short-circuit current below the required values. For both systems, the size of FCLs required was increased by 24.3 % and 28 %, respectively. The results also showed that the hybrid artificial rabbits sine–cosine optimizer was more efficient than other algorithms in solving the investigated problem.

This paper develops a novel methodology for optimal control of islanded microgrids (MGs) based on the coot bird metaheuristic optimizer (CBMO). To this end, the optimum gains for the PI controller are found using the CBMO under a multi-objective optimization framework. The Response Surface Methodology (RSM) is incorporated into the developed procedure to achieve a compromise solution among the different objectives. To prove the effectiveness of the new proposal, a benchmark MG is tested under various scenarios, 1) isolate the system from the grid (autonomous mode), 2) islanded system exposure to load changes, and 3) islanded system exposure to a 3 phase fault. Extensive simulations are performed to validate the new method taking conventional data from PSCAD/EMTDC software. The validity of the suggested optimizer is proved by comparing its results with that achieved using the LMSRE-based adaptive control, sunflower optimization algorithm (SFO), Ziegler-Nichols method and the particle swarm optimization (PSO) techniques. The article shows the superiority of the suggested CBMO over the LMSRE-based adaptive control, SFO, Ziegler-Nichols and the PSO techniques in the transient responses of the system.

Microgrids take a large part in power networks thanks to their operational and economic benefits. This research introduces a novel implementation of an adaptive proportional plus integral (PI) controller to boost the autonomous microgrid operation efficiency. The least mean and square roots of the exponential algorithm are utilized in the adaptive PI control strategy. The multi-objective function for both sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms is obtained by The Response Surface Methodology. The system is evaluated under different environments, which are stated as follows: 1) disconnect the system from the grid (islanding), 2) autonomous system exposure to load variability, and 3) autonomous system exposure to a symmetrical fault. The proposed practicality of the control plan is shown by the data of the simulation, which is extracted from PSCAD/EMTDC software. The strength of the suggested adaptive control is confirmed through matching its results with those obtained using the SFO and PSO based optimal PI controllers.

Power Flow solution of realistic ill-conditioned systems has recently attracted huge attention. Nevertheless, there are still some gaps in this field. For example, most of available references do not provide exhaustive theoretical analysis about convergence properties of proposed approaches. In addition, efficient solution of large-scale ill-conditioned systems is still an open topic. This paper tackles these issues by comprehensively studying the suitability of the Mann Iteration Process for the solution of ill-conditioned systems. A comprehensive theoretical analysis is provided, from which is demonstrated that the Mann Iteration Process has with asymptotic stability, may achieve a high convergence rate and constitutes a robust methodology, improving the contractive properties of the Newton-Raphson method. Moreover, some interesting links with other Power-Flow approaches are obtained as by-product. Several numerical experiments serve to confirm the theoretical findings and to compare the performance of the Mann Iteration Process with other well-known PF solvers. In all cases, the results obtained with the Mann Iteration Process are superior to that obtained using other methodologies, being able to efficiently solve various large-scale ill-conditioned systems.

The power sector of developing countries is the backbone of the economy, and robust implementation of energy policies can play a significant role in its expansion. India is blessed to have ample natural resources; the only necessity is to identify these unseen resources comprehensively. India is an agricultural country with plenty of livestock generated biomass, which is renewable and easily accessible in rural India. The paper estimated the potential of electrical energy through biogas production from livestock generated dung in all states of India. Estimation of biogas from the gross livestock dung generation from all states and largely populated livestock species of the country is annually 2633 million tons which correspondingly projects the annual biogas potential of 265,542 million m3. This magnificent potential unbolts the proposals and implementation of biogas plants as a distributed generation to strengthen India’s power sector.

The problems of increasing electricity consumption and atmospheric pollutants emissions are targeted as critical challenges for power transfer in modern distribution networks. In order to tackle this issue, many researchers have proposed the incorporation of Distributed Generator (DG) units, as well as different types of reactive power compensation devices, which will contribute to the increase of networks efficiency through loss minimization, voltage profile improvement, or even increasing transmission line’s capacity. This paper addresses the optimal allocation/coordination of DG, Distributed Static VAR Compensator (D-SVC), and Distributed Thyristor-Controlled Series Compensator (D-TCSC) to reduce total power loss in the distribution network. The allocation problem is formulated as a mixed-integer nonlinear programming (MINLP) and is solved using a basic open-source MINLP (BONMIN) solver embedded in GAMS. The proposed method was tested in IEEE 16-bus and 33-bus distribution test systems, where two cases were considered. Firstly, BONMIN solver is used for allocation of D-SVC, and D-TCSC, respectively, where the results showed that incorporation of these devices leads to reduction of the active power losses, while the minimal losses and highest efficiency increase for both test networks occurred in the case where 3 D-TCSCs allocated. Secondly, a coordinated allocation of DGs and D-SVCs, as well as DGs and D-TCSCs, is presented, where it was shown that coordination of these devices leads to even greater loss reduction, i.e. increased efficiency. Also, it was concluded that DG and D-TCSC coordination is better suited for loss reduction in the 16-bus system, while DG and D-SVC coordination leads to greater loss reduction for the 33-bus system. Furthermore, the comparison between BONMIN and metaheuristic methods was performed for DG allocation and DG and D-TCSC coordination in IEEE 33-bus system, where it was concluded that BONMIN performs better in terms of minimal power loss, compared to the results which were presented in the available literature. Finally, the BONMIN solver’s performance is evaluated using three parameters: number of nodes, number of iterations, and total execution time, where the numerical analysis showed that BONMIN is efficient in solving the addressed problems.

The demonstrated research work analyses the technoeconomic modelling and sensitivity analysis of the available resources for the rural community in India. The various resources used in this study are solar, wind, hydro, battery and utility grid-connected system. The usefulness of the on-grid system in the rural sector is that excess amount of electricity produced through renewable energy sources (RES) could be sold back to the utility grid. A total of 12 possible configurations of various resources with and without a grid-connected system was analyzed for minimum Levelized Cost of Energy (LCOE) and Total Net Present Cost (TNPC). Further, sensitivity analysis is accomplished for different sensitive variables to understand the nature of the system for wider application in rural communities. The solar-wind-hydro-based utility grid-connected network is observed to be the best optimal configuration with a minimum value of LCOE of 0.056 $/kWh. The simulation results reveal that the effective utilization of RES has been a cost-efficient and reliable system to the power supply in remote communities.

This paper proposes the use of two recent optimization techniques, called the Local Unimodal Sampling (LUS) and the Spotted Hyena Optimizer (SHO), to optimally tune cascaded PID controller designed while controlling the speed and reducing the torque ripples of the switched reluctance motor. The proposed implementation of these algorithms has the advantage of better convergence and lower computational efforts when compared to other optimization techniques. To demonstrate the effectiveness of the proposed cascaded PID controller using LUS and SHO, they are compared to other controller designs present in previous literature, specifically the cascaded fractional-order PID (FOPID) controller. Results show that the PID controller is more superior to the FOPID controller and that the SHO-based cascaded PID controller leads to less current and torque ripples with better speed response. The study is implemented at different loading conditions. The results are presented in the form of a time-domain simulation conducted via MATLAB/SIMULINK.

Three-diode model (TDM) of photovoltaic (PV) cells is more accurate model that includes most of the optical and electrical losses of PV module. However, the nonlinearity and parameters of this TDM are higher, which make it complex and challengeable model. This article utilizes an efficient algorithm called the Circle Search Algorithm (CSA) for accurate electrical modeling of PV modules. The CSA is inspired by the circle's well-known geometrical properties and the perpendicular relationship between its radius and tangent line. The CSA and other four algorithms are applied to determine the nine parameters of the TDM of three commercial PV modules. The objective function is the root mean square error (RMSE) between the estimated and measured output currents. The statistical optimization results and convergence curves proved the robustness and speed of CSA in determining the optimum TDM among the other methods. The predicted I-V curves are plotted against observed I-V curves at various temperatures and solar irradiance to demonstrate the CSA's superiority under various scenarios. The absolute current error of the CSA-TDM at maximum voltage is less than that of other algorithms.

Mathematical design of a new fourth-order harmonic passive filter is presented and its optimal strategy is proposed with regards to various design scenarios. The considered objective function is total demand distortion minimization of the supply current. The crow spiral-based search algorithm (CSSA) is applied to solve the design problem formulated. This CSSA relies on a spiral search mechanism that improves the convergence and balances the exploration and exploitation abilities of the conventional crow search algorithm. Various power quality indices are taken into consideration like the individual and total harmonic voltage distortion values, individual and total current harmonic distortion values, active power loss, actual and displacement power factor percentages, and resonance damping capabilities. These indices were handled as non-linear constraints in the design. Additionally, a comparative investigation of the different damped filters is presented to assess the planned filter performance. The results obtained confirm the success of the filter design proposed in this paper.

In order to ensure efficient, bulk-power transmission over a long distance, need of increasing transmission-level voltage is being thought for the past many years. As a result, Ultra High Voltage AC (UHVAC) Transmission has appeared as a feasible solution. Many countries like Italy, Japan, USA, USSR, Brazil, China, India are developing technology for UHVAC transmission systems. Operating at 1000 kV (and above voltage level) demands careful design of transmission structure, switchgear, and equipment design. This paper presents a review of several studies carried out to analyze the electro-magnetic environment in UHVAC lines. The electromagnetic environment (Field, Audible Noise, Corona Loss, Radio Interference) of 1200 kV, Wardha-Aurangabad single-circuit transmission line, India is also evaluated using FACE (Field and Corona Effect) software. The results of the study of static AC field and magnetic field of the line are found to be within prescribed limits suggested by International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines.

Usually, electric vehicle systems are based on various modules that should ensure the high power and stability of the vehicle on the track. The majority of these components are linked to the charging mechanism. In this regard, dynamic wireless power transfer is a practical method to solve electric vehicle range anxiety and reduce the cost of onboard batteries. Wireless recharging has long been common with pure electric vehicles and is designed to allow charging even when the vehicle is in motion. However, it is difficult to analyze this method since its operating philosophy is complex, particularly with the existence of several variables and parameters. Also, the state of the vehicle, whether it is in motion or not, defines several parameters such as the vehicle speed as well as the sizes and dimensions of the coil receivers. This paper presents a novel method to improve the performance of the dynamic wireless recharge system. In the proposed system, receiver coils have been added to maximize charging power by offering a dynamic mathematical model that can describe and measure source-to-vehicle power transmission even though it is in motion. In the proposed mathematical model, all physical parameters describing the model were presented and discussed. The results showed the effectiveness of the proposed model. Also, the experimental tests confirmed the validity of the simulation results obtained by providing two coil receivers under the vehicle.

Currently, solar energy is one of the leading renewable energy sources that help support energy transition into decarbonized energy systems for a safer future. This work provides a comprehensive review of mathematical modeling used to simulate the performance of photovoltaic (PV) modules. The meteorological parameters that influence the performance of PV modules are also presented. Various deterministic and probabilistic mathematical modeling methodologies have been investigated. Moreover, the metaheuristic methods used in the parameter extraction of diode models of the PV equivalent circuits are addressed in this article to encourage the adoption of algorithms that can predict the parameters with the highest precision possible. With the significant increase in the computational power of workstations and personal computers, soft computing algorithms are expected to attract more attention and dominate other algorithms. The different error expressions used in formulating objective functions that are employed in extracting the parameters of PV models are comprehensively expressed. Finally, this work aims to develop a comprehensive layout for the previous, current, and possible future areas of PV module modeling.

Integrating renewable energy sources (RESs) into modern electric power systems offers various techno-economic benefits. However, the inconsistent power profile of RES influences the power flow of the entire distribution network, so it is crucial to optimize the power flow in order to achieve stable and reliable operation. Therefore, this paper proposes a newly developed circle search algorithm (CSA) for the optimal solution of the probabilistic optimal power flow (OPF). Our research began with the development and evaluation of the proposed CSA. Firstly, we solved the OPF problem to achieve minimum generation fuel costs; this used the classical OPF. Then, the newly developed CSA method was used to deal with the probabilistic power flow problem effectively. The impact of the intermittency of solar and wind energy sources on the total generation costs was investigated. Variations in the system’s demands are also considered in the probabilistic OPF problem scenarios. The proposed method was verified by applying it to the IEEE 57-bus and the 118-bus test systems. This study’s main contributions are to test the newly developed CSA on the OPF problem to consider stochastic models of the RESs, providing probabilistic modes to represent the RESs. The robustness and efficiency of the proposed CSA in solving the probabilistic OPF problem are evaluated by comparing it with other methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the hybrid machine learning and transient search algorithm (ML-TSO) under the same parameters. The comparative results showed that the proposed CSA is robust and applicable; as evidence, an observable decrease was obtained in the costs of the conventional generators’ operation, due to the penetration of renewable energy sources into the studied networks.

The fuel cell is vital in electrical distribution networks as a distributed generation in today’s world. A precise model of a fuel cell is extensively required as it rigorously affects the simulation studies’ transient and dynamic analyses of the fuel cell. This appears in several microgrids and smart grid systems. This paper introduces a novel attempt to optimally determine all unknown factors of the polymer exchange membrane (PEM) fuel cell model using a meta-heuristic algorithm termed the Lightning search algorithm (LSA). In this model, the current–voltage relationship is heavily nonlinear, including several unknown factors because of the shortage of fuel cell data from the manufacturer’s side. This issue can be treated as an optimization problem, and LSA is applied to detect its ability to solve this problem accurately. The objective function is the sum of the squared error between the estimated output voltage and the measured output voltage of the fuel cell. The constraints of the optimization problem involve the factors range (lower and upper limit). The LSA is utilized in minimizing the objective function. The effectiveness of the LSA-PEM fuel cell model is extensively verified using the simulation results performed under different operating conditions. The simulation results of the proposed model are compared with the measured results of three commercial fuel cells, such as Ballard Mark V 5 kW, BCS 500 W and Nedstack PS6 6 kW, to obtain a realistic study. The results of the proposed algorithm are also compared with different optimized models to validate the model and, further, to determine where LSA stands in terms of precision. In this regard, the proposed model can yield a lower SSE by more than 5% in some cases and high performance of the LSA-PEMFC model. With the results obtained, it can be concluded that LSA prevails as a potential optimization algorithm to develop a precise PEM fuel cell model.

This paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems.

This paper presents a novel metaheuristic optimization algorithm inspired by the geometrical features of circles, called the circle search algorithm (CSA). The circle is the most well-known geometric object, with various features including diameter, center, perimeter, and tangent lines. The ratio between the radius and the tangent line segment is the orthogonal function of the angle opposite to the orthogonal radius. This angle plays an important role in the exploration and exploitation behavior of the CSA. To evaluate the robustness of the CSA in comparison to other algorithms, many independent experiments employing 23 famous functions and 3 real engineering problems were carried out. The statistical results revealed that the CSA succeeded in achieving the minimum fitness values for 21 out of the tested 23 functions, and the p-value was less than 0.05. The results evidence that the CSA converged to the minimum results faster than the comparative algorithms. Furthermore, high-dimensional functions were used to assess the CSA’s robustness, with statistical results revealing that the CSA is robust to high-dimensional problems. As a result, the proposed CSA is a promising algorithm that can be used to easily handle a wide range of optimization problems.

This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters’ uncertainty compared with other techniques.

This article introduces an application of the recently developed hunger games search (HGS) optimization algorithm. The HGS is combined with chaotic maps to propose a new Chaotic Hunger Games search (CHGS). It is applied to solve the optimal power flow (OPF) problem. The OPF is solved to minimize the generation costs while satisfying the systems’ constraints. Moreover, the article presents optimal siting for mixed renewable energy sources, photovoltaics, and wind farms. Furthermore, the effect of adding renewable energy sources on the overall generation costs value is investigated. The exploration field of the optimization problem is the active output power of each generator in each studied system. The CHGS also obtains the best candidate design variables, which corresponds to the minimum possible cost function value. The robustness of the introduced CHGS algorithm is verified by performing the simulation 20 independent times for two standard IEEE systems—IEEE 57-bus and 118-bus systems. The results obtained are presented and analyzed. The CHGS-based OPF was found to be competitive and superior to other optimization algorithms applied to solve the same optimization problem in the literature. The contribution of this article is to test the improvement done to the proposed method when applied to the OPF problem, as well as the study of the addition of renewable energy sources on the introduced objective function.

This paper presents a novel minimum seeking algorithm referred to as the Hunger Games Search (HGS) algorithm. The HGS is used to obtain optimal values in the model describing proton exchange membrane fuel cells (PEMFCs). The PEMFC model has many parameters that are linked in a nonlinear manner, as well as a set of constraints. The HGS was used with the aforementioned model to test its performance against nonlinear models. The main aim of the optimization problem was to obtain accurate values of PEMFC parameters. The proposed heuristic algorithm was used with two commercial PEMFCs: the Ballard Mark V and the BCS 500 W. The simulation results obtained using the HGS-based model were compared to the experimental results. The effectiveness of the proposed model was verified under various temperature and partial pressure conditions. The numerical output results of the HGS-based fuel cell model were compared with other optimization algorithm-based models with respect to their efficiency. Moreover, the parametric t-test and other statistical analysis methods were employed to check the robustness of the proposed algorithm under various independent runs. Using the proposed HGS-based PEMFC model, a model with very high precision could be obtained, affecting the operation and control of the fuel cells in the simulation analyses.