Saad Alghuwainem

This study combines the metaheuristic algorithm Transient Search Optimization (TSO) with the Levy flight distribution to find the optimal proportional-integral (PI) controllers for robust operation of islanded microgrids. The first step is to use the response surface methodology (RSM) to empirically express the multi-objective function. This function includes the transient variations of the terminal voltages of the microgrids. To demonstrate the efficacy of the hybrid Levyflight and TSO (LTSO), a benchmark microgrid system undergoes rigorous testing under different operational scenarios: i) transitioning the system into autonomous mode by disconnecting from the main grid; ii) adapting to varying load conditions while isolated; and iii) responding to a 3-phase fault while operating in islanded mode. Numerous simulations are run to verify the suggested methodology, employing conventional data extracted from the PSCAD/EMTDC software. The study’s findings are further reinforced through a comparative analysis with established optimization techniques such as the least mean and the square root of exponential approaches, the enhanced block-sparse adaptive Bayesian algorithm, the adaptive-width generalized correntropy diffusion algorithm, the sunflower optimization algorithm, the Coot bird metaheuristic optimizer, and particle swarm optimization. The results collectively underscore the superiority of the LTSO algorithm in enhancing the transient response of the terminal voltages of islanded microgrids, thereby offering a promising avenue for optimizing the control and stability of such systems.

For process control in engineering applications, the fewer the coding lines of optimization algorithms, the more applications there are. Therefore, this work develops a new straightforward metaheuristic optimization algorithm named the propagation search algorithm (PSA), stirred by the wave propagation of the voltage and current along long transmission lines. The mathematical models of the voltage and current are utilized in modeling the PSA, where the voltage and current are the search agents. The propagation constant of the transmission line is the control parameter for the exploitation and exploration of the PSA. After that, the robustness of the PSA is verified using 23 famous testing functions. The statistical tests, comprising mean, standard deviation, and p-values, for 20 independent optimization experiments are utilized to confirm the robustness of the PSA to find the best result and the significant difference between the outcomes of the PSA and those of the compared algorithms. Finally, the proposed PSA is applied to find the optimum design parameters of four engineering design problems, including a three-bar truss, compression spring, pressure vessel, and welded beam. The outcomes show that the PSA converges to the best solutions very quickly, which can be applied to those applications that require a fast response.

Although the main concern of consumers is to reduce the cost of energy consumption, zero-energy buildings are the main concern of governments, which reduce the carbon footprint of the residential sector. Therefore, homeowners are motivated to install distributed renewable energy resources such as solar energy, which includes photovoltaics (PVs), solar concentrators, and energy storage systems (ESSs); these installations are intended to maintain the homeowners’ energy consumption, and the excess energy can be sold to the grid. In light of the comfort consumption suggestions made by users, this paper presents an optimal home energy management (HEM) for zero-energy buildings and low energy consumption. Firstly, this paper proposes a new optimization algorithm called random integer search optimization (RISO). Afterwards, we propose a new objective function to enable zero energy consumption from the grid and lower costs. Therefore, in this study, the primary energy resources for homes are PVs and ESSs, while the grid is on standby during the intermittency of the primary resources. Then, the HEM applies the RISO algorithm for an optimal day-ahead load schedule based on the day-ahead weather forecast and consumers’ comfort time range schedule. The proposed HEM is investigated using a schedule of habits for residential customers living in Hong Kong, where the government subsidizes the excess clean energy from homes to the grid. Three scenarios were studied and compared in this work to verify the effectiveness of the proposed HEM. The results revealed that the load schedule within the comfort times decreased the cost of energy consumption by 25% of the cost without affecting the users’ comfort.

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.

The aim of this article is to introduce a novel Circle Search Algorithm (CSA) with the purpose of obtaining a precise electrical model of a proton exchange membrane fuel cell (PEMFC). Current-voltage and current-power curves are used to characterize the performance of PEMFCs. A nonlinear model with seven unknown parameters is used to describe these polarization curves. Estimating these unknown parameters is a critical issue because they influence the dynamic analysis of fuel cells in a variety of applications such as transportation and smart grids. The suggested method is based on minimizing the fitness function (the sum of the squared errors (SSE)) between estimated and measured voltage values. The CSA is compared to the neural network algorithm (NNA), grey wolf optimization (GWO), and the sine cosine algorithm (SCA). The optimization results reveal that the simulation times of the CSA, NNA, GWO, and SCA are 5.2, 6, 5.8, and 5.75 s, respectively. Moreover, the CSA converges to the best minimum within the first 100 iterations, which is faster than the other algorithms. The robustness of the CSA is verified using 20 independent runs, where the CSA achieves the smallest average and standard deviation. In addition, the t-test proves the superiority of the CSA compared to the other algorithms, where all p-values are less than 5%. The simulated I-V and I-P curves of the CSA-PEMFC model match the measured curves very closely. Moreover, the efficacy of the CSA-PEMFC model is evaluated under a variety of temperature and pressure conditions. Therefore, the suggested CSA-PEMFC model has the potential to be an accurate and efficient model.

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 depicts a new attempt to apply a novel transient search optimization (TSO) algorithm to optimally design the proportional-integral (PI) controllers. Optimal PI controllers are utilized in all converters of a grid-linked permanent magnet synchronous generator (PMSG) powered by a variable-speed wind turbine. The converters of such wind energy systems contain a generator-side converter (GSC) and a grid-side inverter (GSI). Both of these converters are optimally controlled by the proposed TSO-based PI controllers using a vector control scheme. The GSC is responsible for regulating the maximum power point, the reactive generator power, and the generator currents. In addition, the GSI is essentially controlled to control the point of common coupling (PCC) voltage, DC link voltage, and the grid currents. The TSO is applied to minimize the fitness function, which has the sum of these variables’ squared error. The optimization problem’s constraints include the range of the proportional and integral gains of the PI controllers. All the simulation studies, including the TSO code, are implemented using PSCAD software. This represents a salient and new contribution of this study, where the TSO is coded using Fortran language within PSCAD software. The TSO-PI control scheme’s effectiveness is compared with that achieved by using a recent grey wolf optimization (GWO) algorithm–PI control scheme. The validity of the proposed TSO–PI controllers is tested under several network disturbances, such as subjecting the system to balanced and unbalanced faults. With the optimal TSO–PI controller, the low voltage ride-through ability of the grid-linked PMSG can be further improved.