Recently, building an accurate mathematical model with the help of the experimentally measured data of solar cells and Photovoltaic (PV) modules, as a tool for simulation and performance evaluation of the PV systems, has attracted the attention of many researchers. In this work, Coyote Optimization Algorithm (COA) has been applied for extracting the unknown parameters involved in various models for the solar cell and PV modules, namely single diode model, double diode model, and three diode model. The choice of COA algorithm for such an application is made because of its good tracking characteristics and the balance creation between the exploration and exploitation phases. Additionally, it has only two control parameters and such a feature makes it very simple in application. The Root Mean Square Error (RMSE) value between the data based on the optimized parameters for each model and those based on the measured data of the solar cell and PV modules is adopted as the objective function. Parameters' estimation for various types of PV modules (mono-crystalline, thin-film, and multi-crystalline) under different operating scenarios such as a change in intensity of solar radiation and cell temperature is studied. Furthermore, a comprehensive statistical study has been performed to validate the accurateness and stability of the applied COA as a competitor to other optimization algorithms in the optimal design of PV module parameters. Simulation results, as well as the statistical measurement, validate the superiority and the reliability of the COA algorithm not only for parameter extraction of different PV modules but also under different operating scenarios. With the COA, precise PV models have been established with acceptable RMSE of 7.7547x10-4 , 7.64801x10-4 , and 7.59756 x10-4 for SDM, DDM, and TDM respectively considering R.T.C. France solar cell.

In this paper, a simulation model describing the operation of a PV/wind/diesel hybrid microgrid system with battery bank storage has been proposed. Optimal sizing of the proposed system has been presented to minimize the cost of energy (COE) supplied by the system while increasing the reliability and efficiency of the system presented by the loss of power supply probability (LPSP). Novel optimization algorithms of Whale Optimization Algorithm (WOA), Water Cycle Algorithm (WCA), Moth-Flame Optimizer (MFO), and Hybrid particle swarm-gravitational search algorithm (PSOGSA) have been applied for designing the optimized microgrid. Moreover, a comprehensive comparison has been accomplished between the proposed optimization techniques. The optimal sizing of the system components has been carried out using real-time meteorological data of Abu-Monqar village located in the Western Desert of Egypt for the first time for developing this promising remote area. Statistical study for determining the capability of the optimization algorithm in finding the optimal solution has been presented. Simulation results confirmed the promising performance of the hybrid WOA over the other algorithms. INDEX TERMS Isolated microgrids, cost of energy (COE), loss of power supply probability (LPSP), optimization.

In this paper, an efficient optimization technique called Chaotic Harris Hawks optimization (CHHO) is proposed and applied for estimating the accurate operating parameters of proton exchange membrane fuel cell (PEMFC), which simulate and mimic its electrical performance. The conventional Harris Hawks optimization (HHO) is a recent optimization technique that is based on the hunting approach of Harris hawks. In this proposed optimization technique, ten chaotic functions are applied for tackling with the studied optimization problem. The CHHO is proposed to enhance the search capability of conventional HHO and avoid its trapping into local optima. The sum of squared errors (SSE) between the experimentally measured output voltage and the corresponding simulated ones is adopted as the objective function. The developed CHHO technique is tested on four various commercial PEMFC stacks to assess and validate its effectiveness compared with other well-known optimization techniques. A statistical study is performed to appreciate the stability and reliability of the proposed CHHO technique. However, the results show the effectiveness and superiority of proposed CHHO compared with the conventional HHO and other competitive metaheuristic optimization algorithms under the same study cases.INDEX TERMS Proton exchange membrane fuel cell, parameter estimation, Harris Hawks optimization, sum of squared errors.

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Among all renewable energy sources, solar cells are considered the most popular solution for a clean source of energy and have a wide range of applications from few watts to Megawatt industrial and domestic loads. Building a precise mathematical model based on nonlinear equations for solar cells as well as photovoltaic (PV) modules is an essential issue for reasonable performance assessment, control and optimal operation of PV energy systems. In the current study, a novel optimization algorithm, Tree Growth Algorithm (TGA), is applied for accurate and efficient extraction of the unknown solar cell and PV module parameters. TGA is applied for identifying the values of the unknown parameters of various solar cells and PV modules based on different diode models. Single diode model (SDM), double diode model (DDM) and three diode model (TDM) are investigated in the mathematical models of both solar cells and PV modules. The obtained results from the application of TGA to achieve this objective are compared with different algorithms reported in the literature. Moreover, the results demonstrated that the proposed algorithm of TGA superior to other reported methods. The good matching of the I-V characteristic curve of the computed parameters with those of the measured data from the manufacturer's PV modules/cells datasheet proved that the proposed TGA may function as a competitor to the methods provided in literature for parameters' identification of PV of solar cells.

Recently, extracting the precise values of unknown parameters of the polymer electrolyte membrane fuel cell (PEMFC) is considered one of the most widely nonlinear and semi-empirical optimization problems. This paper proposes and applies a Modified Artificial Ecosystem Optimization (MAEO) algorithm to solve the problem of PEMFC parameters extraction. The conventional AEO is a novel optimization technique that is inspired by the energy flow in a natural ecosystem which is defined as abiotic, which includes non-living bodies and elements such as light, water and air. The proposed optimization algorithm, MAEO, is used to enhance the performance of conventional AEO and provide faster convergence rate as well as to be far away from falling into the local optima. In the proposed MAEO, an operator is suggested to improve the balance between exploitation and Exploration phases. The accurate estimation of PEMFC unknown parameters leads to develop a precise mathematical model which simulates the electrochemical and electrical characteristics of PEMFC. The objective function of the studied optimization problem is formulated as the sum of squared errors (SSE) between the measured and simulated stack voltages. To prove the reliability and capability of the proposed MAEO algorithm in solving this problem compared with other recent algorithms, it is tested on four different PEMFC stack models, namely, BCS-500W, SR-12 500W, 250W and Temasek 1 kW stacks. Moreover, statistical measures are performed to assess the superiority and robustness of the proposed algorithm. In addition, the accuracy of optimized parameters is assessed through the dynamic characteristics of PEMFCs under varying the reactants' pressures and temperature of the cell. However, the simulation results confirm that the proposed MAEO algorithm has high accuracy and reliability in extracting the PEMFC optimal parameters compared with the conventional AEO and other effective algorithms. INDEX TERMS Polymer electrolyte membrane fuel cell, parameters extraction, modified artificial ecosystem optimization, sum of squared errors, polarization curves.

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In this paper, the impact of integrating photovoltaic plants (PVPs) with high penetration levels into the national utility grid of Egypt is demonstrated. Load flow analysis is used to examine the grid capacity in the case of integrating the desired PVPs and computer simulations are also used to assess the upgrading of the transmission network to increase its capacity. Furthermore, the impact of increasing the output power generated from PVPs, during normal conditions, on the static voltage stability was explored. During transient conditions of operation (three-phase short circuit and outage of a large generating station), the impact of high penetration levels of PVPs on the voltage and frequency stability has been presented. Professional DIgSILENT PowerFactory simulation package was used for implementation of all simulation studies. The results of frequency stability analysis proved that the national grid could be maintained stable even when the PVPs reached a penetration level up to 3000 MW of the total generation in Egypt. Transmission network upgrading to accommodate up to 3000 MW from the proposed PV power plants by 2025 is suggested. In addition, analysis of voltage stability manifests that the dynamic behavior of the voltage depends remarkably on the short circuit capacity of the grid at the point of integrating the PVPs.