There are three standard equivalent circuit models of solar cells in the literature—single-diode, double-diode, and triple-diode models. In this paper, first, a modified version of the single diode model, called the Improved Single Diode Model (ISDM), is presented. This modification is realized by adding resistance in series with the diode to enable better power loss dissipation representation. Second, the mathematical expression for the current–voltage relation of this circuit is derived in terms of Lambert’s W function and solved by using the special trans function theory. Third, a novel hybrid algorithm for solar cell parameters estimation is proposed. The proposed algorithm, called SA-MRFO, is used for the parameter estimation of the standard single diode and improved single diode models. The proposed model’s accuracy and the proposed algorithm’s efficiency are tested on a standard RTC France solar cell and SOLAREX module MSX 60. Furthermore, the experimental verification of the proposed circuit and the proposed solar cell parameter estimation algorithm on a solar laboratory module is also realized. Based on all the results obtained, it is shown that the proposed circuit significantly improves current–voltage solar cell representation in comparison with the standard single diode model and many results in the literature on the double diode and triple diode models. Additionally, it is shown that the proposed algorithm is effective and outperforms many literature algorithms in terms of accuracy and convergence speed.

In the literature, all investigations dealing with regulator design in the AVR loop observe the AVR system as a single input single output (SISO) system, where the input is the generator reference voltage, while the output is the generator voltage. Besides, the regulator parameters are determined by analyzing the terminal generator voltage response for a step change from zero to the rated value of the generator voltage reference. Unlike literature approaches, in this study, tuning of the AVR controllers is conducted while modeling the AVR system as a double input single output (DISO) system, where the inputs are the setpoint of the generator voltage and the step disturbance on the excitation voltage, while the output is the generator voltage. The transfer functions of the generator voltage dependence on the generator voltage reference value and the excitation voltage change were derived in the developed DISO-AVR model. A novel objective function for estimating DISO-AVR regulator parameters is proposed. Also, a novel metaheuristic algorithm named hybrid simulated annealing and gorilla troops optimization is employed to solve the optimization problem. Many literature approaches are compared using different regulator structures and practical limitations. Furthermore, the experimental results of 120 MVA synchronous generators in HPP Piva (Montenegro) are presented to show the drawbacks of the literature approaches that observe generator setpoint voltage change from zero to the rated value. Based on the presented results, the proposed procedure is efficient and strongly applicable in practice.

Over the last decades, the deployment of large solar-based photovoltaic power plants has grown tremendously. The undesirable impact of high integration level of photovoltaic systems has led energy stakeholders to regulate such penetration to avoid this negative impact. One major concern with regard to photovoltaic penetration is the issue of power quality. Poor power quality can be a source of system disturbance and major economic losses. However, the power quality analysis is not widely discussed in the literature, with most of the studies focusing on the harmonic issues as potential power quality problem, but this study shows that there are a number of power quality issues, such as undervoltage, overvoltage, power fluctuation, and power factor. This study presents practical approaches to a grid-connected solar photovoltaic plant with associated control circuits developed in the time-domain. The power quality of a grid-connected solar photovoltaic plant is investigated by an analysis of the inverter output voltage and nominal current for different photovoltaic plant sizes. Also, the effect of different conditions of solar irradiance and ambient temperature on the power quality is analyzed. To identify power quality issues, a photovoltaic plant time-domain model is developed using Power Systems Computer Aided Design software. Various solar photovoltaic plant controls such as maximum power point tracking and modulation signals sinusoidal pulse width modulation and pulse width modulation for direct current-to-direct current boost converter are developed and integrated into the simulation environment. Several case studies are performed taking into account different photovoltaic plant ratings such as 250 kW–3 MW, where the point of common coupling is monitored.

A fuzzy cascaded PI−PD (FCPIPD) controller is proposed in this paper to optimize load frequency control (LFC) in the linked electrical network. The FCPIPD controller is composed of fuzzy logic, proportional integral, and proportional derivative with filtered derivative mode controllers. Utilizing renewable energy sources (RESs), a dual-area hybrid AC/DC electrical network is used, and the FCPIPD controller gains are designed via secretary bird optimization algorithm (SBOA) with aid of a novel objective function. Unlike the conventional objective functions, the proposed objective function is able to specify the desired LFCs response. Under different load disturbance situations, a comparison study is conducted to compare the performance of the SBOA-based FCPIPD controller with the one-to-one (OOBO)-based FCPIPD controller and the earlier LFC controllers published in the literature. The simulation’s outcomes demonstrate that the SBOA-FCPIPD controller outperforms the existing LFC controllers. For instance, in the case of variable load change and variable RESs profile, the SBOA-FCPIPD controller has the best integral time absolute error (ITAE) value. The SBOA-FCPIPD controller’s ITAE value is 0.5101, while sine cosine adopted an improved equilibrium optimization algorithm-based adaptive type 2 fuzzy PID controller and obtained 4.3142. Furthermore, the work is expanded to include electric vehicle (EV), high voltage direct current (HVDC), generation rate constraint (GRC), governor dead band (GDB), and communication time delay (CTD). The result showed that the SBOA-FCPIPD controller performs well when these components are equipped to the system with/without reset its gains. Also, the work is expanded to include a four-area microgrid system (MGS), and the SBOA-FCPIPD controller excelled the SBOA-CPIPD and SBOAPID controllers. Finally, the SBOA-FCPIPD controller showed its superiority against various controllers for the two-area conventionally linked electrical network.

The most commonly used model of solar cells is the single-diode model, with five unknown parameters. First, this paper proposes three variants of the single-diode model, which imply the voltage dependence of the series resistance, parallel resistance, and both resistors. Second, analytical relationships between the current and the voltage expressed were derived using the Lambert W function for each proposed model. Third, the paper presents a hybrid algorithm, Chaotic Snake Optimization (Chaotic SO), combining chaotic sequences with the snake optimization algorithm. The application of the proposed models and algorithm was justified on two well-known solar photovoltaic (PV) cells—RTC France solar cell and Photowatt-PWP201 module. The results showed that the root-mean-square-error (RMSE) values calculated by applying the proposed equivalent circuit with voltage dependence of both resistors are reduced by 20% for the RTC France solar cell and 40% for the Photowatt-PWP201 module compared to the standard single-diode equivalent circuit. Finally, an experimental investigation was conducted into the applicability of the proposed models to a solar laboratory module, and the results obtained proved the relevance and effectiveness of the proposed models.

The present paper provides an optimal design for load frequency control (LFC) in the interconnected power system. To obtain an adequate LFC response alongside shortening implementation time and minimizing costs, an integral (I) controller is used. A deep analysis of the I controller-based LFC is presented. At first, a two-area interconnected power system is used, and to enhance the LFC response, the I controller and frequency bias parameters are optimized using three novel optimization algorithms, which are the incomprehensible but intelligible-in-time logic algorithm (ILA), the coati optimization algorithm (COA), and the brown-bear optimization algorithm (BOA). Also, five well-known techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), pattern search (PS), and nonlinear programming (NP), are used. A new objective function utilizing the integral of squared error (ISE), settling time, settling-max, and settling-min of the dynamic response is used to increase the efficacy of estimating the parameters. The presented results in this paper showed that the optimized I controller outperforms the classic I controller. After considering a load change in one area by 18.75%, the optimized I controller achieved the lowest ISE values. ISE values were: 0.00582, 0.00179, 0.00176, 0.00178, 0.00321, 0.00304, 0.00179, 0.00185, and 0.00181, for classic I, PSO-I, GA-I, SA-I, PS-I, NP-I, ILA-I, COA-I, and BOA-I. Then, the proposed method is applied to a nonlinear two-area system, demonstrating that the proposed strategies can deal with nonlinearity. As the purpose of the hybrid power system is to create a robust energy infrastructure that adheres to sustainability standards, the proposed algorithms are analyzed in a three-area multi-source power system comprising renewable energy sources (RESs) such as photovoltaic (PV) and wind turbine (WT), a battery energy storage system (BESS), and an electric vehicle (EV).