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This paper offers an electromagnetism-like (IEM) algorithm to estimate the five parameters of a single-diode PV module's model. IEM uses local search and improves movement step to increase the convergence to optimal solutions. The key of improvement is performed by adding a nonlinear equation to adjust the length of the particle in each iteration. Moreover, the total force formula is simplified to speed up the exploration for an optimal solution. Analyses are carried out by experimental data points at various operational conditions to show the stability and reliability of the proposed methods. The results of the proposed IEM algorithm show a better convergence speed and high accuracy compared with other models in the literature, which involves various statistical errors. The values of average root mean square error, mean bias error, standard deviation, average absolute error, and average test statistic of the proposed method are 589%, 0.51%, 0.19%, 46%, and 0.53, respectively. As a conclusion, the IEM algorithm presents better performance than other methods in the literature in terms of accuracy and convergence. [ABSTRACT FROM AUTHOR]

The paper presents a new multiple scenario multi-objective salp swarm optimization (MS-MOSS) algorithm to optimally size a standalone PV system. An accurate estimation of the number of PV modules and storage battery is crucial as it affects the system reliability and cost. Three scenarios have been presented focusing on Pareto optimal solutions by minimizing two conflicting objectives. Loss of load probability (LLP) and life-cycle cost (LLC) are considered to obtain the Pareto front. The iterative method is employed for validation of the superiority results of the proposed MS-MOSS algorithm. The results show that the scenarios are able to find Pareto optimal configuration at a high level of accuracy and at a very low cost. The proposed three scenarios are faster than iterative approach approximately by 158, 194.2, and 141.6 times, respectively. The third scenario outperforms other scenarios in terms of coverage and convergence of the distribution of solution to the Pareto front. As a conclusion, The MS-MOSS algorithm is found to be very effective in sizing of SAPV system. • A new multiple scenario multi-objective salp swarm optimization (MS-MOSS) is proposed. • An optimal size for a standalone PV system is found using MS-MOSS. • Three scenarios have been presented to obtain on Pareto optimal solutions. • The results demonstrate the efficiency of MS-MOSS for sizing of SAPV system. [ABSTRACT FROM AUTHOR]

• BHHO method is proposed to extract the parameters of the PV module's model. • Two different intervals of EE are used to improve the research direction. • Random steps inspired by FPA with keep using the LF concept to enrich the diversity of the algorithm. • An effective mutation scheme of the DE approach with the 2-Opt algorithm is introduced. • An adapting F in the mutation phase is proposed. In order to realize the performance of the PV model before being installed, it is often indispensable to develop reliable and accurate parameter identification methods for dealing with the PV models. Up to now, several stochastic methods have been proposed to analyze the feature space of this problem. However, some of the stochastic-based methods may present unsatisfactory results due to their insufficient exploration and exploitation inclinations, and the multimodal and nonlinearity existed in PV parameters extraction problems. In this paper, a Boosted Harris Hawk's Optimization (BHHO) technique is proposed to achieve a more stable model and effectively estimate the parameters of the single diode PV model. The BHHO method combines random exploratory steps of evolution inspired by the flower pollination algorithm (FPA) and a powerful mutation scheme of the differential evolution (DE) with 2-Opt algorithms. The proposed strategies not only help BHHO algorithm to accelerate the convergence rate but also assist it in scanning new regions of the search basins. The results demonstrate that the proposed BHHO is more accurate and reliable compared to the basic version and several well-established methods. The BHHO method was rigorously validated by using real experimental data under seven sunlight and temperature conditions. Furthermore, the statistical criteria indicate that the proposed BHHO method has lower errors among other peers, which is highly useful for real-world applications. [ABSTRACT FROM AUTHOR]

Solar energy is considered one of the most important renewable energy resources, and can be used to power a stand-alone photovoltaic (SAPV) system for supplying electricity in a remote area. However, inconstancy and unpredictable amounts of solar radiation are considered major obstacles in designing SAPV systems. Therefore, an accurate sizing method is necessary to apply in order to find an optimal configuration and fulfil the required load demand. In this study, a novel hybrid sizing approach was developed on the basis of techno-economic objectives to optimally size the SAPV system. The proposed hybrid method consisted of an intuitive method to estimate initial numbers of PV modules and storage battery, an iterative approach to accurately generate a set of wide ranges of optimal configurations, and a Pareto envelope-based selection algorithm (PESA-II) to reduce large configuration by efficacy obtaining a set of Pareto front (PF) solutions. Subsequently, the optimal configurations were ranked by using an integrated analytic hierarchy process (AHP) and vlsekeriterijumskaoptimizacija i kompromisonoresenje (VIKOR). The techno-economic objectives were loss of load probability, life cycle cost, and levelized cost of energy. The performance analysis results demonstrated that the lead–acid battery was reliable and more cost-effective than the other types of storage battery. [ABSTRACT FROM AUTHOR]

[Display omitted] • A hybrid algorithm for reliable and efficient parameter estimation of the PV model. • Significant advancements are achieved in methodology and objective function design. • Performance of the algorithm is analyzed with partial shading and noise effects. • Proposed approach achieves enhancements in the overall performance and accuracy. • It outperforms other algorithms in terms of accuracy, consistency, and convergence. A reliable methodology is essential for accurately estimating the parameters of PV models, enabling reliable performance evaluations, effective control studies, accurate analysis of partial shading effects, and optimal optimization of Photovoltaic (PV) systems. It ensures that the obtained parameters reflect the true characteristics of the PV system, leading to more accurate and reliable results in various applications. The existing literature extensively explores the utilization of powerful Metaheuristic Algorithms (MAs) to address the complex constrained optimization problem in PV systems and achieve optimal solutions. However, it is important to note that a significant portion of these MAs primarily concentrates on the development of methodologies, often overlooking the design of the objective function tailored for PV systems. This oversight has created a theoretical gap in this research domain, underscoring the necessity for additional exploration and investigation to address this limitation. To address the existing theoretical gap, this study focused on developing an objective function that accurately estimates the initial root parameters of Photovoltaic (PV) models. This objective function was designed by incorporating the first-order Berndt-Hall-Hall-Hausman (BHHH) numerical method, along with the non-linear damping parameter of the Levenberg-Marquardt technique (LM). By implementing this approach, the study aimed to significantly improve the precision and reliability of estimating the initial root parameters in PV models, effectively filling the theoretical void in this specific research area. Then in terms of methodology, the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is combined with the Sine-Cosine mutualism phase of Symbiotic Organisms Search (SOS) for efficiently estimating the unknown parameters of PV models. The keystone of EHGSO in terms of methodology enhances exploration at the beginning of optimization and intensifies exploitation in later iterations. The proposed EHGSO methodology based on the adaptive damping BHHH technique (EHGSO AdBHHH) is tested on Single Diode (SD), and Double Diode (DD) PV models using actual experimental data. EHGSO AdBHHH exhibits outstanding accordance with attained experimental data compared with other algorithms, and its superiority is validated using several statistical criteria. [ABSTRACT FROM AUTHOR]