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• Comparison of optimized spiral and staggered layouts for different heliostat shapes. • The GOA algorithm is used for the first time to optimize the heliostat layout. • A circular heliostat shape reduces the land area by 25.9% & 28.4% for both layouts. • A circular heliostat shape improves the efficiency to 71% & 71.1% for both layouts. During the design and characterization of Solar Power Tower (SPT) systems, the selection and optimization of the heliostat field distribution are critical. Additionally, selecting the appropriate heliostat shape is an important factor to consider because of the optical losses linked to the heliostat shape. In the current study, the optimization and comparison between biomimetic spiral and Campo radial-staggered layout are carried out by considering various heliostat shapes. The optimization is carried out for the PS10 plant installed in Spain. The Grasshopper Optimization Algorithm (GOA) approach is used, and the heliostat field efficiency is assessed. The optimized heliostat field showed that the Campo radial-staggered layout provides better efficiency and the lowest land area when compared to the biomimetic spiral layout. In addition, using a circular heliostat shape for the original PS10 field, the land area could be reduced by 25.86% (about 113,538 m2) and 28.39% (about 124,633 m2) for the biomimetic spiral and staggered fields, respectively. On the other hand, for the circular heliostat, the yearly efficiency is improved to ∼ 71 and ∼ 71.1% for biomimetic spiral and staggered fields, respectively, compared to the conventional rectangular heliostat with an efficiency equal to ∼ 69.3%. Indeed, the registered gains in land area and yearly efficiency will reduce the Levelized cost of energy. [ABSTRACT FROM AUTHOR]

Summary: The present study focuses on the optimization of solar tower power plant heliostat field by considering different heliostat shapes including rectangular, square, pentagon, hexagon, heptagon, octagon, and circular heliostat shapes. The optimization is carried out using an in‐house developed code‐based MATLAB program. The developed in‐house code is validated first on a well‐known PS10 Solar Thermal Power plant having rectangular heliostats shape and the resulting yearly unweighted heliostat field efficiency of about 64.43% could be obtained. The optimized PS10 heliostat field using different heliostat shapes showed that the circular and octagon heliostat shapes provide better efficiency with minimum land area. The yearly efficiency is increased from 69.65% for the rectangular heliostat shape to 70.96% and 71% for the octagon and circular shapes, respectively. In addition, the calculated field area (land area) is reduced for the case of circular and octagon heliostat shapes with a gain of about 11.10% and 10.93% (about 42.0436 × 103 and 41.4036 × 103 m2), respectively, in comparison with the PS10 field area. [ABSTRACT FROM AUTHOR]

Summary: The present study focuses on the optimization of solar tower power plant heliostat field by considering different heliostat shapes including rectangular, square, pentagon, hexagon, heptagon, octagon, and circular heliostat shapes. The optimization is carried out using an in‐house developed code‐based MATLAB program. The developed in‐house code is validated first on a well‐known PS10 Solar Thermal Power plant having rectangular heliostats shape and the resulting yearly unweighted heliostat field efficiency of about 64.43% could be obtained. The optimized PS10 heliostat field using different heliostat shapes showed that the circular and octagon heliostat shapes provide better efficiency with minimum land area. The yearly efficiency is increased from 69.65% for the rectangular heliostat shape to 70.96% and 71% for the octagon and circular shapes, respectively. In addition, the calculated field area (land area) is reduced for the case of circular and octagon heliostat shapes with a gain of about 11.10% and 10.93% (about 42.0436 × 103 and 41.4036 × 103 m2), respectively, in comparison with the PS10 field area. [ABSTRACT FROM AUTHOR]

• •Development of an enhanced hybrid model for multi-hour ahead DNI forecasting. • •Comprehensive performance analysis using four different DNI databases. • •Implementation of the GA-GOA Algorithm for CSP Optimization. • •Assessment of electricity generation from CSP plants for multi-hour ahead forecasting. This study addresses the critical need for precise Direct Normal Irradiation forecasting in concentrating solar power systems to enhance performance and manage power generation intermittency. We propose a novel hybrid model that combines Variation Mode Decomposition, Swarm Decomposition Algorithm, Random Forest for feature selection, and Deep Convolutional Neural Networks, aiming to improve the forecasting accuracy. This model covers the entire process from Direct Normal Irradiation forecasting to heliostat field optimization and electricity generation. We validated the model across four globally diverse regions, taking into account their distinct climates and meteorological conditions. The results show that our model aligns closely with actual measurements and outperforms existing forecasting methods in terms of precision and stability. The forecasting performance was assessed using normalized Root Mean Square Error, with results ranging from 0.75% to 3.4% across different regions. This demonstrates the model's potential for real-world application in concentrating solar power systems, optimizing heliostat field effectiveness, and reliably forecasting electricity production for grid management. [ABSTRACT FROM AUTHOR]

A comparative analysis has been carried out between eight metaheuristic algorithms, namely; genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), grey wolf optimization (GWO), improved grey wolf optimization (IGWO), artificial bee colony (ABC), grasshopper optimization algorithm (GOA), and a proposed hybrid genetic-grasshopper (GA-GOA) to optimize the staggered heliostat field of the PS10 plant. The annual weighted efficiency is taken as the objective function for field layout optimization. In addition, the investigated algorithms have been assessed in terms of best energy yield, levelized cost of energy (LCOE), land use factor (LUF), and computational cost. It has been found that evolutionary algorithms outperform swarm intelligence algorithms in terms of efficiency, whereas GOA and GWO converge faster. To get high efficiency with low computational cost, a hybrid GA-GOA algorithm has been proposed. This study found that the hybrid GA-GOA algorithm does indeed deliver improved performance, with an optimum weighted efficiency boosted by 1.45% at a computation cost of ∼63.7 h. In addition, it provides the best optimum LCOE of 26.22 c€/kWh and successfully enhances LUF by 11.2% compared to the PS10 reference plant. Based on these results, the authors can conclude that the proposed hybrid GA-GOA algorithm represents a suitable tool to cost-effectively optimize the design of heliostat field layouts and reduce their land footprint. [ABSTRACT FROM AUTHOR]

Accurate and effective forecasting of short-term global solar radiation is critical for the development of photovoltaic systems, particularly for integration into existing grid systems. However, its non-stationary characteristics caused by climatic factors make its estimation extremely challenging. In this regard, a newly designed learning technique for multi-hour global solar radiation forecasting is proposed based on a time-varying filter-empirical mode decomposition (TVF-EMD), feature selection, and extreme learning machine (ELM) as an essence regression. The proposed hybridization strategy consists of three main steps for understanding the fundamental behavioral aspects of hourly global solar radiation data. The first phase employs the TVF-EMD algorithm to deal with the variability of global solar radiation data by separating it into a series of more stable and constant subseries. Then, the feature selection step is employed to evaluate and identify distinctive features set from the whole decomposed subseries by means of the RReliefF algorithm. The selected feature sets are used to train and optimize our forecasting extreme learning machine model, then the tuned ELM model is used to assess the forecasting accuracy. The proposed TVF-EMD-RF-ELM model is evaluated and validated in different regions in Algeria with various climate conditions. The forecasting findings of the TVF-EMD algorithm demonstrate high accuracy compared to the recent version of empirical mode decomposition CEEMDAN. Overall forecasting periods, the TVF-EMD-RF-ELM model produces an error less than 8.3% in terms of normalized root mean square error nRMSE in all studied regions. • A detailed review of different decomposition techniques for global solar radiation forecasting. • An improved artificial intelligence model for multi-hour solar radiation forecasting has been developed. • A regional examination is established, with three sites. • The proposed hybrid model is validated against several models. • The suggested model has been validated for its ability to forecast hourly global solar radiation. [ABSTRACT FROM AUTHOR]