Genetic algorithm optimization of heliostat field layout for the design of a central receiver solar thermal power plant

Haris, Muhammad; Rehman, Atiq Ur; Iqbal, Sheeraz; Athar, Syed Owais; Kotb, Hossam; AboRas, Kareem M.; Alkuhayli, Abdulaziz; Ghadi, Yazeed Yasin; Kitmo,

doi:10.1016/j.heliyon.2023.e21488

Cited by 7 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The photovoltaic energy system is based on the efficiency of the solar panels [14]. This system includes module parameters such as irradiance and temperature under standard conditions, defined by Eqs (1), ( 2) and (3).…”

Section: System Based Photovoltaic Plantmentioning

confidence: 99%

Optimal sizing and power losses reduction of photovoltaic systems using PSO and LCL filters

F. Elnaggar,

Duvalier Péné,

Boussaibo

et al. 2024

PLoS ONE

Self Cite

View full text Add to dashboard Cite

The integration of renewable energy systems into electricity grids is a solution for strengthening electricity distribution networks (SEDNs). Renewable energies such as solar photovoltaics are suitable for reinforcing a low-voltage line by offering an electrical energy storage system. However, the integration of photovoltaic systems can lead to problems of harmonic distortion due to the presence of direct current or non-linear feedback in networks from other sources. Therefore, connection standards exist to ensure the quality of the energy before injection at a point of common coupling (PCC). In this work, particle swarm optimization (PSO) is used to control a boost converter and to evaluate the power losses and the harmonic distortion rate. The test on the IEEE 14 bus standard makes it possible to determine the allocation or integration nodes for other sources such as biomass, wind or hydrogen generators, in order to limit the impact of harmonic disturbances (LIHs). The evaluation of the harmonic distortion rate, the power losses as well as the determination of the system size is done using an objective function defined based on the integration and optimization constraints of the system. The proposed model performs better since the grid current and voltage are stabilized in phase after the photovoltaic source is injected.

show abstract

Section: System Based Photovoltaic Plantmentioning

confidence: 99%

Optimal sizing and power losses reduction of photovoltaic systems using PSO and LCL filters

F. Elnaggar,

Duvalier Péné,

Boussaibo

et al. 2024

PLoS ONE

Self Cite

View full text Add to dashboard Cite

show abstract

“…[41] combines engineering optimization and heuristic algorithm optimization to propose a co-optimization method based on coupling instantaneous optical, thermal and mechanical models, and then uses the obtained system model for design optimization based on genetic algorithm. A genetic algorithm (GA) based on rejection sampling [42] to optimize the field layout has also been applied to the heliostat field in Quetta, Pakistan, and this method has been shown to be effective in reducing the number of heliostats and improving efficiency.…”

Section: B Heliostat Field Layout Optimization Methodologymentioning

confidence: 99%

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

Zou,

Zhou,

2024

IEEE Access

View full text Add to dashboard Cite

Confronted with the challenges posed by climate change and the ongoing energy transition, solar energy is one of the important new energy sources, and the tower solar power plant has become an innovative solution to promote clean energy development. The optimization of heliostat field layout constitutes a crucial aspect in enhancing the operational efficiency of a concentrated solar power tower plant. Currently, the optimization of heliostat field layout has garnered widespread attention. In this paper, we propose the swarm optimization algorithm with niching and elite competition called NECSO to solve the large-scale heliostat field layout optimization. First, aiming to increase diversity and heterogeneity within the population, we employ a random grouping strategy to partition the population into distinct sub-swarms. Then, we design a niching and elite competition mechanism to harmonize the performance of the exploration. The niching competition is carried out within any sub-swarm to enhance the explorability of particles. The elite competition occurs between the elites which select from each sub-swarm to improve the convergence of particles. Additionally, we develop a mathematical model for the optimization of heliostat field layout. This model employs currently advanced computational methods, facilitating prompt and precise calculation of the optical efficiency in the heliostat field layout. To evaluate the performance of NECSO, we design 15 practical cases of heliostat field layout with varying scales. And then, we conduct comparative experiments with eight currently mainstream and excellent algorithms. The results indicate that NECSO exhibits competitive performance in solving the heliostat field layout optimization, particularly in large-scale cases.

show abstract

Optimal arrangement of fixed heliostat field based on genetic algorithm and greedy algorithm

Peng,

Chen,

Zhou

et al. 2024

2024 4th International Conference on Computer Communication and Information Systems (CCCIS)

View full text Add to dashboard Cite

Genetic algorithm optimization of heliostat field layout for the design of a central receiver solar thermal power plant

Cited by 7 publications

References 30 publications

Optimal sizing and power losses reduction of photovoltaic systems using PSO and LCL filters

Optimal sizing and power losses reduction of photovoltaic systems using PSO and LCL filters

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

Optimal arrangement of fixed heliostat field based on genetic algorithm and greedy algorithm

Contact Info

Product

Resources

About