Optimization of a Heliostat Field Layout on Annual Basis Using a Hybrid Algorithm Combining Particle Swarm Optimization Algorithm and Genetic Algorithm

Li, Chao; Zhai, Rongrong; Yang, Yongping

doi:10.3390/en10111924

Cited by 22 publications

(3 citation statements)

References 29 publications

(35 reference statements)

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“…As dλ is proportional to time, the derivative of Equation (1) shows the time spent in the ring of declinations with width dδ. The density grows for large declinations as shown in Figure 1.…”

Section: Sunpath Approach To Facilitate Energy Yield Analysesmentioning

confidence: 99%

“…Depending on the approach, the optimization of such systems could involve the optimization of hundreds or even thousands of parameters. The ability to perform the techno-economic optimizations quickly depends on how fast and accurately the annual performance of a CST system or some of their key subsystems can be found [1]. Increasingly, ray tracing is used to model the optical behaviour of the light collection and concentration (LCC) subsystem of CST systems.…”

Section: Introductionmentioning

confidence: 99%

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Minimizing the Computational Effort to Optimize Solar Concentrators with the Open-Source Tools SunPATH and Tonatiuh++

et al. 2021

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Integrals that are of interest in the analysis, design, and optimization of concentrating solar thermal systems (CST), such as the annual optical efficiency of the light collection and concentration (LCC) subsystem, can be accurately computed or estimated in two distinct ways: on the time domain and on the spatial domain. This article explores these two ways, using a case study that is highly representative of the commercial CST systems being deployed worldwide. In the time domain, the computation of these integrals are explored using 1-min, 10-min, and 1-h solar DNI input data and using The Cyprus Institute (CyI)’s High-Performance Computing (HPC) system and an open-source ray tracer, Tonatiuh++, being actively developed at CyI. In the spatial domain, the computation of these integrals is explored using SunPATH, another open-source software tool being actively developed at CyI, in tandem with Tonatiuh++. The comparison between the time and spatial domain approach clearly indicate that the spatial domain approach using SunPATH is dramatically more computationally efficient than the time domain approach. According to the results obtained, at least for the case study analyzed in this article, to compute the annual energy delivered by the LCC subsystem with a relative error less than 0.1%, it is enough to provide SunPATH with 1-h DNI data as input, request from SunPATH the sun position and weights of just 30 points in the celestial sphere, and run Tonatiuh++ to simulate these 30 points using 15 million rays per run. As the test case is highly representative, it is expected that this approach will yield similar results for most CST systems of interest.

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Section: Sunpath Approach To Facilitate Energy Yield Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Minimizing the Computational Effort to Optimize Solar Concentrators with the Open-Source Tools SunPATH and Tonatiuh++

et al. 2021

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“…A general description of the GA is presented in [20]. The GA is inspired by Darwin's principle of natural selection and the survival of the fittest, and has been successfully used to solve different optimization problems, recently achieving substantial progresses [21][22][23][24]. The GA was selected as the main solution strategy for the formulated optimization problem due to its advantages over other optimization algorithms, As mentioned in Section 1, a genetic algorithm (GA) has successfully been used in [13] to solve a similar problem, in comparison with conventional calculation methods.…”

Section: Solution Strategymentioning

confidence: 99%

Calculation of Distance Protection Settings in Mutually Coupled Transmission Lines: A Comparative Analysis

Serna

López-Lezama

2019

Energies

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Protection of mutually coupled transmission lines poses special challenges to protection engineers, since the general principles outlining the expected performance of the protection elements are based on simplified versions of the issues affecting them. Distance protection elements are among the most commonly accepted methods for protecting mutually coupled transmission lines, however, the calculation of settings and the validation of their performance is a complex task, involving lots of simulations and analysis of results to come up with an acceptable solution. Though various methods have been proposed to deal with the problem of calculating the optimal settings of distance protection elements, there is still room for improvement, since these methods have been formulated for the general case considering simple transmission lines. In this paper, a variation of one of these methods is introduced to enhance the computing times and reliability of the solutions for the specific application of quadrilateral distance protection to mutually coupled transmission lines, through the characterization of the expected performance of the operating characteristic, and the formulation of the optimization problem and the solution method. The obtained results show the improvements in computing times and quality of the solution provided by the proposed algorithm.

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Artificial Neural Network–Particle Swarm Optimization (ANN-PSO) Approach for Behaviour Prediction and Structural Optimization of Lightweight Sandwich Composite Heliostats

2021

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The necessity to diminish the heliostats’ cost so that central tower concentrating solar power (CSP) systems can stride to the forefront to become the technology of choice for generating renewable electricity is obliging the industry to consider innovative designs, leading to new materials being implemented into the development of heliostats. Honeycomb sandwich composites offer a lightweight but stiff structure that appear to be an ideal substitute for existing heliostat mirrors and their steel supporting trusses, avoiding large drive units and reducing energy consumption. However, realizing a honeycomb sandwich composite as a heliostat, among a multitude of possible combinations can be tailored from, that delivers the best trade-off between the panel’s weight reduction (broadly equates to cost) and structural integrity is cumbersome and challenging due to the complex nonlinear material behaviour, along with the large number of design variables and performance constraints. We herein offer a simulation–optimization model for behaviour prediction and structural optimization of lightweight honeycomb sandwich composite heliostats utilizing artificial neural network (ANN) technique and particle swarm optimization (PSO) algorithm. Considering various honeycomb core configurations and several loading conditions, a thorough investigation was carried out to optimally choose the training algorithm, number of neurons in the hidden layer, activation function in a network and the suitable swarm size that delivers the best performance for convergence and processing time. Carried out for three case scenarios, each with different design requirements, the results showed that the proposed integrated ANN-PSO approach provides a useful, flexible and time-efficient tool for heliostat designers to predict and optimize the structural performance of honeycomb sandwich composite-based heliostats as per desired requirements. Knowing that heliostats in the field are not all subjected to the same wind conditions, this method offers flexibility to tailor heliostats independently, allowing them to be made lighter depending on the local wind speed in the field. This could lead to reductions in the size of drive units used to track the heliostat, and the foundations required to support these structures. Such reductions would deliver real cost savings, which are currently an impediment to the wider spread use of CSP systems.

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Optimization of a Heliostat Field Layout on Annual Basis Using a Hybrid Algorithm Combining Particle Swarm Optimization Algorithm and Genetic Algorithm

Cited by 22 publications

References 29 publications

Minimizing the Computational Effort to Optimize Solar Concentrators with the Open-Source Tools SunPATH and Tonatiuh++

Minimizing the Computational Effort to Optimize Solar Concentrators with the Open-Source Tools SunPATH and Tonatiuh++

Calculation of Distance Protection Settings in Mutually Coupled Transmission Lines: A Comparative Analysis

Artificial Neural Network–Particle Swarm Optimization (ANN-PSO) Approach for Behaviour Prediction and Structural Optimization of Lightweight Sandwich Composite Heliostats

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