Rose pattern for heliostat field optimization with a dynamic speciation‐based mutation differential evolution

Abstract

Summary Concentrated solar power technology is one of the most promising technologies in energy field. Arguably, the heliostat field layout is a crucial component in solar power tower system. Numerous studies have been developed for heliostat field optimization. However, most of the existing layouts which utilize radial staggered patterns are based on only two or four variables, leading to relatively rigid modes due to strong configuration constraints. In this article, we propose a new method called rose layout, which divides the regular radial staggered pattern into six sectors and they are optimized separately. Therefore, the radial increments between consecutive rows are not restricted to zones or rows, only relevant to which sector they belong to. This arrangement is more flexible and also efficient. Furthermore, a new differential evolution algorithm with a dynamic speciation‐based mutation strategy (DSM‐DE) is developed to solve this high‐dimensional problem. In order to validate the proposed rose pattern and DSM‐DE, three sets of comparative experiments were carried out. The first set of tests were operated with the conventional four optimization variables, the second series optimized total 43 radial increments between consecutive rows, whereas the third series employed the rose layout. All sets of cases were optimized by four competitive variants of differential evolution algorithm, ie, JADE, SHADE, EB‐LSHADE, and DSM‐DE. Experimental results verified that the rose layout can obtain higher overall optical efficiency and less land coverage than previous methods and DSM‐DE is superior to other DE variants for this high‐dimensional problem. The heliostat field studied in this article is simulated in Qinghai, China. By integrating rose layout with DSM‐DE, the field unweighted efficiency progressed from 44.386% to 53.972%, and the annual weighted efficiency reached 59.091%, which was 0.318% higher than the 43‐variable optimization.

Problem of Heliostat Field Efficiency Calculation Based on Innovative Monte Carlo Simulation

“…To calculate the collector truncation efficiency, we adopted the method proposed by Collado and Guallar, based on the HFLCAL model [3]:…”

Numerical Simulation and Design of Multi-Tower Concentrated Solar Power Fields

“…The proposed algorithm, the Artificial Bee Colony Algorithm (IABCA), finds the best position for each heliostat on the field in which the maximum efficiency for both the heliostat and field is reached within a limited area. L Deng et al [23] proposed a new pattern for the heliostat field layout: a rose pattern based on the classic radial staggered configuration. The pattern divides the radial staggered configuration into six sectors, and some of those sectors are then optimized separately using advanced differential equation algorithm, thus increasing the optimization variables.…”

Design optimization of a solar tower power plant heliostat field by considering different heliostat shapes

“…The computed daily and monthly averaged annual optical efficiency were found equal to 0.5023 and 0.5025, respectively. Very recently, Deng et al 26 proposed a new method named rose layout based on the split of the regular radial staggered pattern into six sectors, then the optimization process is carried out separately for each divided sector. Authors reported that the advantage of this method is that it is more efficient and flexible as the radial increments between consecutive rows are relevant its corresponding sector and are not constrained to zones or rows.…”