Heliostat Field Layout Optimization Research Articles

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

The heliostat field layout in a central receiver solar thermal power plant has significant optical losses that can ultimately affect the overall output power of the plant. In this paper, an optimized heliostat field layout based on annual efficiency and power of 50 MW for the local coordinates of Quetta, Pakistan, is proposed. The performance of two different heliostat field layouts such as radial staggered and Fermat's spiral distribution are evaluated and different design points in a year are considered for the analysis. The field layouts are then optimized using a rejection sampling based Genetic Algorithm (GA). It considers the output power and mean overall efficiency for vernal equinox, summer solstice, autumnal equinox, and winter solstice as objective functions. The GA optimizes the heliostat field parameters, namely, security distance (DS), tower height (TH), heliostat width to length ratio (WR), and the length of heliostats (LH). The study system was developed in MATLAB for validation. It was observed that for the radial staggered layout, the number of heliostats decreased by 364 and the efficiency was improved by 8.52 % using GA optimization relative to unoptimized results field layout. The annual efficiency for Fermat's spiral configuration was improved by 14.62 % and correspondingly, the number of heliostats decreased by 434.

Accurate interpolation methods for the annual simulation of solar central receiver systems using celestial coordinate system

Heliostat field layout optimization bases on simulations of the annual energy production. To reduce the computation time of the optimization process, one can try to reduce the number of simulation points of the annual domain, while keeping similar accuracy. For the temporal domain, there exist already different approaches as aggregation of days. To further reduce the number of needed simulation points, in this paper we decouple the power computation from the irradiation, such that we just regard the computation of the power plant efficiency. This time-dependent parameter is transformed into a celestial coordinate system where the solar angle-dependent efficiency will be approximated using suitable multi-dimensional interpolation methods.We distinguish between an accurate approximation of the received annual optical energy and the electrical energy of each moment of a year. These methods are demonstrated for the existing heliostat field layouts PS10 and Gemasolar in Seville, while using realistic weather conditions. With this new approach just around 40 simulation points suffice to reach an accuracy of 99.9% for the received power for smaller power plants as PS10, and for larger plants as Gemasolar. Compared to a state-of-the-art method, this investigation helps to accelerate the simulation by factor three.

Pattern-free heliostat field layout optimization using physics-based gradient

In this paper, the alternating direction method is proposed to perform heliostat field layout optimization, which includes two new strategies: (1) pattern-free layout optimization using the proposed physics-based gradient of optical efficiencies, and (2) new initial designs obtained by changing a scaling factor (k). To obtain the physics-based gradient, insolation weighted optical efficiency is divided into two terms – shading/blocking efficiency and non-shading/blocking efficiency, and for more efficient and accurate layout optimization, the proposed alternating direction method performs layout optimization along alternating directions obtained from the physics-based gradient. The proposed alternating direction method is validated using two field layouts – PS10 and a field layout obtained by SolarPILOT – with two objective functions, optical efficiency and levelized cost of electricity (LCOE). The validation results show that both the insolation weighted optical efficiency and LCOE are much improved by the proposed optimization method.

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

Of all the renewable power generation technologies, solar tower power system is expected to be the most promising technology that is capable of large-scale electricity production. However, the optimization of heliostat field layout is a complicated process, in which thousands of heliostats have to be considered for any heliostat field optimization process. Therefore, in this paper, in order to optimize the heliostat field to obtain the highest energy collected per unit cost (ECUC), a mathematical model of a heliostat field and a hybrid algorithm combining particle swarm optimization algorithm and genetic algorithm (PSO-GA) are coded in Matlab and the heliostat field in Lhasa is investigated as an example. The results show that, after optimization, the annual efficiency of the heliostat field increases by approximately six percentage points, and the ECUC increases from 12.50 MJ/USD to 12.97 MJ/USD, increased about 3.8%. Studies on the key parameters indicate that: for un-optimized filed, ECUC first peaks and then decline with the increase of the number of heliostats in the first row of the field (Nhel1). By contrast, for optimized field, ECUC increases with Nhel1. What is more, for both the un-optimized and optimized field, ECUC increases with tower height and decreases with the cost of heliostat mirror collector.

Optimization of heliostat field layout in solar central receiver systems on annual basis using differential evolution algorithm

Optimization of a heliostat field is an essential task to make a solar central receiver system effective because major optical losses are associated with the heliostat fields. In this study, a mathematical model was developed to effectively optimize the heliostat field on annual basis using differential evolution, which is an evolutionary algorithm. The heliostat field layout optimization is based on the calculation of five optical performance parameters: the mirror or the heliostat reflectivity, the cosine factor, the atmospheric attenuation factor, the shadowing and blocking factor, and the intercept factor. This model calculates all the aforementioned performance parameters at every stage of the optimization, until the best heliostat field layout based on annual performance is obtained. Two different approaches were undertaken to optimize the heliostat field layout: one with optimizing insolation weighted annual efficiency and the other with optimizing the un-weighted annual efficiency. Moreover, an alternate approach was also proposed to efficiently optimize the heliostat field in which the number of computational time steps was considerably reduced. It was observed that the daily averaged annual optical efficiency was calculated to be 0.5023 as compared to the monthly averaged annual optical efficiency, 0.5025. Moreover, the insolation weighted daily averaged annual efficiency of the heliostat field was 0.5634 for Dhahran, Saudi Arabia. The code developed can be used for any other selected location.

Heliostat field optimization: A new computationally efficient model and biomimetic layout

Abstract In this article, a new model and a biomimetic pattern for heliostat field layout optimization are introduced. The model, described and validated herein, includes a detailed calculation of the annual average optical efficiency accounting for cosine losses, shading and blocking, aberration and atmospheric attenuation. The model is based on a discretization of the heliostats and can be viewed as ray tracing with a carefully selected distribution of rays. The prototype implementation is sufficiently fast to allow for field optimization. Parameters are introduced for the radially staggered layout and are optimized with the objective of maximizing the annual insolation weighted heliostat field efficiency. In addition, inspired by the spirals of the phyllotaxis disc pattern, a new biomimetic placement heuristic is described and evaluated, which generates layouts of both higher insolation-weighted efficiency and higher ground coverage than radially staggered designs. Specifically, this new heuristic is shown to improve the existing PS10 field by 0.36% points in efficiency while simultaneously reducing the land area by 15.8%. Moreover, the new pattern achieves a better trade-off between land area usage and efficiency, i.e., it can reduce the area requirement significantly for any desired efficiency. Finally, the improvement in area becomes more pronounced with an increased number of heliostats, when maximal efficiency is the objective. While minimizing the levelized cost of energy (LCOE) is typically a more practical objective, results of the case study presented show that it is possible to both reduce the land area (i.e. footprint) of the plant and number of heliostats for fixed energy collected. By reducing the capital cost of the plant at no additional costs, the effect is a reduction in LCOE.

塔式太阳能热发电站镜场的优化设计

塔式太阳能热发电站镜场的优化设计