Abstract
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.
Highlights
In China, over 60% of power generated from the coal-fired power plant, which contributes 40%of total national emissions [1]
The change of the optimization parameters of radial distance in zone 3 and the additional safety distance lead to the number of the heliostats decreasing from 3450 to 2850, but the heliostat field area increases from 1.10 × 106 m2 to 1.20 × 106 m2
The optimization parameters of radial distances in zones 1 and 2 are still equal to 1, the heliostat field efficiencies of zones 1 and 2 slightly increase from 0.6907 to 0.6924 and from 0.6281 to 6311, respectively. This is caused by the increase of the optimization parameter of the additional separation distance, which influences ∆R1 and ∆R2
Summary
In China, over 60% of power generated from the coal-fired power plant, which contributes 40%of total national emissions [1]. In China, over 60% of power generated from the coal-fired power plant, which contributes 40%. Because of the severe pressure from the international community to reduce carbon emissions, China has started to focus on generating power from renewable energy [2]. Of all the renewable energy, solar energy is arguably one of the most favorable solutions for the green power generation and concentrating solar power (CSP) technologies are expected to be the most promising technologies that are capable of large-scale electricity production [3,4,5]. It is estimated that CSP could meet approximately 11% of the global demand for electricity by 2050 [5]. In a solar tower power plant (STPP), solar energy is collected by the heliostat to produce steam to drive electrical generators. The design and optimization of the heliostat field layout are very important and the heliostat field remains an active research field
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