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Research Papers

Optimization of Heliostat Aim Point Selection for Central Receiver Systems Based on the Ant Colony Optimization Metaheuristic

[+] Author and Article Information
Boris Belhomme

Belhomme Engineering and Consulting,
Kufsteiner Platz 4,
Munich 81679, Germany
e-mail: bb@belhomme-engineering.de

Robert Pitz-Paal

e-mail: robert.pitz-paal@dlr.de

Peter Schwarzbözl

e-mail: peter.schwarzboezl@dlr.de
German Aerospace Center (DLR),
Institute of Solar Research,
Linder Höhe,
Cologne 51147, Germany

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the Journal of Solar Energy Engineering. Manuscript received November 10, 2010; final manuscript received May 21, 2013; published online xx xx, xxxx. Assoc. Editor: Manuel Romero Alvarez.

J. Sol. Energy Eng 136(1), 011005 (Jul 16, 2013) (7 pages) Paper No: SOL-10-1168; doi: 10.1115/1.4024738 History: Received November 10, 2010; Revised May 21, 2013

The optimization of the selection of heliostat aim points in a solar power tower plant with the objective of an increased overall efficiency represents a NP-hard optimization problem of high dimension. This paper presents a universal procedure for the purpose of aim point optimization based on the ant colony optimization metaheuristic that uses the principles of swarm intelligence. The applicability of the developed aim point optimization procedure to central receiver systems is demonstrated on a test case, for which the electrical power of a concentrated photovoltaic (CPV) receiver is maximized for a selected operating point. The example of a CPV receiver was chosen due to its nonlinear and nonmonotonous dependency of efficiency and flux density. It is shown that the optimization result is very close to the theoretical maximum.

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Copyright © 2014 by ASME
Topics: Optimization , Density
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References

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Figures

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Fig. 1

Visualization of the optimization loop: entire calculation sequence with full ray tracing (1) and shortened calculation sequence feeding the receiver model with partial results (2)

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Fig. 2

One specific aim point configuration visualized as a path through the heliostat–aim point matrix. Heliostats 1 and 5 are aimed to aim point 2, heliostats 2 and 3 are aimed to aim point 3, and heliostat 4 is aimed to aim point 1.

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Fig. 3

(a) Efficiency of a MIM-module as a function of solar concentration ratio c at different ambient temperatures Tamb and at a constant MIM-module temperature Tref; (b) MIM-module temperatures TMIM module as a function of solar concentration ratio c for different ambient temperatures Tamb

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Fig. 4

Heliostat field with different focal lengths in the range between 100 and 240 m

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Fig. 5

Equidistant aim point grids with different densities inside of the rectangular aperture of the receiver

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Fig. 6

Flux density distribution with one single central aim point (a) and with the optimized aim point configuration on the 7 × 7 raster

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Fig. 7

Optimized aim point configuration on the 7 × 7 raster and the corresponding heliostat positions

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Fig. 8

Optimization progress for different aim point grids without constrains (a) and with symmetric constrains (b)

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Fig. 9

Hourly values of electric power of the CPV receiver for one day (June 21st). Dot-dashed are the theoretical maximum values calculated as explained above, dotted are the results from the optimization method using a 7 × 7 grid and dashed are the values for the case where all heliostats are aimed at the center. The DNI is depicted in solid line according to the right y-axis.

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