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.

Issue Section:
Research Papers
Topics:
Optimization, Density

References

References
1.
Jones
,
S. A.
,
Lumia
,
R.
,
Davenport
,
R.
,
Thomas
,
R. C.
,
Gorman
,
D.
,
Kolb
,
G. J.
, and
Donnelly
,
M. W.
,
2007
, “
Heliostat Cost Reduction Study
,” Technical Report No. SAND2007-3293.
2.
Vant-Hull
,
L. L.
,
2002
, “
The Role of “Allowable Flux Density” in the Design and Operation of Molten-Salt Solar Central Receivers
,”
ASME J. Sol. Energy Eng.
,
124
(
2
), pp.
165
–
169
.10.1115/1.1464124
Google Scholar
Crossref
Search ADS
 
3.
Smith
,
D. C.
,
1992
, “
Design and Optimization of Tube-Type Receiver Panels for Molten Salt Applications
,”
ASME Solar Energy Conference
,
Maui, HI, April 5–9, ASME, NY
.
4.
García-Martín
,
F. J.
,
Berenguel
,
M.
,
Valverde
,
A.
, and
Camacho
,
E. F.
,
1999
, “
Heuristic Knowledge-Based Heliostat Field Control for the Optimization of the Temperature Distribution in a Volumetric Receiver
,”
Sol. Energy
,
66
(
5
), pp.
355
–
369
.10.1016/S0038-092X(99)00024-9
Google Scholar
Crossref
Search ADS
 
5.
Haeger
,
M.
,
Keller
,
L.
,
Monterreal
,
R.
, and
Valverde
,
A.
,
1994
, “
Operational Experiences With the Experimental Set-Up of a 2,5 MWth Volumetric Receiver (TSA) at the Plataforma Solar de Almería
,” Almería, PSA Technical Report PSA-TR02/94.
6.
Belhomme
,
B.
,
2010
,
Bewertung und Optimierung von Zielpunktstrategien für solare Turmkraftwerke
,
Shaker Verlag
,
Aachen
, Germany.
7.
Belhomme
,
B.
,
Pitz-Paal
,
R.
,
Schwarzbözl
,
P.
, and
Ulmer
,
S.
,
2009
, “
A New Fast Ray Tracing Tool for High-Precision Simulation of Heliostat Fields
,”
ASME J. Sol. Energy Eng.
,
131
(
3
), p.
031002
.10.1115/1.3139139
Google Scholar
Crossref
Search ADS
 
8.
Kellerer
,
H.
,
Pferschy
,
U.
, and
Pisinger
,
D.
,
2004
,
Knapsack Problems
,
Springer
,
Berlin
.
9.
Dorigo
,
M.
,
1992
,
Optimization, Learning and Natural Algorithms
, Ph.D. thesis,
Politecnico de Milano
,
Milano, Italy
.
10.
Dorigo
,
M.
, and
Gambardella
,
L.
,
1997
, “
Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem
,”
IEEE Trans. Evol. Comput.
,
1
(
1
), pp.
53
–
66
.10.1109/4235.585892
Google Scholar
Crossref
Search ADS
 
11.
Pisinger
,
D.
,
1995
, “
Algorithms for Knapsack Problems
,” Ph.D. thesis, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
12.
Bertsekas
,
D.
,
2005
,
Dynamic Programming and Optimal Control
, Vol.
1
,
3rd ed.
,
Athena Scientific
,
Belmont, MA
.
13.
Cordón
,
O.
,
Herrera
,
F.
, and
Stützle
,
T.
,
2002
, “
A Review on the Ant Colony Optimization Metaheuristic: Basis, Models, and New Trends
,”
Mathware Soft Comput.
,
9
, pp.
141
–
175
.
14.
Padmanabhan
,
S.
,
Chandrasekaran
,
M.
, and
Raman
,
S.
,
2012
, “
Evaluation of the Performance of Ant Colony Optimization Over Particle Swarm Optimization
,”
Int. J. Comput. Appl.
,
38
(
6
), pp.
12
–
18
.10.5120/4691-6826
15.
Putha
,
R.
,
Quadrifoglio
,
L.
, and
Zechman
,
E.
,
2012
, “
Comparing Ant Colony Optimization and Genetic Algorithm Approaches for Solving Traffic Signal Coordination Under Oversaturation Conditions
,”
Comput. Aided Civ. Infrastruct. Eng.
,
27
, pp.
14
–
28
.10.1111/j.1467-8667.2010.00715.x
Google Scholar
Crossref
Search ADS
 
16.
Maier
,
H.
,
Simpson
,
A.
,
Zecchin
,
A.
,
Foong
,
W.
,
Phang
,
K.
,
Seah
,
H.
, and
Tan
,
C.
,
2003
, “
Ant Colony Optimization for Design of Water Distribution Systems
,”
J. Water Resour. Plann. Manage.
,
129
(
3
), pp.
200
–
209
.10.1061/(ASCE)0733-9496(2003)129:3(200)
Google Scholar
Crossref
Search ADS
 
17.
Hong-Bo
,
S.
, and
Shuxia
,
L.
,
2008
, “
The Comparison Between Genetic Simulated Annealing Algorithm and Ant Colony Optimization Algorithm for ASP
,”
4th International Conference on Wireless Communications, Networking and Mobile Computing
(
WiCOM'08
), Dalian, China, October 12–14.10.1109/WiCom.2008.2953
18.
Löckenhoff
,
R.
,
Dibowski
,
G.
,
Dimroth
,
F.
,
Meusel
,
M., V.
Riesen
,
S.
, and
Bett
,
A.
,
2006
, “
1000 Sun, Compact Receiver Based on Monolithic Interconnected Modules (MIMs)
,”
IEEE 4th World Conference on Photovoltaic Energy Conversion
(
WCPEC-4
),
Waikoloa, HI
, May 7–12.10.1109/WCPEC.2006.279561
19.
Neumann
,
A.
,
Witzke
,
A.
,
Jones
,
S. A.
, and
Schmitt
,
G.
,
2002
, “
Representative Terrestrial Solar Brightness Profiles
,”
ASME J. Sol. Energy Eng.
,
124
(
2
), pp.
198
–
204
.10.1115/1.1464880
Google Scholar
Crossref
Search ADS
 
Copyright © 2014 by ASME
You do not currently have access to this content.