Energy is vital to social and economic development. Increased energy demand and reduced fossil fuel resources led to use of renewable energy (RE) resources, whose intermittence and high investment cost spur research into optimal sizing of hybrid systems. Advancements in computer hardware and software enable solution of optimization problems through algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), etc. The recently introduced Imperialistic Competition Algorithm (ICA) has shown excellent capability in solving various optimization problems. This paper introduces it and shows its benefits to an optimal-sizing problem of a hybrid RE system. The results will be shown and the effect of changing optimization's parameters will be discussed. To test the potential of proposed algorithm for minimum cost solution finding, a comparison between ICA and PSO algorithm will be provided. Results show the advantage of using ICA algorithm to find a better optimum solution for hybrid power system.
REFERENCES
1.
J.
Chang
, D. Y. C.
Leung
, C. Z.
Wu
, and Z. H.
Yuan
, “A review on the energy production, consumption, and prospect of renewable energy in China
,” Renewable Sustainable Energy Rev.
7
, 453
–468
(2003
).2.
M.
Lukenmez
and E.
Demireli
, “Renewable energy policy in Turkey with the new legal regulations
,” Renewable Energy
39
, 1
–9
(2012
).3.
G. M.
Shafiullah
, M. T. O.
Amanullah
, A.
Ali
, D.
Jarvis
, and P.
Wolfs
, “Prospects of renewable energy—A feasibility study in the Australian context
,” Renewable Energy
39
, 183
–197
(2012
).4.
T.
Abbasi
and S. A.
Abbasi
, “Decarbonization of fossil fuels as a strategy to control global warming
,” Renewable Sustainable Energy Rev.
15
, 1828
–1834
(2011
).5.
T.
Abbasi
, M.
Premalatha
, and S. A.
Abbasi
, “The return to renewables: Will it help in global warming control?
,” Renewable Sustainable Energy Rev.
15
, 891
–894
(2011
).6.
L.
Zhipeng
, W.
Fushuan
, and G.
Ledwich
, “Optimal sitting and sizing of distributed generators in distribution systems considering uncertainties
,” IEEE Trans. Power Deliv.
26
, 2541
–2551
(2011
).7.
E. I.
Vrettos
and S. A.
Papathanassiou
, “Operating policy and optimal sizing of a high penetration RES-BESS system for small isolated grids
,” IEEE Trans. Energy Convers.
26
, 744
–756
(2011
).8.
G. B.
Shrestha
and L.
Goel
, “A study on optimal sizing of stand-alone photovoltaic stations
,” IEEE Trans. Energy Convers.
13
, 373
–378
(1998
).9.
R. L.
Haupt
and S. E.
Haupt
, Practical Genetic Algorithms
, 2nd ed. (John Wiley & Sons
, 2004
).10.
H.
Golpira
and H.
Bevrani
, “Application of GA optimization for automatic generation control design in an interconnected power system
,” Energy Convers. Manage.
52
, 2247
–2255
(2011
).11.
P.
Bhatt
, R.
Roy
, and S. P.
Ghoshal
, “GA/particle swarm intelligence based optimization of two specific varieties of controller devices applied to two-area multi-units automatic generation control
,” Int. J. Electr. Power Energy Syst.
32
, 299
–310
(2010
).12.
R. K.
Jatoth
and G. A.
Reddy
, “A hybrid GA-adaptive particle swarm optimization based tuning of unscented Kalman filter for harmonic estimation
,” in Swarm, Evolutionary, and Memetic Computing
, edited by B. K.
Panigrahi
, S.
Das
, P. N.
Suganthan
, and S. S.
Dash
(Springer-Verlag
, Berlin
, 2010
), Vol. 6466
, pp. 380
–388
.13.
R. A.
Hooshmand
and S.
Soltani
, “Fuzzy optimal phase balancing of radial and meshed distribution networks using BF-PSO algorithm
,” IEEE Trans. Power Syst.
27
(1
), 47
–57
(2012
).14.
M.
Melanie
, “An introduction to genetic algorithms
,” in A Bradford Book
(The MIT Press Cambridge
, Massachusetts London, England
, 1999
), Fifth printing.15.
A.
Azadeh
, S. F.
Ghaderi
, B. P.
Nokhandan
, and M.
Sheikhalishahi
, “A new genetic algorithm approach for optimizing bidding strategy viewpoint of profit maximization of a generation company
,” Expert Sys. Applic.
39
, 1565
–1574
(2012
).16.
Y.-H.
Wang
, Y.-C.
Li
, and H.-C.
Liao
, “Using a genetic algorithm to determine optimal complementary learning clusters for ESL in Taiwan
,” Expert Sys. Applic.
38
, 14832
–14837
(2011
).17.
N.
Dong
, C.-H.
Wu
, W.-H.
Ip
, Z.-Q.
Chen
, C.-Y.
Chan
, and K.-L.
Yung
, “An improved species based genetic algorithm and its application in multiple template matching for embroidered pattern inspection
,” Expert Sys. Applic.
38
, 15172
–15182
(2011
).18.
M.
Dorigo
and C.
Blum
, “Ant colony optimization theory: A survey
,” Theor. Comput. Sci.
344
, 243
–278
(2005
).19.
S.-M.
Chen
and C.-Y.
Chien
, “Solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques
,” Expert Sys. Applic.
38
, 14439
–14450
(2011
).20.
Y.
Betul
, “Mixed-model assembly line balancing using a multi-objective ant colony optimization approach
,” Expert Sys. Applic.
38
, 12453
–12461
(2011
).21.
J.
Tian
, L.
Ma
, and W.
Yu
, “Ant colony optimization for wavelet-based image interpolation using a three-component exponential mixture model
,” Expert Sys. Applic.
38
, 12514
–12520
(2011
).22.
A.
Fetanat
and G.
Shafipour
, “Generation maintenance scheduling in power systems using ant colony optimization for continuous domains based 0–1 integer programming
,” Expert Sys. Applic.
38
, 9729
–9735
(2011
).23.
S.
Mukhopadhyay
and S.
Banerjee
, “Global optimization of an optical chaotic system by chaotic multi swarm particle swarm optimization
,” Expert Sys. Applic.
39
, 917
–924
(2012
).24.
R. C.
Green Ii
, L.
Wang
, and M.
Alam
, “Training neural networks using central force optimization and particle swarm optimization: Insights and comparisons
,” Expert Sys. Applic.
39
, 555
–563
(2012
).25.
W.
Qi
, “Hybrid wavelet ν-support vector machine and chaotic particle swarm optimization for regression estimation
,” Expert Sys. Applic.
38
, 14624
–14632
(2011
).26.
H.
Jiang
, C. K.
Kwong
, Z.
Chen
, and Y. C.
Ysim
, “Chaos particle swarm optimization and T–S fuzzy modeling approaches to constrained predictive control
,” Expert Sys. Applic.
39
, 194
–201
(2012
).27.
M.
Moradi
and M.
Abedini
, “A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems
,” Int. J. Electr. Power Energy Syst.
34
, 66
–74
(2012
).28.
S. M.
Hakimi
and S. M.
Moghaddas-Tafreshi
, “Optimal sizing of a stand-alone hybrid power system via particle swarm optimization for Kahnouj area in south-east of Iran
,” Renewable Energy
34
, 1855
–1862
(2009
).29.
H.
Sepehri Rad
and C.
Lucas
, “Application of imperialistic competition algorithm in recommender systems
,” in Proceedings of the 3rd International CSI Computer Conference (CSICC'08)
, Iran, 2008
.30.
R.
Rajabioun
, F.
Hashemzadeh
, E.
Atashpaz-Gargari
, B.
Mesgari
, and F.
Rajaei Salmasi
, “Identification of a MIMO evaporator and its decentralized PID controller tuning using colonial competitive algorithm
,” in IFAC World Congress
, 2008
.31.
H.
Mozafari
, B.
Abdi
, and A.
Ayob
, “Optimization of transmission conditions for thin interphase layer based on imperialist competitive algorithm
,” Int. J. Comput. Sci. Eng.
2
, 2486
–2490
(2010
).32.
T.
Weise
, Global Optimization Algorithms–Theory and Application (2008
); online at http://www.it-weise.de.33.
E. H.
Nasab
, M.
Khezri
, M. S.
Khodamoradi
, and E. A.
Gargari
, “An application of imperialist competitive algorithm to simulation of energy demand based on economic indicators: Evidence from Iran
,” Eur. J. Sci. Res.
43
, 495
–506
(2010
).34.
E.
Atashpaz-Gargari
and C.
Lucas
, “Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition
,” in IEEE Congress on Evolutionary Computation CEC2007
(IEEE, 2007
), pp. 4661
–4667
.35.
M. M.
Hadji
and B.
Vahidi
, “A solution to the unit commitment problem using imperialistic competition algorithm
,” IEEE Trans. Power Syst.
27
(1
), 117
–124
(2012
).36.
D.
Feldman
, G.
Barbose
, R.
Margolis
, R.
Wiser
, N.
Darghouth
, and A.
Goodrich
, Photovoltaic (PV) Pricing Trends: Historical, Recent, and Near-Term Projections
, (National Renewable Energy Laboratory (NREL)
, Golden, CO
, 2012
).37.
R.
Tidball
, J.
Bluestein
, N.
Rodriguez
, and S.
Knoke
, “Cost and performance assumptions for modeling electricity generation technologies
,” Contract.
303
, 1
–201
(2010
).38.
E.
Lantz
, R.
Wiser
, and M.
Hand
, “The past and future cost of wind energy
,” in IEA Wind Task
(NREL, 2012
), Vol. 26.© 2013 AIP Publishing LLC.
2013
AIP Publishing LLC
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