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Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

CN 10-1205/N

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Front. Eng    2018, Vol. 5 Issue (4) : 533-540    https://doi.org/10.15302/J-FEM-2018025
RESEARCH ARTICLE
Network-based optimization techniques for wind farm location decisions
Jorge Ignacio CISNEROS-SALDANA1, Seyedmohammadhossein HOSSEINIAN2, Sergiy BUTENKO2()
1. Department of Electrical and Computer Engineering, Texas A&M University, 3128 TAMU, College Station, TX 77843-3128, USA
2. Department of Industrial and Systems Engineering, Texas A&M University, 3131 TAMU, College Station, TX 77843-3131, USA
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Abstract

This study aims to find appropriate locations for wind farms that can maximize the overall energy output while controlling the effects of wind speed variability. High wind speeds are required to obtain the maximum possible power output of a wind farm. However, balancing the wind energy supplies over time by selecting diverse locations is necessary. These issues are addressed using network-based models. Hence, actual wind speed data are utilized to demonstrate the advantages of the proposed approach.
Keywords wind energy      wind farm location      network analysis      optimization      clique      s-plex     
Corresponding Author(s): Sergiy BUTENKO   
Just Accepted Date: 22 August 2018   Online First Date: 16 November 2018    Issue Date: 29 November 2018
 Cite this article:   
Jorge Ignacio CISNEROS-SALDANA,Seyedmohammadhossein HOSSEINIAN,Sergiy BUTENKO. Network-based optimization techniques for wind farm location decisions[J]. Front. Eng, 2018, 5(4): 533-540.
 URL:  
https://academic.hep.com.cn/fem/EN/10.15302/J-FEM-2018025
https://academic.hep.com.cn/fem/EN/Y2018/V5/I4/533
Fig.1  Geographical locations considered in the study
Fig.2  Average wind speed for each of the 201 sites
Fig.3  Cumulative average wind speeds for the considered sites
Fig.4  Distribution of the correlation coefficient values
Fig.5  Threshold-based wind speed graph with θ=0
Fig.6  Degree distribution for the threshold-based wind speed graph with θ= 0
Solution Number of vertices Average speed (m/s)
Wind speed graph 201 2.70
Max clique 6 2.36
Max weight clique 5 4.53
Max 2-plex 9 2.22
Max weight 2-plex 6 5.25
Max 3-plex 12 2.67
Max weight 3-plex 9 4.60
Tab.1  Summary of the results for the entire wind speed graph
Fig.7  Average monthly wind speeds for the maximum clique, 2-plex, and 3-plex and their weighted versions
Solution Number of vertices Average speed (m/s)
Feasible wind speed graph 39 5.19
Max weight clique 2 7.05
Max weight 2-plex 4 7.10
Max weight 3-plex 6 6.50
Tab.2  Summary of the results for the feasible wind speed graph
Fig.8  Maximum 3-plex solution for the feasible wind speed graph
Fig.9  Average monthly wind speeds for the maximum weight clique, 2-plex, and 3-plex in the reduced wind speed graph
Locations Latitude Longitude Average speed (m/s)
Calamarca −16.90 −68.12 8.13
Huacullani −16.47 −68.73 6.92
Julaca −20.95 −67.95 6.69
Aeropuerto −17.60 −61.13 6.54
Samaipata −18.17 −63.95 6.36
Chorocona −16.93 −67.17 6.19
Villa Puni −15.67 −69.20 5.99
Caquiaviri −17.02 −68.60 5.89
Tacagua–Challapata −18.88 −66.77 5.67
Huaraco −17.35 −67.65 5.66
Santa Cruz Senamhi −17.78 −63.17 5.63
Santa Cruz Trompillo −17.75 −63.17 5.63
San Antonio −20.00 −63.18 5.57
Santa Cruz Viru Viru −17.67 −63.18 5.53
Comarapa −17.88 −64.53 5.43
El Vallecito −17.77 −63.15 5.25
Catacora −17.20 −69.45 5.07
Viru Viru −17.63 −63.13 5.02
San Agustin −21.17 −67.67 4.92
Tiquipaya −19.22 −65.82 4.92
El Salvador −20.62 −63.17 4.91
Redencion −18.82 −64.60 4.9
Irpa Chico −16.73 −68.37 4.9
Ayo Ayo −17.10 −68.00 4.78
Vitichi −20.12 −65.48 4.74
J. Molino–Pilancho −17.65 −65.45 4.63
Entre Rios −21.50 −64.17 4.62
Caripe −18.01 −68.84 4.61
Comujo–Cipasa −19.22 −66.39 4.5
Ulla Ulla −15.02 −69.25 4.46
Samasa −19.48 −65.68 4.46
Robore −18.32 −59.75 4.35
Huarina −16.20 −68.63 4.27
Uyuni −20.45 −66.82 4.27
Carabuco −15.75 −69.17 4.24
Santana A −13.77 −65.43 4.23
S. Juan Huancollo −16.58 −68.97 4.23
Chachacomani −18.36 −68.95 4.21
Viacha −16.65 −68.30 4.17
  Table A1 List of the locations included in the feasible wind speed graph
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