Assessing the energy benefit of using a wind turbine micro-siting model

L. Parada, C. Herrera, P. Flores, V. Parada

Research output: Contribution to journalArticle

  • 1 Citations

Abstract

Wind farm layouts are often designed based on simple rules that give rise to regular arrays. Several studies have stated that these arrays may not be efficient due to high wake losses for some wind directions. Recently, wind turbine micro-siting models have been compared with respect to regularly arrayed layouts. However, these studies have considered just a single regular layout configuration and a fixed number of turbines. In this paper, an approach is proposed to design highly efficient wind farms and is further compared to different configurations of regularly arrayed layouts considering different spacings and number of wind turbines. The proposed approach maximizes the power of a wind farm and efficiently incorporates the use of irregular terrain boundaries and real wind data. The proposed approach is first compared to the Horns Rev I layout. Subsequently, the proposed approach is further compared to different regular layout configurations using wind data measured at a site located in Northern Chile. The results suggest that regularly arrayed wind farms are sub-optimal and may be subjected to high wake losses, particularly for some wind directions. With the proposed approach, 4.09% and 2.18% higher efficiencies on average were obtained compared to aligned and staggered layouts, respectively. © 2017 Elsevier Ltd
LanguageEnglish
Pages591-601
Number of pages11
JournalRenewable Energy
Volume118
DOIs
Publication statusPublished - 2018

Fingerprint

Wind turbines
Farms
Turbines

Keywords

  • Genetic algorithm
  • Layout optimization
  • Micro-siting
  • Wind farm
  • Wind turbine
  • Electric utilities
  • Genetic algorithms
  • Wakes
  • Wind power
  • Energy benefits
  • Higher efficiency
  • Irregular terrain
  • Turbine micro-siting
  • Wind farm layouts
  • Wind turbines
  • array
  • assessment method
  • experimental design
  • genetic algorithm
  • numerical model
  • optimization
  • wind farm
  • wind turbine
  • Chile

Cite this

Assessing the energy benefit of using a wind turbine micro-siting model. / Parada, L.; Herrera, C.; Flores, P.; Parada, V.

In: Renewable Energy, Vol. 118, 2018, p. 591-601.

Research output: Contribution to journalArticle

Parada, L. ; Herrera, C. ; Flores, P. ; Parada, V. / Assessing the energy benefit of using a wind turbine micro-siting model. In: Renewable Energy. 2018 ; Vol. 118. pp. 591-601.
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title = "Assessing the energy benefit of using a wind turbine micro-siting model",
abstract = "Wind farm layouts are often designed based on simple rules that give rise to regular arrays. Several studies have stated that these arrays may not be efficient due to high wake losses for some wind directions. Recently, wind turbine micro-siting models have been compared with respect to regularly arrayed layouts. However, these studies have considered just a single regular layout configuration and a fixed number of turbines. In this paper, an approach is proposed to design highly efficient wind farms and is further compared to different configurations of regularly arrayed layouts considering different spacings and number of wind turbines. The proposed approach maximizes the power of a wind farm and efficiently incorporates the use of irregular terrain boundaries and real wind data. The proposed approach is first compared to the Horns Rev I layout. Subsequently, the proposed approach is further compared to different regular layout configurations using wind data measured at a site located in Northern Chile. The results suggest that regularly arrayed wind farms are sub-optimal and may be subjected to high wake losses, particularly for some wind directions. With the proposed approach, 4.09{\%} and 2.18{\%} higher efficiencies on average were obtained compared to aligned and staggered layouts, respectively. {\circledC} 2017 Elsevier Ltd",
keywords = "Genetic algorithm, Layout optimization, Micro-siting, Wind farm, Wind turbine, Electric utilities, Genetic algorithms, Wakes, Wind power, Energy benefits, Higher efficiency, Irregular terrain, Turbine micro-siting, Wind farm layouts, Wind turbines, array, assessment method, experimental design, genetic algorithm, numerical model, optimization, wind farm, wind turbine, Chile",
author = "L. Parada and C. Herrera and P. Flores and V. Parada",
note = "Export Date: 11 April 2018 Correspondence Address: Parada, L.; Department of Mechanical Engineering, University of Concepcion, Casilla 160 – C, Correo 3, Ciudad Universitaria, Chile; email: lparada@udec.cl Funding details: FB0816, CONICYT, Consejo Nacional de Innovaci{\'o}n, Ciencia y Tecnolog{\'i}a Funding details: P05-004-F Funding text: This research was partially funded by the Complex Engineering Systems Institute (Grant number: P05-004-F ), CONICYT (Grant number: FB0816 ). References: Samorani, M., The wind farm layout optimization problem (2013) Handbook of Wind Power Systems, pp. 21-38. , http://link.springer.com/10.1007/978-3-642-41080-2_2, P.M. Pardalos S. Rebennack M.V.F. Pereira N.A. Iliadis V. Pappu Springer Berlin Heidelberg Berlin, Heidelberg URL; Group, P.A.E.P., Best Practice Guidance to Planning Policy Statement 18 ‘Renewable Energy (2009), http://www.planningni.gov.uk/index/news/news_policy/planning_policy_statement_18_renewable_energy_3.htm, Tech. rep URL; Song, M., Chen, K., Zhang, X., Wang, J., Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction (2016) Renew. Energy, 85, pp. 57-65. , http://linkinghub.elsevier.com/retrieve/pii/S0960148115300562, URL; Park, J., Law, K.H., Layout optimization for maximizing wind farm power production using sequential convex programming (2015) Appl. Energy, 151, pp. 320-334. , http://linkinghub.elsevier.com/retrieve/pii/S0306261915004560, URL; Serrano Gonz{\'a}lez, J., Burgos Pay{\'a}n, M., Santos, J.M.R., Gonz{\'a}lez-Longatt, F., A review and recent developments in the optimal wind-turbine micro-siting problem (2014) Renew. Sustain. Energy Rev., 30, pp. 133-144. , http://linkinghub.elsevier.com/retrieve/pii/S1364032113006989, URL; Herbert-Acero, J., Probst, O., R{\'e}thor{\'e}, P.-E., Larsen, G., Castillo-Villar, K., A review of methodological approaches for the design and optimization of wind farms (2014) Energies, 7 (11), pp. 6930-7016. , http://www.mdpi.com/1996-1073/7/11/6930/, URL; Grady, S., Hussaini, M., Abdullah, M., Placement of wind turbines using genetic algorithms (2005) Renew. Energy, 30 (2), pp. 259-270. , http://linkinghub.elsevier.com/retrieve/pii/S0960148104001867, URL; Şişbot, S., Turgut, Z., Tun{\cc}, M., {\cC}amdalI, N., Optimal positioning of wind turbines on G{\"o}k{\cc}eada using multi-objective genetic algorithm (2010) Wind Energy, 13 (4), pp. 297-306. , http://onlinelibrary.wiley.com/doi/10.1002/we.339/abstract, URL; Montoya, F.G., Manzano-Agugliaro, F., L{\'o}pez-M{\'a}rquez, S., Hern{\'a}ndez-Escobedo, Q., Gil, C., Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms (2014) Expert Syst. Appl., 41 (15), pp. 6585-6595. , http://linkinghub.elsevier.com/retrieve/pii/S095741741400267X, URL; Wang, L., Tan, A.C., Gu, Y., Yuan, J., A new constraint handling method for wind farm layout optimization with lands owned by different owners (2015) Renew. Energy, 83, pp. 151-161. , http://linkinghub.elsevier.com/retrieve/pii/S0960148115003092, URL; Mittal, P., Kulkarni, K., Mitra, K., A novel hybrid optimization methodology to optimize the total number and placement of wind turbines (2016) Renew. Energy, 86, pp. 133-147. , http://www.sciencedirect.com/science/article/pii/S0960148115301956, URL; Tong, W., Chowdhury, S., Messac, A., Multi-objective windfarm optimization simultaneously optimizing coe and land footprint of wind farms under different land plot availability https://arc.aiaa.org/doi/abs/10.2514/6.2015-1802, 56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, American Institute of Aeronautics and Astronautics, dOI: 10.2514/6.2015-1802. URL; Wan, C., Wang, J., Yang, G., Zhang, X., Optimal micro-siting of wind farms by particle swarm optimization (2010) Advances in Swarm Intelligence, 6145, pp. 198-205. , http://link.springer.com/10.1007/978-3-642-13495-1_25, D. Hutchison T. Kanade J. Kittler J.M. Kleinberg F. Mattern J.C. Mitchell M. Naor O. Nierstrasz C. Pandu Rangan B. Steffen M. Sudan D. Terzopoulos D. Tygar M.Y. Vardi G. Weikum Y. Tan Y. Shi K.C. Tan Springer Berlin Heidelberg Berlin, Heidelberg URL; Pookpunt, S., Ongsakul, W., Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients (2013) Renew. Energy, 55, pp. 266-276. , http://linkinghub.elsevier.com/retrieve/pii/S0960148112007604, URL; Zhang, P.Y., Romero, D.A., Beck, J.C., Amon, C.H., Solving wind farm layout optimization with mixed integer programs and constraint programs (2014) EURO J. Comput. Optim., 2 (3), pp. 195-219. , https://link.springer.com/article/10.1007/s13675-014-0024-5, URL; Guirguis, D., Romero, D.A., Amon, C.H., Toward efficient optimization of wind farm layouts: utilizing exact gradient information (2016) Appl. Energy, 179, pp. 110-123. , http://linkinghub.elsevier.com/retrieve/pii/S0306261916308765, URL; Yamani Douzi Sorkhabi, S., Romero, D.A., Yan, G.K., Gu, M.D., Moran, J., Morgenroth, M., Amon, C.H., The impact of land use constraints in multi-objective energy-noise wind farm layout optimization (2016) Renew. Energy, 85, pp. 359-370. , http://www.sciencedirect.com/science/article/pii/S0960148115300495, URL; Kuo, J.Y., Romero, D.A., Beck, J.C., Amon, C.H., Wind farm layout optimization on complex terrains – integrating a CFD wake model with mixed-integer programming (2016) Appl. Energy, 178, pp. 404-414. , http://linkinghub.elsevier.com/retrieve/pii/S0306261916308595, URL; Saavedra-Moreno, B., Salcedo-Sanz, S., Paniagua-Tineo, A., Prieto, L., Portilla-Figueras, A., Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms (2011) Renew. Energy, 36 (11), pp. 2838-2844. , http://www.sciencedirect.com/science/article/pii/S096014811100190X, URL; Parada, L., Herrera, C., Flores, P., Parada, V., Wind farm layout optimization using a Gaussian-based wake model (2017) Renew. Energy, 107, pp. 531-541. , http://www.sciencedirect.com/science/article/pii/S0960148117300952, URL; Gao, X., Yang, H., Lu, L., Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model (2016) Appl. Energy, 174, pp. 192-200. , http://linkinghub.elsevier.com/retrieve/pii/S0306261916305633, URL; Wan, C., Wang, J., Yang, G., Gu, H., Zhang, X., Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy (2012) Renew. Energy, 48, pp. 276-286. , http://linkinghub.elsevier.com/retrieve/pii/S0960148112003096, URL; Gebraad, P., Thomas, J.J., Ning, A., Fleming, P., Dykes, K., Maximization of the annual energy production of wind power plants by optimization of layout and yaw-based wake control (2017) Wind Energy, 20 (1), pp. 97-107. , http://onlinelibrary.wiley.com/doi/10.1002/we.1993/abstract, 1993. URL; Ainslie, J.F., Calculating the flowfield in the wake of wind turbines (1988) J. Wind Eng. Ind. Aerodyn., 27 (1-3), pp. 213-224. , http://www.sciencedirect.com/science/article/pii/0167610588900372, URL; Vermeer, L.J., Sϕrensen, J.N., Crespo, A., Wind turbine wake aerodynamics (2003) Prog. Aerosp. Sci., 39 (6-7), pp. 467-510. , http://www.sciencedirect.com/science/article/pii/S0376042103000782, URL; Tian, L., Zhu, W., Shen, W., Zhao, N., Shen, Z., Development and validation of a new two-dimensional wake model for wind turbine wakes (2015) J. Wind Eng. Ind. 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year = "2018",
doi = "10.1016/j.renene.2017.11.018",
language = "English",
volume = "118",
pages = "591--601",
journal = "Renewable Energy",
issn = "0960-1481",
publisher = "Elsevier Ltd",

}

TY - JOUR

T1 - Assessing the energy benefit of using a wind turbine micro-siting model

AU - Parada, L.

AU - Herrera, C.

AU - Flores, P.

AU - Parada, V.

N1 - Export Date: 11 April 2018 Correspondence Address: Parada, L.; Department of Mechanical Engineering, University of Concepcion, Casilla 160 – C, Correo 3, Ciudad Universitaria, Chile; email: lparada@udec.cl Funding details: FB0816, CONICYT, Consejo Nacional de Innovación, Ciencia y Tecnología Funding details: P05-004-F Funding text: This research was partially funded by the Complex Engineering Systems Institute (Grant number: P05-004-F ), CONICYT (Grant number: FB0816 ). References: Samorani, M., The wind farm layout optimization problem (2013) Handbook of Wind Power Systems, pp. 21-38. , http://link.springer.com/10.1007/978-3-642-41080-2_2, P.M. Pardalos S. Rebennack M.V.F. Pereira N.A. Iliadis V. Pappu Springer Berlin Heidelberg Berlin, Heidelberg URL; Group, P.A.E.P., Best Practice Guidance to Planning Policy Statement 18 ‘Renewable Energy (2009), http://www.planningni.gov.uk/index/news/news_policy/planning_policy_statement_18_renewable_energy_3.htm, Tech. rep URL; Song, M., Chen, K., Zhang, X., Wang, J., Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction (2016) Renew. Energy, 85, pp. 57-65. , http://linkinghub.elsevier.com/retrieve/pii/S0960148115300562, URL; Park, J., Law, K.H., Layout optimization for maximizing wind farm power production using sequential convex programming (2015) Appl. Energy, 151, pp. 320-334. , http://linkinghub.elsevier.com/retrieve/pii/S0306261915004560, URL; Serrano González, J., Burgos Payán, M., Santos, J.M.R., González-Longatt, F., A review and recent developments in the optimal wind-turbine micro-siting problem (2014) Renew. Sustain. Energy Rev., 30, pp. 133-144. , http://linkinghub.elsevier.com/retrieve/pii/S1364032113006989, URL; Herbert-Acero, J., Probst, O., Réthoré, P.-E., Larsen, G., Castillo-Villar, K., A review of methodological approaches for the design and optimization of wind farms (2014) Energies, 7 (11), pp. 6930-7016. , http://www.mdpi.com/1996-1073/7/11/6930/, URL; Grady, S., Hussaini, M., Abdullah, M., Placement of wind turbines using genetic algorithms (2005) Renew. Energy, 30 (2), pp. 259-270. , http://linkinghub.elsevier.com/retrieve/pii/S0960148104001867, URL; Şişbot, S., Turgut, Z., Tunç, M., ÇamdalI, N., Optimal positioning of wind turbines on Gökçeada using multi-objective genetic algorithm (2010) Wind Energy, 13 (4), pp. 297-306. , http://onlinelibrary.wiley.com/doi/10.1002/we.339/abstract, URL; Montoya, F.G., Manzano-Agugliaro, F., López-Márquez, S., Hernández-Escobedo, Q., Gil, C., Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms (2014) Expert Syst. Appl., 41 (15), pp. 6585-6595. , http://linkinghub.elsevier.com/retrieve/pii/S095741741400267X, URL; Wang, L., Tan, A.C., Gu, Y., Yuan, J., A new constraint handling method for wind farm layout optimization with lands owned by different owners (2015) Renew. Energy, 83, pp. 151-161. , http://linkinghub.elsevier.com/retrieve/pii/S0960148115003092, URL; Mittal, P., Kulkarni, K., Mitra, K., A novel hybrid optimization methodology to optimize the total number and placement of wind turbines (2016) Renew. Energy, 86, pp. 133-147. , http://www.sciencedirect.com/science/article/pii/S0960148115301956, URL; Tong, W., Chowdhury, S., Messac, A., Multi-objective windfarm optimization simultaneously optimizing coe and land footprint of wind farms under different land plot availability https://arc.aiaa.org/doi/abs/10.2514/6.2015-1802, 56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, American Institute of Aeronautics and Astronautics, dOI: 10.2514/6.2015-1802. URL; Wan, C., Wang, J., Yang, G., Zhang, X., Optimal micro-siting of wind farms by particle swarm optimization (2010) Advances in Swarm Intelligence, 6145, pp. 198-205. , http://link.springer.com/10.1007/978-3-642-13495-1_25, D. Hutchison T. Kanade J. Kittler J.M. Kleinberg F. Mattern J.C. Mitchell M. Naor O. Nierstrasz C. Pandu Rangan B. Steffen M. Sudan D. Terzopoulos D. Tygar M.Y. Vardi G. Weikum Y. Tan Y. Shi K.C. Tan Springer Berlin Heidelberg Berlin, Heidelberg URL; Pookpunt, S., Ongsakul, W., Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients (2013) Renew. Energy, 55, pp. 266-276. , http://linkinghub.elsevier.com/retrieve/pii/S0960148112007604, URL; Zhang, P.Y., Romero, D.A., Beck, J.C., Amon, C.H., Solving wind farm layout optimization with mixed integer programs and constraint programs (2014) EURO J. Comput. Optim., 2 (3), pp. 195-219. , https://link.springer.com/article/10.1007/s13675-014-0024-5, URL; Guirguis, D., Romero, D.A., Amon, C.H., Toward efficient optimization of wind farm layouts: utilizing exact gradient information (2016) Appl. Energy, 179, pp. 110-123. , http://linkinghub.elsevier.com/retrieve/pii/S0306261916308765, URL; Yamani Douzi Sorkhabi, S., Romero, D.A., Yan, G.K., Gu, M.D., Moran, J., Morgenroth, M., Amon, C.H., The impact of land use constraints in multi-objective energy-noise wind farm layout optimization (2016) Renew. Energy, 85, pp. 359-370. , http://www.sciencedirect.com/science/article/pii/S0960148115300495, URL; Kuo, J.Y., Romero, D.A., Beck, J.C., Amon, C.H., Wind farm layout optimization on complex terrains – integrating a CFD wake model with mixed-integer programming (2016) Appl. Energy, 178, pp. 404-414. , http://linkinghub.elsevier.com/retrieve/pii/S0306261916308595, URL; Saavedra-Moreno, B., Salcedo-Sanz, S., Paniagua-Tineo, A., Prieto, L., Portilla-Figueras, A., Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms (2011) Renew. Energy, 36 (11), pp. 2838-2844. , http://www.sciencedirect.com/science/article/pii/S096014811100190X, URL; Parada, L., Herrera, C., Flores, P., Parada, V., Wind farm layout optimization using a Gaussian-based wake model (2017) Renew. Energy, 107, pp. 531-541. , http://www.sciencedirect.com/science/article/pii/S0960148117300952, URL; Gao, X., Yang, H., Lu, L., Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model (2016) Appl. Energy, 174, pp. 192-200. , http://linkinghub.elsevier.com/retrieve/pii/S0306261916305633, URL; Wan, C., Wang, J., Yang, G., Gu, H., Zhang, X., Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy (2012) Renew. Energy, 48, pp. 276-286. , http://linkinghub.elsevier.com/retrieve/pii/S0960148112003096, URL; Gebraad, P., Thomas, J.J., Ning, A., Fleming, P., Dykes, K., Maximization of the annual energy production of wind power plants by optimization of layout and yaw-based wake control (2017) Wind Energy, 20 (1), pp. 97-107. , http://onlinelibrary.wiley.com/doi/10.1002/we.1993/abstract, 1993. URL; Ainslie, J.F., Calculating the flowfield in the wake of wind turbines (1988) J. Wind Eng. Ind. Aerodyn., 27 (1-3), pp. 213-224. , http://www.sciencedirect.com/science/article/pii/0167610588900372, URL; Vermeer, L.J., Sϕrensen, J.N., Crespo, A., Wind turbine wake aerodynamics (2003) Prog. Aerosp. Sci., 39 (6-7), pp. 467-510. , http://www.sciencedirect.com/science/article/pii/S0376042103000782, URL; Tian, L., Zhu, W., Shen, W., Zhao, N., Shen, Z., Development and validation of a new two-dimensional wake model for wind turbine wakes (2015) J. Wind Eng. Ind. Aerodyn., 137, pp. 90-99. , http://linkinghub.elsevier.com/retrieve/pii/S0167610514002505, URL; Mittal, A., Optimization of the Layout of Large Wind Farms Using a Genetic Algorithm (2010), http://rave.ohiolink.edu/etdc/view?acc_num=case1270056861, Ph.D. thesis Case Western Reserve University URL; Wang, L., Tan, A.C.C., Gu, Y., Comparative study on optimizing the wind farm layout using different design methods and cost models (2015) J. Wind Eng. Ind. Aerodyn., 146, pp. 1-10. , http://www.sciencedirect.com/science/article/pii/S0167610515001725, URL; Chen, Y., Li, H., Jin, K., Song, Q., Wind farm layout optimization using genetic algorithm with different hub height wind turbines (2013) Energy Convers. Manag., 70, pp. 56-65. , http://linkinghub.elsevier.com/retrieve/pii/S0196890413000873, URL; Kallioras, N.A., Lagaros, N.D., Karlaftis, M.G., Pachy, P., Optimum layout design of onshore wind farms considering stochastic loading (2015) Adv. Eng. Softw., 88, pp. 8-20. , http://linkinghub.elsevier.com/retrieve/pii/S0965997815000757, URL; Rajper, S., Amin, I.J., Optimization of wind turbine micrositing: a comparative study (2012) Renew. Sustain. Energy Rev., 16 (8), pp. 5485-5492. , http://linkinghub.elsevier.com/retrieve/pii/S1364032112004017, URL; Kusiak, A., Song, Z., Design of wind farm layout for maximum wind energy capture (2010) Renew. Energy, 35 (3), pp. 685-694. , http://linkinghub.elsevier.com/retrieve/pii/S0960148109003796, URL; Bartl, J., Pierella, F., Sætrana, L., Wake measurements behind an array of two model wind turbines (2012) Energy Procedia, 24, pp. 305-312. , http://www.sciencedirect.com/science/article/pii/S1876610212011538, URL; Bastankhah, M., Porté-Agel, F., A new analytical model for wind-turbine wakes (2014) Renew. 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PY - 2018

Y1 - 2018

N2 - Wind farm layouts are often designed based on simple rules that give rise to regular arrays. Several studies have stated that these arrays may not be efficient due to high wake losses for some wind directions. Recently, wind turbine micro-siting models have been compared with respect to regularly arrayed layouts. However, these studies have considered just a single regular layout configuration and a fixed number of turbines. In this paper, an approach is proposed to design highly efficient wind farms and is further compared to different configurations of regularly arrayed layouts considering different spacings and number of wind turbines. The proposed approach maximizes the power of a wind farm and efficiently incorporates the use of irregular terrain boundaries and real wind data. The proposed approach is first compared to the Horns Rev I layout. Subsequently, the proposed approach is further compared to different regular layout configurations using wind data measured at a site located in Northern Chile. The results suggest that regularly arrayed wind farms are sub-optimal and may be subjected to high wake losses, particularly for some wind directions. With the proposed approach, 4.09% and 2.18% higher efficiencies on average were obtained compared to aligned and staggered layouts, respectively. © 2017 Elsevier Ltd

AB - Wind farm layouts are often designed based on simple rules that give rise to regular arrays. Several studies have stated that these arrays may not be efficient due to high wake losses for some wind directions. Recently, wind turbine micro-siting models have been compared with respect to regularly arrayed layouts. However, these studies have considered just a single regular layout configuration and a fixed number of turbines. In this paper, an approach is proposed to design highly efficient wind farms and is further compared to different configurations of regularly arrayed layouts considering different spacings and number of wind turbines. The proposed approach maximizes the power of a wind farm and efficiently incorporates the use of irregular terrain boundaries and real wind data. The proposed approach is first compared to the Horns Rev I layout. Subsequently, the proposed approach is further compared to different regular layout configurations using wind data measured at a site located in Northern Chile. The results suggest that regularly arrayed wind farms are sub-optimal and may be subjected to high wake losses, particularly for some wind directions. With the proposed approach, 4.09% and 2.18% higher efficiencies on average were obtained compared to aligned and staggered layouts, respectively. © 2017 Elsevier Ltd

KW - Genetic algorithm

KW - Layout optimization

KW - Micro-siting

KW - Wind farm

KW - Wind turbine

KW - Electric utilities

KW - Genetic algorithms

KW - Wakes

KW - Wind power

KW - Energy benefits

KW - Higher efficiency

KW - Irregular terrain

KW - Turbine micro-siting

KW - Wind farm layouts

KW - Wind turbines

KW - array

KW - assessment method

KW - experimental design

KW - genetic algorithm

KW - numerical model

KW - optimization

KW - wind farm

KW - wind turbine

KW - Chile

U2 - 10.1016/j.renene.2017.11.018

DO - 10.1016/j.renene.2017.11.018

M3 - Article

VL - 118

SP - 591

EP - 601

JO - Renewable Energy

T2 - Renewable Energy

JF - Renewable Energy

SN - 0960-1481

ER -