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A Review of Methodological Approaches for the Design and Optimization of Wind Farms

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Listed:
  • José F. Herbert-Acero

    () (Chair for Wind Energy, Physics Department, Tecnológico de Monterrey, Eugenio Garza Sada 2501 Sur, 64849 Monterrey, N.L., Mexico)

  • Oliver Probst

    () (Chair for Wind Energy, Physics Department, Tecnológico de Monterrey, Eugenio Garza Sada 2501 Sur, 64849 Monterrey, N.L., Mexico)

  • Pierre-Elouan Réthoré

    () (Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark)

  • Gunner Chr. Larsen

    () (Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark)

  • Krystel K. Castillo-Villar

    () (Department of Mechanical Engineering, the University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA)

Abstract

This article presents a review of the state of the art of the Wind Farm Design and Optimization (WFDO) problem. The WFDO problem refers to a set of advanced planning actions needed to extremize the performance of wind farms, which may be composed of a few individual Wind Turbines (WTs) up to thousands of WTs. The WFDO problem has been investigated in different scenarios, with substantial differences in main objectives, modelling assumptions, constraints, and numerical solution methods. The aim of this paper is: (1) to present an exhaustive survey of the literature covering the full span of the subject, an analysis of the state-of-the-art models describing the performance of wind farms as well as its extensions, and the numerical approaches used to solve the problem; (2) to provide an overview of the available knowledge and recent progress in the application of such strategies to real onshore and offshore wind farms; and (3) to propose a comprehensive agenda for future research.

Suggested Citation

  • José F. Herbert-Acero & Oliver Probst & Pierre-Elouan Réthoré & Gunner Chr. Larsen & Krystel K. Castillo-Villar, 2014. "A Review of Methodological Approaches for the Design and Optimization of Wind Farms," Energies, MDPI, Open Access Journal, vol. 7(11), pages 1-87, October.
  • Handle: RePEc:gam:jeners:v:7:y:2014:i:11:p:6930-7016:d:41777
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    References listed on IDEAS

    as
    1. Punt, Maarten J. & Groeneveld, Rolf A. & van Ierland, Ekko C. & Stel, Jan H., 2009. "Spatial planning of offshore wind farms: A windfall to marine environmental protection?," Ecological Economics, Elsevier, vol. 69(1), pages 93-103, November.
    2. van Haaren, Rob & Fthenakis, Vasilis, 2011. "GIS-based wind farm site selection using spatial multi-criteria analysis (SMCA): Evaluating the case for New York State," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(7), pages 3332-3340, September.
    3. Kiranoudis, C. T. & Voros, N. G. & Maroulis, Z. B., 2001. "Short-cut design of wind farms," Energy Policy, Elsevier, vol. 29(7), pages 567-578, June.
    4. Zhao, M. & Chen, Z. & Blaabjerg, F., 2006. "Probabilistic capacity of a grid connected wind farm based on optimization method," Renewable Energy, Elsevier, vol. 31(13), pages 2171-2187.
    5. Manchado, Cristina & Otero, César & Gómez-Jáuregui, Valentín & Arias, Rubén & Bruschi, Viola & Cendrero, Antonio, 2013. "Visibility analysis and visibility software for the optimisation of wind farm design," Renewable Energy, Elsevier, vol. 60(C), pages 388-401.
    6. Florian Krug & Bastian Lewke, 2009. "Electromagnetic Interference on Large Wind Turbines," Energies, MDPI, Open Access Journal, vol. 2(4), pages 1-12, November.
    7. Pattanariyankool, Sompop & Lave, Lester B., 2010. "Optimizing transmission from distant wind farms," Energy Policy, Elsevier, vol. 38(6), pages 2806-2815, June.
    8. Rodman, Laura C. & Meentemeyer, Ross K., 2006. "A geographic analysis of wind turbine placement in Northern California," Energy Policy, Elsevier, vol. 34(15), pages 2137-2149, October.
    9. Oliver Probst & Diego Cárdenas, 2010. "State of the Art and Trends in Wind Resource Assessment," Energies, MDPI, Open Access Journal, vol. 3(6), pages 1-55, June.
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    Cited by:

    1. Wędzik, Andrzej & Siewierski, Tomasz & Szypowski, Michał, 2016. "A new method for simultaneous optimizing of wind farm’s network layout and cable cross-sections by MILP optimization," Applied Energy, Elsevier, vol. 182(C), pages 525-538.
    2. repec:gam:jeners:v:9:y:2016:i:5:p:352:d:69650 is not listed on IDEAS
    3. repec:gam:jeners:v:9:y:2016:i:3:p:216:d:66066 is not listed on IDEAS
    4. Silvio Rodrigues & Carlos Restrepo & George Katsouris & Rodrigo Teixeira Pinto & Maryam Soleimanzadeh & Peter Bosman & Pavol Bauer, 2016. "A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures," Energies, MDPI, Open Access Journal, vol. 9(3), pages 1-42, March.
    5. Mayo, Michael & Daoud, Maisa, 2016. "Informed mutation of wind farm layouts to maximise energy harvest," Renewable Energy, Elsevier, vol. 89(C), pages 437-448.
    6. Guirguis, David & Romero, David A. & Amon, Cristina H., 2016. "Toward efficient optimization of wind farm layouts: Utilizing exact gradient information," Applied Energy, Elsevier, vol. 179(C), pages 110-123.
    7. Lo Brutto, Ottavio A. & Thiébot, Jérôme & Guillou, Sylvain S. & Gualous, Hamid, 2016. "A semi-analytic method to optimize tidal farm layouts – Application to the Alderney Race (Raz Blanchard), France," Applied Energy, Elsevier, vol. 183(C), pages 1168-1180.
    8. repec:eee:renene:v:123:y:2018:i:c:p:748-755 is not listed on IDEAS
    9. Shafiqur Rehman & Salman A. Khan, 2016. "Fuzzy Logic Based Multi-Criteria Wind Turbine Selection Strategy—A Case Study of Qassim, Saudi Arabia," Energies, MDPI, Open Access Journal, vol. 9(11), pages 1-26, October.
    10. repec:eee:appene:v:209:y:2018:i:c:p:140-152 is not listed on IDEAS
    11. Rodrigues, S. & Restrepo, C. & Kontos, E. & Teixeira Pinto, R. & Bauer, P., 2015. "Trends of offshore wind projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1114-1135.
    12. repec:eee:appene:v:199:y:2017:i:c:p:187-204 is not listed on IDEAS
    13. Parada, Leandro & Herrera, Carlos & Flores, Paulo & Parada, Victor, 2017. "Wind farm layout optimization using a Gaussian-based wake model," Renewable Energy, Elsevier, vol. 107(C), pages 531-541.
    14. Li, Wenwen & Özcan, Ender & John, Robert, 2017. "Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation," Renewable Energy, Elsevier, vol. 105(C), pages 473-482.
    15. Ju Feng & Wen Zhong Shen, 2015. "Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction," Energies, MDPI, Open Access Journal, vol. 8(4), pages 1-18, April.
    16. Kuo, Jim Y.J. & Romero, David A. & Amon, Cristina H., 2015. "A mechanistic semi-empirical wake interaction model for wind farm layout optimization," Energy, Elsevier, vol. 93(P2), pages 2157-2165.
    17. repec:eee:appene:v:197:y:2017:i:c:p:279-291 is not listed on IDEAS
    18. repec:eee:renene:v:118:y:2018:i:c:p:591-601 is not listed on IDEAS
    19. Souma Chowdhury & Ali Mehmani & Jie Zhang & Achille Messac, 2016. "Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes," Energies, MDPI, Open Access Journal, vol. 9(5), pages 1-31, May.
    20. repec:eee:appene:v:205:y:2017:i:c:p:1283-1297 is not listed on IDEAS
    21. Rodrigues, S. & Bauer, P. & Bosman, Peter A.N., 2016. "Multi-objective optimization of wind farm layouts – Complexity, constraint handling and scalability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 587-609.
    22. Soukissian, Takvor H. & Karathanasi, Flora E., 2017. "On the selection of bivariate parametric models for wind data," Applied Energy, Elsevier, vol. 188(C), pages 280-304.

    More about this item

    Keywords

    wind farm design; micro-siting; wind farm layout; wind farm performance modeling; nonlinear optimization; metaheuristic optimization;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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