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Offshore wind farm layout optimization using mathematical programming techniques

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  • Pérez, Beatriz
  • Mínguez, Roberto
  • Guanche, Raúl

Abstract

Offshore wind power is a renewable energy of growing relevance in current electric energy systems, presenting favorable wind conditions in comparison with the sites on land. However, the higher energy yield has to compensate the increment in installation and maintenance costs, thus the importance of optimizing resources. One relevant aspect to increase profitability is the wind farm layout. The aim of this paper is to propose a new method to maximize the expected power production of offshore wind farms by setting the appropriate layout, i.e. minimizing the wake effects. The method uses a sequential procedure for global optimization consisting of two steps: i) an heuristic method to set an initial random layout configuration, and ii) the use of nonlinear mathematical programming techniques for local optimization, which use the random layout as an initial solution. The method takes full advantage of the most up-to-date mathematical programming techniques while performing a global optimization approach, which can be easily parallelized. The performance of the proposed procedure is tested using the German offshore wind farm Alpha Ventus, located in the North Sea, yielding an increment of expected annual power production of 3.52% with respect to the actual configuration. According to current electricity prices in Germany, this constitutes an expected profit increment of almost 1 M€ per year.

Suggested Citation

  • Pérez, Beatriz & Mínguez, Roberto & Guanche, Raúl, 2013. "Offshore wind farm layout optimization using mathematical programming techniques," Renewable Energy, Elsevier, vol. 53(C), pages 389-399.
  • Handle: RePEc:eee:renene:v:53:y:2013:i:c:p:389-399
    DOI: 10.1016/j.renene.2012.12.007
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    References listed on IDEAS

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    1. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2012. "Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation," Renewable Energy, Elsevier, vol. 38(1), pages 16-30.
    2. Grady, S.A. & Hussaini, M.Y. & Abdullah, M.M., 2005. "Placement of wind turbines using genetic algorithms," Renewable Energy, Elsevier, vol. 30(2), pages 259-270.
    3. Kusiak, Andrew & Song, Zhe, 2010. "Design of wind farm layout for maximum wind energy capture," Renewable Energy, Elsevier, vol. 35(3), pages 685-694.
    4. Saavedra-Moreno, B. & Salcedo-Sanz, S. & Paniagua-Tineo, A. & Prieto, L. & Portilla-Figueras, A., 2011. "Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms," Renewable Energy, Elsevier, vol. 36(11), pages 2838-2844.
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