Articles | Volume 54
https://doi.org/10.5194/adgeo-54-79-2020
https://doi.org/10.5194/adgeo-54-79-2020
17 Oct 2020
 | 17 Oct 2020

Layout optimization for offshore wind farms in India using the genetic algorithm technique

Narender Kangari Reddy and Somnath Baidya Roy

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Cited articles

Atlas, R., Hoffman, R. N., Ardizzone, J., Leidner, S. M., Jusem, J. C., Smith, D. K., and Gombos, D.: A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications, B. Am. Meteorol. Soc., 92, 157–74, https://doi.org/10.1175/2010BAMS2946.1, 2011. 
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Charhouni, N., Sallaou, M., and Mansouri, K.: Realistic Wind Farm Design Layout Optimization with Different Wind Turbines Types, Int. J. Energ. Environ. Eng., 10, 307–318, https://doi.org/10.1007/s40095-019-0303-2, 2019. 
Chen, K., Song, M. X., Zhang, X., and Wang, S. F.: Wind turbine layout optimization with multiple hub height wind turbines using Greedy Algorithm, Renew. Energ., 96, 676–686, https://doi.org/10.1016/j.renene.2016.05.018, 2016. 
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Short summary
In this study, we apply the Genetic Algorithm technique that mimics the natural selection process observed in nature to design optimal layouts for massive wind farms off the southeastern coast of India using real wind data. Our results show that layout optimization leads to large improvements in power generation (up to 28 %), efficiency (up to 34 %), and cost (up to 25 %) due to the reduction in wake losses.