A code ‘Wind Farm Optimization using a Genetic Algorithm’ (referred as WFOG) was developed for optimizing the placement of wind turbines in large wind farms. It utilizes an analytical wake model (by Jensen et al.) to minimize the cost per unit power for the wind farm. In this study, a new wake model by Ishihara et al. is tested in WFOG. The wake model takes into account the effect of atmospheric turbulence and rotor generated turbulence on the wake recovery. Results of the two wake models are compared with data from Horns Rev and Nysted wind farm. The maximum error (Horns Rev wind farm) for Ishihara’s wake model was 7% as compared to 15% for Jensen’s wake model. The optimal results obtained in earlier studies (using Jensen’s wake model) are compared to wind farm configurations obtained for Ishihara’s wake model. The optimization is carried out for the simplest wind regime: Constant wind speed and fixed wind direction.

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