Due to increasing penetration of wind energy in the recent times, wind farm owners tend to generate increasing amount of energy out of wind farms. In order to meet targets, many wind farms are operated with a layout of numerous turbines placed close to each other in a limited area leading to greater energy losses due to ‘wake effects’ instead of generating more power. To solve the probl em in the most optimal way, these turbines need to satisfy many other constraints such as topological constraints, minimum allowable capacity factors, inter-turbine distances etc. Existing methods to solve this complex turbine placement problem typically assume knowledge about the total number of turbines to be placed in the farm, which might be unrealistic. This study proposes a novel hybrid optimization methodology, a combination of evolutionary and classical optimization approaches, to simultaneously determine the optimum number of turbines to be placed in a wind farm along with their optimal locations. Application of the proposed method on a representative case study yields 43% higher Annual Energy Production (AEP) than the results found by one of the existing methods.
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