Abstract
An optimization process based on Monte Carlo sampling of the variables of the objective function is developed. The technique is applied to a grouping of wind turbine generators. The permissible dimensions of the wind turbines and the range of spacing between adjacent machines allows a search for a maximum profit over a wide mix of system configurations. The computer program for the optimization algorithm finds cluster configurations for maximum profit with a high degree of certainty for the relatively simple cost and design constraints used in the model.
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