Wind Farm Layout Optimization Under Uncertainty using Bayesian Approach

The uncertain nature of wind reduces the ability to produce rated power from a wind farm on a sustainable basis. Such wind nature variations are generally ignored by the wind farm practitioners while designing the layout thereby leading to overestimation of rated power calculations. These variations in wind are considered in the form of a probability mass function of speed and direction, known as Wind Frequency Maps (WFMs), which have a direct impact on the power production of wind farm. To determine the true capacity of wind farm under uncertainty, several WFMs depicting different wind scenarios need to be considered. Therefore, in this work, a robust optimization formulation has been formulated which can find the layout of wind farm that is immune to uncertain scenarios expressed through different WFMs. The worst-case robust formulation is solved using the Bayesian optimization framework which balances the exploration and exploitation of decision variable space and fuses a best guess with characterization of considered uncertainty. The results have shown the effectiveness of Bayesian optimization to optimize the layout of wind farm for various number of turbines with lower computational costs.