Decomposition based multi-objective optimization to simultaneously determine the number and the optimum locations of wind turbines in a wind farm

Abstract

Wind energy is one of the most progressive types of renewable energies deployed using wind turbines, which convert wind power into electrical energy. Various methodologies and layouts have been proposed to determine the optimal layout (numbers and locations) of wind turbines inside a wind farm to extract maximum energy. However, limited availability of land area has resulted in the construction of these wind farms near to human habitats causing a negative impact on human health. Compared to other factors, noise has become an immense concern for wind farm designers, as it needs to be constrained within the mandatory limits. Using a well-established wake model and ISO-9613-2 noise calculation, this study performs a wind farm layout optimization (WFLO) based on a multi-objective trade-off between minimization of noise propagation and maximization of energy generation. A novel hybrid methodology is proposed as a combination of proposed probabilistic variable decomposed multi-objective evolutionary algorithm (VdRBNSGA-II) and a newly developed gradient-based non-dominated normalized normal constraint (nD-NNC) method. As opposed to previous works, generated non-dominated front provides various alternative energy-noise solutions along with an additional information on turbine layouts, which allows a decision maker to select among different competing solutions based on the existing noise and other regulations.