Combined wind turbine design and wind farm layout optimisation under wind resource uncertainty

Abstract

Applications of the wind farm layout optimisation problem focus on optimally positioning a certain number of turbines within a wind farm so that annual energy production (AEP) is maximised. This study addresses an earlier stage in the wind farm development process. Instead of optimising the individual positions of a certain number of turbines of a selected model, the control variables in the optimisation are rotor diameter of a wind turbine of fixed nominal power, number of turbines in a wind farm of fixed area and orientation angle of turbines in the farm. In addition to AEP, this study considers capital and operational expenses of a wind farm to calculate the levelized cost of energy (LCoE), which is the objective function. Given the stage of development addressed, it is also essential that uncertainty is considered; here the focus is on the impact of wind resource uncertainty. The optimisation is performed with a recently developed state-of-the-art stochastic gradient based method (StoSAG) which has shown in different domains to be computationally efficient and accurate when dealing with optimisation problems under uncertainty. Our results show non-trivial optimal designs with LCoE reductions of ∼0.5% compared to the most optimal solution from a sensitivity analysis.