Optimization of wind farm operation with a noise constraint

Abstract

Abstract. This article presents a method for performing noise constrained optimization of wind farms by changing the operational modes of the individual wind turbines. The optimization is performed by use of the TopFarm framework and the PyWake wind farm modeling as well as two sound propagation models: the ISO 9613-2 model and the Parabolic Equation model, WindSTAR. The two sound propagation models introduce different levels of complexity to the optimization problem with the WindSTAR model taking a broader range of parameters, like the acoustic ground impedance, the complex terrain elevation and the flow field from the noise source to the receptor, into account. Thus, as the WindSTAR model introduces a higher complexity of the sound propagation computations, it likewise introduces a higher computational time. Wind farm optimization using each of the two sound propagation models is therefore performed in different atmospheric conditions and for different source/receptor setups, and compared through this study in order to evaluate the advantage of using a more complex sound propagation model. The article focuses on artificial wind farms in flat terrain as well as arbitrarily chosen dwellings at which the noise constraints are applied. By this, the study presents the potential of an optimization algorithm focusing on the sound propagation and wind farm operation trade-off.