Multi-Objective Optimization of Solar Tower Heliostat Fields

Abstract

We introduce a model to compute the annual performance of a heliostat field. We take into account topography, tracking errors, and the position and intensity of the sun. An approach is introduced, which improves on the otherwise expensive pairwise comparison to calculate shading and blocking. Because the computational time is reduced significantly, the presented implementation is sufficiently fast to allow for heliostat field layout optimization within a couple of hours. The optimization is executed via a genetic algorithm, which optimizes the heliostat positioning parameters as well as other design parameters, e.g. receiver tilt angle. A novel approach is used to reduce the search domain. Because the search domain delivers several local optima with comparable values of the objective function, the objective function is augmented. We use smoothing functionals to disperse the local optima. A field layout is optimized on a hilly ground in South Africa, with additional constraints on the heliostat positions.