A heuristic method for optimizing a solar power tower system is proposed, in which both heliostat field (heliostat locations and number) and the tower (tower height and receiver size) are simultaneously considered.Maximizing the thermal energy collected per unit cost leads to a difficult optimization problem due to its characteristics: it has a nonconvex black-box objective function with computationally expensive evaluation and nonconvex constraints.The proposed method sequentially optimizes the field layout for a given tower configuration and then, the tower design is optimized for the previously obtained field layout. A greedy-based heuristic algorithm is presented to address the heliostat location problem. This algorithm follows a pattern-free method. The only constraints to be considered are: the field region and the nonconvex constraints * Corresponding author. Instituto de Matemáticas de la Universidad de Sevilla (IMUS) Edificio Celestino Mutis-1 Planta-D10.5 Avda. Reina Mercedes, s/n, 41012 Sevilla, Spain. Tel: +34 955420870.Email address: carmenanadb@us.es (C. Domínguez-Bravo)Preprint submitted to Computers & Operations Research November 25, 2014 (which allow heliostats to not collide).The absence of a geometrical pattern to design the field and the simultaneous optimization of the field and the tower designs make this approach different from the existing ones. Our method is compared against other proposals in the literature of heliostat field optimization.

The design of a Solar Power Tower plant involves the optimization of the heliostat field layout. Fields are usually designed to have all heliostats of identical size. Although the use of a single heliostat size has been questioned in the literature, there are no tools to design fields with heliostats of several sizes at the same time.In this paper, the problem of optimizing the heliostat field layout of a system with heliostats of different sizes is addressed. We present an optimization tool to design solar plants allowing two heliostat sizes. The methodology is illustrated with a particular example considering different heliostat costs.

In this article a new procedure to optimize the design of a Multiple Receivers Solar Power Tower system is presented. The proposed procedure allows to optimize the different receivers (height, aperture tilt angle, azimuth angle and aperture size) as well as the heliostat field layout, seeking to minimize the levelized cost of thermal energy.The optimization problem is high dimensional, with a black-box nonconvex objective function that is hard to compute.Our method is based on an alternating greedy-based heuristic method, already used by the authors to design a system with a single receiver, which simultaneously optimizes the receivers and the heliostat field. The proposed procedure allows one to determine the overall number of heliostats, their locations and the aiming region of each field.

Abstract. In this paper the optimization of a heliostat field with triangular heliostat pods is addressed. The use of structures which allow the combination of several heliostats into a common pod system aims to reduce the high costs associated with the heliostat field and therefore reduces the Levelized Cost of Electricity value. A pattern-based algorithm and two pattern-free algorithms are adapted to handle the field layout problem with triangular heliostat pods. Under the Helio100 project in South Africa, a new small-scale Solar Power Tower plant has been recently constructed. The Helio100 plant has 20 triangular pods (each with 6 heliostats) whose positions follow a linear pattern. The obtained field layouts after optimization are compared against the reference field Helio100.

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