A parallel Teaching-Learning-Based Optimization procedure for automatic heliostat aiming

The flux distribution generated by the heliostat field of solar central receiver system (SCRS) over the receiver needs to be carefully controlled. It is necessary to avoid dangerous radiation peaks and temperature distributions to maximize the efficiency and keep the system in a safe state. These tasks imply both selecting the subset of heliostats to be activated and assigning each one to a certain aiming point at the receiver. The heliostat field is usually under human control and supervision, what is a potential limiting factor. Thus, there is an active research line to define automatic aiming procedures. In fact, a general and autonomous methodology is being developed by the authors of this work. However, the mathematical modeling leads to face a complex large-scale optimization problem. In this work, applying Teaching-Learning-Based Optimization (TLBO), a population-based large-scale optimizer, is considered. It is intended to serve to perform large explorations of the search-space to finally deploy further local optimizers over the most promising results. Considering the computational cost of the objective function, a parallel version of TLBO has been developed. It significantly accelerates the procedure, and the possibility of being included in a more complex process remains viable. Additionally, the parallel version of TLBO is also linked as a generic open-source library.