Optical characterization of heliostat facets based on Computational Optimization

In solar power tower plants, knowing the optical quality of heliostats makes it possible to predict relevant information on the receiver surface, such as the irradiance concentration factor and spillage. However, there are no standardized routines for optical characterization in commercial facilities because the process is challenging, multidisciplinary, and time-demanding. This article revises the traditional optical characterization methodology followed at the Solar Platform of Almeria (PSA). The process starts with the acquisition of the image of the studied optical system. After that, the picture must be fitted to an analytical model, which requires finding the variables that best reproduce the reality. The traditional method for accomplishing this task is iterative, semi-automatic, and contains trial-and-error components. This work studies how to replace this part with heuristic optimizers and considers using the state-of-the-art methods TLBO, UEGO, and Multi-Start Interior -Point (MSIP). Their effectiveness has been compared to the results manually achieved by an expert with three different heliostat facets. According to the results obtained, the parameter sets found by TLBO and UEGO outperform those obtained through the traditional method.