Optimization of V-Trough photovoltaic concentrators through genetic algorithms with heuristics based on Weibull distributions

Photovoltaic V-Troughs use simple and low-cost non-imaging optics, namely flat mirrors, to increase the solar harvesting area by concentrating the sunlight towards regular solar cells. The geometrical dispositions of the V-Trough's elements, and the way in which they are dynamically adjusted to track the sun, condition the optical performance. In order to improve their harvesting capacity, their geometrical set-up can be tailored to specific conditions and performance priorities. Given the large number of possible configurations and the interdependence of the multiple parameters involved, this work studies genetic algorithms as a heuristic approach for navigating the space of possible solutions. Among the algorithms studied, a new genetic algorithm named “GA-WA” (Genetic Algorithm-Weibull Arias) is proposed. GA-WA uses new heuristic processes based on Weibull distributions. Several V-Trough performance indicators are proposed as objective functions that can be optimized with genetic algorithms: (i) Ce‾ (average effective concentration); (ii) Cost (cost of materials) and (iii) Tsp (space required). Moreover, from the integration of these indicators, three multi-objective indices are proposed: (a) ICOE (Ce‾ versus Cost); (b) MICOE (Ce‾ versus Cost and Ce‾ versus Tsp combined) and (c) MDICOE (similar to MICOE but with discretization considerations). The heuristic parameters of the studied genetic algorithms are optimized and their capacities are explored in a case study. The results are compared against reported V-Trough set-ups designed with the interactive software VTDesign for the same case study. It was found that genetic algorithms, such as the ones developed in this work, are effective in the performance indicators improvement, as well as efficient and flexible tools in the problem of defining the set-up of solar V-Troughs in personalized scenarios. The intuition and the more holistic exploration of a trained engineer with an interactive software can be complemented with the broader and less biased evolutionary optimization of a tool like GA-WA.