Study on the annual optical comprehensive performance of linear Fresnel reflector concentrators with an effective multi-objective optimization model

In this paper, a more general multi-objective optimization model is developed to efficiently investigate the annual optical comprehensive performance of the entire linear Fresnel reflector concentrator (LFRC). It was carried out by combining the multi-objective genetic algorithm (MOGA) and the Monte Carlo ray-tracing (MCRT) method, with an effective objective function verified to efficiently characterize the annual solar flux non-uniformity of LFRCs, named as the nominal annual solar flux non-uniformity. With this indicator, trade-off studies can be realized for considering the common conflicting relationship between the optical efficiency and the solar flux non-uniformity on annual time scale. It was validated and proved to make the costly infeasible LFRC MOGA-MCRT optimization feasible, which significantly reduces the computing time of the originally time-consuming optimization almost two orders of magnitude. After validation, this optimization model was successfully applied to optimize LFRCs of different mirror shapes and tracking modes at different latitudes. The recommended geometrical parameters combination was obtained from each Pareto front, by using a method called Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Through continuously expanding the application scope and reusing TOPSIS, better parameters combination was further recommended. For the most recommended parameters combination studied in this paper, the nominal annual solar flux non-uniformity decreased by 38.04% while the nominal annual optical efficiency increased by 133.60%, as compared with the original LFRC baseline system. This time-reducing optimization model is thus proved to be effective, which could also provide guidance for the optical optimization of other similar solar thermal applications.