Abstract

Solar thermal combisystems can significantly reduce primary energy consumption for residential buildings and therefore cut down greenhouse gas emissions; however, the overall performance of such systems depends on their design (i.e., configuration and sizing of their components) and operating conditions. Designing solar thermal combisystems can be improved by using optimization methods. Therefore, this doctoral thesis introduces a multi-objective optimization framework for optimizing the configuration and equipment sizing of solar thermal combisystems. A micro-time variant multiobjective particle swarm optimization (micro-TVMOPSO) algorithm is developed for handling engineering optimization problems, such as the multi-objective optimization of solar combisystems, where evaluating objective functions is time-consuming. The proposed framework uses a generic solar combisystem model coupled with the micro-TVMOPSO algorithm to find a set of optimized combisystem designs. Applied to two case studies, the multi-objective optimization framework was able to find designs reducing the life cycle cost, life cycle energy use, and life cycle exergy destroyed of solar thermal combisystems. The proposed multi-objective optimization framework can therefore be used to get the most out of solar thermal combisystems given specificc economic and environmental conditions.