Multi-objective optimization of a residential solar thermal combisystem

Solar thermal systems for domestic hot water and space heating, referred to as solar combisystems, can significantly reduce primary energy consumption for residential buildings. In most studies, single-objective optimization algorithms are used for the design and operation strategies of such complex systems. This paper presents the use of two conflicting objective functions, the life cycle cost and life cycle energy, for the optimization of a residential solar combisystem in Montreal, Quebec, Canada. Since the design the solar combisystem is treated as a multi-objective optimization problem, two different approaches for solving such problems are presented and compared: the weighted sum method (WSM) using a hybrid particle swarm optimization/Hooke-Jeeves (PSO/HJ) algorithm, and a multi-objective particle swarm optimization (MOPSO). Finally, a hybrid (MOPSO/HJ) is proposed to enhance the local search of MOPSO. The results show that the WSM was time-consuming for such an optimization problem. MOPSO/HJ was more than six times faster than the WSM. Compared with the base case combisystem, MOPSO/HJ found different design options, where the life cycle cost and life cycle energy were reduced by up to 88.6% and 63.9%, respectively.