(182f) Multi-Objective Optimization of Cchp Systems Using Particle Swarm Algorithms
According to the characteristics of energy use from specific buildings, most CCHP systems may operate following the electric load (FEL), the thermal load (FTL) or the hybrid electric-thermal load (FHL). In this study, thermal modeling and optimal design of a typical CCHP system for a designated office building was conducted using the multi-objective particle swarm optimization (MOPSO) algorithm. The cooling capacity of electrical and absorption chillers as well as electric cooling ratio were optimized as the design variables. The Pareto principle was applied to evaluate the system indicators under different operate strategies, including the energy, economy, and environmental parameters simultaneously. It was observed that the improvement of energy efficiency would inevitably lead to the increase of investment. The performance of CCHP system under different operate strategies during a year was also compared. Finally, the results of this study would provide a certain technical reference for related planning and design.