(182f) Multi-Objective Optimization of Cchp Systems Using Particle Swarm Algorithms

The development of the global economy and the growth of population have brought about an ever-increasing demand for energy consumption. At the same time, related studies have pointed out that generally there are more than one-third of global energy consumption comes from building energy supply systems. Since the excellent performance on energy saving and emission reduction, the combined cooling heating and power (CCHP) system has been widely used to supply energy in residential houses, offices, shopping malls, hotels and other buildings or regions. However, in the actual design planning process, there is a lack of scientific and rational optimization methods and management strategies for many CCHP systems.

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.