Optimization of vapor compression cycle based on genetic algorithm

This paper presents a model-based optimization strategy for vapor compression refrigeration cycle. The optimization problem is formulated as minimizing the total operating cost of all energy consuming devices with mechanical limitations, component interactions, environment conditions and cooling load demands as constraints. Genetic algorithm is utilized to calculate optimal set point under different operating conditions. The simulation results comparison between the proposed algorithm and traditional on-off control verifies the energy saving effect of the proposed method.