Decentralized optimization for vapor compression refrigeration cycle - Zhao, Lei; Cai, Wen-Jian; Ding, Xu-dong; Chang, Wei-chung (Applied thermal engineering, 2013)
This paper presents a model based decentralized optimization method for vapor compression refrigeration cycle (VCC). The overall system optimization problem is formulated and separated into minimizing the energy consumption of three interactive individual subsystems subject to the constraints of hybrid model, mechanical limitations, component interactions, environment conditions and cooling load demands. Decentralized optimization method from game theory is modified and applied to VCC optimization to obtain the Perato optimal solution under different working conditions. Simulation and experiment results comparing with traditional on–off control and genetic algorithm are provided to show the satisfactory prediction accuracy and practical energy saving effect of the proposed method. For the working hours, its computation time is steeply reduced to 1% of global optimization algorithm with consuming only 1.05% more energy consumption. ► The decentralized optimization problem of VCC is formulated. ► Decentralized optimization technique is modified and applied to the problem. ► Experiments show proposed method energy consumption is close to global optimization. ► Experiments show proposed method is much faster than global optimization.