Global optimization of a vapor compression refrigeration system with a self-adaptive differential evolution algorithm

• Published in: Applied thermal engineering 2021-10, Vol.197, p.117427, Article 117427
• Main Authors: Kong, Dehao, Yin, Xiaohong, Ding, Xudong, Fang, Ning, Duan, Peiyong
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Air conditioning; Algorithms; Cooling loads; Energy consumption; Energy savings; Evolutionary algorithms; Evolutionary computation; Global optimization; Global optimization strategy; Heat transfer; Optimization; Refrigeration; Self-adaptive differential evolution algorithm; System effectiveness; Vapor compression refrigeration; Vapor compression refrigeration system
• ISSN: 1359-4311; 1873-5606
• DOI: 10.1016/j.applthermaleng.2021.117427

•A model to predict the performance and energy consumption of VCRS is established.•A global optimization strategy avoiding local minimum is proposed.•The SADE algorithm is adopted to solve the optimization problem with fast speed.•Average 15.57% energy savings can be obtained by the global optimization strategy. This paper presents a global optimization strategy for the vapor compression refrigeration system (VCRS) based on self-adaptive differential evolution (SADE) algorithm to minimize the system energy consumption with the indoor cooling requirements fulfilled. The simplified hybrid VCRS model is established based on the thermodynamics and heat transfer theory, together with the parameter regression method, to predict the system performance and energy consumption under different working conditions. Then considering the interactions between components, the influences of varying indoor loads and outdoor air conditions, a global optimization problem with constraints is formulated. The SADE algorithm is used to solve the global optimization problem, and the optimal variables settings are obtained. Simulation results demonstrate that the proposed global optimization strategy can improve the VCRS energy efficiency in different time periods while satisfying the cooling demands. Averagely 15.57% energy saving on the typical testing day can be achieved. Moreover, significant energy savings can be obtained during the morning and evening periods with partial indoor cooling loads. Furthermore, to show the effectiveness of proposed SADE strategy, the DE and classical PSO algorithms are compared, and the results indicate that the SADE algorithm can significantly reduce the calculation time, and can avoid local minimum. This research brings a feasible methodology, to reduce the energy consumption of air conditioning systems effectively.