Decentralized optimization for vapor compression refrigeration cycle

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 hyb...

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Vydáno v:Applied thermal engineering Ročník 51; číslo 1-2; s. 753 - 763
Hlavní autoři: Zhao, Lei, Cai, Wen-Jian, Ding, Xu-dong, Chang, Wei-chung
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ltd 01.03.2013
Elsevier
Témata:
Algorithms
Applied sciences
Compressing
Decentralized
Decentralized optimization
Decentralized problem formulation
Energy
Energy consumption
Energy. Thermal use of fuels
Exact sciences and technology
Genetic algorithms
Hybrid component models
Optimization
Refrigerating engineering
Refrigerating engineering. Cryogenics. Food conservation
Refrigeration
Techniques. Materials
Thermal engineering
Vapor compression refrigeration cycle
Decentralized optimization
Decentralized problem formulation
Vapor compression refrigeration cycle
Hybrid component models
ISSN:1359-4311
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Shrnutí: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.
Bibliografie:ObjectType-Article-2
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ISSN:1359-4311
DOI:10.1016/j.applthermaleng.2012.10.001