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...

Full description

Saved in:
Bibliographic Details
Published in:Applied thermal engineering Vol. 51; no. 1-2; pp. 753 - 763
Main Authors: Zhao, Lei, Cai, Wen-Jian, Ding, Xu-dong, Chang, Wei-chung
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.03.2013
Elsevier
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1359-4311
DOI:10.1016/j.applthermaleng.2012.10.001