Abstract
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. © 2012 Elsevier Ltd. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 753-763 |
| Number of pages | 11 |
| Journal | Applied Thermal Engineering |
| Volume | 51 |
| Issue number | 1-2 |
| DOIs | |
| Publication status | Published - 2013 |
| Externally published | Yes |
Keywords
- Component interaction
- Computation time
- Cooling load
- Decentralized optimization
- Energy-saving effect
- Environment conditions
- Global optimization algorithm
- Hybrid component
- Hybrid model
- Model-based OPC
- On-off control
- Optimal solutions
- Problem formulation
- Satisfactory predictions
- System optimizations
- Vapor-compression refrigeration cycle
- Working hours, Algorithms
- Energy utilization
- Game theory
- Global optimization
- Optimization
- Vapor compression refrigeration, Vapors