Research Papers

Solar Hybrid Air Conditioner: Model Validation and Optimization

[+] Author and Article Information
Ali Al-Alili

Department of Mechanical Engineering,
The Petroleum Institute,
P.O. Box 2533,
Abu Dhabi, UAE
e-mail: alialalili@pi.ac.ae

Yunho Hwang

Center for Environmental Energy Engineering,
University of Maryland,
4164 Glenn Martin Hall Building,
College Park, MD 20742
e-mail: yhhwang@umd.edu

Reinhard Radermacher

Center for Environmental Energy Engineering,
University of Maryland,
4164 Glenn Martin Hall Building,
College Park, MD 20742,
e-mail: raderm@umd.edu

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received June 7, 2015; final manuscript received January 11, 2016; published online February 23, 2016. Assoc. Editor: Jorge E. Gonzalez.

J. Sol. Energy Eng 138(3), 031003 (Feb 23, 2016) (10 pages) Paper No: SOL-15-1174; doi: 10.1115/1.4032683 History: Received June 07, 2015; Revised January 11, 2016

Solar air conditioners (A/Cs) have attracted much attention in research, but their performance and cost have to be optimized in order to become a real alternative to conventional A/C systems. In this study, a hybrid solar A/C is simulated using the transient systems simulation program(trnsys), which is coupled with matlab in order to carry out the optimization study. The trnsys model is experimentally validated prior to the optimization study. Two optimization problems are formulated with the following design variables: solar collector area, solar collector mass flow rate, solar thermal energy storage volume, and solar electrical energy storage size. The genetic algorithm (GA) is selected to solve the single-objective optimization problem and find the global optimum design for the lowest electrical consumption. To optimize the two objective functions simultaneously, energy consumption and total cost (TC), a multi-objective genetic algorithm (MOGA) is used to find the Pareto curve within the design variables' bounds while satisfying the constraints. The overall cost of the optimized solar A/C design is also compared to a standard vapor compression cycle (VCC). The results show that coupling trnsys and matlab expands trnsys optimization capability in solving more complex optimization problems. The results also show that the optimized solar hybrid A/C is not very competitive when the electricity prices are low and no governmental support is provided.

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Fig. 2

GA working principle [22]

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Fig. 1

The hybrid solar A/C

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Fig. 3

Thermal network of the CPVT collector [26]

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Fig. 8

The solar hybrid A/C Pareto front solutions

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Fig. 9

Comparing ISOC and matlab MOGA

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Fig. 10

The effect of the population size

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Fig. 11

The variation of the design variables in the Pareto solution (population size = 50)

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Fig. 12

The performance of the CPVT collector in the selected optimized system

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Fig. 13

The monthly loads on the DW and the VCC

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Fig. 19

TC of the proposed system versus conventional VCC

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Fig. 20

The hybrid A/C cost breakdown

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Fig. 14

Monthly sensible and latent loads of the standalone VCC

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Fig. 15

Hourly space conditions during the cooling season (solar hybrid A/C)

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Fig. 16

Hourly space conditions during the cooling season (standalone VCC)

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Fig. 17

The hourly electrical performance of the complete solar hybrid A/C

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Fig. 18

The hourly thermal performance of the complete solar hybrid A/C

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Fig. 4

DW and EW outlet temperatures

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Fig. 5

DW and EW outlet humidity ratios

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Fig. 6

Evaporator cooling capacity

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Fig. 7

Compressor electricity consumption



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