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Research Papers

Scenario-Based Multi-Objective Optimization of an Air-Based Building-Integrated Photovoltaic/Thermal System

[+] Author and Article Information
Mahsa Khaki, Amin Shahsavar

Department of Mechanical Engineering,
Kermanshah University of Technology,
Kermanshah 6715685420, Iran

Shoaib Khanmohammadi

Department of Mechanical Engineering,
Kermanshah University of Technology, Kermanshah 6715685420, Iran e-mail: sh.khanmohammadi@kut.ac.ir

1Corresponding author.

Manuscript received April 8, 2017; final manuscript received September 6, 2017; published online October 17, 2017. Assoc. Editor: Jorge Gonzalez.

J. Sol. Energy Eng 140(1), 011003 (Oct 17, 2017) (13 pages) Paper No: SOL-17-1130; doi: 10.1115/1.4038050 History: Received April 08, 2017; Revised September 06, 2017

In this paper, a genetic algorithm-based multi-objective optimization of a building-integrated photovoltaic/thermal (BIPV/T) system is carried out to find the best system configurations which lead to maximum energetic and exergetic performances for Kermanshah, Iran climatic condition. In the proposed BIPV/T system, the cooling potential of ventilation and exhaust airs are used in buildings for cooling the PV panels and also heating the ventilation air by heat rejection of PV panels. Four scenarios with various criteria in the form of system efficiencies and useful outputs are considered to reflect all possible useful outputs in the optimization procedure. This study models a glazed BIPV/T system with various collector areas (Apv=10,15,25,and30m2) and different length to width ratio (L/W=0.5,1,1.5,and2) to determine the optimum air mass flow rate, bottom heat loss coefficient, depth of the channel as well as the optimum depth of the air gap between PV panel and glass cover that maximize two defined objective functions in different scenarios. Results showed that using fourth scenario (with the annual total useful thermal and electrical outputs as objective functions) and first scenario (with the annual average first- and second-law efficiencies as objective functions) for optimizing the proposed BIPV/T system leads to the highest amount of useful thermal and overall outputs, respectively. Moreover, it was concluded that, if the electrical output of the system is more important than the thermal output, the first scenario gives better results.

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Figures

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Fig. 1

View of the proposed BIPV/T system integrated into the roof of a building

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Fig. 2

Schematic of the studied BIPV/T system

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Fig. 3

Heat transfer coefficients along the surfaces of the studied BIPV/T system

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Fig. 4

Comparison between the results obtained from this study and experimental results [31]

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Fig. 5

Decision variables mapping form Rp space to Rq the space of objective functions

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Fig. 6

Genetic algorithm flowchart

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Fig. 7

Pareto front for first scenario in the case of a BIPV/T system with Apv=10m2 and various L/W ratios

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Fig. 8

Pareto front for second scenario in the case of a BIPV/T system with Apv=10m2 and various L/W ratios

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Fig. 9

Pareto front for third scenario in the case of a BIPV/T system with Apv=10m2 and various L/W ratios

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Fig. 10

Pareto front for fourth scenario in the case of a BIPV/T system with Apv=10m2 and various L/W ratios

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