Multi-Objective Optimization of a Solar Combined Power Generation and Multi-Cooling System Using CO2 as a Refrigerant

This paper proposes a new combined multi-cooling and power generation system (CMCP) driven by solar energy. Carbon dioxide is used as a refrigerant. A parabolic trough collector (PTC) is employed to collect solar radiation and convert it into thermal energy. The system includes a supercritical CO2 p...

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Bibliographic Details
Published in:Energies (Basel) 2023-02, Vol.16 (4), p.1585
Main Authors: Hammemi, Rania, Elakhdar, Mouna, Tashtoush, Bourhan, Nehdi, Ezzedine
Format: Article
Language:English
Subjects:
Alternative energy sources
Capital costs
Carbon dioxide
CO2 power cycle
combined system
Cooling
Cooling systems
Design parameters
ejector
Ejectors
Emissions
Energy consumption
Energy efficiency
Energy resources
exergoeconomic analysis
Exergy
Gases
Genetic algorithms
Heat
Heat transfer
Heating
Industrial plant emissions
Mathematical models
multi-cooling
multi-objective optimization
Multiple objective analysis
One dimensional models
Optimization
Payback periods
Refrigerants
Refrigeration
Renewable resources
Simulation
Software
Solar collectors
Solar energy
Solar radiation
Thermal energy
Weather
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Summary:This paper proposes a new combined multi-cooling and power generation system (CMCP) driven by solar energy. Carbon dioxide is used as a refrigerant. A parabolic trough collector (PTC) is employed to collect solar radiation and convert it into thermal energy. The system includes a supercritical CO2 power system for power production and an ejector refrigeration system with two ejectors to provide cooling at two different evaporating temperatures. The CMCP system is simulated hourly with weather conditions for Tunisia. The PTC mathematical model is used to calculate the heat transfer fluid outlet temperature and the performance of the CMCP system on a specific day of the year. A 1D model of an ejector with a constant area is adopted to evaluate the ejector performance. The system’s performance is evaluated by an energetic and exergetic analysis. The importance of the system’s components is determined by an exergoeconomic analysis. The system is modeled using MATLAB software. A genetic algorithm is used for multi-objective optimization to determine the best values and solutions for the system’s design parameters. The optimal energy and exergy efficiencies were found to be 13.7 percent and 37.55 percent, respectively, and the total product unit cost was 31.15 USD/GJ.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16041585