The techno-economic and environmental analysis of genetic algorithm (GA) optimized cold thermal energy storage (CTES) for air-conditioning applications

Thermal energy storage can be employed for air conditioning system load management, i.e., load shifting and leveling, to serve the peak electricity demand for the air-conditioning system with high capacity utilization. Ice and phase change material-based thermal energy storage systems were modeled and optimized for air-conditioning applications. The mathematical modeling involved energy, exergy, environmental and economic analysis of both the systems at full and partial operating modes. The system is then optimized for a commercial building to give maximized exergy efficiency and minimized total annual investment and operating cost over five different system temperatures as decision variables. The full operating mode strategy resulted in a higher exergy efficiency for both systems, whereas partial operating mode proved to be a more economical operating strategy. The multi-objective genetic algorithm-based optimization is carried out with two different refrigerants (R134a and R717) in the vapor compression refrigeration cycle of the systems. A single system design point is then selected using a multi-criteria decision-making technique. The electricity consumption while utilizing the thermal energy storage based system was lower as compared to the conventional system for air-conditioning applications. The two modeled systems are compared based on storage media, operating strategies, and the refrigerant used.

Automated summary

Thermal energy storage systems for air-conditioning applications have been modeled and optimized, with analysis conducted on energy, exergy, environmental, and economic aspects in both full and partial operating modes. Optimization results showed that full operating mode led to higher exergy efficiency for both systems, while partial operating mode may be a more cost-effective operational strategy.