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

Abstract

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.