A multi-objective analysis for enhanced energy and exergy performances of an integrated compressed air energy storage system using the meta-heuristic whale optimization algorithm

This study investigates the optimization of energy and exergy efficiencies in a compressed air energy storage integrated energy system using the meta-heuristic whale optimization algorithm. The analysis focuses on the effects of key operating parameters, including current density, utilization factor, and temperature, on the system's performance. The whale optimization algorithm identifies seven multi-objective optimum points, with processing conditions ranging from a current density of 3000 A/m2 to 5237 A/m2, utilization factors between 0.740 and 0.747, and temperatures from 700 °C to 900 °C. Desirability analysis reveals that some points, with a desirability value of 1, are the most optimal. Among these, one point characterized by a utilization factor of 0.747, a current density of 3000 A/m2, and a temperature of 700 °C, is identified as the optimal point. Under these conditions, the system achieves an energy efficiency of 66.63 % and an exergy efficiency of 34.85 %. The study highlights the significant potential of the meta-heuristic whale optimization algorithm in navigating complex search spaces to identify optimal processing conditions. The results underscore the importance of balancing electrical and thermal efficiencies to achieve overall system optimization using meta-heuristic optimization algorithms.