Identification of Optimal Parameters for a Small-Scale Compressed-Air Energy Storage System Using Real Coded Genetic Algorithm

Compressed-Air energy storage (CAES) is a well-established technology for storing the excess of electricity produced by and available on the power grid during off-peak hours. A drawback of the existing technique relates to the need to burn some fuel in the discharge phase. Sometimes, the design parameters used for the simulation of the new technique are randomly chosen, making their actual construction difficult or impossible. That is why, in this paper, a small-scale CAES without fossil fuel is proposed, analyzed, and optimized to identify the set of its optimal design parameters maximizing its performances. The performance of the system is investigated by global exergy efficiency obtained from energy and exergy analyses methods and used as an objective function for the optimization process. A modified Real Coded Genetic Algorithm (RCGA) is used to maximize the global exergy efficiency depending on thirteen design parameters. The results of the optimization indicate that corresponding to the optimum operating point, the consumed compressor electric energy is 103.83kWh and the electric energy output is 25.82kWh for the system charging and discharging times of about 8.7 and 2 h, respectively. To this same optimum operating point, a global exergy efficiency of 24.87% is achieved. Moreover, if the heat removed during the compression phase is accounted for in system efficiency evaluation based on the First Law of Thermodynamics, an optimal round-trip efficiency of 79.07%can be achieved. By systematically analyzing the variation of all design parameters during evolution in the optimization process, we conclude that the pneumatic motor mass flow rate can be set as constant and equal to its smallest possible value. Finally, a sensitivity analysis performed with the remaining parameters for the change in the global exergy efficiency shows the impact of each of these parameters