Comprehensive techno-economic assessment and tri-objective optimization of an innovative integration of compressed air energy storage system and solid oxide fuel cell

The growing integration of renewable energy into the power system causes the electricity profile to differ from the power demand. This results in a deficit of electricity at peak hours and excess electricity during off-peak hours. Accordingly, the purpose of this research is to investigate the concept of peak shaving storing energy at a cheap cost in off-peak hours and using it at a high cost for peak hours. The present study proposes a novel storage configuration for electricity generation by combining a solid oxide fuel cell (SOFC), compressed air energy storage (CAES), and a water desalination unit. The objective is to deal with power failures and interruptions in power grids that have a high level of renewable resource penetration while reducing the emissions produced by CAES systems. The suggested system also combines a gas turbine and a fuel cell to create electrical energy during discharge, increasing efficiency and lowering pollutants. An economic, environmental, and thermodynamic analysis of the proposed system's performance is conducted. The suggested system is then optimized using the gray wolf algorithm to determine the optimal way to balance thermodynamic performance with economic and environmental factors. Lastly, it is determined that at the TOPSIS point, this system's exergy round trip efficiency is 71.03%, its total cost is 34.07 $/h, and its pollution rate is 0.184 kg/kWh.

Automated summary

This study investigates the concept of peak shaving by storing energy at a low cost during off-peak hours and using it during peak hours. A novel storage configuration combining solid oxide fuel cell (SOFC), compressed air energy storage (CAES), and water desalination unit is proposed. The system aims to address power failures and reduce emissions while increasing efficiency. An economic, environmental, and thermodynamic analysis is conducted, and the system is optimized using the gray wolf algorithm.