Optimal Design of a Hybrid Liquid Air Energy Storage System Utilizing Waste Heat Recovery for Hydrogen and Power Production

Liquid air energy storage (LAES) provides a high volumetric energy density and overcomes geographical constraints more effectively than other extensive energy storage systems such as compressed air and pumped hydro storage. However, LAES faces challenges such as lower efficiency rates, restricted economic feasibility, and potential environmental impacts. Integrating and recovering waste heat to produce power and additional products (e.g., hydrogen and fresh water) can reduce the drawbacks of LAES systems. This study introduces a novel integrated LAES system combining a liquefied natural gas (LNG) vaporization unit, a solid oxide fuel cell process, the magnesium-chlorine thermochemical plant, and a Kalina thermal power cycle. During the period of low electricity demand, purified air using the power produced by wind turbines is pressurized and liquefied within the Linde–Hampson process. Liquid air and LNG after cold energy recovery during periods of high electricity demand are fed into gas turbines and fuel cell systems, respectively. The heat produced from the solid oxide fuel cell system is used to produce electricity and hydrogen within the power plants and thermochemical units. The round trip and exergy efficiencies of the hybrid LAES process are obtained at 67.98 and 65.25%, respectively. The economic investigation indicates that the prime cost of electricity during on-peak times, the return on investment, and net annual profit are 0.0771 US$/kWh, 3.579 years, and 4.884 MMUS$/yr, respectively. The results reveal that utilizing the fuel cell unit and Mg–Cl thermochemical process during on-peak times achieves a 62.81% reduction in carbon dioxide emissions compared to the base process. Due to the complexity of the proposed structure, an optimization framework based on machine learning and multiobjective optimization is employed to optimize thermodynamic and economic variables. Different decision criteria including TOPSIS, LINMAP, and fuzzy Bellman–Zadeh methods are utilized to identify the best operating states within the Pareto frontier. The variability in the target functions is evaluated using uncertainty quantification techniques through the Monte Carlo method.