Soft computing optimization of a renewable energy-integrated multigeneration system with liquid air energy storage

The presented study provides the results of a comprehensive assessment of a multigeneration system integrating renewable energy with liquid air energy storage systems, through exergoeconomic and exergoenvironmental evaluations. Applying soft-computing techniques for optimization powered by artificial neural networks, the research aims to improve the introduced configuration's efficiency, economic feasibility, and environmental sustainability. The system aims to generate power, desalinated water, heating/cooling loads, and other products to leverage the synergies between renewable energy contributions and the advanced Liquid air energy storage (LAES) for energy storage. From the exergoenvironmental evaluation, the sustainability index for energy storage facilities, desalination systems, and multigeneration systems is 1.92, 1.43, and 1.88, respectively. The obtained outcomes from the technical analysis indicate that the exergetic term of the round-trip efficiency and exergy destruction are 61.11 % and 15.59 MW, respectively. The optimized values for the levelized costs of hydrogen and water from exergoeconomic analysis are 1.52 and 5.22. The obtained findings from the optimization process revealed that the produced hydrogen and freshwater can exceed 6.49 ×10⁷m³ and 7.59 ×10⁴ m³ per year. Besides, the optimum working condition pushes the system toward 74.75 % exergetic round-trip efficiency and a 0.48 US$/kWh levelized cost of production.