Thermodynamic optimization with multi objectives and parameters for liquid air energy storage system based on the particle swarm optimization (PSO)

Liquid air energy storage is a promising large-scale energy storage technology for the grid with the increasing penetration of renewable energy. However, most of the previous researches focusing on the thermodynamic optimization relied on different kinds of simulation software, which were commonly single-objective and singleparameter, and needed complex manual test. Thus, an intelligent optimization algorithm based on the particle swarm optimization (PSO) was proposed to achieve the optimal system performance. The thermodynamic model was developed based on MATLAB, and the round-trip efficiency (RTE) and exergy efficiency were selected as performance evaluation indexes. The adiabatic efficiency of the compressor and expander and the ambient temperature were set as optimization variables to obtain the optimal combination of multi parameters, especially the compression and expansion pressures. The comparison between the optimization results and that derived from a traditional simulation software demonstrates that the algorithm has high reliability and adaptability for the multi-parameter and multi-objective optimization of the liquid air energy storage system. The optimal RTE and exergy efficiency of the system are 0.6220 and 0.6576, respectively, and the corresponding compression pressure and expansion pressure are 8.6 MPa and 7.4 MPa, respectively.

Automated summary

Liquid air energy storage is a promising large-scale energy storage technology, and research shows that the intelligent optimization algorithm based on PSO has high reliability and adaptability for multi-parameter and multi-objective optimization.