Thermo-economic multi-objective optimization of the liquid air energy storage system

Abstract

Liquid Air Energy Storage (LAES) is a promising energy storage technology for large-scale application in future energy systems with a higher renewable penetration. However, most studies focused on the thermodynamic analysis of LAES, few studies on thermo-economic optimization of LAES have been reported so far. Thus, this work focuses on the multi-objective optimization of LAES by using non-dominated sorting genetic algorithm (NSGA-II), taking the round trip efficiency (RTE) and economic indicators as the optimization objectives. Three major results were obtained from this work. First, the NSGA-II-based optimization framework is able to determine the optimal design and operational parameters of LAES under different configurations and scenarios, including the optimal charging and discharging pressure, heat transfer areas, and mass flow rates of hot and cold storage media etc. It is shown that the increase of energy efficiency by 9 % ~ 14 % and a decrease of exergy destruction by 16 % were seen. Secondly, the design and operational guidelines of LAES can be derived from the optimization. The optimal charging pressure was found to vary between 13 and 15 MPa, and the optimal discharging pressure is between 9 and 12 MPa, depending on the machines' efficiencies and pinch points of heat exchangers of a LAES system. Finally, the Pareto Fronts of capital expenditure, energy efficiency and the occupied space energy density of a LAES system provide system operators good operational and investment advice. Specifically, the RTE of the LAES increasing by 1 % is at the cost of increasing the capital cost by 0.5–1 %. In this study, if the investment budget is over 48 M£, a LAES system with three-stage compressors and four-stage turbines presents better trade-off between RTE and investment cost than the three-stage and four-stage systems. Overall, this work provides, for the first time, an NSGA-II-based optimization framework capable of determining the optimal design and operational parameters of LAES, informing the stakeholders of the design guidelines and investment guidance under different scenarios.