A comprehensive study and multi-criteria optimization of a novel sub-critical liquid air energy storage (SC-LAES). (15th April 2022)

• Record Type: Journal Article
• Authors: Bashiri Mousavi, Shadi; Nabat, Mohammad Hossein; Razmi, Amir Reza; Ahmadi, Pouria
• Is Part Of: Energy conversion and management. Volume 258(2022)
• Publication Date: 2022-04-15
• Subjects: Compressed Air Energy Storage -- Liquid Air Energy Storage -- Artificial Neural Network -- Multi-Objective Optimization -- 5E Analyses; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2022.115549
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Introducing a sub-critical liquid air energy storage and performing a comprehensive analyses. Multi-objective optimization based on a combination of artificial neural network and genetic algorithm. A fundamental comparison of the proposed system with the conventional technologies. The round trip energy and exergy efficiencies of 77.1% and 68.8% are achieved. Achieving a payback period of 1.8 years with total profit of $ 151 M for the California state. Abstract: Liquid air energy storage, as a bulk-scale energy storage technology, has recently attracted much attention for the development and sustainability of smart grids. In the present study, a sub-critical liquid air energy storage system is designed and comprehensively investigated in terms of energy, exergy, environmental, economic, and exergoeconomic. The objective of this design is to minimize the number of equipment in the liquid air energy storage to reduce its structural complexity, increase its applications, improve the performance, and recover waste cold exergy at the liquefied natural gas regasification terminals. Moreover, a fundamental comparison is performed between five storage systems including the proposed system, the conventional liquid air energy storage, and compressed air energy storage by single-stage and two-stage expansion with reheating to highlight the specific characteristics of the proposed system. In addition, using a combination of the artificial neural network and genetic algorithm, optimal design conditions in terms of thermodynamic and economic performance are presented. The proposed system presents a remarkable performance from various points of view compared to other alternatives by storing 27.20 MWh of excess grid power as liquid air during off-peak periods and exploiting it to generate a power of 182.70 MWh at peak demand times. The results show that the electrical efficiency, round trip efficiency, and exergetic round trip efficiency of the system are 563.80%, 77.10%, and 68.80%, respectively. The payback period and net profit of the system in the states of California, USA are around 1.8 years and $ 151 M. At the optimum condition, the round trip efficiency and TOTAL cost rate are 68% and 461 $/h respectively.