A novel optimization strategy for designing cryogenic energy storage systems

Liquid Air Energy Storage offers several advantages over other energy storage systems, including high energy density, scalability, cost-competitiveness, and non-geographical constraints. This study develops a computational tool for optimizing such systems. A deterministic, non-linear mathematical mo...

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Bibliographic Details
Published in:Energy (Oxford) Vol. 332; p. 136490
Main Authors: Manassaldi, Juan I., Incer-Valverde, Jimena, Morosuk, Tatiana, Mussati, Miguel C., Mussati, Sergio F.
Format: Journal Article
Language:English
Published: Elsevier Ltd 30.09.2025
Subjects:
Adiabatic cryogenic energy storage
Cost
Exergy
NLP mathematical programming
Simultaneous optimization
Solution strategy
Simultaneous optimization
NLP mathematical programming
Solution strategy
Exergy
Cost
Adiabatic cryogenic energy storage
ISSN:0360-5442
Online Access:Get full text
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Summary:Liquid Air Energy Storage offers several advantages over other energy storage systems, including high energy density, scalability, cost-competitiveness, and non-geographical constraints. This study develops a computational tool for optimizing such systems. A deterministic, non-linear mathematical model was implemented in an object-oriented, equation-based programming language and solved using a derivative-based optimization algorithm. The model was verified using a reference case from the literature and successfully applied to solve three optimization objectives: maximizing round-trip efficiency (RTE), minimizing total exergy destruction, and minimizing total cost. Compared to the reference case, the optimization achieved simultaneous improvements, including nearly a 20 % higher RTE (42.3 %–50.6 %) and a 21 % increase in liquid air yield (from 0.6190 to 0.7481). Additionally, reduced heat transfer area requirements and overall cost reductions were realized. The detailed models developed for all process units, including the air liquefaction process, enable their combination or integration to investigate any adiabatic cryogenic energy system, supporting several optimization criteria such as efficiency or cost. This study marks a major advancement in mathematical modeling from a Process Systems Engineering perspective. It highlights the effectiveness of simultaneous optimization, gradient-based algorithms, and precise property estimation via dynamic-link libraries in enhancing the performance of cryogenic energy storage systems. •Derivative-based optimization is applied to LAES sizing and operation.•Detailed models and DLLs enhance LAES system optimization efficiency.•High-level modeling and DLLs enable large-scale LAES optimization.•Compared to a base case, optimization increased RTE by 20 %.
ISSN:0360-5442
DOI:10.1016/j.energy.2025.136490