Optimization of Liquid Air Energy Storage (LAES) using a Genetic Algorithm (GA)

Renewable energy sources have a growing share in the energy market due to the threat from climate change, which is caused by emissions from fossil fuels. A future energy scenario that is likely to be realized is distributed energy systems (DES), where renewable energy sources play an increasing role. Energy storage technologies must be adopted to achieve these two expectations. Liquid Air Energy Storage (LAES), is a cryogenic technology that is discussed in this paper. Two cases are considered in this work to represent different operating modes for the LAES process: with and without an extra amount of hot oil in the discharging process. The performance of the LAES system will be analyzed with different number of compression stages and expansion stages in each mode. A Genetic Algorithm (GA) is used to optimize the LAES process. The round-trip efficiency is 63.1 % after flowsheet improvement and optimization.