NUMERICAL MODELING AND DESIGN OPTIMIZATION OF A MICRO-CAES SYSTEM

With the increasing demand for electrical energy storage to balance intermittent renewable energy generation, there is a growing interest in efficient and cost-effective storage technologies.Compressed Air Energy Storage (CAES) has emerged as a promising solution due to its long lifetime, low environmental impact and mature technology base.This paper presents the modelling and the optimization of a micro-scale Adiabatic CAES system.Accurately modelling the time-variant behaviour and off-design performance of various components is necessary to estimate the system's performance properly and, consequently, to optimize the design.A model of a micro-CAES was developed using the open-source, object-oriented, software OpenModelica.The storage system is coupled with a PV energy source and an energy user.The model simulates plant behaviour over a year to properly account for the seasonal variations.The model is employed to study the influence of the size of key components on system performance in order to enhance its efficiency through optimized component sizing.The optimization is performed using an external algorithm that guides and steers the optimization process.Six main parameters are selected as design variables such as compressor and expander size, volume and maximum bared pressure of the air storage, TES size, and photovoltaic installed power while round-trip efficiency and energy coverage are selected as a metric for optimization.The sensitivity analysis has shown that the mass of oil exhibits the least influence on system performance among the parameters examined, suggesting its exclusion from the optimization procedure.Through optimization, parameters enabling the achievement of maximal values for each variable have been identified.Additionally, it is shown the utility of the surrogate model developed during optimization for expedited evaluation of system performance throughout the design spectrum.