Compressed air energy storage (CAES) is a large-scale energy storage system with long-term capacity for utility applications. This study evaluates different business models' economic feasibility of CAES pre-selected reservoir case studies. It assesses several scenarios for each case study and analyzes two business models: one for the storage of excess renewable energy sources (RES) and another for energy arbitrage. The novelty of this work is pe. Compressed air energy storage (CAES) is a large-scale energy storage system with long-term capacity for utility applications. This study evaluates different business models' economic feasibility of CAES pre-selected reservoir case studies. It assesses several scenarios for each case study and analyzes two business models: one for the storage of excess renewable energy sources (RES) and another for energy arbitrage. The novelty of this work is performing the economic investment assessment using a Monte Carlo Simulation (MCS) methodology applied to CAES, considering the uncertainties associated with such types of projects and evaluating different business models for the technology.The results suggest a better performance from the CAES RES business model than the CAES arbitrage business model. Furthermore, the diabatic CAES assessed scenarios seem to have more attractive results than their equivalent adiabatic CAES systems in the CAES RES business model. However, adiabatic CAES can be economically feasible in both business models. In addition, it was observed that CAES is viable in specific scenarios and can be profitable for the storage of energy from RES, facilitating the management of their variability, decreasing their dependence on weather, and helping their integration into the grid. However, CAES does not seem a good fit for grid energy arbitrage in the. ••Assessment of different business models for Compressed Air Energy Storage (CAES).••CAES feasibility for renewable energies integration is higher than for arbitrage.••Adiabatic CAES viability in different business models.••Underlines CAES's importance as a feasible energy storage solution for RES.Compressed air energy storageEconomic analysisBusiness modelsMonte Carlo simulationThe decarbonization of world economies relies on several pillars from which the increase of energy production from clean sources such as renewable energy sources (RES) plays a key role. However, RES can be a challenge for the energy grids in balancing supply and demand or power adequacy. Thus, energy storage is one of the possible solutions for those challenges and is an essential component of future energy grids.Compressed air energy storage (CAES) is one of the few large-scale energy storage technologies that support grid applications having the ability to store tens or hundreds of MW of power capacity, which may be used to store excess energy from RES, according to.In a CAES plant, when power is abundant and demand is low, the off-peak power from the grid or the electricity generated from RES is used to compress ambient air. This compressed air is stored under pressure in underground geological reservoirs (for large-scale CAES) or at surface reservoirs such as tanks or pipes (for small-scale CAES). Later, when power demand requirements are high, the pressurized air is released back up to the surface, heated, and expanded, rushing through a turbine and driving a generator to produce electricity,. For large-scale CAES, the underground reservoirs are geolo. The methodology used for conducting the business models' economic assessment of the pre-selected CAES case studies is based on the evaluation of financial and investment projects and is adapted to the particular case of CAES projects in Portugal.The methodology for the CAES scenarios is divided into steps (Fig. 3). The first step is setting all the CAES assumptions, costs, and revenues. Step two establishes probable scenarios for each of the two case studies. Moreover, the first and second steps of the adopted methodology are intrinsically linked and happen simultaneously. In step three, the stochastic analysis of every scenario and uncertain inputs through Monte Carlo Simulation (MCS) is conducted based on a discounted cash flow (DCF) approach. Finally, the results of this stochastic analysis are evaluated in step four, calculating the financial indicators for every probable scenario assumed, including case studies and business models.A DCF approach estimates an investment's value using its expected future cash flows. DCF is equal to the sum of the cash flow in each period divided by one plus the discount rate or cost of capital raised to the power of the period number. Therefore, DCF is represented by Eq. (1).(1)DCF=(CF/(1+k)1)+(CF/(1+k)2)+(CF/(1+k)3)+. +(CF/(1+k)t)where CF is t.