Compressed air energy storages (CAESs) have many advantages in the utility level as an emerging technology and thus, they can be taken into account in energy and reserve markets. One of the most important features of CAES is its fast response ability, which makes it an attractive option to alleviate the uncertainties of renewable energy resources (RERs) and demands. This paper proposes a two-stage mathematical optimization model for optimally day operation of generation units as well as CAESs in energy and reserve market in a stochastic way. The features of the presented reserve model of CAESs are as follows: (a) considering two constraints in order to model the CAES reserve for providing capability at every hour through six operation modes; (b) considering the limitations related to the state of charge (SOC) in the CAESs. Moreover, the generator reliability is not generally considered during scheduling thermal generation units. Therefore, a model to take into account generator reliability in the scheduling problem is presented by this paper. Regarding the stochastic behavior of some variables in the power system such as demands and RERs, this paper presents a stochastic optimal operation model on the basis of information gap decision theory (IGDT) together with risk averse (RA) strategy in order to overcome this information gap and to help independent system operator (ISO). As demand response program (DRP), the curtailed demand is considered for enhancing the market flexibility. The proposed model is formulated as a mixed integer nonlinear problem (MINLP), which is solved by CPLEX solver of the GAMS software. Employing the presented model in the 6-bus test system shows the efficacy of the proposed model. Simulation results show that considering restrictions on reserve deliverability across multiple hours lessens the total reserve by 21.34 MW and increases the operation cost by $434.54. Moreover, considering the reliability index rises total operation costs by almost 10%.

压缩空气储能 (CAES) 作为一项新兴技术在公用事业层面具有许多优势，因此可以在能源和储备市场中加以考虑。CAES 最重要的特性之一是其快速响应能力，这使其成为缓解可再生能源 (RER) 和需求不确定性的有吸引力的选择。本文提出了一种两阶段数学优化模型，用于以随机方式优化发电机组以及能源和储备市场中的 CAES 的日间运行。所提出的 CAES 储备模型的特点如下： (a) 考虑两个约束，以便对 CAES 储备建模，以便通过六种操作模式每小时提供能力；(b) 考虑与 CAES 中充电状态 (SOC) 相关的限制。而且，在调度火电机组时一般不考虑发电机的可靠性。因此，本文提出了一个在调度问题中考虑发电机可靠性的模型。针对电力系统中一些变量如需求和RERs的随机行为，本文提出了基于信息差距决策理论（IGDT）和风险规避（RA）策略的随机最优运行模型，以克服这些信息差距并帮助独立系统运营商 (ISO)。作为需求响应计划（DRP），减少的需求被认为是为了提高市场的灵活性。所提出的模型被表述为混合整数非线性问题 (MINLP)，该问题由 GAMS 软件的 CPLEX 求解器求解。在 6 总线测试系统中使用所提出的模型显示了所提出模型的有效性。模拟结果表明，考虑对多个小时的储备可交付性的限制，总储备减少了 21.34 MW，并增加了 434.54 美元的运营成本。此外，考虑到可靠性指标，总运营成本增加了近 10%。