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In this paper, a two-stage stochastic day-ahead (DA) scheduling model isproposed incorporating wind power units and compressed air energy storage(CAES) to clear a co-optimized energy and reserve market. The two-stagestochastic programming method is employed to deal with the wind powergeneration uncertain nature. A linearized AC optimal power flow (LAC-OPF)approach with consideration of network losses, reactive power, and voltagemagnitude constraints is utilized in the proposed two-stage stochastic DAscheduling model. Using an engineering insight, a two-level LAC-OPF(TL-LAC-OPF) approach is proposed to (i) reduce the number of binary variablesof the LAC-OPF approach which decreases the computational burden, and (ii)obtain LAC-OPF pre-defined parameters adaptively so that the accuracy ofLAC-OPF approach is increased as a result of reducing artificial losses.Furthermore, as the CAES efficiency depends on its thermodynamic andoperational conditions, the proposed two-stage stochastic DA scheduling modelis developed by considering its thermodynamic characteristics to obtain a morerealistic market decision at the first place. The proposed model is applied toIEEE 30-bus and 57-bus test systems using GAMS software, and is compared withthree traditional approaches, i.e., AC-OPF, DC-OPF, and LAC-OPF. Simulationresults demonstrate effectiveness of the proposed methodology.

Stochastic AC Network-constrained Scheduling of CAES and Wind Power Generation in Joint Energy and reserve market: Toward More Realistic Results