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