Spinning reserve stochastic model of compressed air energy storage in day-ahead joint energy and reserve market using information gap decision theory method

Abstract

Regarding numerous benefits of the compressed air energy storage (CAES) in the utility level, these devices 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 and demands. This paper proposes a two-stage mathematical optimization model for optimally day ahead operation of generation units as well as CAESs in energy and reserve markets in a stochastic way. The features of presented reserve model of CAESs are as follows: (a) considering two constraints in order to model the reserve of CAES for providing capability at each hour by six operation modes; (b) considering the limitations related to the state of charge in the CAESs. It is clear that there is an intrinsic deviation between predicted and actual uncertainty variables in a power system. This paper presents a stochastic optimal operation model on the basis of information gap decision theory together with risk averse strategy in order to overcome this information gap and to help independent system operator. As demand response program, the curtailed demand is considered for enhancing the market flexibility. The proposed model is formulated as a mixed-integer nonlinear problem, which is solved by CPLEX solver of the GAMS software. Employing the presented model in the 6-bus test system demonstrates the efficacy of the proposed model. Simulation results show that considering restrictions on reserve deliverability across multiple hours, lessens the total reserve by 22.75 MW and increases the operation cost by $438.26.