Reliability and reserve in day ahead joint energy and reserve market stochastic scheduling in presence of compressed air energy storage

摘要

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.