Stochastic programming-based optimal bidding of compressed air energy storage with wind and thermal generation units in energy and reserve markets

Abstract

One effective way to compensate for uncertainties is the use and management of energy storage. Therefore, a new method based on stochastic programming (SP) is proposed here, for optimal bidding of a generating company (GenCo) owning a compressed air energy storage (CAES) along with wind and thermal units to maximize profits. This scheduling has been presented for the GenCo's participation in day-ahead energy and spinning reserve (SR) markets and CVaR is also considered as a risk-controlling index. Firstly, the obtained results are validated by comparing with those of two previous studies. Then, the complete results of the proposed method are presented on a real power system, which indicate the capability of SP in scheduling CAES units. Furthermore, it is observed that CAES units can gain greater profits in joint energy and reserve markets due to their high ramp rates. In addition, the value of stochastic solution (VSS) is used to quantify the advantage of the stochastic method over a deterministic one, which illustrates the advantage of SP-based optimal bidding method especially for CAES and wind units and also for risk-averse GenCos. Overall, it is concluded that the stochastic method is efficient for optimal-bidding of GenCos owning CAES and wind units.