Stochastic Model Predictive Control-based Real-time Operation of a Transmission Constrained Joint Wind-PHS System

Trading wind energy in deregulated markets is a challenging task due to uncertainties involved. To cope with this complication, a significant body of work is devoted to the development of day-ahead bidding strategies based on stochastic programming. However, the problem of real-time operation, which can be defined as the management of the system in balancing markets after day-ahead bidding phase is completed, is not studied well in the literature. Motivated by this fact, in the present work, a stochastic model predictive control (SMPC) based real-time operation method is developed for a transmission-constrained joint wind-PHS system. It is assumed that the generation company participates in the day-ahead market and balancing market as a price taker player. Since real-time operation depends on contracts made a priori, day-ahead bidding is also considered as an integral part and modeled as mixed-integer linear programming (MILP) based stochastic program. Main features of the proposed framework, which distinguish it from the previous studies, are the application of an SMPC strategy for real-time operation and inclusion of transmission constraints in bidding and operation phases.