Optimal self-scheduling of a wind power producer in energy and ancillary services markets using a multi-stage stochastic programming

Abstract

Wind power is expected to deliver a significant part of power generation in future smart grid. However, many economic challenges have arisen from the intermittent nature of wind power. In this paper, a multi-stage stochastic model is proposed for self-scheduling problem of Wind Power Producers (WPPs) in competitive electricity markets. The proposed model includes three trading levels namely; forward, day-ahead, and balancing sessions. The problem uncertainties, such as wind power, market prices and quantity of activated reserve by ISO are considered by the Monte Carlo method. Moreover, Conditional Value-at-Risk (CVaR) is employed in the model as an appropriate risk measuring technique. The proposed model yields the optimal behavior of WPPs to participate in day-ahead energy and ancillary services markets (i.e. spinning reserve and regulation). Simulation results indicate that simultaneous participation of the WPPs in the mentioned markets not only augments their profit but also can significantly decrease the associated risks.