LAUSR

This article presents a contingency-based stochastic security-constrained unit commitment to address the integration of wind power producers to the joint energy and reserve markets. The model considers ancillary services as a solution to cope with the uncertainties of the problem. In this regard, a comprehensive model is considered that maintains the profit of supplementary services. The contingency ranking is a popular method for reducing the computation burden of the unit commitment problem, but performing the contingency analysis changes the high-impact events in previous ranking methods. This article employs an intelligent contingency ranking technique to address the above issue and to find the actual top-ranked outages based on the final solution. The proposed algorithm simultaneously clears the energy and reserve based on the mechanism of the day-ahead market. The main idea of this article is to develop a framework for considering the most effective outages in the presence of the uncertainty of wind power without a heavy computation burden. Also, energy storage systems are considered to evaluate the impact of the scheduling of storage under uncertainties. Also, an accelerated Benders decomposition technique is applied to solve the problem. Numerical results on a six-bus and the IEEE 118-bus test systems show the effectiveness of the proposed approach. Furthermore, it shows that utilizing both wind farms and storage devices will reduce the total operational cost of the system, while the intelligent contingency ranking analysis and enough reserves ensure the security of power supply.