Demand response resources (DRRs) have been recently introduced as one of the most economic tools of congestion alleviation in power systems. Nevertheless, the severe uncertainty of multiple DRRs constituting a demand response firm (DRF) is an indispensable issue to be considered for the resilient operation of future power systems. Consequently, the information gap decision theory (IGDT) technique is utilized for addressing the uncertainty of consumers’ participation in demand response programs. This article presents a novel hybrid scenario-IGDT-based framework, designated as SIGDT, for corrective transmission congestion management (CM) in the presence of large-scale uncertain wind farms and DRFs as well as the uncertainty of conventional generating units. A reliability network modeling of a repairable N-component wind turbine (WT) is presented, considering failure and repair rates of turbines’ components, and then the uncertainty of wind farms’ generation is handled using the scenario-based approach. The proposed framework is applied to the IEEE-reliability test system (RTS) system to demonstrate its accuracy and capability. The results discuss the impact of failure and repair rates of WTs components on the proposed SIGDT-based CM and emphasize the application of the proposed framework for decision-makers to ensure the optimal operation of power systems under uncertainties of DRFs, wind farms, and conventional units.