Electrical power scheduling typically occurs in two stages: Day-Ahead (DA) planning and Real-Time (RT) balancing. In DA scheduling, generation and reserve capacities are determined for the following day, with reserves used in RT to balance power under variable operating conditions. Due to the increasing demand for electricity, power systems often operate near their stability margins, which raises the likelihood of equipment failures (contingencies). These uncertainties can lead to potential changes in network topology. The challenge becomes even more complex with the integration of Renewable Energy Sources (RESs), owing to their inherently uncertain nature. If these combined uncertainties are not explicitly accounted for during the scheduling process, there is a risk of network collapse. To address this, the paper presents a general formulation of the optimal power flow (OPF) problem under uncertainty. This problem is solved using both two-stage robust optimization and two-stage stochastic programming to model wind generation uncertainty. It is shown that two-stage stochastic optimization is more efficient and practical compared to two-stage robust optimization. Building on this, the paper incorporates line outage uncertainty alongside wind generation uncertainty and proposes a Security Constrained Stochastic Optimal Power Flow (SC-SOPF) method for scheduling generation and reserves. Furthermore, to avoid unintended relay tripping caused by load encroachment, the report proposes a minimal and cost-effective adjustment in generation settings from the base-case values. These adjustments ensure that, after a contingency, limited post-contingency generation rescheduling is sufficient to prevent unintended relay operations due to load encroachment or overloading.