## 1 Introduction  
The emerging trend of wind power integration into bulk power systems has raised great concern worldwide over the past decade [1]. Although wind generation is regarded as an eco-friendly energy resource with zero marginal cost, the uncertain and intermittent nature of wind power has posed grand challenges to utility operators in terms of keeping the system reliable and cost-effective [2]. More specifically, the network under high wind penetration is prone to congestion issues, i.e., large fluctuations of line flows due to inherent wind power uncertainty, making it difficult to promote the deployment of wind energy in the grid. The undesired consequences of congestion such as generation reserve undeliverability, wind curtailment, and load shedding would add extra operational costs, diminish security margins, or even further demoralize investment in the wind industry. Therefore, there is a need for methods that allow an effective utilization of the increasing wind generation when faced with transmission congestion, as well as other technical limits of thermal generators (e.g., capacity limits, ramping limits). The existing strategies that are applied to alleviate the uncertainty-induced congestion can be categorized into two groups: improving the accuracy of wind power forecasting as well as modeling on the wind farm side, and providing more operational flexibility from the power system side. It is generally acknowledged that forecast accuracy deteriorates as the time horizon grows. Specifically, the error for a day-ahead forecast can vary from 15-25% using the current NWP-based approach [3; 4], and this value could be even higher during the period of extreme events such as unexpected wind power ramps [5]. The forecast uncertainty is taken into account by modeling the forecast errors statistically with various types of distributions (e.g., Gaussian [6], Weibull [7] or Beta distributions [8]). More deep research has found that forecast errors are conditional to the given point forecasts, and thus, the error distribution should be versatile with respect to different forecast levels [8]. In [9], copula theory is employed to depict the spatial correlation of forecast errors among different wind farms in a neighboring area. [PERSON] et al. [10] argue that half of the extra cost due to the inaccuracy of forecast could be saved if forecast errors are properly considered in a liberalized electricity market. As for the latter category, we seek to come up with solutions in a systematic manner relying on either infrastructural supplements (e.g., energy storage systems (ESS) [11], demand response (DR) resources [12], and flexible alternating current (AC) transmission systems (FACTS) [13]), or enhancement of utilization of the existing infrastructure and methodology framework. The formal effort is straightforward and effective in mitigating wind power uncertainty; however, it requires a huge amount of capital investment, as well as other operation and maintenance costs relating to the equipment [14]. To circumvent such a problem, a less expensive approach to congestion management is achieved by modifying the network topology with transmission switching [15]. In [16], different types of DR programs are explored to evaluate their impacts on the reliability of the wind integrated power system. Scheduling adequate generation spinning reserves is the most commonly-used way of providing the required flexibility to hedge against the risk of unexpected real-time branch congestion [17]. Then, the question of how to quantify the reserve capacity to avoid assuming a fixed reserve rate arises naturally, as in the traditional deterministic framework [18]. Reference [19] presents a dynamic reserve scheduling approach on an hourly basis according to the realistic wind power conditions, and the results are compared with the static reserve method.