## 1 Introduction  
Wind power, which is clean and renewable, has been integrated with the grid on a large scale to replace fossil fuels [1; 2]. A lot of countries have set goals for high-penetration wind power levels in the grid to increase the wind power utilization. China aims to achieve an average wind power utilization rate of 95% in 2020. However, the randomness and intermittence of wind power have increased the tremendous challenges in wind energy utilization as well as the power system operators and planners [3; 4]. The traditional dispatching operating mode of a power system, designed to address limited uncertainty in the system, encounters challenges in accommodating the high penetration of large-scale wind power. Thus, new methods should be developed to cope with the situation [5]. The chance-constrained optimization technique has been adopted to manage the problems involved stochastic variables in some previous studies [6; 7]. In [7], the chance constraints in the optimization formulations guarantees that the failure probability of the EV charging plan fulfilling the driving requirement is below the predetermined confidence parameter. In [6], a chance constrained programing approach was presented to ensure desired confidence levels of meeting future stochastic power and natural gas demands while minimizing the investment cost. In the present study, the chance constraint is applied to ensure the probability of system imbalance caused by too little system reserve capacity is less than a certain probability. These types of constraints could not be inverted to obtain equivalent deterministic equalities. Thus, the method developed in [7] could not be directly applied here to solve our problem. In addition, the joint chance constrained problem in [6] are formulated through mixed-integer quadratic programming (MIQP), which is not efficient method to solve our problem related to not joint chance constraints. Therefore, we propose the sample average approximation (SAA) algorithm [8] to solve the problem. The approach can provide a solution that converges to the optimal one as the number of samples increases. During the Spring Festival, the load is expected to drop sharply which will decrease the usage of power. In 2018, to ensure the safe and stable operation of the power grid, Shandong Electric Power Company took measures to limit renewable energy output, which means that a large amount of wind power would be abandoned. However, Jiangsu Electric Power Company actively encourages enterprises to produce, which not only increases the load of 9000 MW for trough load periods but also promotes the consumption of renewable energy. In the first three days of the Spring Festival, a total of 72000 MWh of renewable energy consumption was added. In the smart grid, the load, as an important responsive resource [9; 10], is an excellent alternative for wind power consumption [11; 12]. Extensive research has been done to increase wind power utilization through the participation of responsive loads. In [13], a model for solving the combined effect of PEVs and wind power integration with a DR (demand response) program on static transmission network expansion planning was proposed, but the uncertainty of wind power has not been considered. A two-stage stochastic model which handling the forecast errors of renewable generations in a microgrid with responsive loads was implemented in [14], however, the objective of the model is only operation cost not including the renewable energy utilization. [PERSON] [15] investigated the potential of DR as an emerging alternative in systems with significant amounts of wind power. A comprehensive set of DR programs including tariff-based, incentive-based and combinational DR programs are considered in a stochastic network-constrained market clearing framework. [PERSON] [16] provided a stochastic method to conduct the optimal scheduling of the combination of wind power and energy storage with considering the DR program in the electricity market.