中文摘要：

为解决风电功率不确定性对系统稳定运行带来的影响,在含风电的系统优化调度问题中引入需求响应和储能系统。首先利用区间法模拟风电场景并构建了基于Kantorovich距离的场景削减策略,然后分别在需求侧和发电侧引入需求响应和储能系统,结合2阶段优化理论,以风电日前预测功率和超短期预测功率作为随机变量及其实现,构建了计及需求响应的风电储能2阶段调度优化模型。为求解该模型,在传统二进制粒子群算法中引入混沌搜索,构建了混沌二进制粒子群算法。最后,以IEEE 36节点10机系统进行算例仿真。结果表明,混沌二进制粒子群算法能够得到全局最优解,适用于风电储能系统2阶段模型求解;利用需求响应和储能系统的协作效应,可以抑制风电功率的不确定性,提高系统风电利用效率,降低系统发电煤耗水平,因此综合效益显著。

英文摘要：

In allusion to the effects on system stability brought by wind power uncertainty, the energy storage system and demand response are led into the optimal dispatching of power grid containing wind farms. Firstly, the interval method is utilized to simulate the scene of wind farm and a Kantorovich distance based scene cut strategy is constructed; secondly, the demand response and energy storage system are led into the demand side and generation side respectively; thirdly, combining with two-stage optimization theory and taking thg day-ahead predicted wind power and ultra-short term predicted wind power as random variable and its implementation a two-stage scheduling optimization model for wind farm and energy storage system, in which the demand response is taken into account, is constructed. To solve the constructed model, the chaos searching is led into traditional binary particle swarm optimization （PSO） algorithm to a construct chaotic binary PSO algorithm; finally, the simulation based on IEEE 36-bus 10-machine system, to which a wind farm with capacity of 650 MW is connected, is performed. Simulation results show that the global optimal solution can be obtained by chaotic binary PSO algorithm, thus this algorithm is suitable to solve the two-stage scheduling optimization model for wind farm and energy storage system; utilizing the synergetic effect of demand response with energy storage system the uncertainty of wind power can be suppressed and the wind energy utilization efficiency can be improved, meanwhile the coal consumption for grid power generation can be reduced, so the comprehensive benefits of the proposed strategy are obvious.