机会约束随机动态经济调度的凸松弛迭代求解法

大规模风电并网给电力系统运行带来了重大技术挑战,其出力的随机性和波动性增加了线路传输能力和备用调节能力不足的风险,传统的确定性经济调度方法已不完全适用。首先提出了一种基于混合高斯分布的机会约束随机动态经济调度模型,该模型将最小备用约束和线路传输容量约束建模为机会约束,并引入调频机组的仿射控制策略以实时平衡风功率的波动。然后提出了一种机会约束规划的松弛迭代求解方法,将随机调度模型转化为二次规划模型以实现快速有效求解。利用中国西南某省25个风电场的现场数据以及IEEE 118节点系统进行算例分析,验证了混合高斯分布对风电预测误差的拟合精度,以及所提方法的可行性。

Convex Relaxation Based Iterative Solution Method for Stochastic Dynamic Economic Dispatch with Chance Constrains

The integration of massive wind power brings significant technical challenges to power system operation. The randomness and volatility of wind power output increase the risk of insufficient transmission or regulation capacity, and the traditional determined economic dispatch methods are no longer applicable in this condition. First, a chance constrained stochastic economic dispatch model is proposed based on Gaussian mixture distribution. In this model, the minimum reserve constraints and transmission capacity constraints are represented by chance constraints, and the affine control strategy of regulation units is adopted to balance the real-time wind power fluctuation. And then, a convex relaxation based iterative solution method for chance- constrained programming model is proposed, by which, stochastic dispatch model is converted into a quadratic programming model to achieve the fast and effective solution. Taking data of 25 wind farms in Southwest China and the IEEE 118-bus test system as the analysis cases, the fitting accuracy of Gaussian mixture distribution for wind power forecasting error and the efficiency of the proposed method are verified.