基于随机分层分布式模型预测控制的风电集群频率控制规划方法

A Programming Method for Wind Power Cluster Frequency Control Based on S-H-DMPC

大规模风电集群功率输出不确定性对接入电网频率造成不利影响,为了使风电集群与传统电源协调配合共同参与系统调频,提出一种基于随机分层分布式模型预测控制(stochastic-hierarchical-distributed model predictive control,S-H-DMPC)的风电集群频率控制机会约束目标滚动规划方法(chance constrained goal rolling programming,CCGRP)。首先,建立考虑功率波动相关性的风电集群功率预测误差模型;其次,提出考虑风电集群功率预测误差随机向量的双层机会约束目标滚动规划方法,上层规划侧重电网拓扑结构及全区系统经济性,下层规划侧重平均系统频率增广模型(average system frequency augmented model,ASFAM)及分区运行安全性;最后,提出基于蒙特卡罗随机模拟的模型求解方法,该方法采用仿射变换算法,通过对风电集群功率预测误差随机向量进行抽样实现机会约束条件的处理。仿真算例表明,所提控制方法能有效提高风电集群参与系统调频的准确性,证明了方法的可行性与鲁棒性。

The uncertainty of large-scale wind power cluster power imposes an adverse effect on power system frequency. In order to make wind power cluster coordinate with traditional power in frequency control, a method of chance constrained goal rolling programming(CCGRP) based on stochastic-hierarchical-distributed model predictive control(S-H-DMPC) for wind power cluster frequency control was proposed. Firstly, a wind power cluster forecasting error model considering wind power temporal correlation was established. Secondly, a bi-level chance constrained goal rolling programming method considering wind power cluster forecasting error random vector was proposed, in which power system topology and system economic objective were focused in upper-level, and average system frequency augmented model(ASFAM) and system secure objective were focused in lower-level. Finally, a solution method based on Monte Carlo stochastic simulation was proposed, in which an affine transformation algorithm was utilized to sample wind power cluster forecasting error random vector, and to handle chance constraints. Case study results demonstrated that the proposed control method could effectively improve the accuracy of wind power cluster participating in power system frequency control.