一种风电集群分层模型预测控制策略研究

Research on a Hierarchical Model Predictive Control Strategy of Wind Power Cluster

针对分布式风电场大规模风电集群(WPC)在电力系统并网中存在的有功功率调度与控制问题,提出了一种基于动态电力调度的分层模型预测控制(HMPC)策略。首先,将调度框架分为日内调度、实时调度、集群优化和风电场调制四个层次,使得风电调度具有精细的时间尺度。其次,利用下行旋转和传输路径最大限度地提高风电场调制层的风电输出,并通过数值特征方法进行分层分析,提高优化模型中风电预报数据的准确性。最后,利用真实风电场数据进行仿真实验,验证所提控制策略的有效性。实验结果表明,所提HMPC策略能充分利用风电集群的多个时间尺度和适应因子的预测信息,能有效提高风力发电集群传输路径的利用率,提高了风电的可容纳空间和调度的准确性。

Aiming at the problem of active power dispatch and control in the grid-connected power system of large-scale wind power clusters(WPC)of distributed wind farms,a hierarchical model predictive control(HMPC)strategy based on dynamic power dispatch was proposed. Firstly,the dispatch framework was divided into four levels:intraday dispatch,real-time dispatch,cluster optimization and wind farm modulation,so that wind power dispatch could have a fine time scale. Secondly,the downstream rotation and transmission path were used to maximize the wind power output of the wind farm modulation layer,and the numerical feature method was used to perform layered analysis to improve the accuracy of the wind power forecast data in the optimization model. Finally,simulation experiments were conducted using real wind farm data to verify the effectiveness of the proposed control strategy. Experimental results show that the proposed HMPC strategy can make full use of the forecast information of multiple time scales and adaptation factors of wind power clusters,effectively improve the utilization of wind power cluster transmission paths,and improve the accommodating space of wind power and the accuracy of dispatching.