Active Power Cooperative Control for Wind Power Clusters with Multiple Temporal and Spatial Scales

To improve the control of active power in wind power clusters, an active power hierarchical predictive control method with multiple temporal and spatial scales is proposed. First, the method from the spatial scale divides the wind power clusters into the cluster control layer, sub-cluster coordination layer and single wind farm power regulation layer. Simultaneously, from the temporal scale, the predicted data are divided layer by layer: the 15 min power prediction is deployed for the first layer; the 5 min power prediction is employed for the second layer; the 1 min power prediction is adopted for the third layer. Secondly, the prediction model was developed, and each hierarchical prediction was optimized using MPC. Thirdly, wind farms are dynamically clustered, and then the output power priority of wind farms is established. In addition, the active power of each wind farm is controlled according to the error between the dispatch value and the real-time power with feedback correction so that each wind farm achieves cooperative control with optimal power output. Finally, combined with the simulation of practical wind power clusters, the results show that the wind abandonment rate was reduced by 2.13%, and the dispatch of the blindness was overcome compared with the fixed proportional strategy. Therefore, this method can improve the efficiency of cooperative power generation.