Large-scale wind power cluster with distributed wind farms has generated the active power dispatch and control problems in the power system. In this paper, a novel hierarchical model predictive control (HMPC) strategy based on dynamic active power dispatch is proposed to improve wind power schedule and increase wind power accommodation. The strategy consists of four layers with refined time scales, including intra-day dispatch, real-time dispatch, cluster optimization and wind farm modulation layer. A dynamic grouping strategy is specifically developed to allocate the schedule for wind farms in cluster optimization layer. In order to maximize wind power output, downward spinning reserve and transmission pathway utilization are developed in wind farm modulation layer. Meanwhile, a stratification analysis approach for ultra-short-term wind power forecasting error is presented as feedback correction to increase forecasting accuracy. The proposed strategy is evaluated by a case study in the IEEE network with wind power cluster integration. Results show that wind power accommodation has been enhanced by use of the proposed HMPC strategy, compared with the conventional dispatch and allocation methods.

Wind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares support vector machine model (LSSVM), and gravitational search algorithm (GSA), is proposed to improve accuracy of ultra-short-term wind power forecasting. To process the data, original wind power series were decomposed by EEMD-PE techniques into a number of subsequences with obvious complexity differences. Then, a new heuristic GSA algorithm was utilized to optimize the parameters of the LSSVM. The optimized model was developed for wind power forecasting and improved regression prediction accuracy. The proposed model was validated with practical wind power generation data from the Hebei province, China. A comprehensive error metric analysis was carried out to compare the performance of our method with other approaches. The results showed that the proposed model enhanced forecasting performance compared to other benchmark models.

With large scale wind integration and increasing wind penetration in power systems, relying solely on conventional generators for frequency control is not enough to satisfy system frequency stability requirements. It is imperative that wind power have certain capabilities to participate in frequency control by cooperating with conventional power sources. Firstly, a multi-area interconnected power system frequency response model containing wind power clusters and conventional generators is established with consideration of several nonlinear constraints. Moreover, a distributed model predictive control (DMPC) strategy considering Laguerre functions is proposed, which implements online rolling optimization by using ultra-short-term wind power forecasting data in order to realize advanced frequency control. Finally, a decomposition-coordination control algorithm considering Nash equilibrium is presented, which realizes online fast optimization of multivariable systems with constraints. Simulation results demonstrate the feasibility and effectiveness of the proposed control strategy and algorithm.