The ever-increasing number of wind farms has brought challenges and opportunities in wind power forecasting techniques to take advantage of interdependencies between hundreds of spatially distributed wind farms, e.g., over a region. In this paper, a Sparsity-Controlled Vector Autoregressive (SC-VAR) model is introduced to obtain sparse model structures in a spatio-temporal wind power forecasting framework by reformulating the original VAR model into a constrained Mixed Integer Non-Linear Programming (MINLP) problem. It allows controlling the sparsity of the coefficient matrices in direct manner. However this original SC-VAR is difficult to implement due to its complicated constraints and the lack of guidelines for setting its parameters. To reduce the complexity of this MINLP and to make it possible to incorporate prior expert knowledge to benefit model building and forecasting, the original SC-VAR is modified and a Correlation-Constrained SC-VAR (CCSC-VAR) is proposed based on spatial correlation information about wind farms. Our approach is evaluated based on a case study of very-short-term forecasting for 25 wind farms in Denmark. Comparison is performed with a set of traditional local methods and spatio-temporal methods. Results show the proposed CCSC-VAR has better overall performance than both the original SC-VAR and other benchmark methods, taking into account all evaluation indicators, including sparsity-control ability, sparsity, accuracy and efficiency.

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.

Extreme meteorological phenomena (e.g., cold waves) are low-probability, high-impact events that present significant operational and dispatch challenges for new-energy power systems. 1) Insufficient data on wind and photovoltaic power generation hinder data-driven modelling. 2) The uncertainty of small-sample wind and photovoltaic data is difficult to characterize. Therefore, a two-stage distributionally robust optimization dispatch strategy for a power system based on small-sample wind and solar data with uncertainty is developed. First, a generalized modelling method for a conditional generative adversarial network (CGAN) is proposed, which increases the generation of hourly output data for wind and photovoltaic power. Considering the drawback of large prediction errors for wind power and photovoltaic power in small-sample cases, a two-stage distributionally robust optimization model based on the Wasserstein metric is constructed, which embeds hard constraints (e.g., the capacity constraint of wind and solar transmission lines) and soft constraints (e.g., prediction errors). This model is nonconvex, and exactly solving it incurs a heavy computational burden. Duality theory and relaxation approximation are adopted here; the robust two-stage-distribution model is transformed into a mixed-integer linear programming model, and the iterative Taylor formula algorithm is used to linearize the constraint conditions. A numerical example is provided to demonstrate that the proposed model and solution algorithm can not only address the uncertainty of small-sample wind and photovoltaic data but also reduce the system operating costs and increase the model-solving efficiency.

Most renewable energy power systems are created to provide more resilient, reliable, economical, sustainable and secure power support services for loads. However, owing to the inherent forecasting errors of wind and photovoltaic (PV) power, existing optimal dispatch decisions based on forecasting errors have biases. To address this issue, this paper proposes the distributed proximal policy optimization (DPPO) model with embedded dual rules for optimal power dispatch that considers wind and PV power forecasting error correction. The proposed model embeds forecasting and error correction information into the DPPO state space. Moreover, considering the physical characteristics and operational security constraints of the power grid, power balance and flow constraints are embedded in the DPPO network in a regular form. Finally, by integrating the established rules, wind and PV forecasting, and error correction information, the proposed model achieves optimal dispatch decisions through the calculation of state and execution of prescribed actions. The proposed method is applied and tested on a modified IEEE-30 bus system using actual data from a provincial power grid. The numerical results demonstrate that the proposed method effectively addresses optimal dispatch decisions caused by wind and PV forecasting errors. Compared with three other advanced methods, the proposed approach has significant advantages in promoting wind power accommodation, reducing operating costs, and enhancing the adaptability of optimal dispatch to uncertainty.