This study explores the smoothing effect on the uncertainty of the wind power fluctuations that affect a power system at points of common coupling. Set pair analysis is applied to evaluate the similarity between the power fluctuations of a single wind turbine and those of all aggregated wind turbines, based on which a quantitative index describing the wind power smoothing effect is proposed. The smoothing effect characteristics of a wind farm cluster are investigated for different numbers of wind turbines, different wind speeds, different seasons and multiple sampling intervals. A significant smoothing effect is usually observed at a shorter sampling interval and a higher wind speed, and the smoothing effect index increases with an increase in the numbers of wind turbines and farms. The experimental results indicate that the wind power forecast accuracy varies with the smoothing effect index, which is influenced by the number of wind farms. Furthermore, the aggregated output from a wind farm cluster with a higher smoothing effect index exhibits better forecasting performance than that from a single wind farm, showing that the trend of the wind power series becomes smoother due to the smoothing effect, thus enabling 1-step-ahead wind power forecasting with higher accuracy

At present, the installed capacity of wind power has increased significantly. Improving the power forecasting accuracy of Wind Farm Cluster (WFC) is of great significance to promote the consumption of renewable energy. How to effectively extract the complex spatio-temporal correlation between wind farms is the key to improve the power forecasting accuracy of WFC. Aiming at the above problems, this paper proposes a short-term WFC power forecasting method based on global information adaptive perception graph convolution. Compared with the traditional static graph structure, this paper designs the construction method of multiple Characteristic Graph Structures (CGSs) according to the correlation between farms. Firstly, a set of calculation methods suitable for Dynamic Correlation Coefficient (DCC) between wind farms is proposed, and according to this method, the dynamic correlation of WFC is described from multiple perspectives to obtain the graph structure at each moment, then, the graph embedding and clustering algorithm are used to obtain multiple CGSs containing key features of WFC. Finally, by establishing an Adaptive Graph Convolution Network (AGCN), the prediction accuracy is improved by graph switching. Compared with the traditional WFC forecasting method, the root mean square error of the proposed forecasting method is reduced by 1.14%-3.42%.

Wind power forecasting (WPF) systems are essential to maintain the safe and stable operation of the power system in case of large-scale grid-connected wind farms. However, the current forecasting has the problem of disunity between statistical value and application value, that is, it only pays attention to its forecasting accuracy and ignores the risks caused by it in the power system. In order to solve the above problems, this study proposes a framework of wind supply power forecasting (WSPF) for wind farm cluster, which takes into account the risk scenario perception. First of all, aiming at the predicted risk phenomenon in WPF, TimesNet combined with the fluctuation information of Numerical Weather Prediction (NWP) wind speed is used to identify the corresponding risk scenarios. Secondly, the effective consumption area and power supply risk area evaluation index, as well as the accuracy of WSPF are defined, and the optimal forecasting curve correction scheme is fitted according to the index. Thirdly, taking into account the correction scheme and identification results, a variety of predictors are used to verify the WSPF according to the above framework. Finally, the proposed method is applied to a wind farm cluster in Inner Mongolia Autonomous region of China, the average accuracy of WSPF has increased by 37%, which verifies the effectiveness and universality of this method.

Big wind farms must be integrated to power system. Wind power from big wind farms, with randomness, volatility and intermittent, will bring adverse impacts on the connected power system. High precision wind power forecasting is helpful to reduce above adverse impacts. There are two kinds of wind power forecasting. One is to forecast wind power only based on its time series data. The other is to forecast wind power based on wind speeds from weather forecast. For a big wind farm, due to its spatial scale and dynamics of wind, wind speeds at different wind turbines are obviously different, that is called wind speed spatial dispersion. Spatial dispersion of wind speeds and its influence on the wind power forecasting errors have been studied in this paper. An error evaluation framework has been established to account for the errors caused by wind speed spatial dispersion. A case study of several wind farms has demonstrated that even if the forecasting average wind speed is accurate, the error caused by wind speed spatial dispersion cannot be ignored for the wind power forecasting of a wind farm.

With the centralization of wind power development, power-prediction technology based on wind power clusters has become an important means to reduce the volatility of wind power, so a large-scale power-prediction method of wind power clusters is proposed considering the prediction stability. Firstly, the fluctuating features of wind farms are constructed by acquiring statistical features to further build a divided model of wind power clusters using fuzzy clustering algorithm. Then the spatiotemporal features of the data of wind power are obtained using a spatiotemporal attention network to train the prediction model of wind power clusters in a large scale. Finally, the stability of predictive performance of wind power is analyzed using the comprehensive index evaluation system. The results show that the RMSE of wind power prediction is lower than 0.079 at large-scale wind farms based on the prediction method of wind power proposed in this paper using experience based on the data of 159 wind farms in the Nei Monggol Autonomous Region in China and the extreme error is better than 25% for the total capacity of wind farms, which indicates high stability and accuracy.