Renewable energy is the development direction of power system of China. To further boost the development of China's power industry, the digital transformation of the power is underway. This study examined the panorama of digital transformation of power system of China. Through analyzing the effect of digital transformation on renewable energy utilization, three business applications were identified: short-term prediction of renewable energy output, renewable energy equipment maintenance, and intelligent power-grid maintenance. The implementation process and effect of the three business applications are analyzed by using the data covering 2012-2019 from three aspects: the short-term prediction accuracy of renewable energy output, the reduction of the fault restoration mean time for renewable energy equipment to the industry average, the reduction ratio of the mean time of power grid fault restoration to the industry average, and the rate of renewable energy utilization. The results indicated that digital transformation boosted the efficiency of renewable energy utilization. Based on the findings, the following suggestions are proposed for improving the digital transformation of the power industry and thereby promoting renewable energy utilization: 1) the first is to emphasize the scale efficiency of digital application in the early stage of digitization and renewable energy development, and emphasize the technical performance of a digital application in the middle and later stages of development; 2) the second is to enhance data accuracy, such as meteorological data, historical data for renewable energy power stations, power equipment status data, and strengthen the application of algorithms and computational models; 3) the third suggestion is to improve the overall digitalization rate of the power industry.

With the increasing of wind power penetration rate in the power grid, power dispatching including wind curtailment often happens to guarantee the safety of power system. Considering the fatigue load of wind turbines during de-loading operation, how to optimally dispatch them along with the required power instructions becomes a hot issue. Under this background, this paper mainly studies the active power dispatching based on fatigue load optimization. In order to reduce the calculation complexity of fatigue load, this paper proposes a simplified active power dispatching model for wind turbines via look-up table. Then, the ultra-short-term wind speed prediction is performed using the least squares support vector machine (LS-SVM) for the dynamic’s prediction of wind turbines, which will be applied in fatigue load pre-calculation. Moreover, in order to fast solve the optimal dispatching problem, the accelerated particle swarm optimization (APSO) algorithm is adopted. Under different de-loading scenes, the simulation experiments show that the fatigue load of the wind turbines has certain regularity while different de-loading strategies yield different effects on the fatigue load. Finally, based on the evaluation of Damage Equivalent Load (DEL), the proposed optimal dispatching strategy in this paper is proved to be effective to meet the external dispatching from power grid while reducing the total fatigue load level of wind farm via optimizing the power instruction to each wind turbine.

The active power fluctuation of wind turbines is not only related to their friendliness to the grid, but also to their fatigue damage. In this paper, the active power of wind turbines in wind farms is optimally scheduled to achieve the suppression of fatigue load of wind turbines. Considering the complexity of fatigue load calculation, it is difficult to apply to real-time active scheduling using metrics that directly characterize fatigue load. To address this problem, a data-driven modeling method for wind turbine fatigue based on deep neural network (DNN) is proposed in this paper, and the relationship between wind speed, power and other easily measurable parameters and fatigue load is established. Further, an improved multi-objective grey wolf optimizer (MOGWO) is designed to achieve the wind farm active scheduling process with the data-driven fatigue calculation results as the optimization objective. The results show that: The fatigue load prediction model of data-driven fatigue calculation proposed in this paper has a satisfactory effect, and the fatigue load of wind turbines can be effectively reduced by adjusting the active power.

With the rapid development of wind power industry recently, huge data source are accumulated by the widespread Supervisory Control And Data Acquisition systems. The data-driven wind turbine power curve plays an important role in many fields whereas it is sensitive to data quality. The invalid and unnatural data need to be reasonably eliminated. Considering the complex influences to data records, probabilistic description is effective to represent the data uncertainty. Initially, raw data are preliminarily cleaned in the three-dimensional Copula space. On this basis, in divisional operation regions of the variable-pitch wind turbine, the weighted mixture of Archimedes Copula functions are estimated by expectation maximization to establish the joint probabilistic distribution. Then, the confidence boundary modeling procedure of WTPC is presented to identify abnormal data while an evaluation system is proposed for adaptive modeling with guaranteed performance. After outliers elimination by the boundary, a bi-directional Markov Chain interpolation method is proposed to recover consecutive missing data with optimized weights. Finally, the operation data from different wind turbines are processed and used for validation. The simulation results show that more accurate power curves and theoretical power can be obtained which suggests effectiveness of the proposed methods and their great application potential.

With the continuous increase of grid-connected photovoltaic (PV) installed capacity and the urgent demand of synergetic utilization with the other power generation forms, the high-precision prediction of PV power generation is increasingly important for the optimal scheduling and safe operation of the grid. In order to improve the power prediction accuracy, using the response characteristics of PV array under different environmental conditions, a data driven multi-model power prediction method for PV power generation is proposed, based on the seasonal meteorological features. Firstly, through the analysis of PV power characteristics in typical seasons and seasonal distribution of the weather factors, such as solar irradiance and ambient temperature, the influences of different weather factors on PV power prediction are studied. Then, according to the meteorology characteristics of Beijing, different seasons can be divided. The historical data corresponding to different seasons are acquired and then the seasonal PV power forecasting models are established based on optimized multi-layer back propagation neural network (BPNN), realizing the multi-model prediction of PV power. Finally, effectiveness of the seasonal PV power forecasting method is compared and validated. The performance analysis of the neural network forecasting model under typical seasonal conditions shows that the multi-model forecasting method based on seasonal characteristics of PV power generation is better than that of single power forecasting model for the whole year. The results show that the proposed method can effectively improve the power forecasting accuracy of PV power.