It is a trend to use virtual power plant technology to realize demand response and participate in electricity trading. We design and implement the software control platform of virtual power plant for demand response. For this software platform, we analysed the requirements and got the overall architecture of the platform. On this basis, we design and implement the microservice architecture, interface design, basic application function design, advanced application function design, hardware architecture, communication architecture and security encryption of the platform. Finally, we summarize the application of the platform, and put forward the direction of further research and development.

No abstract

The ultrashort-term wind power prediction (USTWPP) technology assists the grid to arrange spare capacity, which is important to optimize power investment reasonably. To improve the accuracy of USTWPP and optimize power investment requirements, a USTWPP method with dynamic switching of multiple models is proposed. For high wind speed fluctuation samples, the wind speed-power curve (WSPC) is fitted in a large sample of historical data, and the corrected wind speed is the input of WSPC. The spatiotemporal attentive network model (STAN) is built for the prediction of low wind speed fluctuation samples. According to the real-time fluctuation characteristics of the correction wind speed, a switching mechanism between multiple models is established to reconstruct the prediction results along the time axis direction, and the predicted power is set to zero for the samples whose correction wind speed is lower than the cut-in wind speed. We conducted simulation experiments with data provided by a wind farm with an installed capacity of 130.5 MW in China. The normalized root mean square error (NRMSE) for the 4 h ahead predicted power reaches 0.0907, which verified the validity and applicability of the proposed model.

The integration of a high proportion of wind power has brought disorderly impacts on the stability of the power system. Accurate wind power forecasting technology is the foundation for achieving wind power dispatchability. To improve the stability of the power system after the high proportion of wind power integration, this paper proposes a steady-state deduction method for the power system based on large-scale wind power cluster power forecasting. First, a wind power cluster reorganization method based on an improved DBSCAN algorithm is designed to fully use the spatial correlation of wind resources in small-scale wind power groups. Second, to extract the temporal evolution characteristics of wind power data, the traditional GRU network is improved based on the Huber loss function, and a wind power cluster power prediction model based on the improved GRU network is constructed to output ultra-short-term power prediction results for each wind sub-cluster. Finally, the wind power integration stability index is defined to evaluate the reliability of the prediction results and further realize the steady-state deduction of the power system after wind power integration. Experimental analysis is conducted on 18 wind power farms in a province of China, and the simulation results show that the RMSE of the proposed method is only 0.0869 and the probability of extreme error events is low, which has an important reference value for the stability evaluation of large-scale wind power cluster integration.

SummaryHigh penetration of renewable energy is the development trend of the future power system. As one of the clean energy sources, wind power generation has an increasing share in the energy market. However, due to the harsh working environment, the high fault rate and poor accessibility of the wind farms, resulting in the difficult maintenance process and high cost. This article proposes a fault diagnosis (FD) method based on long short‐term memory (LSTM) and feature optimization strategies for wind turbines (WTs), thus reducing the operation and maintenance costs of WTs. First, Pearson correlation coefficient analysis is performed on the collected data features to remove redundant features, and wavelet transform is adopted to remove the redundant data, so as to optimize the fault features and fault data. Then the selected features samples are used to train LSTM‐based FD model. Finally, the actual production data is adopted to verify the proposed method. The proposed method can effectively locate the faults, and provide data support for wind farms, thus improving the reliability, safety, and economic benefits of wind farms.