Study On Short - Term Load Forecasting Model Of Electric Vehicle Charging Station In Smart Grid

With the implementation of UHV strategy and the advance of smart grid, the intelligent and interactive of China’s power grid is increased gradually. However, the smooth connection to the grid of large-scale solar power and wind power farm, the growing popularity of electric vehicles and the rapid development of micro grid, etc,which will have a huge impact on load mode of the grid, and increase the difficulty of load forecasting. As such, improving the accuracy of the load forecasting of the Smart Grid has great significance for the security and stability of grid in the future. The paper studies from one part of load forecasting of the Smart Grid, which is called the short-term load forecasting of electric vehicle charging stations, exploring ways to improve the short-term load forecasting precision of electric vehicle charging stations.Popularity of electric vehicles is the inevitable trend of social development, a large number of electric vehicle charging load will have a greater impact on the regional power grid, as such, the load forecasting of electric vehicle is an important part of the load forecasting of Smart Grid in the future. The RBF-NN short-term load forecasting model has the features of faster training speed and willing not fall into the local minimum value,the paper uses the RBF-NN short-term load forecasting model for predicting, and the predicted results are compared with BP-NN short-term load forecasting model, which demonstrates the effectiveness of RBF-NN short-term load forecasting model.As the electric vehicle charging load has characteristics of randomness and volatile,the prediction accuracy of only using RBF-NN short-term load forecasting model is still large。In order to further improve the accuracy of short-term load forecasting of electric vehicle charging station, the paper proposes a method of correcting RBF-NN short-term load forecasting model online with fuzzy control, the method uses fuzzy control theory correct RBF-NN short-term load forecasting model online, compared to single RBF-NN short-term load forecasting model, the prediction accuracy has been further improved,which proves the superiority of the method of correcting RBF-NN short-term load forecasting model online, and provides a strong theoretical basis for application practice of short-term load forecasting of electric vehicles.