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Aiming at the power fluctuation problem for the randomness of the time-space transfer of electric vehicles (EV), taking into account the gender difference of car owners, different real-time travel purpose and two-way travel of car owners, the charge-discharge dispatch strategy based on the two-way travel chain for the travel randomness of EV is proposed. Firstly, the two-way travel chain of EV is established by considering the actual travel process of car owners. Secondly, the real-time travel probability model is given by considering the gender differences of the male and female owners travel. Then, the dispatch model is set up by reducing the power fluctuation as the objective function according to the real-time travel demand of the owner and the real-time state of charge (SOC) of EV. Finally, the charge and discharge of EV are dispatched according to the established model, and the simulation if given by analyzing the cars in a area of 280 square meters. The simulation results show that the two-way travel model is closer to the actual travel law of owners, and the dispatch strategy based on the dispatched model of the two-way travel chain can better restrain the power fluctuation.

Power load is an important part of power system, and power load forecasting has an important impact on power system analysis, design and control. With the development of smart micro grid, load forecasting has gradually become an important module in the energy management system, It is “source, network, load and storage” “An important link in energy flow matching. The staged combined demand forecasting model of power grid based on neural network and polynomial regression is adopted, and judgment conditions are added to the neural network. If the training sample data does not converge in the neural network training process, the neural network forecasting is terminated, and the data is automatically transferred to the polynomial regression model to obtain the forecasting results. This method can be initially used for annual and monthly load forecasting. It is an intelligent micro grid The planning of has laid a certain technical foundation.