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Renewable energy resources and electric vehicles give pressure on the smart grid. Once a large number of electric vehicles do batch charging at peak time, the dynamic resource allocation ability of the smart grid appears to be very important. This article proposes one composite game model to describe the interaction process during the charging. The smart grid area covers numbers of charging stations which satisfy spatial non-equilibrium distribution. Each vehicle has enough remaining energy to reach each charging station in the considering field. This paper considers preferential policies to the cost-sensitive drivers. From the view of energy management, preferential policies from charging stations will fluctuate the energy demand. To do dynamic dispatching strategy in power supply, the model describe two type of interactions, one is between stations and vehicles, the other is between vehicles. The model help strategy managers understand more about energy demand which hopefully can assist them do reasonable dynamic dispatching strategy.

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.