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A large quantity of Electric Vehicle (EV) charging station loads connected to the power grid will aggravate the peak-valley difference and reduce the stability of the power system and the economic benefits of operation. The connection of EV charging station loads will also inject a large quantity of harmonics into the power grid, further aggravating the risk of power supply equipment failure. EV can also be regarded as distributed energy storage devices. Under special circumstances, EV can feed back electric energy to the power grid through power conversion devices and participate in frequency modulation of the power grid. In order to reduce the idle rate of charging stations and promote the distributed photovoltaic absorption capacity of the system, this paper proposes an electric load forecasting model based on Particle Swarm Optimization Neural Network (PSO-NN). The charging and discharging power of EV cluster and energy storage equipment are solved in turn by PSO algorithm, and when the regulation capacity of EV cluster is insufficient or limited, it is supplemented by energy storage equipment. The simulation results show that with the increase of the quantity of experiments, the accuracy of this algorithm is stable at about 95%, and the real-time wavelength tends to be stable. Therefore, the scheduling strategy can effectively improve the safety and economic performance of power grid and the capacity of transportation system, which is conducive to improving the operational performance of power grid and transportation network and providing algorithm and technical support for the construction of charging station number planning model.
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