KEYWORDS: Roads, Resistance, Networks, Computer simulations, Neural networks, Monte Carlo methods, Error analysis, Telecommunications, System on a chip, Solids
Electric vehicle load forecasting is the basis for the safe and stable operation of the distribution network, and it is also a prerequisite for the planning and layout of electric vehicle infrastructure. Firstly, considering the characteristics of electric vehicles as participants in the transportation network and the characteristics and mobile load characteristics of electric vehicles as vehicles, a method for forecasting the temporal and spatial distribution of electric vehicle charging load considering traffic flow is proposed. This method first establishes a road network model that considers the flow-density-speed model and the road section impedance and node impedance based on the traffic flow of the road section based on the characteristics of the urban road network multiple intersections and the traffic flow of each section. Secondly, introduce the time function of charging probability and Freud's path search algorithm to assign start and end nodes and plan the driving path for electric vehicles to simulate its dynamic driving process and charging behavior. Finally, a simulation experiment of charging load prediction is carried out with a typical regional road network. The result shows that the distribution of electric vehicle charging load in different functional areas is different and the time distribution is uneven, which verifies the effectiveness and feasibility of the proposed method.
KEYWORDS: Roads, Feature selection, Neural networks, Monte Carlo methods, Power supplies, Data modeling, Artificial neural networks, Temperature metrology, Fuzzy logic, Fusion energy
In recent years, the electric vehicle market has been expanding rapidly. By the end of 2021, the number of new energy vehicles in the country will reach 7.84 million, accounting for 2.60% of the total number of vehicles, an increase of 59.25% over 2020. A small number of electric vehicles will not have an impact on the power grid. But in the future, with the electrification of all passenger cars, large-scale electric vehicles will emerge in the future. The access to large-scale electric vehicles and charging piles to the power grid will bring new challenges to the normal operation and control of the power grid. Therefore, charging load forecasting is essential. This paper proposes a load forecasting model based on a multilayer perceptron. It can predict the charging load of electric vehicles in a specific area and provide a reference for urban infrastructure planning and construction, optimal power flow of power systems, and economic dispatch of power grids.
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