Load forecasting method for electric vehicle

Abstract

The invention provides a load forecasting method for an electric vehicle. The load forecasting method comprises the following steps: classifying influence factors for charging power of the electric vehicle and modeling the influence factors; confirming the influence factors for the charging power of the electric vehicle and adopting a Monte-Carlo simulation method for forecasting the charging power of a single electric vehicle in each time period per day under different influence factors; confirming the quantity of different living spaces and the quantity of electric vehicles contained in each living space, acquiring the charging power of the electric vehicle group charged in each living space in each time period per day according to the summation of the charging power of the single electric vehicle in each time period per day under different influence factors, and superposing, thereby acquiring the charging load of the whole electric vehicle group in each time period per day. According to the load forecasting method, the research is respectively performed for the factors, such as user behavior, charging modes, power battery characteristics, permeability and electrovalence, the influences of all the factors on the charging power characteristics of the electric vehicle are confirmed, and the expectation value of the charging power is forecasted.

Description

technical field [0001] The invention relates to a method in the field of grid steady-state analysis, in particular to an electric vehicle load forecasting method. Background technique [0002] The Monte Carlo simulation method is a calculation method based on the theory and method of probability and statistics. It connects the problem to be solved with a certain probability model, and uses a computer to achieve statistical simulation or sampling to obtain an approximate solution to the problem. Also known as statistical simulation method or statistical test method. [0003] The fluctuation of electricity price and demand influence each other, and the user's sensitivity to electricity price determines the user's electricity consumption strategy. The price elasticity coefficient (the ratio of the percentage change of electricity consumption to the corresponding percentage change of electricity price within a certain period of time) can approximately describe this quantitative...

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Problems solved by technology

[0005] In the prior art, the load forecasting of most electric vehicles assumes that the driving time and driving distance of electric vehicle users obey a certain probability distribution, and obtains the initial charging time and duration of electric vehicles through stochastic simulation, and assumes that the power battery pack is charged at a constant power. Charging then approximates the charging power of electric vehicles, without analyzing and classifying the charging habits of electric vehicle users, without analyzing the diversity of user behaviors in different occupational spaces, and without comprehensively analyzing the impact of various factors on the charging power of electric vehicles. Therefore, the research results can only approximate the charging power of electric vehicles, and cannot truly reflect the power consumption of electric vehicles.

When the penetration rate of electric vehicles is high, inaccurate load forecasting of electric vehicles will have a negative impact on the dispatching operation of the power system, which may cause frequency fluctuations in the grid; inaccurate load forecasting of electric vehicles will affect power system planning, which may lead to peak charging periods. Overloading of lines and transformers in some areas

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