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Electric vehicle load prediction method and system

An electric vehicle and load forecasting technology, applied in forecasting, system integration technology, information technology support system, etc., can solve the problems of inability to accurately describe the load forecasting situation of electric vehicles, inability to adjust, and unrepresentative

Active Publication Date: 2020-03-27
YANSHAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Both the former and the latter forecasting methods are too unrepresentative to accurately explain the load forecast of electric vehicles in a certain area, so that targeted adjustments can not be made based on the impact of electric vehicles on the grid

Method used

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  • Electric vehicle load prediction method and system
  • Electric vehicle load prediction method and system
  • Electric vehicle load prediction method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0084] figure 1 This is a method flowchart of the electric vehicle load prediction method according to Embodiment 1 of the present invention.

[0085] see figure 1 , the electric vehicle load forecasting method includes:

[0086] Step 101: Predict the number of electric vehicles in the target area.

[0087] This step 101 specifically includes:

[0088] Establish a neural network model of the relationship between the influencing factors of the holding quantity and the holding quantity;

[0089] Obtain the value of each said holding quantity influencing factor;

[0090] The value of each of the factors influencing the ownership is input into the neural network model to obtain the ownership of electric vehicles in the target area.

[0091] Step 102: Predict the random path of the electric vehicle in the target area.

[0092] The step 102 specifically includes:

[0093] Establish the main chain with the destination as the first factor affecting the random path;

[0094] Th...

Embodiment 2

[0121] This Embodiment 2 is a more specific embodiment of Embodiment 1.

[0122] The basic principle of the second embodiment is as follows: predict the number of electric vehicles based on the BP neural network, establish a Markov model of multi-chain electric vehicle paths, and use Monte Carlo to predict the distribution of the load for one day. Optimized using V2G (Vehicle-to-grid) at the peak of the predicted load.

[0123] The steps of this embodiment 2 are as follows:

[0124] (1) Classify the factors that affect the number of electric vehicles, and establish a BP neural network model to predict the number of electric vehicles.

[0125] (2) Establish a random path model for electric vehicles, divide the factors that affect the random path into primary factors and secondary factors, and conduct simulation modeling by establishing a multi-chain Markov model.

[0126] The main influencing factor of the random path of electric vehicles in a region is weather, and the seconda...

Embodiment 3

[0211] image 3 It is a system structure diagram of the electric vehicle load prediction system according to the third embodiment of the present invention.

[0212] see image 3 , the electric vehicle load forecasting system, including:

[0213] An inventory prediction module 301, used to predict the electric vehicle inventory in the target area;

[0214] a path prediction module 302, configured to predict a random path of the electric vehicle in the target area;

[0215] A load distribution prediction module 303, configured to predict the load distribution of the electric vehicle in one day based on the inventory and the random path;

[0216] The optimization module 304 is configured to optimize the load distribution situation based on the electric energy interaction relationship between the vehicle and the power grid to obtain the optimized load distribution of the electric vehicle.

[0217] Optionally, the holding quantity prediction module 301 includes:

[0218] The n...

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Abstract

The invention discloses an electric vehicle load prediction method and system. The electric vehicle load prediction method comprises the steps: predicting the electric vehicle ownership of a target area; predicting a random path of the electric vehicle in the target area; predicting the load distribution condition of the electric vehicle within one day based on the retention amount and the randompath; and optimizing the load distribution condition based on the electric energy interaction relationship between the vehicle and the power grid to obtain optimized electric vehicle load distribution. The electric vehicle load prediction method can achieve the prediction of the load of the electric vehicle in a certain region.

Description

technical field [0001] The invention relates to the field of load forecasting, in particular to an electric vehicle load forecasting method and system. Background technique [0002] In the case of global resource shortage and global warming, promoting the development of electric vehicles can alleviate the pressure on global resources and climate, which makes electric vehicles attract people's attention. However, with the increase in the number of electric vehicles and the large-scale connection of electric vehicles to the power grid, the impact on the power grid cannot be ignored. The number of electric vehicles in a region has a greater impact on the charging load of electric vehicles in this area, and there are many factors that affect the growth of the number of electric vehicles every year. The factors affecting the charging load of electric vehicles are mainly in space and time. Under the influence of the space factor of electric vehicle charging, the daily driving ro...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/12
CPCG06Q10/04G06N3/126Y04S10/50Y02E40/70
Inventor 窦春霞张雷雷张博张立国赵朋毕晓璇
Owner YANSHAN UNIV
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