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Electric car charging-discharging behavior prediction method

A technology of electric vehicles and prediction methods, which is applied in the field of power transmission and distribution, and can solve problems such as the influence of prediction accuracy and the failure to consider the influence of MSN

Active Publication Date: 2015-09-09
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this solution does not consider the impact of MSN on user charging and discharging plans, which affects the prediction accuracy to a certain extent, so it is necessary to improve it

Method used

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  • Electric car charging-discharging behavior prediction method
  • Electric car charging-discharging behavior prediction method
  • Electric car charging-discharging behavior prediction method

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Embodiment Construction

[0048] The invention includes two parts: the influence of MSN on the charging and discharging behavior of the electric vehicle; and the charging and discharging plan of the electric vehicle under the influence. Combine below figure 2 , image 3 The present invention is described in detail:

[0049] (1) figure 2 It is a conceptual diagram of the influence of MSN on the charging and discharging behavior of electric vehicles. As a component particle in MSN, electric vehicles are affected by the information disseminated by information dissemination nodes in the network (called external influence p), and the ideas and behaviors of associated individuals (other electric vehicles) in the network (called internal influence q) . p and q together constitute the influence of MSN on the charging and discharging of electric vehicles, expressed as:

[0050] ω 2 =p+q (1)

[0051] In the formula, ω 2 That is the influence of MSN on the charging and discharging of electric vehicles. ...

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Abstract

The invention provides an electric car charging-discharging behavior prediction method. The method includes the steps of a, setting electric car charging-discharging basic constraint; b, setting a target function; c, calculating MSN influence omega 2; d, using omega 2 to correct cross inheritance particle swarm algorithm parameters; e, using the electric car charging-discharging basic constraint as premise, and using the corrected cross inheritance particle swarm algorithm so solve the target function so as to obtain an electric car charging-discharging plan and power distribution network load. The method has the advantages that the cross inheritance particle swarm algorithm is used to predict the charging-discharging plan of an electric car user, influence of a mobile social network on the charging-discharging plan is fully considered, prediction result accuracy is increased greatly, and reliable reference data can be provided to the power supply department during power grid load adjusting.

Description

technical field [0001] The invention relates to a method for predicting charging and discharging behavior of an electric vehicle considering the influence of a mobile social network, and belongs to the technical field of power transmission and distribution. Background technique [0002] The large-scale use of electric vehicles has formed a huge charging demand, and has also brought great challenges to the planning and operation of the power grid. Therefore, it is particularly important to predict and study the charging and discharging behavior of electric vehicles. [0003] With the improvement of the time-of-use electricity price strategy and the V2G (Vehicle-to-grid abbreviation), it describes such a system: when the hybrid electric vehicle or the pure electric vehicle is not running, the electric motor connected to the grid uses energy In turn, when the battery of an electric vehicle needs to be fully charged, the current can be extracted from the grid and fed to the batt...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06
Inventor 李刚董耀众宋雨申金波
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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