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Urban electric vehicle charging decision-making method based on typical load dynamic game

An electric vehicle, dynamic game technology, applied in electric vehicle charging technology, constraint-based CAD, electrical digital data processing, etc., can solve problems such as power system operation and control hazards

Active Publication Date: 2019-02-19
NORTHEASTERN UNIV
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Problems solved by technology

With the popularization of electric vehicles in the future, large-scale electric vehicles will be connected to the

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  • Urban electric vehicle charging decision-making method based on typical load dynamic game
  • Urban electric vehicle charging decision-making method based on typical load dynamic game
  • Urban electric vehicle charging decision-making method based on typical load dynamic game

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Embodiment Construction

[0101] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0102] In order to study the composition of the load in a certain area, it is necessary to know the real-time load data of all users, as well as the topological association information of each load line or special or common change in the area, and it is necessary to clarify where the various coded load properties belong. a category. Through the power grid SCADA system and information information system, information such as power grid topology, user load, load nature, and grid structure can be obtained respectively, so that the proportion of industrial, agricultural, commercial, and residential loads can be calculated, and the load of the entire system can be realized. Str...

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Abstract

The invention provides an urban electric vehicle charging decision-making method based on a typical load dynamic game, which relates to the technical field of charging electric vehicles. According tothe short-term load curve clustering of power network, the typical load types of users in a certain area are obtained, and the ideal charging load curve of electric vehicle is calculated, subject to charge demand constraint, charging time constraint, electric vehicle charging quantity limit, power load curve, distribution network capacity as a boundary condition, the minimum error between the charging load curve and the ideal charging load curve as a constraint condition, the lowest user cost as an objective function, the establishment of optimization equation, and solution, to obtain the charging strategy, through the dynamic game decision-making results to determine whether to charge the electric vehicle. The method provided by the invention can improve the load characteristic of the electric network, minimize the fluctuation of the electric network after the electric vehicle is connected to the electric network, improve the load rate of the electric equipment, and on this basis, minimize the charging cost of the electric vehicle user.

Description

technical field [0001] The invention relates to the technical field of electric vehicle charging, in particular to an urban electric vehicle charging decision-making method based on a typical load dynamic game. Background technique [0002] The global energy crisis and climate change have driven the rapid development and application of electric vehicles worldwide. With the popularity of electric vehicles in the future, large-scale electric vehicles will be connected to the grid for charging and discharging. The entry will bring significant harm to the operation and control of the power system. How to accommodate large-scale electric vehicles connected to the grid without exceeding the capacity of the grid has become a major problem that must be solved in the development and popularization of electric vehicles in the future. In order to solve these difficulties, it is necessary to use reasonable means to regulate the charging and discharging behavior of electric vehicles. A...

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Application Information

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IPC IPC(8): G06F17/50G06K9/62G06Q10/06
CPCG06Q10/06393G06F2111/06G06F2111/04G06F30/20G06F18/23213Y02T90/167Y04S30/12
Inventor 冯健于洋马大中张化光刘金海李云博
Owner NORTHEASTERN UNIV
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