Electric vehicle charging dynamic pricing method and system based on user consumption psychology

Abstract

The invention discloses an electric vehicle charging dynamic pricing method and system based on user consumption psychology, and the method comprises the steps: S1, calculating the response time period and the shortest charging time of a user according to the charging behavior data of an electric vehicle user, and screening the user with the demand response capability; s2, constructing a three-dimensional psychological feature vector based on the user with the demand response capability, performing weighted calculation on a user type decision parameter, and dividing user types; s3, constructing a differentiated comprehensive satisfaction model for different types of users; s4, constructing an electric vehicle charging double-layer pricing model based on a stackelberg game; and making a charging station price strategy, thereby realizing flexible regulation and control of the charging load of the electric vehicle. The method has the advantages of reducing the deviation between the scheduling instruction and the actual response, remarkably improving the regulation and control efficiency of the price signal on the load and the like.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of electric vehicles, and in particular to a method and system for dynamic pricing of electric vehicle charging based on user consumption psychology. Background Art

[0002] As electric vehicles are connected to the grid on a large scale, their disordered charging behavior poses significant challenges to the stability and economic viability of the power system. Traditional electricity price machines fail to consider the demand response of electric vehicles. Existing pricing strategies generally ignore differences in user psychology and fail to quantitatively assess the differences in the responses of different types of users to electricity prices. This lack of user preference modeling leads to significant deviations between dispatch instructions and actual responses, weakening the effectiveness of price signals in load regulation. There is an urgent need to establish a dynamic pricing model that integrates user psychology with grid dispatch requirements, enabling flexible regulation of electric vehicle charging loads through price leverage and improving the operational efficiency of microgrids. Summary of the Invention

[0003] In response to the technical problems existing in the prior art, the present invention provides a dynamic pricing method and system for electric vehicle charging based on user consumption psychology, which reduces the deviation between dispatch instructions and actual responses and significantly improves the effectiveness of price signals in regulating loads.

[0004] In order to solve the above technical problems, the technical solution proposed by the present invention is: A dynamic pricing method for electric vehicle charging based on user consumption psychology includes the following steps: S1. Based on the charging behavior data of electric vehicle users, calculate the user's available response time and minimum charging time, and screen users with demand response capabilities. Charging behavior data includes travel time, charging probability selection, and demand response available time. S2. Based on users with demand response capabilities, a three-dimensional psychological feature vector is constructed, and user type decision parameters are calculated through weighted calculation. , according to the decision parameters The value of categorizes users into emergency, normal, or economic user types; S3. Based on the user type in step S2, a differentiated comprehensive satisfaction model is constructed for different types of users; the comprehensive satisfaction model is generated by weighting the user's economic satisfaction and electricity convenience satisfaction; S4. Based on the comprehensive satisfaction model in step S3, a two-tier pricing model for electric vehicle charging based on the Stackelberg game is constructed. The outer layer model is optimized with the goal of minimizing the cost of distribution network operators to determine the transaction electricity price. The inner layer model is optimized with the goal of maximizing the revenue of charging stations and minimizing the charging cost of EV users to formulate a charging station pricing strategy, thereby achieving flexible regulation of electric vehicle charging load.

[0005] Preferably, in step S1, calculating the user response period and the shortest charging time includes: in Dynamically calculated from battery parameters:

[0006] Where, is the battery capacity; is the maximum charging power; The target state of charge set by the user; is the initial state of charge; Construct the demand response available set, including the charging response set S ch and discharge response set S dis , and verifies that the user belongs to the schedulable set by matching the response type.

[0007] Preferably, the charging response set S ch satisfy and , is the anxiety threshold; discharge response set S dis satisfy , is the discharge safety threshold.

[0008] Preferably, the specific process of step S2 is: User psychological feature vector V i It consists of three dimensions, including travel volatility , battery anxiety index and peak shifting potential , each dimension corresponds to a specific consumer psychology characteristic, specifically:

[0009]

[0010]

[0011]

[0012]

[0013] Where, is the standard deviation of the Gaussian distribution of electric vehicle SOC; The user's battery anxiety index; is the user's peak shifting potential value, is the vehicle grid-connected period; N represents the number of historical charging times; K represents the charging event index; Indicates daily mileage; Indicates the arithmetic mean of the user's historical charging SOC; Indicates the arithmetic mean of the user's overall charging SOC; Indicates the user's battery anxiety value; Assign different weights to user psychological feature vectors 、 、 , get the user type decision parameters :

[0014] Where, is the weight of travel volatility; is the weight of the battery anxiety index, is the weight of the peak shifting potential value; according to The value of divides users into three categories: emergency, normal, and economic , specifically:

[0015] Indicates that the user is an emergency type. Indicates that the user is normal. Indicates that the user is economical.

[0016] Preferably, the specific process of generating the comprehensive satisfaction model is: User economic satisfaction index of electric vehicles i and user convenience satisfaction index Specifically:

[0017]

[0018] in, is the electricity price influencing factor; The charging price given to the grid; for Expected charging price; After implementing dynamic time-sharing charging prices, In the time period Charging load within Peak-valley price In the time period Charging load within is the total time of the day; Overall satisfaction of electric vehicle users Characterized by user economic satisfaction and electricity convenience satisfaction, it reflects the different charging strategy choices of electric vehicle users with different preferences, specifically:

[0019] in, for User satisfaction The preference coefficient of represents the economic satisfaction and electricity convenience satisfaction of electric vehicle user i. When j = 1, it represents the economic satisfaction of user i; when j = 2, it represents the electricity convenience satisfaction of user i.

[0020] Preferably, in a two-tier pricing model for electric vehicle charging based on the Stackelberg game, in the outer layer, the operator is the leader, and the charging station and EV are followers; in the inner layer, the charging station is the leader, and the EV is the follower; first, the operator updates the access load of each distribution network node with the optimization goal of minimizing the operator's operating cost, obtains each transaction electricity price, and then sends it to the charging station for the inner layer game; in the inner layer, the charging station formulates a charging and discharging plan with the goal of maximizing profits, and publishes the price to the EV; the EV arranges the charging and discharging plan based on the charging and discharging price information with the goal of minimizing the charging cost; after the inner layer game is over, the load of each node is fed back to the outer layer operator model, and the two-tier game is coordinated and iterated to finally achieve a balance of interests.

[0021] Preferably, the constraints of the inner model charging station model are the charging and discharging power and the charging and discharging electricity price, with the goal of maximizing its own profit, including the electricity sales income and electricity purchase cost. The objective function is:

[0022] Where, 、 and Respectively represent charging stations Profits, electricity sales revenue and electricity purchase costs;

[0023]

[0024] and for Time-limited charging stations The charging and discharging power; and is the decision variable, which represents the charging and discharging price of the charging station; and They represent the electricity prices for charging stations to purchase electricity from the grid and sell electricity to the grid respectively; Compensation costs for charging stations to mobilize EVs to participate in V2G.

[0025] Preferably, the minimum charging cost model for EV users is:

[0026] Where, Indicates the The total cost of charging an EV; 、 and They represent the charging cost, discharging benefit and battery loss cost of EV respectively;

[0027]

[0028]

[0029] is the decision variable, indicating The charging and discharging power of electric vehicles during the period, Indicates charging, Indicates discharge; and They are Electric vehicles during the time period The charge and discharge status binary variable, 1 means charging / discharging, 0 means no charging or discharging; and They represent the energy loss coefficient of battery charging and discharging respectively.

[0030] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described above are executed.

[0031] The present invention further discloses a dynamic pricing system for electric vehicle charging based on user consumption psychology, comprising a memory and a processor connected to each other, wherein a computer program is stored on the memory, and when the computer program is run by the processor, the steps of the method described above are executed.

[0032] Compared with the prior art, the advantages of the present invention are: The present invention accurately screens the set of dispatchable users based on charging behavior data (travel time, charging probability, and available response time period). By calculating the intersection of the responsive time periods and the shortest charging time, it dynamically identifies user groups with charging and discharging response capabilities. Secondly, it innovatively constructs a three-dimensional psychological characteristic vector to quantify user preferences. Combined with weighted decision parameters, users are divided into emergency, normal, and economic types, addressing the defect of traditional models that ignore psychological differences. Finally, a differentiated comprehensive satisfaction model is established based on user type, and a Stackelberg game two-tier pricing framework is driven: the upper-level power grid optimizes the electricity price strategy to improve economy, and the lower-level users adjust the charging and discharging plans to minimize costs while meeting satisfaction constraints. Ultimately, the peak-to-valley difference in load is reduced, the average charging cost of users is lowered, and the response deviation of dispatch instructions is reduced, forming a closed-loop mechanism of "psychological quantification-dynamic pricing-resource collaboration".

[0033] This invention dynamically divides user types through three-dimensional psychological feature vectors (travel volatility / battery anxiety / peak shifting potential), transforming traditional fuzzy "user preferences" into computable decision parameters. , improving the accuracy of response behavior predictions; this invention utilizes a two-tier pricing model based on the Stackelberg game, taking user satisfaction as a core constraint, reducing the deviation between dispatch instructions and actual responses, and significantly improving the effectiveness of price signals in regulating load. This method accurately quantifies the consumer psychology of electric vehicle users and provides decision support for dynamic pricing models and methods for electric vehicle charging based on user consumer psychology. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a diagram of the double-layer Stackelberg game model of the present invention.

[0035] Figure 2 This is a flow chart of an embodiment of the electric vehicle charging dynamic pricing method based on user consumption psychology of the present invention. DETAILED DESCRIPTION

[0036] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0037] like Figure 2 As shown, the electric vehicle charging dynamic pricing method based on user consumption psychology provided by the embodiment of the present invention includes the following steps: S1. Based on the charging behavior data of electric vehicle users, including travel time, charging probability selection, and demand response available time, calculate the user's available response time and shortest charging time, and screen users with demand response capabilities; Dynamically calculate the shortest charging time based on battery parameters The specific process is: Travel time analysis, i.e. user grid connection time Grid demand response period There is a time intersection:

[0038] Charging capacity analysis, i.e. the user's shortest charging time Not exceeding the grid demand response period:

[0039] in Dynamically calculated from battery parameters:

[0040] Where: is the battery capacity; is the maximum charging power; The target state of charge set by the user (in percentage form, such as 80%); is the initial state of charge.

[0041] Construct the demand response available set, including the charging response set S ch and discharge response set S dis and verify that the user belongs to the schedulable set by matching the response type; The charging response set S ch :Users can accept the charging time offset, satisfying and ( is the anxiety threshold, the default value is 60%); Discharge Response Collection S dis :Users have V2G capabilities and meet ( is the discharge safety threshold, the default value is 70%); Response type matches: Verify that the user belongs to the dispatchable set: .

[0042] S2. Based on the schedulable user set, a three-dimensional psychological feature vector is constructed and the user type decision parameters are calculated by weighting. , according to the decision parameters The value of categorizes users into emergency, normal, or economic user types; User psychological feature vector V i It consists of three dimensions: travel volatility, battery anxiety index, and peak-shifting potential. Each dimension corresponds to a specific consumer psychology characteristic:

[0043]

[0044]

[0045]

[0046]

[0047] Where, is the standard deviation of the Gaussian distribution of electric vehicle SOC, This shows that users are more sensitive to convenience. This shows that users are more sensitive to economics; For the user's battery anxiety index, The smaller it is, the more relaxed the user is about the SOC value and the higher their acceptance of delayed charging. The larger the value, the more anxious the user is about the SOC value and the lower their acceptance of delayed charging. is the user's peak shifting potential value, The vehicle grid connection period, The larger the value, the higher the user's potential for peak shifting response. N represents the number of historical charging events, which is used to calculate the total number of historical charging events with volatility. K represents the charging event index, which is the number of a single charging event. Indicates daily mileage; Indicates the arithmetic mean of the user's historical charging SOC; Indicates the arithmetic mean of the user's overall charging SOC; Indicates the user's battery anxiety value, which is the ratio of actual battery life to advertised battery life.

[0048] Assign different weights to the above user psychological feature vectors 、 、 , get the user type decision parameters :

[0049] Where, is the weight of travel volatility; is the weight of the battery anxiety index, is the weight of the peak shifting potential value.

[0050] according to The value of divides users into three categories: emergency, normal, and economic , specifically:

[0051] Indicates that the user is an emergency type. Indicates that the user is normal. This indicates that the user is an economy user. Emergency users have a short grid connection window, low peak-shifting potential, are insensitive to price, and have low responsiveness. Economy users have a long grid connection window, high peak-shifting potential, and high responsiveness. Normal users fall in between the two, with moderate responsiveness.

[0052] S3. Based on the user types divided in S2, build differentiated comprehensive satisfaction models for different types of users.

[0053] User economic satisfaction and electricity convenience satisfaction are selected as two indicators to measure comprehensive satisfaction. Electric vehicle user economic satisfaction measures the relationship between the actual charging price given by the power grid and the user's expected charging price; user electricity convenience satisfaction indicates the degree of change in EV charging behavior:

[0054]

[0055]

[0056]

[0057] in, is the economic satisfaction index, is the electricity price influencing factor; The charging price given to the grid; for Expected charging price, normal, emergency, economy Different users can set it according to actual situation; is the user convenience satisfaction index, After implementing dynamic time-sharing charging prices, In the time period Charging load within Peak-valley price In the time period Charging load within The total time of the day.

[0058] Overall satisfaction of electric vehicle users It is represented by user economic satisfaction and electricity convenience satisfaction, reflecting the different charging strategy choices of electric vehicle users with different preferences.

[0059]

[0060] in, for User satisfaction The preference coefficient of represents the economic satisfaction and electricity convenience satisfaction of electric vehicle user i. When j = 1, it represents the economic satisfaction of user i; when j = 2, it represents the electricity convenience satisfaction of user i.

[0061] At the same time, electric vehicle users The preference coefficient should satisfy:

[0062] S4. Based on comprehensive user satisfaction, a two-tier pricing model for electric vehicle charging is constructed based on the Stackelberg game: the outer model is optimized to minimize the cost of distribution network operators and determine the transaction electricity price; the inner model is optimized to maximize the revenue of charging stations and minimize the charging cost of EV users, and a charging station pricing strategy is formulated. EV users arrange charging and discharging plans based on the electricity price.

[0063] In the two-tier pricing model for electric vehicle charging, the interaction process among charging stations, EVs, and operators can be established as a two-tier Stackelberg game model, such as Figure 1 As shown in the figure. In the outer layer, the operator is the leader, and the charging station and EV are followers. In the inner layer, the charging station is the leader, and the EV is the follower. First, the operator updates the access load of each distribution network node with the optimization goal of minimizing the operator's operating costs. The operator obtains the transaction electricity price, which is then distributed to the charging station for the inner layer game. In the inner layer, the charging station formulates a charging and discharging plan with the goal of maximizing profits, and the price is distributed to the EV. The EV arranges the charging and discharging plan based on the charging and discharging price information with the goal of minimizing charging costs. After the inner layer game concludes, the load of each node is fed back to the outer layer operator model. The two-layer game is coordinated and iterated to ultimately achieve a balance of interests.

[0064] The outer model takes the operator's operating cost as the optimization goal, satisfies the power balance constraints and node voltage constraints, obtains the transaction electricity prices, and sends them to the charging stations for inner-layer game.

[0065] The constraints of the inner model charging station model are charging and discharging power and charging and discharging electricity price. The goal is to maximize its own profit, including electricity sales revenue and electricity purchase cost. The objective function is:

[0066]

[0067]

[0068] Where, 、 and Respectively represent charging stations Profits, electricity sales revenue and electricity purchase costs; and for Time-limited charging stations The charging and discharging power; and is the decision variable, which represents the charging and discharging price of the charging station; and They represent the electricity prices for charging stations to purchase electricity from the grid and sell electricity to the grid respectively; Compensation costs for charging stations to mobilize EVs to participate in V2G.

[0069] The minimum charging cost model for EV users is:

[0070]

[0071]

[0072]

[0073] Where, Indicates the The total cost of charging an EV; 、 and They represent the charging cost, discharging benefit and battery loss cost of EV respectively; is the decision variable, indicating The charging and discharging power of electric vehicles during the period, Indicates charging, Indicates discharge; and They are Electric vehicles during the time period The charge and discharge status binary variable, 1 means charging / discharging, 0 means no charging or discharging; and They represent the energy loss coefficient of battery charging and discharging respectively.

[0074]

[0075]

[0076] The EV model also needs to meet the user's comprehensive satisfaction constraints:

[0077] in for Overall user satisfaction, for The minimum comprehensive satisfaction level for participating in scheduling. The minimum value of the comprehensive satisfaction level can be set according to actual conditions.

[0078] Based on user charging behavior data (including travel time, charging probability selection, and demand response available time period), the present invention dynamically screens users with demand response capabilities by calculating the user's responsive time period and the shortest charging time. Secondly, a three-dimensional user psychological characteristic vector is constructed, and weighted user type decision parameters are generated to accurately classify users into different types. Next, a differentiated comprehensive satisfaction model is established for different user types. Finally, a two-tier pricing model based on Stackelberg game is constructed in conjunction with the satisfaction model, enabling users to independently adjust their charging and discharging plans, providing decision support for the power market that balances grid economy and user satisfaction.

[0079] The present invention accurately screens the set of dispatchable users based on charging behavior data (travel time, charging probability, and available response time period). By calculating the intersection of the responsive time periods and the shortest charging time, it dynamically identifies user groups with charging and discharging response capabilities. Secondly, it innovatively constructs a three-dimensional psychological characteristic vector to quantify user preferences. Combined with weighted decision parameters, users are divided into emergency, normal, and economic types, addressing the defect of traditional models that ignore psychological differences. Finally, a differentiated comprehensive satisfaction model is established based on user type, and a Stackelberg game two-tier pricing framework is driven: the upper-level power grid optimizes the electricity price strategy to improve economy, and the lower-level users adjust the charging and discharging plans to minimize costs while meeting satisfaction constraints. Ultimately, the peak-to-valley difference in load is reduced, the average charging cost of users is lowered, and the response deviation of dispatch instructions is reduced, forming a closed-loop mechanism of "psychological quantification-dynamic pricing-resource collaboration".

[0080] This invention dynamically divides user types through three-dimensional psychological feature vectors (travel volatility / battery anxiety / peak shifting potential), transforming traditional fuzzy "user preferences" into computable decision parameters. , improving the accuracy of response behavior predictions; this invention utilizes a two-tier pricing model based on the Stackelberg game, taking user satisfaction as a core constraint, reducing the deviation between dispatch instructions and actual responses, and significantly improving the effectiveness of price signals in regulating load. This method accurately quantifies the consumer psychology of electric vehicle users and provides decision support for dynamic pricing models and methods for electric vehicle charging based on user consumer psychology.

[0081] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the above-described method. The present invention further provides a dynamic pricing system for electric vehicle charging based on user consumption psychology, comprising an interconnected memory and a processor, the memory having a computer program stored thereon, which, when executed by the processor, performs the steps of the above-described method. The medium and system of the present invention correspond to the above-described method and also have the advantages described for the method.

[0082] The present invention can implement all or part of the process steps in the above-described method embodiments through hardware associated with computer program instructions. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-described method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. Computer-readable storage media include any entity or device capable of carrying computer program code, recording media, USB flash drives, removable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Memory is used to store computer programs and / or modules. The processor implements various functions by running or executing the computer programs and / or modules stored in the memory and accessing data stored in the memory. The memory may include a high-speed random access memory and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0083] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A dynamic pricing method for electric vehicle charging based on user consumption psychology, characterized in that: Including steps: S1. Based on the charging behavior data of electric vehicle users, calculate the user's available response time and minimum charging time, and screen users with demand response capabilities. Charging behavior data includes travel time, charging probability selection, and demand response available time. S2. Based on users with demand response capabilities, a three-dimensional psychological feature vector is constructed, and user type decision parameters are calculated through weighted calculation. , according to the decision parameters The value of categorizes users into emergency, normal, or economic user types; S3. Based on the user type in step S2, a differentiated comprehensive satisfaction model is constructed for different types of users; the comprehensive satisfaction model is generated by weighting the user's economic satisfaction and electricity convenience satisfaction; S4. Based on the comprehensive satisfaction model in step S3, a two-tier pricing model for electric vehicle charging based on the Stackelberg game is constructed. The outer layer model is optimized with the goal of minimizing the cost of distribution network operators to determine the transaction electricity price. The inner layer model is optimized with the goal of maximizing the revenue of charging stations and minimizing the charging cost of EV users to formulate a charging station pricing strategy, thereby achieving flexible regulation of electric vehicle charging load.

2. The electric vehicle charging dynamic pricing method based on user consumption psychology according to claim 1 is characterized in that: In step S1, the user response period and the shortest charging time are calculated. include: in Dynamically calculated from battery parameters: Where, is the battery capacity; is the maximum charging power; The target state of charge set by the user; is the initial state of charge; Build demand response availability sets, including charging response sets S ch and discharge response set S dis , and verifies that the user belongs to the schedulable set by matching the response type.

3. The electric vehicle charging dynamic pricing method based on user consumption psychology according to claim 2 is characterized in that: The charging response set S ch satisfy and , is the anxiety threshold; the discharge response set Sdis satisfies , is the discharge safety threshold; Grid-connected period for users Grid demand response period The time intersection.

4. The electric vehicle charging dynamic pricing method based on user consumption psychology according to claim 1, 2 or 3, characterized in that: The specific process of step S2 is: User psychological feature vector V i It consists of three dimensions, including travel volatility , battery anxiety index and peak shifting potential , each dimension corresponds to a specific consumer psychology characteristic, specifically: Where, is the standard deviation of the Gaussian distribution of electric vehicle SOC; The user's battery anxiety index; is the user's peak shifting potential value, is the vehicle grid-connected period; N represents the number of historical charging times; K represents the charging event index; Indicates daily mileage; Indicates the arithmetic mean of the user's historical charging SOC; Indicates the arithmetic mean of the user's overall charging SOC; Indicates the user's battery anxiety value; Assign different weights to user psychological feature vectors 、 、 , get the user type decision parameters : Where, is the weight of travel volatility; is the weight of the battery anxiety index, is the weight of the peak shifting potential value; according to The value of divides users into three categories: emergency, normal, and economic , specifically: Indicates that the user is an emergency type. Indicates that the user is normal. Indicates that the user is economical.

5. The electric vehicle charging dynamic pricing method based on user consumption psychology according to claim 1, 2 or 3, characterized in that: The specific process of generating a comprehensive satisfaction model is as follows: User economic satisfaction index of electric vehicles i and user convenience satisfaction index Specifically: in, is the electricity price influencing factor; The charging price given to the grid; for Expected charging price; After implementing dynamic time-sharing charging prices, In the time period Charging load within Peak-valley price In the time period Charging load within is the total time of the day; Overall satisfaction of electric vehicle users Characterized by user economic satisfaction and electricity convenience satisfaction, it reflects the different charging strategy choices of electric vehicle users with different preferences, specifically: in, for User satisfaction The preference coefficient of represents the economic satisfaction and electricity convenience satisfaction of electric vehicle user i. When j = 1, it represents the economic satisfaction of user i; when j = 2, it represents the electricity convenience satisfaction of user i.

6. The electric vehicle charging dynamic pricing method based on user consumption psychology according to claim 1, 2 or 3, characterized in that: In the two-tier pricing model for electric vehicle charging based on the Stackelberg game, in the outer layer, the operator is the leader, and the charging station and EV are followers; in the inner layer, the charging station is the leader, and the EV is the follower. First, the operator updates the access load of each distribution network node with the optimization goal of minimizing the operator's operating cost, obtains each transaction electricity price, and sends it to the charging station for the inner layer game. In the inner layer, the charging station formulates a charging and discharging plan with the goal of maximizing profits, and the price is released to the EV. The EV arranges the charging and discharging plan based on the charging and discharging price information with the goal of minimizing the charging cost. After the inner layer game is completed, the load of each node is fed back to the outer layer operator model. The two-tier game coordinates and iterates to ultimately achieve a balance of interests.

7. The electric vehicle charging dynamic pricing method based on user consumption psychology according to claim 6 is characterized in that: The constraints of the inner model charging station model are charging and discharging power and charging and discharging electricity price. The goal is to maximize its own profit, including electricity sales revenue and electricity purchase cost. The objective function is: Where, 、 and Respectively represent charging stations Profits, electricity sales revenue and electricity purchase costs; and for Time-limited charging stations The charging and discharging power; and is the decision variable, which represents the charging and discharging price of the charging station; and They represent the electricity prices for charging stations to purchase electricity from the grid and sell electricity to the grid respectively; Compensation costs for charging stations to mobilize EVs to participate in V2G.

8. The electric vehicle charging dynamic pricing method based on user consumption psychology according to claim 7 is characterized in that: The minimum charging cost model for EV users is: Where, Indicates the The total cost of charging an EV; 、 and They represent the charging cost, discharging benefit and battery loss cost of EV respectively; is the decision variable, indicating The charging and discharging power of electric vehicles during the period, Indicates charging, Indicates discharge; and They are Electric vehicles during the time period The charge and discharge status binary variable, 1 means charging / discharging, 0 means no charging or discharging; and They represent the energy loss coefficient of battery charging and discharging respectively.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computer program performs the steps of the method according to any one of claims 1 to 8.

10. A dynamic pricing system for electric vehicle charging based on user consumption psychology, comprising a memory and a processor connected to each other, wherein a computer program is stored on the memory, characterized in that: When the computer program is executed by a processor, the computer program performs the steps of the method according to any one of claims 1 to 8.

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