Vehicle network interaction user cost analysis method, system, device and medium

Abstract

The invention discloses a vehicle network interaction user cost analysis method, system and device, and a medium. The method comprises the following steps: obtaining a discharge cost selected by an electric vehicle user; and comparing the discharge cost selected by the electric vehicle user with a relation mapping curve between the optimal discharge price of the electric vehicle user and the unit discharge cost to obtain the optimal discharge price of the electric vehicle user, the relation mapping curve of the optimal discharge price and the unit discharge cost is obtained based on a behavior-cost model of an electric vehicle user. The method, the system, the equipment and the medium can determine the optimal discharge price of the electric vehicle.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle discharge electricity price pricing, and relates to a vehicle-grid interactive user cost analysis method, system, equipment and medium. Background Art

[0002] With the continuous increase in the number of electric vehicles, a large number of electric vehicles and charging stations will be put into use in the future. Electric vehicles and new energy charging piles will become the most high-quality adjustable resources on the demand side. Rational use of these resources and encouraging resources to participate in power system load regulation in a market-oriented manner will promote the transformation and further development of new power systems. With a large number of electric vehicles and charging stations put into use, the effective management of charging stations has become a difficult problem, especially how to manage discharge prices.

[0003] At present, when staff conduct discharge pricing at electric vehicle charging stations, they generally use a fixed charging standard or set the discharge electricity fee based on the user-side time-of-use electricity price. This method is not sensitive to price, and demand-side personnel are not clear about the optimal price range and cannot accurately determine the optimal discharge price of electric vehicles. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method, system, device and medium for analyzing user cost of vehicle-grid interaction, which can determine the optimal discharge price of an electric vehicle.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme:

[0006] In one aspect of the present invention, the vehicle-network interaction user cost analysis method of the present invention comprises:

[0007] Obtain the discharge cost of electric vehicle users;

[0008] The discharge cost of the electric vehicle user is compared with a relationship mapping curve between an optimal discharge price and unit discharge cost of the electric vehicle user to obtain an optimal discharge price for the electric vehicle user, wherein the relationship mapping curve between the optimal discharge price and unit discharge cost is obtained based on a behavior-cost model of the electric vehicle user.

[0009] A further improvement of the vehicle-network interaction user cost analysis method of the present invention is:

[0010] Furthermore, before comparing the discharge cost of the electric vehicle user with the relationship mapping curve between the optimal discharge price and unit discharge cost of the electric vehicle user, the method further includes:

[0011] Obtain historical operating data of electric vehicle charging stations;

[0012] constructing a behavior-cost model of electric vehicle users based on the historical operation data of the electric vehicle charging station;

[0013] According to the behavior-cost model of the electric vehicle user, the optimal discharge price corresponding to different discharge costs is calculated;

[0014] According to the optimal discharge prices corresponding to the different discharge costs, a relationship mapping curve between the optimal discharge price and the unit discharge cost is constructed.

[0015] Furthermore, the behavior-cost model of the electric vehicle user is expressed as:

[0016]

[0017] Among them, C d =(P c +C loss )Q V +C f +C DT +C L , C I is the income that electric vehicle users obtain by participating in vehicle-network interaction in unit time, η user is the user’s willingness to participate in grid aggregation, E i is the discharge power of the electric vehicle user on the i-th day, Pr d is the discharge price of electric vehicle users, α is the user's preference level for participating in vehicle-grid interaction, P c is the charging price of electric vehicles, C loss is the energy loss cost during the charging and discharging process of electric vehicles, Q V is the total discharge power of the electric vehicle, C f is the loss cost of electric vehicles, C DT is the idling loss cost when the user needs to discharge, C L It is the queuing time cost when the user needs to discharge.

[0018] Furthermore, the constraints of the behavior-cost model of the electric vehicle user include driving time constraints, driving power constraints, user discharge electricity price constraints and electric vehicle discharge constraints.

[0019] Furthermore, the electric vehicle discharge constraint condition is:

[0020]

[0021] Among them, S n,t is the SOC of the nth electric vehicle, η dis is the discharge efficiency of electric vehicles, The upper and lower limits of the battery SOC of the nth electric vehicle.

[0022] In a second aspect of the present invention, the vehicle-network interaction user cost analysis system of the present invention comprises:

[0023] The first acquisition module is used to obtain the discharge cost of the electric vehicle user;

[0024] The comparison module is used to compare the discharge cost of the electric vehicle user with the relationship mapping curve between the optimal discharge price and the unit discharge cost of the electric vehicle user to obtain the optimal discharge price of the electric vehicle user, wherein the relationship mapping curve between the optimal discharge price and the unit discharge cost is obtained based on the behavior-cost model of the electric vehicle user.

[0025] As a further improvement of the vehicle-network interaction user cost analysis system of the present invention,

[0026] Furthermore, it also includes:

[0027] The second acquisition module is used to acquire the historical operation data of the electric vehicle charging station;

[0028] A first building module is used to build a behavior-cost model of electric vehicle users based on the historical operation data of the electric vehicle charging station;

[0029] A calculation module, used for calculating the optimal discharge price corresponding to different discharge costs according to the behavior-cost model of the electric vehicle user;

[0030] The second construction module is used to construct a relationship mapping curve between the optimal discharge price and the unit discharge cost according to the optimal discharge prices corresponding to the different discharge costs.

[0031] Furthermore, the behavior-cost model of the electric vehicle user is expressed as:

[0032]

[0033] Among them, C d =(P c +C loss )Q V +C f +C DT +C L , C I is the income that electric vehicle users obtain by participating in vehicle-network interaction in unit time, η user is the user’s willingness to participate in grid aggregation, E i is the discharge power of the electric vehicle user on the i-th day, Pr d is the discharge price of electric vehicle users, α is the user's preference level for participating in vehicle-grid interaction, P cis the charging price of electric vehicles, C loss is the energy loss cost during the charging and discharging process of electric vehicles, Q V is the total discharge power of the electric vehicle, C f is the loss cost of electric vehicles, C DT is the idling loss cost when the user needs to discharge, C L It is the queuing time cost when the user needs to discharge.

[0034] Furthermore, the constraints of the behavior-cost model of the electric vehicle user include driving time constraints, driving power constraints, user discharge electricity price constraints and electric vehicle discharge constraints.

[0035] Furthermore, the electric vehicle discharge constraint condition is:

[0036]

[0037]

[0038] Among them, S n,t is the SOC of the nth electric vehicle, η dis is the discharge efficiency of electric vehicles, The upper and lower limits of the battery SOC of the nth electric vehicle.

[0039] In a third aspect of the present invention, the computer device of the present invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the vehicle-network interaction user cost analysis method when executing the computer program.

[0040] In a fourth aspect of the present invention, the computer-readable storage medium of the present invention stores a computer program, and when the computer program is executed by a processor, the steps of the vehicle-grid interaction user cost analysis method are implemented.

[0041] The present invention has the following beneficial effects:

[0042] The vehicle-grid interactive user cost analysis method, system, device and medium of the present invention, when in specific operation, obtains the relationship mapping curve between the optimal discharge price and unit discharge cost of the electric vehicle user by reverse deduction through the electric vehicle traveler behavior-cost model. When in use, the discharge cost of the electric vehicle user and the relationship mapping curve between the optimal discharge price and unit discharge cost of the electric vehicle user are compared to obtain the optimal discharge price of the electric vehicle user, so as to provide an effective reference basis for the business personnel of the electric vehicle charging station to formulate the discharge electricity price, thereby making the quotation more reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0044] Figure 1 is a flow chart of the method of the present invention;

[0045] Figure 2 is a schematic diagram of the user value function in the present invention;

[0046] Figure 3 It is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only an embodiment of a part of the present invention, not all embodiments, and is not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concepts disclosed in the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present invention.

[0048] The accompanying drawings show schematic diagrams of structures according to embodiments disclosed in the present invention. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0049] Embodiment 1

[0050] refer to Figure 1 The vehicle-network interaction user cost analysis method of the present invention comprises the following steps:

[0051] 1) Obtaining historical operation data of an electric vehicle charging station, wherein the historical operation data of the electric vehicle charging station includes the electricity purchase cost of electric vehicle users, charging and discharging queue time, electric vehicle user discharge amount, and user driving mileage to reach the electric vehicle charging station.

[0052] 2) constructing a behavior-cost model of electric vehicle travelers based on the historical operation data of the electric vehicle charging station, and obtaining a relationship mapping curve between the optimal discharge price and the unit discharge cost based on the behavior-cost model of the electric vehicle travelers;

[0053] The process of step 2) is:

[0054] 21) Construct a behavior-cost model and calculate the total cost C of electric vehicles participating in discharge based on the behavior-cost model d for:

[0055] C d =(P c +C loss )Q V +C f +C DT +C L

[0056] Among them, P c is the charging price of electric vehicles, C loss is the energy loss cost during the charging and discharging process of electric vehicles, Q V is the total discharge power of the electric vehicle, C f is the loss cost of electric vehicles, C DT is the idling loss cost when the user needs to discharge, C L It is the queuing time cost when the user needs to discharge.

[0057] Figure 2 is the user value function. When car owners receive the discharge electricity price and choose to participate in the vehicle-grid interaction, they will generate benefits and loss utility caused by participating in the vehicle-grid interaction. Research data shows that people's risk preferences will reverse near the reference point. They tend to be risk-averse in the face of benefits and risk-seeking in the face of losses. The farther away from the reference point, the smaller the impact of marginal changes in benefits or losses on people's psychology. This results in an S-shaped value function that describes the limited rationality of decision makers.

[0058] According to the user value function, the behavior-cost model is further modified as follows:

[0059]

[0060] Among them, C d is the cost per unit time for electric vehicle users to participate in vehicle-grid interaction, C I is the income that electric vehicle users obtain by participating in vehicle-network interaction in unit time, η user is the user’s willingness to participate in grid aggregation, E i is the discharge power of the electric vehicle user on the i-th day, Pr dis the discharge price of electric vehicle users, α is the user's preference level for participating in vehicle-grid interaction, and the larger the α value, the higher the user's willingness to participate in vehicle-grid interaction.

[0061] Among them, the energy loss cost C during the discharge process of electric vehicles loss for:

[0062]

[0063] Among them, η c is the charging efficiency of electric vehicles, η d The discharge efficiency of electric vehicles.

[0064] The vehicle loss cost C during the discharge process of electric vehicles f for:

[0065] C f =ξQ V

[0066] Where ξ represents the loss coefficient during the discharge process of the electric vehicle, Q V Indicates the total discharge of the electric vehicle.

[0067] The user's idle driving loss cost C DT for:

[0068] C DT =365∑γS ij nE e V

[0069] Among them, C DT is the idle driving loss cost when the user needs to charge, γ is the road tortuosity coefficient, S ij is the distance from user location i to destination charging station j, n is the average number of vehicles that need to be discharged at each intersection per day, E e is the average power consumption per kilometer of travel, and V is the station discharge electricity price.

[0070] The user's queuing time cost C L for:

[0071] C L =365ξ∑(W qi ΣVn)

[0072] Among them, W qi is the expected waiting time of each charging station, V is the discharge electricity price of the station, and n is the average number of vehicles that need to be discharged per day.

[0073] The constraints of the electric vehicle user's behavior-cost model include driving time constraints, driving power constraints, user discharge electricity price constraints and electric vehicle discharge constraints, specifically.

[0074] a) Driving time constraints:

[0075] Users have a certain tolerance limit for driving time, and EV users have an upper limit constraint on the time cost of charging and discharging travel routes, that is,

[0076]

[0077] Among them, t n The maximum tolerable time required for the nth user to reach the charging station from the current location.

[0078] b) Driving power constraints:

[0079] The selection of charging stations and their driving routes should be within the allowable range of the remaining basic power to avoid the battery power being too low, that is,

[0080]

[0081] Where ω is the energy loss coefficient, D n,s is the driving distance of the nth EV to the charging station s, and are the charging threshold and basic lower limit of the SOC of the battery of the nth electric vehicle respectively; E is the rated capacity of the battery of a single EV.

[0082] c) The user discharge price constraint condition is:

[0083]

[0084] Among them, V min and V max To determine the upper and lower limits of electricity prices for discharge

[0085] d) The electric vehicle discharge constraint conditions are:

[0086]

[0087] Among them, S n,t is the SOC of the nth electric vehicle, η dis is the discharge efficiency of electric vehicles, The upper and lower limits of the battery SOC of the nth electric vehicle.

[0088] 22) According to the behavior-cost model of the electric vehicle user, the optimal discharge price corresponding to different discharge costs is calculated.

[0089] 23) According to the optimal discharge prices corresponding to the different discharge costs, a relationship mapping curve between the optimal discharge price and the unit discharge cost is constructed.

[0090] 3) Obtain the user's discharge cost, compare the user's electricity cost with the relationship mapping curve between the optimal discharge price and unit discharge cost, and determine the optimal discharge price.

[0091] Specifically, after selecting the unit discharge cost, the optimal discharge price can be obtained by querying the relationship mapping curve between the optimal discharge price and the unit discharge cost, thereby guiding the demand side through the design side to determine the optimal discharge price. Then, business personnel provide electric vehicle users with the discharge electricity price for participating in the vehicle-grid interaction during this period based on the optimal discharge price, making the electricity price more instructive.

[0092] According to the present invention, by means of reverse deduction, the electric vehicle traveler behavior-cost model is used to analyze the willingness of electric vehicle travelers to participate in vehicle-grid interaction and the cost of participating in vehicle-grid interaction within a certain driving range, queuing time, and charging electricity price range. Then, based on the participation cost and willingness, the business personnel of the electric vehicle charging station are guided to discuss the optimal discharge electricity price provided to users participating in the vehicle-grid interaction. That is, by guiding the demand side through the design side, the optimal discharge price can be determined according to the optimal cost, thereby making the quotation more reasonable.

[0093] Embodiment 2

[0094] refer to Figure 3 The vehicle-network interaction user cost analysis system of the present invention comprises:

[0095] The first acquisition module is used to obtain the discharge cost of the electric vehicle user;

[0096] The comparison module is used to compare the discharge cost of the electric vehicle user with the relationship mapping curve between the optimal discharge price and the unit discharge cost of the electric vehicle user to obtain the optimal discharge price of the electric vehicle user, wherein the relationship mapping curve between the optimal discharge price and the unit discharge cost is obtained based on the behavior-cost model of the electric vehicle user.

[0097] As an embodiment of the present invention, it also includes:

[0098] The second acquisition module is used to acquire the historical operation data of the electric vehicle charging station;

[0099] A first building module is used to build a behavior-cost model of electric vehicle users based on the historical operation data of the electric vehicle charging station;

[0100] A calculation module, used for calculating the optimal discharge price corresponding to different discharge costs according to the behavior-cost model of the electric vehicle user;

[0101] The second construction module is used to construct a relationship mapping curve between the optimal discharge price and the unit discharge cost according to the optimal discharge prices corresponding to the different discharge costs.

[0102] As an implementation mode of the present invention, the behavior-cost model of the electric vehicle user is expressed as:

[0103]

[0104] Among them, C d =(P c +C loss )Q V +C f +C DT +C L , C I is the income that electric vehicle users obtain by participating in vehicle-network interaction in unit time, η user is the user’s willingness to participate in grid aggregation, E i is the discharge power of the electric vehicle user on the i-th day, Pr d is the discharge price of electric vehicle users, α is the user's preference level for participating in vehicle-grid interaction, P c is the charging price of electric vehicles, C loss is the energy loss cost during the charging and discharging process of electric vehicles, Q V is the total discharge power of the electric vehicle, C f is the loss cost of electric vehicles, C DT is the idling loss cost when the user needs to discharge, C L It is the queuing time cost when the user needs to discharge.

[0105] As an implementation mode of the present invention, the constraint conditions of the behavior-cost model of the electric vehicle user include driving time constraint conditions, driving power constraint conditions, user discharge electricity price constraint conditions and electric vehicle discharge constraint conditions.

[0106] As an implementation mode of the present invention, the electric vehicle discharge constraint condition is:

[0107]

[0108] Among them, S n,t is the SOC of the nth electric vehicle, η dis is the discharge efficiency of electric vehicles, The upper and lower limits of the battery SOC of the nth electric vehicle.

[0109] The division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present application may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0110] Embodiment 3

[0111] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the vehicle-grid interactive user cost analysis method are implemented, for example, including: obtaining historical operation data of an electric vehicle charging station; constructing a behavior-cost model of an electric vehicle user based on the historical operation data of the electric vehicle charging station; calculating the optimal discharge price corresponding to different discharge costs based on the behavior-cost model of the electric vehicle user; constructing a relationship mapping curve between the optimal discharge price and the unit discharge cost based on the optimal discharge price corresponding to the different discharge costs; obtaining the discharge cost of the electric vehicle user; comparing the discharge cost of the electric vehicle user with the relationship mapping curve between the optimal discharge price and the unit discharge cost of the electric vehicle user to obtain the optimal discharge price of the electric vehicle user, wherein the relationship mapping curve between the optimal discharge price and the unit discharge cost is obtained based on the behavior-cost model of the electric vehicle user. The memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc. The processor, the network interface, and the memory are interconnected through an internal bus, and the internal bus may be an industrial standard architecture bus, a peripheral component interconnection standard bus, an extended industrial standard architecture bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs, specifically, the programs may include program codes, and the program codes include computer operation instructions. The memory may include internal memory and non-volatile memory, and provide instructions and data to the processor.

[0112] Embodiment 4

[0113] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of implementing the vehicle-grid interactive user cost analysis method include: obtaining historical operation data of an electric vehicle charging station; constructing a behavior-cost model of an electric vehicle user according to the historical operation data of the electric vehicle charging station; calculating the optimal discharge price corresponding to different discharge costs according to the behavior-cost model of the electric vehicle user; constructing a relationship mapping curve between the optimal discharge price and the unit discharge cost according to the optimal discharge price corresponding to the different discharge costs; obtaining the discharge cost selected by the electric vehicle user; comparing the discharge cost selected by the electric vehicle user with the relationship mapping curve between the optimal discharge price and the unit discharge cost of the electric vehicle user, and obtaining the optimal discharge price of the electric vehicle user, wherein the relationship mapping curve between the optimal discharge price and the unit discharge cost is obtained based on the behavior-cost model of the electric vehicle user. Specifically, the computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may include a random access memory (RAM) and / or a cache memory, etc. The non-volatile memory may include a read-only memory (ROM), a hard disk, a flash memory, an optical disk, a magnetic disk, etc.

[0114] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0115] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for analyzing the user cost of vehicle-network interaction, characterized in that: include: Obtain the discharge cost of electric vehicle users; The discharge cost of the electric vehicle user is compared with a relationship mapping curve between an optimal discharge price and unit discharge cost of the electric vehicle user to obtain an optimal discharge price for the electric vehicle user, wherein the relationship mapping curve between the optimal discharge price and unit discharge cost is obtained based on a behavior-cost model of the electric vehicle user.

2. The vehicle-network interaction user cost analysis method according to claim 1 is characterized in that: Before comparing the discharge cost selected by the electric vehicle user with the relationship mapping curve between the optimal discharge price and the unit discharge cost of the electric vehicle user, the following further comprises: Obtain historical operating data of electric vehicle charging stations; constructing a behavior-cost model of electric vehicle users based on the historical operation data of the electric vehicle charging station; According to the behavior-cost model of the electric vehicle user, the optimal discharge price corresponding to different discharge costs is calculated; According to the optimal discharge prices corresponding to the different discharge costs, a relationship mapping curve between the optimal discharge price and the unit discharge cost is constructed.

3. The vehicle-network interaction user cost analysis method according to claim 2 is characterized in that: The behavior-cost model of electric vehicle users is expressed as: Among them, C d =(P c +C loss )Q v +C f +C DT +C L , C I is the income that electric vehicle users obtain by participating in vehicle-network interaction in unit time, η user is the user’s willingness to participate in grid aggregation, E i is the discharge power of the electric vehicle user on the i-th day, Pr d is the discharge price of electric vehicle users, α is the user's preference level for participating in vehicle-grid interaction, P c is the charging price of electric vehicles, C loss is the energy loss cost during the charging and discharging process of electric vehicles, Q v is the total discharge power of the electric vehicle, C f is the loss cost of electric vehicles, C DT is the idling loss cost when the user needs to discharge, C L It is the queuing time cost when the user needs to discharge.

4. The vehicle-network interaction user cost analysis method according to claim 2 is characterized in that: The constraints of the behavior-cost model of the electric vehicle user include driving time constraints, driving power constraints, user discharge electricity price constraints and electric vehicle discharge constraints.

5. The method for analyzing the cost of vehicle-network interaction users according to claim 4 is characterized in that: The electric vehicle discharge constraint condition is: Among them, S n，t is the SOC of the nth electric vehicle, η dis is the discharge efficiency of electric vehicles, The upper and lower limits of the battery SOC of the nth electric vehicle.

6. A vehicle-network interaction user cost analysis system, characterized in that: include: The first acquisition module is used to obtain the discharge cost of the electric vehicle user; The comparison module is used to compare the discharge cost of the electric vehicle user with the relationship mapping curve between the optimal discharge price and the unit discharge cost of the electric vehicle user to obtain the optimal discharge price of the electric vehicle user, wherein the relationship mapping curve between the optimal discharge price and the unit discharge cost is obtained based on the behavior-cost model of the electric vehicle user.

7. The vehicle-network interaction user cost analysis system according to claim 6 is characterized in that: Also includes: The second acquisition module is used to acquire the historical operation data of the electric vehicle charging station; A first building module is used to build a behavior-cost model of electric vehicle users based on the historical operation data of the electric vehicle charging station; A calculation module, used for calculating the optimal discharge price corresponding to different discharge costs according to the behavior-cost model of the electric vehicle user; The second construction module is used to construct a relationship mapping curve between the optimal discharge price and the unit discharge cost according to the optimal discharge prices corresponding to the different discharge costs.

8. The vehicle-network interaction user cost analysis system according to claim 6 is characterized in that: The behavior-cost model of electric vehicle users is expressed as: Among them, C d =(P c +C loss )Q v +C f +C DT +C L , C I is the income that electric vehicle users obtain by participating in vehicle-network interaction in unit time, η user is the user’s willingness to participate in grid aggregation, E i is the discharge power of the electric vehicle user on the i-th day, Pr d is the discharge price of electric vehicle users, α is the user's preference level for participating in vehicle-grid interaction, P c is the charging price of electric vehicles, C loss is the energy loss cost during the charging and discharging process of electric vehicles, Q v is the total discharge power of the electric vehicle, C f is the loss cost of electric vehicles, C DT is the idling loss cost when the user needs to discharge, C L It is the queuing time cost when the user needs to discharge.

9. The vehicle-network interaction user cost analysis system according to claim 6, characterized in that: The constraints of the behavior-cost model of the electric vehicle user include driving time constraints, driving power constraints, user discharge electricity price constraints and electric vehicle discharge constraints.

10. The vehicle-network interaction user cost analysis system according to claim 9, characterized in that: The electric vehicle discharge constraint condition is: Among them, S n，t is the SOC of the nth electric vehicle, η dis is the discharge efficiency of electric vehicles, The upper and lower limits of the battery SOC of the nth electric vehicle.

11. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the vehicle-network interaction user cost analysis method as described in any one of claims 1 to 5 are implemented.

12. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the vehicle-network interaction user cost analysis method as described in any one of claims 1 to 5 are implemented.

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