Reverse discharge ordered control method based on user heterogeneity and multi-element benefit cooperation

Abstract

The application discloses a reverse discharge order control method based on user heterogeneity and multi-element benefit cooperation and belongs to the technical field of transportation and power combination. In order to solve the problem of optimal cooperation of power grid, user and traffic benefit in reverse discharge, the application constructs basic parameters of electric vehicle users with reverse discharge function, establishes user discharge decision, establishes power grid regulation power constraint condition based on discharge power of the vehicle, establishes regional road traffic flow constraint condition based on superimposed discharge traffic, establishes a reverse discharge price multi-objective optimization model, carries out price feasibility check on the reverse discharge price multi-objective optimization model by using the constraint condition, carries out price release and user response instruction after the check, adjusts the dynamic price strategy after the check fails, executes the charging and discharging operation based on the user response instruction, synchronously updates multi-dimensional operation data of the Internet of Vehicles platform, the power grid dispatching system and the traffic hub monitoring platform, and evaluates the effect of the multi-objective optimization model.

Description

Technical Field

[0001] This invention belongs to the field of transportation and power integration technology, specifically relating to a reverse discharge orderly control method based on user heterogeneity and multi-benefit synergy. Background Technology

[0002] In existing technologies, V2G electricity price optimization models mainly focus on grid power balance and maximizing user revenue. For example, they incentivize users to participate in charging by setting fixed or time-of-use prices, or construct optimal scheduling models aimed at grid load easing. Some studies have considered the coupling between charging pile deployment and transportation networks, but the following problems still exist:

[0003] 1. The complexity of factors influencing electric vehicle users' discharge decisions was not considered;

[0004] 2. If a large number of electric vehicles concentrate on going to charging stations to discharge during the evening rush hour, the increased discharge traffic will be superimposed on the background traffic, which may easily lead to regional traffic congestion.

[0005] 3. Existing optimization objective functions consider only one factor, mostly maximizing the benefits on the user side, and lack comprehensive consideration of the negative effects on the power grid side and traffic congestion. Summary of the Invention

[0006] The problem this invention aims to solve is to achieve optimal synergy among the power grid, users, and transportation benefits. It proposes a reverse discharge orderly control method based on user heterogeneity and the synergy of multiple benefits.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A reverse discharge ordered control method based on user heterogeneity and the synergy of multiple benefits includes the following steps:

[0009] S1. Establish basic parameters for users of electric vehicles with reverse discharge function, including user heterogeneity parameters, vehicle status and discharge behavior patterns;

[0010] The specific implementation method of step S1 includes the following steps:

[0011] S1.1. Define user heterogeneity parameters, including income level coefficients. Battery loss sensitivity coefficient Travel purpose and itinerary rigidity coefficient Discharge driving time cost;

[0012] The formula for calculating the cost of discharge driving time is:

[0013]

[0014] in, The discharge driving time cost for the j-th user. Let j be the discharge driving distance of the j-th user. Average vehicle speed For the j-th user's time value, Positively correlated with income level;

[0015] S1.2. Define vehicle state and discharge behavior pattern:

[0016] For vehicles in a travel state, the discharge decision only considers whether to discharge before departure or after arrival at the destination. Users then choose the discharge decision based on convenience, or choose not to discharge, thus obtaining the discharge distance for the j-th user in a travel state. Where min() is the minimum value function, Let j be the one-way distance from the starting point to the charging station for the j-th user. Let be the one-way distance from the destination to the charging station for the j-th user;

[0017] For a stationary vehicle, the travel distance of the j-th user discharging electricity while stationary is: ,in, Let be the distance from the j-th user's parking spot to the nearest charging station;

[0018] S2. Based on the basic parameters obtained in step S1, establish user discharge decisions;

[0019] S3. For vehicles that are determined to be discharging using the user discharge strategy in step S2, multi-dimensional operation data are collected in real time through the vehicle network platform, power grid dispatch system, traffic hub monitoring platform or equipment to establish dual constraints, including power grid regulation power constraints based on the discharge power of the discharging vehicle and regional road traffic flow constraints based on the superimposed discharge traffic.

[0020] S4. Establish a multi-objective optimization model for reverse discharge electricity pricing to generate dynamic electricity pricing strategies;

[0021] S5. Use dual constraints to verify the feasibility of the reverse discharge electricity price multi-objective optimization model. If the verification is successful, issue the electricity price and issue user participation reverse discharge response instructions. If the verification fails, adjust the dynamic electricity price strategy until the verification is successful.

[0022] S6. Based on user response commands, the user performs charging and discharging operations, and then synchronously updates multi-dimensional operational data from the vehicle network platform, power grid dispatching system, and transportation hub monitoring platform;

[0023] S7. Based on the data updated in step S6, evaluate the effectiveness of the multi-objective optimization model for reverse discharge electricity price. If the evaluation is passed, the current round of regulation ends; otherwise, return to step S3 for iterative calculation.

[0024] Furthermore, the specific implementation method of step S2 includes the following steps:

[0025] S2.1. Establish the expected utility function of discharge behavior, including the expected utility function of vehicle discharge behavior in travel states. The expected utility function of the discharge behavior of a stationary vehicle The expression is:

[0026]

[0027]

[0028] in, Net revenue per unit discharge, This is the discharge amount. The cost is calculated based on battery wear per unit of discharge. This is the risk cost coefficient for insufficient power. For mathematical expectation, Based on the anticipated electricity demand for future travel, The current acceptable remaining battery level is represented by `max()`, which is the maximum value function.

[0029] S2.2. Establish the expected utility function for the non-discharge behavior, expressed as:

[0030]

[0031] in, The power reserve utility coefficient, Let be the expected utility function of the j-th user's non-discharge behavior;

[0032] S2.3. Constructing the user discharge decision as Otherwise, it will not discharge. Let be the expected utility function of the discharge behavior of the j-th user.

[0033] Furthermore, the specific implementation method of step S3 includes the following steps:

[0034] S3.1. Collect multi-dimensional operational data in real time through platforms or equipment such as vehicle-to-everything (V2X) platforms, power grid dispatching systems, and transportation hub monitoring systems, including real-time power grid load, frequency and voltage regulation demand, new energy output, charging station vehicle scale, road network traffic flow, vehicle dwell time, and battery SOC status.

[0035] S3.2. Establish double constraints;

[0036] Based on the discharge power of the discharging vehicle, the power constraint condition for grid regulation is established, and its expression is:

[0037]

[0038] in, for The total discharge power of all discharging vehicles at any given moment; The total number of users in the region who can participate in reverse discharge; for The actual discharge power of the j-th user at time j; for The indicator function for the j-th user at time j is 1 if the user is participating in the discharge, and 0 otherwise. for The total power demand for grid discharge at any given time;

[0039] Based on the superimposed discharge traffic, the regional road traffic flow constraint condition is established, and the expression is:

[0040]

[0041] in, for Traffic flow in the area at any given time; for Traffic flow in the area at any given time; for Traffic impact coefficient of the j-th user at time j; To assess the traffic carrying capacity of the regional road network.

[0042] Furthermore, the expression for the multi-objective optimization model of reverse discharge electricity price established in step S4 is as follows:

[0043]

[0044] in, For the total social benefits of reverse discharge, To save costs on the power grid side For user-side discharge revenue, The social costs of traffic congestion , , These are the weight coefficients for each item;

[0045]

[0046] in, For the cost of traditional power generation, The discharge cost is T, which represents the total number of hours the power grid operates throughout the day.

[0047]

[0048] in, The net revenue per unit discharge for the j-th user; Let J be the discharge amount of the j-th user.

[0049]

[0050] in, This is the social cost conversion factor per unit time. The additional travel time caused by the discharge detour for the j-th user; In the background traffic flow Additional delays caused by vehicle interference from discharge vehicles; The total number of vehicles in the background traffic flow.

[0051] Furthermore, the specific implementation method of step S5 includes the following steps:

[0052] S5.1. A multi-objective optimization model for reverse discharge electricity pricing generates a dynamic electricity pricing strategy. This strategy is input into traffic simulation software to simulate user response behavior and traffic impact. The simulation output is the social cost of traffic congestion. and Traffic flow in the area at any time This feedback is then fed back to the multi-objective optimization model for reverse discharge electricity pricing, which adjusts the pricing strategy until... convergence;

[0053] S5.2. Perform a feasibility check on the optimized output electricity pricing strategy obtained in step S5.1 to determine whether the electricity price is within a reasonable range, whether the corresponding discharge power matches the grid regulation requirements, whether the vehicle aggregation scale exceeds the regional road network capacity limit, and whether the user revenue reaches the expected threshold. If the check fails, return to step S5.1 for a new iteration.

[0054] Furthermore, step S7 quantifies the effect of this round of electricity price regulation from three dimensions: peak shaving and valley filling effect on the grid side, traffic hub efficiency on the transportation side, and discharge revenue achievement rate on the user side. It calculates the regulation deviation and forms evaluation indicators to provide a basis for correction in the next round of electricity price optimization.

[0055] The beneficial effects of this invention are:

[0056] The reverse discharge orderly control method based on user heterogeneity and synergy of multiple benefits described in this invention constructs a hierarchical utility model that considers user income, battery wear awareness, travel purpose and time cost in order to accurately characterize user behavior in real scenarios, providing a micro-behavioral basis for dynamic electricity price optimization.

[0057] The present invention discloses a reverse discharge orderly control method based on user heterogeneity and synergy of multiple benefits. By introducing traffic carrying capacity constraints, it avoids local traffic congestion caused by concentrated vehicle travel induced by high discharge benefits, solves the impact of existing V2G electricity prices on traffic, and ensures that the regional road traffic flow after urban traffic plus discharge traffic is less than the traffic carrying capacity.

[0058] This invention discloses a reverse discharge orderly control method based on user heterogeneity and the synergy of multiple benefits. It constructs a reverse discharge electricity price optimization model and establishes an objective function based on grid-side cost savings, user-side discharge revenue, and the negative social costs of traffic congestion. Under the constraints of meeting grid regulation needs and traffic carrying capacity, while satisfying the grid's peak-shaving and valley-filling requirements, it smooths out discharge power fluctuations through dynamic electricity pricing, ensuring network operating efficiency and achieving optimal synergy among the grid, users, and traffic benefits. This improves the quality of grid operation and enhances the overall social benefits of the system. Attached Figure Description

[0059] Figure 1 This is a flowchart of a reverse discharge ordered control method based on user heterogeneity and the synergy of multiple benefits, as described in this invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described specific embodiments are merely a part of the embodiments of the invention, and not all of them. The components of the specific embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations, and the invention may also have other embodiments.

[0061] Therefore, the following detailed description of specific embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected specific embodiments of the invention. All other specific embodiments obtained by those skilled in the art based on these specific embodiments without inventive effort are within the scope of protection of this invention.

[0062] To further understand the invention's content, features, and effects, the following specific embodiments are provided, along with accompanying drawings. Figure 1 Detailed explanation is as follows:

[0063] Example 1:

[0064] A reverse discharge ordered control method based on user heterogeneity and the synergy of multiple benefits, characterized by the following steps:

[0065] S1. Establish basic parameters for users of electric vehicles with reverse discharge function, including user heterogeneity parameters, vehicle status and discharge behavior patterns;

[0066] The specific implementation method of step S1 includes the following steps:

[0067] S1.1. Define user heterogeneity parameters, including income level coefficients. Battery loss sensitivity coefficient Travel purpose and itinerary rigidity coefficient Discharge driving time cost;

[0068] Furthermore, income class coefficient low-income groups (1) Highly sensitive to electricity price incentives, high-income groups ( A value close to 0 indicates a weak response to electricity prices. This coefficient can be categorized by household annual income.

[0069] Furthermore, the battery loss sensitivity coefficient This data reflects users' concerns about the potential for accelerated battery degradation due to discharge. The survey shows that users using the battery swapping model have lower sensitivity to degradation due to the transfer of asset risk. Among non-battery swapping users, those expecting high vehicle replacement frequency (<3-4 years) have lower concerns, while those expecting low replacement frequency have higher concerns. Highly sensitive groups are more concerned about battery warranty and long-term vehicle ownership, exhibiting a higher sensitivity coefficient to battery degradation. The battery loss sensitivity coefficient is close to 1); the low-sensitivity group has a lower sensitivity coefficient. (close to 0)

[0070] Furthermore, the rigidity coefficient of travel purpose and itinerary in rigid travel throughout the entire time period These users are constantly on the go during the evening rush hour, with no available time window for charging; they also travel flexibly during certain periods. These users, while having travel plans during the evening rush hour, have a flexible window of time available. They can remain idle and available throughout the day. These users have no travel plans during the evening rush hour and their vehicles are parked. They can drive to a charging station to discharge their vehicles, without being restricted by travel time.

[0071] The formula for calculating the cost of discharge driving time is:

[0072]

[0073] in, The discharge driving time cost for the j-th user. Let j be the discharge driving distance of the j-th user. Average vehicle speed For the j-th user's time value, Positively correlated with income level;

[0074] S1.2. Define vehicle state and discharge behavior pattern:

[0075] For vehicles in a travel state, the discharge decision only considers whether to discharge before departure or after arrival at the destination. Users then choose the discharge decision based on convenience, or choose not to discharge, thus obtaining the discharge distance for the j-th user in a travel state. Where min() is the minimum value function, Let j be the one-way distance from the starting point to the charging station for the j-th user. Let be the one-way distance from the destination to the charging station for the j-th user;

[0076] For a stationary vehicle, the travel distance of the j-th user discharging electricity while stationary is: ,in, Let be the distance from the j-th user's parking spot to the nearest charging station;

[0077] Furthermore, the impact of state distinction on decision-making lies in the core difference between the two types of states:

[0078] Travel distance cost: When stationary, round trips are required, resulting in higher distance costs; when traveling, only one-way trips are needed, and additional mileage can be reduced through trip planning.

[0079] Time constraints: Users in the travel state are constrained by the time window of their travel destination and have low tolerance for detours; users in the stationary state have relatively free time.

[0080] Trip Purpose Coefficient: User's Travel Status If the user remains stationary and has no further travel plans for the evening, then... .

[0081] S2. Based on the basic parameters obtained in step S1, establish user discharge decisions;

[0082] Furthermore, the specific implementation method of step S2 includes the following steps:

[0083] S2.1. Establish the expected utility function of discharge behavior, including the expected utility function of vehicle discharge behavior in travel states. The expected utility function of the discharge behavior of a stationary vehicle The expression is:

[0084]

[0085]

[0086] in, Net revenue per unit discharge, This is the discharge amount. The cost is calculated based on battery wear per unit of discharge. This is the risk cost coefficient for insufficient power. For mathematical expectation, Based on the anticipated electricity demand for future travel, The current acceptable remaining battery level is represented by `max()`, which is the maximum value function.

[0087] S2.2. Establish the expected utility function for the non-discharge behavior, expressed as:

[0088]

[0089] in, The power reserve utility coefficient, Let be the expected utility function for the j-th user's non-discharge behavior; for vehicles in a stationary state with no further travel plans, , ;

[0090] S2.3. Constructing the user discharge decision as Otherwise, it will not discharge. Let be the expected utility function of the discharge behavior of the j-th user; if the inequality does not hold, then the discharge is rejected or postponed.

[0091] S3. For vehicles that are determined to be discharging using the user discharge strategy in step S2, multi-dimensional operation data are collected in real time through the vehicle network platform, power grid dispatch system, traffic hub monitoring platform or equipment to establish dual constraints, including power grid regulation power constraints based on the discharge power of the discharging vehicle and regional road traffic flow constraints based on the superimposed discharge traffic.

[0092] Furthermore, the specific implementation method of step S3 includes the following steps:

[0093] S3.1. Collect multi-dimensional operational data in real time through platforms or equipment such as vehicle-to-everything (V2X) platforms, power grid dispatching systems, and transportation hub monitoring systems, including real-time power grid load, frequency and voltage regulation demand, new energy output, charging station vehicle scale, road network traffic flow, vehicle dwell time, and battery SOC status.

[0094] S3.2. Establish double constraints;

[0095] Based on the discharge power of the discharging vehicle, the power constraint condition for grid regulation is established, and its expression is:

[0096]

[0097] in, for The total discharge power of all discharging vehicles at any given moment; The total number of users in the region who can participate in reverse discharge; for The actual discharge power of the j-th user at time j; for The indicator function for the j-th user at time j is 1 if the user is participating in the discharge, and 0 otherwise. for The total power demand for grid discharge at any given time;

[0098] Based on the superimposed discharge traffic, the regional road traffic flow constraint condition is established, and the expression is:

[0099]

[0100] in, for Traffic flow in the area at any given time; for Traffic flow in the area at any given time; for Traffic impact coefficient of the j-th user at time j; To assess the traffic carrying capacity of the regional road network.

[0101] S4. Establish a multi-objective optimization model for reverse discharge electricity pricing to generate dynamic electricity pricing strategies;

[0102] Furthermore, the expression for the multi-objective optimization model of reverse discharge electricity price established in step S4 is as follows:

[0103]

[0104] in, For the total social benefits of reverse discharge, To save costs on the power grid side For user-side discharge revenue, The social costs of traffic congestion , , These are the weight coefficients for each item;

[0105]

[0106] in, For the cost of traditional power generation, The discharge cost is T, which represents the total number of hours the power grid operates throughout the day.

[0107]

[0108] in, The net revenue per unit discharge for the j-th user; Let J be the discharge amount of the j-th user.

[0109]

[0110] in, The social cost per unit time conversion factor; The additional travel time caused by the discharge detour for the j-th user; In the background traffic flow Additional delays caused by vehicle interference from discharge vehicles; Given the total number of vehicles in the background traffic flow, the impact of vehicle detours due to V2G discharge is calculated using traffic simulation software (such as SUMO / VISSIM). The social cost of traffic congestion is defined as the value of the additional travel time delay incurred by all vehicles due to V2G discharge behavior.

[0111] Furthermore, the reverse discharge electricity price model can be expressed as:

[0112] ;

[0113] The constraints are:

[0114] ;

[0115] S5. Use dual constraints to verify the feasibility of the reverse discharge electricity price multi-objective optimization model. If the verification is successful, issue the electricity price and issue user participation reverse discharge response instructions. If the verification fails, adjust the dynamic electricity price strategy until the verification is successful.

[0116] Furthermore, the specific implementation method of step S5 includes the following steps:

[0117] S5.1. A multi-objective optimization model for reverse discharge electricity pricing generates a dynamic electricity pricing strategy. This strategy is input into traffic simulation software to simulate user response behavior and traffic impact. The simulation output is the social cost of traffic congestion. and Traffic flow in the area at any time This feedback is then fed back to the multi-objective optimization model for reverse discharge electricity pricing, which adjusts the pricing strategy until... convergence;

[0118] S5.2. Perform a feasibility check on the optimized output electricity pricing strategy obtained in step S5.1 to determine whether the electricity price is within a reasonable range, whether the corresponding discharge power matches the grid regulation requirements, whether the vehicle aggregation scale exceeds the regional road network capacity limit, and whether the user revenue reaches the expected threshold. If the check fails, return to step S5.1 for a new iteration.

[0119] S6. Based on user response commands, the user performs charging and discharging operations, and then synchronously updates multi-dimensional operational data from the vehicle network platform, power grid dispatching system, and transportation hub monitoring platform;

[0120] Furthermore, the dynamic real-time electricity price generated based on the multi-objective optimization model of reverse discharge electricity price is simultaneously released through the vehicle network platform, hub information system and vehicle terminal. Combined with discharge benefit prompts and grid regulation guidance, it drives electric vehicle users to respond autonomously and complete charging and discharging decisions, forming a positive transmission from electricity price signal to user behavior to charging and discharging execution.

[0121] Furthermore, based on user response commands, V2G reverse discharge operations are executed, and data such as the vehicle's actual discharge power, discharge duration, grid-connected electricity, and user execution deviations are collected in real time to form a closed-loop feedback of user behavior execution results, providing a real operational basis for system status updates.

[0122] Furthermore, based on charging and discharging execution feedback and real-time monitoring data, the grid load level, frequency regulation support effect, traffic congestion, vehicle SOC changes, and parking status are updated synchronously.

[0123] S7. Based on the data updated in step S6, evaluate the effectiveness of the multi-objective optimization model for reverse discharge electricity price. If the evaluation is passed, the current round of regulation ends; otherwise, return to step S3 for iterative calculation.

[0124] Furthermore, step S7 quantifies the effect of this round of electricity price regulation from three dimensions: peak shaving and valley filling effect on the grid side, traffic hub efficiency on the transportation side, and discharge revenue achievement rate on the user side. It calculates the regulation deviation and forms evaluation indicators to provide a basis for correction in the next round of electricity price optimization.

[0125] Furthermore, a new round of optimization is initiated based on preset time periods (such as 15min / 30min) or abnormal events (sudden changes in power grid load, excessive traffic congestion, insufficient user response); if the triggering conditions are not met, continuous monitoring and data transmission are carried out; if the conditions are met, the current round of regulation ends or enters rolling iteration.

[0126] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0127] Although this application has been described above with reference to specific embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of this application. In particular, as long as there is no structural conflict, the features in the specific embodiments disclosed in this application can be combined with each other in any way. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, this application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A reverse discharge ordered control method based on user heterogeneity and synergy of multiple benefits, characterized in that, The steps include the following: S1. Establish basic parameters for users of electric vehicles with reverse discharge function, including user heterogeneity parameters, vehicle status and discharge behavior patterns; The specific implementation method of step S1 includes the following steps: S1.

1. Define user heterogeneity parameters, including income level coefficients. Battery loss sensitivity coefficient Travel purpose and itinerary rigidity coefficient Discharge driving time cost; The formula for calculating the cost of discharge driving time is: in, The discharge driving time cost for the j-th user. Let j be the discharge driving distance of the j-th user. Average vehicle speed For the j-th user's time value, Positively correlated with income level; S1.

2. Define vehicle state and discharge behavior pattern: For vehicles in a travel state, the discharge decision only considers whether to discharge before departure or after arrival at the destination. Users then choose the discharge decision based on convenience, or choose not to discharge, thus obtaining the discharge distance for the j-th user in a travel state. Where min() is the minimum value function, Let j be the one-way distance from the starting point to the charging station for the j-th user. Let be the one-way distance from the destination to the charging station for the j-th user; For a stationary vehicle, the travel distance of the j-th user discharging electricity while stationary is... ,in, Let be the distance from the j-th user's parking spot to the nearest charging station; S2. Based on the basic parameters obtained in step S1, establish user discharge decisions; S3. For vehicles that are determined to be discharging based on the user's discharge decision in step S2, multi-dimensional operation data are collected in real time through the vehicle network platform, power grid dispatch system, traffic hub monitoring platform or equipment to establish dual constraints, including power grid regulation power constraints based on the discharge power of the discharging vehicle and regional road traffic flow constraints based on the superimposed discharge traffic. S4. Establish a multi-objective optimization model for reverse discharge electricity pricing to generate dynamic electricity pricing strategies; S5. Use dual constraints to verify the feasibility of the reverse discharge electricity price multi-objective optimization model. If the verification is successful, issue the electricity price and issue user participation reverse discharge response instructions. If the verification fails, adjust the dynamic electricity price strategy until the verification is successful. S6. Based on user response commands, the user performs charging and discharging operations, and then synchronously updates multi-dimensional operational data from the vehicle network platform, power grid dispatching system, and transportation hub monitoring platform; S7. Based on the data updated in step S6, evaluate the effect of the multi-objective optimization model for reverse discharge electricity price. If the evaluation is passed, the current round of regulation ends; if the evaluation is not passed, return to step S3 for iterative calculation. The specific implementation method of step S2 includes the following steps: S2.

1. Establish the expected utility function of discharge behavior, including the expected utility function of vehicle discharge behavior in travel states. The expected utility function of the discharge behavior of a stationary vehicle The expression is: in, Net revenue per unit discharge, This is the discharge amount. The cost is calculated based on battery wear per unit of discharge. This is the risk cost coefficient for insufficient power. For mathematical expectation, Based on the anticipated electricity demand for future travel, The current acceptable remaining battery level is represented by `max()`, which is the maximum value function. S2.

2. Establish the expected utility function for the non-discharge behavior, expressed as: in, The power reserve utility coefficient, Let be the expected utility function of the j-th user's non-discharge behavior; S2.

3. Constructing the user discharge decision as Otherwise, it will not discharge. Let be the expected utility function of the discharge behavior of the j-th user.

2. The reverse discharge ordered control method based on user heterogeneity and synergy of multiple benefits as described in claim 1, characterized in that, The specific implementation method of step S3 includes the following steps: S3.

1. Collect multi-dimensional operational data in real time through vehicle network platforms, power grid dispatching systems, transportation hub monitoring platforms or equipment, including real-time power grid load, frequency and voltage regulation demand, new energy output, charging station vehicle scale, road network traffic flow, vehicle dwell time, and battery SOC status; S3.

2. Establish double constraints; Based on the discharge power of the discharging vehicle, the power constraint condition for grid regulation is established, and its expression is: in, for The total discharge power of all discharging vehicles at any given moment; The total number of users in the region who can participate in reverse discharge; for The actual discharge power of the j-th user at time j; for The indicator function for the j-th user at time j is 1 if the user is participating in the discharge, and 0 otherwise. for The total power demand for grid discharge at any given time; Based on the superimposed discharge traffic, the regional road traffic flow constraint condition is established, and the expression is: in, for Traffic flow in the area at any given time; for Traffic flow in the area at any given time; for Traffic impact coefficient of the j-th user at time j; To assess the traffic carrying capacity of the regional road network.

3. The reverse discharge ordered control method based on user heterogeneity and synergy of multiple benefits as described in claim 2, characterized in that, The expression for the multi-objective optimization model of reverse discharge electricity price established in step S4 is as follows: in, For the total social benefits of reverse discharge, To save costs on the power grid side For user-side discharge revenue, The social costs of traffic congestion , , These are the weight coefficients for each item; in, For the cost of traditional power generation, The discharge cost is T, which represents the total number of hours the power grid operates throughout the day. in, The net revenue per unit discharge for the j-th user; Let J be the discharge amount of the j-th user. in, This is the social cost conversion factor per unit time. The additional travel time caused by the discharge detour for the j-th user; In the background traffic flow Additional delays caused by vehicle interference from discharge vehicles; The total number of vehicles in the background traffic flow.

4. The reverse discharge ordered control method based on user heterogeneity and synergy of multiple benefits as described in claim 3, characterized in that, The specific implementation method of step S5 includes the following steps: S5.

1. A multi-objective optimization model for reverse discharge electricity pricing generates a dynamic electricity pricing strategy. This strategy is input into traffic simulation software to simulate user response behavior and traffic impact. The simulation output is the social cost of traffic congestion. and Traffic flow in the area at any time This feedback is then fed back to the multi-objective optimization model for reverse discharge electricity pricing, which adjusts the pricing strategy until... convergence; S5.

2. Perform a feasibility check on the optimized output electricity pricing strategy obtained in step S5.1 to determine whether the electricity price is within a reasonable range, whether the corresponding discharge power matches the grid regulation requirements, whether the vehicle aggregation scale exceeds the regional road network capacity limit, and whether the user revenue reaches the expected threshold. If the check fails, return to step S5.1 for a new iteration.

5. The reverse discharge ordered control method based on user heterogeneity and synergy of multiple benefits according to claim 4, characterized in that, Step S7 quantifies the effect of this round of electricity price regulation from three dimensions: peak shaving and valley filling effect on the grid side, traffic efficiency of transportation hubs on the transportation side, and discharge revenue achievement rate on the user side. It calculates the regulation deviation and forms evaluation indicators to provide a basis for correction in the next round of electricity price optimization.

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