A vehicle-network interaction aggregation scheduling and double-layer incentive pricing method and related equipment

Abstract

The present application relates to a kind of vehicle-network interaction aggregation scheduling and double-layer incentive pricing method and related equipment.The method obtains multiple-source operating state data to construct state vector;Based on the state of charge dynamic model, off-site constraint and safety window evaluation single vehicle adjustment boundary, and determine the lower limit of discharge compensation in combination with battery attenuation cost;Generalized utility model containing multi-dimensional cost is constructed, and user response probability is calculated in combination with price elasticity;Based on the boundary and response probability, superimposed distribution network capacity and slope constraint aggregation generates the commitment adjustment capacity;Rolling horizon control is used to solve the time-sharing service fee, compensation price and charge-discharge power instruction jointly and execute, and utility model parameters are calibrated online according to execution deviation.The present application solves the problem that the existing scheme is separated from physical limit and user's willingness, and improves the robustness of system.

Description

Technical Field

[0001] This invention belongs to the field of vehicle-to-grid (V2G) technology, specifically relating to a V2G interactive aggregation scheduling and two-layer incentive pricing method and related equipment. Background Technology

[0002] With the large-scale integration of electric vehicles into the power grid, the load fluctuations at the end of the distribution network are significant, and the charging load exhibits characteristics of random arrival and peak-hour overlap, which can easily lead to transformer overload and feeder overruns. Relying on the development of bidirectional charging and discharging equipment and aggregated operation mode, electric vehicles can participate in grid demand response and peak shaving and valley filling through orderly charging and reverse discharging, providing the grid with flexible and adjustable resources.

[0003] Existing vehicle-to-grid (V2G) scheduling solutions mostly employ fixed compensation pricing or simple rule-based control mechanisms. Some solutions introduce centralized optimization scheduling algorithms in single-site or single-region scenarios, planning vehicle charging schedules within the depot under macro-level power constraints to achieve peak shaving and valley filling of local grid loads. However, in actual industrial applications and engineering implementations, these V2G scheduling solutions have revealed many limitations. Existing solutions overly idealize the physical assessment of adjustable resources at the bottom level, often estimating resources solely based on the number of connected vehicles and static power thresholds. They fail to incorporate the dynamic evolution of the vehicle's state of charge, stringent off-site compliance constraints, safety window limits, and battery degradation costs into the underlying physical and economic considerations, which can easily lead to the system overestimating the actual available discharge energy and duration.

[0004] Existing scheduling and pricing strategies also fail to establish an effective quantitative correlation between price signals and the uncertainty of user participation. Traditional solutions typically lack analysis of price elasticity and generalized utility for users under different electricity service fees, discharge compensation benefits, and range risk, neglecting the dynamic mapping of users' actual response probabilities. This causes the ideal control strategy output by the optimized model to fail extensively after deployment due to insufficient actual user response willingness. Traditional scheduling models suffer from severe cross-level fragmentation in constraint handling, failing to deeply coordinate single-vehicle adjustment boundaries, user response probabilities, and transformer capacity and distribution network net load ramping constraints at the power station. This makes the scheduling system highly susceptible to local power surges and equipment thermal stress risks in scenarios with tightening distribution network constraints. Furthermore, existing open-loop control architectures lack effective online calibration and re-optimization iteration mechanisms for underlying parameters when facing unavoidable power execution deviations and user participation rate prediction deviations during the execution phase, making them prone to model drift as the system continues to operate. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a vehicle-to-everything (V2X) interactive aggregation scheduling and two-layer incentive pricing method and related equipment, aiming to solve the problem of low fulfillment rate caused by existing solutions deviating from physical limits and user willingness.

[0006] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: According to a first aspect of the present invention, a method for vehicle-to-everything (V2X) interaction aggregation scheduling and two-tier incentive pricing is provided, comprising: Acquire multi-source operational status data of electric vehicles, charging piles, and power distribution networks at power stations, construct operational status vectors, and filter out a set of controllable objects; Based on the dynamic model of the state of charge, off-site constraints, and safety window of each electric vehicle in the controllable object set, the executable adjustment boundary of a single vehicle is evaluated, and the compensation lower limit for participating in reverse discharge is determined in combination with the battery degradation cost. A generalized utility model is constructed that includes electricity service fees, discharge compensation revenue and battery degradation costs. Users are grouped based on price elasticity, and the response probability of each electric vehicle participating in vehicle-to-grid interaction is calculated. Based on the single vehicle's executable adjustment boundary and the response probability, and superimposed with the station transformer capacity constraint and net load ramping constraint, the station-level promised adjustment capacity is aggregated and generated. Based on the promised adjustable capacity, rolling time-domain control is used for collaborative optimization to jointly solve the station time-sharing service fee, reverse discharge compensation price, and charging and discharging power command for the next time window. The system issues instructions on the time-sharing service fee, reverse discharge compensation price, and charging / discharging power to the user terminal and the charging pile for control execution. When the actual execution deviation exceeds a preset threshold, the system triggers online calibration and re-optimization of the parameters in the generalized utility model.

[0007] In one possible implementation of the first aspect, the steps of acquiring multi-source operational status data of electric vehicles, charging piles, and power distribution networks at power stations, constructing operational status vectors, and filtering out a set of controllable objects specifically include: The expected departure time of electric vehicles, target state of charge and historical participation records, as well as the communication link quality and execution status of charging piles are collected as the multi-source operating status data; For data in the multi-source operating status data that are missing the expected departure time or target state of charge, probability estimation is used to fill in the missing data using the historical dwell time distribution. The collected and completed multi-source operational status data are correlated and integrated to construct the operational status vector. Based on the data integrity, communication quality, and historical instruction execution success rate extracted from the operating status vector, a comprehensive credibility score for the electric vehicle and charging pile is calculated. Data that does not meet the consistency check in the running state vector is removed, and objects with a comprehensive credibility score lower than the credibility threshold are downgraded to conservative scheduling mode. The remaining objects are then grouped into the controllable object set.

[0008] In one possible implementation of the first aspect, the step of evaluating the executable adjustment boundary of a single vehicle based on the dynamic model of the state of charge of each electric vehicle in the set of controllable objects, the off-site constraint, and the safety window, and determining the compensation lower limit for participating in reverse discharge in conjunction with the battery degradation cost, specifically includes: Based on the aforementioned departure constraints, priority is given to ensuring the rigid energy replenishment needs that reach the target state of charge before the expected departure time are met. Based on the dynamic model of the state of charge and the safety state limit defined by the safety window, and combined with the charging and discharging efficiency, the energy time window for delayed charging, as well as the energy for reverse discharge, the duration of continuous discharge, and the upper limit of discharge power are calculated to form the adjustable boundary that the vehicle can execute. The battery degradation cost for a single discharge is calculated by mapping discharge energy to discharge rate. The sum of the battery degradation cost and the preset range risk cost is used as the lower limit of compensation for the corresponding electric vehicle to participate in reverse discharge.

[0009] In one possible implementation of the first aspect, the step of constructing a generalized utility model that includes electricity service fees, discharge compensation revenue, and battery degradation costs, segmenting users based on price elasticity, and calculating the response probability of each electric vehicle participating in vehicle-to-grid interaction specifically includes: The price elasticity is characterized by calculating the user's response elasticity parameter to price signals based on historical response records, and users are divided into sensitive, adaptive, and inert groups according to the range of the response elasticity parameter. By combining the expenditure costs of electricity service fees, the convenience costs of waiting time, the costs of range risk, the benefits of discharge compensation, and the costs of battery degradation, a generalized utility model with parameters specific to the corresponding group is constructed. The output of the generalized utility model is transformed into the response probability of the user choosing to charge only in an orderly manner, participate in reverse discharge, or not participate in regulation using the Logit probabilistic selection model.

[0010] In one possible implementation of the first aspect, the step of aggregating and generating the station-level promised adjustable capacity based on the single-vehicle operable adjustment boundary and the response probability, and superimposing the station transformer capacity constraint and net load ramping constraint, specifically includes: Predict the basic rigid charging load of the power station and the corresponding uncertainty prediction margin; Multiply the upper limit of the dischargeable power in the single vehicle's adjustable boundary by the corresponding response probability, and sum them up for all controllable electric vehicles in the depot to obtain the depot-level expected reverse discharge capacity. A conservative reduction factor, determined by the uncertainty prediction margin and the comprehensive confidence score, is introduced. The expected reverse discharge capacity is reduced by multiplying the conservative reduction factor to generate the promised adjustable capacity. The upper limit of the promised adjustable capacity is limited by the difference between the total capacity of the station transformer and the basic rigid charging load, and the change in the station net load in adjacent time windows is limited by the ramp limit defined by the net load ramp constraint.

[0011] In one possible implementation of the first aspect, the step of using rolling time-domain control for collaborative optimization based on the promised adjustable capacity to jointly solve for the station time-sharing service fee, reverse discharge compensation price, and charge / discharge power command for the next time window specifically includes: A comprehensive optimization objective function is constructed, which includes minimizing the power purchase cost of the power station, minimizing the compensation expenditure cost, minimizing the net load peak-valley difference penalty, minimizing the ramp penalty term, and maximizing the ancillary service revenue term. Under the premise of satisfying the single-vehicle executable adjustment boundary, the upper bound of the promised adjustment capacity, the set total compensation budget constraint, and the price smoothing and anti-jitter constraint, the station time-sharing service fee, the reverse discharge compensation price, and the charging and discharging power command specific to a single charging pile are jointly solved to minimize the comprehensive optimization objective function.

[0012] In one possible implementation of the first aspect, the step of sending the station time-sharing service fee, reverse discharge compensation price, and charging / discharging power instructions to the user terminal and the charging pile for control execution, and triggering online calibration and re-optimization of the parameters in the generalized utility model when the actual execution deviation exceeds a preset threshold, further includes a real-time hardware protection mechanism during system operation: Real-time monitoring of the actual state of charge and parking status of electric vehicles; If the system detects that a user has triggered an early departure event or that the battery level has dropped to the safe state of charge limit, the station controller will immediately block the reverse discharge command sent to the corresponding charging pile and force the corresponding charging pile to switch to a unidirectional full-power charging strategy that only requires reaching the target state of charge.

[0013] In one possible implementation of the first aspect, the step of sending the station time-sharing service fee, reverse discharge compensation price, and charging / discharging power instructions to the user terminal and the charging pile for control execution, and triggering online calibration and re-optimization of the parameters in the generalized utility model when the actual execution deviation exceeds a preset threshold, specifically includes: At the end of each rolling cycle, calculate the power execution deviation between the planned charging and discharging power and the actual metered power, as well as the participation rate prediction deviation between the planned response probability and the actual participation rate. When the power execution deviation or the participation rate prediction deviation is greater than the preset threshold, the difference between the actual participation rate and the planned response probability is used as a feedback quantity, and the response elasticity parameter in the generalized utility model is updated online in combination with the step size parameter, and the conservative reduction coefficient during the aggregation calculation is adjusted down simultaneously. Based on the updated generalized utility model and the conservative reduction coefficient, a re-optimization iteration calculation is forcibly triggered to enter the next rolling cycle in order to generate a revised distribution control strategy.

[0014] According to a second aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the aforementioned vehicle-to-everything (V2X) interactive aggregation scheduling and two-tier incentive pricing method.

[0015] According to a third aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned vehicle-to-grid interactive aggregation scheduling and two-layer incentive pricing method.

[0016] According to a fourth aspect of the present invention, a computer program product is provided, which, when executed by a processor, implements the aforementioned vehicle-to-grid interactive aggregation scheduling and two-layer incentive pricing method.

[0017] Compared with the prior art, the present invention has at least the following beneficial effects: This invention introduces a dynamic model of the vehicle's state of charge, off-site constraints, and a safe operating window, combined with the underlying battery degradation cost, to establish a lower limit for reverse discharge compensation, thus changing the traditional static and coarse-grained resource assessment method. This deep physical state-economic cost binding mechanism can accurately define the truly executable adjustment boundaries for a single vehicle while ensuring users' rigid travel needs and battery health and safety. This effectively avoids the scheduling system's blind overestimation of available discharge energy and duration.

[0018] A generalized utility model integrating electricity service fees, discharge compensation revenue, and battery degradation costs was constructed, and user segmentation was performed using price elasticity. This transformed the uncertain subjective willingness of users to participate into continuous and calculable response probability values. This enables the scheduling algorithm to accurately capture the true behavioral tendencies of different vehicle owner groups under multi-dimensional incentive conditions, solving the problem of widespread failure of scheduling commands after their issuance due to the detachment from actual user intentions in previous static strategies.

[0019] In the aggregation and generation of station-level regulation capacity and the joint solution process, this invention deeply couples the microscopic physical regulation boundary of a single vehicle and the user response probability with the macroscopic station transformer capacity and distribution network net load ramping constraints across multiple levels. By relying on rolling time-domain control to jointly solve for time-sharing pricing incentives and charging / discharging power commands, it achieves coordinated linkage between price signal induction and underlying hardware physical constraints. This mechanism ensures that the final generated joint dispatch strategy naturally meets the safety baseline of the power grid hardware, fundamentally eliminating the risk of local power surges and equipment thermal loads in the distribution network caused by blindly pursuing economic benefits.

[0020] To address the unavoidable random disturbances and errors during strategy execution, this invention establishes a closed-loop control mechanism for online parameter calibration and re-optimization triggered by actual execution deviations. When a deviation in actual operating power or participation rate from a preset safety threshold is detected, the system can automatically utilize real feedback data from the execution site to fine-tune and correct hidden parameters within the generalized utility model online and iteratively optimize the scheme. This effectively overcomes the defect of traditional open-loop control architectures where predictive models are prone to distortion and drift over time. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the specific embodiments of the present invention, the drawings used in the description of the specific embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a vehicle-to-everything (V2X) interactive aggregation scheduling and two-layer incentive pricing method according to the present invention.

[0023] Figure 2 This is a schematic diagram illustrating user segmentation and response characteristics based on price elasticity.

[0024] Figure 3 This is a schematic diagram of iterative updates and load feedback guided by a two-tier time-of-use pricing system. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] like Figure 1 As shown, this invention provides a vehicle-to-grid (V2G) interactive aggregation scheduling and two-layer incentive pricing method. This method is executed by a computer system or depot-side control equipment during operation. By fusing multi-source data, modeling user responses, and strongly binding them with underlying physical constraints of the equipment, it achieves synergy between resource scheduling and pricing. The method specifically includes the following steps: S110. Obtain multi-source operating status data of electric vehicles, charging piles and power distribution networks at the site, construct an operating status vector and select a set of controllable objects.

[0027] Specifically, the system collects the operating parameters and status information of each physical terminal in real time through a standard communication interface. After integration, it forms a digitally represented operating status vector, and based on this, it filters out unreliable or uncontrollable devices to determine the objects that actually participate in the scheduling in the current period.

[0028] S120. Based on the dynamic model of the state of charge of each electric vehicle in the controllable object set, the off-site constraints and safety window, evaluate the adjustable boundary that a single vehicle can perform, and determine the lower limit of compensation for participating in reverse discharge in combination with the battery degradation cost.

[0029] The system no longer uses static battery capacity for evaluation. Instead, it transforms the rigid travel demand when the vehicle leaves the station and the battery safety baseline into a physical constraint model to calculate the real and feasible charging and discharging power and energy boundary. At the same time, it transforms the hardware loss caused by reverse discharge into the economic cost of battery degradation, thereby establishing the minimum incentive threshold for equipment to participate in scheduling.

[0030] S130. Construct a generalized utility model that includes electricity service fees, discharge compensation revenue and battery degradation costs, and group users based on price elasticity to calculate the response probability of each electric vehicle participating in vehicle-to-grid interaction.

[0031] In other words, the system quantifies and integrates economic incentives with users' subjective perceived costs, and uses a probability model to predict users' tendency to choose corresponding scheduling strategies under different price signals.

[0032] S140. Based on the single vehicle's executable adjustment boundary and response probability, and superimposed with the station transformer capacity constraint and net load ramping constraint, the station-level promised adjustable capacity is aggregated and generated.

[0033] Specifically, the system aggregates the probabilistic available resources at the individual level to the substation level, and introduces safety boundary limits for the operation of distribution network hardware in the process to ensure that the aggregated regulation capacity is absolutely safe at the physical execution level.

[0034] S150. Based on the committable adjustable capacity, a rolling time-domain control is used for collaborative optimization to jointly solve for the station time-sharing service fee, reverse discharge compensation price, and charging / discharging power command for the next time window. Within a rolling window, the system uses pricing strategy and power allocation as joint decision variables and seeks the optimal solution through an optimization algorithm.

[0035] S160 sends instructions on the time-sharing service fee, reverse discharge compensation price, and charging / discharging power to the user terminal and charging pile for control and execution. When the actual execution deviation exceeds the preset threshold, it triggers online calibration and re-optimization of the parameters in the generalized utility model.

[0036] This embodiment acquires multi-source operational status data and constructs a constraint system by combining physical boundaries and economic costs. It then uses a generalized utility model to predict response probabilities, superimposes hardware power distribution constraints when aggregating capacity, and finally jointly solves for pricing and power commands, executing them in a closed loop. This mechanism solves the technical problem of significant deviations between actual and planned responses after policy release due to the traditional rule-based control model's failure to explicitly consider equipment physical status and user participation. It improves the actual fulfillment rate of vehicle-to-grid (V2G) interactive scheduling commands, and the optimization results are practically feasible.

[0037] In one possible implementation, the steps of acquiring multi-source operational status data of electric vehicles, charging piles, and power distribution networks at power stations, constructing operational status vectors, and filtering out a set of controllable objects include: S210. Collect the expected departure time of electric vehicles, target state of charge and historical participation records, as well as the communication link quality and execution status of charging piles, as multi-source operation status data.

[0038] Optionally, as a supplement to the deep perception of multi-source operational status data, the system also simultaneously acquires event-related information and edge physical states during data collection. Specifically, this includes: acquiring the authorized reverse discharge identifier, remaining mileage requirement, and default records including abnormal exits from the vehicle side; acquiring the charging pile side's bidirectional charging and discharging support identifier and communication packet loss rate; acquiring the branch limit, phase limit, and local conventional load power sequence within the station; and acquiring the demand response capacity assessment rules and response time limits issued by the power grid side.

[0039] S220. For data on the expected departure time or target state of charge that is missing in the multi-source operating status data, probability estimation is used to complete the missing data using the historical dwell time distribution.

[0040] Optionally, the specific processing logic for probability estimation completion is as follows: For vehicles that have not reported their expected departure time, the system performs feature matching based on the current scenario type of the station (such as a park, community, or public station), weekday attributes, and arrival time, maps it to the historical dwell time distribution matrix, and outputs the point estimate and confidence interval of the departure time; For vehicles that lack the current state of charge, the system uses the most recent effective state of charge plus the metered integrated energy at the charging pile end for short-term completion, and labels them as low-confidence samples in the system.

[0041] S230. The collected and completed multi-source operation status data are correlated and integrated to construct and generate an operation status vector.

[0042] The formula for constructing the running state vector is as follows:

[0043] In the formula, Indicates time period The overall operating state vector; Represents the vehicle-side state data matrix; This represents the status data matrix of the charging pile. This represents a matrix of power distribution status data on the station side; This indicates power grid dispatch instructions and power rationing information; This represents the current time-of-use electricity price and ancillary service assessment price signal vector.

[0044] S240. Calculate the comprehensive credibility score of electric vehicles and charging piles based on the data integrity, communication quality, and historical instruction execution success rate extracted from the running state vector.

[0045] The formula for calculating the overall credibility score is as follows:

[0046] In the formula, Indicates equipment The overall credibility score; This represents the data completeness score; Indicates the communication link quality score; This represents the historical execution success rate score; This indicates the score for measurement accuracy; , , , These represent the preset weight parameters corresponding to each score, and satisfy the following conditions: .

[0047] S250. Remove data from the operating state vector that does not meet the consistency verification, and downgrade objects with a comprehensive credibility score below the credibility threshold to conservative scheduling mode. The remaining objects are then grouped into a controllable object set. The consistency verification includes comparing the physical matching degree between the slope of the state of charge change and the metered energy integral. Devices downgraded to conservative scheduling mode will be forcibly deprived of reverse discharge privileges and will only be allowed to participate in unidirectional ordered charging or limited discharge.

[0048] This implementation introduces a historical probability estimation completion mechanism and a multi-dimensional cross-validation comprehensive credibility scoring calculation logic to eliminate and downgrade inconsistent data to generate a reliable operational state vector. This mechanism addresses the technical problem of distortion in the upper-level resource evaluation system caused by missing dimensions, abrupt changes, or communication interruptions in the underlying perception data, preventing invalid vehicles and faulty pile groups from undermining the feasibility of global optimization from the source.

[0049] It should be noted that the off-site constraint mentioned in this invention refers to the physical limitation that, when making any charging and discharging scheduling decisions, the system must ensure that the actual charge of the vehicle's battery is not lower than the preset target state of charge at the expected off-site time (i.e., the moment the charging gun is disconnected and the vehicle leaves the depot). The safety window mentioned in this invention refers to the safe operating range of battery state of charge (SOC) strictly defined by the battery management system (BMS) and the vehicle manufacturer to prevent thermal runaway, overcharging, or over-discharging of the power battery. It is usually defined by the lower and upper limits of the safe state of charge.

[0050] In one possible implementation, the step of evaluating the executable adjustment boundary of a single vehicle based on the dynamic model of the state of charge of each electric vehicle in the controllable object set, off-site constraints, and safety windows, and determining the compensation lower limit for participating in reverse discharge in conjunction with battery degradation costs, includes: S310. Based on the departure constraint, priority is given to ensuring the rigid energy replenishment requirement reaches the target state of charge before the expected departure time. The calculation formula for the rigid energy replenishment requirement is as follows:

[0051] In the formula, Indicates electric vehicles The rigid energy replenishment requirement that must be met before leaving the station; Indicates the target state of charge set by the user; This represents the state of charge quantity, which serves as a risk buffer to prevent insufficient recharging due to anticipated departure errors. Indicates the current time period The state of charge; This indicates the rated usable capacity of the battery.

[0052] S320, based on the dynamic model of state of charge and the safety state of charge limit defined by the safety window, combined with the charging and discharging efficiency, calculates the energy time window for delayed charging, as well as the energy for reverse discharge, the duration of continuous discharge, and the upper limit of discharge power, forming the adjustable boundary that a single vehicle can execute.

[0053] The calculation logic for the dynamic model of the state of charge and the charge / discharge power boundary includes the following expressions:

[0054]

[0055]

[0056]

[0057]

[0058] In the formula, Indicates the state of charge for the next time period; and These represent charging efficiency and discharging efficiency, respectively. and These represent charging power commands and discharging power commands, respectively. Indicates the control step size; Indicates the lower limit of the safe state of charge; Indicates the upper limit of the state of charge; and These are mutually exclusive Boolean control variables for charge and discharge states; and Indicates the maximum allowable charging and discharging power at the vehicle end; and This indicates the maximum allowable charging and discharging power at the charging pile terminal.

[0059] Based on the above physical conditions, the formulas for calculating the reverse discharge energy and its duration are as follows:

[0060]

[0061] In the formula, Indicates the energy that can be discharged in reverse; This indicates the maximum sustainable reverse discharge duration.

[0062] S330: Calculate the battery degradation cost for a single discharge by mapping the discharge energy to the discharge rate, and use the sum of the battery degradation cost and the preset range risk cost as the lower limit of compensation for the corresponding electric vehicle to participate in reverse discharge.

[0063] It should be noted that the battery degradation cost refers to the economic value of equipment loss per single call action, which is calculated by converting the battery cycle life of an electric vehicle into a reduction due to its participation in reverse discharge.

[0064] The formula for calculating the relevant economic costs is as follows:

[0065]

[0066] In the formula, This represents the battery degradation cost incurred during the current time period when participating in reverse scheduling; This represents the unit attenuation coefficient obtained by mapping and converting the current discharge depth, discharge rate, and temperature parameters. This indicates the minimum compensation price that the system will pay to the vehicle. This represents compensation to users for the cost of worrying about battery life.

[0067] This embodiment establishes a state of charge evolution model and explicitly defines dual-end power boundary limits and safe operating windows. Combined with the rigid demand for vehicle travel, it transforms the underlying electrochemical degradation cost into a compensation price floor, solving the technical problem of existing technologies that only make rough estimates, leading to a systematic overestimation of available discharge resources and completely ignoring battery health loss.

[0068] In one possible implementation, it should be noted that the price elasticity described in this invention is a concept from behavioral economics, referring to the sensitivity of users to changes in electricity prices or compensation prices, reflecting the proportional relationship between changes in unit price and changes in users' willingness to participate in vehicle-to-grid interaction. The generalized utility model described in this invention refers to a comprehensive evaluation model that transcends the measurement of a single economic benefit, placing the explicit financial gains and losses (service fees, discharge compensation) faced by users and the implicit psychological and physical losses (time-waiting anxiety, range anxiety, battery life degradation) under a unified mathematical dimension for weighted summation.

[0069] Combination Figure 2 The steps of constructing a generalized utility model that includes electricity service fees, discharge compensation revenue, and battery degradation costs, segmenting users based on price elasticity, and calculating the response probability of each electric vehicle participating in vehicle-to-grid interaction include: S410. Calculate the user's response elasticity parameter to price signals based on historical response records to characterize price elasticity. Divide users into sensitive, adaptive, and inert groups based on the range of the response elasticity parameter.

[0070] The formula for calculating the response elasticity parameter is as follows:

[0071] In the formula, Indicates user The elasticity parameter in response to price signals; This represents the historical probability of actual participation. The level of price incentives faced historically. When At that time, they were classified as a highly responsive sensitive group; when When, they were classified as an adaptive group with moderate response; when At that time, they were classified into a weakly responsive inert group (of which...) and These are the preset high and low elasticity thresholds.

[0072] S420, the expenditure cost of comprehensive electricity price service fees, the convenience cost of waiting time, the cost of range risk, the discharge compensation benefit and the battery degradation cost, and construct a generalized utility model with parameters specific to the corresponding group.

[0073] The functional expression of the generalized utility model is as follows:

[0074]

[0075]

[0076] In the formula, Indicates user During the period Regarding selection behavior The overall generalized utility value derived from the assessment; behavioral type ; , , , , Each weight corresponds to a sensitivity penalty weight for cost and benefit factors, and this set of weights is independently assigned based on the different group types mentioned above; This indicates the electricity service fee that the vehicle needs to pay; The basic electricity price for the power grid; For station Dynamically charged service fee rates; The time cost for convenience includes queuing and access success rate; This is a risk item related to the remaining driving range; This represents the discharge compensation benefit obtained by discharging in the reverse direction to the power grid; The price rate is compensated for by reverse discharge. This represents the battery degradation cost calculated from the underlying data.

[0077] S430: The Logit probabilistic choice model is used to transform the output of the generalized utility model into the response probability of the user choosing to only charge in an orderly manner, participate in reverse discharge, or not participate in regulation.

[0078] The formula for the nonlinear transformation of the response probability is as follows:

[0079] In the formula, This represents the probability of the calculated output response. The scaling parameter is used to adjust for the randomness of different groups in the decision-making process; the denominator is the set of behaviors. The sum of the utility indices of all possible behaviors within the context.

[0080] By introducing price elasticity parameters to segment the characteristics of car owner groups and incorporating the cost and benefit structures of multidimensional games into the generalized utility functions specific to each group, the problem of large execution deviations and frequent failures of optimization solutions caused by treating users as deterministic machine tools after the release of static strategies is solved. Uncertain subjective intentions are transformed into machine-readable predictions that can be used as hard constraint mathematical variables in subsequent collaborative computing.

[0081] In one possible implementation, it should be noted that the uncertainty prediction margin mentioned in this invention refers to a safety buffer power band set through a probability statistical confidence interval to address the mathematical prediction error caused by random factors such as weather and traffic flow affecting the basic charging load of the power station. The conservative reduction coefficient mentioned in this invention is a dynamic multiplier with a value range typically between (0,1]. Its physical meaning is to actively downgrade the expected discharge capacity calculated theoretically by the system to offset the risk of failure to fulfill the grid contract due to underlying uncertainties such as communication interruptions and user defaults on vehicle delivery. Specifically, the step of aggregating and generating the station-level committable adjustable capacity based on the single-vehicle executable adjustment boundary and response probability, and superimposed with the station transformer capacity constraint and net load ramping constraint, includes: S510, Predict the basic rigid charging load of the power station and the corresponding uncertainty prediction margin.

[0082] The specific prediction formula for the basic rigid charging load is as follows:

[0083] In the formula, Indicates station During the period The predicted value of the basic rigid charging load; Record the historical load sequence of the station; This indicates the current status of the station's gun insertion order. Indicates the local operating status of the station; This represents the margin of uncertainty in prediction given by the statistical distribution.

[0084] Optionally, in this step, the system will perform a deep decoupling operation on the basic requirements, separating the total charging requirements into the amount of deferred charging that does not affect the departure target and the amount of mandatory charging that must be started immediately to meet the target. In the aggregation calculation, the mandatory charging amount is physically locked to prevent it from being called by the scheduling algorithm.

[0085] S520. Multiply the upper limit of the dischargeable power in the single vehicle's adjustable boundary by the corresponding response probability, and sum them up for all controllable electric vehicles in the depot to obtain the depot-level expected reverse discharge capacity.

[0086] The formula for the aggregate accumulation of the expected reverse discharge capacity is as follows:

[0087] In the formula, This represents the total expected reverse discharge capacity at the field station level. This refers to the total set of controllable vehicles connected to the station. This represents the response probability of a single vehicle participating in reverse discharge, output through the generalized utility model.

[0088] S530. Introduce a conservative reduction factor determined by the uncertainty prediction margin and the comprehensive credibility score. Use the conservative reduction factor to multiply and reduce the expected reverse discharge capacity to generate a promiseable adjustable capacity.

[0089] The formula for dewatering degradation with a guaranteed adjustable capacity is as follows:

[0090] In the formula, This indicates the risk mitigation and regulation capacity that can be committed to and reported to the grid side; This is a dynamically generated conservative reduction factor.

[0091] S540 limits the upper limit of the commensurable adjustable capacity to the difference between the total capacity of the station transformers and the basic rigid charging load, and limits the change in the station net load in adjacent time windows to the ramp limit defined by the net load ramp constraint.

[0092] The superimposed hardware distribution network constraint expression is as follows:

[0093]

[0094]

[0095] In the formula, This indicates the real-time physical net load of the station; This indicates the physical upper limit of the total capacity of the station feeders or transformers; This indicates the maximum ramp limit set by the net load ramp constraint, used to suppress power surges and prevent thermal stress damage to equipment.

[0096] This embodiment solves the problems of high frequency regulation defaults caused by directly accumulating "number of units × power" and causing overload of distribution transformers due to frequent local adjustments. It extracts the response probability and combines it with the physical limits of the equipment through mathematical product fusion and aggregation. It also constructs a reduction coefficient based on the credibility score to actively squeeze out redundant false power.

[0097] In one possible implementation, the step of collaboratively optimizing based on committable adjustable capacity using rolling time-domain control to jointly solve for the station time-sharing service fee, reverse discharge compensation price, and charge / discharge power command for the next time window includes: S610. Construct a comprehensive optimization objective function, which includes minimizing the power purchase cost of the power station, minimizing the compensation expenditure cost, minimizing the net load peak-valley difference penalty, minimizing the ramp penalty term, and maximizing the ancillary service revenue term.

[0098] Comprehensive optimization objective function The structure is as follows:

[0099] In the formula, ~ These are the system weight coefficients for various costs, penalties, and benefits. The decomposition terms within the objective function are calculated as follows: Electricity purchase cost: ; Compensation expenditure costs: ; Uphill penalty items: ; Net load peak-to-valley difference penalty: ; Ancillary service revenue items: ,in The settlement price for ancillary services issued to the external power grid; The base for the penalty for user experience loss introduced.

[0100] S620, under the premise of satisfying the single-vehicle executable adjustment boundary, the upper limit of the promised adjustment capacity, the set total compensation budget constraint, and the price smoothing and anti-jitter constraint, jointly solve the station time-sharing service fee, reverse discharge compensation price, and charging and discharging power command specific to a single charging pile to achieve the minimum value of the comprehensive optimization objective function.

[0101] When the solver engine runs, the system will forcibly attach business budget and strategy smoothing stability constraint equations:

[0102]

[0103]

[0104]

[0105]

[0106] In the formula, This indicates the upper limit of the total compensation budget set by the platform for a specific period; and These represent the price smoothing and anti-shaking constraint limits that are forcibly set to avoid excessive stimulation that could lead to sudden changes in group behavior.

[0107] Optionally, to prevent the solution engine from crashing under extreme circumstances such as a sudden drop in power distribution limits, a feasibility-priority recovery mechanism is embedded in the system's underlying layer. When the above comprehensive constraints trigger an infeasible solution, the system initiates a step-by-step backoff algorithm: the first priority is to forcibly ensure that the vehicle's off-site charge status meets the standard and that the transformer capacity does not exceed the limit; the second priority is to gradually relax economic constraints and expected revenue targets, and automatically reduce the volume of the capacity pool by adjusting external commitments.

[0108] This embodiment utilizes a multi-weighted collaborative objective function to quantitatively unify operating cost control, performance budget locking, and grid hardware ramp-up suppression within a single multi-dimensional mathematical solution space. Furthermore, it prevents numerical oscillations in control commands by forcibly imposing variable slope constraints. This solves the problems that single peak-shaving scheduling is prone to causing localized periodic fluctuations and that high subsidies lead to long-term operational unsustainability.

[0109] In one possible implementation, the step of sending the station time-sharing service fee, reverse discharge compensation price, and charging / discharging power instructions to the user terminal and the charging pile for control execution, and triggering online calibration and re-optimization of the parameters in the generalized utility model when the actual execution deviation exceeds a preset threshold, also includes a real-time hardware protection mechanism during system operation: The S710 system monitors the actual state of charge and parking status of electric vehicles in real time. It establishes a direct communication thread with the underlying site's microcontroller to poll the physical interface handshake status and BMS-reported parameters at a second-level granularity.

[0110] S720 If it is detected that a user has triggered an early departure event or the battery power has dropped to the safe state of charge limit, the station controller will immediately block the reverse discharge command sent to the corresponding charging pile and force the corresponding charging pile to switch to a unidirectional full-power charging strategy with the only constraint of reaching the target state of charge.

[0111] This embodiment directly embeds the highest priority underlying physical action defense line and logic fuse in the execution stage of the station-side hardware controller, so that the hardware boundary forcibly cuts off the upper-level economic instructions. This overcomes the problem that when there is communication delay, data is not refreshed, or the user does not perform any warning operation, the discharge instructions calculated by the historical model are still forcibly executed, thereby breaking through the battery health limit and causing the user to be unable to complete the trip.

[0112] In one feasible approach, combining Figure 3 As can be seen from the electricity price guidance iteration process, the steps of issuing the time-sharing service fee, reverse discharge compensation price, and charging / discharging power instructions to the user terminal and the charging pile for control execution, and triggering online calibration and re-optimization of the parameters in the generalized utility model when the actual execution deviation exceeds a preset threshold, specifically include: S810. At the end of each rolling cycle, calculate the power execution deviation between the planned charging and discharging power and the actual metered power, and the participation rate prediction deviation between the planned response probability and the actual participation rate.

[0113] The specific formula for calculating monitoring deviation is defined as follows:

[0114]

[0115] In the formula, For power execution deviation; The summary value of the actual metered power uploaded by the smart meter; The power value of the planned charge / discharge command issued by the system in the preceding sequence; This is due to bias in participation rate prediction; This represents the statistical proportion of actual response discharges within the station. This represents the estimated probability of the planned response output from the early stages of the model.

[0116] S820. When the power execution deviation or participation rate prediction deviation is greater than the set preset threshold (i.e., the condition is met). When the actual participation rate and the planned response probability are used as feedback, the response elasticity parameter in the generalized utility model is updated online in combination with the step size parameter, and the conservative reduction factor during aggregation calculation is adjusted simultaneously.

[0117] The online gradient update iteration formula for the core response model is as follows:

[0118] In the formula, This is the updated and corrected group response parameter set matrix for the next control cycle; The old parameter set matrix that caused the current bias; This represents the online update step size for characterizing the feedback suppression learning rate. Simultaneously, the system forcibly reduces the conservative reduction factor for the total aggregated water level at the control stations, significantly locking up energy storage quotas available for external sale.

[0119] S830, based on the updated generalized utility model and conservative reduction coefficient, forcibly triggers the re-optimization iteration calculation to enter the next rolling cycle, in order to generate the corrected distribution control strategy.

[0120] By establishing a mathematical error detection channel connecting the meter feedback and the feedforward model, gradient feedback is introduced to perform online micro-step iteration to cover the implicit parameters of the utility matrix, and this is correlated with the voltage drop system commitment red line. This mechanism solves the technical problem of parameter drift in static open-loop control models during operation in vehicle-to-grid environments caused by the dynamic time-varying nature of user behavior and psychology, as well as sudden equipment failures.

[0121] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of a vehicle-to-grid interactive aggregation scheduling and two-layer incentive pricing method.

[0122] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be Random Access Memory (RAM) or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the vehicle-to-grid interactive aggregation scheduling and two-layer incentive pricing method in the above embodiments.

[0123] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0124] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0127] This invention also provides a computer program product for executing any of the above-described vehicle-to-grid (V2G) interaction aggregation scheduling and two-tier incentive pricing methods. Since the computer program product provided by this invention belongs to the same inventive concept as the above-described V2G interaction aggregation scheduling and two-tier incentive pricing method, it possesses all the advantages of the above-described V2G interaction aggregation scheduling and two-tier incentive pricing method. Therefore, the beneficial effects of the computer program product provided by this invention will not be elaborated upon here.

[0128] In this invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0129] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention.

Claims

1. A vehicle-to-everything (V2X) interactive aggregation scheduling and two-tier incentive pricing method, characterized in that, include: Acquire multi-source operational status data of electric vehicles, charging piles, and power distribution networks at power stations, construct operational status vectors, and filter out a set of controllable objects; Based on the dynamic model of the state of charge, off-site constraints, and safety window of each electric vehicle in the controllable object set, the executable adjustment boundary of a single vehicle is evaluated, and the compensation lower limit for participating in reverse discharge is determined in combination with the battery degradation cost. A generalized utility model is constructed that includes electricity service fees, discharge compensation revenue and battery degradation costs. Users are grouped based on price elasticity, and the response probability of each electric vehicle participating in vehicle-to-grid interaction is calculated. Based on the single vehicle's executable adjustment boundary and the response probability, and superimposed with the station transformer capacity constraint and net load ramping constraint, the station-level promised adjustment capacity is aggregated and generated. Based on the promised adjustable capacity, rolling time-domain control is used for collaborative optimization to jointly solve the station time-sharing service fee, reverse discharge compensation price, and charging and discharging power command for the next time window. The system issues instructions on the time-sharing service fee, reverse discharge compensation price, and charging / discharging power to the user terminal and the charging pile for control execution. When the actual execution deviation exceeds a preset threshold, the system triggers online calibration and re-optimization of the parameters in the generalized utility model.

2. The vehicle-to-grid interactive aggregation scheduling and two-tier incentive pricing method according to claim 1, characterized in that, The steps of acquiring multi-source operational status data of electric vehicles, charging piles, and power distribution networks at power stations, constructing operational status vectors, and selecting a set of controllable objects specifically include: The expected departure time of electric vehicles, target state of charge and historical participation records, as well as the communication link quality and execution status of charging piles are collected as the multi-source operating status data; For data in the multi-source operating status data that are missing the expected departure time or target state of charge, probability estimation is used to fill in the missing data using the historical dwell time distribution. The collected and completed multi-source operational status data are correlated and integrated to construct the operational status vector. Based on the data integrity, communication quality, and historical instruction execution success rate extracted from the operating status vector, a comprehensive credibility score for the electric vehicle and charging pile is calculated. Data that does not meet the consistency check in the running state vector is removed, and objects with a comprehensive credibility score lower than the credibility threshold are downgraded to conservative scheduling mode. The remaining objects are then grouped into the controllable object set.

3. The vehicle-to-grid interactive aggregation scheduling and two-tier incentive pricing method according to claim 1, characterized in that, The step of evaluating the executable adjustment boundary of a single vehicle based on the dynamic model of the state of charge of each electric vehicle in the controllable object set, the off-site constraint, and the safety window, and determining the compensation lower limit for participating in reverse discharge in combination with the battery degradation cost, specifically includes: Based on the aforementioned departure constraints, priority is given to ensuring the rigid energy replenishment needs that reach the target state of charge before the expected departure time are met. Based on the dynamic model of the state of charge and the safety state limit defined by the safety window, and combined with the charging and discharging efficiency, the energy time window for delayed charging, as well as the energy for reverse discharge, the duration of continuous discharge, and the upper limit of discharge power are calculated to form the adjustable boundary that the vehicle can execute. The battery degradation cost for a single discharge is calculated by mapping discharge energy to discharge rate. The sum of the battery degradation cost and the preset range risk cost is used as the lower limit of compensation for the corresponding electric vehicle to participate in reverse discharge.

4. The vehicle-to-grid interactive aggregation scheduling and two-tier incentive pricing method according to claim 1, characterized in that, The steps of constructing a generalized utility model that includes electricity service fees, discharge compensation revenue, and battery degradation costs, segmenting users based on price elasticity, and calculating the response probability of each electric vehicle participating in vehicle-to-grid interaction specifically include: The price elasticity is characterized by calculating the user's response elasticity parameter to price signals based on historical response records, and users are divided into sensitive, adaptive, and inert groups according to the range of the response elasticity parameter. By combining the expenditure costs of electricity service fees, the convenience costs of waiting time, the costs of range risk, the benefits of discharge compensation, and the costs of battery degradation, a generalized utility model with parameters specific to the corresponding group is constructed. The output of the generalized utility model is transformed into the response probability of the user choosing to charge only in an orderly manner, participate in reverse discharge, or not participate in regulation using the Logit probabilistic selection model.

5. The vehicle-to-grid interactive aggregation scheduling and two-tier incentive pricing method according to claim 2, characterized in that, The step of aggregating and generating the station-level promised adjustable capacity based on the single-vehicle executable adjustment boundary and the response probability, and superimposing the station transformer capacity constraint and net load ramping constraint, specifically includes: Predict the basic rigid charging load of the power station and the corresponding uncertainty prediction margin; Multiply the upper limit of the dischargeable power in the single vehicle's adjustable boundary by the corresponding response probability, and sum them up for all controllable electric vehicles in the depot to obtain the depot-level expected reverse discharge capacity. A conservative reduction factor, determined by the uncertainty prediction margin and the comprehensive confidence score, is introduced. The expected reverse discharge capacity is reduced by multiplying the conservative reduction factor to generate the promised adjustable capacity. The upper limit of the promised adjustable capacity is limited by the difference between the total capacity of the station transformer and the basic rigid charging load, and the change in the station net load in adjacent time windows is limited by the ramp limit defined by the net load ramp constraint.

6. The vehicle-to-grid interactive aggregation scheduling and two-tier incentive pricing method according to claim 1, characterized in that, The steps of using rolling time-domain control for collaborative optimization based on the promised adjustable capacity to jointly solve for the station time-sharing service fee, reverse discharge compensation price, and charge / discharge power command for the next time window specifically include: A comprehensive optimization objective function is constructed, which includes minimizing the power purchase cost of the power station, minimizing the compensation expenditure cost, minimizing the net load peak-valley difference penalty, minimizing the ramp penalty term, and maximizing the ancillary service revenue term. Under the premise of satisfying the single-vehicle executable adjustment boundary, the upper bound of the promised adjustment capacity, the set total compensation budget constraint, and the price smoothing and anti-jitter constraint, the station time-sharing service fee, the reverse discharge compensation price, and the charging and discharging power command specific to a single charging pile are jointly solved to minimize the comprehensive optimization objective function.

7. The vehicle-to-grid interactive aggregation scheduling and two-tier incentive pricing method according to claim 3, characterized in that, The step of issuing the station time-sharing service fee, reverse discharge compensation price, and charging / discharging power instructions to the user terminal and the charging pile for control execution, and triggering online calibration and re-optimization of the parameters in the generalized utility model when the actual execution deviation exceeds a preset threshold, also includes a real-time hardware protection mechanism during system operation: Real-time monitoring of the actual state of charge and parking status of electric vehicles; If the system detects that a user has triggered an early departure event or that the battery level has dropped to the safe state of charge limit, the station controller will immediately block the reverse discharge command sent to the corresponding charging pile and force the corresponding charging pile to switch to a unidirectional full-power charging strategy that only requires reaching the target state of charge.

8. The vehicle-to-grid interactive aggregation scheduling and two-tier incentive pricing method according to claim 5, characterized in that, The steps of issuing the station time-sharing service fee, reverse discharge compensation price, and charging / discharging power commands to the user terminal and the charging pile for control execution, and triggering online calibration and re-optimization of the parameters in the generalized utility model when the actual execution deviation exceeds a preset threshold, specifically include: At the end of each rolling cycle, calculate the power execution deviation between the planned charging and discharging power and the actual metered power, as well as the participation rate prediction deviation between the planned response probability and the actual participation rate. When the power execution deviation or the participation rate prediction deviation is greater than the preset threshold, the difference between the actual participation rate and the planned response probability is used as a feedback quantity, and the response elasticity parameter in the generalized utility model is updated online in combination with the step size parameter, and the conservative reduction coefficient during the aggregation calculation is adjusted down simultaneously. Based on the updated generalized utility model and the conservative reduction coefficient, a re-optimization iteration calculation is forcibly triggered to enter the next rolling cycle in order to generate a revised distribution control strategy.

9. 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, it implements a vehicle-to-everything (V2X) interactive aggregation scheduling and two-tier incentive pricing method as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a vehicle-to-everything (V2X) interactive aggregation scheduling and two-tier incentive pricing method as described in any one of claims 1 to 8.

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