V2g electric vehicle aggregator demand response coordinated dispatching method based on user willingness to correct dispatchable capacity

Abstract

The application provides a V2G electric vehicle aggregator demand response cooperative scheduling method based on user willingness correction schedulable capacity, and belongs to the technical field of electric vehicle charging and discharging scheduling. The method first acquires electric vehicle basic state and user travel data, and initially determines the objective scheduling capacity of a single vehicle; secondly, the response degree model and the probability model of accepting scheduling of users participating in V2G scheduling are established by comprehensively considering the battery health state, the remaining stagnation time, the remaining SOC and the discharge price incentive; the single theoretical schedulable capacity is calculated under the conditions of meeting the subsequent travel demand and the minimum SOC constraint, and the user response degree is introduced for correction to obtain the single effective schedulable capacity; then, the corrected single capacity is aggregated according to the cluster to form the schedulable power boundary and the capacity boundary at the aggregator level; a multi-objective scheduling optimization model is constructed, a multi-objective particle swarm algorithm is used to solve the charging and discharging scheme, and a dynamic correction is realized through a rolling update mechanism.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle charging and discharging scheduling technology, and in particular relates to a V2G electric vehicle aggregator demand response collaborative scheduling method based on user willingness to modify schedulable capacity. Background Technology

[0002] With the continuous growth of new energy vehicle ownership, large-scale electric vehicle grid connection has become a significant factor affecting power system operation. Electric vehicles possess both load and energy storage attributes, and centralized charging can exacerbate peak-valley differences and localized overloads in the distribution network. However, V2G technology can enable aggregators to manage distributed vehicles in a unified manner, participating in peak shaving, valley filling, and demand response.

[0003] Existing V2G scheduling methods typically involve aggregators formulating charging and discharging plans based on grid signals, time-of-use pricing, and vehicle access status. However, these methods have significant shortcomings in practical applications: 1. The dispatchable capacity of a single electric vehicle is small, and the access time, disconnection time, and state of charge are highly uncertain. Judging dispatchable capacity solely based on access status can easily lead to overestimation of capacity. 2. Users' willingness to participate in V2G discharging is influenced not only by compensation prices but also by factors such as battery health, remaining SOC, travel demand, and battery wear anxiety. If all connected vehicles are assumed to be dispatchable resources, the aggregator's scheduling plan may deviate significantly from actual response behavior. 3. While some existing methods involve user response probability or compensation incentives, they often remain at the level of willingness assessment or capacity estimation. They fail to effectively embed the actual dispatchable capacity, adjusted for user willingness, into the aggregator's demand response scheduling model. The lack of coupling between capacity assessment and charging / discharging optimization means that scheduling decisions remain based on idealized capacity, making it difficult to reflect the executable response capability under real operating conditions. Summary of the Invention

[0004] The main objective of this invention is to provide a V2G electric vehicle aggregator demand response collaborative scheduling method based on user willingness to modify schedulable capacity, which solves the problems of insufficient characterization of user participation willingness, low accuracy of schedulable capacity assessment, disconnect between capacity assessment and demand response scheduling, and low adaptability of scheduling schemes.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a V2G electric vehicle aggregator demand response collaborative scheduling method based on user willingness to adjust schedulable capacity, comprising the following steps: S1: Obtain basic status data, user behavior data, and demand response-related data of electric vehicles connected to the V2G aggregator platform, make a preliminary judgment on the objective scheduling capability of individual electric vehicles, and identify whether they meet the time and SOC conditions for participating in V2G scheduling. S2: Based on the objective vehicle status in S1, and taking into account battery health status, remaining idle time, remaining SOC and discharge price incentives, a subjective response willingness model for users to participate in V2G scheduling is constructed. The user response rate is quantified through fuzzy logic rules, and a Probit model based on subsidy amount and SOC is established to calculate the probability of users accepting the aggregator's scheduling. S3: For a single electric vehicle with objective scheduling capability, calculate its theoretical schedulable capacity under the conditions of meeting subsequent travel demand, minimum SOC limit and dwell time constraint, and introduce the user responsiveness obtained in S2 into the correction to obtain the actual schedulable capacity of the single vehicle. S4: The schedulable capacity of each individual unit, after being modified according to the user's wishes, is accumulated according to the aggregation range to form the schedulable power boundary and capacity boundary at the cluster level and the aggregator level. S5: Based on the actual schedulable capacity, construct a demand response scheduling optimization model that takes into account the arbitrage revenue of aggregators, the revenue of demand response compensation, the revenue of users, the cost of battery loss and the goal of grid load smoothing, and satisfy the corresponding constraints. Use the multi-objective particle swarm optimization algorithm to solve the charging and discharging scheduling schemes for each time period. Based on the changes in vehicle status and user behavior during the actual execution process, update the user willingness parameters and schedulable capacity on a rolling basis, and solve the scheduling model again.

[0006] In the preferred scheme, the basic status data in step S1 includes at least: the current stored energy of the electric vehicle, the maximum stored energy, the current state of charge, the access duration, the access time, the expected disconnection time, the remaining idle time at the current destination, the remaining SOC, the subsidy amount, the discharge price incentive, the time-of-use price, the response power reported by the aggregator, and the baseline load. The formula for calculating the state of charge of the nth electric vehicle is: ; In the formula, For the first The state of charge of an electric vehicle; For the first The amount of electricity currently stored in the electric vehicle; For the first The maximum amount of electricity a fully charged electric vehicle can store. Actual depth of discharge of the vehicle: ; In the formula, For the first The actual depth of discharge of an electric vehicle; Shortest charge / discharge time: ; In the formula, For the first The shortest charging and discharging time for a single electric vehicle; For the first The actual depth of discharge of an electric vehicle; For the first The maximum amount of electricity a fully charged electric vehicle can store. For the first The rated charging and discharging power of an electric vehicle; Charge / discharge scheduling response coefficient: ; In the formula, For the first electric vehicles at all times Objective capabilities to participate in V2G scheduling; For the first The moment when an electric vehicle is connected to the power grid; For the first The moment when an electric vehicle leaves the power grid; The shortest time required for the vehicle to reach the desired SOC; when The vehicle was determined to meet the objective conditions for dispatch. Battery degradation anxiety costs: ; In the formula, For individual user batteries The cost of loss and anxiety; Additional battery losses incurred by individual electric vehicles participating in V2G scheduling; This represents the psychological resistance users have to battery aging caused by discharge, and ; The variable is a binomial distribution with values ​​ranging from 0 to 1; v=0 represents the user group that accepts the loss, and v=1 represents the user group that resists the loss.

[0007] In the preferred scheme, step S2 involves constructing a user responsiveness model: ; In the formula, For the first electric vehicles at all times The responsiveness of participating in V2G scheduling; The extent to which battery health status affects user willingness to participate; The degree to which the remaining time spent at the current destination affects users' willingness to participate; The extent to which the remaining SOC affects users' willingness to participate; The extent to which discharge electricity price incentives affect users' willingness to participate; For the first electric vehicles at all times Objective scheduling capabilities; User responsiveness function; Normalize the input factor x: ; In the formula, These are the normalized input variables; These are the original input variables; and These are the minimum and maximum values ​​of the variable, respectively. Furthermore, user response intentions were divided into five fuzzy sets using fuzzy logic rules, yielding a value range of... The response results; At the same time, a model of the strength of users' willingness to accept scheduling is established: ; In the formula, For the first The strength of each user's willingness to accept scheduling; , , These are the regression coefficients; For the first The subsidy amount corresponding to each user; For the first The state of charge of each user's vehicle; The error term is a random error term and follows a standard normal distribution; The probability that a user accepts the scheduling is: ; In the formula, For the first The probability that a user will accept the scheduling; The distribution function of the standard normal distribution; , , These are the regression coefficients; For the first The subsidy amount corresponding to each user; For the first The state of charge of each user's vehicle.

[0008] In the preferred scheme, in step S3, after the k-th stroke, the SOC at the end of the discharge scheduling must satisfy: ; In the formula, For the first SOC at the start of the next trip; For the first SOC at the end of the next discharge scheduling; This represents the charging power after the discharge scheduling process has ended. The charging time after the discharge scheduling ends; For charge and discharge efficiency; For electric vehicle battery capacity; For the first SOC upon arrival at the destination of the next trip; And minimum discharge SOC safety constraints: ; In the formula, For the first SOC at the end of the next discharge scheduling; For the first SOC upon arrival at the destination of the next trip; For discharge control power; Discharge scheduling duration; For charge and discharge efficiency; For electric vehicle battery capacity; The minimum SOC allowed throughout the entire process; Meet the conditions At that time, the discharge SOC is: ; In the formula, For the first The State of Charge (SOC) of the electric vehicle during the second dwell phase; For the first SOC upon arrival at the destination of the next trip; This represents the charging power after the discharge scheduling process has ended. The charging time after the discharge scheduling ends; For charge and discharge efficiency; For electric vehicle battery capacity; For discharge control power; Discharge scheduling duration; Then the theoretical schedulable capacity of the nth electric vehicle at time t is given by With battery capacity Common characteristics; After introducing user responsiveness correction, the contribution of a single unit to the schedulable capacity of the cluster is: ; For the first electric vehicles at all times Dischargeable SOC; For the first electric vehicles at all times User responsiveness; For electric vehicle battery capacity; The contributors to the cluster discharge power are: ; This refers to the discharge power level of the electric vehicle in the parking area.

[0009] In the preferred embodiment, in step S4, The V2G schedulable discharge power of the m-th electric vehicle cluster at time t is: ; In the formula, For the first A cluster of electric vehicles at time V2G discharge power; For the first The number of electric vehicles contained in each cluster; For the first electric vehicles at all times User responsiveness; This refers to the discharge power level of the electric vehicle in the parking area. A certain type of functional area A A The schedulable discharge power is: ; In the formula, For a certain type of functional area At any moment V2G discharge power; For type function area The number of electric vehicle clusters participating in the aggregation; For the first A cluster of electric vehicles at time V2G discharge power; The total dispatchable discharge power of all functional areas is: ; In the formula, For a moment Total dispatchable discharge power of the entire electric vehicle cluster; For a certain type of functional area At any moment V2G discharge power; , , and These areas are divided into residential areas, work areas, shopping and leisure areas, and other areas. The schedulable capacity of the m-th cluster is: ; In the formula, For the first A cluster of electric vehicles at time schedulable capacity; For the first electric vehicles at all times Dischargeable SOC; For the first electric vehicles at all times User responsiveness; For electric vehicle battery capacity; For the first The number of electric vehicles contained in each cluster; The schedulable capacity of functional area A is: ; In the formula, For a certain type of functional area At any moment schedulable capacity; For type function area The number of electric vehicle clusters participating in the aggregation; For the first A cluster of electric vehicles at time schedulable capacity; The total schedulable capacity is: ; In the formula, For a moment Total schedulable capacity of all electric vehicle clusters; For a certain type of functional area At any moment schedulable capacity; Furthermore, the actual charging and discharging power available to the aggregator is calculated based on the user's acceptance probability of scheduling: ; ; In the formula, For a moment The charging power of the aggregated electric vehicle cluster; The number of electric vehicles aggregated; For the first electric vehicles at all times The charging power; For a moment The discharge power of the aggregated electric vehicle cluster; For the first electric vehicles at all times The discharge power; For the first The probability that a user will accept the scheduling.

[0010] In the preferred scheme, in step S5, the objective function of the scheduling optimization model includes: Arbitrage profits for aggregators: ; In the formula, For aggregators' arbitrage profits in the electricity market; The profit-sharing ratio between aggregators and users; For a moment Time-of-use electricity pricing; For a moment Discharge power after polymerization; For a moment The combined charging power; The charging and discharging duration of the aggregated electric vehicle cluster; Aggregator demand response compensation revenue: ; In the formula, Compensation for aggregators' demand response; To compensate for the number of times; For aggregators at any time Reported response power; For a moment The response compensation price; The duration of the scheduling; User arbitrage profit allocation: ; In the formula, The arbitrage profits distributed to users; The percentage of revenue allocated to users; For a moment Time-of-use electricity pricing; and These are discharge power and charging power, respectively. The duration of the scheduling; Total cost of user compensation for aggregators: ; In the formula, For battery wear and tear costs; The battery usage allocation ratio between aggregators and users; Battery purchase cost; For a moment The polymer discharge power; This represents the maximum depth of discharge cycle count of the battery. This refers to the maximum depth of discharge of the battery. This is the maximum amount of energy the battery can store. The optimization objective on the grid side is to minimize the daily variance of the load curve after dispatch; the total user revenue is... - ,in Bearing the cost of loss for users To compensate aggregator users for total costs.

[0011] In the preferred scheme, the scheduling model constraints in step S5 include: Output balance constraints: ; In the formula, For aggregators at any time The actual power after participating in demand response; For a moment Baseline load power; Aggregate charging power; This refers to the aggregated discharge power; SOC security constraints: ; In the formula, and These are the minimum and maximum values ​​of SOC, respectively. For a moment The vehicle's state of charge; SOC state update constraints: ; In the formula, and The vehicles at the time and The state of charge; and These are the battery charging efficiency and discharging efficiency, respectively. The scheduling time step; This is the maximum amount of energy the battery can store. Charge and discharge mutual exclusion constraint: ; In the formula, For a moment The discharge power; For a moment The charging power; Demand response output constraint: The power adjustment and charging / discharging power reported by the aggregator at each time period fall within the preset adjustment range, meeting the average output target.

[0012] In the preferred scheme, the rolling update mechanism in step S5 is specifically as follows: The system iterates in a closed loop based on the scheduling cycle. After the cycle ends, the steps are repeated based on the actual response results, changes in vehicle status, and changes in price incentives. The user responsiveness, acceptance probability, cluster effective power, and capacity boundaries are recalculated. The updated boundary parameters are then substituted into the multi-objective particle swarm optimization model to resolve the charging and discharging scheduling scheme, thus achieving rolling updates.

[0013] In the preferred scheme, in step S5, the demand response compensation cost paid by the aggregator to the user includes peak shaving compensation and valley filling compensation: Peak shaving compensation cost is: ; The cost of filling the valley is: ; The cost of responding to user demands and compensating for such demands is: ; In the formula, To compensate for peak shaving costs; To compensate for the cost of filling the valley; The total demand response compensation cost paid by the aggregator to the user; For aggregators at any time The compensation price offered to the user; The discharge power after polymerization; The charging power after aggregation; The battery wear and tear costs borne by the user are: ; In the formula, Battery wear and tear costs borne by the user; The percentage of battery usage borne by the user; Battery purchase cost; The discharge power after polymerization; This represents the maximum depth of discharge cycle count of the battery. This refers to the maximum depth of discharge of the battery. This is the maximum amount of energy the battery can store.

[0014] In the preferred embodiment, the aggregation scope is divided according to functional areas, site management units, or aggregator jurisdiction units, and the functional areas include residential areas, work areas, shopping and leisure areas, and other areas.

[0015] This invention provides a V2G electric vehicle aggregator demand response collaborative scheduling method based on user willingness to adjust schedulable capacity. It addresses the problems in existing V2G electric vehicle aggregator scheduling, such as insufficient characterization of user participation willingness, idealized schedulable capacity assessment, and disconnect between schedulable capacity and demand response scheduling optimization. These problems lead to overestimation of schedulable capacity, large deviations in actual execution of scheduling schemes, and a lack of dynamic adaptability. First, basic vehicle status and travel data are acquired to determine the objective scheduling capacity of individual vehicles. Second, considering battery health, dwell time, SOC, and electricity price incentives, user responsiveness is quantified using fuzzy logic, and a Probit model is established to calculate the probability of accepting scheduling. Then, the theoretical schedulable capacity that meets travel and minimum SOC constraints is calculated, and user responsiveness is incorporated for adjustment. Next, the adjusted capacity is aggregated by cluster and functional area, forming a dual boundary of capacity and power. Finally, a multi-objective optimization model that considers aggregator arbitrage, compensation revenue, user revenue, battery loss, and grid smoothing is constructed to solve the scheduling scheme, and dynamic closed-loop adjustment is achieved through rolling updates. This improves the accuracy of schedulable capacity assessment, enhances the executability of scheduling schemes, improves the completeness of input data by forming a dual boundary of capacity and power, and enhances economic efficiency, safety, and scenario adaptability. Attached Figure Description

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is the overall flowchart of the method of the present invention; Figure 2 This is a schematic diagram illustrating the objective scheduling capability determination and user participation willingness assessment of a single electric vehicle according to the present invention; Figure 3 This is a schematic diagram illustrating the schedulable capacity correction of a single electric vehicle according to the present invention; Figure 4 This is a schematic diagram illustrating the electric vehicle cluster aggregation and the schedulable capacity assessment of the aggregator in this invention; Figure 5 This is a schematic diagram of the aggregator demand response scheduling optimization and rolling update of the present invention. Detailed Implementation

[0017] Example 1 like Figure 1-5 As shown, a V2G electric vehicle aggregator demand response collaborative scheduling method based on user willingness to adjust schedulable capacity includes the following steps: S1: Basic Data Acquisition and Initial Assessment of Vehicle Scheduling Capability. Acquire basic status data, user behavior data, and demand response-related data of electric vehicles connected to the V2G aggregator platform. This data includes at least the vehicle's current state of charge, current stored capacity, maximum stored capacity, access time, estimated disconnection time, remaining dwell time at the current destination, battery health status, remaining SOC, subsidy amount, discharge price incentives, time-of-use pricing, aggregator response power, and baseline load. Then, conduct a preliminary assessment of the objective scheduling capability of individual electric vehicles, identifying whether they meet the time and SOC conditions for participating in V2G scheduling. This forms the basic input required for subsequent user willingness modeling and schedulable capacity assessment.

[0018] S2: Modeling and Calculating the User's Willingness to Participate in V2G Scheduling and the Probability of Accepting Scheduling. Based on the objective vehicle status obtained in S1, and considering factors such as battery health status, remaining idle time, remaining SOC, and discharge price incentives, a subjective response willingness model for user participation in V2G scheduling is constructed. The user's response level is quantified using fuzzy logic rules to obtain the user's responsiveness to V2G scheduling at the current moment. Furthermore, a Probit model is established by combining the subsidy amount and SOC to calculate the probability of the user accepting the aggregator's scheduling, thus describing user behavior from two perspectives: the strength of subjective willingness and whether or not to accept scheduling.

[0019] S3: Calculation of the dispatchable capacity of a single electric vehicle and correction of user willingness. For a single electric vehicle with objective dispatch capability, under the conditions of meeting users' subsequent travel needs, minimum SOC limit and dwell time constraint, calculate its theoretical capacity or theoretical discharge capacity that can participate in V2G discharge dispatch; then, the user responsiveness or dispatch acceptance probability obtained in S2 is introduced into the single-unit capacity calculation process to correct the theoretical dispatchable capacity and obtain the actual dispatchable capacity of the single unit that is closer to the actual execution situation.

[0020] S4: Electric Vehicle Cluster Aggregation and Aggregator Scheduled Capacity Assessment. Individual electric vehicles are aggregated according to the aggregator's management scope, functional area, or access cluster. The scheduled capacity of each individual electric vehicle, after being adjusted according to user preferences, is accumulated to obtain the scheduled discharge power and scheduled capacity of different clusters in different time periods. This further forms a capacity boundary at the aggregator level that can be used to participate in demand response.

[0021] S5: Aggregator Demand Response Scheduling Optimization and Rolling Update. Based on the actual schedulable capacity of aggregators obtained in S4, a scheduling optimization model for V2G aggregators participating in demand response is constructed. The model simultaneously considers objectives such as aggregator arbitrage revenue, demand response compensation revenue, user revenue, battery loss cost, and grid load fluctuations, while satisfying SOC constraints, charging and discharging power constraints, charging and discharging mutual exclusion constraints, and output constraints for participating in demand response. Subsequently, a multi-objective particle swarm optimization algorithm is used to solve for the charging and discharging power and response capacity in each time period to obtain the aggregator scheduling scheme. During actual execution, based on the actual user response results, vehicle status changes, and price incentive changes, the user willingness, acceptance probability, and cluster schedulable capacity are rolled over and updated. The updated results are then fed back into the scheduling model, achieving dynamic correction.

[0022] This embodiment improves the accuracy of schedulable capacity assessment and enhances the feasibility of scheduling schemes; it establishes dual boundaries of capacity and power, improving the integrity of input data; and it enhances economy, security, and scenario adaptability.

[0023] The following steps will be explained in detail.

[0024] In step S1, such as Figure 1-2 As shown, the basic status identification and initial objective scheduling capability assessment of individual electric vehicles connected to the aggregation platform includes the following steps: S1.1: Obtaining the state of charge of electric vehicles For the A vehicle, its state of charge for: ; In the formula, For the first The state of charge of an electric vehicle; For the first The amount of electricity currently stored in the electric vehicle; For the first The maximum energy storage capacity of an electric vehicle when fully charged; the above formula determines the current energy level of the vehicle, providing a basis for subsequent judgment on whether the vehicle has the basic energy conditions to participate in V2G scheduling.

[0025] S1.2: Calculation of actual discharge depth After obtaining the vehicle's current state of charge, in order to further determine the space available for the vehicle to participate in discharge scheduling, the first step is to calculate... Actual depth of discharge of electric vehicles Its expression is: ; In the formula, For the first The actual depth of discharge of an electric vehicle; For the first The state of charge (SOC) of an electric vehicle is given by this formula. This formula shows that the higher the current SOC of the vehicle, the greater the potential depth of discharge scheduling available; when the SOC is low, the space available for participation in V2G discharge scheduling decreases accordingly.

[0026] S1.3: Calculation of the shortest charge / discharge time After determining the actual discharge depth, the next step is to calculate the... The time required for an electric vehicle to complete one shortest charge and discharge cycle Its expression is: ; In the formula, For the first The shortest charging and discharging time for a single electric vehicle; For the first The actual depth of discharge of an electric vehicle; For the first The maximum amount of electricity a fully charged electric vehicle can store. For the first The rated charging and discharging power of the electric vehicle. The above formula yields the shortest time required for the vehicle to complete basic dispatching actions under the current battery level.

[0027] S1.4: Calculation of scheduling response coefficient based on access duration After obtaining the shortest charge / discharge time, calculate the first... Charging and discharging scheduling response coefficient of electric vehicles Its expression is: ; In the formula, For the first The charging and discharging scheduling response coefficient of an electric vehicle; For the first The connection time of each electric vehicle; For the first The shortest charging and discharging time for an electric vehicle. This coefficient characterizes whether the vehicle's time margin for grid connection is sufficient to support its participation in demand response scheduling. A small coefficient indicates insufficient effective time for scheduling; a large coefficient indicates good time conditions for the vehicle to participate in scheduling.

[0028] S1.5: Objective scheduling capability determination based on access time and disconnection time In addition to determining based on access duration, the objective vehicle dispatching capability can be further characterized by combining the vehicle's access time, disconnection time, and the shortest time required to reach the desired SOC. electric vehicles at all times Objective scheduling capability for: ; In the formula, For the first electric vehicles at all times Objective capabilities to participate in V2G scheduling; For the first The moment when an electric vehicle is connected to the power grid; For the first The moment when an electric vehicle leaves the power grid; The shortest time required for the vehicle to reach the desired State of Charge (SOC). This indicates that the vehicle meets the basic time requirements to participate in V2G scheduling during its current stop; when This indicates that vehicles are unlikely to participate in dispatching without affecting subsequent travel demand.

[0029] S1.6: Acquisition of basic data related to user participation intention While completing the initial assessment of the vehicle's objective capabilities, we further obtain the basic variables needed for subsequent user participation intention modeling, including the impact of battery health status, remaining idle time, remaining SOC, and discharge price incentives. Among these, the battery wear anxiety cost is: ; In the formula, For individual user batteries The cost of loss and anxiety; Additional battery losses incurred by individual electric vehicles participating in V2G scheduling; This represents the psychological resistance users have to battery aging caused by discharge, and ; Let be a variable whose values ​​range from 0 to 1 and follow a binomial distribution, where For users who are accepting of battery degradation, This refers to a user group that strongly opposes battery degradation.

[0030] Through the above steps, the current state of charge, actual depth of discharge, shortest charging and discharging time, dispatch response coefficient, objective dispatch capability assessment results, and basic influence quantities required for subsequent willingness modeling are obtained for each electric vehicle. Then, candidate vehicles that meet the basic conditions for participating in V2G dispatching in the current time period are selected, and then step S2 is entered to model the user's willingness to participate in V2G dispatching and calculate the probability of accepting dispatching. This avoids directly including all vehicles connected to the power grid into the dispatching resource pool, and improves the accuracy of subsequent dispatchable capacity assessment and aggregator demand response dispatching.

[0031] In step S2, user willingness to participate in V2G scheduling and the probability of accepting scheduling are modeled and calculated, as follows: Based on the initial determination of the objective scheduling capability of individual electric vehicles completed in step S1, the user's willingness to participate in V2G scheduling is further characterized from the perspective of subjective behavior, such as... Figure 2 As shown.

[0032] S2.1: User participation in V2G scheduling response model construction No. electric vehicles at all times The response model for V2G scheduling is as follows: ; In the formula, For the first electric vehicles at all times The responsiveness of participating in V2G scheduling; The extent to which battery health status affects user willingness to participate; The degree to which the remaining time spent at the current destination affects users' willingness to participate; The extent to which the remaining SOC affects users' willingness to participate; The extent to which discharge electricity price incentives affect users' willingness to participate; For the first electric vehicles at all times Objective scheduling capabilities; Let be the user responsiveness function. The above formula couples the objective dispatchability of vehicles with the user's subjective willingness to participate.

[0033] S2.2: Normalization of influencing factors To eliminate the impact of differences in the dimensions of different input factors on the response model, the input factors were... The normalization process is performed, and its expression is: ; In the formula, These are the normalized input variables; These are the original input variables; and These are the minimum and maximum values ​​of the variable, respectively. This process maps battery health status, remaining idle time, remaining SOC, and discharge price incentives to the same numerical range, facilitating subsequent fuzzy rule reasoning.

[0034] S2.3: Obtaining the impact of battery health status Among the various factors influencing users' willingness to participate in V2G scheduling, battery health status can be quantified through battery wear and tear anxiety costs, expressed as follows: ; In the formula, For individual user batteries The cost of loss and anxiety; Additional battery losses incurred by individual electric vehicles participating in V2G scheduling; The coefficient representing the user's aversion to battery aging caused by discharge, and ; Let be a variable that follows a binomial distribution, where For users who are accepting of battery degradation, This refers to the user group that expresses strong resistance to battery degradation. This formula is used to characterize the degree of subjective resistance users feel due to concerns about reduced battery life.

[0035] S2.4: The participation mechanism of remaining stagnation time, remaining SOC and discharge price incentives In the response model for user participation in V2G dispatch, remaining dwell time, remaining State of Charge (SOC), and discharge price incentives serve as key input factors in response evaluation. A longer remaining dwell time indicates a more sufficient time the user has to stay at their current destination, making them more likely to participate in V2G dispatch. A higher remaining SOC means a greater amount of battery capacity can be released by the vehicle to meet subsequent travel needs, making the user more likely to accept discharge dispatch. A higher discharge price incentive means stronger economic compensation for user participation, typically leading to a higher willingness to participate. These three factors, along with battery health status, constitute the core inputs to the user's subjective willingness to participate.

[0036] S2.5: Response Measurement Based on Fuzzy Logic Rules After input normalization, fuzzy logic rules are used to quantify the responsiveness of users participating in V2G scheduling. User willingness to respond to S2 is divided into five fuzzy sets: extremely weak, weak, moderate, strong, and extremely strong. A membership function is then used to convert the user's subjective participation willingness into a range of values. The response rate results. This transforms the originally subjective and uncertain user participation intentions into numerical variables that can be directly used in subsequent capacity calculations and aggregation scheduling. The aforementioned responsiveness The larger the value, the stronger the user's willingness to participate in V2G scheduling at the current moment; the responsiveness The smaller the value, the weaker the user's willingness to participate in V2G scheduling.

[0037] S2.6: Construction of a model for the strength of user willingness to accept scheduling In addition to the responsiveness model, to further describe whether users accept aggregator scheduling from a probabilistic perspective, a user willingness-to-accept scheduling strength model is established. The subsidy amount and vehicle state of charge are used as explanatory variables. The strength of each user's willingness to accept scheduling is: ; In the formula, For the first The strength of each user's willingness to accept scheduling; , , These are the regression coefficients; For the first The subsidy amount corresponding to each user; For the first The state of charge of each user's vehicle; It is a random error term that follows a standard normal distribution.

[0038] S2.7: Binary classification decision on whether a user accepts the scheduling Introducing binary random variables Characterizing the first Whether a user accepts scheduling is determined by the following relationship: When When, it is assumed that the user accepts the scheduling; when At that time, it is assumed that the user does not accept the scheduling. Among them, In order to accept scheduling, This indicates a refusal to accept scheduling. The formula transforms the continuous intensity of willingness into a discrete scheduling acceptance behavior.

[0039] S2.8: Calculation of the probability of a user accepting the schedule Based on the standard normal distribution function, we can obtain the first... The probability that a user will accept the scheduling is: ; In the formula, For the first The probability that a user will accept the scheduling; The distribution function of the standard normal distribution; , , These are the regression coefficients; For the first The subsidy amount corresponding to each user; For the first The state of charge of a user's vehicle. This formula is used to probabilistically characterize the likelihood of a user participating in the aggregator's scheduling.

[0040] Through the above steps, two types of key behavioral parameters for subsequent capacity adjustment are obtained: 1. User responsiveness in V2G scheduling. 2. The probability that a user will accept the aggregator's scheduling. .in, Used to characterize the strength of a user's willingness to participate in scheduling. These parameters are used to characterize the probability level of whether a user accepts scheduling. Combining the above two types of parameters with the objective vehicle scheduling capability results obtained in step S1 can serve as an important basis for correcting the theoretical schedulable capacity of a single vehicle in step S3, thereby avoiding directly treating all connected vehicles as schedulable resources.

[0041] like Figure 3 As shown, in step S3, the schedulable capacity of a single electric vehicle is calculated and the user's willingness is corrected. Specifically, for a single electric vehicle that has been determined to have objective scheduling capability in step 1 and whose user participation willingness has been quantified in step S2, its schedulable discharge capacity under the conditions of meeting subsequent travel needs, minimum SOC limit and stationary time constraint is further calculated.

[0042] Step S3 first determines the maximum discharge space that a single electric vehicle can provide without affecting subsequent travel. Then, the user responsiveness obtained in step S2 is introduced into the capacity calculation process to obtain a single effective schedulable capacity that is closer to the actual execution situation.

[0043] S3.1: SOC constraint at the end of discharge scheduling No. After the trip, if the vehicle participates in V2G discharge dispatch at the destination, it must still have the ability to recharge and meet the needs of the next trip after the discharge dispatch ends. Its SOC relationship is as follows: ; In the formula, For the first SOC at the start of the next trip; For the first SOC at the end of the next discharge scheduling; This represents the charging power after the discharge scheduling process has ended. The charging time after the discharge scheduling ends; For charge and discharge efficiency; For electric vehicle battery capacity; For the first The SOC (State of Charge) at the destination after the next trip. This formula is used to ensure that after participating in V2G dispatch, the vehicle can still recover to the SOC level that meets the needs of the next trip after subsequent charging.

[0044] S3.2: Minimum SOC constraint at the end of discharge scheduling During the discharge process, to prevent over-discharge of the battery, the State of Charge (SOC) at the end of the discharge schedule must also meet the minimum SOC constraint, the expression of which is: ; In the formula, For the first SOC at the end of the next discharge scheduling; For the first SOC upon arrival at the destination of the next trip; For discharge control power; Discharge scheduling duration; For charge and discharge efficiency; For electric vehicle battery capacity; This is the minimum SOC allowed throughout the entire process. This formula is used to ensure that the vehicle's SOC does not drop below the battery's safe range after V2G discharge scheduling is implemented.

[0045] S3.3: Determination of State of Charge (SOC) for Individual Electric Vehicles To determine whether a vehicle's State of Charge (SOC) upon reaching its destination is sufficient to support discharge scheduling, the SOC upon arrival at the destination must be greater than its dischargeable SOC. The expression for this is: ; In the formula, For the first SOC upon arrival at the destination of the next trip; This refers to the State of Charge (SOC) available for vehicle discharge scheduling during this stop phase. This determination relationship explains that a vehicle can only participate in V2G discharge scheduling if it has sufficient remaining charge upon reaching its destination.

[0046] S3.4: Calculation of the State of Charge (SOC) of a Single Electric Vehicle After meeting the above conditions, the discharge SOC of a single electric vehicle during the current dwell phase is: ; In the formula, For the first The State of Charge (SOC) of the electric vehicle during the second dwell phase; For the first SOC upon arrival at the destination of the next trip; This represents the charging power after the discharge scheduling process has ended. The charging time after the discharge scheduling ends; For charge and discharge efficiency; For electric vehicle battery capacity; For discharge control power; This represents the discharge scheduling duration. This formula is used to quantify the theoretical discharge space of a single electric vehicle during the current dwell time, under the premise of satisfying subsequent travel and minimum SOC constraints.

[0047] S3.5: Determination of the theoretical dispatchable capacity of a single electric vehicle In this invention, the theoretical dispatchable capacity of a single electric vehicle is determined by both its dischargeable state of charge (SOC) and battery capacity. In other words, when the first... electric vehicles at all times The dischargeable SOC is At that time, its theoretically releaseable scheduling space is from With battery capacity Common characterization. This processing method is consistent with the single-vehicle capacity composition in the subsequent cluster capacity summation formula and is used to introduce user responsiveness correction.

[0048] S3.6: Adjustment of single-unit effective schedulable capacity based on user responsiveness After obtaining the theoretical discharge space of a single electric vehicle, the user responsiveness obtained in step 2 will be used... A monomer capacity correction process is introduced. For the first... A car, at any time The contribution to the cluster's schedulable capacity is as follows: In the formula, For the first electric vehicles at all times Dischargeable SOC; For the first electric vehicles at all times User responsiveness; This refers to the battery capacity of electric vehicles. This contribution indicates that the actual effective dispatchable capacity of a single electric vehicle is not solely determined by its theoretical discharge capacity, but is also influenced by whether users are willing to participate in V2G scheduling. The higher the user responsiveness, the greater the actual contribution of the vehicle to the cluster's dispatchable capacity; conversely, the lower the responsiveness, the smaller the contribution.

[0049] S3.7: Intentional Correction of Dispatch Power for Individual Electric Vehicles In addition to capacity-based correction, the discharge power of a single vehicle can also be directly corrected based on user responsiveness during discharge power aggregation. Specifically, the... electric vehicles at all times The contributors to the cluster discharge power are: In the formula, This refers to the discharge power level of the electric vehicle in the parking area. For the first electric vehicles at all times This improves user responsiveness. This processing method ensures that the power contribution and capacity contribution of individual vehicles remain consistent in the correction logic, thus providing a unified expression basis for cluster aggregation in step S4.

[0050] Through the above steps, the discharge SOC, theoretical schedulable capacity, and effective schedulable capacity contribution (corrected for user responsiveness) of a single electric vehicle at the current moment or during its current dwell period can be obtained. Compared to methods that calculate capacity solely based on vehicle physical states, this step explicitly incorporates user subjective participation into the single-unit capacity assessment process, thereby making the subsequent cluster aggregation results closer to the actual scheduling execution scenario. The output of step S3 will directly serve as the input for the electric vehicle cluster schedulable capacity assessment and aggregator capacity boundary formation in step S4.

[0051] In step S4, electric vehicle cluster aggregation and aggregator schedulable capacity assessment are performed.

[0052] The scope of aggregation can be divided according to functional areas, site management units, or aggregator jurisdiction units.

[0053] S4.1: Calculation of V2G Dispatch Power for a Single Electric Vehicle Cluster For the A cluster of electric vehicles, at time The discharge power participating in V2G scheduling is: ; In the formula, For the first A cluster of electric vehicles at time V2G discharge power; For the first The number of electric vehicles contained in each cluster; For the first electric vehicles at all times User responsiveness; This represents the discharge power level of electric vehicles in the stationary area. It indicates that the cluster discharge power is not simply the sum of the rated discharge power of all connected vehicles, but rather requires correction for user responsiveness before aggregation, thus more accurately reflecting the effective discharge capacity of the vehicle cluster at the current moment.

[0054] S4.2: Calculation of dispatchable discharge power for electric vehicle clusters in a certain functional area For a certain type of functional area At any given moment The V2G schedulable discharge power is: ; In the formula, For a certain type of functional area At any moment V2G discharge power; For type function area The number of electric vehicle clusters participating in the aggregation; For the first A cluster of electric vehicles at time The V2G discharge power. This formula is used to further aggregate multiple electric vehicle clusters within the same functional area to form a regionally dispatchable discharge power.

[0055] S4.3: Calculation of the total dispatchable discharge power of the entire electric vehicle cluster For all types of functional areas, at time The total schedulable discharge power is: ; In the formula, For a moment Total dispatchable discharge power of the entire electric vehicle cluster; For a certain type of functional area At any moment V2G discharge power; , , and These areas are designated as residential, work, shopping and leisure, and other zones. This formula allows for the creation of a global, schedulable discharge power boundary at the system level.

[0056] S4.4: For the first A cluster of electric vehicles, at time The schedulable capacity is: ; In the formula, For the first A cluster of electric vehicles at time schedulable capacity; For the first electric vehicles at all times Dischargeable SOC; For the first electric vehicles at all times User responsiveness; For electric vehicle battery capacity; For the first The number of electric vehicles included in a cluster. This indicates that the schedulable capacity of a cluster is determined by the discharge state of each vehicle, its battery capacity, and user responsiveness. It is the cumulative result at the cluster level after adjusting for individual theoretical discharge capacity based on user preferences.

[0057] like Figure 4 As shown, the effective schedulable capacity of a single unit is accumulated and aggregated according to the cluster and functional area, and the cluster discharge power is formed synchronously. ,capacity The data is aggregated into regional and total schedulable capacity; it also illustrates the dual aggregation logic that adjusts the actual available charging and discharging power of the aggregator based on the user's acceptance probability P (Y=1).

[0058] S4.5: Calculation of the schedulable capacity of a certain type of functional area For a certain type of functional area At any given moment The schedulable capacity is: ; In the formula, For a certain type of functional area At any moment schedulable capacity; For type function area The number of electric vehicle clusters participating in the aggregation; For the first A cluster of electric vehicles at time The schedulable capacity. This formula is used to further aggregate the capacity of multiple clusters within the same type of functional area to form a regional schedulable capacity result.

[0059] S4.6: Calculation of the total schedulable capacity of the entire electric vehicle cluster For all functional areas, at time The total schedulable capacity is: ; In the formula, For a moment Total schedulable capacity of all electric vehicle clusters; For a certain type of functional area At any moment The schedulable capacity is obtained through this formula. The total capacity boundary available for aggregators to participate in demand response scheduling at the system level is obtained through this formula.

[0060] S4.7: Aggregator Charging Power Calculation Based on Acceptance Scheduling Probability At the aggregator level, the aggregated charging power can also be calculated based on the user's acceptance probability of scheduling, and its expression is as follows: ; In the formula, For a moment The charging power of the aggregated electric vehicle cluster; The number of electric vehicles aggregated; For the first electric vehicles at all times The charging power; For the first The probability that a user accepts scheduling. This formula is used to characterize the aggregator at time [time] from a probabilistic perspective. The actual charging capacity that can be utilized.

[0061] S4.8: Calculation of discharge power by aggregator based on acceptance scheduling probability Similarly, aggregators at any time The discharge power is: ; In the formula, For a moment The discharge power of the aggregated electric vehicle cluster; For the first electric vehicles at all times The discharge power; For the first The probability that a user will accept the scheduling; The number of electric vehicles aggregated. This indicates that the effective discharge capacity at the aggregator level is not only constrained by the physical discharge capacity of the vehicles, but also affected by the probability of whether users accept the dispatch.

[0062] S4.9: Dual Aggregation Logic Based on Responsiveness and Acceptance Probability In this invention, two types of behavioral parameters are used for cluster capacity calculation and aggregator power calculation: one is the responsiveness of user participation in V2G scheduling. One type is used to correct the schedulable capacity of a single unit and aggregate it to the cluster capacity level; the other type is the user's acceptance probability of scheduling. Used to correct aggregators at specific times. Available charging and discharging power. The former is better suited to characterizing the impact of users' subjective willingness to participate on "capacity space," while the latter is better suited to characterizing the impact of whether users accept scheduling on "instant power dispatch." Through this dual aggregation logic, both capacity boundaries and power boundaries can be formed simultaneously, providing a more complete input for aggregators' demand response scheduling.

[0063] Through the above steps, the schedulable discharge power and schedulable capacity of a single electric vehicle cluster, a certain type of functional area, and all functional areas at each time point are obtained. Simultaneously, the aggregated charging power and aggregated discharging power results at the aggregator level are also obtained. This leads to... , , , as well as Together, they constitute the capacity and power boundaries when aggregators participate in demand response scheduling, and serve as the core input for constructing the demand response scheduling optimization model in step S5.

[0064] In step S5, such as Figure 5As shown, the aggregator demand response scheduling optimization and rolling update are performed as follows: S5.1: Modeling Electricity Market Arbitrage Profits for Aggregators Aggregators profit from arbitrage under the time-of-use pricing mechanism by charging at low prices and discharging at high prices, as expressed in the following formula: ; In the formula, For aggregators' arbitrage profits in the electricity market; The profit-sharing ratio between aggregators and users; For a moment Time-of-use electricity pricing; For a moment Discharge power after polymerization; For a moment The combined charging power; This represents the charging and discharging duration of the aggregated electric vehicle cluster. This formula is used to characterize the direct economic benefits of the aggregator in the electricity market.

[0065] S5.2: Modeling the Revenue Compensation for Aggregators' Demand Response When aggregators adjust power according to grid demand, they can obtain demand response compensation revenue, the expression of which is: ; In the formula, Compensation for aggregators' demand response; To compensate for the number of times; For aggregators at any time Reported response power; For a moment The response compensation price; The duration is the scheduling duration. This formula describes the direct compensation benefits that aggregators receive for participating in demand response.

[0066] S5.3: User Demand Response Compensation Cost Modeling To incentivize user participation in scheduling, aggregators need to pay users peak-shaving and valley-filling compensation. The cost of peak-shaving compensation is: ; The cost of filling the valley is: ; The cost of responding to user demands and compensating for such demands is: ; In the formula, To compensate for peak shaving costs; To compensate for the cost of filling the valley; The total demand response compensation cost paid by the aggregator to the user; For aggregators at any time The compensation price offered to the user; The discharge power after polymerization; This represents the aggregated charging power. This set of formulas quantifies the cost aggregators must pay to incentivize users to participate in demand response.

[0067] S5.4: Battery Loss Cost Modeling During demand response, frequent charging and discharging of electric vehicles leads to battery life degradation. Therefore, a battery loss cost model needs to be established, the expression of which is: ; In the formula, For battery wear and tear costs; The battery usage allocation ratio between aggregators and users; Battery purchase cost; For a moment The polymer discharge power; This represents the maximum depth of discharge cycle count of the battery. This refers to the maximum depth of discharge of the battery. This represents the maximum storage capacity of the battery. This formula is used to characterize the cost of battery life loss caused by participation in V2G scheduling.

[0068] S5.5: User Revenue Modeling On the user side, the distribution benefits that users can obtain are: ; In the formula, The arbitrage profits distributed to users; The percentage of revenue allocated to users; For a moment Time-of-use electricity pricing; and These are discharge power and charging power, respectively. The duration of the scheduling is [duration]. Users also receive demand response compensation and battery wear compensation, expressed as follows:

[0069] In the formula, Compensation for users' demand response; This refers to the battery degradation compensation revenue received by the user. The aforementioned revenue corresponds to the demand response compensation and battery degradation compensation paid by the aggregator to the user.

[0070] S5.6: Modeling of Battery Degradation Costs Beared by Users In cases where battery degradation costs are shared by the aggregator and the user, the user's share of battery degradation costs is: ; In the formula, Battery wear and tear costs borne by the user; The percentage of battery usage borne by the user; Battery purchase cost; The discharge power after polymerization; This represents the maximum depth of discharge cycle count of the battery. This refers to the maximum depth of discharge of the battery. This represents the maximum battery capacity. This formula is used to characterize the battery depreciation cost borne by the user side due to participation in V2G scheduling.

[0071] S5.7: Construction of Aggregator Economic Goals and User Economic Goals In the scheduling optimization model, the aggregator's economic objective is determined by arbitrage profits. Demand response compensation revenue User demand response compensation cost and battery wear and tear costs Together they constitute; the user's economic goal is to distribute revenue among users. Demand response compensation revenue Battery loss compensation revenue And the battery wear and tear costs borne by the user Together they constitute the economic interests of both the aggregator and the user in demand response scheduling, as described above through the revenue and cost items.

[0072] S5.8: Construction of Power Grid Technology Objectives In addition to economic objectives, the scheduling model also includes grid-side technical objectives, namely minimizing the daily average variance of the load curve. These technical objectives aim to reduce load fluctuations and smooth peak-to-valley differences, thereby improving the stability of grid operation. This objective, together with the aforementioned aggregator economic objectives and user economic objectives, constitutes a multi-objective demand response scheduling problem.

[0073] S5.9: Constructing Constraints for the Scheduling Model To ensure the physical feasibility of the aggregator scheduling scheme, the scheduling model must satisfy the following constraints: (1) Output balance constraint: ; In the formula, For aggregators at any time The actual power after participating in demand response; For a moment Baseline load power; Aggregate charging power; The aggregated discharge power. This formula is used to characterize the balance between the actual power of the system after participation in dispatch and the baseline load. (2) SOC constraint: ; In the formula, and These are the minimum and maximum values ​​of SOC, respectively. For a moment The vehicle's state of charge (SOC). This formula is used to ensure that the vehicle's SOC remains within a safe and feasible range during the scheduling process. (3) SOC state update constraints: ; In the formula, and The vehicles at the time and The state of charge; and These are the battery charging efficiency and discharging efficiency, respectively. The scheduling time step; This is the maximum storage capacity of the battery. This formula is used to describe the effect of charging and discharging behavior on the change of SOC. (4) Charging and discharging mutual exclusion constraint: ; In the formula, For a moment The discharge power; For a moment The charging power. This formula is used to ensure that vehicles cannot be charged and discharged at the same time. (5) Participation in demand response constraints: When aggregators participate in demand response at each stage, their load, aggregated charging power, aggregated discharging power, and reported response output must meet the preset adjustment range constraints and average output requirements to ensure that the adjustment capacity provided by the aggregator can meet the demand response requirements.

[0074] S5.10: Scheduling Model Solving and Rolling Update Based on the above objective function and constraints, a multi-objective particle swarm optimization algorithm is used to solve the demand response scheduling model. This algorithm yields the charging power, discharging power, and response capacity of aggregators participating in demand response under different scheduling periods, thus forming the final scheduling scheme for the aggregators.

[0075] In actual operation, the vehicle's remaining SOC, dwell time, price incentives, and actual user response all change over time. Therefore, at the end of each scheduling cycle, steps 1 to 5 are re-executed. That is, vehicle status and travel constraint data are re-acquired, and user responsiveness is recalculated. and the probability of accepting scheduling The schedulable capacity of individual electric vehicles is revised, and the cluster aggregation results are updated. Then, the demand response scheduling model is resolved based on the updated schedulable capacity and power boundaries. Figure 5 As shown, by using a rolling update method, the aggregator's scheduling scheme can continuously adapt to changes in vehicle status and user behavior, thereby improving the real-time performance and executability of the scheduling results.

[0076] In this embodiment, through a rolling update mechanism, the user intention parameters and schedulable capacity are recalculated and the model is resolved after each scheduling cycle based on changes in vehicle status and actual user response results. This allows the scheduling scheme to continuously adapt to changes in operating conditions, reduces the accumulation of execution deviations caused by open-loop control, and improves the feasibility of the project.

[0077] Through the above steps, the aggregated charging power, aggregated discharging power, demand response output, and corresponding economic benefits and technical adjustment effects of the aggregator in each scheduling period are obtained. Simultaneously, through a rolling update mechanism, user behavior parameters and schedulable capacity boundaries can be continuously corrected, thereby ensuring that the aggregator always makes scheduling decisions based on response resources that are closer to reality when participating in demand response. The above results together constitute the final output of this invention.

[0078] In summary, this invention improves the accuracy, economy, and engineering feasibility of scheduling schemes by objectively determining scheduling capabilities, quantifying user participation willingness, correcting individual capacity, clustering aggregation, optimizing demand response, and rolling updates, enabling aggregators to participate in demand response with responsive resources that more closely approximate the actual operating state.

[0079] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A method for V2G electric vehicle aggregator demand response coordinated dispatching based on user willingness to modify dispatchable capacity, characterized in that, Includes the following steps: S1: Obtain basic status data, user behavior data, and demand response-related data of electric vehicles connected to the V2G aggregator platform, make a preliminary judgment on the objective scheduling capability of individual electric vehicles, and identify whether they meet the time and SOC conditions for participating in V2G scheduling. S2: Based on the objective vehicle status in S1, and taking into account battery health status, remaining idle time, remaining SOC and discharge price incentives, a subjective response willingness model for users to participate in V2G scheduling is constructed. The user response rate is quantified through fuzzy logic rules, and a Probit model based on subsidy amount and SOC is established to calculate the probability of users accepting the aggregator's scheduling. S3: For a single electric vehicle with objective scheduling capability, calculate its theoretical schedulable capacity under the conditions of meeting subsequent travel demand, minimum SOC limit and dwell time constraint, and introduce the user responsiveness obtained in S2 into the correction to obtain the actual schedulable capacity of the single vehicle. S4: The schedulable capacity of each individual unit, after being modified according to the user's wishes, is accumulated according to the aggregation range to form the schedulable power boundary and capacity boundary at the cluster level and the aggregator level. S5: Based on the actual schedulable capacity, construct a demand response scheduling optimization model that takes into account the arbitrage revenue of aggregators, the revenue of demand response compensation, the revenue of users, the cost of battery loss and the goal of grid load smoothing, and satisfy the corresponding constraints. Use the multi-objective particle swarm optimization algorithm to solve the charging and discharging scheduling schemes for each time period. Based on the changes in vehicle status and user behavior during the actual execution process, update the user willingness parameters and schedulable capacity on a rolling basis, and solve the scheduling model again. 2.The V2G electric vehicle aggregator demand response co-scheduling method based on user willingness to revise dispatchable capacity according to claim 1, wherein, The basic status data in step S1 includes at least the following: the current stored energy of the electric vehicle, the maximum stored energy, the current state of charge, the access duration, the access time, the expected disconnection time, the remaining idle time at the current destination, the remaining SOC, the subsidy amount, the discharge price incentive, the time-of-use price, the response power reported by the aggregator, and the baseline load. The formula for calculating the state of charge of the nth electric vehicle is: ； In the formula, For the first The state of charge of an electric vehicle; For the first The amount of electricity currently stored in the electric vehicle; For the first The maximum amount of electricity a fully charged electric vehicle can store. Actual depth of discharge of the vehicle: ； In the formula, For the first The actual depth of discharge of an electric vehicle; Shortest charge / discharge time: ； In the formula, For the first The shortest charging and discharging time for a single electric vehicle; For the first The actual depth of discharge of an electric vehicle; For the first The maximum amount of electricity a fully charged electric vehicle can store. For the first The rated charging and discharging power of an electric vehicle; Charge / discharge scheduling response coefficient: ； In the formula, For the first electric vehicles at all times Objective capabilities to participate in V2G scheduling; For the first The moment when an electric vehicle is connected to the power grid; For the first The moment when an electric vehicle leaves the power grid; The shortest time required for the vehicle to reach the desired SOC; when The vehicle was determined to meet the objective conditions for dispatch. Battery degradation anxiety costs: ； In the formula, For individual user batteries The cost of loss and anxiety; Additional battery losses incurred by individual electric vehicles participating in V2G scheduling; This represents the psychological resistance users have to battery aging caused by discharge, and ; The variable is a binomial distribution with values ​​ranging from 0 to 1; v=0 represents the user group that accepts the loss, and v=1 represents the user group that resists the loss.

3. The V2G electric vehicle aggregator demand response collaborative scheduling method based on user willingness to modify schedulable capacity according to claim 1, characterized in that, In step S2, a user responsiveness model is constructed: ； In the formula, For the first electric vehicles at all times The responsiveness of participating in V2G scheduling; The extent to which battery health status affects user willingness to participate; The degree to which the remaining time spent at the current destination affects users' willingness to participate; The extent to which the remaining SOC affects users' willingness to participate; The extent to which discharge electricity price incentives affect users' willingness to participate; For the first electric vehicles at all times Objective scheduling capabilities; User responsiveness function; Normalize the input factor x: ； In the formula, These are the normalized input variables; These are the original input variables; and These are the minimum and maximum values ​​of the variable, respectively. Furthermore, user response intentions were divided into five fuzzy sets using fuzzy logic rules, yielding a value range of... The response results; At the same time, a model of the strength of users' willingness to accept scheduling is established: ； In the formula, For the first The strength of each user's willingness to accept scheduling; , , These are the regression coefficients; For the first The subsidy amount corresponding to each user; For the first The state of charge of each user's vehicle; The error term is a random error term and follows a standard normal distribution; The probability that a user accepts the scheduling is: ； In the formula, For the first The probability that a user will accept the scheduling; The distribution function of the standard normal distribution; , , These are the regression coefficients; For the first The subsidy amount corresponding to each user; For the first The state of charge of each user's vehicle.

4. The V2G electric vehicle aggregator demand response collaborative scheduling method based on user willingness to modify schedulable capacity according to claim 1, characterized in that, In step S3, after the k-th stroke, the SOC at the end of the discharge scheduling must satisfy: ； In the formula, For the first SOC at the start of the next trip; For the first SOC at the end of the next discharge scheduling; This represents the charging power after the discharge scheduling process has ended. The charging time after the discharge scheduling ends; For charge and discharge efficiency; For electric vehicle battery capacity; For the first SOC upon arrival at the destination of the next trip; And the minimum SOC safety constraint for discharge: ； In the formula, For the first SOC at the end of the next discharge scheduling; For the first SOC upon arrival at the destination of the next trip; For discharge control power; Discharge scheduling duration; For charge and discharge efficiency; For electric vehicle battery capacity; The minimum SOC allowed throughout the entire process; Meet the conditions At that time, the discharge SOC is: ； In the formula, For the first The State of Charge (SOC) of the electric vehicle during the second dwell phase; For the first SOC upon arrival at the destination of the next trip; This represents the charging power after the discharge scheduling process has ended. The charging time after the discharge scheduling ends; For charge and discharge efficiency; For electric vehicle battery capacity; For discharge control power; Discharge scheduling duration; Then the theoretical schedulable capacity of the nth electric vehicle at time t is given by With battery capacity Common characteristics; After introducing user responsiveness correction, the contribution of a single unit to the schedulable capacity of the cluster is: ; For the first electric vehicles at all times Dischargeable SOC; For the first electric vehicles at all times User responsiveness; For electric vehicle battery capacity; The contributors to the cluster discharge power are: ; This refers to the discharge power level of the electric vehicle in the parking area.

5. The V2G electric vehicle aggregator demand response collaborative scheduling method based on user willingness to modify schedulable capacity according to claim 1, characterized in that, In step S4, The V2G schedulable discharge power of the m-th electric vehicle cluster at time t is: ； In the formula, For the first A cluster of electric vehicles at time V2G discharge power; For the first The number of electric vehicles contained in each cluster; For the first electric vehicles at all times User responsiveness; This refers to the discharge power level of the electric vehicle in the parking area. A certain type of functional area A A The schedulable discharge power is: ； In the formula, For a certain type of functional area At any moment V2G discharge power; For type function area The number of electric vehicle clusters participating in the aggregation; For the first A cluster of electric vehicles at time V2G discharge power; The total dispatchable discharge power of all functional areas is: ； In the formula, For a moment Total dispatchable discharge power of the entire electric vehicle cluster; For a certain type of functional area At any moment V2G discharge power; , , and These areas are divided into residential areas, work areas, shopping and leisure areas, and other areas. The schedulable capacity of the m-th cluster is: ； In the formula, For the first A cluster of electric vehicles at time schedulable capacity; For the first electric vehicles at all times Dischargeable SOC; For the first electric vehicles at all times User responsiveness; For electric vehicle battery capacity; For the first The number of electric vehicles contained in each cluster; The schedulable capacity of functional area A is: ； In the formula, For a certain type of functional area At any moment schedulable capacity; For type function area The number of electric vehicle clusters participating in the aggregation; For the first A cluster of electric vehicles at time schedulable capacity; The total schedulable capacity is: ； In the formula, For a moment Total schedulable capacity of all electric vehicle clusters; For a certain type of functional area At any moment schedulable capacity; Furthermore, the actual charging and discharging power available to the aggregator is calculated based on the user's acceptance probability of scheduling: ； ； In the formula, For a moment The charging power of the aggregated electric vehicle cluster; The number of electric vehicles aggregated; For the first electric vehicles at all times The charging power; For a moment The discharge power of the aggregated electric vehicle cluster; For the first electric vehicles at all times The discharge power; For the first The probability that a user will accept the scheduling.

6. The V2G electric vehicle aggregator demand response collaborative scheduling method based on user willingness to modify schedulable capacity according to claim 1, characterized in that, In step S5, the objective function of the scheduling optimization model includes: Arbitrage profits for aggregators: ； In the formula, For aggregators' arbitrage profits in the electricity market; The profit-sharing ratio between aggregators and users; For a moment Time-of-use electricity pricing; For a moment Discharge power after polymerization; For a moment The combined charging power; The charging and discharging duration of the aggregated electric vehicle cluster; Aggregator demand response compensation revenue: ； In the formula, Compensation for aggregators' demand response; To compensate for the number of times; For aggregators at any time Reported response power; For a moment The response compensation price; The duration of the scheduling; User arbitrage profit allocation: ； In the formula, The arbitrage profits distributed to users; The percentage of revenue allocated to users; For a moment Time-of-use electricity pricing; and These are discharge power and charging power, respectively. The duration of the scheduling; Total cost of user compensation for aggregators: ； In the formula, For battery wear and tear costs; The battery usage allocation ratio between aggregators and users; Battery purchase cost; For a moment The polymer discharge power; This represents the maximum depth of discharge cycle count of the battery. This refers to the maximum depth of discharge of the battery. This is the maximum amount of energy the battery can store. The optimization objective on the grid side is to minimize the daily variance of the load curve after dispatch; the total user revenue is... - ,in Bearing the cost of loss for users To compensate aggregator users for total costs.

7. The V2G electric vehicle aggregator demand response collaborative scheduling method based on user willingness to modify schedulable capacity according to claim 1, characterized in that, In step S5, the constraints of the scheduling model include: Output balance constraints: ； In the formula, For aggregators at any time The actual power after participating in demand response; For a moment Baseline load power; Aggregate charging power; This refers to the aggregated discharge power; SOC security constraints: ； In the formula, and These are the minimum and maximum values ​​of SOC, respectively. For a moment The vehicle's state of charge; SOC state update constraints: ； In the formula, and The vehicles at the time and The state of charge; and These are the battery charging efficiency and discharging efficiency, respectively. The scheduling time step; This is the maximum amount of energy the battery can store. Charge and discharge mutual exclusion constraint: ； In the formula, For a moment The discharge power; For a moment The charging power; Demand response output constraint: The power adjustment and charging / discharging power reported by the aggregator at each time period fall within the preset adjustment range, meeting the average output target.

8. The V2G electric vehicle aggregator demand response collaborative scheduling method based on user willingness to modify schedulable capacity according to claim 1, characterized in that, The rolling update mechanism in step S5 is as follows: The system iterates in a closed loop based on the scheduling cycle. After the cycle ends, the steps are repeated based on the actual response results, changes in vehicle status, and changes in price incentives. The user responsiveness, acceptance probability, cluster effective power, and capacity boundaries are recalculated. The updated boundary parameters are then substituted into the multi-objective particle swarm optimization model to resolve the charging and discharging scheduling scheme, thus achieving rolling updates.

9. The V2G electric vehicle aggregator demand response collaborative scheduling method based on user willingness to modify schedulable capacity according to claim 1, characterized in that, In step S5, the demand response compensation cost paid by the aggregator to the user includes peak shaving compensation and valley filling compensation: Peak shaving compensation cost is: ； The cost of filling the valley is: ； The cost of responding to user demands and compensating for such demands is: ； In the formula, To compensate for peak shaving costs; To compensate for the cost of filling the valley; The total demand response compensation cost paid by the aggregator to the user; For aggregators at any time The compensation price offered to the user; The discharge power after polymerization; The charging power after aggregation; The battery wear and tear costs borne by the user are: ; In the formula, Battery wear and tear costs borne by the user; The percentage of battery usage borne by the user; Battery purchase cost; The discharge power after polymerization; This represents the maximum depth of discharge cycle count of the battery. This refers to the maximum depth of discharge of the battery. This is the maximum amount of energy the battery can store.

10. The V2G electric vehicle aggregator demand response collaborative scheduling method based on user willingness to modify schedulable capacity according to claim 1, characterized in that, The aggregation scope is divided according to functional areas, site management units, or aggregator jurisdiction units. The functional areas include residential areas, work areas, shopping and leisure areas, and other areas.

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