Electric vehicle cluster participation income distribution method based on aggregator mode

Abstract

The invention provides an electric vehicle cluster participation income distribution method based on an aggregator mode, and the method comprises the following steps: S01, building an electric vehicle user willingness model through the evaluation of the willingness of an electric vehicle user; s02, through a monomer EV charging and discharging model, an aggregator regulates and controls the EV, and participates in a frequency modulation market to obtain benefits; and S03, based on a Shapley value theory, distributing the obtained income to the aggregator and the EV vehicle owner. An electric vehicle user intention model solves the problem of influence factors of participation and response of electric vehicle users, provides an EV real-time margin, solves the problems of unreasonable income distribution among the electric vehicle users, unreasonable user contribution and income and the like, and considers energy resource occupation of the EV in energy cost distribution and frequency modulation contribution of the EV in frequency modulation income based on a Shapley value theory. The problem of income distribution fairness between EVA and EV vehicle owners is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system operation and control technology, and relates to a method for electric vehicle cluster participation in income distribution based on an aggregator mode, which is suitable for coordinated control and market participation after large-scale electric vehicles are connected to a power grid. BACKGROUND

[0002] With the increasing penetration of renewable energy, the problem of power grid frequency stability is increasingly prominent. Traditional frequency regulation services mainly rely on generator units, but there are problems such as slow response speed and high regulation cost. Electric vehicles, as a kind of distributed energy storage resource, have fast response capability and can participate in the frequency regulation market through the form of an aggregator (EVA). However, there are the following problems in the prior art: first, a single electric vehicle has small capacity and is difficult to directly participate in the market; second, there is a conflict between user charging demand and frequency regulation services; third, there is a lack of a reasonable income distribution mechanism; and fourth, the battery wear cost is not fully considered.

[0003] The common electric vehicle aggregation control method currently mainly adopts a centralized control strategy, which realizes the frequency regulation target by accurately controlling each electric vehicle. This method has high computational complexity, strict communication requirements, and is difficult to guarantee the individualized needs of users. In addition, the existing method does not adequately consider the user participation willingness and battery wear cost, which affects the actual application effect. SUMMARY

[0004] 1. Technical problems to be solved:

[0005] The current control of electric vehicles has the problems of large differences in EV owner response willingness, many factors affecting the willingness of the owner, complex user behavior patterns, and low user participation response due to unreasonable income distribution.

[0006] 2. Technical solutions:

[0007] In order to solve the above problems, the present application provides a method for electric vehicle cluster participation in income distribution based on an aggregator mode, comprising the following steps:

[0008] Step S01: establishing an electric vehicle user willingness model by evaluating the willingness of electric vehicle users;

[0009] Step S02: participating in the frequency regulation market to obtain income by the aggregator regulating and controlling the EV through a single EV charging and discharging model;

[0010] Step S03: distributing the obtained income to the aggregator and the EV owner based on the shapley value theory.

[0011] In step S01, the electric vehicle user willingness model comprises the following steps:

[0012] Step S11: Quantify the single EV's extra battery loss under the aggregator's regulation , which is given by

[0013] (12)

[0014] where and are the power loss per unit of energy during charging and discharging, respectively; and are the extra charging and discharging power, respectively; is the time when the single EV starts to participate in the aggregator's regulation; and are the durations of extra charging and discharging, respectively; and are the starting charging and discharging times during the extra charging and discharging period, respectively; and are the charging and discharging time periods during the extra charging and discharging period, respectively;

[0015] Step S12: Establish the basic loss model, which is given by

[0016] (13)

[0017] (14)

[0018] where is the individual rejection coefficient, represents the direct wear of the battery life caused by the charging and discharging amplitude; represents the accelerated loss caused by frequent and high-power charging and discharging; is the discharging indicator variable: 1 when discharging, otherwise 0;

[0019] Step S13: EV charging time in the grid The user's anxiety level for flexible car use is measured by the EV's time in the grid, and the single user's flexible car use time anxiety cost is defined as

[0020] (15)

[0021] where is the total time of the vehicle staying; is the maximum staying duration; is the upper limit of the anxiety cost; is the sensitivity index;

[0022] Step S14: Obtain the comprehensive willingness function

[0023] The user comprehensive anxiety cost is defined based on battery loss anxiety cost and flexible use time anxiety cost Reflecting the willingness of EV users to participate in regulation:

[0024] (16)

[0025] In the formula, is the income of a single user participating in the aggregator regulation; is the conversion factor; is the maximum flexible use anxiety cost of a single user participating in the aggregator regulation, reflects the psychological trade-off between the income and the time cost sacrificed by the user in participating in the aggregator regulation. The greater the value, the stronger the willingness of the corresponding user to participate in the regulation, and vice versa.

[0026] In step S02, the single EV charging and discharging model includes

[0027] According to the changes between various states of EVs accessing the grid, the battery SOC value changes, as shown in the following formula:

[0028] (1)

[0029] In the formula, and are the SOC values of the battery of the i-th EV at the current time and the next time, respectively; is the time interval; and are the charging and discharging efficiencies of the EV, respectively; When the single EV accesses the grid, the power satisfies the constraint condition as shown in the following formula:

[0030]

[0031] (2)

[0032] In the formula, is the output power of the i-th EV; and are the rated charging and discharging powers of the i-th EV, respectively; The SOC operating boundary of the single EV in the charging period is as shown in the following formula:

[0033]

[0034] (3)

[0035] (4)

[0036] In the formula, and​​​ respectively the moment SOC upper and lower limits of the EV; the moment the EV is expected to be off-grid;

[0037] the single EV battery SOC constraint, as shown in the following formula:

[0038] (5);

[0039] Assuming the charging period of the EV is , considering that the EV accepts the control of the aggregator, the minimum time :

[0040] (6).

[0041] The aggregator aggregates and controls a large number of EVs in a certain area, and the total power and power boundaries of all EVs are used to represent the total power and power boundaries of the aggregator, and the power and power boundaries of the aggregator are:

[0042] (7)

[0043] (8)

[0044] (9)

[0045] (10)

[0046] In the formula, and are the upper and lower boundaries of the power and power of the aggregator at moment; is the number of EVs under the jurisdiction of the aggregator.

[0047] The aggregator controls the EV, specifically, under the premise of actively controlling the charging power of the user, the user participates in different charging modes, and the user is promised to meet the charging demand, and the charging mode includes a charging mode based on a guaranteed power, specifically as follows:

[0048] The purpose of accessing the power grid is only to obtain electric energy, and the aggregator does not make any constraints on the charging behavior of the EV, and charges at any time on-grid and off-grid;

[0049] The guaranteed power of each charging EV is promised every period, and the power charged in the period is not less than the guaranteed power at the end of each standby period, so as to avoid insufficient charging when the vehicle is off-grid;

[0050] Let the set of time slots of the hth segment of the day for vehicle j be H, then the floor constraint is:

[0051] (11)

[0052] wherein, the battery power of the vehicle at the hth segment; the minimum power that the vehicle is committed to charge at the hth segment.

[0053] The revenue from the frequency regulation market includes the revenue of the aggregator participating in the frequency regulation auxiliary market, the real-time margin of the EV, the revenue of the aggregator, and the revenue of the EV owner.

[0054] The revenue of the aggregator participating in the frequency regulation auxiliary market is specifically: let the charging power of the aggregator at each segment be , which is the benchmark power reported to the system, and the frequency regulation will increase or decrease the charging power based on the benchmark power; the uplink and downlink capacities reported are and , wherein the subscripts all represent the th segment, then the aggregator needs to pay the energy cost of purchasing power , obtains the capacity revenue of providing backup , and obtains the mileage revenue of real-time frequency response , which are defined as follows:

[0055] (17)

[0056] (18)

[0057] (19)

[0058] wherein, , , are the energy market price (unit: yuan / kwh) and the uplink and downlink backup capacity price (unit: yuan / kwh), respectively; represents the duration of a backup segment; is the mileage price (unit: yuan / kwh); is the frequency regulation performance index; is the frequency regulation mileage;

[0059] The real-time margin of the EV is used to constrain the power curve to ensure user demand, and serves as the basis for real-time power scheduling and revenue distribution,

[0060] the real-time minimum power is defined as , for the jth electric vehicle, if it is off-grid at the time point t, the total power thereof needs to be not lower than ,

[0061] (20)

[0062] The real-time margin of EV at time point t is defined as the power at this time Relative to The additional power:

[0063] (21);

[0064] The aggregator's income is the frequency adjustment income minus the compensation to the car owner due to insufficient charging: in the time period ,

[0065] (22)

[0066] (23)

[0067] In the formula, represents the proportion of the frequency adjustment income, represents the power shortage relative to the guaranteed charging power promised to the car owner after the end of the time period, is the cumulative actual charging power of vehicle j in the current segment, represents the penalty coefficient;

[0068] The EV owner's income is specifically: for EVs with charging records, the charging cost in the time period can be recorded as , the frequency adjustment income is recorded as , and the income allocated to the aggregator is deducted. The total energy cost and the total frequency adjustment income of all EVs are given by the following formulas, where represents the total number of EVs with charging records in the time period,

[0069] (24)

[0070] (25)

[0071] For a single EV, the total cost due to charging in the time period includes the energy cost due to charging, the income due to the frequency adjustment service, and the compensation fee charged from the aggregator due to insufficient charging, as shown in the following formula:

[0072] (26).

[0073] In step S03, the calculation of the shapley value of the charging cost of the EV and the calculation of the shpley value of the frequency adjustment income are included.

[0074] ​The charging cost for each EV is allocated proportionally based on its respective marginal cost, as shown in the following formula:

[0075] (28)

[0076] The marginal cost mentioned is the guarantee provided by the aggregator for each additional EV connected to the grid for charging. The amount of electricity;

[0077] Frequency modulation revenue is divided into two parts: capacity revenue and range revenue. The frequency modulation revenue of a single EV is recorded as capacity revenue. and mileage benefits Two parts, as shown in the following formula:

[0078] (29)

[0079] The more EVs charged, the more actual available capacity is. This benefit is directly based on the charging time allocation during that period, as shown in the following formula.

[0080] (30)

[0081] Each moment Mileage benefits Break it down, and then do each The profit distribution is as follows:

[0082] (31)

[0083] The marginal benefit of a single EV to frequency regulation is: when this EV participates in frequency regulation, it shares the burden of the change in the charge margin of other EVs by changing its own charge margin. Let the charging power of the j-th EV at time t be... The margin change between time t and time t+1 is:

[0084] (32).

[0085] Based on the basic definition of contribution based on Shpley values, the relationship between aggregator and EV is as follows: 1) EV frequency modulation contribution is 0 when the margin remains unchanged; when upward adjustment is needed, a positive change in margin results in a positive contribution, and a negative change in margin results in a negative contribution; when downward adjustment is needed, a negative change in margin results in a positive contribution, and a positive change in margin results in a negative contribution.

[0086] For the aggregator as a whole, the change in total margin has the same sign as the frequency regulation demand: an upward adjustment increases the total margin, and a downward adjustment decreases the total margin. As a contribution, the mileage gain accumulated by the j-th EV in time period t is allocated proportionally as follows:

[0087] (33)

[0088] In the formula, is the frequency modulation demand symbol, if the demand is up, it is +1, otherwise it is -1.

[0089] 3. Beneficial effects:

[0090] The application provides an electric vehicle cluster participation income distribution method based on an aggregator mode, establishes a single EV model and an EVA model, reduces control complexity through the charging and discharging trajectory of the single EV and the aggregator mode, comprehensively considers battery loss and anxiety cost, proposes a comprehensive user willingness model, improves user participation, ensures user charging demand through guaranteed power and real-time margin management, formulates a transaction strategy, distributes the income of the aggregator participating in the frequency modulation market to each vehicle owner based on a Shapley value theory distribution mechanism, and ensures the fairness of income distribution. BRIEF DESCRIPTION OF DRAWINGS

[0091] Figure 1 is an EV participation energy-frequency modulation market framework diagram.

[0092] Figure 2 is an SOC curve of whether to participate in regulation. DETAILED DESCRIPTION

[0093] The application will be described in detail below with reference to the drawings.

[0094] The application provides an electric vehicle cluster participation income distribution method based on an aggregator mode, including the following steps:

[0095] Step S01: through the evaluation of the willingness of electric vehicle users, an electric vehicle user willingness model is established.

[0096] Step S02: through the single EV charging and discharging model, the aggregator regulates and controls the EV to participate in the frequency modulation market to obtain income.

[0097] Step S03: based on the shapley value theory, the obtained income is distributed to the aggregator and the EV owner.

[0098] The application proposes a user willingness model, solves the problem of influencing factors of electric vehicle user participation response, proposes an EV real-time margin, solves the problems of unreasonable income distribution among electric vehicle users and unreasonable user contribution and income, and based on the shapley value theory, considers the energy resource occupation of the EV in energy cost distribution and considers the frequency modulation contribution of the EV in frequency modulation income, and solves the problem of fairness of income distribution between the EVA and the EV owner.

[0099] As Figure 1As shown, EVA acts as an intermediate link between EVs and the grid, and makes profits by pursuing price difference in the reserve market and energy market. In the day-ahead energy market, the aggregator evaluates the participation of the EV cluster in response to the charging and discharging incentive, and reports and purchases the planned electricity energy of the next day to the market; in the frequency regulation market, the aggregator reports the frequency regulation capacity, and adjusts the charging and discharging power of the EV according to the frequency regulation signal released by the frequency regulation market.

[0100] The charging and discharging model of the single EV includes the change of the battery SOC value according to the change between various states of the EV entering the grid, the constraint condition of the power of the single EV accessing the grid satisfying the formula, and the SOC constraint of the battery of the single EV.

[0101] According to the change between various states of the EV entering the grid, the change formula of the battery SOC value is as follows:

[0102] (1)

[0103] In the formula, and are the SOC values of the battery of the i-th EV at the current time and the next time, respectively; is the time interval; and are the charging and discharging efficiencies of the EV, respectively. The constraint condition of the power of the single EV accessing the grid satisfies the formula as follows:

[0104]

[0105] (2)

[0106] In the formula, is the output power of the i-th EV; and are the rated charging and discharging powers of the i-th EV, respectively. The SOC running boundary of the single EV in the charging period is as follows:

[0107] (3)

[0108] (4)

[0109] In the formula, and

[0110] are the upper and lower limits of the SOC of the i-th EV at the time t; is the state of charge of the electric vehicle at the time t when the electric vehicle is expected to be off-grid. ​​​​​​

[0111] Monomer EV battery SOC constraints:

[0112] (5).

[0113] Assume the charging period of the first EV is , considering that the EV accepts the regulation of the EVA, the minimum time of the EV charging demand :

[0114] (6).

[0115] The EVA can aggregate and control a large number of EVs in a certain area, and the total power and power boundary of the EVA can be represented by the sum of the power and power boundary of all EVs. The power and power boundary of the EVA is:

[0116] (7)

[0117] (8)

[0118] (9)

[0119] (10)

[0120] In the formula, and are the upper and lower boundaries of the power and power of the EVA at time; is the number of EVs under the jurisdiction of the EVA.

[0121] According to the purchase price of the day-ahead energy market, the EV aggregator formulates the planned charging power of the next day according to its marginal revenue and charging demand prediction. In the real-time energy market, the EV aggregator formulates the charging and discharging price according to the real-time energy market price, frequency modulation income and actual charging condition.

[0122] Since the charging time of the EV is unknown from the perspective of the aggregator, in order to meet the charging demand of the user, the aggregator needs to let the user participate in different charging modes under the premise that the user's charging power can be actively regulated, and promise to meet the user's charging demand. Therefore, the present application proposes a charging mode based on guaranteed power, as follows:

[0123] (1) The purpose of accessing the power grid is only to obtain electric energy, and the aggregator does not make any constraints on the charging behavior of the EV, which can be charged at any time and disconnected from the grid.

[0124] ​(2) A guaranteed minimum amount of charging for each EV in each time period (at the end of each backup time period, the amount of charging in that time period is not less than the guaranteed minimum amount), to avoid insufficient charging when the vehicle is off-grid.

[0125] Let the set of time periods in which vehicle j is divided in a day be H, then the guaranteed minimum constraint is

[0126] (11)

[0127] where, Battery capacity of vehicle in time period h; Guaranteed minimum amount of charging for vehicle in time period h.

[0128] The additional charging and discharging behavior of EVs will reduce the service life of electric vehicle batteries, thereby affecting the willingness of users to participate in regulation. As shown in FIG. 2, curve A represents that when the user is unwilling to participate in regulation, the SOC of the EV does not change after entering the grid and charging to full capacity; curve B represents the SOC change of the EV after entering the grid and charging and discharging when the user is willing to participate in regulation. When the user participates in regulation, the EV charges to the time t1 and begins to perform charging and discharging behavior other than normal charging, and ends the charging and discharging behavior at the time t2 and returns to normal charging behavior until it is full and off-grid. Therefore, the time period is the additional charging and discharging time of the electric vehicle willing to participate in regulation compared to the normal charging electric vehicle Figure 2

[0129] Quantifying the additional battery loss of a single EV participating in EVA regulation is:

[0130] (12)

[0131] where, and are the power loss per degree of EV charging and discharging, respectively; and are the additional charging and discharging power, respectively; is the time at which a single EV participating in EVA regulation begins to perform additional charging and discharging behavior; and are the additional charging and discharging time, respectively; and are the start charging and discharging time in the additional charging and discharging time, respectively; and are the charging and discharging time periods in the additional charging and discharging time, respectively.

[0132] Basic loss model

[0133] ​​​​(13)

[0134] (14)

[0135] where, is the individual rejection coefficient. represents the direct wear of the battery life caused by the charge and discharge amplitude; represents the accelerated wear caused by frequent and high-power charging and discharging; is the discharge indication variable (1 when discharging, otherwise 0)

[0136] EV charging time on the network

[0137] The user's anxiety level for flexible use of the vehicle is measured by the EV time on the network, and the individual user's flexible use of the vehicle time anxiety cost is defined as:

[0138] (15)

[0139] where, is the total time of the vehicle staying; is the maximum staying time; is the upper limit of the anxiety cost; is the sensitivity index.

[0140] Comprehensive willingness function

[0141] Based on the battery wear anxiety cost and the flexible use of the vehicle time anxiety cost, the user's comprehensive anxiety cost is defined to reflect the willingness of the EV user to participate in the regulation:

[0142] (16)

[0143] where, is the individual user's income participating in the EVA regulation; is the conversion factor; is the maximum flexible use of the vehicle anxiety cost of the individual user participating in the EVA regulation. reflects the psychological trade-off between the user's income and the time cost sacrificed in participating in the EVA regulation. The greater the value, the stronger the willingness of the corresponding user to participate in the regulation, and vice versa.

[0144] As an EV aggregator, EVA will represent all the EVs that are willing to participate in frequency regulation, report the reserve capacity of each period to the system, and be responsible for the charging power scheduling of all the charging piles in the real-time response stage, and settle the charging cost and frequency regulation income with each EV. In this EVA-led operation mode, EVA will participate in frequency regulation reserve under the premise of guaranteeing the charging demand by signing a contract with EV owners, thereby obtaining income. EVA must promise the owners that before the battery is fully charged, a pre-agreed minimum amount of power should be charged every period. In terms of benefit distribution, the charging cost is shared by the owners with charging records, and the income from frequency regulation is shared by EVA and the owners.

[0145] Let the aggregator's charging power in each period be , which is the reference power reported to the system, and the frequency regulation will be adjusted up and down based on this power; the uplink and downlink capacities reported are and , where the subscript represents the th period. The aggregator's total energy cost for purchasing power is , the capacity income obtained by providing reserve is , and the mileage income of real-time response frequency is , which are defined as follows.

[0146] (17)

[0147] (18)

[0148] (19)

[0149] In the formula, , , are the energy market price (unit: yuan / kwh) and the uplink and downlink reserve capacity price (unit: yuan / kwh), respectively; represents the duration of a reserve period; is the mileage price (unit: yuan / kwh); is the frequency regulation performance index; is the frequency regulation mileage.

[0150] A real-time margin parameter is defined for a single EV to constrain its power curve to ensure user demand, and also as the basis for real-time power scheduling and income distribution.

[0151] First, define the real-time minimum power . For the jth electric vehicle, if it is disconnected at time t, its total power must not be less than .

[0152] (20)

[0153] The real-time margin of EV at time point t can be defined as the power at this time Relative to The additional power:

[0154] (21).

[0155] EVA will maximize its own interests as the goal of energy purchase and capacity reporting. The energy cost of the aggregator due to charging is borne by all car owners, and all the benefits due to frequency modulation are distributed by all car owners and EVA, but EVA needs to guarantee the minimum power of each EV. Therefore, in the time period , the income of the aggregator should be the frequency modulation income share minus the compensation to the car owner due to insufficient charging:

[0156] (22)

[0157] (23)

[0158] In the formula, represents the sharing ratio of frequency modulation income; represents the power shortage relative to the minimum charging power promised to the car owner after the end of the period; is the cumulative actual charging power of vehicle j in the current segment; represents the penalty coefficient.

[0159] After optimization, the total energy cost and capacity income of the aggregator can be determined, and after the end of real-time operation, the system can also settle the frequency modulation mileage income to the aggregator. For EVs with charging records, the charging cost in the time period can be recorded as , and the frequency modulation income can be recorded as . After deducting the income allocated to EVA, the total energy cost and total frequency modulation income of all EVs are given by the following formula, where represents the total number of EVs with charging records in the period.

[0160] (24)

[0161] (25)

[0162] Then for a single EV, the total fee due to charging in the time period is This includes energy costs incurred due to charging, revenue generated from frequency regulation services, and compensation fees collected from EVA when charging is insufficient.

[0163] (26)

[0164] Therefore, as the aggregator manager, EVA needs to fairly allocate the total cost and total revenue to each EV and calculate... and Based on the Shapley value theory, the energy resource occupation of EVs is considered in energy cost allocation, and the frequency regulation contribution of EVs is considered in frequency regulation revenue, thus fairly allocating energy costs and frequency regulation revenue. The basic equations for the shapley values ​​of the energy cost and frequency regulation revenue of an EV are as follows:

[0165] (27)

[0166] In the formula, For all A collection of EVs; for The middle does not include the first One of all non-empty subsets of EVs; This is a utility function with set independent variables. Essentially, this formula calculates the marginal cost or marginal benefit of an EV, that is, the difference between the cost or benefit incurred when the EV is charged and when it is not charged.

[0167] Because EVA promises a certain amount of charge per unit time to the EV (charging time is...) For every additional EV connected to the grid for charging, EVA needs to ensure... The amount of electricity is the first unit, and any amount exceeding this amount does not require additional payment for extra EVA; therefore, this amount of electricity is the first unit. The marginal cost of an EV, because Since the cost is a fixed value, the marginal cost is only proportional to the time spent on the network. Furthermore, because the charging of each EV does not interfere with each other, the charging demand of different EVs is monotonically additive. Therefore, the charging cost of each EV can be allocated proportionally according to its respective marginal cost.

[0168] (28).

[0169] Frequency modulation revenue is divided into two parts: capacity revenue and range revenue. Therefore, the frequency modulation revenue of a single EV is also recorded as capacity revenue. and mileage benefits Two parts.

[0170] (29)

[0171] Since the more EVs are charging, the more actual available capacity is, this part of the benefit can be directly based on the charging time allocation of the period.

[0172] (30)

[0173] For the mileage benefit, the different factors affecting the benefit of each electric vehicle owner is the actual power change, on this basis, considering the impact of real-time margin changes on mileage benefits. Can be split into each time t mileage benefit , then each benefit allocation.

[0174] (31)

[0175] The essence of the marginal benefit of a single EV on the frequency modulation benefit is: when the EV participates in frequency modulation, it shares the remaining EV's power margin change by changing its own power margin. Denote the charging power of the jth EV at time t as , the margin change between time t and time t+1 as

[0176] (32).

[0177] Based on the basic definition of shpley value based on contribution, and the relationship between EVA and EV in this paper should be: 1) the frequency modulation contribution of EV with unchanged margin is 0. 2) when it needs to be adjusted upwards, the margin change is positive, and the contribution is positive. The margin change is negative, and the contribution is negative. 3) when it needs to be adjusted downwards, the margin change is negative, and the contribution is positive. The margin change is positive, and the contribution is negative.

[0178] For the entire aggregator, it is obvious that the total margin change of the entire aggregator is the same sign as the frequency modulation demand: when it needs to be adjusted upwards, the total margin becomes larger, and when it needs to be adjusted downwards, the total margin becomes smaller. Therefore, the as the contribution degree can be allocated in proportion to meet the above conditions. Therefore, for the jth EV, the mileage benefit accumulated at time t is

[0179] (33)

[0180] In the formula, is the sign of frequency modulation demand, +1 if the demand is up, and -1 otherwise.

Claims

1. A method for participating in the distribution of benefits based on the aggregator model of electric vehicle clusters, characterized by: Comprising the following steps: Step S01: Establishing an electric vehicle user willingness model by evaluating the electric vehicle user willingness; Step S02: Through the single EV charging and discharging model, the aggregator regulates the EV to participate in the frequency regulation market to obtain income; Step S03: Based on the shapley value theory, the obtained income is distributed to the aggregator and the EV owner.

2. The method for aggregators mode-based electric vehicle cluster participation revenue distribution according to claim 1, wherein: In step S01, the electric vehicle user willingness model comprises the following steps: Step S11: Quantifying the single-vehicle EV extra battery wear that participates in the aggregator regulation The formula is as follows: （12） wherein, and are the degree of loss of electricity during EV charging and discharging, respectively; and are the additional charging and discharging power, respectively; is the time when a single EV participating in the aggregator regulation starts the additional charging and discharging behavior; and are the additional charging and discharging duration, respectively; and are the start charging and discharging time within the additional charging and discharging time, respectively; and are the charging and discharging time period within the additional charging and discharging time, respectively. Step S12: Establishing a basic loss model, the formula is as follows: （13） （14） wherein is the individual rejection coefficient, represents the direct wear of the battery life by the charge and discharge amplitude; represents the accelerated wear caused by frequent, high-power charge and discharge; is the discharge indicator variable: 1 when discharging, otherwise 0; Step S13: EV charging time on the network Users' anxiety about flexible vehicle use is measured by the EV's online usage time, thus reducing the time anxiety cost of flexible vehicle use for individual users. Defined as: （15） wherein is the total time the vehicle stays; is the maximum stay duration; is the upper limit of the anxiety cost; is the sensitivity index; Step S14: Obtaining a comprehensive willingness function Defining the user's comprehensive anxiety cost based on battery depletion anxiety cost and flexible use of vehicle time anxiety cost To reflect the willingness of EV users to participate in regulation: （16） In the formula, is the maximum flexible car anxiety cost of a single user participating in the aggregator regulation, is the conversion factor, is the maximum flexible car anxiety cost of a single user participating in the aggregator regulation, embodies the psychological trade-off of the user between obtaining income and sacrificing time cost in participating in the aggregator regulation. The greater the value, the stronger the willingness of the corresponding user to participate in the regulation, and vice versa.

3. The method of claim 1, wherein the method is based on a model of aggregators, and wherein the method further comprises: receiving a request for a reward from a user of the electric vehicle; and providing the reward to the user of the electric vehicle. In step S02, the single EV charging and discharging model comprises According to the change between the various states of EV access to the grid, the battery SOC value changes, as shown in the following formula: （1） In the formula: and The first The SOC value of the battery of an EV at the current moment and the next moment; For time intervals; and The charging and discharging efficiencies of the EVs are respectively; The power of single EV access to the grid satisfies the constraint condition as shown in the following formula: （2） In the formula, is the output power of the first vehicle EV; and is the rated charge-discharge power of the first vehicle EV; Monomer EVs in charging period The SOC operating boundary within the battery, as shown by the following equation: （3） （4） wherein and are respectively the upper and lower limits of the SOC of the EV at the moment; the state of charge of the electric vehicle at the moment when it is desired to go off-grid; the state of charge of the electric vehicle at the moment when it is desired to go off-grid;​ The single EV battery SOC constraint is as shown in the following formula: （5）； Assume that the charging period of the EV is , consider that the EV accepts to participate in the aggregator's regulation, the minimum time , the EV charging demand : （6）。 4. The electric vehicle cluster income distribution method based on the aggregator mode according to claim 3, wherein: The aggregator aggregates and controls a large number of EVs in a certain area, and the total power and power boundary of the aggregator is represented by the sum of the power and power boundary of all EVs, and the power and power boundary of the aggregator is: （7） （8） （9） （10） wherein, and are the upper and lower boundaries of the state of charge and power at time is the number of EVs under the aggregator's jurisdiction.​ 5. The method of claim 4, wherein the method is based on a model of aggregators, and wherein the method further comprises: determining a total amount of the participation benefit; and determining a total amount of the participation benefit to be distributed to the aggregators based on the total amount of the participation benefit and the participation benefit distribution ratio. The aggregator regulates the EV, specifically, under the premise of actively regulating the user's charging power, the user participates in different charging modes, and the user is promised to meet the charging demand, and the charging mode includes a charging mode based on a guaranteed power, and the charging mode is as follows: The purpose of accessing the grid is only to obtain electric energy, and the aggregator does not make any constraints on the charging behavior of the EV, and the EV can access the grid at any time for charging and off-grid; A guaranteed power is promised to each charging EV in each period, and the power charged in each standby period is not less than the guaranteed power, so as to avoid insufficient charging when the vehicle off-grid; Let the vehicle j be divided into the hth time interval set H in a day, and the guaranteed constraint is: （11） wherein Battery charge of the vehicle at the time period h; The minimum charge that the vehicle is committed to take on at the hth segment.

6. The method of claim 5, wherein the method is based on a model of aggregators, and wherein the method further comprises: determining a total amount of the participation benefit; and determining a total amount of the participation benefit to be distributed to the aggregators based on the total amount of the participation benefit and the participation benefit distribution ratio. The income obtained from the frequency regulation market includes the income of the aggregator participating in the frequency regulation auxiliary market, the real-time margin of the EV, the income of the aggregator and the income of the EV owner.

7. The method of claim 6, wherein the method is based on a model of aggregators, and wherein the method further comprises: receiving, by the aggregator, a request for a reward from the electric vehicle cluster; and providing, by the aggregator, the reward to the electric vehicle cluster. The income of the mobile car aggregator participating in the frequency modulation auxiliary market is specifically: recording the aggregator charging power per period as The power is used as the reference power reported to the system, and the frequency modulation will be adjusted up and down based on the power; the reported uplink and downlink capacities are and , wherein the subscripts all represent the th period, then the aggregator needs to pay the energy cost of purchasing power , obtains the capacity income of providing backup , and obtains the mileage income of real-time response frequency , which are defined as follows: （17） （18） （19） In the formula, , , are the energy market price (unit: yuan / kwh) and the uplink and downlink reserve capacity price (unit: yuan / kwh), respectively; represents the duration of a reserve period; is the mileage price (unit: yuan / kwh); is the frequency modulation performance index; is the frequency modulation mileage; The real-time margin of the EV is used to constrain its power curve to ensure user demand, and is used as the basis for real-time power scheduling and income distribution, Definition of real-time minimum power For the jth electric vehicle, if it goes off-grid at time point t, its total power should not be less than , （20） The real-time margin of EV at time point t is defined as the electric quantity at this time with respect to excess electric quantity: （21）； The aggregator's revenue is the FM revenue share minus the compensation to the car owner for the undercharge: in period , （22） （23） wherein, represents the sharing ratio of the frequency modulation benefit, represents the power shortage relative to the guaranteed charging power promised to the vehicle owner after the end of the period, is the cumulative actual charging power of the vehicle j in the current segment, represents the penalty coefficient; The EV owner's benefits are specifically: for EVs with charging records, their charging cost in a time period is noted as , the frequency regulation benefit is noted as , and after deducting the benefit allocated to the aggregator, the total energy cost and total frequency regulation benefit of all EVs are given by the following equations, where represents the total number of EVs with charging records in the time period, （24） （25） For a single EV, the total cost incurred due to charging Total cost incurred due to charging The total cost incurred due to charging includes the cost of energy, the revenue from frequency regulation services, and the compensation fee from the aggregator when the EV is undercharged, as shown in the following equation: （26）。 8. The method of claim 7, wherein the method is based on a model of aggregators, and wherein the method further comprises: receiving, by the aggregator, a request for a reward from the electric vehicle cluster; and providing, by the aggregator, the reward to the electric vehicle cluster. In step S03, the shapley value of the charging cost of the EV and the shpley value of the frequency regulation income are calculated.

9. The method of claim 8, wherein the method is based on a model of aggregators, and wherein the method further comprises: receiving, by the aggregator, a request for a reward from the electric vehicle cluster; and providing, by the aggregator, the reward to the electric vehicle cluster. The charging cost of each EV is proportionally distributed according to the marginal cost, as shown in the following formula: （28） The marginal cost is the amount of power the aggregator guarantees for each additional EV charging on the grid. The frequency modulation benefit is divided into capacity benefit and mileage benefit, and the frequency modulation benefit of a single EV is recorded as capacity benefit and mileage benefit , as shown in the following formula: （29） The more the number of charging EVs, the more the actual available capacity, and this part of the income is directly distributed based on the charging time of the period, as shown in the following formula （30） Each moment Mileage benefits Break it down, and then do each The profit distribution is as follows: （31） The marginal benefit of a single EV on the frequency regulation benefit is: when the EV participates in frequency regulation, it shares the amount of change in the power margin of the remaining EVs by changing its own power margin. The charging power of the jth EV at time t is The change in the margin between time t and time t+1 is: （32）。 10. The method of claim 9, wherein the method is based on a model of aggregators, and wherein the method further comprises: receiving a request for a reward from a user of the electric vehicle; and providing the reward to the user of the electric vehicle. Based on the basic definition of shpley value based on contribution, the contribution of EV to the aggregator is: 1) the frequency modulation contribution of the EV with unchanged margin is 0; when it needs to be adjusted upwards, the margin change is positive, so the contribution is positive; when the margin change is negative, the contribution is negative; when it needs to be adjusted downwards, the margin change is negative, so the contribution is positive; when the margin change is positive, the contribution is negative. For the whole aggregator, the total margin change and the frequency modulation demand are of the same sign: when it is adjusted upwards, the total margin becomes larger; when it is adjusted downwards, the total margin becomes smaller, so the total margin change is proportional to the frequency modulation demand: As the contribution is distributed in proportion, the mileage benefit accumulated by the jth EV in period t is divided into: （33） In the formula, is the frequency modulation demand symbol, +1 if the demand is to increase, -1 otherwise.

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