A control method for electric vehicle distribution network based on V2G technology

Abstract

The present invention provides an electric vehicle distribution network regulation method based on V2G technology, including: S1) collecting regional load data and individual electric vehicle charging data; S2) establishing an evaluation model for the regulation potential of electric vehicle charging and discharging; S3) establishing a distribution network regulation model involving electric vehicles; S4) obtaining a regulation plan based on actual data. The present invention proposes an evaluation model for the regulation potential of electric vehicle charging and discharging. On this basis, with the goal of minimizing the daily load fluctuation of the power grid, considering the quantitative calculation and analysis of the benefits of multiple parties including the grid, merchants, and vehicles, a distribution network regulation model involving electric vehicles is established to obtain the optimal regulation plan. During the peak period of regional load, electric vehicles will reduce charging or discharge. Compared with the orderly charging that only reduces the charging power, it has a better peak shaving effect, and at the same time, the improvement of load fluctuation also has a better regulation effect than the existing orderly charging technology.

Description

Technical Field

[0001] The present invention relates to the technical field of economic dispatching of power systems, and in particular to a method for regulating and controlling an electric vehicle distribution network based on V2G technology. Background Art

[0002] With the increasingly severe situation of environmental pollution and energy shortage, electric vehicles that do not rely on fossil fuels have begun to receive extensive attention from society. Electric vehicles have characteristics such as high energy efficiency, low noise, and zero exhaust emissions, and are more environmentally friendly than traditional fuel vehicles. Large-scale application of electric vehicles can effectively alleviate crises such as energy shortage and environmental pollution and contribute to the realization of the low-carbon economy goal.

[0003] Currently, electric vehicles can be divided into three categories: buses, taxis, and private cars. The battery capacity of electric buses and private cars is difficult to meet the needs of a day's operation, so it is necessary to supplement electric energy by replacing the battery or fast charging. Private cars are mainly used for the owner's commuting, and their charging locations are mostly fixed in the unit parking lot or the community garage. Since their daily driving mileage is relatively small, slow charging is mostly used. The large-scale application of electric vehicles will form a huge charging load during specific periods of the day, impacting the power grid and affecting the safety of the power grid. The charging times of the three types of electric vehicles are determined by their respective travel habits and have strong regularity, which means that the charging process of electric vehicles can be orderly controlled through certain means to reduce the charging load and improve the reliability and safety of the power grid.

[0004] V2G technology is the latest technology in the field of electric vehicle technology. Its core is to utilize the characteristics of the rechargeable batteries of electric vehicles, regard a large number of electric vehicles as a distributed energy storage system and manage them, so as to discharge during the peak period of the power grid load and charge during the valley period, thereby reducing the peak-valley difference rate and realizing the improvement of the safety and economy of the power grid. In addition, a large number of batteries can also serve as a buffer for renewable energy to achieve the consumption of intermittent energy such as photovoltaic.

[0005] However, the existing research models related to the technology do not fully analyze and consider the interests of all participating parties, especially the benefit analysis of users and aggregators is relatively one-sided. Summary of the Invention

[0006] In view of the deficiencies of the existing technology, the present invention provides a method for regulating and controlling an electric vehicle distribution network based on V2G technology.

[0007] The technical solution of the present invention is as follows: A method for regulating and controlling an electric vehicle distribution network based on V2G technology, including the following steps:

[0008] S1), Collect regional load data and individual electric vehicle charging data;

[0009] S2), Establish an evaluation model for the regulation potential of electric vehicle charging and discharging;

[0010] S3), Establish a distribution network regulation model based on the participation of electric vehicles;

[0011] S4), Obtain a regulation plan according to the actual data.

[0012] Preferably, in step S1), the electric vehicle charging data includes the SOC state, travel demand, and charging power.

[0013] Preferably, in step S2), the establishment of the evaluation model for the regulation potential of electric vehicle charging and discharging includes the establishment of constraint conditions, and the specific steps are as follows:

[0014] S201), Battery safety constraint

[0015] The newly added △SOCt in the t-th period should ensure that the SOCt after charging and discharging is always within the safe constraint range of the battery, that is:

[0016]

[0017] Among them, represents the initial SOC value of the electric vehicle, , , respectively represent the lower limit, upper limit of the electric vehicle battery state, and the power difference in the i-th period;

[0018] When the electric vehicle has been charging at the maximum power within the constraint in each period before the t-th moment, and , at this time, the of the electric vehicle is the smallest, and at this time, the discharge capacity of the electric vehicle is the largest:

[0019]

[0020] In the formula, represents the minimum increment of SOC in the t-th period under the first constraint condition; represents the maximum SOC increment in the i-th period;

[0021] When the electric vehicle has been discharging at the maximum power within the constraint in each period before the t-th moment, and , at this time, the of the electric vehicle is the largest, and at this time, the charging capacity of the electric vehicle is the largest:

[0022]

[0023] In the formula, represents the maximum increment of the SOC at time t under the first constraint condition; represents the minimum increment of the SOC at time i.

[0024] S202), Travel demand constraint. After the user finishes charging, the battery power of the electric vehicle should at least be able to support the user's one-day journey, that is:

[0025]

[0026] In the formula, is the time period when the regulation of the electric vehicle ends; then the minimum increment of the SOC at time t under the second constraint condition can be calculated by the following formula:

[0027]

[0028] In the formula, respectively represent the user's daily driving mileage, the cruising range of the electric vehicle, and the minimum increment of the SOC at time t under the second constraint condition; respectively represent the rated charging power of the electric vehicle charging pile, the charging and discharging power of the charging pile at time t, and the time period when the regulation of the electric vehicle ends;

[0029] If continuous charging in the time period after time t can meet the user's travel demand, the battery power of the user at time t can be discharged to the minimum value; if continuous charging in the time period after time t cannot meet the user's travel demand, the battery increment of the user at time t needs to consider the limitation of the travel demand.

[0030] S203), Charging and discharging power constraint. In addition to the battery and demand constraints, the charging and discharging process of the electric vehicle is also limited by the power of the charging pile, that is, it cannot exceed the rated limit:

[0031]

[0032]

[0033] Among them, respectively represent the minimum and maximum values of the SOC increment at time t of the electric vehicle under the third constraint condition.

[0034] S204), Considering the above three constraint conditions comprehensively, the upper and lower limit calculation formulas of the SOC increment of the electric vehicle are as follows:

[0035]

[0036]

[0037] The charge and discharge adjustable potential corresponding to the electric vehicle at time t is:

[0038] 。

[0039] Preferably, in step S3), the establishment of the distribution network regulation model involving electric vehicles is as follows: aiming at suppressing the load fluctuation of the power grid, with the battery capacity, charge and discharge power, charge and discharge time of each electric vehicle, the power at the time of the electric vehicle leaving the station, the net profit of the aggregator, and the net charging cost of the electric vehicle user as constraints, and taking △t as the duration of responding to the dispatching in units, the charge and discharge powers of each electric vehicle at each moment in the parked state are used as the optimization control variables, and the following charge and discharge scheduling model in the V2G mode of electric vehicles is established:

[0040] S301), the objective function is to minimize the daily load fluctuation of the power grid:

[0041] In the formula, F1 is the root mean square of the daily load fluctuation of the power grid, N is the number of electric vehicles; T is the number of moments for measuring the charge and discharge load; P(t) is the conventional load of the system at the t-th moment; is the charge and discharge power of the i-th electric vehicle at the t-th moment:

[0042] When , it means that the electric vehicle is charging;

[0043] When , it means that the electric vehicle is discharging;

[0044] When , it means that there is no power flow between the vehicle and the grid;

[0045] The daily average load of the power grid considering the charge and discharge power can be obtained by the following formula:

[0046]

[0047] S302), the constraint conditions include

[0048] ① State of charge constraint. When the battery is over-discharged, it will cause serious damage to the battery performance. Therefore, the minimum value of the state of charge is set as SOC min , when it is lower than this value, the battery stops discharging, and the maximum value of the state of charge is set as SOC max , when it is higher than this value, charging stops, that is:

[0049]

[0050] In the formula is the charge of the i-th electric vehicle at the t-th moment; is the minimum power limit of the i-th electric vehicle; is the maximum power limit of the i-th electric vehicle;

[0051] Among them, the state of charge of the i-th electric vehicle at the (t + 1)-th moment can be obtained by the following formula:

[0052]

[0053] In the formula, is the charging and discharging power of the i-th electric vehicle at the t-th moment; Ci is the battery capacity of the i-th electric vehicle, is the duration of unit response scheduling.

[0054] ② Charging and discharging power constraint and regulation potential constraint. The V2G technology can realize the bidirectional flow of energy between electric vehicles and the power grid. During the low-load period, the power grid charges the electric vehicle, and during the high-load period, the electric vehicle discharges to the power grid. Therefore, the charging and discharging rate of the electric vehicle varies between the maximum discharge power (negative value) and the maximum charging power (positive value):

[0055]

[0056] In the formula, is the maximum discharge power of the i-th electric vehicle at the t-th moment; is the charging and discharging power of the i-th electric vehicle at the t-th moment; is the maximum charging power of the i-th electric vehicle at the t-th moment.

[0057] ③ Owner's charging demand constraint. To ensure that the battery power of the electric vehicle can meet the owner's travel needs after leaving the charging station, the owner can customize the target battery capacity when the vehicle leaves:

[0058]

[0059] In the formula, is the state of charge of the i-th electric vehicle when leaving the station; is the state of charge set to meet the travel needs of the owner of the i-th electric vehicle. In practice, it can be set according to the owner's next travel needs;

[0060] is the charging and discharging power of the i-th electric vehicle at the t-th moment; is the inbound time of the i-th electric vehicle;

[0061] is the outbound time of the i-th electric vehicle; is the duration of unit response scheduling.

[0062] ④ Aggregator revenue constraint. The costs for the aggregator to participate in dispatching responses include subsidies for users' participation in orderly charging and discharging dispatching responses (divided into charging response and discharging response parts), and the revenue comes from the compensation fees and service fees from the grid company. The calculation formula for the aggregator's service fee revenue is:

[0063]

[0064]

[0065] In the formula, is the aggregator's service fee revenue when the vehicle owner participates in the demand response of orderly charging and discharging; is the charging service price at the t-th moment; is the charging power of the i-th electric vehicle responding to the dispatch at the t-th moment; is the duration of unit response dispatch;

[0066] The compensation fees for the aggregator to users' participation in dispatch responses include two parts: users' participation in orderly charging responses and orderly discharging responses. The charging response compensation fees are as follows:

[0067]

[0068] In the formula, is the subsidy revenue for the charging response part when users participate in orderly charging and discharging dispatching; is the charging power of the i-th electric vehicle at the t-th moment; is the total charging load of electric vehicles at the t-th moment under disorderly charging; is the duration of unit response dispatch; is the subsidy standard for users' participation in charging responses;

[0069] The discharging response compensation fees are as follows:

[0070]

[0071] In the formula, is the subsidy revenue for the discharging response part when users participate in orderly charging and discharging dispatching; is the discharging incentive electricity price at the t-th moment; is the discharging power of the i-th electric vehicle responding to the dispatch at the t-th moment; is the unit response dispatch duration;

[0072] In summary, the compensation fees for the aggregator to users' participation in orderly charging and discharging dispatching responses are:

[0073]

[0074] To ensure that the net revenue of aggregators participating in orderly charging and discharging scheduling responses is greater than the service fee income of aggregators during disorderly charging, the following constraints are imposed:

[0075] 。

[0076] The beneficial effects of the present invention are as follows:

[0077] 1. The present invention proposes an evaluation model for the regulation potential of electric vehicle charging and discharging. On this basis, with the goal of minimizing the daily load fluctuation of the power grid, considering the quantitative calculation and analysis of the benefits of multiple parties including the grid, merchants, and vehicles, a distribution network regulation model involving electric vehicles is established to obtain the optimal regulation plan;

[0078] 2. During the peak period of regional load, electric vehicles will reduce charging or discharge. Compared with orderly charging that only reduces the charging power, it has a better peak shaving effect, and at the same time, the improvement of load fluctuation also has a better regulation effect than the existing orderly charging technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is the basic load curve diagram in Embodiment 2 of the present invention;

[0080] Figure 2 It is the comparison diagram of the total regional load before and after regulation in Embodiment 2 of the present invention;

[0081] Figure 3 It is the comparison diagram of the regional charging load before and after regulation in Embodiment 2 of the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0082] The following further describes the specific embodiments of the present invention with reference to the drawings:

[0083] Embodiment 1

[0084] This embodiment provides a method for regulating an electric vehicle distribution network based on V2G technology, including the following steps:

[0085] S1). Collect regional load data and individual electric vehicle charging data; among them, the electric vehicle charging data includes the SOC state, travel demand, and charging power.

[0086] S2). Establish an evaluation model for the regulation potential of electric vehicle charging and discharging; including the establishment of multiple constraint conditions, specific steps:

[0087] S201). Battery safety constraint

[0088] The newly added △SOCt at time t should ensure that the SOCt after charging and discharging is always within the safe constraint range of the battery, that is:

[0089]

[0090] Among them, represents the initial SOC value of the electric vehicle, , , respectively represent the lower limit, upper limit of the electric vehicle battery state, and the power difference in the i-th period;

[0091] When the electric vehicle has been charging at the maximum power within the constraints in each period before time t, and , at this time, the of the electric vehicle is the smallest, and at this time, the discharge capacity of the electric vehicle is the largest:

[0092]

[0093] In the formula, represents the minimum increment of SOC in the t period under the first constraint condition; represents the maximum increment of SOC in the i period;

[0094] When the electric vehicle has been discharging at the maximum power within the constraints in each period before time t, and , at this time, the of the electric vehicle is the largest, and at this time, the charging capacity of the electric vehicle is the largest:

[0095]

[0096] In the formula, represents the maximum increment of SOC in the t period under the first constraint condition; [[ID=4,5]]represents the minimum increment of SOC in the i period. ;

[0097] S202), travel demand constraint. After the user finishes charging, the battery power of the electric vehicle should at least be able to support the user's one-day journey, that is:

[0098]

[0099] In the formula, is the period when the electric vehicle finishes regulation; then the minimum increment of SOC in the t period under the second constraint condition can be calculated by the following formula:

[0100]

[0101] In the formula, respectively represent the user's daily driving mileage, the cruising range of the electric vehicle, and the minimum increment of SOC in the t period under the second constraint condition; respectively represent the rated charging power of the electric vehicle charging pile, the charging and discharging power of the charging pile at time t, and the time period when the electric vehicle ends regulation;

[0102] If continuous charging in the time period after t can meet the travel needs of users, the battery power of users in the t time period can be discharged to the minimum value; if continuous charging in the time period after t cannot meet the travel needs of users, the battery increment of users in the t time period needs to consider the limitation of travel needs.

[0103] S203), Charging and discharging power constraint. In addition to battery and demand constraints, the charging and discharging process of electric vehicles is also restricted by the power of the charging pile, that is, it cannot exceed the rated limit:

[0104]

[0105]

[0106] Among them, respectively represent the minimum and maximum values of the SOC increment of the electric vehicle in the t time period under the third constraint condition.

[0107] S204), Considering the above three constraint conditions comprehensively, the upper and lower limit calculation formulas of the electric vehicle SOC increment are as follows:

[0108]

[0109]

[0110] The adjustable potential of the charging and discharging of the electric vehicle corresponding to time t is:

[0111]

[0112] S3), Establish a distribution network regulation model based on the participation of electric vehicles; aiming at suppressing the load fluctuation of the power grid, taking the battery capacity, charging and discharging power, charging and discharging time of each electric vehicle, the power of the electric vehicle when leaving the station, the net profit of the aggregator, and the net charging cost of the electric vehicle user as constraints, and taking △t as the unit response scheduling duration, taking the charging and discharging power of each electric vehicle at each moment in the parked state as the optimization control variable, establish the following charging and discharging scheduling model under the V2G mode of electric vehicles:

[0113] S301), The objective function is to minimize the daily load fluctuation of the power grid:

[0114]

[0115] In the formula, F1 is the root mean square of the daily load fluctuation of the power grid, N is the number of electric vehicles; T is the number of moments for measuring the charging and discharging load; P(t) is the conventional load of the system at the t-th moment; is the charging and discharging power of the i-th electric vehicle at the t-th moment:

[0116] When it means that the electric vehicle is charging;

[0117] When it means that the electric vehicle is discharging;

[0118] When it means that there is no power flow between the vehicle and the grid;

[0119] is the daily average load of the power grid considering the charging and discharging power, which can be obtained by the following formula:

[0120]

[0121] S302) The constraint conditions include:

[0122] ① State of charge constraint. When the battery is over-discharged, it will cause serious damage to the battery performance. Therefore, the minimum value of the state of charge is set as . When it is lower than this value, the battery stops discharging, and the maximum value of the state of charge is set as . When it is higher than this value, charging stops, that is:

[0123]

[0124] In the formula is the charge of the i-th electric vehicle at the t-th moment; is the minimum charge limit of the i-th electric vehicle; is the maximum charge limit of the i-th electric vehicle;

[0125] Among them, the charge of the i-th electric vehicle at the (t + 1)-th moment can be obtained by the following formula:

[0126]

[0127] In the formula, is the charging and discharging power of the i-th electric vehicle at the t-th moment; Ci is the battery capacity of the i-th electric vehicle, is the duration of unit response scheduling.

[0128] ② Charging and discharging power constraint and regulation potential constraint. The V2G technology can realize the bidirectional flow of energy between the electric vehicle and the power grid. During the low load period, the power grid charges the electric vehicle, and during the high load period, the electric vehicle discharges to the power grid. Therefore, the charging and discharging rate of the electric vehicle varies between the maximum discharge power (negative value) and the maximum charging power (positive value):

[0129]

[0130] In the formula, is the maximum discharge power of the \(i\)-th electric vehicle at the \(t\)-th moment; is the charge-discharge power of the \(i\)-th electric vehicle at the \(t\)-th moment; is the maximum charge power of the \(i\)-th electric vehicle at the \(t\)-th moment.

[0131] ③ Constraint on the charging demand of the vehicle owner. To ensure that the battery power of the electric vehicle can meet the travel demand of the vehicle owner after leaving the charging station, the vehicle owner can customize the target battery capacity when the vehicle leaves:

[0132]

[0133] In the formula, is the state of charge of the \(i\)-th electric vehicle at the outbound moment; is the state of charge set to meet the travel demand of the owner of the \(i\)-th electric vehicle, which can be set according to the owner's next travel demand in practice;

[0134] is the charge-discharge power of the \(i\)-th electric vehicle at the \(t\)-th moment; is the inbound time of the \(i\)-th electric vehicle;

[0135] is the outbound moment of the \(i\)-th electric vehicle; is the duration of unit response scheduling.

[0136] ④ Aggregator revenue constraint. The cost for the aggregator to participate in the dispatching response includes the subsidy for users to participate in the orderly charge-discharge dispatching response (divided into two parts: charge response and discharge response), and the revenue comes from the compensation fee and service fee from the power grid company. The calculation formula for the service fee income of the aggregator is:

[0137]

[0138]

[0139] In the formula, is the service fee income of the aggregator when the vehicle owner participates in the demand response of orderly charge-discharge; is the charging service price at the \(t\)-th moment; is the charging power of the \(i\)-th electric vehicle responding to the dispatching at the \(t\)-th moment; is the duration of unit response scheduling;

[0140] The compensation cost for the aggregator to users participating in the dispatching response includes two parts: users participating in the orderly charge response and the orderly discharge response. The charge response compensation cost is as follows:

[0141]

[0142] Wherein, is the subsidy income of the charging response part when the user participates in the orderly charging and discharging scheduling; is the charging power of the i-th electric vehicle at the t-th moment; is the total charging load of electric vehicles at the t-th moment under disordered charging; is the duration of unit response scheduling; is the subsidy standard for users to participate in the charging response;

[0143] The compensation cost for the discharge response is as follows:

[0144]

[0145] Wherein, is the subsidy income of the discharge response part when the user participates in the orderly charging and discharging scheduling; is the discharge incentive electricity price at the t-th moment; is the discharge power of the i-th electric vehicle that responds to the scheduling at the t-th moment; is the unit response scheduling duration;

[0146] In summary, the compensation cost of the aggregator for the user to participate in the orderly charging and discharging scheduling response is:

[0147]

[0148] In order to ensure that the net income of the aggregator participating in the orderly charging and discharging scheduling response is greater than the service fee income of the aggregator during disordered charging, the following constraints are available:

[0149] .

[0150] S4), Obtain the regulation and control plan according to the actual data, obtain the charging power regulation value of each time period on the premise of meeting the constraint conditions, and actually regulate the charging and discharging states and power values of the charging piles according to the regulation value, so as to achieve the purpose of optimal regulation.

[0151] Embodiment 2

[0152] This embodiment takes the typical daily load curve of a certain area on a summer working day as the regulation and control target curve, as Figure 1 shown. The charging, discharging and self-parameters of electric vehicles are set as shown in Table 1. The number of electric vehicles is set to 1200, and the subsidy standards for the aggregator to participate in the discharge and charging responses are 2.5 yuan / kWh and 1 yuan / kWh respectively. The time T covered by the calculation example is 24 regulation and control time periods for the day-ahead invitation response.

[0153] Table 1 Charging, discharging and self-parameters of electric vehicles

[0154]

[0155] In this embodiment, pre-regulation, orderly charging, and orderly charge and discharge (the method of this embodiment) are set to compare the regulation effects and indicators. The results of the regional load situation after regulation are as Figure 2 shown. The charging load results before and after regulation in the two methods are as Figure 3 shown. The comparison of relevant indicators before and after regulation is shown in Table 2. After regulation in this embodiment and after general orderly charging regulation, the peak regional loads are reduced by 3884.4 kW and 3152.8 kW respectively, which are 17.7% and 14.4% lower than the original respectively. The method of the present invention has a larger adjustable space compared with general orderly charging. After the method of the present invention is regulated, during the peak period of regional load, electric vehicles will reduce charging or discharge. Compared with orderly charging that only reduces the charging power, it has a better peak shaving effect. At the same time, the improvement of load fluctuation also has a better regulation effect than general orderly charging, thus verifying the feasibility and practicality of the method in this article.

[0156] Table 2 Comparison of relevant indicators before and after regulation

[0157]

[0158] The above embodiments and descriptions in the specification only illustrate the principles and the best embodiments of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

Claims

1. An electric vehicle distribution network regulation method based on V2G technology, characterized in that, It includes the following steps: S1), Collect regional load data and individual electric vehicle charging data; S2), Establish an evaluation model for the regulation potential of electric vehicle charging and discharging, including three constraint conditions: battery safety constraint, travel demand constraint, and charging and discharging power constraint; By comprehensively considering the above three constraint conditions, the upper and lower limit calculation formulas for the increment of the electric vehicle's state of charge (SOC) are as follows: The adjustable charging and discharging potential corresponding to the electric vehicle at time t is: Wherein, represents the minimum increment of the SOC at time t under the first constraint condition; Indicates the minimum increment of SOC at time t under the second constraint condition; Indicates the minimum increment of SOC at time t under the third constraint condition; Indicates the maximum increment of SOC at time t under the first constraint condition; Represents the maximum increment of SOC at time t under the third constraint condition; Indicates the maximum SOC increment during period t; Indicates the minimum SOC increment during period t; Indicates the battery capacity of an electric vehicle; S3), Establish a distribution network regulation model involving electric vehicles, aiming to smooth the load fluctuations of the power grid, with the battery capacity, charging and discharging power, charging and discharging time of each electric vehicle, the battery power when the electric vehicle leaves the station, the net profit of the aggregator, and the net charging cost of the electric vehicle user as constraints, and taking △t as the duration of the unit response to the dispatch. Regarding the charging and discharging power of each electric vehicle at each moment in the parked state as the optimization control variable, establish the following charging and discharging dispatch model in the vehicle-to-grid (V2G) mode: S301), The objective function is to minimize the daily load fluctuation of the power grid: Wherein, F1 is the root mean square of the daily load fluctuation of the power grid, N is the number of electric vehicles; T is the number of moments for measuring the charging and discharging load; P(t) is the conventional load of the system at the t-th moment; is the charging and discharging power of the i-th electric vehicle at the t-th moment: When it indicates that the electric vehicle is charging; When it indicates that the electric vehicle is discharging; When it indicates that there is no power flow between the vehicle and the grid; The daily average load of the power grid considering the charge and discharge power can be obtained by the following formula: S302), Establish multiple constraint conditions, and the said constraint conditions include: ① State of charge constraint; ② Charging and discharging power constraint and regulation potential constraint; ③ Vehicle owner's charging demand constraint; ④ Aggregator's revenue constraint; S4), Obtain the regulation plan according to the actual data, obtain the charging power regulation value for each time period on the premise of meeting the constraint conditions, and actually regulate the charging and discharging state and power value of the charging pile according to this regulation value, so as to achieve the purpose of optimal regulation.

2. The electric vehicle distribution network regulation method based on V2G technology according to claim 1, characterized in that: In step S1), the said electric vehicle charging data includes the SOC state, travel demand, and charging power.

3. The electric vehicle distribution network regulation method based on V2G technology according to claim 1, characterized in that: The said battery safety constraint is: Newly added during the t period It is necessary to ensure that after charge and discharge Always within the safety constraints of the battery, that is: Among them, represents the initial SOC value of the electric vehicle, , , respectively represent the lower limit, upper limit of the electric vehicle battery state, and the power difference in the i-th period; When the electric vehicle has been charging at the maximum power within the constraints in each time period before time t, and , at this time, the of the electric vehicle is the smallest. At this time, the discharge capacity of the electric vehicle is the largest: In the formula, represents the minimum increment of the SOC at time t under the first constraint condition; represents the maximum increment of the SOC at time i. When the electric vehicle discharges at the maximum power within the constraint during each period before time t, and , at this time, the of the electric vehicle is the largest, and at this time, the charging capacity of the electric vehicle is the largest: Wherein, represents the maximum increment of the SOC at time t under the first constraint condition; represents the minimum increment of the SOC at time i.

4. The electric vehicle distribution network regulation method based on V2G technology according to claim 3, characterized in that: The said travel demand constraint is: After the user finishes charging, the battery power of the electric vehicle should at least be able to support the user's one-day journey, that is: In the formula, is the time period when the regulation of the electric vehicle ends; then the minimum SOC increment at time t under the second constraint can be calculated by the following formula: Wherein, respectively represent the daily driving mileage of the user, the cruising range of the electric vehicle, and the minimum SOC increment in time period t under the second constraint condition; respectively represent the rated charging power of the electric vehicle charging pile, the charging and discharging power of the charging pile at time t, and the time period when the electric vehicle ends regulation; If continuous charging in the time period after time t can meet the travel demand of the user, the battery power of the user at time t can be discharged to the minimum value; If continuous charging in the time period after time t cannot meet the travel demand of the user, the battery increment of the user at time t needs to consider the limitation of the travel demand.

5. A method for regulating an electric vehicle distribution network based on V2G technology according to claim 4, characterized in that: The said charging and discharging power constraint is: In addition to the battery and demand constraints, the charging and discharging process of the electric vehicle is also restricted by the power of the charging pile, that is, it cannot exceed the rated limit: Among them, respectively represent the minimum and maximum values of the SOC increment of the electric vehicle at time t under the third constraint condition.

6. The electric vehicle distribution network regulation method based on V2G technology according to claim 1, characterized in that: The said state of charge constraint is: When the battery is over-discharged, it will cause serious damage to the battery performance. Therefore, the minimum value of the state of charge is set to , when the value is lower than this, the battery stops discharging, and the maximum value of the state of charge is set to , when the value is higher than this, charging stops, that is: where is the state of charge of the i-th electric vehicle at the t-th moment; is the minimum state of charge limit of the i-th electric vehicle; is the maximum state of charge limit of the i-th electric vehicle; Among them, the state of charge of the i-th electric vehicle at the (t + 1)-th moment can be obtained by the following formula: Wherein, is the charging and discharging power of the i-th electric vehicle at the t-th moment; Ci is the battery capacity of the i-th electric vehicle, is the duration of unit response scheduling.

7. A method for regulating an electric vehicle distribution network based on V2G technology according to claim 6, characterized in that: The charging and discharging power constraint and regulation potential constraint are: The V2G technology can realize the bidirectional flow of energy between the electric vehicle and the power grid. The power grid charges the electric vehicle during the low load period, and the electric vehicle discharges to the power grid during the high load period. Therefore, the charging and discharging rate of the electric vehicle changes between the maximum discharge power (negative value) and the maximum charging power (positive value): Wherein, is the maximum discharge power of the i-th electric vehicle at the t-th moment; is the charge and discharge power of the i-th electric vehicle at the t-th moment; is the maximum charge power of the i-th electric vehicle at the t-th moment.

8. A method for regulating an electric vehicle distribution network based on V2G technology according to claim 7, characterized in that: The said vehicle owner's charging demand constraint is: [[ID= wherein, is the state of charge of the i-th electric vehicle at the time of leaving the station; is the state of charge of the i-th electric vehicle at the time of entering the station; is the state of charge set to meet the travel demand of the owner of the i-th electric vehicle, and can be set according to the owner's next travel demand in practice; is the charging and discharging power of the i-th electric vehicle at the t-th moment; is the arrival time of the i-th electric vehicle at the station; is the departure time of the i-th electric vehicle; is the duration of unit response to dispatching.

9. A method for regulating an electric vehicle distribution network based on V2G technology according to claim 8, characterized in that: The aggregator revenue constraint. The costs for the aggregator to participate in the dispatching response include the subsidies for users to participate in the orderly charging and discharging dispatching responses (divided into two parts: charging response and discharging response), and the revenue comes from the compensation fees and service fees from the power grid company. The calculation formula for the service fee income of the aggregator is as follows: ； Wherein, is the service fee income of the aggregator when the vehicle owner participates in the demand response of orderly charging and discharging; is the charging service price at the t-th moment; is the charging power of the i-th electric vehicle responding to the dispatch at the t-th moment; is the charging service price in the T-th time period; is the duration of unit response dispatch; respectively represent the charging power of the \(i\)-th electric vehicle during the regulation of the first time period and the charging power of the \(i\)-th electric vehicle during the regulation of the \(T\)-th time period; respectively represent the charging power of the \(i\)-th electric vehicle in response to the regulation of the first time period and the charging power of the \(i\)-th electric vehicle in response to the regulation of the \(T\)-th time period; The compensation fees for the aggregator for users to participate in the dispatching response include two parts: users' participation in the orderly charging response and the orderly discharging response. The charging response compensation fees are as follows: In the formula, is the subsidy income of the charging response part when the user participates in the orderly charging and discharging scheduling; is the charging power of the i-th electric vehicle at the t-th moment; is the total charging load of electric vehicles at the t-th moment under unordered charging; is the duration of the unit response scheduling; is the subsidy standard for users to participate in the charging response; The discharging response compensation fees are as follows: Wherein, is the subsidy income of the discharge response part when the user participates in the orderly charge and discharge scheduling; is the discharge incentive electricity price at the t-th moment; is the discharge power of the i-th electric vehicle responding to the scheduling at the t-th moment; is the unit response scheduling duration; In summary, the compensation fees for the aggregator for users to participate in the orderly charging and discharging dispatching responses are: To ensure that the net revenue of the aggregator participating in the orderly charging and discharging dispatching response is greater than the service fee income of the aggregator during disorderly charging, the following constraints are available: 。

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