Electric vehicle service network low-carbon charging method based on carbon emission flow

Abstract

The invention discloses an electric vehicle service network low-carbon charging method based on carbon emission flow, which comprises the following steps of: 1, establishing a daily charging load model of an electric private car by considering travel starting time, travel ending time, daily driving mileage and air conditioner energy consumption of the electric private car; step 2, considering the basic load of the network node and the charging load of the electric vehicle, obtaining the dynamic carbon potential of the node of the service network of the electric vehicle, and recording the carbon emission obtained by combining the charging amount and the corresponding carbon potential; step 3, dividing a service network step carbon emission responsibility range and a corresponding step carbon price interval based on a Shapley value; and 4, establishing a low-carbon charging optimization model of the electric vehicle service network, and redistributing the charging load capacity of the electric vehicle. For an electric vehicle service network, carbon emission caused by charging of the electric vehicle is obviously reduced, the carbon emission cost, namely the operation cost, is reduced, and the low-carbon property of the electric vehicle is fully exerted.

Description

Technical Field

[0001] The present invention relates to the field of optimization of low-carbon charging at electric vehicle service outlets, and in particular to a low-carbon charging method at electric vehicle service outlets based on carbon emission flow. Background Art

[0002] Electric vehicles are driven by electricity, have the advantages of high efficiency and zero pollution, can effectively reduce carbon emissions, and are an important means to solve traffic and environmental problems. However, the cleanliness of the source of electric vehicle charging directly determines whether the electric vehicle has low-carbon properties. As of 2021, coal-fired thermal power generation accounts for 61% of my country's power generation, and continues to dominate my country's power grid energy structure for a long time. If there is a lack of effective response measures, the clean level of electric vehicle charging will be difficult to guarantee, which will only transfer carbon emissions from the transportation side to the power generation side, which is contrary to the original intention of my country to develop electric vehicles. At the same time, the inherent volatility and randomness of new energy output and the coupling of electric vehicle charging time also pose certain challenges in the current power system. Therefore, it is necessary to achieve the optimal scheduling of electric vehicle charging and clean energy by orderly regulating the charging behavior of electric vehicles.

[0003] This field has conducted extensive research on clean energy and optimal scheduling of electric vehicle loads, but in the modeling process, only the charging cost of electric vehicles is considered, and the carbon emissions of the power system caused by electric vehicle charging are not considered, which does not reflect the low-carbon nature of electric vehicles. Even if the carbon emissions caused by the charging process of electric vehicles are taken into account in the modeling process, only a rough calculation is made using the proportion of thermal power generation, which lacks a certain degree of accuracy. Therefore, the present invention proposes a low-carbon charging optimization method for electric vehicle service outlets based on carbon emission flows. The carbon emission flow theory of the power system is used to accurately calculate the carbon emissions caused by electric vehicle charging, so as to accurately guide and constrain the charging behavior of electric vehicles. Summary of the invention

[0004] The purpose of the present invention is to propose a low-carbon charging method for electric vehicle service outlets based on carbon emission flows, which incorporates the carbon emission costs and demand response costs of electric vehicles into the total costs of service outlets, guides and encourages service outlets to arrange electric vehicle charging during clean energy output periods, so as to reduce the carbon emissions of the power system and the carbon emission costs paid by the service outlets.

[0005] The technical solution to achieve the purpose of the present invention is:

[0006] A low-carbon charging method for electric vehicle service outlets based on carbon emission flow, comprising the following steps:

[0007] Step 1: Considering the start time, end time, daily mileage and air conditioning energy consumption of electric vehicles, a daily charging load model for electric vehicles is established;

[0008] Step 2: Taking into account the basic load of the network nodes and the charging load of electric vehicles, the dynamic carbon potential of the electric vehicle service network nodes is obtained, and the carbon emissions obtained by combining the charging amount with the corresponding carbon potential are recorded;

[0009] Step 3: Divide the service outlets’ tiered carbon emission responsibility scope and corresponding tiered carbon price ranges based on the Shapley value;

[0010] Step 4: Establish a low-carbon charging optimization model for electric vehicle service outlets to redistribute the charging load of electric vehicles.

[0011] The advantages and effects of the present invention are as follows:

[0012] For electric vehicle service outlets, the present invention significantly reduces carbon emissions caused by charging electric vehicles, reduces carbon emission costs, namely operating costs, and gives full play to the low-carbon attributes of electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of a process of the present invention;

[0014] Figure 2 It is a schematic diagram of the IEEE14 improved node system of the present invention;

[0015] Figure 3 is the original load and electric vehicle charging load curve of the present invention;

[0016] Figure 4 is the predicted output value of the wind turbine generator set of the present invention;

[0017] Figure 5 It is the scope of carbon emission responsibility of service outlet A of the present invention;

[0018] Figure 6 It is the scope of carbon emission responsibility of service outlet B of the present invention;

[0019] Figure 7 It is the scope of carbon emission responsibility of service outlet C of the present invention;

[0020] Figure 8 It is the comparison before and after the optimization of the charging load of service outlet A of the present invention;

[0021] Fig. 9 It is the comparison before and after the optimization of the charging load of the service outlet B of the present invention;

[0022] Fig.10 This is a comparison of the charging load of service point C before and after optimization of the present invention. DETAILED DESCRIPTION

[0023] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0024] Example

[0025] A low-carbon charging optimization method for electric vehicle service outlets based on carbon emission flow, such as Figure 1 As shown, the following steps are included:

[0026] Step 1: Considering the start time, end time, daily mileage and air conditioning energy consumption of electric private cars, the Monte Carlo sampling method is used to establish the daily charging load model of electric private cars, and then the charging of electric vehicle clusters is handed over to the nearest service outlets for management;

[0027] Step 2: Taking into account the basic load of the network nodes and the charging load of electric vehicles, the dynamic carbon potential of the electric vehicle service network nodes is obtained, and the carbon emissions obtained by combining the charging amount with the corresponding carbon potential are recorded;

[0028] Step 3: Divide the service outlets’ tiered carbon emission responsibility range and the corresponding tiered carbon price range based on the Shapley value. Due to the different node locations of electric vehicle service outlets, it is unfair and unreasonable to use the same range of tiered carbon price ranges. Therefore, the present invention introduces the Shapley value to help complete the division of the tiered carbon emission responsibility range of each service outlet. Similarly, a tiered carbon price is formulated to encourage and guide service outlets to optimize the distribution of electric vehicle charging loads.

[0029] Step 4: Establish a low-carbon charging optimization model for electric vehicle service outlets. Using the stepped carbon price as a signal, reallocate the electric vehicle charging load without affecting the normal travel of electric vehicles. Transfer the load from periods with higher carbon potential to periods with lower carbon potential, reduce carbon emissions, and achieve the goal of maximizing economic benefits.

[0030] Furthermore, step 1 is as follows:

[0031] Step 1.1, it is assumed that the electric vehicle starts charging after the trip ends. The end time of the trip is the start time of charging. The end time of the trip satisfies the normal distribution, and its probability density function f e (t 1 )for:

[0032]

[0033] Where, t 1 is the end time of private car travel, i.e. the start time of electric vehicle charging; μ t1 =17.6,σ t1 =3.4.

[0034] Step 1.2, travel start time, its probability density function f s (t 2 )for:

[0035]

[0036] Where, t 2 is the start time of private car travel, i.e. the mandatory end time of electric vehicle charging; μ t2 =8.92,σ t2 =3.24.

[0037] Step 1.3, the daily mileage satisfies the log-normal distribution, and its probability density function f(x) is:

[0038]

[0039] Where x is the daily mileage of private cars; μ x =3.20,σ x =0.88.

[0040] Step 1.4, due to the climate characteristics of Liaoning, car owners will turn on the air conditioner in the car to ensure driving comfort in different seasons. The air conditioning energy consumption of pure electric vehicles is supplied by the battery, so the air conditioning energy consumption model in different seasons is also different:

[0041]

[0042] p is the probability of turning on the air conditioner, v is the average speed of the electric vehicle, P air The power of the air conditioner.

[0043] The temperature in spring and autumn is suitable, and the impact of air conditioning energy consumption is not considered. a =0; in winter, p win =1,P air-1 is the heating power; in summer, n che is the number of vehicles that need air conditioning, N is the total number of vehicles, P air-2 is the cooling power.

[0044] Step 1.5, randomly extract the electric vehicle travel end time, travel start time and daily mileage parameters through the Monte Carlo algorithm, and model the charging load of an electric vehicle as follows:

[0045]

[0046] Where D is the daily mileage of the electric vehicle, in km; S is the rated mileage of the electric vehicle, which is 300 km; ET is the battery capacity of the electric vehicle, which is 62 kWh; t is the charging time; W is the amount of electricity required to fully charge the electric vehicle; P c is the charging power, which is 5kW; η is the charging efficiency, which is 0.9.

[0047] Step 1.6, according to the calculation method of the charging load of a single electric vehicle, the daily charging load of the electric vehicle cluster can be obtained, and the owner will hand over the charging of the electric vehicle to the nearest service outlet for management.

[0048] In order to track the carbon emissions corresponding to the electricity consumed on the user side, the power system carbon emission flow theory came into being. The carbon emission flow theory can attribute the carbon emission responsibility generated on the power generation side to the user side that consumes electricity through the principle of proportional sharing, and specifically quantify the carbon emissions caused by the electricity consumed on the load side. Therefore, the present invention can accurately calculate the power system carbon emissions caused by electric vehicles during the charging process through the power system carbon emission flow theory. Step 2 is as follows:

[0049] Step 2.1, basic definition of carbon emission flow:

[0050]

[0051] Where F represents the carbon flow rate and R represents the carbon flow rate.

[0052]

[0053] In the formula, ρ represents the carbon flow density, which is the combination of carbon emission flow and power flow, and the carbon emission corresponding to the unit of electricity consumption, in units of tCO 2 / kWh.

[0054] Calculate the node carbon potential. Since the power generation and consumption links in the power system exist in the form of nodes in the power system, the physical quantity of node carbon potential is defined to describe the node carbon emission intensity, represented by the symbol e. The carbon emission value caused by the consumption of a unit of electricity at this node is equivalent to the carbon emission value on the power generation side. The commonly used unit is tCO 2 / kWh.

[0055]

[0056] Where N+ is the set of all branches connected to node n that have flow flowing into node n; l is the branch number.

[0057] Step 2.2, carbon emissions calculation:

[0058] The carbon emissions per unit time of electric vehicle charging load are calculated based on the dynamic node carbon potential of the service outlets. The specific calculation formula is as follows:

[0059] E n =e n P n (10)

[0060] Where P n Indicates the EV charging load.

[0061] At present, when the power system uses carbon prices to constrain electricity consumption on the load side, it mostly adopts the form of tiered carbon prices, and the same tiered carbon prices are used for the entire power system load nodes. In the power system network, the node where the generator set is located is fixed, but due to factors such as the different locations and power consumption of each electric vehicle service outlet node, the actual carbon responsibility allocated to each service outlet is different. At this time, it is unfair to set a tiered carbon price for each service outlet based on the same carbon emission range.

[0062] Based on the calculation results based on the carbon emission flow theory, the present invention introduces the apportionment method of Shapley value, calculates the reasonable range of carbon emissions of each electric vehicle service outlet by apportioning the carbon emission responsibility, and more reasonably apportions the carbon emission responsibility of each service outlet, and then proposes a reasonable ladder carbon price range to calculate the carbon emission cost of the service outlet. Step 3 is as follows:

[0063] Step 3.1: Calculate the carbon emission responsibility of each service outlet based on the Shapley value

[0064]

[0065] Where xi is the carbon responsibility emission allocated to alliance member i; S is the sub-alliance composed of sub-alliance members ranked before alliance member i; P(S) is the probability of sub-alliance S occurring; S∪{i} is the formation of a new alliance by incorporating alliance member i into alliance S; c(S∪{i})-c(S) is the marginal impact generated when alliance member i joins sub-alliance S.

[0066]

[0067] The carbon emission responsibility assumed by the sub-alliance members should be within a certain range and not greater than the maximum marginal contribution x of the sub-alliance members. max , nor less than the minimum value x of its marginal effect min , that is, x min ≤x≤x max ,in

[0068] x max (i)=max(c(S∪{i})-c(S))(13)

[0069] x min (i) = min(c(S∪{i})-c(S))

[0070] Step 3.2: Based on the maximum value x of the marginal effect of carbon emission responsibility max , minimum value x min , and the mean value x ave, we can divide the reasonable range of carbon emission responsibility of each service outlet and formulate a tiered carbon price within this range. In each period, the carbon emission responsibility of each service outlet is divided into 4 intervals: 0~x min Free carbon emission responsibility range, x min ~x ave For low carbon emission responsibility scope, x ave ~x max For China's carbon emission responsibility scope, x max ~∞ is the high carbon emission responsibility range. Within different carbon emission responsibility ranges, the carbon emissions of service outlets are subject to tiered carbon prices according to different ranges, i.e.

[0071]

[0072] Where α is the step-wise carbon emission cost, in units of $ / kgCO 2 ; E n,t is the total carbon emission per unit time of the load node, and the carbon emission cost is calculated according to the step carbon price as C n,t

[0073]

[0074] The step 4 is specifically as follows:

[0075] Step 4.1: Using the step carbon price as a signal and minimizing the carbon emission cost and demand response cost as the objective function

[0076]

[0077] In the formula, C emi,n,t is the carbon emission cost of service point n at time t, in $ / kW; c dis The cost coefficient for each unit of power is $ / kW; D dis,n,t It is the response quantity of negative n at time t, in kW.

[0078] Step 4.2, Constraints

[0079] Formula (17) is the electric vehicle charging load constraint:

[0080]

[0081] Where D tr,n,t It is the charging load after response at time t. Generally, it is assumed that the upper and lower limits of the change are 20% of the load.

[0082]

[0083] Formula (18) ensures that the total charging capacity of electric vehicles remains unchanged before and after optimization, and does not affect normal travel.

[0084] Formula (19-20) is the carbon emission constraint

[0085]

[0086]

[0087] In the formula, E n,t is the total carbon emissions of the service outlets at time t, in kgCO 2 ;e g,t is the carbon potential of the generating unit node at time t, in kgCO 2 / kWh;e n,t is the carbon potential of the load node at time t, in kgCO 2 / kWh; l,t is the branch carbon emission rate at time t, in kgCO 2 / kWh.

[0088] Formula (21) is the carbon emission cost constraint of the service outlets:

[0089]

[0090] The following is an example analysis of this embodiment:

[0091] 1 Basic Data

[0092] The present invention uses MATLAB2016b module and calls CPLEX solver to solve the model. The example uses IEEE14 improved node system as an example for example analysis. The network structure of the system is as follows Figure 2 As shown in the figure, it mainly includes 3 coal-fired and gas-fired units and 2 new energy units. Its carbon emission intensity (unit: gCO 2 / kWh) as shown in Table 1, assuming that the number of electric vehicles in the area is 2,000, represented by 3 service outlets, and the low-carbon charging optimization cycle is set to 24 hours, and each time period is set to 1 hour. The original load and electric vehicle charging load curves are shown in Figure 3 shown.

[0093] From the network topology diagram, we can see that the charging of electric vehicles in this area is managed uniformly by three electric vehicle service outlets, located on nodes 5, 9, and 14, respectively, called service outlet A, service outlet B, and service outlet C. The number of managed electric vehicles accounts for 30%, 30%, and 40% of the total number. The predicted output values ​​of the two wind turbines are as follows: Figure 4 shown.

[0094] Based on the Shapley value method, the carbon emission responsibility of each service outlet is apportioned, and then the reasonable range of carbon emission responsibility of each service outlet in 24 hours is calculated, and four tiered carbon price charging standard intervals are divided, 0~x min For free carbon emission responsibility range, at the bottom; x min ~x ave For low carbon emission responsibility range, in the middle; x ave ~x max The top part is the medium carbon emission responsibility scope; the part beyond the top part is the high carbon emission responsibility scope. The tiered carbon emission responsibility scopes of the three service outlets are as follows: Figure 5-7 shown.

[0095] By analyzing Figure 5-7 and Figure 3 The forecast of electric vehicle charging load is available. The trend of the total carbon emission responsibility scope of the service outlets is roughly consistent with the trend of the daily charging load of electric vehicles. The number of electric vehicles managed by service outlets A and B is similar, and the total carbon emission responsibility scope is also roughly the same. The number of electric vehicles managed by service outlet C is more than that of A and B, and its total carbon emission responsibility scope is also larger. It can be obtained that the charging volume of electric vehicles at the service outlet determines the size of its total carbon emission scope. However, due to the different node locations of service outlets in the power system and the interaction between various sub-alliances, there are large differences in the ladder carbon emission responsibility scope and changes assigned to different service outlets.

[0096] Service outlet A is close to wind turbine G1 and coal-fired unit G2. Since the output of wind turbines is smaller than that of coal-fired units and the carbon emission intensity of coal-fired units is the highest, service outlet A has the highest proportion of free carbon responsibility. Service outlet B is closer to wind turbine G5 due to its geographical location. Its electric vehicle load charging is mostly provided by the output of wind turbines, so the proportion of free carbon emission responsibility interval is the smallest. Most of the electricity consumed by service outlet C comes from gas-fired unit G4 with lower carbon emission intensity, so the proportion of free carbon emission responsibility is smaller than that of service outlet A but larger than that of service outlet B.

[0097] analyze Figure 5 , during the time period of 20:00-24:00, the output of wind turbine G1 increases significantly, and the stepped carbon emission responsibility range of service outlet A is smaller than the stepped carbon emission responsibility range of the time period of 13:00-17:00 with the same electric vehicle charging load. Figure 6 , 7 ,During the period from 18:00 to 20:00, the charging capacity of electric vehicles increased rapidly and reached the maximum value, but due to the rapid increase in the output value of wind turbine G5 at this time, the carbon emission responsibility ranges of service outlets B and C decreased to varying degrees. Figure 6 and Figure 7At 20:00, due to the sudden and substantial reduction in output of wind turbine G5, the free and low-carbon responsibility ranges of service points B and C increased to varying degrees.

[0098] Based on the proposed low-carbon incentive guidance strategy, the carbon emissions and carbon emission costs of the service outlets before and after the incentives are compared, as shown in Tables 2 and 3.

[0099] At the same time, the optimized load distribution of service outlets A, B and C is obtained, such as Figure 8-10 .

[0100] analyze Figure 5-7 and Figure 8-10 It can be seen that since the electricity consumed by service point A mainly comes from the thermal power unit G1 with high carbon emission intensity, the low-carbon guidance method is not very effective in reducing emissions and reducing carbon emission costs for service point A; between 15:00 and 19:00, the charging load of electric vehicles gradually increased and reached a peak. Service points B and C chose to reduce the charging load during this period and transfer the load to the period between 19:00 and 24:00 when the output of wind turbines increased to absorb wind power. The effect was more significant after low-carbon guidance, with service points A and B reducing 360.7 and 123 kg of CO respectively. 2 Emissions. After responding to the low-carbon policy, the city’s electric vehicles emit CO2 in a day’s charging cycle. 2 Emissions were reduced by 490kg. As the carbon emissions from electric vehicle load charging were reduced, the carbon emission costs that the service outlets should pay were also reduced by $250.2, bringing benefits to the service outlets.

[0101] It can be seen that after electric vehicle service outlets obey the low-carbon incentive guidance mechanism, electric vehicle service outlets will tend to choose time periods with higher wind power output to arrange more electric vehicles for charging, increasing the proportion of clean energy in the charging power of electric vehicles, making the low-carbon attributes of electric vehicles better reflected, while reducing the carbon emissions of the power system, it also reduces the cost paid by electric vehicle service outlets for carbon emissions.

[0102] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

[0103] Table 1 Carbon emission intensity of units

[0104] Generator Set Carbon emission intensity Wind turbine G1 0 Coal-fired unit G2 875 Gas Generator G3 525 Gas Generator G4 520 Wind turbine G5 0

[0105] Table 2 Comparison of carbon emissions of each service outlet before and after incentive guidance

[0106]

[0107] Table 3 Comparison of carbon emissions and operating costs before and after incentive guidance

[0108]

Claims

1. A low-carbon charging method for electric vehicle service outlets based on carbon emission flow, characterized in that The following steps are involved: Step 1: Considering the start time, end time, daily mileage and air conditioning energy consumption of electric vehicles, a daily charging load model for electric vehicles is established; Step 2: Taking into account the basic load of the network nodes and the charging load of electric vehicles, the dynamic carbon potential of the electric vehicle service network nodes is obtained, and the carbon emissions obtained by combining the charging amount with the corresponding carbon potential are recorded; Step 3: Divide the service outlets’ tiered carbon emission responsibility scope and corresponding tiered carbon price ranges based on the Shapley value; Step 4: Establish a low-carbon charging optimization model for electric vehicle service outlets to redistribute the charging load of electric vehicles.

2. A low-carbon charging method for electric vehicle service outlets based on carbon emission flow according to claim 1, characterized in that The step 1 is specifically as follows: Step 1.1: It is assumed that the electric vehicle starts charging after the trip ends. The end time of the trip is the start time of charging. The end time of the trip satisfies the normal distribution, and its probability density function f e (t1) is: In the formula, t1 is the end time of private car travel, that is, the start time of electric vehicle charging; Step 1.2, travel start time, its probability density function f s (t2) is: In the formula, t2 is the start time of private car travel, that is, the mandatory end time of electric vehicle charging; Step 1.3, the daily mileage satisfies the log-normal distribution, and its probability density function f(x) is: Where x is the daily mileage of private cars; μ x =3.20,σ x =0.88; Step 1.4, due to the climate characteristics of different regions, car owners will turn on the air conditioner in the car to ensure driving comfort in different seasons. The air conditioning energy consumption of pure electric vehicles is supplied by the battery, so the air conditioning energy consumption model in different seasons is also different: p is the probability of turning on the air conditioner, v is the average speed of the electric vehicle, P air is the power of the air conditioner; The temperature in spring and autumn is suitable, and the impact of air conditioning energy consumption is not considered. a =0; in winter, p win =1,P air-1 is the heating power; in summer, n che is the vehicle that needs to be air-conditioned, N is the total number of vehicles, P air-2 is the cooling power; Step 1.5, randomly extract the electric vehicle travel end time, travel start time and daily mileage parameters through the Monte Carlo algorithm, and model the charging load of an electric vehicle as follows: Where D is the daily mileage of the electric vehicle, in km; S is the rated mileage of the electric vehicle, which is 300 km; ET is the battery capacity of the electric vehicle, which is 62 kWh; t is the charging time; W is the amount of electricity required to fully charge the electric vehicle; P c is the charging power, which is 5kW; η is the charging efficiency, which is 0.9; Step 1.6, according to the calculation method of the charging load of a single electric vehicle, the daily charging load of the electric vehicle cluster is obtained, and the owner of the electric vehicle will hand over the charging of the electric vehicle to the nearest service outlet for management.

3. A low-carbon charging method for electric vehicle service outlets based on carbon emission flow according to claim 1, characterized in that The step 2 is specifically as follows: Step 2.1, calculate the carbon flow. The carbon flow is used to indicate the size of the carbon flow on the branch, and is represented by F. It is defined as: the corresponding carbon emission accumulation amount when the active power flow passes through a branch within a certain period of time, in kgCO2 or tCO2; Step 2.2, calculate the carbon flow rate, represented by the symbol R, which is defined as: the carbon flow rate passing through a branch with active power per unit time, in units of tCO2 / h; Step 2.3, calculate the carbon flow density. The carbon emission flow in the power system is combined with the power flow, represented by the symbol ρ, which is defined as: the carbon emission corresponding to the consumption of unit electricity, in units of tCO2 / kWh; Step 2.4, calculate the node carbon potential. Since the power generation and power consumption links in the power system exist in the form of nodes in the power system, the node carbon potential is defined as a physical quantity to describe the node carbon emission intensity. It is represented by the symbol e, which is the carbon emission value equivalent to the power generation side caused by the consumption of unit electricity at the node, and the unit is tCO2 / kWh; Nodal carbon potential e of node n n The calculation is as follows: Where N+ is the set of all branches connected to node n with flow flowing into node n; l is the branch number; Step 2.5, calculate the carbon emissions of EV charging load per unit time based on the carbon potential of the dynamic service network node. The specific calculation formula is as follows: AND n =and n P n (10) Where P n Indicates the EV charging load.

4. According to the method for optimizing low-carbon charging of electric vehicle service outlets based on carbon emission flow in claim 1, the specific steps of step 3 are as follows: Step 3.1: Calculate the carbon emission responsibility of each service outlet based on the Shapley value In the formula, x i is the carbon emission responsibility allocated to alliance member i; S is the sub-alliance composed of sub-alliance members ranked before alliance member i; P(S) is the probability of sub-alliance S occurring; S∪{i} is the formation of a new alliance by incorporating alliance member i into alliance S; c(S∪{i})-c(S) is the marginal impact generated when alliance member i joins sub-alliance S; The carbon emission responsibility assumed by the sub-alliance members should be within a certain range and not greater than the maximum marginal contribution x of the sub-alliance members. max , nor less than the minimum value x of its marginal effect min , that is, x min ≤x≤x max ,in Step 3.2: Based on the maximum value x of the marginal effect of carbon emission responsibility max , minimum value x min , and the mean value x ave , divide the reasonable range of carbon emission responsibility of each service outlet, and formulate a ladder carbon price within this range; divide the carbon emission responsibility of each service outlet into 4 intervals in each time period: 0~x min Free carbon emission responsibility range, x min ~x ave For low carbon emission responsibility scope, x ave ~x max For China's carbon emission responsibility scope, x max ~∞ is the high carbon emission responsibility range; within different carbon emission responsibility ranges, the carbon emissions of service outlets are subject to a tiered carbon price according to different ranges, that is, Where α is the step-wise carbon emission cost, in units of $ / kgCO2; E n,t is the total carbon emission per unit time of the load node, and the carbon emission cost is calculated according to the step carbon price as C n,t 5. A low-carbon charging method for electric vehicle service outlets based on carbon emission flow according to claim 1, characterized in that The step 4 is specifically as follows: Step 4.1: Using the step carbon price as a signal and minimizing the carbon emission cost and demand response cost as the objective function In the formula, C emi,n,t is the carbon emission cost of service point n at time t, in $ / kW; c dis The cost coefficient for each unit of power is $ / kW; D dis,n,t is the response of load n at time t, in kW; Step 4.2, Constraints Formula (17) is the electric vehicle charging load constraint: Where D tr,n,t is the charging load after the response at time t. Generally, the upper and lower limits of the change are assumed to be 20% of the load; Formula (18) ensures that the total charging capacity of electric vehicles remains unchanged before and after optimization, and does not affect normal travel; Formula (19-20) is the carbon emission constraint In the formula, E n,t is the total carbon emissions of the service outlets at time t, in kgCO2; e g,t is the carbon potential of the generating unit node at time t, in kgCO2 / kWh; e n,t is the carbon potential of the load node at time t, in kgCO2 / kWh; ρ l,t is the branch carbon emission rate at time t, in kgCO2 / kWh; Formula (21) is the carbon emission cost constraint of the service outlets:

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