Electric vehicle charging price setting method of coupling carbon signal and time-of-use electricity price

Abstract

The invention belongs to the technical field of price making, and particularly relates to a carbon signal and time-of-use electricity price coupled electric vehicle charging price making method, which comprises the following steps: S1, estimating a dynamic carbon emission factor (CEF), and describing the carbon emission intensity of each node in a power system by adopting the dynamic CEF, wherein the dynamic CEF is equivalent carbon emission generated at a power generation end when a node consumes unit electric quantity in different time periods; s2, coupling the dynamic CEF and the time-of-use electricity price, and formulating the electric vehicle EV charging price of the residential user; and S3, based on the made EV charging price, constructing an EV optimal charging decision based on price signal guidance. According to the method, the EV charging price comprehensively considering the load management target and the low-carbon target is designed, and the peak-valley difference and indirect carbon emission of the power distribution network in the EV charging process can be reduced while the user is orderly guided to make an economic charging decision.

Description

Technical Field

[0001] The present invention belongs to the technical field of price setting, and in particular relates to a method for setting electric vehicle charging prices by coupling carbon signals with time-of-use electricity prices. Background Art

[0002] In recent years, climate issues caused by excessive greenhouse gas emissions have received increasing attention. With the continuous implementation of the "dual carbon" goals and the improvement of residents' awareness of carbon emission reduction, the number of new energy vehicles represented by electric vehicles (EVs) has continued to rise. EV charging demand has multi-dimensional randomness in space, time, quantity, and power. When a large number of EVs are connected to the distribution network for charging in an unordered manner, it will bring severe challenges to the flexible regulation of the system. Therefore, there are existing literatures that study the orderly charging guidance of EVs from the perspective of load management. However, the original intention of promoting the use of EVs is to "reduce carbon", that is, to achieve carbon emission reduction goals through the near-zero carbon emissions of EVs during driving, rather than as an object of demand response. It should be pointed out that under the premise that the power generation energy structure is still dominated by fossil energy, EVs will still generate indirect carbon emissions during the charging process. Therefore, when designing a guidance mechanism for orderly EV charging, not only load management factors but also low-carbon factors should be considered.

[0003] Regarding the accounting of carbon emission reductions of EVs, traditional research mainly draws on the "Methodology for Voluntary Greenhouse Gas Emission Reduction Projects" to calculate certified emission reductions and participate in carbon emission rights trading to obtain income. However, the above methods are mainly aimed at voluntary emission reduction projects, and are currently only applicable to electric bus projects, and are not very operational for massive household EVs. In fact, the carbon emissions of electricity load can also be indirectly calculated based on the carbon emission factor CEF, but the static CEF cannot accurately depict the differences in carbon emissions caused by the output proportion of different types of generators in different time periods, nor can it allow electricity users to perceive the differences in carbon emissions from electricity usage behaviors in different time periods. The carbon emission flow theory links carbon emissions with the electricity usage behaviors of electricity users, providing a new perspective for the accounting of carbon emissions.

[0004] As a special type of electricity load, EV can also obtain dynamic CEF through carbon flow tracking, which can be used as a signal to guide electricity users to respond to low-carbon demand. The paper "Carbon efficient smart charging using forecasts of marginal emission factors" established a short-term prediction model of dynamic CEF to provide real-time guidance signals for EV users to charge low-carbon and realize EV intelligent low-carbon charging. The paper "Dynamic carbon emission factor based interactive control of distribution network by a generalized regression neural network assisted optimization" proposed an interactive control method between distribution network operators and EV aggregators based on dynamic CEF, which can reduce the peak-to-valley difference of charging load in the distribution network and the indirect carbon emissions of EV by adjusting the EV charging power in different time periods. The paper "Sustainable electric vehicle charging coordination: Balancing CO2 emission reduction and peak power demand shaving" established a dual-objective optimization model to balance EV indirect carbon emissions and charging load peak-to-valley difference, providing a reference for achieving sustainable EV charging.

[0005] However, the above literature does not fully utilize the guiding role of price signals and ignores the autonomous willingness of EV users in charging decisions. In actual operation, users often only pay attention to price signals and prefer to make EV charging decisions from the perspective of cost minimization. Therefore, how to couple carbon signals with electricity price signals and guide EVs to perform low-carbon and orderly charging with a single charging price is a question worth exploring. Summary of the invention

[0006] In view of the above deficiencies in the prior art, the purpose of the present invention is to provide an electric vehicle charging price setting method that couples carbon signals and time-of-use electricity prices, and designs an EV charging price that comprehensively considers load management goals and low-carbon goals. While guiding users to make economical charging decisions in an orderly manner, it can reduce the peak-to-valley difference and indirect carbon emissions of the distribution network during EV charging.

[0007] To achieve the above objectives, the present invention provides a method for setting electric vehicle charging prices by coupling carbon signals with time-of-use electricity prices, comprising the following steps: S1. Estimation of the dynamic carbon emission factor CEF is performed. The dynamic CEF is used to describe the carbon emission intensity of each node in the power system. The dynamic CEF is the equivalent carbon emission generated by the node consuming unit electricity at the power generation end in different time periods. The steps include: S1.1. Calculate the power system active flux matrix, line flow distribution matrix and generator output matrix; S1.2, estimate the dynamic CEF matrix of nodes in the power system and obtain the dynamic CEF in the distribution network area; S2. Couple dynamic CEF with time-of-use electricity prices to set charging prices for residential electric vehicles (EVs); S3. Based on the set EV charging price, construct an EV optimal charging decision guided by price signals, the steps include: S3.1, Analyze EV travel patterns; S3.2. Construct the optimal charging decision for EV, including setting the objective function and constraints, and using dynamic CEF to estimate the indirect carbon emissions of EV.

[0008] As a preferred embodiment of the present invention, in S1.1, the calculation method is: S1.1.1. Calculation of the active flux matrix of the power system , expressed as: (1); In the formula, represents the active flux from node i to node j during period t; when i=j, represents the active flux of node i in period t; represents the set of branches where the active power flow enters node i, and s represents one of the branches; represents the active power flow of branch s during period t; represents the injected active power flow of the generator set at node k during period t; I represents the total number of nodes in the power system; S1.1.2. Calculate the line flow distribution matrix , expressed as: (2); In the formula, represents the active power flow on branch s from node i to node j; when i=j, , It means equivalent to; S1.1.3. Calculate the generator output matrix , expressed as: (3); In the formula, represents the injected active power flow of the generator set at node i during period t; K represents the number of nodes connected to the generator set in the power system.

[0009] As a preferred solution of the present invention, in S1.2, the method for obtaining the dynamic CEF in the distribution network area is: S1.2.1. Estimation of the dynamic CEF matrix of nodes in the power system , expressed as: (4); In the formula, represents the carbon emission intensity matrix of the generator set, represents the carbon emission intensity of the generator set at node k during period t; represents the dynamic CEF at node i during period t; T T is the total number of time periods; the superscript T indicates transposition; In formula (4), Defined , where the elements are ,pass Calculate the dynamic CEF matrix; S1.2.2, in order to ensure the consistency of indirect CEF of users at different nodes in the same area in different time periods, the dynamic CEF of each node The dynamic CEF in the region is obtained by spatial averaging, which is expressed as: (5); In the formula, It represents the dynamic CEF of the entire distribution network area during period t; represents the active load of node i during period t.

[0010] As a preferred solution of the present invention, in S2, the process of formulating the charging price of electric vehicles (EV) for residential users is as follows: S2.1. Design EV charging price according to dynamic CEF distribution interval, expressed as: (6); In the formula, represents the EV charging price during period t; is the time-sharing charging price for residents during period t; Adjustment amount for EV charging price; S2.2 Calculation , calculated as: (7); In the formula, , , They represent the minimum, average, and maximum values ​​of the dynamic CEF in the distribution network area, respectively; , They are the price reduction and increase coefficients respectively.

[0011] As a preferred embodiment of the present invention, in S2.2, for , In order to balance the electricity price adjustment during high-carbon emission and low-carbon emission periods, the total increase in electricity price in the high-carbon emission interval is equal to the total decrease in electricity price in the low-carbon emission interval, then: (8); In the formula, , They respectively represent the number of time periods when the dynamic CEF of the distribution network area is in the high carbon emission range and the low carbon emission range.

[0012] As a preferred embodiment of the present invention, in S3.1, the charging of the EV depends on the time of returning home. , Time away from home and daily mileage x, the method for analyzing EV travel patterns is: S3.1.1. Time of returning home ,assumed Obey expectations , standard deviation is The normal distribution of: (9); In the formula, express The probability density function of ; exp is the exponential function; S3.1.2. Time away from home ,assumed Obey expectations , standard deviation is The normal distribution of: (10); In the formula, express The probability density function of S3.1.3 For daily mileage x, assume that x follows the expectation , standard deviation is The normal distribution of: (11); In the formula, represents the probability density function of x; ln is the logarithmic function, which represents the logarithm with base e; S3.1.4. Charging time required for the nth EV It is expressed as: (12); In the formula, is the daily driving distance of the nth EV; , are the battery capacity and range of the nth EV respectively; , They are the charging power and charging efficiency of the charging pile respectively.

[0013] As a preferred solution of the present invention, in S3.2, the process of constructing the EV optimal charging decision is: S3.2.1 Objective function of EV optimal charging decision for: (13); Where N is the total number of EVs in the charging area; represents the charging status of the nth EV in period t. , indicating that charging is performed during the t period. , indicating no charging during period t; S3.2.2. Set constraints, including battery energy constraints, state of charge constraints, and charging time constraints; S3.2.3. Based on the characteristics of EV power load, the dynamic CEF of the distribution network area is used to estimate the indirect carbon emissions of EV, expressed as: (14); In the formula, Indicates the indirect carbon emissions of EVs.

[0014] As a preferred solution of the present invention, in S3.2.2, the constraint conditions are specifically: Battery energy constraint: When the EV is fully charged, the battery energy must meet the user's expectations and meet the user's travel requirements, expressed as: (15); In the formula, The power of the nth EV when it starts charging; is the target power that the nth EV is expected to achieve; The state of charge constraint is expressed as: (16); (17); In the formula, , are the charge states of the nth EV at time period t and time period t-1 respectively; represents the charging status of the nth EV at time t-1. , indicating that charging is performed during the t-1 period. If , indicating no charging during the t-1 period; , They are the upper and lower limits of the EV state of charge; The charging time constraint is expressed as: (18).

[0015] The algorithm involved in the present invention can be executed by an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the above algorithm calculation is implemented by executing the software by the processor.

[0016] The beneficial effects of the present invention are: The present invention takes into account the load management goal and the low-carbon goal, and designs a new charging price system for electric vehicles (EVs) by combining the dynamic carbon emission factor (CEF) of the distribution network with the time-of-use electricity price for residents. This price system can not only effectively guide users to charge at economically more reasonable time periods and reduce charging costs, but also reduce indirect carbon emissions during the charging process of electric vehicles and promote the low-carbon operation of the power system.

[0017] The present invention achieves orderly guidance of electric vehicle charging demand and optimizes the load management of the power system by dynamically adjusting the charging price. While reducing the peak-to-valley difference of the distribution network load, it also increases the absorption rate of clean energy such as photovoltaics, which is of great significance for promoting the optimization of the energy structure and achieving the "dual carbon" goal. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 is a schematic diagram of the IEEE33 node system during the verification process of the present invention; Figure 3 It is a schematic diagram of the main grid power purchase and flexible unit output during the verification process of the present invention; Figure 4 It is a schematic diagram of the dynamic CEF estimation results of the distribution network during the verification process of the present invention; Figure 5 It is a schematic diagram of EV charging prices in various scenarios during the verification process of the present invention; Figure 6 It is a schematic diagram of EV charging load in various scenarios during the verification process of the present invention; Figure 7 It is a comprehensive comparison chart of the guiding effect of EV charging prices in the verification process of the present invention. DETAILED DESCRIPTION

[0019] The embodiments of the present invention are further described below in conjunction with the accompanying drawings: like Figure 1 As shown, the method for formulating electric vehicle charging prices by coupling carbon signals with time-of-use electricity prices includes the following steps: S1. Estimation of the dynamic carbon emission factor CEF is performed. The dynamic CEF is used to describe the carbon emission intensity of each node in the power system. The dynamic CEF is the equivalent carbon emission generated by the node consuming unit electricity at the power generation end in different time periods. The steps include: S1.1. Calculate the power system active flux matrix, line flow distribution matrix and generator output matrix; S1.2, estimate the dynamic CEF matrix of nodes in the power system and obtain the dynamic CEF in the distribution network area; S2. Couple dynamic CEF with time-of-use electricity prices to set charging prices for residential electric vehicles (EVs); S3. Based on the set EV charging price, construct an EV optimal charging decision guided by price signals, the steps include: S3.1, Analyze EV travel patterns; S3.2. Construct the optimal charging decision for EV, including setting the objective function and constraints, and using dynamic CEF to estimate the indirect carbon emissions of EV.

[0020] Carbon emission flow theory is a carbon emission flow estimation theory similar to the power system flow calculation theory.

[0021] In S1.1, the calculation method is: S1.1.1. Calculation of the active flux matrix of the power system , expressed as: (1); In the formula, It represents the active flux from node i to node j in period t, in kW; when i=j, It represents the active flux of node i in period t, in kW; represents the set of branches where the active power flow enters node i, and s represents one of the branches; It represents the active power flow of branch s in period t, in kW; represents the injected active power flow of the generator set at node k in period t, in kW; I represents the total number of nodes in the power system; S1.1.2. Calculate the line flow distribution matrix , expressed as: (2); In the formula, It represents the active power flow on the branch s from node i to node j, in kW; when i=j, , It means equivalent to; All non-diagonal elements of are the active power flows between branch s from node i to node j, and all diagonal elements are 0; S1.1.3. Calculate the generator output matrix , expressed as: (3); In the formula, It represents the injected active power flow of the generator set at node i during period t, in kW; K represents the number of nodes connected to the generator set in the power system.

[0022] In S1.2, the method for obtaining the dynamic CEF in the distribution network area is: S1.2.1. Estimation of the dynamic CEF matrix of nodes in the power system , expressed as: (4); In the formula, represents the carbon emission intensity matrix of the generator set, It represents the carbon emission intensity of the generator set at node k during period t, in kg / kW·h; represents the dynamic CEF at node i during period t, in kg / kW·h; T T is the total number of time periods; the superscript T indicates transposition; Carbon emission intensity matrix for power generation units , can be obtained through public channels, for example, according to the document "Low-carbon dispatch strategy of distributed resources in distribution network based on node carbon potential", which provides a table of generator set parameters, as shown in Table 1: Table 1 Generator parameters in the literature

[0023] Then, the corresponding Can be set to: .

[0024] In formula (4), Defined , where the elements are ,pass Calculate the dynamic CEF matrix; S1.2.2, in order to ensure the consistency of indirect CEF of users at different nodes in the same area in different time periods, the dynamic CEF of each node The dynamic CEF in the region is obtained by spatial averaging, which is expressed as: (5); In the formula, It represents the dynamic CEF of the entire distribution network area during period t, in kg / kW·h; Represents the active load of node i in period t, in kW.

[0025] The higher the dynamic CEF in the distribution network, the greater the carbon emissions generated by consuming a unit of electricity. In order to encourage EV users to take responsibility for carbon emission reduction, the EV charging price is designed according to the dynamic CEF distribution range based on the residential EV time-sharing charging price formulated mainly considering load management goals and user charging habits.

[0026] In S2, the process of setting the charging price for residential electric vehicles (EVs) is as follows: S2.1. Design EV charging price according to dynamic CEF distribution interval, expressed as: (6); In the formula, represents the EV charging price at time period t, in RMB / kW·h; is the time-sharing charging price for residents in period t, in Yuan / kW·h; is the EV charging price adjustment, unit: RMB / kW·h; S2.2 Calculation , calculated as: (7); In the formula, , , They represent the minimum, average and maximum values ​​of the dynamic CEF of the distribution network area, in kg / kW·h; , They are respectively the price reduction and increase coefficients, unit: Yuan / ton.

[0027] In S2.2, since the number of time periods in the high and low carbon emission ranges is generally not equal, , In order to balance the electricity price adjustment during high-carbon emission and low-carbon emission periods, the total increase in electricity price in the high-carbon emission interval is equal to the total decrease in electricity price in the low-carbon emission interval, then: (8); In the formula, , They respectively represent the number of time periods when the dynamic CEF of the distribution network area is in the high carbon emission range and the low carbon emission range.

[0028] In S3.1, EV charging depends on the time of returning home , Time away from home and daily mileage x, the method for analyzing EV travel patterns is: S3.1.1. Time of returning home ,assumed Obey expectations , standard deviation is The normal distribution of: (9); In the formula, express The probability density function of ; exp is the exponential function; S3.1.2. Time away from home ,assumed Obey expectations , standard deviation is The normal distribution of: (10); In the formula, express The probability density function of S3.1.3 For daily mileage x, assume that x follows the expectation , standard deviation is The normal distribution of: (11); In the formula, represents the probability density function of x; ln is the logarithmic function, which represents the logarithm with base e; S3.1.4. Charging time required for the nth EV It is expressed as: (12); In the formula, is the daily driving distance of the nth EV, in km; , are the battery capacity and range of the nth EV, in kW·h and km respectively; , They are the charging power (in kW) and charging efficiency of the charging pile respectively.

[0029] In S3.2, the process of constructing the optimal charging decision for EV is: S3.2.1 Objective function of EV optimal charging decision for: (13); Where N is the total number of EVs in the charging area; represents the charging status of the nth EV in period t. , indicating that charging is performed during the t period. , indicating no charging during period t; S3.2.2. Set constraints, including battery energy constraints, state of charge constraints, and charging time constraints; S3.2.3. Based on the characteristics of EV power load, the dynamic CEF of the distribution network area is used to estimate the indirect carbon emissions of EV, expressed as: (14); In the formula, Indicates the indirect carbon emissions of EVs, in tons.

[0030] In S3.2.2, the constraints are: Battery energy constraint: When the EV is fully charged, the battery energy must meet the user's expectations and meet the user's travel requirements, which can be expressed as: (15); In the formula, The power of the nth EV when it starts charging, in kW·h; is the target power that the nth EV is expected to achieve, in kW·h; The state of charge constraint is expressed as: (16); (17); In the formula, , are the state of charge (%) of the nth EV at period t and period t-1 respectively; represents the charging status of the nth EV at time t-1. , indicating that charging is performed during the t-1 period. If , indicating no charging during the t-1 period; , They are the upper and lower limits of the EV state of charge respectively; The charging time constraint is expressed as: (18).

[0031] The verification process is: 1. Basic data: The improved IEEE33 node system is used for simulation analysis. Its topology and distributed power supply locations are as follows: Figure 2 The grid structure parameters such as the system line impedance value are consistent with the standard IEEE33 node system, the reference voltage is 12.66kV, and other parameters are shown in Table 2. Figure 2In the figure, DG1 and DG2 represent distributed generators, WT is a wind turbine, PV is photovoltaic, numbers represent nodes, and nodes connected by yellow charging pile marks represent residential community nodes with charging piles.

[0032] Table 2 System parameters

[0033] The relevant parameters of the distributed flexible unit are shown in Table 3.

[0034] Table 3 Flexible unit parameters

[0035] System load data is shown in Appendix 4. Nodes 10-18 are residential nodes with a total of 2,000 EVs. Related parameters are shown in Table 5. The main grid electricity purchase price and main grid carbon potential are shown in Table 6.

[0036] Table 4 System load data

[0037] Table 5 EV parameters

[0038] Table 6 Main grid carbon potential and main grid electricity purchase time-of-use price

[0039] 2. Distribution network operators’ optimized dispatch and dynamic CEF estimation results: The main grid (upper grid) power purchase and flexible unit output are as follows Figure 3 It can be seen that the output of flexible units is mainly concentrated in the period from 17 to 21, and some flexible units also produce output in the period from 7 to 9.

[0040] The dynamic CEF estimation results of the distribution network are as follows: Figure 4 As shown. It can be seen that the low carbon emission interval appears in the 11-15 period, which is mainly the period when the photovoltaic output is large; the high carbon emission interval is mainly concentrated in the 5-7 and 17-21 periods, which is mainly the period when the main grid carbon potential is high or the flexible unit output is large; the rest of the period is the flat carbon emission interval. The distribution network operator cost is shown in Table 7.

[0041] Table 7 Total costs of distribution network operators

[0042] 3. Scene settings: In order to verify the impact of the EV charging price designed in this embodiment on the charging decision of EV users, three charging price scenarios are set.

[0043] Scenario 1: EV users charge their cars when they get home at a fixed charging price.

[0044] Scenario 2: Current residential EV time-sharing charging prices guide charging.

[0045] Scenario 3: EV charging price-guided charging that couples dynamic CEF with the current residential EV time-of-use charging price.

[0046] EV charging prices in various scenarios Figure 5 Among them, the EV charging prices in scenarios 1 and 2 are set according to the "Notice on Further Improving the Time-of-Use Electricity Price Policy for Residential Electric Vehicle Charging Piles".

[0047] Depend on Figure 5 It can be seen that the design of EV charging prices under scenario 3 not only considers factors such as load management and user charging habits, but also carbon emission factors. In the 5th to 7th period, the photovoltaic output is small, and the distribution network operator mainly purchases electricity from the main grid to meet the load demand. The carbon potential of the main grid is at a high level, resulting in a high dynamic CEF of the distribution network. Therefore, raising the charging price makes EV users who charge during this period bear the responsibility for carbon emissions. In the 11th to 15th period, the photovoltaic output is large and the carbon potential of the main grid is at a low level. The dynamic CEF of the distribution network is low, so lowering the charging price guides users to charge actively. In the 17th to 19th period, due to the ramp constraint, the distribution network operator increases the flexible unit to balance the power shortage. In the 20th to 21st period, the output of the flexible unit is reduced, but the carbon potential of the main grid is high, resulting in a high dynamic CEF of the distribution network in the 17th to 21st period. Raising the charging price makes EV users who charge during this period bear the responsibility for carbon emissions.

[0048] 4. Result analysis: EV charging load in each scenario Figure 6 The comparison of the peak-to-valley difference of the total load of the distribution network after the EV charging load is superimposed on the industrial, commercial and residential loads is shown in Table 8.

[0049] Table 8 Peak-to-valley difference of total load of distribution network

[0050] It can also be seen from Table 8 that compared with scenario 1, scenarios 2 and 3 both reduce the peak-to-valley difference of the total load of the distribution network after superimposing the EV charging load. This is because the electricity prices of scenarios 2 and 3 both take into account the factors of load management. Among them, compared with scenario 2, scenario 3 not only considers the time-sharing distribution of the load curve when guiding the load shift, but also considers the dynamic CEF distribution of the distribution network. Therefore, the peak-to-valley difference of the total load of the distribution network in scenario 3 is slightly higher than that in scenario 2.

[0051] The EV charging costs under various scenarios are shown in Table 9.

[0052] Table 9 EV charging costs in various scenarios

[0053] As shown in Table 4, the EV charging cost in scenarios 2 and 3 is reduced compared to scenario 1. Among them, the EV charging cost in scenario 3 is reduced the most, which means that the EV charging price designed in this embodiment is more economically attractive to users than the current EV time-sharing charging price.

[0054] The indirect carbon emissions of EV under each scenario are shown in Table 10.

[0055] Table 10 Indirect carbon emissions of EVs under various scenarios

[0056] A comprehensive analysis was conducted under each scenario, and the guiding effects of several EV charging prices were compared from three aspects: peak load shaving, charging cost saving, and reducing indirect carbon emissions. The results are as follows: Figure 7 As shown. Figure 7 It can be seen that the EV charging price designed in this embodiment is more attractive to EV users in terms of reducing charging costs, and the peak-shaving and valley-filling effect under its guidance is basically the same as the current EV time-sharing charging price, and the carbon reduction guidance effect is further improved. It should be pointed out that the number of EVs in the example of this embodiment is limited. If the ownership and growth trend of EVs in the whole society are taken into account, the charging price considering the low-carbon target will have a more significant "carbon reduction" guidance effect on the EV charging process.

Claims

1. A method for setting electric vehicle charging prices by coupling carbon signals with time-of-use electricity prices, characterized in that The following steps are involved: S1. Estimation of the dynamic carbon emission factor CEF is performed. The dynamic CEF is used to describe the carbon emission intensity of each node in the power system. The dynamic CEF is the equivalent carbon emission generated by the node consuming unit electricity at the power generation end in different time periods. The steps include: S1.

1. Calculate the power system active flux matrix, line flow distribution matrix and generator output matrix; S1.2, estimate the dynamic CEF matrix of nodes in the power system and obtain the dynamic CEF in the distribution network area; S2. Couple dynamic CEF with time-of-use electricity prices to set charging prices for residential electric vehicles (EVs); S3. Based on the set EV charging price, construct an EV optimal charging decision guided by price signals, the steps include: S3.1, Analyze EV travel patterns; S3.

2. Construct the optimal charging decision for EV, including setting the objective function and constraints, and using dynamic CEF to estimate the indirect carbon emissions of EV.

2. The electric vehicle charging price setting method of coupling carbon signal and time-of-use electricity price according to claim 1 is characterized in that: In S1.1, the calculation method is: S1.1.

1. Calculation of the active flux matrix of the power system , expressed as: （1）； In the formula, represents the active flux from node i to node j during period t; When i=j, represents the active flux of node i in period t; represents the set of branches where the active power flow enters node i, and s represents one of the branches; represents the active power flow of branch s during period t; represents the injected active power flow of the generator set at node k during period t; I represents the total number of nodes in the power system; S1.1.

2. Calculate the line flow distribution matrix , expressed as: （2）； In the formula, represents the active power flow on branch s from node i to node j; When i=j, , It means equivalent to; S1.1.

3. Calculate the generator output matrix , expressed as: （3）； In the formula, represents the injected active power flow of the generator set at node i during period t; K represents the number of nodes connected to the generator set in the power system.

3. The electric vehicle charging price setting method of coupling carbon signal and time-of-use electricity price according to claim 2 is characterized in that: In S1.2, the method for obtaining the dynamic CEF in the distribution network area is: S1.2.

1. Estimation of the dynamic CEF matrix of nodes in the power system , expressed as: （4）； In the formula, represents the carbon emission intensity matrix of the generator set, represents the carbon emission intensity of the generator set at node k during period t; represents the dynamic CEF at node i during period t; T T is the total number of time periods; the superscript T indicates transposition; In formula (4), Defined , where the elements are ,pass Calculate the dynamic CEF matrix; S1.2.2, in order to ensure the consistency of indirect CEF of users at different nodes in the same area in different time periods, the dynamic CEF of each node The dynamic CEF in the region is obtained by spatial averaging, which is expressed as: （5）； In the formula, It represents the dynamic CEF of the entire distribution network area during period t; represents the active load of node i during period t.

4. The electric vehicle charging price setting method of coupling carbon signal and time-of-use electricity price according to claim 3 is characterized in that: In S2, the process of formulating the charging price of electric vehicles (EV) for residential users is as follows: S2.

1. Design EV charging price according to dynamic CEF distribution interval, expressed as: （6）； In the formula, represents the EV charging price during period t; is the time-sharing charging price for residents during period t; Adjustment amount for EV charging price; S2.2 Calculation , calculated as: （7）； In the formula, , , They represent the minimum, average, and maximum values ​​of the dynamic CEF in the distribution network area, respectively; , They are the price reduction and increase coefficients respectively.

5. The electric vehicle charging price setting method of coupling carbon signal and time-of-use electricity price according to claim 4 is characterized in that: In S2.2, for , In order to balance the electricity price adjustment during high-carbon emission and low-carbon emission periods, the total increase in electricity price in the high-carbon emission interval is equal to the total decrease in electricity price in the low-carbon emission interval, then: （8）； In the formula, , They respectively represent the number of time periods when the dynamic CEF of the distribution network area is in the high carbon emission range and the low carbon emission range.

6. The electric vehicle charging price setting method of coupling carbon signal and time-of-use electricity price according to claim 4 is characterized in that: In S3.1, the charging of EV depends on the time of returning home. , Time away from home and daily mileage x, the method for analyzing EV travel patterns is: S3.1.

1. Time of returning home ,assumed Obey expectations , standard deviation is The normal distribution of: （9）； In the formula, express The probability density function of ; exp is the exponential function; S3.1.

2. Time away from home ,assumed Obey expectations , standard deviation is The normal distribution of: （10）； In the formula, express The probability density function of S3.1.3 For daily mileage x, assume that x follows the expectation , standard deviation is The normal distribution of: （11）； In the formula, represents the probability density function of x; ln is the logarithmic function, which represents the logarithm with base e; S3.1.

4. Charging time required for the nth EV It is expressed as: （12）； In the formula, is the daily driving distance of the nth EV; , are the battery capacity and range of the nth EV respectively; , They are the charging power and charging efficiency of the charging pile respectively.

7. The electric vehicle charging price setting method of coupling carbon signal and time-of-use electricity price according to claim 6 is characterized in that: In S3.2, the process of constructing the optimal charging decision for EV is: S3.2.1 Objective function of EV optimal charging decision for: （13）； Where N is the total number of EVs in the charging area; represents the charging status of the nth EV in period t. , indicating that charging is performed during the t period. , indicating no charging during period t; S3.2.

2. Set constraints, including battery energy constraints, state of charge constraints, and charging time constraints; S3.2.

3. Based on the characteristics of EV power load, the dynamic CEF of the distribution network area is used to estimate the indirect carbon emissions of EV, expressed as: （14）； In the formula, Indicates the indirect carbon emissions of EVs.

8. The electric vehicle charging price setting method of coupling carbon signal and time-of-use electricity price according to claim 7 is characterized in that: In S3.2.2, the constraints are specifically: Battery energy constraint: When the EV is fully charged, the battery energy must meet the user's expectations and meet the user's travel requirements, which can be expressed as: （15）； In the formula, The power of the nth EV when it starts charging; is the target power that the nth EV is expected to achieve; The state of charge constraint is expressed as: （16）； （17）； In the formula, , are the charge states of the nth EV at time period t and time period t-1 respectively; represents the charging status of the nth EV at time t-1. , indicating that charging is performed during the t-1 period. If , indicating no charging during the t-1 period; , They are the upper and lower limits of the EV state of charge respectively; The charging time constraint is expressed as: （18）。

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