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

Abstract

The present invention belongs to the field of price setting technology, specifically to a method for setting electric vehicle charging prices by coupling carbon signals with time-of-use electricity prices. The method comprises the following steps: S1. Estimating a dynamic carbon emission factor (CEF), using the dynamic CEF to describe the carbon emission intensity of each node in the power system, where the dynamic CEF represents the equivalent carbon emissions generated at the power generation end per unit of electricity consumed by the node during different time periods; S2. Coupling the dynamic CEF with time-of-use electricity prices to set residential electric vehicle (EV) charging prices; and S3. Based on the set EV charging prices, constructing an optimal EV charging decision guided by price signals. The present invention designs an EV charging price that comprehensively considers load management objectives and low-carbon goals. This method can guide users in making economical charging decisions in an orderly manner while reducing peak-to-valley differences in the distribution network and indirect carbon emissions during EV charging.

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 change issues caused by excessive greenhouse gas emissions have garnered increasing attention. With the continued implementation of the "dual carbon" goals and growing public awareness of carbon reduction, the number of new energy vehicles (EVs), particularly electric vehicles (EVs), has continued to rise. EV charging demand exhibits multi-dimensional randomness, including spatial, temporal, quantity, and power consumption. When massive numbers of EVs connect to the distribution network for charging in an unordered manner, this poses a significant challenge to the system's flexible regulation. Therefore, existing literature has explored the issue of orderly EV charging guidance from the perspective of load management. However, the primary motivation for promoting EV use is carbon reduction—that is, achieving carbon reduction targets through near-zero carbon emissions during driving, rather than as a target for demand response. It should be noted that, given that the power generation energy mix remains primarily fossil fuel-based, EV charging still generates indirect carbon emissions. Therefore, when designing mechanisms to guide orderly EV charging, it is important to consider not only load management but also decarbonization.

[0003] Regarding the accounting of EV carbon emission reductions, traditional research has primarily drawn on the "Voluntary Greenhouse Gas Emission Reduction Project Methodology" to calculate certified emission reductions and participate in carbon emission rights trading to generate revenue. However, this method primarily targets voluntary emission reduction projects and is currently only applicable to electric bus projects, making it less practical for the large number of household EVs. In fact, the carbon emissions of electricity loads can also be indirectly calculated based on the carbon emission factor (CEF). However, the static CEF cannot accurately depict the differences in carbon emissions caused by the output share of different types of generator sets in different time periods, nor can it allow electricity users to perceive the differences in carbon emissions from electricity usage behavior in different time periods. The carbon emission flow theory links carbon emissions with electricity users' electricity usage behavior, providing a new perspective for carbon emission accounting.

[0004] As a special type of electricity load, EVs can also use dynamic CEFs, obtained through carbon flow tracking, as signals to guide electricity users in responding to low-carbon demand. The paper "Carbon efficient smart charging using forecasts of marginal emission factors" establishes a short-term dynamic CEF forecast model to provide real-time guidance signals for EV users' low-carbon charging, enabling smart low-carbon EV charging. The paper "Dynamic carbon emission factor based interactive control of distribution network by a generalized regression neural network assisted optimization" proposes an interactive control method between distribution network operators and EV aggregators based on dynamic CEF. By regulating EV charging power during different time periods, it aims to reduce the peak-to-valley difference in charging load within the distribution network and reduce EV indirect carbon emissions. The paper "Sustainable electric vehicle charging coordination: Balancing CO2 emission reduction and peak power demand shaving" establishes a dual-objective optimization model to balance EV indirect carbon emissions and peak-to-valley difference in charging load, providing a reference for achieving sustainable EV charging.

[0005] However, the aforementioned literature fails to fully leverage the guiding role of price signals and overlooks the autonomy of EV users in charging decisions. In practice, users often focus solely on price signals and prefer to make EV charging decisions based on cost minimization. Therefore, how to couple carbon signals with electricity price signals to guide EV charging in a low-carbon and orderly manner using a single charging price is a question worth exploring. Summary of the Invention

[0006] In view of the above shortcomings in the existing technology, 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. An EV charging price is designed 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 of the distribution network and indirect carbon emissions 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:

[0008] S1. Estimating the dynamic carbon emission factor (CEF). Dynamic CEF is used to describe the carbon emission intensity of each node in the power system. Dynamic CEF is the equivalent carbon emission generated at the power generation end per unit of electricity consumed by the node in different time periods. The steps include:

[0009] S1.1. Calculate the power system active flux matrix, line power flow distribution matrix, and generator output matrix;

[0010] S1.2, estimate the dynamic CEF matrix of nodes in the power system and obtain the dynamic CEF within the distribution network area;

[0011] S2. Couple dynamic CEF with time-of-use electricity prices to formulate charging prices for residential electric vehicles (EVs);

[0012] S3. Based on the established EV charging price, construct an EV optimal charging decision guided by price signals. The steps include:

[0013] S3.1. Analyze EV travel patterns;

[0014] S3.2. Construct the optimal EV charging decision, including setting the objective function and constraints, and using dynamic CEF to estimate the indirect carbon emissions of EV.

[0015] As a preferred embodiment of the present invention, in S1.1, the calculation method is:

[0016] S1.1.1. Calculation of the power system active flux matrix , expressed as:

[0017] (1);

[0018] Where, 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;

[0019] S1.1.2. Calculate the line flow distribution matrix , expressed as:

[0020] (2);

[0021] Where, represents the active power flow on branch s from node i to node j; when i=j, , It means equivalent to;

[0022] S1.1.3. Calculate the generator output matrix , expressed as:

[0023] (3);

[0024] Where, 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.

[0025] As a preferred embodiment of the present invention, in S1.2, the method for obtaining the dynamic CEF within the distribution network area is:

[0026] S1.2.1. Estimation of the dynamic CEF matrix of nodes in the power system , expressed as:

[0027] (4);

[0028] Where, 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;

[0029] In formula (4), Defined , where the elements are ,pass Calculate the dynamic CEF matrix;

[0030] S1.2.2, in order to ensure the consistency of indirect CEF of different node users in the same area in each period, the dynamic CEF of each node The spatial averaging process is used to obtain the dynamic CEF within the region, which is expressed as:

[0031] (5);

[0032] Where, represents the dynamic CEF of the entire distribution network area during period t; represents the active load of node i during period t.

[0033] As a preferred embodiment of the present invention, in S2, the process of formulating the charging price of electric vehicles (EVs) for residential users is as follows:

[0034] S2.1. Design EV charging price based on dynamic CEF distribution interval, expressed as:

[0035] (6);

[0036] Where, 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;

[0037] S2.2 Calculation , calculated as:

[0038] (7);

[0039] Where, 、 、 They represent the minimum, average, and maximum values of the dynamic CEF of the distribution network area; 、 They are the price reduction and increase coefficients respectively.

[0040] As a preferred embodiment of the present invention, in S2.2, for 、 In order to balance the electricity price adjustments during high-carbon emission and low-carbon emission periods, the total amount of electricity price increases in the high-carbon emission interval is equal to the total amount of electricity price decreases in the low-carbon emission interval. Then:

[0041] (8);

[0042] Where, 、 They represent the number of periods when the dynamic CEF of the distribution network area is in the high carbon emission range and the low carbon emission range, respectively.

[0043] As a preferred solution 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:

[0044] S3.1.1. Regarding the time of returning home ,assumed Obey expectations , the standard deviation is Normal distribution of:

[0045] (9);

[0046] Where, express The probability density function of ; exp is the exponential function;

[0047] S3.1.2. Time away from home ,assumed Obey expectations , the standard deviation is Normal distribution of:

[0048] (10);

[0049] Where, express The probability density function of

[0050] S3.1.3 For daily mileage x, assume that x follows the expectation , the standard deviation is Normal distribution of:

[0051] (11);

[0052] Where, represents the probability density function of x; ln is the logarithmic function, which represents the logarithm with base e;

[0053] S3.1.4. Charging time required for the nth EV Expressed as:

[0054] (12);

[0055] Where, 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.

[0056] As a preferred embodiment of the present invention, in S3.2, the process of constructing the EV optimal charging decision is as follows:

[0057] S3.2.1 Objective Function for EV Optimal Charging Decision for:

[0058] (13);

[0059] Where N is the total number of EVs in the charging area; represents the charging status of the nth EV in period t, if , indicating that charging is carried out during the t period. If , indicating no charging during period t;

[0060] S3.2.2. Set constraints, including battery energy constraints, state of charge constraints, and charging time constraints;

[0061] S3.2.3. Based on the characteristics of EV electricity load, the dynamic CEF of the distribution network area is used to estimate the indirect carbon emissions of EVs, expressed as:

[0062] (14);

[0063] Where, Indicates the indirect carbon emissions of EVs.

[0064] As a preferred embodiment of the present invention, in S3.2.2, the constraints are specifically:

[0065] Battery energy constraint: After 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:

[0066] (15);

[0067] Where, The power of the nth EV when it starts charging; is the target power that the nth EV expects to achieve;

[0068] The state of charge constraint is expressed as:

[0069] (16);

[0070] (17);

[0071] Where, 、 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, if , indicating that charging is carried out during the t-1 period. If , indicating no charging during the t-1 period; 、 They are the upper and lower limits of EV state of charge;

[0072] Charging time constraint, expressed as:

[0073] (18).

[0074] 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 capable of running on the processor. The above-mentioned algorithm calculation is realized by executing the software through the processor.

[0075] The beneficial effects of the present invention are:

[0076] This paper comprehensively considers both load management and low-carbon objectives. By combining the distribution network's dynamic carbon emission factor (CEF) with residential time-of-use electricity prices, it designs a new electric vehicle (EV) charging pricing system. This pricing system not only effectively guides users to charge during economically reasonable times, reducing charging costs, but also reduces indirect carbon emissions during EV charging, promoting low-carbon operation of the power system.

[0077] By dynamically adjusting charging prices, this invention achieves orderly guidance of electric vehicle charging demand and optimizes load management in the power system. While reducing peak-to-valley load variations in the distribution network, it also increases the absorption rate of clean energy sources such as photovoltaics. This is of great significance for promoting the optimization of the energy structure and achieving the "dual carbon" goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is a schematic diagram of the process of the present invention;

[0079] Figure 2 It is a schematic diagram of the IEEE33 node system during the verification process of the present invention;

[0080] Figure 3 This is a schematic diagram of the main grid power purchase and flexible unit output during the verification process of the present invention;

[0081] 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;

[0082] Figure 5 This is a schematic diagram of EV charging prices in various scenarios during the verification process of the present invention;

[0083] Figure 6 It is a schematic diagram of EV charging load in various scenarios during the verification process of the present invention;

[0084] Figure 7 This is a comprehensive comparison chart of the guiding effect of EV charging prices during the verification process of the present invention. DETAILED DESCRIPTION

[0085] The embodiments of the present invention are further described below with reference to the accompanying drawings:

[0086] like Figure 1 As shown in FIG, the method for formulating electric vehicle charging prices by coupling carbon signals with time-of-use electricity prices includes the following steps:

[0087] S1. Estimating the dynamic carbon emission factor (CEF). Dynamic CEF is used to describe the carbon emission intensity of each node in the power system. Dynamic CEF is the equivalent carbon emission generated at the power generation end per unit of electricity consumed by the node in different time periods. The steps include:

[0088] S1.1. Calculate the power system active flux matrix, line power flow distribution matrix, and generator output matrix;

[0089] S1.2, estimate the dynamic CEF matrix of nodes in the power system and obtain the dynamic CEF within the distribution network area;

[0090] S2. Couple dynamic CEF with time-of-use electricity prices to formulate charging prices for residential electric vehicles (EVs);

[0091] S3. Based on the established EV charging price, construct an EV optimal charging decision guided by price signals. The steps include:

[0092] S3.1. Analyze EV travel patterns;

[0093] S3.2. Construct the optimal EV charging decision, including setting the objective function and constraints, and using dynamic CEF to estimate the indirect carbon emissions of EV.

[0094] Carbon emission flow theory is a carbon emission flow estimation theory similar to the power system flow calculation theory.

[0095] In S1.1, the calculation method is:

[0096] S1.1.1. Calculation of the power system active flux matrix , expressed as:

[0097] (1);

[0098] Where, It represents the active flux from node i to node j in period t, in kW; when i=j, 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 during period t, in kW; represents the injected active power flow of the generator set at node k during period t, in kW; I represents the total number of nodes in the power system;

[0099] S1.1.2. Calculate the line flow distribution matrix , expressed as:

[0100] (2);

[0101] Where, Represents the active power flow on 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;

[0102] S1.1.3. Calculate the generator output matrix , expressed as:

[0103] (3);

[0104] Where, 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.

[0105] In S1.2, the method for obtaining the dynamic CEF within the distribution network area is:

[0106] S1.2.1. Estimation of the dynamic CEF matrix of nodes in the power system , expressed as:

[0107] (4);

[0108] Where, 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, 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;

[0109] For the carbon emission intensity matrix of 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:

[0110] Table 1 Generator parameters in the literature

[0111]

[0112] Then, the corresponding Can be set to:

[0113] .

[0114] In formula (4), Defined , where the elements are ,pass Calculate the dynamic CEF matrix;

[0115] S1.2.2, in order to ensure the consistency of indirect CEF of different node users in the same area in each period, the dynamic CEF of each node The spatial averaging process is used to obtain the dynamic CEF within the region, which is expressed as:

[0116] (5);

[0117] Where, It represents the dynamic CEF of the entire distribution network area during period t, in kg / kW·h; It represents the active load of node i in period t, in kW.

[0118] The higher the dynamic CEF in the distribution network, the greater the carbon emissions generated per unit of electricity consumed. To incentivize EV users to assume responsibility for carbon reduction, EV charging prices are designed based on the dynamic CEF distribution range, building on the residential EV time-of-use charging prices that primarily consider load management goals and user charging habits.

[0119] In S2, the process of setting the charging price for residential electric vehicles (EVs) is as follows:

[0120] S2.1. Design EV charging price based on dynamic CEF distribution interval, expressed as:

[0121] (6);

[0122] Where, represents the EV charging price during period t, in RMB / kW·h; is the time-sharing charging price for residents during period t, in Yuan / kW·h; is the EV charging price adjustment, unit: RMB / kW·h;

[0123] S2.2 Calculation , calculated as:

[0124] (7);

[0125] Where, 、 、 They represent the minimum, average, and maximum values of the dynamic CEF of the distribution network area, respectively, in kg / kW·h; 、 They are price reduction and increase coefficients respectively, unit: Yuan / ton.

[0126] In S2.2, since the number of periods in the high and low carbon emission ranges is generally not equal, 、 In order to balance the electricity price adjustments during high-carbon emission and low-carbon emission periods, the total amount of electricity price increases in the high-carbon emission interval is equal to the total amount of electricity price decreases in the low-carbon emission interval. Then:

[0127] (8);

[0128] Where, 、 They represent the number of periods when the dynamic CEF of the distribution network area is in the high carbon emission range and the low carbon emission range, respectively.

[0129] 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:

[0130] S3.1.1. Regarding the time of returning home ,assumed Obey expectations , the standard deviation is Normal distribution of:

[0131] (9);

[0132] Where, express The probability density function of ; exp is the exponential function;

[0133] S3.1.2. Time away from home ,assumed Obey expectations , the standard deviation is Normal distribution of:

[0134] (10);

[0135] Where, express The probability density function of

[0136] S3.1.3 For daily mileage x, assume that x follows the expectation , the standard deviation is Normal distribution of:

[0137] (11);

[0138] Where, represents the probability density function of x; ln is the logarithmic function, which represents the logarithm with base e;

[0139] S3.1.4. Charging time required for the nth EV Expressed as:

[0140] (12);

[0141] Where, 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 respectively the charging power (in kW) and charging efficiency of the charging pile.

[0142] In S3.2, the process of constructing the optimal EV charging decision is:

[0143] S3.2.1 Objective Function for EV Optimal Charging Decision for:

[0144] (13);

[0145] Where N is the total number of EVs in the charging area; represents the charging status of the nth EV in period t, if , indicating that charging is carried out during the t period. If , indicating no charging during period t;

[0146] S3.2.2. Set constraints, including battery energy constraints, state of charge constraints, and charging time constraints;

[0147] S3.2.3. Based on the characteristics of EV electricity load, the dynamic CEF of the distribution network area is used to estimate the indirect carbon emissions of EVs, expressed as:

[0148] (14);

[0149] Where, Indicates the indirect carbon emissions of EVs, in tons.

[0150] In S3.2.2, the constraints are specifically:

[0151] Battery energy constraint: After 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:

[0152] (15);

[0153] Where, The power consumption of the nth EV when it starts charging, in kW·h; is the target power that the nth EV expects to achieve, in kW·h;

[0154] The state of charge constraint is expressed as:

[0155] (16);

[0156] (17);

[0157] Where, 、 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, if , indicating that charging is carried out during the t-1 period. If , indicating no charging during the t-1 period; 、 They are the upper and lower limits of EV state of charge;

[0158] Charging time constraint, expressed as:

[0159] (18).

[0160] The verification process is:

[0161] 1. Basic data:

[0162] 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 2 In 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.

[0163] Table 2 System parameters

[0164]

[0165] The relevant parameters of the distributed flexible unit are shown in Table 3.

[0166] Table 3 Flexible unit parameters

[0167]

[0168] 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.

[0169] Table 4 System load data

[0170]

[0171] Table 5 EV parameters

[0172]

[0173] Table 6 Main grid carbon potential and main grid electricity purchase time-of-use price

[0174]

[0175] 2. Distribution network operator optimization dispatch and dynamic CEF estimation results:

[0176] The main grid (upper grid) power purchase and flexible unit output are as follows Figure 3 As shown in the figure, the output of flexible units is mainly concentrated in the period from 17 to 21, and some flexible units also output in the period from 7 to 9.

[0177] The dynamic CEF estimation results of the distribution network are as follows: Figure 4 As shown in the table, low carbon emissions occur between 11 and 15 o'clock, primarily during periods of high PV output. High carbon emissions occur primarily between 5 and 7 and 17 and 21 o'clock, primarily during periods of high grid carbon potential or high flexible unit output. The remaining periods are characterized by flat carbon emissions. Distribution network operator costs are shown in Table 7.

[0178] Table 7 Total costs of distribution network operators

[0179]

[0180] 3. Scene settings:

[0181] 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.

[0182] Scenario 1: EV users charge their cars upon returning home at a fixed charging price.

[0183] Scenario 2: Current residential EV time-sharing charging prices guide charging.

[0184] Scenario 3: EV charging price-guided charging that couples dynamic CEF with the current residential EV time-of-use charging price.

[0185] EV charging prices in various scenarios Figure 5 The EV charging prices in scenarios 1 and 2 are set according to the Notice on Further Improving the Time-of-Use Electricity Pricing Policy for Residential Electric Vehicle Charging Piles.

[0186] Depend on Figure 5 As can be seen, the EV charging price design in Scenario 3 not only considers load management and user charging habits, but also carbon emissions. During periods 5 to 7, PV output is low, and distribution network operators primarily purchase electricity from the main grid to meet load demand. However, the main grid's carbon potential is high, resulting in a high dynamic CEF for the distribution network. Therefore, increasing charging prices places a higher carbon footprint on EV users charging during this period. During periods 11 to 15, PV output is high and the main grid's carbon potential is low, resulting in a lower dynamic CEF for the distribution network. Therefore, lowering charging prices encourages users to charge more actively. During periods 17 to 19, due to ramping constraints, distribution network operators increase the use of flexible units to balance power shortages. During periods 20 to 21, flexible unit output is reduced, but the main grid's carbon potential is high, resulting in a higher dynamic CEF for the distribution network. Therefore, increasing charging prices places a higher carbon footprint on EV users charging during this period.

[0187] 4. Result Analysis

[0188] 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.

[0189] Table 8 Peak-to-valley difference of total load of distribution network

[0190]

[0191] Table 8 also shows that, compared to Scenario 1, the addition of EV charging loads in Scenarios 2 and 3 reduces the peak-to-valley difference in the total distribution network load. This is because the electricity prices in Scenarios 2 and 3 take load management into account. Scenario 3, compared to Scenario 2, considers not only the time-sharing distribution of the load curve but also the dynamic CEF distribution of the distribution network when guiding load shifting. Therefore, the peak-to-valley difference in the total distribution network load in Scenario 3 is slightly higher than that in Scenario 2.

[0192] The EV charging costs under various scenarios are shown in Table 9.

[0193] Table 9 EV charging costs in various scenarios

[0194]

[0195] As shown in Table 4, EV charging costs are lower in both Scenario 2 and 3 compared to Scenario 1. Scenario 3 shows the largest reduction in EV charging costs, indicating that the EV charging price designed in this embodiment is more economically attractive to users than the current EV time-sharing charging price.

[0196] The indirect carbon emissions of EVs under each scenario are shown in Table 10.

[0197] Table 10 Indirect carbon emissions of EVs under various scenarios

[0198]

[0199] Comprehensive analysis under each scenario, respectively, from the peak shaving and valley filling, saving charging costs, and reducing indirect carbon emissions, the guiding role of several EV charging prices is comprehensively compared. The results are as follows Figure 7 As shown. Figure 7 As can be seen, the EV charging price designed in this embodiment is more attractive to EV users in terms of reducing charging costs. The peak-shaving and valley-filling effect achieved through this design is essentially the same as that achieved with current EV time-of-use charging prices, further enhancing the carbon reduction effect. It should be noted that the number of EVs used in this example is limited. If the overall EV ownership and growth trend are considered, a charging price designed with low-carbon targets would have an even more significant carbon reduction effect on the EV charging process.

Claims

1. The electric vehicle charging price setting method that couples carbon signals with time-of-use electricity prices is characterized by The following steps are involved: S1. Estimating the dynamic carbon emission factor (CEF). Dynamic CEF is used to describe the carbon emission intensity of each node in the power system. Dynamic CEF is the equivalent carbon emission generated at the power generation end per unit of electricity consumed by the node in different time periods. The steps include: S1.

1. Calculate the power system active flux matrix, line power 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 within the distribution network area; S2. Couple dynamic CEF with time-of-use electricity prices to formulate charging prices for residential electric vehicles (EVs); S3. Based on the established 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 EV charging decision, including setting the objective function and constraints, and estimating the indirect carbon emissions of EVs using dynamic CEF; In S2, the process of setting the charging price for residential electric vehicles (EVs) is as follows: S2.

1. Design EV charging price based on dynamic CEF distribution interval, expressed as: （6）； Where, 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）； Where, 、 、 They represent the minimum, average, and maximum values of the dynamic CEF of the distribution network area; 、 are the price reduction and increase coefficients respectively; It represents the dynamic CEF of the entire distribution network area during period t.

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 power system active flux matrix , expressed as: （1）； Where, represents the active flux from node i to node j in 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）； Where, 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）； Where, 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）； Where, 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 different node users in the same area in each period, the dynamic CEF of each node The spatial averaging process is used to obtain the dynamic CEF within the region, which is expressed as: （5）； Where, 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 1 is characterized in that: In S2.2, for 、 In order to balance the electricity price adjustments during high-carbon emission and low-carbon emission periods, the total amount of electricity price increases in the high-carbon emission interval is equal to the total amount of electricity price decreases in the low-carbon emission interval. Then: （8）； Where, 、 They represent the number of periods when the dynamic CEF of the distribution network area is in the high carbon emission range and the low carbon emission range, respectively.

5. 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 S3.1, the charging time 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. Regarding the time of returning home ,assumed Obey expectations , the standard deviation is Normal distribution of: （9）； Where, express The probability density function of ; exp is the exponential function; S3.1.

2. Time away from home ,assumed Obey expectations , the standard deviation is Normal distribution of: （10）； Where, express The probability density function of S3.1.3 For daily mileage x, assume that x follows the expectation , the standard deviation is Normal distribution of: （11）； Where, 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 Expressed as: （12）； Where, 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.

6. The electric vehicle charging price setting method of coupling carbon signal and time-of-use electricity price according to claim 5 is characterized in that: In S3.2, the process of constructing the optimal EV charging decision is as follows: S3.2.1 Objective Function for 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, if , indicating that charging is carried out during the t period. If , 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 electricity load, the dynamic CEF of the distribution network area is used to estimate the indirect carbon emissions of EVs, expressed as: （14）； Where, Indicates the indirect carbon emissions of EVs.

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 S3.2.2, the constraints are: Battery energy constraint: After 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）； Where, The power of the nth EV when it starts charging; is the target power that the nth EV expects to achieve; The state of charge constraint is expressed as: （16）； （17）； Where, 、 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, if , indicating that charging is carried out during the t-1 period. If , indicating no charging during the t-1 period; 、 They are the upper and lower limits of EV state of charge; Charging time constraint, expressed as: （18）。

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