Distributed interaction method and system for participation of electric vehicle in demand response

Abstract

The invention discloses a distributed interaction method and system for participation of an electric vehicle in demand response, and relates to the technical field of vehicle network interaction, and the method comprises the steps: determining the daily driving and charging modes of the electric vehicle based on the dynamic use condition of the electric vehicle; based on the daily driving and charging mode of the electric vehicle, establishing a degradation cost model of the battery of the electric vehicle, and performing piecewise linearization processing; based on the degradation cost model of the battery of the electric vehicle, combining the charging and discharging cost of the electric vehicle and the income of participating in the demand response to construct an optimization interaction model of participating in the demand response of the electric vehicle; decomposing the optimization interaction model into a plurality of sub-problems, and constructing a distributed interaction model; solving the distributed interaction model to obtain an electric vehicle participation demand response interaction scheme; according to the invention, the balance between the income of the electric vehicle user and the battery degradation cost is realized; and an effective electric vehicle demand response charging and discharging scheduling scheme is provided on the premise of protecting user privacy information.

Description

Technical Field

[0001] The present invention relates to the field of vehicle-grid interaction technology, and in particular to a distributed interaction method and system for electric vehicles to participate in demand response. Background Art

[0002] In recent years, in order to achieve the "dual carbon" goals, the country has vigorously promoted the development of the new energy vehicle industry and the construction of charging infrastructure, and the number of electric vehicles and charging piles has increased year by year. However, disorderly charging of large-scale electric vehicles will lead to problems such as widening peak-to-valley differences, deteriorating power quality, and increased network losses. Therefore, it is of great significance to promote orderly charging, promote in-depth vehicle-grid interaction, and promote the safe, stable and economic operation of the power grid. As a flexible and adjustable resource on the user side, electric vehicles have both storage and load attributes, and there is huge potential for the development of large-scale two-way intelligent interaction between electric vehicles and power grids. Electric vehicles can participate in a variety of differentiated demand response scenarios such as peak shaving and valley filling, renewable energy consumption, frequency regulation, and backup support.

[0003] At present, the existing charging and discharging optimization scheduling of electric vehicles participating in demand response is mostly aimed at economy. However, the charging and discharging behavior of electric vehicles will affect the battery life and cause battery degradation, and the dynamic vehicle usage of electric vehicle users will affect the demand response effect. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: how to provide a solution for the demand response charging and discharging scheduling of electric vehicles in the process of carbon neutrality and carbon peak, and promote the sustainable and efficient development of vehicle-grid interaction.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a distributed interactive method for electric vehicles to participate in demand response, including:

[0008] Determine the daily driving and charging patterns of electric vehicles based on the dynamic usage of electric vehicles;

[0009] Based on the daily driving and charging patterns of electric vehicles, a degradation cost model of electric vehicle batteries is established and piecewise linearized.

[0010] Based on the degradation cost model of electric vehicle batteries, combined with the charging and discharging costs of electric vehicles and the benefits of participating in demand response, an optimized interactive model for electric vehicles to participate in demand response is constructed;

[0011] Decompose the optimization interaction model into multiple sub-problems and build a distributed interaction model;

[0012] Solve the distributed interaction model and obtain the interactive scheme for electric vehicles to participate in demand response.

[0013] As a preferred solution for the distributed interactive method of electric vehicles participating in demand response,

[0014] Determining the daily driving and charging mode of the electric vehicle based on the dynamic usage of the electric vehicle includes:

[0015] Assume that the vehicle starts from A at time t1, arrives at B at time t2, starts from B at time t3, and returns to A at time t4, that is, [t1, t2] and [t3, t4] are the travel time periods of electric vehicles, and [t2, t3] and [t4, t1] are the plug-in time periods of electric vehicles; assume that t1, t2, t3, and t4 follow the following normal distribution:

[0016]

[0017] Where, t represents t1, t2, t3 and t4; μ and σ are the mean and standard deviation respectively.

[0018] As a preferred solution for the distributed interactive method of electric vehicles participating in demand response,

[0019] The degradation cost model of electric vehicle batteries is established by:

[0020] The electric vehicle battery degradation cost model is expressed as:

[0021]

[0022] Where DC is the degradation cost of electric vehicle battery; C b is the battery purchase cost; LC is the battery replacement cost; E0 is the battery capacity; DOD = 1-SOC is the battery discharge depth; k is a constant used for calibration; C L Cycle life refers to the number of times a battery can be charged and discharged before capacity loss.

[0023] As a preferred solution for the distributed interactive method of electric vehicles participating in demand response,

[0024] The piecewise linearization process comprises:

[0025] The battery degradation cost is linearized using a piecewise linear function, which is expressed as:

[0026]

[0027] In the formula, a1, a2, a3, a4, a5 and b1, b2, b3, b4, b5 are constants.

[0028] As a preferred solution for the distributed interactive method of electric vehicles participating in demand response,

[0029] The construction of an optimized interactive model for electric vehicles to participate in demand response includes:

[0030] The objective function including the benefits of electric vehicles participating in demand response, the charging and discharging costs of electric vehicles, and the battery degradation costs is established, which is specifically expressed as:

[0031]

[0032] In the formula, is the battery power of the i-th electric vehicle in period t, where positive indicates charging and negative indicates discharging; For the corresponding The slack variable r i,t is the profit coefficient of the ith electric vehicle participating in demand response in period t; c i,t is the charging and discharging cost coefficient of the i-th electric vehicle in period t; ζ is the penalty coefficient; M is the total number of electric vehicles; T is the total number of time periods.

[0033] As a preferred solution for the distributed interactive method of electric vehicles participating in demand response,

[0034] The construction of an optimized interactive model for electric vehicles to participate in demand response also includes:

[0035] The dynamic use of electric vehicles and related constraints are established, expressed as:

[0036]

[0037]

[0038] In the formula, is the rated power of the electric vehicle; [t i,2 ,t i,3 ]∪[t i,4 ,t i,1 ] is the set of time periods when the electric vehicle is plugged in; ND indicates that it needs to be discharged, that is, the set of time periods when the electric vehicle is in a standby or discharging state; NC indicates that it needs to be charged, that is, the set of time periods when the electric vehicle is in a standby or discharging state; [t i,1 ,t i,2 ]∪[t i,3 ,t i,4 ] is the travel time set of electric vehicles; E i,0 is the battery capacity of the i-th electric car; β i ≤1 is the battery capacity ratio of the ith electric vehicle used for travel; SOC i,tis the state of charge of the i-th electric vehicle in period t; SOC0 is the initial state of charge of the electric vehicle; SOC max and SOC min SOC is the upper and lower limits of the state of charge of electric vehicles; acc is the limit of the state of charge of the electric vehicle allowed before traveling; Equations (6) and (7) are the discharge and charging power constraints of the electric vehicle during the plug-in period; Equation (8) is the power consumption of the electric vehicle during the travel period; Slack variables are introduced in Equation (9) to offset the travel stage The influence of the discharge depth is shown in Figure 1. The influence of the discharge depth is shown in Figure 1. The influence of the discharge depth is shown in Figure 1. The influence of the discharge depth is shown in Figure 1. The influence of the discharge depth is shown in Figure 1. The influence of the discharge depth is shown in Figure 1. The influence of the discharge depth is shown in Figure 1.

[0039] The relevant constraints for demand response requirements are:

[0040]

[0041] In the formula, is the target power of demand response during period t; The power provided to the electricity market during period t.

[0042] As a preferred solution for the distributed interactive method of electric vehicles participating in demand response,

[0043] Decomposing the optimization interaction model into multiple sub-problems and constructing a distributed interaction model includes:

[0044] The optimization interaction model of electric vehicles participating in demand response is updated as follows:

[0045]

[0046] In the formula, λ and γ are the first and second penalty coefficients respectively;

[0047] The updated optimization interaction model is decomposed into multiple sub-problems to obtain a distributed interaction model of electric vehicles participating in demand response.

[0048] In a second aspect, an embodiment of the present invention provides a distributed interactive system for electric vehicles to participate in demand response, including:

[0049] An analysis module for determining the daily driving and charging patterns of electric vehicles based on the dynamic usage of electric vehicles;

[0050] The cost linearization module is used to establish the degradation cost model of electric vehicle batteries based on the daily driving and charging patterns of electric vehicles, and perform piecewise linearization processing;

[0051] The optimization modeling module is used to build an optimization interaction model for electric vehicles to participate in demand response based on the degradation cost model of electric vehicle batteries, combined with the charging and discharging costs of electric vehicles and the benefits of participating in demand response;

[0052] Distributed module, used to decompose the optimization interaction model into multiple sub-problems and build a distributed interaction model;

[0053] The solution module is used to solve the distributed interaction model and obtain the interactive solution of electric vehicles participating in demand response.

[0054] In a third aspect, an embodiment of the present invention provides a computing device, including:

[0055] Memory and processor;

[0056] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the one or more programs are executed by the one or more processors, the one or more processors implement the distributed interactive method for electric vehicles to participate in demand response as described in any embodiment of the present invention.

[0057] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the distributed interactive method for electric vehicles to participate in demand response.

[0058] Beneficial effects of the invention: The invention takes into account the impact of dynamic usage of electric vehicles and battery degradation costs on vehicle-grid interaction, achieving a balance between electric vehicle user benefits and battery degradation costs; proposes a distributed method to decompose and solve the optimization interaction problem of electric vehicles participating in demand response, and provides an effective electric vehicle demand response charging and discharging scheduling solution under the premise of protecting user privacy information. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0060] Figure 1 It is an overall flow chart of the distributed interactive method for electric vehicles to participate in demand response according to the present invention;

[0061] Figure 2 It is a target power curve diagram of electric vehicles participating in demand response in a simulation example of the distributed interactive method for electric vehicles participating in demand response described in the present invention. DETAILED DESCRIPTION

[0062] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0063] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0064] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0065] Example 1

[0066] Reference Figure 1 , which is the first embodiment of the present invention, and provides a distributed interactive method for electric vehicles to participate in demand response, including:

[0067] S1: Determine the daily driving and charging mode of electric vehicles based on the dynamic usage of electric vehicles;

[0068] S2: Based on the daily driving and charging patterns of electric vehicles, a degradation cost model of electric vehicle batteries is established and piecewise linearized;

[0069] S3: Based on the degradation cost model of electric vehicle batteries, combined with the charging and discharging costs of electric vehicles and the benefits of participating in demand response, an optimized interactive model for electric vehicles to participate in demand response is constructed;

[0070] S4: Decompose the optimization interaction model into multiple sub-problems and construct a distributed interaction model;

[0071] S5: Solve the distributed interaction model and obtain the interactive scheme for electric vehicles to participate in demand response.

[0072] It should be noted that through steps S1-S5, the present invention focuses on the vehicle-grid interaction problem for demand response, proposes a distributed interaction method for electric vehicles to participate in demand response, provides a solution for electric vehicle demand response charging and discharging scheduling in the process of carbon neutrality and carbon peak, and promotes the sustainable and efficient development of vehicle-grid interaction.

[0073] Example 2

[0074] Reference Figure 1 , which is an embodiment of the present invention, provides a distributed interactive method for electric vehicles to participate in demand response based on the previous embodiment, including:

[0075] In the embodiment of the present application, in the above step S1, determining the daily driving and charging mode of the electric vehicle based on the dynamic usage of the electric vehicle includes:

[0076] Assume that the vehicle starts from A at time t1, arrives at B at time t2, starts from B at time t3, and returns to A at time t4, that is, [t1, t2] and [t3, t4] are the travel time periods of electric vehicles, and [t2, t3] and [t4, t1] are the plug-in time periods of electric vehicles; assume that t1, t2, t3, and t4 follow the following normal distribution:

[0077]

[0078] Where, t represents t1, t2, t3 and t4; μ and σ are the mean and standard deviation respectively.

[0079] In the embodiment of the present application, the degradation cost model of the electric vehicle battery is established in the above step S2, and the piecewise linearization processing includes:

[0080] The electric vehicle battery degradation cost model is expressed as:

[0081]

[0082] Where DC is the degradation cost of electric vehicle battery; C b is the battery purchase cost; LC is the battery replacement cost; E0 is the battery capacity; DOD = 1-SOC is the battery discharge depth; k is a constant used for calibration; C L Cycle life refers to the number of times a battery can be charged and discharged before capacity loss.

[0083] C L Mainly depends on DOD. When the DOD value is lower, the cycle life of the battery will increase and the battery degradation cost will decrease. L The relationship with DOD is expressed as:

[0084] C L =L0e α(1-DOD) (33)

[0085] Where L0 is the battery cycle life when DOD is 100%; α is the attenuation coefficient.

[0086] In order to reduce the complexity of calculation, a piecewise linear function is used to linearize the battery degradation cost:

[0087]

[0088] In the formula, a1, a2, a3, a4, a5 and b1, b2, b3, b4, b5 are constants.

[0089] In the embodiment of the present application, the optimization interaction model for electric vehicles to participate in demand response in the above step S3 includes:

[0090] Electric vehicles participate in demand response through load aggregators, with the economic benefits of electric vehicles participating in demand response as the objective function, which includes the benefits of electric vehicles participating in demand response, the charging and discharging costs of electric vehicles, and the battery degradation costs, which can be specifically expressed as:

[0091]

[0092] In the formula, is the battery power of the i-th electric vehicle in period t, where positive indicates charging and negative indicates discharging; For the corresponding The slack variable is used to offset the power of electric vehicles during travel; r i,t is the profit coefficient of the ith electric vehicle participating in demand response in period t; c i,t is the charging and discharging cost coefficient of the i-th electric vehicle in period t; ζ is the penalty coefficient; M is the total number of electric vehicles; T is the total number of time periods.

[0093] The dynamic use of electric vehicles and related constraints are:

[0094]

[0095]

[0096] In the formula, is the rated power of the electric vehicle; [t i,2 ,t i,3 ]∪[t i,4 ,t i,1 ] is the set of time periods when the electric vehicle is plugged in; ND indicates that it needs to be discharged, that is, the set of time periods when the electric vehicle is in a standby or discharging state; NC indicates that it needs to be charged, that is, the set of time periods when the electric vehicle is in a standby or discharging state; [t i,1 ,t i,2 ]∪[t i,3 ,ti,4 ] is the travel time set of electric vehicles; E i,0 is the battery capacity of the i-th electric car; β i ≤1 is the battery capacity ratio of the ith electric vehicle used for travel; SOC i,t is the state of charge of the i-th electric vehicle in period t; SOC0 is the initial state of charge of the electric vehicle; SOC max and SOC min SOC is the upper and lower limits of the state of charge of electric vehicles; acc is the limit of the state of charge of electric vehicles allowed before traveling. Equations (6) and (7) are the discharge and charging power constraints of electric vehicles during the plug-in period, respectively; Equation (8) is the power consumption of electric vehicles during the travel period; since electric vehicles cannot participate in demand response interaction during the travel period, a slack variable is introduced in Equation (9) to offset the travel stage The influence of the discharge depth is given in Figure 1. Equation (10) is the change of the state of charge of the electric vehicle. Equations (11)-(13) are the state of charge constraints of the electric vehicle. Equation (14) is the relationship between the discharge depth and the state of charge.

[0097] The relevant constraints for demand response requirements are:

[0098]

[0099] Where P t target is the target power of demand response during period t; P t market It is the power provided by the electricity market during period t. When the aggregated power of electric vehicles cannot meet the demand response requirements, the power provided by the electricity market will make up for it.

[0100] In the embodiment of the present application, the optimization interaction model is decomposed into multiple sub-problems in the above step S4, and the construction of the distributed interaction model includes:

[0101] Considering that electric vehicle users are usually reluctant to disclose private information such as battery state of charge and travel time, a distributed approach is adopted to decompose the optimization interaction model into multiple sub-problems, and optimal charging and discharging scheduling is performed for each electric vehicle based on demand response targets and information such as the overall situation of other electric vehicles.

[0102] In order to decompose the optimization interaction model, constraint (15) is relaxed and incorporated into the objective function. The optimization interaction model of electric vehicles participating in demand response is updated as follows:

[0103]

[0104] Where λ and γ are the first-order and second-order penalty coefficients respectively.

[0105] By decomposing the updated optimization interaction model into multiple sub-problems, a distributed interaction model of electric vehicles participating in demand response can be obtained.

[0106] The subproblem for the i-th electric car is:

[0107]

[0108]

[0109] In the formula, the sub-term in formula (16) is In formula (17), it is simplified to

[0110] The sub-problems of the electricity market are:

[0111]

[0112] In the embodiment of the present application, solving the distributed interaction model in the above step S5 includes:

[0113] The alternating direction multiplier method is used to solve the interactive problem of electric vehicles participating in demand response. The specific process is as follows:

[0114] (1) Initialization, setting the initial value of the variable and P t market(0) , set the initial value of the penalty coefficient λ (0) , set the quadratic penalty coefficient γ and error threshold ε, and take the iteration number ν = 1.

[0115] (2) Input the time point parameters t1, t2, t3 and t4 of each electric vehicle user’s vehicle usage, input the number of electric vehicles M, and set the maximum number of iterations N.

[0116] (3) Solve subproblems (17) and (6)-(14) for each electric vehicle, and update the charging and discharging power of each electric vehicle

[0117] (4) Solve the subproblem (18) of the electricity market and update the power of the electricity market

[0118] (5) Update λ according to formula (19):

[0119]

[0120] (6) Determine whether the convergence condition is met. If it does not meet equation (20), take ν = ν + 1 and return to step (3); if it meets equation (20), end the iteration.

[0121]

[0122] (7) Output the final result and

[0123] Thus, the optimal interactive scheme for electric vehicles to participate in demand response can be obtained.

[0124] Example 3

[0125] The above is a schematic scheme of the distributed interactive method for electric vehicles to participate in demand response in this embodiment. It should be noted that the technical scheme of the distributed interactive system for electric vehicles to participate in demand response and the technical scheme of the distributed interactive method for electric vehicles to participate in demand response belong to the same concept. For details not described in detail in the technical scheme of the distributed interactive system for electric vehicles to participate in demand response in this embodiment, please refer to the description of the technical scheme of the distributed interactive method for electric vehicles to participate in demand response.

[0126] This embodiment also provides a system based on a distributed interactive method for electric vehicles to participate in demand response, including:

[0127] An analysis module for determining the daily driving and charging patterns of electric vehicles based on the dynamic usage of electric vehicles;

[0128] The cost linearization module is used to establish the degradation cost model of electric vehicle batteries based on the daily driving and charging patterns of electric vehicles, and perform piecewise linearization processing;

[0129] The optimization modeling module is used to build an optimization interaction model for electric vehicles to participate in demand response based on the degradation cost model of electric vehicle batteries, combined with the charging and discharging costs of electric vehicles and the benefits of participating in demand response;

[0130] Distributed module, used to decompose the optimization interaction model into multiple sub-problems and build a distributed interaction model;

[0131] The solution module is used to solve the distributed interaction model and obtain the interactive solution of electric vehicles participating in demand response.

[0132] This embodiment further provides a computing device, which is applicable to a distributed interactive method in which electric vehicles participate in demand response, and includes:

[0133] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the distributed interactive method for electric vehicles to participate in demand response as proposed in the above embodiment.

[0134] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the distributed interactive method for electric vehicles to participate in demand response as proposed in the above embodiment is implemented.

[0135] The storage medium proposed in this embodiment and the distributed interactive method for electric vehicles to participate in demand response proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0136] Example 4

[0137] Refer to Table 1 and Figure 2 , which is an embodiment of the present invention, provides a distributed interactive method for electric vehicles to participate in demand response. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0138] Assume that 50 electric vehicles participate in demand response. Figure 2 is the target power of demand response, and the relevant parameters are shown in Table 1.

[0139] Table 1 Parameter settings

[0140]

[0141]

[0142] The proposed distributed interactive method for electric vehicles participating in demand response considering battery degradation cost is compared with the case where battery degradation cost is not considered in the objective function. Compared with the case where battery degradation is not considered in the objective function, the charging cost of electric vehicles under the proposed method is reduced from 158.55USD to 150.7USD, the actual degradation cost of the battery is reduced from 51.27USD to 49.3USD, and the total revenue of electric vehicle users is increased from 55.87USD to 55.87USD.

[0143] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distributed interactive method for electric vehicles to participate in demand response, characterized in that: include: Determine the daily driving and charging patterns of electric vehicles based on the dynamic usage of electric vehicles; Based on the daily driving and charging patterns of electric vehicles, a degradation cost model of electric vehicle batteries is established and piecewise linearized. Based on the degradation cost model of electric vehicle batteries, combined with the charging and discharging costs of electric vehicles and the benefits of participating in demand response, an optimized interactive model for electric vehicles to participate in demand response is constructed; Decompose the optimization interaction model into multiple sub-problems and build a distributed interaction model; Solve the distributed interaction model and obtain the interactive scheme for electric vehicles to participate in demand response.

2. The distributed interactive method for electric vehicles to participate in demand response as claimed in claim 1, characterized in that: Determining the daily driving and charging mode of the electric vehicle based on the dynamic usage of the electric vehicle includes: Assume that the vehicle starts from A at time t1, arrives at B at time t2, starts from B at time t3, and returns to A at time t4, that is, [t1, t2] and [t3, t4] are the travel time periods of electric vehicles, and [t2, t3] and [t4, t1] are the plug-in time periods of electric vehicles; assume that t1, t2, t3, and t4 follow the following normal distribution: Where, t represents t1, t2, t3 and t4; μ and σ are the mean and standard deviation respectively.

3. The distributed interactive method for electric vehicles to participate in demand response as claimed in claim 2, characterized in that: The degradation cost model of electric vehicle batteries is established by: The electric vehicle battery degradation cost model is expressed as: Where DC is the degradation cost of electric vehicle battery; C b is the battery purchase cost; LC is the battery replacement cost; E0 is the battery capacity; DOD = 1-SOC is the battery discharge depth; k is a constant used for calibration; C L Cycle life refers to the number of times a battery can be charged and discharged before capacity loss.

4. The distributed interactive method for electric vehicles to participate in demand response as claimed in claim 3, characterized in that: The piecewise linearization process comprises: The battery degradation cost is linearized using a piecewise linear function, which is expressed as: In the formula, a1, a2, a3, a4, a5 and b1, b2, b3, b4, b5 are constants.

5. The distributed interactive method for electric vehicles to participate in demand response as claimed in claim 4, characterized in that: The construction of an optimized interactive model for electric vehicles to participate in demand response includes: The objective function including the benefits of electric vehicles participating in demand response, the charging and discharging costs of electric vehicles, and the battery degradation costs is established, which is specifically expressed as: In the formula, is the battery power of the i-th electric vehicle in period t, where positive indicates charging and negative indicates discharging; For the corresponding The slack variable r i,t is the profit coefficient of the ith electric vehicle participating in demand response in period t; c i,t is the charging and discharging cost coefficient of the i-th electric vehicle in period t; ζ is the penalty coefficient; M is the total number of electric vehicles; T is the total number of time periods.

6. The distributed interactive method for electric vehicles to participate in demand response as claimed in claim 5, characterized in that: The construction of an optimized interactive model for electric vehicles to participate in demand response also includes: The dynamic use of electric vehicles and related constraints are established, expressed as: In the formula, is the rated power of the electric vehicle; [t i,2 ,t i,3 ]∪[t i,4 ,t i,1 ] is the set of time periods when the electric vehicle is plugged in; ND indicates that it needs to be discharged, that is, the set of time periods when the electric vehicle is in a standby or discharging state; NC indicates that it needs to be charged, that is, the set of time periods when the electric vehicle is in a standby or discharging state; [t i,1 ,t i,2 ]∪[t i,3 ,t i,4 ] is the travel time set of electric vehicles; E i,0 is the battery capacity of the i-th electric car; β i ≤1 is the battery capacity ratio of the ith electric vehicle used for travel; SOC i,t is the state of charge of the i-th electric vehicle in period t; SOC0 is the initial state of charge of the electric vehicle; SOC max and SOC min SOC is the upper and lower limits of the state of charge of electric vehicles; acc is the limit of the state of charge of the electric vehicle allowed before traveling; Equations (6) and (7) are the discharge and charging power constraints of the electric vehicle during the plug-in period; Equation (8) is the power consumption of the electric vehicle during the travel period; Slack variables are introduced in Equation (9) to offset the travel stage The influence of the discharge depth is shown in Figure 1. The influence of the discharge depth is shown in Figure 1. The influence of the discharge depth is shown in Figure 1. The influence of the discharge depth is shown in Figure 1. The influence of the discharge depth is shown in Figure 1. The influence of the discharge depth is shown in Figure 1. The influence of the discharge depth is shown in Figure 1. The relevant constraints for demand response requirements are: Where P t target is the target power of demand response during period t; P t market The power provided to the electricity market during period t.

7. The distributed interactive method for electric vehicles to participate in demand response as claimed in claim 6, characterized in that: Decomposing the optimization interaction model into multiple sub-problems and constructing a distributed interaction model includes: The optimization interaction model of electric vehicles participating in demand response is updated as follows: In the formula, λ and γ are the first and second penalty coefficients respectively; The updated optimization interaction model is decomposed into multiple sub-problems to obtain a distributed interaction model of electric vehicles participating in demand response.

8. A system using the distributed interactive method for electric vehicles to participate in demand response as claimed in any one of claims 1 to 7, characterized in that: include: An analysis module for determining the daily driving and charging patterns of electric vehicles based on the dynamic usage of electric vehicles; The cost linearization module is used to establish the degradation cost model of electric vehicle batteries based on the daily driving and charging patterns of electric vehicles, and perform piecewise linearization processing; The optimization modeling module is used to build an optimization interaction model for electric vehicles to participate in demand response based on the degradation cost model of electric vehicle batteries, combined with the charging and discharging costs of electric vehicles and the benefits of participating in demand response; Distributed module, used to decompose the optimization interaction model into multiple sub-problems and build a distributed interaction model; The solution module is used to solve the distributed interaction model and obtain the interactive solution of electric vehicles participating in demand response.

9. A computing device comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the distributed interactive method for electric vehicles to participate in demand response as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the distributed interactive method for electric vehicles to participate in demand response as described in any one of claims 1 to 7.

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