Electric vehicle load demand response strategy based on precise excitation

Abstract

The invention relates to the technical field of electric vehicle load demand response, solves the technical problems that incentive distribution is not uniform and user response intentions are not obvious in the process that electric vehicle users participate in demand response, and particularly relates to an electric vehicle load demand response strategy based on precise incentive. An electric vehicle user load decision-making scheme and a load aggregator decision-making scheme are obtained by constructing a charging and discharging behavior evaluation model, a single user precise excitation model, a single user charging and discharging characteristic model, a demand response task constraint model and an optimized objective function model and constructing an electric vehicle power utilization load decision-making algorithm. According to the invention, through interaction between the electric vehicle load aggregator and the electric vehicle, the excitation size which should be given to the electric vehicle user by the load aggregator during demand response is negotiated, and the charging strategy can be adjusted according to the power grid load condition and the electricity price change, so that the power consumption cost is reduced and the load pressure in the peak period is reduced; and effective utilization of renewable energy sources is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle load demand response, and in particular to an electric vehicle load demand response strategy based on precise incentives. Background Art

[0002] Electric vehicles participating in demand response have significant advantages and far-reaching significance. There are several ways for electric vehicles to participate in demand response: First, take advantage of the real-time fluctuations in electricity prices, choose to charge during periods of low electricity prices, reduce charging costs, and reduce charging demand during peak electricity price periods; second, receive and respond to demand response signals sent by grid operators, adjust charging power or delay charging in real time to respond to changes in grid demand; third, when grid demand peaks, feed back the stored electrical energy in electric vehicle batteries to the grid to support grid load balancing.

[0003] In the existing technical solutions, the basic regulation method for electric vehicles is that load aggregators use fixed incentives to guide electric vehicle users to adjust charging power. However, this regulation method does not consider giving specific incentives for different response behaviors of electric vehicles, does not try to change the electric vehicle power when the electric vehicle leaves the charging station, and does not determine the incentive size through interactive negotiation between load aggregators and electric vehicles. As a result, electric vehicle users face uneven incentive distribution and low user response willingness during the process of participating in demand response. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides an electric vehicle load demand response strategy based on precise incentives, which solves the technical problems faced by electric vehicle users in the process of participating in demand response, such as uneven incentive distribution and low user response willingness.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an electric vehicle load demand response strategy based on precise incentives, setting load aggregators to obtain benefits by adjusting loads to absorb renewable energy, and then participating in power demand response. The electric vehicle load demand response strategy includes the following steps:

[0006] Construct a charging and discharging behavior evaluation model for load aggregators to analyze the charging and discharging behavior of individual electric vehicle users;

[0007] Obtain the parameters reported by electric vehicle single users based on the charging and discharging behavior evaluation model;

[0008] Based on the reported parameters, a single-user precise incentive model is constructed for load aggregators to consider the charging and discharging behavior of single electric vehicle users;

[0009] Construct a single user charging and discharging characteristic model for load aggregators to evaluate the upper and lower limits of charging and discharging power of each electric vehicle in each time period;

[0010] Construct a demand response task constraint model for load aggregators to absorb new energy by adjusting load during demand response period;

[0011] Construct an optimization objective function model based on the charging and discharging behavior evaluation model, the single user charging and discharging characteristic model, and the demand response task constraint model;

[0012] Construct an electric vehicle power load decision algorithm to obtain the electric vehicle user load decision plan and the load aggregator decision plan.

[0013] Furthermore, the charge and discharge behavior evaluation model includes:

[0014] There are three ways for electric vehicle users to adjust their electric vehicles to participate in demand response, namely:

[0015] Allow the expected power to be adjusted down to W i ET- In the first regulation mode, the electric vehicle's power level when leaving the charging station meets the following conditions:

[0016] W i,t =W i ET- ,t=T i ET

[0017] Where W i,t is the power of electric vehicle i in time period t; T i ET The estimated time for the i-th electric vehicle user to leave the charging station;

[0018] Adjust the expected power up to W i ET+ The second regulation method is that the power of the electric vehicle when leaving the charging station meets the following conditions:

[0019] W i,t =W i ET+ ,t=T i ET

[0020] In the first and second regulation modes, it is set that electric vehicles must reach a specified power threshold before participating in demand response. The power threshold meets the following conditions:

[0021] W i ST ≤W i RECH ≤W i ET- <W iET <W i ET+

[0022] Where W i ST W is the power of the electric vehicle when it arrives at the charging station; i RECH is the power threshold for electric vehicle i to participate in demand response; W i ET The expected charge level of an electric vehicle when it leaves the charging station.

[0023] Furthermore, the single user precise incentive model is:

[0024] α i =u 3,i (u 1,i α 1,i +u 2,i α 2,i +α 3,i )

[0025] in,

[0026]

[0027] In the formula, α i represents the unit incentive given by the load aggregator to electric vehicle i in time period t; α 1,i , α 2,i and α 3,i Both represent the excitation gain coefficient; u 1,i Indicates whether the electric vehicle adopts the first adjustment method, if yes, it is 1, otherwise it is 0; u 2,i Indicates whether to adopt the second adjustment method, if yes, it is 1, otherwise it is 0; u 3,i Indicates whether the electric vehicle user participates in demand response, the value is 1 if yes, otherwise 0;

[0028] The constraints of the single user precise incentive model are:

[0029]

[0030] In the formula, M is a maximum value, z 1,i and z 2,i is an introduced auxiliary variable.

[0031] Furthermore, the single user charging and discharging characteristic model is:

[0032]

[0033] Where W i ST W is the power of the electric vehicle when it arrives at the charging station;i ET is the expected power of the electric vehicle when it leaves the charging station; T i ST is the time when the i-th electric vehicle user arrives at the charging station under the jurisdiction of the load aggregator; T i ET The estimated time for electric vehicles to leave the charging station; is the charging power of electric vehicle i in time period t; P i CHMIN represents the slow charging power of electric vehicle i; Δt is the time interval for the load aggregator to dispatch electric vehicles; η CH Indicates the charging efficiency of the charging station.

[0034] Furthermore, the charging power P of the electric vehicle i in time period t is i, C t H for:

[0035]

[0036] In the formula, s i,t Indicates whether the electric vehicle is charging, 1 if charged, otherwise 0; v i,t It is a 0-1 variable, indicating whether the electric vehicle adopts the fast charging mode when charging. The fast charging mode is 1, otherwise it is 0; P i CHMAX represents the fast charging power of electric vehicle i;

[0037] Charging power The constraints are:

[0038]

[0039] In the formula, h i,t is the introduced auxiliary variable.

[0040] Furthermore, the demand response task constraint model is:

[0041]

[0042] Where P t CH P represents the total charging power of the electric vehicle group aggregated by the load aggregator in time period t; t GRID Indicates the power supply of the power grid company; P t REN The amount of renewable energy consumed by electric vehicles; P t RENMAX Maximize the output of renewable energy.

[0043] Furthermore, the optimization objective function model includes the optimization objective function of the load aggregator and the optimization objective function of the electric vehicle single user;

[0044] The optimization objective function of the load aggregator is:

[0045]

[0046] In the formula, C AGG is the revenue of the load aggregator participating in demand response during the period; REN P is the unit subsidy given to load aggregators for absorbing renewable energy during the electric vehicle demand response period; q is the price charged by electric vehicle load aggregators for charging electric vehicles; t REN The amount of renewable energy consumed by electric vehicles; α i represents the unit incentive given by the load aggregator to electric vehicle i in time period t; is the charging power of electric vehicle i in time period t;

[0047] After deducting the subsidy given by the load aggregator to the user, the optimization objective function of the electric vehicle user is:

[0048]

[0049] In the formula, C USER Represents the charging cost for electric vehicle users.

[0050] Furthermore, the construction of the electric vehicle power load decision algorithm specifically includes the following steps:

[0051] S71, load aggregator based on demand response task constraint model P t REN ≤P t RENMAX , randomly initialize the decision variable P t REN , and substitute it into the demand response task constraint model and optimization objective function model;

[0052] S72. Electric vehicle users use the single user precise incentive model, the single user charging and discharging characteristic model, and the demand response task constraint model as constraint conditions, and the optimization objective function model as the optimization objective to optimize the optimization objective function C of electric vehicle users. USER , calculate the decision variable u 3,i 、u 2,i 、u 1,i ;

[0053] S73, electric vehicle users will make the decision variable u 3,i 、u 2,i 、u1,i Send it to the load aggregator, and the load aggregator will send P t REN Set as variables, take the single user precise incentive model, single user charging and discharging characteristic model and demand response task constraint model as constraints, and optimize the objective function model as the optimization target to optimize the load aggregator's optimization objective function C AGG , calculate the optimal P t ' REN ;

[0054] S74. Load aggregator sets convergence threshold ΔP t REN , judge P t REN and the optimal P t ' REN Is the difference less than the specified threshold ΔP t REN , if it is less than, go to step S75, otherwise return to step S71;

[0055] S75. Generate electric vehicle user load decision plan u 3,i 、u 2,i 、u 1,i , and the load aggregator decision scheme P t REN .

[0056] By means of the above technical solution, the present invention provides an electric vehicle load demand response strategy based on precise incentives, which has at least the following beneficial effects:

[0057] 1. The present invention considers the interactive negotiation between load aggregators and electric vehicles to determine the size of incentives, so that electric vehicle load aggregators can use flexible and changeable incentives to accurately guide electric vehicle users to adjust charging power, thereby solving the problem of uneven incentive distribution and lack of user response willingness faced by electric vehicle users in the process of participating in demand response.

[0058] 2. The present invention negotiates the incentives that load aggregators should give to electric vehicle users during demand response through the interaction between electric vehicle load aggregators and electric vehicles. Electric vehicle users change their response behaviors to obtain different degrees of incentives based on the negotiation situation, and can adjust their charging strategies based on grid load conditions and changes in electricity prices, thereby reducing electricity costs and load pressure during peak periods, and promoting the effective use of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0060] Figure 1 The figure is a flow chart of the electric vehicle load demand response strategy of the present invention. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods, so that the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0062] The participation of electric vehicles in demand response has significant advantages and far-reaching significance. First, through intelligent charging and on-board energy storage feedback, electric vehicles can feed back the stored electricity to the grid when the grid load is peak, helping to balance the power supply and improve the stability of the grid. Secondly, dynamic pricing mechanisms and demand response signals enable electric vehicle users to adjust their charging strategies according to grid load conditions and changes in electricity prices, thereby reducing electricity costs and reducing load pressure during peak periods. In addition, the participation of electric vehicles also promotes the effective use of renewable energy, promotes the development of green energy, and supports the sustainable development of the energy system. In general, the demand response capability of electric vehicles not only helps to improve the intelligent management of the power grid, but also contributes to environmental protection and energy conservation. At present, data centers are basically built in industrial parks, which are equipped with a large number of integrated energy facilities and distributed renewable energy.

[0063] Regarding the current problem of uneven incentive distribution and low willingness of electric vehicle users in the process of demand response, please refer to Figure 1 This embodiment proposes an electric vehicle load demand response strategy based on precise incentives. It is set that the load aggregator obtains benefits by adjusting the load to absorb renewable energy and then participates in the power demand response. The demand response start time is T DRS , the deadline is T DRE The electric vehicle load demand response strategy includes the following steps:

[0064] S1. Construct a charging and discharging behavior evaluation model for load aggregators to analyze the charging and discharging behavior of electric vehicle single users. In this embodiment, the load aggregator plans to charge users during the demand response period, where the number of users expected to connect is I, and the time for the i-th electric vehicle user to arrive at the charging station under the jurisdiction of the load aggregator is T i ST , the estimated time to leave the charging station is T i ET , the electric car reaches the charging station with a charge of W i ST , the expected power of the electric vehicle when leaving the charging station is W i ET, the original charging mode of electric vehicles is slow charging.

[0065] Load aggregators analyze electric vehicle users, who have three ways to adjust electric vehicles to participate in demand response. The first is to allow the expected power to be adjusted down to W i ET- The second is to increase the expected power to W i ET+ .

[0066] In the first and second methods, electric vehicles are required to reach a specified power threshold before participating in demand response. The power threshold meets the following conditions:

[0067] W i ST ≤W i RECH ≤W i ET- <W i ET <W i ET+

[0068] Where W i RECH is the power threshold for electric vehicle i to participate in demand response. i,t is the power of electric vehicle i in time period t.

[0069] In the first regulation mode, the power of the electric vehicle when leaving the charging station meets the following conditions:

[0070] W i,t =W i ET- ,t=T i ET

[0071] In the second regulation mode, the power of the electric vehicle when leaving the charging station meets the following conditions:

[0072] W i,t =W i ET+ ,t=T i ET

[0073] S2. Obtain the parameters reported by the electric vehicle single user based on the charging and discharging behavior evaluation model. The reported parameters include T i ET , T i ST , W i RECH , W i,t , W i ET-, W i ET+ 、I.

[0074] S3. Constructing a precise incentive model for individual electric vehicle users based on the reported parameters for the load aggregator to consider the charging and discharging behaviors of individual electric vehicle users; the load aggregator in this embodiment constructs a precise incentive model for individual electric vehicle users based on the parameters reported by the individual electric vehicle users in step S2.

[0075] Specifically, the incentives that electric vehicle i receives from participating in demand response depend on the expected power adjustment when leaving the charging station (W i ET- and W i ET+ ), the power threshold W for participating in demand response i RECH .

[0076]

[0077] In the formula, α 1,i , α 2,i and α 3,i Represents the excitation gain coefficient.

[0078] According to the gain coefficient of electric vehicle users, load aggregators give incentives to electric vehicles based on user response. The precise incentive model for single users is:

[0079] α i =u 3,i (u 1,i α 1,i +u 2,i α 2,i +α 3,i )

[0080] In the formula, α i u represents the unit incentive given by the load aggregator to electric vehicle i in time period t. 1,i Indicates whether the electric vehicle adopts the first adjustment method, if yes, it is 1, otherwise it is 0. 2,i Indicates whether to adopt the second adjustment method, if yes, it is 1, otherwise it is 0. 3,i Indicates whether the electric vehicle user participates in demand response. The value is 1 if participating, and 0 otherwise.

[0081] Single user precise incentive model α i =u3(u1α 1,i +u2α 2,i +α 3,i ) can be transformed into the following constraints, namely:

[0082]

[0083] In the formula, M is a maximum value, z 1,i and z 2,i is an introduced auxiliary variable.

[0084] S4. Construct a single-user charging and discharging characteristic model for load aggregators to evaluate the upper and lower limits of charging and discharging power of each electric vehicle in each time period; load aggregators need to evaluate the upper and lower limits of charging and discharging power of each electric vehicle in each time period.

[0085] The power model and charging power of electric vehicles at each time period t meet the following constraints:

[0086]

[0087] The above formula can be converted into:

[0088]

[0089] In the formula, is the charging power of electric vehicle i in time period t.

[0090]

[0091] Where η CH represents the charging efficiency of the charging station, s i,t Indicates whether the electric vehicle is charging, 1 if charged, 0 otherwise. i,t It is a 0-1 variable, indicating whether the electric vehicle is charged in fast charging mode. Fast charging is 1, otherwise it is 0. i CHMAX represents the fast charging power of electric vehicle i, P i CHMIN represents the slow charging power of electric vehicle i. Δt is the time interval for the load aggregator to dispatch electric vehicles.

[0092] Charging power of electric vehicle i in time period t It can be transformed into the following constraints:

[0093]

[0094] Where h i,t is the introduced auxiliary variable.

[0095] The discharge power model of the aggregated electric vehicle group is:

[0096]

[0097] Where P t CH It represents the total charging power of the electric vehicle group aggregated by the load aggregator in time period t.

[0098] S5. Construct a demand response task constraint model for load aggregators to absorb new energy by adjusting load during demand response. The purpose of load aggregator charging and discharging power adjustment is to achieve overall load adjustment during demand response and absorb new energy. The constraints are:

[0099]

[0100] Where P t GRID Indicates the power supply of the power grid company. t REN is the amount of renewable energy consumed by electric vehicles. t RENMAX Maximize the output of renewable energy.

[0101] S6. Based on the charging and discharging behavior evaluation model, the charging and discharging characteristic model of the single user, and the demand response task constraint model, the optimization objective function of the load aggregator and the single electric vehicle user is constructed; the optimization objective function of the load aggregator is:

[0102]

[0103] In the formula, C AGG is the revenue of the load aggregator participating in demand response during the period; REN P is the unit subsidy given to load aggregators for absorbing renewable energy during the electric vehicle demand response period; q is the price charged by electric vehicle load aggregators for charging electric vehicles; t REN The amount of renewable energy consumed by electric vehicles; α i represents the unit incentive given by the load aggregator to electric vehicle i in time period t; is the charging power of electric vehicle i in time period t.

[0104] After deducting the subsidy given by the load aggregator to the user, the optimization objective function of the electric vehicle user is:

[0105]

[0106] In the formula, C USER Represents the charging cost for electric vehicle users.

[0107] S7. Construct an electric vehicle power load decision algorithm to obtain the electric vehicle user load decision solution u 3,i 、u 2,i 、u 1,i And the load aggregator's decision plan P t REN The decision variable of the electric vehicle load aggregator is the amount of renewable energy consumed by electric vehicles P tREN , the decision variable of the electric vehicle user group is u 3,i 、u 2,i 、u 1,i , the algorithm implementation process is:

[0108] S71, load aggregator based on demand response task constraint model P t REN ≤P t RENMAX , randomly initialize the decision variable P t REN , and substitute it into the demand response task constraint model and optimization objective function model;

[0109] S72. Electric vehicle users use the single user precise incentive model, the single user charging and discharging characteristic model, and the demand response task constraint model as constraint conditions, and the optimization objective function model as the optimization objective to optimize the optimization objective function C of electric vehicle users. USER , calculate the decision variable u 3,i 、u 2,i 、u 1,i In this embodiment, the optimization algorithm used here is mixed integer nonlinear programming, heuristic algorithm, etc. The decision variables can be directly obtained by using the optimization algorithm according to the constraint conditions and the optimization target. As a technical means that can be implemented by those skilled in the art using known optimization algorithms, it will not be described in detail again.

[0110] S73, electric vehicle users will make the decision variable u 3,i 、u 2,i 、u 1,i Send it to the load aggregator, and the load aggregator will send P t REN Set as variables, take the single user precise incentive model, single user charging and discharging characteristic model and demand response task constraint model as constraints, and optimize the objective function model as the optimization target to optimize the load aggregator's optimization objective function C AGG , calculate the optimal P t ' REN The optimization algorithm used here is mixed integer nonlinear programming, heuristic algorithm, etc. The decision variables can be directly obtained by using the optimization algorithm according to the constraints and the optimization target. As a technical means that can be implemented by those skilled in the art using known optimization algorithms, it will not be described in detail again.

[0111] S74. Load aggregator sets convergence threshold ΔP t REN , judge P t REN and the optimal P t ' RENIs the difference less than the specified threshold ΔP t REN , if it is less than, go to step S75, otherwise return to step S71;

[0112] S75. Generate electric vehicle user load decision plan u 3,i 、u 2,i 、u 1,i , and the load aggregator decision scheme P t REN , and generate the P t GRID .

[0113] In this embodiment, u 1,i Indicates whether the electric vehicle adopts the first adjustment method, if yes, it is 1, otherwise it is 0; u 2,i Indicates whether to adopt the second adjustment method, if yes, it is 1, otherwise it is 0; u 3,i Indicates whether the electric vehicle user participates in demand response, the value is 1 if yes, otherwise 0; is the charging power of electric vehicle i in time period t; P t GRID Indicates the power supply of the power grid company; P t REN is the amount of renewable energy consumed by electric vehicles. Threshold ΔP t REN The value of is determined by the load aggregator and is not limited here.

[0114] The present invention considers the interactive negotiation between load aggregators and electric vehicles to determine the size of incentives, so that electric vehicle load aggregators can use flexible and changeable incentives to accurately guide electric vehicle users to adjust charging power, give specific incentives considering different response behaviors of electric vehicles, and try to change the electric vehicle power when the electric vehicle leaves the charging station. This solves the problem of uneven incentive distribution and low user response willingness faced by electric vehicle users in the process of participating in demand response.

[0115] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, so the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0116] The above implementation methods have been described in detail. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An electric vehicle load demand response strategy based on precise incentives, characterized in that: The load aggregator is set to obtain benefits by participating in the power demand response by adjusting the load to absorb renewable energy. The electric vehicle load demand response strategy includes the following steps: Construct a charging and discharging behavior evaluation model for load aggregators to analyze the charging and discharging behavior of individual electric vehicle users; Obtain the parameters reported by electric vehicle single users based on the charging and discharging behavior evaluation model; Based on the reported parameters, a single-user precise incentive model is constructed for load aggregators to consider the charging and discharging behavior of single electric vehicle users; Construct a single user charging and discharging characteristic model for load aggregators to evaluate the upper and lower limits of charging and discharging power of each electric vehicle in each time period; Construct a demand response task constraint model for load aggregators to absorb new energy by adjusting load during demand response period; Construct an optimization objective function model based on the charging and discharging behavior evaluation model, the single user charging and discharging characteristic model, and the demand response task constraint model; Construct an electric vehicle power load decision algorithm to obtain the electric vehicle user load decision plan and the load aggregator decision plan.

2. The electric vehicle load demand response strategy according to claim 1, characterized in that: The charge and discharge behavior evaluation model includes: There are three ways for electric vehicle users to adjust their electric vehicles to participate in demand response, namely: Allow the expected power to be adjusted down to W i ET- In the first regulation mode, the electric vehicle's power level when leaving the charging station meets the following conditions: W i,t =W i ET- ,t=T i ET Where W i,t is the power of electric vehicle i in time period t; T i ET The estimated time for the i-th electric vehicle user to leave the charging station; Adjust the expected power up to W i ET+ The second regulation method is that the power of the electric vehicle when leaving the charging station meets the following conditions: W i,t =W i ET+ ,t=T i ET In the first and second regulation modes, it is set that electric vehicles must reach a specified power threshold before participating in demand response. The power threshold meets the following conditions: IN i ST ≤In i RECH ≤In i ET- <In i ET <In i ET+ Where W i ST W is the power of the electric vehicle when it arrives at the charging station; i RECH is the power threshold for electric vehicle i to participate in demand response; W i ET The expected charge level of an electric vehicle when it leaves the charging station.

3. The electric vehicle load demand response strategy according to claim 2, characterized in that: The single user precise incentive model is: a i =u 3,i (u 1,i a 1,i +u 2,i a 2,i +a 3,i ) in, In the formula, α i represents the unit incentive given by the load aggregator to electric vehicle i in time period t; α 1,i , α 2,i and α 3,i Both represent the excitation gain coefficient; u 1,i Indicates whether the electric vehicle adopts the first adjustment method, if yes, it is 1, otherwise it is 0; u 2,i Indicates whether to adopt the second adjustment method, if yes, it is 1, otherwise it is 0; u 3,i Indicates whether the electric vehicle user participates in demand response, the value is 1 if yes, otherwise 0; The constraints of the single user precise incentive model are: In the formula, M is a maximum value, z 1,i and z 2,i is an introduced auxiliary variable.

4. The electric vehicle load demand response strategy according to claim 3, characterized in that: The single user charging and discharging characteristic model is: Where W i ST W is the power of the electric vehicle when it arrives at the charging station; i ET is the expected power of the electric vehicle when it leaves the charging station; T i ST is the time when the i-th electric vehicle user arrives at the charging station under the jurisdiction of the load aggregator; T i ET The estimated time for electric vehicles to leave the charging station; is the charging power of electric vehicle i in time period t; P i CHMIN represents the slow charging power of electric vehicle i; Δt is the time interval for the load aggregator to dispatch electric vehicles; η CH Indicates the charging efficiency of the charging station.

5. The electric vehicle load demand response strategy according to claim 4, characterized in that: The charging power of the electric vehicle i in time period t for: In the formula, s i,t Indicates whether the electric vehicle is charging, 1 if charged, otherwise 0; v i,t It is a 0-1 variable, indicating whether the electric vehicle adopts the fast charging mode when charging. The fast charging mode is 1, otherwise it is 0; P i CHMAX represents the fast charging power of electric vehicle i; Charging power The constraints are: In the formula, h i,t is the introduced auxiliary variable.

6. The electric vehicle load demand response strategy according to claim 1, characterized in that: The demand response task constraint model is: Where P t CH P represents the total charging power of the electric vehicle group aggregated by the load aggregator in time period t; t GRID Indicates the power supply of the power grid company; P t REN The amount of renewable energy consumed by electric vehicles; P t RENMAX Maximize the output of renewable energy.

7. The electric vehicle load demand response strategy according to claim 3, characterized in that: The optimization objective function model includes the optimization objective function of the load aggregator and the optimization objective function of the electric vehicle single user; The optimization objective function of the load aggregator is: In the formula, C AGG is the revenue of the load aggregator participating in demand response during the period; REN P is the unit subsidy given to load aggregators for absorbing renewable energy during the electric vehicle demand response period; q is the price charged by electric vehicle load aggregators for charging electric vehicles; t REN The amount of renewable energy consumed by electric vehicles; α i represents the unit incentive given by the load aggregator to electric vehicle i in time period t; is the charging power of electric vehicle i in time period t; After deducting the subsidy given by the load aggregator to the user, the optimization objective function of the electric vehicle user is: In the formula, C USER Represents the charging cost for electric vehicle users.

8. The electric vehicle load demand response strategy according to claim 1, characterized in that: The specific process of constructing the electric vehicle power load decision algorithm includes the following steps: S71, load aggregator based on demand response task constraint model P t REN ≤P t RENMAX , randomly initialize the decision variable P t REN , and substitute it into the demand response task constraint model and optimization objective function model; S72. Electric vehicle users use the single user precise incentive model, the single user charging and discharging characteristic model, and the demand response task constraint model as constraint conditions, and the optimization objective function model as the optimization objective to optimize the optimization objective function C of electric vehicle users. USER , calculate the decision variable u 3,i 、u 2,i 、u 1,i ; S73, electric vehicle users will make the decision variable u 3,i 、u 2,i 、u 1,i Send it to the load aggregator, and the load aggregator will send P t REN Set as variables, take the single user precise incentive model, single user charging and discharging characteristic model and demand response task constraint model as constraints, and optimize the objective function model as the optimization target to optimize the load aggregator's optimization objective function C AGG , calculate the optimal P t ' REN ; S74. Load aggregator sets convergence threshold ΔP t REN , judge P t REN and the optimal P t ' REN Is the difference less than the specified threshold ΔP t REN , if it is less than, go to step S75, otherwise return to step S71; S75. Generate electric vehicle user load decision plan u 3,i 、u 2,i 、u 1,i , and the load aggregator decision scheme P t REN .

9. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, can implement the electric vehicle load demand response strategy described in any one of claims 1 to 8.

10. A computer device, characterized in that: The invention comprises a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the electric vehicle load demand response strategy according to any one of claims 1 to 8 is implemented.

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