A precise excitation-based electric vehicle load demand response method

Abstract

The present application relates to the technical field of electric vehicle load demand response, solves the technical problem that electric vehicle users face uneven incentive distribution and insignificant user response willingness in the process of participating in demand response, and particularly relates to an electric vehicle load demand response strategy based on precise incentive, which builds a charging and discharging behavior evaluation model, a single user precise incentive model, a single user charging and discharging characteristic model, a demand response task constraint model and an optimization objective function model, and builds an electric vehicle power load decision algorithm to obtain an electric vehicle user load decision scheme and a load aggregator decision scheme. Through the interaction between the electric vehicle load aggregator and the electric vehicle, the size of the incentive that the load aggregator should give to the electric vehicle user during demand response is negotiated, the charging strategy can be adjusted according to the power grid load condition and the electricity price change, thereby reducing the electricity cost and reducing the load pressure in the peak period, and promoting the effective utilization of renewable energy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle load demand response, and particularly relates to an electric vehicle load demand response method based on precise incentive. BACKGROUND

[0002] Electric vehicles participating in demand response has significant advantages and far-reaching significance. The main ways of electric vehicles participating in demand response are as follows: one is to use the real-time fluctuation of electricity price, to choose to charge at a lower electricity price period, to reduce charging cost, and to reduce charging demand at a peak electricity price period; two is to receive and respond to the demand response signal sent by the power grid operator, to adjust the charging power or delay charging in real time, so as to respond to the change of power grid demand; three is to feed back the stored electric energy in the electric vehicle battery to the power grid at the peak demand of the power grid, to support the load balance of the power grid.

[0003] In the prior art, the basic adjustment mode of electric vehicles is that the load aggregator uses fixed incentive to guide electric vehicle users to adjust the charging power, however, this adjustment mode does not consider giving specific incentive to different response behaviors of electric vehicles, does not try to change the electric quantity of the electric vehicle when the electric vehicle leaves the charging station, and does not determine the incentive size through the interactive agreement between the load aggregator and the electric vehicle. Thus, the electric vehicle users face the problem of uneven distribution of incentives in the process of participating in demand response, and the user response willingness is not significant. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides an electric vehicle load demand response method based on precise incentive, which solves the technical problem that electric vehicle users face uneven distribution of incentives in the process of participating in demand response, and the user response willingness is not significant.

[0005] To solve the above technical problems, the present application provides the following technical scheme: an electric vehicle load demand response method based on precise incentive, setting that the load aggregator participates in power demand response by adjusting load consumption of renewable energy to obtain income, the electric vehicle load demand response method comprising the following steps:

[0006] A charging and discharging behavior evaluation model for the load aggregator to analyze the charging and discharging behavior of individual electric vehicle users is constructed;

[0007] Parameters reported by individual electric vehicle users according to the charging and discharging behavior evaluation model are obtained;

[0008] An individual user precise incentive model for the load aggregator to consider the charging and discharging behavior of individual electric vehicle users is constructed according to the reported parameters;

[0009] An individual user charging and discharging characteristic model for the load aggregator to evaluate the upper and lower limits of the charging and discharging electric quantity of each electric vehicle at each period is constructed.

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

[0011] An optimization objective function model is constructed based on the charging and discharging behavior evaluation model, the individual user charging and discharging characteristic model, and the demand response task constraint model.

[0012] An electric vehicle power load decision algorithm is constructed to obtain electric vehicle user load decision schemes and load aggregation quotient decision schemes.

[0013] Furthermore, the charging and discharging behavior evaluation model includes:

[0014] Individual electric vehicle users have three ways to adjust their electric vehicle participation in demand response:

[0015] Allow the desired power level to be lowered to The first adjustment method requires the electric vehicle to meet the following conditions when leaving the charging station:

[0016]

[0017] In the formula, For electric vehicles During the period The amount of electricity; For the first The estimated time for each electric vehicle user to leave the charging station;

[0018] Increase the expected power consumption to The second adjustment method requires that the electric vehicle's charge level meets the following conditions when leaving the charging station:

[0019]

[0020] Furthermore, under both the first and second regulation methods, electric vehicles are only considered for participation in demand response if they reach a specified energy threshold. This energy threshold must meet the following conditions:

[0021]

[0022] In the formula, The battery level of the electric vehicle when it arrives at the charging station; For electric vehicles The power threshold for user-involved demand response; This refers to the expected charge level of an electric vehicle when it leaves a charging station.

[0023] Furthermore, the precise incentive model for individual users is as follows:

[0024]

[0025] in,

[0026]

[0027]

[0028]

[0029] In the formula, This indicates that the load aggregator supplies electric vehicles During the period Unit incentives; , and Both represent excitation gain coefficients; This indicates whether the electric vehicle adopts the first adjustment method; 1 indicates adoption, and 0 indicates otherwise. This indicates whether the second adjustment method should be adopted; 1 indicates adoption, and 0 indicates otherwise. This indicates whether electric vehicle users participate in demand response; a value of 1 indicates participation, and 0 indicates otherwise.

[0030] The constraints of the individual user precision incentive model are:

[0031]

[0032] In the formula, It is a maximum value. and These are the auxiliary variables introduced.

[0033] Furthermore, the individual user charge / discharge characteristic model is as follows:

[0034]

[0035] In the formula, The battery level of the electric vehicle when it arrives at the charging station; The expected charge level when the electric vehicle leaves the charging station; For the first The time it takes for an electric vehicle user to arrive at a charging station managed by a load aggregator; The estimated time for electric vehicles to leave the charging station; For electric vehicles During the period The charging power; Indicates electric vehicles slow charging power; The time interval for load aggregators to schedule electric vehicles; This indicates the charging efficiency of the charging station.

[0036] Furthermore, the electric vehicle During the period charging power for:

[0037]

[0038] In the formula, This indicates whether the electric vehicle is charging; 1 indicates charging, and 0 indicates otherwise. This is a 0-1 variable, indicating whether the electric vehicle uses fast charging mode; 1 indicates fast charging, and 0 indicates otherwise. Indicates electric vehicles Fast charging power;

[0039] Charging power The constraints are:

[0040]

[0041] In the formula, These are the auxiliary variables introduced.

[0042] Furthermore, the demand response task constraint model is as follows:

[0043]

[0044] In the formula, This indicates the electric vehicle fleet aggregated by the load aggregator during the time period. The total charging power; Indicates the power supply capacity of the power grid company; The amount of renewable energy consumed by electric vehicles; To maximize the contribution of renewable energy.

[0045] Furthermore, the optimization objective function model includes the optimization objective function of the load aggregator and the optimization objective function of the individual electric vehicle user;

[0046] The objective function for optimizing the load aggregator is:

[0047]

[0048] In the formula, The revenue of load aggregators participating in demand response during a given period; Unit subsidies will be provided to load aggregators to absorb renewable energy from the grid during the electric vehicle demand response period; The price charged by electric vehicle load aggregators for charging electric vehicles; The amount of renewable energy consumed by electric vehicles; This indicates that the load aggregator supplies electric vehicles During the period Unit incentives; For electric vehicles During the period The charging power;

[0049] After deducting the subsidies provided by the load aggregator to users, the objective function for electric vehicle users is:

[0050]

[0051] In the formula, This indicates the charging costs for electric vehicle users.

[0052] Furthermore, the specific process of constructing the electric vehicle electricity load decision algorithm includes the following steps:

[0053] S71, The load aggregator, based on the demand response task constraint model... Randomly initialize decision variables And substitute them into the demand response task constraint model and the optimization objective function model;

[0054] S72. Electric vehicle users take the individual user precise incentive model, the individual user charging and discharging characteristic model, and the demand response task constraint model as constraints, and the optimization objective function model as the optimization objective, to optimize the electric vehicle user's optimization objective function. Calculate the decision variables , , ;

[0055] S73, Electric vehicle users will make decision variables , , Send it to the load aggregator, and the load aggregator will... Let these be variables, and let the individual user precise incentive model, individual user charging and discharging characteristic model, and demand response task constraint model be used as constraints. Let the optimization objective function model be used as the optimization objective function to optimize the load aggregator. Calculate the optimal ;

[0056] S74, Load aggregator sets convergence threshold ,judge and optimal Is the difference less than the specified threshold? If the value is less than 0, proceed to step S75; otherwise, return to step S71.

[0057] S75, Generate Electric Vehicle User Load Decision Scheme , , and load aggregator decision-making scheme .

[0058] By employing the above technical solution, the present invention provides a method for load demand response of electric vehicles based on precise incentives, which has at least the following beneficial effects:

[0059] 1. This invention considers the method of determining the incentive size through interaction and negotiation between load aggregators and electric vehicles, so that electric vehicle load aggregators can use flexible and varied incentives to accurately guide electric vehicle users to adjust the charging power, thereby solving the problems of uneven incentive distribution and insignificant user response willingness faced by electric vehicle users in the process of participating in demand response.

[0060] 2. This invention facilitates the interaction between electric vehicle load aggregators and electric vehicles to negotiate the level of incentives that load aggregators should provide to electric vehicle users when responding to demand. Electric vehicle users can change their response behavior to obtain different levels of incentives based on the negotiation. This allows them to adjust their charging strategies according to grid load conditions and electricity price changes, thereby reducing electricity costs and reducing peak-hour load pressure, and promoting the effective utilization of renewable energy. Attached Figure Description

[0061] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0062] Figure 1 This is a flowchart of the electric vehicle load demand response method of the present invention. Detailed Implementation

[0063] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This will allow for a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects, and to facilitate its implementation.

[0064] Electric vehicles (EVs) have significant advantages and far-reaching implications for demand response. First, through smart charging and onboard energy storage, EVs can feed stored energy back into the grid during peak load periods, helping to balance power supply and improve grid stability. Second, dynamic pricing mechanisms and demand response signals enable EV users to adjust their charging strategies based on grid load and electricity price changes, thereby reducing electricity costs and alleviating peak load pressure. Furthermore, EV participation promotes the effective use of renewable energy, drives the development of green energy, and supports the sustainable development of the energy system. In summary, the demand response capabilities of EVs not only contribute to the intelligent management of the grid but also to environmental protection and energy conservation. Currently, data centers are primarily located in industrial parks, which are equipped with numerous integrated energy facilities and distributed renewable energy sources.

[0065] To address the current issues of uneven incentive distribution and low user willingness to respond in the electric vehicle user demand response process, please refer to... Figure 1 This embodiment proposes a method for electric vehicle load demand response based on precise incentives. It sets a load aggregator to participate in electricity demand response and generate revenue by adjusting load to absorb renewable energy. The demand response start time is... The deadline is The electric vehicle load demand response method includes the following steps:

[0066] S1. Construct a charging and discharging behavior evaluation model for load aggregators to analyze the charging and discharging behavior of individual electric vehicle users. In this embodiment, the load aggregator plans to charge users during demand response periods, where the expected number of users is [number missing]. , No. The time it takes for an electric vehicle user to arrive at a charging station managed by the load aggregator is The estimated time to leave the charging station is The electric vehicle's charge level when it arrives at the charging station is The expected charge level of an electric vehicle when leaving a charging station is Electric vehicles are originally charged using slow charging.

[0067] Load aggregators analyze electric vehicle (EV) users, who have two ways to adjust EV participation in demand response. The first is to allow the desired charge level to be reduced to [a lower limit]. The second option is to increase the desired battery capacity. .

[0068] In both the first and second methods, electric vehicles are only considered for demand response if they reach a specified energy threshold. This energy threshold must meet the following conditions:

[0069]

[0070] In the formula For electric vehicles The power threshold for user participation in demand response. For electric vehicles During the period The amount of electricity.

[0071] Under the first adjustment method, the electric vehicle's charge level when leaving the charging station meets the following conditions:

[0072]

[0073] Under the second adjustment method, the electric vehicle's charge level when leaving the charging station meets the following conditions:

[0074]

[0075] S2. Obtain the parameters reported by individual electric vehicle users based on the charging and discharging behavior evaluation model. The reported parameters include... , , , , , , .

[0076] S3. Based on the reported parameters, construct a precise incentive model for individual electric vehicle users to consider the charging and discharging behavior of individual electric vehicle users. In this embodiment, the load aggregator constructs a precise incentive model for individual electric vehicle users based on the parameters reported by individual electric vehicle users in step S2.

[0077] Specifically, electric vehicles The incentives for participating in demand response depend on the expected charge adjustment when leaving the charging station. and ), the power threshold for participating in demand response .

[0078]

[0079]

[0080]

[0081] In the formula, , and This represents the excitation gain coefficient.

[0082] Based on the gain coefficient of electric vehicle users, the load aggregator provides incentives to electric vehicles in conjunction with user response. The precise incentive model for individual users is as follows:

[0083]

[0084] In the formula, This indicates that the load aggregator supplies electric vehicles During the period Unit incentives. This indicates whether the electric vehicle adopts the first adjustment method; 1 indicates adoption, and 0 indicates otherwise. This indicates whether the second adjustment method should be adopted; 1 indicates adoption, and 0 indicates otherwise. This indicates whether electric vehicle users participate in demand response; a value of 1 indicates participation, and 0 indicates otherwise.

[0085] Individual User Precision Incentive Model This can be transformed into the following constraint conditions, namely:

[0086]

[0087] In the formula, It is a maximum value. and These are the auxiliary variables introduced.

[0088] S4. Construct a single-user charging and discharging characteristic model for load aggregators to evaluate the upper and lower limits of the charging and discharging capacity of each electric vehicle in each time period; load aggregators need to evaluate the upper and lower limits of the charging and discharging capacity of each electric vehicle in each time period.

[0089] Electric vehicles at different times The power model and charging power satisfy the following constraints:

[0090]

[0091] The above formula can be converted into:

[0092]

[0093] In the formula, For electric vehicles During the period The charging power.

[0094]

[0095] In the formula, This indicates the charging efficiency of the charging station. This indicates whether the electric vehicle is charging; 1 indicates charging, and 0 indicates otherwise. This is a 0-1 variable, indicating whether the electric vehicle uses fast charging mode; 1 indicates fast charging, and 0 indicates otherwise. Indicates electric vehicles Fast charging power, Indicates electric vehicles The slow charging power. The time interval for scheduling electric vehicles for load aggregators.

[0096] electric vehicles During the period charging power This can be transformed into the following constraints:

[0097]

[0098] In the formula These are the auxiliary variables introduced.

[0099] The discharge power model of the aggregated electric vehicle group is as follows:

[0100]

[0101] In the formula, This indicates the electric vehicle fleet aggregated by the load aggregator during the time period. The total charging power.

[0102] S5. Construct a demand response task constraint model for load aggregators to absorb renewable energy by adjusting load during demand response; the purpose of load aggregators' charging and discharging power adjustment is to achieve overall load adjustment during demand response and thus absorb renewable energy. The constraints are as follows:

[0103]

[0104] In the formula, This indicates the power supply capacity of the power grid company. The amount of renewable energy consumed by electric vehicles. To maximize the contribution of renewable energy.

[0105] S6. Based on the charging and discharging behavior evaluation model, the individual user charging and discharging characteristic model, and the demand response task constraint model, construct the optimization objective functions for the load aggregator and the individual electric vehicle user; the optimization objective function for the load aggregator is:

[0106]

[0107] In the formula, The revenue of load aggregators participating in demand response during a given period; Unit subsidies will be provided to load aggregators to absorb renewable energy from the grid during the electric vehicle demand response period; The price charged by electric vehicle load aggregators for charging electric vehicles; The amount of renewable energy consumed by electric vehicles; This indicates that the load aggregator supplies electric vehicles During the period Unit incentives; For electric vehicles During the period The charging power.

[0108] After deducting the subsidies provided by the load aggregator to users, the objective function for electric vehicle users is:

[0109]

[0110] In the formula, This indicates the charging costs for electric vehicle users.

[0111] S7. Construct an electric vehicle electricity load decision algorithm to obtain electric vehicle user load decision schemes. , , and load aggregation decision-making solutions The decision variable for electric vehicle load aggregators is the amount of renewable energy consumed by electric vehicles. The decision variables for electric vehicle user groups are , , The algorithm implementation process is as follows:

[0112] S71, The load aggregator, based on the demand response task constraint model... Randomly initialize decision variables And substitute them into the demand response task constraint model and the optimization objective function model;

[0113] S72. Electric vehicle users take the individual user precise incentive model, the individual user charging and discharging characteristic model, and the demand response task constraint model as constraints, and the optimization objective function model as the optimization objective, to optimize the electric vehicle user's optimization objective function. Calculate the decision variables , , In this embodiment, the optimization algorithm used is a mixture of integer nonlinear programming and heuristic algorithms. The decision variables can be directly obtained using the optimization algorithm based on the constraints and optimization objectives. This is a technical means that can be implemented by those skilled in the art using known optimization algorithms, and will not be described in detail again.

[0114] S73, Electric vehicle users will make decision variables , , Send it to the load aggregator, and the load aggregator will... Let these be variables, and let the individual user precise incentive model, individual user charging and discharging characteristic model, and demand response task constraint model be used as constraints. Let the optimization objective function model be used as the optimization objective function to optimize the load aggregator. Calculate the optimal The optimization algorithms used here include mixed-integer nonlinear programming and heuristic algorithms. The decision variables can be directly obtained from the constraints and optimization objectives using these algorithms. This is a technical means that can be implemented by those skilled in the art using known optimization algorithms, and will not be elaborated further.

[0115] S74, Load aggregator sets convergence threshold ,judge and optimal Is the difference less than the specified threshold? If the value is less than 0, proceed to step S75; otherwise, return to step S71.

[0116] S75, Generate Electric Vehicle User Load Decision Scheme , , and load aggregator decision-making scheme Simultaneously generate steps S72 and S73 , .

[0117] In this embodiment, This indicates whether the electric vehicle adopts the first adjustment method; 1 indicates adoption, and 0 indicates otherwise. This indicates whether the second adjustment method should be adopted; 1 indicates adoption, and 0 indicates otherwise. This indicates whether electric vehicle users participate in demand response; a value of 1 indicates participation, and 0 indicates otherwise. For electric vehicles During the period The charging power; Indicates the power supply capacity of the power grid company; The amount of renewable energy consumed for electric vehicles. Threshold. The value of is determined by the load aggregator and is not limited here.

[0118] This invention considers a method of interactive negotiation between load aggregators and electric vehicles to determine the incentive magnitude. This allows electric vehicle load aggregators to use flexible and varied incentives to accurately guide electric vehicle users to adjust charging power. It considers different response behaviors of electric vehicles and provides specific incentives, attempting to change the battery level of electric vehicles when they leave the charging station. This solves the problems of uneven incentive distribution and insignificant user response willingness faced by electric vehicle users in demand response processes.

[0119] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this 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 code.

[0120] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for load demand response of electric vehicles based on precise incentives, characterized in that, Load aggregators are configured to absorb renewable energy by adjusting load, thereby participating in electricity demand response and generating revenue. The electric vehicle load demand response method includes the following steps: A charging and discharging behavior evaluation model is constructed for load aggregator analysis of the charging and discharging behavior of individual electric vehicle users, including: Individual electric vehicle users have three ways to adjust their electric vehicle participation in demand response: Allow the desired power level to be lowered to The first adjustment method requires the electric vehicle to meet the following conditions when leaving the charging station: In the formula, For electric vehicles During the period The amount of electricity; For the first The estimated time for each electric vehicle user to leave the charging station; Increase the expected power consumption to The second adjustment method requires that the electric vehicle's charge level meets the following conditions when leaving the charging station: Furthermore, under both the first and second regulation methods, electric vehicles are only considered for participation in demand response if they reach a specified energy threshold. This energy threshold must meet the following conditions: In the formula, The battery level of the electric vehicle when it arrives at the charging station; For electric vehicles The power threshold for user-involved demand response; The expected charge level when the electric vehicle leaves the charging station; Obtain parameters reported by individual electric vehicle users based on the charging and discharging behavior evaluation model; Based on the reported parameters, a precise incentive model for individual electric vehicle users is constructed to account for the charging and discharging behavior of individual users in the load aggregator. in, In the formula, This indicates that the load aggregator supplies electric vehicles During the period Unit incentives; , and Both represent excitation gain coefficients; This indicates whether the electric vehicle adopts the first adjustment method; 1 indicates adoption, and 0 indicates otherwise. This indicates whether the second adjustment method should be adopted; 1 indicates adoption, and 0 indicates otherwise. This indicates whether electric vehicle users participate in demand response; a value of 1 indicates participation, and 0 indicates otherwise. The constraints of the individual user precision incentive model are: In the formula, It is a maximum value. and For the introduction of auxiliary variables; A single-user charging and discharging characteristic model is constructed to evaluate the upper and lower limits of the charging and discharging capacity of each electric vehicle in different time periods for load aggregators. In the formula, The battery level of the electric vehicle when it arrives at the charging station; The expected charge level when the electric vehicle leaves the charging station; For the first The time it takes for an electric vehicle user to arrive at a charging station managed by a load aggregator; The estimated time for electric vehicles to leave the charging station; For electric vehicles During the period The charging power; Indicates electric vehicles slow charging power; The time interval for load aggregators to schedule electric vehicles; Indicates the charging efficiency of the charging station; A demand response task constraint model is constructed for load aggregators to absorb renewable energy by adjusting loads during demand response, as follows: In the formula, This indicates the electric vehicle fleet aggregated by the load aggregator during the time period. The total charging power; Indicates the power supply capacity of the power grid company; The amount of renewable energy consumed by electric vehicles; To maximize the contribution of renewable energy; An optimization objective function model is constructed based on the charging and discharging behavior evaluation model, the individual user charging and discharging characteristic model, and the demand response task constraint model. The optimization objective function model includes the optimization objective function of the load aggregator and the optimization objective function of the individual electric vehicle user; The objective function for optimizing the load aggregator is: In the formula, The revenue of load aggregators participating in demand response during a given period; Unit subsidies will be provided to load aggregators to absorb renewable energy from the grid during the electric vehicle demand response period; The price charged by electric vehicle load aggregators for charging electric vehicles; The amount of renewable energy consumed by electric vehicles; This indicates that the load aggregator supplies electric vehicles During the period Unit incentives; For electric vehicles During the period The charging power; , These are the start and end times for the demand response, respectively. Number of users; After deducting the subsidies provided by the load aggregator to users, the objective function for electric vehicle users is: In the formula, This indicates the charging costs for electric vehicle users; An electric vehicle power load decision algorithm is constructed to obtain electric vehicle user load decision schemes and load aggregation quotient decision schemes.

2. The electric vehicle load demand response method according to claim 1, characterized in that, The electric vehicle During the period charging power for: In the formula, This indicates whether the electric vehicle is charging; 1 indicates charging, and 0 indicates otherwise. This is a 0-1 variable, indicating whether the electric vehicle uses fast charging mode; 1 indicates fast charging, and 0 indicates otherwise. Indicates electric vehicles Fast charging power; Charging power The constraints are: In the formula, These are auxiliary variables introduced.

3. The electric vehicle load demand response method according to claim 1, characterized in that, The specific process of constructing the electric vehicle electricity load decision algorithm includes the following steps: S71, The load aggregator, based on the demand response task constraint model... Randomly initialize decision variables And substitute them into the demand response task constraint model and the optimization objective function model; S72. Electric vehicle users take the individual user precise incentive model, the individual user charging and discharging characteristic model, and the demand response task constraint model as constraints, and the optimization objective function model as the optimization objective, to optimize the electric vehicle user's optimization objective function. Calculate the decision variables , , ; S73, Electric vehicle users will make decision variables , , Send it to the load aggregator, and the load aggregator will... Let these be variables, and let the individual user precise incentive model, individual user charging and discharging characteristic model, and demand response task constraint model be used as constraints. Let the optimization objective function model be used as the optimization objective function to optimize the load aggregator. Calculate the optimal ; S74, Load aggregator sets convergence threshold ,judge and optimal Is the difference less than the specified threshold? If the value is less than 0, proceed to step S75; otherwise, return to step S71. S75, Generate Electric Vehicle User Load Decision Scheme , , and load aggregator decision-making scheme .

4. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the electric vehicle load demand response method according to any one of claims 1 to 3.

5. A computer device, characterized in that, It includes a processor and a memory, the memory being used to store a computer program that, when executed by the processor, implements the electric vehicle load demand response method as described in any one of claims 1 to 3.

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