Electric vehicle charging optimization method and system considering multi-party interest subject game

Abstract

The invention discloses an electric vehicle charging optimization method and system considering a multi-party interest subject game. The method comprises the following steps: acquiring electric load data and sampling to obtain an initial electric load scene set; reducing scenes in the initial electric load scene set by using K-means clustering to obtain typical electric load scenes, and calculating the probability corresponding to each typical electric load scene; on the basis of the classic electric load scene and the corresponding probability, the income of the power grid and the charging station and the charging cost of the electric vehicle user are obtained through calculation; establishing a cooperative game model by taking the maximum income of the power grid and the charging station and the minimum charging cost of the electric vehicle user as optimization objectives; and solving the cooperative game model by adopting a KKT condition, a duality theory and a linear relaxation technology, and outputting a globally optimal solution, namely an equilibrium solution of the game. According to the method, the electric vehicle charging scheme is optimized on the premise of balancing the interests of multiple subjects.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging control technology, and in particular to an electric vehicle charging optimization method and system that considers the game between multiple stakeholders. Background Technology

[0002] The number of electric vehicles in China continues to grow. According to a research report on the development strategy of electric vehicles, it is predicted that my country's electric vehicle ownership will reach 60 million by 2030. Furthermore, with the implementation of unified construction and operation of charging stations, electric vehicles will serve as a high-quality demand response resource, achieving peak shaving and valley filling, and improving the load level of the power grid.

[0003] Vigorously promoting the participation of electricity users and load aggregators in the electricity market demand response is a key focus of the current deepening of the power system reform. Electric vehicles (EVs), as a high-quality demand response resource, require consideration of multiple decision-making entities involved in charging and discharging scheduling scenarios, primarily the power grid, charging stations, and EV users. Charging stations provide cheaper electricity to EV users through bidding, profiting from the price difference; simultaneously, EV users, through the electricity prices set by charging stations, adjust their charging and discharging times to achieve peak shaving and valley filling, reducing the pressure on the power grid for peak regulation, thus benefiting all three parties. In reality, the power grid, charging stations, and EV users, as different stakeholders, have different optimization objectives in their respective decision-making processes. While each entity independently optimizes its own objectives, it is also influenced by the decisions of other entities, forming a complex game of interests among them. Summary of the Invention

[0004] This invention aims to address the limitations of existing technologies in ensuring a balance of interests among multiple stakeholders. While ensuring the safe and stable operation of the system, it maximizes the revenue of the power grid and charging stations while minimizing user expenses. It provides an electric vehicle charging optimization method and system that considers the game between multiple stakeholders.

[0005] Firstly, the present invention provides an electric vehicle charging optimization method that considers the game between multiple stakeholders, including:

[0006] S1: Acquire electrical load data and sample it to obtain an initial electrical load scenario set;

[0007] S2: Use K-means clustering to reduce the scenarios in the initial electrical load scenario set to obtain typical electrical load scenarios, and calculate the probability corresponding to each typical electrical load scenario;

[0008] S3: Based on classic electrical load scenarios and corresponding probabilities, calculate and obtain the revenue of the power grid and charging stations, as well as the charging costs for electric vehicle users;

[0009] S4: Establish a cooperative game model, which includes taking the maximization of the revenue of the power grid and charging stations and the minimization of the charging cost for electric vehicle users as the optimization objective function and constraints;

[0010] S5: Solve the cooperative game model using KKT conditions, duality theory, and linear relaxation techniques, and output the global optimal solution, i.e., the equilibrium solution of the game.

[0011] Furthermore, the initial electrical load scenario set in S1 is obtained by Latin hypercube sampling of electrical load data based on the electrical load probability distribution function.

[0012] Furthermore, the specific process of reducing the initial electrical load scenario set using K-means clustering in S2 to obtain typical electrical load scenarios is as follows:

[0013] S21: Assume the initial number of electrical load scenarios is M, and the number of clusters is N;

[0014] S22: From the initial electrical load scenario set N scenes were selected as the initial cluster centers. and will be The set of scenes centered on this point is denoted as ;

[0015] S23: Calculate the Euclidean distance between the remaining scenes (excluding the cluster centers) and each cluster center. And according to the principle of minimum distance, the remaining scenes are assigned to the set of nearest scenes. middle;

[0016] S24: Statistical analysis of each scene set Number of scenes in The cluster centers are calculated and updated, with the number of iterations k = k + 1. The formula for calculating the cluster centers is as follows:

[0017]

[0018] S25: Determine the convergence criterion function value for the current iteration number k. Does it meet the requirements? , The preset convergence accuracy is set as follows: if the accuracy is met, clustering is complete and the result is output; otherwise, return to S22; where the convergence criterion function value is... The calculation formula is:

[0019]

[0020] S26: Perform a convergence check: If |f(n)-f(n-1)|≤ε, then convergence accuracy has been achieved, clustering is complete, and the classic electrical load scenario is output; otherwise, return to S22; where, the set of scenarios after clustering is complete. This corresponds to a typical electrical load scenario.

[0021] Furthermore, the calculation process for the probabilities corresponding to each typical electrical load scenario in S2 is as follows:

[0022]

[0023] in, For classic electrical load scenarios The probability of occurrence; For each scene set The number of scenarios in the equation; M is the initial number of electrical load scenarios.

[0024] Furthermore, the revenue of the power grid and charging stations, and the charging costs for electric vehicle users in S3 are specifically as follows:

[0025] Benefits of the power grid:

[0026]

[0027] Revenue from charging stations:

[0028]

[0029] Charging costs for electric vehicle users:

[0030]

[0031] in, For the benefit of the power grid; The revenue of the charging station is represented by T; the scheduling period is represented by E; and the number of electric vehicles (EVs) is represented by E. For the scene The probability of occurrence; For the scene The electricity price that the power grid feeds back to individual charging stations during the next t-period; For the scene The amount of electricity sold by the power grid to individual charging stations during the next t-period; For the scene The contract electricity price between the power grid and the charging station for the next time period t; For the scene The contracted electricity volume between the power grid and the charging station in the next time period t; For the scene The electricity purchase price of the power grid during the next time period t; For the scene The electricity purchased by the power grid during the next time period t; For the scene The unit price of charging for electric vehicles (EVs) during time period t; For the scene The charging power of the i-th electric vehicle during time period t; For the scene The electrical load below; Charging costs for electric vehicle users.

[0032] Furthermore, the objective function of the cooperative game model in S4 is specifically as follows:

[0033]

[0034] in, The total revenue of the power grid-charging station alliance model; For profit; Charging costs for electric vehicle users.

[0035] Furthermore, in the cooperative game model in S4, the same scenario The constraints of the power grid and charging station alliance model are as follows:

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] in, These represent the lower and upper limits of the electric vehicle charging price during time period t, respectively. The unit price for charging electric vehicles during time period t; The average daily electricity price for charging; M is a constant; The contracted electricity volume between the power grid and the charging station during time period t; The electricity sold by the power grid to individual charging stations during time period t; This is a Boolean variable representing the electricity purchase and sale status during time period t; It is a Boolean variable representing the storage and discharge state during time period t; Electricity purchased from the power grid during time period t; Let be the charging power of the i-th electric vehicle during time period t; For the electrical load of the power grid; and The upper limit of the storage / discharge power of the energy storage device during time period t; and The charging / discharging efficiency of the energy storage device; S0 represents the upper limit of the energy storage device's capacity; S0 represents the initial capacity of the energy storage device.

[0044] Furthermore, in the cooperative game model in S4, the same scenario The constraints for electric vehicle users are:

[0045]

[0046]

[0047]

[0048] in, Let e ​​be the battery capacity of the eth electric vehicle; Let be the initial battery charge of the i-th electric vehicle; T represents the upper limit of charging power for the i-th electric vehicle; a The charging period for electric vehicles.

[0049] Secondly, the present invention provides an electric vehicle charging optimization system that considers the game between multiple stakeholders, wherein the system employs the method described above, including:

[0050] Initial Electric Load Scenario Set Acquisition Module: Used to acquire electric load data and sample it to obtain an initial electric load scenario set;

[0051] Classic electrical load scenario acquisition module: Used to reduce the scenarios in the initial electrical load scenario set using K-means clustering to obtain typical electrical load scenarios, and calculate the probability corresponding to each typical electrical load scenario;

[0052] Multi-stakeholder benefit acquisition module: used to calculate and obtain the revenue of the power grid, charging stations and electric vehicle users based on classic electrical load scenarios and corresponding probabilities;

[0053] Cooperative game model construction module: Establish a cooperative game model, which includes the optimization objective function and constraints of maximizing the revenue of the power grid and charging stations and minimizing the charging cost for electric vehicle users;

[0054] Model Solving Module: Used to solve cooperative game models using KKT conditions, duality theory, and linear relaxation techniques, and output the global optimal solution, i.e. the equilibrium solution of the game.

[0055] This invention proposes an electric vehicle charging optimization method and system that considers the game theory among multiple stakeholders. The method takes a win-win perspective from the viewpoint of the power grid, charging stations, and electric vehicle users, aiming to maximize the total revenue of the power grid and charging stations while minimizing the charging cost for electric vehicle users. A cooperative game model is established to optimize electric vehicle charging. Addressing the needs of power grid operation, the uncertainty of electrical load is considered in the game theory problem. The cooperative game model is solved using KKT conditions, duality theory, and linear relaxation techniques, outputting the globally optimal solution, i.e., the equilibrium solution of the game, effectively coordinating the interests of the power grid, charging stations, and EV users. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a flowchart of an electric vehicle charging optimization method that considers the game between multiple stakeholders, provided in an embodiment of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0059] Example 1

[0060] like Figure 1 As shown, this embodiment provides an electric vehicle charging optimization method that considers the game between multiple stakeholders, including:

[0061] S1: Acquire electrical load data and sample it to obtain an initial electrical load scenario set. In specific implementation, the methods for acquiring electrical load data include, but are not limited to, from the power system, public datasets, or other relevant sources. This data may include historical load data, real-time load data, and predicted load data.

[0062] Specifically, the initial set of electrical load scenarios is obtained by Latin hypercube sampling of the electrical load data based on the electrical load probability distribution function. The electrical load scenario set represents the uncertainty of the electrical load problem; too many scenarios complicate the solution, while too few scenarios lead to low solution accuracy. Latin hypercube sampling is a stratified random sampling method that can efficiently sample from the distribution range of variables, ensuring the comprehensiveness of the sample results by forcibly extracting samples from each stratum.

[0063] S2: Use K-means clustering to reduce the scenarios in the initial electrical load scenario set to obtain typical electrical load scenarios, and calculate the probability corresponding to each typical electrical load scenario;

[0064] Specifically, reducing the initial electrical load scenario using K-means clustering includes the following steps:

[0065] S21: Assume the initial number of electrical load scenarios is M, and the number of clusters is N;

[0066] S22: From the initial electrical load scenario set N scenes were selected as the initial cluster centers. and will be The set of scenes centered on this point is denoted as ;

[0067] S23: Calculate the Euclidean distance between the remaining scenes (excluding the cluster centers) and each cluster center. And according to the principle of minimum distance, the remaining scenes are assigned to the set of nearest scenes. middle;

[0068] S24: Statistical analysis of each scene set Number of scenes in The cluster centers are calculated and updated, with the number of iterations k = k + 1. The formula for calculating the cluster centers is as follows:

[0069]

[0070] S25: Determine the convergence criterion function value for the current iteration number k. Does it meet the requirements? , The preset convergence accuracy is set as follows: if the accuracy is met, clustering is complete and the result is output; otherwise, return to S22; where the convergence criterion function value is... The calculation formula is:

[0071]

[0072] S26: Perform a convergence check: If |f(n)-f(n-1)|≤ε, then convergence accuracy has been achieved, clustering is complete, and the classic electrical load scenario is output; otherwise, return to S22; where, the set of scenarios after clustering is complete. This corresponds to a typical electrical load scenario.

[0073] Furthermore, the calculation process for the probabilities corresponding to each typical electrical load scenario is as follows:

[0074]

[0075] in, For classic electrical load scenarios The probability of occurrence; For each scene set The number of scenarios in the equation; M is the initial number of electrical load scenarios.

[0076] S3: Based on classic electrical load scenarios and corresponding probabilities, calculate and obtain the revenue of the power grid and charging stations, as well as the charging costs for electric vehicle users;

[0077] Benefits of the power grid:

[0078]

[0079] Revenue from charging stations:

[0080]

[0081] Charging costs for electric vehicle users:

[0082]

[0083] in, For the benefit of the power grid; The revenue of the charging station is represented by T; the scheduling period is represented by E; and the number of electric vehicles (EVs) is represented by E. For the scene The probability of occurrence; For the scene The electricity price that the power grid feeds back to individual charging stations during the next t-period; For the scene The amount of electricity sold by the power grid to individual charging stations during the next t-period; For the scene The contract electricity price between the power grid and the charging station for the next time period t; For the scene The contracted electricity volume between the power grid and the charging station in the next time period t; For the scene The electricity purchase price of the power grid during the next time period t; For the scene The electricity purchased by the power grid during the next time period t; For the scene The unit price of charging for electric vehicles (EVs) during time period t; For the scene The charging power of the i-th electric vehicle during time period t; For the scene The electrical load below; Charging costs for electric vehicle users.

[0084] S4: Establish a cooperative game model, which includes maximizing the revenue of the power grid and charging stations and minimizing the charging cost for electric vehicle users as the optimization objective function and constraints.

[0085] The power grid, charging stations, and electric vehicle (EV) users are independent stakeholders who formulate trading strategies based on their respective interests to maximize their gains. In a multi-party game model, EV users primarily interact with the power grid through charging stations. Assume that during this interaction, EV owners report their available charging times and charging power to the grid. First, the charging station, based on the grid's real-time electricity price and the information reported by the EVs, sets the electricity price for EV users and the amount of electricity to purchase from the grid. Then, EV users adjust their charging times and power based on the real-time price and feed this information back to the charging station. Meanwhile, the grid, based on the electricity purchases reported by the charging station, sets a new electricity price and feeds this information back to the charging station. After receiving feedback from the grid and EV users, the charging station readjusts its energy purchase strategy and electricity price, feeding this information back to both the grid and EV users. This trading process conforms to the characteristics of a dynamic game; the charging station's energy purchase strategy from the grid and the electricity price it sells to EV users influence the grid's electricity price setting and the EV users' energy purchase strategy. Conversely, changes in the electricity price charged by the power grid and the energy purchase strategies of EV users will lead to charging stations readjusting their energy purchase strategies from the grid and their electricity pricing strategies for EV users. The game between the power grid and charging stations is a cooperative game, with the optimization objective maximizing the benefits of the alliance formed by the grid and charging stations. A master-slave game exists between EV users and charging stations, with charging stations as the leaders and EV users as the followers, whose optimization objective is to minimize their own charging costs.

[0086] The specific objective function in the cooperative game model is:

[0087]

[0088] in, The total revenue of the power grid-charging station alliance model; For profit; Charging costs for electric vehicle users.

[0089] Specifically, in the cooperative game model in S4, the same scenario The constraints of the power grid and charging station alliance model are as follows (since the constraints are the same for each scenario, subscripts are omitted in the following analysis). ):

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097] in, These represent the lower and upper limits of the electric vehicle charging price during time period t, respectively. The unit price for charging electric vehicles during time period t; The average daily electricity price for charging; M is a constant (this constant takes a large value to ensure that the inequality is satisfied); The contracted electricity volume between the power grid and the charging station during time period t; The electricity sold by the power grid to individual charging stations during time period t; This is a Boolean variable representing the electricity purchase and sale status during time period t; It is a Boolean variable representing the storage and discharge state during time period t; Electricity purchased from the power grid during time period t; Let be the charging power of the i-th electric vehicle during time period t; For the electrical load of the power grid; and The upper limit of the storage / discharge power of the energy storage device during time period t; and The charging / discharging efficiency of the energy storage device; S0 represents the upper limit of the energy storage device's capacity; S0 represents the initial capacity of the energy storage device.

[0098] Specifically, in the cooperative game model in S4, the same scenario The constraints for electric vehicle users are as follows (since the constraints are the same for each scenario, subscripts are omitted in the following analysis). ):

[0099]

[0100]

[0101]

[0102] in, Let e ​​be the battery capacity of the eth electric vehicle; Let be the initial battery charge of the i-th electric vehicle; T represents the upper limit of charging power for the i-th electric vehicle; a The charging period for electric vehicles.

[0103] S5: The cooperative game model is solved using KKT conditions, duality theory, and linear relaxation techniques to output the global optimal solution, i.e., the equilibrium solution of the game. In this specific implementation, the Cplex solver is used for the solution.

[0104] Example 2

[0105] This embodiment provides an electric vehicle charging optimization system that considers the game between multiple stakeholders, the system comprising:

[0106] The initial load scenario set acquisition module is used to acquire load data and sample it to obtain an initial load scenario set. This module is responsible for acquiring load data from the power system, public datasets, or other relevant sources. This data may include historical load data, real-time load data, and predicted load data, ensuring the accuracy and completeness of the data to lay the foundation for subsequent analysis and processing. Latin hypercube sampling technology is used to sample the load probability distribution function to obtain a typical load scenario set.

[0107] Classic electrical load scenario acquisition module: Used to reduce the scenarios in the initial electrical load scenario set using K-means clustering to obtain typical electrical load scenarios, and calculate the probability corresponding to each typical electrical load scenario.

[0108] Multi-stakeholder benefit acquisition module: used to calculate and obtain the revenue of the power grid, charging stations and electric vehicle users based on classic electrical load scenarios and corresponding probabilities;

[0109] Cooperative game model construction module: used to build a cooperative game model, which includes the optimization objective function and constraints of maximizing the revenue of the power grid and charging stations and minimizing the charging cost for electric vehicle users;

[0110] Model Solving Module: Used to solve cooperative game models using KKT conditions, duality theory, and linear relaxation techniques, and output the global optimal solution, i.e. the equilibrium solution of the game.

[0111] Example 3

[0112] This embodiment provides an electronic terminal including a processor and a memory, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the method described above.

[0113] Example 4

[0114] This embodiment provides a readable storage medium storing a computer program that, when invoked by a processor, performs the steps of the method described above.

[0115] It should be understood that, in the embodiments of the present invention, the processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.

[0116] The readable storage medium is a computer-readable storage medium, which can be an internal storage unit of the controller described in any of the foregoing embodiments, such as the controller's hard drive or memory. The readable storage medium can also be an external storage device of the controller, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the controller. Further, the readable storage medium can include both the controller's internal storage unit and external storage devices. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium can also be used to temporarily store data that has been output or will be output.

[0117] Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0119] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. An electric vehicle charging optimization method considering the game among multiple stakeholders, characterized in that, include: S1: Acquire electrical load data and sample it to obtain an initial electrical load scenario set; S2: Use K-means clustering to reduce the scenarios in the initial electrical load scenario set to obtain typical electrical load scenarios, and calculate the probability corresponding to each typical electrical load scenario; S3: Based on classic electrical load scenarios and corresponding probabilities, calculate and obtain the revenue of the power grid and charging stations, as well as the charging costs for electric vehicle users; S4: Establish a cooperative game model, which includes taking the maximization of the revenue of the power grid and charging stations and the minimization of the charging cost for electric vehicle users as the optimization objective function and constraints; S5: Solve the cooperative game model using KKT conditions, duality theory, and linear relaxation techniques, and output the global optimal solution, i.e., the equilibrium solution of the game.

2. The method according to claim 1, characterized in that, The initial electrical load scenario set in S1 is obtained by Latin hypercube sampling of electrical load data based on the electrical load probability distribution function.

3. The method according to claim 1, characterized in that, The specific process of reducing the initial electrical load scenario set using K-means clustering in S2 to obtain typical electrical load scenarios is as follows: S21: Assume the initial number of electrical load scenarios is M, and the number of clusters is N; S22: From the initial electrical load scenario set N scenes were selected as the initial cluster centers. and will be The set of scenes centered on this point is denoted as ; S23: Calculate the Euclidean distance between the remaining scenes (excluding the cluster centers) and each cluster center. And according to the principle of minimum distance, the remaining scenes are assigned to the set of nearest scenes. middle; S24: Statistical analysis of each scene set Number of scenes in The cluster centers are calculated and updated, with the number of iterations k = k + 1. The formula for calculating the cluster centers is as follows: ； S25: Determine the convergence criterion function value for the current iteration number k. Does it meet the requirements? , The preset convergence accuracy is set as follows: if the accuracy is met, clustering is complete and the result is output; otherwise, return to S22; where the convergence criterion function value is... The calculation formula is: ； S26: Perform a convergence check: If |f(n)-f(n-1)|≤ε, then convergence accuracy has been achieved, clustering is complete, and the classic electrical load scenario is output; otherwise, return to S22; where, the set of scenarios after clustering is complete. This corresponds to a typical electrical load scenario.

4. The method according to claim 1, characterized in that, The calculation process for the probabilities corresponding to each typical electrical load scenario in S2 is as follows: ； in, For classic electrical load scenarios The probability of occurrence; For each scene set The number of scenarios in the equation; M is the initial number of electrical load scenarios.

5. The method according to claim 1, characterized in that, The revenue of the power grid and charging stations, and the charging costs for electric vehicle users in S3 are specifically as follows: Benefits of the power grid: ； Revenue from charging stations: ； Charging costs for electric vehicle users: ； in, For the benefit of the power grid; The revenue of the charging station is represented by T; the scheduling period is represented by E; and the number of electric vehicles (EVs) is represented by E. For the scene The probability of occurrence; For the scene The electricity price that the power grid feeds back to individual charging stations during the next t-period; For the scene The amount of electricity sold by the power grid to individual charging stations during the next t-period; For the scene The contract electricity price between the power grid and the charging station for the next time period t; For the scene The contracted electricity volume between the power grid and the charging station in the next time period t; For the scene The electricity purchase price of the power grid during the next time period t; For the scene The electricity purchased by the power grid during the next time period t; For the scene The unit price of charging for electric vehicles (EVs) during time period t; For the scene The charging power of the i-th electric vehicle during time period t; For the scene The electrical load below; Charging costs for electric vehicle users.

6. The method according to claim 5, characterized in that, The specific optimization objective function in the cooperative game model in S4 is as follows: ； in, The total revenue of the power grid-charging station alliance model; For profit; Charging costs for electric vehicle users.

7. The method according to claim 6, characterized in that, In the cooperative game model in S4, the same scenario The constraints of the power grid and charging station alliance model are as follows: ； ； ； ； ； ； ； in, These represent the lower and upper limits of the electric vehicle charging price during time period t, respectively. The unit price for charging electric vehicles during time period t; The average daily electricity price for charging; M is a constant; The contracted electricity volume between the power grid and the charging station during time period t; The electricity sold by the power grid to individual charging stations during time period t; This is a Boolean variable representing the electricity purchase and sale status during time period t; It is a Boolean variable representing the storage and discharge state during time period t; Electricity purchased from the power grid during time period t; Let be the charging power of the i-th electric vehicle during time period t; For the electrical load of the power grid; and The upper limit of the storage / discharge power of the energy storage device during time period t; and The charging / discharging efficiency of the energy storage device; S0 represents the upper limit of the energy storage device's capacity; S0 represents the initial capacity of the energy storage device.

8. The method according to claim 6, characterized in that, In the cooperative game model in S4, the same scenario The constraints for electric vehicle users are: ； ； ； in, Let e ​​be the battery capacity of the eth electric vehicle; Let be the initial battery charge of the i-th electric vehicle; T represents the upper limit of charging power for the i-th electric vehicle; a The charging period for electric vehicles.

9. An electric vehicle charging optimization system considering the game theory among multiple stakeholders, wherein the system employs the method described in any one of claims 1-8, characterized in that, include: Initial Electric Load Scenario Set Acquisition Module: Used to acquire electric load data and sample it to obtain an initial electric load scenario set; Classic electrical load scenario acquisition module: Used to reduce the scenarios in the initial electrical load scenario set using K-means clustering to obtain typical electrical load scenarios, and calculate the probability corresponding to each typical electrical load scenario; Multi-stakeholder benefit acquisition module: used to calculate and obtain the revenue of the power grid, charging stations and electric vehicle users based on classic electrical load scenarios and corresponding probabilities; Cooperative game model construction module: Establish a cooperative game model, which includes the optimization objective function and constraints of maximizing the revenue of the power grid and charging stations and minimizing the charging cost for electric vehicle users; Model Solving Module: Used to solve cooperative game models using KKT conditions, duality theory, and linear relaxation techniques, and output the global optimal solution, i.e. the equilibrium solution of the game.

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