Vehicle network interaction-oriented operation optimization method of optical storage and charging integrated station

Abstract

The invention specifically relates to an operation optimization method of an optical storage and charging integrated station for vehicle network interaction, and belongs to the technical field of operation optimization of optical storage and charging integrated stations. According to the invention, the operation model of the optical storage and charging integrated station is established by considering the operation of the optical storage and charging integrated station and the participation of the vehicle network interaction, then the constraint condition is obtained, and the operation proportionality coefficient of the optical storage and charging integrated station and the participation of the vehicle network interaction proportionality coefficient are optimized by substituting the historical data of the past year into the fuzzy controller. And solving the optimized operation model of the optical storage and charging integrated station to obtain the optimal operation method of the optical storage and charging integrated station.

Description

Technical Field

[0001] This invention specifically relates to an operation optimization method for an integrated photovoltaic, energy storage, and charging station oriented towards vehicle-to-grid interaction, belonging to the technical field of operation optimization for integrated photovoltaic, energy storage, and charging stations. Background Technology

[0002] Integrated photovoltaic-storage-charging stations not only provide charging services for electric vehicles (EVs), but also enable vehicle-to-grid functionality through bidirectional charging piles, feeding the EV's battery energy back into the grid to provide ancillary services. The application of V2G technology provides the grid with additional flexibility, supports the integration of renewable energy, and helps reduce grid load pressure during peak hours. However, bidirectional charging piles are more expensive, typically about three times the cost of unidirectional charging piles, and there is currently a lack of clear evidence that most EV users are willing to participate in vehicle-to-grid (V2G) programs. The main reason is user concerns about battery degradation. On the one hand, V2G may raise concerns about risks such as shortened battery life and insufficient remaining charge; on the other hand, the current lack of reasonable incentive mechanisms to compensate users for their "opportunity cost" leads to low participation and unstable responses.

[0003] Furthermore, most existing models currently separate the investment planning and operation scheduling of integrated photovoltaic-storage-charging stations, failing to establish a closed-loop optimization mechanism between "planning, behavior, and economic benefits." While some models consider investment returns and operational benefits, they fail to incorporate behavioral response modeling or incentive design mechanisms, making it difficult to accurately reflect the heterogeneity and uncertainty of users in real-world applications. Summary of the Invention

[0004] The technical problem to be solved by this invention is: how to achieve the optimal balance between the integrated photovoltaic, energy storage and charging station and vehicle-to-grid interaction and its own operation.

[0005] To solve the above-mentioned technical problems, the technical solution proposed in this invention is: an operation optimization method for an integrated photovoltaic, energy storage, and charging station oriented towards vehicle-to-grid interaction, comprising the following steps:

[0006] Step 1: Establish an operation model for the integrated photovoltaic-storage-charging station, taking the benefits of participating in vehicle-to-grid interaction and its own operation as the objective, as shown in the following formula (1).

[0007] (1)

[0008] In equation (1), T is the operating time of the integrated photovoltaic storage and charging station. It is the time step within the operating time of the integrated photovoltaic, energy storage and charging station; It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. The charging power of electric vehicles when providing charging services to them; This refers to the unit revenue generated by the integrated photovoltaic, energy storage, and charging station for providing charging services. It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. The discharge power of electric vehicles during the process of obtaining electrical energy from electric vehicle users through vehicle-to-grid interaction; This refers to the unit compensation income of the integrated photovoltaic, energy storage and charging station participating in vehicle-to-grid interaction services; It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. The demand response capacity provided to the power grid; This refers to the unit demand response service revenue of the integrated photovoltaic, energy storage, and charging station participating in demand response; It is the unit cost of obtaining electrical energy from the grid by the integrated photovoltaic, energy storage and charging station; This refers to the unit incentive cost paid by the integrated photovoltaic, energy storage, and charging station to electric vehicle users for participating in vehicle-to-grid interaction; It is the ratio between the operating cost and the investment cost of the integrated photovoltaic, energy storage and charging station; and These are the conversion coefficients for the one-time costs and annualized costs related to transformers and charging piles, respectively. It refers to the transformer's capacity; It is the unit capacity cost of the transformer; This indicates a parking space in a group of integrated photovoltaic, energy storage, and charging stations. One of the parking spaces in the integrated photovoltaic, energy storage and charging station ; It is a parking space for an integrated photovoltaic, energy storage and charging station. Service status of bidirectional charging piles; This indicates the unit cost of a bidirectional charging pile; These are the ratios of the integrated photovoltaic-storage-charging station's own operation and its participation in vehicle-to-grid interaction; The relaxation variables of the integrated photovoltaic-storage-charging station in its own operation and the relaxation variables involved in vehicle-to-grid interaction are respectively defined. It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. Underneath the integrated photovoltaic, energy storage and charging station parking space The charging power of electric vehicles when charging services are provided to them; It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. The discharge power of electric vehicles during the process of obtaining electric energy from electric vehicle users through vehicle-to-grid interaction at the integrated photovoltaic, energy storage and charging station parking space c; This is the minimum revenue per unit provided by the integrated photovoltaic, energy storage, and charging station for charging services; This is the maximum unit incentive cost paid by the photovoltaic-storage-charging integrated station to electric vehicle users for participating in vehicle-to-grid interaction;

[0009] Step 2: Obtain the charging and discharging power constraints of the integrated photovoltaic-storage-charging station operation model, as shown in equation (2) below.

[0010] (2)

[0011] In equation (2), It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. Underneath the integrated photovoltaic, energy storage and charging station parking space The maximum charging power of an electric vehicle when charging services are provided; It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. The maximum discharge power of electric vehicles during the process of obtaining power from electric vehicle users through vehicle-to-grid interaction at the integrated photovoltaic, energy storage and charging station parking space c; It is the ratio of charging and discharging power loss;

[0012] The firmware constraints of the operation model of the integrated photovoltaic storage and charging station are obtained as shown in equation (3) below.

[0013] (3)

[0014] In equation (3), Indicates the capacity limit of the power distribution system;

[0015] The self-operation and participation in vehicle-to-grid interaction constraints of the integrated photovoltaic-storage-charging station operation model are obtained as shown in equation (4) below.

[0016]

[0017] In equation (4), and These are the minimum participation coefficients for the operation of the integrated photovoltaic-storage-charging station itself and its participation in vehicle-to-grid interaction; These are the costs of controlling and guiding electric vehicles using the integrated photovoltaic, energy storage, and charging station;

[0018] The demand response constraints of the operation model of the integrated photovoltaic storage and charging station are obtained as shown in equation (5) below.

[0019] (5)

[0020] In equation (5), It is the maximum discharge power of electric vehicles that enter the integrated photovoltaic-storage-charging station to participate in vehicle-to-grid interaction;

[0021] Step 3: Establish the electric vehicle operation model for entering the integrated photovoltaic-storage-charging station to participate in vehicle-to-grid interaction as shown in the following formula (6).

[0022] (6)

[0023] In equation (6), Indicates parking space at the photovoltaic-storage-charging integrated station The battery status of the electric vehicle upon arrival; Indicates parking space at the photovoltaic-storage-charging integrated station The battery status of the electric vehicle when it requests to leave; and These represent parking spaces at the integrated photovoltaic, energy storage, and charging station. Below are the arrival and departure times of electric vehicles; Indicates parking space at the photovoltaic-storage-charging integrated station The battery capacity of electric vehicles; This represents the truncated Gaussian distribution function. This represents the mean. Represents variance. Indicates the range of stages in the distribution;

[0024] The constraints of the electric vehicle operation model are obtained as shown in equation (7) below.

[0025] (7)

[0026] In equation (7), This indicates the maximum charging power of the photovoltaic-storage-charging integrated station;

[0027] Step 4: Obtain the historical operation data of the integrated photovoltaic-storage-charging station over the past year, and substitute the historical operation data into the following formula (8) to calculate the historical hierarchical analysis score of the integrated photovoltaic-storage-charging station. ,

[0028] (8)

[0029] In equation (8), , and These are the weights of the technical indicators, economic indicators, and social benefit indicators of the integrated photovoltaic-storage-charging station. , and These are the technical indicator scores, economic indicator scores, and social benefit indicator scores of the integrated photovoltaic, energy storage, and charging station. It is the remaining capacity of the power grid connected to the integrated photovoltaic-storage-charging station; This is the average maximum power requirement of each parking space within the integrated photovoltaic, energy storage, and charging station; It is the total area within the aforementioned integrated photovoltaic, energy storage, and charging station; It is the carbon factor of traditional thermal power; It is the average charging time for electric vehicle users who participate in vehicle-to-grid interaction;

[0030] The historical hierarchical analysis score In the input fuzzy controller, the optimal values ​​of the self-operation ratio coefficient α and the participation ratio coefficient β in vehicle-to-grid interaction in the operation model of the integrated photovoltaic-storage-charging station are obtained through rule superposition and the centroid method. and ;

[0031] Step 5: Place the above and Substituting the photovoltaic-storage-charging integrated station operation model into the obtained photovoltaic-storage-charging integrated station operation optimization model, the photovoltaic-storage-charging integrated station operation optimization model and constraints and the electric vehicle operation model and constraints are substituted into the MATLAB software platform to solve the problem with the objective of maximizing the benefits of the photovoltaic-storage-charging integrated station, thereby obtaining the operation scheme of the photovoltaic-storage integrated station, and then executing it.

[0032] Furthermore, in step 4, the historical hierarchical analysis score is... The process of obtaining the optimal values ​​of the self-operation ratio coefficient α and the participation ratio coefficient β of the integrated photovoltaic-storage-charging station in the operation model of the input fuzzy controller through rule superposition and the centroid method is as follows:

[0033] Step 4.1: Analyze the historical hierarchy and score it. The data was divided into three fuzzy subsets after fuzzification. , respectively representing smaller, medium, and larger ranges;

[0034] The historical hierarchy analysis score is calculated using the following formula (9). Smaller fuzzy subset membership value Membership value of medium fuzzy subset and membership values ​​of larger fuzzy subsets ,

[0035] (9)

[0036] Step 4.2: Obtain the historical hierarchical analysis score. The fuzzy inference rule base between the self-operation ratio coefficient α and the participation in vehicle-to-network interaction ratio coefficient β will be used to determine the historical hierarchical analysis score. Substituting the membership values ​​of the three fuzzy subsets into the fuzzy inference rule base for fuzzy inference, we obtain the membership value matrix of the self-running ratio coefficient α. And the membership value matrix of the participation vehicle-to-network interaction ratio coefficient β As shown in equation (10),

[0037] (10)

[0038] In equation (7), , and The historical hierarchical analysis scores are described separately. Smaller fuzzy subset membership value Membership value of medium fuzzy subset and membership values ​​of larger fuzzy subsets The first, second, and third membership values ​​of the self-running ratio coefficient α obtained through fuzzy inference using the fuzzy inference rule base; , and The historical hierarchical analysis scores are described separately. Smaller fuzzy subset membership value Membership value of medium fuzzy subset and membership values ​​of larger fuzzy subsets The first, second, and third membership values ​​of the participation vehicle-to-network interaction ratio coefficient β are obtained by fuzzy reasoning through the fuzzy reasoning rule base.

[0039] Step 4.3: Calculate the membership value matrix of the self-running scaling factor α. And the membership value matrix of the participation vehicle-to-network interaction ratio coefficient β Substituting all the data into the following equation (11), the optimal value of the self-operating proportional coefficient α is calculated. And the optimal value of the participation rate coefficient β in vehicle-to-everything (V2X) interaction. ,

[0040] (11)

[0041] In equation (11), It is the independent variable of the self-operating ratio coefficient; It is the membership composition function of the self-running ratio coefficient; It is the kth rule intensity among the three rule intensities of the self-operating ratio coefficient; It is the kth membership function among the three membership functions of the self-operating proportional coefficient; Indicates that the above Each point value in the data is related to the stated value. The smaller value is retained during the comparison, and the membership function is regenerated from the retained point values. This means comparing each point value in the three regenerated membership functions, keeping the maximum value, and then using the retained point values ​​to generate a membership composition function. It is the independent variable of the participation rate coefficient of the vehicle-to-network interaction; It is the membership composition function of the participation ratio coefficient of the vehicle-to-network interaction; It is the kth rule strength among the three rule strengths of the participation ratio coefficient of vehicle-to-network interaction; It is the kth membership function among the three membership functions of the vehicle-to-network interaction ratio coefficient; Indicates that the above Each point value in the data is related to the stated value. The smaller value is retained during the comparison, and the membership function is regenerated from the retained point values. This means comparing each point value in the three regenerated membership functions, keeping the maximum value, and then using the kept point values ​​to generate a membership composition function.

[0042] The beneficial effects of this invention are as follows: This invention establishes an integrated photovoltaic, energy storage, and charging station operation model by considering both its own operation and participation in vehicle-to-grid interaction. Then, it obtains the constraints and uses historical data from the past year to optimize the self-operation ratio coefficient and the participation in vehicle-to-grid interaction ratio coefficient of the integrated photovoltaic, energy storage, and charging station operation model in the fuzzy controller. Finally, it solves the optimized integrated photovoltaic, energy storage, and charging station operation model to obtain the optimal operation method of the integrated photovoltaic, energy storage, and charging station. Attached Figure Description

[0043] Figure 1 This is a flowchart from an embodiment of the present invention.

[0044] Figure 2 This is a schematic block diagram illustrating the optimization process in an embodiment of the present invention. Detailed Implementation

[0045] The following description, in conjunction with the accompanying drawings and specific embodiments, further illustrates the operation optimization method of the integrated photovoltaic-storage-charging station for vehicle-to-grid interaction according to the present invention.

[0046] Example

[0047] The operation optimization method in this embodiment, such as Figure 1 and Figure 2 As shown, it includes the following steps:

[0048] Step 1: Establish an operation model for the integrated photovoltaic-storage-charging station, with the goal of benefiting from participating in vehicle-to-grid interaction and operating itself, as shown in equation (1) below.

[0049] (1)

[0050] In equation (1), T is the operating time of the integrated photovoltaic-storage-charging station. It is the time step within the operating time of the integrated photovoltaic, energy storage, and charging station; It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. The charging power of electric vehicles when providing charging services to them; This refers to the unit revenue generated by the integrated photovoltaic, energy storage, and charging station providing charging services. It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. The discharge power of electric vehicles during the process of obtaining electrical energy from electric vehicle users through vehicle-to-grid interaction; It is the compensation income for units participating in vehicle-to-grid interaction services at integrated photovoltaic, energy storage, and charging stations. It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. The demand response capacity provided to the power grid; It refers to the demand response service revenue of the unit participating in demand response at the integrated photovoltaic, energy storage and charging station. It is the unit cost of obtaining electricity from the grid for the photovoltaic-storage-charging integrated station; It is the unit incentive cost paid by the photovoltaic, energy storage and charging integrated station to electric vehicle users to participate in vehicle-to-grid interaction; It is the ratio between the operating cost and the investment cost of the integrated photovoltaic, energy storage and charging station; and These are the conversion coefficients for the one-time costs and annualized costs related to transformers and charging piles, respectively. It refers to the transformer's capacity; It is the unit capacity cost of the transformer; This indicates a parking space in a group of integrated photovoltaic, energy storage, and charging stations. One of the parking spaces in the integrated photovoltaic, energy storage and charging station ; It is a parking space for an integrated photovoltaic, energy storage and charging station. Service status of bidirectional charging piles; This indicates the unit cost of a bidirectional charging pile; These are the ratios of the integrated photovoltaic-storage-charging station's own operation and its participation in vehicle-to-grid interaction; The relaxation variables of the integrated photovoltaic-storage-charging station and its own operation, as well as the relaxation variables involved in vehicle-to-grid interaction, are separated. It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. Underneath the integrated photovoltaic, energy storage and charging station parking space The charging power of electric vehicles when charging services are provided to them; It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. The discharge power of electric vehicles during the process of obtaining electric energy from electric vehicle users through vehicle-to-grid interaction at the integrated photovoltaic, energy storage and charging station parking space c; It is the minimum revenue per unit provided by the integrated photovoltaic, energy storage and charging station for charging services. It is the maximum incentive cost paid by the photovoltaic-storage-charging integrated station to electric vehicle users to participate in vehicle-to-grid interaction;

[0051] Step 2: Obtain the charging and discharging power constraints of the integrated photovoltaic-storage-charging station operation model, as shown in equation (2) below.

[0052] (2)

[0053] In equation (2), It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. Underneath the integrated photovoltaic, energy storage and charging station parking space The maximum charging power of an electric vehicle when charging services are provided; It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. The maximum discharge power of electric vehicles during the process of obtaining power from electric vehicle users through vehicle-to-grid interaction at the integrated photovoltaic, energy storage and charging station parking space c; It is the ratio of charging and discharging power loss;

[0054] The firmware constraints for the operation model of the integrated photovoltaic, energy storage and charging station are obtained as shown in equation (3) below.

[0055] (3)

[0056] In equation (3), Indicates the capacity limit of the power distribution system;

[0057] The self-operation and participation in vehicle-to-grid interaction of the integrated photovoltaic-storage-charging station operation model are obtained as shown in equation (4) below.

[0058]

[0059] In equation (4), and These are the minimum participation coefficients for the operation of the integrated photovoltaic, energy storage, and charging station itself and for participating in vehicle-to-grid interaction; These are the costs of using integrated photovoltaic, energy storage, and charging stations to control and guide electric vehicles;

[0060] The demand response constraints of the integrated photovoltaic-storage-charging station operation model are obtained as shown in equation (5) below.

[0061] (5)

[0062] In equation (5), It is the maximum discharge power of electric vehicles entering the photovoltaic-storage-charging integrated station to participate in vehicle-to-grid interaction;

[0063] Step 3: Establish the electric vehicle operation model for entering the photovoltaic-storage-charging integrated station to participate in vehicle-to-grid interaction as shown in the following formula (6).

[0064] (6)

[0065] In equation (6), Indicates parking space at the photovoltaic-storage-charging integrated station The battery status of the electric vehicle upon arrival; Indicates parking space at the photovoltaic-storage-charging integrated station The battery status of the electric vehicle when it requests to leave; and These represent parking spaces at the integrated photovoltaic, energy storage, and charging station. Below are the arrival and departure times of electric vehicles; Indicates parking space at the photovoltaic-storage-charging integrated station The battery capacity of electric vehicles; This represents the truncated Gaussian distribution function. This represents the mean. Represents variance. Indicates the range of stages in the distribution;

[0066] The constraints of the electric vehicle operation model are obtained as shown in equation (7) below.

[0067] (7)

[0068] In equation (7), This indicates the maximum charging power of the photovoltaic-storage-charging integrated station;

[0069] Step 4: Obtain the historical operation data of the integrated photovoltaic-storage-charging station for the past year, and substitute the historical operation data into the following formula (8) to calculate the historical hierarchical analysis score of the integrated photovoltaic-storage-charging station. ,

[0070] (8)

[0071] In equation (8), , and These are the weights of the technical indicators, economic indicators, and social benefit indicators for the integrated photovoltaic, energy storage, and charging station. , and These are the technical indicator scores, economic indicator scores, and social benefit indicator scores for the integrated photovoltaic, energy storage, and charging station. It refers to the remaining capacity of the power grid connected to the integrated photovoltaic-storage-charging station; This is the average maximum power requirement of each parking space within the integrated photovoltaic, energy storage, and charging station. It is the total area within the photovoltaic, energy storage, and charging integrated station; It is the carbon factor of traditional thermal power; It is the average charging time for electric vehicle users who participate in vehicle-to-grid interaction;

[0072] Historical level analysis score In the input fuzzy controller, the optimal values ​​of the self-operation ratio coefficient α and the participation ratio coefficient β in vehicle-to-grid interaction in the operation model of the integrated photovoltaic-storage-charging station are obtained through rule superposition and the centroid method. and ;

[0073] The process is as follows:

[0074] Step 4.1: Score the historical hierarchy analysis The data was divided into three fuzzy subsets after fuzzification. , respectively representing smaller, medium, and larger ranges;

[0075] The historical hierarchy analysis score is calculated using the following formula (9). Smaller fuzzy subset membership value Membership value of medium fuzzy subset and membership values ​​of larger fuzzy subsets ,

[0076] (9)

[0077] Step 4.2: Obtain historical hierarchical analysis scores The fuzzy inference rule base between its own operating ratio coefficient α and the participation ratio coefficient β in vehicle-to-network interaction will be used to analyze historical hierarchical scores. Substituting the membership values ​​of the three fuzzy subsets into the fuzzy inference rule base for fuzzy inference, we obtain the membership value matrices of their respective running ratio coefficients α. Membership matrix of the participation rate coefficient β of vehicle-to-everything (V2X) interaction As shown in equation (10),

[0078] (10)

[0079] In equation (7), , and Historical Hierarchical Analysis Scores Smaller fuzzy subset membership value Membership value of medium fuzzy subset and membership values ​​of larger fuzzy subsets The first, second, and third membership values ​​of the self-running proportional coefficient α obtained through fuzzy inference using the fuzzy inference rule base; , and Historical Hierarchical Analysis Scores Smaller fuzzy subset membership value Membership value of medium fuzzy subset and membership values ​​of larger fuzzy subsets The first, second, and third membership values ​​of the participation vehicle-to-network interaction ratio coefficient β obtained through fuzzy inference using the fuzzy inference rule base;

[0080] Step 4.3: The membership matrix of its own scaling factor α Membership matrix of the participation rate coefficient β of vehicle-to-everything (V2X) interaction Substituting all the data into the following equation (11), the optimal value of its own operating ratio coefficient α is calculated. The optimal value of the participation rate coefficient β in vehicle-to-everything (V2X) interaction. ,

[0081] (11)

[0082] In equation (11), It is the independent variable of its own operating ratio coefficient; It is the membership composition function of its own operating proportional coefficient; It is the kth rule strength among the three rule strengths of its own operating ratio coefficient; It is the kth membership function among the three membership functions of its own operating proportional coefficient; Indicates will Each point value in the and The smaller value is retained during the comparison, and the membership function is regenerated from the retained point values. This means comparing each point value in the three regenerated membership functions, keeping the maximum value, and then using the retained point values ​​to generate a membership composition function. It is the independent variable of the participation rate coefficient of vehicle-to-network interaction; It is the membership composition function of the participation ratio coefficient of vehicle-to-network interaction; It is the kth rule strength among the three rule strengths involved in the vehicle-to-network interaction ratio coefficient; It is the kth membership function among the three membership functions that participate in the vehicle-to-network interaction ratio coefficient; Indicates will Each point value in the and The smaller value is retained during the comparison, and the membership function is regenerated from the retained point values. This means comparing each point value in the three regenerated membership functions, keeping the maximum value, and then using the kept point values ​​to generate a membership composition function.

[0083] Step 5: and Substituting the photovoltaic-storage-charging integrated station operation model into the MATLAB software platform, we obtain the photovoltaic-storage-charging integrated station operation optimization model. We then substitute the photovoltaic-storage-charging integrated station operation optimization model and constraints, along with the electric vehicle operation model and constraints, into the MATLAB software platform to solve the problem with the objective of maximizing the benefits of the photovoltaic-storage-charging integrated station. This yields the operation scheme for the photovoltaic-storage integrated station, which is then executed.

Claims

1. An operation optimization method for an integrated photovoltaic-storage-charging station oriented towards vehicle-to-grid interaction, characterized in that: Includes the following steps: Step 1: Establish an operation model for the integrated photovoltaic-storage-charging station, taking the benefits of participating in vehicle-to-grid interaction and its own operation as the objective, as shown in the following formula (1). （1） In equation (1), T is the operating time of the integrated photovoltaic storage and charging station. It is the time step within the operating time of the integrated photovoltaic, energy storage and charging station; It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. The charging power of electric vehicles when providing charging services to them; This refers to the unit revenue generated by the integrated photovoltaic, energy storage, and charging station for providing charging services. It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. The discharge power of electric vehicles during the process of obtaining electrical energy from electric vehicle users through vehicle-to-grid interaction; This refers to the unit compensation income of the integrated photovoltaic, energy storage and charging station participating in vehicle-to-grid interaction services; It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. The demand response capacity provided to the power grid; This refers to the unit demand response service revenue of the integrated photovoltaic, energy storage, and charging station participating in demand response; It is the unit cost of obtaining electrical energy from the grid by the integrated photovoltaic, energy storage and charging station; This refers to the unit incentive cost paid by the integrated photovoltaic, energy storage, and charging station to electric vehicle users for participating in vehicle-to-grid interaction; It is the ratio between the operating cost and the investment cost of the integrated photovoltaic, energy storage and charging station; and These are the conversion coefficients for the one-time costs and annualized costs related to transformers and charging piles, respectively. It refers to the transformer's capacity; It is the unit capacity cost of the transformer; This indicates a parking space in a group of integrated photovoltaic, energy storage, and charging stations. One of the parking spaces in the integrated photovoltaic, energy storage and charging station ; It is a parking space for an integrated photovoltaic, energy storage and charging station. Service status of bidirectional charging piles; This indicates the unit cost of a bidirectional charging pile; These are the ratios of the integrated photovoltaic-storage-charging station's own operation and its participation in vehicle-to-grid interaction; The relaxation variables of the integrated photovoltaic-storage-charging station in its own operation and the relaxation variables involved in vehicle-to-grid interaction are respectively defined. It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. Underneath the integrated photovoltaic, energy storage and charging station parking space The charging power of electric vehicles when charging services are provided to them; It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. The discharge power of electric vehicles during the process of obtaining electric energy from electric vehicle users through vehicle-to-grid interaction at the integrated photovoltaic, energy storage and charging station parking space c; This is the minimum revenue per unit provided by the integrated photovoltaic, energy storage, and charging station for charging services; This is the maximum unit incentive cost paid by the photovoltaic-storage-charging integrated station to electric vehicle users for participating in vehicle-to-grid interaction; Step 2: Obtain the charging and discharging power constraints of the integrated photovoltaic-storage-charging station operation model, as shown in equation (2) below. （2） In equation (2), It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. Underneath the integrated photovoltaic, energy storage and charging station parking space The maximum charging power of an electric vehicle when charging services are provided; It is the time step within the operating time T of the integrated photovoltaic, energy storage, and charging station. The maximum discharge power of electric vehicles during the process of obtaining power from electric vehicle users through vehicle-to-grid interaction at the integrated photovoltaic, energy storage and charging station parking space c; It is the ratio of charging and discharging power loss; The firmware constraints of the operation model of the integrated photovoltaic storage and charging station are obtained as shown in equation (3) below. （3） In equation (3), Indicates the capacity limit of the power distribution system; The self-operation and participation in vehicle-to-grid interaction constraints of the integrated photovoltaic-storage-charging station operation model are obtained as shown in equation (4) below. In equation (4), and These are the minimum participation coefficients for the operation of the integrated photovoltaic-storage-charging station itself and its participation in vehicle-to-grid interaction; These are the costs of controlling and guiding electric vehicles using the integrated photovoltaic, energy storage, and charging station; The demand response constraints of the operation model of the integrated photovoltaic storage and charging station are obtained as shown in equation (5) below. （5） In equation (5), It is the maximum discharge power of electric vehicles that enter the integrated photovoltaic-storage-charging station to participate in vehicle-to-grid interaction; Step 3: Establish the electric vehicle operation model for entering the integrated photovoltaic-storage-charging station to participate in vehicle-to-grid interaction as shown in the following formula (6). （6） In equation (6), Indicates parking space at the photovoltaic-storage-charging integrated station The battery status of the electric vehicle upon arrival; Indicates parking space at the photovoltaic-storage-charging integrated station The battery status of the electric vehicle when it requests to leave; and These represent parking spaces at the integrated photovoltaic, energy storage, and charging station. Below are the arrival and departure times of electric vehicles; Indicates parking space at the photovoltaic-storage-charging integrated station The battery capacity of electric vehicles; This represents the truncated Gaussian distribution function. This represents the mean. Represents variance. Indicates the range of stages in the distribution; The constraints of the electric vehicle operation model are obtained as shown in equation (7) below. （7） In equation (7), This indicates the maximum charging power of the photovoltaic-storage-charging integrated station; Step 4: Obtain the historical operation data of the integrated photovoltaic-storage-charging station over the past year, and substitute the historical operation data into the following formula (8) to calculate the historical hierarchical analysis score of the integrated photovoltaic-storage-charging station. , （8） In equation (8), , and These are the weights of the technical indicators, economic indicators, and social benefit indicators of the integrated photovoltaic-storage-charging station. , and These are the technical indicator scores, economic indicator scores, and social benefit indicator scores of the integrated photovoltaic, energy storage, and charging station. It is the remaining capacity of the power grid connected to the integrated photovoltaic-storage-charging station; This is the average maximum power requirement of each parking space within the integrated photovoltaic, energy storage, and charging station; It is the total area within the aforementioned integrated photovoltaic, energy storage, and charging station; It is the carbon factor of traditional thermal power; It is the average charging time for electric vehicle users who participate in vehicle-to-grid interaction; The historical hierarchical analysis score In the input fuzzy controller, the optimal values ​​of the self-operation ratio coefficient α and the participation ratio coefficient β in vehicle-to-grid interaction in the operation model of the integrated photovoltaic-storage-charging station are obtained through rule superposition and the centroid method. and ; Step 5: Place the above and Substituting the photovoltaic-storage-charging integrated station operation model into the obtained photovoltaic-storage-charging integrated station operation optimization model, the photovoltaic-storage-charging integrated station operation optimization model and constraints and the electric vehicle operation model and constraints are substituted into the MATLAB software platform to solve the problem with the objective of maximizing the benefits of the photovoltaic-storage-charging integrated station, thereby obtaining the operation scheme of the photovoltaic-storage integrated station, and then executing it.

2. The operation optimization method according to claim 1, characterized in that: In step 4, the historical hierarchical analysis score is... The process of obtaining the optimal values ​​of the self-operation ratio coefficient α and the participation ratio coefficient β of the integrated photovoltaic-storage-charging station in the operation model of the input fuzzy controller through rule superposition and the centroid method is as follows: Step 4.1: Analyze the historical hierarchy and score it. The data was divided into three fuzzy subsets after fuzzification. , respectively representing smaller, medium, and larger ranges; The historical hierarchy analysis score is calculated using the following formula (9). Smaller fuzzy subset membership value Membership value of medium fuzzy subset and membership values ​​of larger fuzzy subsets , （9） Step 4.2: Obtain the historical hierarchical analysis score. The fuzzy inference rule base between the self-operation ratio coefficient α and the participation in vehicle-to-network interaction ratio coefficient β will be used to determine the historical hierarchical analysis score. Substituting the membership values ​​of the three fuzzy subsets into the fuzzy inference rule base for fuzzy inference, we obtain the membership value matrix of the self-running ratio coefficient α. And the membership value matrix of the participation vehicle-to-network interaction ratio coefficient β As shown in equation (10), （10） In equation (7), , and The historical hierarchical analysis scores are described separately. Smaller fuzzy subset membership value Membership value of medium fuzzy subset and membership values ​​of larger fuzzy subsets The first, second, and third membership values ​​of the self-running ratio coefficient α obtained through fuzzy inference using the fuzzy inference rule base; , and The historical hierarchical analysis scores are described separately. Smaller fuzzy subset membership value Membership value of medium fuzzy subset and membership values ​​of larger fuzzy subsets The first, second, and third membership values ​​of the participation vehicle-to-network interaction ratio coefficient β are obtained by fuzzy reasoning through the fuzzy reasoning rule base. Step 4.3: Calculate the membership value matrix of the self-running scaling factor α. And the membership value matrix of the participation vehicle-to-network interaction ratio coefficient β Substituting all the data into the following equation (11), the optimal value of the self-operating proportional coefficient α is calculated. And the optimal value of the participation rate coefficient β in vehicle-to-everything (V2X) interaction. , （11） In equation (11), It is the independent variable of the self-operating ratio coefficient; It is the membership composition function of the self-running ratio coefficient; It is the kth rule intensity among the three rule intensities of the self-operating ratio coefficient; It is the kth membership function among the three membership functions of the self-operating proportional coefficient; Indicates that the above Each point value in the data is related to the stated value. The smaller value is retained during the comparison, and the membership function is regenerated from the retained point values. This means comparing each point value in the three regenerated membership functions, keeping the maximum value, and then using the retained point values ​​to generate a membership composition function. It is the independent variable of the participation rate coefficient of the vehicle-to-network interaction; It is the membership composition function of the participation ratio coefficient of the vehicle-to-network interaction; It is the kth rule strength among the three rule strengths of the participation ratio coefficient of vehicle-to-network interaction; It is the kth membership function among the three membership functions of the vehicle-to-network interaction ratio coefficient; Indicates that the above Each point value in the data is related to the stated value. The smaller value is retained during the comparison, and the membership function is regenerated from the retained point values. This means comparing each point value in the three regenerated membership functions, keeping the maximum value, and then using the kept point values ​​to generate a membership composition function.

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