Electric vehicle charging and discharging coordinated regulation and control system considering time-of-use electricity price

Abstract

The invention discloses an electric vehicle charging and discharging coordinated regulation and control system considering time-of-use electricity price, and relates to the technical field of new energy development and utilization economic index evaluation, and the method comprises the steps: carrying out the classification and measurement of the charging and discharging response capability of a single electric vehicle based on SOC; establishing an evaluation model by adopting a fuzzy reasoning technology of a fuzzy reasoning method; and the corresponding electric vehicle charging and discharging response potential is measured. According to the method, the uncertainty and subjective preference of user behaviors are effectively fused; the method overcomes the defects of high calculation complexity, strict data requirements and poor generalization caused by dependence on individual trip chain simulation in a traditional method, significantly improves the practicability and adaptability of the model, and provides scientific and reliable decision support for the subsequent development of large-scale vehicle network interactive scheduling, demand response management and power grid planning of a power system.

Description

Technical Field

[0001] This invention relates to the field of economic indicator evaluation technology for the development and utilization of new energy sources, and in particular to a coordinated control system for charging and discharging of electric vehicles that takes into account time-of-use pricing. Background Technology

[0002] With the advancement of dual-carbon goals and the acceleration of the construction of new power systems, electric vehicles, as mobile energy storage units with large scale, wide distribution and high dispatchability potential, are receiving increasing attention for the orderly guidance of their charging behavior. In recent years, the number of electric vehicles has continued to rise, and the proportion of charging load in the power grid has been increasing day by day. How to effectively tap their potential to participate in demand response has become a key issue in improving the grid regulation capacity and promoting the consumption of renewable energy.

[0003] Currently, existing technologies generally rely on constructing the complete travel chain of a single electric vehicle to simulate its movement and charging / discharging process in different functional areas. This requires accurately tracking the micro-behaviors of each vehicle, such as departure time, destination, driving route, and parking duration. The modeling complexity is high, the computational resources are large, and the data quality requirements are extremely high, making it difficult for the model to be quickly migrated and applied across different regions. In addition, it often focuses on the detailed simulation of individual behaviors, ignoring the overall statistical characteristics of regional electric vehicle clusters. It is difficult to effectively reflect the macro-level differences in the distribution of charging demand in functional areas such as residential areas, commercial areas, and work areas, resulting in measurement results that lack representativeness and universality, and cannot meet the actual needs of rapid assessment of the response potential of electric vehicles in a wide range and multiple scenarios. Summary of the Invention

[0004] In view of the problems existing in the current methods and systems for measuring the charging and discharging response potential of electric vehicles, this invention is proposed.

[0005] Therefore, the problem to be solved by this invention is: how to overcome the shortcomings of existing technologies that rely on the simulation of a single electric vehicle travel chain, are difficult to reflect the overall characteristics of regional clusters, and have poor universality, so as to accurately and efficiently measure the charging and discharging response potential of regional electric vehicle clusters under incentives such as time-of-use pricing, taking into account users' battery capacity, travel characteristics, and response willingness.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a method for coordinated regulation of electric vehicle charging and discharging considering time-of-use pricing, comprising,

[0008] Based on SOC (State of Charge), the charging and discharging response capabilities of individual electric vehicles are classified and measured. SOC refers to battery capacity. The charging and discharging response intentions of electric vehicles are assessed using fuzzy inference methods, taking into account factors influencing user decisions. A fuzzy inference technique is used to establish an evaluation model. All electric vehicles in different functional zones are considered as a whole, and the travel characteristics of electric vehicles in different functional zones are used as input parameters for the evaluation model. Within the framework of the unified evaluation model, input parameters are applied to different functional zones to measure the corresponding electric vehicle charging and discharging response potential.

[0009] As a preferred embodiment of the electric vehicle charging and discharging coordinated control method considering time-of-use pricing described in this invention, the classification of the charging and discharging response capabilities of individual electric vehicles includes three categories: charging-only, discharging-only, and rechargeable / dischargeable.

[0010] The "charge-only" setting applies when the electric vehicle's battery level is too low, i.e., the battery percentage is below the minimum acceptable level for the user. At this time, electric vehicles connected to the power grid are only allowed to charge.

[0011] The provision allowing only discharge applies when the electric vehicle is fully charged, i.e., when the battery percentage is at the user's maximum charge limit. At time 1, electric vehicles connected to the power grid are only allowed to discharge.

[0012] The rechargeable / dischargeable capability is used when the remaining battery power of the electric vehicle is below the minimum discharge capacity limit. Higher than the maximum charging capacity At this time, the electric vehicle is both discharging and charging;

[0013] Based on the classification of the charging and discharging response capabilities of individual electric vehicles, the charging and discharging capacity and power response capabilities of three types of electric vehicles were measured respectively.

[0014] As a preferred embodiment of the electric vehicle charging and discharging coordinated control method considering time-of-use pricing described in this invention, wherein: in the electric vehicle only allowed to discharge state, the electric vehicle is only allowed to discharge, and the formula for calculating the discharge response capacity and discharge power of the electric vehicle only allowed to discharge during the parking time is:

[0015] The formula for the discharge response capacity of the electric vehicle that is only allowed to discharge during the parking period is:

[0016] ;

[0017] The formula for the discharge power of the electric vehicle that is only allowed to discharge during the parking time is:

[0018]

[0019] in, For electric vehicles The discharge capacity response capability, For the battery capacity of electric vehicles, The minimum battery level acceptable to the user. The maximum battery capacity desired by the user. For electric vehicles Arrival time, The duration of electric vehicle dwell time, For vehicles Discharge regulation power, This refers to the vehicle's permissible discharge power.

[0020] As a preferred embodiment of the electric vehicle charging and discharging coordinated control method considering time-of-use pricing described in this invention, wherein: in the electric vehicle only allowed to charge state, the electric vehicle is only allowed to charge, and the formula for calculating the charging response capacity and charging power of the electric vehicle only allowed to charge during the parking time is:

[0021] The formula for the charging response capacity of the electric vehicle that is only allowed to charge during the parking period is:

[0022] ;

[0023] The formula for the charging power of electric vehicles that are only allowed to charge during the parking period is:

[0024] ;

[0025] in, For electric vehicles Charging capacity response capability This refers to the battery's rated capacity. For vehicles The charging response power, The permitted charging power for the vehicle.

[0026] As a preferred embodiment of the electric vehicle charging and discharging coordinated control method considering time-of-use pricing described in this invention, wherein: in the state of a rechargeable and discharging electric vehicle;

[0027] If the minimum battery capacity allowed by electric vehicle users is The maximum battery capacity is the battery's rated capacity. When the battery level is between its minimum and maximum, the formulas for the capacity and power response of an electric vehicle under charging and discharging conditions are as follows:

[0028] The capacity formula for charging and discharging electric vehicles is as follows:

[0029] )

[0030] The formula for the response capability of electric vehicle charging and discharging is:

[0031]

[0032] in, For the discharge capacity response capability of electric vehicles, The charging capacity response capability for electric vehicles.

[0033] As a preferred embodiment of the electric vehicle charging and discharging coordinated control method considering time-of-use pricing described in this invention, the step of establishing the evaluation model using fuzzy inference technology includes the following steps:

[0034] Establishment of variable fuzzy language and membership function;

[0035] Definition of fuzzy inference rules;

[0036] Selection of fuzzy computing and defuzzification methods;

[0037] The establishment of the variable fuzzy language and membership function includes using charging price, discharging price, battery capacity, and parking duration as inputs for fuzzy inference, and charging response intention and discharging response intention as outputs. The linguistic variables for both inputs and outputs are divided into three fuzzy subsets, with the linguistic variable set for the charging price and discharging price input variables being... ;

[0038] Each subset represents a charging price or discharging price at a high, medium, or low level, respectively; the linguistic variable sets for the output quantities of charging response intention and discharging response intention are: Each subset represents a user's charging or discharging willingness at a high, medium, or low level, respectively.

[0039] The triangular membership function is used to characterize the membership degree of each subset. The formula for the triangular membership function is:

[0040] ;

[0041] in, This represents the minimum value of the triangular distribution. This represents the maximum value of the triangular distribution. Indicates the peak point. Indicates an interval;

[0042] when Towards When driving, the highest membership degree is 1. Exceeding or At that time, the membership degree is 0;

[0043] The fuzzy inference rule definition includes rule 1, rule 2, and rule 3;

[0044] The selection of fuzzy computing and defuzzification methods includes, in terms of fuzzy inference rule calculation methods, adopting the Mamdani fuzzy inference calculation method to calculate the charging response intention of electric vehicle users after defuzzification and to calculate the different levels of the response intention of electric vehicle users after defuzzification;

[0045] The formula for the defuzzified charging response intention of electric vehicle users is:

[0046] ;

[0047] The formula for calculating the different levels of response intention of electric vehicle users after defuzzification is as follows:

[0048] ;

[0049] in, This represents the charging response intention of electric vehicle users calculated after defuzzification. X, Y, and Z represent the degree to which the user's intended response, calculated after deblurring, belongs to different levels, and represent triples. Represents the tuple X corresponding to Fuzzy sets Represents the tuple Y corresponding to Fuzzy sets Represents the tuple Z corresponding to Fuzzy sets A set representing the charging response states. This represents the membership degree of state v;

[0050] As a preferred embodiment of the electric vehicle charging and discharging coordinated control method considering time-of-use pricing described in this invention, the different functional areas include residential areas, work areas, and commercial areas. The charging and discharging response potential of the functional areas is simulated using Python 3.9 software, and the charging and discharging response potential of the three types of functional areas is analyzed to complete the measurement of the corresponding electric vehicle charging and discharging response potential.

[0051] Secondly, embodiments of the present invention provide an electric vehicle charge / discharge response potential measurement system, comprising: a classification module, which classifies and measures the charge / discharge response capability of individual electric vehicles based on SOC, wherein SOC refers to battery capacity; an evaluation module, which uses fuzzy inference methods to assess the electric vehicle's charge / discharge response intention, comprehensively considers factors influencing user decisions, and establishes an evaluation model using fuzzy inference techniques; and a measurement module, which treats all electric vehicles in different functional areas as a whole, uses the travel characteristics of electric vehicles in different functional areas as input parameters for establishing the evaluation model, and, within the framework of a unified evaluation model, inputs parameters to measure the corresponding electric vehicle charge / discharge response potential for different functional areas.

[0052] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the above-described method for coordinated control of electric vehicle charging and discharging considering time-of-use pricing.

[0053] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the above-described method for coordinated control of electric vehicle charging and discharging considering time-of-use pricing.

[0054] The beneficial effects of this invention are as follows: By classifying electric vehicles into three categories based on battery capacity—charging-only, discharging-only, and rechargeable / dischargeable—this invention accurately characterizes the individual response capability under different battery capacity states. Combined with fuzzy inference technology, it comprehensively considers factors such as battery capacity, parking duration, and charging / discharging electricity prices to quantify the degree of user willingness to participate in the response, using this as the core decision-making basis for intelligent agent simulation, effectively integrating the uncertainty and subjective preferences of user behavior. Furthermore, by using the macroscopic travel characteristics of different functional areas such as residential areas, commercial areas, and work areas as model inputs, it achieves rapid measurement of the overall response potential of regional clusters within a unified simulation framework. This overcomes the shortcomings of traditional methods that rely on individual travel chain simulation, resulting in high computational complexity, stringent data requirements, and poor generalization, significantly improving the model's practicality and adaptability. This provides scientific and reliable decision support for subsequent large-scale vehicle-grid interactive scheduling, demand response management, and power grid planning in the power system. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments 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. Wherein:

[0056] Figure 1 The present invention provides a measurement flowchart for a method for coordinated control of electric vehicle charging and discharging considering time-of-use pricing.

[0057] Figure 2 This is a schematic diagram of a system for a time-of-use electricity pricing-based coordinated control method for electric vehicle charging and discharging, provided as an embodiment of the present invention.

[0058] Figure 3 This is a graph showing the relationship between battery capacity and charge / discharge in a method for coordinated control of electric vehicle charging and discharging that considers time-of-use pricing, provided as an embodiment of the present invention.

[0059] Figure 4 This diagram illustrates the membership function of the input and output variables in a time-of-use (TOU) electric vehicle charging and discharging coordinated control method that considers time-of-use pricing, as provided in an embodiment of the present invention.

[0060] Figure 5 This is a schematic diagram of the simulation results of the working area charge and discharge response potential of a time-of-use electricity pricing-based electric vehicle charge and discharge coordinated control method provided in an embodiment of the present invention.

[0061] Figure 6 This is a schematic diagram of the simulation results of the charging and discharging response potential in a residential area, which is a method for coordinated control of electric vehicle charging and discharging considering time-of-use pricing, provided in an embodiment of the present invention.

[0062] Figure 7 This is a schematic diagram of the simulation results of the charging and discharging response potential in a commercial area, which is a method for coordinated control of electric vehicle charging and discharging considering time-of-use pricing, provided as an embodiment of the present invention.

[0063] Figure 8 This is a schematic diagram of the structure of a medium for a method of coordinated control of electric vehicle charging and discharging considering time-of-use pricing, provided in an embodiment of the present invention.

[0064] Figure 9 This is a schematic diagram of a computing device for a time-of-use electricity pricing-based coordinated control method for electric vehicle charging and discharging, as provided in an embodiment of the present invention. Detailed Implementation

[0065] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0066] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0067] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0068] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0069] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0070] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0071] Example 1

[0072] Reference Figures 1-9 This is the first embodiment of the present invention, which provides a method for coordinated control of electric vehicle charging and discharging considering time-of-use pricing, comprising:

[0073] S1: Classify and measure the charging and discharging response capability of a single electric vehicle based on SOC, where SOC refers to battery capacity.

[0074] Among them, the charging and discharging response capabilities of individual electric vehicles are classified into three categories: charging-only, discharging-only, and rechargeable / dischargeable.

[0075] Charging is only permitted when the electric vehicle's battery is too low, i.e., when the battery percentage is below the minimum acceptable level for the user. At this time, electric vehicles connected to the power grid are only allowed to charge.

[0076] Discharge is only permitted when the electric vehicle is fully charged, i.e., when the battery percentage is at the user's maximum charge limit. At time 1, electric vehicles connected to the power grid are only allowed to discharge.

[0077] Rechargeable / dischargeable is used when the remaining charge of an electric vehicle is below the minimum charge limit for discharge only. Higher than the maximum charging capacity At this time, the electric vehicle is both discharging and charging;

[0078] Based on the classification of the charging and discharging response capabilities of individual electric vehicles, the charging and discharging capacity and power response capabilities of three types of electric vehicles were measured respectively.

[0079] S1.1: In the state where electric vehicles are only allowed to discharge, the formula for calculating the discharge response capacity and discharge power of an electric vehicle that is only allowed to discharge during the parking time is as follows:

[0080] The formula for the discharge response capacity of an electric vehicle that is only allowed to discharge during the parking period is:

[0081] ;

[0082] The formula for the discharge power of an electric vehicle that is only allowed to discharge during the parking period is:

[0083]

[0084] in, For electric vehicles The discharge capacity response capability, For the battery capacity of electric vehicles, The minimum battery level acceptable to the user. The maximum battery capacity desired by the user. For electric vehicles Arrival time, The duration of electric vehicle dwell time, For vehicles Discharge regulation power, This refers to the vehicle's permissible discharge power.

[0085] S1.2: In the electric vehicle only charging mode, the electric vehicle is only allowed to charge. The formula for calculating the charging response capacity and charging power of the electric vehicle only charging mode during the parking time is as follows:

[0086] The formula for the charging response capacity of electric vehicles that are only allowed to charge during the parking period is:

[0087] ;

[0088] The formula for the charging power of electric vehicles that are only allowed to charge during the parking period is:

[0089] ;

[0090] in, For electric vehicles Charging capacity response capability This refers to the battery's rated capacity. For vehicles The charging response power, The permitted charging power for the vehicle.

[0091] S1.3: In the state of a rechargeable electric vehicle;

[0092] If the minimum battery capacity allowed by electric vehicle users is The maximum battery capacity is the battery's rated capacity. When the battery level is between its minimum and maximum, the formulas for the capacity and power response of an electric vehicle under charging and discharging conditions are as follows:

[0093] The formula for the charging and discharging capacity of electric vehicles is:

[0094] );

[0095] The formula for the response capability of electric vehicles during charging and discharging is:

[0096] ;

[0097] in, For the discharge capacity response capability of electric vehicles, The charging capacity response capability for electric vehicles.

[0098] S2: Using fuzzy reasoning methods to assess the charging and discharging response intentions of electric vehicles, and taking into account factors that influence user decisions, an evaluation model is established using fuzzy reasoning techniques.

[0099] The fuzzy reasoning technique for establishing an evaluation model using fuzzy reasoning methods includes the following steps:

[0100] Establishment of variable fuzzy language and membership function;

[0101] Definition of fuzzy inference rules;

[0102] Selection of fuzzy computing and defuzzification methods;

[0103] The establishment of the variable fuzzy language and membership function involves using charging price, discharging price, battery capacity, and parking duration as inputs for fuzzy inference, and charging and discharging response intentions as outputs. The linguistic variables for both inputs and outputs are divided into three fuzzy subsets. The linguistic variable sets for the input variables of charging price and discharging price are as follows: ;

[0104] Each subset represents a charging price or discharging price at a high, medium, or low level, respectively; the linguistic variable sets for the output quantities of charging response intention and discharging response intention are: Each subset represents a user's charging or discharging willingness at a high, medium, or low level, respectively.

[0105] The triangular membership function is used to characterize the membership degree of each subset. The formula for the triangular membership function is:

[0106] ;

[0107] in, This represents the minimum value of the triangular distribution. This represents the maximum value of the triangular distribution. Indicates the peak point. Indicates an interval;

[0108] when Towards When driving, the highest membership degree is 1. Exceeding or At that time, the membership degree is 0;

[0109] The fuzzy inference rule definitions include Rule 1, Rule 2, and Rule 3;

[0110] The selection of fuzzy computing and defuzzification methods includes, in terms of fuzzy inference rule calculation methods, the adoption of the Mamdani fuzzy inference calculation method to calculate the charging response intention of electric vehicle users after defuzzification and to calculate the different levels of the response intention of electric vehicle users after defuzzification;

[0111] The formula for the defuzzified charging response intention of electric vehicle users is:

[0112] ;

[0113] The formula for calculating the different levels of willingness to respond from electric vehicle users after defuzzification is as follows:

[0114] ;

[0115] in, This represents the charging response intention of electric vehicle users calculated after defuzzification. X, Y, and Z represent the degree to which the user's intended response, calculated after deblurring, belongs to different levels, and represent triples. Represents the tuple X corresponding to Fuzzy sets Represents the tuple Y corresponding to Fuzzy sets Represents the tuple Z corresponding to Fuzzy sets A set representing the charging response states. This represents the membership degree of state v;

[0116] S3: Treat all electric vehicles in different functional areas as a whole, use the travel characteristics of electric vehicles in different functional areas as input parameters to establish an evaluation model, and input parameters within the framework of a unified evaluation model for different functional areas to measure the charging and discharging response potential of the corresponding electric vehicles.

[0117] The different functional areas include residential areas, work areas and commercial areas. The charging and discharging response potential of each functional area is simulated using Python 3.9 software. The charging and discharging response potential of the three types of functional areas is analyzed, and the corresponding charging and discharging response potential of electric vehicles is measured.

[0118] S4: Based on the corresponding electric vehicle charging and discharging response potential, predict the dispatchable capacity of charging stations for user response capabilities.

[0119] The prediction of the schedulable charging station capacity for user response includes constructing a road network model, analyzing factors influencing electric vehicle charging behavior, evaluating electric vehicle response capabilities, and predicting the schedulable charging station capacity.

[0120] The formula for constructing the road network model is as follows:

[0121] ;

[0122] in, It represents the set of road networks. This represents the set of road network nodes, which has a total of One node; Indicated Represents the set of road segment weight values. Indicates the first One road network node; This indicates the connection of road network nodes. , The section of road; Indicates in Time period and road section The weights, road network set The connection relationships between nodes are represented by an adjacency matrix. To describe.

[0123] Furthermore, the elements of matrix D The expression is:

[0124] ;

[0125] In this model, travel time is selected as the road resistance, and a speed-flow model is introduced, including the electric vehicle's travel speed. The expression is:

[0126] ;

[0127] In the formula, For roads Zero flow velocity; Let be the road's traffic capacity, and let be the road's capacity during time period t. Traffic flow; , and This is the adaptive coefficient for road grade.

[0128] Path resistance model The expression is:

[0129] ;

[0130] In the formula, For roads Length; For the intersection Traffic light waiting time.

[0131] The analysis of factors influencing electric vehicle charging behavior includes information on electric vehicle travel time, state of charge, and charging probability selection.

[0132] Furthermore, the travel time information of electric vehicles is a linear combination of multiple Gaussian distributions, which can characterize complex data distribution features. In order to represent the differences in driving habits of different types of electric vehicles, a Gaussian mixture model is used to describe the distribution of electric vehicle travel time:

[0133] ;

[0134] In the formula, K is the number of Gaussian distributions; , and These represent the weights, mean, and standard deviation of the Gaussian distribution, respectively.

[0135] The initial state of charge of an electric vehicle is a random variable with a Gaussian distribution, and its probability density function is:

[0136] ;

[0137] in, This represents the initial state of charge of the electric vehicle.

[0138] The charging probability selection of electric vehicle users is mainly influenced by their State of Charge (SOC). The lower the SOC, the higher the probability that the user will choose to charge. The charging probability function is as follows:

[0139] ;

[0140] In the formula, , These are the upper and lower thresholds for the state of charge that triggers charging selection, respectively.

[0141] The assessment of electric vehicle responsiveness refers to the assessment based on demand response time periods. and the Electric vehicle grid connection time , obtained the Electric vehicles participate in demand response period The calculation formula is:

[0142] ;

[0143] According to the Initial SOC value of electric vehicles connected to charging piles And the expected value of ending charging It can calculate the shortest charging time. :

[0144] ;

[0145] In the formula, and They represent the first The battery capacity and maximum charging power of an electric vehicle;

[0146] like In line with the charging demand response period published by the power grid Or the period of discharge demand If the results overlap, the electric vehicle is considered to have responsiveness.

[0147] The dynamic pricing decision for charging stations that considers dynamic impedance includes dynamic impedance calculation and constructing a dynamic pricing decision model for charging stations.

[0148] Furthermore, dynamic impedance is calculated as dynamic traffic impedance.

[0149] For each path segment, its time consumption can be obtained using a traffic flow model, with nodes as the unit of time. To the node Taking the path as an example for analysis, based on the relationship between traffic flow, density, and speed, the free-flow speed Represented as:

[0150] ;

[0151] In the formula, For route traffic flow, Given the route traffic density and the maximum capacity, Critical density It can be represented as:

[0152] ;

[0153] Assuming that vehicles in the free-flow portion of the road segment are uniformly distributed along the path, and combining this with the generated electric vehicle behavior information, It can be represented as:

[0154] ;

[0155] In the formula, This represents the number of vehicles in the path. This represents the path length. This indicates the number of vehicles that can be accommodated per unit distance;

[0156] Known traffic density Free flow velocity and minimum driving speed driving distance It can be represented as:

[0157] ;

[0158] α and β are model parameters, and the time spent traversing the path can be expressed as:

[0159] ;

[0160] Considering the traffic flow prediction time interval, it can be expressed as:

[0161] ;

[0162] Where τ is the time of the next traffic flow model prediction; Let the natural numbers satisfy the following formula:

[0163] ;

[0164] At this point, the average speed of the path can be obtained. :

[0165] ;

[0166] Low temperatures have a comprehensive impact on electric vehicles, not only limiting their performance in cold weather but also potentially adversely affecting the long-term health and lifespan of the battery. In other words, different ambient temperatures affect the charging and discharging efficiency of electric vehicles. Therefore, this section discusses the temperature at time t. and discharge efficiency By fitting the curve, the following relationship is obtained:

[0167] ;

[0168] The energy consumption of electric vehicles varies depending on the traffic conditions.

[0169] The remaining battery power of the electric vehicle upon arrival at the charging station can be calculated using the following expression:

[0170] ;

[0171] In the formula, E represents the battery capacity of the electric vehicle. This is the initial state of charge. To encourage electric vehicles to use less congested charging stations. To increase the revenue of charging station operators, it is necessary to study how electric vehicles individually select their target charging stations. Based on Reilly's retail gravity law and considering the influence of external factors such as the charging environment of electric vehicles, a charging station gravity function is constructed:

[0172] ;

[0173] In the formula, For charging stations The appeal of electric vehicle j For charging stations The total number of charging plugs in the middle. To correspond to the charging level of the charging station Rated charging capacity, For the electricity price of charging, For electric vehicles to reach charging stations distance, Parameters can be shared with electric vehicle users via a mobile application, and electric vehicle users can select charging stations. Furthermore, the time and cost associated with traveling from the current location to the selected charging station are displayed via these parameters. This is reflected in the attraction function.

[0174] Furthermore, the boundary points of the charging station can be calculated using the following formula:

[0175] ;

[0176] Charging stations generate revenue by providing charging services to electric vehicles during their daily operations; however, they also incur certain operating costs, primarily the cost of purchasing electricity from the electricity market and maintenance costs. Specifically, the revenue function of a charging station is: ;

[0177] In the formula, For the first The revenue obtained by each charging station For the first The charging price set by each charging station at time t. For the first The number of charging plugs occupied at a charging station at time t. For the first The average charging energy of each charging station.

[0178] The profit function of a charging station can be expressed as:

[0179] ;

[0180] In the formula, for The total revenue of each charging station, and .

[0181] The formula for the decision constraint of charging price is: ;

[0182] In the formula, For the first Each charging station sets a lower limit for charging prices. For the first Each charging station sets an upper limit on charging prices.

[0183] The formula for the operating constraints of energy storage batteries is:

[0184] ;

[0185] In the formula, For the first The energy storage capacity of the energy storage battery equipment in each charging station This represents the upper limit of the capacity of energy storage battery devices. This represents the lower limit of the capacity of energy storage battery devices. and These refer to the charging and discharging efficiencies of energy storage battery devices. For the first The maximum charging capacity of the energy storage battery equipment in each charging station. For the first The maximum discharge capacity of the energy storage battery equipment in each charging station. and The first The charging and discharging states of energy storage battery devices in each charging station are binary variables.

[0186] Constraints on purchasing electricity from the external electricity market, based on the system power balance constraint formula:

[0187] ;

[0188] ;

[0189] At this point, the dynamic robust pricing decision model for electric vehicles regarding charging stations has been completed.

[0190] In a preferred embodiment, an electric vehicle charge / discharge response potential measurement system includes a classification module that classifies and measures the charge / discharge response capability of individual electric vehicles based on SOC (Solution Capacity), where SOC refers to battery capacity; an evaluation module that uses fuzzy inference methods to assess the electric vehicle's charge / discharge response intention, comprehensively considering factors influencing user decisions, and establishes an evaluation model using fuzzy inference techniques; and a measurement module that treats all electric vehicles in different functional areas as a whole, uses the travel characteristics of electric vehicles in different functional areas as input parameters for establishing the evaluation model, and, within the framework of a unified evaluation model, inputs parameters to measure the corresponding electric vehicle's charge / discharge response potential for different functional areas.

[0191] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or independent of it, or they can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0192] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface of the computer device is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen of the computer device may be an LCD screen or an e-ink display screen. The input device of the computer device may be a touch layer covering the display screen, or buttons, a trackball, or a touchpad located on the casing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0193] In summary, this invention accurately characterizes the individual response capability of electric vehicles under different battery charge states by classifying them into three categories based on battery capacity: charging-only, discharging-only, and rechargeable / dischargeable. Combined with fuzzy inference technology, it comprehensively considers factors such as battery capacity, parking duration, and charging / discharging electricity prices to quantify the degree of user willingness to participate in the response, using this as the core decision-making basis for intelligent agent simulation, effectively integrating the uncertainty and subjective preferences of user behavior. Furthermore, by using the macroscopic travel characteristics of different functional areas such as residential areas, commercial areas, and work areas as model inputs, it achieves rapid measurement of the overall response potential of regional clusters within a unified simulation framework. This overcomes the shortcomings of traditional methods that rely on individual travel chain simulation, resulting in high computational complexity, stringent data requirements, and poor generalization, significantly improving the model's practicality and adaptability. It provides scientific and reliable decision support for power systems to conduct large-scale vehicle-grid interactive scheduling, demand response management, and power grid planning.

[0194] Example 2

[0195] Reference Figures 1-9 This is the second embodiment of the present invention, which provides a method for coordinated control of electric vehicle charging and discharging considering time-of-use pricing. To verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0196] The membership functions of the input and output variables of the electric vehicle's charging and discharging response intention are designed. Fuzzy inference is used to calculate the charging and discharging response intention of the electric vehicle. After completing the design of the basic parameters, the membership functions of the input and output variables in the fuzzy inference are designed according to the actual situation of the simulation example.

[0197] To facilitate calculations, membership functions for input and output variables are designed based on simulation model data. Since the electric vehicles used in this invention all have a battery capacity of 60 kWh, the battery capacity range is designed to be 0–60 kWh, the vehicle parking time range is set to 0–24 hours, and the charging / discharging price range is set to 0–1. The membership functions for each input and output variable are as follows: Figure 4 As shown.

[0198] The charging and discharging response potential of three functional areas is analyzed. The vehicle time-sharing distribution based on the time-sharing charging distribution of electric vehicles in different functional areas varies in each scenario. Other parameters are designed according to the basic parameters of the model. To ensure the reliability of the simulation results, this paper conducts 10 simulations for each scenario and summarizes and analyzes all simulation results under each scenario.

[0199] Simulation Results and Analysis of Operating Region Charge-Discharge Response Potential: Simulations were performed on the operating region charge-discharge response potential under different scenarios, and the results are as follows. Figure 5 As shown, influenced by factors such as vehicle time distribution, parking duration, charging and discharging electricity price, and battery capacity, the charging and discharging response potential of electric vehicles in the working area exhibits significant time-based differences. The charging power response potential and charging capacity response potential of electric vehicles in the working area fluctuate. The charging power and capacity response potential of electric vehicles are at their maximum at 15:00, with the maximum charging power response potential exceeding 2000 kW and the maximum charging capacity response potential exceeding 400 kWh. The charging response power and capacity are at their minimum at 20:00. The charging power and capacity response potential of electric vehicles are at a relatively high level from 12:00 to 16:00, with a relatively large range of variation, indicating that the adjustment potential of charging power and capacity is relatively high at this time. Conversely, the charging response power and capacity are at a relatively low level from 0:00 to 3:00 and from 19:00 to 20:00, with a relatively small range of variation during these periods, indicating that the response potential of charging power and capacity is low at these times.

[0200] The discharge power and capacity response potential of electric vehicles (EVs) in the working area both show a trend of first increasing and then decreasing. The discharge response power and capacity of EVs reach their maximum values ​​between 3 PM and 4 PM, with the maximum discharge response power exceeding 800 kW and the maximum discharge response capacity exceeding 350 kWh. At 2 AM, the discharge response power and capacity reach their minimum values, with the minimum discharge response power below 200 kW and the minimum discharge response capacity below 50 kWh. Compared to the charging response potential, the overall discharge potential level decreases. In terms of overall variation, the discharge response power and capacity of EVs are at a relatively high level between 1 PM and 4 PM, during which the range of variation is also relatively large; while the discharge response power and capacity are at a relatively low level between 12 AM and 3 AM, during which the range of variation is also relatively small.

[0201] Through multiple simulations of the working area, it was found that the charging and discharging response potential of the working area has significant characteristics. In terms of charging response potential, the charging power response potential and capacity response potential of the working area are at a high level from 12:00 to 16:00 and the range of charging response potential variation is large; while they are at a low level from 0:00 to 3:00 and from 19:00 to 20:00 and the range of charging response potential variation is small. Further analysis reveals three main reasons for the higher charging response potential between 12:00 and 16:00: First, the charging price is relatively low between 12:00 and 16:00, compared to the peak charging price between 6:00 and 7:00, leading to increased user willingness to charge. Second, vehicle distribution is higher during this period, resulting in a higher rate of residents at work and the highest number of vehicles in work areas. Third, parking duration is longer, as there is still some time before people leave work. These factors contribute to the higher charging response potential of electric vehicle users. Regarding discharging response potential, the working area's discharging response potential is higher between 14:00 and 16:00 and lower between 0:00 and 3:00. This phenomenon is attributed to both vehicle distribution and discharging price. Similar to charging response, although the discharging price peaks between 17:00 and 20:00, the limited number of vehicles in the working area during this period makes it difficult to achieve large-scale discharging regulation potential.

[0202] Simulation results and analysis of residential areas: Multiple simulations of the charging and discharging response potential of residential areas are summarized, and the results are as follows: Figure 6 As shown, in terms of charging response, the time-of-day variation trend of charging response potential in residential areas differs significantly from that in working areas. The time-of-day variation characteristics of charging response potential in residential areas are stronger. The charging power response potential and charging capacity response potential of electric vehicles in residential areas surge at 23:00, with the maximum charging power response potential approaching 5000 kW and the maximum charging capacity response potential exceeding 2500 kWh, significantly higher than the maximum charging response potential of working areas. It should be noted that in addition to exhibiting extremely high levels of charging power and capacity response potential at 23:00, the charging power and capacity response potential of electric vehicles in residential areas are also at relatively high levels from 0 to 1:00, 11:00, and from 16:00 to 17:00. The charging power and capacity response potential are at relatively low levels from 8:00 to 10:00 and from 18:00 to 23:00, and the variation range of charging power and capacity during these periods is also smaller.

[0203] Regarding the discharge response in residential areas, the discharge power and capacity response potential of electric vehicles both exhibited fluctuating changes. The discharge response power and capacity of electric vehicles reached their maximum values ​​at 18:00, with the maximum response discharge power around 1200 kW and the maximum response discharge capacity exceeding 100 kWh. The discharge response power and capacity of electric vehicles reached their minimum values ​​at 5:00 AM, with the minimum discharge regulation power and minimum discharge response capacity both below 200 kW. In terms of overall changes, the discharge response power and capacity of electric vehicles were at a relatively high level from 19:00 to 22:00, while they were at a relatively low level from 5:00 to 6:00.

[0204] Summarizing the results of multiple simulations of residential areas, it was found that the charging and discharging response potential of residential areas has significant temporal distribution characteristics. The charging power regulation potential and capacity regulation potential of residential areas surge at 23:00, and remain at high levels from 0:00 to 1:00, 11:00, and 16:00 to 17:00. The charging regulation power and capacity remain at low levels from 8:00 to 10:00 and from 18:00 to 23:00. This invention believes that the combined effects of charging electricity price, vehicle distribution, and parking time lead to this distribution. Typically, residents return to their residences after get off work in the evening, resulting in a higher vehicle distribution in residential areas at night. In addition, the charging electricity price is at its off-peak level from 23:00 to 6:00 the next morning. Therefore, without considering the convenience and availability of charging, people's willingness to charge is concentrated at 23:00, reaching its maximum value.

[0205] Regarding discharge response potential, the discharge response potential in residential areas is highest at 6 PM, and remains at a relatively high level from 6 PM to 10 PM. This paper argues that vehicle distribution and discharge electricity prices are the main reasons for this characteristic distribution of discharge response potential. Unlike work areas, people mostly return to residential areas after 6 PM, at which time the number of electric vehicles in residential areas increases rapidly. Furthermore, the discharge electricity price peaks between 6 PM and 8 PM, leading to a higher willingness to discharge during this period, thus resulting in a higher discharge response potential during this time.

[0206] Simulation results and analysis of the commercial area: Based on two scenarios of time-sharing charging distribution in the commercial area, the charging and discharging response potential of the commercial area is simulated. Specific simulation results are as follows: Figure 7As shown, compared with other functional areas, the overall charging and discharging response potential of the commercial area is lower. Except for a few time points, the charging response power of electric vehicles does not exceed 1000 kW, and the charging response power and capacity of electric vehicles show a fluctuating trend. The charging response potential increases significantly at 11:00 and 23:00 compared with other time points, and reaches its maximum at 23:00, with the maximum charging response power approaching 2500 kW and the maximum charging response capacity exceeding 1200 kWh. In terms of overall distribution, the charging response power and capacity of the commercial area are at a relatively high level from 11:00 to 17:00; while the charging response power and capacity are at a relatively low level from 1:00 to 10:00 and from 18:00 to 22:00, with a smaller range of variation, indicating that the charging adjustment potential is relatively small during these periods.

[0207] The electric vehicle cluster reaches its maximum discharge response potential at 11 PM, with a maximum discharge response power approaching 1000 kW and a maximum discharge response capacity approaching 700 kWh. In terms of overall distribution, the discharge response power and capacity in the commercial area exhibit a two-tiered distribution. From 8 AM to 11 PM, the discharge regulation potential and capacity in the commercial area are generally at a high level, with discharge response power and capacity mostly fluctuating around 300 kW and 300 kWh respectively; from 12 AM to 7 AM, the discharge response power and capacity mostly fluctuate around 200 kW and 100 kWh respectively.

[0208] Under the combined influence of vehicle time-sharing distribution, parking duration, and charging / discharging electricity prices, the charging / discharging response potential of commercial areas exhibits a more complex trend compared to other areas, and this trend simultaneously reflects the characteristics of both residential and workplace areas. Further analysis suggests this is closely related to the complexity of the population in commercial areas. Compared to other functional areas, commercial areas have a more diverse population, including not only shoppers and entertainment venues but also workers. This more complex and variable behavior leads to the complex variations in the charging / discharging response potential of electric vehicles in commercial areas. Although the variations in charging / discharging response potential in commercial areas are complex, they exhibit higher charging response potential between 11:00 and 17:00 and at 23:00, and higher discharging response potential at 18:00 and 23:00 under both scenarios.

[0209] After introducing the method and system of exemplary embodiments of the present invention, the following references are made. Figure 8 A computer-readable storage medium according to exemplary embodiments of the present invention will be described, please refer to... Figure 8The computer-readable storage medium shown is an optical disc 30, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it implements the steps described in the above-described method implementation. For example, it classifies and measures the charging and discharging response capability of individual electric vehicles based on SOC (Solution Capacity), where SOC refers to battery capacity; it uses fuzzy inference to determine the charging and discharging response intention of electric vehicles, comprehensively considers factors affecting user decisions, and establishes an evaluation model using fuzzy inference technology; it treats all electric vehicles in different functional areas as a whole, uses the travel characteristics of electric vehicles in different functional areas as input parameters for establishing the evaluation model, and, within the framework of the unified evaluation model, inputs parameters to measure the corresponding electric vehicle charging and discharging response potential for different functional areas. The specific implementation methods of each step will not be repeated here. It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0210] After introducing the methods and media of exemplary embodiments of the present invention, the following references are made. Figure 9 A computational device for adaptive recovery of low-voltage power grid self-healing control according to an exemplary embodiment of the present invention.

[0211] Figure 9 A block diagram is shown of an exemplary computing device 40 suitable for implementing embodiments of the present invention. The computing device 40 may be a computer system or a server. Figure 9 The computing device 40 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0212] like Figure 9 As shown, the components of computing device 40 may include, but are not limited to: one or more processors or processing units 401, system memory 402, and bus 403 connecting different system components (including system memory 402 and processing unit 401).

[0213] The computing device 40 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 40, including volatile and non-volatile media, and removable and non-removable media.

[0214] System memory 402 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 4021 and / or cache memory 4022. Computing device 40 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 4023 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 9 (Not shown in the image, usually referred to as "hard drive"). Although not shown in... Figure 9 The diagram illustrates that disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) can be provided. In these cases, each drive can be connected to bus 403 via one or more data media interfaces. System memory 402 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0215] A program / utility 4025 having a set (at least one) of program modules 4024 may be stored, for example, in system memory 402, and such program modules 4024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment. Program modules 4024 typically perform the functions and / or methods described in the embodiments of the present invention.

[0216] The computing device 40 can also communicate with one or more external devices 404 (such as a keyboard, pointing device, display, etc.). This communication can be performed via the input / output (I / O) interface 405. Furthermore, the computing device 40 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 406. Figure 9 As shown, network adapter 406 communicates with other modules of computing device 40 (such as processing unit 401) via bus 403. It should be understood that, although... Figure 9 As not shown, it can be used in conjunction with computing device 40 with other hardware and / or software modules.

[0217] The processing unit 401 executes various functional applications and data processing by running programs stored in the system memory 402. For example, it classifies and measures the charging and discharging response capabilities of individual electric vehicles based on SOC (Solution Capacity), where SOC refers to battery capacity; it uses fuzzy inference methods to assess the charging and discharging response intentions of electric vehicles, comprehensively considers factors affecting user decisions, and establishes an evaluation model using fuzzy inference techniques; it treats all electric vehicles in different functional areas as a whole, uses the travel characteristics of electric vehicles in different functional areas as input parameters for establishing the evaluation model, and, within the framework of the unified evaluation model, inputs parameters to measure the corresponding electric vehicle charging and discharging response potential for different functional areas.

[0218] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0219] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0220] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0221] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0222] If the functionality is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion 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 of the various embodiments of this invention. The aforementioned 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.

[0223] Finally, it should be noted that the above embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0224] Furthermore, although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0225] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for coordinated regulation of charging and discharging of electric vehicles considering time-of-use electricity price, characterized in that: include, Based on SOC, the charging and discharging response capability of a single electric vehicle is classified and measured, where SOC refers to battery capacity; The willingness to charge and discharge electric vehicles is assessed using fuzzy reasoning methods. Taking into account factors that influence user decisions, an evaluation model is established using fuzzy reasoning techniques. All electric vehicles in different functional areas are treated as a whole. The travel characteristics of electric vehicles in different functional areas are used as input parameters to establish an evaluation model. Within the framework of the unified evaluation model, the input parameters are used to measure the charging and discharging response potential of the corresponding electric vehicles in different functional areas. Based on the corresponding electric vehicle charging and discharging response potential, the dispatchability of charging stations is predicted based on user response capabilities, and dynamic pricing decisions for charging stations are made considering dynamic impedance. 2.The method of claim 1, wherein the method comprises: The classification of the charging and discharging response capabilities of individual electric vehicles includes three categories: charging-only, discharging-only, and rechargeable / dischargeable. The only allowed charging is used when the electric car has a low charge, i.e. the battery charge is below a minimum charge accepted by the user At this time, the electric car connected to the grid is only allowed to charge. The discharging-only is used when the electric vehicle is in a full charge state, i.e. the battery charge percentage is at the upper limit of the user's charge To 1, at which time the electric vehicle connected to the power grid is only allowed to discharge; The chargeable and dischargeable is used when the remaining electric quantity of the electric automobile is lower than the lower limit of the discharging electric quantity only The upper limit of the charging electric quantity only At this time, the electric automobile is both discharging and charging. Based on the classification of the charging and discharging response capabilities of individual electric vehicles, the charging and discharging capacity and power response capabilities of three types of electric vehicles were measured respectively. 3.The method of claim 2, wherein the method comprises: In the state where electric vehicles are only allowed to discharge, the formulas for calculating the discharge response capacity and discharge power of an electric vehicle that is only allowed to discharge during the parking period are as follows: The formula for the discharge response capacity of the electric vehicle that is only allowed to discharge during the parking period is: ； The formula for the discharge power of the electric vehicle that is only allowed to discharge during the parking time is: ； wherein, a discharge capacity response capability of the electric vehicle, a discharge capacity response capability of the electric vehicle, a battery charge of the electric vehicle, a minimum charge acceptable to the user, an upper limit of the battery charge desired by the user, an arrival time of the electric vehicle, a stay duration of the electric vehicle, a discharge adjustment power of the vehicle, a discharge adjustment power of the vehicle, a discharge adjustment power of the vehicle, a discharge adjustment power of the vehicle. 4.The method of claim 3, wherein the method further comprises: determining the time interval of the time-of-use electricity price; and determining the time interval of the time-of-use electricity price. In the charging-only electric vehicle mode, the electric vehicle is only allowed to perform charging operations. The formulas for calculating the charging response capacity and charging power of the electric vehicle during the parking period are as follows: The formula for the charging response capacity of the electric vehicle that is only allowed to charge during the parking period is: ； The formula for the charging power of electric vehicles that are only allowed to charge during the parking period is: ； wherein, for an electric vehicle a charging capacity response capability, a rated power of the battery, for a vehicle a charging response power, a charging power allowed for the vehicle. 5.The method of claim 4, wherein the method further comprises: determining the time interval of the time-of-use electricity price; and determining the time interval of the time-of-use electricity price. In the state of a rechargeable electric vehicle; If the minimum battery level allowed by the electric vehicle user is and the maximum battery level is the rated capacity of the battery , then the capacity and power response capability formula for both charging and discharging of the electric vehicle when the battery level is between the minimum and maximum levels is: The capacity formula for charging and discharging electric vehicles is as follows: )； The formula for the response capability of electric vehicle charging and discharging is: ； wherein, is a discharging capacity response capability of the electric vehicle, is a charging capacity response capability of the electric vehicle. 6.The method of claim 5, wherein the method further comprises: determining the time interval of the time-of-use electricity price; and determining the time interval of the time-of-use electricity price. The fuzzy reasoning technique for establishing the evaluation model using fuzzy reasoning methods includes the following steps: Establishment of variable fuzzy language and membership function; Definition of fuzzy inference rules; Selection of fuzzy computing and defuzzification methods; The variable fuzzy language and membership function establishment includes taking the charging price, discharging price, battery power and parking time as the input variables of fuzzy reasoning, taking the charging response willingness and discharging response willingness as the output variables, and the language variables of the input variables and the output variables are all divided into three fuzzy subsets, wherein the language variable set of the charging price and discharging price input variables is ; Each subset respectively represents that the charging price or discharging price is at high, medium or low level; the language variable set of charging response willingness and discharging response willingness output quantity is , and each subset respectively represents that the charging response willingness or discharging response willingness of the user is at high, medium or low level. The triangular membership function is used to characterize the membership degree of each subset. The formula for the triangular membership function is: ； wherein, denotes the minimum value of the triangular distribution, denotes the maximum value of the triangular distribution, denotes the peak point, denotes the interval; When To The membership is highest 1 when driving, and is 0 when Exceeding Or ; The fuzzy inference rule definition includes rule 1, rule 2, and rule 3; The selection of fuzzy computing and defuzzification methods includes, in terms of fuzzy inference rule calculation methods, adopting the Mamdani fuzzy inference calculation method to calculate the charging response intention of electric vehicle users after defuzzification and to calculate the different levels of the response intention of electric vehicle users after defuzzification; The formula for the defuzzified charging response intention of electric vehicle users is: ； The formula for calculating the different levels of response intention of electric vehicle users after defuzzification is as follows: ； wherein, represents the calculated charging response willingness of the electric vehicle user after deblurring, represents the degree to which the calculated user response willingness after deblurring belongs to different levels, X, Y, Z represent a triple, represents the fuzzy set corresponding to the triple X, represents the fuzzy set corresponding to the triple Y, represents the fuzzy set corresponding to the triple Z, represents the set of charging response states, represents the membership degree of state v;​​​ The different functional areas include residential areas, work areas, and commercial areas. The charging and discharging response potential of each functional area is simulated using Python 3.9 software. The charging and discharging response potential of the three types of functional areas is analyzed, and the corresponding charging and discharging response potential of electric vehicles is measured.

7. The method of claim 6, wherein the method further comprises: determining the time interval of the time-of-use electricity price; and determining the time interval of the time-of-use electricity price. The prediction of the schedulable charging station capacity for user response includes constructing a road network model, analyzing factors influencing electric vehicle charging behavior, evaluating electric vehicle response capabilities, and predicting the schedulable charging station capacity. The formula for constructing the road network model is as follows: ； in, It represents the set of road networks. This represents the set of road network nodes, which has a total of One node; Indicated Represents the set of road segment weight values. Indicates the first One road network node; This indicates the connection of road network nodes. , The section of road; Indicates in Time period and road section The weights, road network set The connection relationships between nodes are represented by an adjacency matrix. To describe; The analysis of factors influencing electric vehicle charging behavior includes information on electric vehicle travel time, state of charge information, and charging probability selection. The assessment of electric vehicle responsiveness refers to the assessment based on demand response time periods. and the Electric vehicle grid connection time , obtained the Electric vehicles participate in demand response period ; The prediction of the dispatchable capacity of charging stations includes setting road network parameters and demand response time periods, generating the travel time and initial state of charge of each electric vehicle through Monte Carlo sampling, and reading the origin and destination information from the OD matrix. Calculate road travel time based on speed-flow model, simulate EV driving, select the nearest charging station to enter and record load information, and report the EV's demand response capability. The dynamic pricing decision for charging stations that considers dynamic impedance includes dynamic impedance calculation and constructing a dynamic pricing decision model for charging stations.

8. A system for coordinated regulation of charging and discharging of electric vehicles considering time-of-use electricity price, based on the method for coordinated regulation of charging and discharging of electric vehicles considering time-of-use electricity price according to any one of claims 1-7, characterized in that: include, The classification module classifies and measures the charging and discharging response capabilities of individual electric vehicles based on SOC, where SOC refers to battery capacity. The evaluation module utilizes fuzzy reasoning methods to assess the charging and discharging response intentions of electric vehicles, comprehensively considers factors influencing user decisions, and establishes an evaluation model using fuzzy reasoning techniques. The measurement module treats all electric vehicles in different functional areas as a whole, uses the travel characteristics of electric vehicles in different functional areas as input parameters to build an evaluation model, and inputs parameters within the framework of a unified evaluation model to measure the charging and discharging response potential of the corresponding electric vehicles for different functional areas. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: When the processor executes the computer program, it implements the steps of the electric vehicle charging and discharging coordinated control method considering time-of-use pricing as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the electric vehicle charging and discharging coordinated control method considering time-of-use pricing as described in any one of claims 1 to 7.

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