Electric vehicle cluster frequency modulation capacity evaluation method, system and equipment considering user behavior characteristics, and medium

Abstract

The invention discloses an electric vehicle cluster frequency modulation capacity evaluation method, system and device considering user behavior characteristics and a medium, and the method comprises the steps: building a charging power influence factor probability model considering driving characteristics through collecting vehicle data and user behavior data, and calculating model parameters through employing a parameter estimation algorithm; establishing a classification power model for different vehicle type charging modes and probability features; frequency modulation / charging cluster groups are divided based on the frequency modulation time margin, the charging power of frequency modulation clusters is calculated through Monte Carlo simulation of Latin hypercube sampling, and the charging power is substituted into the evaluation model to obtain the frequency modulation capacity of the clusters at each moment in the future; and dynamic data updating and model rolling optimization continuously improve the evaluation precision. Limitation of a traditional single distribution model is broken through, calculation speed and simulation precision are both considered, and key technical support is provided for electric vehicles to participate in the frequency modulation auxiliary service market.

Description

Technical Field

[0001] This invention belongs to the field of charging pile cluster frequency regulation technology, specifically involving a method, system, equipment, and medium for evaluating the frequency regulation capacity of electric vehicle clusters that takes into account user behavior characteristics. Background Technology

[0002] With the large-scale grid integration of new energy sources, the new power system, dominated by new energy sources, will face significant challenges in frequency regulation. Electric vehicles (EVs), as user-end devices with autonomous behavior, cannot be directly controlled by the grid; instead, vehicle owners actively participate in and accept grid dispatch. The energy storage characteristics of EV batteries allow them to function as distributed energy storage devices for bidirectional power transmission with the grid, but priority must be given to meeting users' travel needs. How to assess the grid frequency regulation capacity of existing large EV clusters to fully utilize their energy storage characteristics is a highly valuable research topic.

[0003] Currently, there are some research results on utilizing the energy storage characteristics of electric vehicles to participate in power system frequency regulation, mainly focusing on participation in system frequency regulation control, with fewer research results on frequency regulation capacity prediction. The literature [Liu Kezhen, et al. Two-stage optimized scheduling of electric vehicles dynamically participating in frequency regulation services considering user willingness [J]. Power System Technology] uses limited user willingness data collected through questionnaires as the research object. Based on the support vector machine-random forest algorithm under snake optimization, it constructs a user willingness classification prediction model, integrates user willingness and frequency regulation capability, divides electric vehicles into clusters, and establishes a dynamic frequency regulation output model for the clusters to evaluate the frequency regulation potential of the frequency regulation clusters. This research addresses the problem that scheduling plans are formulated before frequency regulation, leading to the inability to collect user willingness data, by predicting user willingness, but it does not consider the driving characteristics of different types of electric vehicles. The literature [Zang Hanzhou, et al. Constrained Electric Vehicle Charging and Discharging Strategies Based on Interior Point Strategy Optimization [J]. Power Grid Technology] establishes a scheduling model considering the enthusiasm of electric vehicles to provide frequency regulation ancillary services from two aspects: user travel charging demand and battery degradation cost compensation. It analyzes the impact of electric vehicle frequency regulation compensation prices on users' enthusiasm to participate in frequency regulation ancillary services. However, this literature only provides a simple model of electric vehicle user behavior characteristics, and the accuracy of frequency regulation capacity assessment needs to be verified. How to conduct frequency regulation capacity assessment of electric vehicle clusters without affecting the travel charging demand of electric vehicle users is a technical challenge. It requires considering both the different travel characteristics of different electric vehicle users and the reality that individual user travel characteristics are highly random. However, if electric vehicle clusters want to participate in the frequency regulation ancillary service market, they need to predict the frequency regulation capacity at specific future moments in advance; otherwise, accurate application for frequency regulation capacity cannot be made. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method, system, device, and medium for evaluating the frequency regulation capacity of electric vehicle clusters that takes into account user behavior characteristics.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for evaluating the frequency regulation capacity of electric vehicle clusters that takes into account user behavior characteristics, comprising the following steps:

[0007] Step S1: Collect electric vehicle-related data, including electric vehicle data itself and user behavior data;

[0008] Step S2: Establish a probabilistic model of the factors affecting the charging power of electric vehicles that take into account the user's driving characteristics. Based on the electric vehicle data and user behavior data collected in Step S1, use a parameter estimation algorithm to estimate the parameters of the probabilistic model of the factors affecting the charging power of different types of electric vehicles.

[0009] Step S3: Establish a probabilistic model for charging power of different types of electric vehicles, taking into account charging mode and charging probability;

[0010] Step S4: Based on the electric vehicle frequency regulation time margin, electric vehicles are divided into frequency regulation clusters and charging clusters. Combining the charging power probability model of different types of electric vehicles in the frequency regulation clusters and the parameter estimation results of step S2, the charging power values ​​of electric vehicles are calculated using the Monte Carlo simulation method based on Latin hypercube sampling. The charging power values ​​are then input into the established electric vehicle cluster frequency regulation capacity evaluation model to obtain the electric vehicle cluster frequency regulation capacity at each time point in the next day.

[0011] Step S5: Based on the electric vehicle cluster's participation in power system frequency regulation, dynamically collect the latest electric vehicle-related data, dynamically repeat steps S1 to S4, continuously update the charging power probability models and parameters of different types of electric vehicles based on the latest data, and conduct rolling assessments of the electric vehicle cluster's frequency regulation capacity.

[0012] Furthermore, in step S1, the electric vehicle's own data includes electric vehicle type, battery capacity, state of charge, charging mode and its corresponding charging power, discharging power, charging efficiency, discharging efficiency, and maximum driving range data; the electric vehicle type includes four types: electric bus, electric taxi, electric private car and electric official vehicle; the charging mode includes two types: regular charging and fast charging; the user behavior data includes daily driving mileage, starting charging time, expected time to leave the charging station, and set state of charge data.

[0013] Furthermore, in step S2, the factors influencing the electric vehicle charging power include the state of charge before charging, charging duration, initial charging time, electric vehicle type, and charging mode. The established probabilistic models for the factors influencing the electric vehicle charging power considering user driving characteristics include a probabilistic model of the state of charge before charging, a probabilistic model of charging duration, and a probabilistic model of initial charging time; wherein the probabilistic model of the state of charge before charging is:

[0014]

[0015] In the formula, E t0 E represents the state of charge before charging. set0 D represents the state of charge of the electric vehicle after its last charge, and D represents the maximum driving range of the electric vehicle in pure electric mode. , These represent the expected value and standard deviation of the daily mileage of electric vehicles, respectively.

[0016] The charging time required for an electric vehicle to reach its set state of charge is:

[0017]

[0018] In the formula, T ch For the required charging time, E set1 For the set state of charge, Q n For rated capacity, P ch For charging power, η ch For charging efficiency;

[0019] The probabilistic model for the charging time required to bring an electric vehicle to a set state of charge is as follows:

[0020]

[0021] A finite mixture model is used to construct a probabilistic model for the initial charging time of electric vehicles, namely:

[0022]

[0023] In the formula, t0 is the initial charging time of the electric vehicle; M is the number of hybrid components in the finite hybrid model; This is the k-th component function of a finite mixture model; Let be the parameter vector of the k-th component function; Let the weight of the k-th component function be given, and let it satisfy the following requirements:

[0024]

[0025] The component functions of the hybrid model with finite initial charging time for electric vehicles adopt a log-normal distribution on weekdays and a normal distribution on rest days, i.e.:

[0026]

[0027] In the formula, This is the k-th component function of the finite mixture model of working days; This is the k-th component function of the finite mixture model for rest days; and These are the expected value and standard deviation of the logarithm of the k-th component function variable in the finite mixture model of working days, respectively. and Let $\begin{pmatrix}$ be the expected value and standard deviation of the k-th component function variable in the finite mixture model of rest days, respectively.

[0028] Based on the electric vehicle data and user behavior data collected in step S1, the maximum likelihood estimation algorithm is used to estimate the expected value and standard deviation of the daily mileage for four types of electric vehicles: electric buses, electric taxis, electric private cars, and electric official vehicles. This yields the probability distribution of the pre-charging state of charge and charging time for different types of electric vehicles. The expectation-maximization algorithm is used to estimate the starting charging time for the four types of electric vehicles: electric buses, electric taxis, electric private cars, and electric official vehicles. This yields the expected value and standard deviation of the starting charging time for different types of electric vehicles. This yields the probability distribution of the starting charging time for different types of electric vehicles.

[0029] Furthermore, in step S3, a day is divided into 1440 / a time periods at intervals of a minutes, and the charging power P of the electric vehicle at time t in the future day is calculated. ch.t The probability model is as follows:

[0030]

[0031] In the formula, P ch The charging power of the electric vehicle; φ is the charging probability; w t For the charging status of electric vehicles, w t =1 indicates that charging is in progress. t =0 indicates that the battery is not charging or is fully charged.

[0032] There are four scenarios for electric vehicle charging power, as shown below:

[0033]

[0034] In the formula, P ch.1 P represents the charging power in normal charging mode. ch.2 This refers to the charging power in fast charging mode.

[0035] The probability of an electric vehicle being charged at time t within the next day is:

[0036]

[0037] In the formula, Let be the joint probability distribution function of charging start time and charging duration, i.e.:

[0038]

[0039] In the formula, Let be the probability distribution function for the charging start time. Let the charging time probability distribution function be used.

[0040] For electric buses, a two-charge-per-day charging method is generally used, with fast charging during the day and regular charging at night. The charging power P of the electric bus at time t on a future day is... GJ.ch.t The probability model is as follows:

[0041]

[0042] In the formula, P GJ.ch.1 P GJ.ch.2 These are the charging powers corresponding to the regular charging and fast charging modes for electric buses, respectively.

[0043] For electric taxis, a two-charge-per-day charging schedule is generally adopted, using fast charging mode. The charging power P of an electric taxi at time t on a future day is... CZ.ch.t The probability model is as follows:

[0044]

[0045] In the formula, P CZ.ch.2 The charging power corresponding to the fast charging mode of electric taxis;

[0046] For electric private cars, a multi-day charging pattern is generally adopted. During the day, fast charging is used at work or shopping malls, while regular charging is used at night in residential areas. The charging power P of the electric private car at time t on the next day is... SJ.ch.t The probability model is as follows:

[0047]

[0048] In the formula, P SJ.ch.1 P SJ.ch.2 These are the charging powers corresponding to regular charging and fast charging modes for electric private vehicles, respectively.

[0049] For electric official vehicles, a daily charging schedule is generally adopted, using a conventional charging mode. The charging power P of the electric official vehicle at time t on a future day is... GW.ch.t The probability model is as follows:

[0050]

[0051] In the formula, P GW.ch.1 The charging power corresponding to the normal charging mode of electric official vehicles.

[0052] Furthermore, in step S4, the frequency regulation time margin of the electric vehicle is defined as follows:

[0053]

[0054] In the formula, δ represents the frequency regulation time margin of the electric vehicle. A δ greater than 1 indicates that the electric vehicle can participate in frequency regulation, and it is marked as R=1; a δ less than 1 indicates that the electric vehicle cannot participate in frequency regulation, and it is marked as R=0; t LK Estimated time for electric vehicle users to leave charging stations;

[0055] Based on the frequency regulation time margin of electric vehicles, different types of electric vehicles are divided into frequency regulation cluster groups or charging cluster groups; for electric vehicles in the frequency regulation cluster group, the charging power probability model of different types of electric vehicles established in step S3 and the parameter estimation results in step S2 are combined, and the Monte Carlo simulation method based on Latin hypercube sampling is used to calculate the charging demand of electric vehicles at 1440 / a time points in the next day.

[0056] The Latin hypercube sampling steps are as follows: First, calculate the cumulative distribution function of the random variable b. The value interval [0,1] is divided into N equal parts to obtain N sub-intervals; then, a sample value of H is selected from each sub-interval. This sample value can be selected from the midpoint of the sub-interval or a boundary point close to the expected value of b; then, N sample values ​​of the random variable b are obtained through the inverse function of the cumulative distribution function, and finally, the random variable sample matrix B is obtained. 1×N ;

[0057] The calculated charging demand of electric vehicles at 1440 / a times within the next day is used as input data and substituted into the electric vehicle cluster frequency regulation capability assessment model to obtain the electric vehicle cluster frequency regulation capacity at 1440 / a times within the next day. The specific formula is as follows:

[0058]

[0059] In the formula, , N represents the maximum up-regulation power and maximum down-regulation power of the electric vehicle cluster in the region at time t, respectively, for participating in frequency regulation; k.ch N represents the number of electric vehicles connected to charging stations that only offer charging functionality; k.dc This indicates the number of electric vehicles connected to charging stations that have both charging and discharging capabilities. , Let be the charging power of electric vehicles i and j at time t, respectively; , These are the rated charging powers of electric vehicles i and j, respectively; Let be the discharge power of electric vehicle j at time t; Let J be the rated discharge power of electric vehicle j. , These represent the charging and discharging states of an electric vehicle connected to a charging station with charging and discharging functions, respectively. When in charging state... ,otherwise When in a discharge state ,otherwise ;

[0060] In addition, electric vehicles in frequency modulation trunking groups should also meet the following requirements:

[0061]

[0062] In the formula, E min E max These represent the minimum and maximum states of charge of an electric vehicle battery, respectively. , These represent the charging efficiency and discharging efficiency of electric vehicle j, respectively. This indicates the state of charge of the electric vehicle at the user-defined departure time.

[0063] Furthermore, in step S5, based on the electric vehicle cluster's participation in power system frequency regulation, when assessing the electric vehicle cluster's frequency regulation capacity for the next two days on the next natural day, the relevant electric vehicle data collected on the previous natural day is dynamically input into step S1. The models and parameters of steps S2 and S3 are dynamically updated based on the latest relevant electric vehicle data. On this basis, the electric vehicle cluster's frequency regulation capacity at each time point on the next two days is obtained through step S4. Also, the time scale of the electric vehicle cluster's frequency regulation capacity assessment is dynamically adjusted according to the frequency regulation requirements.

[0064] Secondly, the present invention provides an electric vehicle cluster frequency regulation capacity assessment system that takes into account user behavior characteristics, comprising:

[0065] The data collection module is used to collect data related to electric vehicles. The electric vehicle frequency tuning time includes data about the electric vehicle itself and user behavior data.

[0066] The influencing factor probability model and parameter estimation module is used to establish a probability model of the influencing factors of electric vehicle charging power considering user driving characteristics. Based on the collected data of the electric vehicle itself and user behavior data, the parameter estimation algorithm is used to estimate the parameters of the probability model of the influencing factors of charging power of different types of electric vehicles.

[0067] The charging power probability model module is used to establish charging power probability models for different types of electric vehicles that take into account charging modes and charging probabilities.

[0068] The electric vehicle cluster frequency regulation capacity assessment module divides electric vehicles into frequency regulation clusters and charging clusters based on the frequency regulation time margin of electric vehicles. Combining the charging power probability model of different types of electric vehicles in the frequency regulation clusters and the parameter estimation results of step S2, the module uses the Monte Carlo simulation method based on Latin hypercube sampling to calculate the charging power value of electric vehicles. The charging power value is then input into the established electric vehicle cluster frequency regulation capacity assessment model to obtain the electric vehicle cluster frequency regulation capacity at each time point in the next day.

[0069] The dynamic evaluation module is used to dynamically collect the latest electric vehicle-related data based on the electric vehicle cluster's participation in power system frequency regulation. It dynamically and repeatedly executes the data collection module, the influencing factor probability model and parameter estimation module, the charging power probability model module, and the electric vehicle cluster frequency regulation capacity evaluation module. Based on the latest data, it continuously updates the charging power probability models and parameters of different types of electric vehicles and conducts rolling evaluations of the electric vehicle cluster frequency regulation capacity.

[0070] Thirdly, the present invention provides an electronic device including a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the above-described method for evaluating the frequency regulation capacity of electric vehicle clusters that takes into account user behavior characteristics.

[0071] Fourthly, the present invention provides a computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the above-described method for evaluating the frequency regulation capacity of electric vehicle clusters taking into account user behavior characteristics.

[0072] This invention establishes a probabilistic model of the factors influencing electric vehicle charging power considering user driving characteristics. A finite mixture model is used to construct a probabilistic model of the initial charging time of electric vehicles, overcoming the limitations of traditional single-distribution models using Weibull distributions to describe the initial charging time, which lack the ability to describe multi-peak and skewed characteristics. This model aligns with the randomness, asymmetry, and multi-peak characteristics of electric vehicle initial charging time. Two types of parameter estimation algorithms are used to estimate parameters based on the characteristics of different probabilistic models. Taking into full account the actual charging characteristics of four types of electric vehicles, probabilistic models of charging power for different types of electric vehicles considering multiple charging modes and charging probabilities are established, making the models more realistic. Based on the frequency modulation time margin, electric vehicles are divided into frequency modulation clusters and charging clusters. To address the lack of analytical solutions in the probabilistic model of electric vehicle charging, this invention employs a Monte Carlo simulation method based on Latin hypercube sampling to calculate the charging power of electric vehicles in frequency-modulated clusters. By utilizing Latin hypercube sampling for stratified sampling based on the cumulative distribution function of the input random variables, the calculation speed is significantly improved while maintaining the accuracy of the Monte Carlo simulation method. Simultaneously, based on the established electric vehicle cluster frequency regulation capacity assessment model, the frequency regulation capacity of the electric vehicle cluster at various future times can be obtained. According to the frequency regulation needs of the electric vehicle cluster participating in the system, the latest data is dynamically collected and the model and parameters are updated, continuously evaluating the frequency regulation capacity of the electric vehicle cluster and continuously improving the model and parameter fitting accuracy, thereby continuously improving the accuracy of the electric vehicle cluster frequency regulation capacity assessment. This invention lays the foundation for electric vehicle clusters to participate in the frequency regulation ancillary service market. Attached Figure Description

[0073] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0074] Figure 1 A flowchart illustrating an exemplary embodiment of the present invention for evaluating the frequency regulation capacity of an electric vehicle cluster that takes into account user behavior characteristics;

[0075] Figure 2 A schematic diagram of the structure of an electric vehicle cluster frequency regulation capacity assessment system that considers user behavior characteristics, provided as an exemplary embodiment of the present invention;

[0076] Figure 3 A structural block diagram of an electronic device provided as an exemplary embodiment of the present invention. Detailed Implementation

[0077] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0078] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0079] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of the present invention are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0080] It should also be understood that in the embodiments of the present invention, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0081] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more unless explicitly defined or given contrary instructions in the context.

[0082] In addition, the term "and / or" in this invention is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0083] In addition, in this invention, the character " / " generally indicates that the objects before and after it are in an "or" relationship.

[0084] It should also be understood that the description of the various embodiments in this invention emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0085] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0086] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0087] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0088] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0089] The embodiments of this invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose computing system environments or configurations. Well-known examples of terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.

[0090] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0091] Exemplary methods

[0092] Figure 1 This is a flowchart illustrating an exemplary embodiment of the present invention regarding a method for assessing the frequency regulation capacity of electric vehicle clusters that considers user behavior characteristics. This embodiment can be applied to electronic devices, such as... Figure 1 As shown, the method for evaluating the frequency regulation capacity of electric vehicle clusters considering user behavior characteristics includes the following steps:

[0093] Step S1: Collect electric vehicle-related data, including electric vehicle data itself and user behavior data.

[0094] Specifically, the collected data on the electric vehicles themselves includes electric vehicle type, battery capacity, state of charge, charging mode and its corresponding charging power, discharging power, charging efficiency, discharging efficiency, and maximum driving range; electric vehicle types include four types: electric buses, electric taxis, electric private cars and electric official vehicles; charging modes include two types: regular charging and fast charging; user behavior data includes daily driving mileage, starting charging time, expected time to leave the charging station, and set state of charge data.

[0095] Step S2: Establish a probabilistic model of the factors affecting the charging power of electric vehicles that take into account the user's driving characteristics. Based on the electric vehicle data and user behavior data collected in Step S1, use a parameter estimation algorithm to estimate the parameters of the probabilistic model of the factors affecting the charging power of different types of electric vehicles.

[0096] Specifically, since the factors affecting electric vehicle charging power include the state of charge before charging, charging duration, initial charging time, electric vehicle type, and charging mode; and for a single electric vehicle, the state of charge before charging, charging duration, and initial charging time are highly random, a probabilistic model considering the user's driving characteristics and the factors affecting electric vehicle charging power is established, including a probabilistic model of the state of charge before charging, a probabilistic model of charging duration, and a probabilistic model of initial charging time. The probabilistic model of the state of charge before charging is as follows:

[0097]

[0098] In the formula, E t0 E represents the state of charge before charging. set0 D represents the state of charge of the electric vehicle after its last charge, and D represents the maximum driving range of the electric vehicle in pure electric mode. , These represent the expected value and standard deviation of the daily mileage of electric vehicles, respectively.

[0099] The charging time required for an electric vehicle to reach its set state of charge is:

[0100]

[0101] In the formula, T ch For the required charging time, E set1 For the set state of charge, Q n For rated capacity, P ch For charging power, η ch For charging efficiency;

[0102] The probabilistic model for the charging time required for an electric vehicle to reach a set state of charge is as follows:

[0103]

[0104] Because the initial charging time of electric vehicles exhibits strong randomness, asymmetry, and multi-peak characteristics, a single distribution model is insufficient to describe multi-peak and skewed patterns. Therefore, a finite mixture model is used to construct a probabilistic model for the initial charging time of electric vehicles, namely:

[0105]

[0106] In the formula, t0 is the initial charging time of the electric vehicle; M is the number of hybrid components in the finite hybrid model; This is the k-th component function of a finite mixture model; Let be the parameter vector of the k-th component function; Let the weight of the k-th component function be given, and let it satisfy the following requirements:

[0107]

[0108] The component functions of the hybrid model with finite initial charging time for electric vehicles adopt a log-normal distribution on weekdays and a normal distribution on rest days, i.e.:

[0109]

[0110] In the formula, This is the k-th component function of the finite mixture model of working days; This is the k-th component function of the finite mixture model for rest days; and These are the expected value and standard deviation of the logarithm of the k-th component function variable in the finite mixture model of working days, respectively. and Let $\begin{pmatrix}$ be the expected value and standard deviation of the k-th component function variable in the finite mixture model of rest days, respectively.

[0111] Based on the electric vehicle data and user behavior data collected in step S1, the maximum likelihood estimation algorithm is used to estimate the expected value and standard deviation of the daily mileage for four types of electric vehicles: electric buses, electric taxis, electric private cars, and electric official vehicles. This yields the probability distribution of the pre-charging state of charge and charging time for different types of electric vehicles. The expectation-maximization algorithm is used to estimate the starting charging time for the four types of electric vehicles: electric buses, electric taxis, electric private cars, and electric official vehicles. This yields the expected value and standard deviation of the starting charging time for different types of electric vehicles. This yields the probability distribution of the starting charging time for different types of electric vehicles.

[0112] Step S3: Establish a probabilistic model for charging power of different types of electric vehicles, taking into account charging modes and charging probabilities.

[0113] Specifically, if a day is divided into 1440 / a time periods at intervals of a minutes, then the charging power P of an electric vehicle at time t in the next day is... ch.t The probability model is as follows:

[0114]

[0115] In the formula, P ch The charging power of the electric vehicle; φ is the charging probability; w tFor the charging status of electric vehicles, w t =1 indicates that charging is in progress. t =0 indicates that the battery is not charging or is fully charged.

[0116] There are four scenarios for electric vehicle charging power, as shown below:

[0117]

[0118] In the formula, P ch.1 P represents the charging power in normal charging mode. ch.2 This refers to the charging power in fast charging mode.

[0119] The probability of an electric vehicle being charged at time t within the next day is:

[0120]

[0121] In the formula, Let be the joint probability distribution function of charging start time and charging duration, i.e.:

[0122]

[0123] In the formula, Let be the probability distribution function for the charging start time. Let the charging time probability distribution function be used.

[0124] For electric buses, a two-charge-per-day charging method is generally used, with fast charging during the day and regular charging at night. The charging power P of the electric bus at time t on a future day is... GJ.ch.t The probability model is as follows:

[0125]

[0126] In the formula, P GJ.ch.1 P GJ.ch.2 These are the charging powers corresponding to the regular charging and fast charging modes for electric buses, respectively.

[0127] For electric taxis, a two-charge-per-day charging schedule is generally adopted, using fast charging mode. The charging power P of an electric taxi at time t on a future day is... CZ.ch.t The probability model is as follows:

[0128]

[0129] In the formula, P CZ.ch.2 The charging power corresponding to the fast charging mode of electric taxis;

[0130] For electric private cars, a multi-day charging pattern is generally adopted. During the day, fast charging is used at work or shopping malls, while regular charging is used at night in residential areas. The charging power P of the electric private car at time t on the next day is... SJ.ch.t The probability model is as follows:

[0131]

[0132] In the formula, P SJ.ch.1 P SJ.ch.2 These are the charging powers corresponding to regular charging and fast charging modes for electric private vehicles, respectively.

[0133] For electric official vehicles, a daily charging schedule is generally adopted, using a conventional charging mode. The charging power P of the electric official vehicle at time t on a future day is... GW.ch.t The probability model is as follows:

[0134]

[0135] In the formula, P GW.ch.1 The charging power corresponding to the normal charging mode of electric official vehicles.

[0136] Step S4: Based on the electric vehicle frequency regulation time margin, electric vehicles are divided into frequency regulation clusters and charging clusters. Combining the charging power calculation model of different types of electric vehicles in the frequency regulation cluster and the parameter estimation results of step S2, the charging power value of electric vehicles is calculated using the Monte Carlo simulation method based on Latin hypercube sampling. The charging power value is then input into the established electric vehicle cluster frequency regulation capacity evaluation model to obtain the electric vehicle cluster frequency regulation capacity at each time point in the next day.

[0137] Specifically, the frequency regulation time margin of electric vehicles is defined as follows:

[0138]

[0139] In the formula, δ represents the frequency regulation time margin of the electric vehicle. A δ greater than 1 indicates that the electric vehicle can participate in frequency regulation, and it is marked as R=1; a δ less than 1 indicates that the electric vehicle cannot participate in frequency regulation, and it is marked as R=0; t LK Estimated time for electric vehicle users to leave charging stations;

[0140] Based on the frequency regulation time margin of electric vehicles, different types of electric vehicles are divided into frequency regulation cluster groups or charging cluster groups; for electric vehicles in the frequency regulation cluster group, the charging power probability model of different types of electric vehicles established in step S3 and the parameter estimation results in step S2 are combined, and the Monte Carlo simulation method based on Latin hypercube sampling is used to calculate the charging demand of electric vehicles at 1440 / a time points in the next day.

[0141] The Latin hypercube sampling steps are as follows: First, the cumulative distribution function of the random variable b is... The value interval [0,1] is divided into N equal parts to obtain N sub-intervals; then, a sample value of H is selected from each sub-interval. This sample value can be selected from the midpoint of the sub-interval or a boundary point close to the expected value of b; then, N sample values ​​of the random variable b are obtained through the inverse function of the cumulative distribution function, and finally, the random variable sample matrix B is obtained. 1×N ;

[0142] Using the calculated charging demand of electric vehicles at 1440 / a times within the next day as input data, and substituting it into the electric vehicle cluster frequency regulation capacity assessment model, the frequency regulation capacity of the electric vehicle cluster at 1440 / a times within the next day can be obtained. The specific formula is as follows:

[0143]

[0144] In the formula, , N represents the maximum up-regulation power and maximum down-regulation power of the electric vehicle cluster in the region at time t, respectively, for participating in frequency regulation; k.ch N represents the number of electric vehicles connected to charging stations that only offer charging functionality; k.dc This indicates the number of electric vehicles connected to charging stations that have both charging and discharging capabilities. , Let be the charging power of electric vehicles i and j at time t, respectively; , These are the rated charging powers of electric vehicles i and j, respectively; Let be the discharge power of electric vehicle j at time t; Let J be the rated discharge power of electric vehicle j. , These represent the charging and discharging states of an electric vehicle connected to a charging station with charging and discharging functions, respectively. When in charging state... ,otherwise When in a discharge state ,otherwise ;

[0145] In addition, electric vehicles in frequency modulation trunking groups should also meet the following requirements:

[0146]

[0147] In the formula, E min E max These represent the minimum and maximum states of charge of an electric vehicle battery, respectively. , These represent the charging efficiency and discharging efficiency of electric vehicle j, respectively. This indicates the state of charge of the electric vehicle at the user-defined departure time.

[0148] Step S5: Based on the electric vehicle cluster's participation in power system frequency regulation, dynamically collect the latest electric vehicle-related data, dynamically repeat steps S1 to S4, continuously update the charging power probability models and parameters of different types of electric vehicles based on the latest data, and conduct rolling assessments of the electric vehicle cluster's frequency regulation capacity.

[0149] Specifically, based on the electric vehicle cluster's demand for participating in power system frequency regulation, when assessing the electric vehicle cluster's frequency regulation capacity for the next two days on the next natural day, the relevant electric vehicle data collected on the previous natural day is dynamically input into step S1. The models and parameters in steps S2 and S3 are dynamically updated based on the latest relevant electric vehicle data. Based on this, step S4 obtains the electric vehicle cluster's frequency regulation capacity for each time point on the next two days. Furthermore, the time scale for the electric vehicle cluster's frequency regulation capacity assessment can be dynamically adjusted according to the frequency regulation demand; for example, it can be dynamically adjusted to assess the electric vehicle cluster's frequency regulation capacity for the next four hours.

[0150] Exemplary System

[0151] Figure 2 This is a schematic diagram of the structure of an electric vehicle cluster frequency regulation capacity assessment system that considers user behavior characteristics, provided as an exemplary embodiment of the present invention. Figure 2 As shown, the device 200 includes:

[0152] The data collection module 201 is used to collect data related to electric vehicles, including data about the electric vehicles themselves and user behavior data.

[0153] The influencing factor probability model and parameter estimation module 202 is used to establish a probability model of the influencing factors of electric vehicle charging power considering user driving characteristics, and to use a parameter estimation algorithm to estimate the parameters of the probability model of the influencing factors of charging power of different types of electric vehicles.

[0154] The charging power probability model module 203 is used to establish charging power probability models for different types of electric vehicles that take into account charging modes and charging probabilities.

[0155] The electric vehicle cluster frequency regulation capacity assessment module 204 is used to divide electric vehicles into frequency regulation clusters and charging clusters according to the electric vehicle frequency regulation time margin. Combining the charging power calculation model of different types of electric vehicles in the frequency regulation cluster, the module uses the Monte Carlo simulation method based on Latin hypercube sampling to calculate the electric vehicle charging power value, and inputs the charging power value into the established electric vehicle cluster frequency regulation capacity assessment model to obtain the electric vehicle cluster frequency regulation capacity at each time of the next day.

[0156] The dynamic evaluation module 205 is used to dynamically collect the latest electric vehicle-related data based on the electric vehicle cluster's participation in power system frequency regulation needs. It dynamically and repeatedly executes the data collection module 201, the influencing factor probability model and parameter estimation module 202, the charging power probability model module 203, and the electric vehicle cluster frequency regulation capacity evaluation module 204. Based on the latest data, it continuously updates the charging power probability models and parameters of different types of electric vehicles and conducts rolling evaluations of the electric vehicle cluster frequency regulation capacity.

[0157] Optionally, the data collection module 201 collects data about the electric vehicle itself, including electric vehicle type, battery capacity, state of charge, charging mode and its corresponding charging power, discharging power, charging efficiency, discharging efficiency, and maximum driving range. The electric vehicle type includes four types: electric bus, electric taxi, electric private car and electric official vehicle. The collected charging modes include two types: regular charging and fast charging. The user behavior data includes daily driving mileage, starting charging time, expected time to leave the charging station, and set state of charge data.

[0158] Optionally, the influencing factor probability model and parameter estimation module 202 establishes a probability model for the influencing factors of electric vehicle charging power considering user driving characteristics, including a probability model of the electric vehicle's pre-charging state of charge, a probability model of charging duration, and a probability model of the initial charging time. The probability model for the electric vehicle's pre-charging state of charge is as follows:

[0159]

[0160] In the formula, E t0 E represents the state of charge before charging. set0 D represents the state of charge of the electric vehicle after its last charge, and D represents the maximum driving range of the electric vehicle in pure electric mode. , These represent the expected value and standard deviation of the daily mileage of electric vehicles, respectively.

[0161] The charging time required for an electric vehicle to reach its set state of charge is:

[0162]

[0163] In the formula, Tch For the required charging time, E set1 For the set state of charge, Q n For rated capacity, P ch For charging power, η ch For charging efficiency;

[0164] The probabilistic model for the charging time required for an electric vehicle to reach a set state of charge is as follows:

[0165]

[0166] A probabilistic model of the initial charging time of electric vehicles is constructed using a finite mixture model, namely:

[0167]

[0168] In the formula, t0 is the initial charging time of the electric vehicle; M is the number of hybrid components in the finite hybrid model; This is the k-th component function of a finite mixture model; Let be the parameter vector of the k-th component function; Let the weight of the k-th component function be given, and let it satisfy the following requirements:

[0169]

[0170] The component functions of the hybrid model with finite initial charging time for electric vehicles adopt a log-normal distribution on weekdays and a normal distribution on rest days, i.e.:

[0171]

[0172] In the formula, This is the k-th component function of the finite mixture model of working days; This is the k-th component function of the finite mixture model for rest days; and These are the expected value and standard deviation of the logarithm of the k-th component function variable in the finite mixture model of working days, respectively. and Let $\begin{pmatrix}$ be the expected value and standard deviation of the k-th component function variable in the finite mixture model of rest days, respectively.

[0173] Based on the electric vehicle data and user behavior data collected by the data collection module 201, the maximum likelihood estimation algorithm is used to estimate the expected value and standard deviation of the daily mileage for four types of electric vehicles: electric buses, electric taxis, electric private cars, and electric official vehicles. This yields the probability distribution of the pre-charging state of charge and charging time for different types of electric vehicles. The expectation-maximization algorithm is used to estimate the starting charging time for the four types of electric vehicles: electric buses, electric taxis, electric private cars, and electric official vehicles. This yields the expected value and standard deviation of the starting charging time for different types of electric vehicles. This yields the probability distribution of the starting charging time for different types of electric vehicles.

[0174] Optionally, the charging power probability model module 203 divides a day into 1440 / a time periods at intervals of a minutes, and calculates the charging power P of the electric vehicle at time t on a future day. ch.t The probability model is as follows:

[0175]

[0176] In the formula, P ch The charging power of the electric vehicle; φ is the charging probability; w t For the charging status of electric vehicles, w t =1 indicates that charging is in progress. t =0 indicates that the battery is not charging or is fully charged.

[0177] There are four scenarios for electric vehicle charging power, as shown below:

[0178]

[0179] In the formula, P ch.1 P represents the charging power in normal charging mode. ch.2 This refers to the charging power in fast charging mode.

[0180] The probability of an electric vehicle being charged at time t within the next day is:

[0181]

[0182] In the formula, Let be the joint probability distribution function of charging start time and charging duration, i.e.:

[0183]

[0184] In the formula, Let be the probability distribution function for the charging start time. Let the charging time probability distribution function be used.

[0185] For electric buses, a two-charge-per-day charging method is generally used, with fast charging during the day and regular charging at night. The charging power P of the electric bus at time t on a future day is... GJ.ch.t The probability model is as follows:

[0186]

[0187] In the formula, P GJ.ch.1 P GJ.ch.2 These are the charging powers corresponding to the regular charging and fast charging modes for electric buses, respectively.

[0188] For electric taxis, a two-charge-per-day charging schedule is generally adopted, using fast charging mode. The charging power P of an electric taxi at time t on a future day is... CZ.ch.t The probability model is as follows:

[0189]

[0190] In the formula, P CZ.ch.2 The charging power corresponding to the fast charging mode of electric taxis;

[0191] For electric private cars, a multi-day charging pattern is generally adopted. During the day, fast charging is used at work or shopping malls, while regular charging is used at night in residential areas. The charging power P of the electric private car at time t on the next day is... SJ.ch.t The probability model is as follows:

[0192]

[0193] In the formula, P SJ.ch.1 P SJ.ch.2 These are the charging powers corresponding to regular charging and fast charging modes for electric private vehicles, respectively.

[0194] For electric official vehicles, a daily charging schedule is generally adopted, using a conventional charging mode. The charging power P of the electric official vehicle at time t on a future day is... GW.ch.t The probability model is as follows:

[0195]

[0196] In the formula, P GW.ch.1 The charging power corresponding to the normal charging mode of electric official vehicles.

[0197] Optionally, in the electric vehicle cluster frequency regulation capacity assessment module 204, the electric vehicle frequency regulation time margin is defined as follows:

[0198]

[0199] In the formula, δ represents the frequency regulation time margin of the electric vehicle. A δ greater than 1 indicates that the electric vehicle can participate in frequency regulation, and it is marked as R=1; a δ less than 1 indicates that the electric vehicle cannot participate in frequency regulation, and it is marked as R=0; t LK Estimated time for electric vehicle users to leave charging stations;

[0200] Based on the frequency regulation time margin of electric vehicles, different types of electric vehicles are divided into frequency regulation cluster groups or charging cluster groups. For electric vehicles in the frequency regulation cluster group, the charging power probability model of different types of electric vehicles established by the charging power probability model module 203, the probability model of influencing factors, and the parameter estimation results of the parameter estimation module 202 are combined to calculate the charging demand of electric vehicles at 1440 / a time points in the next day using the Monte Carlo simulation method based on Latin hypercube sampling.

[0201] The Latin hypercube sampling steps are as follows: First, the cumulative distribution function of the random variable b is... The value interval [0,1] is divided into N equal parts to obtain N sub-intervals; then, a sample value of H is selected from each sub-interval. This sample value can be selected from the midpoint of the sub-interval or a boundary point close to the expected value of b; then, N sample values ​​of the random variable b are obtained through the inverse function of the cumulative distribution function, and finally, the random variable sample matrix B is obtained. 1×N ;

[0202] Using the calculated charging demand of electric vehicles at 1440 / a times within the next day as input data, and substituting it into the electric vehicle cluster frequency regulation capacity assessment model, the frequency regulation capacity of the electric vehicle cluster at 1440 / a times within the next day can be obtained. The specific formula is as follows:

[0203]

[0204] In the formula, , N represents the maximum up-regulation power and maximum down-regulation power of the electric vehicle cluster in the region at time t, respectively, for participating in frequency regulation; k.ch N represents the number of electric vehicles connected to charging stations that only offer charging functionality; k.dc This indicates the number of electric vehicles connected to charging stations that have both charging and discharging capabilities. , Let be the charging power of electric vehicles i and j at time t, respectively; , These are the rated charging powers of electric vehicles i and j, respectively; Let be the discharge power of electric vehicle j at time t; Let J be the rated discharge power of electric vehicle j. , These represent the charging and discharging states of an electric vehicle connected to a charging station with charging and discharging functions, respectively. When in charging state... ,otherwise When in a discharge state ,otherwise ;

[0205] In addition, electric vehicles in frequency modulation trunking groups should also meet the following requirements:

[0206]

[0207] In the formula, E min E max These represent the minimum and maximum states of charge of an electric vehicle battery, respectively. , These represent the charging efficiency and discharging efficiency of electric vehicle j, respectively. This indicates the state of charge of the electric vehicle at the user-defined departure time.

[0208] Optionally, the dynamic evaluation module 205, based on the electric vehicle cluster's participation in power system frequency regulation needs, dynamically inputs the electric vehicle-related data collected on the previous day into the data collection module 201 when conducting the frequency regulation capacity assessment of the electric vehicle cluster for the next natural day. Based on the latest electric vehicle-related data, the models and parameters of the influencing factor probability model and parameter estimation module 202 and the charging power probability model module 203 are dynamically updated. On this basis, the electric vehicle cluster frequency regulation capacity assessment module 204 obtains the electric vehicle cluster frequency regulation capacity for each time point on the next natural day. Furthermore, the time scale of the electric vehicle cluster frequency regulation capacity assessment can be dynamically adjusted according to frequency regulation needs, for example, it can be dynamically adjusted to assess the electric vehicle cluster frequency regulation capacity for the next 4 hours.

[0209] Exemplary electronic devices

[0210] Figure 3 This is a structural block diagram of an electronic device provided as an exemplary embodiment of the present invention. (See diagram below.) Figure 3 As shown, the electronic device 300 includes one or more processors 301 and memory 302.

[0211] The processor 301 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0212] The memory 302 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM), cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 301 may execute the program instructions to implement the methods of the software programs in the various embodiments of the present invention described above, and / or other desired functions. In one example, the electronic device may further include an input device 303 and an output device 304, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0213] In addition, the input device 303 may also include a keyboard, mouse, etc.

[0214] The output device 304 can output various information to the outside. The output device 304 may include a display, a speaker, a printer, a communication network, and remote output devices connected to it.

[0215] Of course, for the sake of simplicity, Figure 3 Only some of the components of this electronic device relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0216] Exemplary computer program products and computer-readable storage media

[0217] In addition to the methods and apparatus described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0218] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0219] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0220] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0221] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0222] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus embodiments, since they largely correspond to the method embodiments, the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0223] The block diagrams of devices, systems, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0224] The methods and systems of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the present invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers recording media storing programs for performing the methods according to the present invention.

[0225] It should also be noted that in the systems, apparatus, and methods of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered equivalents of the present invention. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0226] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize that certain variations, modifications, alterations, additions, and sub-combinations thereof should be covered within the scope of the claims of this invention.

Claims

1. A method for evaluating the frequency regulation capacity of electric vehicle clusters considering user behavior characteristics, characterized in that, include: Step S1: Collect electric vehicle-related data, including electric vehicle data itself and user behavior data; Step S2: Establish a probabilistic model of the factors affecting the charging power of electric vehicles that take into account the user's driving characteristics. Based on the electric vehicle data and user behavior data collected in Step S1, use a parameter estimation algorithm to estimate the parameters of the probabilistic model of the factors affecting the charging power of different types of electric vehicles. Step S3: Establish a probabilistic model for charging power of different types of electric vehicles, taking into account charging mode and charging probability; Step S4: Based on the electric vehicle frequency regulation time margin, electric vehicles are divided into frequency regulation clusters and charging clusters. Combining the charging power probability model of different types of electric vehicles in the frequency regulation clusters and the parameter estimation results of step S2, the charging power values ​​of electric vehicles are calculated using the Monte Carlo simulation method based on Latin hypercube sampling. The charging power values ​​are then input into the established electric vehicle cluster frequency regulation capacity evaluation model to obtain the electric vehicle cluster frequency regulation capacity at each time point in the next day. Step S5: Based on the electric vehicle cluster's participation in power system frequency regulation, dynamically collect the latest electric vehicle-related data, dynamically repeat steps S1 to S4, continuously update the charging power probability models and parameters of different types of electric vehicles based on the latest data, and conduct rolling assessments of the electric vehicle cluster's frequency regulation capacity.

2. The method according to claim 1, characterized in that, In step S1, the electric vehicle's own data includes electric vehicle type, battery capacity, state of charge, charging mode and its corresponding charging power, discharging power, charging efficiency, discharging efficiency, and maximum driving range data; the electric vehicle type includes four types: electric bus, electric taxi, electric private car and electric official vehicle; the charging mode includes two types: regular charging and fast charging; the user behavior data includes daily driving mileage, starting charging time, expected time to leave the charging station, and set state of charge data.

3. The method according to claim 1, characterized in that, In step S2, the factors affecting the electric vehicle charging power include the state of charge before charging, charging duration, initial charging time, electric vehicle type, and charging mode. The established probabilistic models for the factors affecting the electric vehicle charging power considering user driving characteristics include a probabilistic model of the state of charge before charging, a probabilistic model of charging duration, and a probabilistic model of initial charging time; wherein the probabilistic model of the state of charge before charging is: ； In the formula, E t0 E represents the state of charge before charging. set0 D represents the state of charge of the electric vehicle after its last charge, and D represents the maximum driving range of the electric vehicle in pure electric mode. , These represent the expected value and standard deviation of the daily mileage of electric vehicles, respectively. The charging time required for an electric vehicle to reach its set state of charge is: ； In the formula, T ch For the required charging time, E set1 For the set state of charge, Q n For rated capacity, P ch For charging power, η ch For charging efficiency; The probabilistic model for the charging time required to bring an electric vehicle to a set state of charge is as follows: ； A finite mixture model is used to construct a probabilistic model for the initial charging time of electric vehicles, namely: ； In the formula, t0 is the initial charging time of the electric vehicle; M is the number of hybrid components in the finite hybrid model; This is the k-th component function of a finite mixture model; Let be the parameter vector of the k-th component function; Let the weight of the k-th component function be given, and let it satisfy the following requirements: ； The component functions of the hybrid model with finite initial charging time for electric vehicles adopt a log-normal distribution on weekdays and a normal distribution on rest days, i.e.: ； In the formula, This is the k-th component function of the finite mixture model of working days; This is the k-th component function of the finite mixture model for rest days; and These are the expected value and standard deviation of the logarithm of the k-th component function variable in the finite mixture model of working days, respectively. and Let $\begin{pmatrix}$ be the expected value and standard deviation of the k-th component function variable in the finite mixture model of rest days, respectively. Based on the electric vehicle data and user behavior data collected in step S1, the maximum likelihood estimation algorithm is used to estimate the expected value and standard deviation of the daily mileage for four types of electric vehicles: electric buses, electric taxis, electric private cars, and electric official vehicles. This yields the probability distribution of the pre-charging state of charge and charging time for different types of electric vehicles. The expectation-maximization algorithm is used to estimate the starting charging time for the four types of electric vehicles: electric buses, electric taxis, electric private cars, and electric official vehicles. This yields the expected value and standard deviation of the starting charging time for different types of electric vehicles. This yields the probability distribution of the starting charging time for different types of electric vehicles.

4. The method according to claim 1, characterized in that, In step S3, a day is divided into 1440 / a time periods at intervals of a minutes, and the charging power P of the electric vehicle at time t in the next day is calculated. ch.t The probability model is as follows: ； In the formula, P ch The charging power of the electric vehicle; φ is the charging probability; w t For the charging status of electric vehicles, w t =1 indicates that charging is in progress. t =0 indicates that the battery is not charging or is fully charged. There are four scenarios for electric vehicle charging power, as shown below: ； In the formula, P ch.1 P represents the charging power in normal charging mode. ch.2 This refers to the charging power in fast charging mode. The probability of an electric vehicle being charged at time t within the next day is: ； In the formula, Let be the joint probability distribution function of charging start time and charging duration, i.e.: ； In the formula, Let be the probability distribution function for the charging start time. Let the charging time probability distribution function be used. For electric buses, a two-charge-per-day charging method is generally used, with fast charging during the day and regular charging at night. The charging power P of the electric bus at time t on a future day is... GJ.ch.t The probability model is as follows: ； In the formula, P GJ.ch.1 P GJ.ch.2 These are the charging powers corresponding to the regular charging and fast charging modes for electric buses, respectively. For electric taxis, a two-charge-per-day charging schedule is generally adopted, using fast charging mode. The charging power P of an electric taxi at time t on a future day is... CZ.ch.t The probability model is as follows: ； In the formula, P CZ.ch.2 The charging power corresponding to the fast charging mode of electric taxis; For electric private cars, a multi-day charging pattern is generally adopted. During the day, fast charging is used at work or shopping malls, while regular charging is used at night in residential areas. The charging power P of the electric private car at time t on the next day is... SJ.ch.t The probability model is as follows: ； In the formula, P SJ.ch.1 P SJ.ch.2 These are the charging powers corresponding to regular charging and fast charging modes for electric private vehicles, respectively. For electric official vehicles, a daily charging schedule is generally adopted, using a conventional charging mode. The charging power P of the electric official vehicle at time t on a future day is... GW.ch.t The probability model is as follows: ； In the formula, P GW.ch.1 The charging power corresponding to the normal charging mode of electric official vehicles.

5. The method according to claim 1, characterized in that, In step S4, the frequency regulation time margin of the electric vehicle is defined as follows: ； In the formula, δ represents the frequency regulation time margin of the electric vehicle. A δ greater than 1 indicates that the electric vehicle can participate in frequency regulation, and it is marked as R=1; a δ less than 1 indicates that the electric vehicle cannot participate in frequency regulation, and it is marked as R=0; t LK Estimated time for electric vehicle users to leave charging stations; Based on the frequency regulation time margin of electric vehicles, different types of electric vehicles are divided into frequency regulation cluster groups or charging cluster groups; for electric vehicles in the frequency regulation cluster group, the charging power probability model of different types of electric vehicles established in step S3 and the parameter estimation results in step S2 are combined, and the Monte Carlo simulation method based on Latin hypercube sampling is used to calculate the charging demand of electric vehicles at 1440 / a time points in the next day. The Latin hypercube sampling steps are as follows: First, calculate the cumulative distribution function of the random variable b. The value interval [0,1] is divided into N equal parts to obtain N sub-intervals; then, a sample value of H is selected from each sub-interval. This sample value can be selected from the midpoint of the sub-interval or a boundary point close to the expected value of b; then, N sample values ​​of the random variable b are obtained through the inverse function of the cumulative distribution function, and finally, the random variable sample matrix B is obtained. 1×N ; The calculated charging demand of electric vehicles at 1440 / a times within the next day is used as input data and substituted into the electric vehicle cluster frequency regulation capability assessment model to obtain the electric vehicle cluster frequency regulation capacity at 1440 / a times within the next day. The specific formula is as follows: ； In the formula, , N represents the maximum up-regulation power and maximum down-regulation power of the electric vehicle cluster in the region at time t, respectively, for participating in frequency regulation; k.ch N represents the number of electric vehicles connected to charging stations that only offer charging functionality; k.dc This indicates the number of electric vehicles connected to charging stations that have both charging and discharging capabilities. , Let be the charging power of electric vehicles i and j at time t, respectively; , These are the rated charging powers of electric vehicles i and j, respectively; Let be the discharge power of electric vehicle j at time t; Let J be the rated discharge power of electric vehicle j. , These represent the charging and discharging states of an electric vehicle connected to a charging station with charging and discharging functions, respectively. When in charging state... ,otherwise When in a discharge state ,otherwise ; In addition, electric vehicles in frequency modulation trunking groups should also meet the following requirements: ； In the formula, E min E max These represent the minimum and maximum states of charge of an electric vehicle battery, respectively. , These represent the charging efficiency and discharging efficiency of electric vehicle j, respectively. This indicates the state of charge of the electric vehicle at the user-defined departure time.

6. The method according to claim 1, characterized in that, In step S5, based on the electric vehicle cluster's participation in power system frequency regulation, when assessing the electric vehicle cluster's frequency regulation capacity for the next two days on the next natural day, the relevant electric vehicle data collected on the previous natural day is dynamically input into step S1. The models and parameters of steps S2 and S3 are dynamically updated based on the latest relevant electric vehicle data. On this basis, the electric vehicle cluster's frequency regulation capacity at each time point on the next two days is obtained through step S4. The time scale for assessing the electric vehicle cluster's frequency regulation capacity can also be dynamically adjusted according to the frequency regulation requirements.

7. A frequency regulation capacity assessment system for electric vehicle clusters considering user behavior characteristics, characterized in that, include: The data collection module is used to collect data related to electric vehicles. The electric vehicle frequency tuning time includes data about the electric vehicle itself and user behavior data. The influencing factor probability model and parameter estimation module is used to establish a probability model of the influencing factors of electric vehicle charging power considering user driving characteristics. Based on the collected data of the electric vehicle itself and user behavior data, the parameter estimation algorithm is used to estimate the parameters of the probability model of the influencing factors of charging power of different types of electric vehicles. The charging power probability model module is used to establish charging power probability models for different types of electric vehicles that take into account charging modes and charging probabilities. The electric vehicle cluster frequency regulation capacity assessment module divides electric vehicles into frequency regulation clusters and charging clusters based on the frequency regulation time margin of electric vehicles. Combining the charging power probability model of different types of electric vehicles in the frequency regulation clusters and the parameter estimation results of step S2, the module uses the Monte Carlo simulation method based on Latin hypercube sampling to calculate the charging power value of electric vehicles. The charging power value is then input into the established electric vehicle cluster frequency regulation capacity assessment model to obtain the electric vehicle cluster frequency regulation capacity at each time point in the next day. The dynamic evaluation module is used to dynamically collect the latest electric vehicle-related data based on the electric vehicle cluster's participation in power system frequency regulation. It dynamically and repeatedly executes the data collection module, the influencing factor probability model and parameter estimation module, the charging power probability model module, and the electric vehicle cluster frequency regulation capacity evaluation module. Based on the latest data, it continuously updates the charging power probability models and parameters of different types of electric vehicles and conducts rolling evaluations of the electric vehicle cluster frequency regulation capacity.

8. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the electric vehicle cluster frequency regulation capacity assessment method considering user behavior characteristics as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the electric vehicle cluster frequency regulation capacity assessment method considering user behavior characteristics as described in any one of claims 1 to 6.

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