Frequency modulation market-oriented electric vehicle package design and parameter formulation method and system

Abstract

The invention relates to the technical field of electric power information, and provides a frequency modulation market-oriented electric vehicle package design and parameter formulation method and system. The frequency modulation market-oriented electric vehicle package design and parameter formulation method comprises the following steps of: obtaining user historical data, and constructing a user preference fuzzy set of a user under an evaluation criterion according to the acceptance degree of the user on the evaluation criterion; based on all retail frequency modulation packages, calculating the conformity degree of the packages and the evaluation criteria, and constructing a package intuitionistic fuzzy set of each package under the evaluation criteria; matching the user with the package to obtain a similarity measurement matrix, and calculating the probability that the user selects a certain package to obtain a user package selection probability matrix; based on the user plan selection probability matrix, plan subsidy cost is determined; and constructing a target function based on the energy purchase cost, the package subsidy cost, the expected risk cost and the frequency modulation income, and solving an optimal scheme in combination with constraint conditions. And a user selection behavior can be simulated through the fuzzy set to formulate package parameters.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electric power information technology, and in particular to a frequency regulation market-oriented electric vehicle package design and parameter setting method and system. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] Vehicle-to-grid (V2G) technology is developing rapidly, and electric vehicles (EV) capable of bidirectional energy flow with the power grid can be seen as a large number of distributed energy storage resources providing frequency regulation support for the power grid. EV participation in power grid frequency regulation has become a new business model. With the increasing penetration of renewable energy, the demand for power grid frequency regulation is increasing. EVs with distributed characteristics and rapid response capabilities have great potential in stabilizing renewable energy fluctuations and improving power grid stability. Therefore, how an electric vehicle aggregator (EVA) adopts strategies to encourage users to participate in the frequency regulation market has become a key factor in its success in business competition. As a win-win value-added service of the power selling party, the package can solve the problems of multiple power selling parties and complex electricity pricing mechanisms that users encounter in the process of seeking power selling agents. However, unreasonable package design can result in deep discharge, high rate charge and discharge, and low user benefits, thereby reducing the willingness of users to participate in frequency regulation. Therefore, for an EVA, it is very important to design a frequency regulation package and use an appropriate method to analyze package selection to optimize parameter settings.

[0004] In terms of package design, existing research focuses on scheduling targets such as peak shaving and low carbon, and designs packages based on load charging characteristics. However, no research has considered the frequency regulation characteristics of EVs and designed corresponding frequency regulation subsidy packages.

[0005] In terms of parameter setting, existing research only considers the response willingness of users and does not combine a global optimization model with bidding revenue in the electricity market. Moreover, simple models are used to describe the selection behavior of users, which cannot accurately depict the complex psychology of scattered users when making selections.

[0006] In terms of user selection psychology, there have been many studies abroad on the use of fuzzy decision-making for qualitative recommendations. These studies have been proven effective in charging scheduling, site selection, road traffic analysis, and vehicle path decision-making. The above studies are based on different influencing factors and evaluate the preferences of EV users through fuzzy theory. However, the above studies lack a fuzzy evaluation system under the influence of frequency regulation participation, and the evaluation of fuzzy set membership lacks a theoretical basis. SUMMARY

[0007] To solve the technical problems in the background art, the application provides a method and system for designing and formulating parameters of electric vehicle packages for frequency modulation markets.

[0008] To achieve the above-mentioned purposes, the application adopts the following technical solutions: The first aspect of the application provides a method for designing and formulating parameters of electric vehicle packages for frequency modulation markets.

[0009] The method for designing and formulating parameters of electric vehicle packages for frequency modulation markets comprises the following steps: Based on the discharge frequency modulation participation, the mandatory retention time length and the privacy information, a plurality of retail frequency modulation packages are constructed, and a charging subsidy price is configured for each package. According to the provision of charging information, participation in discharge frequency modulation, willingness to stay at the charging station for a long time and user price sensitivity, four evaluation criteria are set in turn. The historical data of users are obtained, and the user preference fuzzy set of the users under the evaluation criteria is constructed according to the acceptance degree of the users to the evaluation criteria. Based on the user package selection probability matrix, the package subsidy cost is determined.

[0010] The application quantifies the fuzzy relationship between users and package attributes by introducing fuzzy sets to solve the uncertainty in user preferences and construct a user satisfaction evaluation model to determine the user's package selection.

[0011] Further, the four evaluation criteria include: a first evaluation criterion, a second evaluation criterion, a third evaluation criterion, and a fourth evaluation criterion, wherein the first evaluation criterion is used to evaluate the degree of perfection of providing charging information, the second evaluation criterion is used to evaluate whether the user participates in discharge frequency modulation after selecting a package, the third evaluation criterion is used to evaluate the deviation degree of the forced retention time of the package from the ideal residence time of the user; and the fourth evaluation criterion is used to evaluate the suitability of the package price parameter to the price preference of the user.

[0012] Further, the method includes: based on the direct correlation between the first evaluation criterion, the second evaluation criterion, and the package attribute, calculating the first compliance degree and the second compliance degree; based on the third evaluation criterion, using a first compliance degree evaluation function to quantify the matching degree of the retention time, and calculating the third compliance degree; based on the fourth evaluation criterion, using a second compliance degree function to quantify the acceptance degree of the user to the price point of the subsidy pricing range, and calculating the fourth compliance degree.

[0013] Further, the objective function is expressed by the following formula:

[0014] wherein, is the purchase cost, is the package subsidy cost, is the expected risk cost, is the frequency modulation benefit.

[0015] Further, the expected risk cost is expressed by the following formula:

[0016]

[0017]

[0018] wherein, is a unit deviation penalty coefficient, is a frequency modulation capacity deviation, and are the frequency modulation capacities based on the predicted time and the actual time respectively, is the predicted arrival time, is the actual arrival time, is a time deviation term, is a binary variable, indicating whether the user is willing to provide information.

[0019] Further, the frequency modulation benefit is expressed by the following formula:

[0020]

[0021]

[0022] wherein, , is the aggregate up-down regulation capacity of EVs that purchase package k, , is the up-down regulation capacity price at time t, respectively, , is the capacity up-down limit specified when providing ancillary frequency regulation services, denotes the time difference.

[0023] Further, the constraint condition includes an equivalent energy storage model boundary limit, which is expressed by the following formula:

[0024]

[0025]

[0026] wherein, denotes that the EV numbered belongs to cluster the overall maximum power and minimum power, denotes that the EV numbered belongs to cluster the maximum and minimum capacity, , is the equivalent energy storage energy of cluster j at time t and t+1, is the equivalent energy storage power of cluster j at time t, , is the grid-connected and off-grid boundary energy of aggregate k, denotes the time difference.

[0027] Further, before constructing the objective function, the following are included: calculating the maximum power, minimum power, maximum capacity, and minimum capacity of the EV aggregate model cluster as a whole, defining the energy boundary in the grid-connected state and the energy boundary in the off-grid state, and calculating the equivalent energy storage energy of the EV cluster.

[0028] Further, the retail frequency regulation package includes: a first package: no discharging frequency regulation, a retention duration of , no provision of relevant information, and a charging subsidy price of ; a second package: no discharging frequency regulation, a retention duration of , not providing relevant information, the charging subsidy price is ; The third package: discharging frequency modulation is carried out, and the retention time is , not providing relevant information, the charging subsidy price is ; The fourth package: discharging frequency modulation is carried out, and the retention time is , not providing relevant information, the charging subsidy price is ; The fifth package: discharging frequency modulation is not carried out, and the retention time is , providing relevant information, the charging subsidy price is ; The sixth package: discharging frequency modulation is not carried out, and the retention time is , providing relevant information, the charging subsidy price is ; The seventh package: discharging frequency modulation is carried out, and the retention time is , providing relevant information, the charging subsidy price is ; The eighth package: discharging frequency modulation is carried out, and the retention time is , providing relevant information, the charging subsidy price is .

[0029] The application extracts the behavior characteristics affecting the EV frequency modulation capability based on the grid-connected model, and designs eight types of frequency modulation subsidy packages to cover the multi-dimensional requirements of user behavior.

[0030] The second aspect of the application provides an electric vehicle package design and parameter setting system for a frequency modulation market.

[0031] An electric vehicle package design and parameter setting system for a frequency modulation market, comprising: The package construction module is configured to: based on discharging frequency modulation participation, forced retention time and privacy information provision, construct multiple retail frequency modulation packages, and configure a charging subsidy price for each package; The evaluation criterion setting module is configured to: according to providing charging information, participating in discharging frequency modulation, willingness to stay for a long time at the charging station and user price sensitivity, four evaluation criteria are set in turn; The fuzzy processing module is configured to: acquire user historical data, construct a user preference fuzzy set of the user under the evaluation criteria according to an acceptance degree of the user to the evaluation criteria; calculate a degree of conformity of a package to the evaluation criteria based on all retail frequency modulation packages, and construct a package intuitionistic fuzzy set of each package under the evaluation criteria; match the user and the package based on a user preference fuzzy matrix of the user under the evaluation criteria and a package intuitionistic fuzzy matrix of each package under the evaluation criteria, obtain a similarity measure matrix, calculate a probability of the user selecting a certain package, and obtain a user package selection probability matrix; The output module is configured to: determine a package subsidy cost based on the user package selection probability matrix; construct an objective function based on a purchase cost, the package subsidy cost, an expected risk cost, and a frequency modulation benefit, and solve an optimal scheme in combination with a constraint condition.

[0032] A third aspect of the present application provides a computer device, the device comprising: a processor adapted to execute a computer program; a computer readable storage medium, the computer readable storage medium storing a computer program, the computer program being executed by the processor to implement the steps in the method for designing and parameterizing a package for a frequency modulation market-oriented electric vehicle according to the first aspect.

[0033] A fourth aspect of the present application provides a computer readable storage medium, the computer readable storage medium storing a computer program, the computer program being adapted to be loaded and executed by a processor to implement the steps in the method for designing and parameterizing a package for a frequency modulation market-oriented electric vehicle according to the first aspect.

[0034] A fifth aspect of the present application provides a computer program product or a computer program.

[0035] The present application provides a computer program product or a computer program, the computer program product or the computer program comprising computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the steps in the method for designing and parameterizing a package for a frequency modulation market-oriented electric vehicle according to the first aspect.

[0036] Compared with the prior art, the present application has the following beneficial effects: The application proposes a dynamic decision model based on a double-layer optimization framework, aiming to realize intelligent optimization of electric vehicle frequency modulation package parameters. Through data interaction and iterative calculation between the upper and lower layers, the model effectively balances the complex relationship between user selection preferences and aggregator economic benefits. In the lower layer model, the system first aggregates user groups with similar selection tendencies based on different package selection results of users. By analyzing the combined characteristics of discharge participation and retention time, users are divided into four typical clusters. In the upper layer model, the aggregator establishes a multi-objective optimization decision system by comprehensively considering frequency modulation market revenue and various operating costs. This system not only needs to calculate the frequency modulation capacity value provided by each user cluster, but also needs to fully consider key factors such as electricity purchase cost, package subsidy expenditure, and risk cost due to user behavior prediction deviation. By dynamically adjusting key variables such as retention time and subsidy price in the package parameters, and feeding back the optimization results to the lower layer model for a new round of user selection probability calculation, a closed-loop optimization mechanism is formed. The improved particle swarm algorithm is used for solving, and through multiple iterations, the optimal balance solution between user satisfaction and aggregator revenue is finally obtained. The model verification results show that this two-way feedback optimization mechanism can significantly improve the overall performance of the system, while ensuring the user's selection freedom, maximizing the economic benefits of the aggregator, and providing a scientific and effective decision support tool for electric vehicle frequency modulation services in the power market environment. This model not only solves the limitations of traditional optimization methods that cannot balance the interests of multiple parties, but also provides an important reference for demand response mechanism design in new power systems through intelligent dynamic adjustment of parameters. BRIEF DESCRIPTION OF DRAWINGS

[0037] The drawings accompanying the specification of this application form a part thereof, serve to further understand the application, and together with the description, explain the application and do not limit the application in any way.

[0038] Figure 1 is a flowchart of the electric vehicle package design and parameter setting method for the frequency modulation market according to an embodiment of the application; Figure 2 is a structure diagram of an electric vehicle grid-connected model according to an embodiment of the application; Figure 3 is a schematic diagram of the support degree of users for each criterion according to an embodiment of the application; Figure 4 is a schematic diagram of user package selection probability according to an embodiment of the application; Figure 5 is a schematic diagram of the up-regulation capacity of aggregated EVs according to an embodiment of the application; Figure 6 is a schematic diagram of the down-regulation capacity of aggregated EVs according to an embodiment of the application; Figure 7 is a structural diagram of the frequency modulation market-oriented electric vehicle package design and parameter setting system shown in the embodiment of the present application; Figure 8 is a structural diagram of the computer device shown in the embodiment of the present application. DETAILED DESCRIPTION

[0039] The present application will be further described below in conjunction with the accompanying drawings and embodiments.

[0040] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0041] It should be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and furthermore, it should be understood that when the terms "comprise" and / or "include" are used in the specification, there is a feature, step, operation, device, component, and / or combination thereof.

[0042] As introduced in the background, (1) existing researches mostly focus on peak shaving, low carbon, and other dispatching targets, and design packages according to load charging characteristics, but there is no research based on the frequency modulation characteristics of EVs and the corresponding frequency modulation subsidy package is designed. (2) Existing technologies only consider price optimization. (3) EV aggregators need to establish a decision-making model to estimate the day-ahead frequency modulation capacity. However, existing research lacks a fuzzy evaluation system under the influence of participation in frequency modulation, and the evaluation of fuzzy set membership lacks theoretical basis.

[0043] In order to solve the above problems, the present application provides a frequency modulation market-oriented electric vehicle package design and parameter setting method and system. The scheme of the present application will be described in detail below through several embodiments.

[0044] Figure 1 is a flowchart of the frequency modulation market-oriented electric vehicle package design and parameter setting method shown in the embodiment of the present application; refer to Figure 1 , the method comprises: Based on the discharge frequency modulation participation, the forced retention time, and the privacy information, a plurality of retail frequency modulation packages are constructed, and a charging subsidy price is configured for each package; According to the provision of charging information, participation in discharge frequency modulation, willingness to stay for a long time at the charging station, and user price sensitivity, four evaluation criteria are set in turn; Obtaining user historical data, constructing user preference fuzzy set of the user under the evaluation criteria according to the acceptance degree of the user to the evaluation criteria; calculating the degree of conformity of the package to the evaluation criteria based on all retail frequency modulation packages, constructing package intuitionistic fuzzy set of each package under the evaluation criteria; matching the user and the package based on the user preference fuzzy matrix of the user under the evaluation criteria and the package intuitionistic fuzzy matrix of each package under the evaluation criteria, obtaining a similarity measure matrix, calculating the probability of the user selecting a certain package, and obtaining a user package selection probability matrix; Based on the user package selection probability matrix, the package subsidy cost is determined; based on the energy purchase cost, the package subsidy cost, the expected risk cost and the frequency modulation income, a target function is constructed, and the optimal scheme is solved combined with the constraint condition.

[0045] The application establishes a double-layer optimization model: the upper layer takes the maximum income of EVA participating in the day-ahead market as the target, and dynamically optimizes the package parameters; the lower layer establishes a package selection model considering user satisfaction, and transmits the selection result to the upper layer for iteration.

[0046] 1. Frequency modulation package design 1) EV grid-connected model In the intelligent charging station, the charging pile is equipped with an edge, which can receive the real-time frequency modulation signal of the upper power grid and control the EV to start or stop charging and discharging, and monitor the cloud server and sensor operation state of the charging vehicle, so that the battery energy can always be kept in the operable area. The grid-connected state of the electric vehicle equipped with V2G function can be divided into three modes of charging-idling-discharging. The existing related research describes the scheduling flexibility of a single EV through a grid-connected model, as shown in the figure. The red line encloses the upper and lower boundaries of the operable area. Figure 2

[0047] 2) Frequency modulation package design As can be seen from the grid-connected model, if the vehicle can enter the discharging mode, the range of its operable area will be widened, that is, the frequency modulation capacity will be improved. At the same time, appropriately prolonging the user's stay time in the charging station can improve the EV frequency modulation capacity. Some users are unwilling to upload key information such as battery capacity and next day entering charging station time, and the lack of these information will increase the risk loss in the operation process. By providing subsidies to these users, information sharing can be encouraged, and the risk loss caused by prediction error can be reduced.

[0048] Based on the user discharging mode participation, the difference in stay time and the demand for privacy protection, eight kinds of retail frequency modulation packages are designed as shown in Table 1, each package has different subsidies. Among them, the stay time and the subsidy price are the package parameters that change within a certain range.

[0049] Table 1: Frequency modulation package settings ​

[0050] The application is based on in-depth analysis of electric vehicle user behavior characteristics and systematic research on grid-connected model of electric vehicles, and proposes an innovative differentiated frequency modulation package design scheme. By establishing a coupling model of electric vehicles participating in power grid frequency modulation, three core user behavior characteristics are first identified and quantified, including: (1) the willingness of users to participate in discharge frequency modulation; (2) the time preference of users at the charging station; (3) the tendency of users to provide vehicle privacy information. These characteristics not only directly determine the potential ability of electric vehicles to participate in power grid frequency modulation, but also have a significant impact on the operation and dispatch of power systems.

[0051] Based on this, the research innovatively constructs a multi-dimensional frequency modulation package system, which includes 8 types of differentiated combination schemes. Each package is composed of four key dimension parameters: first, the discharge frequency modulation participation dimension, which directly determines whether the vehicle can provide discharge service through V2G technology, thereby significantly expanding the frequency modulation capacity of the power grid; second, the mandatory residence time dimension, which can greatly improve the flexibility and response ability of system frequency modulation by appropriately extending the vehicle's residence time at the charging station; third, the privacy information provision dimension, which encourages users to share key data such as battery capacity and arrival time at the station to more accurately predict and dispatch frequency modulation resources and reduce uncertainty risks; and fourth, the charging subsidy price dimension, which provides differentiated economic incentives according to different combinations of the first three dimensions.

[0052] Based on the grid-connected model, the application extracts three user behavior characteristics that affect the frequency regulation capability of electric vehicles. For these characteristics, eight types of packages are designed that cover discharge frequency adjustment needs, time flexibility preferences, and privacy protection tendencies.

[0053] 2. Lower layer fuzzy set-based package selection model 1) User fuzzy set establishment Set Four evaluation criteria are used to evaluate users and packages respectively. They represent providing charging information, participating in discharge frequency modulation, willingness to stay at the charging station for a long time, and user price sensitivity. Assuming there are EV users and criteria, their historical data upload permission, V2G function installation, reservation charging period selection, historical data such as charging price sensitivity, and past package selection are collected through questionnaires to obtain the user Fuzzy set of user under criterion (1) In the formula, The physical meaning is the user's criteria. The level of acceptance. express users The fuzzy matrix composed of fuzzy sets under the criteria: (2) 2) Fuzzy evaluation of packages Assuming the number of packages is Get the package In the guidelines Fuzzy sets below: (3) (1) Standard : Guidelines and These respectively represent providing charging information and participating in discharge frequency regulation, which are directly related to the package attributes and are evaluated as binary constants. (In the criteria...) Under these circumstances, packages one, two, three, and four do not require any information, therefore their compliance rate is 0. (Based on the criteria...) The attributes of packages three, four, seven, and eight indicate that users must participate in discharge frequency modulation after selecting these packages, so their compliance rate is 1. Other packages do not have this requirement, so their compliance rate is 0.

[0054] (4) (5) (2) Standard : Guidelines The purpose is to assess the mandatory stay time of the package. Ideal user dwell time The degree of deviation was assessed. A first conformity function was constructed using statistical methods to quantify the degree of matching in terms of dwell time. First conformity evaluation function. It adopts a Gaussian-like distribution form: (6) In the formula, The mandatory stay time for the package This is the adjustment parameter for the time deviation. This function describes how close the residence time is to the ideal time: when... near When the two deviate significantly, the degree of agreement reaches its maximum; when the two deviate significantly, the degree of agreement decreases exponentially.

[0055] (3) Standard

[0056] Guidelines Evaluate the suitability of the package pricing parameters for users' price preferences. Similarly, design a second suitability evaluation function. This is to quantify users' acceptance of the price points within the range of subsidized pricing.

[0057] The second conformity function uses an adjusted Sigmoid function to represent the trend that the degree of conformity to the standard gradually increases as the price rises.

[0058] (7) in, For package pricing parameters, Steepness is used to control the growth rate of the second conformity function as price changes. The center value of the price is used to adjust the translation position of the Sigmoid function. , This is the price compliance assessment coefficient.

[0059] Thus, you can get a package deal. In the guidelines The following evaluation value .use express Each package is available at The fuzzy matrix composed of the evaluation values ​​under each criterion is shown in Equation (8).

[0060] (8) 3) Similarity assessment Define a similarity metric based on weighted Euclidean distance to match users with packages, where, Here, we define the weight for each criterion, assuming all criteria have the same weight. In the formula... This represents the similarity between users and service packages under the Euclidean distance.

[0061] (9) Thus, the similarity metric matrix is ​​obtained. : (10) This represents user satisfaction with the package. Based on this, user... Select Package probability : (11) This yields the probability matrix of user plan selection. : (12) To effectively address the fuzziness and uncertainty of user preferences, this study constructs a multidimensional evaluation model based on fuzzy set theory. This model quantifies user preferences from four key dimensions and calculates the user's matching degree and selection probability for different FM packages accordingly: (1) Information support level: reflects the degree of user acceptance of sharing privacy information, and directly affects the system's prediction accuracy of frequency modulation resources; (2) Discharge frequency regulation participation: measures whether users are willing to participate in reverse discharge frequency regulation and determines their potential to expand the grid regulation capacity; (3) Dwell time matching degree: assess the degree of fit between the user's actual charging behavior and the package requirements. This factor significantly affects the continuity and flexibility of frequency modulation service; (4) Price sensitivity: This describes the user's response to economic incentives and determines the actual attractiveness of the package subsidy.

[0062] 3. Upper-level EVA Day-to-Day Market Bidding Strategy Model 1) EV aggregation model Assume the number is The EV belongs to the cluster The cluster's overall maximum and minimum power Maximum and minimum capacity It can be represented as: (13) (14) in, For clusters The number of EVs.

[0063] Since the grid connection and off-grid operation of EVs will affect the energy boundary of the cluster, equations (15)-(16) define the energy boundary under grid connection conditions. Energy boundary in the off-grid state In the formula and They are numbered as follows EV represents the maximum energy at time t and time t+1.

[0064] (15) (16) in, Indicates EV in The grid connection status at any given time is 1 for grid connection and 0 for off-grid. The corrected energy storage capacity of the EV cluster, etc. As shown in equation (17). In the equation... Let be the equal energy storage power of cluster j at time t.

[0065] (17) 2) EVA's participation in the day-ahead energy-frequency modulation market model EVAs can participate in day-ahead electricity market bidding and trading as resource agents, bidding in a clustered aggregation format. Their day-ahead objective is to minimize the sum of energy purchase costs, package subsidy costs, and risk prediction costs while meeting user charging needs, and to maximize revenue in the frequency regulation market. The objective function can be written as: (18) in, To purchase energy costs, The cost of the package subsidy is shown in equations (19) and (20), respectively.

[0066] (19) (20) in, To purchase the aggregate power of EVs in package k, The price for purchasing energy. For package parameters, Choose the option for the user.

[0067] To quantify the impact of time deviations of users who did not provide charging information on the system's frequency regulation capacity, a risk function is defined. First, for user i, their predicted arrival time is... Actual arrival time satisfy: (twenty one) in, This is the time deviation term. This is a binary variable representing whether a user is willing to provide information. The frequency modulation capacity deviation is defined. for: (twenty two) Then the expected risk cost for: (twenty three) in, The unit deviation penalty coefficient, and These are the frequency modulation capacities based on predicted time and actual time, respectively.

[0068] The time-domain distribution of reserve capacity available for auxiliary frequency regulation services is calculated using an equal energy storage model and used as the frequency regulation capacity to be reported to the frequency regulation market. (twenty four) (25) In the formula, , Adjust the aggregate capacity of EVs for cluster j. , Capacity limits are specified for providing auxiliary frequency modulation (FM) services. This allows for the calculation of FM revenue. In the formula and The capacity price is adjusted up or down at time t.

[0069] (26) The constraints are the boundary limits of the equivalent energy storage model: (27) (28) (29) in, , Let j be the equivalent energy storage power and energy boundary of aggregate j. , This refers to the grid-connected and off-grid boundary energy of aggregate k. , Let be the same energy storage capacity of cluster j at time t and time t+1. Let be the equal energy storage power of cluster j at time t. The time difference is represented. This formula represents the relationship between energy and power under the constraint of equal energy storage, and the specific derivation is shown in equation (17).

[0070] This invention utilizes fuzzy sets to represent the uncertainty in user choice behavior and constructs a user package selection model. Ultimately, it establishes a two-layer optimization model with maximizing aggregator revenue as the upper-level objective and user choice behavior as the lower-level guide.

[0071] This invention first establishes a fuzzy set of user preferences using questionnaires and historical behavioral data to quantify their acceptance of each dimension. Based on this, two types of key functions are designed to evaluate the suitability of the service packages: The dwell time matching degree is modeled using a Gaussian function-like method. The matching degree reaches its peak when the mandatory duration of the package is close to the user's ideal time, and then decays exponentially as the deviation increases, accurately capturing the user's sensitivity to time flexibility. Price sensitivity is calculated based on an improved Sigmoid function. The higher the subsidy price, the faster the user acceptance grows, but the marginal utility diminishes, ensuring the rationality of economic incentives.

[0072] By employing a weighted Euclidean distance algorithm, the similarity of user satisfaction with each service plan under multi-dimensional criteria is comprehensively calculated and transformed into a probability distribution of user selection of different plans. This model not only effectively solves the ambiguity problem in user decision-making but also provides a quantitative basis for system operators to optimize plan design, achieving precise matching between supply and demand.

[0073] Finally, a dynamic decision-making model based on a two-layer optimization framework is proposed to achieve intelligent optimization of electric vehicle frequency regulation package parameters. This model effectively balances the complex relationship between user preferences and aggregator economic benefits through data interaction and iterative calculation between the upper and lower layers. In the lower-layer model, the system first aggregates user groups with similar selection tendencies based on different package choices. By analyzing the combined characteristics of discharge participation and dwell time, users are divided into four typical clusters. In the upper-layer model, the aggregator establishes a multi-objective optimization decision-making system by comprehensively considering frequency regulation market revenue and various operating costs. This system not only needs to calculate the frequency regulation capacity value that each user cluster can provide, but also fully considers key factors such as electricity purchase costs, package subsidy expenditures, and risk costs due to user behavior prediction biases. By dynamically adjusting key variables such as dwell time and subsidy prices in the package parameters, and feeding the optimization results back to the lower-layer model for a new round of user selection probability calculation, a continuously iterative closed-loop optimization mechanism is formed. The study uses an improved particle swarm optimization algorithm for solving the problem, and through multiple iterations, a solution that achieves the optimal balance between user satisfaction and aggregator revenue is finally obtained.

[0074] Model validation results demonstrate that this bidirectional feedback optimization mechanism significantly improves the overall system performance, maximizing the economic benefits for aggregators while ensuring user freedom of choice. This provides a scientific and effective decision support tool for electric vehicle frequency regulation services in the electricity market environment. The model not only overcomes the limitations of traditional optimization methods in balancing the interests of multiple parties but also provides an important reference for the design of demand response mechanisms in new power systems through intelligent dynamic parameter adjustment.

[0075] To verify the scheme of this invention, charging behavior data of 50 EVA vehicles with a battery capacity of 60 kWh and maximum charging and discharging powers of 6 kW and 5 kW respectively were selected. Based on their travel patterns, the Monte Carlo method was used to simulate the users' arrival time and minimum expected power upon departure. A survey was conducted on these users to obtain their fuzzy coefficients. The current pricing references the pricing of demand-side resource reserves in foreign electricity markets.

[0076] In the case study analysis, the impact of providing charging information, participating in discharge frequency regulation, willingness to stay at charging stations for extended periods, and the user's price sensitivity on the user is set to be 0.2, 0.3, 0.3, and 0.2, respectively. Through comprehensive evaluation and analysis, the following settings are established: It is 8. It is 1.4. The value is 2. It is 0.6. It is 0.5. It is 16.

[0077] Through questionnaire surveys and collected user history data, we can obtain information about users'... Fuzzy preferences under four criteria, such as Figure 3 As shown in the figure, users are generally highly sensitive to price and dwell time, while their support for providing personal information is mostly in the middle range, indicating a relatively neutral preference. Furthermore, most users have a low willingness to participate in frequency modulation for discharge, but there is considerable room for hesitation.

[0078] User package selection results are as follows: Figure 4 As shown in the results, packages 3, 5, and 7 have the highest user satisfaction. Furthermore, some users who are hesitant about participating in discharge frequency regulation may still choose packages that include this feature. Meanwhile, packages 6 and 8 also have high satisfaction rates, indicating that most users are willing to adjust their behavior to support charging solutions that improve EV frequency regulation capabilities, provided there are appropriate subsidies or incentives.

[0079] Since the EV grid-connected model is directly affected by the frequency regulation method and charging time, users who have selected different packages will be aggregated according to whether they participate in discharge frequency regulation and the different times they stay at the charging station: Aggregation 1: Users who choose Package 1 and Package 5, which do not accept discharge frequency modulation and have short dwell times.

[0080] Aggregation 2: Users who choose Package 2 and Package 6 who do not accept discharge frequency modulation and have long dwell times.

[0081] Aggregation 3: Users who choose to accept discharge frequency modulation and have short dwell time in Package 3 and Package 7.

[0082] Aggregation 4: Users who choose to accept discharge frequency modulation and have a long dwell time in Package 4 and Package 8.

[0083] Upward and downward regulation capacity obtained in the energy-frequency regulation market, such as Figure 5 and Figure 6 As shown.

[0084] Combination Figure 5 and Figure 6Looking at the capacity increases, the capacity increase for aggregated EVs is stronger during the early morning (0:00 to 7:00) and afternoon (12:00 to 16:00) hours than at other times. This is because vehicle driving demand increases during other times, while charging resources are relatively reduced. Packages 7 and 8 are the most popular choices. These packages all provide discharge frequency regulation and charging information, indicating that many users are willing to choose packages with better frequency regulation to obtain more subsidies.

[0085] In terms of capacity reduction, the reduction capacity is significantly better during nighttime hours, especially between 9:00 PM and midnight. The capacity reduction during the early morning hours is lower because most charging vehicles have already reached their charging limits, making further power reduction impossible. Furthermore, due to... Figure 5 It is known that the price increase reached its peak during this period, and providing resources with higher prices yielded higher returns.

[0086] To investigate the impact of different package parameter settings on revenue and user satisfaction, three scenarios were designed (as shown in Table 2): Scenario 1 represents the optimal package parameters obtained through two-level iterative optimization; Scenario 2 represents the minimum value of the package parameters; and Scenario 3 represents the maximum value of the package parameters. The revenue, subsidy costs, and user satisfaction under the three scenarios are shown in Table 3.

[0087] Table 2 Parameters of Packages for Different Scenarios

[0088] Table 3 Comparison of Parameters and Scenarios for Different Packages

[0089] In scenario two, the package parameters are set to the minimum, resulting in low subsidy amounts and low user dwell time, leading to a negative total subsidy revenue. However, because low dwell time better meets the needs of most users, user satisfaction is maximized. This approach of simply pursuing user satisfaction at the expense of subsidies and service time fails to fully leverage users' frequency tuning potential and also impacts the aggregator's economic benefits.

[0090] In scenario three, the package parameters are set to the maximum value, the subsidy amount increases significantly, and the user's dwell time is significantly prolonged. However, the long dwell time affects the user experience, reduces users' willingness to choose the response package, and the higher subsidy cost also reduces the overall revenue.

[0091] This invention comprehensively considers the balance between EVA (Economic Value Added) benefits and user satisfaction through parameters obtained through two-layer optimization. The optimized parameter design ensures an appropriate subsidy amount and a reasonable user retention time, maximizing total benefits. In terms of user satisfaction, Scenario 1 also maintains a high level. This indicates that the optimized package can balance user experience and EVA benefits, demonstrating the effectiveness of the parameter optimization model.

[0092] The above combination Figure 1 The present invention provides a detailed description of the electric vehicle package design and parameter setting method for the frequency modulation market. Next, the electric vehicle package design and parameter setting system for the frequency modulation market provided by the present invention will be described in conjunction with the accompanying drawings.

[0093] Figure 7 This is a schematic diagram of the structure of an electric vehicle package design and parameter setting system for the frequency modulation market, as shown in an embodiment of the present invention. Figure 7 The system described in this invention includes: The package building module is configured to: build a variety of retail frequency modulation packages based on discharge frequency modulation participation, mandatory dwell time and privacy information provision, and configure a charging subsidy price for each package; The evaluation criteria setting module is configured to set four evaluation criteria in sequence based on providing charging information, participating in discharge frequency regulation, willingness to stay at the charging station for a long time, and user price sensitivity. The fuzzy processing module is configured to: acquire historical user data; construct a fuzzy set of user preferences under the evaluation criteria based on the user's acceptance of the evaluation criteria; calculate the degree of conformity between the packages and the evaluation criteria based on all retail frequency tuning packages, and construct a package intuition fuzzy set for each package under the evaluation criteria; match users and packages based on the user preference fuzzy matrix under the evaluation criteria and the package intuition fuzzy matrix for each package under the evaluation criteria, obtain a similarity metric matrix, calculate the probability of a user choosing a certain package, and obtain a user package selection probability matrix. The output module is configured to: determine the package subsidy cost based on the user package selection probability matrix; construct an objective function based on energy purchase cost, package subsidy cost, expected risk cost, and frequency regulation revenue; and solve for the optimal solution by combining the constraints.

[0094] In some embodiments, the four evaluation criteria include: a first evaluation criterion, a second evaluation criterion, a third evaluation criterion, and a fourth evaluation criterion. The first evaluation criterion is used to evaluate the completeness of the charging information provided; the second evaluation criterion is used to evaluate whether the user participates in discharge frequency regulation after selecting a package; the third evaluation criterion is used to evaluate the degree of deviation between the mandatory dwell time of the package and the user's ideal dwell time; and the fourth evaluation criterion is used to evaluate the suitability of the package price parameters for the user's price preferences.

[0095] In some embodiments, the method for calculating the degree of compliance of all retail FM packages with the evaluation criteria includes: Based on the direct correlation between the first evaluation criterion, the second evaluation criterion, and the package attributes, the first compliance degree and the second compliance degree are calculated; Based on the third assessment criterion, the first compliance assessment function is used to quantify the degree of matching of the stay time and calculate the third compliance. Based on the fourth evaluation criterion, the second compliance function is used to quantify the degree of user acceptance of the price points within the subsidy pricing range and calculate the fourth compliance degree.

[0096] In some embodiments, the objective function is expressed by the following formula:

[0097] in, To purchase energy costs, To subsidize the cost of the package, For the expected risk cost, For frequency modulation revenue.

[0098] In some embodiments, the expected risk cost is expressed by the following formula:

[0099]

[0100]

[0101] in, The unit deviation penalty coefficient, For frequency modulation capacity deviation, and These are the frequency modulation capacities based on predicted time and actual time, respectively. To predict arrival time, This is the actual arrival time. For time deviation, This is a binary variable representing whether a user is willing to provide information.

[0102] In some embodiments, the frequency modulation benefit is expressed by the following formula:

[0103]

[0104]

[0105] in, , To adjust the aggregate capacity of EVs purchased under package k, , Adjust the capacity price up or down at time t. , Capacity limits specified for providing auxiliary frequency modulation services. Indicates time difference.

[0106] In some embodiments, the constraints include boundary restrictions of the equivalent energy storage model, expressed by the following formula:

[0107]

[0108]

[0109] in, Indicates the number is The EV belongs to the cluster Overall maximum and minimum power, Indicates the number is The EV belongs to the cluster Maximum and minimum capacity , To aggregate the grid-connected and off-grid boundary energy of k, , Let be the same energy storage capacity of cluster j at time t and time t+1. Let be the equal energy storage power of cluster j at time t. Indicates time difference.

[0110] In some embodiments, before constructing the objective function, the method includes: calculating the maximum power, minimum power, maximum capacity, and minimum capacity of the entire EV aggregation model cluster; defining the energy boundary under grid-connected and off-grid conditions; and calculating the energy storage energy of the EV cluster and other energy storage components.

[0111] In some embodiments, the retail FM package includes: Package 1: No discharge frequency modulation, dwell time is... No relevant information is provided; the charging subsidy price is... ; Second package: No discharge frequency modulation, dwell time is... No relevant information is provided; the charging subsidy price is... ; Third package: Perform discharge frequency modulation, dwell time is... No relevant information is provided; the charging subsidy price is... ; Fourth package: Discharge frequency modulation, dwell time is No relevant information is provided; the charging subsidy price is... ; Fifth package: No discharge frequency modulation, dwell time is Provide relevant information and charging subsidy prices. ; Package 6: No discharge frequency modulation, dwell time is Provide relevant information and charging subsidy prices. ; Package 7: Perform discharge frequency modulation, and the dwell time is... Provide relevant information and charging subsidy prices. ; Package 8: Perform discharge frequency modulation and stay duration is Provide relevant information and charging subsidy prices. .

[0112] According to embodiments of the present invention, the electric vehicle package design and parameter setting system for the frequency modulation market can correspond to the execution of the method described in the embodiments of the present invention, and the above and other operations and / or functions of each module of the electric vehicle package design and parameter setting system for the frequency modulation market are respectively for the purpose of implementing Figure 1 For the sake of brevity, the corresponding processes of each method in the code will not be elaborated here.

[0113] See Figure 8 The diagram shows the structure of a computer device, which includes a processor, a communication interface, and a computer-readable storage medium. The processor, communication interface, and computer-readable storage medium are connected via a bus or other means. The communication interface is used to receive and send data. The computer-readable storage medium can be stored in the computer device's memory. The computer-readable storage medium stores computer programs, including program instructions, and the processor executes the program instructions stored in the computer-readable storage medium. The processor (or CPU, Central Processing Unit) is the computing and control core of the computer device, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement the corresponding steps in the embodiment of the method for designing and setting parameters for electric vehicle packages targeting the frequency modulation market.

[0114] This embodiment provides a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the processing system of the computer device. Furthermore, this storage space also contains one or more instructions suitable for loading and execution by the processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM memory or non-volatile memory, such as at least one disk storage device; optionally, it can also be at least one computer-readable storage medium located remotely from the aforementioned processor.

[0115] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes one or more instructions stored in the computer-readable storage medium to implement the corresponding steps in the above embodiment of the electric vehicle package design and parameter setting method for the frequency modulation market.

[0116] This embodiment provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the corresponding steps in the above embodiment of the electric vehicle package design and parameter setting method for the frequency modulation market.

[0117] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0118] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0119] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes ​ The steps of the function specified in one or more boxes.

[0121] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0122] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for designing and parameterizing a package for electric vehicles oriented to the frequency market, characterized by, The method comprises the following steps: Based on the participation of discharge frequency modulation, the forced residence time and the provision of privacy information, a plurality of retail frequency modulation packages are constructed, and a charging subsidy price is configured for each package; According to the provision of charging information, the participation in discharge frequency modulation, the willingness to stay at the charging station for a long time and the price sensitivity of the user, four evaluation criteria are sequentially set; The historical data of the user is obtained, the user preference fuzzy set of the user under the evaluation criteria is constructed according to the acceptance degree of the user to the evaluation criteria, and the conformity of the package to the evaluation criteria is calculated based on all retail frequency modulation packages, and the package intuitionistic fuzzy set of each package under the evaluation criteria is constructed; Based on the user preference fuzzy matrix of the user under the evaluation criteria and the package intuitionistic fuzzy matrix of each package under the evaluation criteria, the user and the package are matched to obtain a similarity matrix, the probability of the user selecting a certain package is calculated, and a user package selection probability matrix is obtained; Based on the user package selection probability matrix, the package subsidy cost is determined; Based on the energy purchasing cost, the package subsidy cost, the expected risk cost and the frequency modulation benefit, a target function is constructed, and the optimal scheme is solved combined with the constraint condition.

2. The method of designing and parameterizing a package for a frequency market-oriented electric vehicle according to claim 1, wherein, The four evaluation criteria include a first evaluation criterion, a second evaluation criterion, a third evaluation criterion and a fourth evaluation criterion, wherein the first evaluation criterion is used to evaluate the perfection degree of the provision of charging information, the second evaluation criterion is used to evaluate whether the user participates in discharge frequency modulation after selecting the package, the third evaluation criterion is used to evaluate the deviation degree of the forced residence time of the package and the ideal residence time of the user, and the fourth evaluation criterion is used to evaluate the appropriateness of the package price parameter to the price preference of the user.

3. The method of designing and parameterizing a package for a frequency market-oriented electric vehicle according to claim 2, wherein, The method for calculating the conformity of the package to the evaluation criteria based on all retail frequency modulation packages comprises the following steps: Based on the direct correlation between the first evaluation criterion, the second evaluation criterion and the package attribute, the first conformity and the second conformity are calculated; Based on the third evaluation criterion, the matching degree of the residence time is quantified by using a first conformity evaluation function, and the third conformity is calculated; Based on the fourth evaluation criterion, the acceptance degree of the user to the price point in the subsidy pricing range is quantified by using a second conformity function, and the fourth conformity is calculated.

4. The method of designing and parameterizing a package for a frequency market-oriented electric vehicle according to claim 1, wherein, The target function is represented by the following formula: wherein, is the cost of purchasing power, is the cost of the package subsidy, is the cost of expected risk, is the tuning benefit.

5. The method of designing and parameterizing a package for a frequency market oriented electric vehicle according to claim 4, wherein, The expected risk cost is represented by the following formula: wherein, is a unit bias penalty coefficient, is a frequency modulation capacity bias, and are frequency modulation capacities based on predicted and actual times, respectively, is a predicted arrival time, is an actual arrival time, is a time bias term, is a binary variable indicating whether the user is willing to provide information.

6. The method of designing and parameterizing a package for a frequency market-oriented electric vehicle according to claim 4, wherein, The frequency modulation benefit is represented by the following formula: wherein, , is the aggregate up / down capacity for EVs purchasing package k, , are the up / down capacity prices at time t, respectively, , is the capacity up / down limit specified when providing auxiliary frequency modulation service, denotes the time difference.

7. The method of designing and parameterizing a package for a frequency market-oriented electric vehicle according to claim 1, wherein, The constraint condition includes the boundary limitation of the equivalent energy storage model, which is represented by the following formula: wherein, EVs numbered belong to cluster Overall maximum and minimum power, EVs numbered belong to cluster Maximum minimum capacity, , is the grid-connected, off-grid boundary energy for aggregation k, , is the equal energy storage energy for cluster j at time t and t+1, is the equal energy storage power for cluster j at time t, denotes the time difference.

8. The method of designing and parameterizing a package for a frequency market oriented electric vehicle according to claim 1, wherein, Before constructing the target function, the following steps are included: calculating the maximum power, the minimum power, the maximum capacity and the minimum capacity of the EV aggregation model cluster as a whole, defining the energy boundary in the grid-connected state and the energy boundary in the off-grid state, and calculating the equivalent energy of the EV cluster.

9. The method of designing and parameterizing a package for a frequency market-oriented electric vehicle according to claim 1, wherein, The retail frequency modulation package comprises: The first set meal: not to discharge frequency, the length of stay is , not to provide relevant information, the price of charging subsidies is ; The second set meal: not to discharge frequency, the length of stay is , not to provide relevant information, the price of charging subsidies is ; The third set meal: carry out discharge frequency modulation, the residence time is , do not provide relevant information, the price of charging subsidy is ; The fourth package: discharging frequency adjustment, the retention time is , no related information, the price of charging subsidies is ; The fifth package: no discharge frequency adjustment, the retention time is , providing relevant information, the price of charging subsidies is ; The sixth package: no discharge frequency adjustment, the retention time is , providing relevant information, the price of charging subsidies is ; The seventh package: performing discharge frequency modulation, the retention time is , providing relevant information, and the charging subsidy price is . The eighth package: performing discharge frequency modulation, the retention time is , providing relevant information, and the charging subsidy price is .

10. A system for designing and parameterizing packages for electric vehicles in a frequency market, characterized in that, The method comprises the following steps: A package construction module is configured to construct a plurality of retail frequency modulation packages based on the participation of discharge frequency modulation, the forced residence time and the provision of privacy information, and to configure a charging subsidy price for each package; An evaluation criterion setting module is configured to sequentially set four evaluation criteria according to the provision of charging information, the participation in discharge frequency modulation, the willingness to stay at the charging station for a long time and the price sensitivity of the user; The fuzzy processing module is configured to: acquire user historical data, construct a user preference fuzzy set of the user under the evaluation criteria according to an acceptance degree of the user to the evaluation criteria; and calculate a degree of compliance of each package with the evaluation criteria based on all retail frequency modulation packages, and construct a package intuitionistic fuzzy set of each package under the evaluation criteria; Based on the user preference fuzzy matrix of the user under the evaluation criteria and the package intuitionistic fuzzy matrix of each package under the evaluation criteria, the user and the package are matched to obtain a similarity measure matrix, the probability of the user selecting a certain package is calculated, and a user package selection probability matrix is obtained; The output module is configured to: determine a package subsidy cost based on the user package selection probability matrix; construct a target function based on a purchase cost, the package subsidy cost, an expected risk cost, and a frequency modulation benefit, and solve an optimal scheme in combination with a constraint condition. 11.A computer device, characterized in that, a processor adapted to execute a computer program; a computer readable storage medium having a computer program stored therein, the computer program being executed by the processor to implement the steps of the method for designing and parameterizing a frequency modulation market-oriented electric vehicle package according to any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is adapted to be loaded and executed by the processor to implement the steps of the method for designing and parameterizing a frequency modulation market-oriented electric vehicle package according to any one of claims 1-9.

13. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is executed by the processor to implement the steps of the method for designing and parameterizing a frequency modulation market-oriented electric vehicle package according to any one of claims 1-9.

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