New energy vehicle grid interaction frequency modulation control method based on power margin and related device

Abstract

This application discloses a method and related apparatus for frequency regulation control of new energy vehicles based on power margin, relating to the field of power system frequency regulation control. The method includes: acquiring the status information and user participation information of the power batteries of each electric vehicle connected to the power grid; calculating the available charging and discharging capacity of each electric vehicle based on the status information; determining the adjustable capacity and power margin of the electric vehicle cluster within a target area based on the available charging and discharging capacity; calculating the change in active power of the electric vehicle cluster based on the power system frequency deviation and power margin, using a preset active power frequency regulation coefficient and frequency-power droop characteristics; allocating the change in active power to each electric vehicle based on a weighting coefficient, according to the current power of each electric vehicle and its contribution to the total power of the electric vehicle cluster, to obtain additional frequency regulation active power; and controlling each electric vehicle to perform charging or discharging operations based on the additional frequency regulation active power.

Description

Technical Field

[0001] This application relates to the field of power system frequency regulation control, and in particular to a method and related device for vehicle-to-grid frequency regulation control of new energy vehicles based on power margin. Background Technology

[0002] With the high proportion of new energy connected to the grid leading to a decrease in the equivalent inertia of the power system and an increase in frequency fluctuations, the power grid’s demand for flexible frequency regulation resources is becoming increasingly urgent. The large-scale development of new energy vehicles makes them potential frequency regulation resources through vehicle-to-grid (V2G) technology, with great potential to participate in system frequency regulation.

[0003] Currently, there are explorations into V2G systems participating in grid frequency regulation. However, existing methods mostly adopt a centralized control architecture, which suffers from problems such as slow response and poor adaptability to distributed access of new energy vehicle groups. Some strategies do not fully consider the state of charge (SOC) of new energy vehicle power batteries, which can easily lead to overcharging and discharging and accelerated battery aging. At the same time, frequency regulation power allocation mostly adopts a fixed ratio mode, without combining the actual adjustable capacity of the vehicle and the dynamic optimization based on user participation. Furthermore, there is a lack of differentiated control strategies for different SOC ranges of the battery. As a result, the frequency regulation potential of new energy vehicles has not been fully released, resource utilization is low, and it is difficult to achieve efficient and stable regulation of system frequency while ensuring battery life and user needs. Therefore, there is an urgent need for a V2G frequency regulation control scheme that is suitable for distributed access scenarios and takes into account both system frequency regulation requirements and battery safety. Summary of the Invention

[0004] The purpose of this application is to provide a power margin-based frequency modulation control method and related devices for new energy vehicles participating in vehicle-to-grid interaction. By calculating the power margin of the electric vehicle cluster, the method achieves dynamic allocation of frequency modulation power, taking into account both battery safety and frequency modulation effect, and improving the resource utilization and response flexibility of new energy vehicles participating in vehicle-to-grid interaction frequency modulation.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a new energy vehicle vehicle-to-grid frequency modulation control method based on power margin, including: S1. Obtain the status information and user participation information of each electric vehicle's power battery connected to the power grid; the status information includes battery capacity and current state of charge (SOC); S2. Based on the status information, calculate the available charging and discharging capacity of each electric vehicle; S3. Based on the available charging and discharging capacity of each electric vehicle, determine the adjustable capacity of the electric vehicle cluster within the target area and the power margin of the electric vehicle cluster. S4. Based on the power system frequency deviation and the power margin, the active power change of the electric vehicle cluster is calculated using a preset active frequency regulation coefficient and frequency-power droop characteristics. S5. Based on the current power of each electric vehicle and its contribution to the total power of the electric vehicle cluster, and using the weighting coefficient for electric vehicles participating in power system frequency regulation, the change in active power is allocated to each electric vehicle to obtain the additional frequency regulation active power of each electric vehicle; the weighting coefficient is determined by the current state of charge (SOC) in the power battery of each electric vehicle. S6. Based on the additional frequency-modulated active power, control each electric vehicle to perform charging or discharging operations.

[0006] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the power margin-based new energy vehicle vehicle-to-grid interactive frequency modulation control method described in any one of the above applications.

[0007] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described power margin-based new energy vehicle vehicle-to-grid interactive frequency modulation control method.

[0008] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described power margin-based new energy vehicle vehicle-to-grid frequency modulation control method.

[0009] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a new energy vehicle vehicle-to-grid (V2G) frequency regulation control method and related devices based on power margin. It first acquires the state of the power batteries of each electric vehicle and user participation information, calculates the available charging and discharging capacity of a single vehicle, then integrates these to obtain the power margin of the electric vehicle cluster. Combined with the system frequency deviation, it calculates the required frequency regulation active power change for the cluster. Finally, it allocates the frequency regulation power using weighting coefficients determined based on the battery SOC range. This solves the problems of lag and poor adaptability in existing centralized control methods, achieving flexible response of new energy vehicle frequency regulation resources in distributed access scenarios. Simultaneously, by dividing the power battery into different SOC ranges using differentiated weighting coefficients, it addresses the issues of existing... Some strategies fail to adequately consider battery status, leading to overcharging and over-discharging and accelerated battery aging. This approach ensures battery safety and extends battery life while meeting grid frequency regulation requirements. Furthermore, this application dynamically allocates frequency regulation power based on the vehicle's actual adjustable capacity, addressing the issues of insufficient frequency regulation potential and low resource utilization in fixed-ratio allocation modes. It balances user participation and battery status, fully releasing the frequency regulation potential of new energy vehicles while ensuring user needs and battery safety. This improves resource utilization and control stability of vehicle-grid interactive frequency regulation, better adapting to the flexible frequency regulation needs of the grid in scenarios with a high proportion of new energy vehicles connected to the grid. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is an application environment diagram of a new energy vehicle vehicle-to-grid interactive frequency modulation control method based on power margin in one embodiment of this application; Figure 2 A flowchart illustrating a power margin-based frequency modulation control method for new energy vehicles using vehicle-to-grid interaction, provided as an embodiment of this application. Figure 3 A framework diagram of a V2G electric vehicle group participating in frequency modulation system provided in an embodiment of this application; Figure 4 This is a schematic diagram of a system frequency control model provided in an embodiment of this application; Figure 5 This is a schematic diagram of the frequency modulation droop characteristics of an electric vehicle group according to an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0014] The power margin-based frequency modulation control method for new energy vehicles based on vehicle-to-grid interaction provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server.

[0015] Server 104 can feed back the obtained status information and user participation information of each electric vehicle power battery to terminal 102. Furthermore, in some embodiments, the power margin-based new energy vehicle-to-grid interaction frequency modulation control method can also be implemented independently by server 104 or terminal 102. For example, terminal 102 can directly perform power margin-based new energy vehicle-to-grid interaction frequency modulation control based on the status information and user participation information of each electric vehicle power battery; alternatively, server 104 can obtain the status information and user participation information of each electric vehicle power battery from the data storage system and perform power margin-based new energy vehicle-to-grid interaction frequency modulation control based on the status information and user participation information of each electric vehicle power battery.

[0016] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0017] In one exemplary embodiment, such as Figure 2 As shown, a method for frequency modulation control of new energy vehicles based on power margin is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1Taking server 104 as an example, the explanation includes steps S1-S6: S1. Obtain the status information and user participation information of each electric vehicle's power battery connected to the power grid; the status information includes battery capacity and current state of charge (SOC); S2. Based on the status information, calculate the available charging and discharging capacity of each electric vehicle; S3. Based on the available charging and discharging capacity of each electric vehicle, determine the adjustable capacity of the electric vehicle cluster within the target area and the power margin of the electric vehicle cluster. S4. Based on the power system frequency deviation and the power margin, the active power change of the electric vehicle cluster is calculated using a preset active frequency regulation coefficient and frequency-power droop characteristics. S5. Based on the current power of each electric vehicle and its contribution to the total power of the electric vehicle cluster, and using the weighting coefficient for electric vehicles participating in power system frequency regulation, the change in active power is allocated to each electric vehicle to obtain the additional frequency regulation active power of each electric vehicle; the weighting coefficient is determined by the current state of charge (SOC) in the power battery of each electric vehicle. S6. Based on the additional frequency-modulated active power, control each electric vehicle to perform charging or discharging operations.

[0018] In one exemplary embodiment, when executing S1-S6, the specific steps can be as follows: This embodiment proposes a new energy vehicle-to-grid (V2G) frequency regulation control method based on power margin. The V2G system participates in frequency regulation using a hierarchical control structure. Electric vehicle (EV) groups are distributedly connected to the power system, and chargers respond locally based on the EV energy storage zones. EV charging station operators act as a link between EVs and the grid, only performing capacity calculations and distributing droop coefficients locally. Distributed EVs can adjust their charging and discharging power in real time according to system frequency fluctuations, participating in system frequency regulation. The frequency regulation control center typically allocates system frequency regulation tasks rationally based on the EV power adjustment characteristics, controlling the EV clusters to participate in system frequency regulation, maximizing support for bidirectional V2G, and achieving single-area system frequency regulation. The frequency regulation control center develops a P / f frequency droop curve and then adjusts the EV output power based on load changes to smooth the system frequency. When the system frequency is higher than the rated frequency fn, EVs can absorb grid power; when it is lower... f n To maintain system frequency stability, the charging power of electric vehicles (EVs) can be reduced, or EVs can discharge into the grid when idle. In this way, a large fleet of EVs can be considered as frequency-regulating power plants, participating in system frequency regulation services. A specific framework for distributed V2G participation in system frequency regulation is as follows: Figure 3 As shown.

[0019] exist Figure 3 The charging interface of electric vehicles generally includes an on-board communication interface and an energy transmission interface. After the electric vehicle is connected to the power grid, the status information of the electric vehicle's power battery (including battery capacity, current state of charge (SOC), frequency regulation participation information, etc.) and the participation of the vehicle user can be uploaded to the charging operator's charging pile. At this time, the charging operator's charging pile acts as a virtual power plant to estimate the available capacity of each electric vehicle.

[0020] Design a frequency regulation model for a single-area system involving a fleet of electric vehicles, such as Figure 4 The basic model for frequency control of a regional system containing a fleet of electric vehicles is shown. The expression for the frequency modulation model is given by equation (1). Figure 4 The frequency control strategy in the equation. The adjustable capacity of the electric vehicle is calculated using equations (2) and (3). Corresponding to Figure 4 The section on the observation of the operating status of new energy vehicles.

[0021] Among them, the V2G charging interface calculates the given power based on the frequency detected by the local system and the droop curve (the calculation method is as shown in Equation (1)). The grid connection interface can then output active power to the grid. The primary frequency regulation response is shown in Equation (1). Regarding the formulation of the droop curve for electric vehicles participating in grid frequency regulation, based on the capacity of the vehicle group, the electric vehicle group participating in frequency regulation service is equivalent to a frequency regulation power plant or an adjustable load, forming a curve as shown in Equation (1). Figure 5 The drooping curve shown.

[0022] (1).

[0023] In the formula: Δ P This represents the change in active power of the electric vehicle fleet. K f Δ is the active frequency regulation coefficient; f For power system frequency deviation, f N The rated frequency of the power system P t This is the current adjustable power. P t It can also be expressed as the overall adjustable charging and discharging power of the cluster of electric vehicles, where the power flowing from the electric vehicle to the grid is defined as positive. After the electric vehicle is connected to the grid, it obtains various information about the power battery through the charging and discharging interface. V2G calculates the upper and lower limits of the total charging and discharging capacity of the participating vehicles. E max , E min ], respectively representing the first i car The appropriate charging and discharging capacity of electric vehicles and the available capacity of distributed V2G in the region are shown in equations (2) and (3).

[0024] (2).

[0025] (3).

[0026] In the formula, E i Indicates the first i car The current available discharge capacity of electric vehicles, E Li Indicates the first i car Minimum usable discharge capacity of electric vehicles E Ni Indicates the first i car Maximum usable discharge capacity of electric vehicles E max This indicates the maximum discharge capacity of the electric vehicle fleet. E min This indicates the maximum charging capacity participating in the electric vehicle fleet. A frequency dead zone needs to be set; during the dead zone, electric vehicles do not participate in frequency regulation and only charge and discharge according to user demand. When the system frequency increases (i.e., Δ...), f >0), requiring new energy vehicles to be charged from the power grid, then the current adjustable power in equation (1) P t As shown in equation (4); when the system frequency decreases (i.e., Δ f <0, requiring new energy vehicles to discharge from the grid, then the current adjustable power in equation (1) P t As shown in equation (5).

[0027] (4).

[0028] (5).

[0029] Based on the power contribution of new energy vehicles, the additional active power for frequency regulation of a single unit participating in frequency regulation is initially allocated as shown in equation (6). This allocation is as follows: Figure 4 The active power allocation part of the medium frequency control strategy.

[0030] (6).

[0031] In the formula, α is the weighting coefficient for electric vehicles participating in system frequency regulation. The extent to which the weighting coefficient α deviates from 1 reflects the magnitude of electric vehicle charging and discharging, indicating its ability to participate in frequency adjustment under the user's power battery energy storage zone regulation strategy requirements.

[0032] A fixed power allocation ratio between electric vehicles (EVs) and traditional power units reduces the utilization rate of EV resources. This paper proposes a load frequency control strategy based on real-time capacity prediction of EV clusters. This strategy can adjust the power allocation of EV clusters in real time, meeting the driving needs of EV users while improving grid frequency stability through source-load interaction. When considering the participation of EV batteries in system frequency regulation, avoiding high-power charging and discharging of batteries at excessively high or low SOC states effectively maintains the battery's State of Health (SOH) and reduces battery aging time.

[0033] Therefore, it is necessary to partition the SOC of the power battery and further set different control strategies. Based on the current SOC of the power battery, it can be divided into four working zones: normal charge / discharge zone, charging priority zone, discharging priority zone, and disabled zone. Combined with a fuzzy controller, the output of the energy storage system is smoothed, assisting traditional generator sets in participating in power system frequency regulation.

[0034] The control strategies for each working zone are as follows.

[0035] (1) When the SOC of the power battery is at a reasonable level, i.e. min ≤SOC≤SOC max时， The power battery is in the normal charge and discharge range, and the battery capacity is at a reasonable level.

[0036] (2) When the power battery is at SOC max When the battery's charge level is below the state of charge (SOC), it is in the discharge priority zone, suitable for discharging but not for charging. When the power battery assists the generator set in participating in system frequency regulation, the discharge power of the power battery should be appropriately increased, and the charging process should be reduced, or even stopped altogether.

[0037] (3) When the power battery is at SOC L ≤SOC<SOC min When the battery is in the charging priority zone, if the power battery continues to discharge, the battery's SOC will be lower than the lifespan of the energy storage system. Therefore, when the power battery system assists the traditional thermal power unit in participating in the power system frequency regulation, the energy storage system should participate in the charging process in a way that minimizes the discharge process.

[0038] (4) When the SOC of the power battery is less than or equal to the SOC L At this time, the power battery is already in a restricted area, and the energy storage system is prohibited from discharging. Otherwise, it will seriously affect the lifespan of the energy storage system; only charging is permitted.

[0039] Electric vehicles adjust their frequency regulation participation level based on the State of Charge (SOC) of the power battery by assigning a weighting coefficient α. When the power battery is in the normal charge / discharge zone, its charging and discharging performance is relatively ideal, and the weighting coefficient α can be set to 1. When the power battery is in the discharge priority zone, if the system frequency increases and the power vehicle needs to charge, the weighting coefficient α is set to a value less than 1; if the system frequency decreases and the power vehicle needs to discharge, the weighting coefficient α is set to a value greater than 1. When the power battery is in the charging priority zone, if the system frequency increases and the power vehicle needs to charge, the weighting coefficient α is set to a value greater than 1; if the system frequency decreases and the power vehicle needs to discharge, the weighting coefficient α is set to a value less than 1. When the power battery is in the disabled zone, if the system frequency increases and the power vehicle needs to charge, the weighting coefficient α is set to a value greater than 1; if the system frequency decreases and the power vehicle needs to discharge, the weighting coefficient α is 0.

[0040] This application also provides an application scenario that utilizes the aforementioned power margin-based frequency regulation control method for new energy vehicles interacting with the grid. Specifically, this application scenario involves electric vehicles participating in grid frequency regulation. A certain scale of distributed new energy electric vehicle charging piles are connected to the regional power grid. After a large number of electric vehicles are connected to the grid, the grid operator obtains the power battery status information and user participation information for all connected electric vehicles through the frequency regulation control center. Based on the aforementioned method, the power margin of the entire electric vehicle cluster is calculated. Combined with the current frequency deviation of the grid, the total active power change that the cluster needs to adjust is obtained. Then, based on the SOC partition of each electric vehicle's power battery, the frequency regulation weighting coefficient for each vehicle is determined. Finally, the active power adjustment is allocated to individual electric vehicles, controlling each vehicle to perform the corresponding charging or discharging operation. This process, while meeting the daily charging and discharging needs of users and ensuring the lifespan of power batteries, can quickly respond to the grid's frequency regulation needs, smooth grid frequency fluctuations, improve grid operational stability, and also provide electric vehicle users with a revenue channel for participating in grid ancillary services.

[0041] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 6As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores the additional frequency-modulated active power of each electric vehicle. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a new energy vehicle vehicle-to-grid interactive frequency modulation control method based on power margin.

[0042] Figure 6 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0043] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0044] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0045] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations and be carried out with the authorization of the corresponding device owner.

[0046] 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 computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0047] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0048] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0049] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for frequency modulation control of new energy vehicles based on power margin and vehicle-to-grid interaction, characterized in that, include: S1. Obtain the status information and user participation information of each electric vehicle's power battery connected to the power grid; The status information includes battery capacity and current state of charge (SOC). S2. Based on the status information, calculate the available charging and discharging capacity of each electric vehicle; S3. Based on the available charging and discharging capacity of each electric vehicle, determine the adjustable capacity of the electric vehicle cluster within the target area and the power margin of the electric vehicle cluster. S4. Based on the power system frequency deviation and the power margin, the active power change of the electric vehicle cluster is calculated using a preset active frequency regulation coefficient and frequency-power droop characteristics. S5. Based on the current power of each electric vehicle and its contribution to the total power of the electric vehicle cluster, and using the weighting coefficient for electric vehicles participating in power system frequency regulation, the change in active power is allocated to each electric vehicle to obtain the additional frequency regulation active power of each electric vehicle; the weighting coefficient is determined by the current state of charge (SOC) in the power battery of each electric vehicle. S6. Based on the additional frequency-modulated active power, control each electric vehicle to perform charging or discharging operations.

2. The new energy vehicle-to-grid interaction frequency modulation control method based on power margin according to claim 1, characterized in that, The power margin is determined by the maximum charging capacity and maximum discharging capacity of the electric vehicle cluster. The formula for calculating the maximum charging capacity is as follows: ； The formula for calculating the maximum discharge capacity is as follows: ； In the formula, E Ei E represents the current available discharge capacity of the i-th electric vehicle. Li E represents the minimum available discharge capacity of the i-th electric vehicle. Ni E represents the maximum available discharge capacity of the i-th electric vehicle. max E represents the maximum discharge capacity of the electric vehicle fleet. min This represents the maximum charging capacity of the electric vehicle group, where n represents the total number of electric vehicles.

3. The new energy vehicle-to-grid interaction frequency modulation control method based on power margin according to claim 2, characterized in that, The formula for the change in active power of the electric vehicle cluster is as follows: ； In the formula, Δ P This represents the change in active power of the electric vehicle fleet. K f Δ is the active frequency regulation coefficient. f For the frequency deviation of the power system, f N P is the rated frequency of the power system. t This is the current adjustable power.

4. The new energy vehicle-to-grid interaction frequency modulation control method based on power margin according to claim 3, characterized in that, When the power system frequency increases, the current adjustable power is determined by the maximum charging capacity of the electric vehicle cluster; The first formula for calculating the current adjustable power is: ； When the power system frequency decreases, the current adjustable power is determined by the maximum discharge capacity of the electric vehicle cluster; The second formula for calculating the current adjustable power is: ； In the formula, P Ei This represents the current power of the i-th electric vehicle.

5. The new energy vehicle-to-grid interactive frequency modulation control method based on power margin according to claim 4, characterized in that, The formula for calculating the additional frequency-regulated active power of each of the electric vehicles is as follows: ； In the formula, α represents the weighting coefficient for electric vehicles participating in system frequency regulation.

6. The new energy vehicle-to-grid interactive frequency modulation control method based on power margin according to claim 5, characterized in that, The weighting coefficients are determined as follows: The current state of charge (SOC) of the electric vehicle's power battery is divided into four working zones: normal charge / discharge zone, discharge priority zone, charging priority zone, and disabled zone. When SOC min ≤SOC≤SOC max At this time, the SOC is in the normal charge / discharge region, and α = 1; When SOC max When the SOC is less than the SOC, the SOC is in the discharge priority region. If charging is required, α < 1; if discharging is required, α > 1. When SOC L ≤SOC<SOC min When the SOC is in the charging priority zone, if charging is required, α > 1; if discharging is required, α < 1. When SOC≤SOC L When the SOC is in the disabled zone, if charging is required, α > 1; if discharging is required, α = 0. In the formula, SOC max This indicates the maximum SOC (State of Charge) of the power battery; SOC L This indicates the minimum SOC (State of Charge) of the power battery. SOC min This indicates a low SOC value for the power battery.

7. The new energy vehicle-to-grid interaction frequency modulation control method based on power margin according to claim 1, characterized in that, The status information of the power batteries of each electric vehicle and the user participation information are uploaded to the charging operator through the charging pile via the vehicle communication interface of each electric vehicle.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the power margin-based vehicle-to-grid interactive frequency modulation control method for new energy vehicles according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the new energy vehicle-to-grid interactive frequency modulation control method based on power margin as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the new energy vehicle-to-grid interactive frequency modulation control method based on power margin as described in any one of claims 1-7.

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