Virtual inertia power compensator control method, system and medium applied to electric vehicle charging station

Abstract

This invention relates to a virtual inertial power compensator control method, system, and medium for electric vehicle charging stations. Addressing the problems of insufficient system inertia and underutilization of electric vehicle load storage capacity caused by the widespread access of electric vehicle charging stations and the increased capacity of power electronic devices in the system, this invention creatively constructs a dynamic adjustment capability assessment method for electric vehicle charging stations, confirming that electric vehicle charging stations possess generalized energy storage characteristics and have power system inertial support capabilities. Based on the VSG algorithm, the rotor power equation is decoupled, and a virtual inertial power compensation control strategy is designed for application in the inverter control at the charging station level. Inertial support is introduced at the load connection point to fully utilize the excellent load storage capacity of electric vehicles, improve system inertia, and enhance the system's resistance to load disturbances.

Description

Technical Field

[0001] This invention pertains to the power industry and designs inverter control strategies, specifically relating to a virtual inertial power compensator control method, system, and medium applied to electric vehicle charging stations. Background Technology

[0002] Currently, the electric vehicle market is experiencing a golden age of rapid development. The number of electric vehicles in my country is growing rapidly, and corresponding electric vehicle charging infrastructure is constantly improving. More and more electric vehicle charging stations are being put into operation, and the proportion of energy consumption for electric vehicle charging load in the power grid is gradually increasing. As a type of load with relatively random energy consumption, electric vehicle load brings many uncertainties to the new power system, posing a significant challenge to power quality. Furthermore, a high proportion of renewable energy grid-connected equipment and power electronic equipment is a major characteristic of the new power system. With the introduction of dual-carbon goals, more and more renewable energy generation is being integrated into the grid, reducing the proportion of traditional synchronous generators and increasing the proportion of power electronic grid-connected equipment. This leads to a significant decrease in system inertia, weakening the system's ability to withstand disturbances. The charging piles in electric vehicle charging stations, also power electronic equipment, further challenge the system's inertia. Current research on electric vehicle load focuses more on optimization to reduce the peak-to-valley ratio of charging load and achieve peak-shaving and valley-filling goals. However, the utilization of the real-time dynamic regulation capacity of the equivalent generalized energy storage equipment is still insufficient. Regarding system inertia support, many studies focus on grid-connected control algorithms for new energy power generation to improve system inertia. However, this often requires high-capacity energy storage resources and coordination of inverter control algorithms. Not only are the equipment deployment costs too high, but the control algorithm design is also complex and not conducive to the expansion of new energy grid-connected equipment.

[0003] With the continuous development and improvement of electric vehicle battery and charging technologies, more and more charging piles in operation have V2G capabilities, which enables electric vehicles to give full play to their good load storage characteristics and provides a foundation for electric vehicles to participate in grid support as equivalent energy storage devices. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a virtual inertia power compensator control method for electric vehicle charging stations. This method fully utilizes the dynamic adjustment capabilities of electric vehicles and enables them to exhibit excellent load storage characteristics, serving as an inertia power compensation resource to provide inertia power support to the power grid.

[0005] The specific technical solution of this invention is a modeling method for the regulating capacity of electric vehicle charging stations and an equivalent energy storage control strategy for charging stations, along with its adaptive parameter design, specifically including the following steps:

[0006] To obtain the dynamic adjustable range of electric vehicle clusters within an electric vehicle charging station, specifically, all grid-connected electric vehicles are treated as generalized energy storage devices, and their equivalent adjustable capacity and maximum charging / discharging power boundaries are calculated.

[0007] The control algorithm based on rotor inertial power provides inertial compensation to the power grid. Specifically, it uses the obtained boundary data to tune the virtual inertia and virtual damping in the control algorithm of rotor inertial power. Based on the tuned virtual inertia and virtual damping, the control algorithm of rotor inertial power provides inertial compensation to the power grid. The boundary data includes the equivalent adjustable capacity and the maximum charge and discharge power boundary.

[0008] Preferably, the calculation of the equivalent adjustable capacity and the maximum charge / discharge power boundary adopts the following formula:

[0009]

[0010] in, Let represent the charging and discharging power of the generalized energy storage at time t, respectively; Let represent the maximum charging and discharging power of the generalized energy storage at time t, respectively; This represents the equivalent SoC (System-on-Chips) of the equivalent generalized energy storage at time t. Let represent the upper and lower boundaries of the battery capacity at time t, respectively, representing the equivalent generalized energy storage capacity. This represents the change in the generalized energy storage equivalent battery capacity at time t due to the change in the grid connection status of electric vehicles within the station.

[0011] Preferably, the specific process of the rotor inertial power control algorithm includes:

[0012] The time-domain relationship between the system's angular frequency and power is obtained by analyzing the VSG rotor motion equation and electrical power angle characteristic equation, clarifying the inertial support effect of the VSG rotor on frequency disturbances during transient processes.

[0013] After analyzing the transient response process of VSG, and combining it with the law of conservation of energy, the expression for rotor inertial power is derived through power decoupling.

[0014] P J As the reference input in the power control loop, the equivalent generalized energy storage power boundary is P J The limiting effect, combined with voltage and current dual closed-loop control, is applied to the control of the generalized energy storage inverter in the charging station, enabling the generalized energy storage of the charging station to have inertial power support capability.

[0015] Preferably, the rotor inertial power is expressed as:

[0016]

[0017] Among them, P J Where ω is the rotor inertial power, J is the rotor moment of inertia, D is the damping coefficient, and ω is the rotor moment of inertia. n ω is the rated angular frequency, and ω is the grid angular frequency.

[0018] Preferably, the specific process for tuning the virtual inertia and virtual damping in the rotor inertial power control algorithm includes:

[0019] Tuning virtual inertia and virtual damping based on the optimal second-order system mode of second-order self-oscillating response process;

[0020] Taking into account the system's required response speed and the need for a small overshoot, ξ is set as the optimal damping ratio, and the value of the virtual damping during stable operation is calculated.

[0021] Based on the current power demand of the charging station, the virtual inertia J is calculated, and based on the virtual inertia J, the value of the virtual damping D during stable operation is obtained.

[0022] The tuned virtual inertia and virtual damping are updated in the rotor inertia power compensation control algorithm.

[0023] As a preferred option, the natural oscillation angular frequency ω n The damping coefficient ξ is expressed as:

[0024]

[0025] As a preferred method, based on the rotor inertial energy formula, the derivative of both sides of the equation is taken to determine the formula for calculating the rotor's moment of inertia in steady state. The value of the virtual damping during steady operation is then calculated using the following formula:

[0026]

[0027] As a preferred option, the virtual inertia J is obtained according to the following formula:

[0028]

[0029] A virtual inertial power compensator control system for electric vehicle charging stations includes

[0030] The first module is configured to obtain the dynamic adjustable range of electric vehicle clusters within an electric vehicle charging station. Specifically, it treats all grid-connected electric vehicles as generalized energy storage devices and calculates their equivalent adjustable capacity and maximum charge / discharge power boundaries.

[0031] The second module is configured to provide inertial compensation to the power grid based on the rotor inertial power control algorithm. Specifically, it tunes the virtual inertia and virtual damping in the rotor inertial power control algorithm using the obtained boundary data, and provides inertial compensation to the power grid based on the tuned virtual inertia and virtual damping through the rotor inertial power control algorithm.

[0032] A medium storing a computer program capable of performing any of the methods described herein.

[0033] The advantages of this invention are as follows: The virtual inertia power compensator control strategy for electric vehicle charging stations proposed in this invention effectively evaluates the dynamic adjustment capability of electric vehicle clusters in charging stations and fully utilizes the ability of electric vehicles as adjustable resources to provide inertial power support to the power grid. Inertia is directly introduced at the electric vehicle load connection point, thereby improving the system frequency stability. Furthermore, since the control algorithm is easier to implement than the inertia introduction method of VSG, it avoids the large investment in energy storage equipment required for VSG design, reducing equipment implementation costs and making this invention easy to promote and apply. Attached Figure Description

[0034] Figure 1 This is a design flowchart of the present invention;

[0035] Figure 2 These are the constraints of a single adjustable electric vehicle model;

[0036] Figure 3 This is a schematic diagram of the equivalent generalized energy storage dynamic adjustment capability of a charging station;

[0037] Figure 4 It is an inertial power compensation control strategy;

[0038] Figure 5 It is based on the inertial power compensator structure of the charging station;

[0039] Figure 6 It is an adaptive parameter tuning process. Detailed Implementation

[0040] To facilitate understanding and implementation of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to embodiments and accompanying drawings. The embodiments described herein are for illustrative purposes only and are not intended to limit the present invention.

[0041] Example 1

[0042] The specific technical solution of this invention is a modeling method for the regulating capacity of electric vehicle charging stations and an equivalent energy storage control strategy for charging stations, along with its adaptive parameter design, specifically including:

[0043] Step 1: Determine the dynamic adjustable range of the electric vehicle cluster within the electric vehicle charging station. Due to limitations in their own charging and discharging power and capacity constraints, electric vehicles within the charging station require an assessment of their cluster's adjustable potential to be considered as an equivalent generalized energy storage device.

[0044] Step 2: Establish a rotor inertial power model and propose a control strategy for a virtual inertial power compensator for electric vehicle charging stations based on the rotor inertial power model. Electric vehicles connect to the grid via inverters and lack rotating mechanical structures. To provide inertial support to the grid, the rotor inertial power model needs to be incorporated into the inverter control algorithm to achieve equivalent inertia.

[0045] Step 3: Design an adaptive adjustment scheme for the compensator parameters based on the dynamically adjustable capacity of the electric vehicle cluster within the charging station. Since the dynamic adjustment capability of the electric vehicle cluster within the charging station changes over time, the coefficients in the virtual inertial power compensator algorithm need to be tuned to optimize its inertial power support results.

[0046] In step 1, a basic model of the adjustable space of a single electric vehicle is first established, and then the adjustable space model of the equivalent generalized energy storage of the electric vehicle cluster is obtained by using the Minkowski method.

[0047] The load power of a single electric vehicle charging at a four-quadrant adjustable power charging station should meet the following constraints:

[0048]

[0049] in, It is expressed as the grid connection state of electric vehicle n at time t, and is a Boolean value. When the electric vehicle is in grid connection state, the value is 1, and otherwise it is 0. and Let represent the maximum charging and discharging power that an electric vehicle can accept, and represent the power boundary constraints for a single electric vehicle. and These represent the actual charging power and discharging power of electric vehicle n during time period t, respectively.

[0050] The SoC constraints for a single electric vehicle can be expressed as:

[0051]

[0052] in, The battery charge status of electric vehicle n at time t; and These are represented by Boolean variables indicating the charging and discharging states of electric vehicle n at time t; s n,min and s n,maxΔt represents the upper and lower boundaries of the battery charge of electric vehicle n, i.e., the boundary constraints of battery capacity during the charging process; Δt is the unit time interval.

[0053] In reality, electric vehicles do not charge and discharge simultaneously, therefore, additional charging and discharging constraints are needed to complete the charging model for a single electric vehicle.

[0054]

[0055] Due to Boolean constraints, when the electric vehicle is in an off-grid state, there is At this time there is When electric vehicles are connected to the grid By the properties of Boolean quantities, it is obvious that at this time... There can be at most one value of 1, indicating the charging and discharging status of the electric vehicle.

[0056] Because the dynamic adjustment power capacity of a single electric vehicle is too small and its frequency support capability is insufficient, charging stations, as natural electric vehicle load aggregators, treat electric vehicle loads as equivalent generalized energy storage devices at the charging station level. This not only provides greater support power but also avoids the problem of calculating a large amount of data from individual electric vehicles.

[0057] The Minkowski sum is a method for summing spatially expanded sets of variables with the same domain applied to Euclidean space. Although there are time differences in the grid connection times of different electric vehicles, Boolean variables can be used to solve this problem. The charging behavior can be extended to the same time domain, thereby equating electric vehicle clusters to generalized energy storage devices at the charging station level. The model can be represented as follows:

[0058]

[0059]

[0060] in, These represent the battery charge levels at the start and end times of grid connection for electric vehicle n, respectively.

[0061] The above model realizes the energy aggregation management of multiple electric vehicle units. At the charging station level, all grid-connected electric vehicles are equivalent to a generalized energy storage device with large capacity and high charging and discharging power. This enables the assessment of the dynamic adjustment capability of the electric vehicle cluster. The variables and parameters of the generalized energy storage are expressed as follows:

[0062]

[0063] in, Let represent the charging and discharging power of the generalized energy storage at time t, respectively; Let represent the maximum charging and discharging power of the generalized energy storage at time t, respectively; This represents the equivalent SoC (System-on-Chips) of the equivalent generalized energy storage at time t. Let represent the upper and lower boundaries of the battery capacity at time t, respectively, representing the equivalent generalized energy storage capacity. This represents the change in the generalized energy storage equivalent battery capacity at time t due to the change in the grid connection status of electric vehicles within the station.

[0064] Step 2: Decouple the rotor motion equation from the power equation of the virtual synchronous machine, and design a specific virtual inertial power compensation control strategy based on the rotor inertial power equation, as follows:

[0065] The droop characteristic equation of VSG is:

[0066] P m =P ref +K f (ω n -ω)

[0067] Among them, P m P is the mechanical power input to VSG. ref K is the rated active power. f ω is the droop coefficient. n ω is the rated angular frequency, and ω is the grid angular frequency.

[0068] The rotor motion equation of VSG can be expressed as:

[0069]

[0070] Where J is the rotor moment of inertia, P e Where is the output power of the VSG, and D is the damping coefficient.

[0071] The electrical power angle characteristic of VSG can be expressed as:

[0072]

[0073]

[0074] Among them, U l E0 is the output voltage of the VSG; E0 is the filter port voltage of the VSG; X f This refers to the reactance of the grid-connected filter.

[0075] Therefore, the transfer function between the VSG output power and the rated active power is expressed as:

[0076]

[0077] Therefore, the approximate expression for the relationship between the system's angular frequency and power can be written as:

[0078]

[0079] Therefore, when the system experiences a power disturbance ΔP ref Then, the time-domain expression for the system frequency can be obtained:

[0080]

[0081] When a disturbance occurs in the system, the rotor angular velocity changes. The rotor actually balances the torque by absorbing or releasing power, which is reflected in the change of the rotor kinetic energy. The rotor's inertial power P can be defined. J According to the principle of energy conservation, the expression for rotor inertial power can be obtained:

[0082]

[0083] Thus, the step disturbance response of the rotor inertial power is obtained:

[0084]

[0085] Therefore, K in the power control loop of the VSG f and P ref Setting it to 0 will yield the aforementioned rotor inertia power command input. By changing the power command input in the VSG control loop to the aforementioned power command and applying it to the control of the generalized energy storage inverter in the charging station, the energy storage of the charging station will have inertia power support capability.

[0086] Step 3: Considering that the steady-state charging power of the electric vehicle cluster at the charging station changes over time, the parameters of the inertial power compensator also need to be tuned as needed. The specific tuning steps are as follows:

[0087] Based on the rotor inertial power expression, the step disturbance response process of the inertial power compensator is a second-order self-oscillating decay response process. Its stable operating parameters J and D can be tuned according to the optimal second-order system method, with a natural oscillation angular frequency ω. n The damping coefficient ξ is expressed as:

[0088]

[0089] To achieve a better response speed and a smaller overshoot, ξ is first set as the optimal damping ratio. Then, D during stable operation can be tuned as follows:

[0090]

[0091] The rotor inertial energy can be expressed as:

[0092] E J =0.5Jω 2

[0093] Differentiating both sides, we can determine the rotor's moment of inertia in steady state as:

[0094]

[0095] Among them, P cs This represents the power demand of the charging station at the current scheduling moment.

[0096] Example 2

[0097] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0098] As attached Figure 1 As shown, the design process of the virtual inertial power compensator control strategy applied to electric vehicle charging stations specifically includes the following steps:

[0099] Step 1: Establish a single electric vehicle charging decision model (see attached) Figure 2 (This demonstrates the constraints of a single adjustable electric vehicle model.) Multiple individual decision models are superimposed using Minkowski algorithms to evaluate the dynamic adjustment capability of an equivalent generalized energy storage device at the charging station level. (See appendix) Figure 3 (This demonstrates the dynamic adjustment capability of the charging station's equivalent generalized energy storage).

[0100] Step 2: Decouple the rotor inertial power from the VSG transient process, and design an inertial power compensation control strategy based on the formula derivation (see appendix). Figure 4 It is an inertial power compensation control strategy structure), and this strategy is applied to the control of the generalized energy storage inverter in the charging station (see attached). Figure 5 The structure of an inertial power compensator applied to a charging station is presented.

[0101] Step 3: Import the electric vehicle data within the station, calculate its current dynamic adjustment capability and charging load power demand within the station, and determine the inertial power compensator parameter values ​​for the current cycle based on the optimal second-order system (see attached). Figure 6 The parameter tuning process is given.

[0102] Example 3

[0103] This embodiment relates to a virtual inertial power compensator control system applied to electric vehicle charging stations, including...

[0104] The first module is configured to obtain the dynamic adjustable range of electric vehicle clusters within an electric vehicle charging station. Specifically, it treats all grid-connected electric vehicles as generalized energy storage devices and calculates their equivalent adjustable capacity and maximum charge / discharge power boundaries.

[0105] The second module is configured to provide inertial compensation to the power grid based on the rotor inertial power control algorithm. Specifically, it tunes the virtual inertia and virtual damping in the rotor inertial power control algorithm using the obtained boundary data, and provides inertial compensation to the power grid based on the tuned virtual inertia and virtual damping through the rotor inertial power control algorithm.

[0106] Example 3

[0107] This embodiment relates to a medium characterized in that it stores a computer program capable of executing any of the methods described in Embodiment 1.

[0108] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0109] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0110] 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.

[0111] 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 Figure 1 The steps of the function specified in one or more boxes.

[0112] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0113] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A virtual inertial power compensator control method applied to electric vehicle charging stations, characterized in that, include: To obtain the dynamic adjustable range of electric vehicle clusters within an electric vehicle charging station, specifically, all grid-connected electric vehicles are treated as generalized energy storage devices, and their equivalent adjustable capacity and maximum charging / discharging power boundaries are calculated. The control algorithm based on rotor inertial power provides inertial compensation to the power grid. Specifically, it uses the obtained boundary data to tune the virtual inertia and virtual damping in the control algorithm of rotor inertial power. Based on the tuned virtual inertia and virtual damping, the control algorithm of rotor inertial power provides inertial compensation to the power grid. The boundary data includes the equivalent adjustable capacity and the maximum charge and discharge power boundary. The calculation of the equivalent adjustable capacity and the maximum charge / discharge power boundary is performed using the following formula: in, , They represent in t The charging and discharging power of generalized energy storage at all times; , They represent in t The maximum charging and discharging power of generalized energy storage at any given moment; This indicates that the equivalent generalized energy storage t Equivalent SoC at any given moment , They represent equivalent generalized energy storage. t The upper and lower boundaries of the battery capacity at any given time. Indicates in t The change in the generalized energy storage equivalent battery capacity caused by the change in the grid connection status of electric vehicles within the station at all times; and They represent in t Electric vehicles during the time period n The actual charging power and discharging power, and Referred to as electric vehicles n exist t Boolean variables representing the charging and discharging states at any given time. For electric vehicles n exist t Real-time battery status. Described as electric vehicle n exist t The grid connection status at any time. , Electric vehicles n Battery charge levels at the start and end of grid connection; The specific process of the rotor inertial power control algorithm includes: The time-domain relationship between the system's angular frequency and power is obtained by analyzing the VSG rotor motion equation and electrical power angle characteristic equation, clarifying the inertial support effect of the VSG rotor on frequency disturbances during transient processes. After analyzing the transient response process of VSG, and combining it with the law of conservation of energy, the expression for rotor inertial power is derived through power decoupling. Will P J As the reference input in the power control loop, the equivalent generalized energy storage power boundary is P J The limiting effect, combined with voltage and current dual closed-loop control, is applied to the control of the generalized energy storage inverter in the charging station, enabling the generalized energy storage of the charging station to have inertial power support capability. The rotor inertial power is expressed as: in, P J For rotor inertial power, J The moment of inertia of the rotor. D The damping coefficient is... The rated angular frequency, The angular frequency of the power grid; The specific process of tuning the virtual inertia and virtual damping in the control algorithm for rotor inertial power includes: Tuning virtual inertia and virtual damping based on the optimal second-order system mode of second-order self-oscillating response process; Taking into account the system's required response speed and minimum overshoot, the setting is as follows: To determine the optimal damping ratio, calculate the value of the virtual damping during stable operation. Based on the current power demand of the charging station, the virtual inertia is calculated. J And based on virtual inertia J To obtain the virtual damping during stable operation D The value; The tuned virtual inertia and virtual damping are updated in the rotor inertia power compensation control algorithm.

2. The virtual inertial power compensator control method applied to electric vehicle charging stations according to claim 1, characterized in that, Natural oscillating angular frequency and damping coefficient Represented as: ； in, The output voltage of the VSG. This refers to the filter port voltage of the VSG. For the reactance of the grid-connected filter, J The moment of inertia of the rotor. D is the damping coefficient.

3. The virtual inertial power compensator control method applied to electric vehicle charging stations according to claim 1, characterized in that, Based on the rotor inertial energy formula, differentiating both sides of the equation determines the formula for calculating the rotor's moment of inertia in steady state. The value of the virtual damping during steady-state operation is then calculated using the following formula: ； in, The output voltage of the VSG. This refers to the filter port voltage of the VSG. For the reactance of the grid-connected filter, J This represents the rotor's moment of inertia.

4. The virtual inertial power compensator control method applied to electric vehicle charging stations according to claim 1, characterized in that, Virtual Inertia J Obtain it using the following formula: ； in, P cs This represents the power demand of the charging stations at the current scheduling moment. This is the angular frequency of the power grid.

5. A virtual inertial power compensator control system applied to electric vehicle charging stations, characterized in that, include The first module is configured to obtain the dynamic adjustable range of electric vehicle clusters within an electric vehicle charging station. Specifically, it treats all grid-connected electric vehicles as generalized energy storage devices and calculates their equivalent adjustable capacity and maximum charge / discharge power boundaries. The second module is configured to provide inertial compensation to the power grid based on the rotor inertial power control algorithm. Specifically, it tunes the virtual inertia and virtual damping in the rotor inertial power control algorithm using the obtained boundary data, and provides inertial compensation to the power grid based on the tuned virtual inertia and virtual damping through the rotor inertial power control algorithm. The calculation of the equivalent adjustable capacity and the maximum charge / discharge power boundary is performed using the following formula: in, , They represent in t The charging and discharging power of generalized energy storage at all times; , They represent in t The maximum charging and discharging power of generalized energy storage at any given moment; This indicates that the equivalent generalized energy storage t Equivalent SoC at any given moment , They represent equivalent generalized energy storage. t The upper and lower boundaries of the battery capacity at any given time. Indicates in t The change in the generalized energy storage equivalent battery capacity caused by the change in the grid connection status of electric vehicles within the station at all times; and They represent in t Electric vehicles during the time period n The actual charging power and discharging power, and Referred to as electric vehicles n exist t Boolean variables representing the charging and discharging states at any given time. For electric vehicles n exist t Real-time battery status. Described as electric vehicle n exist t The grid connection status at any time. , Electric vehicles n Battery charge levels at the start and end of grid connection; The specific process of the rotor inertial power control algorithm includes: The time-domain relationship between the system's angular frequency and power is obtained by analyzing the VSG rotor motion equation and electrical power angle characteristic equation, clarifying the inertial support effect of the VSG rotor on frequency disturbances during transient processes. After analyzing the transient response process of VSG, and combining it with the law of conservation of energy, the expression for rotor inertial power is derived through power decoupling. Will P J As the reference input in the power control loop, the equivalent generalized energy storage power boundary is P J The limiting effect, combined with voltage and current dual closed-loop control, is applied to the control of the generalized energy storage inverter in the charging station, enabling the generalized energy storage of the charging station to have inertial power support capability. The rotor inertial power is expressed as: in, P J For rotor inertial power, J The moment of inertia of the rotor. D The damping coefficient is... The rated angular frequency, The angular frequency of the power grid; The specific process of tuning the virtual inertia and virtual damping in the control algorithm for rotor inertial power includes: Tuning virtual inertia and virtual damping based on the optimal second-order system mode of second-order self-oscillating response process; Taking into account the system's required response speed and minimum overshoot, the setting is as follows: To determine the optimal damping ratio, calculate the value of the virtual damping during stable operation. Based on the current power demand of the charging station, the virtual inertia is calculated. J And based on virtual inertia J To obtain the virtual damping during stable operation D The value; The tuned virtual inertia and virtual damping are updated in the rotor inertia power compensation control algorithm.

6. A medium, characterized in that: The device contains a computer program capable of performing the method according to any one of claims 1 to 4.

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