Network construction type energy storage online parameter identification virtual circuit closed-loop modeling method and system

Abstract

The invention provides a network construction type energy storage online parameter identification virtual circuit closed-loop modeling method and system, and relates to the technical field of power system optimization control and modeling, and the method comprises the steps: building a black box model according to a signal interaction relation between a network construction type energy storage control system and an electrical system; based on the PWM pulse signal output by the black box model, calculating the internal potential of the network-forming energy storage equivalent power supply of the virtual circuit; establishing a mathematical relationship between the internal potential and terminal voltage, impedance, load power and disturbance power to obtain a virtual circuit; taking the control signal output by the black box model as an input signal of the virtual circuit model, taking the electrical quantity signal output by the virtual circuit model as an input signal of the black box model, and establishing a closed-loop identification model; and online parameter identification is realized through a prediction-correction-update rolling optimization strategy. According to the invention, an identification model based on an equivalent circuit is improved to be based on a virtual circuit, and the flexible adaptation and identification capability of a complex power grid scene and an operation state is comprehensively improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power system optimization control and modeling, and in particular to a virtual circuit closed-loop modeling method and system for online parameter identification of grid-connected energy storage. Background Art

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

[0003] Grid-connected energy storage has active support capabilities and can improve the problems of short-circuit capacity reduction and insufficient inertia support caused by large-scale renewable energy power generation connected to the grid. However, due to the technical confidentiality of manufacturers, there is currently a lack of white-box models that can accurately characterize grid-connected energy storage devices, making it difficult to conduct in-depth analysis of the stability characteristics of grid-connected energy storage after grid connection. Therefore, it is of great practical significance to study the parameter identification of grid-connected energy storage devices.

[0004] In the existing parameter identification methods of grid-connected energy storage devices, frequency sweep analysis (impedance method) or time domain simulation (data-driven method) is generally performed based on equivalent circuit models to obtain observation data describing the frequency response characteristics or disturbance response characteristics of the system, and finally parameter identification is achieved based on the pre-established transfer function or state space mathematical formula containing the parameters to be estimated. However, the equivalent circuit of the existing identification method will have a significant impact on the observed data and identification results. Due to the complexity and diversity of the actual power grid, it is difficult to establish an equivalent circuit model for all possible power grid scenarios, so the universality of the identification results for different power grid scenarios cannot be guaranteed. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a virtual circuit closed-loop modeling method and system for online parameter identification of grid-type energy storage. In view of the insufficient universality of parameter identification models under different power grid scenarios, starting from the physical essence of the grid-related characteristics of the grid-type energy storage converter and its control system, the traditional identification model based on equivalent circuits is improved to one based on virtual circuits, and a highly abstract mathematical model is established according to the physical relationship between the control system and the electrical system, which comprehensively improves the flexible adaptation and identification capabilities for complex power grid scenarios and a wide range of operating conditions.

[0006] According to some embodiments, the present disclosure adopts the following technical solutions:

[0007] A virtual circuit closed-loop modeling method for online parameter identification of grid-connected energy storage includes:

[0008] Obtain the operating data of the grid-type energy storage control system, and establish a black box model based on the signal interaction relationship between the grid-type energy storage control system and the electrical system;

[0009] Based on the electrical quantity signals collected by the black box model, the output data interface of the virtual circuit is constructed; based on the PWM pulse signal output by the black box model, the internal potential of the network-type energy storage equivalent power supply of the virtual circuit is calculated;

[0010] Based on the power transmission relationship between the grid-type energy storage equivalent power source and the electrical system, a virtual circuit model is constructed, and a closed-loop identification model is established based on the virtual circuit model;

[0011] A Kalman filter containing the control parameters to be estimated is constructed based on the closed-loop identification model. The disturbance power and impedance variables are adjusted to flexibly change the grid scenario and operating conditions to achieve online parameter identification.

[0012] Among them, the process of establishing a closed-loop identification model based on the virtual circuit model includes: establishing virtual circuit models for different scenarios for grid-connected energy storage independent power supply and power supply in conjunction with other power sources, and based on the input-output relationship between the virtual circuit model and the black box model, using the control signal output by the black box model as the input signal of the virtual circuit model, and using the electrical quantity signal output by the virtual circuit model as the input signal of the black box model to establish a closed-loop identification model.

[0013] According to some embodiments, the present disclosure adopts the following technical solutions:

[0014] The virtual circuit closed-loop modeling system for online parameter identification of grid-connected energy storage includes:

[0015] A black box model building module for grid-type energy storage is used to obtain the operating data of the grid-type energy storage control system and establish a black box model based on the signal interaction relationship between the grid-type energy storage control system and the electrical system;

[0016] The signal interface module is used to construct the output data interface of the virtual circuit based on the electrical quantity signal collected by the black box model; based on the PWM pulse signal output by the black box model, the internal potential of the network-type energy storage equivalent power supply of the virtual circuit is calculated;

[0017] A virtual circuit model building module is used to build a virtual circuit model based on the power transmission relationship between the grid-type energy storage equivalent power source and the electrical system;

[0018] A closed-loop identification model construction module is used to establish a closed-loop identification model based on a virtual circuit model; wherein the process of establishing a closed-loop identification model based on a virtual circuit model includes: establishing virtual circuit models in different scenarios for grid-connected energy storage independent power supply and power supply in combination with other power sources, and based on the input-output relationship between the virtual circuit model and the black box model, using the control signal output by the black box model as the input signal of the virtual circuit model, and using the electrical quantity signal output by the virtual circuit model as the input signal of the black box model, to establish a closed-loop identification model;

[0019] The online parameter identification module is used to construct a Kalman filter containing the control parameters to be estimated based on the closed-loop identification model, and realize online parameter identification by adjusting the disturbance power and impedance variables to flexibly change the power grid scenario and operating conditions.

[0020] According to some embodiments, the present disclosure adopts the following technical solutions:

[0021] A computer program product includes a computer program, and when the computer program is executed by a processor, the virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage is implemented.

[0022] According to some embodiments, the present disclosure adopts the following technical solutions:

[0023] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage is implemented.

[0024] According to some embodiments, the present disclosure adopts the following technical solutions:

[0025] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage disclosed in the present invention aims at the lack of versatility and accuracy of parameter identification of grid-type energy storage in complex power grid scenarios, and provides a closed-loop identification model including a control system black box and an electrical system. The traditional identification model based on equivalent circuits is improved to one based on virtual circuits, and a highly abstract mathematical model is established based on the physical relationship between the control system and the electrical system. It has the advantages of clear physical meaning and high computational efficiency, and can comprehensively improve the flexible adaptation and identification capabilities for complex power grid scenarios and wide operating conditions.

[0028] The disclosed virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage is based on a closed-loop identification model to construct a Kalman filter containing control parameters to be estimated, flexibly set system disturbances by adjusting power and impedance variables, calculate the estimated value of the black box model output based on the prediction model, and solve the optimization problem through the parameter estimation model to achieve the optimization update of the parameters to be estimated. The present invention is suitable for rolling execution in the form of scheduled tasks or periodic tasks, and can improve the online application performance of parameter identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0030] Figure 1 The closed-loop identification model structure and variable diagram of the embodiment of the present disclosure;

[0031] Figure 2 This is a flow chart of a virtual circuit closed-loop modeling method for online parameter identification of a grid-connected energy storage system according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0033] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0034] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0035] Example 1

[0036] In one embodiment of the present disclosure, a virtual circuit closed-loop modeling method for online parameter identification of a grid-type energy storage is provided, comprising:

[0037] Step 1: Obtain the operating data of the grid-type energy storage control system, and establish a black box model based on the signal interaction relationship between the grid-type energy storage control system and the electrical system;

[0038] Step 2: Based on the electrical quantity signal collected by the black box model, construct the output data interface of the virtual circuit; based on the PWM pulse signal output by the black box model, calculate the internal potential of the grid-type energy storage equivalent power supply of the virtual circuit;

[0039] Step 3: Based on the power transmission relationship between the grid-type energy storage equivalent power source and the electrical system, a virtual circuit model is constructed, and a closed-loop identification model is established based on the virtual circuit model;

[0040] Step 4: Construct a Kalman filter containing the control parameters to be estimated based on the closed-loop identification model, and flexibly change the grid scenario and operating conditions by adjusting the disturbance power and impedance variables to achieve online parameter identification;

[0041] Among them, the process of establishing a closed-loop identification model based on the virtual circuit model includes: establishing virtual circuit models for different scenarios for grid-connected energy storage independent power supply and power supply in conjunction with other power sources, and based on the input-output relationship between the virtual circuit model and the black box model, using the control signal output by the black box model as the input signal of the virtual circuit model, and using the electrical quantity signal output by the virtual circuit model as the input signal of the black box model to establish a closed-loop identification model.

[0042] As an embodiment, the virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage disclosed in the present invention has a basic structure and variables as follows: Figure 1 As shown. The black box model refers to an unidentified grid-connected energy storage control system, and the virtual circuit refers to an abstract mathematical model that characterizes the relationship between the internal potential and the terminal voltage, impedance, load power, and disturbance power, which is established based on the power transmission relationship between the equivalent power source and the electrical network. The present disclosure is based on the input-output relationship between the virtual circuit model and the black box model, and uses the control signal output by the black box model as the input signal of the virtual circuit model, and uses the electrical quantity signal output by the virtual circuit model as the input signal of the black box model to establish a closed-loop identification logic framework. The specific implementation process is as follows:

[0043] Step 1: Obtain the operating data of the grid-type energy storage control system, and establish a black box model based on the signal interaction relationship between the grid-type energy storage control system and the electrical system;

[0044] Specifically, the upper-level dispatching instructions of the grid-type energy storage control system are obtained as input data, including voltage reference value, active power reference value, and reactive power reference value. A black box model is established based on the signal interaction relationship between the grid-type energy storage control system and the electrical system. Among them, the mathematical form of the control system black box model is as follows:

[0045]

[0046] y b =PWM(e b );

[0047] Among them, e b is the reference value of the output modulation voltage of the grid-type energy storage converter, u bs and u bv They represent the dispatching instruction input signal vector and the grid-connected electrical quantity input signal vector, θ b represents the control parameter vector to be estimated, y bIt represents the PWM modulated pulse signal output by the black box model, which is obtained by calculating the voltage reference value through the carrier wave and is used to control the on and off of the converter switching element; Represents the equivalent mathematical model of the controller.

[0048] Step 2: Based on the electrical quantity signal collected by the black box model, construct the output data interface of the virtual circuit; based on the PWM pulse signal output by the black box model, calculate the internal potential of the grid-type energy storage equivalent power supply of the virtual circuit;

[0049] Specifically, based on the electrical quantity signals collected by the black box model, the output data interface of the virtual circuit is constructed, including the three-phase voltage and current signals of the grid connection point. Based on the PWM pulse signal output by the black box model, the internal potential of the grid-type energy storage equivalent power supply of the virtual circuit is calculated.

[0050] Among them, the output data interface of the virtual circuit is as follows:

[0051]

[0052] Among them, y v The grid-connected electrical quantity data output by the virtual circuit, including the positive sequence component of the three-phase voltage and negative sequence component Positive sequence component of three-phase current and negative sequence component The specific calculation method of positive and negative sequence components is:

[0053]

[0054]

[0055] in, and are the positive and negative sequence components of the effective value, respectively, and are respectively the positive and negative sequence components of the effective value of the current, θ is the voltage phase of phase a, is the power factor angle.

[0056] The method for calculating the internal potential of the grid-type energy storage equivalent power supply in the virtual circuit is as follows:

[0057] E G =PWM -1 (y b );

[0058] Among them, E G It is the internal potential of the grid-type energy storage equivalent power supply in the virtual circuit, which is calculated by inverse transformation of the PWM modulation signal output by the black box model.

[0059] Step 3: Construct a virtual circuit model based on the power transmission relationship between the grid-type energy storage equivalent power source and the electrical system;

[0060] Specifically, based on the power transmission relationship between the equivalent power source and the electrical network, a mathematical relationship between the internal potential and the terminal voltage, impedance, load power and disturbance power is established to obtain a virtual circuit model.

[0061] The process of establishing a closed-loop identification model based on a virtual circuit model includes: establishing virtual circuit models for different scenarios for grid-connected energy storage independent power supply and grid power supply scenarios in combination with other power sources;

[0062] (1) For the grid scenario of independent power supply of grid-connected energy storage, a virtual circuit model of a single-machine system is established, and its mathematical expression is:

[0063]

[0064] θ=θ G -δ;

[0065]

[0066] Among them, P and Q are the active and reactive power of the load, x is the equivalent internal reactance of the grid-type energy storage, V G is the effective value of the grid connection point voltage, k sc is the short-circuit fault voltage drop coefficient.

[0067] (2) For the grid scenario where grid-connected energy storage is jointly powered by other power sources, a virtual circuit model of the two-machine system is established, and its mathematical expression is:

[0068] δ2=θ1-θ2;

[0069]

[0070] P = P1 + P2;

[0071] Q = Q1 + Q2;

[0072]

[0073] θ1[t+1]=θ-δ1[t+1];

[0074]

[0075] Where x1 represents the reactance inside the grid power source, r2 and x2 represent the resistance and reactance between the two power sources respectively, δ1 is the power angle between the potential inside the grid-type energy storage equivalent power source and the grid connection point voltage, δ2 is the power angle between the grid connection point voltage and the parallel power source terminal voltage, V G1 and θ1 are the voltage amplitude and phase of the equivalent power grid connection point, VG2 and θ2 are the voltage amplitude and phase of the parallel power supply node, P and Q are the active and reactive power output by the equivalent power supply, P1 and Q1 are the active and reactive power of the load connected to the equivalent power supply bus, P2 and Q2 are the active and reactive power between the equivalent power supply and the parallel power supply, and the label [t+1] represents the data of the next time step.

[0076] Step 4: Establish a closed-loop identification model based on the virtual circuit model;

[0077] Specifically, based on the input-output relationship between the virtual circuit model and the black box model, the control signal output by the black box model is used as the input signal of the virtual circuit model, and the electrical quantity signal output by the virtual circuit model is used as the input signal of the black box model to establish a closed-loop identification model.

[0078] The closed-loop identification model is as follows:

[0079]

[0080] y b =PWM(e b );

[0081] E G =PWM -1 (y b );

[0082] u bv =[V G1 ,θ1];

[0083] Step 5: Construct a Kalman filter containing the control parameters to be estimated based on the closed-loop identification model, and flexibly change the grid scenario and operating conditions by adjusting the disturbance power and impedance variables to achieve online parameter identification;

[0084] Specifically, a Kalman filter containing the control parameters to be estimated is constructed based on the closed-loop identification model. The grid scenario and operating conditions are flexibly changed by adjusting the disturbance power and impedance variables, and online parameter identification is achieved through a prediction-correction-update rolling optimization strategy.

[0085] The method of constructing a Kalman filter based on a closed-loop identification model is as follows:

[0086] (1) A Kalman filter prediction model including a networked energy storage control system is established, and its mathematical expression is:

[0087]

[0088] sσ1=V set -V G1 ;

[0089] Q ref=K p1 (V set -V G1 )+K i1 σ1;

[0090] sσ2=Q ref -Q;

[0091] e b =K p2 (Q ref -Q)+K i2 σ2;

[0092] Among them, Δω is the offset of the grid-type energy storage output frequency, V set and P set are the voltage and active output setting values, σ1 and σ2 are auxiliary state variables, Q ref are intermediate variables, D, H, K p1 , K p2 , K i1 , K i2 is the parameter to be estimated.

[0093] (2) Based on the electrical quantity data output by the virtual circuit, an objective function is established to characterize the online calculation prediction error:

[0094]

[0095] Among them, A and B are the state matrix and input matrix of the prediction model respectively, x is the state vector of the prediction model, u is the input vector of the black box model, that is, the output vector of the virtual circuit model, the superscript “+” represents the next time step, the superscript “-” represents the previous time step, and “^” represents the predicted value.

[0096] (3) Establishing the optimal parameter estimation model:

[0097]

[0098] Among them, M is the optimization objective function established in the previous step, and E and e are the 1 / 0 vector and boundary value representing the upper and lower limit constraints respectively.

[0099] The method of flexibly changing the grid scenario and operating conditions by adjusting the disturbance power and impedance variables is as follows:

[0100] (1) Establish the mathematical expressions of power and impedance disturbance terms:

[0101] P1[t+1]=P1+ΔP1;

[0102] Q1[t+1]=Q1+ΔQ1;

[0103] θ2[t+1]=θ2+Δθ2;

[0104] x1[t+1]=x1+Δx1;

[0105] x2[t+1]=x2+Δx2;

[0106] (2) In the steady state, the s term in the prediction model is assumed to be 0, and we get:

[0107]

[0108] (3) Further, the Kalman filter prediction model under new scenarios and operating conditions is obtained:

[0109]

[0110] The method for realizing online parameter identification through the prediction-correction-update rolling optimization strategy is as follows:

[0111] The black box model output of the next time step is calculated based on the prediction model as the estimated value, and the estimated value and the observed value are substituted into the parameter estimation model to obtain the new parameter value by solving the optimization problem. The above calculation process is iterated in subsequent time steps to realize the online parameter identification of the rolling update method.

[0112] Example 2

[0113] In one embodiment of the present disclosure, a virtual circuit closed-loop modeling system for online parameter identification of a grid-connected energy storage is provided, comprising:

[0114] A black box model building module for grid-type energy storage is used to obtain the operating data of the grid-type energy storage control system and establish a black box model based on the signal interaction relationship between the grid-type energy storage control system and the electrical system;

[0115] The signal interface module is used to construct the output data interface of the virtual circuit based on the electrical quantity signal collected by the black box model; based on the PWM pulse signal output by the black box model, the internal potential of the network-type energy storage equivalent power supply of the virtual circuit is calculated;

[0116] A virtual circuit model building module is used to build a virtual circuit model based on the power transmission relationship between the grid-type energy storage equivalent power source and the electrical system;

[0117] A closed-loop identification model construction module is used to establish a closed-loop identification model based on a virtual circuit model; wherein the process of establishing a closed-loop identification model based on a virtual circuit model includes: establishing virtual circuit models in different scenarios for grid-connected energy storage independent power supply and power supply in combination with other power sources, and based on the input-output relationship between the virtual circuit model and the black box model, using the control signal output by the black box model as the input signal of the virtual circuit model, and using the electrical quantity signal output by the virtual circuit model as the input signal of the black box model, to establish a closed-loop identification model;

[0118] The online parameter identification module is used to construct a Kalman filter containing the control parameters to be estimated based on the closed-loop identification model, and realize online parameter identification by adjusting the disturbance power and impedance variables to flexibly change the power grid scenario and operating conditions.

[0119] The specific implementation method of the above modules is the same as that in Example 1 and will not be described in detail here.

[0120] Example 3

[0121] In one embodiment of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, implements the virtual circuit closed-loop modeling method for online parameter identification of grid-connected energy storage.

[0122] Example 4

[0123] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage is implemented.

[0124] Example 5

[0125] In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage.

[0126] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0128] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage, characterized in that: include: Obtain the operating data of the grid-type energy storage control system, and establish a black box model based on the signal interaction relationship between the grid-type energy storage control system and the electrical system; Based on the electrical quantity signals collected by the black box model, the output data interface of the virtual circuit is constructed; based on the PWM pulse signal output by the black box model, the internal potential of the network-type energy storage equivalent power supply of the virtual circuit is calculated; Based on the power transmission relationship between the grid-type energy storage equivalent power source and the electrical system, a virtual circuit model is constructed, and a closed-loop identification model is established based on the virtual circuit model; A Kalman filter containing the control parameters to be estimated is constructed based on the closed-loop identification model. The disturbance power and impedance variables are adjusted to flexibly change the grid scenario and operating conditions to achieve online parameter identification. Among them, the process of establishing a closed-loop identification model based on the virtual circuit model includes: establishing virtual circuit models for different scenarios for grid-connected energy storage independent power supply and power supply in conjunction with other power sources, and based on the input-output relationship between the virtual circuit model and the black box model, using the control signal output by the black box model as the input signal of the virtual circuit model, and using the electrical quantity signal output by the virtual circuit model as the input signal of the black box model to establish a closed-loop identification model.

2. The virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage according to claim 1, characterized in that: The upper-level dispatching instructions of the grid-type energy storage control system are obtained as input data, including voltage reference value, active power reference value, and reactive power reference value. A black box model is established based on the signal interaction relationship between the grid-type energy storage control system and the electrical system. Specifically: y b =PWM(e b ); Among them, e b is the reference value of the output modulation voltage of the grid-type energy storage converter, u bs and u bv They represent the dispatching instruction input signal vector and the grid-connected electrical quantity input signal vector, θ b represents the control parameter vector to be estimated, y b It represents the PWM modulated pulse signal output by the black box model, which is obtained by calculating the voltage reference value through the carrier and is used to control the on and off of the converter switching elements.

3. The virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage according to claim 1, characterized in that: Based on the electrical quantity signals collected by the black box model, the output data interface of the virtual circuit is constructed, specifically: Among them, y v The grid-connected electrical quantity data output by the virtual circuit, including the positive sequence component of the three-phase voltage and negative sequence component Positive sequence component of three-phase current and negative sequence component 4. The virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage according to claim 1, characterized in that: Based on the PWM pulse signal output by the black box model, the internal potential of the network-type energy storage equivalent power supply of the virtual circuit is calculated, specifically: E G =PWM -1 (y b ); Among them, E G It is the internal potential of the grid-type energy storage equivalent power supply in the virtual circuit, which is calculated by inverse transformation of the PWM modulation signal output by the black box model.

5. The virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage according to claim 1, characterized in that: Based on the power transmission relationship between the equivalent power source of the grid-type energy storage and the electrical system, a virtual circuit model is constructed. For the grid scenario where the grid-type energy storage is independently powered, a virtual circuit model of the single-machine system is established, specifically: θ=θ G -d; Among them, P and Q are the active and reactive power of the load, x is the equivalent internal reactance of the grid-type energy storage, V G is the effective value of the grid connection point voltage, k sc is the short-circuit fault voltage drop coefficient.

6. The virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage according to claim 1, characterized in that: For the grid scenario where grid-connected energy storage is jointly powered by other power sources, a virtual circuit model of the two-machine system is established, specifically: δ2=θ1-θ2; P = P1 + P2; Q = Q1 + Q2; θ1[t+1]=θ-δ1[t+1]; Among them, δ1 is the power angle between the potential inside the grid-type energy storage equivalent power source and the grid connection point voltage, δ2 is the power angle between the grid connection point voltage and the parallel power supply terminal voltage, V G1 and θ1 are the voltage amplitude and phase of the equivalent power grid connection point, V G2 and θ2 are the voltage amplitude and phase of the parallel power supply node, P and Q are the active and reactive power output by the equivalent power supply, P1 and Q1 are the active and reactive power of the load connected to the equivalent power supply bus, P2 and Q2 are the active and reactive power between the equivalent power supply and the parallel power supply, and the label [t+1] represents the updated data for the next time step.

7. A virtual circuit closed-loop modeling system for online parameter identification of grid-type energy storage, characterized in that: include: A black box model building module for grid-type energy storage is used to obtain the operating data of the grid-type energy storage control system and establish a black box model based on the signal interaction relationship between the grid-type energy storage control system and the electrical system; The signal interface module is used to construct the output data interface of the virtual circuit based on the electrical quantity signal collected by the black box model; based on the PWM pulse signal output by the black box model, the internal potential of the network-type energy storage equivalent power supply of the virtual circuit is calculated; A virtual circuit model building module is used to build a virtual circuit model based on the power transmission relationship between the grid-type energy storage equivalent power source and the electrical system; A closed-loop identification model construction module is used to establish a closed-loop identification model based on a virtual circuit model; wherein the process of establishing a closed-loop identification model based on a virtual circuit model includes: establishing virtual circuit models in different scenarios for grid-connected energy storage independent power supply and power supply in combination with other power sources, and based on the input-output relationship between the virtual circuit model and the black box model, using the control signal output by the black box model as the input signal of the virtual circuit model, and using the electrical quantity signal output by the virtual circuit model as the input signal of the black box model, to establish a closed-loop identification model; The online parameter identification module is used to construct a Kalman filter containing the control parameters to be estimated based on the closed-loop identification model, and realize online parameter identification by adjusting the disturbance power and impedance variables to flexibly change the power grid scenario and operating conditions.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the virtual circuit closed-loop modeling method for online parameter identification of grid-connected energy storage described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage as described in any one of claims 1 to 6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the virtual circuit closed-loop modeling method for online parameter identification of grid-type energy storage as described in any one of claims 1-6.

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