Network construction energy storage linear quadratic optimization parameter identification method and system

Abstract

The invention provides a network construction energy storage linear quadratic optimization parameter identification method and system, and relates to the technical field of power system optimization control and identification, and the method comprises the steps: building a network construction energy storage control black box equivalent mathematical model, and abstractly representing a control structure and a to-be-identified parameter through an input and output relation; a hardware-in-the-loop simulation model is constructed, active disturbance, reactive disturbance, voltage disturbance and frequency disturbance are used as typical disturbance effects, the operation state of the power grid is continuously changed, and a time domain dynamic response database under massive operation conditions is constructed; constructing an identification error solver and a linear quadratic optimization parameter identification model; calculating and controlling the deviation between the prediction data of the black box equivalent mathematical model and the time domain dynamic response data; and the identification result is updated in a rolling manner through linear quadratic optimization calculation. According to the method, a traditional time domain identification mode is improved to be nested fusion of time domain data, a control model and an optimization model, and the accuracy of parameter identification under the wide operation condition is comprehensively improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power system optimization control and identification, and in particular to a method and system for identifying linear quadratic optimization parameters of a grid-connected energy storage system. 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] The large-scale application of grid-connected energy storage is beneficial to improving the voltage and frequency stability problems caused by large-scale grid-connected renewable energy generation. However, there is currently a lack of standardized constraints on the grid-connected technical solutions of various manufacturers, resulting in large differences in the grid-related characteristics of different types of grid-connected energy storage equipment, and it is difficult to obtain white box models of various types of equipment, which cannot meet the needs of modeling and analysis of new power systems. Therefore, it is of great practical significance to study the parameter identification of grid-connected energy storage equipment.

[0004] Existing identification methods generally perform sweep frequency analysis (impedance method) or time domain simulation (data-driven method) on the steady-state operating point of the system from the perspective of frequency domain or time domain, so as to obtain observation data describing the frequency response characteristics or disturbance response characteristics of the system, and finally implement parameter identification based on the pre-established model containing the parameters to be estimated. However, the identification process of existing methods relies on the linearization of specific operating points of the system. Since the operating state of the actual system may change dramatically over a large range, the observation data will deviate from the linearization characterization interval of the operating point, and therefore cannot guarantee accuracy under a wide range of operating conditions. Summary of the invention

[0005] In order to solve the above problems, the present disclosure proposes a linear quadratic optimization parameter identification method and system for grid-building energy storage. Aiming at a wide range of operating conditions, a control black box equivalent mathematical model is established based on the input-output relationship of the grid-building energy storage control system. The control parameters to be estimated in the control black box equivalent mathematical model are used as optimization variables to construct a linear quadratic optimization parameter estimation model for grid-building energy storage. Based on the linear quadratic optimization parameter estimation model and the Kalman filter, online rolling identification is realized to improve the accuracy of parameter identification of grid-building energy storage.

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

[0007] The linear quadratic optimization parameter identification method for grid energy storage includes:

[0008] Obtain the internal and external input data of the grid-building energy storage control system, and establish a control black box equivalent mathematical model based on the input-output relationship of the grid-building energy storage control system;

[0009] Based on the connection relationship between the grid-connected energy storage control system and the power grid, a closed-loop simulation model based on hardware in the loop is established. Based on the controller input signal and controller output signal of the closed-loop simulation model, a time-domain dynamic response database under massive operating conditions is constructed by continuously changing the operating state.

[0010] Based on the time domain dynamic response database and the equivalent relationship between the control black box equivalent mathematical model and the controller input and output, an identification error solver is established;

[0011] The estimated control parameters in the control black box equivalent mathematical model are taken as optimization variables, the minimum output deviation of the identification error solver is taken as the optimization target, and a linear quadratic optimization parameter identification model is constructed.

[0012] The time domain dynamic response database data corresponding to different operating states is adjusted to flexibly change the operating conditions. The estimated control parameters are updated through the linear quadratic optimization parameter identification model. Online rolling parameter identification is achieved through time-step prediction, correction and update.

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

[0014] The linear quadratic optimization parameter identification system for grid energy storage includes:

[0015] A control black box equivalent mathematical model building module is used to obtain the internal and external input data of the grid-building energy storage control system, and to establish a control black box equivalent mathematical model based on the input-output relationship of the grid-building energy storage control system;

[0016] The hardware-in-the-loop simulation module is used to establish a closed-loop simulation model based on hardware-in-the-loop based on the connection relationship between the grid-building energy storage control system and the power grid;

[0017] A time domain dynamic response database construction module is used to construct a time domain dynamic response database under massive operating conditions by continuously changing the operating state based on the controller input signal and controller output signal of the closed-loop simulation model;

[0018] An identification error solver module is used to establish an identification error solver based on a time domain dynamic response database and an equivalent relationship between a control black box equivalent mathematical model and controller input and output;

[0019] The linear quadratic optimization parameter identification model construction module is used to take the estimated control parameters in the control black box equivalent mathematical model as optimization variables, take the minimum output deviation of the identification error solver as the optimization target, and construct the linear quadratic optimization parameter identification model;

[0020] The online parameter identification module is used to adjust the time domain dynamic response database data corresponding to different operating states to flexibly change the operating conditions. The estimated control parameters are updated through the linear quadratic optimization parameter identification model. Online rolling parameter identification is achieved through step-by-step prediction, correction and update.

[0021] According to some embodiments, the present disclosure adopts the following technical solutions:

[0022] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the linear quadratic optimization parameter identification method for network energy storage is implemented.

[0023] According to some embodiments, the present disclosure adopts the following technical solutions:

[0024] 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 linear quadratic optimization parameter identification method for network energy storage.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] A linear quadratic optimization parameter identification method for grid-building energy storage disclosed in the present invention aims to solve the problem of decreased accuracy in identification of grid-building energy storage parameters under a wide range of operating conditions. A massive time-domain dynamic response database of operating conditions based on a control black box equivalent mathematical model and a closed-loop simulation model is provided, and the traditional time-domain identification method is improved into a combination of a mathematical model and an optimization model. The method has the advantages of strong flexibility in adjusting operating conditions and high precision in parameter matching, and can comprehensively improve the accurate identification capability under a wide range of operating conditions.

[0027] The present invention discloses a linear quadratic optimization parameter identification method for network energy storage. Based on the equivalent relationship between the control black box equivalent mathematical model and the controller input and output, a control black box output prediction model, an identification error solver and a linear quadratic optimization parameter identification model are constructed. Online parameter identification is achieved through prediction estimation-identification correction-parameter updating, and is executed in a step-by-step rolling manner, which can significantly improve the online performance of the parameter identification model. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] 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.

[0029] Figure 1 The overall structure and relationship diagram of the identification method of the embodiment of the present disclosure;

[0030] Figure 2 This is a flow chart of a method for identifying linear quadratic optimization parameters for grid-building energy storage according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0032] 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.

[0033] 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.

[0034] Example 1

[0035] In one embodiment of the present disclosure, a method for identifying linear quadratic optimization parameters of a grid-building energy storage system is provided, comprising:

[0036] Step 1: Obtain the internal and external input data of the grid-building energy storage control system, and establish a control black box equivalent mathematical model based on the input-output relationship of the grid-building energy storage control system;

[0037] Step 2: Based on the connection relationship between the grid-connected energy storage control system and the power grid, a closed-loop simulation model based on hardware in the loop is established. Based on the controller input signal and controller output signal of the closed-loop simulation model, a time-domain dynamic response database under massive operating conditions is constructed by continuously changing the operating state;

[0038] Step 3: Establish an identification error solver based on the time domain dynamic response database and the equivalent relationship between the control black box equivalent mathematical model and the controller input and output;

[0039] Step 4: Take the estimated control parameters in the control black box equivalent mathematical model as optimization variables, take the minimum output deviation of the identification error solver as the optimization goal, and construct a linear quadratic optimization parameter identification model;

[0040] Step 5: Adjust the time domain dynamic response database data corresponding to different operating states to flexibly change the operating conditions, update the estimated control parameters through the linear quadratic optimization parameter identification model, and realize online rolling parameter identification through time-step prediction, correction and update.

[0041] As an embodiment, the present invention discloses a method for identifying parameters of linear quadratic optimization of grid energy storage, and its overall structure and signal relationship are as follows: Figure 1 As shown, the controller black box refers to an unidentified grid-connected energy storage control system, and the equivalent mathematical model refers to an abstract mathematical model that describes the controller structure and unknown parameters to be identified based on the input-output relationship of the controller. The simulation model refers to an equivalent calculation model for the power system, and the time-domain dynamic response database is a digitized description of the response relationship between the controller and the simulation model. The identification error solver obtains the identification error by analyzing the equivalent mathematical model prediction data and the time-domain dynamic response data. The linear quadratic optimization parameter identification model provides new identification parameter values ​​to the equivalent mathematical model through optimization calculation, thereby realizing an online rolling closed-loop parameter identification logic structure. The specific implementation process of the present disclosure is as follows. Figure 2 As shown, including:

[0042] Step 1: Obtain the internal and external input data of the grid-building energy storage control system, and establish a control black box equivalent mathematical model based on the input-output relationship of the grid-building energy storage control system;

[0043] Specifically, the voltage reference value, active power reference value, and reactive power reference value of the power dispatch instruction are obtained as external input data, and the three-phase voltage value and three-phase current value of the grid-connected energy storage point are obtained as internal input data. Based on the input-output relationship of the grid-connected energy storage control system, an equivalent mathematical model of the control black box is established.

[0044] Among them, the expression of the control black box equivalent mathematical model is as follows:

[0045] y ctrl =Φ(u abc ,i abc ,λ ctrl )

[0046] In the formula, y ctrl is the output signal of the grid energy storage converter, u abc and i abc Respectively represent the three-phase voltage and current signals of the grid connection point, λ ctrl represents the control parameter vector to be identified.

[0047] Step 2: Based on the connection relationship between the grid-connected energy storage control system and the power grid, a closed-loop simulation model based on hardware in the loop is established;

[0048] Specifically, based on the connection relationship between the grid-connected energy storage control system and the grid, a real-time simulation model of the grid equivalent circuit connected to the energy storage controller is constructed, the converter starting pulse signal output by the controller is used as the input signal of the real-time simulation model, and the grid connection point voltage and current values ​​of the real-time simulation model are used as the controller input signal to establish a closed-loop simulation model based on hardware in the loop;

[0049] Among them, (1) the construction method of the real-time simulation model of the power grid equivalent circuit is as follows:

[0050] [u abc ,i abc ]=Θ(e abc ,z g,abc ,z s,abc )

[0051]

[0052] θ=θ G -δ

[0053] In the formula, e abc is the internal potential of the network energy storage, e rms is the effective value of the internal potential, z g,abc and z s,abc are the equivalent impedance of the power supply and the system respectively, P and Q are the active and reactive power of the load, x is the equivalent internal reactance of the grid energy storage, u rms is the effective value of the grid-connected point voltage, θ is the internal potential phase angle, θ G is the grid-connected point voltage phase angle, and δ is the phase angle difference.

[0054] (2) The construction method of the closed-loop simulation system based on hardware in the loop is as follows:

[0055] y ctrl =Φ(u abc ,i abc ,λ ctrl )

[0056] e abc =SPWM -1 (y ctrl )

[0057] Among them, SPWM -1 It represents the inverse transformation of sinusoidal pulse width modulation, and the internal potential of the converter output is calculated through the converter trigger pulse signal. Φ represents the controller part of the simulation model.

[0058] Step 3: Based on the controller input signal and controller output signal of the closed-loop simulation model, a time-domain dynamic response database under massive operating conditions is constructed by continuously changing the operating state;

[0059] Specifically, active disturbance, reactive disturbance, voltage disturbance and frequency disturbance are taken as typical disturbance effects, and disturbance signals are injected into the real-time simulation model. Based on the equivalent circuit output signal (controller input signal) and the equivalent circuit input signal (controller output signal) of the closed-loop simulation model, a time domain dynamic response database under massive operating conditions is constructed by continuously changing the operating state.

[0060] Among them, the construction method of the time domain dynamic response database under massive operating conditions is as follows:

[0061] [U abc (k),I abc (k)] = Ψ[P(k),Q(k),z s (k)]

[0062] Y ctrl (k)=Φ[U abc (k),I abc (k)]

[0063] P(k)=P(k-1)+ΔP

[0064] Q(k)=Q(k-1)+ΔQ

[0065] z s (k) = z s (k-1)+Δz s

[0066] In the formula, k represents the kth operating condition, P and Q are the active and reactive power of the load, and z s is the equivalent impedance of the system. abc and I abc are the voltage signal vector and current signal vector of the grid-connected energy storage point corresponding to the operating conditions, which are calculated by the equivalent circuit simulation model. ctrl It is the controller time domain dynamic output data corresponding to the operating conditions; ΔP, ΔQ, Δz s They represent active disturbance, reactive disturbance and system impedance change disturbance respectively, Ψ represents the circuit part of the simulation model, and Φ represents the controller part of the simulation model.

[0067] Step 4: Establish an identification error solver based on the time domain dynamic response database and the equivalent relationship between the control black box equivalent mathematical model and the controller input and output;

[0068] Specifically, based on the equivalent relationship between the control black box equivalent mathematical model and the controller input and output, the controller input signal of the time domain dynamic response database is used as the input data of the control black box equivalent mathematical model. By performing deviation analysis between the output data of the control black box equivalent mathematical model and the controller output signal of the time domain dynamic response database, an identification error solver is established.

[0069] Among them, the identification error solver is constructed as follows:

[0070]

[0071] Where y represents the actual output vector of the controller in the time domain dynamic response data, and the superscript “^” represents the predicted value of the control black box equivalent mathematical model.

[0072] Step 5: Take the estimated control parameters in the control black box equivalent mathematical model as optimization variables, take the minimum output deviation of the identification error solver as the optimization goal, and construct a linear quadratic optimization parameter identification model;

[0073] Specifically, the control parameters to be estimated in the equivalent mathematical model of the control black box are used as optimization variables, and a control black box output prediction model is established based on the internal transfer function expression of the equivalent mathematical model. The minimization of the output deviation of the identification error solver is taken as the optimization goal, and a linear quadratic optimization parameter identification model is constructed.

[0074] Among them, (1) the specific method of establishing the control black box output prediction model is as follows:

[0075]

[0076] sx1=u ref -u rms

[0077] sx2=k p1 (u ref -u rms )+k i1 x1-Q

[0078]

[0079] Where Δf is the frequency change, u ref is the grid connection point voltage reference value, P set is the active power command value, x1 and x2 are auxiliary state variables, d, h, k p1 , k p2 , k i1 , k i2 is the control parameter to be identified; s represents the Laplace operator, and Δf represents the small frequency signal.

[0080] (2) The specific method of constructing the linear quadratic optimization parameter identification model is as follows:

[0081]

[0082] st

[0083] θ i ≤θ i,max

[0084] θ i ≥θ i,min

[0085] Where W is the identification error solver, which is used as the objective function of the optimal parameter identification model. i,max and θ i,min are the parameters to be estimated θ i The upper and lower boundary values.

[0086] Step 6: Adjust the time domain dynamic response database data corresponding to different operating states to flexibly change the operating conditions, update the estimated control parameters through the linear quadratic optimization parameter identification model, and realize online rolling parameter identification through step-by-step prediction, correction and update.

[0087] Specifically, the time-domain dynamic response database data corresponding to different operating states are adjusted to achieve flexible changes in operating conditions. The time-domain dynamic response data under the current operating conditions and disturbances are selected, and online parameter identification is achieved through the strategy of predictive model estimation-identification error solver correction-optimal parameter identification model update.

[0088] Among them, the strategy of updating the prediction model through prediction model estimation-identification error solver correction-optimization parameter identification model is as follows:

[0089] (1) Adjust the time domain dynamic response database data corresponding to different operating states to achieve flexible changes in operating conditions:

[0090] P(k)=P(k-1)+ΔP

[0091] Q(k)=Q(k-1)+ΔQ

[0092] z s (k) = z s (k-1)+Δz s

[0093] In the formula, k represents the kth operating condition, P and Q are the active and reactive power of the load, and z s is the equivalent impedance of the system.

[0094] (2) Select the time domain dynamic response data under the current operating conditions and disturbances:

[0095] [U abc (k),I abc (k)] = Ψ[P(k),Q(k),z s (k)]

[0096] Where U abc and I abc They are respectively the voltage signal vector and current signal vector of the grid-connected energy storage point corresponding to the operating conditions, which are calculated through the equivalent circuit simulation model.

[0097] (3) Estimate the control black box output data under the current working conditions through the prediction model:

[0098] Y ctrl (k)=Φ[U abc (k),I abc (k)]

[0099] Among them, Y ctrl Output prediction data for the controller under the kth operating condition.

[0100] (4) Correct the estimation deviation through the identification error solver:

[0101]

[0102] Where y represents the actual output vector of the controller in the time domain dynamic response data, and the superscript “^” represents the predicted value of the control black box equivalent mathematical model.

[0103] (5) Update the estimated control parameters through the linear quadratic optimization parameter identification model:

[0104]

[0105] Where W is the objective function of the optimal parameter identification model, t+1 represents the next time step, and t represents the current time step. Online rolling parameter identification is achieved through time-step prediction, correction, and update.

[0106] Example 2

[0107] In one embodiment of the present disclosure, a linear quadratic optimization parameter identification system for grid-building energy storage is provided, comprising:

[0108] A control black box equivalent mathematical model building module is used to obtain the internal and external input data of the grid-building energy storage control system, and to establish a control black box equivalent mathematical model based on the input-output relationship of the grid-building energy storage control system;

[0109] The hardware-in-the-loop simulation module is used to establish a closed-loop simulation model based on hardware-in-the-loop based on the connection relationship between the grid-building energy storage control system and the power grid;

[0110] A time domain dynamic response database construction module is used to construct a time domain dynamic response database under massive operating conditions by continuously changing the operating state based on the controller input signal and controller output signal of the closed-loop simulation model;

[0111] An identification error solver module is used to establish an identification error solver based on a time domain dynamic response database and an equivalent relationship between a control black box equivalent mathematical model and controller input and output;

[0112] The linear quadratic optimization parameter identification model construction module is used to take the estimated control parameters in the control black box equivalent mathematical model as optimization variables, take the minimum output deviation of the identification error solver as the optimization target, and construct the linear quadratic optimization parameter identification model;

[0113] The online parameter identification module is used to adjust the time domain dynamic response database data corresponding to different operating states to flexibly change the operating conditions. The estimated control parameters are updated through the linear quadratic optimization parameter identification model. Online rolling parameter identification is achieved through step-by-step prediction, correction and update.

[0114] Example 3

[0115] 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 linear quadratic optimization parameter identification method for networked energy storage is implemented.

[0116] Example 4

[0117] 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, 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 linear quadratic optimization parameter identification method for network energy storage.

[0118] 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.

[0119] 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.

[0120] 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 linear quadratic optimization parameter identification method for grid energy storage, characterized in that: include: Obtain the internal and external input data of the grid-building energy storage control system, and establish a control black box equivalent mathematical model based on the input-output relationship of the grid-building energy storage control system; Based on the connection relationship between the grid-connected energy storage control system and the power grid, a closed-loop simulation model based on hardware in the loop is established. Based on the controller input signal and controller output signal of the closed-loop simulation model, a time-domain dynamic response database under massive operating conditions is constructed by continuously changing the operating state. Based on the time domain dynamic response database and the equivalent relationship between the control black box equivalent mathematical model and the controller input and output, an identification error solver is established; The estimated control parameters in the control black box equivalent mathematical model are taken as optimization variables, the minimum output deviation of the identification error solver is taken as the optimization target, and a linear quadratic optimization parameter identification model is constructed. The time domain dynamic response database data corresponding to different operating states is adjusted to flexibly change the operating conditions. The estimated control parameters are updated through the linear quadratic optimization parameter identification model. Online rolling parameter identification is achieved through time-step prediction, correction and update.

2. The linear quadratic optimization parameter identification method for network energy storage according to claim 1, characterized in that: The internal and external input data of the grid-connected energy storage control system are obtained, and an equivalent mathematical model of the control black box is established based on the input-output relationship of the grid-connected energy storage control system, including: obtaining the voltage reference value, active power reference value, and reactive power reference value of the power dispatch instruction as external input data, obtaining the three-phase voltage value and three-phase current value of the grid-connected energy storage point as internal input data, and establishing an equivalent mathematical model of the control black box based on the input-output relationship of the grid-connected energy storage control system.

3. The linear quadratic optimization parameter identification method for network energy storage according to claim 1, characterized in that: Based on the connection relationship between the grid-building energy storage control system and the power grid, a closed-loop simulation model based on hardware in the loop is established, including: based on the connection relationship between the grid-building energy storage control system and the power grid, a real-time simulation model of the power grid equivalent circuit connected to the grid-building energy storage controller is constructed, the converter starting pulse signal output by the grid-building energy storage controller is used as the real-time simulation model input signal, the grid connection point voltage and current values ​​of the real-time simulation model are used as the controller input signal, and a closed-loop simulation model based on hardware in the loop is established.

4. The linear quadratic optimization parameter identification method for network energy storage according to claim 1, characterized in that: Based on the controller input signal and controller output signal of the closed-loop simulation model, a time-domain dynamic response database under massive operating conditions is constructed by continuously changing the operating state, including: taking active disturbance, reactive disturbance, voltage disturbance and frequency disturbance as typical disturbance effects, injecting disturbance signals into the real-time simulation model, and based on the equivalent circuit output signal and equivalent circuit input signal of the closed-loop simulation model, a time-domain dynamic response database under massive operating conditions is constructed by continuously changing the operating state.

5. The linear quadratic optimization parameter identification method for network energy storage according to claim 1, characterized in that: Based on the time domain dynamic response database and the equivalent relationship between the control black box equivalent mathematical model and the controller input and output, an identification error solver is established, including: based on the equivalent relationship between the control black box equivalent mathematical model and the controller input and output, the controller input signal of the time domain dynamic response database is used as the input data of the control black box equivalent mathematical model, and the identification error solver is established by performing deviation analysis between the output data of the control black box equivalent mathematical model and the controller output signal of the time domain dynamic response database.

6. The linear quadratic optimization parameter identification method for network energy storage according to claim 1, characterized in that: The control parameters to be estimated in the control black box equivalent mathematical model are taken as optimization variables, the output deviation of the identification error solver is minimized as the optimization target, and a linear quadratic optimization parameter identification model is constructed, including: taking the control parameters to be estimated in the control black box equivalent mathematical model as optimization variables, establishing a control black box output prediction model based on the internal transfer function expression of the equivalent mathematical model, minimizing the output deviation of the identification error solver as the optimization target, and constructing a linear quadratic optimization parameter identification model.

7. The linear quadratic optimization parameter identification method for network energy storage according to claim 1, characterized in that: The time-domain dynamic response database data corresponding to different operating states are adjusted to realize flexible changes in operating conditions. The time-domain dynamic response data under the current operating conditions and disturbances are selected, and online rolling parameter identification is realized by controlling the black-box output prediction model estimation, identification error solver correction, and linear quadratic optimization parameter identification model update strategy.

8. A linear quadratic optimization parameter identification system for grid energy storage, characterized in that: include: A control black box equivalent mathematical model building module is used to obtain the internal and external input data of the grid-building energy storage control system, and to establish a control black box equivalent mathematical model based on the input-output relationship of the grid-building energy storage control system; The hardware-in-the-loop simulation module is used to establish a closed-loop simulation model based on hardware-in-the-loop based on the connection relationship between the grid-connected energy storage control system and the power grid; A time domain dynamic response database construction module is used to construct a time domain dynamic response database under massive operating conditions by continuously changing the operating state based on the controller input signal and controller output signal of the closed-loop simulation model; An identification error solver module is used to establish an identification error solver based on a time domain dynamic response database and an equivalent relationship between a control black box equivalent mathematical model and controller input and output; The linear quadratic optimization parameter identification model construction module is used to take the estimated control parameters in the control black box equivalent mathematical model as optimization variables, take the minimum output deviation of the identification error solver as the optimization target, and construct the linear quadratic optimization parameter identification model; The online parameter identification module is used to adjust the time domain dynamic response database data corresponding to different operating states to flexibly change the operating conditions. The estimated control parameters are updated through the linear quadratic optimization parameter identification model. Online rolling parameter identification is achieved through step-by-step prediction, correction and update.

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 linear quadratic optimization parameter identification method for grid energy storage as described in any one of claims 1 to 7 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 linear quadratic optimization parameter identification method for network energy storage as described in any one of claims 1 to 7.

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