A network-constructing and network-following energy storage hybrid power station control method and system

Abstract

The application discloses a network-keeping energy storage hybrid power station control method and system, belongs to the electrical engineering technical field, and comprises the following steps: based on the circuit topology and control strategy of the network-keeping energy storage hybrid power station, constructing a state space structure model of the network-keeping energy storage hybrid power station and grid connection; based on the state space structure model of the network-keeping energy storage hybrid power station and grid connection, constructing an H∞ robust controller; solving the H∞ robust controller to obtain a to-be-solved matrix required by the H∞ robust controller; verifying whether the to-be-solved matrix required by the H∞ robust controller meets the system performance; and outputting the H∞ robust controller meeting the system performance to control the network-keeping energy storage hybrid power station. The method constructs a state space structure model, designs an H∞ robust controller, and significantly enhances the robustness of the system, and has obvious advancement in adaptability of a complex power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrical engineering, in particular to a network-constructing and network-following hybrid energy storage power station control method and system. BACKGROUND

[0002] Existing energy storage power stations mostly adopt a single control mode: network-following control or network-constructing control. Network-following energy storage has fast response speed, but lacks inertia support and is prone to cause wide-band oscillation in weak power grids; network-constructing control has voltage / frequency support capability, but has slow dynamic response, and a traditional fixed PID controller (Proportional-Integral-Derivative Controller) is mostly used in the control link, which has insufficient robustness to model parameter perturbation and external interference. The control strategies of current hybrid energy storage power stations are mostly based on fixed mode switching or simple gain scheduling, and the adaptive adjustment capability and robustness of the system are limited when facing complex power systems, and the influence of power grid uncertainty (such as impedance change and load fluctuation) on system stability and system performance is not fully considered.

[0003] The application with Chinese patent publication No. CN117559479A discloses a network-following / network-constructing hybrid distribution network energy storage inverter control method, a mode control module of the energy storage inverter receives an externally given distribution network strength judgment instruction; when the distribution network strength judgment instruction is a strong network instruction of the distribution network, the energy storage inverter adopts a network-following control mode to output power in a steady-state power balance mode, a transient fluctuation suppression mode and a transient and steady-state active support mode; when the distribution network strength judgment instruction is a weak network instruction of the distribution network, the energy storage inverter adopts a network-constructing control mode to output power in a steady-state active support mode and a transient and steady-state fluctuation suppression mode. However, this method relies on the externally given distribution network strength judgment instruction for network-following and network-constructing control mode switching, has high dependence on the communication system, and has limited system adaptability. In actual power grid operation, the distribution network strength judgment may have errors or delays, and communication failure or delay may cause the control instruction to be unable to be conveyed in time, resulting in untimely or incorrect mode switching. Frequent mode switching when the strength of the power grid is in a critical state will impact the stability of the energy storage power station and may cause power fluctuation, voltage transient change and other problems, affecting power quality and stable operation of the power grid. These factors limit the adaptability of the method when dealing with complex power grid environments and internal problems of the energy storage power station, and may affect the long-term stable operation of the hybrid energy storage power station.

[0004] The application discloses a grid-connected converter control method based on hybrid control of grid-connection and grid-following, flexibly adjusts the proportion of grid-connection and grid-following characteristics by constructing an adaptive hybrid synchronization control model, ensures smooth and efficient grid-connection process, constructs a voltage and current double-loop control model to accurately regulate output voltage and current, meets the high-standard power quality requirements of the power grid on new energy power generation, meanwhile, introduces power grid impedance estimation for virtual impedance control, and further enhances the anti-interference ability and response speed of the system. The model established is relatively complex, resulting in a large amount of calculation of the control device in real-time operation, the model established by the application includes an adaptive hybrid synchronization control model, a voltage and current double-loop model and a virtual synchronous impedance model, the calculation amount of the control device is large during system operation, which may cause delay of control response and unable to track the change of the power grid in time. Meanwhile, the application has a high dependence on the accuracy of short-circuit ratio estimation, however, the capacity of each part of the system required in the short-circuit ratio calculation formula is difficult to obtain accurate real-time data. In addition, the ratio adaptive adjustment model established by the application has certain limitations whether through simulation of hysteresis loop or lookup table, the short-circuit ratio obtained by simulation of hysteresis loop is approximately related to the grid-following characteristic ratio, which may not accurately reflect the actual power grid situation, and the lookup table method depends on the corresponding relationship set in advance, and is difficult to adapt to complex and variable power grid scenes, when the power grid operation state is at the boundary of the lookup table or special situations occur, the grid-following characteristic ratio may not be accurately determined, affecting the effect of the control strategy. SUMMARY

[0005] The application aims to provide a grid-connection and grid-following hybrid energy storage power station control method and system, which constructs a state space structure model, designs an H∞ robust controller, and introduces a nonlinear compensation, so that the system has the functions of active support of grid-connection type and rapid response of grid-following type, and the robustness of the system is significantly enhanced, and the adaptability to complex power systems is obviously advanced.

[0006] To achieve the above-mentioned purpose, the application provides the following technical solutions.

[0007] The application provides a grid-connection and grid-following hybrid energy storage power station control method, which comprises the following steps:

[0008] Based on the circuit topology and control strategy of the grid-connection and grid-following hybrid energy storage power station, a state space structure model of the grid-connection and grid-following hybrid energy storage power station and grid-connection is constructed;

[0009] Based on the state space structure model of the grid-connection and grid-following hybrid energy storage power station and grid-connection, an H∞ robust controller is constructed; the H∞ robust controller is solved to obtain a to-be-solved matrix required by the H∞ robust controller;

[0010] The to-be-solved matrix required by the H∞ robust controller satisfies the system performance condition; when the system performance is not satisfied, the nonlinear term in the H∞ robust controller is regarded as an uncertain disturbance to the system for nonlinear compensation until the simulation verification requirement is met;

[0011] The H∞ robust controller satisfying the system performance is output, and is used for constructing a grid-following energy storage hybrid power station control.

[0012] As a further improvement of the application, the circuit topology and control strategy based on the grid-following energy storage hybrid power station are used to construct a state space structure model of the grid-following energy storage hybrid power station and grid connection, which comprises:

[0013] Based on the circuit topology and control strategy of the grid-following energy storage hybrid power station, a small signal model of the grid-connected energy storage power ring is obtained from a small signal model of the grid-connected energy storage system; a small signal model of the grid-following energy storage system is obtained from the active and reactive power control relationship of the grid-following energy storage system; a small signal model of the energy storage converter filter and the connecting line is obtained by small signal modeling of the grid connection line and the power grid.

[0014] According to the small signal model of the grid-connected energy storage power ring, the small signal model of the grid-following energy storage system and the small signal model of the energy storage converter filter and the connecting line, and based on the circuit equation between the grid connection point and the power grid, a state space structure model of the grid-following energy storage hybrid power station and grid connection is obtained.

[0015] As a further improvement of the application, the state space structure model based on the grid-following energy storage hybrid power station and grid connection is used to construct an H∞ robust controller; the to-be-solved matrix required by the H∞ robust controller is solved, which comprises:

[0016] Based on the grid-following energy storage hybrid power station, three perturbations are determined, which are power grid impedance perturbation, load perturbation and energy storage battery parameter perturbation, wherein the energy storage battery parameter perturbation specifically comprises battery equivalent resistance perturbation and battery capacity perturbation, and the norm limit of various perturbations is determined.

[0017] Various perturbations are uniformly represented as norm-bounded uncertainty; based on the norm-bounded uncertainty, the relationship between the system matrix, the disturbance input matrix and the output matrix is constructed based on the positive definite matrix and the scalar, and the closed-loop system equation is obtained by introducing the controller.

[0018] A scalar function of system stability is constructed, and the stability trend relationship of the closed-loop system under the action of uncertainty is obtained after substituting the closed-loop system equation; the stability trend relationship of the closed-loop system under the action of uncertainty is converted to a linear matrix inequality by applying the matrix inverse auxiliary theorem.

[0019] The to-be-solved matrix required by the H∞ robust controller is obtained by solving the linear matrix inequality.

[0020] As a further improvement of the application, the application matrix inversion auxiliary theorem converts the stability trend relationship of the closed-loop system under the action of uncertainty into a linear matrix inequality, including:

[0021] When dealing with uncertainty, the Schur complement lemma is used to reorganize the matrix inverse operation or cross terms involved in the inequality in the stability relationship of the closed-loop system under uncertain disturbance, eliminate the nonlinear structure, and obtain an inequality with a standard form;

[0022] Variable substitution is performed on the inequality with a standard form, and the relevant variables are applied to the matrix inversion auxiliary theorem to obtain a linear matrix inequality.

[0023] As a further improvement of the application, the solving H∞ robust controller includes:

[0024] The H∞ robust controller is solved by using a convex optimization tool to obtain the to-be-solved matrix required by the H∞ robust controller.

[0025] As a further improvement of the application, the nonlinear term in the H∞ robust controller is regarded as an uncertain disturbance to the system for nonlinear compensation, including:

[0026] The nonlinear compensation is performed by a sliding mode controller, and the nonlinear term considered in the nonlinear compensation includes the dead zone effect of the converter , the saturation voltage of the IGBT , and the filter parasitic parameters .

[0027] The application also proposes a network-keeping energy storage hybrid power station control system based on the network-keeping energy storage hybrid power station control method, including:

[0028] A first construction module is configured to construct a state space structure model of the network-keeping energy storage hybrid power station and the grid based on the circuit topology and control strategy of the network-keeping energy storage hybrid power station;

[0029] A second construction module is configured to construct an H∞ robust controller based on the state space structure model of the network-keeping energy storage hybrid power station and the grid, and solve the H∞ robust controller to obtain a to-be-solved matrix required by the H∞ robust controller;

[0030] A verification compensation module is configured to verify whether the to-be-solved matrix required by the H∞ robust controller satisfies the system performance, and if not, regard the nonlinear term in the H∞ robust controller as an uncertain disturbance to the system for nonlinear compensation until the simulation verification requirement is met.

[0031] An output control module is configured to output an H∞ robust controller satisfying system performance, and is configured to construct the grid-following and grid-forming hybrid energy storage power station control.

[0032] The application further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the grid-following and grid-forming hybrid energy storage power station control method when executing the computer program.

[0033] The application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the grid-following and grid-forming hybrid energy storage power station control method.

[0034] The application further provides a computer program product, which includes computer instructions, and the computer instructions instruct a computer to execute the grid-following and grid-forming hybrid energy storage power station control method.

[0035] Compared with the prior art, the application has the following advantages:

[0036] The application provides a grid-following and grid-forming hybrid energy storage power station control method, which constructs a state space structure model, designs an H∞ robust controller, and introduces a nonlinear compensation, so that the system has both stability support of the grid-forming type and rapid response function of the grid-following type, and the robustness of the system can be significantly enhanced, and the adaptability in a complex power system is obviously advanced. The control link and parameters used in the method are taken into account in the design of the power grid and the energy storage side disturbance at the beginning, the influence of the disturbance on the stability of the energy storage power station is minimized by using the H∞ robust control method, the method does not depend on external communication and power grid parameter identification, the calculation amount is small, and the requirement for device performance is not high. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The application provides a design process of the grid-following and grid-forming hybrid energy storage power station control method.

[0038] Figure 2 The application provides a grid-following and grid-forming hybrid energy storage power station grid connection topology and control strategy schematic diagram.

[0039] Figure 3 The application provides a state space structure diagram of the grid-following and grid-forming hybrid energy storage power station.

[0040] Figure 4 The application provides an H∞ robust controller design process.

[0041] Figure 5 The application provides an example working condition simulation grid connection point voltage and current result diagram, (a) is before a power flow mutation, and (b) is after a power flow mutation.

[0042] Figure 6 To simulate the active power output of the grid-parallel hybrid energy storage power station of the example working condition of the present application. DETAILED DESCRIPTION

[0043] The technical solutions in the present application will be clearly and completely described below in combination with the drawings in the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. The components of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0044] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms first, second, etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0045] At present, in the control technology of energy storage systems, the limitation of single control mode is obvious. The response of the grid-following energy storage is fast, but it lacks inertia support in the weak power grid, which easily causes wide-frequency oscillation. The grid-forming control can provide voltage / frequency support, but the dynamic response is slow, and the traditional fixed PID controller has poor robustness in the face of model parameter perturbation and external disturbance. The existing control strategy of hybrid energy storage power station is mostly based on fixed mode switching or simple scheduling, which is difficult to cope with the uncertainty of complex power systems. The technical innovation of the present application is to construct a state space structure model, design an H∞ robust controller, and introduce a nonlinear compensation, so that the system has both the active support of the grid-forming type and the fast response function of the grid-following type, significantly enhancing the robustness of the system and having obvious advancement in adaptability to complex power systems.

[0046] Among them, the essence of the H∞ robust controller is to take the H∞ norm as the optimization index, to ensure the stability of the system and to meet the performance under the worst uncertainty / disturbance. It does not pursue the optimal performance under the accurate model, but pursues the reliable performance under the uncertain environment, which is one of the control methods for coping with complex engineering problems.

[0047] The present application mainly proposes a grid-parallel hybrid energy storage power station control method and system, which makes the operating characteristics of the energy storage power station applicable to complex power system application scenarios.

[0048] In a first aspect, the present application provides a grid-parallel hybrid energy storage power station control method, which comprises the following steps: Figure 1As shown, it is the design process of the grid-keeping and grid-following energy storage hybrid power station control method and system. Through the establishment of the state space small signal model, the design of the H∞ robust controller for the disturbance and perturbation, the nonlinear compensation of the system and other methods, the robust stability control method is introduced into the control structure of the grid-keeping and grid-following energy storage hybrid system, and simulation verification is carried out. Part of the step details used in the implementation process of the method are as follows.

[0049] Step 1: Based on the circuit topology and control strategy of the grid-keeping and grid-following energy storage hybrid power station, the state space structure model of the grid-keeping and grid-following energy storage hybrid power station and the grid-connected is constructed; the state space structure model of the grid-keeping and grid-following energy storage hybrid power station and the grid-connected is constructed, and the linearization processing is performed on the nonlinear system, and the performance index is converted into a quantitative index;

[0050] In the above scheme, based on the circuit topology and control strategy of the grid-keeping and grid-following energy storage hybrid power station, the state space structure model of the grid-keeping and grid-following energy storage hybrid power station and the grid-connected is constructed, including:

[0051] Based on the circuit topology and control strategy of the grid-keeping and grid-following energy storage hybrid power station; the small signal model of the grid-keeping type energy storage power ring is obtained from the small signal model of the grid-keeping type energy storage system; the small signal model of the grid-following type energy storage system is obtained from the active and reactive control relationship of the grid-following type energy storage system; the small signal model of the energy storage converter filter and the connection line is obtained by small signal modeling of the grid-connected line and the grid;

[0052] According to the small signal model of the grid-keeping type energy storage power ring, the small signal model of the grid-following type energy storage system and the small signal model of the energy storage converter filter and the connection line, and based on the circuit equation between the grid-connected point and the grid, the state space structure model of the grid-keeping and grid-following energy storage hybrid power station and the grid-connected is obtained.

[0053] Specifically, the circuit topology and control strategy of the grid-keeping and grid-following energy storage hybrid power station are as shown in Figure 2 . Figure 2 , , , The grid-keeping type energy storage and the grid-following type energy storage grid-connected point voltage and current, , , , The grid-keeping type energy storage and the grid-following type energy storage grid-connected point output real-time power, , , , The d-axis and q-axis components of the grid-keeping type energy storage and the grid-following type energy storage modulation wave, The potential phase angle of the grid-keeping type energy storage system.

[0054] ​The small-signal model of the grid-type energy storage system is as follows:

[0055] (1)

[0056] In the formula, For the self-synchronization angular frequency of the grid-type energy storage system, For virtual rotational inertia, For virtual damping, The rated angular frequency, This is the droop factor for frequency modulation. This is the voltage sag coefficient. and For the system electromagnetic power, and The d-axis and q-axis components of the potential within a grid-type energy storage system. and The d-axis and q-axis components of the grid connection point voltage of the grid-connected energy storage system are shown. and These are the d-axis and q-axis components of the branch current in a grid-type energy storage system.

[0057] The small-signal model of the grid-type energy storage power loop is thus obtained as follows:

[0058] (2)

[0059] In the formula, This refers to the small-signal disturbance at the self-synchronization angular frequency of a grid-type energy storage system. Internal potential of grid-type energy storage system d , q Small-signal disturbance of axis components This is the coefficient matrix in the state-space model. This is the input matrix in the state-space model. Describe the dynamic coupling relationship of the state variables themselves; This reflects the impact of input disturbances on state variables; , This represents small-signal disturbances related to grid-connected energy storage branch currents, system grid connection point currents, etc., and indicates the current disturbance input in the power loop. These are the output matrix coefficients, used to extract the mapping relationship between state variables and output physical quantities. To directly transmit matrix coefficients, this method describes the impact of input disturbances on the output without passing through state variables, thus improving the linearized expression of the small-signal model of the power loop. The rate of change of small-signal disturbances representing the self-synchronization angular frequency of a grid-type energy storage system; Represents branch current dq The rate of change of small-signal disturbances in the axis components.

[0060] The active and reactive power control links of a grid-connected energy storage system are as follows:

[0061] (3)

[0062] In the formula, This is the active power command value. and To represent the d-axis and q-axis components of the branch current at the grid connection point of the grid-connected energy storage system, , , and These are the parameters for the inner loop PI controller. and These are the d-axis and q-axis components of the grid connection point voltage. and For grid-connected line inductance parameters, and These are the d-axis and q-axis components of the modulated wave; s For the Laplace operator, 、 The controlled current component of the inner current loop (the d-axis and q-axis components of the converter outlet current or the filtered current).

[0063] Therefore, its small-signal model is obtained as follows:

[0064] (4)

[0065] In the formula, The prefix represents the amount of small-signal perturbation.

[0066] Small-signal modeling of grid-connected lines and the power grid:

[0067] (5)

[0068] In the formula, and For the filter inductor and parasitic resistance, It is the filter capacitor. and These are grid-connected inductors and resistors. , These are the d-axis and q-axis components of the current flowing through the filter inductor. , This refers to the current flowing into the grid (or the filtered component of the current at the grid connection point). d shaft and q Axial components. ω Synchronous rotational angular velocity, in dq In a rotating coordinate system, this is used to describe the synchronous rotational relationship between voltage and current.E d , E q The converter output potential (or the modulation potential of the inner loop control output) d shaft and q Axial components.

[0069] This leads to the small-signal model of the energy storage converter filter and connecting lines:

[0070] (6)

[0071] In the formula, The prefix represents the amount of small-signal perturbation.

[0072] The circuit equation between the grid connection point and the power grid is:

[0073] (7)

[0074] In the formula, For filter inductor current d , q Small-signal disturbance of the axis component; Voltage drop of the grid-connected filter d , q Small-signal disturbance of the axis component; Current flowing into the grid side d , q Small-signal disturbance of the axis component; To provide the output potential of the converter (or the modulated potential of the control loop). d , q Small-signal disturbance of the axis component; Voltage at grid connection point d , q Small-signal disturbance of the axis component; This refers to a small signal disturbance to the synchronous rotational angular velocity. It is the input perturbation of the model; The state matrix describes the state variables ( The internal dynamic coupling relationship between them. Given the input matrix, characterize the input perturbation quantities respectively. The influence of state variables on the state variables; , These are the inductance and resistance of the connection line between the grid connection point and the power grid, respectively. , Injecting grid current into the grid connection point respectively d Axis (active power related) and q Axis (reactive power dependent) components; , are the d-axis and q-axis components of the grid-side voltage d axis and q axis components. , are the d-axis and q-axis components of the grid-side voltage d axis and q axis components.

[0075] The state space structure diagram of the grid-connected-following hybrid energy storage power station and the grid-connected state is shown in Figure 3 . Figure 3 , , and represent the input quantities of the grid-connected energy storage, the following energy storage and the grid-connected line state equation respectively, , and represent the state quantities of the grid-connected energy storage, the following energy storage and the grid-connected line state equation respectively, , and represent the output quantities of the grid-connected energy storage, the following energy storage and the grid-connected line state equation respectively. , , , are the command values of active power, reactive power, grid-connected voltage and self-synchronous angle frequency, and are the active and reactive power output by the grid-connected energy storage to the grid, , are the d-axis and q-axis components of the filter capacitor current of the following energy storage, , are the d-axis and q-axis components of the grid-connected point current of the following energy storage.

[0076] Step 2: Based on the state space structure model of the grid-connected-following hybrid energy storage power station and the grid-connected, an H∞robust controller is constructed; the H∞robust controller is solved to obtain the to-be-solved matrix required by the H∞robust controller; the H∞robust controller is designed, which can be synthesized by constructing an augmented system considering the generalized object to comprehensively analyze the system.

[0077] In the above scheme, based on the state space structure model of the grid-connected-following hybrid energy storage power station and the grid-connected, an H∞robust controller is constructed; the H∞robust controller is solved to obtain the to-be-solved matrix required by the H∞robust controller, including:

[0078] Based on the grid-connected-following hybrid energy storage power station, three perturbations are determined, which are grid impedance perturbation, load perturbation and energy storage battery parameter perturbation, wherein the energy storage battery parameter perturbation specifically includes battery equivalent resistance perturbation and battery capacity perturbation, and the norm limit of various perturbations is determined;

[0079] The various perturbations are uniformly represented as norm-bounded uncertainties; based on the norm-bounded uncertainties, a relationship between the system matrix, the disturbance input matrix and the output matrix is constructed based on a positive definite matrix and a scalar, and a controller is introduced to obtain a closed-loop system equation;

[0080] A scalar function of system stability is constructed, and after being substituted into the closed-loop system equation, a stability trend relationship of the closed-loop system under the action of uncertainties is obtained; the stability trend relationship of the closed-loop system under the action of uncertainties is converted to a linear matrix inequality by applying the matrix inverse auxiliary theorem;

[0081] The linear matrix inequality is solved to obtain the to-be-solved matrix required by the H∞ robust controller.

[0082] The design process of the H∞ robust controller is shown in FIG. 1. Figure 4 The specific process is illustrated by examples as follows:

[0083] The grid-following energy storage hybrid power station mainly considers the following three perturbations: grid impedance perturbation, load perturbation and energy storage battery parameter perturbation, wherein the energy storage battery parameter perturbation specifically includes battery equivalent resistance perturbation and battery capacity perturbation, and the norm limits of various perturbations are determined.

[0084] The grid impedance perturbation , the load perturbation , the battery equivalent resistance perturbation , and the battery capacity perturbation . Let be the perturbation of the grid resistance, be the perturbation of the grid inductance, s be the Laplace operator.

[0085] Next, the above perturbations are uniformly represented as norm-bounded uncertainties, and satisfy the corresponding conditions. At the same time, there is a positive definite matrix and a scalar, so that the system matrix, the disturbance input matrix and the output matrix satisfy a certain relationship. The norm limits of various perturbations are as follows:

[0086] (8)

[0087] In the formula, is the H∞ norm (infinite norm), which is used to measure the maximum gain of the perturbation in the frequency domain. : represents the grid impedance perturbation , and its maximum value of amplitude in the entire frequency domain does not exceed ; indicates the Euclidean norm (2-norm), which is used to measure the size of a vector / scalar.

[0088] The above perturbations are unified as a norm-bounded uncertainty: is a block diagonal matrix constructed by diagonalizing each perturbation, satisfying . is the unified uncertainty matrix.

[0089] Suppose there exist a positive definite matrix and a scalar such that , where is the system matrix, is the disturbance input matrix, is the output matrix.

[0090] Then introduce the controller, which contains the to-be-solved matrices. Based on this controller, the closed-loop system equation is shown in equation (9). Introduce the controller , where is the controller matrix, and are the to-be-solved matrices.

[0091] The closed-loop system equation is:

[0092] (9)

[0093] where x is the state vector, representing the core dynamic variables of the system, such as current, voltage, frequency disturbance; A+BK: closed-loop system matrix, BK reflects the correction of the controller K to the natural characteristics A of the system; D: disturbance input matrix, d is the disturbance vector (including grid impedance, load, battery terminal voltage, etc. perturbation. z is the output evaluation variable (used to design performance indicators, such as requiring z energy to be less than the energy of disturbance d multiplied by , to ensure anti-interference ability). C+EK: output mapping matrix, EK reflects the modification of the controller to the output mapping, E is the matrix related to performance evaluation.

[0094] Construct Lyapunov function: ; used to analyze system stability. P is a positive definite matrix; represents the energy norm of the state vector x;

[0095] Substitute into the closed-loop system equation:

[0096] (10)

[0097] The first term : is the core term of , representing the energy change rate of the state x itself; the second term : related to the energy of the evaluation output z, embodying the performance constraint; the third term : embodying the requirement of disturbance d suppression, ensuring the disturbance energy is attenuated, is an index of the disturbance suppression level, the smaller the anti-interference ability is stronger.

[0098] For the analysis of system stability, construct Lyapunov function, substitute it into the closed loop equation to get equation (10). When dealing with uncertainty, use Schur complement lemma to convert the relevant inequality into equation (11).

[0099] When dealing with uncertainty, use Schur complement lemma to convert the inequality into:

[0100] (11)

[0101] In the formula, The state energy attenuation term + evaluation output energy term corresponding to the derivative of Lyapunov function embodies the joint constraint of the state and performance of the closed loop system, (A+BK) is the closed loop system matrix, (C+EK) is the evaluation output matrix. The upper right block: PD, the lower left block: DTP: describes the coupling relationship between the disturbance input d and the state x, D is the disturbance injection matrix, P is the Lyapunov matrix. is the upper limit of directly constraining the disturbance energy, is performance index, the smaller the stronger the disturbance suppression ability is; I is the unit matrix.

[0102] Then make variable substitution, let the relevant variables, apply matrix inverse auxiliary theorem to get linear matrix inequality (LMI) as equation (12). Make variable substitution: let , , apply matrix inverse auxiliary theorem to get linear matrix inequality (Linear Matrix Inequality, LMI for short):

[0103] (12)

[0104] In the formula, Embody the coupling constraint of the closed loop system matrix and the Lyapunov matrix. Embody the constraint of the evaluation output matrix E and the system, The constraint of the disturbance injection relationship turns the influence of the disturbance d into the coupling of Q and D. 、 Respectively constrain the upper limit of the evaluation output energy and the disturbance energy.

[0105] Solve by convex optimization tool (such as MATLAB LMI Toolbox) and , the to-be-solved matrix required for obtaining the H∞ robust controller and .

[0106] In control theory, Lyapunov function is a scalar function used to analyze the stability of a system, usually denoted as V(x) (where x is the state variable of the system). In the design process of H∞ robust controller, the core role of Lyapunov function is to analyze the stability of the closed-loop system. By constructing Lyapunov function and substituting it into the closed-loop system equation, the derivative expression as shown in equation (10) can be obtained. According to the sign property of the derivative, it can be judged whether the system can still maintain stability in the presence of uncertainty (such as perturbation). If the derivative of Lyapunov function satisfies the negative definite condition, it means that the system still has asymptotic stability under the action of disturbance, which lays a theoretical foundation for subsequent transformation of inequalities and solving of controller parameters by using Schur complement lemma and other tools.

[0107] The application of matrix inverse auxiliary theorem converts the stability trend relationship of the closed-loop system under the action of uncertainty into a linear matrix inequality, including:

[0108] When dealing with uncertainty, the Schur complement lemma is used to reorganize the matrix inverse operation or cross terms involved in the inequality in the stability trend relationship of the closed-loop system under the action of uncertainty, eliminate the nonlinear structure, and obtain an inequality with a standard form;

[0109] Variable substitution is performed on the inequality with a standard form, and relevant variables are applied to obtain a linear matrix inequality by using the matrix inverse auxiliary theorem.

[0110] Equation (10) is the derivative expression of Lyapunov function with respect to time after substituting the constructed Lyapunov function into the closed-loop system equation. Its core purpose is to quantitatively analyze the stability trend of the closed-loop system under the action of uncertainty. Through this expression, the relationship between the sign property of Lyapunov function derivative and the system state, disturbance can be observed directly. In the design of H∞ robust controller, the role of equation (10) is to provide a basis for the subsequent derivation of stability criterion: if it can be proved that it satisfies the negative definite condition (or the corresponding attenuation condition) by processing this expression, it can be confirmed that the closed-loop system can still maintain stability in the presence of uncertainty such as grid impedance perturbation, load perturbation, and energy storage battery parameter perturbation. This result is the key prerequisite for further using the Schur complement lemma to transform the stability condition into a solvable LMI, and finally provides a theoretical basis for the solution of controller parameters.

[0111] The method of the application unifies grid impedance perturbation, load perturbation, battery equivalent internal resistance perturbation and battery capacity perturbation into norm-bounded uncertainty (satisfying a certain norm condition) and is a core pre-step in the H∞ robust controller design process, which is closely logically associated with the subsequent links.

[0112] Firstly, the unified representation provides a standard form for the mathematical modeling of system uncertainty. The subsequent introduction of positive definite matrices and scalars and the relationship formula of system matrix, disturbance input matrix and output matrix is based on the characteristics of norm-bounded uncertainty, which lays the foundation for quantifying the influence of uncertainty on the system.

[0113] Secondly, in the process of constructing Lyapunov function and substituting into the closed-loop system equation to obtain equation (10), the expression of norm-bounded uncertainty is the key basis for deriving the relationship between the derivative of Lyapunov function and the system state and the disturbance. Only by determining the norm limit of uncertainty, the influence range of disturbance can be accurately considered in analyzing the sign property of the derivative.

[0114] Furthermore, the processing of equation (10) and the subsequent transformation into inequality (11) by using Schur complement lemma relies on the mathematical structure of norm-bounded uncertainty. This unified form enables the uncertainty to be included in the convex optimization framework, providing feasibility for subsequent variable substitution to obtain LMI and using convex optimization tools to solve the controller parameters, ensuring that the controller design process is carried out under the strict support of mathematical theory.

[0115] For grid-parallel energy storage hybrid power station, the system frequency or voltage may be unstable due to factors such as phase synchronization failure, wideband coupling of power electronic equipment and power grid, transient shock in the process of switching between grid-parallel and grid-following modes or other factors. Wideband oscillation, frequency drop, voltage instability and other problems may occur. The application proposes a robust stable operation control method for grid-parallel energy storage hybrid power station. Without significantly increasing the system modeling cost and changing the existing control strategy framework too much, the H∞ robust controller is introduced to ensure that the system control law can still guarantee stable operation of the system and has a certain stability margin under complex working conditions of the system facing a wide range of external disturbances and system model perturbations.

[0116] Step 3: simulation verification and system optimization: verify whether the to-be-solved matrix required by the H∞ robust controller satisfies the system performance; if not, compensate the system for nonlinear disturbance by regarding the nonlinear term in the H∞ robust controller as an uncertain disturbance, until the simulation verification requirement is met;

[0117] The application takes grid flow sudden change working condition as an example to show the application effect by simulation results. The grid flow suddenly changes at 1s, 2s and 3s, respectively. The voltage and current measurement results of the grid-parallel energy storage hybrid power station under the example working condition are as followsFigure 5 are shown, where (a) is before the power flow mutation, and (b) is after the power flow mutation. Figure 6 It can be seen that the power flow mutation is obvious at 1s, 2s and 3s. Figure 6 In the figure, the power grid load suddenly increases at 1s, the power grid load returns to that before 1s at 2s, and the grid impedance decreases at 3s.

[0118] Step 4: If the system performance still cannot meet the requirements after the above steps, the nonlinear terms of the model are regarded as uncertain disturbances to make nonlinear compensation for the system until the simulation verification requirements are met.

[0119] For the nonlinear compensation in the application, a sliding mode controller can be used for nonlinear compensation. The nonlinear terms include the dead zone effect of the converter , the saturation voltage of the IGBT and the parasitic parameters of the filter.

[0120] wherein the dead zone voltage

[0121] current tracking error :

[0122] (13)

[0123] integral sliding surface s :

[0124] (14)

[0125] sliding mode switching term :

[0126] (15)

[0127] is the dead zone current threshold, and the dead zone voltage compensation amount is:

[0128] (16)

[0129] the saturation voltage compensation term is:

[0130] (17)

[0131] the feedforward compensation term of the parasitic resistance uff is:

[0132] (18)

[0133] wherein, is the DC voltage, which is the voltage of the DC bus of the power electronic device such as a converter, and is used as a basic quantity in the dead-zone voltage calculation and reflects the power storage and power supply capability of the DC side. i is the current of the converter output or the related circuit, which is used to determine the polarity (i>0 or i<0) of the dead-zone voltage and determine the compensation direction of the dead-zone voltage. is a reference current, is a three-phase alternating current output by the converter abc ; Λ is a diagonal matrix, and λ1, λ2, and λ3 are positive coefficients; K s is a control gain, and sign(s) is a sign function that outputs ±1 according to the positive and negative of the sliding mode surface s to realize switching of the control quantity. V sat is the saturation voltage of the IGBT; is a saturation voltage compensation term; sat(i abc ) is a saturation function; I rated is a rated current, R f : filter parasitic resistance; u ff is a feedforward compensation term of the parasitic resistance, Δi f is a current change, and max|| represents the maximum value of the absolute value.

[0134] The control method has been partially verified on a semi-physical simulation platform. In some areas where the power grid stability is poor and the power grid strength is not strong, the operation characteristics of the energy storage power station can be effectively improved by using this technology, and the power grid stability can be improved. With the large-scale access of new energy, the complexity of the power grid is increasing, which makes the requirements for the control and output characteristics of the energy storage power station more stringent. In the field of distributed energy grid connection and microgrid construction, the application scale of this technology will continue to expand. It is expected that under the promotion of the comprehensive construction of new-type power systems, it will be widely used in grid-following-grid-constructing hybrid energy storage power stations and play a key role in ensuring the stable and efficient operation of power systems.

[0135] In a second aspect, the application discloses a grid-following-grid-constructing hybrid energy storage power station control system based on the above-mentioned grid-following-grid-constructing hybrid energy storage power station control method, which comprises a first construction module, a second construction module, a verification compensation module, and an output control module.

[0136] The first construction module is configured to construct a state space structure model of the grid-following and grid-forming energy storage hybrid power station and the grid based on a circuit topology and a control strategy of the grid-following and grid-forming energy storage hybrid power station.

[0137] The first construction module is specifically configured to:

[0138] Based on the circuit topology and the control strategy of the grid-following and grid-forming energy storage hybrid power station, a small-signal model of a grid-forming energy storage power loop is obtained from a small-signal model of the grid-forming energy storage system, a small-signal model of the grid-following energy storage system is obtained from an active and reactive control relationship of the grid-following energy storage system, and a small-signal model of a filter of the energy storage converter and a connecting line is obtained by small-signal modeling of a grid connection line and a power grid.

[0139] The state space structure model of the grid-following and grid-forming energy storage hybrid power station and the grid is obtained based on the small-signal model of the grid-forming energy storage power loop, the small-signal model of the grid-following energy storage system, and the small-signal model of the filter of the energy storage converter and the connecting line, and based on a circuit equation between a grid connection point and the power grid.

[0140] The second construction module is configured to construct an H∞ robust controller based on the state space structure model of the grid-following and grid-forming energy storage hybrid power station and the grid, and to obtain a to-be-solved matrix required by the H∞ robust controller by solving the H∞ robust controller.

[0141] The second construction module is specifically configured to:

[0142] Based on the grid-following and grid-forming energy storage hybrid power station, three perturbations are determined, which are a power grid impedance perturbation, a load perturbation, and an energy storage battery parameter perturbation, wherein the energy storage battery parameter perturbation specifically includes a battery equivalent internal resistance perturbation and a battery capacity perturbation, and norm limits of various perturbations are determined.

[0143] Various perturbations are uniformly represented as norm-bounded uncertainty, and based on the norm-bounded uncertainty, a relationship formula of a system matrix, a disturbance input matrix, and an output matrix is constructed based on a positive definite matrix and a scalar, and a closed-loop system equation is obtained by introducing a controller.

[0144] A scalar function of system stability is constructed, and after being substituted into the closed-loop system equation, a stability trend relationship of the closed-loop system under the action of the uncertainty is obtained, and the stability trend relationship of the closed-loop system under the action of the uncertainty is converted to a linear matrix inequality by applying a matrix inverse auxiliary theorem.

[0145] The to-be-solved matrix required by the H∞ robust controller is obtained by solving the linear matrix inequality.

[0146] As a preferred solution, the application of the matrix inverse auxiliary theorem to convert the stability trend relationship of the closed-loop system under the action of the uncertainty to the linear matrix inequality includes:

[0147] In the process of dealing with uncertainty, the Schur complement lemma is used to reorganize the matrix inverse operation or cross terms involved in the inequality of the stability relationship of the closed-loop system under uncertain disturbance, eliminate the nonlinear structure, and obtain an inequality with a standard form;

[0148] Variable substitution is performed on the inequality with a standard form, and the related variables are applied to the matrix inverse auxiliary theorem to obtain a linear matrix inequality.

[0149] Further, the solving H∞ robust controller obtains the to-be-solved matrix required by the H∞ robust controller, including:

[0150] The H∞ robust controller is solved by a convex optimization tool to obtain the to-be-solved matrix required by the H∞ robust controller.

[0151] The compensation module is used to verify whether the to-be-solved matrix required by the H∞ robust controller satisfies the system performance, and if not, the nonlinear term in the H∞ robust controller is regarded as an uncertain disturbance to the system for nonlinear compensation until the simulation verification requirement is met.

[0152] The nonlinear term in the H∞ robust controller is regarded as an uncertain disturbance to the system for nonlinear compensation, including:

[0153] The nonlinear compensation is performed by a sliding mode controller, and the nonlinear term considered in the nonlinear compensation includes the dead zone effect of the converter , the saturation voltage of the IGBT , and the filter parasitic parameters .

[0154] The output control module is used to output the H∞ robust controller satisfying the system performance, and is used to construct a grid-following hybrid energy storage power station control.

[0155] The above-mentioned scheme proposes a grid-following hybrid energy storage power station control system, which aims to solve the robustness problem of the system under complex working conditions when connected to the grid. By constructing a small signal model containing grid-connected and grid-following energy storage, integrating grid impedance perturbation, load disturbance and battery parameter uncertainty modeling, an H∞ robust controller is designed to minimize the disturbance transfer function norm. Linear matrix inequality (LMI) optimization is used to solve the controller parameters to ensure the robust stability of the system when facing disturbances. For the nonlinear part of the model, a sliding mode compensator is designed to compensate for the dead zone effect of the converter, parasitic parameters, etc. The stability and robustness of the control method are verified through simulation tests. The present application can effectively improve the robustness of the system, thereby improving the adaptability of the energy storage power station to complex grid conditions.

[0156] The third object of the embodiments of the present application is to provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the network-keeping and network-following energy storage hybrid power station control method when executing the computer program.

[0157] The processor implements the network-keeping and network-following energy storage hybrid power station control method when executing the computer program, and specifically comprises:

[0158] Based on the circuit topology and control strategy of the network-keeping and network-following energy storage hybrid power station, a state space structure model of the network-keeping and network-following energy storage hybrid power station and grid connection is constructed.

[0159] Based on the state space structure model of the network-keeping and network-following energy storage hybrid power station and grid connection, an H∞ robust controller is constructed; the H∞ robust controller is solved to obtain a to-be-solved matrix required by the H∞ robust controller.

[0160] The to-be-solved matrix required by the H∞ robust controller is verified to meet the system performance; when the system performance is not met, a nonlinear term in the H∞ robust controller is regarded as an uncertain disturbance to make nonlinear compensation to the system until the simulation verification requirement is met.

[0161] The H∞ robust controller meeting the system performance is outputted and used for network-keeping and network-following energy storage hybrid power station control.

[0162] The fourth object of the embodiments of the present application is to provide a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the network-keeping and network-following energy storage hybrid power station control method.

[0163] The computer program is executed by the processor to implement the network-keeping and network-following energy storage hybrid power station control method, and specifically comprises:

[0164] Based on the circuit topology and control strategy of the network-keeping and network-following energy storage hybrid power station, a state space structure model of the network-keeping and network-following energy storage hybrid power station and grid connection is constructed.

[0165] Based on the state space structure model of the network-keeping and network-following energy storage hybrid power station and grid connection, an H∞ robust controller is constructed; the H∞ robust controller is solved to obtain a to-be-solved matrix required by the H∞ robust controller.

[0166] The to-be-solved matrix required by the H∞ robust controller is verified to meet the system performance; when the system performance is not met, a nonlinear term in the H∞ robust controller is regarded as an uncertain disturbance to make nonlinear compensation to the system until the simulation verification requirement is met.

[0167] The H∞ robust controller meeting the system performance is outputted and used for network-keeping and network-following energy storage hybrid power station control.

[0168] A fifth object of the embodiments of the present application is to provide a computer program product comprising computer instructions instructing a computer to execute the grid-following and grid-forming hybrid energy storage power station control method.

[0169] The computer instructions instruct the computer to execute the grid-following and grid-forming hybrid energy storage power station control method, and specifically comprise:

[0170] Based on the circuit topology and control strategy of the grid-following and grid-forming hybrid energy storage power station, a state space structure model of the grid-following and grid-forming hybrid energy storage power station and grid-connected is constructed;

[0171] Based on the state space structure model of the grid-following and grid-forming hybrid energy storage power station and grid-connected, an H∞ robust controller is constructed; the H∞ robust controller is solved to obtain a to-be-solved matrix required by the H∞ robust controller;

[0172] The to-be-solved matrix required by the H∞ robust controller is verified to meet the system performance; when the system performance is not met, the nonlinear term in the H∞ robust controller is regarded as an uncertain disturbance to the system for nonlinear compensation until the simulation verification requirement is met;

[0173] The H∞ robust controller meeting the system performance is output and used for grid-following and grid-forming hybrid energy storage power station control.

[0174] These computer program instructions can also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer readable memory produce a product comprising instruction devices, which realize the functions specified in the flow Figure 1 One flow or multiple flows and / or blocks Figure 1 One block or multiple blocks.

[0175] These computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in the flow Figure 1 One flow or multiple flows and / or blocks Figure 1 One block or multiple blocks.

[0176] The present application can adopt a completely hardware embodiment, a completely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, readable storage media, optical storage, etc.) containing computer usable program code.

[0177] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more functions specified in the flowchart block or blocks and / or combination of flowchart blocks. Figure 1 one or more functions specified in the flowchart block or blocks and / or combination of flowchart blocks.

[0178] Obviously, the embodiments described above are only a part but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work shall fall within the protection scope of the present application.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still make modifications to the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features therein. Such modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for controlling a hybrid power station of grid-forming and grid-following energy storage, characterized in that, The method comprises the following steps: Based on the circuit topology and control strategy of the grid-connected and grid-following energy storage hybrid power station, a state space structure model of the grid-connected and grid-following energy storage hybrid power station and grid is constructed; Based on the state space structure model of the grid-connected and grid-following energy storage hybrid power station and grid, an H∞ robust controller is constructed; the H∞ robust controller is solved to obtain a to-be-solved matrix required by the H∞ robust controller; The to-be-solved matrix required by the H∞ robust controller is verified to meet the system performance; if the system performance is not met, the nonlinear term in the H∞ robust controller is regarded as an uncertain disturbance to the system for nonlinear compensation until the simulation verification requirement is met; The H∞ robust controller meeting the system performance is output and used for controlling the grid-connected and grid-following energy storage hybrid power station; The method of constructing the state space structure model of the grid-connected and grid-following energy storage hybrid power station and grid based on the circuit topology and control strategy of the grid-connected and grid-following energy storage hybrid power station comprises the following steps: Based on the circuit topology and control strategy of the grid-connected and grid-following energy storage hybrid power station; a small-signal model of the grid-connected energy storage power ring is obtained from a small-signal model of the grid-connected energy storage system; a small-signal model of the grid-following energy storage system is obtained from an active and reactive control relationship of the grid-following energy storage system; a small-signal model of the energy storage converter filter and connection line is obtained by small-signal modeling of the grid connection line and grid; According to the small-signal model of the grid-connected energy storage power ring, the small-signal model of the grid-following energy storage system and the small-signal model of the energy storage converter filter and connection line, and based on the circuit equation between the grid connection point and the grid, a state space structure model of the grid-connected and grid-following energy storage hybrid power station and grid is obtained; The method of constructing the H∞ robust controller based on the state space structure model of the grid-connected and grid-following energy storage hybrid power station and grid comprises the following steps: Based on the grid-connected and grid-following energy storage hybrid power station, three perturbations are determined, which are grid impedance perturbation, load perturbation and energy storage battery parameter perturbation, wherein the energy storage battery parameter perturbation specifically includes battery equivalent resistance perturbation and battery capacity perturbation, and the norm limit of various perturbations is determined; Various perturbations are uniformly represented as norm-bounded uncertainty; based on the norm-bounded uncertainty, the relationship formula of the system matrix, the disturbance input matrix and the output matrix is constructed based on the positive definite matrix and the scalar, and the closed-loop system equation is obtained by introducing the controller; A scalar function of system stability is constructed, and the stability trend relationship of the closed-loop system under the action of uncertainty is obtained after the closed-loop system equation is substituted; the stability trend relationship of the closed-loop system under the action of uncertainty is converted to a linear matrix inequality by applying the matrix inverse auxiliary theorem; The linear matrix inequality is solved to obtain the to-be-solved matrix required by the H∞ robust controller. 2.The network-constructing and network-following energy storage hybrid power station control method according to claim 1, characterized in that, The method of converting the stability trend relationship of the closed-loop system under the action of uncertainty to a linear matrix inequality by applying the matrix inverse auxiliary theorem comprises the following steps: In the process of dealing with uncertainty, the Schur complement lemma is used to reorganize the matrix inverse operation or cross term involved in the inequality in the stability relationship of the closed-loop system under the action of uncertain disturbance, so as to eliminate the nonlinear structure and obtain an inequality with a standard form. The variable substitution is performed for the inequality with the canonical form, the related variable is applied, and the linear matrix inequality is obtained by using the matrix inversion auxiliary theorem. 3.The network-constructing and network-following energy storage hybrid power station control method according to claim 1, characterized in that, The solving of the H∞ robust controller includes obtaining a to-be-solved matrix required by the H∞ robust controller. The H∞ robust controller is solved by using a convex optimization tool to obtain the to-be-solved matrix required by the H∞ robust controller. 4.The network-constructing and network-following energy storage hybrid power station control method according to claim 1, characterized in that, The nonlinear term in the H∞ robust controller is regarded as an uncertain disturbance to perform nonlinear compensation on the system. Nonlinear compensation is performed with a sliding mode controller, the nonlinear terms considered by the nonlinear compensation including converter dead-time effect , IGBT saturation voltage , and filter parasitic parameters .

5. A network- and grid-following energy storage hybrid power plant control system, characterized by, The network-following energy storage hybrid power station control method according to any one of claims 1-4 comprises: The first construction module is configured to construct a state space structure model of the network-following energy storage hybrid power station and the grid according to a circuit topology and a control strategy of the network-following energy storage hybrid power station. The second construction module is configured to construct an H∞ robust controller based on the state space structure model of the network-following energy storage hybrid power station and the grid. The verification compensation module is configured to verify whether the to-be-solved matrix required by the H∞ robust controller satisfies system performance. The output control module is configured to output the H∞ robust controller satisfying the system performance, and to control the network-following energy storage hybrid power station.

6. An electronic device, comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the network-following energy storage hybrid power station control method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the network-following energy storage hybrid power station control method according to any one of claims 1-4.

8. A computer program product comprising computer instructions, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the network-following energy storage hybrid power station control method according to any one of claims 1-4.

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