Method and system for controlling network construction-network following energy storage hybrid power station

Abstract

The invention discloses a network construction-following network energy storage hybrid power station control method and system, and belongs to the technical field of electrical engineering, and the control method comprises the steps: constructing a network construction-following network energy storage hybrid power station and grid-connected state space structure model based on the circuit topology and control strategy of the network construction-following network energy storage hybrid power station; constructing an H-infinity robust controller based on a state space structure model of the network construction-network following energy storage hybrid power station and grid connection; solving the H-infinity robust controller to obtain a to-be-solved matrix required by the H-infinity robust controller; verifying that a to-be-solved matrix required by the H-infinity robust controller meets the performance condition of the system; and outputting an H-infinity robust controller meeting the system performance, wherein the H-infinity robust controller is used for controlling the network construction-network following energy storage hybrid power station. According to the method, the state space structure model is constructed, the H-infinity robust controller is designed, the robustness of the system is remarkably enhanced, and the method has obvious advancement in the aspect of adaptability of a complex power system.

Description

Technical Field

[0001] The present application relates to the field of electrical engineering technology, and more specifically to a control method and system for a grid-building and grid-following energy storage hybrid power station. Background Art

[0002] Existing energy storage power stations mostly use a single control mode: grid-following control or grid-forming control. Grid-following energy storage has a fast response speed, but lacks inertia support, which can easily cause broadband oscillations in weak power grids. Grid-forming control has voltage / frequency support capabilities, but its dynamic response is slow, and the control link often uses traditional fixed PID controllers (Proportional-Integral-Derivative Controllers), which lack robustness to model parameter perturbations and external interference. Current control strategies for hybrid energy storage power stations are mostly based on fixed mode switching or simple gain scheduling. This limits the system's adaptive regulation capabilities and robustness when faced with complex power systems, and does not fully consider the impact of grid uncertainties (such as impedance changes and load fluctuations) on system stability and performance.

[0003] The Chinese patent application CN117559479A discloses a hybrid grid-following / grid-forming distribution network energy storage inverter control method. The energy storage inverter mode control module receives an externally given distribution network strength judgment instruction. When the distribution network strength judgment instruction is a strong distribution network instruction, the energy storage inverter adopts a grid-following control mode, outputting power in a steady-state power balance mode, a transient fluctuation smoothing mode, and a transient-stable active support mode. When the distribution network strength judgment instruction is a weak distribution network instruction, the energy storage inverter adopts a grid-forming control mode, outputting power in a steady-state active support mode and a transient-stable fluctuation smoothing mode. However, this method relies on an externally given distribution network strength judgment instruction to switch between the grid-following and grid-forming control modes, has a high dependence on the communication system, and has limited system adaptability. In actual grid operation, there may be errors or delays in the distribution network strength judgment. Communication failures or delays may also prevent the control instruction from being transmitted in a timely manner, resulting in untimely or erroneous mode switching. Frequently switching modes when grid strength is critical can impact the stability of energy storage plants, potentially causing power fluctuations and voltage transients, impacting power quality and grid stability. These factors limit the adaptability of this approach to complex grid environments and internal issues within energy storage plants, potentially impacting the long-term stable operation of hybrid energy storage plants.

[0004] Chinese patent application CN119401547A discloses a grid-connected converter control method based on hybrid grid-following and grid-forming control. By constructing an adaptive hybrid synchronous control model, the ratio of grid-following and grid-forming characteristics can be flexibly adjusted, ensuring a smooth and efficient grid connection process. A voltage-current dual-loop control model is constructed to precisely regulate output voltage and current, meeting the grid's high-standard power quality requirements for renewable energy generation. Furthermore, grid impedance estimation is introduced for virtual impedance control, further enhancing the system's anti-interference capability and response speed. The established model is relatively complex, resulting in a large amount of computational effort for the control device during real-time operation. The model includes an adaptive hybrid synchronous control model, a voltage-current dual-loop closed-loop model, and a virtual synchronous impedance model. This large amount of computational effort for the control device during system operation can lead to delayed control responses and an inability to track grid changes in a timely manner. Furthermore, the invention relies heavily on the accuracy of the grid short-circuit ratio estimation. However, accurate real-time data on the capacity of each system component required for the proposed short-circuit ratio calculation formula is difficult to obtain. In addition, the ratio adaptive regulation model established by the invention has certain limitations whether it is through simulating the hysteresis loop or the lookup table. The relationship between the short-circuit ratio and the grid-following characteristic ratio obtained by simulating the hysteresis loop is approximate and may not accurately reflect the actual grid situation. The lookup table method relies on a pre-set correspondence and is difficult to adapt to complex and changeable grid scenarios. When the grid operation state is at the boundary of the lookup table or special circumstances occur, it may not be possible to accurately determine the grid-following characteristic ratio, affecting the effectiveness of the control strategy. Summary of the Invention

[0005] The purpose of this application is to provide a control method and system for a grid-building-grid-following energy storage hybrid power station. This method constructs a state space structure model, designs an H∞ robust controller, and introduces nonlinear compensation, so that the system has both the active support of the grid-building type and the rapid response function of the grid-following type, significantly enhancing the robustness of the system and having obvious advancement in adaptability to complex power systems.

[0006] To achieve the above objectives, this application provides the following technical solutions: This application proposes a control method for a grid-connected and grid-following energy storage hybrid power station, including: Based on the circuit topology and control strategy of the grid-connected energy storage hybrid power station, a state space structure model of the grid-connected energy storage hybrid power station and the grid connection is constructed; Based on the state space structure model of the grid-connected energy storage hybrid power station and the grid-connected energy storage system, an H∞ robust controller is constructed. The H∞ robust controller is solved to obtain the matrix required by the H∞ robust controller. Verify that the matrix required by the H∞ robust controller meets the system performance requirements. If the system performance is not met, treat the nonlinear terms in the H∞ robust controller as uncertain disturbances and perform nonlinear compensation on the system until the simulation verification requirements are met. Output the H∞ robust controller satisfying the system performance for the grid-following-grid-tracking hybrid energy storage power station control.

[0007] As a further improvement of the application, the circuit topology and control strategy based on the grid-following-grid-tracking hybrid energy storage power station, the state space structure model of the grid-following-grid-tracking hybrid energy storage power station and grid is constructed, comprising: On the basis of the circuit topology and control strategy of the grid-following-grid-tracking hybrid energy storage power station; the small signal model of the grid-connected energy storage power ring is obtained from the small signal model of the grid-connected energy storage system; the small signal model of the grid-tracking energy storage system is obtained from the active and reactive control relationship of the grid-tracking energy storage system; the small signal model of the energy storage converter filter and connecting line is obtained by small signal modeling of the grid-connected line and power grid; According to the small signal model of the grid-connected energy storage power ring, the small signal model of the grid-tracking energy storage system and the small signal model of the energy storage converter filter and connecting line, and based on the circuit equation between the grid-connected point and the power grid, the state space structure model of the grid-following-grid-tracking hybrid energy storage power station and grid is obtained.

[0008] As a further improvement of the application, the state space structure model of the grid-following-grid-tracking hybrid energy storage power station and grid is constructed, and the H∞ robust controller is constructed; the required matrix of the H∞ robust controller is solved, comprising: Based on the grid-following-grid-tracking 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; 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; The 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 into the closed-loop system equation; the linear matrix inequality is obtained by applying the matrix inverse auxiliary theorem to convert the stability trend relationship of the closed-loop system under the action of uncertainty; Solving the linear matrix inequality, the required matrix of the H∞ robust controller is obtained.

[0009] As a further improvement of the application, the linear matrix inequality is obtained by applying the matrix inverse auxiliary theorem to convert the stability trend relationship of the closed-loop system under the action of uncertainty, comprising: 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. Variable substitution is performed on the inequality with a standard form, relevant variables are applied, and the matrix inverse auxiliary theorem is applied to obtain a linear matrix inequality.

[0010] As a further improvement of the application, the solving of the H∞ robust controller includes: 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.

[0011] 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: The nonlinear compensation is performed by using 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 parasitic parameters of the filter .

[0012] 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, which includes: 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 the control strategy of the network-keeping energy storage hybrid power station. 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 to solve the H∞ robust controller to obtain a to-be-solved matrix required by the H∞ robust controller. 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 to 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 satisfied when the system performance is not satisfied. An output control module is configured to output the H∞ robust controller satisfying the system performance, and to control the network-keeping energy storage hybrid power station.

[0013] The application also proposes an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the network-keeping energy storage hybrid power station control method described above when executing the computer program.

[0014] The present application also proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned grid-building-grid-following energy storage hybrid power station control method.

[0015] The present application also proposes a computer program product, which includes computer instructions, and the computer instructions instruct a computer to execute the above-mentioned grid-building-grid-following energy storage hybrid power station control method.

[0016] Compared with the existing technology, this application has the following advantages: This invention proposes a control method for a hybrid grid-connected energy storage power station. By constructing a state-space structural model, designing an H∞ robust controller, and introducing nonlinear compensation, the system combines the stability support of a grid-connected system with the rapid response of a grid-connected system. This significantly enhances system robustness and demonstrates significant advancements in adaptability to complex power systems. The control elements and parameters used in this method incorporate perturbations on both the grid and energy storage sides at the outset of the design. H∞ robust control is used to minimize the impact of disturbances on the stability of the energy storage power station. This method does not rely on external communications or grid parameter identification, requires minimal computation, and places low demands on device performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is the design process of a grid-connected and grid-following energy storage hybrid power station control method of this application; Figure 2 Schematic diagram of the grid-connected topology and control strategy of the grid-connected energy storage hybrid power station of this application; Figure 3 This is the state space structure diagram of the grid-connected energy storage hybrid power station of this application; Figure 4 The H∞ robust controller design process for this application; Figure 5 The voltage and current results of the grid connection point simulation for the example working condition of this application are shown in Figure 1. (a) is before the sudden change in the power flow, and (b) is after the sudden change in the power flow. Figure 6 The example working condition simulation of this application is to construct a grid-following energy storage hybrid power station with active power output. DETAILED DESCRIPTION

[0018] The technical solutions in the present application will be described clearly and completely below in conjunction 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 protection of the present application.

[0019] 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.

[0020] At present, in the control technology of energy storage systems, the limitation of single control mode is obvious. Although the response of the grid-following energy storage is fast, 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 when facing 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 grid-forming and the fast response function of grid-following, significantly enhancing the robustness of the system and having obvious advancement in adaptability to complex power systems.

[0021] 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, and is one of the control methods for coping with complex engineering problems.

[0022] The present application mainly proposes a grid-following energy storage hybrid power station control method and system, which makes the operating characteristics of the energy storage power station applicable to complex power system application scenarios.

[0023] In a first aspect, the present application provides a grid-following energy storage hybrid power station control method, which comprises the following steps: Figure 1The figure shows the design process of the control method and system for a hybrid power station with grid-connected energy storage. By establishing a state-space small-signal model, designing an H∞ robust controller for disturbances and perturbations, and performing nonlinear compensation for the system, a robust stability control method is introduced into the control structure of the hybrid system with grid-connected energy storage, and simulation verification is performed. Details of some of the steps used in implementing the method proposed in this application are as follows.

[0024] Step 1: Based on the circuit topology and control strategy of the grid-connected hybrid power station, a state-space structural model of the grid-connected hybrid power station and the grid connection is constructed. The state-space structural model of the grid-connected hybrid power station and the grid connection is constructed, and the nonlinear system is linearized to convert the performance indicators into quantitative indicators. In the above scheme, based on the circuit topology and control strategy of the grid-connected energy storage hybrid power station, a state space structure model of the grid-connected energy storage hybrid power station and the grid connection is constructed, including: Based on the circuit topology and control strategy of the grid-connected energy storage hybrid power station; the small signal model of the grid-connected energy storage system is used to obtain the small signal model of the grid-connected energy storage power loop; the small signal model of the grid-connected energy storage system is obtained from the active and reactive power control relationship of the grid-connected energy storage system; the small signal model of the grid-connected line and the power grid is modeled to obtain the small signal model of the energy storage converter filter and the connecting line; Based on the small signal model of the grid-connected energy storage power loop, the small signal model of the grid-following energy storage system, 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, the state space structure model of the grid-connected energy storage hybrid power station and the grid connection is obtained.

[0025] Specifically, the circuit topology and control strategy diagram of the grid-connected energy storage hybrid power station is as follows: Figure 2 . Figure 2 middle, 、 、 、 To build grid-type energy storage and grid-following energy storage, the grid connection point voltage and current are: 、 、 、 Output real-time power to grid-connected energy storage and grid-following energy storage points. 、 、 、 are the d-axis and q-axis components of the modulation wave of the grid-type energy storage and the grid-following energy storage, is the potential phase angle in the grid-type energy storage system.

[0026] Among them, the small signal model of the grid-type energy storage system is as follows: (1) Where, is the self-synchronization angular frequency of the grid-type energy storage system, is the virtual moment of inertia, is the virtual damping, is the rated angular frequency, is the FM droop coefficient, is the voltage regulation droop coefficient, and is the system electromagnetic power, and are the d-axis and q-axis components of the potential in the grid-type energy storage system, and are the d-axis and q-axis components of the grid-connected point voltage of the grid-connected energy storage system, and are the d-axis and q-axis components of the branch current of the grid-type energy storage system.

[0027] The small signal model of the grid-type energy storage power loop is obtained as follows: (2) Where, is the small signal disturbance of the self-synchronization angular frequency of the grid-type energy storage system, The potential inside the grid-type energy storage system d 、 q The small signal disturbance of the axis component, is the coefficient matrix in the state-space model, is the input matrix in the state-space model, Describe the dynamic coupling relationship between the state quantity itself; Reflect the impact of input disturbance on state quantity; 、 It is the small signal disturbance quantity related to the grid-type energy storage branch current, system grid-connected point current, etc., representing the current disturbance input in the power loop. is the output matrix coefficient, which is used to extract the mapping relationship from state quantity to output physical quantity. The matrix coefficients are directly transferred to describe the impact of the input disturbance on the output without passing through the state quantity, and the linear expression of the power loop small signal model is improved. The rate of change of the small signal disturbance representing the self-synchronization angular frequency of the grid-type energy storage system; Representative branch current dq The rate of change of the small signal disturbance of the axis component.

[0028] The active and reactive power control links of the grid-connected energy storage system are as follows: (3) Where, is the active power command value, and are the d-axis and q-axis components of the branch current at the grid-connected point of the grid-following energy storage system, 、 、 and are the current inner loop PI controller parameters, and are the d-axis and q-axis components of the grid-connected point voltage, and is the grid-connected line inductance parameter, and are the d-axis and q-axis components of the modulated wave; s is the Laplace operator, 、 The controlled current components of the inner current loop (the d-axis and q-axis components of the converter output current or the filtered current).

[0029] The small signal model is obtained as follows: (4) Where, The prefix stands for small signal disturbance amount.

[0030] Small signal modeling of grid-connected lines and power grids: (5) Where, and is the filter inductance and parasitic resistance of the filter, is the filter capacitor, and are the grid inductance and resistance. 、 are the d-axis and q-axis components of the current flowing through the filter inductor, 、 is the current flowing into the grid side (or the filtered component of the grid connection point current) d Axis and q Axis component. ω Synchronous rotation angular velocity, dq In the rotating coordinate system, it is used to describe the rotating synchronous relationship between voltage and current. E d 、 E q : The output potential of the converter (or the modulation potential of the inner loop control output) d Axis and q Axis component.

[0031] This results in the small signal model of the energy storage converter filter and connecting lines: (6) Where, The prefix stands for small signal disturbance amount.

[0032] The circuit equation between the grid connection point and the power grid is: (7) Where, is the filter inductor current d 、 q Small signal disturbance of the axis component; is the voltage drop of the grid-connected filter d 、 q Small signal disturbance of the axis component; The current flowing into the grid side d 、 q Small signal disturbance of the axis component; Output potential for the converter (or modulation potential for the control link) d 、 q Small signal disturbance of the axis component; The grid voltage d 、 q Small signal disturbance of the axis component; is the small signal disturbance of synchronous rotation angular velocity; is the input disturbance of the model; is the state matrix, describing the state variables ( ), is the input matrix, which respectively characterizes the input disturbance ( ) influence on state variables; 、 are the inductance and resistance of the connection line between the grid connection point and the grid respectively; 、 The grid current injected into the grid connection point is d axis (active power related) and q axis (reactive related) components; 、 They are the voltage at the grid connection point side (or converter side) and d Axis and q Axis component; 、 The grid side voltage is d Axis and q Axis component.

[0033] The state space structure diagram of the grid-connected energy storage hybrid power station and the grid-connected energy storage hybrid power station is as follows: Figure 3 shown. Figure 3 middle, 、 and They represent the input quantities of the state equations of grid-connected energy storage, grid-following energy storage and grid-connected lines, respectively. 、 and They represent the state quantities of the state equations of grid-connected energy storage, grid-following energy storage and grid-connected lines, 、 and They represent the output quantities of the state equations of grid-connected energy storage, grid-following energy storage and grid-connected lines respectively. 、 、 、 are the command values ​​of active power, reactive power, grid voltage and self-synchronous angular frequency respectively. and To construct the active and reactive power output of grid-type energy storage to the grid, 、 are the d-axis and q-axis components of the capacitor current of the grid-type energy storage filter, 、 are the d-axis and q-axis components of the grid-connected point current of the grid-following energy storage.

[0034] Step 2: Based on the state space structure model of the grid-connected energy storage hybrid power station and the grid-connected energy storage system, an H∞ robust controller is constructed; the H∞ robust controller is solved to obtain the matrix required by the H∞ robust controller; the H∞ robust controller can be designed by constructing an augmented system considering generalized objects to conduct a comprehensive analysis of the system.

[0035] In the above scheme, an H∞ robust controller is constructed based on the state space structure model of the grid-connected hybrid power station and the grid-connected energy storage system. The H∞ robust controller is solved to obtain the required matrix for the H∞ robust controller, including: Based on a grid-connected and grid-connected energy storage hybrid power station, three perturbations are determined: grid impedance perturbation, load perturbation, and energy storage battery parameter perturbation. The energy storage battery parameter perturbation specifically includes the battery equivalent internal resistance perturbation and battery capacity perturbation, and the norm limits of various perturbations are determined. Various perturbations are uniformly expressed as norm-bounded uncertainty. Based on norm-bounded uncertainty, the relationship between the system matrix, the disturbance input matrix, and the output matrix is ​​constructed based on positive definite matrices and scalars, and a controller is introduced to obtain the closed-loop system equation. Construct a scalar function of system stability and substitute it into the closed-loop system equation to obtain the stability trend relationship of the closed-loop system under uncertainty. Apply the auxiliary theorem of matrix inversion to transform the stability trend relationship of the closed-loop system under uncertainty into a linear matrix inequality. Solve the linear matrix inequality to obtain the matrix required for the H∞ robust controller.

[0036] The design process of the H∞ robust controller is as follows: Figure 4 The specific process is described as follows: The grid-connected and grid-following energy storage hybrid power station mainly considers the following three perturbations: grid impedance perturbation, load perturbation, and energy storage battery parameter perturbation. The energy storage battery parameter perturbation specifically includes the battery equivalent internal resistance perturbation and battery capacity perturbation, and the norm limits of various perturbations are determined.

[0037] Grid impedance perturbation , load perturbation , the battery equivalent internal resistance perturbation , battery capacity perturbation . is the grid impedance perturbation, is the perturbation of the grid resistance, is the perturbation of the grid inductance, s is the Laplace operator.

[0038] Next, the above perturbations are uniformly expressed as norm-bounded uncertainties that satisfy the corresponding conditions. At the same time, there exist positive definite matrices and scalars such that the system matrix, the perturbation input matrix, and the output matrix satisfy specific relationships. The norm bounds of various perturbations are as follows: (8) Where, is the H∞ norm (infinity norm), which is used to measure the maximum gain of the perturbation in the frequency domain. : Indicates the grid impedance perturbation In the entire frequency domain, the maximum value of its amplitude does not exceed ; Refers to the Euclidean norm (2-norm), which is used to measure the size of a vector / scalar.

[0039] The above perturbations are uniformly expressed as norm-bounded uncertainty: , is to diagonalize various perturbations and construct an uncertainty block diagonal matrix that satisfies . is the unified uncertainty matrix.

[0040] Assume that there is a positive definite matrix and scalar Make ,in is the system matrix, is the perturbation input matrix, is the output matrix.

[0041] Then the controller is introduced, which contains the matrix to be solved. Based on this controller, the closed-loop system equation is shown in Equation (9). ,in, is the controller matrix, and is the matrix to be determined.

[0042] The closed-loop system equation is: (9) in, x is the state vector, representing the core dynamic variables of the system, such as current, voltage, and frequency disturbances; A+BK: closed-loop system matrix, BK reflects the correction of the system's natural characteristic A by the controller K; D: disturbance input matrix, d is the disturbance vector (including perturbations such as grid impedance, load, and battery terminal voltage). z is the output evaluation variable (used for design Performance indicators, such as requirements z The energy of the disturbance d is less than the energy of the disturbance d multiplied by , ensuring anti-disturbance capability). C+EK: output mapping matrix, EK reflects the controller's correction of the output mapping, and E is a matrix related to performance evaluation.

[0043] Construct the Lyapunov function: ; Used to analyze system stability. P is a positive definite matrix; represents the energy norm of the state vector x; Substituting into the closed-loop system equation: (10) Item 1 :yes The core term represents the rate of change of energy of state x itself; the second term : It is related to the energy of the evaluation output z and reflects the performance constraint; the third item : reflects the requirement for suppressing the disturbance d and ensures that the disturbance energy is attenuated. is an indicator of the disturbance rejection level, The smaller it is, the stronger the anti-interference ability is.

[0044] To analyze the stability of the system, a Lyapunov function is constructed and substituted into the closed-loop equation to obtain Equation (10). When dealing with uncertainty, the Schur complement lemma is used to transform the relevant inequality into Equation (11).

[0045] To deal with uncertainty, use Schur's complement lemma to transform the inequality into: (11) Where, The state energy decay term and the evaluation output energy term, corresponding to the derivative of the Lyapunov function, reflect the joint constraints on the closed-loop system state and performance. (A + BK) is the closed-loop system matrix, and (C + EK) is the evaluation output matrix. The upper right block (PD) and the lower left block (DTP) describe the coupling relationship between the disturbance input d and the state x. D is the disturbance injection matrix, and P is the Lyapunov matrix. is the upper limit of the direct constraint perturbation energy, yes Performance index, the smaller it is, the stronger the disturbance suppression ability is; I is the unit matrix.

[0046] Then, we replace the variables and set the relevant variables. Apply the matrix inversion auxiliary theorem to obtain the linear matrix inequality (LMI) as shown in Equation (12). , , applying the auxiliary theorem of matrix inversion to obtain the linear matrix inequality (LMI): (12) Where, It reflects the coupling constraints between the closed-loop system matrix and the Lyapunov matrix. Reflects the evaluation output matrix E and the constraints of the system, Constraints are injected into the disturbance relationship, converting the impact of the disturbance d into the coupling of Q and D. 、 Constrain the upper limit of evaluation output energy and disturbance energy respectively.

[0047] Solved by convex optimization tools (such as MATLAB LMI Toolbox) and , we get the matrix required by the H∞ robust controller and .

[0048] In control theory, the Lyapunov function is a scalar function used to analyze system stability, usually denoted as V(x) (where x is the system state variable). In the H∞ robust controller design process, the core role of the Lyapunov function is to analyze the stability of the closed-loop system. By constructing the Lyapunov function and substituting it into the closed-loop system equation, the derivative expression shown in Equation (10) can be obtained. Based on the sign of the derivative, it can be determined whether the system can still remain stable in the presence of uncertainty (such as perturbations). If the derivative of the Lyapunov function satisfies the negative definiteness condition, it means that the system is still asymptotically stable under the influence of disturbances, which lays a theoretical foundation for the subsequent transformation of inequalities and solution of controller parameters through tools such as Schur's complement lemma.

[0049] The matrix inversion auxiliary theorem is used to convert the stability trend relationship of the closed-loop system under uncertainty into a linear matrix inequality, including: When dealing with uncertainty, the Schur complement lemma is used to reorganize the matrix inversion operations or cross terms involved in the inequality in the stability trend relationship of the closed-loop system under uncertainty, eliminating the nonlinear structure and obtaining an inequality with a canonical form. For the inequality with normal form, we replace the variables, set the relevant variables, and apply the matrix inversion auxiliary theorem to obtain the linear matrix inequality.

[0050] Equation (10) is the expression for the time derivative of the Lyapunov function obtained by 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 uncertainty. Through this expression, the relationship between the sign of the derivative of the Lyapunov function and the system state and disturbance can be intuitively observed. In the design of H∞ robust controllers, the role of Equation (10) is to provide a basis for the derivation of subsequent stability criteria: if it can be proved that it satisfies the negative definiteness (or the corresponding attenuation condition) by processing this expression, it can be confirmed that the closed-loop system can remain stable in the presence of uncertainties such as grid impedance perturbations, load perturbations, and energy storage battery parameter perturbations. This result is the key prerequisite for further using the Schur complement lemma to transform the stability condition into a solvable LMI, ultimately providing a theoretical basis for solving the controller parameters.

[0051] The method of this application uniformly expresses the grid impedance perturbation, load perturbation, battery equivalent internal resistance perturbation and battery capacity perturbation as norm-bounded uncertainty (satisfying specific norm conditions). It is the core prerequisite step in the H∞ robust controller design process and has a close logical connection with subsequent links. First, this unified representation provides a standard form for mathematical modeling of system uncertainty. The subsequent introduction of relationships between positive definite matrices and scalars and the system matrix, perturbation input matrix, and output matrix is ​​precisely based on the properties of norm-bounded uncertainty, laying the foundation for quantifying the impact of uncertainty on the system. Secondly, in the process of constructing the Lyapunov function and substituting it 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 the Lyapunov function and the system state and disturbance. Only by clearly defining the norm bounds of uncertainty can we accurately consider the scope of the disturbance's influence when analyzing the sign properties of the derivative. Furthermore, the treatment of Equation (10) and its subsequent transformation into inequality (11) using the Schur complement lemma relies on the mathematical structure of norm-bounded uncertainty. This unified form allows uncertainty to be incorporated into the convex optimization framework, making it feasible to subsequently obtain the LMI through variable substitution and solve the controller parameters using convex optimization tools, ensuring that the controller design process is carried out under the support of rigorous mathematical theory.

[0052] For hybrid power stations with grid-connected and grid-following energy storage, the frequency or voltage of the system may become unstable due to factors such as phase synchronization failure, broadband coupling of power electronic equipment with the grid, and transient shocks during the hybrid system's switching between grid-connected and off-grid modes or grid-connected and grid-following modes, resulting in problems such as broadband oscillations, frequency drops, and voltage instability. The present invention proposes a robust and stable operation control method for hybrid power stations with grid-connected and grid-following energy storage. Without significantly increasing the system modeling cost or excessively changing the existing control strategy framework, the system control law is introduced to ensure stable operation and a certain stability margin under complex operating conditions where the system faces a wide range of external disturbances and system model perturbations.

[0053] Step 3: Simulation verification and system optimization: Verify that the matrix required by the H∞ robust controller meets the system performance requirements. If the system performance is not met, treat the nonlinear terms in the H∞ robust controller as uncertain disturbances and perform nonlinear compensation on the system until the simulation verification requirements are met. The present invention uses the power grid sudden change working condition as an example to demonstrate the effect of the invention with simulation results. The sudden change of power flow occurs at 1s, 2s and 3s respectively. The voltage and current measurement results of the grid connection point of the grid-following energy storage hybrid power station under the example working condition are as follows: Figure 5 As shown in the figure, (a) is before the sudden change of the power flow, and (b) is after the sudden change of the power flow. It can be seen that the sudden change of the power flow is obvious at 1s, 2s and 3s. The active output of the grid-connected energy storage hybrid power station is as follows: Figure 6 shown. Figure 6 In the example, the grid load suddenly increases at 1 second, returns to the level before 1 second at 2 seconds, and decreases at 3 seconds. This shows that the proposed robust control method for a grid-connected and grid-following hybrid power station exhibits good robustness against grid disturbances and can effectively support the grid.

[0054] Step 4: If the system performance still does not meet the requirements after the above steps, treat the model nonlinear terms as uncertain disturbances and perform nonlinear compensation on the system until the simulation verification requirements are met. Output an H∞ robust controller that meets the system performance requirements and is used to control the grid-connected energy storage hybrid power station.

[0055] For the nonlinear compensation in the present invention, a sliding mode controller can be used for nonlinear compensation. Considering the nonlinear term includes the dead zone effect of the converter , IGBT saturation voltage and filter parasitics .

[0056] Among them, the dead zone voltage

[0057] Current tracking error : (13) Integral sliding surface s : (14) Sliding mode switching term : (15) is the dead zone current threshold, dead zone voltage compensation for: (16) The saturation voltage compensation term is: (17) Feedforward compensation term for parasitic resistance u ff for: (18) Where, It is the DC side voltage, which is the voltage of the DC bus of power electronic devices such as converters. It is used as the basic quantity in the dead zone voltage calculation and reflects the DC side's electrical energy storage and power supply capabilities. i Refers to the current of the converter output or related circuit, which is used to determine the polarity of the dead zone voltage (i>0 or i<0) and decide the compensation direction of the dead zone voltage. is the reference current, is the three-phase AC current output by the converter ( abc represents three phases); Λ is a diagonal matrix, λ1,λ2,λ3 are positive coefficients; K s is the control gain, sign(s) is the sign function, according to the sliding surface s The positive and negative output ±1 realizes the switching of the control quantity. V sat is the IGBT saturation voltage; is the saturation voltage compensation term; sat(i abc ) is a saturation function; I rated is the rated current, R f : filter parasitic resistance; u ffis the feedforward compensation term of parasitic resistance, Δi f is the current change, and max|| represents the maximum absolute value.

[0058] This control method has been partially validated in a hardware-in-the-loop simulation platform. In areas with poor grid stability and weak grid strength, this technology can effectively improve the operating characteristics of energy storage power stations and enhance grid stability. With the large-scale integration of new energy sources, the complexity of the grid continues to increase, placing increasingly stringent requirements on the control and output characteristics of energy storage power stations. The application of this technology will continue to expand in areas such as distributed energy grid integration and microgrid construction. Driven by the comprehensive development of new power systems, it is expected to be widely used in hybrid energy storage power stations with grid-connected and grid-connected connections, playing a key role in ensuring the stable and efficient operation of the power system.

[0059] In the second aspect, the present application discloses a grid-connected and grid-following energy storage hybrid power station control system, which is based on the above-mentioned grid-connected and grid-following energy storage hybrid power station control method and includes: a first construction module, a second construction module, a verification and compensation module, and an output control module.

[0060] The first construction module is used to construct a state space structure model of the grid-connected energy storage hybrid power station and the grid connection based on the circuit topology and control strategy of the grid-connected energy storage hybrid power station; The first building block is specifically used to: Based on the circuit topology and control strategy of the grid-connected energy storage hybrid power station; the small signal model of the grid-connected energy storage system is used to obtain the small signal model of the grid-connected energy storage power loop; the small signal model of the grid-connected energy storage system is obtained from the active and reactive power control relationship of the grid-connected energy storage system; the small signal model of the grid-connected line and the power grid is modeled to obtain the small signal model of the energy storage converter filter and the connecting line; Based on the small signal model of the grid-connected energy storage power loop, the small signal model of the grid-following energy storage system, 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, the state space structure model of the grid-connected energy storage hybrid power station and the grid connection is obtained.

[0061] The second construction module is used to construct an H∞ robust controller based on the state space structure model of the grid-connected energy storage hybrid power station and the grid connection; solve the H∞ robust controller to obtain the matrix required by the H∞ robust controller; The second constitutive model is specifically used for: Based on a grid-connected and grid-connected energy storage hybrid power station, three perturbations are determined: grid impedance perturbation, load perturbation, and energy storage battery parameter perturbation. The energy storage battery parameter perturbation specifically includes the battery equivalent internal resistance perturbation and battery capacity perturbation, and the norm limits of various perturbations are determined. Various perturbations are uniformly expressed as norm-bounded uncertainty. Based on norm-bounded uncertainty, the relationship between the system matrix, the disturbance input matrix, and the output matrix is ​​constructed based on positive definite matrices and scalars, and a controller is introduced to obtain the closed-loop system equation. Construct a scalar function of system stability and substitute it into the closed-loop system equation to obtain the stability trend relationship of the closed-loop system under uncertainty. Apply the auxiliary theorem of matrix inversion to transform the stability trend relationship of the closed-loop system under uncertainty into a linear matrix inequality. Solve the linear matrix inequality to obtain the matrix required for the H∞ robust controller.

[0062] As a preferred solution, the application of the matrix inversion auxiliary theorem to transform the stability trend relationship of the closed-loop system under uncertainty into a linear matrix inequality includes: When dealing with uncertainty, the Schur complement lemma is used to reorganize the matrix inversion operations or cross terms involved in the stability relationship of the closed-loop system under uncertain disturbances, eliminating the nonlinear structure and obtaining an inequality with a canonical form. For the inequality with normal form, we replace the variables, set the relevant variables, and apply the matrix inversion auxiliary theorem to obtain the linear matrix inequality.

[0063] Furthermore, the solving of the H∞ robust controller to obtain the matrix required by the H∞ robust controller includes: The H∞ robust controller is solved by convex optimization tools, and the required matrix of the H∞ robust controller is obtained.

[0064] The verification and compensation module is used to verify that the matrix required by the H∞ robust controller meets the system performance requirements. If the system performance is not met, the nonlinear terms in the H∞ robust controller are treated as uncertain interference and nonlinear compensation is performed on the system until the simulation verification requirements are met. The method of treating the nonlinear term in the H∞ robust controller as an uncertain disturbance to perform nonlinear compensation on the system includes: The nonlinear compensation is performed using a sliding mode controller. The nonlinear terms considered in the nonlinear compensation include the dead zone effect of the converter. , IGBT saturation voltage and filter parasitics .

[0065] The output control module is used to output an H∞ robust controller that meets the system performance and is used to control the grid-following energy storage hybrid power station.

[0066] The above scheme proposes a grid-connected and grid-following energy storage hybrid power station control system, which aims to solve the problem of robustness under complex working conditions when the system is connected to the grid. By constructing a small signal model containing grid-connected and grid-following energy storage, integrating the grid impedance perturbation, load disturbance and battery parameter uncertainty modeling, an H∞ robust controller is designed to minimize the norm of the disturbance transfer function. The linear matrix inequality (LMI) is used to optimize the controller parameters to ensure the robust stability of the system in the face of disturbances. For the nonlinear links of the model, a sliding mode compensator is designed to compensate for the dead zone effect and parasitic parameters of the converter, and the stability and robustness of the control method are verified through simulation tests. The present invention can effectively improve the robustness of the system, thereby improving the adaptability of the energy storage power station to complex grid working conditions.

[0067] The third purpose of an embodiment of the present application is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned grid-building-grid-following energy storage hybrid power station control method when executing the computer program.

[0068] When the processor executes the computer program, the control method of the grid-building-grid-following energy storage hybrid power station is implemented, which specifically includes: Based on the circuit topology and control strategy of the grid-connected energy storage hybrid power station, a state space structure model of the grid-connected energy storage hybrid power station and the grid connection is constructed; Based on the state space structure model of the grid-connected energy storage hybrid power station and the grid-connected energy storage system, an H∞ robust controller is constructed. The H∞ robust controller is solved to obtain the matrix required by the H∞ robust controller. Verify that the matrix required by the H∞ robust controller meets the system performance requirements. If the system performance is not met, treat the nonlinear terms in the H∞ robust controller as uncertain disturbances and perform nonlinear compensation on the system until the simulation verification requirements are met. The output is an H∞ robust controller that meets the system performance and is used to control the grid-following energy storage hybrid power station.

[0069] The fourth object of the embodiment of the present application is to provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the control method of the grid-building-grid-following energy storage hybrid power station.

[0070] When the computer program is executed by the processor, the control method of the grid-building-grid-following energy storage hybrid power station is implemented, which specifically includes: Based on the circuit topology and control strategy of the grid-connected energy storage hybrid power station, a state space structure model of the grid-connected energy storage hybrid power station and the grid connection is constructed; Based on the state space structure model of the grid-connected energy storage hybrid power station and the grid-connected energy storage system, an H∞ robust controller is constructed. The H∞ robust controller is solved to obtain the matrix required by the H∞ robust controller. Verify that the matrix required by the H∞ robust controller meets the system performance requirements. If the system performance is not met, treat the nonlinear terms in the H∞ robust controller as uncertain disturbances and perform nonlinear compensation on the system until the simulation verification requirements are met. The output is an H∞ robust controller that meets the system performance and is used to control the grid-following energy storage hybrid power station.

[0071] A fifth object of the embodiments of the present application is to provide a computer program product, which includes computer instructions, and the computer instructions instruct a computer to execute the control method of the grid-building-grid-following energy storage hybrid power station.

[0072] The computer instructions instruct the computer to execute the control method of the grid-building-grid-following energy storage hybrid power station, which specifically includes: Based on the circuit topology and control strategy of the grid-connected energy storage hybrid power station, a state space structure model of the grid-connected energy storage hybrid power station and the grid connection is constructed; Based on the state space structure model of the grid-connected energy storage hybrid power station and the grid-connected energy storage system, an H∞ robust controller is constructed. The H∞ robust controller is solved to obtain the matrix required by the H∞ robust controller. Verify that the matrix required by the H∞ robust controller meets the system performance requirements. If the system performance is not met, treat the nonlinear terms in the H∞ robust controller as uncertain disturbances and perform nonlinear compensation on the system until the simulation verification requirements are met. The output is an H∞ robust controller that meets the system performance and is used to control the grid-following energy storage hybrid power station.

[0073] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0075] The present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, readable storage media, optical storage, etc.) containing computer-usable program code.

[0076] The present application 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 application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0077] Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A control method for a grid-connected and grid-following energy storage hybrid power station, characterized in that: include: Based on the circuit topology and control strategy of the grid-connected energy storage hybrid power station, a state space structure model of the grid-connected energy storage hybrid power station and the grid connection is constructed; Based on the state space structure model of the grid-connected energy storage hybrid power station and the grid-connected energy storage system, an H∞ robust controller is constructed. The H∞ robust controller is solved to obtain the matrix required by the H∞ robust controller. Verify that the matrix required by the H∞ robust controller meets the system performance requirements. If the system performance is not met, treat the nonlinear terms in the H∞ robust controller as uncertain disturbances and perform nonlinear compensation on the system until the simulation verification requirements are met. The output is an H∞ robust controller that meets the system performance and is used to control the grid-following energy storage hybrid power station.

2. The control method of the grid-connected and grid-following hybrid power station according to claim 1 is characterized in that: The circuit topology and control strategy of the grid-connected energy storage hybrid power station are used to construct a state space structure model of the grid-connected energy storage hybrid power station and the grid connection, including: Based on the circuit topology and control strategy of the grid-connected energy storage hybrid power station; the small signal model of the grid-connected energy storage system is used to obtain the small signal model of the grid-connected energy storage power loop; the small signal model of the grid-connected energy storage system is obtained from the active and reactive power control relationship of the grid-connected energy storage system; the small signal model of the grid-connected line and the power grid is modeled to obtain the small signal model of the energy storage converter filter and the connecting line; Based on the small signal model of the grid-connected energy storage power loop, the small signal model of the grid-following energy storage system, 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, the state space structure model of the grid-connected energy storage hybrid power station and the grid connection is obtained.

3. The control method of the grid-connected and grid-following hybrid power station according to claim 1 is characterized in that: The state space structure model of the grid-connected energy storage hybrid power station and the grid connection is used to construct an H∞ robust controller; the H∞ robust controller is solved to obtain the matrix required by the H∞ robust controller, including: Based on a grid-connected and grid-connected energy storage hybrid power station, three perturbations are determined: grid impedance perturbation, load perturbation, and energy storage battery parameter perturbation. The energy storage battery parameter perturbation specifically includes the battery equivalent internal resistance perturbation and battery capacity perturbation, and the norm limits of various perturbations are determined. Various perturbations are uniformly expressed as norm-bounded uncertainty. Based on norm-bounded uncertainty, the relationship between the system matrix, the disturbance input matrix, and the output matrix is ​​constructed based on positive definite matrices and scalars, and a controller is introduced to obtain the closed-loop system equation. Construct a scalar function of system stability and substitute it into the closed-loop system equation to obtain the stability trend relationship of the closed-loop system under uncertainty. Apply the auxiliary theorem of matrix inversion to transform the stability trend relationship of the closed-loop system under uncertainty into a linear matrix inequality. Solve the linear matrix inequality to obtain the matrix required for the H∞ robust controller.

4. The control method of the grid-connected and grid-following energy storage hybrid power station according to claim 3 is characterized in that: The matrix inversion auxiliary theorem is used to transform the stability trend relationship of the closed-loop system under uncertainty into a linear matrix inequality, including: When dealing with uncertainty, the Schur complement lemma is used to reorganize the matrix inversion operations or cross terms involved in the stability relationship of the closed-loop system under uncertain disturbances, eliminating the nonlinear structure and obtaining an inequality with a canonical form. For the inequality with normal form, we replace the variables, set the relevant variables, and apply the matrix inversion auxiliary theorem to obtain the linear matrix inequality.

5. The control method of the grid-connected and grid-following energy storage hybrid power station according to claim 3 is characterized in that: The method of solving the H∞ robust controller to obtain the matrix required by the H∞ robust controller includes: The H∞ robust controller is solved by convex optimization tools, and the required matrix of the H∞ robust controller is obtained.

6. The control method of the grid-connected and grid-following energy storage hybrid power station according to claim 1 is characterized in that: The nonlinear term in the H∞ robust controller is regarded as an uncertain disturbance to compensate the system nonlinearly, including: A sliding mode controller is used for nonlinear compensation. The nonlinear terms considered in nonlinear compensation include the dead zone effect of the converter. , IGBT saturation voltage and filter parasitics .

7. A grid-connected and grid-following energy storage hybrid power station control system, characterized in that: The control method of a grid-connected and grid-following energy storage hybrid power station according to any one of claims 1 to 6 comprises: The first construction module is used to construct a state space structure model of the grid-connected energy storage hybrid power station and the grid connection based on the circuit topology and control strategy of the grid-connected energy storage hybrid power station; The second construction module is used to construct an H∞ robust controller based on the state space structure model of the grid-connected energy storage hybrid power station and the grid connection; solve the H∞ robust controller to obtain the matrix required by the H∞ robust controller; The verification and compensation module is used to verify that the matrix required by the H∞ robust controller meets the system performance requirements. If the system performance is not met, the nonlinear terms in the H∞ robust controller are treated as uncertain interference and nonlinear compensation is performed on the system until the simulation verification requirements are met. The output control module is used to output an H∞ robust controller that meets the system performance and is used to control the grid-following energy storage hybrid power station.

8. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the control method of the grid-connected and grid-following energy storage hybrid power station as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for controlling a grid-connected and grid-following energy storage hybrid power station according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising computer instructions, characterized in that: The computer instructions instruct the computer to execute the control method of a grid-connected and grid-following energy storage hybrid power station as described in any one of claims 1-6.

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