Energy storage converter frequency response characteristic analysis method and system

Abstract

The application belongs to the technical field of novel power system control, and specifically discloses a method and system for analyzing frequency response characteristics of an energy storage converter, wherein the method comprises: establishing a small signal model of different controllers in the energy storage converter with virtual inertia control under a direct current voltage time scale, constructing an amplitude-phase motion model of unbalanced power input based on the small signal model; extracting an equivalent inertia expression of the energy storage converter based on the amplitude-phase motion model; and drawing a Bode diagram and analyzing the frequency response characteristics of the energy storage converter under different virtual inertia control modes based on the amplitude-phase characteristics of the Bode diagram by changing the controller parameters corresponding to the small signal model and the operating parameters of the energy storage converter; wherein the amplitude of the Bode diagram is used to represent the equivalent inertia time constant of the energy storage converter, and the phase of the Bode diagram is used to represent the inertia response speed of the energy storage converter. The application realizes the analysis of the frequency response characteristics of the energy storage converter with virtual inertia control.

Description

Technical Field

[0001] This application belongs to the field of new power system control technology, and more specifically, relates to a method and system for analyzing the frequency response characteristics of an energy storage converter. Background Technology

[0002] In recent years, with the increasing penetration of new energy sources in the power grid, more and more new energy equipment has been connected to the power system. Unlike synchronous generators, new energy equipment cannot provide backup spinning capacity to ensure stable system operation, which makes the new power system lack sufficient inertia. When the power system experiences significant disturbances due to load switching and frequent generator switching, traditional frequency regulation units cannot meet the regulation needs of the power system.

[0003] To enhance the regulation capability of power systems, energy storage converters with virtual inertia control have become an effective means of addressing the low inertia of new energy sources due to their rapid frequency regulation capabilities. To clarify the inertia mechanism, evaluate the inertia response capability of energy storage converters, and facilitate the optimized design of virtual inertia control parameters, accurate analysis of the inertia characteristics of energy storage converters with virtual inertia control under parameter variations is crucial. The energy provided by energy storage converters during the inertia response phase differs from the spontaneous response of traditional generator units, where inertia originates from the actual rotor. It is primarily influenced by the control of the energy storage converter and its control parameters, making it difficult to quantitatively characterize its inertia support capability.

[0004] Current research mainly evaluates the inertia support capability of energy storage from the perspective of frequency stability and system inertia requirements of new power systems. It extracts two indicators, the lowest frequency point and the maximum frequency change rate, to evaluate the frequency change suppression performance of energy storage converters. However, it does not specifically quantify the ability of energy storage converters with virtual inertia control to provide virtual inertia. There is still a lack of mature theoretical methods for analyzing the inertia response characteristics of energy storage converters with virtual inertia control and evaluating the system inertia support capability. Summary of the Invention

[0005] In view of the above-mentioned defects in the existing technology, this application provides a method and system for analyzing the frequency response characteristics of energy storage converters, aiming to solve the problem of inertia response characteristic analysis of energy storage converters with virtual inertia control.

[0006] In a first aspect, this application provides a method for analyzing the frequency response characteristics of an energy storage converter, including:

[0007] Small-signal models of different controllers in an energy storage converter with virtual inertia control are established under the DC voltage time scale. Based on the small-signal models, an amplitude-phase motion model of unbalanced power input is constructed.

[0008] Based on the amplitude-phase motion model, the equivalent inertia expression of the energy storage converter is extracted;

[0009] Based on the equivalent inertia expression, Bode plots are drawn by changing the controller parameters and operating parameters of the energy storage converter corresponding to the small signal model, and the frequency response characteristics of the energy storage converter under different virtual inertia control modes are analyzed based on the amplitude and phase characteristics of the Bode plots.

[0010] Among them, the amplitude of the Bode plot is used to characterize the equivalent inertia time constant of the energy storage converter, and the phase of the Bode plot is used to characterize the inertia response speed of the energy storage converter.

[0011] Secondly, this application also provides a frequency response characteristic analysis system for energy storage converters, comprising:

[0012] The modeling module is used to establish small-signal models of different controllers in an energy storage converter with virtual inertia control under the DC voltage time scale, and to construct an amplitude-phase motion model of unbalanced power input based on the small-signal models.

[0013] The equivalent inertia extraction module is used to extract the equivalent inertia expression of the energy storage converter based on the amplitude-phase motion model.

[0014] The analysis module is used to draw Bode plots based on the equivalent inertia expression, by changing the controller parameters and operating parameters of the energy storage converter corresponding to the small-signal model, and to analyze the frequency response characteristics of the energy storage converter under different virtual inertia control modes based on the amplitude and phase characteristics of the Bode plots.

[0015] Among them, the amplitude of the Bode plot is used to characterize the equivalent inertia time constant of the energy storage converter, and the phase of the Bode plot is used to characterize the inertia response speed of the energy storage converter.

[0016] Thirdly, this application also provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation thereof.

[0017] Fourthly, this application also provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0018] Fifthly, this application also provides a computer program product that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0019] This application provides a method and system for analyzing the frequency response characteristics of an energy storage converter. It establishes small-signal models of different controllers in an energy storage converter with virtual inertia control at the DC voltage time scale, constructing an amplitude-phase motion model of unbalanced power input. This amplitude-phase motion model has a clear physical process and good scalability; that is, when changing the inertia control strategy, only the modeling of the inertia part needs to be modified. Based on the amplitude-phase motion model, an equivalent inertia expression for the energy storage converter with virtual inertia control is extracted. Using this equivalent inertia expression, by changing the controller parameters corresponding to the small-signal model and the operating parameters of the energy storage converter, a Bode plot is obtained as the basis for analyzing the inertia characteristics of the energy storage converter with virtual inertia control. This method is applicable to the analysis of the inertia characteristics of energy storage converters with virtual inertia control under different virtual inertia control methods. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this application or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the frequency response characteristic analysis method for energy storage converters provided in this application embodiment;

[0022] Figure 2 This is a small-signal model of an energy storage converter with virtual inertia control provided in the embodiments of this application;

[0023] Figure 3 This is a comparison diagram of the active power output obtained by the amplitude-phase motion model provided in the embodiments of this application and the real model;

[0024] Figure 4 This is a comparison diagram of the system frequencies obtained by the amplitude-phase motion model provided in the embodiments of this application and the actual model;

[0025] Figure 5 This is a schematic diagram illustrating the process of extracting equivalent inertia based on a synchronous machine model, as provided in an embodiment of this application.

[0026] Figure 6 This is a schematic diagram illustrating the process of extracting equivalent inertia based on the amplitude-phase motion model provided in this application embodiment;

[0027] Figure 7 The different virtual inertia control droop coefficients provided in the embodiments of this application are the lower inertia coefficients. A schematic diagram of the amplitude and phase characteristics;

[0028] Figure 8The equivalent inertia under different virtual inertia control droop coefficients provided in the embodiments of this application is... A schematic diagram of the amplitude and phase characteristics;

[0029] Figure 9 The inertia coefficients under different virtual inertia control time constants provided in the embodiments of this application are the inertia coefficients. A schematic diagram of the amplitude and phase characteristics;

[0030] Figure 10 The equivalent inertia under different virtual inertia control time constants provided in the embodiments of this application are A schematic diagram of the amplitude and phase characteristics;

[0031] Figure 11 The inertia coefficient under different DC voltage control proportional coefficients provided in the embodiments of this application. A schematic diagram of the amplitude and phase characteristics;

[0032] Figure 12 The equivalent inertia under different DC voltage control proportional coefficients provided in the embodiments of this application is... A schematic diagram of the amplitude and phase characteristics;

[0033] Figure 13 The inertia coefficients of the energy storage converter with virtual inertia control under different DC voltage control differential coefficients provided in the embodiments of this application are as follows: A schematic diagram of the amplitude and phase characteristics;

[0034] Figure 14 The equivalent inertia under different DC voltage control differential coefficients provided in the embodiments of this application is... A schematic diagram of the amplitude and phase characteristics;

[0035] Figure 15 The inertia coefficients under different phase-locked loop controller bandwidths provided in the embodiments of this application are A schematic diagram of the amplitude and phase characteristics;

[0036] Figure 16 This application provides equivalent inertia under different phase-locked loop controller bandwidths in its embodiments. A schematic diagram of the amplitude and phase characteristics;

[0037] Figure 17 The inertia coefficients under different phase-locked loop controller damping ratios provided in the embodiments of this application are... A schematic diagram of the amplitude and phase characteristics;

[0038] Figure 18 The equivalent inertia under different phase-locked loop controller damping ratios provided in the embodiments of this application are A schematic diagram of the amplitude and phase characteristics;

[0039] Figure 19This is a schematic diagram illustrating the changes in active power output and system frequency of an energy storage converter with virtual inertia control under different virtual inertia control droop coefficients provided in this application embodiment during grid frequency disturbances.

[0040] Figure 20 This is a schematic diagram illustrating the changes in active power output and system frequency of an energy storage converter with virtual inertia control under different virtual inertia control time constants provided in the embodiments of this application during grid frequency disturbances.

[0041] Figure 21 This is a schematic diagram showing the changes in active power output and system frequency of an energy storage converter with virtual inertia control under different DC voltage controller proportional coefficients provided in the embodiments of this application during grid frequency disturbances.

[0042] Figure 22 This is a schematic diagram showing the changes in active power output and system frequency of an energy storage converter with virtual inertia control under different DC voltage controller integral coefficients provided in the embodiments of this application during grid frequency disturbances.

[0043] Figure 23 This is a schematic diagram illustrating the changes in active power output and system frequency of an energy storage converter with virtual inertia control under different phase-locked loop controller bandwidths provided in this application embodiment during grid frequency disturbances;

[0044] Figure 24 This is a schematic diagram showing the changes in active power output and system frequency of an energy storage converter with virtual inertia control under different phase-locked loop controller damping ratios provided in the embodiments of this application during grid frequency disturbances.

[0045] Figure 25 This is a schematic diagram of the structure of the energy storage converter frequency response characteristic analysis system provided in this embodiment of the application;

[0046] Figure 26 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0048] Figure 1 This is a flowchart illustrating the frequency response characteristic analysis method for energy storage converters provided in this application embodiment, as shown below. Figure 1 As shown, the method includes at least the following steps (Step):

[0049] S101. Establish small-signal models of different controllers in an energy storage converter with virtual inertia control under the DC voltage time scale, and construct an amplitude-phase motion model of unbalanced power input based on the small-signal models.

[0050] S102. Based on the amplitude-phase motion model, extract the equivalent inertia expression of the energy storage converter;

[0051] S103. Based on the equivalent inertia expression, Bode plots are drawn by changing the controller parameters and operating parameters of the energy storage converter corresponding to the small-signal model, and the frequency response characteristics of the energy storage converter under different virtual inertia control modes are analyzed based on the amplitude and phase characteristics of the Bode plots.

[0052] The frequency response characteristic analysis method for energy storage converters provided in this application establishes small-signal models of different controllers in an energy storage converter with virtual inertia control at the DC voltage time scale, and constructs an amplitude-phase motion model of unbalanced power input. This amplitude-phase motion model has a clear physical process and good scalability, meaning that only the modeling of the inertia part needs to be modified when changing the inertia control strategy. Based on the amplitude-phase motion model, the equivalent inertia expression of the energy storage converter with virtual inertia control is extracted. Using this equivalent inertia expression, by changing the controller parameters corresponding to the small-signal model and the operating parameters of the energy storage converter, a Bode plot is obtained as the basis for the inertia characteristic analysis of the energy storage converter with virtual inertia control. This method is applicable to the analysis of the inertia characteristics of energy storage converters with virtual inertia control under different virtual inertia control methods.

[0053] The technical solution provided in this application embodiment is described below using a three-machine, nine-node circuit as an example. The circuit consists of two synchronous machines and one energy storage converter, wherein the energy storage converter is composed of 200 500kW sub-converters; the reference parameters of the energy storage converter with virtual inertia control are shown in Table 1.

[0054] Table 1

[0055]

[0056] For S101, establish small-signal models of different controllers in the energy storage converter with virtual inertia control under the DC voltage time scale, and construct an amplitude-phase motion model of unbalanced power input based on the small-signal models.

[0057] Specifically, as a key device connecting energy storage systems and the power grid, the performance of energy storage converters directly affects the stability and reliability of the power system. At the DC voltage time scale (typically on the order of 0.1s), the dynamic response characteristics of energy storage converters play a crucial role in power system frequency regulation and voltage support. Establishing small-signal models of different controllers in an energy storage converter with virtual inertia control at the DC voltage time scale helps to gain a deeper understanding of the dynamic behavior of energy storage converters.

[0058] Energy storage converters typically consist of a main circuit and a control circuit, with the control circuit responsible for implementing various control strategies. Figure 2 This is a small-signal model of an energy storage converter with virtual inertia control provided in the embodiments of this application, such as... Figure 2 As shown, a small-signal model of an energy storage converter with virtual inertia control is established on the DC voltage time scale. This includes establishing small-signal models of the virtual inertia controller, DC voltage controller, energy storage controller, and phase-locked loop controller. Using the controller parameters and operating parameters of the energy storage converter with virtual inertia control, and considering its structure, a small-signal model of the energy storage converter with virtual inertia control on the DC voltage time scale is established.

[0059] The amplitude-phase motion model uses the change in internal potential driven by unbalanced power to describe the characteristics of equipment and reflects the operating essence of AC power systems. In power systems, the dynamic process of the phase and amplitude of the internal potential of equipment has a significant impact on the stability and performance of the system. By structurally decomposing an energy storage converter with virtual inertia control, the small-signal models of each controller are modeled. The controllers interact through electrical connections. Based on the small-signal models of each controller and the interactions between them, an amplitude-phase motion model of an energy storage converter with virtual inertia control and unbalanced power input is established. This amplitude-phase motion model has a clear physical process and good scalability; that is, when changing the inertia control strategy, only the modeling of the inertia part needs to be modified.

[0060] Figure 3 This is a comparison diagram of the active power output obtained from the amplitude-phase motion model provided in the embodiments of this application and the actual model. Figure 4 This is a comparison diagram of the system frequency obtained by the amplitude-phase motion model and the actual model provided in the embodiments of this application. Taking the connection of an energy storage converter with virtual inertia control to a three-machine nine-node power grid as an example, it verifies the simulation accuracy of the amplitude-phase motion model of the energy storage converter with virtual inertia control provided in the embodiments of this application. It is assumed that 10% of the active load is connected to the power grid at t=60s. Figure 3 and Figure 4As shown, the comparative responses of the amplitude-phase motion model and the real model are provided respectively. It can be seen that the amplitude-phase motion model provided in this application embodiment can accurately reflect the active power output of the energy storage converter with virtual inertia control and the dynamic frequency of the system, and can be used to study inertia problems.

[0061] For S102, based on the amplitude-phase motion model, the equivalent inertia expression of the energy storage converter is extracted.

[0062] Figure 5 This is a schematic diagram illustrating the process of extracting equivalent inertia based on a synchronization machine model, as provided in an embodiment of this application. Figure 6 This is a schematic diagram illustrating the process of extracting equivalent inertia based on the amplitude-phase motion model provided in this application embodiment, analogous to... Figure 5 The process of extracting equivalent inertia using a synchronous machine model is shown. In this embodiment, the equivalent inertia is extracted using an amplitude-phase motion model of an energy storage converter with virtual inertia control, as shown below. Figure 6 As shown, the extracted equivalent inertia expression for the energy storage converter with virtual inertia control satisfies:

[0063]

[0064] in, This represents the equivalent inertia expression. , , and These represent the inertia coefficients extracted from the amplitude-phase motion model. These inertia coefficients are related to the multi-loop control parameters and the operating parameters of the energy storage converter. Specifically:

[0065] Related to the inherent characteristics of energy storage converters, the following conditions must be met:

[0066]

[0067] in, The rated angular velocity representing the power grid frequency. Indicates the power factor. This represents the Laplace operator.

[0068] Related to the control parameters of the virtual inertia controller, satisfying:

[0069]

[0070] in, K 0 indicates the virtual inertia control droop coefficient. T K This represents the virtual inertia control time constant.

[0071] Related to the control parameters of the DC voltage controller and energy storage controller, the following conditions must be met:

[0072]

[0073] in, C Indicates the DC capacitance value. U dc0 Indicates the magnitude of the DC bus voltage. X f Indicates the magnitude of the filter inductance. k pu This represents the proportional coefficient for DC voltage control. k iu This represents the differential coefficient for DC voltage control. K D Indicates the energy storage coefficient. E 0 represents the steady-state value of the output voltage of the energy storage converter.

[0074] Related to the control parameters of the phase-locked loop controller, satisfying:

[0075]

[0076] in, k ppll This represents the proportional coefficient of the phase-locked loop control. k ipll This represents the differential coefficient of the phase-locked loop control. ω n This represents the oscillation frequency of a second-order system. ξ This indicates the damping ratio.

[0077] For a phase-locked loop (PLL) controller, the PLL bandwidth satisfies:

[0078]

[0079] in, This represents the angular frequency corresponding to the bandwidth angle of the phase-locked loop.

[0080] For S103, based on the equivalent inertia expression, by changing the controller parameters corresponding to the small-signal model and the operating parameters of the energy storage converter, Bode plots are drawn, and the frequency response characteristics of the energy storage converter under different virtual inertia control modes are analyzed based on the amplitude and phase characteristics of the Bode plots.

[0081] Specifically, the amplitude of the Bode plot is used to characterize the equivalent inertia time constant of an energy storage converter with virtual inertia control, while the phase of the Bode plot is used to characterize the inertia response speed of the energy storage converter with virtual inertia control. A higher Bode plot amplitude indicates a larger active power of the energy storage converter, and a smaller phase distance from 0 indicates a shorter time for the energy storage converter to reach its peak output power.

[0082] By changing the controller parameters and operating parameters of the energy storage converter corresponding to the small-signal model, the Bode plot of the energy storage converter with virtual inertia control is obtained. Then, the frequency response characteristics of the energy storage converter under different virtual inertia control modes are analyzed using the amplitude and phase characteristics of the Bode plot.

[0083] Specifically, equivalent inertia Inertia coefficient , , and The cumulative effect of the combined action means that different controller parameters and energy storage converter operating parameters have different effects on the inertia coefficient. , , and The impact is reflected in the equivalent inertia of the energy storage converter with virtual inertia control. The impact of this method is discussed in the embodiments of this application. Each inertia coefficient is analyzed separately. By analyzing the expression of a single inertia coefficient, the controller parameters and energy storage converter operating parameters are changed accordingly. Bode plots are obtained for the equivalent inertia and inertia coefficients. The frequency response characteristics of the energy storage converter under different virtual inertia control methods can be analyzed by combining the amplitude and phase characteristics of the Bode plots.

[0084] For inertia coefficient Its Bode plot reflects the inherent coefficients in an energy storage converter with virtual inertia control: the rated angular velocity at the grid frequency. and power coefficient Changing the controller parameters affects the inertia coefficient. No impact.

[0085] For inertia coefficient The inertia coefficient can be obtained by changing the virtual inertia control droop coefficient and the virtual inertia control time constant of the virtual inertia controller. and equivalent inertia Bode plot; select the oscillation frequency under the DC voltage time scale, analyze the amplitude and phase characteristics of the Bode plot under different control parameters, and obtain the controller parameters that affect the inertial response of the energy storage converter.

[0086] Figure 7 The different virtual inertia control droop coefficients provided in the embodiments of this application are the lower inertia coefficients. A schematic diagram of the amplitude and phase characteristics. Figure 8 The equivalent inertia under different virtual inertia control droop coefficients provided in the embodiments of this application is... A schematic diagram of the amplitude and phase characteristics. Figure 9 The inertia coefficients under different virtual inertia control time constants provided in the embodiments of this application are the inertia coefficients. A schematic diagram of the amplitude and phase characteristics. Figure 10 The equivalent inertia under different virtual inertia control time constants provided in the embodiments of this application are A schematic diagram of the amplitude and phase characteristics, as shown below. Figures 7 to 10 As shown in the figure, the amplitude characteristics indicate that the virtual inertia control droop coefficient... K The larger 0 is, the larger the virtual inertia control time constant. T K The smaller the value, the stronger the inertial response of the energy storage converter; the phase characteristics show that the virtual inertia control droop coefficient... K 0 has little impact on the inertial response speed of the energy storage converter, while the virtual inertial control time constant... T K The smaller the inertia, the faster the inertia response speed.

[0087] That is, virtual inertia control droop coefficient K 0 is positively correlated with the inertial response of the energy storage converter; virtual inertial control time constant T K It is negatively correlated with both the inertial response and the inertial response speed of the energy storage converter.

[0088] For inertia coefficient By changing the PI coefficient of the DC voltage controller and the energy storage coefficient of the energy storage controller K D Obtain the inertia coefficient and equivalent inertia Bode plot; select the oscillation frequency under the DC voltage time scale, analyze the amplitude and phase characteristics of the Bode plot under different control parameters, and obtain the controller parameters that affect the inertial response of the energy storage converter.

[0089] Figure 11 The inertia coefficient under different DC voltage control proportional coefficients provided in the embodiments of this application. A schematic diagram of the amplitude and phase characteristics. Figure 12 The equivalent inertia under different DC voltage control proportional coefficients provided in the embodiments of this application is... A schematic diagram of the amplitude and phase characteristics. Figure 13 The inertia coefficients of the energy storage converter with virtual inertia control under different DC voltage control differential coefficients provided in the embodiments of this application are as follows: A schematic diagram of the amplitude and phase characteristics. Figure 14 The equivalent inertia under different DC voltage control differential coefficients provided in the embodiments of this application is... A schematic diagram of the amplitude and phase characteristics, as shown below. Figures 11 to 14 As shown, the amplitude and phase characteristics reveal the PI coefficient of the DC voltage controller and the energy storage coefficient of the energy storage controller. K DThe inertial response of the energy storage converter is almost unaffected, meaning that the PI coefficient of the DC voltage controller and the energy storage coefficient of the energy storage controller are independent of the inertial response of the energy storage converter.

[0090] For inertia coefficient By changing the bandwidth (bandwidth is related to the damping ratio) and damping ratio of the phase-locked loop controller. ξ Obtain the inertia coefficient and equivalent inertia Bode plot; select the oscillation frequency under the DC voltage time scale, analyze the amplitude and phase characteristics of the Bode plot under different control parameters, and obtain the controller parameters that affect the inertial response of the energy storage converter.

[0091] Figure 15 The inertia coefficients under different phase-locked loop controller bandwidths provided in the embodiments of this application are A schematic diagram of the amplitude and phase characteristics. Figure 16 This application provides equivalent inertia under different phase-locked loop controller bandwidths in its embodiments. A schematic diagram of the amplitude and phase characteristics. Figure 17 The inertia coefficients under different phase-locked loop controller damping ratios provided in the embodiments of this application are... A schematic diagram of the amplitude and phase characteristics. Figure 18 The equivalent inertia under different phase-locked loop controller damping ratios provided in the embodiments of this application are A schematic diagram of the amplitude and phase characteristics, as shown below. Figures 15 to 18 As shown in the figure, it can be seen from the amplitude and phase characteristics that the bandwidth and damping ratio of the phase-locked loop controller have almost no effect on the inertial response of the energy storage converter, that is, the bandwidth and damping ratio of the phase-locked loop controller are independent of the inertial response of the energy storage converter.

[0092] Figure 19 This is a schematic diagram illustrating the changes in active power output and system frequency of an energy storage converter with virtual inertia control under different virtual inertia control droop coefficients provided in the embodiments of this application during grid frequency disturbances. Figure 20 This is a schematic diagram illustrating the changes in active power output and system frequency of an energy storage converter with virtual inertia control under different virtual inertia control time constants provided in the embodiments of this application during grid frequency disturbances. Figure 21 This is a schematic diagram illustrating the changes in active power output and system frequency of an energy storage converter with virtual inertia control under different proportional coefficients of DC voltage controllers provided in the embodiments of this application during grid frequency disturbances. Figure 22 This is a schematic diagram illustrating the changes in active power output and system frequency of an energy storage converter with virtual inertia control under different DC voltage controller integral coefficients provided in the embodiments of this application during grid frequency disturbances. Figure 23 This is a schematic diagram illustrating the changes in active power output and system frequency of an energy storage converter with virtual inertia control under different phase-locked loop controller bandwidths provided in this application embodiment during grid frequency disturbances. Figure 24 This is a schematic diagram illustrating the changes in active power output and system frequency of an energy storage converter with virtual inertia control under different phase-locked loop controller damping ratios provided in this application embodiment during grid frequency disturbances. Figures 19 to 24 The paper illustrates the changes in active power output and system frequency of an energy storage converter with virtual inertia control under different control parameter variations during grid frequency disturbances, further verifying the frequency response characteristic analysis method of the energy storage converter provided in the embodiments of this application.

[0093] The frequency response characteristic analysis system for energy storage converters provided in this application is described below. The frequency response characteristic analysis system for energy storage converters described below can be referred to in correspondence with the frequency response characteristic analysis method for energy storage converters described above.

[0094] Figure 25 This is a schematic diagram of the structure of the energy storage converter frequency response characteristic analysis system provided in this application embodiment. As shown in the figure, the system includes at least:

[0095] Modeling module 2501 is used to establish small-signal models of different controllers in an energy storage converter with virtual inertia control under the DC voltage time scale, and to construct an amplitude-phase motion model of unbalanced power input based on the small-signal model.

[0096] The equivalent inertia extraction module 2502 is used to extract the equivalent inertia expression of the energy storage converter based on the amplitude-phase motion model.

[0097] Analysis module 2503 is used to draw Bode plots based on the equivalent inertia expression, by changing the controller parameters and operating parameters of the energy storage converter corresponding to the small signal model, and to analyze the frequency response characteristics of the energy storage converter under different virtual inertia control modes based on the amplitude and phase characteristics of the Bode plots.

[0098] Among them, the amplitude of the Bode plot is used to characterize the equivalent inertia time constant of the energy storage converter, and the phase of the Bode plot is used to characterize the inertia response speed of the energy storage converter.

[0099] In some embodiments, the energy storage converter with virtual inertia control includes a virtual inertia controller, a DC voltage controller, an energy storage controller, and a phase-locked loop controller.

[0100] In some embodiments, the equivalent inertia expression of the energy storage converter satisfies:

[0101]

[0102] in, This represents the equivalent inertia of the energy storage converter. , , and These represent the inertia coefficients extracted from the amplitude-phase motion model;

[0103] in, Related to the inherent characteristics of energy storage converters, the following conditions must be met:

[0104]

[0105] in, The rated angular velocity representing the power grid frequency. Indicates the power factor. Represents the Laplace operator;

[0106] Related to the control parameters of the virtual inertia controller, satisfying:

[0107]

[0108] in, K 0 indicates the virtual inertia control droop coefficient. T K Indicates the virtual inertia control time constant;

[0109] Related to the control parameters of the DC voltage controller and energy storage controller, the following conditions must be met:

[0110]

[0111] in, C Indicates the DC capacitance value. U dc0 Indicates the magnitude of the DC bus voltage. X f Indicates the magnitude of the filter inductance. k pu This represents the proportional coefficient for DC voltage control. k iu This represents the differential coefficient for DC voltage control. K D Indicates the energy storage coefficient. E 0 represents the steady-state value of the output voltage of the energy storage converter;

[0112] Related to the control parameters of the phase-locked loop controller, satisfying:

[0113]

[0114] in, k ppll This represents the proportional coefficient of the phase-locked loop control. k ipll This represents the differential coefficient of the phase-locked loop control. ωn This represents the oscillation frequency of a second-order system. ξ This indicates the damping ratio.

[0115] In some embodiments, the inertia coefficient Related to the inherent characteristics of the energy storage converter, changing the controller parameters corresponding to the small-signal model and the operating parameters of the energy storage converter affects the inertia coefficient. No impact.

[0116] In some embodiments, the analysis module 2503 is specifically used for:

[0117] The inertia coefficient is obtained by changing the virtual inertia control droop coefficient and the virtual inertia control time constant of the virtual inertia controller. and equivalent inertia The Bird diagram;

[0118] Based on inertia coefficient and equivalent inertia Bode plots show that the virtual inertia control droop coefficient is positively correlated with the inertia response of the energy storage converter, while the virtual inertia control time constant is negatively correlated with both the inertia response and the inertia response speed of the energy storage converter.

[0119] In some embodiments, the analysis module 2503 is specifically used for:

[0120] The inertia coefficient is obtained by changing the PI coefficient of the DC voltage controller and the energy storage coefficient of the energy storage controller. and equivalent inertia The Bird diagram;

[0121] Based on inertia coefficient and equivalent inertia Bode plots show that the PI coefficient of the DC voltage controller and the energy storage coefficient of the energy storage controller are independent of the inertial response of the energy storage converter.

[0122] In some embodiments, the analysis module 2503 is specifically used for:

[0123] The inertia coefficient can be obtained by changing the bandwidth and damping ratio of the phase-locked loop controller. and equivalent inertia The Bird diagram;

[0124] Based on inertia coefficient and equivalent inertia Bode plots show that the bandwidth and damping ratio of the phase-locked loop controller are independent of the inertial response of the energy storage converter.

[0125] It is understood that the detailed functional implementation of each of the above units / modules can be found in the description in the aforementioned method embodiments, and will not be repeated here.

[0126] It should be understood that the above system is used to execute the methods in the above embodiments. The corresponding program modules in the system are similar in implementation principle and technical effect to those described in the above methods. The working process of the system can be referred to the corresponding process in the above methods, and will not be repeated here.

[0127] Based on the methods described in the above embodiments, this application provides an electronic device. The device may include at least one memory for storing a program and at least one processor for executing the program stored in the memory. When the program stored in the memory is executed, the processor performs the methods described in the above embodiments.

[0128] Figure 26 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application, such as... Figure 26 As shown, the electronic device may include a processor 2601, a communications interface 2602, a memory 2603, and a communication bus 2604, wherein the processor 2601, the communications interface 2602, and the memory 2603 communicate with each other via the communication bus 2604. The processor 2601 can call software instructions in the memory 2603 to execute the methods described in the above embodiments.

[0129] Furthermore, the logical instructions in the aforementioned memory 2603 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application.

[0130] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0131] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0132] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0133] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0134] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0135] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0136] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for analyzing the frequency response characteristics of an energy storage converter, characterized in that, include: Small-signal models of different controllers in an energy storage converter with virtual inertia control are established under the DC voltage time scale. Based on the small-signal models, an amplitude-phase motion model of unbalanced power input is constructed. Based on the amplitude-phase motion model, the equivalent inertia expression of the energy storage converter is extracted; Based on the equivalent inertia expression, by changing the controller parameters corresponding to the small-signal model and the operating parameters of the energy storage converter, a Bode plot is drawn, and the frequency response characteristics of the energy storage converter under different virtual inertia control modes are analyzed based on the amplitude and phase characteristics of the Bode plot. The amplitude of the Bode plot is used to characterize the equivalent inertia time constant of the energy storage converter, and the phase of the Bode plot is used to characterize the inertia response speed of the energy storage converter. The energy storage converter with virtual inertia control includes a virtual inertia controller, a DC voltage controller, an energy storage controller, and a phase-locked loop controller. The equivalent inertia expression of the energy storage converter satisfies: in, This represents the equivalent inertia of the energy storage converter. , , and These represent the inertia coefficients extracted from the amplitude-phase motion model, respectively. in, Related to the inherent characteristics of the energy storage converter, the following conditions are met: in, The rated angular velocity representing the power grid frequency. Indicates the power factor. Represents the Laplace operator; Related to the control parameters of the virtual inertia controller, the following conditions must be met: in, This represents the droop coefficient for virtual inertia control. Indicates the virtual inertia control time constant; Related to the control parameters of the DC voltage controller and the energy storage controller, the following conditions must be met: in, C Indicates the DC capacitance value. Indicates the magnitude of the DC bus voltage. Indicates the magnitude of the filter inductance. This represents the proportional coefficient for DC voltage control. This represents the differential coefficient for DC voltage control. Indicates the energy storage coefficient. This represents the steady-state value of the output voltage of the energy storage converter; Related to the control parameters of the phase-locked loop controller, the following conditions must be met: in, This represents the proportional coefficient of the phase-locked loop control. This represents the differential coefficient of the phase-locked loop control. This represents the oscillation frequency of a second-order system. This indicates the damping ratio.

2. The method for analyzing the frequency response characteristics of an energy storage converter according to claim 1, characterized in that, Inertia coefficient Related to the inherent characteristics of the energy storage converter, changing the controller parameters corresponding to the small-signal model and the operating parameters of the energy storage converter affects the inertia coefficient. No impact.

3. The method for analyzing the frequency response characteristics of an energy storage converter according to claim 1, characterized in that, The analysis of the frequency response characteristics of the energy storage converter under different virtual inertia control methods includes: The inertia coefficient is obtained by changing the virtual inertia control droop coefficient and the virtual inertia control time constant of the virtual inertia controller. and equivalent inertia The Bird diagram; Based on inertia coefficient and equivalent inertia Bode plots show that the virtual inertia control droop coefficient is positively correlated with the inertia response of the energy storage converter, and the virtual inertia control time constant is negatively correlated with both the inertia response and the inertia response speed of the energy storage converter.

4. The method for analyzing the frequency response characteristics of an energy storage converter according to claim 1, characterized in that, The analysis of the frequency response characteristics of the energy storage converter under different virtual inertia control methods includes: The inertia coefficient is obtained by changing the PI coefficient of the DC voltage controller and the energy storage coefficient of the energy storage controller. and equivalent inertia The Bird diagram; Based on inertia coefficient and equivalent inertia Bode plots show that the PI coefficient of the DC voltage controller and the energy storage coefficient of the energy storage controller are independent of the inertial response of the energy storage converter.

5. The method for analyzing the frequency response characteristics of an energy storage converter according to claim 1, characterized in that, The analysis of the frequency response characteristics of the energy storage converter under different virtual inertia control methods includes: The inertia coefficient is obtained by changing the bandwidth and damping ratio of the phase-locked loop controller. and equivalent inertia The Bird diagram; Based on inertia coefficient and equivalent inertia Bode plots show that the bandwidth and damping ratio of the phase-locked loop controller are independent of the inertial response of the energy storage converter.

6. A frequency response characteristic analysis system for an energy storage converter, characterized in that, include: The modeling module is used to establish small-signal models of different controllers in an energy storage converter with virtual inertia control under the DC voltage time scale, and to construct an amplitude-phase motion model of unbalanced power input based on the small-signal models. The equivalent inertia extraction module is used to extract the equivalent inertia expression of the energy storage converter based on the amplitude-phase motion model. The analysis module is used to draw a Bode plot based on the equivalent inertia expression, by changing the controller parameters corresponding to the small-signal model and the operating parameters of the energy storage converter, and to analyze the frequency response characteristics of the energy storage converter under different virtual inertia control modes based on the amplitude and phase characteristics of the Bode plot. The amplitude of the Bode plot is used to characterize the equivalent inertia time constant of the energy storage converter, and the phase of the Bode plot is used to characterize the inertia response speed of the energy storage converter. The energy storage converter with virtual inertia control includes a virtual inertia controller, a DC voltage controller, an energy storage controller, and a phase-locked loop controller. The equivalent inertia expression of the energy storage converter satisfies: in, This represents the equivalent inertia of the energy storage converter. , , and These represent the inertia coefficients extracted from the amplitude-phase motion model, respectively. in, Related to the inherent characteristics of the energy storage converter, the following conditions are met: in, The rated angular velocity representing the power grid frequency. Indicates the power factor. Represents the Laplace operator; Related to the control parameters of the virtual inertia controller, the following conditions must be met: in, This represents the droop coefficient for virtual inertia control. Indicates the virtual inertia control time constant; Related to the control parameters of the DC voltage controller and the energy storage controller, the following conditions must be met: in, C Indicates the DC capacitance value. Indicates the magnitude of the DC bus voltage. Indicates the magnitude of the filter inductance. This represents the proportional coefficient for DC voltage control. This represents the differential coefficient for DC voltage control. Indicates the energy storage coefficient. This represents the steady-state value of the output voltage of the energy storage converter; Related to the control parameters of the phase-locked loop controller, the following conditions must be met: in, This represents the proportional coefficient of the phase-locked loop control. This represents the differential coefficient of the phase-locked loop control. This represents the oscillation frequency of a second-order system. This indicates the damping ratio.

7. An electronic device, characterized in that, include: At least one memory for storing computer programs; At least one processor is configured to execute a program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to perform the method as described in any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is run on the processor, it causes the processor to perform the method as described in any one of claims 1-5.

Images