A method and system for controlling fluid energy storage

Abstract

This invention belongs to the field of flow energy storage control technology, specifically relating to a flow energy storage control method and system. The method includes: real-time acquisition of grid frequency and calculation of frequency deviation and frequency change rate; obtaining baseline adjustment amounts for virtual inertia and damping coefficient using symmetric fuzzy logic control; constructing a multi-dimensional directional factor for frequency disturbance based on frequency deviation and frequency change rate, and obtaining the final adjustment amount in combination with the baseline adjustment amount; and updating the virtual inertia and damping coefficient of the virtual synchronous machine online based on the final adjustment amount. This invention improves the flow energy storage system's ability to support grid frequency declines by constructing an asymmetric enhanced control strategy, effectively improving grid stability and security.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of liquid flow energy storage control. More particularly, the present application relates to a liquid flow energy storage control method and system. BACKGROUND

[0002] With the increasing proportion of renewable energy generation in the power grid, large-scale energy storage systems, especially liquid flow energy storage systems, play an increasingly important role in maintaining the stability of the power grid. In order to enable the energy storage system to actively support the power grid, the grid-connected inverter of the energy storage system widely adopts a virtual synchronous machine control strategy. This strategy can effectively smooth the frequency fluctuations of the power grid and enhance the stability of the power grid by simulating the inertia and damping characteristics of a traditional synchronous generator.

[0003] In order to improve the adaptability of the virtual synchronous machine control strategy to complex power grid conditions, intelligent algorithms such as fuzzy logic control are often used in the prior art to adjust the virtual inertia and damping parameters online according to the real-time dynamic changes of the grid frequency. However, when designing fuzzy rules, this kind of method usually follows the principle of symmetry, that is, for the frequency deviation with the same amplitude but opposite directions, the control action with the same size and opposite direction is applied.

[0004] This symmetrical design ignores the characteristic that the power grid is more sensitive to the frequency drop caused by power shortage than to the frequency rise caused by power surplus. Frequency drop is more likely to trigger a chain reaction and even cause system instability. This is because when the system has a power shortage, the rapid drop in frequency will cause the generator speed to decrease, increasing the mechanical stress of the generator set, which may trigger the action of the protection device and cause cascading trips. In contrast, when the system has a power surplus, the frequency rise usually has a larger adjustment margin. Therefore, the traditional symmetrical control strategy is insufficient in terms of the strength and speed of the support response when dealing with critical frequency drop events, and cannot achieve the optimal support effect of the power grid, which poses a challenge to the safety of the power grid under extreme conditions. SUMMARY

[0005] To solve the technical problem of insufficient support response of the existing virtual synchronous machine symmetrical control strategy when dealing with critical power grid frequency drop events, the present application provides solutions in the following aspects.

[0006] In a first aspect, the present application provides a liquid flow energy storage control method, comprising:

[0007] real-time collecting the grid frequency of a grid-connected point, and calculating a frequency deviation and a frequency change rate based on the collected grid frequency;

[0008] obtaining a reference adjustment amount of virtual inertia and damping coefficient by using symmetrical fuzzy logic control according to the frequency deviation and the frequency change rate;

[0009] constructing a frequency disturbance multi-dimension direction factor according to the frequency deviation and the frequency change rate, the value of the frequency disturbance multi-dimension direction factor being greater than 1 only when both the frequency deviation and the frequency change rate are negative values;

[0010] multiplying the frequency disturbance multi-dimension direction factor by the reference adjustment amount to obtain a final adjustment amount of the virtual inertia and the damping coefficient;

[0011] updating the virtual inertia and the damping coefficient of the virtual synchronous machine on-line according to the final adjustment amount.

[0012] Preferably, the frequency deviation and the frequency change rate are calculated based on the collected grid frequency, comprising:

[0013] obtaining a real-time frequency of the grid through a phase-locked loop unit, and subtracting a rated frequency of the grid from the real-time frequency to obtain the frequency deviation;

[0014] applying a least square method to linearly fit the frequency data point sequence, and taking a slope of the fitted straight line as the frequency change rate at the current time.

[0015] Preferably, the reference adjustment amount of the virtual inertia and the damping coefficient is obtained by using the symmetric fuzzy logic control, comprising:

[0016] constructing a fuzzy logic controller with the frequency deviation and the frequency change rate as double inputs, and with the reference adjustment amount of the virtual inertia and the reference adjustment amount of the damping coefficient as double outputs;

[0017] fuzzifying the input amounts, and performing fuzzy reasoning according to a symmetric fuzzy rule base to obtain the reference adjustment amount of the virtual inertia and the damping coefficient through defuzzification.

[0018] Preferably, the frequency disturbance multi-dimension direction factor satisfies the expression:

[0019]

[0020] In the expression, denotes the frequency disturbance multi-dimension direction factor; denotes the real-time frequency deviation; denotes a normalized reference value of the frequency deviation; denotes the real-time frequency change rate; denotes an asymmetric gain coefficient of the frequency deviation; denotes an asymmetric gain coefficient of the frequency change rate; denotes a scaling coefficient of denotes a hyperbolic tangent function.

[0021] ​Preferably, obtaining the final adjustment amount for the virtual inertia and damping coefficient includes:

[0022] Multiply the frequency perturbation multidimensional direction factor by the virtual inertia reference adjustment amount to obtain the final virtual inertia adjustment amount;

[0023] Multiply the frequency disturbance multidimensional direction factor by the damping coefficient reference adjustment amount to obtain the final adjustment amount of the damping coefficient.

[0024] Preferably, the online update of the virtual inertia and damping coefficient of the virtual synchronizer based on the final adjustment includes:

[0025] The final adjustment of the virtual inertia is added to the virtual inertia before the update to obtain the updated virtual inertia. The final adjustment of the damping coefficient is added to the damping coefficient before the update to obtain the updated damping coefficient.

[0026] Preferably, the online update further includes:

[0027] Boundary constraints are applied to the updated parameters. If the updated virtual inertia is greater than the preset maximum virtual inertia value, it is forced to be equal to the maximum virtual inertia value; if it is less than the preset minimum virtual inertia value, it is forced to be equal to the minimum virtual inertia value. If the updated damping coefficient is greater than the preset maximum damping coefficient value, it is forced to be equal to the maximum damping coefficient value; if it is less than the preset minimum damping coefficient value, it is forced to be equal to the minimum damping coefficient value.

[0028] Preferably, the fuzzification of the input includes:

[0029] The domain of the frequency deviation and the frequency change rate are divided into five fuzzy subsets, and the membership degree of the input value on the fuzzy subsets is determined by the triangular membership function.

[0030] Preferably, the defuzzification employs the area centroid method.

[0031] Secondly, the present invention provides a fluid flow energy storage control system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned fluid flow energy storage control method is implemented.

[0032] By adopting the above technical solution, a computer program for the above-mentioned liquid flow energy storage control method is generated and stored in a memory so that it can be loaded and executed by a processor. A terminal device can then be made based on the memory and the processor for convenient use.

[0033] The beneficial effects of this invention are as follows: By real-time acquisition of grid frequency and calculation of frequency deviation and frequency change rate, this invention constructs symmetric fuzzy logic control to obtain the benchmark adjustment amount of virtual inertia and damping coefficient. Then, based on the frequency drop depth and speed characteristics, it constructs a multi-dimensional directional factor of frequency disturbance to achieve asymmetric enhanced control for grid frequency drop conditions. When the grid frequency is in a dropping state, it automatically enhances the parameter adjustment amplitude, improving the support capability of the flow hydride energy storage system for grid frequency drops. For frequency rise conditions, it maintains the conventional control strategy. This differentiated response mechanism enables the flow hydride energy storage system to provide stronger support in the most vulnerable frequency drop scenarios of the grid, effectively cope with the chain reaction risk caused by rapid grid frequency drops, and avoid unnecessary parameter adjustments in non-critical operating conditions. This ensures efficient utilization of control resources and long-term system stability, thereby improving the overall stability of the grid and providing more reliable energy storage support for the large-scale integration of renewable energy. Attached Figure Description

[0034] Figure 1 This is a schematic flowchart illustrating a fluid flow energy storage control method according to the present invention;

[0035] Figure 2 This is the power grid frequency curve;

[0036] Figure 3 The curve represents the frequency deviation versus the rate of change of frequency.

[0037] Figure 4 A graph showing the variation of the multidimensional directional factor of frequency perturbation;

[0038] Figure 5 According to Figure 2 The curve showing the change of virtual inertia after dynamic adaptive adjustment of the power grid frequency;

[0039] Figure 6 According to Figure 2 The curve showing the change of damping coefficient after dynamic adaptive adjustment of the power grid frequency. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0042] This invention discloses a liquid flow energy storage control method, referring to... Figure 1This includes steps S1-S4:

[0043] S1. Real-time acquisition of the grid frequency at the grid connection point, and calculation of frequency deviation and frequency change rate based on the acquired grid frequency.

[0044] It should be noted that when the grid frequency fluctuates, the frequency deviation reflects the severity of the power imbalance, while the frequency change rate reflects the speed at which the power imbalance develops. For flow storage systems, a decrease in grid frequency represents a power deficit condition, in which case the flow storage system needs to rapidly release energy to support the grid; conversely, an increase in frequency represents a power surplus condition, in which the flow storage system needs to absorb the excess energy. The grid is far more sensitive to frequency decreases than to frequency increases because a rapid frequency decrease can easily trigger a cascading failure, while a frequency increase usually has more buffer space. Therefore, this invention obtains the frequency deviation and frequency change rate to achieve differentiated control of frequency increases and decreases.

[0045] Specifically, the phase-locked loop unit deployed at the grid connection point of the flow storage system tracks the grid voltage phase angle in real time to obtain the real-time frequency of the grid. For example, Figure 2 This is the power grid frequency curve.

[0046] Furthermore, based on the real-time frequency and the rated frequency of the power grid The real-time frequency deviation is calculated as follows:

[0047]

[0048] In the formula, Indicates the real-time frequency of the power grid; This indicates the rated frequency of the power grid, with a standard value of 50 Hz. Indicates frequency deviation. When When the value is positive, it indicates that the grid frequency is higher than the rated value, and the system is in a state of excess power; when When the value is negative, it indicates that the power grid frequency is lower than the rated value and the system is in a power deficit condition.

[0049] A linear fit is performed using the least squares method on the frequency data point sequence collected in the recent period, and the slope of the fitted line is taken as the rate of change of frequency at the current moment. ,when When the value is negative and the absolute value is large, it indicates that the power grid frequency is rapidly decreasing and the system is in an emergency state; when... A positive value indicates that the power grid frequency is rising. In this embodiment, the frequency data point sequence includes the power grid frequency collected within the last 100 milliseconds. This time length effectively balances measurement noise suppression and dynamic response speed. In other embodiments, implementers can choose the time length according to the actual implementation situation, for example, a value between 50 milliseconds and 200 milliseconds.

[0050] It should be noted that the least squares method is used in this invention because it can effectively filter out measurement noise and high-frequency disturbances compared to simple two-point difference, resulting in a smoother estimate of the frequency change rate that better reflects the true trend. In other embodiments, implementers can choose the frequency data processing method according to the actual implementation situation, such as using Kalman filtering or moving average filtering.

[0051] For example, Figure 3 for Figure 2 The frequency deviation and frequency change rate curves corresponding to the power grid frequency.

[0052] S2. Based on the frequency deviation and the rate of change of frequency, the reference adjustment amounts of virtual inertia and damping coefficient are obtained by using symmetrical fuzzy logic control.

[0053] It should be noted that the dynamic characteristics of power grid frequency are highly nonlinear. Fuzzy logic can effectively handle this nonlinear relationship. In order to establish a benchmark for asymmetric control, this invention first constructs a standard symmetric fuzzy logic controller. The controller outputs parameter adjustment amounts that do not consider the directional characteristics of disturbances, which serve as the basis for subsequent asymmetric correction.

[0054] Specifically, constructing based on frequency deviation and rate of change of frequency It is a dual-input system, with adjustment based on a virtual inertia reference. and damping coefficient reference adjustment amount This is a dual-output fuzzy logic controller. The input is fuzzified, and its domain of discourse is divided into five fuzzy subsets: {negative large (NB), negative small (NS), zero (ZE), positive small (PS), and positive large (PB)}. The membership degree of the precise input value on these subsets is determined using a triangular membership function. The domain of discourse is usually set as , The domain of discourse is usually set as The specific range can be adjusted according to the actual power grid characteristics. In other embodiments, implementers can select the number and type of fuzzy subsets according to the actual implementation situation, such as using 7 fuzzy subsets or Gaussian membership functions, etc.

[0055] Based on control engineering experience, a symmetrical "IF-THEN" fuzzy rule base was established, containing 25 rules that cover all input combinations. For example, the core rule "IF" is NB AND is NB, THEN is PB AND "isPB" indicates that when the frequency is in a state of significant decrease, the maximum positive adjustment should be given to enhance the system's inertia and damping. Through Mamdani fuzzy inference and the area centroid method for defuzzification, the fuzzy conclusions are converted into precise reference adjustment amounts. and ,in, This indicates the adjustment amount of the virtual inertia reference. This represents the baseline adjustment amount for the damping coefficient. This baseline control strategy symmetrically handles both rising and falling frequency conditions; that is, for frequency deviations and rates of change of the same amplitude, regardless of whether they are positive or negative, it generates an adjustment amount of equal magnitude but opposite direction.

[0056] S3. Construct a multi-dimensional directional factor for frequency disturbance based on frequency deviation and frequency change rate, and obtain the final adjustment amount by combining it with the benchmark adjustment amount.

[0057] It is important to note that the danger of a grid frequency drop event depends not only on the depth of the drop but also, and perhaps more importantly, on its speed. In actual grid operation, a frequency drop of 0.3 Hz occurring within 2 seconds is far more dangerous than one of the same magnitude occurring within 10 seconds. This is because a rapid drop often signifies a sudden disconnection of a large-capacity generator or a sudden connection of a large load, potentially triggering a chain reaction. As a critical component supporting the grid, flow storage systems must respond more strongly to such rapid frequency drop disturbances. Therefore, this invention constructs a multi-dimensional directional factor for frequency disturbances to assess the degree of danger of frequency drops, enabling the system to provide stronger support under critical operating conditions while avoiding over-adjustment under non-critical conditions.

[0058] Specifically, construct a multi-dimensional directional factor for frequency perturbation:

[0059]

[0060] In the formula, This represents the multidimensional directional factor of frequency perturbation; Indicates real-time frequency deviation; This represents the normalized reference value for frequency deviation, used to... Converted to a dimensionless quantity, it is set to 0.5 in this embodiment. This value is determined based on power grid operation standards, because a power grid frequency deviation exceeding ±0.5 Hz is generally considered a serious anomaly requiring emergency handling; therefore, 0.5 was chosen. As a normalized reference value, it can ensure that under typical power grid fault conditions... The value is within a reasonable range, making The function can effectively distinguish frequency deviations of different degrees of severity; Indicates the real-time rate of change of frequency; The asymmetric gain coefficient, representing the frequency deviation, typically ranges from 100 to 1000. To ensure sufficient asymmetric enhancement under typical power grid conditions while avoiding over-response, this embodiment... Setting it to 0.3 provides sufficient enhancement during severe frequency drops without having an excessive impact during minor fluctuations; The asymmetric gain coefficient, representing the rate of frequency change, typically ranges from [value range missing]. The value is determined based on the statistical characteristics of the power grid frequency change rate, so that the system has a stronger response to rapid frequency changes. In this embodiment, it is set to 0.5. express The scaling factor is used to adjust... The numerical range, eliminate Its dimensions, making it consistent with The effective scope matching, the value range is usually 1. s / Hz, when The system enters an emergency state when the frequency exceeds ±0.5 Hz per second. This embodiment is configured as follows: s / Hz, making exist Within the range, with The effective scope of the function matches, ensuring that... hour, A value close to 1 provides the maximum enhancement effect; Denotes the hyperbolic tangent function, which smoothly maps any real number to... Range. In other embodiments, implementers can select specific values ​​for the parameters based on actual implementation conditions, such as adjusting them according to factors like grid inertia level and load characteristics.

[0061] This invention uses conditional judgment and The control enhancement range is precisely limited to the frequency decline range; that is, the asymmetric enhancement mechanism is activated only when the grid frequency is below the rated value and continues to decline. When the condition is met, the multi-dimensional directional factor of the frequency disturbance increases with the depth and speed of frequency decline. The absolute value increases or When the absolute value increases, and As it increases, it leads to Increase. This invention employs a hyperbolic tangent function, making the enhancement effect sensitive in the early stages of disturbance, while tending to smooth and saturate when the disturbance is extremely large, effectively preventing excessive instantaneous impacts on the control quantity due to measurement abrupt changes or noise. For example, when and At that time, if taken , , , ,but This indicates a 39.1% increase in control; while when and hour, This indicates a 68.0% increase in control, reflecting a stronger response to more severe disturbances.

[0062] For example, Figure 4 for Figure 2 A graph showing the variation of the multi-dimensional directional factor of frequency disturbance corresponding to the frequency of the China Power Grid.

[0063] Furthermore, the frequency perturbation multi-dimensional direction factor Applying the baseline adjustment, calculate the final parameter adjustment:

[0064]

[0065]

[0066] In the formula, This represents the final adjustment amount of the virtual inertia; This indicates the final adjustment amount of the damping coefficient; This represents the multi-dimensional direction factor of frequency disturbance. When the system is in a frequency descent condition... The final adjustment is amplified, causing the virtual synchronizer to exhibit stronger inertia and damping characteristics, thereby providing greater power support; under other operating conditions, The adjustment mechanism in this invention maintains the baseline adjustment amount. It ensures that the flow energy storage system provides the strongest support during the most vulnerable moments of the power grid, while avoiding wasting regulation capacity under non-critical operating conditions.

[0067] S4. Update the virtual inertia and damping coefficient of the virtual synchronizer online based on the final adjustment.

[0068] It should be noted that, as a key component supporting the power grid, the control parameters of a flow energy storage system must be able to respond quickly to the dynamic demands of the grid while preventing excessive parameter drift that could lead to system instability. This is especially true when the grid experiences continuous disturbances, as parameters may cumulatively drift beyond the stable operating range, affecting system stability. Therefore, this invention incorporates a boundary constraint mechanism to prevent the control system from becoming unstable under extreme or continuous disturbances.

[0069] Specifically, the final adjustment amount of virtual inertia And the virtual inertia before the update Accumulate to obtain the updated virtual inertia. The final adjustment amount of the damping coefficient Damping coefficient before update Accumulate the results and update the damping coefficient. .

[0070] Apply boundary constraints to the updated virtual inertia and damping coefficient. The safe operating range is Damping coefficient The safe operating range is . This represents the minimum virtual inertia value, typically set as the minimum inertia required for stable system operation, with a value range of [value missing]. In this embodiment, it is set to 2; This represents the maximum virtual inertia value, set based on the physical overload capacity of the flow storage system and the requirements for safe operation of the power grid. Its value range is [range missing]. In this embodiment, it is set to 15; This represents the minimum damping coefficient, typically set as the minimum damping required to prevent system oscillations, and its value ranges from [value missing]. In this embodiment, it is set to 5; This represents the maximum damping coefficient, set according to the dynamic response characteristics of the flow storage system and grid connection technical standards, with a range of [value missing]. In this embodiment, the value is set to 30. In other embodiments, implementers can set specific values ​​for the boundary parameters according to the actual implementation situation, such as adjusting them based on factors like the capacity of the flow energy storage system and the short-circuit capacity of the power grid.

[0071] The control system will make a judgment immediately after each parameter update. Whether it has escaped the range, if Greater than Then a mandatory order equal ;like Less than Then a mandatory order equal .right Perform the same logic for boundary constraint processing.

[0072] It should be noted that boundary constraints ensure that control parameters are always within a safe range. When the power grid experiences continuous disturbances, they prevent excessive parameter accumulation that could lead to system instability. When the power grid returns to normal, they allow parameters to gradually return to the normal range, thus preventing the system from being in a state of high stress continuously.

[0073] For example, Figure 5 According to Figure 2 The curve showing the change of virtual inertia after dynamic adaptive adjustment of the power grid frequency.Figure 6 According to Figure 2 The curve showing the change of damping coefficient after dynamic adaptive adjustment of the power grid frequency.

[0074] Furthermore, the parameter values ​​after boundary constraints are sent to the core computing module of the virtual synchronous machine to complete the adaptive control closed loop, thereby achieving precise and efficient support for power grid frequency fluctuations.

[0075] This invention also discloses a fluid energy storage control system, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a fluid energy storage control method according to the present invention.

[0076] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0077] In the description of this specification, "multiple" or "several" means at least two, such as two, three or more, unless otherwise expressly and specifically defined.

[0078] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A liquid flow energy storage control method, characterized by, The method comprises the steps of: collecting the grid frequency of a grid point in real time, and calculating the frequency deviation and the frequency change rate based on the collected grid frequency; obtaining the reference adjustment amount of the virtual inertia and the damping coefficient by using the symmetric fuzzy logic control according to the frequency deviation and the frequency change rate, comprising: constructing a fuzzy logic controller with the frequency deviation and the frequency change rate as double inputs, and the reference adjustment amount of the virtual inertia and the damping coefficient as double outputs; fuzzying the input quantity, and performing fuzzy reasoning according to the symmetric fuzzy rule base to obtain the reference adjustment amount of the virtual inertia and the damping coefficient by defuzzification; the symmetric fuzzy logic control symmetrically processes the frequency rising and falling conditions, and generates adjustment amounts of equal size and opposite direction for the same amplitude of frequency deviation and change rate, regardless of positive or negative; constructing a frequency disturbance multi-dimensional direction factor according to the frequency deviation and the frequency change rate, and the value of the frequency disturbance multi-dimensional direction factor is greater than 1 only when the frequency deviation and the frequency change rate are both negative; the frequency disturbance multi-dimensional direction factor satisfies the expression: In the formula, represents a frequency disturbance multi-dimensional direction factor; represents a real-time frequency deviation; represents a frequency deviation normalized reference value; represents a real-time frequency change rate; represents an asymmetric gain coefficient of the frequency deviation; represents an asymmetric gain coefficient of the frequency change rate; represents a scaling coefficient of represents a hyperbolic tangent function; multiplying the frequency disturbance multi-dimensional direction factor and the reference adjustment amount to obtain the final adjustment amount of the virtual inertia and the damping coefficient; updating the virtual inertia and the damping coefficient of the virtual synchronous machine online according to the final adjustment amount.

2. The liquid flow energy storage control method of claim 1, wherein The method for calculating the frequency deviation and the frequency change rate based on the collected grid frequency comprises: obtaining the real-time frequency of the grid by a phase-locked loop unit, and obtaining the frequency deviation by subtracting the rated frequency of the grid from the real-time frequency; applying the least square method to linear fitting of the frequency data point sequence, and taking the slope of the fitted straight line as the frequency change rate at the current time.

3. The liquid flow energy storage control method of claim 1, wherein The method for obtaining the final adjustment amount of the virtual inertia and the damping coefficient comprises: multiplying the frequency disturbance multi-dimensional direction factor and the reference adjustment amount of the virtual inertia to obtain the final adjustment amount of the virtual inertia; multiplying the frequency disturbance multi-dimensional direction factor and the reference adjustment amount of the damping coefficient to obtain the final adjustment amount of the damping coefficient.

4. The liquid flow energy storage control method of claim 1, wherein, The method for updating the virtual inertia and the damping coefficient of the virtual synchronous machine online according to the final adjustment amount comprises: accumulating the final adjustment amount of the virtual inertia and the virtual inertia before updating to obtain the updated virtual inertia, and accumulating the final adjustment amount of the damping coefficient and the damping coefficient before updating to obtain the updated damping coefficient.

5. The liquid flow energy storage control method of claim 4, wherein, The online updating further comprises: applying boundary constraints to the updated parameters, and forcibly setting the updated virtual inertia equal to the preset maximum virtual inertia if the updated virtual inertia is greater than the preset maximum virtual inertia, or equal to the preset minimum virtual inertia if the updated virtual inertia is less than the preset minimum virtual inertia; forcibly setting the updated damping coefficient equal to the preset maximum damping coefficient if the updated damping coefficient is greater than the preset maximum damping coefficient, or equal to the preset minimum damping coefficient if the updated damping coefficient is less than the preset minimum damping coefficient.

6. The liquid flow energy storage control method of claim 1, wherein, The method for fuzzying the input quantity comprises: dividing the value domain of the frequency deviation and the frequency change rate into five fuzzy subsets, and determining the membership of the input value in the fuzzy subsets by using a triangular membership function.

7. The liquid flow energy storage control method of claim 1, wherein The defuzzification adopts the area barycenter method.

8. A liquid flow energy storage control system, characterized by, The method comprises the steps of: A processor and a memory, the memory storing computer program instructions which, when executed by the processor, implement a liquid flow energy storage control method according to any one of claims 1-7.

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