Energy storage control method and system for new energy power fluctuation suppression and related equipment

Abstract

The invention is suitable for the technical field of energy storage control, and provides an energy storage control method and system for new energy power fluctuation suppression and related equipment, and the method comprises the steps: collecting the active power outputted by a plurality of new energy equipment in a first preset period time, and carrying out the preprocessing of the collected data; determining an initial value of active power output by the new energy equipment in the first preset period time according to the preprocessed data; on the basis of the initial value of the active power output by the new energy equipment in the first preset period time, calculating a predicted value of the active power output by the new energy equipment in the second preset period time by using a sliding linear regression filtering algorithm; and determining a power reference value of the energy storage controller according to the predicted value of the total output active power of the new energy equipment in the second preset period time. According to the method, the reference value of the power is calculated through the sliding linear regression filtering algorithm, and the fluctuation suppression effect of the new energy plant station is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of energy storage control technology, and in particular to an energy storage control method, system and related equipment for suppressing power fluctuation of new energy. Background Art

[0002] Generally speaking, configuring energy storage on the new energy plant side can suppress fluctuations in new energy power generation, improve the ability of new energy to track planned power generation output, and reduce wind and solar power abandonment; configuring energy storage on the grid side can delay investment in transmission equipment and network upgrades and expansions, alleviate congestion in transmission channels, and improve grid stability; configuring energy storage on the load side can reduce the peak-to-valley difference, improve power quality, and play a role in emergency power supply.

[0003] In the prior art, the energy storage control algorithms for suppressing power fluctuations of new energy sources mainly include filtering control algorithms, sliding average algorithms, model prediction control algorithms, etc. Among them, filtering control algorithms are the most widely used algorithms for smoothing power fluctuations of new energy sources. Filtering control algorithms can be divided into Kalman filtering, wavelet transform, filtering algorithms, etc. Among them, Kalman filtering is suitable for real-time data processing and calculation of wind power and photovoltaics, but it is difficult to handle high-frequency power fluctuations; wavelet transform has the advantage of handling short-term power fluctuations, but the algorithm is complex, and the selection of wavelet basis functions and the number of decomposition layers are the difficulties of the algorithm; filtering algorithms have the advantages of strong applicability and simple algorithm principles. Through a certain filtering algorithm, the target power of wind power and photovoltaics is obtained, and then the smoothing effect and energy storage capacity configuration under different filtering parameters are obtained. The difficulty of the algorithm lies in the reasonable selection of filtering parameters. More importantly, the phase deviation of the low-pass filter will cause serious power lag of the output power, resulting in a significant increase in the charging and discharging power of the battery, reducing the battery life.

[0004] In view of this, there is a need for an energy storage control method, system and related equipment for suppressing power fluctuations of new energy. Summary of the invention

[0005] The embodiments of the present application provide a method, system and related equipment for energy storage control for suppressing power fluctuations of new energy sources, which are used to solve the problem that improper existing energy storage control leads to a significant increase in battery charging and discharging power and a reduction in battery life.

[0006] A first aspect of an embodiment of the present application provides an energy storage control method for suppressing power fluctuations of new energy sources, comprising:

[0007] Collecting the active power output by a number of new energy devices during a first preset cycle time, and preprocessing the collected data;

[0008] Determine the starting value of the total active power output of the new energy equipment at the first preset cycle time according to the preprocessed data;

[0009] Based on the initial value of the total active power output of the new energy equipment at the first preset cycle time, a predicted value of the total active power output of the new energy equipment at the second preset cycle time is calculated using a sliding linear regression filtering algorithm;

[0010] The power reference value of the energy storage controller is determined according to the predicted value of the total active power output of the new energy equipment during the second preset cycle time.

[0011] Furthermore, the expression for determining the starting value of the total active power output of the new energy equipment at the first preset cycle time according to the preprocessed data is as follows:

[0012]

[0013] Where: P RE is the total output active power, n is the number of new energy equipment, P REi is the active power output by the i-th new energy device.

[0014] Furthermore, the predicted value of the total active power output of the new energy equipment at the second preset cycle time is calculated based on the initial value of the total active power output of the new energy equipment at the first preset cycle time by using a sliding linear regression filtering algorithm, including:

[0015] Using the Gaussian kernel function as a weight function, constructing a weight matrix corresponding to the initial value of the total active power output of the new energy equipment in the first preset cycle time, and calculating each element in the weight matrix;

[0016] Constructing a design matrix and a target vector based on the initial value of the total active power output of the new energy equipment at the first preset cycle time;

[0017] Calculating the coefficient vector by weighted least squares method based on the weight matrix corresponding to the initial value of the total active power output of the new energy equipment in the first preset cycle time;

[0018] A predicted value of the total active power output of the new energy equipment at the second preset cycle time is calculated based on the coefficient vector.

[0019] Furthermore, the Gaussian kernel function is used as a weight function to construct a weight matrix corresponding to the initial value of the total active power output of the new energy equipment in the first preset cycle time, and each element in the weight matrix is ​​calculated, including:

[0020] Weight matrix W:

[0021] W=diag(w0,w1,…,w k );

[0022] Where: diag is a diagonal matrix, k is the number of data points contained in a time window;

[0023] Calculate each element in the weight matrix:

[0024]

[0025] Where: e is the natural logarithm, τ is the bandwidth parameter, t ―k is the moment corresponding to the first k sampling periods of the first preset cycle time, and t0 is the first preset cycle time.

[0026] Furthermore, the construction of the design matrix and the target vector based on the initial value of the total active power output of the new energy equipment in the first preset cycle time includes:

[0027] Design matrix X and target vector y:

[0028]

[0029]

[0030] Where: P RE (t0) is the starting value of the total active power output of the new energy equipment in the first preset cycle time, P RE (t ―k ) is the measured value of the total active power output of the new energy equipment in the first k sampling cycles of the first preset cycle time.

[0031] Furthermore, the calculation of the predicted value of the total active power output of the new energy equipment at the second preset cycle time based on the coefficient vector includes:

[0032] Coefficient vector β:

[0033] β=(X T WX) ―1 X T Ey;

[0034] Predicted value:

[0035] P RE (t1) = X1β;

[0036] X1=[1t1t1 2 …t1 n ];

[0037] Where: P RE (t1) is the predicted value of the total active power output of the new energy equipment corresponding to the second preset cycle time t1.

[0038] Furthermore, the power reference value of the energy storage controller is determined according to the predicted value of the total active power output of the new energy equipment in the second preset cycle time, including:

[0039] The power reference value of the energy storage controller is determined according to the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant.

[0040] Furthermore, the power reference value of the energy storage controller is determined according to the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant, including:

[0041] When the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant is greater than the upper limit of the energy storage active output capacity, the power reference value of the energy storage controller is the upper limit of the energy storage active output capacity;

[0042] When the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant is greater than the lower limit of the energy storage active output capacity and less than the upper limit of the energy storage active output capacity, the power reference value of the energy storage controller is the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant;

[0043] When the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant is less than the lower limit of the energy storage active output capacity, the power reference value of the energy storage controller is the lower limit of the energy storage active output capacity.

[0044] A second aspect of the embodiment of the present application provides an energy storage control system for suppressing power fluctuations of new energy sources, including:

[0045] A data acquisition and preprocessing unit, used to acquire the active power output by a plurality of new energy devices during a first preset period, and to preprocess the acquired data;

[0046] An active power starting value determination unit, used to determine the starting value of the active power of the total output of the new energy equipment in the first preset cycle time according to the preprocessed data;

[0047] An active power prediction value calculation unit, used to calculate the prediction value of the active power of the total output of the new energy equipment at the second preset cycle time by using a sliding linear regression filtering algorithm based on the initial value of the active power of the total output of the new energy equipment at the first preset cycle time;

[0048] The power reference value determination unit is used to determine the power reference value of the energy storage controller according to the predicted value of the total active power output of the new energy equipment during the second preset cycle time.

[0049] A third aspect of an embodiment of the present application provides a computer device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein when the processor executes the computer-readable instructions, the steps of the energy storage control method for suppressing power fluctuations of new energy as described in any one of the above are implemented.

[0050] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0051] The present application determines the starting value of the total active power output of new energy equipment in the first preset cycle time based on the active power output of several new energy equipment in the first preset cycle time; secondly, a sliding linear regression filtering algorithm is used to calculate the predicted value of the total active power output of new energy equipment in the second preset cycle time; finally, the power reference value of the energy storage controller is determined according to the predicted value of the total active power output of the new energy equipment in the second preset cycle time, and is transmitted to the energy storage controller to achieve the fluctuation suppression effect of the new energy plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic flow chart of an embodiment of an energy storage control method for suppressing power fluctuation of new energy in the present invention;

[0053] Figure 2 It is a simplified diagram of the electrical connection of the primary equipment of the plant in the present invention;

[0054] Figure 3 A schematic diagram of coordinated control of renewable energy power fluctuation suppression in the present invention;

[0055] Figure 4 The structure diagram of an energy storage control system for suppressing power fluctuations of new energy in the present invention. DETAILED DESCRIPTION

[0056] The embodiments of the present application provide a method, system and related equipment for energy storage control for suppressing power fluctuations of new energy sources, which are used to achieve the fluctuation suppression effect of new energy plants.

[0057] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "corresponding to" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0058] Embodiment 1

[0059] The implementation method in this embodiment can be implemented in the system, in the server, or in the terminal, and there is no specific limitation. The following will introduce the energy storage control method for suppressing power fluctuations of new energy in this application from the perspective of system implementation. Figure 1 , the method provided in the embodiment of the present application comprises the following steps:

[0060] S11. Collecting the active power output of several new energy devices in the first preset cycle time, and preprocessing the collected data;

[0061] S12. Determine the starting value of the total active power output of the new energy equipment in the first preset cycle time according to the preprocessed data;

[0062] In this embodiment, the first preset cycle time is the current sampling cycle time. After obtaining the active power output by a number of new energy devices in the current sampling cycle time, the collected data is preprocessed. The preprocessing here includes data cleaning, outlier removal and data normalization to ensure the accuracy and availability of the data, wherein the determination of outliers is based on the degree to which the data deviates from the normal range, and the normal range is determined based on the use of data statistical analysis and equipment technical specifications.

[0063] Specifically, the energy storage control for suppressing power fluctuations of new energy sources is aimed at new energy plants equipped with electrochemical energy storage. The simplified electrical connection diagram of the primary equipment of the plant is as follows: Figure 2 See Figure 2 , where P REi P is the active power of the i-th new energy equipment obtained through measurement and calculation. Assuming that there are n new energy equipment in the plant, then i = 1, 2, ..., n. BSS is the active power of the energy storage device obtained through measurement and calculation, P tot It is the total active power of the entire plant calculated through measurement.

[0064] The active power output of all new energy devices in the current sampling period is added together to obtain the starting value P of the total active power output of the new energy devices. RE , the expression is as follows:

[0065]

[0066] By preprocessing the data and then summing up the active power output of all new energy equipment, the initial value obtained is more accurate, providing an effective data basis for subsequent predictions.

[0067] S13. Based on the starting value of the total active power output of the new energy equipment in the first preset cycle time, a sliding linear regression filter algorithm is used to calculate the predicted value of the total active power output of the new energy equipment in the second preset cycle time;

[0068] In this embodiment, sliding linear regression filtering (local linear regression filtering, MR) is a non-parametric regression method that uses a subset of data points near each prediction point for fitting, giving higher weights to neighboring points. RE is a set of discrete data, and the first preset cycle time (current time) is t0, and P is obtained. RE Represented as P RE (t0), the time corresponding to the previous sampling period is t1, and P RE Represented as P RE (t ―1 ), and so on, the time corresponding to the first k sampling periods is t ―k , P RE Represented as P RE (t ―k ). Sliding linear regression filtering is based on the current sampling period data P RE (t0) and historical data P RE (t ―k ) Calculate the estimated value P of the next sampling period RE (t1), which is the predicted value of the second preset cycle time. The following steps are included:

[0069] 1. Use the Gaussian kernel function as a weight function to construct a weight matrix corresponding to the initial value of the total active power output of the new energy equipment in the first preset cycle time, and calculate each element in the weight matrix;

[0070] Local linear regression uses a weight matrix W to assign different weights to each data point. The weights are usually defined using a kernel function. For the first k sampling periods, time t ―k , using the Gaussian kernel defined as:

[0071]

[0072] Where: e is the natural logarithm, τ is the bandwidth parameter, and the control weight varies with the distance P RE (t0) The speed of change. Generally, τ is taken as half of the time window.

[0073] Assume that the time window of the sliding linear regression filter is T and the sampling frequency is f, then the number of data points contained in a time window is: k = Tf w k As diagonal elements, the weight matrix W is constructed as:

[0074] W=diag(w0,w1,…,w k );

[0075] Where: diag is a diagonal matrix.

[0076] 2. Constructing a design matrix and a target vector based on the initial value of the total active power output of the new energy equipment in the first preset cycle time;

[0077] Specifically, let the fitting order be n, usually n is 2. Design matrix X and target vector y:

[0078]

[0079] 3. Based on the weight matrix corresponding to the initial value of the total active power output of the new energy equipment in the first preset cycle time, the coefficient vector is calculated by weighted least squares method;

[0080] 4. Calculate the predicted value of the total active power output of the new energy equipment at the second preset cycle time based on the coefficient vector.

[0081] Coefficient vector β:

[0082] β=(W T WX) ―1 X T Wy;

[0083] Predicted value:

[0084] P RE (t1) = X1β;

[0085] X1=[1 t1 t1 2 … t1 n ];

[0086] Where: P RE (t1) is the predicted value of the total active power output of the new energy equipment corresponding to the second preset cycle time t1.

[0087] The estimated value P of the total output active power of the plant at time t1 is calculated by the sliding linear regression filtering algorithm. RE(t1), which is the output P of the filtering algorithm tot―ref :P tot―ref =P RE (t1).

[0088] S14. Determine a power reference value of the energy storage controller according to the predicted value of the total active power output of the new energy equipment in the second preset cycle time.

[0089] In this embodiment, determining the power reference value of the energy storage controller according to the predicted value of the total active power output of the new energy equipment in the second preset cycle time includes: determining the power reference value of the energy storage controller according to the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant.

[0090] See also Figure 3 , Figure 3 is the coordinated control diagram, P tot―ref is the reference value of the total active power output of the plant, P BSS―max and P BSS―min are the upper and lower limits of the active output capacity of energy storage, P BSS―ref It is the active power reference value finally output by the energy storage controller.

[0091] Specifically, when the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant is greater than the upper limit of the energy storage active output capacity, the power reference value of the energy storage controller is the upper limit of the energy storage active output capacity;

[0092] When the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant is greater than the lower limit of the energy storage active output capacity and less than the upper limit of the energy storage active output capacity, the power reference value of the energy storage controller is the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant;

[0093] When the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant is less than the lower limit of the energy storage active output capacity, the power reference value of the energy storage controller is the lower limit of the energy storage active output capacity.

[0094] Power reference value P of energy storage controller BSS―ref :

[0095]

[0096] The above sliding linear regression filtering algorithm uses a subset of data points for fitting near each prediction point, giving higher weights to neighboring points. Since the output curve of new energy is nonlinear, sliding linear regression filtering is suitable for situations where data has a nonlinear trend. Therefore, the implementation effect of the present invention has significant advantages over existing algorithms. At the same time, by adjusting multiple parameters to adapt to different types of new energy, the implementation effect has a wider adaptability than existing algorithms.

[0097] Embodiment 2

[0098] See also Figure 4 , an embodiment of an energy storage control system for suppressing power fluctuation of new energy in the present invention comprises the following steps:

[0099] A data acquisition and preprocessing unit, used to acquire the active power output by a plurality of new energy devices during a first preset period, and to preprocess the acquired data;

[0100] An active power starting value determination unit, used to determine the starting value of the active power of the total output of the new energy equipment in the first preset cycle time according to the preprocessed data;

[0101] The predicted value calculation unit of active power is used to calculate the predicted value of the total active power output of the new energy equipment at the second preset cycle time by using a sliding linear regression filtering algorithm based on the initial value of the total active power output of the new energy equipment at the first preset cycle time;

[0102] The power reference value determination unit is used to determine the power reference value of the energy storage controller according to the predicted value of the total active power output of the new energy equipment in the second preset cycle time.

[0103] Embodiment 3

[0104] The present invention provides a computer device, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the above method are implemented.

[0105] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0106] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored. In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0107] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0108] It can be understood that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for controlling energy storage for suppressing power fluctuation of new energy, characterized in that: include: Collecting the active power output by a number of new energy devices during a first preset cycle time, and preprocessing the collected data; Determine the starting value of the total active power output of the new energy equipment at the first preset cycle time according to the preprocessed data; Based on the initial value of the total active power output of the new energy equipment at the first preset cycle time, a predicted value of the total active power output of the new energy equipment at the second preset cycle time is calculated using a sliding linear regression filtering algorithm; The power reference value of the energy storage controller is determined according to the predicted value of the total active power output of the new energy equipment during the second preset cycle time.

2. The energy storage control method for suppressing power fluctuation of new energy according to claim 1 is characterized in that: The expression for determining the starting value of the total active power output of the new energy equipment at the first preset cycle time according to the preprocessed data is as follows: Where: R RE is the total output active power, n is the number of new energy equipment, P REi is the active power output by the i-th new energy device.

3. The energy storage control method for suppressing power fluctuation of new energy according to claim 1 is characterized in that: The method of calculating the predicted value of the total active power output of the new energy equipment at the second preset cycle time by using a sliding linear regression filtering algorithm based on the initial value of the total active power output of the new energy equipment at the first preset cycle time includes: Using the Gaussian kernel function as a weight function, constructing a weight matrix corresponding to the initial value of the total active power output of the new energy equipment in the first preset cycle time, and calculating each element in the weight matrix; Constructing a design matrix and a target vector based on the initial value of the total active power output of the new energy equipment at the first preset cycle time; Calculating the coefficient vector by weighted least squares method based on the weight matrix corresponding to the initial value of the total active power output of the new energy equipment in the first preset cycle time; A predicted value of the total active power output of the new energy equipment at the second preset cycle time is calculated based on the coefficient vector.

4. The energy storage control method for suppressing power fluctuation of new energy according to claim 3 is characterized in that: The Gaussian kernel function is used as a weight function to construct a weight matrix corresponding to the initial value of the total active power output of the new energy equipment in the first preset cycle time, and each element in the weight matrix is ​​calculated, including: Weight matrix W: W=diag(w0,w1,…,w k ); Where: diag is a diagonal matrix, k is the number of data points contained in a time window; Calculate each element in the weight matrix: Where: e is the natural logarithm, τ is the bandwidth parameter, t ―k is the moment corresponding to the first k sampling periods of the first preset cycle time, and t0 is the first preset cycle time.

5. The energy storage control method for suppressing power fluctuation of new energy according to claim 3 is characterized in that: The constructing of a design matrix and a target vector based on the initial value of the total active power output of the new energy equipment during the first preset cycle time comprises: Design matrix X and target vector y: Where: P RE (t0) is the starting value of the total active power output of the new energy equipment in the first preset cycle time, P RE (t ―k ) is the measured value of the total active power output of the new energy equipment in the first k sampling cycles of the first preset cycle time.

6. The energy storage control method for suppressing power fluctuation of new energy according to claim 3 is characterized in that: The calculation of the predicted value of the total active power output of the new energy equipment at the second preset cycle time based on the coefficient vector includes: Coefficient vector β: β=(X T WX) ―1 X T Wy; Predicted value: P RE (t1)=X1β; X1=[1 t1 t1 2 …t1 n ]; Where: P RE (t1) is the predicted value of the total active power output of the new energy equipment corresponding to the second preset cycle time t1.

7. The energy storage control method for suppressing power fluctuation of new energy according to claim 1 is characterized in that: The method of determining the power reference value of the energy storage controller according to the predicted value of the total active power output of the new energy equipment during the second preset cycle time includes: The power reference value of the energy storage controller is determined according to the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant.

8. The energy storage control method for suppressing power fluctuation of new energy according to claim 7 is characterized in that: The step of determining the power reference value of the energy storage controller according to the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant station includes: When the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant is greater than the upper limit of the energy storage active output capacity, the power reference value of the energy storage controller is the upper limit of the energy storage active output capacity; When the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant is greater than the lower limit of the energy storage active output capacity and less than the upper limit of the energy storage active output capacity, the power reference value of the energy storage controller is the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant; When the difference between the predicted value of the total active power output of the new energy equipment in the second preset cycle time and the measured value of the total active power output of the current plant is less than the lower limit of the energy storage active output capacity, the power reference value of the energy storage controller is the lower limit of the energy storage active output capacity.

9. A new energy power fluctuation suppression energy storage control system, characterized in that: include: A data acquisition and preprocessing unit, used to acquire the active power output by a plurality of new energy devices during a first preset period, and to preprocess the acquired data; An active power starting value determination unit, used to determine the starting value of the active power of the total output of the new energy equipment in the first preset cycle time according to the preprocessed data; An active power prediction value calculation unit, used to calculate the prediction value of the active power of the total output of the new energy equipment at the second preset cycle time by using a sliding linear regression filtering algorithm based on the initial value of the active power of the total output of the new energy equipment at the first preset cycle time; The power reference value determination unit is used to determine the power reference value of the energy storage controller according to the predicted value of the total active power output of the new energy equipment during the second preset cycle time.

10. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that: When the processor executes the computer-readable instructions, the steps of the energy storage control method for suppressing new energy power fluctuations as described in any one of claims 1 to 8 are implemented.

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