Double-battery energy storage system optimization control method for stabilizing wind power fluctuation

Abstract

The invention relates to a double-battery energy storage system optimization control method for stabilizing wind power fluctuation, and the method comprises the steps: obtaining the data of the actual output power of a wind power plant, decomposing the power data of the wind power plant through employing an improved adaptive noise complete set empirical mode decomposition method, and obtaining a limited number of IMF components; reconstructing the obtained IMF component in combination with a set grid-connected fluctuation standard, taking the reconstructed low-frequency component as grid-connected power to directly access the network, and taking the reconstructed high-frequency component as target power of the energy storage system to be distributed to the energy storage system; and taking the target power as a power task needing to be stabilized by the dual-battery energy storage system, obtaining an optimal control strategy of the dual-battery energy storage system in combination with the power constraint and the SOC constraint of the energy storage system, and evaluating the cycle life of the dual-battery energy storage system. According to the power data of the wind power plant, the charging and discharging power scheduling strategy of the double-battery energy storage system is obtained, and the service life of the double-battery energy storage system is evaluated by applying the strategy.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage system optimization control, and in particular relates to a dual-battery energy storage system optimization control method for smoothing wind power fluctuations. Background Art

[0002] Renewable energy has become a viable solution to global energy security and environmental degradation. Due to the maturity of wind power technology and low investment costs, wind power has developed rapidly, and wind power generation has increased exponentially year by year. However, like other renewable energy sources (such as solar energy and tidal energy), wind energy is uncertain and uncontrollable. This uncertainty brings technical difficulties to ensuring grid stability, reliability, and power quality. Due to the technological development of battery energy storage systems (BESS), it is widely used in alleviating wind power fluctuations. However, affected by the structure of the single-battery energy storage system, the battery energy storage system will frequently switch between charge and discharge states, and it is difficult to control the SOC charge and discharge cycle interval, which in turn affects the life of the energy storage battery.

[0003] In order to reduce the number of charge and discharge state transitions of BESS and increase the life of the battery energy storage system, the development of dual-battery unit energy storage systems has gradually attracted attention. However, how to schedule the charge and discharge of dual batteries is an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide a dual-battery energy storage system optimization control method for smoothing wind power fluctuations, aiming to obtain a charging and discharging power scheduling strategy for the dual-battery energy storage system based on the power data of the wind farm, and apply the strategy to perform life assessment on the dual-battery energy storage system.

[0005] The present invention provides a dual-battery energy storage system optimization control method for smoothing wind power fluctuations, comprising the following steps:

[0006] Step 1, obtaining the actual output power data of the wind farm, and using the improved adaptive noise complete set empirical mode decomposition method to decompose the wind farm power data to obtain a finite number of IMF components;

[0007] Step 2, reconstruct the obtained IMF components in combination with the set grid-connected fluctuation standard, directly connect the reconstructed low-frequency components to the grid as the grid-connected power, and use the reconstructed high-frequency components as the target power of the dual-battery energy storage system and allocate them to the dual-battery energy storage system;

[0008] Step 3: Use the target power in step 2 as the power task that the dual-battery energy storage system needs to smooth, combine the power constraint and SOC constraint of the dual-battery energy storage system, obtain the optimal control strategy of the dual-battery energy storage system, and evaluate the cycle life of the dual-battery energy storage system.

[0009] Furthermore, the data of the actual output power of the wind farm in step 1 is power data on a 1-minute time scale, that is, there are 1440 sampling points in a day, corresponding to 1440 minutes in a day.

[0010] Furthermore, the improved adaptive noise complete set empirical mode decomposition method described in step 1 decomposes the initial wind power signal to obtain p-order IMF components.

[0011] Furthermore, the specific process of the improved adaptive noise complete set empirical mode decomposition method described in step 1 is as follows:

[0012] 1) Add Gaussian white noise decomposed by EMD to the signal to be decomposed;

[0013] 2) Solve the local mean of the components and obtain the residual signal and IMF components;

[0014] 3) Repeat steps 1) and 2), and the noise signal added in each round is the IMF component of the previous noise signal;

[0015] 4) When there are no components with two adjacent local extreme values ​​in all the decomposed IMF components, the algorithm ends.

[0016] Furthermore, the obtained IMF components described in step 2 are reconstructed in the following specific manner:

[0017] High frequency components:

[0018] Low frequency component:

[0019] Where hfc is the target power of the dual-battery energy storage system, lfc is the low-frequency component reconstructed according to the set grid-connected fluctuation standard, and IMF i is the i-th IMF component, p+1 is the total number of components, and k is the order of the first violation of the volatility limit after reconstruction;

[0020] After reconstruction, the low-frequency component and high-frequency component that meet the specified grid-connected fluctuation limit are obtained, and the low-frequency component that meets the specified grid-connected fluctuation limit is used as the grid-connected component P Grid , the high frequency component is the component that needs to be smoothed by the dual battery energy storage system P Dbess , the relationship is as follows:

[0021] P w =P Grid +P Dbess

[0022] Where P w is the actual output power of the wind farm, PGrid is the low-frequency grid-connected component, P Dbess The dual battery energy storage system needs to smooth the power task.

[0023] Furthermore, the grid-connected fluctuation limit set in step 2 is that the fluctuation within 10 minutes does not exceed 5% of the wind power installed capacity.

[0024] Furthermore, in step 3:

[0025] The power constraint of the energy storage system is:

[0026] When P Dbess > 0, if battery A is discharging and battery B is charging in the system, the power constraints of battery cells A and B should satisfy If battery B is discharged and battery A is charged in the system, the power constraints of battery cells A and B should satisfy When P Dbess When <0, if battery A is discharged and battery B is charged in the system, the power constraints of battery cells A and B should satisfy If battery B is discharged and battery A is charged in the system, the power constraints of battery cells A and B should satisfy When P Dbess =0, the energy storage system is in standby mode and does not operate; is the charge and discharge power of battery cell A, MW; is the charge and discharge power of battery unit B, MW;

[0027] The SOC constraint of the energy storage system is:

[0028]

[0029] In the formula, is the lower limit of SOC of the battery unit in the dual-battery energy storage system, %; The upper limit of battery SOC in the one-charge-one-discharge operation mode, %; SOC ref is the optimal cycle range of the battery SOC; when the SOC of a battery cell in the current mode reaches the cycle range boundary, the charging and discharging trends of the two battery cells are switched; the recursive model of the SOC of the two battery cells over time is expressed as:

[0030]

[0031] Where: SOC A (t), SOC B (t) are the SOC state of charge values ​​of the two battery cells A and B at time t, %; S A , S Bare the charge and discharge status flags of the two battery units A and B at time t, where: the battery discharge status flag is 1, the charge status flag is -1, and the standby status flag is 0; ω1 and ω2 are the energy storage system mode flags at time t, ω1 = 1, ω2 = 0; Δt is the sampling time interval, s; E rat is the rated energy of the battery cell, MJ; η is the conversion efficiency, %;

[0032] The specific method of the cycle life evaluation is as follows:

[0033] The charge and discharge depth is calculated by using the rain flow counting method, and the cycle life is calculated by combining the relationship between the battery cycle life and its charge and discharge depth. The relationship is as follows:

[0034]

[0035] Where: N is the battery cycle life; D OD is the battery discharge depth;

[0036] Using the cycle life relationship, the n cycles of battery working DOD are obtained according to the battery SOC curve, which are recorded as DOD(1), DOD(2), ..., DOD(n). The battery life attenuation rate is expressed as:

[0037]

[0038] Where N max (D OD (i)) represents the battery cycle life corresponding to the i-th discharge depth; if the battery has undergone n cycles, the battery theoretical cycle life is expressed as:

[0039] L = 1 / α;

[0040] At this point, the life span assessment is completed.

[0041] The present invention also provides an optimization control system for a dual-battery energy storage system for smoothing wind power fluctuations, comprising an optimization control module, wherein the optimization control module executes the optimization control method for a dual-battery energy storage system for smoothing wind power fluctuations.

[0042] The present invention also provides a non-transitory computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the dual-battery energy storage system optimization control method for smoothing wind power fluctuations is implemented.

[0043] The present invention also provides an electronic device, comprising:

[0044] A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the dual-battery energy storage system optimization control method for smoothing wind power fluctuations.

[0045] Through the above scheme, the dual-battery energy storage system optimization control method for smoothing wind power fluctuations has the following technical effects:

[0046] 1) The dual-battery energy storage system can avoid multiple switching of charge and discharge states of a single battery in a short period of time when working, thereby extending the service life of the energy storage system.

[0047] 2) ICEEMDAN power decomposition and reconstruction are used to effectively smooth the fluctuation of the original power.

[0048] 3) It can effectively control the volatility of wind power, improve the phenomenon of wind power abandonment, and has a high reference value for improving the economic benefits of wind farms.

[0049] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flow chart of a dual-battery energy storage system optimization control method for smoothing wind power fluctuations according to the present invention;

[0051] Figure 2 A structural diagram of a dual-battery energy storage system in an embodiment of the present invention;

[0052] Figure 3 This is a flowchart of decomposition and reconstruction using ICEEMDAN in an embodiment of the present invention;

[0053] Figure 4 A diagram showing the relationship between the reconstruction order and the fluctuation amount in an embodiment of the present invention;

[0054] Figure 5 1 is a power comparison diagram before and after reconstruction in an embodiment of the present invention;

[0055] Figure 6 is a SOC curve diagram of a battery in an embodiment of the present invention;

[0056] Figure 7 : is a charge and discharge power diagram of battery A in an embodiment of the present invention;

[0057] Figure 8 : is a charge and discharge power diagram of battery B in an embodiment of the present invention;

[0058] Fig. 9 The figure is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0059] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0060] Ginseng Figure 1 As shown, this embodiment provides a dual-battery energy storage system optimization control method for smoothing wind power fluctuations, including the following steps:

[0061] Step S1, obtaining data of actual output power of the wind farm, and decomposing the wind farm power data using an improved adaptive noise complete set empirical mode decomposition method to obtain a finite number of IMF components;

[0062] Step S2, reconstructing the obtained IMF components in combination with the set grid-connected fluctuation standard, directly connecting the reconstructed low-frequency components to the grid as the grid-connected power, and allocating the reconstructed high-frequency components to the dual-battery energy storage system as the target power of the dual-battery energy storage system;

[0063] Step S3, taking the target power in step S2 as the power task that the dual-battery energy storage system needs to smooth, combining the power constraint and SOC constraint of the dual-battery energy storage system, obtaining the optimal control strategy of the dual-battery energy storage system, and evaluating the cycle life of the dual-battery energy storage system.

[0064] The present invention is described in further detail below.

[0065] The dual-battery energy storage system plays a significant role in smoothing the power fluctuations of wind farms. It can absorb wind power that exceeds the fluctuation limit, improve the stability of the power grid, and reap higher economic benefits. In order to better smooth the wind power fluctuations and reasonably adjust the control strategy of the dual-battery energy storage system, this patent proposes a method of using ICEEMDAN to decompose and reconstruct, and finally combine constraints to derive the charging and discharging strategy of the energy storage battery. The specific steps of the dual-battery energy storage system optimization control method for smoothing wind power fluctuations described in the present invention are as follows: first, obtain the actual output power data of the wind farm, and use the improved adaptive noise complete set empirical mode decomposition method (ICEEMDAN) to decompose the wind farm power data to obtain a finite number of IMF components. Secondly, the obtained components are reconstructed (including low-frequency reconstruction and high-frequency reconstruction) in combination with the set grid-connected fluctuation standard, and the reconstructed low-frequency components are directly connected to the grid as the grid-connected power, and the high-frequency components are used as the target power of the dual-battery energy storage system and allocated to the energy storage system. The grid-connected fluctuation limit set by the present invention is that the fluctuation amount within 10 minutes does not exceed 5% of the wind power installed capacity. This limit is set according to the national standard and takes a more refined fluctuation constraint to reserve a fluctuation margin. The specific decomposition and reconstruction flow chart is as follows Figure 3 Finally, the power data P of the dual battery energy storage system obtained in the above steps is Dbess Assigned to the dual-battery energy storage system, combined with the power constraints and SOC constraints of the energy storage system, the optimal control method of the dual-battery energy storage system is given and its cycle life is evaluated. The specific technical solution is as follows:

[0066] (1) Obtain the actual output power data of the wind farm, and use the improved adaptive noise complete ensemble empirical mode decomposition method (ICEEMDAN) to decompose the wind farm power data to obtain a finite number of IMF components. This step includes the following:

[0067] (11) The data of actual wind farm output power is obtained on a 1-minute time scale, that is, there must be 1440 sampling points in a day, corresponding to 1440 minutes in a day.

[0068] Figure 2 This is the structural diagram of the dual-battery energy storage system. The system consists of components such as busbar, dual-battery energy storage system, wind turbine, converter and transformer. Figure 2 Medium W is the power generation of the wind farm; P Dbess is the charge and discharge rate of the dual battery energy storage system; P Dbess1 and P Dbess2 They are the charge and discharge power of battery 1 and battery 2 respectively, which are positive during discharge and negative during charge.

[0069] (12) The improved adaptive noise complete ensemble empirical mode decomposition method is used to decompose the initial wind power signal to obtain the p-order IMF component.

[0070] (2) Reconstruct the obtained components in combination with the set grid-connected fluctuation standard and allocate them to the energy storage system. This step includes the following:

[0071] (21) The obtained components are reconstructed in the following way:

[0072] High frequency components:

[0073] Low frequency component:

[0074] The reconstruction process can be referred to Figure 4 As shown, it can be seen that the fluctuation after the fifth-order reconstruction obviously exceeds the limit, so the fourth-order reconstruction is selected to obtain the power that meets the set grid-connected fluctuation limit.

[0075] Figure 5 The power comparison diagram before and after reconstruction by ICEEMDAN decomposition and combining with the grid-connected fluctuation standard is shown in the figure. Figure 5 It can be seen that the power after reconstruction is smoother and the fluctuation is smaller.

[0076] (22) The low-frequency component that meets the specified grid-connected fluctuation limit is taken as the grid-connected component P Grid , the high frequency component is the component that needs to be smoothed by the dual battery energy storage system P Dbess , the relationship is as follows:

[0077] P w =P Grid +P Dbess

[0078] Where P w is the actual output power of the wind farm, P Grid is the low-frequency grid-connected component, P Dbess The dual battery energy storage system needs to smooth the power task.

[0079] (3) Power allocation and life assessment of dual-battery energy storage system.

[0080] This step includes the following:

[0081] (31) The power constraints of the dual battery energy storage system are as follows:

[0082] When P Dbess > 0, if battery A is discharging and battery B is charging in the system, the power constraints of battery cells A and B should satisfy If battery B is discharged and battery A is charged in the system, the power constraints of battery cells A and B should satisfy When P Dbess When <0, if battery A is discharged and battery B is charged in the system, the power constraints of battery cells A and B should satisfy If battery B is discharged and battery A is charged in the system, the power constraints of battery cells A and B should satisfy When P Dbess =0, the energy storage system is in standby mode and does not operate. In the above content: is the charge and discharge power of battery cell A, MW; is the charge and discharge power of battery unit B, MW.

[0083] (32) The SOC constraints of the dual-battery energy storage system are as follows:

[0084]

[0085] In the formula is the lower limit of SOC of the battery unit in the dual-battery energy storage system, %; Battery SOC upper limit in one-charge-one-discharge operation mode, %, SOC ref is the optimal cycle interval of the battery SOC; when the SOC of a battery cell in the current mode reaches the cycle interval boundary, the charge and discharge trends of the two battery cells are switched. The recursive model of the SOC of the two battery cells over time can be expressed as:

[0086]

[0087] Where: Q A (t), Q B (t) is the SOC state of charge value of battery cells A and B at time t, %; S A , S B is the charge and discharge status flag of the two battery cells A and B at time t (where the battery discharge status flag is "1", the charge status flag is "-1", and the standby status flag is "0"); ω1, ω2——energy storage system mode flag at time t (in the operating mode of the present invention, it is considered that ω1=1, ω2=0); Δt——sampling time interval, s; E rat ——Rated energy of battery cell, MJ; η——Conversion efficiency, %.

[0088] The SOC curves of the two batteries are as follows: Figure 6 As shown, the charge and discharge power curve of the dual-battery energy storage system obtained by the above constraints is as follows Figure 7 and Figure 8 shown.

[0089] (33) The cycle life of the dual-battery energy storage system is evaluated. The rain flow counting method is applied to the charge and discharge depth calculation. The cycle life is calculated by combining the relationship between the battery cycle life and its charge and discharge depth. The specific principle is as follows:

[0090]

[0091] Where: N is the battery cycle life (number of times); D OD is the battery discharge depth.

[0092] Using the above fitting formula, the n cycles of battery working DOD can be obtained according to the battery SOC curve, recorded as DOD(1), DOD(2), ..., DOD(n), and the battery life decay rate can be expressed as:

[0093]

[0094] Where N max (D OD (i)) represents the battery cycle life (number of times) corresponding to the i-th discharge depth. If the battery has undergone n cycles, the battery theoretical cycle life can be expressed as:

[0095] L=1 / α

[0096] After the above calculations, the number of charge and discharge times and available days of the dual battery in a day in the strategy of this article, as well as its comparison with the traditional single battery are shown in the following table.

[0097]

[0098] The optimal control method of the dual-battery energy storage system for smoothing wind power fluctuations first decomposes the actual output power signal of the wind farm into several modal functions (IMFs) using the improved adaptive noise complete ensemble empirical mode decomposition method (ICEEMDAN), reconstructs the obtained components at low frequencies and high frequencies, and selects the direct grid-connected quantity P according to the set grid-connected fluctuation quantity limit standard. grid With dual battery energy storage system power task P Dbess ; Then, the obtained power data is distributed to the dual-battery energy storage system, and the charge and discharge control strategy of the dual-battery energy storage system is formulated in combination with the constraints, and the battery life is evaluated using this strategy. It has the following technical effects:

[0099] 1) The dual-battery energy storage system can avoid multiple switching of charge and discharge states of a single battery in a short period of time when working, thereby extending the service life of the energy storage system.

[0100] 2) ICEEMDAN power decomposition and reconstruction are used to effectively smooth the fluctuation of the original power.

[0101] 3) It can effectively control the volatility of wind power, improve the phenomenon of wind power abandonment, and has a high reference value for improving the economic benefits of wind farms.

[0102] This embodiment also provides an optimization control system for a dual-battery energy storage system for smoothing wind power fluctuations, including an optimization control module, which executes the optimization control method for a dual-battery energy storage system for smoothing wind power fluctuations.

[0103] This embodiment also provides a non-transitory computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the dual-battery energy storage system optimization control method for smoothing wind power fluctuations is implemented.

[0104] Ginseng Fig. 9 As shown, this embodiment also provides an electronic device, including:

[0105] A memory 201 and a processor 202, wherein the memory 201 and the processor 202 are communicatively connected to each other, the memory 201 stores computer instructions, and the processor 202 executes the computer instructions to execute the dual-battery energy storage system optimization control method for smoothing wind power fluctuations.

[0106] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A dual-battery energy storage system optimization control method for smoothing wind power fluctuations, characterized in that: The steps include: Step 1, obtaining the actual output power data of the wind farm, and using the improved adaptive noise complete set empirical mode decomposition method to decompose the wind farm power data to obtain a finite number of IMF components; Step 2: Reconstruct the obtained IMF components in combination with the set grid-connected fluctuation standard, directly connect the reconstructed low-frequency components to the grid as the grid-connected power, and allocate the reconstructed high-frequency components to the dual-battery energy storage system as the target power of the dual-battery energy storage system; Step 3: Use the target power in step 2 as the power task that the dual-battery energy storage system needs to smooth, combine the power constraint and SOC constraint of the dual-battery energy storage system, obtain the optimal control strategy of the dual-battery energy storage system, and evaluate the cycle life of the dual-battery energy storage system.

2. The method for optimizing and controlling a dual-battery energy storage system for smoothing wind power fluctuations according to claim 1 is characterized in that: The data of the actual output power of the wind farm described in step 1 is the power data on a 1-minute time scale, that is, there are 1440 sampling points in a day, corresponding to 1440 minutes in a day.

3. The method for optimizing and controlling a dual-battery energy storage system for smoothing wind power fluctuations according to claim 1 is characterized in that: The improved adaptive noise complete set empirical mode decomposition method described in step 1 decomposes the initial wind power signal into p-order IMF components.

4. The method for optimizing and controlling a dual-battery energy storage system for smoothing wind power fluctuations according to claim 3 is characterized in that: The specific process of the improved adaptive noise complete set empirical mode decomposition method described in step 1 is as follows: 1) Add Gaussian white noise decomposed by EMD to the signal to be decomposed; 2) Solve the local mean of the components and obtain the residual signal and IMF components; 3) Repeat steps 1) and 2), and the noise signal added in each round is the IMF component of the previous noise signal; 4) When there are no components with two adjacent local extreme values ​​in all the decomposed IMF components, the algorithm ends.

5. The method for optimizing and controlling a dual-battery energy storage system for smoothing wind power fluctuations according to claim 4 is characterized in that: The IMF components obtained in step 2 are reconstructed in the following way: High frequency components: Low frequency component: Where hfc is the target power of the dual-battery energy storage system, lfc is the low-frequency component reconstructed according to the set grid-connected fluctuation standard, and IMF i is the i-th IMF component, p+1 is the total number of components, and k is the order of the first violation of the volatility limit after reconstruction; After reconstruction, the low-frequency component and high-frequency component that meet the specified grid-connected fluctuation limit are obtained, and the low-frequency component that meets the specified grid-connected fluctuation limit is used as the grid-connected component P Grid , the high frequency component is the component that needs to be smoothed by the dual battery energy storage system P Dbess , the relationship is as follows: P w =P Grid +P Dbess Where P w is the actual output power of the wind farm, P Grid is the low-frequency grid-connected component, P Dbess The dual battery energy storage system needs to smooth the power task.

6. The method for optimizing and controlling a dual-battery energy storage system for smoothing wind power fluctuations according to claim 1, characterized in that: The grid-connected fluctuation limit set in step 2 is that the fluctuation within 10 minutes does not exceed 5% of the wind power installed capacity.

7. The method for optimizing and controlling a dual-battery energy storage system for smoothing wind power fluctuations according to claim 5 is characterized in that: In step 3: The power constraint of the energy storage system is: When P Dbess > 0, if battery A is discharging and battery B is charging in the system, the power constraints of battery cells A and B should satisfy If battery B is discharged and battery A is charged in the system, the power constraints of battery cells A and B should satisfy When P Dbess When <0, if battery A is discharged and battery B is charged in the system, the power constraints of battery cells A and B should satisfy If battery B is discharged and battery A is charged in the system, the power constraints of battery cells A and B should satisfy When P Dbess =0, the energy storage system is in standby mode and does not operate; is the charge and discharge power of battery cell A, MW; is the charge and discharge power of battery unit B, MW; The SOC constraint of the energy storage system is: In the formula, is the lower limit of SOC of the battery unit in the dual-battery energy storage system, %; The upper limit of battery SOC in the one-charge-one-discharge operation mode, %; SOC ref is the optimal cycle range of the battery SOC; when the SOC of a battery cell in the current mode reaches the cycle range boundary, the charging and discharging trends of the two battery cells are switched; the recursive model of the SOC of the two battery cells over time is expressed as: Where: SOC A (t), SOC B (t) are the SOC state of charge values ​​of the two battery cells A and B at time t, %; S A , S B are the charge and discharge status flags of the two battery units A and B at time t, where: the battery discharge status flag is 1, the charge status flag is -1, and the standby status flag is 0; ω1 and ω2 are the energy storage system mode flags at time t, ω1 = 1, ω2 = 0; Δt is the sampling time interval, s; E rat is the rated energy of the battery cell, MJ; η is the conversion efficiency, %; The specific method of the cycle life evaluation is as follows: The charge and discharge depth is calculated by using the rain flow counting method, and the cycle life is calculated by combining the relationship between the battery cycle life and its charge and discharge depth. The relationship is as follows: Where: N is the battery cycle life; D OD is the battery discharge depth; Using the cycle life relationship, the n cycles of battery working DOD are obtained according to the battery SOC curve, which are recorded as DOD(1), DOD(2), ..., DOD(n). The battery life attenuation rate is expressed as: Where N max (D OD (i)) represents the battery cycle life corresponding to the i-th discharge depth; if the battery has undergone n cycles, the battery theoretical cycle life is expressed as: L＝1 / α； At this point, the life span assessment is completed.

8. A dual-battery energy storage system optimization control system for smoothing wind power fluctuations, characterized in that: It includes an optimization control module, which executes the optimization control method of a dual-battery energy storage system for smoothing wind power fluctuations as described in any one of claims 1-7.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, which, when executed by a processor, implement an optimization control method for a dual-battery energy storage system for smoothing wind power fluctuations as described in any one of claims 1-7.

10. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the optimization control method for a dual-battery energy storage system for smoothing wind power fluctuations as described in any one of claims 1 to 7 by executing the computer instructions.

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