A double battery energy storage system optimization control method for wind power fluctuation suppression

Abstract

The application relates to a kind of double battery energy storage system optimization control methods of wind power fluctuation suppression, comprising: obtaining the data of actual output power of wind farm, using improved adaptive noise complete set empirical mode decomposition method to decompose wind farm power data, obtain a limited IMF component;Combining the set grid fluctuation standard, the obtained IMF component is reconstructed, the low-frequency component after reconstruction is directly connected to the grid as grid power, the high-frequency component after reconstruction is used as the target power of energy storage system, and is distributed to energy storage system;The target power is used as the power task that double battery energy storage system needs to suppress, in combination with the power constraint and SOC constraint of energy storage system, the optimization control strategy of double battery energy storage system is obtained, and the cycle life of double battery energy storage system is evaluated.The application obtains the charge-discharge power scheduling strategy of double battery energy storage system according to the power data of wind farm, and realizes the life evaluation of double battery energy storage system by applying the strategy.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of optimal control of energy storage systems, and particularly relates to a double-battery energy storage system optimal control method for wind power fluctuation suppression. BACKGROUND

[0002] Renewable energy has become a viable solution to global energy security and environmental degradation. Due to the maturity of wind power technology and lower 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 and tidal energy), wind energy is uncertain and uncontrollable. This uncertainty has brought technical difficulties to ensuring grid stability, reliability, and power quality. Due to the development of battery energy storage systems (BESS), it is widely used to alleviate wind power fluctuations. However, due to the structure of the single battery pack energy storage system, the battery energy storage system will frequently switch between charging and discharging states, and it is difficult to control the SOC charging and discharging cycle interval, thereby affecting the service life of the energy storage battery.

[0003] In order to reduce the number of charging and discharging state transitions of the BESS and improve the service life of the battery energy storage system, the development of double-battery unit energy storage systems has gradually attracted attention. However, how to schedule the charging and discharging of the double battery is a problem that needs to be solved urgently. SUMMARY

[0004] The purpose of the present application is to provide a double-battery energy storage system optimal control method for wind power fluctuation suppression, which aims to obtain a charging and discharging power scheduling strategy for a double-battery energy storage system according to wind farm power data, and apply the strategy to evaluate the service life of the double-battery energy storage system.

[0005] The present application provides a double-battery energy storage system optimal control method for wind power fluctuation suppression, comprising the following steps:

[0006] Step 1: Obtain the data of the actual output power of the wind farm, decompose the wind farm power data using an improved adaptive noise complete ensemble empirical mode decomposition method, and obtain a limited number of IMF components;

[0007] Step 2: Reconstruct the obtained IMF components in combination with the set grid fluctuation standard, directly grid the reconstructed low-frequency components as grid power, and distribute the reconstructed high-frequency components as target power of the double-battery energy storage system to the double-battery energy storage system;

[0008] Step 3: Take the target power in step 2 as the power task that needs to be suppressed by the double-battery energy storage system, combine the power constraints and SOC constraints of the double-battery energy storage system, obtain the optimal control strategy of the double-battery energy storage system, and evaluate the cycle life of the double-battery energy storage system.

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

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

[0011] Further, the improved adaptive noise complete ensemble empirical mode decomposition method in step 1 has the following specific process:

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

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

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

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

[0016] Further, the reconstruction of the obtained IMF component in step 2 has the following specific reconstruction method:

[0017] High-frequency component:

[0018] Low-frequency component:

[0019] In the formula, hfc is the target power of the double-battery energy storage system, lfc is the low-frequency component after reconstruction according to the set grid fluctuation standard, IMF i is the i-th IMF component, p+1 is the total number of components, and k is the order of the first time exceeding the fluctuation limit after reconstruction;

[0020] The low-frequency component and the high-frequency component after reconstruction meet the specified grid fluctuation limit, and the low-frequency component meeting the specified grid fluctuation limit is taken as the grid component P Grid , and the high-frequency component is taken as the component P Dbess that needs to be smoothed by the double-battery energy storage system, and their relationship is as follows:

[0021] P w =P Grid +P Dbess

[0022] In the formula, P w is the actual output power of the wind farm, PGrid For low-frequency grid-connected components, P Dbess The power load that needs to be smoothed for dual-battery energy storage systems.

[0023] Furthermore, the grid connection fluctuation limit set in step 2 is that the fluctuation amount in 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 When the power is greater than 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 discharges while battery A is charging in the system, the power constraints of battery cells A and B should satisfy... When P Dbess When <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 discharges while battery A is charging in the system, the power constraints of battery cells A and B should satisfy... When P Dbess When = 0, the energy storage system is in standby mode and does not operate; where: The charging and discharging power of battery cell A is expressed in MW. The charging and discharging power of battery cell B, in MW;

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

[0028]

[0029] In the formula, The lower limit of the SOC of the battery cell in a dual-battery energy storage system, in %. The maximum SOC of the battery in single-charge and single-discharge operation mode, %; SOC ref This represents the optimal cycle range for battery SOC; when a battery cell's SOC reaches the boundary of the cycle range in the current mode, the charging and discharging trends of the two battery cells switch; 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) represents the SOC (State of Charge) values ​​of battery cells A and B at time t, respectively, in %; S A S BRespectively, A, B two battery units at t time of charge and discharge state flag bit, wherein: battery discharge state flag bit is 1, the charging state flag bit is-1, standby state flag bit is 0;ω1, ω2 is t time energy storage system mode flag bit, ω1=1, ω2=0;Δt is sampling time interval, s;E rat For battery unit rated energy, MJ;η is conversion efficiency, %;

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

[0033] The rainflow counting method is used to calculate the charge and discharge depth, and the cycle life of the battery is calculated by combining the relationship between the cycle life of the battery and the charge and discharge depth of the battery, and the relationship is as follows:

[0034]

[0035] In the formula: N is the cycle life of the battery;D OD Is the battery discharge depth;

[0036] Using the cycle life relationship, the n times of battery working DOD are obtained according to the battery SOC curve, which are denoted as DOD(1), DOD(2), …, DOD(n), and the battery life attenuation rate is represented as:

[0037]

[0038] In the formula, N max (D OD (i)) represents the cycle life of the battery corresponding to the i-th discharge depth;If the battery has experienced n cycles, the theoretical cycle life of the battery is represented as:

[0039] L=1 / α;

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

[0041] The application also provides a double-battery energy storage system optimization control system for wind power fluctuation suppression, comprising an optimization control module, which executes the double-battery energy storage system optimization control method for wind power fluctuation suppression.

[0042] The application also provides a non-transitory computer readable storage medium, which stores computer instructions, and the computer instructions are executed by a processor to realize the double-battery energy storage system optimization control method for wind power fluctuation suppression.

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

[0044] Memory and processor, the memory and the processor are connected with each other, the memory has computer instructions, the processor executes the computer instructions, thereby the wind power fluctuation suppression double battery energy storage system optimization control method is executed.

[0045] Through the above scheme, the wind power fluctuation suppression double battery energy storage system optimization control method has the following technical effects:

[0046] 1) double battery energy storage system can avoid single battery in work, short time multiple charge-discharge state switching, thereby prolonging the service life of energy storage system.

[0047] 2) adopt ICEEMDAN power decomposition and reconstruction, effectively smooth the original power fluctuation.

[0048] 3) can effectively control wind power volatility, improve the phenomenon of wind curtailment, and has higher reference value for improving the economic benefit of wind farm.

[0049] The above description is only the summary of the technical scheme of the application, in order to more clearly understand the technical means of the application, and can be implemented according to the content of the specification, as follows with the preferred embodiments of the application and the accompanying drawings are described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 The flow chart of the wind power fluctuation suppression double battery energy storage system optimization control method of the application;

[0051] Figure 2 The structure diagram of double battery energy storage system in the embodiment of the application;

[0052] Figure 3 The flow chart of adopting ICEEMDAN decomposition and reconstruction in the embodiment of the application;

[0053] Figure 4 The relationship diagram of reconstruction order and fluctuation in the embodiment of the application;

[0054] Figure 5 The power comparison chart before and after reconstruction in the embodiment of the application;

[0055] Figure 6 The SOC curve chart of battery in the embodiment of the application;

[0056] Figure 7 The charge-discharge power chart of battery A in the embodiment of the application;

[0057] Figure 8 The charge-discharge power chart of battery B in the embodiment of the application;

[0058] Figure 9 Fig. 1 is a schematic diagram of an electronic device structure according to the present application. DETAILED DESCRIPTION

[0059] The specific embodiments of the present application will be further described in detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate the present application, but are not used to limit the scope of the present application.

[0060] Referring to Figure 1 The present embodiment provides a double battery energy storage system optimization control method for wind power fluctuation suppression, including the following steps:

[0061] Step S1, obtain the data of actual output power of the wind farm, decompose the wind farm power data by using the improved adaptive noise complete ensemble empirical mode decomposition method, and obtain a limited number of IMF components;

[0062] Step S2, combine the set grid fluctuation standard to reconstruct the obtained IMF components, directly grid the reconstructed low-frequency components as grid power, and distribute the reconstructed high-frequency components as target power of the double battery energy storage system to the double battery energy storage system;

[0063] Step S3, take the target power in step S2 as the power task that needs to be suppressed by the double battery energy storage system, combine the power constraint and SOC constraint of the double battery energy storage system, obtain the optimization control strategy of the double battery energy storage system, and evaluate the cycle life of the double battery energy storage system.

[0064] The present application will be further described in detail below.

[0065] The double battery energy storage system plays a significant role in power fluctuation suppression of a wind farm, can accommodate wind power exceeding the fluctuation limit, improve the stability of the power grid, and also can obtain high economic benefits. In order to better suppress the wind power fluctuation and reasonably adjust the control strategy of the double battery energy storage system, the present patent proposes to use ICEEMDAN for decomposition and reconstruction, and finally obtains the method of the charging and discharging strategy of the energy storage battery in combination with the constraints. The specific steps of the double battery energy storage system optimization control method for wind power fluctuation suppression are as follows: firstly, the data of the actual output power of the wind farm is obtained, the improved adaptive noise complete ensemble empirical mode decomposition method (ICEEMDAN) is used to decompose the wind farm power data, and a limited number of IMF components are obtained. Secondly, the obtained components are reconstructed (including low-frequency reconstruction and high-frequency reconstruction) in combination with the set grid-connected fluctuation standard, the reconstructed low-frequency components are directly connected to the grid as the grid-connected power, and the high-frequency components are distributed to the energy storage system as the target power of the double battery energy storage system. The set grid-connected fluctuation limit is that the fluctuation amount of 10 min does not exceed 5% of the wind power installed capacity, which is set according to the national standard, and a more delicate fluctuation constraint is taken to reserve the fluctuation margin. The specific decomposition and reconstruction flowchart is shown in Figure 3 . Finally, the double battery energy storage system power data P Dbess obtained by the above steps is distributed to the double battery energy storage system, the optimization control method of the double battery energy storage system is given in combination with the power constraints and SOC constraints of the energy storage system, and the cycle life of the double battery energy storage system is evaluated. The specific technical scheme is as follows:

[0066] (1) The data of the actual output power of the wind farm is obtained, the improved adaptive noise complete ensemble empirical mode decomposition method (ICEEMDAN) is used to decompose the wind farm power data, and a limited number of IMF components are obtained. This step includes the following contents:

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

[0068] Figure 2 The double battery energy storage system structure diagram, which is composed of a bus, a double battery energy storage system, a wind turbine, a converter and a transformer and other components. Figure 2 P W is the power generation of the wind farm; P Dbess is the charging and discharging rate of the double battery energy storage system; P Dbess1 and P Dbess2 are the charging and discharging power of battery 1 and battery 2 respectively, which is positive when discharging and negative when charging.

[0069] (12) The initial wind power signal is decomposed into p-order IMF components by using the improved adaptive noise complete ensemble empirical mode decomposition method.

[0070] (2) The obtained components are reconstructed according to the set grid fluctuation standard and allocated to the energy storage system. This step includes the following contents:

[0071] (21) The obtained components are reconstructed, and the specific reconstruction method is as follows:

[0072] High-frequency component:

[0073] Low-frequency component:

[0074] The reconstruction process can be referred to Figure 4 It can be seen that the fluctuation after five-order reconstruction is obviously beyond the limit, so four-order reconstruction is selected to obtain the power that meets the set grid fluctuation limit.

[0075] Figure 5 The power comparison chart before and after reconstruction by combining the obtained grid fluctuation standard is shown in 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 fluctuation limit is taken as the grid component P Grid , and the high-frequency component is taken as the component P Dbess that needs to be smoothed by the double-battery energy storage system, and their relationship is as follows:

[0077] P w =P Grid +P Dbess

[0078] In the formula, P w is the actual output power of the wind farm, P Grid is the low-frequency grid component, and P Dbess is the power task that needs to be smoothed by the double-battery energy storage system.

[0079] (3) Power distribution and life evaluation of the double-battery energy storage system.

[0080] This step includes the following contents:

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

[0082] When P Dbess > 0, if battery A discharges and battery B charges in the system, the power constraints of battery units A and B should satisfy If battery B discharges and battery A charges in the system, the power constraints of battery units A and B should be satisfied When P Dbess If battery A discharges and battery B charges in the system, the power constraints of battery units A and B should be satisfied If battery B discharges and battery A charges in the system, the power constraints of battery units A and B should be satisfied When P Dbess = 0, the energy storage system is on standby and does not run. In the above content: is the charging and discharging power of battery unit A, MW; is the charging and discharging power of battery unit B, MW.

[0083] (32) SOC constraints of the double-battery energy storage system, which are specifically as follows:

[0084]

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

[0086]

[0087] In the formula: Q A (t), Q B (t) is the SOC state value of the A and B battery units at time t, %; S A , S B is the charging and discharging state flag of the A and B battery units at time t (wherein the battery discharging state flag is "1", the charging state flag is "-1", and the standby state flag is "0"); ω1, ω2 are the mode flags of the energy storage system at time t (in the operation mode of the present application, ω1 = 1 and ω2 = 0); Δt is the sampling time interval, s; E rat is the rated energy of the battery unit, MJ; η is the conversion efficiency, %.

[0088] The SOC curves of the two batteries are as shown in Figure 6 The charging and discharging power curves of the double-battery energy storage system obtained through the above constraints are as shown in Figure 7 and Figure 8 .

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

[0090]

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

[0092] Using the above fitting formula, the n times of battery working DOD can be obtained according to the battery SOC curve, which is denoted as DOD(1), DOD(2), …, DOD(n). The battery life attenuation rate can be expressed as:

[0093]

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

[0095] L=1 / α

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

[0097]

[0098] The dual battery energy storage system optimization control method for wind power fluctuation suppression first uses the improved adaptive noise complete ensemble empirical mode decomposition method (ICEEMDAN) to decompose the actual output power signal of the wind farm into a plurality of modal functions (IMF). The obtained components are reconstructed at low and high frequencies, and the set grid fluctuation limit value standard is combined to screen out the direct grid amount P grid and the power task P Dbess of the dual battery energy storage system; then, the obtained power data is allocated to the dual battery energy storage system, the charge and discharge control strategy of the dual battery energy storage system is formulated combined with the constraint conditions, and the life of the battery using this strategy is evaluated. It has the following technical effects:

[0099] 1) The dual battery energy storage system can avoid the multiple charge and discharge state switching of the single battery in a short time, thereby prolonging the service life of the energy storage system.

[0100] 2) The ICEEMDAN power decomposition and reconstruction effectively smooth the original power fluctuation.

[0101] 3) can effectively control wind power fluctuation, improve the phenomenon of abandoned wind and electricity, and has high reference value for improving the economic benefit of wind power plant.

[0102] The embodiment also provides a wind power fluctuation flattening double-battery energy storage system optimization control system, which comprises an optimization control module.

[0103] The embodiment also provides a non-transitory computer readable storage medium, which stores computer instructions, and the computer instructions are executed by a processor to implement the wind power fluctuation flattening double-battery energy storage system optimization control method.

[0104] Referring to Figure 9 The embodiment also provides an electronic device, which comprises:

[0105] The memory 201 and the processor 202 are in communication connection with each other, the memory 201 stores computer instructions, and the processor 202 executes the computer instructions to implement the wind power fluctuation flattening double-battery energy storage system optimization control method.

[0106] The above description is only the preferred embodiment of the present application and is not used to limit the present application, and it should be pointed out that, for ordinary skilled in the art, without departing from the technical principles of the present application, a number of improvements and modifications can be made, and these improvements and modifications should be regarded as the protection scope of the present application.

Claims

1. An optimized control method for a dual-battery energy storage system to smooth wind power fluctuations, characterized in that, Includes the following steps: Step 1: Obtain the actual power output data of the wind farm, and use 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 based on the established grid connection fluctuation standards. The reconstructed low-frequency component is directly connected to the grid as grid-connected power, while the reconstructed high-frequency component is allocated to the dual-battery energy storage system as the target power. After reconstruction, low-frequency and high-frequency components that meet the specified grid connection fluctuation limits are obtained, and the low-frequency component that meets the specified grid connection fluctuation limits is used as the grid-connected component P. Grid High-frequency components, such as P, are components that need to be smoothed out in dual-battery energy storage systems. Dbess The relationship is as follows: P w =P Grid +P Dbess In the formula, P w P represents the actual power output of the wind farm. Grid For low-frequency grid-connected components, P Dbess For dual-battery energy storage systems, power tasks that need to be smoothed out; Step 3: Take the target power in Step 2 as the power task that the dual-battery energy storage system needs to smooth. Combine the power constraints and SOC constraints of the dual-battery energy storage system to obtain the optimized control strategy of the dual-battery energy storage system, and evaluate the cycle life of the dual-battery energy storage system. The power constraint of the energy storage system is: When P Dbess When the power is greater than 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 discharges while battery A is charging in the system, the power constraints of battery cells A and B should satisfy... When P Dbess When <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 discharges while battery A is charging in the system, the power constraints of battery cells A and B should satisfy... When P Dbess When = 0, the energy storage system is in standby mode and does not operate; where: The charging and discharging power of battery cell A is expressed in MW. The charging and discharging power of battery cell B, in MW; The SOC constraint of the energy storage system is: In the formula, The lower limit of the SOC of the battery cell in a dual-battery energy storage system is expressed in %. This refers to the upper limit of the battery's SOC (State of Charge) in a charge-discharge operation mode, expressed as a percentage. ref This represents the optimal cycle range for battery SOC; when a battery cell's SOC reaches the boundary of the cycle range in the current mode, the charging and discharging trends of the two battery cells switch; the recursive model of the SOC of the two battery cells over time is expressed as: Where: SOC A (t), SOC B (t) represents the SOC (State of Charge) values ​​of battery cells A and B at time t, respectively, in %; S A S B These are the charging and discharging status flags for battery cells A and B at time t, where: the battery discharge status flag is 1, the charging 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 and ω2 = 0; Δt is the sampling time interval in seconds; E rat Rated energy of the battery cell, in MJ; η is the conversion efficiency, in %; The specific method for cycle life assessment is as follows: The depth of charge and discharge was calculated using the rainflow counting method, and the cycle life was calculated by combining the relationship between battery cycle life and its depth of charge and discharge. The relationship is as follows: In the formula: N is the battery cycle life; D OD This refers to the depth of battery discharge. Using the cycle life formula, the nth cycle of the battery's operating DOD is obtained from the battery's SOC curve, denoted as DOD(1), DOD(2), ..., DOD(n). The battery life degradation rate is expressed as: In the formula, 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 theoretical battery cycle life is expressed as: L＝1 / α； This completes the life assessment.

2. The optimized control method for a dual-battery energy storage system for smoothing wind power fluctuations according to claim 1, characterized in that, The actual power output data of the wind farm mentioned in step 1 is power data on a 1-minute time scale, that is, there should be 1440 sampling points in a day, corresponding to 1440 minutes in a day.

3. The optimized control method for a dual-battery energy storage system for wind power fluctuation mitigation according to claim 1, characterized in that, The improved adaptive noise complete set empirical mode decomposition method described in step 1 decomposes the initial power signal of wind power into p-order IMF components.

4. The optimized control method for a dual-battery energy storage system for wind power fluctuation mitigation according to claim 3, 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) Perform local mean calculation on the components to 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) The algorithm ends when there are no two components with adjacent local extrema among all the decomposed IMF components.

5. The optimized control method for a dual-battery energy storage system for wind power fluctuation mitigation according to claim 4, characterized in that, The reconstruction of the obtained IMF components described in step 2 is specifically performed as follows: High-frequency components: Low-frequency components: In the formula, hfc is the target power of the dual-battery energy storage system, lfc is the low-frequency component after reconstruction according to the set grid connection fluctuation standard, and IMF is the low-frequency component after reconstruction. i Let p be the i-th IMF component, p+1 be the total number of components, and k be the order at which the fluctuation limit is first exceeded after reconstruction.

6. The optimized control method for a dual-battery energy storage system for wind power fluctuation mitigation according to claim 1, characterized in that, The grid connection fluctuation limit set in step 2 is that the fluctuation amount in 10 minutes shall not exceed 5% of the wind power installed capacity.

7. An optimized control system for a dual-battery energy storage system to smooth wind power fluctuations, characterized in that, It includes an optimization control module, which executes the optimization control method for a dual-battery energy storage system for wind power fluctuation smoothing as described in any one of claims 1-6.

8. 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 the dual-battery energy storage system optimization control method for wind power fluctuation smoothing as described in any one of claims 1-6.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform an optimized control method for a dual-battery energy storage system for wind power fluctuation mitigation as described in any one of claims 1-6.

Images