Method for configuring capacity of energy storage system for stabilizing wind power fluctuation

Abstract

The invention relates to a method for configuring capacity of an energy storage system for stabilizing wind power fluctuation, which comprises the following steps of: making a difference between original data and grid-connected power data to obtain data which needs to be stabilized by hybrid energy storage, and obtaining a power task curve and a grid-connected power curve of the hybrid energy storage system under a limit value; decomposing the obtained power data of the hybrid energy storage system by adopting ensemble empirical mode decomposition to obtain a plurality of components, analyzing by adopting sample entropy, and determining a demarcation point of each component; and constructing an HESS capacity configuration model, and determining the optimal capacity of the supercapacitor and the lithium battery in combination with constraint conditions to obtain a hybrid energy storage capacity configuration result with an optimal wind power stabilization effect. The method can adaptively decompose the output power of the wind power plant in different scenes, ensures reasonable distribution of the internal power and capacity of hybrid energy storage, enables the service time of the hybrid energy storage system to be longer, and plays a crucial role in stabilizing the fluctuation of the output power of the wind power plant.

Description

Technical Field

[0001] The present invention belongs to the technical field of capacity configuration of hybrid energy storage systems, and in particular relates to a method for configuring the capacity of an energy storage system for smoothing wind power fluctuations. Background Art

[0002] As a green and clean energy, wind power has been widely used around the world. However, wind power generation has the characteristics of randomness, intermittency and uncertainty, which is not conducive to the stable operation of the power system. With the rapid development of energy storage technology, its application can largely solve the problems of randomness and volatility of wind power generation.

[0003] Existing energy storage media can be divided into two categories: energy type and power type. Energy type storage is represented by battery storage and pumped storage, which have high energy density, low power density, short cycle life, and are not suitable for frequent charge and discharge conversion; power type storage is represented by supercapacitors, superconducting energy storage and flywheel energy storage, which have low energy density, high power density, and frequent charge and discharge will not damage their performance. In view of the excellent complementarity of energy type storage and power type storage in performance, most existing studies use the two to form a hybrid energy storage system (HESS) to smooth out wind power output power fluctuations, among which the battery-supercapacitor hybrid energy storage combination mode is the most typical.

[0004] Since my country proposed to build a new power system on March 15, 2021, a high proportion of new energy units are constantly penetrating the power grid. However, new energy sources, mainly wind power, generally have the problem of excessive volatility and randomness. The configuration of hybrid energy storage systems in wind farms can effectively absorb wind power. Therefore, the configuration of hybrid energy storage systems has become one of the mainstream methods for smoothing wind power fluctuations. However, how to reasonably distribute power between hybrid energy storage systems (batteries and supercapacitors) and how to appropriately configure the capacity of hybrid energy storage systems to avoid insufficient smoothing or excessive costs caused by unreasonable configuration has become an urgent problem to be solved. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for configuring the capacity of an energy storage system for smoothing wind power fluctuations, which can adaptively decompose the output power of a wind farm under different scenarios, ensure the reasonable allocation of power and capacity within the hybrid energy storage, and extend the service life of the hybrid energy storage system, thus playing a vital role in smoothing the fluctuations of the output power of a wind farm.

[0006] The present invention provides a method for configuring the capacity of an energy storage system for smoothing wind power fluctuations. The method is directed to a hybrid energy storage system composed of power-type energy storage and energy-type energy storage, and comprises the following steps:

[0007] Step 1: Obtain all actual output data of the wind farm during the target period, process the obtained data through the SG filtering method and in combination with the national wind power grid connection standard, use the obtained data as wind power grid connection power data, and make a difference between the original data and the grid connection power data to obtain the data that the hybrid energy storage needs to smooth, and then obtain the hybrid energy storage system power task P under the national wind power grid connection standard limit. HESS curve and grid-connected power curve;

[0008] Step 2, using ensemble empirical mode decomposition to decompose the obtained power data of the hybrid energy storage system to obtain several components, and using sample entropy to analyze and determine the dividing points of each obtained component;

[0009] Step 3: Considering the state of charge of the hybrid energy storage system, the HESS capacity configuration model is constructed with the minimum comprehensive cost of the hybrid energy storage system as the objective function, and the optimal capacity of the supercapacitor and lithium battery is determined in combination with the constraints to obtain the hybrid energy storage capacity configuration result with the best wind power suppression effect.

[0010] Further, the SG filtering method in step 1 is as follows:

[0011]

[0012] Where: T is the k-1 order polynomial in the 2n+1 window; T trans is the transposed matrix of T;

[0013] The calculation formula of grid-connected power after SG filtering is as follows:

[0014]

[0015] At this time, the target power of energy storage is:

[0016] P hess (k) = P w (k)-P grid (k)

[0017] Where P Hess (k) is the power that the hybrid energy storage needs to smooth at time k; P w (k) is the wind farm output power at time k; P grid (k) is the grid-connected power of the wind farm at time k.

[0018] Furthermore, the collective empirical mode decomposition described in step 2 includes: adding white noise disturbance to the original signal to be decomposed, then performing EMD decomposition on the original signal with the added white noise disturbance, and integrating and averaging the modal functions obtained by multiple decompositions as the final modal components.

[0019] Furthermore, the collective empirical mode decomposition formula in step 2 is as follows:

[0020] P x (t)+w i (x) = F 1,i (t)+r i (t)

[0021]

[0022] r 1 (t) = P x (t)-F 1 (t)

[0023]

[0024] Where N is the total order; F 1 (t) is the first-order IMF component; t is time; i is the number of times white noise is added; P x (t) is the original power decomposition signal; w i (x) is the added white noise disturbance; F 1,i (t) is the i-th order IMF component in the initial calculation; r i (t) is the residual component.

[0025] Furthermore, the sample entropy is used for analysis in step 2 to determine the cutoff points of the obtained components, and the formula is as follows:

[0026] SampEn(m,r,L)=-lnA m (r) / B m (r)

[0027] Where L is the sequence length; m is the dimension; r is the similarity tolerance; and B m (r) represents the probability that two consecutive subsequences in a time series match m points under the same similarity tolerance, A m (r) represents the probability that two consecutive subsequences in the time series match m+1 points under the same similarity tolerance.

[0028] Furthermore, the objective function of the HESS capacity configuration model in step 3 is:

[0029] C M =C bat +C sc

[0030] Where: Total energy storage configuration cost C M Cost C for lithium battery configuration ba and the configuration cost of supercapacitor C sc The sum of , where:

[0031] C bat =C bat,in +C bat,op

[0032]

[0033] Where: C bat , C bat,in and C bat,op are the configuration cost, investment cost and operation and maintenance cost of lithium batteries respectively; P bat，rated and E bat，rated are the rated charge and discharge power and rated capacity of the lithium battery respectively; C bat,inp and c bat,ine are the power and capacity investment cost coefficients of lithium batteries respectively; r is the discount rate; Y is the system operation life; c bat,op is the operation and maintenance cost coefficient of lithium battery;

[0034] C sc =C sc,in +C sc,op

[0035]

[0036] Where: C sc , C sc,in and C sc,op are the configuration cost, investment cost and operation and maintenance cost of supercapacitors; P sc,rated and E sc,rated are the rated charge and discharge power and rated capacity of the supercapacitor respectively; c sc,inp and c sc,ine are the power and capacity investment cost coefficients of supercapacitors respectively; C sc,op is the operation and maintenance cost coefficient of the supercapacitor.

[0037] Furthermore, the constraints of the HESS capacity configuration model in step 3 are:

[0038]

[0039] In the formula, SOC b (t), SOC sc (t) is the state of charge of the battery and supercapacitor at time t; SOC bmax , SOC bmin SOC is the upper and lower limits of the battery state of charge; scmax , SOC sc,min E is the upper and lower limits of the supercapacitor charge state; b (t), E sc (t) is the remaining power of the battery and supercapacitor at time t, MWh; P B(t), P sc (t) is the charge and discharge power of the battery and supercapacitor at time t, MW; η Bc , η Bd is the charging efficiency and discharging efficiency of the battery; η SCc , η SCd is the charging efficiency and discharging efficiency of the supercapacitor; Δt is the sampling interval, min.

[0040] The present invention also provides a system for configuring the capacity of an energy storage system for smoothing wind power fluctuations, comprising a capacity configuration module, wherein the capacity configuration module executes the method for configuring the capacity of an energy storage system for smoothing wind power fluctuations.

[0041] 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, a method for configuring the capacity of an energy storage system for smoothing wind power fluctuations is implemented.

[0042] The present invention also provides an electronic device, comprising:

[0043] 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 method for configuring the capacity of an energy storage system for smoothing wind power fluctuations by executing the computer instructions.

[0044] By means of the above scheme, the method and system for configuring the capacity of the energy storage system for smoothing wind power fluctuations have the following technical effects:

[0045] (1) Before performing ensemble empirical mode decomposition, the original power data of the wind farm is processed using SG filtering to reduce the errors that may be caused by direct decomposition.

[0046] (2) The present invention adopts ensemble empirical mode decomposition, and integrates and averages the IMFs obtained from multiple decompositions as the final IMF, thereby reducing the error that may be caused by directly using a single IMF component.

[0047] (3) The present invention uses sample entropy to distribute IMF components, thereby improving the credibility of the distribution results.

[0048] 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

[0049] Figure 1It is a structural diagram of a battery-supercapacitor hybrid energy storage system;

[0050] Figure 2 A flow chart of a method for configuring the capacity of an energy storage system for smoothing wind power fluctuations according to the present invention

[0051] Figure 3 This is a comparison chart of power data before and after leveling;

[0052] Figure 4 is the power component diagram of the supercapacitor;

[0053] Figure 5 is the battery power component diagram;

[0054] Figure 6 The figure is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0055] 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.

[0056] This embodiment provides a hybrid energy storage system capacity configuration method for smoothing wind power fluctuations. First, all actual output data of the wind farm within the target period are obtained, and the obtained data are processed by the SG filtering method in combination with the national wind power grid-connected standard. The obtained data is used as wind power grid-connected power data, and the original data is subtracted from the grid-connected data to obtain the data that needs to be smoothed by the hybrid energy storage. Secondly, the power data of the obtained hybrid energy storage system is decomposed using the ensemble empirical mode decomposition (EEMD). Finally, considering the charge state of the hybrid energy storage system, a HESS capacity configuration model is constructed with the minimum comprehensive cost of the hybrid energy storage system as the objective function, and the optimal capacity of the supercapacitor and lithium battery is determined in combination with the constraints, so as to obtain the hybrid energy storage capacity configuration result with the best wind power suppression effect.

[0057] The flow chart of the hybrid energy storage system capacity optimization configuration method for smoothing wind power fluctuations in this embodiment is as follows: Figure 2 As shown, the specific technical solution is as follows:

[0058] (1) Obtain all actual output data of the wind farm during the target period, and process the obtained data using the SG filtering method in combination with the national wind power grid connection standards. This step mainly includes the following contents:

[0059] (1.1) Data acquisition. The power data of the wind farm in one day is selected as the original data. The sampling rule should be once per minute, that is, there should be 1440 sampling points in one day, corresponding to 1440 power data.

[0060] (1.2) Use SG filtering and process the power data according to the grid-connected fluctuation standard specified in the national standard to obtain appropriate grid-connected data. grid With the original data P w By making a difference, we can get the power P that the hybrid energy storage system needs to absorb. hess The data before and after SG processing is compared. Figure 3 shown.

[0061] The national wind power grid connection standards are shown in Table 1;

[0062] Table 1 National wind power grid connection standards

[0063]

[0064] The principle of SG filtering is as follows:

[0065]

[0066] Where: T is the k-1 order polynomial in the 2n+1 window; T trans is the transposed matrix of T.

[0067] The calculation formula of grid-connected power after SG filtering is as follows:

[0068]

[0069] The obtained data is used as the wind power grid-connected power data, and the original data is subtracted from the grid-connected power data to obtain the data that the hybrid energy storage needs to stabilize. Thus, the power task P of the hybrid energy storage system under this limit is obtained. HESS Curve and grid-connected power curve, the power of the hybrid energy storage system is as follows:

[0070] P hess (k) = P w (k)-P grid (k)

[0071] Where P Hess (k) is the power that the hybrid energy storage needs to smooth at time k; P w (k) is the wind farm output power at time k; P grid (k) is the grid-connected power of the wind farm at time k.

[0072] Figure 1 The structure diagram of the battery-supercapacitor hybrid energy storage system is mainly composed of a hybrid energy storage system, a wind power generation system, a busbar and a power grid. b is the charge and discharge power of the battery at a certain moment, P sc is the charging and discharging power of the supercapacitor at a certain moment, P hessis the power of the hybrid energy storage system at a certain moment, P wind is the original wind power, P grid is the grid-connected wind power power.

[0073] The power that the hybrid energy storage system needs to smooth after smoothing, and the relationship between the above-mentioned quantities are as follows:

[0074] P hess =P w -P grid

[0075] P hess =P sc +P ba

[0076] (2) Use ensemble empirical mode decomposition (EEMD) to decompose the power data of the hybrid energy storage system. And use sample entropy to analyze and determine the cutoff points of each component. This step mainly includes the following contents:

[0077] (2.1) Add white noise disturbance to the original signal to be decomposed, then perform EMD decomposition on the original signal with white noise disturbance added, and integrate and average the IMF (modal function) obtained by multiple decompositions as the final IMF. The obtained IMF component diagram is shown in Figure 5 The ensemble empirical mode decomposition method is used, and the decomposed data is the data processed by the SG filtering method. This not only makes the wind power output meet the grid connection standard, but also avoids the phenomenon of over-suppression or under-suppression caused by directly performing the ensemble empirical mode decomposition.

[0078] The principle of ensemble empirical mode decomposition is as follows:

[0079] P x (t)+w i (x) = F 1,i (t)+r i (t)

[0080]

[0081] r 1 (t) = P x (t)-F 1 (t)

[0082]

[0083] Where N is the total order; F 1 (t) is the first-order IMF component: t is time; i is the number of times white noise is added; P x (t) is the original power decomposition signal; w i (x) is the added white noise disturbance; F1,i (t) is the i-th order IMF component in the initial calculation; r i (t) is the residual component.

[0084] (2.2) Using the sample entropy formula:

[0085] SampEn(m,r,L)=-lnA m (r) / B m (r)

[0086] Calculation is performed to find the node with the largest sample entropy gap between nodes as the dividing point between the high-frequency component and the low-frequency component of the hybrid energy storage.

[0087] In the sample entropy formula, L is the sequence length; m is the dimension; r is the similarity tolerance; where B m (r) represents the probability that two consecutive subsequences in a time series match m points under the same similarity tolerance, A m (r) represents the probability that two consecutive subsequences in a time series match m+1 points under the same similarity tolerance. Therefore, sample entropy has the same physical meaning as approximate entropy. For the problem of calculating the complexity of time series, sample entropy is better: first, the calculation process of sample entropy does not depend on the length of the sequence. Second, the changes in m and r have the same impact on the calculation of sample entropy. The calculated sample entropy is shown in Table 2:

[0088] Table 2 Sample entropy results

[0089]

[0090]

[0091] After selecting the cutoff point based on the sample entropy calculation results, the power components of the battery and supercapacitor are obtained as follows: Figure 4 and Figure 5 shown.

[0092] (3) Considering the charge state of the hybrid energy storage system, the HESS capacity configuration model is constructed with the minimum comprehensive cost of the hybrid energy storage system as the objective function, and the optimal capacity of the supercapacitor and lithium battery is determined in combination with the constraints. The baseline variables are introduced to correct them, and the hybrid energy storage capacity configuration result with the optimal wind power suppression effect is obtained.

[0093] Combine the objective function with the constraints to configure the capacity and obtain the appropriate capacity configuration result of the hybrid energy storage system, which specifically includes the following steps:

[0094] The objective function of the HESS capacity configuration model described in (3.1) is:

[0095] C M =Cbat +C sc

[0096] Where: Total energy storage configuration cost C M Cost C for lithium battery configuration ba and the configuration cost of supercapacitor C sc The specific explanation of each part is as follows:

[0097] C bat =C bat,in +C bat,op

[0098]

[0099] Where: C bat , C bat,in and C bat,op are the configuration cost, investment cost and operation and maintenance cost of lithium batteries respectively; P bat，rated and E bat，rated are the rated charge and discharge power and rated capacity of the lithium battery respectively; C bat,inp and c bat,ine are the power and capacity investment cost coefficients of lithium batteries respectively; r is the discount rate; Y is the system operation life; c bat,op is the operation and maintenance cost coefficient of lithium batteries.

[0100] C sc =C sc,in +C sc,op

[0101]

[0102]

[0103] Where: C sc , C sc,in and C sc,op are the configuration cost, investment cost and operation and maintenance cost of supercapacitors; P sc,rated and E sc,rated are the rated charge and discharge power and rated capacity of the supercapacitor respectively; c sc,inp and c sc,ine are the power and capacity investment cost coefficients of supercapacitors respectively; C sc,op is the operation and maintenance cost coefficient of the supercapacitor.

[0104] The constraints of the HESS capacity configuration model described in (3.2) are:

[0105]

[0106] In the formula, SOC b (t), SOC sc(t) is the state of charge of the battery and supercapacitor at time t; SOC bmax , SOC bmin SOC is the upper and lower limits of the battery state of charge; scmax , SOC sc,min E is the upper and lower limits of the supercapacitor charge state; b (t), E sc (t) is the remaining power of the battery and supercapacitor at time t, MWh; P B (t), P sc (t) is the charge and discharge power of the battery and supercapacitor at time t, MW. η Bc , η Bd is the charging efficiency and discharging efficiency of the battery; η SCc , η SCd is the charging efficiency and discharging efficiency of the supercapacitor; Δt is the sampling interval, min.

[0107] (3.3) The optimal capacity configuration results of the hybrid energy storage system are shown in Table 3:

[0108] Table 3 Optimal capacity configuration results of hybrid energy storage system

[0109]

[0110] This hybrid energy storage system capacity configuration method for smoothing wind power fluctuations, for a hybrid energy storage system composed of power-type energy storage and energy-type energy storage, first smoothes the power signal data output by the wind farm through Kalman filtering, so as to obtain the grid-connected data that meets the national wind power grid-connected fluctuation standards and the power data that needs to be absorbed by the hybrid energy storage system. After obtaining the above data, the power data of the hybrid energy storage system is decomposed by the ensemble empirical mode decomposition (EEMD) method to obtain high-frequency components and low-frequency components, and the high-frequency components are allocated to supercapacitors, and the low-frequency components are allocated to batteries. Finally, the solver is called to solve the capacity configuration result of the hybrid energy storage system under the set constraints. Through example verification, the hybrid energy storage capacity optimization configuration method for smoothing wind power fluctuations described in the present invention can adaptively decompose the output power of wind farms in different scenarios, ensure the reasonable allocation of power and capacity inside the hybrid energy storage, and make the hybrid energy storage system have a longer service life, which plays a vital role in smoothing the fluctuation of wind farm output power, and specifically includes the following technical effects:

[0111] (1) Before performing ensemble empirical mode decomposition, the original power data of the wind farm is processed using SG filtering to reduce the errors that may be caused by direct decomposition.

[0112] (2) The present invention adopts ensemble empirical mode decomposition, and integrates and averages the IMFs obtained from multiple decompositions as the final IMF, thereby reducing the error that may be caused by directly using a single IMF component.

[0113] (3) The present invention uses sample entropy to distribute IMF components, thereby improving the credibility of the distribution results.

[0114] This embodiment further provides a capacity configuration system for a hybrid energy storage system for smoothing wind power fluctuations, including a capacity configuration module, wherein the capacity configuration module executes the capacity configuration method for a hybrid energy storage system for smoothing wind power fluctuations.

[0115] 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 hybrid energy storage system capacity configuration method for smoothing wind power fluctuations is implemented.

[0116] Ginseng Figure 6 As shown, this embodiment also provides an electronic device, including:

[0117] 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 hybrid energy storage system capacity configuration method for smoothing wind power fluctuations by executing the computer instructions.

[0118] 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 method for configuring the capacity of an energy storage system for smoothing wind power fluctuations, characterized in that: The method is directed to a hybrid energy storage system consisting of power-type energy storage and energy-type energy storage, and comprises the following steps: Step 1: Obtain all actual output data of the wind farm during the target period, process the obtained data through the SG filtering method and in combination with the national wind power grid connection standard, use the obtained data as wind power grid connection power data, and make a difference between the original data and the grid connection power data to obtain the data that the hybrid energy storage needs to smooth, and then obtain the hybrid energy storage system power task P under the national wind power grid connection standard limit. HESS curve and grid-connected power curve; Step 2, using ensemble empirical mode decomposition to decompose the obtained power data of the hybrid energy storage system to obtain several components, and using sample entropy to analyze and determine the dividing points of each obtained component; Step 3: Considering the state of charge of the hybrid energy storage system, the HESS capacity configuration model is constructed with the minimum comprehensive cost of the hybrid energy storage system as the objective function, and the optimal capacity of the supercapacitor and lithium battery is determined in combination with the constraints to obtain the hybrid energy storage capacity configuration result with the best wind power suppression effect.

2. A method for configuring the capacity of an energy storage system for smoothing wind power fluctuations according to claim 1, characterized in that: The SG filtering method described in step 1 is as follows: Where: T is the k-1 order polynomial in the 2n+1 window; T trans is the transposed matrix of T; The calculation formula of grid-connected power after SG filtering is as follows: At this time, the target power of energy storage is: P hess (k)=P w (k)-P grid (k) Where P Hess (k) is the power that the hybrid energy storage needs to smooth at time k; P w (k) is the wind farm output power at time k; P grid (k) is the grid-connected power of the wind farm at time k.

3. A method for configuring the capacity of an energy storage system for smoothing wind power fluctuations according to claim 2, characterized in that: The ensemble empirical mode decomposition described in step 2 includes: adding white noise disturbance to the original signal to be decomposed, then performing EMD decomposition on the original signal with the added white noise disturbance, and integrating and averaging the modal functions obtained by multiple decompositions as the final modal components.

4. A method for configuring the capacity of an energy storage system for smoothing wind power fluctuations according to claim 3, characterized in that: The collective empirical mode decomposition formula described in step 2 is as follows: P x (t)+w i (x)=F 1,i (t)+r i (t) r1(t)=P x (t)-F1(t) Where N is the total order; F1(t) is the first-order IMF component; t is time; i is the number of times white noise is added; P x (t) is the original power decomposition signal; w i (x) is the added white noise disturbance; F 1,i (t) is the i-th order IMF component in the initial calculation; r i (t) is the residual component.

5. A method for configuring the capacity of an energy storage system for smoothing wind power fluctuations according to claim 4, characterized in that: In step 2, sample entropy is used for analysis to determine the cutoff points of each component obtained. The formula is as follows: SampEn(m,r,L)_-lnA m (r) / B m (r) Where L is the sequence length; m is the dimension; r is the similarity tolerance; and B m (r) represents the probability that two consecutive subsequences in a time series match m points under the same similarity tolerance, A m (r) represents the probability that two consecutive subsequences in the time series match m+1 points under the same similarity tolerance.

6. A method for configuring the capacity of an energy storage system for smoothing wind power fluctuations according to claim 5, characterized in that: The objective function of the HESS capacity configuration model described in step 3 is: C M =C bat +C sc Where: Total energy storage configuration cost C M Cost C for lithium battery configuration ba and the configuration cost of supercapacitor C sc The sum of , where: C bat =C bat,in +C bat,op Where: C bat , C bat,in and C bat,op are the configuration cost, investment cost and operation and maintenance cost of lithium batteries respectively; P bat，rated and E bat，rated are the rated charge and discharge power and rated capacity of the lithium battery respectively; C bat,inp and c bat,ine are the power and capacity investment cost coefficients of lithium batteries respectively; r is the discount rate; Y is the system operation life; c bat,op is the operation and maintenance cost coefficient of lithium battery; C sc =C sc,in +C sc,op Where: C sc , C sc,in and C sc,op are the configuration cost, investment cost and operation and maintenance cost of supercapacitors; P sc,rated and E sc,rated are the rated charge and discharge power and rated capacity of the supercapacitor respectively; c sc,inp and c sc,ine are the power and capacity investment cost coefficients of supercapacitors respectively; C sc,op is the operation and maintenance cost coefficient of the supercapacitor.

7. A method for configuring the capacity of an energy storage system for smoothing wind power fluctuations according to claim 6, characterized in that: The constraints of the HESS capacity configuration model in step 3 are: In the formula, SOC b (t), SOC sc (t) is the state of charge of the battery and supercapacitor at time t; SOC bmax , SOC bmin SOC is the upper and lower limits of the battery state of charge; scmax , SOC sc,min E is the upper and lower limits of the supercapacitor charge state; b (t), E sc (t) is the remaining power of the battery and supercapacitor at time t, MWh; P B (t), P sc (t) is the charge and discharge power of the battery and supercapacitor at time t, MW; η Bc , η Bd is the charging efficiency and discharging efficiency of the battery; η SCc , η SCd is the charging efficiency and discharging efficiency of the supercapacitor; Δt is the sampling interval, min.

8. A system for configuring energy storage system capacity for smoothing wind power fluctuations, characterized in that: It comprises a capacity configuration module, and the capacity configuration module executes a method for configuring the capacity of an 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 a method for configuring the capacity of an 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 method for configuring the capacity of an 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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