Wind power fluctuation stabilizing method based on improved rain flow counting method

Abstract

The invention relates to a wind power fluctuation stabilizing method based on an improved rain flow counting method, and belongs to the technical field of wind power. According to the technical scheme, the method comprises the following steps: (1) designing an improved raccoon optimization algorithm; (2) optimizing a global optimal stress threshold value of the rain flow counting method by using an improved raccoon optimization algorithm; (3) extracting wind power characteristic data points representing wind power output based on the global optimal stress threshold; (4) performing Lagrange interpolation processing on the data points to obtain a wind power characteristic trend; and (5) the deviation between the wind power and the characteristic trend is allocated to the BESS, and the BESS allocates the deviation to an internal battery unit and makes the battery unit respond. According to the method, the wind power fluctuation is effectively stabilized, the charging and discharging state switching frequency of the battery unit is reduced, so that the BESS operation life loss is reduced, the balance of the state of charge of the battery unit is ensured, and the sustainable regulation and control capability of the BESS is improved.

Description

Technical Field

[0001] This invention relates to an improved method for smoothing wind power fluctuations using rainflow counting, belonging to the field of wind power technology. Background Technology

[0002] Wind energy, as a clean and renewable energy source, has received significant attention. With the introduction of the "dual carbon" target, wind power will be integrated into the power grid on a larger scale. However, affected by natural factors such as wind speed, the output power of wind farms is random and intermittent, resulting in large fluctuations in grid-connected power and seriously impacting the safe and stable operation of the power grid. Therefore, how to effectively mitigate the fluctuations in wind farm output power and reduce its impact on the power system is an urgent problem to be solved.

[0003] Battery energy storage systems (BESS) possess rapid charge and discharge characteristics, enabling energy transfer across time and space, and are currently an important means of assisting wind power grid connection. However, without a proper control strategy, BESS can lead to irregular battery cell movements, which not only fail to mitigate fluctuations but also exacerbate grid-connected power fluctuations, resulting in degraded system performance. Furthermore, irregular BESS movements increase the number of charge / discharge state transitions within the BESS and lead to poor state of charge (SOC) balance, increasing BESS lifespan loss and reducing its sustainable control capabilities. Therefore, it is necessary to design a reasonable control strategy for battery energy storage systems to mitigate wind power fluctuations, effectively reducing grid-connected power fluctuations while minimizing BESS lifespan loss and improving its sustainable control capabilities. Summary of the Invention

[0004] This invention proposes an improved rainflow counting method for smoothing wind power fluctuations, reducing the volatility of wind farm grid-connected power and enabling wind power to be safely and stably integrated into the grid. Simultaneously, it ensures that the State of Charge (SOC) of battery cells in the Battery Energy Storage System (BESS) remains consistent during operation, improving the BESS's sustainable control capability. The invention not only designs an improved Raccoon Optimization Algorithm but also, based on this, designs an improved rainflow counting method, combined with Lagrange interpolation, to extract the characteristic trend of wind power output. Furthermore, it utilizes the BESS to smooth wind power fluctuations, reducing grid-connected power fluctuations, minimizing BESS lifespan loss, and improving the BESS's sustainable control capability. Simulations verify the effectiveness of this method, addressing the aforementioned technical problems of existing technologies.

[0005] The technical solution of this invention is:

[0006] An improved method for smoothing wind power fluctuations using rainflow counting includes the following steps:

[0007] (1) Design an improved raccoon optimization algorithm, based on the exponential function to design its position update formula in the local development stage, so as to improve its optimization accuracy;

[0008] (2) Obtaining wind power P w The global optimal stress threshold E of the rainflow counting method was optimized using an improved raccoon optimization algorithm.

[0009] (3) Based on the global optimal stress threshold E, the rainflow counting method is used again to measure P. w The data is processed to extract the characteristic moments and corresponding power data points that characterize wind power.

[0010] (4) The Lagrange interpolation method is used to interpolate the wind power characteristic data points to obtain the wind power characteristic trend P. f ;

[0011] (5) Calculate P w With P f The power deviation P between d and P d The deviation is allocated to the battery energy storage system, which then distributes it to the internal battery cells and allows the battery cells to respond.

[0012] The location update formula for the local development phase in step (1) is as follows:

[0013]

[0014] In the formula, and Let r represent the positions of the i-th optimization individual in the j-th dimension during the (n+1)-th and n-th iterations, respectively, where r is a random number between [0,1]. and , where represents the upper and lower limits of the optimal individual position, and e is the natural constant with a value of 2.718.

[0015] In step (2), the improved raccoon optimization algorithm is used to optimize the global optimal stress threshold E in the rainflow counting method, where the value function is as follows:

[0016]

[0017] In the formula: and The standard deviation and compression ratio represent the trend of wind power characteristics, respectively. and These represent the maximum and minimum values ​​of the standard deviation of the wind power characteristic trend, respectively. and These are the maximum and minimum compression ratios, respectively. The total number of samples. These are sample data values. These are the characteristic trend values ​​after Lagrange interpolation of the characteristic data points. This represents the number of feature data points extracted.

[0018] In step (3), the global optimal stress threshold E obtained in step (2) is combined with the rainflow counting method to calculate the wind power P again. w The data is processed to extract characteristic moments and corresponding power data points that can characterize wind power output.

[0019] In step (4), based on the characteristic time of wind power and the corresponding power data points obtained in step (3), the Lagrange interpolation method is used to interpolate the characteristic data points of wind power, thereby obtaining the characteristic trend P of wind power that can characterize wind power output. f .

[0020] In step (5), the wind power characteristic trend P obtained in step (4) is... f Calculate wind power P w With characteristic trend P f The power deviation P between d and P d The power is allocated to the battery energy storage system, which then distributes the deviation P according to the SOC consistency power allocation method. d It is further allocated to the internal battery cells and the battery cells respond.

[0021] The beneficial effects of this invention are as follows: This invention designs an improved Raccoon Optimization Algorithm and optimizes the rainflow counting method based on the improved Raccoon Optimization Algorithm, thereby effectively extracting wind power characteristic data points. Further Lagrange interpolation processing can accurately extract the wind power characteristic trend representing wind power output. Based on this, a wind power fluctuation smoothing method based on the wind power characteristic trend is designed. This invention can effectively smooth wind power fluctuations, thereby ensuring that wind power can be safely and stably integrated into the grid. For BESS (Battery Energy Storage System), it can reduce the number of charge / discharge state transitions of battery cells during operation, reducing BESS lifespan loss, and also gradually making the SOC (State of Charge) of battery cells more consistent, thus ensuring the consistency of SOC of battery cells within the BESS and improving the sustainable control capability of the BESS. Attached Figure Description

[0022] Figure 1This is a system diagram of an embodiment of the present invention;

[0023] Figure 2 This is an optimization flowchart of the improved raccoon optimization algorithm according to an embodiment of the present invention;

[0024] Figure 3 This is a flowchart illustrating the process of optimizing the rainflow counting method using the improved raccoon optimization algorithm in an embodiment of the present invention.

[0025] Figure 4 This is a diagram illustrating the stress threshold optimization process in an embodiment of the present invention.

[0026] Figure 5 This is a graph showing the results of wind power characteristic data points extracted by the improved rainflow counting method according to an embodiment of the present invention.

[0027] Figure 6 This is a result of wind power characteristic trend extraction according to an embodiment of the present invention;

[0028] Figure 7 This is a diagram showing the SOC changes of each battery cell during operation according to an embodiment of the present invention;

[0029] Figure 8 This is a graph showing the results of smoothing wind power fluctuations according to an embodiment of the present invention;

[0030] Figure 9 for Figure 8 A magnified view of a portion of the image. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0032] An improved method for smoothing wind power fluctuations using rainflow counting includes the following steps: (1) designing an improved Raccoon optimization algorithm; (2) using the improved Raccoon optimization algorithm to optimize the global optimal stress threshold of the rainflow counting method; (3) extracting wind power characteristic data points representing wind power output based on the global optimal stress threshold; (4) performing Lagrange interpolation on the data points to obtain the wind power characteristic trend; (5) allocating the deviation between wind power and the characteristic trend to BESS, and BESS then allocating the deviation to the internal battery cells and allowing the battery cells to respond.

[0033] Appendix Figure 1 The flowchart of this invention includes the following detailed steps.

[0034] Step 1: Design an improved raccoon optimization algorithm:

[0035] The improved raccoon optimization algorithm is designed with an exponential function to improve the position update formula in the local development stage, thereby enhancing its optimization accuracy. The optimization process of the improved raccoon optimization algorithm is as follows:

[0036] 1) Initialization

[0037] (1)

[0038] In the formula: N is the population size, and D is the dimension of the variable. Let be the position of the i-th optimization individual in the j-th dimension, and its calculation formula is as follows:

[0039] (2)

[0040] In the formula: r is a random number between [0,1]. and These are the upper and lower limits of the optimal individual's position, respectively;

[0041] 2) Global Exploration Phase (Hunting and Attack Phase)

[0042] Optimal individuals for tree-climbing attacks Location update:

[0043] (3)

[0044] In the formula: This indicates the position of the optimal individual during the global exploration phase in the nth iteration. This indicates the position of the current optimal solution in the nth iteration. The range of values ​​is Random numbers between;

[0045] Optimal individuals hunting on land Location update:

[0046] (4)

[0047] (5)

[0048] In the formula: and Let f be the upper and lower limits of the position of the individual seeking optimization in the j-th dimension, respectively, and f be the value function of optimization.

[0049] Local optimal solution update:

[0050] (6)

[0051] 3) Partial Development Phase (Escape from Predators Phase)

[0052] Optimization of individuals in the partial development phase Location update:

[0053] (7)

[0054] In the formula, This indicates the position of the optimization individual during the local development phase in the nth iteration;

[0055] Local optimal solution update:

[0056] (8)

[0057] The optimization process of the improved raccoon optimization algorithm is shown in the attached figure. Figure 2 As shown.

[0058] Step 2: Obtain wind power P w The global optimal stress threshold E of the rainflow counting method is optimized using an improved raccoon optimization algorithm. The implementation process of extracting wind power feature data points using the rainflow counting method is as follows:

[0059] 1) Extract peak and valley data points

[0060] When three consecutive adjacent data points do not satisfy the following equation (9) or equation (10), the data point at the intermediate time point is extracted and recorded as a peak-valley data point:

[0061] (9)

[0062] (10)

[0063] 2) Extract full-loop and half-loop data.

[0064] Set counting points Each time a new peak / valley data point is read, let ;

[0065] For peak and valley data points, the magnitude of the change between adjacent peak and valley data points is calculated as shown in the following formula:

[0066] (11)

[0067] In the formula: , , For three consecutive adjacent peak and valley data points, The difference in amplitude between the first peak / valley data point and the second peak / valley data point. The variation amplitude between the second and third peak / valley data points;

[0068] If at this time ,and Then from arrive Extract a half-loop and record its amplitude. Mean, start time and duration, and let ;

[0069] If at this time ,and Then from arrive Extract a full loop and record the amplitude of that half loop. Mean, start time and duration, and let ;

[0070] If at this time ,or If the value is 0, then no counting is performed;

[0071] 3) Delete cycles that have a relatively small impact on the trend of wind power characteristics.

[0072] Set a stress threshold E. If the amplitude of the loop is greater than the stress threshold E, keep the loop; otherwise, delete the loop.

[0073] 4) Record wind power characteristic data points

[0074] For the retained loops, based on the recorded start time and duration, the characteristic moments of wind power are extracted, and then based on the characteristic moments and wind power P... w , thus obtaining the corresponding power data points;

[0075] The process of optimizing the global optimal stress threshold using the improved Raccoon Optimization Algorithm is as follows:

[0076] Using the global optimal stress threshold E of the rainflow counting method as the independent variable for optimization, and employing the improved Raccoon Optimization Algorithm, the optimal value function is as follows:

[0077] (12)

[0078] In the formula: and The standard deviation and compression ratio represent the trend of wind power characteristics, respectively. and These represent the maximum and minimum values ​​of the standard deviation of the wind power characteristic trend, respectively. and These are the maximum and minimum compression ratios, respectively. The total number of samples. These are sample data values. These are the characteristic trend values ​​after Lagrange interpolation of the characteristic data points. The number of feature data points extracted;

[0079] The optimization process of the improved raccoon optimization algorithm for the rainflow counting method is attached. Figure 3 As shown.

[0080] Step 3: Extract wind power characteristic data points:

[0081] Based on the globally optimal stress threshold E obtained by optimizing the rainflow counting method using the improved Raccoon Optimization Algorithm in step 2, the rainflow counting method is used again to calculate the wind power P. w The data is processed to extract the characteristic moments and corresponding power data points that characterize wind power output.

[0082] Step 4: Obtain wind power characteristic trends:

[0083] Based on the wind power characteristic data points obtained in step 3, which can characterize wind power output, the Lagrange interpolation method is used to interpolate them, which is the wind power characteristic trend P. f .

[0084] Step 5: Use BESS to smooth wind power fluctuations:

[0085] Calculate P w With P f The power deviation P between d and P d The deviation is allocated to the battery energy storage system, which then distributes it to the internal battery cells and allows the battery cells to respond.

[0086] The formula for calculating the power regulation command of BESS is as follows:

[0087] (13)

[0088] In the formula: The power regulation command of BESS at time t. Let be the wind power at time t. The trend of wind power characteristics after Lagrange interpolation at time t;

[0089] BESS allocates P based on the SOC consistency power allocation principle. d The process involves allocating resources to the internal battery cells and instructing the battery cells to respond, as follows:

[0090] 1) Calculate the charge / discharge function value characterizing the charge / discharge capability of the battery cell, as shown in the following formula:

[0091] (14)

[0092] In the formula: and Let represent the charging function value and discharging function value of the battery cell at time t-1, respectively. Let e ​​be the SOC of the i-th battery cell at time t-1, and let e be the natural constant with a value of 2.718.

[0093] 2) Based on the battery cell charge / discharge function values ​​under different SOCs, adjust the BESS power regulation command P d The power regulation command allocated to each battery cell is calculated using the following formula:

[0094] (15)

[0095] In the formula: For the power regulation command of the i-th battery cell, N B1 This represents the number of battery cells in the battery energy storage system. Let SOC be the state of charge (SOC) of the i-th battery cell at time t-1;

[0096] 3) Battery cell response

[0097] The battery cell responds to commands while meeting operational constraints:

[0098] Maximum charge / discharge power limit: The charge / discharge power of a battery cell at any given time shall not exceed its maximum charge / discharge power. This constraint can be expressed as:

[0099] (16)

[0100] In the formula: Let t be the actual response power of the battery cell. This represents the maximum charge and discharge power of the battery cell.

[0101] SOC operating limit: The SOC of a battery cell should not exceed the upper or lower limit at any given time, i.e.:

[0102] (17)

[0103] In the formula: Let be the SOC of the i-th battery cell at time t. and These represent the upper and lower limits of the State of Charge (SOC) for the battery cell, respectively; the formula for calculating the SOC of the battery cell is:

[0104] (18)

[0105] In the formula: and Let SOC be the state of charge (SOC) of the i-th battery cell at time t and time t-1, respectively. For time intervals, The rated capacity of the battery cell, , For charging efficiency and discharging efficiency;

[0106] Charge / discharge state constraints: The charge / discharge state flag of battery cell i at any given time should satisfy the constraint of equation (19):

[0107] (19)

[0108] In the formula: Let t be the charging status flag of battery cell i at time t, which is 1 when charging and 0 when not charging; Let t be the discharge state flag of battery cell i at time t, which is 1 when discharging and 0 when not discharging.

[0109] To further understand this invention and verify the effectiveness of the improved rainflow counting method for wind power fluctuation mitigation, a simulation was conducted using a wind farm equipped with a battery energy storage system. The wind farm has a capacity of 100MW, a BESS capacity of 10MW / 10MWh, and other relevant parameters are shown in Table 1 below.

[0110] Table 1 Relevant parameters of battery energy storage power station

[0111]

[0112] The optimization process of finding the global optimal stress threshold using the improved Raccoon Optimization Algorithm for the rainflow counting method is shown in the attached figure. Figure 4 As shown in the attached figure, the wind power characteristic data points extracted by the rainflow counting method using the improved Raccoon Optimization Algorithm are optimized. Figure 5 As shown in the attached figure, the trend of wind power characteristics obtained after Lagrange interpolation of wind power characteristic data points is as follows. Figure 6 As shown, the improved raccoon optimization algorithm only needs 26 iterations to find the optimal solution. At this time, the optimal stress threshold is 0.9092, the corresponding value function value is 2.8811, the standard deviation is 1.7908, and the compression ratio is 0.2181.

[0113] During the process of smoothing wind power fluctuations, the battery cell of the BESS underwent 281 charge-discharge switching operations. This demonstrates that the present invention can effectively reduce the number of charge-discharge switching operations during battery cell operation, thereby reducing the lifespan loss of the BESS. The SOC changes of each battery cell during BESS operation are shown in the attached figure. Figure 7 As shown, the SOC of all battery cells gradually becomes more balanced during the operation of BESS, thus ensuring the consistency of SOC of battery cells within BESS.

[0114] The results of BESS on the smoothing of wind power fluctuations are shown in the attached figure. Figure 8 , 9As shown in Table 2, the average volatility and maximum volatility at the 1-minute and 10-minute levels are as follows. It can be seen that the average volatility and maximum volatility at the 1-minute level under this invention are 1.25% and 20.55%, respectively, and the average volatility and maximum volatility at the 10-minute level are 7.05% and 27.06%, respectively.

[0115] To further highlight the advantages of this invention, a comparison is made between the wind power fluctuation smoothing method based on the improved rainflow counting method proposed in this invention and the method using BESS to smooth wind power fluctuations. The results are shown in Tables 2 and 3. It can be seen that, compared with the traditional method, the method proposed in this invention reduces both the average and maximum fluctuation rates in terms of wind power fluctuation smoothing effect, thus verifying that this invention has a better wind power fluctuation smoothing effect. Regarding the operational lifespan loss and SOC balance of BESS, compared with the traditional method, the method proposed in this invention has lower operational lifespan loss, faster SOC balancing speed, and a higher degree of balancing, thus verifying that the method proposed in this invention is more advantageous for BESS.

[0116] Table 2 Comparison of wind power fluctuation mitigation effects under the two methods

[0117]

[0118] Table 3 Comparison of BESS lifetime loss and SOC balance under the two methods

[0119]

[0120] This invention effectively mitigates wind power fluctuations, reduces the number of battery cell charge / discharge state switching cycles, thereby reducing BESS lifespan loss and ensuring the balance of battery cell state of charge, thus improving the sustainable regulation capability of BESS.

Claims

1. An improved method for smoothing wind power fluctuations using rainflow counting, characterized in that... It includes the following steps: (1) Design an improved raccoon optimization algorithm, based on the exponential function to design its position update formula in the local development stage, so as to improve its optimization accuracy; (2) Obtaining wind power P w The global optimal stress threshold E of the rainflow counting method was optimized using an improved raccoon optimization algorithm. (3) Based on the global optimal stress threshold E, the rainflow counting method is used again to measure P. w The data is processed to extract the characteristic moments and corresponding power data points that characterize wind power. (4) The Lagrange interpolation method is used to interpolate the wind power characteristic data points to obtain the wind power characteristic trend P. f ; (5) Calculate P w With P f The power deviation P between d and P d The deviation is allocated to the battery energy storage system, which then distributes it to the internal battery cells and allows the battery cells to respond.

2. The wind power fluctuation smoothing method based on the improved rainflow counting method according to claim 1, characterized in that: The location update formula for the local development phase in step (1) is as follows: ; In the formula, and Let r represent the positions of the i-th optimization individual in the j-th dimension during the (n+1)-th and n-th iterations, respectively, where r is a random number between [0,1]. and , where represents the upper and lower limits of the optimal individual position, and e is the natural constant with a value of 2.

718.

3. The wind power fluctuation smoothing method based on the improved rainflow counting method according to claim 1, characterized in that, In step (2), the improved raccoon optimization algorithm is used to optimize the global optimal stress threshold E in the rainflow counting method, where the value function is as follows: ; In the formula: and The standard deviation and compression ratio represent the trend of wind power characteristics, respectively. and These represent the maximum and minimum values ​​of the standard deviation of the wind power characteristic trend, respectively. and These are the maximum and minimum compression ratios, respectively. The total number of samples. For sample data values, These are the characteristic trend values ​​after Lagrange interpolation of the characteristic data points. This represents the number of feature data points extracted.

4. The wind power fluctuation smoothing method based on the improved rainflow counting method according to claim 1, characterized in that, In step (3), the global optimal stress threshold E obtained in step (2) is combined with the rainflow counting method to calculate the wind power P again. w The data is processed to extract characteristic moments and corresponding power data points that can characterize wind power output.

5. A wind power fluctuation mitigation method based on an improved rainflow counting method according to claim 1, characterized in that, In step (4), based on the characteristic time of wind power and the corresponding power data points obtained in step (3), the Lagrange interpolation method is used to interpolate the characteristic data points of wind power, thereby obtaining the characteristic trend P of wind power that can characterize wind power output. f .

6. The wind power fluctuation smoothing method based on the improved rainflow counting method according to claim 1, characterized in that, In step (5), the wind power characteristic trend P obtained in step (4) is... f Calculate wind power P w With characteristic trend P f The power deviation P between d and P d The power is allocated to the battery energy storage system, which then distributes the deviation P according to the SOC consistency power allocation method. d It is further allocated to the internal battery cells and the battery cells respond.

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