Wind power real-time smoothing method

Abstract

The application relates to a wind power real-time stabilizing method, which is suitable for the technical field of offshore wind power. The technical scheme of the application is as follows: based on the power of a wind farm connected to a large power grid, the 30-minute wind power fluctuation rate and the output power of the wind farm, the power that should be provided by a hybrid energy storage system is determined; based on the power that should be provided by the hybrid energy storage system and the state of charge of an energy storage battery, the ideal power that should be provided by the energy storage battery is determined; based on the power that should be provided by the energy storage battery, the state of charge of the energy storage battery is determined; based on the state of charge of the energy storage battery, the edge proximity parameter of the energy storage battery is determined; based on the edge proximity parameter of the energy storage battery, the power that should be provided by the energy storage battery is updated; and based on the power that should be provided by the hybrid energy storage system and the power that should be provided by the energy storage battery, the power that should be provided by a super capacitor is determined.

Description

TECHNICAL FIELD

[0001] The application relates to a wind power real-time flattening method. BACKGROUND

[0002] Wind energy has the advantages of being clean and pollution-free. Under the driving of energy saving and emission reduction and atmospheric pollution reduction policies, more and more countries in the world pay attention to wind energy. In recent years, countries in the world have been developing wind power. Offshore wind power is an important direction of world wind power development. From the construction and planning of offshore wind power, it can be seen that the deep sea area with an offshore distance of more than 100 km and a water depth of more than 50 m has more abundant sea area and wind energy resources. Offshore wind resources are mainly concentrated in a small part of the area, and are far away from the load center. The development speed of wind power exceeds the local power demand, and it is difficult to consume locally, resulting in many wind farms abandoning wind and limiting power, which seriously affects the development of wind power.

[0003] In order to improve the problem of abandoning wind and develop wind power on a large scale, the most effective way is to connect wind power to the grid and send it to the power load center to realize the development strategy of “power from a long distance”. However, due to the randomness and volatility of wind power, the impact on the voltage and frequency of the power grid makes it necessary to limit the volatility of wind power when wind power is connected to the grid on a large scale. In addition, China's wind power started late, and there is a lack of historical data of wind resources. The lack of coordination between wind power prediction and power grid planning makes China face a problem that has never been faced by other countries in the world when wind power is connected to the grid.

[0004] At present, wind turbine generators are generally operated in the maximum wind energy capture mode, and the output power of wind power is mainly determined by the real-time wind speed. With the increase of wind power installed capacity and wind power penetration rate, the randomness, unpredictability and volatility of wind energy itself will lead to random fluctuations of wind power, which will have a great impact on the power quality (including voltage and frequency) of the existing power grid and the dispatching of the power grid when wind power is connected to the grid. SUMMARY

[0005] The technical problem to be solved by the application is to provide a wind power real-time flattening method.

[0006] The technical scheme adopted by the application is: a wind power real-time flattening method, comprising:

[0007] Based on the wind farm access power of the power grid at t-1 time and the 30-minute wind power fluctuation rate, and the wind farm output power at t time, a two-time scale power fluctuation relationship model is combined to determine the power that should be provided by the hybrid energy storage system at t time;

[0008] Based on the power that should be provided by the hybrid energy storage system at t time and the state of charge of the energy storage battery at t-1 time, the ideal power that should be provided by the energy storage battery at t time is determined ;

[0009] determining the state of charge of the energy storage battery at the time t based on the power that the energy storage battery should provide at the time t;

[0010] determining the edge proximity parameter of the energy storage battery at the time t based on the state of charge of the energy storage battery at the time t ;

[0011] ;

[0012] updating the power that the energy storage battery should provide based on the edge proximity parameter of the energy storage battery at the time t , comprising:

[0013] ;

[0014] wherein, is a preset edge proximity parameter warning value of the battery;

[0015] determining the power that the super capacitor should provide at the time t based on the power that the hybrid energy storage system should provide at the time t and the power that the energy storage battery should provide at the time t.

[0016] determining the power that the hybrid energy storage system should provide at the time t based on the power of the wind farm connected to the large power grid at the time t-1 and the 30-minute wind power fluctuation rate, and the power of the wind farm output at the time t, in combination with a two-time-scale power fluctuation relationship model, comprising:

[0017] obtaining the power of the wind farm connected to the large power grid at the time t-1, the 30-minute wind power fluctuation rate of the power of the wind farm connected to the large power grid at the time t-1, and the power of the wind farm output at the time t;

[0018] determining the 1-minute wind power fluctuation rate at the time t based on the 30-minute wind power fluctuation rate at the time t-1 in combination with a two-time-scale power fluctuation relationship model;

[0019] determining the first filtering coefficient at the time t based on the 1-minute wind power fluctuation rate at the time t and the power of the wind farm output, and the power of the wind farm connected to the large power grid at the time t-1;

[0020] determining the first power of the wind farm connected to the large power grid at the time t based on the power of the wind farm connected to the large power grid at the time t-1, and the power of the wind farm output at the time t and the first filtering coefficient;

[0021] determining the 30-minute wind power fluctuation rate of the power of the wind farm connected to the large power grid at the time t based on the first power of the wind farm connected to the large power grid at the time t;

[0022] determining whether the 30-minute wind power fluctuation rate at the time t is greater than a preset maximum 30-minute wind power fluctuation rate;

[0023] If yes, determining the second filter coefficient at the time t based on the wind farm output power at the time t, the wind farm grid-connected power at the time t-1 and the preset maximum 30-minute wind power fluctuation rate; otherwise, taking the first filter coefficient as the second filter coefficient at the time t;

[0024] determining the second wind farm grid-connected power at the time t based on the wind farm grid-connected power at the time t-1, the wind farm output power at the time t and the second filter coefficient;

[0025] determining the power to be provided by the hybrid energy storage system at the time t based on the second wind farm grid-connected power at the time t and the wind farm output power at the time t.

[0026] determining the ideal power to be provided by the energy storage battery at the time t based on the power to be provided by the hybrid energy storage system at the time t and the state of charge of the energy storage battery at the time t-1 , comprising:

[0027] determining the loss coefficient of the energy storage battery at the time t-1 based on the state of charge of the energy storage battery at the time t-1;

[0028] determining the filter time coefficient at the time t based on the minimum charge-discharge cycle time of the energy storage battery and the loss coefficient at the time t-1;

[0029] determining the power to be provided by the energy storage battery at the time t based on the power to be provided by the hybrid energy storage system at the time t, the filter time coefficient, the power provided by the energy storage battery at the time t-1 and the minimum charge-discharge cycle time of the battery.

[0030] determining the power to be provided by the energy storage battery at the time t based on the power to be provided by the hybrid energy storage system at the time t, the filter time coefficient, the power provided by the energy storage battery at the time t-1 and the minimum charge-discharge cycle time of the battery, comprises:

[0031] determining the power to be provided by the energy storage battery at the time t based on the power to be provided by the hybrid energy storage system at the time t, the filter time coefficient and the power provided by the energy storage battery at the time t-1;

[0032] determining the power to be provided by the energy storage battery at the time t based on the power to be provided by the hybrid energy storage system at the time t, the filter time coefficient and the power provided by the energy storage battery at the time t-1;

[0033] determining whether the charge-discharge state of the energy storage battery at time t is consistent with that at time t-1, if not, determining whether the duration of the charge-discharge state of the energy storage battery at time t-1 is greater than the minimum charge-discharge cycle time of the battery, if not, the power provided by the energy storage battery at time t is equal to the power provided by the energy storage battery at time t-1.

[0034] determining the loss coefficient of the energy storage battery at time t-1 based on the state of charge of the energy storage battery at time t-1, including:

[0035] ;

[0036] wherein, is the loss coefficient of the energy storage battery at time t-1; is the maximum charge constraint of the energy storage battery; is the initial state of charge of the energy storage battery at time t-1; is the final state of charge of the energy storage battery at time t-1; is the power provided by the energy storage battery at time t-1.

[0037] the two-time-scale power fluctuation relationship model, including:

[0038] ;

[0039] wherein, is the 1-minute wind power fluctuation rate of the power accessed by the wind farm to the large power grid at time t; is the maximum value of the wind power fluctuation rate within 1 minute; is the 30-minute wind power fluctuation rate of the power accessed by the wind farm to the large power grid at time t-1; is the maximum value of the wind power fluctuation rate within 30 minutes.

[0040] determining the first filtering coefficient at time t based on the 1-minute wind power fluctuation rate and the wind farm output power at time t, and the power accessed by the wind farm to the large power grid at time t-1, including:

[0041] ;

[0042] wherein, is the first filtering coefficient at time t; is the 1-minute wind power fluctuation rate of the power accessed by the wind farm to the large power grid at time t; is the rated power of the wind farm; is the minimum value of the power accessed by the wind farm to the large power grid within 1 minute with time t-1 as the end point; is the maximum value of the power accessed by the wind farm to the large power grid within 1 minute with time t-1 as the end point; is the wind farm output power at time t; is the wind farm power connected to the power grid at time t-1.

[0043] A wind power real-time smoothing device, comprising:

[0044] A first module, configured to determine the power provided by the hybrid energy storage system at time t based on the wind farm power connected to the power grid at time t-1, the 30-minute wind power fluctuation rate, and the wind farm output power at time t, and a two-time-scale power fluctuation relationship model;

[0045] A second module, configured to determine the ideal power provided by the energy storage battery at time t based on the power provided by the hybrid energy storage system at time t and the state of charge of the energy storage battery at time t-1 ;

[0046] A third module, configured to determine the state of charge of the energy storage battery at time t based on the power provided by the energy storage battery at time t;

[0047] A fourth module, configured to determine the edge proximity parameter of the energy storage battery at time t based on the state of charge of the energy storage battery at time t ;

[0048] ;

[0049] A fifth module, configured to update the power provided by the energy storage battery at time t based on the edge proximity parameter of the energy storage battery at time t , comprising:

[0050] ;

[0051] wherein, is a preset edge proximity parameter warning value of the battery;

[0052] A sixth module, configured to determine the power provided by the super capacitor at time t based on the power provided by the hybrid energy storage system at time t and the power provided by the energy storage battery at time t.

[0053] A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the wind power real-time smoothing method.

[0054] A computer readable storage medium, having a computer program stored thereon, wherein the computer program is executable by a processor to implement the steps of the wind power real-time smoothing method.

[0055] The beneficial effects of the present application are: the present application determines the target power of wind power after flatness suppression according to the filter coefficient, and calculates the wind power fluctuation under two time scales at this moment. According to the fluctuation of 30-minute target output power and the power fluctuation relationship model of two time scales, the next second wind power one-minute fluctuation limit reference value is determined, and then the filter coefficient is adjusted based on the wind power one-minute fluctuation limit reference value, so as to circulate repeatedly to determine the real-time suppression target value of wind power fluctuation at each moment.

[0056] The present application defines the requirements of wind power grid connection according to the fluctuation characteristics of wind power at different time scales, which helps to more accurately understand the dynamic changes of wind power output, so as to develop more effective control strategies. The present application can reduce the consumption of energy storage while ensuring that wind power can be smoothly and efficiently integrated into the grid, thereby improving the stability and reliability of the entire power system.

[0057] The present application selects a low-pass filter algorithm as the basic algorithm according to the absorption of power of different frequencies by energy storage batteries and supercapacitors, and adaptively adjusts the filter coefficient of the low-pass filter algorithm according to the state of charge of the energy storage battery and the number of charge and discharge changes, to control the charge and discharge of the hybrid energy storage system and reduce the loss of the energy storage battery in the operation process.

[0058] The present application adjusts the power distribution by the edge proximity parameter to prevent the SOC of the battery from exceeding the set limit value. Specifically, if the battery SOC is close to the boundary, the power absorption of the battery and the supercapacitor is adjusted based on to prevent the battery SOC from exceeding the safe range. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 It is a schematic diagram of the wind farm system structure with a hybrid energy storage system in the embodiment;

[0060] Figure 2 It is a schematic diagram of a first-order low-pass filter in the embodiment;

[0061] Figure 3 It is a schematic diagram of the wind power fluctuation relationship of two time scales at adjacent moments in the embodiment;

[0062] Figure 4 It is a flow chart of the wind power suppression algorithm in the embodiment;

[0063] Figure 5 It is a schematic diagram of the initial power and target power of the wind farm in the embodiment;

[0064] Figure 6 It is a schematic diagram of one-minute power fluctuation in the embodiment;

[0065] Figure 7 It is a schematic diagram of thirty-minute power fluctuation in the embodiment;

[0066] Figure 8 The required energy storage output for the example to smooth;

[0067] Figure 9 The initial power and target power of the wind farm for the example;

[0068] Figure 10 The one-minute power fluctuation for the example;

[0069] Figure 11 The thirty-minute power fluctuation for the example;

[0070] Figure 12 The required energy storage for the example using a time constant smoothing algorithm;

[0071] Figure 13 The wind farm wind power fluctuation smoothing control strategy block diagram for the example;

[0072] Figure 14 The edge control diagram of the energy storage coefficient for the example. DETAILED DESCRIPTION

[0073] The embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only, for the purpose of explanation, and are not to be understood as a limitation of the present application. For the step numbers in the following embodiments, they are only set for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0074] In the description of the present application, the meaning of multiple is two or more, and if there is a description of first, second, it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the sequence of indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art.

[0075] As Figure 1 shown, the wind farm system structure in the present embodiment mainly consists of a wind farm and a hybrid energy storage system. The wind farm is generally composed of several wind turbines connected in a certain topological structure, and the output power of all wind turbines after aggregation is the real-time output power of the wind farm . The real-time output power of the wind turbine The function is generated by real-time wind speed, as shown in equation (1), according to the formula, the real-time output power of the fan can be obtained, which provides a reference for grid-connected research of wind farms;

[0076] (1)

[0077] wherein is the rated power of a wind turbine, is the real-time wind speed, is the cut-in wind speed of the fan, is the cut-out wind speed of the fan, is the rated wind speed of the fan.

[0078] The hybrid energy storage system in the wind farm is composed of energy storage batteries and super capacitors, which are used as energy buffer devices to reduce the fluctuation of the active power output of the wind farm, so that the real-time grid-connected active power of the wind farm meets the grid-connected index. The real-time power of the energy storage battery and the real-time power of the super capacitor are determined by the real-time control strategy of the hybrid energy storage system and are obtained by controlling the charging and discharging of the energy storage battery and the super capacitor through a DC / AC power converter. The real-time output power of the hybrid energy storage system is combined with the initial output power of the wind farm to obtain the grid-connected power of the wind farm, which is connected to the power grid.

[0079] In the real-time suppression of wind power active power, the main methods include: average method, average moving method including simple average moving method and exponential average moving method, low-pass filter algorithm, etc.

[0080] In this embodiment, the time scale of wind power active power fluctuation limitation is 1 minute and 30 minutes. Due to the randomness of wind power output, in order to effectively limit the fluctuation of wind power within one minute within the range, the sampling period of wind power is selected as 1s. At the same time, due to the fact that the current short-term prediction error of wind power is still not up to the requirement of real-time adjustment, it is still relatively rare in application; and the sampling period of 1s also limits the application of intelligent algorithms. Therefore, in this embodiment, the short-term prediction of wind power is not involved in determining the wind power suppression target, only the real-time power of wind power is applied to adjust the filter coefficient, and the target of reducing energy storage consumption is taken.

[0081] The low-pass filter algorithm is mainly improved from the principle of a first-order low-pass filter, Figure 2 which is a first-order low-pass filter. The mathematical expression is:

[0082] (2)

[0083] wherein is the filter time constant, is the input signal, The low-pass filter output signal.

[0084] When the low-pass filter algorithm is applied to wind power filtering, the input signal is set as the wind farm output power , and the output signal is set as the wind farm access to the large grid power after low-pass filtering , which meets the wind power grid connection standard. This embodiment mainly studies how to determine the power fluctuation constraints that meet the two time scales when the wind power is connected to the grid by changing the time constant of the low-pass filter. The energy storage system acts as an energy buffer link, and its energy is the difference between the wind farm output power and the wind power target power that meets the grid connection standard.

[0085] In application, the wind farm output power and the access to the large grid power are both obtained by sampling, and the sampling period is set as After discretizing equation (2) and bringing the input and output signals into it, we can get:

[0086] (3)

[0087] Where is the wind farm output power at the tth sampling moment; is the wind farm access to the large grid power after filtering algorithm at the tth sampling moment. It can be seen that the filtered wind power is not only related to the wind power input at this moment, but also affected by the wind power suppression at the last moment. The cut-off angular frequency of the low-pass filter is set as , and according to the knowledge of control theory, the system cut-off angular frequency is:

[0088] (4)

[0089] Assuming that the maximum frequency of the output signal is , i.e. the minimum period is , then

[0090] (5)

[0091] From (4) and (5), we can get:

[0092] (6)

[0093] Since the range of the filter time constant is , it is difficult to optimize during adjustment, so a filter coefficient is redefined as shown in the following formula:

[0094] (7)

[0095] Where , which is convenient for optimization. At this time, the low-pass filter algorithm formula (3) is simplified as:

[0096] (8)

[0097] From the above formula, when the filter coefficient is larger, the wind power output has greater effect, and the filtering effect is small; when the filter coefficient is smaller, the power after suppression of the previous sampling time has greater effect, the filtering effect is better, and the output power after suppression is more stable. Therefore, when the low-pass filtering algorithm is applied to wind power suppression, the filter coefficient is required to be adjusted in real time according to the suppression effect, while considering the real-time use of the energy storage system, the service life of the energy storage system, and the like.

[0098] The embodiment adjusts the filter coefficient of the low-pass filter in real time according to the relationship model of the power fluctuation of wind power in two time scales, and determines the real-time suppression target power of the wind farm that meets the grid connection standard.

[0099] When the wind power is connected to the large power grid, the fluctuation rate of the active power is mainly considered in the embodiment. The sampling time of the embodiment is 1 s, and the wind power active power fluctuation rate of 1 minute at the tthsecond is defined as the ratio of the difference between the maximum power and the minimum power in a 1-minute time window with the tsecond as the end point to the rated power of the wind farm, as shown in formula (9):

[0100] (9)

[0101] wherein represents the wind power sampling value in the 1-minute time window, is the rated power of the wind farm.

[0102] The wind power active power fluctuation rate of 30 minutes at the tthsecond is defined as the ratio of the difference between the maximum and minimum values of the average power of each minute in a 30-minute window with the second as the end point to the rated power of the wind farm, as shown in formula (10):

[0103] (10)

[0104] wherein is the average power of the tthminute in the 30-minute time window.

[0105] ​Based on the definition of 30-minute wind power fluctuation, the 30-minute power fluctuation is accumulated from the 1-minute power fluctuation. Therefore, when determining the target power for wind power level suppression, the 1-minute wind power fluctuation constraint in the grid connection requirements should be considered first, followed by the 30-minute wind power fluctuation. However, the target power is constrained by both 1-minute and 30-minute wind power fluctuations simultaneously; satisfying the 1-minute power fluctuation requirement does not necessarily guarantee that the 30-minute power fluctuation requirement will be met. When adjusting the filter coefficient, if the 1-minute wind power limit can be reset using existing data to obtain a reference that allows the 30-minute wind power fluctuation within that second to meet the limit, the problem of determining the filter coefficient is solved.

[0106] When applying the filtering algorithm to wind power smoothing, the main approach is to change the filtering coefficient to determine the target power for wind power smoothing, so that the smoothed wind power fluctuations meet the constraints under two time scales: 1 minute and 30 minutes.

[0107] The fluctuations in wind power output over 1 minute and 30 minutes are related and influence each other. The 1-minute and 30-minute time windows are shifting. This embodiment found that the 30-minute power fluctuation rate after wind power filtering is within one second. Wind power fluctuation rate in one minute for the next second It has a great influence, and the relationship between the two is as follows: Figure 3 As shown. Therefore, this embodiment is based on Figure 3 A two-timescale power fluctuation relationship model is constructed. Based on the target power determined after the wind level is stabilized in this second, the 30-minute power is used to determine the 1-minute fluctuation reference value of the wind power target power in the next second, thereby determining the reference value of the low-pass filter coefficient.

[0108] Figure 3 In the diagram, the dashed line depicts a simple inverse proportional relationship between power fluctuation rates at two time scales: the maximum limit of wind power fluctuation rate in the 1-minute interval of second t decreases as the wind power fluctuation rate in the previous second increases, and vice versa. However, this simple proportional relationship is not ideal for constraining wind power fluctuation rates when they approach the maximum limit, especially when the 30-minute wind power fluctuation rate in a given second is close to both limits. Specifically, if the 30-minute wind power fluctuation rate in second t is closer to the maximum limit, the allowable 1-minute power fluctuation in the next second should be as small as possible to ensure that the 30-minute wind power fluctuation rate in the next second is within the limit. Conversely, when the 30-minute wind power fluctuation rate in second t is very small, closer to 0, the 1-minute wind power output in the next second reaches the maximum limit, reducing the output of energy storage. This embodiment improves the two-time-scale power fluctuation relationship model as follows: Figure 3 The solid line describes a smoothing effect near the limit value, and its mathematical expression is shown in equation (11):

[0109] (11)

[0110] wherein, and are the maximum values of wind power fluctuation rate of 1 minute and 30 minutes respectively specified by the wind power grid connection standard, then the wind power flattening target power must meet the 1-minute and 30-minute fluctuation limits, as shown in the following formula:

[0111] (12)

[0112] The two-time-scale power fluctuation relationship model in the embodiment links the wind power fluctuations at adjacent moments during wind power flattening, thereby serving as a reference to realize real-time adjustment of the filter coefficient and reduce the output of the energy storage at the same time.

[0113] The low-pass filtering algorithm is adopted in the embodiment, the filter coefficient is adaptively adjusted according to the two-time-scale power fluctuation relationship model at adjacent moments, and the wind power grid connection target output power meeting the fluctuation limits of the two time scales is determined. The wind power real-time flattening method in the embodiment includes the following steps:

[0114] S100, based on the wind farm power connected to the large power grid at t-1 moment and the 30-minute wind power fluctuation rate, and the wind farm output power at t moment, the two-time-scale power fluctuation relationship model is combined to determine the power that should be provided by the hybrid energy storage system at t moment.

[0115] As shown in Figure 4 , specifically includes:

[0116] S110, obtaining the wind farm power connected to the large power grid at t-1 moment , the 30-minute wind power fluctuation rate of the wind farm power connected to the large power grid at t-1 moment , and the wind farm output power at t moment .

[0117] S120, based on the 30-minute wind power fluctuation rate at t-1 moment , the two-time-scale power fluctuation relationship model is combined to determine the 1-minute wind power fluctuation rate at t moment .

[0118] S130, based on the 1-minute wind power fluctuation rate at t moment and the wind farm output power at t moment , and the wind farm power connected to the large power grid at t-1 moment , the first filter coefficient at t moment is determined .

[0119] (13)

[0120] The filter coefficient constraint is obtained:

[0121] (14)

[0122] Guaranteed between and .

[0123] In this embodiment, the formula (13) is discussed in three cases:

[0124] When , let be the maximum value, according to formula (9) to get the filter coefficient, the filter coefficient is the maximum allowable value, reduce the energy storage consumption, where the preset one minute wind power fluctuation rate of this second According to Figure 3 determined. But because There are two cases in this minute window: one exceeds the maximum value, there is no problem according to (9); the other It itself does not exceed the maximum value within one minute time window, if calculated according to formula (9), then At this time (14) plays a constraint role.

[0125] Similarly, when , let be the minimum value within one minute window, and then according to formula (9) to get the filter coefficient, the filter coefficient is the maximum allowable value, reduce the energy storage consumption, where the preset one minute wind power fluctuation rate of this second According to Figure 3 determined. But because There are two cases in this minute window: one less than the allowable minimum value, there is no problem according to (11); the other It itself is within the constraint of one minute time window, if calculated according to formula (11), then At this time (14) plays a constraint role.

[0126] When the wind farm output power of the t second is equal to the last second wind power suppression target power, then according to formula (8) , the filter coefficient does not affect the result. Because Within one minute time window to meet the constraints, this second does not process to meet the constraints. In order to facilitate, let .

[0127] S140, based on the wind farm access to the power grid power at t-1 time , and the wind farm output power at t time And the first filter coefficient ​The first wind farm connected to the power grid at time t is determined using equation (8). .

[0128] S150, Power of the first wind farm connected to the power grid based on time t The 30-minute wind power fluctuation rate of the wind farm connected to the power grid at time t is determined by equation (10). .

[0129] S160. Determine the 30-minute wind power fluctuation rate at time t. Is it greater than the preset maximum value of wind power fluctuation rate over 30 minutes? If it is greater than t, then the wind farm output power at time t is used as the basis. Power of the wind farm connected to the main power grid at time t-1 And the preset maximum value of wind power fluctuation rate in 30 minutes Determine the second filter coefficient at time t. Conversely, the first filter coefficient is used. The second filter coefficient at time t .

[0130] The filter coefficients obtained from (12) and (13) are the reference coefficients derived from the power fluctuation relationship between two time scales at adjacent moments. After calculating the target power for wind level suppression, the maximum allowable value must be determined. Then, it must be verified again whether the constraints are met at this power level over a 30-minute timescale. If the constraints are met, the filter coefficient is... If this condition is not met, the average power at that moment within one minute will be discussed as the minimum and maximum value within 30 minutes, and the maximum wind power fluctuation rate over 30 minutes will be taken into account. Calculate the filter coefficients .

[0131] S170, Power of wind farm connected to the power grid based on time t-1 and the wind farm output power at time t Second filter coefficient The second wind farm's grid connection power at time t is determined using equation (8). .

[0132] S180, Power of the second wind farm connected to the main grid based on time t obtained in step S170 And the wind farm output power at time t obtained in step S110 Determine the power that the hybrid energy storage system should provide at time t. .

[0133] Based on the fluctuation mitigation strategy, after adding a hybrid energy storage system, the block diagram of the real-time power fluctuation mitigation control strategy for the entire wind farm is as follows:Figure 13 The hybrid energy storage system provides energy required for real-time smoothing of fluctuations in the wind farm in real time according to the wind power real-time smoothing strategy and the coordinated control strategy of the energy storage system, so that the wind power grid-connected power meets the grid-connected technical requirements. Figure 13 It can be seen that the hybrid energy storage system provides energy in real time as follows:

[0134] (16)

[0135] It can be seen that the hybrid energy storage system provides energy in real time as follows:

[0136] The wind power real-time smoothing method proposed in the embodiment is simulated on a MATLAB platform to find a target power that meets the grid-connected standard after 24-hour output power of a wind farm is smoothed in real time. The system parameters are shown in Table 1: the rated value of the wind farm output power is 6 MW, and the grid-connected active power fluctuation index of the wind farm is specified as: the power fluctuation within 1 minute does not exceed 2% of the rated power, and the power fluctuation within 30 minutes does not exceed 10% of the rated power, i.e.: .

[0137] Table 1 System parameter settings

[0138] Wind farm rated power (MW) Sampling time (s) 1 minute fluctuation (%) 30 minute power fluctuation (%) 6 1 2 10

[0139] In order to verify the low-pass filtering algorithm based on adaptive adjustment of the filtering coefficient, the determined wind power smoothing target power meets the wind power grid-connected standard and reduces the energy storage consumption. A low-pass filtering algorithm with a constant time constant is also used to process the wind power, as a comparison, and the filtering coefficient is taken as: . The simulation results are shown in Figure 5 - Figure 12 , wherein Figure 5 - Figure 8 is the simulation result obtained by using the filtering algorithm proposed in the embodiment, Figure 9 - Figure 12 is the simulation result obtained by using the constant time constant filtering algorithm.

[0140] Figure 5 The middle red curve is the 24-hour per-second wind power target power obtained after the wind farm output power is smoothed in real time according to the wind power smoothing strategy of the embodiment, and the blue curve is the original output power of the wind farm. It can be seen from the comparison of the two curves that the fluctuation after smoothing is much smoother. Figure 6 and Figure 7 The middle red straight line is the maximum power fluctuation of the grid-connected standard limit value, the blue line is the fluctuation of the obtained target power, and the green curve is the initial power fluctuation. It can be seen that the wind farm target power after the filtering algorithm meets the constraints of the two time scales.Figure 7 and Figure 11 , it is found that the target power is too different from the initial wind power in the case that the initial wind power has met the 30-minute fluctuation index, which is not conducive to reducing the energy storage consumption. The filter algorithm used in this embodiment basically no longer reduces the target power in this case. Therefore, although the target power obtained by the filter algorithm with a fixed time constant is much smoother than the method used in this embodiment, it is at the expense of energy storage consumption, as Figure 5 and Figure 9 , the target power obtained by the filter algorithm with a fixed time constant is much smoother than the method used in this embodiment, but it is at the expense of energy storage consumption, as Figure 8 and Figure 12 It can be seen that it is not conducive to the economic operation of the wind farm.

[0141] In addition, when the filter coefficient is adaptively adjusted, the present embodiment also simulates and compares the different two-time scale relationship models described by the solid curve and the dashed straight line in Figure 3 The results show that the required energy storage is 4.9397 MWh when the wind power connected to the grid is obtained by referring to the dashed straight line, and the required energy storage is 4.654 MWh when the improved solid curve in this embodiment is used. The output of the energy storage is reduced, which is more economical.

[0142] The present embodiment mainly studies the calculation method of the target power in the real-time suppression strategy of wind power fluctuation, so that the active power of the suppressed wind power meets the power fluctuation rate limit of two time scales in the grid connection requirement. The wind power suppression effect is mainly determined by the filter coefficient in the filter algorithm, and the time scale of wind power regulation is 1 second, which limits the play of intelligent algorithms. In view of this, a method of adaptively adjusting the filter coefficient is proposed, which is to update the wind power fluctuation limit of one-minute time scale in real time as a reference according to the different time scale fluctuation relationship of wind power. Through simulation on the MATLAB platform and comparison with the low-pass filter algorithm with a fixed time constant, it is proved that the low-pass filter algorithm with adaptively adjusted filter coefficient proposed in this embodiment has good performance in both wind power suppression effect and reduction of energy storage consumption.

[0143] S200, based on the power that the hybrid energy storage system should provide at time t and the state of charge of the energy storage battery at time t-1, determine the ideal power that the energy storage battery should provide at time t .

[0144] At present, there is no single energy storage with relatively comprehensive characteristics such as charge and discharge cycle times, energy density, and power density. The hybrid energy storage composed of batteries and supercapacitors has complementary characteristics in energy and power, and can suppress wind power fluctuations of different time scales.

[0145] The charge and discharge power of the energy storage system is limited when charging and discharging, and the working state of the energy storage system must be ensured to be normal to prevent overcharging and overdischarging from damaging the energy storage system. The state of charge is an important parameter. It refers to the ratio of the remaining capacity to the rated capacity of the energy storage system after charging and discharging, as shown in equation (17):

[0146] (17)

[0147] wherein is the initial remaining capacity of the energy storage device before charging and discharging, and are the charging and discharging efficiencies of the battery, and are the charging and discharging powers, is the charging and discharging time, is the rated capacity of the energy storage device. In this embodiment, the sampling time of wind power is 1s, the charging and discharging efficiency of the energy storage battery is 70%-90%, and the charging and discharging efficiency of the super capacitor is 90%-95%. The charging and discharging mathematical model of the hybrid energy storage system is shown in equations (18) and (19). In order to ensure that the energy storage battery and the super capacitor work in a normal state and reduce the charging and discharging loss, the energy storage battery and the super capacitor must work within their respective state of charge limits, as shown in equation (20):

[0148] (18)

[0149] (19)

[0150] (20)

[0151] wherein is charging; is discharging; subscript B represents the energy storage battery; subscript SC represents the super capacitor; is the state of charge at time t, which can also be represented by .

[0152] Because the service life of the energy storage battery in the hybrid energy storage system is much shorter than that of the super capacitor, it brings more maintenance and replacement costs, so how to prolong the service life of the battery is also one of the hotspots of energy storage research. Studies have shown that the service life of the battery is related to the number of charging and discharging conversions. In order to calculate the number of charging and discharging conversions, we introduce the charging and discharging state quantity to record the charging and discharging state of the battery, as shown in equation (21):

[0153] (21)

[0154] The battery charging or discharging time Calculated by the duration of the same charge and discharge state 1 or -1. As long as the battery charging or discharging time is limited, the number of charge and discharge conversion of the battery is reduced, and the battery life is prolonged.

[0155] The energy storage battery has a large charge and discharge time scale and absorbs long-time charge and discharge power; while the super capacitor has a small charge and discharge time scale and absorbs high-frequency fluctuations. A low-pass filtering algorithm is applied to effectively distribute low-frequency and high-frequency power to the energy storage battery and the super capacitor respectively. However, the simple low-pass filtering algorithm can only ensure that the energy storage battery absorbs the low-frequency component of the wind power fluctuations, and cannot ensure that it works within the normal state of charge range and prolong the charge and discharge life of the battery.

[0156] The coordination control strategy of the hybrid energy storage system in the embodiment is based on the absorption of wind power fluctuations of different frequencies by the energy storage battery and the super capacitor, and the filtering coefficient of the low-pass filtering algorithm is adjusted adaptively according to the state of charge of the energy storage battery and the number of charge and discharge changes to distribute the hybrid energy storage power.

[0157] In order to reduce the loss of the energy storage battery caused by charging and discharging, the adjustment of the filtering coefficient in the embodiment is defined with reference to the loss parameter. The definition is as follows: is the loss coefficient of the energy storage battery, and its value is represented by the state of charge of the battery, as shown in equation (22):

[0158] (22)

[0159] When , the energy storage battery is in a charging state, and the loss is inversely proportional to the initial and final states of charge of charging. That is, the smaller the initial state of charge of charging, the greater the charging loss; the closer the final state of charge of charging to the maximum state of charge constraint, the more sufficient the charging, and the smaller the charging loss.

[0160] When , the energy storage battery is in a discharging state, and the loss is proportional to the initial state of charge of charging and inversely proportional to the final state of charge. That is, during discharging, the larger the final state of charge, the smaller the discharging loss; the closer the initial state of charge to the maximum state of charge constraint, the smaller the discharging loss.

[0161] In the hybrid energy storage system, the life of the energy storage battery is relatively short, and in order to reduce the maintenance and replacement cost of the energy storage battery, the loss of the energy storage battery must be reduced as much as possible, that is, overcharging and overdischarging must be avoided. Therefore, the real-time adjustment of the total low-pass filtering time constant in the embodiment is to adjust it according to the loss coefficient represented by the state of charge of the energy storage battery.

[0162] S210, based on the state of charge of the energy storage battery at t-1 time , determine the loss coefficient of the energy storage battery at t-1 time .

[0163] S220, determining the minimum charge-discharge cycle time of the battery based on the energy storage battery and the loss coefficient at time t-1 , determining the filter time coefficient at time t by using formula (23) .

[0164] S230, determining the power provided by the hybrid energy storage system at time t based on the power provided by the hybrid energy storage system at time t , the filter time coefficient , the power provided by the energy storage battery at time t-1 , and the minimum charge-discharge cycle time of the battery , determining the power provided by the energy storage battery at time t .

[0165] S231, determining the power provided by the energy storage battery at time t based on the power provided by the hybrid energy storage system at time t , the filter time coefficient , and the power provided by the energy storage battery at time t-1 .

[0166]

[0167] S232, determining whether the charge-discharge state of the energy storage battery at time t is consistent with the charge-discharge state of the hybrid energy storage system at time t based on the power provided by the energy storage battery at time t obtained in step S231, if not, the power provided by the energy storage battery at time t is 0. When

[0168] , then . .

[0169] S233, determining whether the charge-discharge state of the energy storage battery at time t is consistent with the charge-discharge state of the energy storage battery at time t-1, if not, determining whether the duration of the charge-discharge state of the energy storage battery at time t-1 is greater than the minimum charge-discharge cycle time of the battery , if not, the power provided by the energy storage battery at time t is equal to the power provided by the energy storage battery at time t-1.

[0170] When , determining whether the duration T_s of the charge-discharge state corresponding to the energy storage battery at time t-1 at the end of time t-1 is greater than the minimum charge-discharge cycle time of the battery , if T_s≤ , the charge-discharge state of the energy storage battery at time t is continued from the charge-discharge state of the energy storage battery at time t-1, .

[0171] ​​S300, determining the power that the energy storage battery should provide at time t based on the power that the energy storage battery should provide at time t , the state of charge of the energy storage battery at time t is determined using formula (18) .

[0172] S400, determining the edge proximity parameter of the energy storage battery at time t based on the state of charge of the energy storage battery at time t .

[0173] The first part of the real-time control strategy of the offshore wind power hybrid energy storage system aims to allocate the power to be stored to the battery and the super capacitor, while ensuring that the allocation process meets the SOC constraint of the energy storage system, i.e. ensuring that the state of charge of the battery and the super capacitor is maintained within the predetermined safe range.

[0174] In the control process of the algorithm, the SOC state of the energy storage system needs to be considered to make decisions. is defined as the edge proximity parameter of the energy storage battery, whose value is represented by the state of charge of the device, and is used to evaluate the proximity of the state of charge of the battery to its allowed maximum and minimum values, as shown in formula (25)

[0175] (25)

[0176] 1. When , the energy storage battery is in a charging state, and the closer the end-of-charge state of charge is to the maximum charge constraint, represents the distance of the energy storage battery to the maximum state of charge.

[0177] 2. When , the energy storage battery is in a discharging state, and the closer the end-of-charge state of charge is to the minimum charge constraint, represents the distance of the energy storage battery to the minimum state of charge.

[0178] S500, updating the power that the energy storage battery should provide at time t based on the edge proximity parameter of the energy storage battery at time t .

[0179] The SOC "danger value" of the battery can be represented by the parameter that the SOC approaches the edge, and when the warning limit value is reached, a limiting function is selected to limit the power output of the energy storage battery in the corresponding direction. The commonly used limiting function can consider high-order coefficients or power index coefficients. This embodiment adopts a quadratic coefficient, so that the power curve approaches downward and curls. That is:

[0180] (26)

[0181] In the formula, is the ideal power that the energy storage battery should provide at time t,​ is a preset battery edge proximity parameter warning value. The edge control effect of the energy storage coefficient is shown in 14.

[0182] S600, based on the power that the hybrid energy storage system should provide at time t and the power that the energy storage battery should provide at time t, determine the power that the super capacitor should provide at time t.

[0183] (27)

[0184] Embodiment 3: The embodiment is a wind power real-time smoothing device, specifically comprising:

[0185] The first module is configured to determine the power that the hybrid energy storage system should provide at time t based on the wind farm power connected to the large power grid at time t-1 and the 30-minute wind power fluctuation rate, and the wind farm output power at time t, in combination with a two-time scale power fluctuation relationship model.

[0186] The second module is configured to determine the ideal power that the energy storage battery should provide at time t based on the power that the hybrid energy storage system should provide at time t and the state of charge of the energy storage battery at time t-1.

[0187] The third module is configured to determine the state of charge of the energy storage battery at time t based on the power that the energy storage battery should provide at time t.

[0188] The fourth module is configured to determine the edge proximity parameter of the energy storage battery at time t based on the state of charge of the energy storage battery at time t.

[0189]

[0190] The fifth module is configured to update the power that the energy storage battery should provide at time t based on the edge proximity parameter of the energy storage battery at time t, comprising:

[0191]

[0192] wherein, is a preset battery edge proximity parameter warning value;

[0193] The sixth module is configured to determine the power that the super capacitor should provide at time t based on the power that the hybrid energy storage system should provide at time t and the power of the energy storage battery at time t.

[0194] Embodiment 3: The embodiment is a computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the wind power real-time smoothing method of embodiments 1 or 2.​​​​​

[0195] Embodiment 4: A computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the steps of the wind power real-time smoothing method according to Embodiment 1 or 2.

[0196] Furthermore, although the present application is described in the context of functional modules, it is to be understood that one or more of the functions and / or features described above can be integrated in a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules, unless otherwise specified. It is also to be understood that detailed discussion of the actual implementation of each module is unnecessary to an understanding of the present application. Rather, the actual implementation is within the routine skill of engineers familiar with the attributes, functions and internal relationships of the various functional modules disclosed herein. Accordingly, the present application is not limited to purely software implementations, but encompasses firmware, processor, microprocessor, computer or general purpose machine implementations as well. It is also to be understood that the particular concepts disclosed are meant to be illustrative only and not limiting of the scope of the application which is to be determined by the full scope of the claims and equivalents thereof.

[0197] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or partially contribute to the prior art, or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods according to the various embodiments of the present application. The aforementioned storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk.

[0198] The logic and / or steps represented in the flow diagrams or otherwise described herein, for example, can be embodied in non-transitory computer- readable medium using any combination of hardware, software, and / or firmware. The logic and / or steps can be implemented using any of various computer- readable media for storing or transmitting this computer-readable instructions, such as magnetic storage media (e.g., hard disks), optical storage media (e.g., CD-ROMs, DVDs), nonvolatile memory storage media (e.g., ROMs, EPROMs, EEPROMs), and / or flash memory devices.

[0199] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0200] It should be understood that aspects of the application can be implemented in hardware, software, firmware, or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, or combinations thereof, can be used: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), and / or the like.

[0201] In the above description of the present application, the description using the terms "one embodiment", "another embodiment" or "some embodiments", and the like, means that the particular feature, structure, material, or characteristic being described in connection with the embodiment or example is included in at least one embodiment or example of the application. The appearances of the above-described terms in various places in the specification are not necessarily referring to the same embodiment or example. Further, where a particular feature, structure, material, or characteristic is described in connection with an embodiment or example, it is submitted that it is within the purview of one of ordinary skill in the art to effect such feature, structure, material or characteristic in connection with another embodiment or example, whether or not it is described in the specification or shown in the drawings.

[0202] While the embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary and are not to be construed as limiting the scope of the application. The scope of the application is defined by the appended claims and their equivalents.

[0203] The above is the specific description of the preferred embodiment of the application, but the application is not limited to the above-mentioned embodiments, and those skilled in the art can make various equivalent modifications or replacements without departing from the spirit of the application, and these equivalent modifications or replacements are all included in the scope defined by the claims of the application.

Claims

1. A method for real-time wind power mitigation, characterized in that, include: Based on the wind farm's grid connection power at time t-1 and the 30-minute wind power fluctuation rate, as well as the wind farm's output power at time t, and combined with the two-timescale power fluctuation relationship model, the power that the hybrid energy storage system should provide at time t is determined. Based on the power that the hybrid energy storage system should provide at time t and the state of charge of the energy storage battery at time t-1, determine the ideal power that the energy storage battery should provide at time t. ; Based on the power that the energy storage battery should provide at time t, determine the state of charge of the energy storage battery at time t. Based on the state of charge of the energy storage battery at time t, determine the edge proximity parameters of the energy storage battery at time t. ; ； Based on the edge proximity parameters of the energy storage battery at time t, update the power that the energy storage battery should provide. ,include: ； in, The preset warning value for battery edge proximity parameter; Based on the power that the hybrid energy storage system should provide at time t, and the power that the energy storage battery should provide at time t, determine the power that the supercapacitor should provide at time t.

2. The method for real-time wind power mitigation according to claim 1, characterized in that, Based on the wind farm's grid connection power at time t-1 and the 30-minute wind power fluctuation rate, and the wind farm's output power at time t, combined with a two-timescale power fluctuation relationship model, the power that the hybrid energy storage system should provide at time t is determined, including: Obtain the power of the wind farm connected to the grid at time t-1, the 30-minute wind power fluctuation rate of the power of the wind farm connected to the grid at time t-1, and the output power of the wind farm at time t. Based on the 30-minute wind power volatility at time t-1, and combined with the two-time-scale power volatility relationship model, the 1-minute wind power volatility at time t is determined. Based on the 1-minute wind power fluctuation rate and wind farm output power at time t, and the wind farm power connected to the grid at time t-1, the first filter coefficient at time t is determined. Based on the wind farm's grid connection power at time t-1, the wind farm's output power at time t, and the first filter coefficient, the first wind farm's grid connection power at time t is determined. Based on the first wind farm's grid connection power at time t, determine the 30-minute wind power fluctuation rate of the wind farm's grid connection power at time t. Determine whether the 30-minute wind power volatility at time t is greater than the preset maximum 30-minute wind power volatility. If it is greater than the value, then the second filter coefficient at time t is determined based on the wind farm output power at time t, the wind farm grid connection power at time t-1, and the preset maximum value of wind power fluctuation rate over 30 minutes; otherwise, the first filter coefficient is used as the second filter coefficient at time t. Based on the wind farm's grid connection power at time t-1, the wind farm's output power at time t, and the second filter coefficient, the second wind farm's grid connection power at time t is determined. Based on the grid connection power of the second wind farm at time t and the output power of the wind farm at time t, determine the power that the hybrid energy storage system should provide at time t.

3. The method for real-time wind power mitigation according to claim 1, characterized in that, The ideal power that the energy storage battery should provide at time t is determined based on the power that the hybrid energy storage system should provide at time t and the state of charge of the energy storage battery at time t-1. ,include: Based on the state of charge of the energy storage battery at time t-1, determine the loss coefficient of the energy storage battery at time t-1. Based on the minimum charge-discharge cycle time of the energy storage battery and the loss coefficient at time t-1, the filter time coefficient at time t is determined. Based on the power that the hybrid energy storage system should provide at time t, the filtering time coefficient, the power provided by the energy storage battery at time t-1, and the minimum charge-discharge cycle time of the battery, the power that the energy storage battery should provide at time t is determined.

4. The method for real-time wind power mitigation according to claim 3, characterized in that, The determination of the power that the energy storage battery should provide at time t, based on the power that the hybrid energy storage system should provide at time t, the filter time coefficient, the power provided by the energy storage battery at time t-1, and the minimum charge-discharge cycle time of the battery, includes: Based on the power that the hybrid energy storage system should provide at time t, the filtering time coefficient, and the power provided by the energy storage battery at time t-1, determine the power that the energy storage battery should provide at time t. Determine whether the charging and discharging states of the energy storage battery and the hybrid energy storage system at time t are consistent. If they are not consistent, the power that the energy storage battery should provide at time t is 0. Determine whether the charging and discharging states of the energy storage battery at time t and time t-1 are consistent. If they are not consistent, determine whether the duration of the charging and discharging state of the energy storage battery at time t-1 is greater than the minimum charge and discharge cycle time of the battery. If it is not greater, then the power that the energy storage battery should provide at time t is equal to the power that the energy storage battery should provide at time t-1.

5. The method for real-time wind power mitigation according to claim 3, characterized in that, The determination of the loss coefficient of the energy storage battery at time t-1 based on the state of charge of the energy storage battery at time t-1 includes: ； in, Let be the loss coefficient of the energy storage battery at time t-1; The maximum charge constraint for energy storage batteries; The initial state of charge of the energy storage battery at time t-1; This represents the final state of charge of the energy storage battery at time t-1. This represents the power provided by the energy storage battery at time t-1.

6. The method for real-time wind power mitigation according to claim 2, characterized in that, The two-timescale power fluctuation relationship model includes: ； in, Let t be the 1-minute wind power fluctuation rate of the power connected to the main power grid at time t; The maximum value of wind power fluctuation rate within 1 minute; The 30-minute wind power fluctuation rate represents the power output of the electric field connected to the main power grid at time t-1. This represents the maximum fluctuation rate of wind power within 30 minutes.

7. The method for real-time wind power mitigation according to claim 2, characterized in that, The determination of the first filter coefficient at time t, based on the 1-minute wind power volatility and wind farm output power at time t, and the wind farm's grid connection power at time t-1, includes: ； in, The first filter coefficient at time t; Let t be the 1-minute wind power fluctuation rate of the power connected to the main power grid at time t; This refers to the rated power of the wind farm. This represents the minimum power output of the wind farm connected to the main power grid within one minute, ending at time t-1. The maximum power output of the wind farm connected to the main power grid within one minute, ending at time t-1; Let be the output power of the wind farm at time t; Let t-1 be the power of the wind farm connected to the main power grid.

8. A wind power real-time suppression device, characterized in that, include: The first module is used to determine the power that the hybrid energy storage system should provide at time t based on the wind farm's grid connection power and 30-minute wind power fluctuation rate at time t-1, and the wind farm's output power at time t, combined with the two-timescale power fluctuation relationship model. The second module is used to determine the ideal power that the energy storage battery should provide at time t, based on the power that the hybrid energy storage system should provide at time t and the state of charge of the energy storage battery at time t-1. ; The third module is used to determine the state of charge of the energy storage battery at time t based on the power that the energy storage battery should provide at time t. The fourth module is used to determine the edge proximity parameters of the energy storage battery at time t based on the state of charge of the battery at time t. ; ； The fifth module is used to update the power that the energy storage battery should provide based on the edge proximity parameter of the energy storage battery at time t. ,include: ； in, The preset warning value for battery edge proximity parameter; The sixth module is used to determine the power that the supercapacitor should provide at time t, based on the power that the hybrid energy storage system should provide at time t and the power of the energy storage battery at time t.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the real-time wind power suppression method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the real-time wind power suppression method according to any one of claims 1 to 7.

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