Wind power real-time stabilizing method

Abstract

The invention relates to a real-time wind power stabilizing method, and is suitable for the technical field of offshore wind power. According to the technical scheme, the method comprises the following steps of: determining power which should be provided by a hybrid energy storage system based on large power grid access power of a wind power plant, a 30-minute wind power fluctuation ratio and output power of the wind power plant; determining ideal work which should be provided by the energy storage battery based on the power which should be provided by the hybrid energy storage system and the charge state of the energy storage battery; determining the charge state of the energy storage battery based on the power provided by the energy storage battery; based on the charge state of the energy storage battery, determining an edge proximity parameter of the energy storage battery; on the basis of the edge proximity parameter of the energy storage battery, updating the power provided by the energy storage battery; and determining the power which should be provided by the super capacitor based on the power which should be provided by the hybrid energy storage system and the power which should be provided by the energy storage battery.

Description

Technical Field

[0001] The present invention relates to a method for real-time smoothing of wind power, which is applicable to the technical field of offshore wind power. Background Art

[0002] Wind energy has the advantages of being clean and pollution-free. Driven by the policies of energy conservation, emission reduction, and reduction of air pollution, it has been increasingly valued by countries around the world in recent years. Countries around the world have been vigorously developing wind power. Offshore wind power is an important direction for the development of wind power in the world. From the perspective of offshore wind power construction and planning, due to the fact that the deep sea areas with an offshore distance greater than 100 km and a water depth exceeding 50 m have richer sea area and wind energy resources, the offshore wind power resources are mainly concentrated in a small part of the areas and are far from the load centers. The development speed of wind power exceeds the local power demand, and it is difficult to consume locally, resulting in many wind farms having to "abandon wind and limit power", which seriously affects the development of the wind power industry.

[0003] In order to improve the problem of wind abandonment 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 implement the development strategy of "electricity coming from afar". However, due to the impact of the randomness and volatility of wind power on the voltage and frequency of the large power grid, when wind power is connected to the grid on a large scale, it is necessary to limit the volatility rate of wind power; in addition, the wind power in China started late, and there is a lack of historical data on wind resources, and there is a lack of wind power prediction and planning coordination with the power grid, so when wind power is connected to the grid in China, it faces problems that no country in the world has ever faced.

[0004] Currently, wind turbines generally operate in the maximum wind energy capture mode, and the output power of wind power mainly depends on the real-time wind speed. With the increase in the installed capacity of wind power and the penetration rate of wind power, the randomness, unpredictability, and volatility of wind energy itself will cause random fluctuations in wind power, which has a great impact on the power quality (including voltage and frequency) of the existing large power grid and grid dispatching when wind power is connected to the grid. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: in view of the above problems, to provide a method for real-time smoothing of wind power.

[0006] The technical solution adopted by the present invention is: a method for real-time smoothing of wind power, including: Based on the power of the wind farm connected to the large power grid at time t-1, the wind power volatility rate in 30 minutes, and the output power of the wind farm at time t, and combining with the two-time-scale power fluctuation relationship model, determine the power that the hybrid energy storage system 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, determine the ideal 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 power that the energy storage battery should provide at time t; 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 ; ; Update the power that the energy storage battery should provide based on the edge proximity parameter of the energy storage battery at time t , including: ; wherein, is a preset warning value for the battery edge proximity parameter; 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 that the energy storage battery should provide at time t.

[0007] The method for determining the power that the hybrid energy storage system should provide at time t based on the power of the wind farm connected to the large power grid at time t-1, the 30-minute wind power volatility, and the wind farm output power at time t, and combining the two-time-scale power fluctuation relationship model, includes: Obtain the power of the wind farm connected to the large power grid at time t-1, the 30-minute wind power volatility of the wind farm connected to the large power grid at time t-1, and the wind farm output power at time t; Determine the 1-minute wind power volatility at time t based on the 30-minute wind power volatility at time t-1 and combining the two-time-scale power fluctuation relationship model; Determine the first filtering coefficient at time t based on the 1-minute wind power volatility and the wind farm output power at time t, and the power of the wind farm connected to the large power grid at time t-1; Determine the first power of the wind farm connected to the large power grid at time t based on the power of the wind farm connected to the large power grid at time t-1, and the wind farm output power and the first filtering coefficient at time t; Determine the 30-minute wind power volatility of the power of the wind farm connected to the large power grid at time t based on the first power of the wind farm connected to the large power grid at time t; Judge whether the 30-minute wind power volatility at time t is greater than the preset maximum value of the 30-minute wind power volatility; If it is greater, determine the second filtering coefficient at time t based on the wind farm output power at time t, the power of the wind farm connected to the large power grid at time t-1, and the preset maximum value of the 30-minute wind power volatility; otherwise, use the first filtering coefficient as the second filtering coefficient at time t; Determine the second power of the wind farm connected to the large power grid at time t based on the power of the wind farm connected to the large power grid at time t-1, and the wind farm output power and the second filtering coefficient at time t; Determine the power that the hybrid energy storage system should provide at time t based on the power of the second wind farm connected to the large power grid at time t and the output power of the wind farm at time t.

[0008] 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. , including: Determine 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. Determine the filtering time coefficient at time t based on the minimum charge and discharge cycle time of the energy storage battery and the loss coefficient at time t-1. Determine 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 filtering time coefficient, the power provided by the energy storage battery at time t-1, and the minimum charge and discharge cycle time of the battery.

[0009] 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 filtering time coefficient, the power provided by the energy storage battery at time t-1, and the minimum charge and discharge cycle time of the battery includes: Determine 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 filtering time coefficient, and the power provided by the energy storage battery at time t-1. Judge whether the charge and discharge states of the energy storage battery at time t 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. Judge whether the charge and discharge states of the energy storage battery at time t and time t-1 are consistent. If they are not consistent, judge whether the duration of the charge and discharge 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, the power that the energy storage battery should provide at time t is equal to the power provided by the energy storage battery at time t-1.

[0010] 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: ; where 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 end 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.

[0011] Determining the filtering time coefficient at time t based on the minimum charge-discharge cycle time of the energy storage battery and the loss coefficient at time t-1 includes: ; wherein, is the filtering time coefficient at time t; is the minimum charge-discharge cycle time of the energy storage battery; is the loss coefficient of the energy storage battery at time t-1.

[0012] The two-time-scale power fluctuation relationship model includes: ; wherein, is the 1-minute wind power volatility of the power of the electric field connected to the large power grid at time t; is the maximum value of the wind power volatility within 1 minute; is the 30-minute wind power volatility of the power of the electric field connected to the large power grid at time t-1; is the maximum value of the wind power volatility within 30 minutes.

[0013] Determining the first filtering coefficient at time t based on the 1-minute wind power volatility and the wind farm output power at time t, and the power of the wind farm connected to the large power grid at time t-1 includes: ; wherein, is the first filtering coefficient at time t; is the 1-minute wind power volatility of the power of the electric field connected to the large power grid at time t; is the rated power of the wind farm; is the minimum value of the power of the wind farm connected to the large power grid within 1 minute with time t-1 as the end point; is the maximum value of the power of the wind farm connected 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 power of the wind farm connected to the large power grid at time t-1.

[0014] Determining the second filtering coefficient at time t based on the wind farm output power at time t, the power of the wind farm connected to the large power grid at time t-1, and the preset maximum value of the 30-minute wind power volatility includes: ; wherein, is the second filtering coefficient at time t, is the maximum value of the power of the wind farm connected to the large power grid within 30 minutes with time t-1 as the end point; is the minimum value of the power of the wind farm connected to the large power grid within 30 minutes with time t-1 as the end point; is the maximum value of the wind power volatility within 30 minutes; is the output power of the wind farm at time t; is the power of the wind farm connected to the large power grid at time t-1; is the power of the wind farm connected to the large power grid at time t-i; is the minute corresponding to the minimum value of the power of the wind farm connected to the large power grid within 30 minutes, the minute corresponding to the maximum value of the power of the wind farm connected to the large power grid within 30 minutes; is a minute ending at time t.

[0015] A real-time wind power smoothing device, comprising: The first module is used to determine the power that the hybrid energy storage system should provide at time t based on the power of the wind farm connected to the large power grid at time t-1, the 30-minute wind power volatility, and the output power of the wind farm at time t, in combination with the two-time-scale 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 parameter of the energy storage battery at time t based on the state of charge of the energy storage 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 , including: ; Wherein, is a preset warning value for the battery edge proximity parameter; The sixth module is used 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.

[0016] A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the real-time wind power smoothing method are implemented.

[0017] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the real-time wind power smoothing method are implemented.

[0018] The beneficial effects of the present invention are as follows: According to the filtering coefficient, the target power after wind power suppression is determined, and the wind power fluctuations at two time scales at this moment are calculated. According to the fluctuations of the 30-minute target output power and the relationship model of the power fluctuations at the two time scales, the reference value of the one-minute wind power fluctuation limit in the next second is determined, and then the filtering coefficient is adjusted based on the reference value of the one-minute wind power fluctuation limit. By repeating this process, the real-time suppression target value of the wind power fluctuation at each moment is determined.

[0019] According to the fluctuation characteristics of wind power at different time scales, the present invention defines the requirements for wind power grid connection, which helps to more accurately understand the dynamic changes of wind power output, so as to formulate more effective control strategies. The present invention reduces the energy storage consumption while ensuring that the wind power can be smoothly and efficiently incorporated into the power grid, thereby improving the stability and reliability of the entire power system.

[0020] According to the fact that the energy storage battery and the super capacitor absorb power of different frequencies, the present invention selects the low-pass filtering algorithm as the basic algorithm, and adaptively adjusts the filtering coefficient of the low-pass filtering algorithm according to the state of charge of the energy storage battery and the number of charge and discharge changes, controls the charge and discharge of the hybrid energy storage system, and reduces the loss of the energy storage battery during operation.

[0021] The present invention adjusts the power distribution to prevent the SOC of the battery from exceeding the set limit value through the edge proximity parameter . Specifically, if the SOC of the battery is close to the boundary, based on , the power absorption of the battery and the super capacitor is adjusted to prevent the SOC of the battery from exceeding the safe range. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic structural diagram of a wind farm system with a hybrid energy storage system in the embodiment; Figure 2 It is a schematic diagram of a first-order low-pass filter in the embodiment; Figure 3 It is a schematic diagram of the relationship between the wind power fluctuations at two time scales at adjacent moments in the embodiment; Figure 4 It is a flow chart of the wind power suppression algorithm in the embodiment; Figure 5 It is a schematic diagram of the initial power and target power of the wind farm in the embodiment; Figure 6 It is a schematic diagram of the one-minute power fluctuation in the embodiment; Figure 7 It is a schematic diagram of the 30-minute power fluctuation in the embodiment; Figure 8 It is a schematic diagram of the energy storage output required for suppression in the embodiment; Figure 9Schematic diagram of the initial power and target power of the wind farm in the embodiment; Figure 10 Schematic diagram of the power fluctuation within one minute in the embodiment; Figure 11 Schematic diagram of the power fluctuation within thirty minutes in the embodiment; Figure 12 Schematic diagram of the energy storage required for the fixed time constant suppression algorithm in the embodiment; Figure 13 Block diagram of the control strategy for suppressing the wind power fluctuation in the wind farm in the embodiment; Figure 14 Edge control diagram of the energy storage coefficient in the embodiment. Detailed implementation manners

[0023] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, in which the same or similar reference numerals denote 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 and are only used to explain the present invention, and should not be construed as a limitation to the present invention. For the step numbers in the following embodiments, they are only set for the convenience of description and illustration, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0024] In the description of the present invention, the meaning of "a plurality of" is two or more. If the first and second are described, it is only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence of the indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0025] Embodiment 1: As Figure 1 shown, the system structure of the wind farm in this embodiment mainly consists of a wind farm and a hybrid energy storage system. Among them, the wind farm is generally composed of several wind turbines connected according to a certain topological structure. After the output powers of all the wind turbines are aggregated, it is the real-time output power of the wind farm . Among them, the real-time output power of the wind turbine is generated by the real-time wind speed. It is a piecewise function as shown in Equation (1). According to this formula, the real-time output power of the wind turbine can be obtained, providing a reference for the grid connection research of the wind farm; (1) Where is the rated power of a wind turbine, is the real-time wind speed, is the cut-in wind speed of the wind turbine, is the cut-out wind speed of the wind turbine, is the rated wind speed of the wind turbine.

[0026] In a wind farm, the hybrid energy storage system consists of energy storage batteries and supercapacitors. As an energy buffer device, it reduces the fluctuation of the active power output of the wind farm, enabling the real-time grid-connected active power of the wind farm to meet the grid connection index. The real-time power of the energy storage battery and the real-time power of the supercapacitor are determined by the real-time control strategy of the hybrid energy storage system and are obtained by controlling the charge and discharge of the energy storage battery and the supercapacitor through a DC / AC power converter. The real-time output power of the hybrid energy storage system converges with the initial output power of the wind farm to obtain the grid-connected power of the wind farm, which is connected to the large power grid.

[0027] In the real-time suppression of wind power active power, the main methods include: the average value method, the moving average method including the simple moving average method and the exponential moving average method, the low-pass filter algorithm, etc.

[0028] In this embodiment, the time scales for restricting the active power fluctuation of wind power are 1 minute and 30 minutes. Due to the large randomness of the wind power output, in order to effectively limit the fluctuation of wind power within one minute, the sampling period of wind power is selected as 1 s. At the same time, since the current short-term prediction error of wind power still cannot meet the requirements of real-time regulation and is less used in applications; and the 1 s sampling period also limits the application of intelligent algorithms. Therefore, when determining the wind power suppression target in this embodiment, short-term prediction of wind power is not involved, and only the real-time power of wind power is used to adjust the filtering coefficient, with the goal of reducing energy storage consumption.

[0029] The low-pass filter algorithm is mainly improved from the principle of the first-order low-pass filter, Figure 2 which is a first-order low-pass filter. The mathematical expression is: (2) where is the filtering time constant, is the input signal, is the low-pass filter output signal.

[0030] When the low-pass filter algorithm is applied to wind power filtering, the input signal is set as the output power of the wind farm , and the output signal is set as the power of the wind farm connected to the large power grid after low-pass filter suppression , meeting the wind power grid connection standard. This embodiment mainly studies how to determine the power fluctuation constraints that can meet the two time scales during wind power grid connection by changing the time constant of the low-pass filter. The energy storage system, as an energy buffer link, its energy is the difference between the output power of the wind farm and the target power of wind power that meets the grid connection standard.

[0031] During application, the output power of the wind farm and the power connected to the large power grid are both obtained by sampling. Let the sampling period be . After discretizing Equation (2) and substituting the input and output signals, we can obtain: (3) where is the output power of the wind farm at the t-th sampling moment; is the smoothed power of the wind farm connected to the large power grid obtained after the filtering algorithm at the t-th 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 smoothing power at the previous moment. Let the cut-off angular frequency of the low-pass filter be . According to the knowledge of control theory, the cut-off angular frequency of the system is: (4) Assume that the maximum frequency of the output signal is , that is, the minimum period is . Then (5) From (4) and (5), we can get: (6) Since the range of the filtering time constant is , it is difficult to optimize during adjustment. Therefore, a new filtering coefficient is redefined as shown in the following formula: (7) where , which is convenient for optimization. At this time, the low-pass filter algorithm formula (3) is simplified to: (8) It can be seen from the above formula that when the filtering coefficient is larger, the effect of the wind power output is greater and the filtering effect is smaller; when the filtering coefficient is smaller, the effect of the smoothed power at the previous sampling time is greater, the filtering effect is better, and the output smoothed power is more stable. Therefore, when applying the low-pass filter algorithm to wind power smoothing, it is required to adjust the filtering coefficient in real time according to the smoothing effect, taking into account the usage of the energy storage system, the life of the energy storage system, etc.

[0032] This embodiment adjusts the filtering coefficient of the low-pass filter in real time according to the relationship model of the wind power fluctuations on two time scales, and determines the real-time smoothing target power of the wind farm that meets the grid connection standard.

[0033] When wind power is connected to the large power grid, the volatility of its active power is mainly considered in this embodiment. The sampling time of this embodiment is 1 s. The volatility of the wind power active power in one minute at the t-th second is defined as the ratio of the difference between the maximum power and the minimum power in a one-minute time window with the t-th second as the end point to the rated power of the wind farm, as shown in Equation (9): (9) Where represents the sampled value of the wind power in this one-minute time window, is the rated power of the wind farm.

[0034] The volatility of the wind power active power in 30 minutes at the t-th second is defined as: the ratio of the difference between the maximum and minimum values of the average power per minute in a 30-minute window with this second as the end point to the rated power of the wind farm, as shown in Equation (10): (10) Where is the average power in the -th minute within the 30-minute time window.

[0035] From the definition of the 30-minute wind power fluctuation, the 30-minute power fluctuation is accumulated by the 1-minute power fluctuation. Therefore, when determining the target power for suppressing wind power fluctuations, the 1-minute wind power fluctuation constraint in the grid connection requirements should be considered first, and then the 30-minute wind power fluctuation. However, the target power is constrained by both the 1-minute and 30-minute wind power fluctuations. After the 1-minute power fluctuation is satisfied, the 30-minute power fluctuation may not necessarily be satisfied. When adjusting the filtering coefficient, if the 1-minute wind power limit is reset through the existing data in front to obtain a reference that can make the 30-minute wind power fluctuation at this second meet the limit, the problem of determining the filtering coefficient is solved.

[0036] When applying the filtering algorithm to suppress wind power fluctuations, it is mainly to determine the target power for suppressing wind power fluctuations by changing the filtering coefficient, so that the wind power fluctuations after suppression meet the constraints under two time scales of 1 minute and 30 minutes.

[0037] There is a certain relationship between the 1-minute and 30-minute fluctuations of the wind power output, and they affect each other. The 1-minute and 30-minute time windows are moving. This embodiment finds that the 30-minute power volatility of the wind power after filtering at this second has a great influence on the 1-minute volatility Figure 3 of the wind power at the next second. Their relationship is as Figure 3Construct a two-time-scale power fluctuation relationship model. The 1-minute fluctuation reference value of the wind power target for the next second is determined by the 30-minute power determined according to the target power after the wind power is suppressed in this second, so as to determine the reference value of the low-pass filter coefficient.

[0038] Figure 3 In it, the dotted line describes a simple inverse proportional relationship between the power volatility rates of the two time scales, that is, the maximum limit value of the 1-minute wind power volatility at the t-th second decreases as the wind power volatility in the previous second increases, and vice versa. However, this simple proportional relationship cannot achieve an ideal effect on the constraint when the wind power volatility is close to the maximum limit. Especially when the 30-minute wind power volatility in a certain second is close to the two limit values: that is, if the 30-minute wind power volatility at the t-th second is closer to the maximum limit value, in order to ensure that the 30-minute wind power volatility in the next second is within the limit range, the smaller the 1-minute power fluctuation allowed in the next second should be. Conversely, when the 30-minute wind power volatility at the t-th second is very small and closer to 0, the 1-minute wind power in the next second reaches the maximum limit value to reduce the output of the energy storage. In this embodiment, the two-time-scale power fluctuation relationship model is improved to be as Figure 3 described by the solid line in, and a smooth treatment is performed near the limit value. Its mathematical expression is as shown in Equation (11): (11) Wherein, and are respectively the maximum values of the 1-minute and 30-minute wind power volatility rates specified by the wind power grid connection standard. Then the wind power suppression target power must satisfy the 1-minute and 30-minute fluctuation limits, as shown in the following formula: (12)

[0039] In this embodiment, the two-time-scale power fluctuation relationship model in this embodiment links the wind power fluctuations at adjacent moments during wind power suppression, and realizes real-time adjustment of the filter coefficient as a reference, while reducing the output of the energy storage.

[0040] This embodiment adopts a low-pass filter algorithm. The filter coefficient is adaptively adjusted according to the two-time-scale power fluctuation relationship model at adjacent moments to determine the target output power of the wind power grid connection that satisfies the fluctuation limits of the two time scales. The method for real-time suppression of wind power in this example includes the following steps: S100. Based on the power of the wind farm connected to the large power grid at the (t - 1) moment, the 30-minute wind power volatility, and the output power of the wind farm at the t moment, and combining the two-time-scale power fluctuation relationship model, determine the power that the hybrid energy storage system should provide at the t moment.

[0041] As Figure 4 shown, it specifically includes: S110. Obtain the power of the wind farm connected to the large power grid at the (t - 1) moment The 30 - minute wind power volatility of the wind farm connected to the large - scale power grid at time t - 1 , and the output power of the wind farm at time t .

[0042] S120. Based on the 30 - minute wind power volatility at time t - 1 , combined with the two - time - scale power fluctuation relationship model, determine the 1 - minute wind power volatility at time t .

[0043] S130. Based on the 1 - minute wind power volatility at time t and the output power of the wind farm , and the power of the wind farm connected to the large - scale power grid at time t - 1 , determine the first filtering coefficient at time t ; (13) Obtain the filtering coefficient constraint: (14) Ensures that is between and .

[0044] In this embodiment, the in formula (13) is discussed in three cases: When , let be the maximum value. According to formula (9), obtain the filtering coefficient to make the filtering coefficient the allowable maximum value, reducing the energy storage consumption, where the preset 1 - minute wind power volatility of this second is determined according to Figure 3 . However, because has two cases within this one - minute window: one exceeds the maximum value, and there is no problem in calculating according to (9); the other itself does not exceed the maximum value within the one - minute time window. If calculated according to formula (9), then , and at this time (14) plays a constraining role; Similarly, when , then let be the minimum value within the one - minute window, and then inversely deduce the filtering coefficient according to formula (9) to make the filtering coefficient the allowable maximum value, reducing the energy storage consumption, where the preset 1 - minute wind power volatility of this second is determined according to Figure 3 . However, because has two cases within this one - minute window: one is less than the allowable minimum value, and there is no problem in calculating according to (11); the other It is already within the constraints within a one-minute time window. If calculated according to Equation (11), then , at this time (14) plays a constraining role; When the wind farm output power at the t-th second is equal to the wind power smoothing target power of the previous second, it can be known from Equation (8) that , the filtering coefficient does not affect the result. Since It satisfies the constraints within a one-minute time window. Not processing this second means satisfying the constraints. For convenience, let .

[0045] S140. Based on the power of the wind farm connected to the large power grid at the (t - 1) moment , and the wind farm output power at the t moment and the first filtering coefficient , use Equation (8) to determine the first wind farm power connected to the large power grid at the t moment .

[0046] S150. Based on the first wind farm power connected to the large power grid at the t moment , use Equation (10) to determine the 30-minute wind power volatility of the wind farm connected to the large power grid at the t moment .

[0047] S160. Judge whether the 30-minute wind power volatility at the t moment is greater than the preset maximum value of the 30-minute wind power volatility . If it is greater, then based on the wind farm output power at the t moment, the wind farm power connected to the large power grid at the (t - 1) moment and the preset maximum value of the 30-minute wind power volatility , determine the second filtering coefficient at the t moment; otherwise, use the first filtering coefficient as the second filtering coefficient at the t moment.

[0048] From (12) and (13), the filtering coefficient is the reference obtained from the power fluctuation relationship between two time scales at adjacent moments The maximum value allowed. After calculating the wind power smoothing target power, it is necessary to check again whether the constraints are satisfied at the 30-minute time scale under this power. If the constraints are satisfied, the filtering coefficient is . If not, discuss the average power of this moment within one minute as the minimum and maximum values within thirty minutes, and calculate the filtering coefficient according to the maximum 30-minute wind power volatility , as shown in the following formula: (15) Among them, is the minute corresponding to the minimum power of the wind farm connected to the large power grid within 30 minutes, and the minute corresponding to the maximum power of the wind farm connected to the large power grid within 30 minutes; is the minute with t moment as the end point.

[0049] S170. Based on the power of the wind farm connected to the large power grid at t-1 moment , and the output power of the wind farm at t moment and the second filtering coefficient , use Equation (8) to determine the second power of the wind farm connected to the large power grid at t moment .

[0050] S180. Based on the second power of the wind farm connected to the large power grid at t moment obtained in step S170 , and the output power of the wind farm at t moment obtained in step S110 , determine the power that the hybrid energy storage system should provide at t moment .

[0051] According to the fluctuation suppression strategy, after adding the hybrid energy storage system, the real-time flat control strategy block diagram of the entire wind farm power fluctuation is as shown in Figure 13 . The hybrid energy storage system provides the energy required to suppress the wind farm fluctuation in real time according to the real-time flat control strategy of wind power and the coordinated control strategy of the energy storage system, so that the wind power grid-connected power meets the grid connection technical requirements. As can be seen from Figure 13 , the energy provided by the hybrid energy storage system in real time is: (16) Given the grid-connected power and the real-time output power of the wind farm after the real-time flat control strategy of wind power, the power that the hybrid energy storage system should provide per second can be obtained.

[0052] Simulate the real-time flat control method of wind power proposed in this embodiment on the MATLAB platform to find the target power that the output power of a certain wind farm meets the grid connection standard after real-time flat control for 24 hours. The system parameters are as shown in Table 1 below: The rated value of the wind farm output power is 6MW, 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, that is: .

[0053] Table 1 System parameter settings

[0054] To verify the low-pass filtering algorithm based on adaptive adjustment of the filtering coefficient, the determined wind power suppression target power not only meets the wind power grid connection standard but also reduces the energy storage consumption. We also used a low-pass filtering algorithm with a fixed time constant to process the wind power. For comparison, the filtering coefficient value for meeting the grid connection standard is: . The simulation results are as Figure 5 - Figure 12 shown, where Figure 5 - Figure 8 are the simulation results obtained by the filtering algorithm proposed in this embodiment, Figure 9 - Figure 12 are the simulation results obtained by the fixed-time-constant filtering algorithm.

[0055] Figure 5 In , the red curve is the wind power target power per second for 24 hours after real-time suppression of the output power of the wind farm according to the wind power suppression strategy of this embodiment, and the blue is the original output power of the wind farm. Comparing the two curves, it can be seen that the fluctuation is much smoother after suppression. Figure 6 and Figure 7 In , the red straight line is the maximum power fluctuation of the grid connection standard limit value, the blue is the fluctuation of the obtained target power, and the green curve is the initial power fluctuation. It can be seen that the fluctuations of the wind farm target power at two time scales after passing through the filtering algorithm meet the constraints. Comparing Figure 7 and Figure 11 , it is found that when the initial wind power already meets the 30-minute fluctuation index, the target power obtained by using the fixed-time-constant filtering algorithm is too different from it, which is not conducive to reducing the energy storage consumption; while for the filtering algorithm adopted in this embodiment, the target power basically no longer decreases in this case. So although comparing Figure 5 and Figure 9 , the target power obtained by the fixed-time-constant filtering algorithm is much smoother than that of the method adopted in this embodiment, but it is based on sacrificing the energy storage consumption. As can be seen from Figure 8 and Figure 12 , it is not conducive to the economic operation of the wind farm.

[0056] In addition, when adaptively adjusting the filtering coefficient, this example also made a simulation comparison of two different time-scale relationship models described by the solid curve and the virtual straight line in Figure 3 . The results show that for the wind power grid connection power obtained by referring to the virtual straight line, the required energy storage is 4.9397 MWh, while for the solid curve improved in this embodiment, the required energy storage is 4.654 MWh, which reduces the energy storage output and is more economical.

[0057] This embodiment mainly studies the calculation method of the target power in the real-time suppression strategy of wind power fluctuations, so that the active wind power after suppression meets the power volatility limits of two time scales in the grid connection requirements. The effect of wind power suppression is mainly determined by the filtering coefficient in the filtering algorithm, and the time scale of wind power regulation is 1 second, which limits the performance of intelligent algorithms. Therefore, a method is proposed to adaptively adjust the filtering coefficient by updating the wind power fluctuation limit of the one-minute time scale in real time as a reference according to the fluctuation relationship of wind power at different time scales. Through simulation on the MATLAB platform and comparison with the low-pass filtering algorithm with a fixed time constant, it is proved that the low-pass filtering algorithm with adaptive adjustment of the filtering coefficient proposed in this embodiment has good performance both in the effect of wind power suppression and in reducing energy storage consumption.

[0058] S200. 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. .

[0059] Since there is currently no single energy storage with relatively comprehensive characteristics, such as charge-discharge cycle times, energy density, power density, etc., the hybrid energy storage composed of batteries and supercapacitors has complementary characteristics in terms of energy and power, which can suppress wind power fluctuations at different time scales.

[0060] When the energy storage system is charging and discharging, there are limitations on the magnitude of the charging and discharging power. It is necessary to ensure that the energy storage system operates in a normal state to prevent damage to the energy storage system caused by overcharging and over-discharging. Among them, 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): (17) Where is the initial remaining capacity of the energy storage device before charging and discharging, and are the charging and discharging efficiencies of the battery respectively, 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 1 s, the charging and discharging efficiency of the energy storage battery is 70% - 90%, and the charging and discharging efficiency of the supercapacitor is 90% - 95%. Then, the charging and discharging mathematical models of the hybrid energy storage system are shown in Equations (18) and (19). In order to ensure that the energy storage battery and the supercapacitor operate in a normal state and reduce charging and discharging losses, both the energy storage battery and the supercapacitor need to operate within their respective state of charge limits, as shown in Equation (20): (18) (19) (20) Among them, is for charging; is for discharging; the subscript B represents the energy storage battery; the subscript SC represents the super capacitor; is the state of charge at time t, and can also be represented by .

[0061] In the hybrid energy storage system, the service life of the energy storage battery is much shorter than that of the super capacitor, which brings more maintenance and replacement costs. Therefore, how to extend the life of the battery is also one of the research hotspots in energy storage. Research shows that the life of the battery is related to the number of charge-discharge conversion times. To calculate the number of charge-discharge conversion times, we introduce the charge-discharge state quantity , to record the charge-discharge state of the battery, as shown in (21): (21) Then the time for one charge or discharge of the battery is calculated by the duration of the same charge-discharge state 1 or -1. As long as the time for one charge or discharge of the battery is limited, the number of charge-discharge conversion times of the battery can be reduced, and the battery life can be extended.

[0062] The charge-discharge time scale of the energy storage battery is large, and it absorbs the charge-discharge power for a long time; while the charge-discharge time scale of the super capacitor is small, and it absorbs high-frequency fluctuations. Applying the low-pass filtering algorithm can effectively distribute the low-frequency and high-frequency powers 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 fluctuation, and cannot ensure that it works within the normal state of charge range and extend the charge-discharge life of the battery.

[0063] In this embodiment, the coordinated control strategy of the hybrid energy storage system is based on the absorption of wind power fluctuations with different frequencies by the energy storage battery and the super capacitor. The filtering coefficient of the low-pass filtering algorithm should be adaptively adjusted according to the state of charge and the number of charge-discharge changes of the energy storage battery to distribute the hybrid energy storage power.

[0064] To reduce the loss caused by charge and discharge to the energy storage battery, the adjustment of the filtering coefficient in this embodiment is defined with reference to the loss parameter. Define as 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): (22) When When the energy storage battery is in the charging state, the loss is inversely proportional to the state of charge at the beginning and end of charging. That is, the smaller the state of charge at the beginning of charging, the greater the charging loss; the closer the state of charge at the end of charging is to the maximum state of charge constraint, that is, the more fully charged, the smaller the charging loss.

[0065] When the energy storage battery is in the discharging state, the loss is directly proportional to the state of charge at the beginning of charging and inversely proportional to the state of charge at the end. That is, during the discharging process, the greater the state of charge at the end, the smaller the discharging loss; the closer the initial state of charge is to the maximum state of charge constraint, the smaller the discharging loss.

[0066] In the hybrid energy storage system, the lifespan of the energy storage battery is relatively short. In order to reduce the maintenance and replacement costs of the energy storage battery, it is necessary to minimize the loss of the energy storage battery as much as possible, that is, to avoid overcharging and over-discharging. Therefore, the real-time adjustment of the total low-pass filter time constant in this embodiment is adjusted according to the loss coefficient represented by the state of charge of the energy storage battery, as shown in Equation (23): (23) Where is the reference filter time constant determined according to the time scale of the power absorbed by the energy storage battery and the supercapacitor, is the minimum charge-discharge cycle time of the battery. The filter time coefficient at the t-th second is affected by the power loss after charge and discharge in the previous second, and the influence coefficient is , which is inversely proportional to the loss. That is, if the loss of the energy storage battery in the previous second is too large, the smaller the power fluctuation borne in this second, the better, and the smaller the filter time constant. At the same time, due to the limitation of the minimum charge-discharge cycle time, the maximum number of charge-discharge conversions of the energy storage battery within 24 hours a day is theoretically: (24) S210. 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.

[0067] S220. Based on the minimum charge-discharge cycle time of the energy storage battery and the loss coefficient at time t-1, use Equation (23) to determine the filter time coefficient at time t.

[0068] S230. 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 , determine the power that the energy storage battery should provide at time t .

[0069] S231. 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 ; .

[0070] S232. Based on the power that the energy storage battery should provide at time t obtained in step 31 , determine whether the charge and discharge states of the energy storage battery at time t are consistent with those of the hybrid energy storage system at time t. If they are not consistent, the power that the energy storage battery should provide at time t is 0; When , then make .

[0071] S233. Determine whether the charge and discharge 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 charge and discharge 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, 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; When , determine whether the duration Ts of the charge and discharge state corresponding to the energy storage battery at time t-1 when it ends at time t-1 is greater than the minimum charge and discharge cycle time of the battery . If Ts ≤ , then the energy storage battery at time t continues the charge and discharge state of the energy storage battery at time t-1 = .

[0072] S300. Based on the power that the energy storage battery should provide at time t , use Equation (18) to determine the state of charge of the energy storage battery at time t .

[0073] S400. Based on the state of charge of the energy storage battery at time t, determine the edge proximity parameter of the energy storage battery at time t .

[0074] The first part of the real-time control strategy of the offshore wind power hybrid energy storage system aims to distribute the power to be stored into two parts: the battery and the supercapacitor, while ensuring that the distribution process satisfies the SOC constraints of the energy storage system, that is, ensuring that the state of charge of the battery and the supercapacitor is maintained within a predetermined safe range.

[0075] In the control process of the algorithm in this embodiment, it is necessary to consider the SOC state of the energy storage system to make a decision. Define as the edge proximity parameter of the energy storage battery, and its value is represented by the state of charge of the device, which 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): (25) 1. When , the energy storage battery is in the charging state, and the closer the state of charge at the end of charging is to the maximum charge constraint, represents the distance of the energy storage battery to the maximum charge state; 2. When , the energy storage battery is in the discharging state, and the closer the state of charge at the end of charging is to the minimum charge constraint, represents the distance of the energy storage battery to the minimum charge state.

[0076] S500. Update the power that the energy storage battery should provide based on the edge proximity parameter of the energy storage battery at time t ; can characterize the "dangerous value" of the SOC of the battery, that is, the parameter of the SOC close to the edge. 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 the high-order term coefficient or the power exponent coefficient. In this embodiment, a quadratic coefficient is adopted to make the power curve curl downward near . That is: (26) In the formula, is the ideal power that the energy storage battery should provide at time t, is the preset warning value of the battery edge proximity parameter. The edge control effect of the energy storage coefficient is as shown in Figure 14 .

[0077] S600. 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 that the energy storage battery should provide at time t; (27) Embodiment 2: This embodiment is a device for real-time smoothing of wind power, which specifically includes: The first module is used to determine the power that the hybrid energy storage system should provide at time t based on the power of the wind farm connected to the large power grid at time t-1, the 30-minute wind power volatility, and the output power of the wind farm at time t, in combination with the two-time-scale power fluctuation relationship model; 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. ; 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. 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. ; ; The fifth module is configured 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. , including: ; wherein, is a preset warning value for the battery edge proximity parameter; 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.

[0078] Embodiment 3: This embodiment is a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the wind power real-time smoothing method described in Embodiment 1 or 2 are implemented.

[0079] Embodiment 4: This embodiment is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the wind power real-time smoothing method described in Embodiment 1 or 2 are implemented.

[0080] In addition, although the present invention has been described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the above functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It can also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More precisely, considering the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skills of an engineer. Therefore, those skilled in the art can implement the present invention as set forth in the claims without undue experimentation. It can also be understood that the specific concepts disclosed are illustrative only and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0081] When 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 this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This 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 above methods in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0082] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0083] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts (electronic devices) having one or more wirings, portable computer disk cartridges (magnetic devices), random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memories), optical fiber devices, and portable compact disc read-only memories (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the above program can be printed, because the above program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0084] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0085] In the foregoing description of this specification, descriptions with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0086] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

[0087] The above has specifically described the preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A real-time wind power smoothing method, characterized in that, Including: Based on the power of the wind farm connected to the large power grid at time t-1, the 30-minute wind power volatility, and the output power of the wind farm at time t, and combining the two-time-scale power fluctuation relationship model, determine the power that the hybrid energy storage system should provide at time t; 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 ; 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; 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 ; ; Update the power to be provided by the energy storage battery based on the edge proximity parameter of the energy storage battery at time t , including: ; Among them, is a preset warning value for the 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 super capacitor should provide at time t.

2. The real-time wind power smoothing method according to claim 1, wherein The step of determining the power that the hybrid energy storage system should provide at time t based on the power of the wind farm connected to the large power grid at time t-1, the 30-minute wind power volatility, and the output power of the wind farm at time t, and combining the two-time-scale power fluctuation relationship model, includes: Obtain the power of the wind farm connected to the large power grid at time t-1, the 30-minute wind power volatility of the wind farm connected to the large power 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 combining the two-time-scale power fluctuation relationship model, determine the 1-minute wind power volatility at time t; Based on the 1-minute wind power volatility and the output power of the wind farm at time t, and the power of the wind farm connected to the large power grid at time t-1, determine the first filtering coefficient at time t; Based on the power of the wind farm connected to the large power grid at time t-1, and the output power of the wind farm and the first filtering coefficient at time t, determine the first power of the wind farm connected to the large power grid at time t; Based on the first power of the wind farm connected to the large power grid at time t, determine the 30-minute wind power volatility of the wind farm connected to the large power grid at time t; Judge whether the 30-minute wind power volatility at time t is greater than the preset maximum value of the 30-minute wind power volatility; If it is greater, then based on the output power of the wind farm at time t, the power of the wind farm connected to the large power grid at time t-1, and the preset maximum value of the 30-minute wind power volatility, determine the second filtering coefficient at time t; otherwise, use the first filtering coefficient as the second filtering coefficient at time t; Based on the power of the wind farm connected to the large power grid at time t-1, and the output power of the wind farm and the second filtering coefficient at time t, determine the second power of the wind farm connected to the large power grid at time t; Based on the second power of the wind farm connected to the large power grid 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 real-time wind power smoothing method according to claim 1, wherein Determining 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 , including: 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 and discharge cycle time of the energy storage battery and the loss coefficient at time t-1, determine the filtering time coefficient at time t; 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 and discharge cycle time of the battery, determine the power that the energy storage battery should provide at time t.

4. The real-time wind power smoothing method according to claim 3, wherein The step of determining 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 filtering time coefficient, the power provided by the energy storage battery at time t-1, and the minimum charge and 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; Judge whether the charge and discharge states of the energy storage battery at time t are consistent with those of the hybrid energy storage system at time t. If they are inconsistent, the power that the energy storage battery should provide at time t is 0; Judge whether the charge and discharge states of the energy storage battery at time t and time t-1 are consistent. If they are inconsistent, judge whether the duration of the charge and discharge 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, 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 real-time wind power smoothing method according to claim 3, characterized in that The determining 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: ; Among them, 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 end 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.

6. The real-time wind power smoothing method according to claim 3, wherein The determining of the filtering time coefficient at time t based on the minimum charge and discharge cycle time of the energy storage battery and the loss coefficient at time t-1 includes: ; Among them, is the filtering time coefficient at time t; is the minimum charge-discharge cycle time of the energy storage battery; is the loss coefficient of the energy storage battery at time t-1.

7. The real-time smoothing method for wind power according to claim 2, characterized in that, The two-time-scale power fluctuation relationship model includes: ; Among them, is the 1-minute wind power volatility of the electric field connecting to the large power grid at time t; is the maximum value of the wind power volatility within 1 minute; is the 30-minute wind power volatility of the electric field connecting to the large power grid at time t-1; is the maximum value of the wind power volatility within 30 minutes.

8. The real-time wind power smoothing method according to claim 2, wherein The determining of the first filtering coefficient at time t based on the 1-minute wind power volatility and the wind farm output power at time t, and the wind farm power connected to the large power grid at time t-1 includes: ； Among them, is the first filtering coefficient at time t; is the 1-minute wind power volatility of the electric field connecting to the large power grid at time t; is the rated power of the wind farm; is the minimum value of the wind farm connecting to the large power grid power within 1 minute with the end point at time t - 1; is the maximum value of the wind farm connecting to the large power grid power within 1 minute with the end point at time t - 1; is the output power of the wind farm at time t; is the wind farm connecting to the large power grid power at time t - 1.

9. The real-time wind power smoothing method according to claim 2, wherein The determining of the second filtering coefficient at time t based on the wind farm output power at time t, the wind farm power connected to the large power grid at time t-1, and the preset maximum 30-minute wind power volatility includes: ； Among them, is the second filtering coefficient at time t, is the maximum power of the wind farm connected to the large power grid within 30 minutes ending at time t - 1; is the minimum power of the wind farm connected to the large power grid within 30 minutes ending at time t - 1; is the maximum value of the wind power volatility within 30 minutes; is the output power of the wind farm at time t; is the power of the wind farm connected to the large power grid at time t - 1; is the power of the wind farm connected to the large power grid at time t - i; is the minute corresponding to the minimum power of the wind farm connected to the large power grid within 30 minutes, the minute corresponding to the maximum power of the wind farm connected to the large power grid within 30 minutes; is the minute ending at time t.

10. A real-time wind power smoothing device, characterized in that Includes: The first module is used to determine the power that the hybrid energy storage system should provide at time t by combining the two-time-scale power fluctuation relationship model based on the wind farm power connected to the large power grid at time t-1 and the 30-minute wind power volatility, and the wind farm output power at time t; A second module, configured to determine an 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 parameter of the energy storage battery at time t based on the state of charge of the energy storage 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 , including: ; Among them, is a preset warning value for the battery edge proximity parameter; The sixth module is used 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.

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

12. 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 wind power real-time smoothing method according to any one of claims 1 to 9.

Images