Sustainable and stable output control method for grid-connected output power of wind power generation system

Abstract

The invention relates to a sustainable and stable output control method for grid-connected output power of a wind power generation system, and belongs to the technical field of wind power generation. Through an improved self-adaptive moving average filtering algorithm, according to fluctuation characteristics of a wind power predicted value, optimal time-division grid-connected power meeting power fluctuation constraints of different time periods is obtained, and the stable output problem caused by wind power generation power fluctuation is effectively solved; power distribution and capacity configuration are stabilized for energy storage through a complete empirical ensemble mode decomposition algorithm, the randomness of new energy output is stabilized, sustainable and stable output of wind power generation in different time periods is achieved, and the influence of wind power generation output power fluctuation on power grid operation safety and power supply electric energy quality is effectively overcome; the charging and discharging power of the hybrid energy storage system is corrected through an advanced fuzzy correction method, the grid-connected fluctuation of a wind power plant is reduced, and the cycle life of the energy storage system is prolonged; the wind power generation output power can be continuously stable in different time periods, and the configuration cost of the energy storage system is effectively reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of wind power generation, and relates to a method for controlling the sustainable and stable output of grid-connected output power of a wind power generation system. Background Art

[0002] The continuous advancement of wind power generation technology, the transformation to large-capacity, variable pitch adjustment and variable speed constant frequency control wind turbines, etc., has significantly improved the efficiency of converting wind energy into electrical energy, and enhanced the adaptability of the unit to different wind speed conditions, thereby improving the comprehensive utilization rate of wind energy resources. However, the grid connection of wind power is also accompanied by a series of challenges. First, it affects the stability of the power system. Due to the randomness and intermittent nature of wind energy, this will cause fluctuations in power supply, which in turn threatens the stability of the system. In order to meet this challenge, it is necessary to enhance the frequency regulation and peak regulation capabilities of the power system and introduce energy storage technology to provide necessary support. Secondly, the grid connection of wind power may also have an adverse effect on the quality of power. The volatility of wind energy may cause instability in voltage and frequency, thereby affecting the quality of power. In order to maintain the quality of power, the power grid needs to take corresponding measures, such as the use of power quality regulation equipment, such as static synchronous compensator (STATCOM) and static var compensator (SVC). Finally, the unpredictability of wind energy increases the complexity of power generation planning and economic dispatch of the power grid. Dispatchers need to ensure the stable operation of the power grid while also reasonably arranging the operation and maintenance plans of the generator sets to cope with the challenges brought by the fluctuations in wind energy.

[0003] In order to effectively alleviate the uncertainty impact caused by wind power grid connection, the uncertainty range of future wind power can be estimated by estimating the wind power output forecast results obtained from historical wind power data, which can provide more scientific guidance for the preparation of power generation plans and scheduling decisions, thereby significantly reducing the operational risks caused by prediction errors. This measure not only helps to enhance the dominant position of wind power in the power market competition, but also ensures the stability and security of power grid operation. At present, the main strategy adopted by the industry is to combine energy storage equipment with wind power output to smooth the volatility of wind power, and to build a day-ahead optimization scheduling model for active distribution networks to further weaken the impact of wind power fluctuations on the power grid. In addition, there are methods that take advantage of the real-time measurement data in the intraday scheduling stage to fine-tune the control instructions of power sources, power grids and energy storage systems in real time, and realize coordinated optimization scheduling of distribution networks on different time scales. Although these methods of controlling wind power output in different time periods have their own advantages, they all face a common challenge: a large amount of high-quality historical data and real-time data are required as the basis for modeling and prediction. Data loss or noise problems will seriously restrict the accuracy of prediction results. However, the current mainstream control methods mostly focus on how to reduce the operating costs of energy storage systems, such as minimizing pollution costs, minimizing operating costs and maximizing economic benefits. However, there is a relative lack of control strategies for how the power and capacity of energy storage systems specifically affect the output characteristics of wind power after grid connection. Summary of the invention

[0004] The purpose of the present invention is to solve the problem of intermittent random fluctuations in wind power grid-connected output power under the interference of environmental meteorological factors, so as to improve the wind power grid-connected characteristics and maintain continuous and stable power output, while reducing the impact of intermittent random fluctuations in wind power output power caused by environmental meteorological factors on the safety of grid operation and power quality.

[0005] In view of this, the object of the present invention is to provide a ...

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for controlling the sustainable and stable output of grid-connected output power of a wind power generation system comprises the following steps:

[0008] S1. Calculate the grid-connected power of the wind-storage combined system in different time periods: divide the wind power generation prediction curve into time periods, and calculate the average power value of each time period as a reference value for the sustainable and stable power output to the power grid in each time period;

[0009] S2. Establish a wind-storage combined system: Based on the volatility of wind power generation systems, establish a power allocation strategy and quota power capacity configuration for energy storage systems (such as electrochemical energy storage, mechanical energy storage, wind-solar complementarity, photovoltaic complementarity, etc.);

[0010] S3. Setting an advanced fuzzy control strategy for the hybrid energy storage system: Based on the advanced fuzzy control strategy, the charging and discharging operation of the energy storage system is dynamically adjusted in the hybrid energy storage system through advance prediction of power fluctuations.

[0011] Further, the S1 specifically includes the following steps:

[0012] S11: Set the length N of wind power data sampling p , k represents the number of wind power data currently sampled, and let k = 1, N(k) is the window size at time kΔT;

[0013] S12: Determine k≤N p If the condition is satisfied, the process jumps to S13 for processing. If the condition is not satisfied, the current wind power data processing is completed and the grid-connected power P is output. N (k);

[0014] S13: Assume the currently sampled wind power to be the current grid-connected power P N (k);

[0015] S14: Calculate the volatility of the grid-connected power at the dual time scales of 1 minute and 10 minutes and the volatility constraint index; and determine whether the volatility satisfies the corresponding constraint index at the same time. If so, set N(k)=1, k=k+1 and jump to S12; if not, jump to S15;

[0016] S15: Initialize window size N(k)=2;

[0017] S16: Determine N(k)≤k. If so, jump to S17. Otherwise, set N(k)=1, k=k+1 and jump to S12.

[0018] S17: Calculate the grid-connected power at the current moment;

[0019] S18: Calculate the volatility of the grid-connected power at the dual time scales of 1 minute and 10 minutes and the volatility constraint index; and determine whether the volatility satisfies the corresponding constraint index at the same time. If so, set N(k)=1, k=k+1 and jump to S12. If not, set N(k)=N(k)+1 and jump to S16.

[0020] Furthermore, the volatility and volatility constraint index of the grid-connected power at the dual time scales of 1 minute and 10 minutes are calculated in S14, specifically:

[0021]

[0022] Where ΔT is the sampling time, N T1min With NT10min Indicates the number of sampling points required within the 1min and 10min time scales;

[0023] Get the volatility r at 1min and 10min scales at time k xmin (k):

[0024]

[0025] In the formula, r xmin (k) represents x at time k min Volatility under scale, P W (k) is the predicted output power of the wind farm at time k, P nom is the rated output power of the wind farm, Δk is all possible values ​​of k;

[0026] The power fluctuation rate constraint index allowed for grid connection is obtained as follows:

[0027]

[0028] The grid-connected power at the current moment in S17 is specifically:

[0029]

[0030] Further, the method further comprises the following steps:

[0031] The predicted average power of wind power generation during the effective power generation period is formulated as a constant output power reference value to achieve the power grid's regulation of wind power generation output power;

[0032] If there is a large-capacity energy storage system locally, the energy storage system will output a constant average power to reduce the frequency and voltage fluctuations caused by power fluctuations.

[0033] Further, the S2 specifically includes the following steps:

[0034] S21: Establish a wind-storage combined system model: Assume that the relationship between the wind farm output power, grid-connected power, and the charging and discharging reference power of the hybrid energy storage system (HESS) is:

[0035] P out (t) = P W (t)+P Hess (t) (5)

[0036] Where P out is the output power of the wind-storage combined system, P w (t) is the predicted output power of the wind farm at time t, P HESS Represents the output power of the hybrid energy storage system;

[0037] When P out (t)<P W (t), when P Hess When (t) < 0, the HESS system is charged;

[0038] When P out (t)>P W (t), when P Hess When (t)>0, the HESS system discharges;

[0039] The output power fluctuation index of the wind-storage combined system is:

[0040] -D et <ΔP out (t)<D et (6)

[0041] Where D et The upper and lower limits of power fluctuation;

[0042] P N With P W The quantitative relationship is:

[0043] P W (t) = P N (t)+P e (t) (7)

[0044] Where P e Fluctuating power that requires hybrid energy storage system translation;

[0045] If the local energy storage system capacity can meet the needs of smoothing wind power fluctuations, there is no need to configure other energy storage equipment; if not, a hybrid energy storage system consisting of batteries and supercapacitors can be added;

[0046] S22: Use HESS system to smooth power distribution;

[0047] S23: Calculate the rated power and rated capacity configuration of the HESS system;

[0048] S24: Establish HESS system operation constraints.

[0049] Furthermore, the hybrid energy storage system consisting of the supplementary configuration battery and supercapacitor is specifically:

[0050] The HESS system is a combination of batteries and supercapacitors, expressed as:

[0051] P HESS (t) = P ba (t)+P sc (t) (8)

[0052] Where P HESS represents the output power of the hybrid energy storage system, P ba (t) represents the battery output power, P sc Indicates the supercapacitor output power;

[0053] The power direction of hybrid energy storage is the same to avoid energy exchange between different energy storages:

[0054] P ba P sc ≥0(9)

[0055] The dynamic expression of the state of charge of the HESS system is:

[0056]

[0057] Where P HESS Indicates the charging and discharging power of the HESS system, E HESS is the system energy storage capacity, η cv , η ch , η dis They are the converter conversion efficiency, charging efficiency, and discharging efficiency of the energy storage system respectively; SOC=0 means that the energy storage system has been fully discharged, and SOC=1 means that the energy storage system has sufficient power;

[0058] The operating conditions of the energy storage system are:

[0059] SOC min ≤SOC(t)≤SOC max (11)

[0060] In the formula, SOC max With SOC min Indicates the upper and lower limits of the energy storage system's state of charge.

[0061] Further, the S22 is specifically:

[0062] The signal P that needs to be smoothed for the hybrid energy storage system e Using a complete empirical set modal decomposition algorithm to decompose the power of the HESS system so that it is distributed between the supercapacitor and the battery;

[0063] Decomposed P e The signal is:

[0064]

[0065] Where K represents the number of intrinsic mode functions, represents the k-th stage natural mode function, r(t) represents the residual obtained by subtracting each order mode;

[0066] The decomposed P is extracted by Hilbert transform e Signal, get the instantaneous frequency f of each mode function i (t) is:

[0067]

[0068] In the formula, ψ i (t) represents the instantaneous phase;

[0069] Get the instantaneous frequency-time curve c1,…,c1 from high to low frequency range k , select the curve with no mode aliasing or minimal aliasing from the above curves m and c m+1 ; c m With instantaneous frequency higher than c m The modal components of c are accumulated and absorbed by the supercapacitor; m+1 With instantaneous frequency lower than c m+1 The modal components of the power distribution are accumulated and absorbed by the battery; then the HESS power distribution result is:

[0070]

[0071] Where P sc (t) and P ba (t) are the supercapacitor power and battery power used to smooth out fluctuations;

[0072] P sc (t),P ba (t)>0, indicating that the energy storage system is in a discharging state, P sc (t),P ba (t)<0 indicates that the energy storage system is in a charging state.

[0073] Further, the S23 is specifically:

[0074] 1) HESS system rated power configuration:

[0075] The historical energy storage smoothing power is obtained based on the historical wind power output data and the reference grid-connected power, and processed using the S22; in order to smooth the wind power fluctuation, the maximum and Taking into account the conversion efficiency of the energy storage converter and the charging and discharging efficiency of the energy storage system, and The rated power configuration P for energy storage is based on eHESS for:

[0076]

[0077] Where P ch(t) represents the total charging power of the energy storage system, P dis (t) represents the total discharge power of the energy storage system, t0 is the initial sampling time;

[0078] 2) Rated capacity configuration of HESS system:

[0079] From formula (7) and formula (8), we can get:

[0080]

[0081] In the formula, SOC0 represents the initial value of the state of charge, E HESS Indicates the energy storage capacity;

[0082] In order to meet the demand of smoothing wind power output fluctuations, E HESS Take the minimum value in formula (16):

[0083]

[0084] In the formula, E HESS.dis With E HESS.ch Represents the minimum charge and discharge capacity of the energy storage system. The difference between the two is taken to obtain the system energy storage capacity:

[0085]

[0086] Further, the S24 is specifically:

[0087] The operating constraints of the HESS system are:

[0088]

[0089] Where P ba.maxch With P ba.maxdis is the maximum charge and discharge power of the battery, P sc.maxch With P sc.maxdis is the maximum charge and discharge power of the supercapacitor; SOC ≥ SOC max , indicating that the battery or the supercapacitor is in an overcharged state; SOC≤SOC min , indicating that the battery or the supercapacitor is in an over-discharge state.

[0090] 10. A method for controlling the sustainable and stable output of grid-connected output power of a wind power generation system according to claim 1, wherein S3 specifically comprises the following steps:

[0091] S31: Correcting the charging and discharging power of the hybrid energy storage system: Adaptively adjusting the advance charging and discharging parameter β that improves the fluctuation smoothing capability of the hybrid energy storage system at future moments through the advance fuzzy control, so as to correct the charging and discharging power of the hybrid energy storage system;

[0092] S32: Power correction parameter setting: determine the ultra-short-term wind power forecast results within the forward-looking period, determine the advance charging and discharging parameters according to the wind power fluctuation and advance control within the forward-looking period, and correct the output power of the energy storage system;

[0093] The corrected charging and discharging power of the hybrid energy storage system in S31 is:

[0094] P H (t) = P HESS (t)+P β (t)(20)

[0095] Where P H (t) is the output power of the hybrid energy storage system after correction; β is the advance charging and discharging parameter of the hybrid energy storage system's fluctuation smoothing capability at future moments; P β (t) is the power correction value determined by β;

[0096] The S32 is specifically:

[0097] Grid-connected power fluctuation ΔP out (t) is expressed as:

[0098]

[0099] The difference between wind power and grid-connected power at the previous moment ΔP N (t) can be expressed as:

[0100] ΔP N (t) = P W (t)-P out (t-Δt) (22)

[0101] According to the predicted wind power ramp rate and grid-connected power fluctuations in the forward-looking period, the advance charging and discharging parameter β is adjusted, thereby adjusting the SOC state of the hybrid energy storage system to reserve margin for charging and discharging at future times. Let:

[0102]

[0103] In the formula, ΔP1 and ΔP2 are the inputs of fuzzy control. The fuzzy controller input ΔP2 represents the maximum fluctuation value of wind power relative to the grid-connected power at the previous moment in the current look-ahead period; the fuzzy controller input ΔP1 represents the climbing state of wind power at the same moment; P M is the maximum absolute value of the wind power ramp slope in each forward-looking period; is the forward period ΔP N (t) is the maximum absolute value; t+k1Δt is ΔP in the current forward-looking period N(t) The moment of maximum absolute value; the value range of ΔP1 is [-1,1]; the value range of ΔP2 is [-1,1]; the domain of input quantity is the continuous domain of [-1,1];

[0104] The wind power forecast value within the forward-looking period is taken as the ultra-short-term wind power forecast result;

[0105] The weighted average method is used to defuzzify the output fuzzy quantity and obtain the advance charge and discharge parameter β. After advance control, the power of the hybrid energy storage system is adjusted as follows:

[0106]

[0107] Prioritize charging and discharging of supercapacitors; adopt rolling optimization decree control strategy for rolling execution, and the rolling period is equal to the wind power sampling period.

[0108] The beneficial effects of the present invention are:

[0109] Through the improved adaptive sliding average filtering algorithm, according to the fluctuation characteristics of the wind power prediction value, the optimal grid-connected power in different time periods that meets the power fluctuation constraints in different time periods is obtained, thereby effectively solving the problem of stable output caused by wind power fluctuations; the energy storage is smoothed by the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm to smooth the power distribution and capacity configuration, smooth the randomness of the output of new energy, and achieve sustainable and stable output of wind power in different time periods, thereby effectively overcoming the impact of wind power output power fluctuations on the safety of grid operation and power quality; through the advanced fuzzy correction method, the charging and discharging power of the hybrid energy storage system is corrected to reduce the grid-connected fluctuations of the wind farm and improve the cycle life of the energy storage system; the sustainable and stable wind power output power in different time periods can optimize and effectively reduce the configuration cost of the energy storage system.

[0110] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0112] Figure 1 This is a flow chart of an improved adaptive sliding average filtering algorithm according to an embodiment of the present invention;

[0113] Figure 2 The time-divided wind power grid-connected reference power diagram of the embodiment of the present invention;

[0114] Figure 3 A single-period wind power grid-connected reference power diagram according to an embodiment of the present invention;

[0115] Figure 4 A HESS power allocation strategy diagram according to an embodiment of the present invention;

[0116] Figure 5 This is an operating state diagram of a wind power generation system according to an embodiment of the present invention;

[0117] Figure 6 A flow chart of the advanced fuzzy control strategy of the hybrid energy storage system according to an embodiment of the present invention;

[0118] Figure 7 is a membership function diagram of the first input quantity of an embodiment of the present invention;

[0119] Figure 8 is a membership function diagram of the second input quantity of an embodiment of the present invention;

[0120] Fig. 9 It is a membership function diagram of the output quantity of setting β in an embodiment of the present invention;

[0121] Fig.10 This is a flow chart of a method for controlling the sustainable and stable output power of a wind power generation system connected to the grid according to an embodiment of the present invention. DETAILED DESCRIPTION

[0122] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0123] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0124] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0125] The present invention aims to solve the problem that the output power of the wind power generation system is affected by environmental meteorological factors, and the output power presents randomness, intermittency, weak controllability and uncontrollability. This characteristic of wind power will have an impact on the stability and security of the power grid. Therefore, based on the existing wind power prediction, the present method and technology realizes the visual prediction of the grid-connected power of the wind power system by applying intelligent control strategies and advanced energy storage technologies, thereby making the wind power output power more stable and controllable. The present invention can adopt a variety of energy storage methods (not limited to battery energy storage, pumped energy storage, flywheel energy storage, etc.), smooth the wind power fluctuations through the charging and discharging process of the energy storage system, realize continuous and stable control of the output power of the wind power generation system, and ensure that the wind-storage joint system can continuously output a stable power in different time periods. In this way, the wind power system has some basic functions of a traditional power plant, reduces the impact of power uncertainty caused by wind speed fluctuations on the power grid, improves the quality and reliability of grid connection, and makes it possible for the power grid to realize the prediction, adjustment and controllability of wind power output.

[0126] The present invention provides a method for controlling the sustainable and stable output of the grid-connected output power of a wind power generation system, which mainly includes the following steps:

[0127] S1. Calculate the grid-connected power of the wind-storage combined system in different time periods: divide the wind power generation prediction curve into time periods, and calculate the average power value of each time period as a reference value for the sustainable and stable power output to the power grid in each time period;

[0128] S2. Establish a wind-storage combined system: Based on the volatility of wind power generation systems, establish a power allocation strategy and quota power capacity configuration for energy storage systems (such as electrochemical energy storage, mechanical energy storage, wind-solar complementarity, photovoltaic complementarity, etc.);

[0129] S3. Setting an advanced fuzzy control strategy for the hybrid energy storage system: Based on the advanced fuzzy control strategy, the charging and discharging operation of the energy storage system is dynamically adjusted in the hybrid energy storage system through advance prediction of power fluctuations.

[0130] Considering the volatility of wind turbine output, in order to achieve stable operation of the system after wind power is connected to the grid, an improved adaptive sliding average algorithm is used to decompose the predicted output power of wind power and calculate the grid-connected reference power of the wind-storage system. The present invention proposes an improved adaptive sliding average filtering algorithm to solve the grid-connected power of the wind-storage combined system in different time periods. The algorithm can dynamically adjust the window size according to the wind power output power fluctuation rate at each moment, thereby providing the optimal division scheme for each time period, effectively solving the problem of time period division of wind power output power under different fluctuation conditions. The grid-connected power obtained after using the window filtering can not only meet the grid power fluctuation rate requirements, but also track the wind power as much as possible, thereby reducing the burden of the energy storage system and extending the service life of the energy storage system.

[0131] See also Figure 1 , is a flow chart of an improved adaptive sliding average filtering algorithm according to an embodiment of the present invention, and the specific steps of S1 are as follows:

[0132] S11: Set the length N of wind power data sampling p , k represents the number of wind power data currently sampled, and k=1, N(k) is the window size at time kΔT.

[0133] S12: Determine k≤N p If the condition is satisfied, the process jumps to S13 for processing. If the condition is not satisfied, the current wind power data processing is completed and the grid-connected power P is output. N (k).

[0134] S13: Assume the currently sampled wind power to be the current grid-connected power P N (k).

[0135] S14: Calculate the volatility of the grid-connected power at the dual time scales of 1 min and 10 min and the volatility constraint index according to formulas (1) to (4), and determine whether the volatility satisfies the corresponding constraint index. If both are satisfied, set N(k)=1, k=k+1 and jump to S12, otherwise jump to S15.

[0136]

[0137] Where ΔT is the sampling time, N T1min With N T10min Indicates the number of sampling points required within the 1min and 10min time scales.

[0138] Then we get the volatility r at the k time on the 1-min and 10-min scales: xmin (k):

[0139]

[0140] In the formula, r xmin (k) represents x at time k min Volatility under scale, P W (k) is the predicted output power of the wind farm at time k, P nom is the rated output power of the wind farm, and Δk is all possible values ​​of k.

[0141] Therefore, it can be concluded that the power fluctuation rate constraint index allowed for grid connection is:

[0142]

[0143] S15: Initialize window size N(k)=2.

[0144] S16: Determine N(k)≤k. If so, jump to S17; otherwise, set N(k)=1, k=k+1 and jump to S12.

[0145] S17: Calculate the grid-connected power at the current moment according to the following formula:

[0146]

[0147] S18: According to formulas (1) to (4), the volatility of the grid-connected power at the dual time scales of 1 min and 10 min is calculated, and it is determined whether the volatility satisfies the corresponding constraint index. If both are satisfied, set N(k) = 1, k = k + 1 and jump to S12; otherwise, set N(k) = N(k) + 1 and jump to S16.

[0148] By executing the above improved algorithm, the expected grid-connected power and the fluctuating power that needs to be smoothed by the energy storage system can be obtained from the predicted wind power output power. According to the wind power sampled at the current moment, it is judged whether its volatility meets the constraint standard. If it does, it is directly connected to the grid. If it does not, the initial window size is given to be 2, and the window is gradually increased. When the wind power fluctuation rate meets the grid connection standard, the grid connection is executed. This process is repeated for each sampling moment, and then the fluctuating power is obtained by subtracting the wind power from the grid-connected power, and this power is used as the reference input of the energy storage system.

[0149] See also Figure 2 , which is a time-divided wind power grid-connected reference power diagram of an embodiment of the present invention, by formulating the predicted average power of wind power generation in the effective power generation period as a constant output power reference value, the power grid can visually foresee the wind power generation power, and then realize the controllable and adjustable wind power generation output power of the power grid.

[0150] When the local energy storage configuration capacity is sufficient, such as the local large-capacity pumped energy storage, it is possible to use the wind storage output system as a constant value as the overall output, please refer to Figure 3, is a single-period wind power grid-connected reference power diagram of an embodiment of the present invention. The wind storage system can reduce the impact of wind power fluctuations on the power grid with a constant average power output, and reduce the frequency and voltage fluctuations caused by power fluctuations. This constant output method is close to controllable power generation forms such as traditional thermal power plants, which is conducive to the stable operation of the power grid. In addition, the constant output mode helps to increase the access ratio of wind power, so that wind power, a volatile resource, can be connected to the power grid at a higher ratio, promoting the large-scale application of renewable energy.

[0151] However, to achieve constant output, the energy storage system needs to have a large capacity to cope with the random fluctuations and large changes in wind power output, resulting in high construction and operation costs of the energy storage system. In addition, under constant power control, frequent charging and discharging of the energy storage system will cause energy loss, which may reduce the overall system efficiency. Figure 4 , is a HESS power allocation strategy diagram of an embodiment of the present invention; S2 specific steps are as follows:

[0152] S21: Establish a wind-storage combined system model

[0153] Assume that the relationship between wind farm output power, grid-connected power and hybrid energy storage system (HESS) charging and discharging reference power is:

[0154] P out (t) = P W (t)+P Hess (t) (5)

[0155] Where P out is the output power of the wind-storage combined system, P w (t) is the predicted output power of the wind farm at time t, P HESS Represents the output power of the hybrid energy storage system.

[0156] When P out (t)<P W (t), when P Hess When (t)<0, HESS is charged.

[0157] When P out (t)>P W (t), when P Hess When (t)>0, HESS discharges.

[0158] The output power fluctuation index of the wind-storage combined system is:

[0159] -D et <ΔP out (t)<D et (6)

[0160] Where Det The upper and lower limits of power fluctuation.

[0161] P N With P W The quantitative relationship is:

[0162] P W (t) = P N (t)+P e (t) (7)

[0163] Where P e It is the fluctuating power that needs to be shifted by the hybrid energy storage system.

[0164] The present invention takes a hybrid energy storage system composed of batteries and supercapacitors as an example. In practical applications, the existing local energy storage configurations should be considered first, such as pumped storage, flywheel storage, or other energy storage methods with local characteristics, and their existing capacity should be evaluated to see whether they can meet the fluctuation regulation needs of the wind power system. If the local energy storage capacity is sufficient to smooth the power fluctuations of wind power, there is no need to configure other energy storage devices; if the capacity is insufficient, a hybrid energy storage system composed of batteries and supercapacitors should be configured according to system requirements.

[0165] See also Figure 5 , is an operating state diagram of a wind power generation system according to an embodiment of the present invention, showing the operating status of a hybrid energy storage system composed of batteries and supercapacitors participating in wind-storage joint output: the energy storage system is used to smooth out power fluctuations after new energy is connected to the grid due to its fast response speed and charge-discharge characteristics. However, a single battery energy storage system will result in excessively high system investment costs, and frequent charge-discharge will quickly reduce its cycle life. The hybrid energy storage system can deal with the above problems well. This article adopts a HESS system composed of batteries and supercapacitors. The relationship between the HESS system composed of batteries and supercapacitors is as follows:

[0166] P HESS (t) = P ba (t)+P sc (t) (8)

[0167] Where P HESS Represents the output power of the hybrid energy storage system, P ba (t) represents the battery output power, P sc Indicates the supercapacitor output power.

[0168] The power direction of hybrid energy storage should be the same to avoid energy exchange between different energy storages:

[0169] P ba P sc ≥0 (9)

[0170] The dynamic expression of the state of charge of the HESS system is:

[0171]

[0172] Where P HESS Indicates the charging and discharging power of the HESS system, E HESS is the system energy storage capacity, η cv , η ch , η dis They are the converter conversion efficiency, charging efficiency, and discharging efficiency of the energy storage system. SOC = 0 means that the energy storage system has been fully discharged, and SOC = 1 means that the energy storage system has sufficient power.

[0173] The operating conditions of the energy storage system are:

[0174] SOC min ≤SOC(t)≤SOC max (11)

[0175] SOC max With SOC min Indicates the upper and lower limits of the energy storage system's state of charge.

[0176] S22: Using HESS to smooth power distribution

[0177] The signal P that needs to be smoothed for the hybrid energy storage system e The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is used to decompose the power of the HESS system so that it can be effectively distributed between the supercapacitor and the battery. CEEMDAN is an improved empirical mode decomposition (EMD) algorithm that can decompose continuous non-stationary or nonlinear signals into a series of intrinsic mode functions. e The signal decomposes into:

[0178]

[0179] Where K represents the number of intrinsic mode functions, represents the k-th stage natural mode function, and r(t) represents the residual obtained by subtracting each order mode.

[0180] The instantaneous frequency f of each modal function is obtained by extracting CEEMDAN decomposition through Hilbert Transform (HT) i (t) is:

[0181]

[0182] In the formula, ψ i (t) represents the instantaneous phase.

[0183] Get the instantaneous frequency-time curve c1,…,c1 from high to low frequency range k , select the curves with no mode aliasing or minimal aliasing from these curves m and c m+1 , and then c m With instantaneous frequency higher than c m The modal components are accumulated, i.e. reconstructed, and absorbed by the supercapacitor, c m+1 With instantaneous frequency lower than c m+1 The modal components of are accumulated and absorbed by the battery. Then the HESS power allocation result is:

[0184]

[0185] Where P sc (t) and P ba (t) are the supercapacitor power and battery power used to smooth out fluctuations.

[0186] P sc (t),P ba (t)>0, indicating that the energy storage system is in a discharging state, P sc (t),P ba (t)<0 indicates that the energy storage system is in a charging state.

[0187] S23: Calculate the rated power and rated capacity configuration of the HESS system

[0188] 1) HESS system rated power configuration

[0189] The historical energy storage smoothing power is obtained based on the historical wind power output data and the reference grid-connected power, and is processed using the method described in S22 to obtain the maximum power to smooth the wind power fluctuations. and Taking into account the conversion efficiency of the energy storage converter and the charging and discharging efficiency of the energy storage system, and The rated power configuration P for energy storage is based on eHESS for:

[0190]

[0191] Where P ch (t) represents the total charging power of the energy storage system, P dis (t) represents the total discharge power of the energy storage system, and t0 is the initial sampling time.

[0192] 2) Rated capacity configuration of HESS system

[0193] From formula (7) and formula (8), we can get:

[0194]

[0195] In the formula, SOC0 represents the initial value of the state of charge, E HESS Indicates the energy storage quota capacity.

[0196] In order to meet the demand of smoothing wind power output fluctuations, E HESS The minimum value of the above formula should be taken:

[0197]

[0198] In the formula, E HESS.dis With E HESS.ch Represents the minimum charge and discharge capacity of the energy storage system. Taking the difference between the two, we can get the system energy storage capacity:

[0199]

[0200] S24: Establish HESS system operation constraints

[0201] In order to ensure the safe operation of HESS, it is necessary to restrict it to prevent the cycle life from decreasing rapidly. The constraints are:

[0202]

[0203] P ba.maxch With P ba.maxdis is the maximum charge and discharge power of the battery, P sc.maxch With P sc.maxdis is the maximum charge and discharge power of the supercapacitor; at the same time, the charge state of the battery and the supercapacitor needs to be at an appropriate value. When SOC ≥ SOC max , indicating that the battery / supercapacitor is in an overcharged state. When SOC≤SOC min , indicating that the battery / supercapacitor is in an over-discharge state.

[0204] When the HESS system is in a discharging state, if both energy storages are not over-discharged, the energy storage system will discharge normally. When only the battery is over-discharged, the supercapacitor will be responsible for the discharge. Vice versa, when both are in an over-discharge state, the discharge will stop. The discharge power command adjustment of the HESS system is shown in Table 1:

[0205] Table 1

[0206]

[0207] Similarly, the charging power command adjustment of the HESS system is shown in Table 2:

[0208] Table 2

[0209]

[0210] Wind power fluctuations will affect the current charging and discharging behavior of the hybrid energy storage system, and are likely to affect the hybrid energy storage system's ability to smooth fluctuations in the future. Therefore, it is necessary to adjust the hybrid energy storage system output based on the hybrid energy storage system's SOC real-time state and future charging and discharging requirements.

[0211] See also Figure 6 , is a flow chart of the advanced fuzzy control strategy of the hybrid energy storage system according to an embodiment of the present invention, and the specific steps of S3 are as follows:

[0212] S31: The present invention uses the advance fuzzy control to adaptively adjust the advance charging and discharging parameter β that improves the fluctuation smoothing capability of the hybrid energy storage system at future moments, so as to correct the charging and discharging power of the hybrid energy storage system.

[0213] The corrected charging and discharging power of the hybrid energy storage system is:

[0214] P H (t) = P HESS (t)+P β (t) (20)

[0215] Where P H (t) is the output power of the hybrid energy storage system after correction; P β (t) is the power correction value determined by β.

[0216] S32: Power correction parameter setting

[0217] Determine the ultra-short-term wind power forecast results within the look-ahead period, determine the advance charging and discharging parameters based on the wind power fluctuations and advance control within the look-ahead period, and correct the output power of the energy storage system.

[0218] In the real-time control strategy, the output of the hybrid energy storage system is not only limited by the current wind power fluctuation and the state of the hybrid energy storage system, but also affected by the accumulated SOC in the past. If the wind power fluctuation exceeds the limit and needs to be smoothed at a certain moment, and the SOC of the hybrid energy storage system has reached the limit, the hybrid energy storage system cannot continue to charge and discharge due to operational constraints, which will result in the wind power fluctuation not being smoothed.

[0219] Grid-connected power fluctuation ΔP out (t) can be expressed as:

[0220]

[0221] The difference between wind power and grid-connected power at the previous moment ΔP N (t) can be expressed as:

[0222] ΔPN (t) = P W (t)-P out (t-Δt) (22)

[0223] From the above equations (21) and (22), we can see that the grid-connected fluctuating power ΔP out (t) Whether it exceeds the limit and ΔP N (t) and energy storage system output P H (t) is related to the forward-looking period. If ΔP N (t+k1Δt) are all within the fluctuation range, then the output of the hybrid energy storage system is 0. If at a certain moment ΔP N (t+k1Δt) exceeds the limit, and the charge state of the hybrid energy storage system at this moment is not enough to smooth the fluctuation, then even if the current moment ΔP N (t) has not exceeded the limit, and the hybrid energy storage system still needs to charge and discharge a portion of electricity to prepare for smoothing fluctuations in the forward-looking period. According to the predicted wind power ramp rate and grid-connected power fluctuations in the forward-looking period, the advance charge and discharge parameter β is adjusted to adjust the SOC state of the hybrid energy storage system, leaving a margin for future charging and discharging needs, and setting:

[0224]

[0225] In the formula, ΔP1 and ΔP2 are the inputs of fuzzy control. The fuzzy controller input ΔP2 represents the maximum fluctuation value of wind power relative to the grid-connected power at the previous moment in the current look-ahead period; the fuzzy controller input ΔP1 represents the climbing state of wind power at the same moment; P M is the maximum absolute value of the wind power ramp slope in each forward-looking period; P N * is the forward period ΔP N (t) is the maximum absolute value; t+k1Δt is ΔP in the current forward-looking period N (t) The moment of maximum absolute value.

[0226] The above wind power forecast values ​​within the forward-looking period are based on ultra-short-term wind power forecast results.

[0227] According to the above formula (23), the molecule ΔP W There are positive and negative points, P M is the maximum absolute value of the wind power slope in all forward-looking periods, so the value range of ΔP1 is [-1,1]. According to the above formula (24), the numerator ΔP N There are positive and negative values, and the denominator is the ΔP of all forward-looking periods. N The maximum absolute value, so the range of ΔP2 is [-1,1]. Therefore, the domain of input quantity is the continuous domain of [-1,1], and its fuzzy sets are {NB (negative large), NS (negative small), ZO (zero), PS (positive small), PB (positive large)}, please refer to Figure 7 , is the membership function of the first input quantity ΔP1 of the embodiment of the present invention. The output is the advance charge and discharge parameter Δβ, the domain is the discrete domain of [0,0.5], and the fuzzy set is {VS (small), S (small), M (medium), B (large), VB (large)}, please refer to Figure 8 , is the membership function of the second input quantity ΔP2 in the embodiment of the present invention. Fig. 9 , is a membership function diagram of the output of setting β in the embodiment of the present invention; the fuzzy control rules of setting β are shown in Table 3:

[0228] Table 3

[0229]

[0230]

[0231] The weighted average method is used to defuzzify the output fuzzy quantity and obtain the advance charge and discharge parameter β. After advance control, the power of the hybrid energy storage system is adjusted as follows:

[0232]

[0233] In order to extend the battery life, and taking advantage of the supercapacitor's characteristics of frequent charging and discharging and fast response speed, the supercapacitor is charged and discharged first.

[0234] like Figure 8 As shown, a flexible and environmentally adaptable rolling optimization decree control strategy is adopted for rolling execution, and the rolling period is equal to the wind power sampling period, and T is the total operating time.

[0235] See also Fig.10 , which is a flow chart of a method for controlling the sustainable and stable output of the grid-connected output power of a wind power generation system according to an embodiment of the present invention. The present invention utilizes the existing wind power generation power prediction model and formulates a constant output power reference value for each time period, so that the power grid can grasp the changes in wind power generation power in advance and realize the controllability and adjustability of its output. This method effectively solves the problems of randomness, intermittency and uncontrollability of wind power generation systems due to the influence of meteorological conditions. Specifically, through the time period method, the average output power of the wind power generation system in each time period is used as a constant output reference value, which greatly reduces the power fluctuations that need to be adjusted by the energy storage system, thereby reducing the demand for energy storage configuration capacity, reducing system costs, and improving operating efficiency and economy.

[0236] In addition, the present invention proposes a coordinated operation strategy for the wind-storage combined system, which not only enhances the stability and controllability of the wind power generation system, ensures smooth grid connection, but also optimizes the charging and discharging process of the energy storage system. This solution helps promote large-scale access to wind power generation, improve the grid's ability to absorb renewable energy, and enhance the economic benefits of the wind-storage combined system and the reliability of grid operation, so that the wind power generation system gradually possesses some basic characteristics similar to traditional power plants.

[0237] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for controlling the sustainable and stable output power of a wind power generation system connected to the grid, characterized in that: The following steps are involved: S1. Calculate the grid-connected power of the wind-storage combined system in different time periods: divide the wind power generation prediction curve into time periods, and calculate the average power value of each time period as a reference value for the sustainable and stable power output to the power grid in each time period; S2. Establish a wind-storage combined system: Based on the volatility of wind power generation systems, establish a power allocation strategy and quota power capacity configuration for energy storage systems (such as electrochemical energy storage, mechanical energy storage, wind-solar complementarity, photovoltaic complementarity, etc.); S3. Setting an advanced fuzzy control strategy for the hybrid energy storage system: Based on the advanced fuzzy control strategy, the charging and discharging operation of the energy storage system is dynamically adjusted in the hybrid energy storage system through advance prediction of power fluctuations.

2. A method for controlling the sustainable and stable output of grid-connected output power of a wind power generation system according to claim 1, characterized in that: The S1 specifically includes the following steps: S11: Set the length N of wind power data sampling p , k represents the number of wind power data currently sampled, and let k = 1, N(k) is the window size at time kΔT; S12: Determine k≤N p If the condition is satisfied, the process jumps to S13 for processing. If the condition is not satisfied, the current wind power data processing is completed and the grid-connected power P is output. N (k); S13: Assume the currently sampled wind power to be the current grid-connected power P N (k); S14: Calculate the volatility of the grid-connected power at the dual time scales of 1 minute and 10 minutes and the volatility constraint index; and determine whether the volatility satisfies the corresponding constraint index at the same time. If so, set N(k)=1, k=k+1 and jump to S12; if not, jump to S15; S15: Initialize window size N(k)=2; S16: Determine N(k)≤k. If so, jump to S17. Otherwise, set N(k)=1, k=k+1 and jump to S12. S17: Calculate the grid-connected power at the current moment; S18: Calculate the volatility of the grid-connected power at the dual time scales of 1 minute and 10 minutes and the volatility constraint index; and determine whether the volatility satisfies the corresponding constraint index at the same time. If so, set N(k)=1, k=k+1 and jump to S12. If not, set N(k)=N(k)+1 and jump to S16.

3. A method for controlling the sustainable and stable output of grid-connected output power of a wind power generation system according to claim 2, characterized in that: In S14, the fluctuation rate of the grid-connected power and the fluctuation rate constraint index at the dual time scales of 1 minute and 10 minutes are calculated, specifically: Where ΔT is the sampling time, N T1min With N T10min Indicates the number of sampling points required within the 1min and 10min time scales; Get the volatility r at 1min and 10min scales at time k xmin (k): In the formula, r xmin (k) represents x at time k min Volatility under scale, P W (k) is the predicted output power of the wind farm at time k, P nom is the rated output power of the wind farm, Δk is all possible values ​​of k; The power fluctuation rate constraint index allowed for grid connection is obtained as follows: The grid-connected power at the current moment in S17 is specifically:

4. A method for controlling the sustainable and stable output of grid-connected output power of a wind power generation system according to claim 3, characterized in that: The following steps are also included: The predicted average power of wind power generation during the effective power generation period is formulated as a constant output power reference value to achieve the power grid's regulation of wind power generation output power; If there is a large-capacity energy storage system locally, the energy storage system will output a constant average power to reduce the frequency and voltage fluctuations caused by power fluctuations.

5. A method for controlling the sustainable and stable output of grid-connected output power of a wind power generation system according to claim 1, characterized in that: The S2 specifically includes the following steps: S21: Establish a wind-storage combined system model: Assume that the relationship between the wind farm output power, grid-connected power, and the charging and discharging reference power of the hybrid energy storage system (HESS) is: P out (t)=P W (t)+P Hess (t) (5) Where P out is the output power of the wind-storage combined system, P w (t) is the predicted output power of the wind farm at time t, P HESS Represents the output power of the hybrid energy storage system; When P out (t)<P W (t), when P Hess When (t) < 0, the HESS system is charged; When P out (t)>P W (t), when P Hess When (t)>0, the HESS system discharges; The output power fluctuation index of the wind-storage combined system is: -D et <ΔP out (t)<D et (6) Where D et The upper and lower limits of power fluctuation; P N With P W The quantitative relationship is: P W (t)=P N (t)+P e (t) (7) Where P e Fluctuating power that requires hybrid energy storage system translation; If the local energy storage system capacity can meet the needs of smoothing wind power fluctuations, there is no need to configure other energy storage equipment; if not, a hybrid energy storage system consisting of batteries and supercapacitors can be added; S22: Use HESS system to smooth power distribution; S23: Calculate the rated power and rated capacity configuration of the HESS system; S24: Establish HESS system operation constraints.

6. A method for controlling the sustainable and stable output of grid-connected output power of a wind power generation system according to claim 5, characterized in that: The hybrid energy storage system composed of the supplementary configuration battery and supercapacitor is specifically: The HESS system is a combination of batteries and supercapacitors, expressed as: P HESS (t)=P ba (t)+P sc (t) (8) Where P HESS represents the output power of the hybrid energy storage system, P ba (t) represents the battery output power, P sc Indicates the supercapacitor output power; The power direction of hybrid energy storage is the same to avoid energy exchange between different energy storages: P ba P sc ≥0(9) The dynamic expression of the state of charge of the HESS system is: Where P HESS Indicates the charging and discharging power of the HESS system, E HESS is the system energy storage capacity, η cv , η ch , η dis They are the converter conversion efficiency, charging efficiency, and discharging efficiency of the energy storage system respectively; SOC=0 means that the energy storage system has been fully discharged, and SOC=1 means that the energy storage system has sufficient power; The operating conditions of the energy storage system are: SOC min ≤SOC(t)≤SOC max (11) In the formula, SOC max With SOC min Indicates the upper and lower limits of the energy storage system's state of charge.

7. A method for controlling the sustainable and stable output of grid-connected output power of a wind power generation system according to claim 6, characterized in that: The S22 is specifically: The signal P that needs to be smoothed for the hybrid energy storage system e Using a complete empirical set modal decomposition algorithm to decompose the power of the HESS system so that it is distributed between the supercapacitor and the battery; Decomposed P e The signal is: Where K represents the number of intrinsic mode functions, represents the k-th stage natural mode function, r(t) represents the residual obtained by subtracting each order mode; The decomposed P is extracted by Hilbert transform e Signal, get the instantaneous frequency f of each mode function i (t) is: In the formula, ψ i (t) represents the instantaneous phase; Get the instantaneous frequency-time curve c1,…,c1 from high to low frequency range k , select the curve with no mode aliasing or minimal aliasing from the above curves m and c m+1 ; c m With instantaneous frequency higher than c m The modal components of c are accumulated and absorbed by the supercapacitor; m+1 With instantaneous frequency lower than c m+1 The modal components of the power distribution are accumulated and absorbed by the battery; then the HESS power distribution result is: Where P sc (t) and P ba (t) are the supercapacitor power and battery power used to smooth out fluctuations; P sc (t),P ba (t)>0, indicating that the energy storage system is in a discharging state, P sc (t),P ba (t)<0 indicates that the energy storage system is in a charging state.

8. A method for controlling the sustainable and stable output of grid-connected output power of a wind power generation system according to claim 7, characterized in that: The S23 is specifically: 1) HESS system rated power configuration: The historical energy storage smoothing power is obtained based on the historical wind power output data and the reference grid-connected power, and processed using the S22; in order to smooth the wind power fluctuation, the maximum and Taking into account the conversion efficiency of the energy storage converter and the charging and discharging efficiency of the energy storage system, and The rated power configuration P for energy storage is based on eHESS for: Where P ch (t) represents the total charging power of the energy storage system, P dis (t) represents the total discharge power of the energy storage system, t0 is the initial sampling time; 2) Rated capacity configuration of HESS system: From formula (7) and formula (8), we can get: In the formula, SOC0 represents the initial value of the state of charge, E HESS Indicates the energy storage capacity; In order to meet the demand of smoothing wind power output fluctuations, E HESS Take the minimum value in formula (16): In the formula, E HESS.dis With E HESS.ch Represents the minimum charge and discharge capacity of the energy storage system. The difference between the two is taken to obtain the system energy storage capacity:

9. A method for controlling the sustainable and stable output of grid-connected output power of a wind power generation system according to claim 8, characterized in that: The S24 is specifically: The operating constraints of the HESS system are: Where P ba.maxch With P ba.maxdis is the maximum charge and discharge power of the battery, P sc.maxch With P sc.maxdis is the maximum charge and discharge power of the supercapacitor; SOC ≥ SOC max , indicating that the battery or the supercapacitor is in an overcharged state; SOC≤SOC min , indicating that the battery or the supercapacitor is in an over-discharge state.

10. A method for controlling the sustainable and stable output of grid-connected output power of a wind power generation system according to claim 1, characterized in that: The S3 specifically includes the following steps: S31: Correcting the charging and discharging power of the hybrid energy storage system: Adaptively adjusting the advance charging and discharging parameter β that improves the fluctuation smoothing capability of the hybrid energy storage system at future moments through the advance fuzzy control, so as to correct the charging and discharging power of the hybrid energy storage system; S32: Power correction parameter setting: determine the ultra-short-term wind power forecast results within the forward-looking period, determine the advance charging and discharging parameters according to the wind power fluctuation and advance control within the forward-looking period, and correct the output power of the energy storage system; The corrected charging and discharging power of the hybrid energy storage system in S31 is: P H (t)=P HESS (t)+P β (t) (20) Where P H (t) is the output power of the hybrid energy storage system after correction; β is the advance charging and discharging parameter of the hybrid energy storage system's fluctuation smoothing capability at future moments; P β (t) is the power correction value determined by β; The S32 is specifically: Grid-connected power fluctuation ΔP out (t) is expressed as: The difference between wind power and grid-connected power at the previous moment ΔP N (t) can be expressed as: ΔP N (t)=P W (t)-P out (t-Δt) (22) According to the predicted wind power ramp rate and grid-connected power fluctuations in the forward-looking period, the advance charging and discharging parameter β is adjusted, thereby adjusting the SOC state of the hybrid energy storage system to reserve margin for charging and discharging at future times. Let: In the formula, ΔP1 and ΔP2 are the inputs of fuzzy control. The fuzzy controller input ΔP2 represents the maximum fluctuation value of wind power relative to the grid-connected power at the previous moment in the current look-ahead period; the fuzzy controller input ΔP1 represents the climbing state of wind power at the same moment; P M is the maximum absolute value of the wind power ramp slope in each forward-looking period; is the forward period ΔP N (t) is the maximum absolute value; t+k1Δt is ΔP in the current forward-looking period N (t) The moment of maximum absolute value; the value range of ΔP1 is [-1,1]; the value range of ΔP2 is [-1,1]; the domain of input quantity is the continuous domain of [-1,1]; The wind power forecast value within the forward-looking period is taken as the ultra-short-term wind power forecast result; The weighted average method is used to defuzzify the output fuzzy quantity and obtain the advance charge and discharge parameter β. After advance control, the power of the hybrid energy storage system is adjusted as follows: Prioritize charging and discharging of supercapacitors; adopt rolling optimization decree control strategy for rolling execution, and the rolling period is equal to the wind power sampling period.

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