A method for controlling sustainable and stable output of grid-connected output power of a wind power generation system

Abstract

The present application relates to a kind of wind power generation system grid-connected output power sustainable stable output control method, belong to wind power generation technical field.By improved adaptive moving average filtering algorithm, according to the fluctuation characteristics of wind power prediction value, obtain the optimal time-sharing period grid-connected power that satisfies the power fluctuation constraint of different time periods, effectively solve the stable output problem caused by wind power fluctuation;By complete empirical ensemble mode decomposition algorithm, power distribution and capacity configuration are carried out to energy storage, suppress the randomness of new energy output, realize the sustainable stable output of wind power generation in different time periods, effectively overcome the influence of wind power output power fluctuation on power grid operation safety and power quality;By leading fuzzy correction method, charge and discharge power of hybrid energy storage system is corrected, reduce wind farm grid-connected fluctuation, improve the cycle life of energy storage system;Time-sharing period sustainable stable wind power output power effectively reduces the configuration cost of energy storage system.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation technology and relates to a method for controlling the sustainable and stable output power of a wind power generation system connected to the grid. Background Technology

[0002] The continuous advancement of wind power generation technology, particularly the shift towards large-capacity, variable pitch, and variable-speed constant-frequency control wind turbines, has significantly improved the efficiency of converting wind energy into electricity and enhanced the adaptability of turbines to different wind speed conditions, thereby increasing the comprehensive utilization rate of wind energy resources. However, wind power grid connection also brings a series of challenges. First, it impacts the stability of the power system. Due to the randomness and intermittency of wind energy, this can lead to fluctuations in power supply, threatening system stability. To address this challenge, it is necessary to enhance the frequency regulation and peak shaving capabilities of the power system and introduce energy storage technology to provide necessary support. Second, wind power grid connection may also adversely affect power quality. The volatility of wind energy can lead to voltage and frequency instability, thus affecting power quality. To maintain power quality, the grid needs to take corresponding measures, such as using power quality conditioning equipment, including Static Synchronous Compensators (STATCOMs) and Static Var Compensators (SVCs). Finally, the unpredictability of wind energy increases the complexity of grid generation planning and economic dispatch. Dispatchers need to ensure the stable operation of the power grid while also making reasonable arrangements for the operation and maintenance of generator units to cope with the challenges brought about by wind energy fluctuations.

[0003] To effectively mitigate the uncertainties arising from wind power grid connection, estimating the uncertainty range of future wind power output using historical wind power data can provide more scientific guidance for power generation planning and dispatch decisions, thereby significantly reducing operational risks caused by forecast errors. This approach not only helps enhance wind power's competitive advantage in the electricity market but also ensures the stability and security of the power grid. Currently, the industry's main strategy is to combine energy storage devices with wind power output to smooth out wind power volatility and construct day-ahead optimized dispatch models for active distribution networks to further reduce the impact of wind power fluctuations on the grid. In addition, other methods leverage the advantage of real-time measurement data during intraday dispatch to fine-tune control commands for power sources, the grid, and energy storage systems in real time, achieving coordinated and optimized dispatch of the distribution network at different time scales. While these time-segmented wind power output control methods each have their advantages, they all face a common challenge: the need for a large amount of high-quality historical and real-time data as the basis for modeling and forecasting. Data gaps or noise issues will severely limit the accuracy of forecast results. However, most current mainstream control methods focus on how to reduce the operating costs of energy storage systems, such as minimizing pollution costs, minimizing operating costs, and maximizing economic benefits. There is a relative lack of control strategies that address 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 this invention is to solve the problem of intermittent random fluctuations in the output power of wind power grid connection under the interference of environmental meteorological factors, so as to improve the grid connection characteristics of wind power and maintain a continuous and stable power output, while reducing the impact of intermittent random fluctuations in the output power of wind 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] To achieve the above objectives, the present invention provides the following technical solution:

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

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

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

[0010] S3. Set 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 by predicting power fluctuations in advance.

[0011] Furthermore, 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 that have been sampled so far, and k = 1. N(k) is the window size at time kΔT.

[0013] S12: Determine if k≤N p If the conditions are met, proceed to S13 for further processing; otherwise, the current wind power data processing is complete, and the grid-connected power P is output. N (k);

[0014] S13: Assume the currently sampled wind power as the grid-connected power P at the current moment. N (k);

[0015] S14: Calculate the volatility of grid-connected power at dual time scales of 1 min and 10 min and the volatility constraint index; and determine whether the volatility simultaneously meets the corresponding constraint index. If it does, let N(k) = 1, k = k + 1 and jump to S12. If it does not meet the constraint index, jump to S15.

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

[0017] S16: Determine if N(k) ≤ k. If it is satisfied, jump to S17; otherwise, let 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 grid-connected power at dual time scales of 1 min and 10 min and the volatility constraint index; and determine whether the volatility simultaneously meets the corresponding constraint index. If it does, let N(k) = 1, k = k + 1 and jump to S12. If it does not meet the constraint index, let N(k) = N(k) + 1 and jump to S16.

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

[0021]

[0022] In the formula, ΔT is the sampling time, and N is the sampling time. T1min With NT10min This indicates the number of sampling points required within the 1-minute and 10-minute timescales;

[0023] The volatility r at time k on the 1-minute and 10-minute scales is obtained. xmin (k):

[0024]

[0025] In the formula, r xmin (k) represents x at time k. min Volatility at scale P W (k) represents the predicted output power of the wind farm at time k, P nom Let k be the rated output power of the wind farm, and Δk be any possible value of k.

[0026] The power fluctuation rate constraint for permissible grid connection is derived as follows:

[0027]

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

[0029]

[0030] Furthermore, it also includes the following steps:

[0031] The predicted average power of wind power generation during the effective power generation period is set as a constant output power reference value in order to enable the grid to regulate the output power of wind power generation.

[0032] If a large-capacity energy storage system is available locally, it should be used to output a constant average power to reduce frequency and voltage fluctuations caused by power fluctuations.

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

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

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

[0036] In the formula, P out For the output power of the wind-storage combined system, P w (t) represents the predicted output power of the wind farm at time t, P HESS Indicates the output power of the hybrid energy storage system;

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

[0038] When P out (t)>P W When (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] In the formula, D et These are the upper and lower limits for power fluctuations;

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

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

[0044] In the formula, P e For fluctuating power that requires translation by a hybrid energy storage system;

[0045] If the local energy storage system capacity is sufficient to smooth out wind power fluctuations, then no additional energy storage equipment is needed; otherwise, a hybrid energy storage system consisting of batteries and supercapacitors should be added.

[0046] S22: Utilize the HESS system to smooth out power distribution;

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

[0048] S24: Set the operating constraints for the HESS system.

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

[0050] The HESS system is a combination of a battery and a supercapacitor, represented as:

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

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

[0053] Hybrid energy storage systems use the same power direction to avoid energy exchange between different energy storage devices.

[0054] P ba P sc ≥0(9)

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

[0056]

[0057] In the formula, P HESS E represents the charging and discharging power of the HESS system. HESS For the system's energy storage capacity, η cv η ch η dis These represent the converter conversion efficiency, charging efficiency, and discharging efficiency of the energy storage system, respectively; SOC=0 indicates that the energy storage system has been completely discharged, and SOC=1 indicates that the energy storage system has sufficient power.

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

[0059] SOC min ≤SOC(t)≤SOC max (11)

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

[0061] Furthermore, S22 specifically includes:

[0062] The signal P that needs to be smoothed for hybrid energy storage systems e The power of the HESS system is decomposed using a fully empirical set mode decomposition algorithm to distribute it between the supercapacitor and the battery.

[0063] Decomposed P e The signal is:

[0064]

[0065] In the formula, K represents the number of intrinsic mode functions. Let r(t) represent the intrinsic mode function of the k-th stage, and let r(t) represent the residual obtained by subtracting the modes of each order.

[0066] The decomposed P is extracted using Hilbert transform. e The signal is used to obtain the instantaneous frequency f of each mode function. i (t) is:

[0067]

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

[0069] The instantaneous frequency-time curves c1,…,c from high to low frequency range were obtained. k From the above curves, select the c curve with no modal aliasing or the lowest degree of aliasing. m and c m+1 ; will c m With instantaneous frequency higher than c m The modal components are accumulated and absorbed by the supercapacitor; c m+1 With instantaneous frequency below c m+1 The modal components are accumulated and absorbed by the battery; therefore, the HESS power allocation result is:

[0070]

[0071] In the formula, P sc (t) and P ba (t) represents the power of the supercapacitor and the power of the battery used to smooth out fluctuations, respectively;

[0072] P sc (t),P ba (t)>0 indicates 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] Furthermore, S23 specifically includes:

[0074] 1) HESS system power limit configuration:

[0075] Historical energy storage mitigation power is calculated based on historical wind power output data and reference grid-connected power, and then processed using step S22; to mitigate wind power fluctuations, its maximum value is determined. and Balancing the conversion efficiency of the energy storage converter and the charging and discharging efficiency of the energy storage system, in order to and Based on the rated power configuration P for energy storage eHESS for:

[0076]

[0077] In the formula, 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;

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

[0079] From equations (7) and (8), we can obtain:

[0080]

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

[0082] To meet the need to smooth out fluctuations in wind power output, E HESS Take the minimum value in equation (16):

[0083]

[0084] In the formula, E HESS.dis With E HESS.ch This represents the minimum charge / discharge capacity of the energy storage system. The difference between the two values ​​gives the system's rated energy storage capacity.

[0085]

[0086] Furthermore, S24 specifically includes:

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

[0088]

[0089] In the formula, P ba.maxch With P ba.maxdis P is the maximum charge and discharge power of the battery. sc.maxch With P sc.maxdis The maximum charge / discharge power of the supercapacitor; SOC ≥ SOC max This indicates that the battery or the supercapacitor is in an overcharged state; SOC ≤ SOC min This indicates that the battery or the supercapacitor is in an over-discharged state.

[0090] 10. The method for sustainable and stable output control of grid-connected output power of a wind power generation system according to claim 1, characterized in that step S3 specifically includes the following steps:

[0091] S31: Correcting the charging and discharging power of the hybrid energy storage system: The charging and discharging parameter β, which improves the fluctuation smoothing capability of the hybrid energy storage system in the future moment, is adaptively adjusted through advanced fuzzy control to correct the charging and discharging power of the hybrid energy storage system.

[0092] S32: Power correction parameter tuning: Determine the ultra-short-term wind power forecast results within the look-ahead period, determine the advance charging and discharging parameters based on wind power fluctuations and advance control within the look-ahead period, and correct the output power of the energy storage system;

[0093] The corrected charge / discharge power of the hybrid energy storage system in S31 is:

[0094] P H (t)=P HESS (t)+P β (t)(20)

[0095] In the formula, P H (t) represents the corrected output power of the hybrid energy storage system; β represents the advance charge / discharge parameter for the future fluctuation smoothing capability of the hybrid energy storage system; P β (t) is the power correction value determined by β;

[0096] Specifically, S32 is as follows:

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

[0098]

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

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

[0101] Based on the predicted wind power ramp rate and grid-connected power fluctuations within the forward-looking period, the advance charge / discharge parameter β is adjusted to adjust the SOC state of the hybrid energy storage system, thus allowing for a margin for future charge / discharge needs. Let:

[0102]

[0103] In the formula, ΔP1 and ΔP2 are the inputs to the fuzzy control. The fuzzy controller input ΔP2 represents the maximum fluctuation of wind power relative to the grid-connected power at the previous moment within the current look-ahead period; the fuzzy controller input ΔP1 represents the wind power ramp-up status at the same moment; P M This represents the maximum absolute value of the wind power ramp slope for each forward-looking cycle. For the look-ahead period ΔP N (t) is the maximum absolute value; t+k1Δt is ΔP within the current look-ahead period. N(t) The moment of maximum absolute value; the range of ΔP1 is [-1,1]; the range of ΔP2 is [-1,1]; the universe of discourse of the input quantities is a continuous universe of discourse [-1,1].

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

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

[0106]

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

[0108] The beneficial effects of this invention are as follows:

[0109] By employing an improved adaptive moving average filtering algorithm, the optimal time-segmented grid-connected power that satisfies power fluctuation constraints at different times is obtained based on the fluctuation characteristics of wind power prediction, effectively solving the stable output problem caused by wind power fluctuations. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is used to smooth power allocation and capacity configuration for energy storage, mitigating the randomness of new energy output and achieving sustainable and stable wind power output at different times. This effectively overcomes the impact of wind power output fluctuations on grid operation safety and power quality. Furthermore, the advanced fuzzy correction method is used to correct the charging and discharging power of the hybrid energy storage system, reducing wind farm grid connection fluctuations and improving the cycle life of the energy storage system. The sustainable and stable wind power output at different times can optimize and effectively reduce the configuration cost of the energy storage system.

[0110] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0111] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0112] Figure 1 The flowchart below shows the improved adaptive moving average filtering algorithm according to an embodiment of the present invention.

[0113] Figure 2 This is a time-sharing wind power grid connection reference power diagram according to an embodiment of the present invention;

[0114] Figure 3 This is a reference power diagram for wind power grid connection in a single time period, as shown in this embodiment of the invention.

[0115] Figure 4 This is a diagram illustrating the HESS power allocation strategy according to an embodiment of the present invention.

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

[0117] Figure 6 This is a flowchart of the advanced fuzzy control strategy for a hybrid energy storage system according to an embodiment of the present invention;

[0118] Figure 7 This is the membership function diagram of the first input quantity in this embodiment of the invention;

[0119] Figure 8 This is a membership function diagram of the second input quantity in this embodiment of the invention;

[0120] Figure 9 This is a membership function diagram of the output quantity of the tuning β in an embodiment of the present invention;

[0121] Figure 10 This is a flowchart 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 Implementation

[0122] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed 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 are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0123] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0124] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0125] This invention aims to address the issues of randomness, intermittency, weak controllability, and uncontrollability in the output power of wind power generation systems due to environmental and meteorological factors. These characteristics of wind power can impact the stability and security of the power grid. Therefore, this method and technology, building upon existing wind power prediction methods, utilizes intelligent control strategies and advanced energy storage technologies to achieve predictive visibility of the grid-connected power of wind power systems, thereby making wind power output more stable and controllable. This invention can employ various energy storage methods (not limited to battery energy storage, pumped hydro storage, flywheel energy storage, etc.) to smooth wind power fluctuations through the charging and discharging process of the energy storage system, achieving continuous and stable control of the wind power generation system's output power and ensuring that the wind-storage combined system can sustainably output a stable power at different times. In this way, the wind power system acquires some basic functions of a traditional power plant, reducing the impact of power uncertainty caused by wind speed fluctuations on the power grid, improving grid connection quality and reliability, and making it possible for the power grid to predict, adjust, and control the output power of wind power generation.

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

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

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

[0129] S3. Set 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 by predicting power fluctuations in advance.

[0130] Considering the fluctuations in wind turbine output, and to ensure stable system operation after wind power grid connection, an improved adaptive moving average algorithm is used to decompose the predicted wind power output and calculate the grid-connected reference power of the wind-storage system. This invention proposes an improved adaptive moving average filtering algorithm to solve for the time-segmented grid-connected power of the wind-storage integrated system. This algorithm dynamically adjusts the window size according to the fluctuation rate of wind power output at each moment, thus providing an optimal segmentation scheme for each time period. This effectively solves the problem of time-segmentation under different fluctuations in wind power output. The grid-connected power obtained after using this window filtering not only meets the grid-connected power fluctuation requirements but also tracks wind power as closely as possible, thereby reducing the burden on the energy storage system and extending its service life.

[0131] Please see Figure 1 Here is a flowchart of the improved adaptive moving average filtering algorithm according to an embodiment of the present invention. 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 that have been sampled so far, and let k = 1, N(k) is the window size at time kΔT.

[0133] S12: Determine if k≤N p If the conditions are met, proceed to S13 for further processing; otherwise, the current wind power data processing is complete, and the grid-connected power P is output. N (k).

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

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

[0136]

[0137] In the formula, ΔT is the sampling time, and N is the sampling time. T1min With N T10min This indicates the number of sampling points required within the 1-minute and 10-minute timescales.

[0138] This leads to the volatility r at time k on the 1-minute and 10-minute scales. xmin (k):

[0139]

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

[0141] Therefore, the allowable power fluctuation rate constraint for grid connection can be derived as follows:

[0142]

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

[0144] S16: Determine if N(k) ≤ k. If it is satisfied, jump to S17; otherwise, let N(k) = 1, k = k + 1 and jump to S12.

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

[0146]

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

[0148] The improved algorithm described above can be used to obtain the desired grid-connected power and the fluctuating power that needs to be smoothed by the energy storage system from the predicted wind power output. Based on the wind power sampled at the current moment, it is determined whether its volatility meets the constraint criteria. If it does, grid connection is initiated directly. If not, an initial window size of 2 is given, and the window is progressively increased. Grid connection is executed when the wind power volatility meets the grid connection criteria. This process is repeated for each sampling moment. The fluctuating power is then obtained by subtracting the wind power from the obtained grid-connected power, and this power is used as the reference input for the energy storage system.

[0149] Please see Figure 2 This is a time-sharing wind power grid connection reference power diagram in an embodiment of the present invention. By setting the predicted average power of wind power generation during the effective power generation period as a constant output power reference value, the grid can have a visual and predictive view of the wind power generation power, thereby realizing the grid's controllable and adjustable wind power output power.

[0150] When local energy storage capacity is sufficient, such as when there is large-capacity pumped hydro storage, the wind-storage output system may be used as a constant overall output. Please refer to [link to relevant documentation]. Figure 3The above is a reference power diagram for wind power grid connection in a single time period according to an embodiment of the present invention. The wind-storage system can reduce the impact of wind power fluctuations on the power grid by maintaining a constant average power output, and reduce frequency and voltage fluctuations caused by power fluctuations. This constant output mode is close to the controllable power generation form of traditional thermal power plants, which is conducive to the stable operation of the power grid. Furthermore, the constant output mode helps to increase the proportion of wind power connected to the grid, enabling wind power, a fluctuating resource, to be connected to the grid at a higher proportion, thus 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. This results in high construction and operation costs for the energy storage system. Furthermore, under constant power control, frequent charging and discharging of the energy storage system can cause energy loss, potentially reducing the overall system efficiency. Please refer to [link / reference]. Figure 4 Here is a diagram of the HESS power allocation strategy according to an embodiment of the present invention; the specific steps of S2 are as follows:

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

[0153] Let the relationship between the wind farm's output power, grid-connected power, and the charging / discharging reference power of the Hybrid Energy Storage System (HESS) be as follows:

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

[0155] In the formula, P out For the output power of the wind-storage combined system, P w (t) represents the predicted output power of the wind farm at time t, P HESS This indicates the output power of the hybrid energy storage system.

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

[0157] When P out (t)>P W When (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] In the formula, Det These represent the upper and lower limits of power fluctuation.

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

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

[0163] In the formula, P e For fluctuating power that requires translation by a hybrid energy storage system.

[0164] This invention takes a hybrid energy storage system consisting of batteries and supercapacitors as an example. In practical applications, existing local energy storage configurations should be considered first, such as pumped hydro storage, flywheel energy storage, or other locally characteristic energy storage methods. The existing capacity should then be assessed to determine if it can meet the fluctuation regulation needs of the wind power system. If the local energy storage capacity is sufficient to smooth wind power fluctuations, no additional energy storage equipment is needed. If the capacity is insufficient, a hybrid energy storage system consisting of batteries and supercapacitors should be added according to system requirements.

[0165] Please see Figure 5 This is an operational status diagram of a wind power generation system according to an embodiment of the present invention, illustrating the operation of a hybrid energy storage system composed of batteries and supercapacitors participating in the combined wind-storage power output. Energy storage systems are used to mitigate power fluctuations after new energy sources are connected to the grid due to their fast response speed and rechargeable / discharge capabilities. However, a single battery energy storage system can lead to excessively high system investment costs, and frequent charging and discharging can rapidly reduce its cycle life. Hybrid energy storage systems can effectively address these issues. This paper adopts a HESS system combining batteries and supercapacitors. The relationship between the batteries and supercapacitors in the HESS system is as follows:

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

[0167] In the formula, P HESS P represents the output power of the hybrid energy storage system. ba (t) represents the battery output power, P sc This indicates the output power of the supercapacitor.

[0168] Hybrid energy storage should have the same power direction to avoid energy exchange between different energy storage systems.

[0169] P ba P sc ≥0 (9)

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

[0171]

[0172] In the formula, P HESS E represents the charging and discharging power of the HESS system. HESS For the system's energy storage capacity, η cv η ch η dis These represent the converter conversion efficiency, charging efficiency, and discharging efficiency of the energy storage system, respectively. SOC = 0 indicates that the energy storage system is fully discharged, and SOC = 1 indicates that the energy storage system has sufficient charge.

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

[0174] SOC min ≤SOC(t)≤SOC max (11)

[0175] SOC max With SOC min This indicates the upper and lower limits of the state of charge of the energy storage system.

[0176] S22: Utilizing the HESS system to smooth power distribution

[0177] The signal P that needs to be smoothed for hybrid energy storage systems e The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is used to process the power decomposition of the HESS system, enabling efficient power allocation 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. After processing, P... e The signal is decomposed into:

[0178]

[0179] In the formula, K represents the number of intrinsic mode functions. Let r(t) represent the intrinsic mode function of the k-th stage, and let r(t) represent the residual obtained by subtracting the modes of each order.

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

[0181]

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

[0183] The instantaneous frequency-time curves c1,…,c from high to low frequency range were obtained. k From these curves, select the c curve with no modal aliasing or the minimum degree of aliasing. m and c m+1 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 below c m+1 The modal components are accumulated and absorbed by the battery. Therefore, the HESS power allocation result is:

[0184]

[0185] In the formula, P sc (t) and P ba (t) represents the power of the supercapacitor and the power of the battery used to smooth out fluctuations, respectively.

[0186] P sc (t),P ba (t)>0 indicates 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] Based on historical wind power output data and reference grid-connected power, the historical energy storage mitigation power is calculated. This power is then processed using the method described in S22 to determine its maximum value in order to mitigate wind power fluctuations. and Balancing the conversion efficiency of the energy storage converter and the charging and discharging efficiency of the energy storage system, in order to and Based on the rated power configuration P for energy storage eHESS for:

[0190]

[0191] In the formula, 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 the HESS system

[0193] From equations (7) and (8), we can obtain:

[0194]

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

[0196] To meet the need to smooth out fluctuations in wind power output, E HESS The minimum value of the above formula should be taken:

[0197]

[0198] In the formula, E HESS.dis With E HESS.ch This represents the minimum charge / discharge capacity of the energy storage system. Taking the difference between the two values ​​yields the system's rated energy storage capacity.

[0199]

[0200] S24: Establish operating constraints for the HESS system

[0201] To ensure the safe operation of HESS, it is necessary to limit its cycle life to prevent a rapid decline. The constraints are as follows:

[0202]

[0203] P ba.maxch With P ba.maxdis P is the maximum charge and discharge power of the battery. sc.maxch With P sc.maxdis This represents the maximum charge and discharge power of the supercapacitor; simultaneously, the state of charge (SOC) of both the battery and the supercapacitor needs to be at an appropriate value, where SOC ≥ SOC. max This indicates that the battery / supercapacitor is in an overcharged state when SOC ≤ SOC min This indicates that the battery / supercapacitor is in an over-discharged state.

[0204] When the HESS system is in a discharge state, if neither of the two energy storage systems is over-discharged, the energy storage system will discharge normally. If only the battery is over-discharged, the supercapacitor will handle the discharge, and vice versa. When both are over-discharged, 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 can be obtained as shown in Table 2:

[0208] Table 2

[0209]

[0210] Wind power fluctuations can affect the charging and discharging behavior of current hybrid energy storage systems, and may also affect the system's ability to mitigate future fluctuations. Therefore, it is necessary to adjust the output of hybrid energy storage systems based on their current state of charge (SOC) and future charging and discharging needs.

[0211] Please see Figure 6 The flowchart of the advanced fuzzy control strategy for the hybrid energy storage system according to an embodiment of the present invention is shown below. The specific steps of S3 are as follows:

[0212] S31: This invention uses advanced fuzzy control to adaptively adjust the advance charging and discharging parameter β to improve the fluctuation smoothing capability of the hybrid energy storage system in the future, thereby correcting the charging and discharging power of the hybrid energy storage system.

[0213] The corrected charge and discharge power of the hybrid energy storage system is as follows:

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

[0215] In the formula, P H (t) represents the corrected output power of the hybrid energy storage system; 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 forward-looking period, and determine the advance charging and discharging parameters based on wind power fluctuations and advance control within the forward-looking period to correct the output power of the energy storage system.

[0218] In real-time control strategies, the output of a hybrid energy storage system is limited not only by current wind power fluctuations and the system's current state, but also by its accumulated state of charge (SOC). If, at a certain moment, wind power fluctuations exceed the limit and need to be mitigated, but the hybrid energy storage system's SOC has already reached the limit, the system cannot continue charging and discharging due to operational constraints, resulting in unmitigated wind power fluctuations.

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

[0220]

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

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

[0223] From equations (21) and (22) above, it can be seen that the grid-connected fluctuation power ΔP out (t) Whether the limit is exceeded and ΔP N (t) and the output P of the energy storage system H (t) is related to ΔP at each time point within the look-ahead period. N If (t+k1Δt) is within the fluctuation range, then the output of the hybrid energy storage system is 0. If ΔP is within the fluctuation range at a certain moment... N If (t+k1Δt) exceeds the limit, and the state of charge of the hybrid energy storage system at that moment is insufficient to smooth out the fluctuations, then even if ΔP at the current moment... N (t) Even without exceeding the limit, the hybrid energy storage system still needs to charge and discharge a portion of its power to prepare for smoothing fluctuations during the forward cycle. Based on the predicted wind power ramp rate and grid-connected power fluctuations within the forward cycle, the advance charge / discharge parameter β is adjusted to adjust the SOC state of the hybrid energy storage system, leaving a margin for future charge / discharge needs. Let:

[0224]

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

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

[0227] According to the above equation (23), the molecule ΔP W There are positive and negative values, P M The maximum absolute value of the wind power slope for all look-ahead cycles is given, therefore ΔP1 ranges from [-1, 1]. According to equation (24) above, the numerator ΔP N It can be positive or negative, and the denominator is ΔP for all look-ahead periods. N The maximum absolute value, therefore the range of ΔP2 is [-1, 1]. Thus, the universe of discourse for all input quantities is a continuous universe of discourse [-1, 1], and their fuzzy sets are {NB (negative large), NS (negative small), ZO (zero), PS (positive small), PB (positive large)}. Please refer to [link to relevant documentation]. Figure 7 , is the membership function of the first input quantity ΔP1 in this embodiment of the invention. The output is the pre-charge / discharge parameter Δβ, with a discrete universe of discourse of [0, 0.5] and a fuzzy set {VS(small), S(smaller), M(medium), B(larger), VB(larger)}. Please refer to [link to relevant documentation]. Figure 8 , is the membership function of the second input variable ΔP2 in this embodiment of the invention. Please refer to... Figure 9 Table 3 shows the membership function diagram of the output value of the tuned β in this embodiment of the invention; the fuzzy control rules for tuning β are shown in Table 3:

[0228] Table 3

[0229]

[0230]

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

[0232]

[0233] To extend battery life, and taking advantage of the characteristics of supercapacitors—frequent charging and discharging and fast response—the supercapacitors are prioritized for charging and discharging.

[0234] like Figure 8 As shown, a flexible and environmentally adaptable rolling optimization decree control strategy is adopted and executed on a rolling basis, with the rolling period equal to the wind power sampling period, and T being the total running time.

[0235] Please see Figure 10 This is a flowchart illustrating a method for sustainable and stable output control of grid-connected wind power generation systems according to an embodiment of the present invention. The present invention utilizes existing wind power prediction models and establishes constant output power reference values ​​for each time period, enabling the power grid to anticipate changes in wind power generation and achieve controllable and adjustable output. This method effectively solves the problems of randomness, intermittency, and uncontrollability exhibited by wind power generation systems due to weather conditions. Specifically, by using a time-segmented approach, the average output power of the wind power generation system within each time period is used as a constant output reference value, significantly reducing the power fluctuations that the energy storage system needs to adjust, thereby reducing the energy storage capacity requirement, lowering system costs, and simultaneously improving operational efficiency and economy.

[0236] Furthermore, this invention proposes a coordinated operation strategy for wind-storage integrated systems, which not only enhances the stability and controllability of wind power generation systems and ensures smooth grid connection, but also optimizes the charging and discharging process of energy storage systems. This approach helps promote the large-scale integration of wind power generation, improves the grid's capacity to absorb renewable energy, and enhances the economic benefits of wind-storage integrated systems and the reliability of grid operation, thereby enabling wind power generation systems to gradually acquire 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 and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A wind power generation system grid-connected output power sustainable stable output control method, characterized by, The method comprises the following steps: S1, solving the grid-connected power of the wind-storage combined system: based on the wind power prediction curve, the grid-connected power reference value of the time period is calculated by the adaptive moving average algorithm; The grid-connected power reference value is a grid-connected output power target value determined based on a prediction curve and combined with a double-time-scale grid-connected power fluctuation constraint at the beginning of each period and kept unchanged in the period; the grid-connected power fluctuation constraint includes that the fluctuation of the grid-connected power under the double-time-scale of 1 minute and 10 minutes and respectively satisfies and ; S2, establishing the wind-storage combined system to maintain the grid-connected power target: based on the volatility of the wind power generation system, the power distribution strategy and the rated power capacity configuration of the energy storage system are established, The power smoothing strategy mainly comprises the following steps: calculating a wind farm output power prediction value , obtaining a grid-connected output power deviation to be smoothed by calculating a difference between the grid-connected output power target value and the wind farm output power prediction value ; and distributing the power deviation to a hybrid energy storage system for smoothing . S3, setting the hybrid energy storage system ahead fuzzy control strategy: based on the ultra-short-term wind power prediction results in the forward period, the fuzzy control strategy is executed to dynamically correct the charge and discharge power of the hybrid energy storage system; specifically including: S31. Calculate two fuzzy control input quantities: the first input quantity ΔP1 represents the climbing state of the wind power in the forward period, and the second input quantity ΔP2 represents the maximum fluctuation value of the wind power in the forward period relative to the grid-connected power at the last moment; S32. Input ΔP1 and ΔP2 into the fuzzy controller, and obtain the output quantity according to the preset fuzzy rule, that is, the charge and discharge parameter β used to adjust the state of charge SOC margin of the energy storage system in advance; S33. Utilize the parameter β to the current charge-discharge power of the hybrid energy storage system Amend to obtain the corrected system output power To achieve the advance response to future power fluctuations, and reserve the SOC margin for future charge-discharge demand, so that the grid-connected output power continuously meets the grid-connected power fluctuation constraint within the time period.

2. The method for controlling the sustainable and stable output power of a wind power generation system connected to the grid according to claim 1, characterized in that, The S1 specifically comprises the following steps: S11: set the length of wind power data sampling , denotes the number of currently sampled wind power data, and let , be the window size at time t. ​ S12: judging , if satisfied, jump to S13 for processing, if not satisfied, it indicates that the current wind power data processing is completed, output the grid-connected power ; S13: assuming the current sampled wind power as the grid-connected power at the current time ; S14: calculate the fluctuation rate of grid-connected power under the double time scales of 1 min and 10 min and the fluctuation rate constraint index; and determine whether the fluctuation rate meets the corresponding constraint index at the same time, if yes, let , and jump to S12, if not, jump to S15; S15: initialize window size ; S16: judge , if yes, jump to S17, otherwise let , and jump to S12; S17: calculating the grid-connected power at the current moment; S18: calculate the fluctuation rate of grid-connected power under the double time scales of 1 min and 10 min and the fluctuation rate constraint index; and determine whether the fluctuation rate meets the corresponding constraint index at the same time, if yes, let , and jump to S12, if not, let and jump to S16.

3. The method of claim 2, wherein the output power of the wind power system is continuously stabilized and outputted. The fluctuation rate and fluctuation rate constraint index of the grid-connected power in the double time scale of 1min and 10min in the S14 are specifically: ​ wherein is the sampling time, and denotes the number of sampling points required over the 1 min and 10 min time scales; obtained Volatility at the 1 min and 10 min scale : In the formula, denotes the time fluctuation rate under the scale, is the wind farm output power prediction value at the time, is the wind farm output power, is all possible values; The power fluctuation rate constraint index allowed to be connected to the grid is: The current grid-connected power in the S17 is specifically: 。 4. The method of claim 3, wherein the output power of the wind power system is continuously stabilized and outputted. Further comprising the following steps: ​ The average value of the predicted power of the wind power generation in the effective power generation period is formulated as a constant output power reference value to realize the regulation and control of the grid on the output power of the wind power generation; If there is a large-capacity energy storage system in the local area, the energy storage system outputs at a constant average power to reduce the frequency and voltage fluctuations caused by power fluctuations.

5. The method of claim 1, wherein the method further comprises: The S2 specifically comprises the following steps: S21: establishing a wind-storage combined system model: let the relationship among the wind farm output power, the grid-connected power and the charge and discharge reference power of the hybrid energy storage system HESS be: In the formula, is the output power of the wind storage combined system, t is the output power prediction value of the wind farm at the moment, is the output power of the hybrid energy storage system;​ When When the HESS system is charging; When When the HESS system discharges. The wind-storage combined system output power fluctuation index is: In the formula, Pmax, Pminare the upper and lower limits of power fluctuations; The number of the first and the second The number of the first and the second In the formula, is the fluctuating power that needs to be translated by the hybrid energy storage system; If the local energy storage system capacity can meet the power fluctuation smoothing of wind power, no additional energy storage device needs to be configured; if not, a hybrid energy storage system composed of a battery and a super capacitor is supplemented; S22: using the HESS system to suppress power distribution; S23: calculating the rated power and rated capacity configuration of the HESS system; S24: setting the HESS system operation constraint condition.

6. The method of claim 5, wherein the output power of the wind power system is continuously stabilized and outputted. The supplementary configuration of the hybrid energy storage system composed of a battery and a super capacitor is specifically: ​ The HESS system is a combination of a battery and a super capacitor, denoted as: wherein represents the output power of the hybrid energy storage system, represents the output power of the battery, represents the output power of the supercapacitor; The hybrid energy storage power is in the same direction to avoid energy exchange between different energy storages: The state of charge dynamic expression of the HESS system is: In the formula, represents the charging and discharging power of the HESS system, is the energy storage capacity of the system, , , are the converter conversion efficiency, charging efficiency and discharging efficiency of the energy storage system, respectively; represents that the energy storage system is out of power, represents that the energy storage system has sufficient power; The energy storage system operation condition is: SOC min ≤ SOC(t) ≤ SOC max In the formula, With denotes the upper and lower limits of the state of charge of the energy storage system.

7. The method of claim 6, wherein the output power of the wind power system is continuously stabilized and outputted.

8. The method of claim 6, wherein the output power of the wind power system is continuously stabilized and outputted. The S22 is specifically: Signal to be smoothed for hybrid energy storage system The power of the HESS system is decomposed using a complete empirical ensemble mode decomposition algorithm to distribute between the supercapacitor and the battery. Decomposed The signal is: In the formula, K represents the number of inherent modal functions, represents the first k stage inherent modal function, represents the residual amount obtained by differencing each order modal. The decomposed signals are extracted by Hilbert transform to obtain the instantaneous frequency of each mode function is: is: In the formulae, denotes the instantaneous phase; Obtain instantaneous frequency-time curves from high to low frequency range. Select the curves with no modal aliasing or the lowest degree of aliasing from the above curves. and ;Will With instantaneous frequency higher than The modal components are accumulated and absorbed by the supercapacitor; With instantaneous frequency lower than The modal components are accumulated and absorbed by the battery; therefore, the HESS power allocation result is: wherein with respectively the supercapacitor power and battery power used to damp fluctuations; , indicates that the energy storage system is in a discharging state, indicates that the energy storage system is in a charging state.

8. The method of claim 7, wherein the output power of the wind power system is continuously stabilized and outputted. The S23 is specifically: ​ 1) HESS system quota power configuration: According to historical wind power output data and reference grid-connected power, historical energy storage smoothing power is obtained, and the S22 is used for processing; in order to smooth wind power fluctuation, the maximum With , the conversion efficiency of the energy storage converter and the charging and discharging efficiency of the energy storage system are considered, and the rated power configuration of the energy storage is carried out on the basis of With :​ In the formula, denotes the total charging power of the energy storage system, denotes the total discharging power of the energy storage system, is the sampling initial time; 2) HESS system rated capacity configuration: From formula (7) and formula (8): In the formula, represents the initial value of the state of charge, represents the capacity of the energy storage quota; To meet the demand of suppressing wind power output fluctuation, Take the minimum value in formula (16): In the formula, With The minimum charge and discharge capacity of the energy storage system is represented, and the difference between the two is obtained. 。 9. The method of claim 8, wherein the output power of the wind power system is continuously stabilized and outputted.

9. The method of claim 8, wherein the output power of the wind power system is continuously stabilized and outputted. The S24 is specifically: The HESS system operation constraint condition is: wherein with is the maximum charge and discharge power of the battery, with is the maximum charge and discharge power of the supercapacitor; represents that the battery or the supercapacitor is in an overcharged state; represents that the battery or the supercapacitor is in an overdischarged state.

10. The method of claim 1, wherein the method is a method of sustainable and stable output control of grid-connected output power of a wind power generation system, characterized by, In step S31, the calculation formulas of the first input quantity ΔP1 and the second input quantity ΔP2 are respectively: In the formula, and is the input of fuzzy control, represents the maximum fluctuation value of wind power relative to the grid-connected power at the last moment in the current look-ahead period; represents the wind power ramping state at the same moment; is the maximum absolute value of the wind power ramping slope in each look-ahead period; is the look-ahead period maximum absolute value; is the moment in the current look-ahead period maximum absolute value; The value range of 1,1]; The value range of 1,1]; The continuous domain of the input amount theory domain is 1,1]; In step S33, the specific formula for correcting the current charge-discharge power by using the parameter β is as follows: In the formula, D et is the upper limit of the allowable grid-connected power fluctuation.

Images