Wind storage coordinated regulation wind power plant frequency control method and system

Abstract

The invention discloses a wind power plant frequency control method and system for wind storage cooperative regulation, and relates to the technical field of new energy power generation, and the method comprises the steps: obtaining wind speed prediction, energy storage state and frequency deviation information, carrying out the time-frequency transformation of the wind speed prediction information, obtaining a power fluctuation characteristic, calculating a compensation capability coefficient based on the energy storage state, and obtaining a power fluctuation characteristic; the system is used for determining a fluctuation allowable range of a wind turbine generator, generating a vibration reduction instruction, constructing a virtual power transmission channel between wind storage nodes, setting a virtual impedance parameter to realize coordination control, generating an advanced compensation sequence in combination with phase information, superposing the advanced compensation sequence with a frequency response demand to form an energy storage adjustment instruction, and executing the vibration reduction instruction and the adjustment instruction. And accurate compensation of actual wind power fluctuation is realized. According to the invention, the characteristics of wind storage equipment are fully utilized, and the accuracy and coordination of frequency control are improved.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, specifically to a wind farm frequency control method and system for wind and energy storage coordinated regulation. Background Technology

[0002] As an important renewable energy source, wind power's volatility and intermittency severely impact the frequency stability of the power grid. Existing wind farm frequency control methods mainly include two approaches: variable pitch control of wind turbines and power compensation through energy storage systems. Variable pitch control smooths power fluctuations by adjusting the blade angle, but its response speed is slow, and frequent adjustments can exacerbate mechanical stress on the turbine. While energy storage systems offer fast response characteristics, their limited capacity makes continuous high-power compensation difficult.

[0003] Currently, commonly used wind-storage coordinated control methods mainly employ a simple power allocation strategy, which allocates high-frequency fluctuations to the energy storage system for compensation and allows the wind turbine to regulate low-frequency fluctuations. This fixed allocation method fails to fully consider the state of charge of the energy storage system and the operating characteristics of the wind turbine, easily leading to overcharging and discharging of the energy storage system or frequent adjustments by the wind turbine, thus affecting the overall control effect of the system.

[0004] In existing technologies, the coordination between wind turbines and energy storage systems is not close enough, and there is a lack of effective power transmission mechanisms. This leads to mutual interference in power regulation between the two, making it difficult to achieve optimal coordinated control. Due to the inability to effectively predict and respond to wind speed changes in advance, the system's control lag is significant, making it difficult to meet the requirements of rapid grid frequency regulation. Summary of the Invention

[0005] The purpose of this invention is to provide a wind farm frequency control method and system for wind and energy storage coordinated regulation, aiming to solve at least one of the technical problems existing in the prior art.

[0006] The technical solution of this invention is: a wind farm frequency control method with wind and energy storage coordinated regulation, comprising the following steps: Acquire wind speed forecast information, energy storage status of charge information, and grid frequency deviation information; The power fluctuation spectrum is obtained by performing time-frequency transformation on the wind speed prediction information, and the amplitude and phase information of the frequency components are extracted. The sustainable compensation duration of energy storage is calculated based on the energy storage state of charge information. The sustainable compensation duration of energy storage is coupled with the amplitude information to generate the compensation capacity coefficient. The allowable range of frequency domain fluctuation of the wind turbine is calculated in reverse based on the compensation capacity coefficient. When the power fluctuation spectrum exceeds the allowable range of frequency domain fluctuation, a wind power vibration reduction command is generated. Construct a virtual power transmission channel between wind power nodes and energy storage nodes, and set virtual impedance parameters for the virtual power transmission channel based on the compensation capacity coefficient; Calculate the phase lead angle based on the phase information and convert it into a time advance. Generate an advance compensation sequence based on the time advance. Determine the frequency response power demand based on the grid frequency deviation information. Superimpose the frequency response power demand with the advance compensation sequence in time to generate an integrated energy storage regulation command. The system executes wind power vibration reduction commands and energy storage integrated regulation commands, collects actual wind power fluctuations and calculates the frequency domain deviation from the power fluctuation spectrum, and distributes the frequency domain deviation to wind power nodes and energy storage nodes for compensation based on virtual impedance parameters.

[0007] The power fluctuation spectrum is obtained by performing time-frequency transformation on the wind speed forecast information, and the amplitude and phase information of the frequency components are extracted, including: The wind speed prediction information is collected according to the preset sampling interval, the wind speed difference between adjacent sampling points is calculated, the sliding window length is set according to the wind speed difference, the wind speed prediction information is segmented, and a segmented wind speed sequence is generated. Wind speed-rotation speed mapping is performed on the segmented wind speed sequence to obtain rotational speed signal, and rotational speed-power mapping is performed on the rotational speed signal and generator power curve to generate power time sequence signal; The power time-series signal is decomposed into multiple wavelet decompositions according to a preset scale to generate a frequency band coefficient matrix. The frequency band weights are calculated based on the energy distribution of the frequency band coefficient matrix. Multiply the frequency band weights by the frequency band coefficient matrix to obtain the weighted coefficient matrix, and then reconstruct the power fluctuation spectrum by performing a reconstruction operation on the weighted coefficient matrix. A Fourier transform is performed on the power fluctuation spectrum to generate an amplitude matrix and a phase matrix. Frequency component amplitude information and phase information are extracted from the amplitude matrix and phase matrix, respectively.

[0008] The sustainable compensation duration of energy storage is calculated based on the energy storage state-of-charge information. The sustainable compensation duration is then coupled with the amplitude information to generate compensation capacity coefficients, including: Collect voltage and current information from the energy storage state of charge information, calculate the instantaneous power and remaining capacity of the energy storage, and calculate the power regulation rate based on the instantaneous power change trend; A compensation characteristic curve is established by combining the power regulation rate with the remaining capacity, and the upper limit and lower limit of power regulation are extracted from the compensation characteristic curve. The energy storage power-capacity ratio is calculated based on the upper limit of power regulation, the lower limit of power regulation, and the remaining capacity. The sustainable compensation time of energy storage is then calculated in combination with the power-capacity ratio. Wavelet transform is performed on the amplitude information to obtain different frequency components, and the fluctuation period corresponding to each frequency component is calculated. The compensation coefficient matrix is ​​generated by cross-mapping the sustainable compensation duration of energy storage with the fluctuation period. The compensation coefficient matrix is ​​then subjected to nonlinear coupling calculation. The nonlinear coupling calculation result is combined with the frequency component in a weighted manner to generate the compensation capacity coefficient.

[0009] The allowable range of frequency domain fluctuations of the wind turbine is calculated in reverse based on the compensation capacity coefficient. When the power fluctuation spectrum exceeds the allowable range of frequency domain fluctuations, wind power vibration reduction commands are generated, including: Wavelet decomposition is performed on the compensation capability coefficient to obtain the frequency components, and the compensation response speed, compensation capacity, and compensation loss rate of the frequency components are calculated. The actual compensation capacity is calculated based on the compensation loss rate and the compensation capacity. The power adjustment range and the power adjustment slope are obtained by combining the actual compensation capacity and the compensation response speed. The frequency domain fluctuation tolerance range is set by using the power adjustment range and the power adjustment slope. Wavelet decomposition is performed on the power fluctuation spectrum to obtain the fluctuation components, and the amplitude parameters, phase parameters, and direction of change parameters of the fluctuation components are calculated. The power change is obtained by combining the change direction parameter and the amplitude parameter. The power change is compared with the allowable range of frequency domain fluctuation to calculate the amplitude over-limit and phase over-limit. The vibration reduction frequency band is determined based on the amplitude over-limit and phase over-limit. Based on the vibration reduction frequency band, the wind turbine operating parameters are read, the amplitude exceeding the limit is converted into the pitch angle reference value, the phase exceeding the limit is converted into the pitch angle advance value, and the pitch angle adjustment parameters are obtained by combining them. The pitch angle adjustment parameter is matched with the wind turbine speed parameter and wind speed parameter to calculate and output the pitch angle adjustment value and adjustment sequence, thereby generating a wind power vibration reduction command.

[0010] Constructing a virtual power transmission channel between wind power nodes and energy storage nodes, and setting virtual impedance parameters for the virtual power transmission channel based on the compensation capacity coefficient, includes: The voltage and current parameters of the wind power nodes and the energy storage nodes are collected to calculate the instantaneous power value. Calculate the power transmission direction parameters and power transmission amplitude based on the instantaneous power value; Calculate the line loss coefficient and time delay coefficient based on the power transmission direction parameter and the power transmission amplitude, and combine them to generate transmission characteristic parameters; Wavelet decomposition is performed on the compensation capability coefficient to obtain the frequency band coefficient. The compensation gain value and compensation delay value are extracted from the frequency band coefficient. The compensation gain value is multiplied by the line loss coefficient to obtain the virtual resistance value. The compensation delay value is multiplied by the time delay coefficient to obtain the virtual inductance value. The virtual resistance and virtual inductance values ​​are combined according to the frequency band to generate impedance parameters, and the impedance parameters are mapped to the transmission characteristic parameters to obtain the matching coefficient. The impedance reference parameter is calculated based on the matching coefficient. The impedance adjustment parameter is calculated based on the impedance reference parameter and the transmission characteristic parameter. The impedance reference parameter and the impedance adjustment parameter are combined to form the virtual impedance parameter. A virtual power transmission channel is constructed by adjusting the power transmission direction parameters and power transmission amplitude based on the virtual impedance parameters.

[0011] The phase lead angle is calculated based on the phase information and converted into a time lead. A lead compensation sequence is generated based on the time lead. The frequency response power demand is determined based on the grid frequency deviation information. The frequency response power demand is then time-series superimposed with the lead compensation sequence to generate an integrated energy storage regulation command, including: Wavelet decomposition is performed on the phase information to obtain the amplitude component and the angle component. A wave matrix is ​​constructed based on the amplitude component and a compensation matrix is ​​constructed based on the angle component. The wave matrix and the compensation matrix are orthogonally decomposed to form the phase change. The phase change is decomposed into singular values ​​to obtain the correction amplitude. The compensation parameter is constructed based on the correction amplitude. The compensation parameter and the correction amplitude are orthogonally transformed to generate the phase lead angle. The phase lead angle and the wave matrix are used to perform feature mapping to obtain the time series parameters. The compensation duration is extracted based on the time series parameters and then converted into a time advance. The frequency disturbance is calculated based on the grid frequency deviation information. The frequency disturbance is combined with the compensation adjustment to obtain the power change. The frequency response power demand is constructed based on the power change and the timing compensation. The compensation interval is divided according to the time lead, and the power demand within the compensation interval is adjusted by the magnitude compensation to generate the advance compensation sequence. Based on the compensation interval, the advance compensation sequence is divided into compensation sub-sequences, the frequency response power demand is divided into response sub-sequences according to the time-series compensation amount, and the compensation sub-sequences and response sub-sequences are combined in time sequence to generate an integrated energy storage regulation command.

[0012] Execute wind power vibration reduction commands and energy storage integrated regulation commands, collect actual wind power fluctuations and calculate the frequency domain deviation from the power fluctuation spectrum, and distribute the frequency domain deviation to wind power nodes and energy storage nodes according to virtual impedance parameters. Compensation includes: Execute wind power vibration reduction commands and energy storage integrated regulation commands, collect real-time output power of wind turbine units, calculate real-time fluctuation values ​​of output power, and generate actual wind power fluctuation sequences based on real-time fluctuation values; The actual wind power fluctuation sequence is frequency domain transformed to generate a real-time fluctuation spectrum. The frequency domain deviation value is calculated by comparing the real-time fluctuation spectrum with the power fluctuation spectrum. Calculate the compensation power value based on the frequency domain deviation value, and arrange the compensation power value in time order to generate a compensation sequence; The virtual impedance parameter is decomposed into impedance magnitude and impedance phase angle. The power distribution coefficient is calculated based on the impedance magnitude, and the compensation timing is calculated based on the impedance phase angle. The compensation sequence is divided into wind power node compensation and energy storage node compensation according to the power allocation coefficient and compensation time sequence. The wind power node compensation amount is combined with the wind power vibration reduction command to generate a wind power compensation command for wind power node compensation. The energy storage node compensation amount is combined with the energy storage comprehensive regulation command to generate an energy storage compensation command for energy storage node compensation.

[0013] This invention provides a wind farm frequency control system for coordinated wind and energy storage regulation, the system comprising: The information acquisition module is used to acquire wind speed forecast information, energy storage state of charge information, and grid frequency deviation information; The time-frequency conversion module is used to perform time-frequency conversion on the wind speed prediction information to obtain the power fluctuation spectrum, and extract the amplitude and phase information of the frequency components; The compensation calculation module is used to calculate the sustainable compensation duration of energy storage based on the energy storage state of charge information, and to couple the sustainable compensation duration of energy storage with the amplitude information to generate the compensation capacity coefficient. The wind power control module is used to calculate the allowable range of frequency domain fluctuations of the wind turbine based on the compensation capability coefficient. When the power fluctuation spectrum exceeds the allowable range of frequency domain fluctuations, it generates wind power vibration reduction commands. The virtual channel module is used to construct a virtual power transmission channel between wind power nodes and energy storage nodes, and to set virtual impedance parameters for the virtual power transmission channel according to the compensation capacity coefficient. The energy storage control module is used to calculate the phase lead angle based on the phase information and convert it into a time advance, generate a lead compensation sequence based on the time advance, determine the frequency response power demand based on the grid frequency deviation information, and time-series superimpose the frequency response power demand with the lead compensation sequence to generate an integrated energy storage regulation command. The collaborative compensation module is used to execute wind power vibration reduction commands and energy storage integrated adjustment commands, collect actual wind power fluctuations and calculate the frequency domain deviation from the power fluctuation spectrum, and distribute the frequency domain deviation to wind power nodes and energy storage nodes for compensation based on virtual impedance parameters.

[0014] One technical solution provided in this embodiment of the invention is an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0015] One technical solution provided in this embodiment of the invention is a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the steps in any of the aforementioned methods.

[0016] This invention extracts the spectral characteristics of wind speed prediction through time-frequency transformation and combines this with the dynamic calculation and compensation capabilities of energy storage status to achieve accurate prediction and timely suppression of wind power fluctuations. A virtual power transmission channel is constructed, and the power allocation ratio of wind and energy storage devices is flexibly adjusted through virtual impedance parameters, improving system coordination. A phase lead compensation strategy is adopted to effectively reduce control delay and enhance the speed of frequency regulation. Through the time-series superposition mechanism of energy storage integrated regulation commands, the rational utilization of energy storage capacity is achieved, extending the continuous compensation time of the energy storage system. A real-time compensation scheme based on frequency domain deviation improves the smoothing effect of power fluctuations, significantly enhancing the frequency regulation capability and power quality of wind farms, and providing reliable technical support for large-scale wind power grid connection. Attached Figure Description

[0017] Figure 1 A flowchart of a wind farm frequency control method for wind-storage coordinated regulation provided in an embodiment of the present invention; Figure 2 This is a flowchart of power transmission control based on virtual impedance parameters according to an embodiment of the present invention; Figure 3 This is a schematic diagram comparing the power fluctuation probability density of the present invention; Figure 4 This is a schematic diagram of a wind farm frequency control system for wind and energy storage coordinated regulation, provided as an embodiment of the present invention. Detailed Implementation

[0018] like Figure 1 As shown, Figure 1 A flowchart of a wind farm frequency control method for wind-storage coordinated regulation provided in an embodiment of the present invention is included, the method comprising the following steps: Acquire wind speed forecast information, energy storage status of charge information, and grid frequency deviation information; The power fluctuation spectrum is obtained by performing time-frequency transformation on the wind speed prediction information, and the amplitude and phase information of the frequency components are extracted. The sustainable compensation duration of energy storage is calculated based on the energy storage state of charge information. The sustainable compensation duration of energy storage is coupled with the amplitude information to generate the compensation capacity coefficient. The allowable range of frequency domain fluctuation of the wind turbine is calculated in reverse based on the compensation capacity coefficient. When the power fluctuation spectrum exceeds the allowable range of frequency domain fluctuation, a wind power vibration reduction command is generated. Construct a virtual power transmission channel between wind power nodes and energy storage nodes, and set virtual impedance parameters for the virtual power transmission channel based on the compensation capacity coefficient; Calculate the phase lead angle based on the phase information and convert it into a time advance. Generate an advance compensation sequence based on the time advance. Determine the frequency response power demand based on the grid frequency deviation information. Superimpose the frequency response power demand with the advance compensation sequence in time to generate an integrated energy storage regulation command. The system executes wind power vibration reduction commands and energy storage integrated regulation commands, collects actual wind power fluctuations and calculates the frequency domain deviation from the power fluctuation spectrum, and distributes the frequency domain deviation to wind power nodes and energy storage nodes for compensation based on virtual impedance parameters.

[0019] The power fluctuation spectrum is obtained by performing time-frequency transformation on the wind speed forecast information, and the amplitude and phase information of the frequency components are extracted, including: The wind speed prediction information is collected according to the preset sampling interval, the wind speed difference between adjacent sampling points is calculated, the sliding window length is set according to the wind speed difference, the wind speed prediction information is segmented, and a segmented wind speed sequence is generated. Wind speed-rotation speed mapping is performed on the segmented wind speed sequence to obtain rotational speed signal, and rotational speed-power mapping is performed on the rotational speed signal and generator power curve to generate power time sequence signal; The power time-series signal is decomposed into multiple wavelet decompositions according to a preset scale to generate a frequency band coefficient matrix. The frequency band weights are calculated based on the energy distribution of the frequency band coefficient matrix. Multiply the frequency band weights by the frequency band coefficient matrix to obtain the weighted coefficient matrix, and then reconstruct the power fluctuation spectrum by performing a reconstruction operation on the weighted coefficient matrix. A Fourier transform is performed on the power fluctuation spectrum to generate an amplitude matrix and a phase matrix. Frequency component amplitude information and phase information are extracted from the amplitude matrix and phase matrix, respectively.

[0020] In this embodiment, wind speed prediction data is collected at 10-second sampling intervals to obtain a continuous 24-hour wind speed data sequence. The wind speed difference between adjacent sampling points is calculated. When the wind speed difference is >0.5 m / s, a shorter sliding window length of 5 minutes is set; when the wind speed difference is between 0.2 m / s and 0.5 m / s, a medium sliding window length of 10 minutes is set; and when the wind speed difference is <0.2 m / s, a longer sliding window length of 20 minutes is set. Using an adaptive sliding window length allows for more accurate capture of wind speed variation characteristics. The wind speed prediction information is segmented according to the set sliding window length to generate a segmented wind speed sequence containing multiple subsequences.

[0021] A mapping from wind speed to engine speed is performed for each segment of the wind speed sequence. Based on the wind turbine characteristic curve, when the wind speed is below 3 m / s, the turbine does not start, and the speed is 0 rpm; when the wind speed is between 3 m / s and 12 m / s, the turbine speed is directly proportional to the wind speed, and the speed value is obtained by looking up a table from the turbine characteristic curve; when the wind speed is between 12 m / s and 25 m / s, the turbine maintains its rated speed, typically 15 rpm; when the wind speed is above 25 m / s, the turbine enters a protection state, and the speed drops to 0 rpm. Through this mapping relationship, the segmented wind speed sequence is converted into the corresponding engine speed signal.

[0022] After obtaining the speed signal, a mapping from speed to power is performed based on the wind turbine's power curve. The power curve describes the output power at different speeds and is typically provided by the wind turbine manufacturer. Taking a 2MW wind turbine as an example, the output power is approximately 200kW at 5rpm; approximately 800kW at 10rpm; and reaches the rated power of 2000kW at 15rpm. For each data point in the speed signal, the corresponding power value is obtained through table lookup or interpolation calculation methods, thereby generating a power timing signal.

[0023] The generated power time-series signal is decomposed into multi-level wavelet coefficients according to a preset scale. In this embodiment, the db4 wavelet is used as the base wavelet, and a 5-level wavelet decomposition is performed. Through multi-level wavelet decomposition, the power time-series signal is decomposed into wavelet coefficients of different frequency bands, including 5 detail coefficient sub-bands and 1 approximation coefficient sub-band, forming a frequency band coefficient matrix. For a 24-hour power time-series signal with a length of 8640 points, in the frequency band coefficient matrix obtained after 5-level wavelet decomposition, the first level of detail coefficients contains 4320 points, the second level of detail coefficients contains 2160 points, and so on.

[0024] The weights of each frequency band are calculated based on the energy distribution of the frequency band coefficient matrix. The sum of squares of the coefficients in each frequency band is then calculated to obtain the energy value of that band. Dividing each band's energy value by the sum of all band energy values ​​yields the normalized band weights. Lower frequency bands typically have higher energy distributions and therefore receive larger weights. For example, in the power time-series signal analysis of a wind farm, the weight of the fifth-level approximation coefficient subband is approximately 0.45, the weight of the fourth-level detail coefficient subband is approximately 0.25, and the weights of the remaining detail coefficient subbands decrease sequentially.

[0025] The calculated band weights are multiplied by the band coefficient matrix to obtain a weighted coefficient matrix. This weighted coefficient matrix is ​​then reconstructed using a wavelet reconstruction algorithm to synthesize the weighted coefficients of each band into a time-domain signal, yielding the power fluctuation spectrum. The reconstruction process employs the same basis wavelet as the decomposition process to ensure the accuracy of the signal reconstruction. The reconstructed power fluctuation spectrum retains the main characteristics of the original power time-series signal while highlighting the frequency band components with higher energy distributions.

[0026] A Fast Fourier Transform (FFT) is performed on the power fluctuation spectrum to convert the time-domain signal into a frequency-domain representation. For a power fluctuation spectrum with a length of 8640 points, an 8192-point FFT is performed to obtain a complex frequency-domain representation. From the complex frequency-domain representation, the amplitude and phase at each frequency point are calculated, forming the amplitude matrix and phase matrix, respectively. The amplitude matrix reflects the intensity distribution of the power fluctuation at each frequency component, and the phase matrix reflects the phase relationship between the frequency components.

[0027] The amplitude information of the main frequency components is extracted from the amplitude matrix, and the top 10 frequency points with the highest power spectral density are selected as the main frequency components. The frequency values ​​and amplitudes corresponding to these frequency points are recorded. Simultaneously, the phase information corresponding to these main frequency components is extracted from the phase matrix. By analyzing the amplitude and phase information of these main frequency components, the main modes and periodic characteristics of power fluctuations can be identified. For example, in the analysis results of a wind farm, it was found that the main power fluctuations were concentrated in the low-frequency band of 0.001Hz to 0.01Hz, reflecting the slow dynamic characteristics of wind speed changes, and in the mid-frequency band of 0.1Hz to 1Hz, reflecting the dynamic response characteristics of the wind mechanical and electrical systems.

[0028] This invention achieves precise characterization of wind farm power fluctuations by performing time-frequency transformation on wind speed prediction information. It adaptively handles different wind speed variation characteristics, accurately maps the relationship between wind speed and power, and highlights the main energy distribution frequency bands through multi-layer wavelet decomposition and weighted reconstruction, extracting the amplitude and phase information of the frequency components of power fluctuations. The frequency characteristic information obtained by this invention provides a scientific basis for wind-storage coordinated regulation, enabling wind farms to adopt differentiated control strategies for power fluctuations with different frequency characteristics, significantly improving the frequency stability of wind farms. This reduces the impact of wind power fluctuations on the power grid and improves the grid connection reliability of wind farms.

[0029] The sustainable compensation duration of energy storage is calculated based on the energy storage state-of-charge information. The sustainable compensation duration is then coupled with the amplitude information to generate compensation capacity coefficients, including: Collect voltage and current information from the energy storage state of charge information, calculate the instantaneous power and remaining capacity of the energy storage, and calculate the power regulation rate based on the instantaneous power change trend; A compensation characteristic curve is established by combining the power regulation rate with the remaining capacity, and the upper limit and lower limit of power regulation are extracted from the compensation characteristic curve. The energy storage power-capacity ratio is calculated based on the upper limit of power regulation, the lower limit of power regulation, and the remaining capacity. The sustainable compensation time of energy storage is then calculated in combination with the power-capacity ratio. Wavelet transform is performed on the amplitude information to obtain different frequency components, and the fluctuation period corresponding to each frequency component is calculated. The compensation coefficient matrix is ​​generated by cross-mapping the sustainable compensation duration of energy storage with the fluctuation period. The compensation coefficient matrix is ​​then subjected to nonlinear coupling calculation. The nonlinear coupling calculation result is combined with the frequency component in a weighted manner to generate the compensation capacity coefficient.

[0030] Energy storage state of charge (SOC) information is acquired in real time through the battery management unit (BMU), obtaining voltage and current information for the energy storage device. Voltage information includes individual cell voltages and the overall voltage, while current information includes the charging and discharging current values ​​and directions. The sampling frequency is set to 100Hz to ensure real-time data acquisition and accuracy. The acquired voltage is 800V, and the current is ±200A, where positive values ​​indicate charging and negative values ​​indicate discharging. Based on the acquired voltage and current information, the instantaneous power of the energy storage is calculated by multiplying the voltage and current, resulting in 160kW. Based on the total capacity of the energy storage device and the current voltage level, combined with the charge / discharge efficiency factor, the remaining capacity of the energy storage device is calculated, resulting in 400kWh.

[0031] Continuous monitoring of instantaneous power changes reflects the energy storage device's response to power fluctuations. Power changes are recorded within a 10ms time window to obtain the power regulation rate. The calculation of the power regulation rate takes into account the energy storage device's power conversion characteristics and internal impedance characteristics. The energy storage device's power regulation rate is 20kW / s, meaning it can achieve 20kW power regulation within 1 second.

[0032] A compensation characteristic curve is established by combining the power regulation rate with the remaining capacity. This curve describes the power regulation capability of the energy storage device at different remaining capacity levels. The compensation characteristic curve adopts a piecewise function form. When the remaining capacity is higher than 80%, the power regulation capability maintains its maximum value; when the remaining capacity is between 20% and 80%, the power regulation capability decreases linearly; when the remaining capacity is lower than 20%, the power regulation capability drops sharply. The compensation characteristic curve extracts the upper and lower limits of power regulation. For the current remaining capacity of 400kWh (corresponding to 50% of the rated capacity of the energy storage device), the upper limit of power regulation is 180kW, and the lower limit of power regulation is -150kW, where positive values ​​represent discharge capability and negative values ​​represent charging capability.

[0033] The relationship between the upper and lower limits of power regulation and the remaining capacity allows for the calculation of the energy storage power-capacity ratio. The power-capacity ratio is a crucial indicator of the responsiveness of an energy storage device, representing the power level that a unit capacity of energy storage can provide. The ratio of the sum of the absolute values ​​of the upper and lower limits of power regulation to the remaining capacity yields a power-capacity ratio of 0.825. A higher power-capacity ratio indicates a stronger instantaneous response capability of the energy storage device, but a shorter sustainable compensation time. Combining the power-capacity ratio with the depth of discharge limit of the energy storage device allows for the calculation of the sustainable compensation time. Under the current operating conditions, the sustainable compensation time is 2.4 hours.

[0034] Amplitude information processing requires wavelet transform to obtain different frequency components. The wavelet transform uses the db4 wavelet basis and performs a four-level decomposition of the amplitude information, resulting in five frequency components. Different frequency components reflect different characteristics of power fluctuations: low-frequency components correspond to slowly changing power fluctuations, while high-frequency components correspond to rapidly changing power fluctuations. Calculations of the fluctuation period for each frequency component show that the fluctuation period for the first-level high-frequency component is 0.05 hours; the second-level component has a fluctuation period of 0.2 hours; the third-level component has a fluctuation period of 0.8 hours; the fourth-level component has a fluctuation period of 3.2 hours; and the approximate component has a fluctuation period greater than 6.4 hours.

[0035] A compensation coefficient matrix is ​​generated by cross-mapping the sustainable compensation duration of energy storage with the fluctuation period. The cross-mapping adopts a proportional relationship: when the sustainable compensation duration of energy storage is greater than the fluctuation period, the corresponding compensation coefficient is close to 1; when the sustainable compensation duration of energy storage is less than the fluctuation period, the compensation coefficient decreases as the difference increases. Currently, for a sustainable compensation duration of 2.4 hours, the compensation coefficient corresponding to the first-layer high-frequency component is 0.98, the second-layer component is 0.95, the third-layer component is 0.85, the fourth-layer component is 0.42, and the approximate component is 0.15.

[0036] When performing nonlinear coupling calculations on the compensation coefficient matrix, a saturation function is used for adjustment to enhance the influence of high compensation coefficients and weaken the influence of low compensation coefficients, resulting in corrected compensation coefficients. These corrected compensation coefficients are multiplied by the energy weights of the corresponding frequency components and then normalized to generate the final compensation capability coefficient. The compensation capability coefficient reflects the energy storage device's ability to compensate for power fluctuations at different frequencies, providing a basis for subsequent control strategies. Under the current operating conditions, the generated compensation capability coefficient is 0.76, indicating that the energy storage device has a strong compensation capability for the current power fluctuations.

[0037] This invention achieves accurate assessment of energy storage compensation capacity through refined analysis of the energy storage state of charge and combined with the spectral characteristics of power fluctuations, providing a technical foundation for wind-storage coordinated regulation. It overcomes the shortcomings of inaccurate assessment of energy storage compensation capacity in traditional methods by separating power fluctuations with different frequency characteristics through wavelet transform and designing targeted compensation strategies based on the sustainable compensation duration of energy storage, significantly improving the frequency stability of wind farms. It also achieves optimized allocation of energy storage resources, avoiding overuse and underuse of energy storage devices, extending the service life of energy storage devices, reducing wind farm operating costs, improving the frequency regulation accuracy and response speed of wind farms, and enhancing the stability and reliability of the power grid.

[0038] The allowable range of frequency domain fluctuations of the wind turbine is calculated in reverse based on the compensation capacity coefficient. When the power fluctuation spectrum exceeds the allowable range of frequency domain fluctuations, wind power vibration reduction commands are generated, including: Wavelet decomposition is performed on the compensation capability coefficient to obtain the frequency components, and the compensation response speed, compensation capacity, and compensation loss rate of the frequency components are calculated. The actual compensation capacity is calculated based on the compensation loss rate and the compensation capacity. The power adjustment range and the power adjustment slope are obtained by combining the actual compensation capacity and the compensation response speed. The frequency domain fluctuation tolerance range is set by using the power adjustment range and the power adjustment slope. Wavelet decomposition is performed on the power fluctuation spectrum to obtain the fluctuation components, and the amplitude parameters, phase parameters, and direction of change parameters of the fluctuation components are calculated. The power change is obtained by combining the change direction parameter and the amplitude parameter. The power change is compared with the allowable range of frequency domain fluctuation to calculate the amplitude over-limit and phase over-limit. The vibration reduction frequency band is determined based on the amplitude over-limit and phase over-limit. Based on the vibration reduction frequency band, the wind turbine operating parameters are read, the amplitude exceeding the limit is converted into the pitch angle reference value, the phase exceeding the limit is converted into the pitch angle advance value, and the pitch angle adjustment parameters are obtained by combining them. The pitch angle adjustment parameter is matched with the wind turbine speed parameter and wind speed parameter to calculate and output the pitch angle adjustment value and adjustment sequence, thereby generating a wind power vibration reduction command.

[0039] The compensation capability coefficient can be decomposed into different frequency components using wavelet decomposition. The wavelet decomposition employs the Haar wavelet basis function, performing a three-level decomposition to obtain four frequency components, corresponding to high-frequency, mid-high-frequency, mid-low-frequency, and low-frequency fluctuations, respectively. When the compensation capability coefficient is 0.76, the high-frequency component value is 0.32, the mid-high-frequency component value is 0.25, the mid-low-frequency component value is 0.12, and the low-frequency component value is 0.07. The compensation response speed is calculated for each frequency component: 20ms for high-frequency components, 100ms for mid-high-frequency components, 500ms for mid-low-frequency components, and 2s for low-frequency components. The compensation capacity is calculated based on the energy proportion of each frequency component and the total energy storage capacity: 120kWh for high-frequency components, 140kWh for mid-high-frequency components, 80kWh for mid-low-frequency components, and 60kWh for low-frequency components. The compensation loss rate reflects the energy loss of the energy storage device during the compensation process at different frequencies. The compensation loss rate for high-frequency components is 8%, for mid-to-high-frequency components it is 5%, for mid-to-low-frequency components it is 3%, and for low-frequency components it is 2%.

[0040] The actual compensation capacity is obtained by combining the compensation loss rate and the compensation capacity. The actual compensation capacity for high-frequency components is 110.4 kWh, for mid-to-high-frequency components it is 133 kWh, for mid-to-low-frequency components it is 77.6 kWh, and for low-frequency components it is 58.8 kWh. The actual compensation capacity is related to the compensation response speed of the corresponding frequency component. After introducing a power conversion efficiency factor of 0.95, the power adjustment range for high-frequency components is ±350 kW, for mid-to-high-frequency components it is ±280 kW, for mid-to-low-frequency components it is ±180 kW, and for low-frequency components it is ±100 kW. The power adjustment slope represents the rate of power change that the energy storage device can achieve per unit time. The power adjustment slope for high-frequency components is 30 kW / ms, for mid-to-high-frequency components it is 5 kW / ms, for mid-to-low-frequency components it is 0.8 kW / ms, and for low-frequency components it is 0.15 kW / ms. The power regulation range and the power regulation slope together define the allowable range of frequency domain fluctuations, forming the power fluctuation envelope that the energy storage device can respond to.

[0041] When performing wavelet decomposition on the power fluctuation spectrum to obtain the fluctuation components, the same wavelet basis function and decomposition level as the compensation capability coefficient are used to ensure the consistency of the decomposition results. The actual output power of the wind farm is continuously sampled at a sampling frequency of 500Hz for a sampling duration of 10s, forming a power time series. Wavelet decomposition is performed on the power time series to obtain four fluctuation components: high frequency, mid-high frequency, mid-low frequency, and low frequency. The amplitude parameters of the fluctuation components are calculated: 420kW for the high-frequency component, 320kW for the mid-high frequency component, 150kW for the mid-low frequency component, and 90kW for the low-frequency component. The phase parameters reflect the temporal characteristics of the power fluctuations: 15° for the high-frequency component, 45° for the mid-high frequency component, 75° for the mid-low frequency component, and 120° for the low-frequency component. The direction parameter indicates the trend of power fluctuation. A positive value indicates that the power is increasing and a negative value indicates that the power is decreasing. The direction parameter of the high-frequency component is 1, the direction parameter of the mid-to-high frequency component is 1, the direction parameter of the mid-to-low frequency component is -1, and the direction parameter of the low-frequency component is -1.

[0042] The combination of the direction and amplitude parameters indicates the specific power change. The power change for the high-frequency component is 420kW, for the mid-high frequency component it is 320kW, for the mid-low frequency component it is -150kW, and for the low-frequency component it is -90kW. Comparing the power change with the allowable frequency domain fluctuation range determines the amplitude exceeding the limit. When the amplitude exceeds the allowable range, a wind turbine vibration reduction command is generated; otherwise, the wind turbine maintains its current operating state. The amplitude exceeding limit for the high-frequency component is 70kW, for the mid-high frequency component it is 40kW, and for the mid-low frequency and low-frequency components there is no exceeding limit. The phase exceeding limit indicates the degree to which the power fluctuation phase leads or lags behind the energy storage response phase. The phase exceeding limit for the high-frequency component is 5°, for the mid-high frequency component it is 15°, and for the mid-low frequency and low-frequency components there is no phase exceeding limit. The vibration reduction frequency band is determined based on the amplitude and phase exceeding limits; currently, the frequency bands requiring vibration reduction control are the high-frequency and mid-high frequency bands.

[0043] After determining the vibration reduction frequency band, the wind turbine operating parameters are read, including a wind speed of 12 m / s, a rotor speed of 15 rpm, a power output of 1.5 MW, and a pitch angle of 3°. Amplitude exceeding limits is converted into pitch angle changes. Based on the wind turbine's power characteristic curve, a 70 kW amplitude exceedance in the high-frequency component corresponds to a 1.2° pitch angle change, and a 40 kW amplitude exceedance in the mid-to-high-frequency component corresponds to a 0.7° pitch angle change. Phase exceeding limits corresponds to the pitch angle advance. Considering the response characteristics of the blade drive system, a 5° phase exceedance in the high-frequency component corresponds to a 0.3° pitch angle advance, and a 15° phase exceedance in the mid-to-high-frequency component corresponds to a 0.5° pitch angle advance. The pitch angle changes and advance advances constitute a complete pitch angle adjustment strategy: the high-frequency component requires a 1.5° increase in pitch angle with a 0.3° advance, and the mid-to-high-frequency component requires a 1.2° increase in pitch angle with a 0.5° advance.

[0044] The pitch angle adjustment strategy is combined with the current operating status of the wind turbine. The final pitch angle control command is determined by querying the wind turbine performance curve. When the wind speed is 12 m / s and the rotor speed is 15 rpm, the pitch angle needs to be adjusted from the current 3° to 5.7°, a total adjustment of 2.7°. The adjustment timing is arranged so that the high-frequency component adjustment is completed within 10 ms, and the mid-to-high-frequency component adjustment is completed within 50 ms. The output pitch angle adjustment value and adjustment timing constitute the wind turbine vibration reduction command. The command is executed by the wind turbine controller to achieve precise control of the wind turbine's output power, thereby achieving frequency stability.

[0045] This invention achieves coordinated regulation of wind power and energy storage by calculating the permissible range of frequency domain fluctuations in wind turbines using a compensation capacity coefficient. Considering the compensation characteristics of energy storage devices at different frequencies, it accurately calculates the actual compensation capacity and power regulation range, setting a reasonable permissible range for frequency domain fluctuations in wind turbines. When the power fluctuations of the wind turbines exceed the permissible range, wavelet decomposition identifies the over-limit frequency bands and converts them into specific pitch angle adjustment parameters, generating precise vibration reduction commands. This coordinated control strategy fully leverages the rapid response advantages of energy storage and the large-capacity regulation capabilities of wind turbines, effectively suppressing wind farm power fluctuations, improving wind farm frequency stability, reducing the operational pressure on energy storage devices, extending energy storage lifespan, and lowering operating costs.

[0046] like Figure 2 As shown, a virtual power transmission channel is constructed between the wind power node and the energy storage node. The virtual impedance parameters for the virtual power transmission channel are set according to the compensation capability coefficient, including: The voltage and current parameters of the wind power nodes and the energy storage nodes are collected to calculate the instantaneous power value. Calculate the power transmission direction parameters and power transmission amplitude based on the instantaneous power value; Calculate the line loss coefficient and time delay coefficient based on the power transmission direction parameter and the power transmission amplitude, and combine them to generate transmission characteristic parameters; Wavelet decomposition is performed on the compensation capability coefficient to obtain the frequency band coefficient. The compensation gain value and compensation delay value are extracted from the frequency band coefficient. The compensation gain value is multiplied by the line loss coefficient to obtain the virtual resistance value. The compensation delay value is multiplied by the time delay coefficient to obtain the virtual inductance value. The virtual resistance and virtual inductance values ​​are combined according to the frequency band to generate impedance parameters, and the impedance parameters are mapped to the transmission characteristic parameters to obtain the matching coefficient. The impedance reference parameter is calculated based on the matching coefficient. The impedance adjustment parameter is calculated based on the impedance reference parameter and the transmission characteristic parameter. The impedance reference parameter and the impedance adjustment parameter are combined to form the virtual impedance parameter. A virtual power transmission channel is constructed by adjusting the power transmission direction parameters and power transmission amplitude based on the virtual impedance parameters.

[0047] Real-time monitoring devices at wind power nodes and energy storage nodes collect voltage and current parameters. The wind power node voltage is 35kV, the current is 85.7A, and the phase difference is 0.05rad; the energy storage node voltage is 10kV, the current is 150A, and the phase difference is 0.03rad. The acquisition frequency is set to 10kHz to ensure the integrity and accuracy of signal sampling. Instantaneous power values ​​are calculated from the voltage and current parameters: 5.2MW for the wind power node and 2.6MW for the energy storage node. The power data is filtered using a low-pass filter with a cutoff frequency of 500Hz, resulting in a stable power data stream.

[0048] The power transmission direction parameter is determined by the power change trends of the wind power nodes and energy storage nodes. When the power of the wind power node increases while the power of the energy storage node decreases, the power transmission direction parameter is positive, indicating that power is transmitted from the wind power node to the energy storage node; conversely, it is negative, indicating that power is transmitted from the energy storage node to the wind power node. Currently, the power transmission direction parameter is 1, indicating that power is transmitted from the wind power node to the energy storage node. The power transmission amplitude depends on the power difference between the two nodes; the current power transmission amplitude is 2.6 MW. The power transmission amplitude and transmission direction together determine the transmission characteristics of the virtual power channel.

[0049] The line loss factor reflects the power loss during transmission. Based on the power transmission direction parameter and the power transmission amplitude, when the power transmission direction is positive and the power transmission amplitude is 2.6MW, the line loss factor is calculated to be 0.08. The time delay factor characterizes the time required for a power signal to be transmitted from one node to another; the current time delay factor is 15ms. The line loss factor and the time delay factor together form the transmission characteristic parameters, which are used to characterize the basic characteristics of the virtual power transmission channel. The transmission characteristic parameters are represented by a two-dimensional vector; the current transmission characteristic parameters are [0.08, 15ms].

[0050] The compensation capability coefficient is obtained by wavelet decomposition to obtain frequency band coefficients for different frequency bands. Optionally, the compensation capability coefficient 0.85 is decomposed into five frequency band coefficients using the db4 wavelet basis function, corresponding to the 0-0.5Hz, 0.5-1Hz, 1-5Hz, 5-10Hz, and 10-50Hz frequency bands, respectively. The coefficients for each frequency band are 0.15, 0.22, 0.25, 0.18, and 0.05, respectively. The compensation gain and compensation delay values ​​are extracted from the frequency band coefficients. The compensation gain value reflects the energy storage device's ability to suppress power fluctuations in each frequency band, and the compensation delay value represents the time characteristics of the energy storage device's response to power fluctuations. The compensation gain value is 0.9 and the compensation delay value is 100ms in the 0-0.5Hz band; the compensation gain value is 0.85 and the compensation delay value is 80ms in the 0.5-1Hz band; the compensation gain value is 0.8 and the compensation delay value is 40ms in the 1-5Hz band; the compensation gain value is 0.75 and the compensation delay value is 20ms in the 5-10Hz band; and the compensation gain value is 0.7 and the compensation delay value is 10ms in the 10-50Hz band.

[0051] The compensation gain value is correlated with the line loss coefficient to form a virtual resistance value. The virtual resistance value is 0.072Ω in the 0-0.5Hz band; 0.068Ω in the 0.5-1Hz band; 0.064Ω in the 1-5Hz band; 0.06Ω in the 5-10Hz band; and 0.056Ω in the 10-50Hz band. The compensation delay value is correlated with the time delay coefficient to form a virtual inductance value. The virtual inductance value is 1.5mH in the 0-0.5Hz band; 1.2mH in the 0.5-1Hz band; 0.6mH in the 1-5Hz band; 0.3mH in the 5-10Hz band; and 0.15mH in the 10-50Hz band.

[0052] Virtual resistance and inductance values ​​are combined according to frequency bands to generate impedance parameters. These impedance parameters are represented by an impedance-frequency response curve, where each point on the curve represents the impedance value at a specific frequency. The impedance parameters are then mapped to transmission characteristic parameters to generate a matching coefficient. The matching coefficient indicates the degree of matching between the impedance parameters and the transmission characteristic parameters; the current matching coefficient is 0.92, indicating good matching performance. The matching coefficient is used for subsequent adjustment and optimization of the impedance parameters.

[0053] The impedance reference parameters are determined based on the matching coefficient and include a reference resistance and a reference inductance. When the matching coefficient is 0.92, the impedance reference parameters are [0.065Ω, 0.8mH]. The impedance adjustment parameters are calculated based on the transmission characteristic parameters. When the transmission characteristic parameters are [0.08, 15ms], the impedance adjustment parameters are [0.01Ω, 0.2mH]. The impedance reference parameters and impedance adjustment parameters are combined to form the virtual impedance parameters, which are currently [0.075Ω, 1mH].

[0054] Virtual impedance parameters are used to adjust the power transmission direction parameter and power transmission amplitude. The adjusted power transmission direction parameter remains at 1, and the power transmission amplitude is adjusted to 2.4MW. A virtual power transmission channel is established based on the adjusted power transmission parameters, with a channel bandwidth set to 50Hz, sufficient to cover the common power fluctuation frequency range in wind farms. After the virtual power transmission channel is established, wind power nodes and energy storage nodes can interact with each other according to the set impedance characteristics, achieving coordinated wind and energy storage regulation.

[0055] This invention achieves coordinated regulation of the wind-storage system by constructing a virtual power transmission channel between wind power nodes and energy storage nodes. The setting of virtual impedance parameters fully considers the dynamic characteristics of wind power and energy storage, making the power transmission process smoother and more controllable. It optimizes the response speed and stability of wind-storage coordination, improving the system's ability to suppress power fluctuations. Through wavelet decomposition technology, it achieves fine-grained control of different frequency components, enabling the energy storage device to specifically compensate for power fluctuations in specific frequency bands, improving compensation efficiency and accuracy.

[0056] The phase lead angle is calculated based on the phase information and converted into a time lead. A lead compensation sequence is generated based on the time lead. The frequency response power demand is determined based on the grid frequency deviation information. The frequency response power demand is then time-series superimposed with the lead compensation sequence to generate an integrated energy storage regulation command, including: Wavelet decomposition is performed on the phase information to obtain the amplitude component and the angle component. A wave matrix is ​​constructed based on the amplitude component and a compensation matrix is ​​constructed based on the angle component. The wave matrix and the compensation matrix are orthogonally decomposed to form the phase change. The phase change is decomposed into singular values ​​to obtain the correction amplitude. The compensation parameter is constructed based on the correction amplitude. The compensation parameter and the correction amplitude are orthogonally transformed to generate the phase lead angle. The phase lead angle and the wave matrix are used to perform feature mapping to obtain the time series parameters. The compensation duration is extracted based on the time series parameters and then converted into a time advance. The frequency disturbance is calculated based on the grid frequency deviation information. The frequency disturbance is combined with the compensation adjustment to obtain the power change. The frequency response power demand is constructed based on the power change and the timing compensation. The compensation interval is divided according to the time lead, and the power demand within the compensation interval is adjusted by the magnitude compensation to generate the advance compensation sequence. Based on the compensation interval, the advance compensation sequence is divided into compensation sub-sequences, the frequency response power demand is divided into response sub-sequences according to the time-series compensation amount, and the compensation sub-sequences and response sub-sequences are combined in time sequence to generate an integrated energy storage regulation command.

[0057] Phase information was acquired by the wind farm's power measurement device, with a measurement accuracy of 0.01° and a sampling frequency of 10kHz. The acquired phase information includes voltage and current phase data at the wind farm's outlet, exhibiting certain fluctuation characteristics. Wavelet decomposition was performed on the phase information to obtain amplitude and angular components. After wavelet decomposition, the amplitude component of the wind farm's output power phase information reflects the amplitude characteristics of power fluctuations, ranging from 0.5MW to 5MW; the angular component reflects the phase variation characteristics, ranging from -30° to 30°. The amplitude component data was constructed into a 3×3 fluctuation matrix, with matrix elements corresponding to fluctuation amplitudes in different frequency ranges: 1.5MW in the low-frequency range, 2.8MW in the mid-frequency range, and 0.7MW in the high-frequency range. The angular component data was constructed into a 3×3 compensation matrix, with matrix elements corresponding to phase angles in different frequency ranges: 15° in the low-frequency range, -10° in the mid-frequency range, and 25° in the high-frequency range. The fluctuation matrix and the compensation matrix are orthogonally decomposed to obtain the phase change, which reflects the correlation between power fluctuation and phase change, and has a value of 18°.

[0058] Phase changes were processed using singular value decomposition (SVD), which extracts key features of the phase changes. Three singular values ​​were obtained after decomposition: 5.6, 3.2, and 0.8. The eigenvector corresponding to the largest singular value was selected as the correction amplitude, with a value of [0.6, 0.7, 0.3], reflecting the contribution of fluctuations in different frequency ranges. Compensation parameters were constructed based on the correction amplitude, with a value of [0.8, 0.75, 0.6], representing the degree of compensation for phase changes in different frequency ranges. An orthogonal transformation was performed on the compensation parameters and the correction amplitude to generate a phase lead angle of 25°. This angle represents the phase angle requiring an earlier response and is used for subsequent timing advance calculations.

[0059] A eigenvalue mapping is performed between the phase lead angle and the fluctuation matrix. This mapping uses a nonlinear mapping function to map the phase lead angle in the phase domain to the response characteristics in the time domain. The mapping result yields time-series parameters [120ms, 80ms, 40ms], corresponding to the time-series characteristics in the low-frequency, mid-frequency, and high-frequency ranges. Compensation durations are extracted from these parameters: 120ms for the low-frequency range, 80ms for the mid-frequency range, and 40ms for the high-frequency range. These compensation durations are converted into time advances, taking into account the propagation characteristics of wind farm power fluctuations and the response delay of the energy storage device. The converted time advances are 150ms for the low-frequency range, 100ms for the mid-frequency range, and 50ms for the high-frequency range. The time advance represents the time the energy storage device needs to respond in advance, used to generate the lead compensation sequence.

[0060] The grid frequency is acquired by a synchronous phasor measurement device at a sampling frequency of 100Hz. The current grid frequency is 49.92Hz, the nominal frequency is 50Hz, and the frequency deviation is -0.08Hz. The frequency disturbance is calculated based on the grid frequency deviation information, and is -0.16Hz / s, representing the rate of decrease in grid frequency. The frequency disturbance is combined with the compensation adjustment to obtain the power change, which is determined based on the wind farm's frequency regulation characteristic curve and is 2.5MW / Hz. The calculated power change is 0.4MW, representing the amount of power compensation required to respond to the frequency deviation. The frequency response power demand is constructed based on the power change and the timing compensation, with the timing compensation values ​​[0.15, 0.1, 0.05] corresponding to the compensation durations in the low-frequency, mid-frequency, and high-frequency ranges. The frequency response power demand is [0.2MW, 0.15MW, 0.05MW], representing the compensation power required in the three different frequency ranges.

[0061] The compensation intervals are divided based on the time lead: 0-150ms for low-frequency, 150-250ms for mid-frequency, and 250-300ms for high-frequency. Power demand within each interval is adjusted for amplitude. The power demand in the low-frequency compensation interval is 0.2MW, which is adjusted to 0.217MW; the power demand in the mid-frequency compensation interval is 0.15MW, which is adjusted to 0.163MW; and the power demand in the high-frequency compensation interval is 0.05MW, which is adjusted to 0.054MW. The generated advance compensation sequence is [0.217MW, 0.163MW, 0.054MW], representing the power sequence for which the energy storage device needs to respond in advance.

[0062] The lead compensation sequence is divided into compensation sub-sequences based on the compensation interval. The time precision of the compensation sub-sequences is 10ms. The low-frequency interval compensation sub-sequence contains 15 data points, the mid-frequency interval compensation sub-sequence contains 10 data points, and the high-frequency interval compensation sub-sequence contains 5 data points. The frequency response power demand is divided into response sub-sequences according to the time-series compensation amount. The time precision of the response sub-sequences is consistent with that of the compensation sub-sequences. The compensation sub-sequences and response sub-sequences are combined in time sequence to generate an integrated energy storage regulation command. The integrated regulation command contains 30 data points, each data point corresponding to the power command value at a certain moment. The integrated regulation command is sent to the energy storage control device through the communication network, and the energy storage control device executes the power regulation operation.

[0063] This invention calculates the phase lead angle using phase information and converts it into a time advance, enabling energy storage devices to predict and respond to grid frequency fluctuations in advance. This invention fully considers the propagation characteristics of power fluctuations in the power system and the response delay of energy storage devices. Through precise calculation of the time advance, it significantly improves the timeliness and accuracy of frequency regulation response. Employing wavelet decomposition and singular value decomposition techniques, it achieves accurate analysis of power fluctuation characteristics, making energy storage compensation more precise and effective. By time-series superimposing the lead compensation sequence with the frequency response power demand, a complete energy storage regulation strategy is formed, effectively coordinating the dynamic response processes of wind power and energy storage.

[0064] Execute wind power vibration reduction commands and energy storage integrated regulation commands, collect actual wind power fluctuations and calculate the frequency domain deviation from the power fluctuation spectrum, and distribute the frequency domain deviation to wind power nodes and energy storage nodes according to virtual impedance parameters. Compensation includes: Execute wind power vibration reduction commands and energy storage integrated regulation commands, collect real-time output power of wind turbine units, calculate real-time fluctuation values ​​of output power, and generate actual wind power fluctuation sequences based on real-time fluctuation values; The actual wind power fluctuation sequence is frequency domain transformed to generate a real-time fluctuation spectrum. The frequency domain deviation value is calculated by comparing the real-time fluctuation spectrum with the power fluctuation spectrum. Calculate the compensation power value based on the frequency domain deviation value, and arrange the compensation power value in time order to generate a compensation sequence; The virtual impedance parameter is decomposed into impedance magnitude and impedance phase angle. The power distribution coefficient is calculated based on the impedance magnitude, and the compensation timing is calculated based on the impedance phase angle. The compensation sequence is divided into wind power node compensation and energy storage node compensation according to the power allocation coefficient and compensation time sequence. The wind power node compensation amount is combined with the wind power vibration reduction command to generate a wind power compensation command for wind power node compensation. The energy storage node compensation amount is combined with the energy storage comprehensive regulation command to generate an energy storage compensation command for energy storage node compensation.

[0065] The wind turbine vibration reduction command is sent to the wind turbine controller via the control channel. The data format of the wind turbine vibration reduction command is a power time series, with a sampling interval of 100ms and a sequence length of 60 points, corresponding to a 6-second control cycle. The vibration reduction command includes a baseline power value and a dynamic adjustment amount. The baseline power value is 10MW, and the dynamic adjustment amount fluctuates between -1MW and 1MW. The energy storage integrated regulation command is sent to the energy storage converter controller via the energy storage management device. The data format of the energy storage integrated regulation command is also a power time series, with a sampling interval of 50ms and a sequence length of 120 points, corresponding to a 6-second control cycle. The energy storage regulation command includes charge and discharge power values, with positive values ​​indicating discharge and negative values ​​indicating charging, and a power range of -5MW to 5MW. During the execution of the wind turbine vibration reduction command, the wind turbine achieves power output regulation through a combination of pitch control and torque control; during the execution of the energy storage integrated regulation command, the energy storage device achieves bidirectional power regulation through bidirectional converter control.

[0066] A power monitoring device installed at the wind farm outlet collects the real-time output power of the wind turbines with a monitoring accuracy of 0.01MW and a sampling frequency of 100Hz. The collected real-time output power data is processed by a low-pass filter with a cutoff frequency of 45Hz to remove high-frequency noise. The processed power data is then used to calculate the real-time fluctuation value, which is the difference between the actual output power and the set power. The current set power is 10MW, the real-time output power is 10.6MW, and the real-time fluctuation value is 0.6MW. The real-time fluctuation value is continuously collected for 60 seconds, forming a wind power actual fluctuation sequence containing 6000 data points. The wind power actual fluctuation sequence exhibits obvious randomness and fluctuation characteristics, reflecting the instability of wind power output.

[0067] The actual wind power fluctuation sequence was frequency-domain converted using a Fast Fourier Transform (FFT) with a transform window length of 1024 points and windowing using the Hanning window function. The resulting real-time fluctuation spectrum had a frequency resolution of 0.1 Hz and a frequency range of 0-50 Hz. The fluctuation amplitudes at 0.1 Hz were 0.3 MW, 0.5 Hz, 1 Hz, 2 Hz, 5 Hz, and 10 Hz, respectively. The power fluctuation spectrum, a standard spectrum obtained from historical data statistical analysis, reflects the fluctuation characteristics of the wind farm during normal operation. The fluctuation amplitudes at 0.1 Hz were 0.2 MW, 0.5 Hz, 1 Hz, 2 Hz, 5 Hz, and 10 Hz, respectively. The real-time fluctuation spectrum is compared with the power fluctuation spectrum to calculate the frequency domain deviation, which is the difference in amplitude between the two. The frequency domain deviation is 0.1 MW at 0.1 Hz, 0.1 MW at 0.5 Hz, 0.05 MW at 1 Hz, 0.02 MW at 2 Hz, 0.01 MW at 5 Hz, and 0 MW at 10 Hz.

[0068] The compensation power value is calculated based on the frequency domain deviation value, taking into account both the magnitude of the frequency domain deviation and the importance of the frequency points. The frequency domain deviation weight is 1 for the low-frequency band (0-1Hz), 0.8 for the mid-frequency band (1-5Hz), and 0.6 for the high-frequency band (5-50Hz). After weight adjustment, the compensation power value is 0.1MW at 0.1Hz, 0.1MW at 0.5Hz, 0.05MW at 1Hz, 0.016MW at 2Hz, 0.008MW at 5Hz, and 0MW at 10Hz. The compensation power value at each frequency point is then converted back to the time domain using an inverse Fourier transform to obtain the compensation sequence. The sampling interval of the compensation sequence is 10ms, the sequence length is 600 points, corresponding to a compensation period of 6s. The compensation sequence exhibits obvious periodicity, with low-frequency components dominating.

[0069] The virtual impedance parameter encompasses the impedance characteristics at different frequencies, consisting of impedance magnitude and impedance phase angle. It is represented in matrix form, where rows represent different frequencies and columns represent impedance magnitude and phase angle. The virtual impedance parameter at 0.1 Hz is [0.05 Ω, 15°], at 0.5 Hz it is [0.08 Ω, 20°], at 1 Hz it is [0.12 Ω, 25°], at 2 Hz it is [0.18 Ω, 30°], at 5 Hz it is [0.25 Ω, 35°], and at 10 Hz it is [0.35 Ω, 40°]. The virtual impedance parameter is decomposed into impedance magnitude and impedance phase angle. The impedance magnitude reflects the impedance value at different frequencies, while the impedance phase angle reflects the phase relationship of the power response.

[0070] The power allocation factor is calculated based on the impedance amplitude, representing the proportion of compensation power distributed between wind turbine nodes and energy storage nodes. The calculation considers the dynamic characteristics of both wind turbines and energy storage devices; wind turbines are suitable for compensating for low-frequency fluctuations, while energy storage devices are suitable for compensating for medium- and high-frequency fluctuations. The power allocation factor at 0.1Hz is [0.7, 0.3], indicating that 70% of the compensation power is allocated to wind turbine nodes and 30% to energy storage nodes; the power allocation factor at 0.5Hz is [0.6, 0.4]; at 1Hz it is [0.5, 0.5]; at 2Hz it is [0.3, 0.7]; at 5Hz it is [0.1, 0.9]; and at 10Hz it is [0, 1]. The compensation timing is calculated based on the impedance phase angle, representing the difference in response time at different frequency points. The compensation timing at 0.1Hz is [150ms, 100ms], indicating that the wind power node has a 150ms response delay and the energy storage node has a 100ms response delay; the compensation timing at 0.5Hz is [100ms, 80ms]; the compensation timing at 1Hz is [80ms, 50ms]; the compensation timing at 2Hz is [60ms, 30ms]; the compensation timing at 5Hz is [40ms, 20ms]; and the compensation timing at 10Hz is [20ms, 10ms].

[0071] The compensation sequence is divided into wind power node compensation and energy storage node compensation based on power allocation coefficients and compensation timing. Wind power node compensation mainly includes low-frequency components, with a total compensation power of approximately 0.15MW; energy storage node compensation includes components across the entire frequency band, primarily mid-to-high frequencies, with a total compensation power of approximately 0.12MW. Wind power node compensation is combined with wind power vibration reduction commands to generate wind power compensation commands. The combination method is time-series superposition, where the power value at each time point is the sum of the original vibration reduction command value and the compensation amount. The wind power compensation commands are issued to each wind turbine through the wind farm control device to execute wind power node compensation. Energy storage node compensation is combined with energy storage integrated regulation commands to generate energy storage compensation commands. The combination also uses a time-series superposition method. The energy storage compensation commands are issued to the energy storage converter controller through the energy storage management device to execute energy storage node compensation.

[0072] like Figure 3 The diagram illustrates a comparison of power fluctuation probability densities in this embodiment. The existing technology's distribution curve shows a high probability distribution of fluctuation deviations between -0.5MW and +0.5MW, with a large standard deviation, indicating strong random shocks to the grid. In contrast, the fluctuation probability of this technology is highly concentrated near zero. Specifically, over 95% of the fluctuation deviations are successfully limited to an extremely narrow ±0.1MW range. This demonstrates that regardless of random wind conditions (simulating various scenarios such as gusts and gradual winds), the control algorithm can tightly lock the actual output power near the setpoint of 10MW. This statistically significant convergence strongly demonstrates, from a probabilistic perspective, the superior performance of this invention in suppressing random wind power fluctuations, significantly improving grid-connected power quality.

[0073] This invention executes wind power vibration reduction commands and energy storage integrated adjustment commands, collects real-time data on actual wind power fluctuations, calculates the frequency domain deviation from the power fluctuation spectrum, and distributes the frequency domain deviation to wind power nodes and energy storage nodes for compensation based on virtual impedance parameters, thus achieving coordinated operation of wind power and energy storage. This invention fully utilizes the respective response characteristics of wind power and energy storage; wind power is suitable for handling low-frequency fluctuations, while energy storage is suitable for handling medium- and high-frequency fluctuations, forming complementary advantages. Through precise setting of virtual impedance parameters, accurate allocation and coordinated compensation of power fluctuations in different frequency domains are achieved.

[0074] like Figure 4 As shown, Figure 4 This invention provides a schematic diagram of a wind farm frequency control system for coordinated wind and energy storage regulation, comprising: The information acquisition module is used to acquire wind speed forecast information, energy storage state of charge information, and grid frequency deviation information; The time-frequency conversion module is used to perform time-frequency conversion on the wind speed prediction information to obtain the power fluctuation spectrum, and extract the amplitude and phase information of the frequency components; The compensation calculation module is used to calculate the sustainable compensation duration of energy storage based on the energy storage state of charge information, and to couple the sustainable compensation duration of energy storage with the amplitude information to generate the compensation capacity coefficient. The wind power control module is used to calculate the allowable range of frequency domain fluctuations of the wind turbine based on the compensation capability coefficient. When the power fluctuation spectrum exceeds the allowable range of frequency domain fluctuations, it generates wind power vibration reduction commands. The virtual channel module is used to construct a virtual power transmission channel between wind power nodes and energy storage nodes, and to set virtual impedance parameters for the virtual power transmission channel according to the compensation capacity coefficient. The energy storage control module is used to calculate the phase lead angle based on the phase information and convert it into a time advance, generate a lead compensation sequence based on the time advance, determine the frequency response power demand based on the grid frequency deviation information, and time-series superimpose the frequency response power demand with the lead compensation sequence to generate an integrated energy storage regulation command. The collaborative compensation module is used to execute wind power vibration reduction commands and energy storage integrated adjustment commands, collect actual wind power fluctuations and calculate the frequency domain deviation from the power fluctuation spectrum, and distribute the frequency domain deviation to wind power nodes and energy storage nodes for compensation based on virtual impedance parameters.

[0075] One technical solution provided in this embodiment of the invention is an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0076] One technical solution provided in this embodiment of the invention is a computer-readable storage medium storing a computer program, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0077] The specific embodiments described above are preferred embodiments of the present invention and are not intended to limit the specific scope of the present invention. The scope of the present invention includes, but is not limited to, these specific embodiments. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A wind farm frequency control method for coordinated wind and energy storage regulation, characterized in that, Includes the following steps: Acquire wind speed forecast information, energy storage status of charge information, and grid frequency deviation information; The power fluctuation spectrum is obtained by performing time-frequency transformation on the wind speed prediction information, and the amplitude and phase information of the frequency components are extracted. The sustainable compensation duration of energy storage is calculated based on the energy storage state of charge information. The sustainable compensation duration of energy storage is coupled with the amplitude information to generate the compensation capacity coefficient. The allowable range of frequency domain fluctuations of the wind turbine is calculated in reverse based on the compensation capacity coefficient. When the power fluctuation spectrum exceeds the allowable range of frequency domain fluctuations, a wind power vibration reduction command is generated. Construct a virtual power transmission channel between wind power nodes and energy storage nodes, and set virtual impedance parameters for the virtual power transmission channel based on the compensation capacity coefficient; Calculate the phase lead angle based on the phase information and convert it into a time advance. Generate an advance compensation sequence based on the time advance. Determine the frequency response power demand based on the grid frequency deviation information. Superimpose the frequency response power demand with the advance compensation sequence in time to generate an integrated energy storage regulation command. The system executes wind power vibration reduction commands and energy storage integrated regulation commands, collects actual wind power fluctuations and calculates the frequency domain deviation from the power fluctuation spectrum, and distributes the frequency domain deviation to wind power nodes and energy storage nodes for compensation based on virtual impedance parameters.

2. The method according to claim 1, characterized in that, The power fluctuation spectrum is obtained by performing time-frequency transformation on the wind speed forecast information, and the amplitude and phase information of the frequency components are extracted, including: The wind speed prediction information is collected according to the preset sampling interval, the wind speed difference between adjacent sampling points is calculated, the sliding window length is set according to the wind speed difference, the wind speed prediction information is segmented, and a segmented wind speed sequence is generated. Wind speed-rotation speed mapping is performed on the segmented wind speed sequence to obtain rotational speed signal, and rotational speed-power mapping is performed on the rotational speed signal and generator power curve to generate power time sequence signal; The power time-series signal is decomposed into multiple wavelet decompositions according to a preset scale to generate a frequency band coefficient matrix. The frequency band weights are calculated based on the energy distribution of the frequency band coefficient matrix. Multiply the frequency band weights by the frequency band coefficient matrix to obtain the weighted coefficient matrix, and then reconstruct the power fluctuation spectrum by performing a reconstruction operation on the weighted coefficient matrix. A Fourier transform is performed on the power fluctuation spectrum to generate an amplitude matrix and a phase matrix. Frequency component amplitude information and phase information are extracted from the amplitude matrix and phase matrix, respectively.

3. The method according to claim 1, characterized in that, The sustainable compensation duration of energy storage is calculated based on the energy storage state-of-charge information. The sustainable compensation duration is then coupled with the amplitude information to generate compensation capacity coefficients, including: The system collects voltage and current information from the energy storage state of charge information, calculates the instantaneous power and remaining capacity of the energy storage, and calculates the power regulation rate based on the instantaneous power change trend. A compensation characteristic curve is established by combining the power regulation rate with the remaining capacity, and the upper limit and lower limit of power regulation are extracted from the compensation characteristic curve. The energy storage power-capacity ratio is calculated based on the upper limit of power regulation, the lower limit of power regulation, and the remaining capacity. The sustainable compensation time of energy storage is then calculated in combination with the power-capacity ratio. Wavelet transform is performed on the amplitude information to obtain different frequency components, and the fluctuation period corresponding to each frequency component is calculated. The compensation coefficient matrix is ​​generated by cross-mapping the sustainable compensation duration of energy storage with the fluctuation period. The compensation coefficient matrix is ​​then subjected to nonlinear coupling calculation. The nonlinear coupling calculation result is combined with the frequency component in a weighted manner to generate the compensation capacity coefficient.

4. The method according to claim 1, characterized in that, The allowable range of frequency domain fluctuations of the wind turbine is calculated in reverse based on the compensation capacity coefficient. When the power fluctuation spectrum exceeds the allowable range of frequency domain fluctuations, wind power vibration reduction commands are generated, including: Wavelet decomposition is performed on the compensation capability coefficient to obtain the frequency components, and the compensation response speed, compensation capacity, and compensation loss rate of the frequency components are calculated. The actual compensation capacity is calculated based on the compensation loss rate and the compensation capacity. The power adjustment range and the power adjustment slope are obtained by combining the actual compensation capacity and the compensation response speed. The frequency domain fluctuation tolerance range is set by using the power adjustment range and the power adjustment slope. Wavelet decomposition is performed on the power fluctuation spectrum to obtain the fluctuation components, and the amplitude parameters, phase parameters, and direction of change parameters of the fluctuation components are calculated. The power change is obtained by combining the change direction parameter and the amplitude parameter. The power change is compared with the allowable range of frequency domain fluctuation to calculate the amplitude over-limit and phase over-limit. The vibration reduction frequency band is determined based on the amplitude over-limit and phase over-limit. Based on the vibration reduction frequency band, the wind turbine operating parameters are read, the amplitude exceeding the limit is converted into the pitch angle reference value, the phase exceeding the limit is converted into the pitch angle advance value, and the pitch angle adjustment parameters are obtained by combining them. The pitch angle adjustment parameter is matched with the wind turbine speed parameter and wind speed parameter to calculate and output the pitch angle adjustment value and adjustment sequence, thereby generating a wind power vibration reduction command.

5. The method according to claim 1, characterized in that, Constructing a virtual power transmission channel between wind power nodes and energy storage nodes, and setting virtual impedance parameters for the virtual power transmission channel based on the compensation capacity coefficient, includes: The voltage and current parameters of the wind power nodes and the energy storage nodes are collected to calculate the instantaneous power value. Calculate the power transmission direction parameters and power transmission amplitude based on the instantaneous power value; Calculate the line loss coefficient and time delay coefficient based on the power transmission direction parameter and the power transmission amplitude, and combine them to generate transmission characteristic parameters; Wavelet decomposition is performed on the compensation capability coefficient to obtain the frequency band coefficient. The compensation gain value and compensation delay value are extracted from the frequency band coefficient. The compensation gain value is multiplied by the line loss coefficient to obtain the virtual resistance value. The compensation delay value is multiplied by the time delay coefficient to obtain the virtual inductance value. The virtual resistance and virtual inductance values ​​are combined according to the frequency band to generate impedance parameters, and the impedance parameters are mapped to the transmission characteristic parameters to obtain the matching coefficient. The impedance reference parameter is calculated based on the matching coefficient. The impedance adjustment parameter is calculated based on the impedance reference parameter and the transmission characteristic parameter. The impedance reference parameter and the impedance adjustment parameter are combined to form the virtual impedance parameter. A virtual power transmission channel is constructed by adjusting the power transmission direction parameters and power transmission amplitude based on the virtual impedance parameters.

6. The method according to claim 1, characterized in that, The phase lead angle is calculated based on the phase information and converted into a time lead. A lead compensation sequence is generated based on the time lead. The frequency response power demand is determined based on the grid frequency deviation information. The frequency response power demand is then time-series superimposed with the lead compensation sequence to generate an integrated energy storage regulation command, including: Wavelet decomposition is performed on the phase information to obtain the amplitude and angle components. A wave matrix is ​​constructed based on the amplitude components, and a compensation matrix is ​​constructed based on the angle components. The wave matrix and the compensation matrix are orthogonally decomposed to form the phase change. The phase change is decomposed into singular values ​​to obtain the correction amplitude. The compensation parameter is constructed based on the correction amplitude. The compensation parameter and the correction amplitude are orthogonally transformed to generate the phase lead angle. The phase lead angle and the wave matrix are used to perform feature mapping to obtain the time series parameters. The compensation duration is extracted based on the time series parameters and then converted into a time advance. The frequency disturbance is calculated based on the grid frequency deviation information. The frequency disturbance is combined with the compensation adjustment to obtain the power change. The frequency response power demand is constructed based on the power change and the timing compensation. The compensation interval is divided according to the time lead, and the power demand within the compensation interval is adjusted by the magnitude compensation to generate the advance compensation sequence. Based on the compensation interval, the advance compensation sequence is divided into compensation sub-sequences, the frequency response power demand is divided into response sub-sequences according to the time-series compensation amount, and the compensation sub-sequences and response sub-sequences are combined in time sequence to generate an integrated energy storage regulation command.

7. The method according to claim 1, characterized in that, Execute wind power vibration reduction commands and energy storage integrated regulation commands, collect actual wind power fluctuations and calculate the frequency domain deviation from the power fluctuation spectrum, and distribute the frequency domain deviation to wind power nodes and energy storage nodes according to virtual impedance parameters. Compensation includes: Execute wind power vibration reduction commands and energy storage integrated regulation commands, collect real-time output power of wind turbine units, calculate real-time fluctuation values ​​of output power, and generate actual wind power fluctuation sequences based on real-time fluctuation values; The actual wind power fluctuation sequence is frequency domain transformed to generate a real-time fluctuation spectrum. The frequency domain deviation value is calculated by comparing the real-time fluctuation spectrum with the power fluctuation spectrum. Calculate the compensation power value based on the frequency domain deviation value, and arrange the compensation power value in time order to generate a compensation sequence; The virtual impedance parameter is decomposed into impedance magnitude and impedance phase angle. The power distribution coefficient is calculated based on the impedance magnitude, and the compensation timing is calculated based on the impedance phase angle. The compensation sequence is divided into wind power node compensation and energy storage node compensation according to the power allocation coefficient and compensation time sequence. The wind power node compensation amount is combined with the wind power vibration reduction command to generate a wind power compensation command for wind power node compensation. The energy storage node compensation amount is combined with the energy storage comprehensive regulation command to generate an energy storage compensation command for energy storage node compensation.

8. A wind farm frequency control system for coordinated wind and energy storage regulation, used to implement the method described in any one of claims 1-7, characterized in that, The system includes: The information acquisition module is used to acquire wind speed forecast information, energy storage state of charge information, and grid frequency deviation information; The time-frequency conversion module is used to perform time-frequency conversion on the wind speed prediction information to obtain the power fluctuation spectrum, and extract the amplitude and phase information of the frequency components; The compensation calculation module is used to calculate the sustainable compensation duration of energy storage based on the energy storage state of charge information, and to couple the sustainable compensation duration of energy storage with the amplitude information to generate the compensation capacity coefficient. The wind power control module is used to calculate the allowable range of frequency domain fluctuations of the wind turbine based on the compensation capability coefficient. When the power fluctuation spectrum exceeds the allowable range of frequency domain fluctuations, it generates wind power vibration reduction commands. The virtual channel module is used to construct a virtual power transmission channel between wind power nodes and energy storage nodes, and to set virtual impedance parameters for the virtual power transmission channel according to the compensation capacity coefficient. The energy storage control module is used to calculate the phase lead angle based on the phase information and convert it into a time advance, generate a lead compensation sequence based on the time advance, determine the frequency response power demand based on the grid frequency deviation information, and time-series superimpose the frequency response power demand with the lead compensation sequence to generate an integrated energy storage regulation command. The collaborative compensation module is used to execute wind power vibration reduction commands and energy storage integrated adjustment commands, collect actual wind power fluctuations and calculate the frequency domain deviation from the power fluctuation spectrum, and distribute the frequency domain deviation to wind power nodes and energy storage nodes for compensation based on virtual impedance parameters.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 7.

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