CN104201700B - Meter and the regional power grid thermoelectricity frequency modulation crew qiting method of the uncertain fluctuation of wind-powered electricity generation - Google Patents

Abstract

A configuration method for thermal power frequency modulation units in a regional power grid considering wind power uncertainty fluctuations relates to a configuration method for thermal power frequency modulation units considering wind power. In order to solve the problem of affecting the system frequency stability after large-scale wind power is connected to the grid. The present invention uses the Mallat wavelet decomposition and reconstruction algorithm as a tool to decompose and reconstruct the wind power time series, and based on the wavelet multi-scale analysis method, establishes the instantaneous model of the second-level and minute-level wind power uncertainty fluctuations, giving After the time-domain instantaneous expression is obtained, a regional power grid model including wind power for frequency modulation analysis is established. Based on the frequency modulation analysis model, expressions representing the primary and secondary frequency modulation capabilities of the system are given. Based on the frequency modulation capability expressions, the quantitative analysis calculation system is Primary and secondary frequency modulation capabilities under different conditions. The invention can reduce system frequency fluctuations caused by wind power fluctuations and maintain system frequency stability. The invention is applicable to the configuration of thermal power frequency modulation units considering wind power.

Description

Configuration method of thermal power frequency modulation units in regional power grid considering wind power uncertainty fluctuation

technical field

The invention relates to a configuration method of a thermal power frequency regulation unit considering wind power

Background technique

The existing wind power ultra-short-term forecasting technology generally only gives the specific value of wind power at a certain point in time, and the time resolution is 15 minutes, which cannot predict the uncertainty fluctuation of wind power at a smaller time resolution; and the existing model When considering the frequency modulation of wind turbines, active power and reactive power are generally not decoupled, but generally considered, which is not conducive to effective analysis; when analyzing the impact of wind power fluctuations on system frequency modulation, they do not take into account The analysis of the same wind power fluctuation and the corresponding time scale of the secondary frequency regulation is not conducive to targeted analysis; and the existing power system frequency regulation unit configuration method is only configured according to the degree of load fluctuation due to the small wind power access rate Units are not conducive to the stability of the system frequency after large-scale grid-connected wind power.

Contents of the invention

In order to solve the problem of influence on system frequency stability after large-scale wind power is connected to the grid, the present invention further proposes a regional power grid thermal power frequency regulation unit configuration method that takes into account wind power uncertainty fluctuations.

The process of the configuration method of thermal power frequency modulation units in the regional power grid considering the uncertainty fluctuation of wind power is as follows:

Step 1: Use the Mallat wavelet decomposition and reconstruction algorithm as a tool, and select db10 as the wavelet base to decompose and reconstruct the wind power time series: first, perform wavelet decomposition on the measured wind power data with a sampling interval of nS, and specifically decompose The number of layers m is determined by the sampling interval. It should be ensured that the last layer period of decomposition n·2 ^m is exactly 15 min or n·2 ^m is greater than 15 min and the closest to 15 min. If the difference between n·2 ^m and 15 min is within 3 min, the The m-th layer is used as the hourly average wind power at the hourly level. If the difference between n·2 ^m and 15 min is greater than 3 min, the m-th layer and the m-1th layer are reconstructed to obtain the hourly average wind power at the hourly level; Reconstruct the second-level and minute-level decomposition layers of the subsequent periods to obtain the second-level and minute-level power fluctuation residuals after the hourly average wind power is removed from the original wind power sequence;

Based on the wavelet multi-scale analysis method, the instantaneous model of the second-level and minute-level wind power uncertainty fluctuations is established, and the time-domain instantaneous expression is given;

(1) Instantaneous relationship expression between graded wind power fluctuation and hourly average power of wind farm

In the formula, _σm is the standard deviation of the minute-level fluctuation of the output power of the wind farm, is the hourly average power of the wind farm, a, b, and c are the fitting coefficients, which can be determined by least square fitting;

(2) Instantaneous relationship expression between the second-level wind power fluctuation and the hour-level average power of the wind farm

In the formula, σ _s is the standard deviation of the second-level fluctuation of the output power of the wind farm;

Formulas (1) and (2) establish the real-time correspondence between the standard deviation of the second-level and minute-level wind power uncertainty fluctuations and the hourly average power of the wind farm; the wind power prediction value with a time resolution of 15 minutes is exactly is the hour-level wind power, which is used as the formula (1) and (2) Input, real-time forecast for the range of second-level and minute-level wind power plant power uncertainty fluctuations;

Step 2: According to the actual situation of the power plant, establish a regional power grid model including wind power for frequency regulation analysis, and based on the frequency regulation analysis model, give the expressions representing the primary and secondary frequency regulation capabilities of the system:

Establish a regional power grid model with wind power for frequency modulation analysis, as shown in Figure 2; where area A contains wind power, and area B only includes thermal power units;

Each area contains a primary frequency modulation channel and a secondary frequency modulation channel. The output of the system is the current frequency deviation χ _f (s) of each area, and the frequency deviation χ _f (s) of each area is used as a feedback signal through the primary and secondary frequency modulation The channel realizes the primary and secondary adjustment of the frequency in each area; in the figure, α _i is the power generation share coefficient of the i-th unit, δ _i is the adjustment coefficient of the i-th thermal power unit, and R _i is the frequency of the unit participating in the secondary frequency regulation. Power distribution coefficient, B _A , B _B are the frequency deviation coefficients of each area, K _A , K _B are the gains of the secondary frequency modulation integrator in each area respectively; G _i (s) is the transfer function of the i-th generator set, specifically expressed as The formula is as follows:

in, Indicates the dynamic characteristics of the hydraulic servo motor, T _s s is the time constant of the hydraulic servo motor, take T _s s = 0.2s; Indicates the dynamic characteristics of the volume of the steam turbine, T ₀ s is the volume time constant of the high-pressure cylinder; T _a∑ indicates the equivalent rotor time constant, and all synchronous generators in this system except the fan are equivalent to one For a synchronous generator, the equivalent rotor time constant can be obtained by multiplying the power share coefficient by the respective rotor time constant; β _A and β _B represent the equivalent friction coefficients of area A and area B _respectively , and the The calculation method is the same, and it is also calculated according to the power share coefficient; the load fluctuation, wind power fluctuation and tie line power fluctuation are respectively χ _NL (s), χ _Wind (s) and χ _Ptie (s);

The primary frequency regulation capability of the power grid considering the uncertainty fluctuation of wind power, that is, the ratio of the second-level wind power fluctuation variance to the power grid frequency fluctuation variance within a certain period of time under the action of only one frequency regulation, the expression is:

Select the model shown in Figure 2 assuming that there is no load fluctuation in area A, disconnect its secondary frequency modulation channel, that is, only consider the primary frequency modulation effect, and calculate the frequency domain analytical expression of the grid frequency change and second-level wind power fluctuation as follows:

in,

Suppose the input is a zero-mean signal x(t), after the corresponding input-output transfer function H(jω), the output is y(t), j is an imaginary number, and ω is the angular frequency, then the output y(t) The variance of is:

In the formula, S _y (ω)=S _x (ω)|H(jω)| ² , S _y (ω) represents the power spectral density of the output, and S _x (ω) represents the power spectral density of the input;

Considering that the variables involved are deviations with an average value of 0, substituting Equation (5) into Equation (6), the variance of the system frequency fluctuation caused by the second-level wind power fluctuation can be obtained

Among them, S _w1 (ω) is the power spectral density of second-level wind power fluctuations;

It can be obtained from the PSD time-frequency conversion computer algorithm, the sampling of the power spectral density of σ _s (i) in the frequency domain can be approximated as:

Among them, _MFFT (.) is the fast Fourier transform, N is the sampling length, ω _s is the sampling frequency, and E(·) is the mean function; according to the Shannon sampling theorem, when the sampling frequency is more than twice the signal frequency , the continuous signal can be completely reconstructed from the sampling samples; therefore, by selecting an appropriate sampling frequency, the power spectral density of the second-level fluctuation of wind power can be obtained through formula (8); using formula (8) as the power spectral density band of the output signal Entering formula (6), the variance of the second-level wind power fluctuation is obtained as:

After derivation, the frequency-domain analytical expression of the primary frequency regulation capability of the power grid considering the uncertainty fluctuation of wind power can be obtained as:

in, is the variance of the second-level wind power fluctuation, is the variance of the system frequency fluctuation caused by the second-level wind power fluctuation, S _w1 (ω) is the power spectral density of the second-level wind power fluctuation;

The secondary frequency regulation capability of the power grid considering the uncertainty fluctuation of wind power, that is, the ratio of the minute-level wind power fluctuation variance to the power grid frequency fluctuation variance within a certain period of time only under the secondary frequency regulation, the expression is:

Using the above derivation method, the variance of minute-level wind power fluctuations can also be obtained and the variance of system frequency fluctuations caused by minute-scale wind power fluctuations After derivation, the frequency-domain analytical expression of the power grid's secondary frequency regulation capability considering wind power uncertainty fluctuations can be obtained as:

in, is the variance of minute-level wind power fluctuations, is the variance of the system frequency fluctuation, S _w2 (ω) is the power spectral density of the minute-level wind power fluctuation;

Step 3: Based on the frequency modulation capability expression, quantitatively analyze and calculate the primary and secondary frequency modulation capabilities of the system under different conditions.

The establishment of a regional power grid model including wind power for frequency modulation analysis in step three of the thermal power frequency modulation unit configuration method of regional power grid considering wind power uncertainty fluctuations is realized by using Matlab/Simulink software.

The third step of the regional power grid thermal power frequency regulation unit configuration method considering the uncertainty fluctuation of wind power is based on the quantitative analysis of the frequency regulation capability expression and the realization process of the primary and secondary frequency regulation capabilities of the system under different conditions is as follows:

Step 1: Keeping the ratio of thermal power primary frequency regulation units constant, calculate the primary frequency regulation capabilities of the system under different differential coefficients; keep the differential coefficients of each unit constant, and calculate the primary frequency regulation capabilities of the system with different thermal power primary frequency regulation ratios; according to the calculation As a result, draw a table with the proportion of thermal power primary frequency modulation units as the abscissa and the adjustment coefficient as the vertical axis, and from the data in the table, the influence laws of different proportions of thermal power primary frequency modulation units and different difference adjustment coefficients on the primary frequency regulation ability are obtained;

Step 2: Keeping the ratio of thermal power secondary frequency modulation units constant, calculate the secondary frequency modulation capability of the system under different integrator gains; keep the integrator gains of each unit constant, and calculate the secondary frequency modulation of the system under different thermal power secondary frequency modulation unit ratios capacity; according to the calculation results, draw a table with the proportion of thermal power secondary frequency modulation units as the abscissa and the integrator gain as the vertical axis, and calculate the different ratios of thermal power secondary frequency modulation units and different integrator gains for secondary Influence law of frequency modulation capability;

Step 3: Based on the quantitative analysis results of Step 1 and Step 2, provide a thermal power unit configuration method that can effectively suppress system frequency fluctuations caused by wind farm power uncertainty fluctuations.

Step 3 of the regional power grid thermal power frequency modulation unit configuration method considering wind power uncertainty fluctuations Based on the quantitative analysis results of steps 1 and 2, provide thermal power unit configurations that can effectively suppress system frequency fluctuations caused by wind farm power uncertainty fluctuations The implementation process of the method is:

Step 3.1.1: If the adjustment coefficient δ _iA of each unit in area A remains unchanged, the value of D _PFRA is K ₁ , and K ₁ is a constant; when the proportion of primary frequency adjustment units increases from p% to 2p%, the value of D _PFRA increases as large as 2K ₁ ; that is, if the second-level fluctuation of wind power output increases from P to k ₁ ·P at a certain moment, k ₁ is a constant, and the frequency is kept stable at the level when the wind power fluctuation does not increase. Increased from p% to k ₁ ·p%;

Step 3.1.2: If the gain K _A of the integrator in area A remains unchanged, the value of D _SFRA is K ₂ , and K ₂ is an assumed constant; when the proportion of the secondary frequency modulation unit increases from p% to 2p%, the value of D _SFRA increases As large as 2K ₂ ; that is, if the minute-level fluctuation of wind power output increases from P to k ₂ ·P at a certain moment, the frequency is kept stable at the level when the wind power fluctuation does not increase, and the proportion of primary frequency modulation units is increased from p% to k ₂ ·p%.

Step 3 of the regional power grid thermal power frequency modulation unit configuration method considering wind power uncertainty fluctuations Based on the quantitative analysis results of steps 1 and 2, provide thermal power unit configurations that can effectively suppress system frequency fluctuations caused by wind farm power uncertainty fluctuations The implementation process of the method is:

Step 3.2.1: If the proportion of units participating in the primary frequency regulation in area A remains unchanged, adjust the differential rate δ _iA of each unit, and the D _PFRA value and δ _iA have the following approximate relationship, namely:

That is, if the second-level fluctuation of wind power output increases from P to k ₁ P at a certain moment, k ₁ is a hypothetical constant, and the frequency is kept stable at the level when the wind power fluctuation does not increase, and δ _iA is reduced to k ₁ δ _iA ;

Step 3.2.2: If the gain K _A of the integrator in area A remains unchanged, the value of D _SFRA is K ₂ , and K ₂ is an assumed constant; when the proportion of the secondary frequency modulation unit increases from p% to 2p%, the value of D _SFRA increases As large as 2K ₂ ; that is, if the minute-level fluctuation of wind power output increases from P to k ₂ ·P at a certain moment, the frequency is kept stable at the level when the wind power fluctuation does not increase, and the proportion of primary frequency modulation units is increased from p% to k ₂ ·p%.

According to the configuration of thermal power frequency modulation units in the present invention, system frequency fluctuations caused by wind power fluctuations can be reduced, and the system frequency can be kept stable. In the case of the original thermal power frequency modulation unit configuration of the system, the system’s requirement for frequency ±0.1Hz is exceeded at some point in time; when the thermal power frequency modulation unit configuration method provided by the present invention is adopted, the system frequency fluctuation is reduced, and the standard deviation is changed from σ= 0.00042885 reduces to σ = 0.00021798.

Description of drawings

Figure 1. Schematic diagram of the configuration method of thermal power frequency modulation units in regional power grids considering wind power uncertainty fluctuations;

Figure 2. The regional power grid model with wind power for frequency regulation analysis;

Figure 3 System frequency before adopting the configuration method;

Figure 4 System frequency after adopting the configuration method;

Figure 5. Wind farm output power and time series of fluctuation components;

Fig. 6 Schematic diagram of Mallat 8-layer wavelet decomposition and reconstruction algorithm;

Figure 7. Instantaneous fitting model of minute-level wind power plant power uncertainty fluctuations;

Figure 8. Instantaneous fitting model of second-level wind power plant power uncertainty fluctuations.

detailed description

Specific implementation mode 1: This implementation mode is described in conjunction with FIG. 1 and FIG. 2. The process of configuring the thermal power frequency regulation unit configuration method of the regional power grid considering the uncertainty fluctuation of wind power is as follows:

Step 1: Use the Mallat wavelet decomposition and reconstruction algorithm as a tool, and select db10 as the wavelet base to decompose and reconstruct the wind power time series: first, perform wavelet decomposition on the measured wind power data with a sampling interval of nS, and specifically decompose The number of layers m is determined by the sampling interval. It should be ensured that the last layer period of decomposition n·2 ^m is exactly 15 min or n·2 ^m is greater than 15 min and the closest to 15 min. If the difference between n·2 ^m and 15 min is within 3 min, the The m-th layer is used as the hourly average wind power at the hourly level. If the difference between n·2 ^m and 15 min is greater than 3 min, the m-th layer and the m-1th layer are reconstructed to obtain the hourly average wind power at the hourly level; Reconstruct the second-level and minute-level decomposition layers of the subsequent periods to obtain the second-level and minute-level power fluctuation residuals after the hourly average wind power is removed from the original wind power sequence;

Based on the wavelet multi-scale analysis method, the instantaneous model of the second-level and minute-level wind power uncertainty fluctuations is established, and the time-domain instantaneous expression is given;

(1) Instantaneous relationship expression between graded wind power fluctuation and hourly average power of wind farm

In the formula, _σm is the standard deviation of the minute-level fluctuation of the output power of the wind farm, is the hourly average power of the wind farm, a, b, and c are the fitting coefficients, which can be determined by least square fitting;

(2) Instantaneous relationship expression between the second-level wind power fluctuation and the hour-level average power of the wind farm

In the formula, σ _s is the standard deviation of the second-level fluctuation of the output power of the wind farm;

Formulas (1) and (2) establish the real-time correspondence between the standard deviation of the second-level and minute-level wind power uncertainty fluctuations and the hourly average power of the wind farm; the wind power prediction value with a time resolution of 15 minutes is exactly is the hour-level wind power, which is used as the formula (1) and (2) Input, real-time forecast for the range of second-level and minute-level wind power plant power uncertainty fluctuations;

Step 2: According to the actual situation of the power plant, establish a regional power grid model including wind power for frequency regulation analysis, and based on the frequency regulation analysis model, give the expressions representing the primary and secondary frequency regulation capabilities of the system:

Establish a regional power grid model with wind power for frequency modulation analysis, as shown in Figure 2; where area A contains wind power, and area B only includes thermal power units;

Each area contains a primary frequency modulation channel and a secondary frequency modulation channel. The output of the system is the current frequency deviation χ _f (s) of each area, and the frequency deviation χ _f (s) of each area is used as a feedback signal through the primary and secondary frequency modulation The channel realizes the primary and secondary adjustment of the frequency in each area; in the figure, α _i is the power generation share coefficient of the i-th unit, δ _i is the adjustment coefficient of the i-th thermal power unit, and R _i is the frequency of the unit participating in the secondary frequency regulation. Power distribution coefficient, B _A , B _B are the frequency deviation coefficients of each area, K _A , K _B are the gains of the secondary frequency modulation integrator in each area respectively; G _i (s) is the transfer function of the i-th generator set, specifically expressed as The formula is as follows:

in, Indicates the dynamic characteristics of the hydraulic servo motor, T _s s is the time constant of the hydraulic servo motor, take T _s s = 0.2s; Indicates the dynamic characteristics of the volume of the steam turbine, T ₀ s is the volume time constant of the high-pressure cylinder; T _a∑ indicates the equivalent rotor time constant, and all synchronous generators in this system except the fan are equivalent to one For a synchronous generator, the equivalent rotor time constant can be obtained by multiplying the power share coefficient by the respective rotor time constant; β _A and β _B represent the equivalent friction coefficients of area A and area B _respectively , and the The calculation method is the same, and it is also calculated according to the power share coefficient; the load fluctuation, wind power fluctuation and tie line power fluctuation are respectively χ _NL (s), χ _Wind (s) and χ _Ptie (s);

The primary frequency regulation capability of the power grid considering the uncertainty fluctuation of wind power, that is, the ratio of the second-level wind power fluctuation variance to the power grid frequency fluctuation variance within a certain period of time under the action of only one frequency regulation, the expression is:

Select the model shown in Figure 2 assuming that there is no load fluctuation in area A, disconnect its secondary frequency modulation channel, that is, only consider the primary frequency modulation effect, and calculate the frequency domain analytical expression of the grid frequency change and second-level wind power fluctuation as follows:

in,

Suppose the input is a zero-mean signal x(t), after the corresponding input-output transfer function H(jω), the output is y(t), j is an imaginary number, and ω is the angular frequency, then the output y(t) The variance of is:

In the formula, S _y (ω)=S _x (ω)|H(jω)| ² , S _y (ω) represents the power spectral density of the output, and S _x (ω) represents the power spectral density of the input;

Considering that the variables involved are deviations with an average value of 0, substituting Equation (5) into Equation (6), the variance of the system frequency fluctuation caused by the second-level wind power fluctuation can be obtained

Among them, S _w1 (ω) is the power spectral density of second-level wind power fluctuations;

It can be obtained from the PSD time-frequency conversion computer algorithm, the sampling of the power spectral density of σ _s (i) in the frequency domain can be approximated as:

Among them, _MFFT (.) is the fast Fourier transform, N is the sampling length, ω _s is the sampling frequency, and E(·) is the mean function; according to the Shannon sampling theorem, when the sampling frequency is more than twice the signal frequency , the continuous signal can be completely reconstructed from the sampling samples; therefore, by selecting an appropriate sampling frequency, the power spectral density of the second-level fluctuation of wind power can be obtained through formula (8); using formula (8) as the power spectral density band of the output signal Entering formula (6), the variance of the second-level wind power fluctuation is obtained as:

After derivation, the frequency-domain analytical expression of the primary frequency regulation capability of the power grid considering the uncertainty fluctuation of wind power can be obtained as:

in, is the variance of the second-level wind power fluctuation, is the variance of the system frequency fluctuation caused by the second-level wind power fluctuation, S _w1 (ω) is the power spectral density of the second-level wind power fluctuation;

The secondary frequency regulation capability of the power grid considering the uncertainty fluctuation of wind power, that is, the ratio of the minute-level wind power fluctuation variance to the power grid frequency fluctuation variance within a certain period of time only under the secondary frequency regulation, the expression is:

Using the above derivation method, the variance of minute-level wind power fluctuations can also be obtained and the variance of system frequency fluctuations caused by minute-scale wind power fluctuations After derivation, the frequency-domain analytical expression of the power grid's secondary frequency regulation capability considering wind power uncertainty fluctuations can be obtained as:

in, is the variance of minute-level wind power fluctuations, is the variance of the system frequency fluctuation, S _w2 (ω) is the power spectral density of the minute-level wind power fluctuation;

Step 3: Based on the frequency modulation capability expression, quantitatively analyze and calculate the primary and secondary frequency modulation capabilities of the system under different conditions.

Embodiment 2: The establishment of a regional power grid model including wind power for frequency modulation analysis in step 3 described in this embodiment is realized by using Matlab/Simulink software.

Other steps are the same as in the first embodiment.

Specific implementation mode three: Step three described in this embodiment is based on the quantitative analysis of the frequency modulation capability expression and the realization process of the primary and secondary frequency modulation capabilities of the calculation system under different conditions is as follows:

Step 1: Keeping the ratio of thermal power primary frequency regulation unit constant, calculate the primary frequency regulation capability of the system under different differential coefficients; keep the differential coefficient of each unit constant, and calculate the primary frequency regulation capability of the system in different thermal power primary frequency regulation unit ratios; according to the calculation As a result, draw a table with the proportion of thermal power primary frequency modulation units as the abscissa and the adjustment coefficient as the vertical axis, and from the data in the table, the influence rules of different proportions of thermal power primary frequency modulation units and different difference adjustment coefficients on the primary frequency regulation ability are obtained;

Step 2: Keeping the ratio of thermal power secondary frequency modulation units constant, calculate the secondary frequency modulation capability of the system under different integrator gains; keep the integrator gains of each unit constant, and calculate the secondary frequency modulation of the system under different thermal power secondary frequency modulation unit ratios capacity; according to the calculation results, draw a table with the proportion of thermal power secondary frequency modulation units as the abscissa and the integrator gain as the vertical axis, and calculate the different ratios of thermal power secondary frequency modulation units and different integrator gains for secondary Influence law of frequency modulation capability;

Step 3: Based on the quantitative analysis results of Step 1 and Step 2, provide a thermal power unit configuration method that can effectively suppress system frequency fluctuations caused by wind farm power uncertainty fluctuations.

Other steps are the same as in the first embodiment.

Specific Embodiment 4: Step 3 of this embodiment is based on the quantitative analysis results of Step 1 and Step 2 to provide a thermal power unit configuration method that can effectively suppress system frequency fluctuations caused by wind farm power uncertainty fluctuations. The implementation process is as follows:

Step 3.1.1: If the adjustment coefficient δ _iA of each unit in area A remains unchanged, the value of D _PFRA is K ₁ , and K ₁ is a constant; when the proportion of primary frequency adjustment units increases from p% to 2p%, the value of D _PFRA increases as large as 2K ₁ ; that is, if the second-level fluctuation of wind power output increases from P to k ₁ ·P at a certain moment, k ₁ is a constant, and the frequency is kept stable at the level when the wind power fluctuation does not increase. Increased from p% to k ₁ ·p%;

Step 3.1.2: If the gain K _A of the integrator in area A remains unchanged, the value of D _SFRA is K ₂ , and K ₂ is an assumed constant; when the proportion of the secondary frequency modulation unit increases from p% to 2p%, the value of D _SFRA increases As large as 2K ₂ ; that is, if the minute-level fluctuation of wind power output increases from P to k ₂ ·P at a certain moment, the frequency is kept stable at the level when the wind power fluctuation does not increase, and the proportion of primary frequency modulation units is increased from p% to k ₂ ·p%.

Other steps are the same as in the third embodiment.

Embodiment 5: Step 3 described in this embodiment is based on the quantitative analysis results of Step 1 and Step 2 to provide a thermal power unit configuration method that can effectively suppress system frequency fluctuations caused by wind farm power uncertainty fluctuations. The implementation process is as follows:

Step 3.2.1: If the proportion of units participating in the primary frequency regulation in area A remains unchanged, adjust the differential rate δ _iA of each unit, and the D _PFRA value and δ _iA have the following approximate relationship, namely:

That is, if the second-level fluctuation of wind power output increases from P to k ₁ P at a certain moment, k ₁ is a hypothetical constant, and the frequency is kept stable at the level when the wind power fluctuation does not increase, and δ _iA is reduced to k ₁ δ _iA ;

Step 3.2.2: If the gain K _A of the integrator in area A remains unchanged, the value of D _SFRA is K ₂ , and K ₂ is an assumed constant; when the proportion of the secondary frequency modulation unit increases from p% to 2p%, the value of D _SFRA increases As large as 2K ₂ ; that is, if the minute-level fluctuation of wind power output increases from P to k ₂ ·P at a certain moment, the frequency is kept stable at the level when the wind power fluctuation does not increase, and the proportion of primary frequency modulation units is increased from p% to k ₂ ·p%.

Other steps are the same as in the third embodiment.

specific embodiment

The simulation is carried out with the actual day's operation data of a power grid.

Step 1: Using the Mallat wavelet decomposition and reconstruction algorithm as a tool, and selecting db10 as the wavelet base, the wind power time series is decomposed and reconstructed. First, conduct 8-layer decomposition of the measured wind power data (the sampling interval of the patent sample data is 5S, and the time length is one month); reconstruct the first 8 layers and the 7th layer, and obtain the hour-level cycle Average wind power: Reconstruct the following layers 1-3 and 4-6 respectively to obtain the second-level and minute-level power fluctuation residuals after the hourly average wind power is removed from the original wind power sequence (as shown in Figure 5). If the sampling interval of wind speed data is 1S, it needs to be reconstructed after 10 layers of decomposition to obtain the average wind power and the corresponding fluctuation residual in the hour-level period.

The schematic diagram of Mallat wavelet decomposition and reconstruction algorithm is shown in Fig. 5.

Take the absolute value of the second-level and minute-level wind power fluctuation time series and sort them by size, and establish a one-to-one corresponding point-series relationship with the hour-level average power time series of the wind farm. According to the least squares fitting principle, the 3σ principle is used to eliminate outliers, and statistical modeling is carried out on the point column pairs. The data were fitted using the least squares fitting method, and the fitting results are shown in Figures 7 and 8.

Based on the above statistical modeling method, the instantaneous expressions of the second-level and minute-level wind power uncertainty fluctuations of the formulas (1) and (2) can be obtained based on the historical data of different wind farms.

Step 2: The establishment of the frequency modulation model is shown in Figure 2. The derivation process of the expressions representing the primary and secondary frequency modulation capabilities of the system given by the present invention will be analyzed in detail below.

Select the model shown in Figure 2 assuming that there is no load fluctuation in area A, disconnect its secondary frequency modulation channel, that is, only consider the primary frequency modulation effect, and calculate the frequency domain analytical expression of the grid frequency change and second-level wind power fluctuation as follows:

in,

Suppose the input is a zero-mean signal x(t), after the corresponding input-output transfer function H(jω), the output is y(t), j is an imaginary number, and ω is the angular frequency, then the output y(t) The variance of is:

In the formula, S _y (ω)=S _x (ω)|H(jω)| ² , S _y (ω) represents the power spectral density of the output, and S _x (ω) represents the power spectral density of the input;

Considering that the variables involved are deviations with an average value of 0, substituting Equation (3) into Equation (4), the variance of system frequency fluctuations caused by second-level wind power fluctuations can be obtained

Among them, S _w1 (ω) is the power spectral density of second-level wind power fluctuations;

It can be obtained from the wind farm power fluctuation PSD time-frequency conversion computer algorithm, the sampling of the power spectral density of σ _s (i) in the frequency domain can be approximated as:

Among them, _MFFT (.) is the fast Fourier transform, N is the sampling length, ω _s is the sampling frequency, and E(·) is the mean function; according to the Shannon sampling theorem, when the sampling frequency is more than twice the signal frequency , the continuous signal can be completely reconstructed from the sampling samples; therefore, by selecting an appropriate sampling frequency, the power spectral density of the second-level fluctuation of wind power can be obtained through formula (6); using formula (6) as the power spectral density band of the output signal Entering formula (4), the variance of the second-level wind power fluctuation is obtained as:

The primary frequency regulation capability of the power grid considering the uncertainty fluctuation of wind power, that is, the ratio of the second-level wind power fluctuation variance to the power grid frequency fluctuation variance within a certain period of time under the action of only one frequency regulation, the expression is:

The secondary frequency regulation capability of the power grid considering the uncertainty fluctuation of wind power, that is, the ratio of the minute-level wind power fluctuation variance to the power grid frequency fluctuation variance within a certain period of time only under the secondary frequency regulation, the expression is:

Substituting the formula (5) representing the grid frequency variance and the formula (7) representing the second-level wind power fluctuation variance into the dynamic expression (8) of the primary frequency regulation capability, the primary frequency regulation capability of the power grid considering the uncertainty fluctuation of wind power can be obtained The frequency domain analytical expression of is:

in, is the variance of the second-level wind power fluctuation, is the variance of the system frequency fluctuation caused by the second-level wind power fluctuation, S _w1 (ω) is the power spectral density of the second-level wind power fluctuation;

Using the above derivation method, the variance of minute-level wind power fluctuations can also be obtained and the variance of system frequency fluctuations caused by minute-scale wind power fluctuations After derivation, the frequency-domain analytical expression of the power grid's secondary frequency regulation capability considering wind power uncertainty fluctuations can be obtained as:

in, is the variance of minute-level wind power fluctuations, is the variance of the system frequency fluctuation, S _w2 (ω) is the power spectral density of the minute-level wind power fluctuation;

Step 3: Quantitatively analyze the primary and secondary frequency regulation capabilities when the system contains different proportions of thermal power frequency regulation units based on the expression of frequency regulation capability; provide a reference method for the dispatching department to configure thermal power frequency regulation units, and realize the system for wind farm power uncertainty fluctuations Effective control of frequency fluctuations.

According to formula (5) and formula (7), the primary and secondary frequency regulation capabilities of the system when the system contains different proportions of thermal power frequency regulation units are quantitatively analyzed, and the results should be shown in Tables 1 and 2 (for different systems, the calculation results may be vary).

Table 1 Primary frequency modulation capability D _PFCA of the system under different parameters

Among them, A is the percentage of primary frequency regulation units, B is the primary frequency regulation capability D _PFRA of area A, and C is the differential rate of each unit in area A.

Table 2 System secondary frequency modulation capability D _SFRA under different parameters

Among them, A is the percentage of secondary frequency regulation units in area A, B is the secondary frequency regulation capability D _SFRA of area A, and C is the value of integrator gain K _A in area A.

The calculation of D _PFCA and D _SFRA can characterize the current frequency regulation ability of the system for wind power fluctuations. The double value of D _PFCA and D _SFRA indicates that the system's frequency regulation ability for wind power fluctuations is also doubled. For a specific system, the frequency regulation capability of the system is calculated according to the above method, and combined with the current system wind power access situation, the specific configuration method of thermal power frequency regulation units can be given.

In the case of the original thermal power frequency modulation unit configuration of the system, the system frequency fluctuation is shown in Figure 3, and at some point exceeds the system's requirement for frequency ±0.1Hz. When adopting the thermal power frequency regulation unit configuration method provided by the present invention, the system frequency fluctuation is shown in Fig. 4 . The system frequency fluctuation is reduced, and the standard deviation is reduced from σ=0.00042885 to σ=0.00021798.

Simulation analysis results show that the configuration of thermal power frequency modulation units according to the present invention can further reduce system frequency fluctuations caused by wind power fluctuations and maintain system frequency stability.

Claims (5)

1. The thermal power frequency modulation unit configuration method of regional power grid considering wind power uncertainty fluctuation is characterized in that it comprises the following steps: Step 1: Use the Mallat wavelet decomposition and reconstruction algorithm as a tool, and select db10 as the wavelet base to decompose and reconstruct the wind power time series: First, perform wavelet decomposition on the measured wind power data with a sampling interval of nS, specifically The number of decomposition layers m is determined by the sampling interval. It should be ensured that the last layer of decomposition period n·2 ^m is exactly 15 min or n·2 ^m is greater than 15 min and is closest to 15 min. If the difference between n·2 ^m and 15 min is within 3 min, then Take the mth layer as the hourly average wind power at the hourly level, if the difference between n·2 ^m and 15min is greater than 3min, then reconstruct the mth layer and the m-1th layer to obtain the hourly average wind power at the hourly level ;Reconstruct the second-level and minute-level decomposition layers of the subsequent period to obtain the second-level and minute-level power fluctuation residuals after the hourly average wind power is removed from the original wind power sequence; Based on the wavelet multi-scale analysis method, the instantaneous model of the second-level and minute-level wind power uncertainty fluctuations is established, and the time-domain instantaneous expression is given; (1) Instantaneous relationship expression between graded wind power fluctuation and hourly average power of wind farm ${\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; /</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; P</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; \&CenterDot;</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; P</span>}}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$ In the formula, _σm is the standard deviation of the minute-level fluctuation of the output power of the wind farm, is the hourly average power of the wind farm, a, b, and c are the fitting coefficients, which are determined by the least square method; (2) Instantaneous relationship expression between the second-level wind power fluctuation and the hour-level average power of the wind farm ${\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; /</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; P</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; \&CenterDot;</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; P</span>}}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$ In the formula, σ _s is the standard deviation of the second-level fluctuation of the output power of the wind farm; The wind power prediction value with a time resolution of 15 minutes is exactly hour-level wind power, which is input as P in formulas (1) and (2), and the range of uncertainty fluctuations in second-level and minute-level wind power plant power make real-time forecasts; Step 2: According to the actual situation of the power plant, establish a regional power grid model including wind power for frequency regulation analysis, and based on the frequency regulation analysis model, give the expressions representing the primary and secondary frequency regulation capabilities of the system: Establish a regional power grid model with wind power for frequency regulation analysis, where area A contains wind power and area B does not include wind power; Each region contains a primary frequency modulation channel and a secondary frequency modulation channel. The output of the system is the current frequency deviation χ _f (s) of each region, α _i is the power generation share coefficient of the i-th unit, and δ _i is the i-th thermal power unit The adjustment coefficient of the unit, B _A , B _B are the frequency deviation coefficients of each area, K _A , K _B are the gains of the secondary frequency modulation integrator in each area respectively; G _i (s) is the transfer function of the i-th generator set, The specific expression is as follows: ${\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$ in, Indicates the dynamic characteristics of the hydraulic servo motor, T _s s is the time constant of the hydraulic servo motor, take T _s s = 0.2s; Indicates the dynamic characteristics of the volume of the steam turbine, T ₀ s is the volume time constant of the high-pressure cylinder; T _a∑ indicates the equivalent rotor time constant, and all synchronous generators in this system except the fan are equivalent to one For a synchronous generator, multiply the power share coefficient by the respective rotor time constant to obtain the equivalent rotor time constant; β _A and β _B represent the equivalent friction coefficients of area A and area B respectively, and the calculation of T _a∑ The method is the same, and it is also calculated according to the power share coefficient; the primary frequency regulation capability of the power grid considering the uncertainty fluctuation of wind power, that is, only under the action of primary frequency regulation, the second-level wind power fluctuation variance and the power grid frequency fluctuation variance within a certain period of time The ratio of , the expression is: Assuming that there is no load fluctuation in area A, and disconnecting its secondary frequency modulation channel, only the primary frequency modulation effect is considered. The frequency-domain analytical expression of the power grid frequency change and second-level wind power fluctuation is calculated as: ${\mathrm{<span\; class="notranslate">\; \&chi;</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; A</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}\mathrm{<span\; class="notranslate">\; A</span>}}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; \&chi;</span>}}_{\mathrm{<span\; class="notranslate">\; P</span>}\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$ in, α _iA is the power generation share coefficient of the i-th unit in area A, δ _iA is the adjustment coefficient of the i-th thermal power unit in area A; T _a∑A is T _a∑ corresponding to area A, which represents the equivalent Rotor time constant; G _iA (s) is G _i (s) corresponding to area A, which represents the transfer function of the i-th generator set in area A; χ _Pw1 (s) represents the second-level wind power fluctuation, and χ _fA1 (s) represents the grid frequency change corresponding to the second-level wind power fluctuation; Suppose the input is a zero-mean signal x(t), after the corresponding input-output transfer function H(jω), the output is y(t), j is an imaginary number, and ω is the angular frequency, then the output y(t) The variance of is: ${\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}\underset{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{\overset{\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{<span\; class="notranslate">\; \&Integral;</span>}}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$ In the formula, S _y (ω)=S _x (ω)|H(jω)| ² , S _y (ω) represents the power spectral density of the output, and S _x (ω) represents the power spectral density of the input; Considering that the variables involved are deviations with an average value of 0, substituting Equation (5) into Equation (6) to obtain the variance of system frequency fluctuations caused by second-level wind power fluctuations $\begin{array}{c}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; A</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}\underset{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{\overset{\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{<span\; class="notranslate">\; \&Integral;</span>}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; \&chi;</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; A</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}\\ <span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}\underset{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{\overset{\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{<span\; class="notranslate">\; \&Integral;</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}\mathrm{<span\; class="notranslate">\; A</span>}}\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}\end{array}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$ Among them, S _w1 (ω) is the power spectral density of second-level wind power fluctuation; in the frequency domain, s=jω; χ _fA1 (jω) is the frequency domain expression of χ _fA1 (s), G _A (jω) is The frequency domain expression of G _A (s); Obtained by the PSD time-frequency conversion computer algorithm, the sampling of the power spectral density of σ _s (i) in the frequency domain is approximated as: ${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 2</span>}}{{\mathrm{<span\; class="notranslate">\; N\&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$ Among them, σ _s (i) is the sampling sequence composed of the standard deviation of the second-level fluctuation of the wind farm output power at different times, i represents the i-th sampling time point; _MFFT ( ₎ is the fast Fourier transform, and N is Sampling length, ω _s is the sampling frequency, E(·) is the mean function; according to the Shannon sampling theorem, when the sampling frequency is more than twice the signal frequency, the continuous signal is completely reconstructed from the sampling sample; therefore, choose an appropriate Sampling frequency, the power spectral density of wind power second-level fluctuations can be obtained through formula (8); put formula (8) as the power spectral density of the output signal into formula (6) to obtain the variance of second-level wind power fluctuations: ${\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}\underset{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{\overset{\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{<span\; class="notranslate">\; \&Integral;</span>}}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 9</span>}<span\; class="notranslate">\; )</span>$ After derivation, the frequency-domain analytical expression of the primary frequency regulation capability of the power grid considering the uncertainty fluctuation of wind power is: ${\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; P</span>}\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; R</span>}\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; A</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; =</span>\frac{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}\underset{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{\overset{\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{<span\; class="notranslate">\; \&Integral;</span>}}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}}{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}\underset{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{\overset{\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{<span\; class="notranslate">\; \&Integral;</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}\mathrm{<span\; class="notranslate">\; A</span>}}\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$ in, is the variance of the second-level wind power fluctuation, is the variance of the system frequency fluctuation caused by the second-level wind power fluctuation, S _w1 (ω) is the power spectral density of the second-level wind power fluctuation; The secondary frequency regulation capability of the power grid considering the uncertainty fluctuation of wind power is the ratio of the variance of the minute-level wind power fluctuation to the grid frequency fluctuation variance within a certain period of time only under the effect of the secondary frequency regulation. The expression is: Also get the variance of minute-level wind power fluctuations and the variance of system frequency fluctuations caused by minute-scale wind power fluctuations After derivation, the frequency-domain analytical expression of the secondary frequency regulation capability of the power grid considering the uncertainty fluctuation of wind power is obtained as: ${\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; S</span>}\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; R</span>}\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; A</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; =</span>\frac{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}\underset{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{\overset{\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{<span\; class="notranslate">\; \&Integral;</span>}}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}}{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}\underset{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{\overset{\mathrm{<span\; class="notranslate">\; \&infin;</span>}}{<span\; class="notranslate">\; \&Integral;</span>}}<span\; class="notranslate">\; |</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}\mathrm{<span\; class="notranslate">\; A</span>}}\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}}{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 12</span>}<span\; class="notranslate">\; )</span>$ in, is the variance of minute-level wind power fluctuations, is the variance of the system frequency fluctuation, S _w2 (ω) is the power spectral density of the minute-level wind power fluctuation; Step 3: Quantitatively analyze and calculate the primary and secondary frequency regulation capabilities of the regional power grid under different conditions based on the frequency regulation capability expression. 2. The regional power grid thermal power frequency modulation unit configuration method considering wind power uncertainty fluctuation according to claim 1 is characterized in that, the establishment of the regional power grid model containing wind power for frequency modulation analysis in step 2 is to use Matlab/Simulink realized by software. 3. The regional power grid thermal power frequency modulation unit configuration method considering wind power uncertainty fluctuations according to claim 1, characterized in that, step 3 is based on the frequency modulation capability expression, quantitatively analyzing and calculating the first and second values of the regional power grid under different conditions Secondary FM capability: Step 1: Keeping the ratio of thermal power primary frequency regulation units constant, calculate the primary frequency regulation capabilities of the system under different differential coefficients; keep the differential coefficients of each unit constant, and calculate the primary frequency regulation capabilities of the system with different thermal power primary frequency regulation ratios; according to the calculation As a result, draw a table with the proportion of thermal power primary frequency modulation units as the abscissa and the adjustment coefficient as the vertical axis, and from the data in the table, the influence laws of different proportions of thermal power primary frequency modulation units and different difference adjustment coefficients on the primary frequency regulation ability are obtained; Step 2: Keeping the ratio of thermal power secondary frequency modulation units constant, calculate the secondary frequency modulation capability of the system under different integrator gains; keep the integrator gains of each unit constant, and calculate the secondary frequency modulation of the system under different thermal power secondary frequency modulation unit ratios Ability; according to the calculation results, draw a table with the ratio of thermal power secondary frequency modulation units as the abscissa and the integrator gain as the vertical axis, and calculate the different proportions of thermal power secondary frequency modulation units and different integrator gains for secondary Influence law of frequency modulation capability; Step 3: Based on the quantitative analysis results of Step 1 and Step 2, provide a thermal power unit configuration method that can effectively suppress system frequency fluctuations caused by wind farm power uncertainty fluctuations. 4. The regional power grid thermal power frequency modulation unit configuration method considering wind power uncertainty fluctuations according to claim 3, characterized in that, step 3 provides a method that can effectively restrain the wind power generated by the wind farm according to the quantitative analysis results of step 1 and step 2. The realization process of thermal power unit configuration method for system frequency fluctuation caused by uncertainty fluctuation is as follows: Step 3.1.1: If the adjustment coefficient δ _iA of each unit in area A remains unchanged, the value of D _PFRA is K ₁ , and K ₁ is a constant; when the proportion of primary frequency adjustment units increases from p% to 2p%, the value of D _PFRA increases as large as 2K ₁ ; that is, if the second-level fluctuation of wind power output increases from P to k ₁ ·P at a certain moment, k ₁ is a constant, and the frequency is kept stable at the level when the wind power fluctuation does not increase. Increased from p% to k ₁ ·p%; Step 3.1.2: If the gain K _A of the integrator in area A remains unchanged, the value of D _SFRA is K ₂ , and K ₂ is an assumed constant; when the proportion of the secondary frequency modulation unit increases from p% to 2p%, the value of D _SFRA increases As large as 2K ₂ ; that is, if the minute-level fluctuation of wind power output increases from P to k ₂ ·P at a certain moment, the frequency is kept stable at the level when the wind power fluctuation does not increase, and the proportion of primary frequency modulation units is increased from p% to k ₂ ·p%. 5. The regional power grid thermal power frequency modulation unit configuration method considering wind power uncertainty fluctuations according to claim 3, characterized in that, step 3 provides a method that can effectively suppress the wind power generated by the wind farm according to the quantitative analysis results of step 1 and step 2. The realization process of thermal power unit configuration method for system frequency fluctuation caused by uncertainty fluctuation is as follows: Step 3.2.1: If the proportion of units participating in the primary frequency regulation in area A remains unchanged, adjust the differential rate δ _iA of each unit, and the D _PFRA value and δ _iA have the following approximate relationship, namely: ${\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; P</span>}\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; R</span>}\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; \&delta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; A</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 13</span>}<span\; class="notranslate">\; )</span>$ That is, if the second-level fluctuation of wind power output increases from P to k ₁ ·P at a certain moment, k ₁ is a hypothetical constant, and the frequency is kept stable at the level when the wind power fluctuation does not increase, and δ _iA is reduced to k ₁ δ _iA ; Step 3.2.2: If the gain K _A of the integrator in area A remains unchanged, the value of D _SFRA is K ₂ , and K ₂ is an assumed constant; when the proportion of the secondary frequency modulation unit increases from p% to 2p%, the value of D _SFRA increases As large as 2K ₂ ; that is, if the minute-level fluctuation of wind power output increases from P to k ₂ ·P at a certain moment, the frequency is kept stable at the level when the wind power fluctuation does not increase, and the proportion of primary frequency modulation units is increased from p% to k ₂ ·p%.