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

Meter and the regional power grid thermoelectricity frequency modulation crew qiting method of the uncertain fluctuation of wind-powered electricity generation, are related to a kind of thermoelectricity frequency modulation crew qiting method for considering wind-powered electricity generation.In order to solve the problems, such as that system frequency stabilization is impacted after large-scale wind power is grid-connected.The present invention is with Mallat wavelet decompositions and restructing algorithm as instrument, enter the decomposition and reconstruct of sector-style power time series, analysis method based on multi-scale wavelet, set up second level, the instantaneous model of the minute uncertain fluctuation of level wind park power, after providing time domain transient expression formula, set up the model of regional power grid containing wind-powered electricity generation for frequency modulation analysis, based on frequency modulation analysis model, provide the expression formula of the fm capacity of sign system first and second, based on fm capacity expression formula, quantitative analysis system first and second fm capacity at different conditions.The present invention can reduce the system frequency fluctuation that wind power swing is caused, and maintain system frequency stabilization.The present invention is applied to the thermoelectricity frequency modulation crew qiting for considering wind-powered electricity generation.

Description

Regional power grid thermal power frequency modulation unit configuration method considering wind power uncertainty fluctuation

Technical Field

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

Background

The existing wind power ultra-short-term forecasting technology only gives a specific value of wind power at a certain time point generally, the time resolution is 15 minutes, and wind power uncertainty fluctuation under a smaller time resolution cannot be forecasted; when the frequency modulation problem of the wind turbine generator is considered in the existing model, active power and reactive power are generally not decoupled but are comprehensively considered, so that the existing model is not beneficial to effective analysis; when the influence of wind power fluctuation on system frequency modulation is analyzed, the wind power fluctuation under different time scales is not analyzed correspondingly according to the same time scale and the secondary frequency modulation time scale, and the targeted analysis is not facilitated; in addition, the existing configuration method of the frequency modulation unit of the power system is not favorable for the system frequency stabilization after the large-scale wind power grid connection because the wind power access rate is smaller and the unit is configured only according to the fluctuation degree of the load.

Disclosure of Invention

The invention provides a method for configuring a thermal power frequency modulation unit of a regional power grid, which is used for calculating wind power uncertainty fluctuation, and aims to solve the problem that large-scale wind power grid connection affects system frequency stability.

The method for configuring the regional power grid thermal power frequency modulation unit considering wind power uncertainty fluctuation comprises the following steps:

the method comprises the following steps: and (3) decomposing and reconstructing the wind power time series by taking a Mallat wavelet decomposition and reconstruction algorithm as a tool and selecting db10 as a wavelet base: firstly, performing wavelet decomposition on actually measured wind power data with a sampling interval nS, wherein the specific decomposition layer number m is determined by the sampling interval and the last layer period n.2 of the decomposition is ensured^mIs exactly 15min or n.2^mGreater than 15min and closest to 15min, if n.2^mWithin 3min of 15min, the average hourly wind power of the mth layer is in the order of hour, and n.2^mIf the difference between the current time and the 15min is more than 3min, reconstructing the mth layer and the (m-1) th layer to obtain the hour average wind power with the period of hour level; respectively reconstructing the subsequent decomposition layers with the periods of second level and minute level to obtain the second level power and the minute level power of the original wind power sequence after the hour average wind power is removedFluctuating residual errors;

establishing an instantaneous model of the uncertainty fluctuation of the power of the wind power plant at the second level and the minute level based on a wavelet multi-scale analysis method, and giving a time domain instantaneous expression;

(1) instantaneous relational expression of grading wind power fluctuation and wind power plant hour-level average power

In the formula, σ_mIs the standard deviation of the minute-scale fluctuation of the output power of the wind farm,the average power of the wind power plant in the hour level is obtained, and a, b and c are fitting coefficients and can be determined by least square fitting;

(2) instantaneous relational expression of second-level wind power fluctuation and wind power plant hour-level average power

In the formula, σ_sThe standard deviation of second-level fluctuation of the output power of the wind power plant;

the real-time corresponding relation between the standard deviation of uncertainty fluctuation of the second-level and minute-level wind power plant power and the hour-level average power of the wind power plant is established in the formulas (1) and (2); the predicted wind power value with the time resolution of 15 minutes is just the small-level wind power, and is taken as the wind power in the formulas (1) and (2)Inputting, and forecasting the power uncertainty fluctuation range of the wind power plant at the second level and the minute level in real time;

step two: according to the actual situation of a power plant, a wind power-containing regional power grid model for frequency modulation analysis is established, and based on a frequency modulation analysis model, an expression representing the unified system and the secondary frequency modulation capability is given:

establishing a regional power grid model containing wind power for frequency modulation analysis, as shown in fig. 2; the area A contains wind power, and the area B only contains a thermal power generating unit;

each region respectively comprises a primary frequency modulation channel and a secondary frequency modulation channel, and the output of the system is the frequency deviation x of each current region_f(s), frequency deviation χ of each region_f(s) as feedback signal, implementing a first and second adjustment of the respective region frequencies via a first and second chirp channel, α in the figure_iIs the power generation share coefficient of the ith unit,_iis the difference coefficient R of the ith thermal power generating unit_iPower distribution coefficient for participating in secondary frequency modulation units, B_A、B_BIs a frequency deviation coefficient, K, of each region_A、K_BRespectively gaining the secondary frequency modulation integrators in each region; g_i(s) is a transfer function of the ith generating set, and the specific expression is as follows:

wherein,indicating the dynamic characteristics of the hydraulic servomotor, T_ss is the time constant of the hydraulic servo motor, and T is taken_ss＝0.2s；Representing the volumetric dynamic characteristics, T, of the steam turbine₀s is the volume time constant of the high pressure cylinder; t is_a∑Expressing the equivalent rotor time constant, β, where the system is composed of synchronous generators except the wind turbine, all the synchronous generators are equivalent to one synchronous generator, and the power share coefficient is multiplied by the rotor time constant to obtain the equivalent rotor time constant_A、β_BThe equivalent friction coefficients of the region A and the region B, respectively, and T_a∑The same way is adopted for solving the power share coefficient, and the power share coefficient is also used for solving the power share coefficient; the load fluctuation, the wind power fluctuation and the tie line power fluctuation are X_NL(s)、χ_Wind(s) and χ_Ptie(s)；

The power grid primary frequency modulation capability considering wind power uncertainty fluctuation, namely, under the action of primary frequency modulation, the ratio of second-level wind power fluctuation variance to power grid frequency fluctuation variance in a certain period of time is represented as follows:

the model shown in fig. 2 is selected to assume that the load in the area a is not fluctuated, the secondary frequency modulation channel is disconnected, that is, only the primary frequency modulation effect is considered, and the frequency domain analysis expression of the grid frequency variation and the second-level wind power fluctuation can be calculated as follows:

wherein,

the input is a zero-mean signal x (t), and after the input is acted by a corresponding input-output transfer function H (j omega), the output is y (t), j is an imaginary number, omega is an angular frequency, and the variance of the output y (t) is as follows:

in the formula S_y(ω)＝S_x(ω)|H(jω)|²，S_y(ω) represents the power spectral density of the output, S_x(ω) represents the input power spectral density;

considering that the related variable is a deviation amount and the mean value is 0, substituting formula (5) into formula (6) can obtain the variance of system frequency fluctuation caused by second-level wind power fluctuation

Wherein S is_w1(ω) power spectral density for second order wind power fluctuations;

can be obtained by PSD time-frequency conversion computer algorithm_s(i) The power spectral density of (d) in the frequency domain can be approximated as:

wherein M is_FFT(.) is a fast Fourier transform, N is a sample length, ω is_sFor the sampling frequency, E (-) is the mean function; according to the fragrance concentration sampling theorem, when the sampling frequency is more than one time higher than the signal frequency, the continuous signal can be completely reconstructed from the sampling sample; therefore, by selecting a proper sampling frequency, the power spectral density of the wind power second level fluctuation can be obtained through the formula (8); the power spectral density of the output signal with equation (8) is taken into equation (6), and the variance of the second-order wind power fluctuation is obtained as follows:

the frequency domain analytic expression of the primary frequency modulation capability of the power grid considering the wind power uncertainty fluctuation can be obtained through derivation and is as follows:

wherein,is the variance of the second-order wind power fluctuations,for the variance of the system frequency fluctuations caused by second-order wind power fluctuations, S_w1(ω) power spectral density for second order wind power fluctuations;

the power grid secondary frequency modulation capability considering wind power uncertainty fluctuation, namely, under the action of secondary frequency modulation, the ratio of the clock-level wind power fluctuation variance to the power grid frequency fluctuation variance in a certain period of time is represented as follows:

by adopting the derivation method, the variance of minute-level wind power fluctuation can be obtainedAnd variance of system frequency fluctuation caused by minute-scale wind power fluctuationThe frequency domain analytic expression of the secondary frequency modulation capability of the power grid considering the wind power uncertainty fluctuation can be obtained through derivation and is as follows:

wherein,is the variance of the minute-scale wind power fluctuations,is the variance of the system frequency fluctuation, S_w2(ω) power spectral density for minute-scale wind power fluctuations;

step three: and quantitatively analyzing and calculating the primary and secondary frequency modulation capacities of the system under different conditions based on the frequency modulation capacity expression.

The establishment of the wind power-containing regional power grid model for frequency modulation analysis in the third step of the regional power grid thermal power frequency modulation unit configuration method considering wind power uncertainty fluctuation is realized by using Matlab/Simulink software.

The third step of the regional power grid thermal power frequency modulation unit configuration method considering wind power uncertainty fluctuation is based on the realization process of the primary and secondary frequency modulation capacities of the frequency modulation capacity expression quantitative analysis and calculation system under different conditions:

step 1: keeping the proportion of the thermal power primary frequency modulation unit unchanged, and calculating the primary frequency modulation capability of the system under different difference modulation coefficients; keeping the difference adjustment coefficient of each unit unchanged, and calculating the primary frequency modulation capability of the system in different thermal power primary frequency modulation unit proportions; according to the calculation result, drawing a table with the proportion of the thermal power primary frequency modulation unit as an abscissa and the difference adjustment coefficient as an ordinate, and counting the influence rules of the thermal power primary frequency modulation unit with different proportions and different difference adjustment coefficients on the primary frequency modulation capacity from data in the table;

step 2: keeping the proportion of the thermal power secondary frequency modulation unit unchanged, and calculating the secondary frequency modulation capability of the system under different integrator gains; keeping the gain of integrators of all units unchanged, and calculating the secondary frequency modulation capability of the system in different thermal power secondary frequency modulation unit proportions; according to the calculation result, drawing a table with the proportion of the thermal power secondary frequency modulation unit as an abscissa and the gain of the integrator as an ordinate, and counting the influence rules of the thermal power secondary frequency modulation unit with different proportions and the gain of different integrators on the secondary frequency modulation capacity from data in the table;

and step 3: according to the quantitative analysis results of the step 1 and the step 2, the thermal power generating unit configuration method capable of effectively inhibiting system frequency fluctuation caused by uncertainty fluctuation of the wind power plant power is provided.

Step 3 of the regional power grid thermal power frequency modulation unit configuration method considering wind power uncertainty fluctuation provides a thermal power unit configuration method capable of effectively inhibiting system frequency fluctuation caused by wind power plant power uncertainty fluctuation according to the quantitative analysis results of the step 1 and the step 2, and the implementation process comprises the following steps:

step 3.1.1: if the difference adjustment coefficient of each unit in the area A_iARemains unchanged, D_PFRAValue of K₁，K₁Is a constant; when the proportion of the primary frequency modulation unit is increased from p% to 2 p%, D_PFRAIncrease the value to 2K₁(ii) a That is, if the second-order fluctuation quantity of wind power output at a certain moment is increased from P to k₁·P，k₁Is a constant, maintains the frequency stable at the level when the wind power fluctuation is not increased, and increases the proportion of the primary frequency modulation unit from p percent to k₁·p％；

Step 3.1.2: if the gain K of the A-region integrator_AInvariable, D_SFRAValue of K₂，K₂Is a constant of the hypothesis; when the proportion of the secondary frequency modulation unit is increased from p% to 2 p%, D_SFRAIncrease the value to 2K₂(ii) a That is, if the wind power output minute-level fluctuation quantity at a certain moment is increased from P to k₂P, maintaining the frequency stable at the level when the wind power fluctuation is not increased, increasing the proportion of the primary frequency modulation unit from P% to k₂·p％。

Step 3 of the regional power grid thermal power frequency modulation unit configuration method considering wind power uncertainty fluctuation provides a thermal power unit configuration method capable of effectively inhibiting system frequency fluctuation caused by wind power plant power uncertainty fluctuation according to the quantitative analysis results of the step 1 and the step 2, and the implementation process comprises the following steps:

step 3.2.1: if the proportion of the unit participating in the primary frequency modulation in the area A is kept unchanged, the unequal rate of each unit is adjusted_iA，D_PFRAValue and_iAhas the following approximate relationship between:

that is, if the second-order fluctuation quantity of wind power output at a certain moment is increased from P to k₁·P，k₁To maintain the frequency stable at a level where wind power fluctuations are not increased, as a hypothetical constant, will_iAIs reduced to k₁·_iA；

Step 3.2.2: if the gain K of the A-region integrator_AInvariable, D_SFRAValue of K₂，K₂Is a constant of the hypothesis; when the proportion of the secondary frequency modulation unit is increased from p% to 2 p%, D_SFRAIncrease the value to 2K₂(ii) a That is, if the wind power output minute-level fluctuation quantity at a certain moment is increased from P to k₂P, maintaining the frequency stable at the level when the wind power fluctuation is not increased, increasing the proportion of the primary frequency modulation unit from P% to k₂·p％。

The thermal power frequency modulation unit configuration according to the invention can reduce the system frequency fluctuation caused by wind power fluctuation and maintain the system frequency stability. Under the condition of the configuration of an original thermal power frequency modulation unit of the system, the requirement of the system on the frequency of +/-0.1 Hz is exceeded at certain time points; when the thermal power frequency modulation unit configuration method provided by the invention is adopted, the system frequency fluctuation is reduced, and the standard deviation is reduced from 0.00042885 to 0.00021798.

Drawings

FIG. 1 is a flow diagram of a method for configuring a thermal power frequency modulation unit of a regional power grid with wind power uncertainty fluctuation;

FIG. 2 is a power grid model of a wind power-containing region for frequency modulation analysis;

FIG. 3 illustrates system frequencies prior to use of the configuration method;

FIG. 4 illustrates system frequencies after a configuration method is employed;

FIG. 5 is a time series of wind farm output power and fluctuation component;

FIG. 6 is a schematic diagram of a Mallat8 layer wavelet decomposition and reconstruction algorithm;

FIG. 7 is a model of transient fit of minute-scale wind plant power uncertainty fluctuations;

FIG. 8 is a model of transient fit of wind plant power uncertainty fluctuations on the order of seconds.

Detailed Description

The first embodiment is as follows: with reference to fig. 1 and fig. 2, the process of the method for configuring the thermal power frequency modulation unit of the regional power grid for measuring wind power uncertainty fluctuation in the embodiment is as follows:

the method comprises the following steps: and (3) decomposing and reconstructing the wind power time series by taking a Mallat wavelet decomposition and reconstruction algorithm as a tool and selecting db10 as a wavelet base: firstly, performing wavelet decomposition on actually measured wind power data with a sampling interval nS, wherein the specific decomposition layer number m is determined by the sampling interval and the last layer period n.2 of the decomposition is ensured^mIs exactly 15min or n.2^mGreater than 15min and closest to 15min, if n.2^mWithin 3min of 15min, the average hourly wind power of the mth layer is in the order of hour, and n.2^mIf the difference between the current time and the 15min is more than 3min, reconstructing the mth layer and the (m-1) th layer to obtain the hour average wind power with the period of hour level; reconstructing the decomposition layers with the subsequent periods of second level and minute level respectively to obtain second level and minute level power fluctuation residual errors of the original wind power sequence after the hour average wind power is removed;

establishing an instantaneous model of the uncertainty fluctuation of the power of the wind power plant at the second level and the minute level based on a wavelet multi-scale analysis method, and giving a time domain instantaneous expression;

(1) instantaneous relational expression of grading wind power fluctuation and wind power plant hour-level average power

In the formula, σ_mIs the standard deviation of the minute-scale fluctuation of the output power of the wind farm,the average power of the wind power plant in the hour level is obtained, and a, b and c are fitting coefficients and can be determined by least square fitting;

(2) instantaneous relational expression of second-level wind power fluctuation and wind power plant hour-level average power

In the formula, σ_sThe standard deviation of second-level fluctuation of the output power of the wind power plant;

the real-time corresponding relation between the standard deviation of uncertainty fluctuation of the second-level and minute-level wind power plant power and the hour-level average power of the wind power plant is established in the formulas (1) and (2); the predicted wind power value with the time resolution of 15 minutes is just the small-level wind power, and is taken as the wind power in the formulas (1) and (2)Inputting, and forecasting the power uncertainty fluctuation range of the wind power plant at the second level and the minute level in real time;

step two: according to the actual situation of a power plant, a wind power-containing regional power grid model for frequency modulation analysis is established, and based on a frequency modulation analysis model, an expression representing the unified system and the secondary frequency modulation capability is given:

establishing a regional power grid model containing wind power for frequency modulation analysis, as shown in fig. 2; the area A contains wind power, and the area B only contains a thermal power generating unit;

each region respectively comprises a primary frequency modulation channel and a secondary frequency modulation channel, and the output of the system is the frequency deviation x of each current region_f(s), frequency deviation χ of each region_f(s) as feedback signal, implementing a first and second adjustment of the respective region frequencies via a first and second chirp channel, α in the figure_iIs the power generation share coefficient of the ith unit,_iis the difference coefficient R of the ith thermal power generating unit_iPower distribution coefficient for participating in secondary frequency modulation units, B_A、B_BIs a frequency deviation coefficient, K, of each region_A、K_BRespectively gaining the secondary frequency modulation integrators in each region; g_i(s) is a transfer function of the ith generating set, and the specific expression is as follows:

wherein,indicating the dynamic characteristics of the hydraulic servomotor, T_ss is the time constant of the hydraulic servo motor, and T is taken_ss＝0.2s；Representing the volumetric dynamic characteristics, T, of the steam turbine₀s is the volume time constant of the high pressure cylinder; t is_a∑Expressing the equivalent rotor time constant, β, where the system is composed of synchronous generators except the wind turbine, all the synchronous generators are equivalent to one synchronous generator, and the power share coefficient is multiplied by the rotor time constant to obtain the equivalent rotor time constant_A、β_BThe equivalent friction coefficients of the region A and the region B, respectively, and T_a∑The same way is adopted for solving the power share coefficient, and the power share coefficient is also used for solving the power share coefficient; the load fluctuation, the wind power fluctuation and the tie line power fluctuation are X_NL(s)、χ_Wind(s) and χ_Ptie(s)；

The power grid primary frequency modulation capability considering wind power uncertainty fluctuation, namely, under the action of primary frequency modulation, the ratio of second-level wind power fluctuation variance to power grid frequency fluctuation variance in a certain period of time is represented as follows:

the model shown in fig. 2 is selected to assume that the load in the area a is not fluctuated, the secondary frequency modulation channel is disconnected, that is, only the primary frequency modulation effect is considered, and the frequency domain analysis expression of the grid frequency variation and the second-level wind power fluctuation can be calculated as follows:

wherein,

the input is a zero-mean signal x (t), and after the input is acted by a corresponding input-output transfer function H (j omega), the output is y (t), j is an imaginary number, omega is an angular frequency, and the variance of the output y (t) is as follows:

in the formula S_y(ω)＝S_x(ω)|H(jω)|²，S_y(ω) represents the power spectral density of the output, S_x(ω) represents the input power spectral density;

considering that the related variable is a deviation amount and the mean value is 0, substituting formula (5) into formula (6) can obtain the variance of system frequency fluctuation caused by second-level wind power fluctuation

Wherein S is_w1(ω) power spectral density for second order wind power fluctuations;

can be obtained by PSD time-frequency conversion computer algorithm_s(i) The power spectral density of (d) in the frequency domain can be approximated as:

wherein M is_FFT(.) is a fast Fourier transform, N is a sample length, ω is_sFor the sampling frequency, E (-) is the mean function; according to the fragrance concentration sampling theorem, when the sampling frequency is more than one time higher than the signal frequency, the continuous signal can be completely reconstructed from the sampling sample; therefore, by selecting a proper sampling frequency, the power spectral density of the wind power second level fluctuation can be obtained through the formula (8); the power spectral density of the output signal with equation (8) is taken into equation (6), and the variance of the second-order wind power fluctuation is obtained as follows:

the frequency domain analytic expression of the primary frequency modulation capability of the power grid considering the wind power uncertainty fluctuation can be obtained through derivation and is as follows:

wherein,is the variance of the second-order wind power fluctuations,for the variance of the system frequency fluctuations caused by second-order wind power fluctuations, S_w1(ω) power spectral density for second order wind power fluctuations;

the power grid secondary frequency modulation capability considering wind power uncertainty fluctuation, namely, under the action of secondary frequency modulation, the ratio of the clock-level wind power fluctuation variance to the power grid frequency fluctuation variance in a certain period of time is represented as follows:

by adopting the derivation method, the variance of minute-level wind power fluctuation can be obtainedAnd variance of system frequency fluctuation caused by minute-scale wind power fluctuationThe frequency domain analytic expression of the secondary frequency modulation capability of the power grid considering the wind power uncertainty fluctuation can be obtained through derivation and is as follows:

wherein,is the variance of the minute-scale wind power fluctuations,is the variance of the system frequency fluctuation, S_w2(ω) power spectral density for minute-scale wind power fluctuations;

step three: and quantitatively analyzing and calculating the primary and secondary frequency modulation capacities of the system under different conditions based on the frequency modulation capacity expression.

The second embodiment is as follows: in the third step of the present embodiment, the establishment of the wind power-containing regional power grid model for frequency modulation analysis is implemented by using Matlab/Simulink software.

Other steps are the same as in the first embodiment.

The third concrete implementation mode: in the third step of the present embodiment, the implementation process of the first and second frequency modulation capabilities of the quantitative analysis and calculation system based on the frequency modulation capability expression under different conditions is as follows:

step 1: keeping the proportion of the thermal power primary frequency modulation unit unchanged, and calculating the primary frequency modulation capability of the system under different difference modulation coefficients; keeping the difference adjustment coefficient of each unit unchanged, and calculating the primary frequency modulation capability of the system in different thermal power primary frequency modulation unit proportions; according to the calculation result, drawing a table with the proportion of the thermal power primary frequency modulation unit as an abscissa and the difference adjustment coefficient as an ordinate, and counting the influence rules of the thermal power primary frequency modulation unit with different proportions and different difference adjustment coefficients on the primary frequency modulation capacity from data in the table;

step 2: keeping the proportion of the thermal power secondary frequency modulation unit unchanged, and calculating the secondary frequency modulation capability of the system under different integrator gains; keeping the gain of integrators of all units unchanged, and calculating the secondary frequency modulation capability of the system in different thermal power secondary frequency modulation unit proportions; according to the calculation result, drawing a table with the proportion of the thermal power secondary frequency modulation unit as an abscissa and the gain of the integrator as an ordinate, and counting the influence rules of the thermal power secondary frequency modulation unit with different proportions and the gain of different integrators on the secondary frequency modulation capacity from data in the table;

and step 3: according to the quantitative analysis results of the step 1 and the step 2, the thermal power generating unit configuration method capable of effectively inhibiting system frequency fluctuation caused by uncertainty fluctuation of the wind power plant power is provided.

Other steps are the same as in the first embodiment.

The fourth concrete implementation mode: in step 3 of this embodiment, according to the quantitative analysis results in step 1 and step 2, the implementation process of providing the thermal power unit configuration method capable of effectively suppressing system frequency fluctuation caused by uncertainty fluctuation of wind farm power is as follows:

step 3.1.1: if the difference adjustment coefficient of each unit in the area A_iARemains unchanged, D_PFRAValue of K₁，K₁Is a constant; when the proportion of the primary frequency modulation unit is increased from p% to 2 p%, D_PFRAIncrease the value to 2K₁(ii) a That is, if the second-order fluctuation quantity of wind power output at a certain moment is increased from P to k₁·P，k₁Is a constant, maintains the frequency stable at the level when the wind power fluctuation is not increased, and increases the proportion of the primary frequency modulation unit from p percent to k₁·p％；

Step 3.1.2: if the gain K of the A-region integrator_AInvariable, D_SFRAValue of K₂，K₂Is a constant of the hypothesis; when the proportion of the secondary frequency modulation unit is increased from p% to 2 p%, D_SFRAIncrease the value to 2K₂(ii) a That is, if the wind power output minute-level fluctuation quantity at a certain moment is increased from P to k₂P, maintaining the frequency stable at the level when the wind power fluctuation is not increased, increasing the proportion of the primary frequency modulation unit from P% to k₂·p％。

The other steps are the same as those in the third embodiment.

The fifth concrete implementation mode: in step 3 of this embodiment, according to the quantitative analysis results in step 1 and step 2, the implementation process of providing the thermal power unit configuration method capable of effectively suppressing system frequency fluctuation caused by uncertainty fluctuation of wind farm power is as follows:

step 3.2.1: if A area participates in primary frequency modulationThe proportion of the units is kept unchanged, and the unequal ratio of each unit is adjusted_iA，D_PFRAValue and_iAhas the following approximate relationship between:

that is, if the second-order fluctuation quantity of wind power output at a certain moment is increased from P to k₁·P，k₁To maintain the frequency stable at a level where wind power fluctuations are not increased, as a hypothetical constant, will_iAIs reduced to k₁·_iA；

Step 3.2.2: if the gain K of the A-region integrator_AInvariable, D_SFRAValue of K₂，K₂Is a constant of the hypothesis; when the proportion of the secondary frequency modulation unit is increased from p% to 2 p%, D_SFRAIncrease the value to 2K₂(ii) a That is, if the wind power output minute-level fluctuation quantity at a certain moment is increased from P to k₂P, maintaining the frequency stable at the level when the wind power fluctuation is not increased, increasing the proportion of the primary frequency modulation unit from P% to k₂·p％。

The other steps are the same as those in the third embodiment.

DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION

And simulating the actual day of operation data of a certain power grid.

The method comprises the following steps: and (3) decomposing and reconstructing the wind power time series by taking the Mallat wavelet decomposition and reconstruction algorithm as a tool and selecting db10 as a wavelet base. Firstly, performing 8-layer decomposition on actually measured wind power data (the data of the patent example with the sampling interval of 5S and the time length of one month); reconstructing the first 8 layers and the 7 th layer to obtain the hour average wind power with the period of hour level; and respectively reconstructing the following 1-3 layers and 4-6 to obtain second-level and minute-level power fluctuation residual errors (shown in figure 5) of the original wind power sequence after the hour average wind power is removed. If the sampling interval of the wind speed data is 1S, 10 layers of decomposition are needed and then reconstruction is carried out, and the average wind power and the corresponding fluctuation residual error which are in a small-scale period can be obtained.

A schematic diagram of the Mallat wavelet decomposition and reconstruction algorithm is shown in fig. 5.

And (4) taking absolute values of the second-level and minute-level wind power fluctuation quantity time sequences, processing the absolute values, sequencing the absolute values according to the magnitude, and establishing a one-to-one corresponding point-row relation with the hour-level average power time sequence of the wind power plant. According to the least square fitting principle, outliers are removed by using the 3 sigma principle, and statistical modeling is carried out on the point row pairs. The data were fitted using a least squares fit, the results of which are shown in fig. 7 and 8.

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

Step two: the frequency modulation model is established as shown in fig. 2, and the derivation process of the expression representing the secondary frequency modulation capability of the system provided by the invention is specifically analyzed below.

The model shown in fig. 2 is selected to assume that the load in the area a is not fluctuated, the secondary frequency modulation channel is disconnected, that is, only the primary frequency modulation effect is considered, and the frequency domain analysis expression of the grid frequency variation and the second-level wind power fluctuation can be calculated as follows:

wherein,

the input is a zero-mean signal x (t), and after the input is acted by a corresponding input-output transfer function H (j omega), the output is y (t), j is an imaginary number, omega is an angular frequency, and the variance of the output y (t) is as follows:

in the formula S_y(ω)＝S_x(ω)|H(jω)|²，S_y(ω) represents the power spectral density of the output, S_x(ω) represents the input power spectral density;

considering that the related variable is a deviation amount and the mean value is 0, the equation (3) is substituted into the variance of the system frequency fluctuation caused by the second-order wind power fluctuation in the equation (4)

Wherein S is_w1(ω) power spectral density for second order wind power fluctuations;

can be obtained by PSD time-frequency conversion computer algorithm of wind power plant power fluctuation_s(i) The power spectral density of (d) in the frequency domain can be approximated as:

wherein M is_FFT(.) is a fast Fourier transform, N is a sample length, ω is_sFor the sampling frequency, E (-) is the mean function; according to the fragrance concentration sampling theorem, when the sampling frequency is more than one time higher than the signal frequency, the continuous signal can be completely reconstructed from the sampling sample; therefore, by selecting a proper sampling frequency, the power spectral density of the wind power second level fluctuation can be obtained through the formula (6); taking equation (6) as the power spectral density of the output signal to take equation (4) to obtain the variance of second-level wind power fluctuation as follows:

the power grid primary frequency modulation capability considering wind power uncertainty fluctuation, namely, under the action of primary frequency modulation, the ratio of second-level wind power fluctuation variance to power grid frequency fluctuation variance in a certain period of time is represented as follows:

the power grid secondary frequency modulation capability considering wind power uncertainty fluctuation, namely, under the action of secondary frequency modulation, the ratio of the clock-level wind power fluctuation variance to the power grid frequency fluctuation variance in a certain period of time is represented as follows:

substituting a formula (5) representing the frequency variance of the power grid and a formula (7) representing the second-level wind power fluctuation variance into a dynamic expression (8) of the primary frequency modulation capability to obtain a frequency domain analytic expression of the primary frequency modulation capability of the power grid considering the wind power uncertainty fluctuation, wherein the frequency domain analytic expression comprises the following steps:

wherein,is the variance of the second-order wind power fluctuations,for the variance of the system frequency fluctuations caused by second-order wind power fluctuations, S_w1(ω) power spectral density for second order wind power fluctuations;

by adopting the derivation method, the variance of minute-level wind power fluctuation can be obtainedAnd variance of system frequency fluctuation caused by minute-scale wind power fluctuationThe frequency domain analytic expression of the secondary frequency modulation capability of the power grid considering the wind power uncertainty fluctuation can be obtained through derivation and is as follows:

wherein,is the variance of the minute-scale wind power fluctuations,is the variance of the system frequency fluctuation, S_w2(ω) power spectral density for minute-scale wind power fluctuations;

step three: based on the quantitative analysis system of the frequency modulation capability expression, the primary and secondary frequency modulation capabilities of thermal power frequency modulation units with different proportions are obtained; a reference method is provided for a thermal power frequency modulation unit configured by a dispatching department, and effective control of system frequency fluctuation caused by power uncertainty fluctuation of a wind power plant is achieved.

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

TABLE 1 Primary frequency modulation capability D of System under different parameters_PFCA

Wherein A is the primary frequency modulation unit percentage, and B is the primary frequency modulation capability D of the area A_PFRAAnd C is the unequal rate of each unit in the area A.

TABLE 2 Secondary FM Capacity of System under different parameters D_SFRA

Wherein A is the percentage of secondary frequency modulation units in the area A, and B is the secondary frequency modulation capability D of the area A_SFRAAnd C is the integrator gain K of the region A_AThe value of (a).

D_PFCAAnd D_SFRAThe calculation of (A) can characterize the current frequency regulation capability of the system with respect to wind power fluctuations, D_PFCAAnd D_SFRAThe value is doubled, which shows that the frequency regulation capability of the system for wind power fluctuation is doubled. Aiming at a specific system, the system frequency modulation capability is calculated according to the method, and a specific thermal power frequency modulation unit configuration method can be provided by combining the wind power access condition of the current system.

Under the condition of the original configuration of the thermal power frequency modulation unit of the system, the system frequency fluctuation is shown in figure 3, and the requirement of the system on the frequency of +/-0.1 Hz is exceeded at some time points. When the thermal power frequency modulation unit configuration method provided by the invention is adopted, the system frequency fluctuation is shown in figure 4. The system frequency fluctuation is reduced, and the standard deviation is reduced from the value of 0.00042885 to 0.00021798.

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

Claims (5)

1. The method for configuring the regional power grid thermal power frequency modulation unit considering wind power uncertainty fluctuation is characterized by comprising the following steps of:

the method comprises the following steps: and (3) decomposing and reconstructing the wind power time series by taking a Mallat wavelet decomposition and reconstruction algorithm as a tool and selecting db10 as a wavelet base: firstly, the wavelet decomposition is carried out on the actually measured wind power data with the sampling interval of n S, the specific decomposition layer number m is determined by the sampling interval, and the last layer period n.2 of the decomposition is ensured^mIs exactly 15min or n.2^mGreater than 15min and closest to 15min, if n.2^mWithin 3min of 15min, the average hourly wind power of the mth layer is in the order of hour, and n.2^mIf the difference between the current time and the 15min is more than 3min, reconstructing the mth layer and the (m-1) th layer to obtain the hour average wind power with the period of hour level; reconstructing the decomposition layers with the subsequent periods of second level and minute level respectively to obtain second level and minute level power fluctuation residual errors of the original wind power sequence after the hour average wind power is removed;

establishing an instantaneous model of the uncertainty fluctuation of the power of the wind power plant at the second level and the minute level based on a wavelet multi-scale analysis method, and giving a time domain instantaneous expression;

(1) instantaneous relational expression of grading wind power fluctuation and wind power plant hour-level average power

${\mathrm{\σ}}_{rm}={\mathrm{\σ}}_m/\stackrel{\‾}{P}=a\·{\stackrel{\‾}{P}}^b+c---\left(1\right)$

In the formula, σ_mIs the standard deviation of the minute-scale fluctuation of the output power of the wind farm,the average power of the wind power plant in the hour level is determined by least square fitting, and a, b and c are fitting coefficients;

(2) instantaneous relational expression of second-level wind power fluctuation and wind power plant hour-level average power

${\mathrm{\σ}}_{rs}={\mathrm{\σ}}_s/\stackrel{\‾}{P}=a\·{\stackrel{\‾}{P}}^b+c---\left(2\right)$

In the formula, σ_sThe standard deviation of second-level fluctuation of the output power of the wind power plant;

the predicted value of the wind power with the time resolution of 15 minutes is just the small-level wind power, and the wind power is input as P in the formulas (1) and (2) to forecast the uncertain fluctuation range of the second-level and minute-level wind power plants in real time;

step two: according to the actual situation of a power plant, a wind power-containing regional power grid model for frequency modulation analysis is established, and based on a frequency modulation analysis model, an expression representing the unified system and the secondary frequency modulation capability is given:

establishing a regional power grid model containing wind power for frequency modulation analysis, wherein a region A contains wind power and a region B does not contain wind power;

each region respectively comprises a primary frequency modulation channel and a secondary frequency modulation channel, and the output of the system is the frequency deviation x of each current region_f(s)，α_iIs the power generation share coefficient of the ith unit,_iis the difference adjustment coefficient of the ith thermal power generating unit, B_A、B_BIs a frequency deviation coefficient, K, of each region_A、K_BRespectively gaining the secondary frequency modulation integrators in each region; g_i(s) is a transfer function of the ith generating set, and the specific expression is as follows:

$G_i\left(s\right)=\frac{1}{T_{0}s+1}\·\frac{1}{T_{s}s+1}---\left(3\right)$

wherein,indicating the dynamic characteristics of the hydraulic servomotor, T_ss is the time constant of the hydraulic servo motor, and T is taken_ss＝0.2s；Representing the volumetric dynamic characteristics, T, of the steam turbine₀s is the volume time constant of the high pressure cylinder; t is_a∑Expressing equivalent rotor time constant, which is equivalent to synchronous generator except for wind turbine, and multiplying power share coefficient by rotor time constant to obtain equivalent rotor time constant β_A、β_BThe equivalent friction coefficients of the region A and the region B, respectively, and T_a∑The same way is adopted for solving the power share coefficient, and the power share coefficient is also used for solving the power share coefficient; the power grid primary frequency modulation capability considering wind power uncertainty fluctuation, namely, under the action of primary frequency modulation, the ratio of second-level wind power fluctuation variance to power grid frequency fluctuation variance in a certain period of time is represented as follows:

assuming that the load in the area A has no fluctuation, a secondary frequency modulation channel is disconnected, namely only the primary frequency modulation effect is considered, and the frequency domain analytical expression of the grid frequency variation and the second-level wind power fluctuation is calculated as follows:

${\mathrm{\χ}}_{fA1}\left(s\right)=-\frac{1}{T_{a\mathrm{\Σ}A}s+{\mathrm{\β}}_A+G_A\left(s\right)}{\mathrm{\χ}}_{Pw1}\left(s\right)---\left(5\right)$

wherein,α_iAis the power generation share coefficient of the ith unit in the area A,_iAthe difference adjustment coefficient is the difference adjustment coefficient of the ith thermal power generating unit in the area A; t is_a∑AT corresponding to the region A_a∑Represents the equivalent rotor time constant of region a; g_iA(s) is G corresponding to the region A_i(s) representing a transfer function of the ith genset in area a;

χ_Pw1(s) denotes the second order wind power fluctuation,%_fA1(s) represents grid frequency variation corresponding to second order wind power fluctuations;

the input is a zero-mean signal x (t), and after the input is acted by a corresponding input-output transfer function H (j omega), the output is y (t), j is an imaginary number, omega is an angular frequency, and the variance of the output y (t) is as follows:

${\mathrm{\σ}}_y^2=\frac{1}{2\mathrm{\π}}\underset{-\mathrm{\∞}}{\overset{\mathrm{\∞}}{\∫}}{S}_y\left(\mathrm{\ω}\right)d\mathrm{\ω}---\left(6\right)$

in the formula S_y(ω)＝S_x(ω)|H(jω)|²，S_y(ω) represents the power spectral density of the output, S_x(ω) represents the input power spectral density;

considering that the related variable is a deviation amount and the mean value is 0, substituting formula (5) into formula (6) to obtain the variance of system frequency fluctuation caused by second-level wind power fluctuation

$\begin{array}{c}{\mathrm{\σ}}_{fA1}^2=\frac{1}{2\mathrm{\π}}\underset{-\mathrm{\∞}}{\overset{\mathrm{\∞}}{\∫}}|{\mathrm{\χ}}_{fA1}\left(j\mathrm{\ω}\right){|}^{2}d\mathrm{\ω}\\ =\frac{1}{2\mathrm{\π}}\underset{-\mathrm{\∞}}{\overset{\mathrm{\∞}}{\∫}}|-\frac{1}{T_{a\mathrm{\Σ}A}j\mathrm{\ω}+{\mathrm{\β}}_A+G_A\left(j\mathrm{\ω}\right)}{|}^2{S}_{w1}\left(\mathrm{\ω}\right)d\mathrm{\ω}\end{array}---\left(7\right)$

Wherein S is_w1(ω) power spectral density for second order wind power fluctuations; in the frequency domain, s ═ j ω; chi shape_fA1(j ω) is χ_fA1(s) frequency domain representation, G_A(j ω) is G_A(s) frequency domain representation of;

obtained by PSD time-frequency conversion computer algorithm, sigma_s(i) The power spectral density of (a) is approximated as:

$S_{w1}\left(\mathrm{\ω}\right)=\frac{2}{{\mathrm{N\ω}}_s}E\left(\right|M_{FFT}\left(x\left(k\right)\right){|}^2)---\left(8\right)$

wherein σ_s(i) The sampling sequence is formed by standard deviations of second-level fluctuation of the output power of the wind power plant at different moments, and i represents the ith sampling moment point; m_FFT(_·) For fast Fourier transform, N is the sampling length, omega_sFor the sampling frequency, E (-) is the mean function; according to the fragrance concentration sampling theorem, when the sampling frequency is more than one time higher than the signal frequency, the continuous signal is completely reconstructed from the sampling sample; therefore, by selecting a proper sampling frequency, the power spectral density of the wind power second level fluctuation is obtained through the formula (8); the power spectral density of the output signal with equation (8) is taken into equation (6), and the variance of the second-order wind power fluctuation is obtained as follows:

${\mathrm{\σ}}_{pw1}^2=\frac{1}{2\mathrm{\π}}\underset{-\mathrm{\∞}}{\overset{\mathrm{\∞}}{\∫}}{S}_{w1}\left(\mathrm{\ω}\right)d\mathrm{\ω}---\left(9\right)$

the frequency domain analytic expression of the primary frequency modulation capability of the power grid considering the wind power uncertainty fluctuation is obtained through derivation and is as follows:

$D_{PFRA}=\frac{{\mathrm{\σ}}_{pw1}^2}{{\mathrm{\σ}}_{fA1}^2}=\frac{\frac{1}{2\mathrm{\π}}\underset{-\mathrm{\∞}}{\overset{\mathrm{\∞}}{\∫}}{S}_{w1}\left(\mathrm{\ω}\right)d\mathrm{\ω}}{\frac{1}{2\mathrm{\π}}\underset{-\mathrm{\∞}}{\overset{\mathrm{\∞}}{\∫}}|-\frac{1}{T_{a\mathrm{\Σ}A}j\mathrm{\ω}+{\mathrm{\β}}_A+G_A\left(j\mathrm{\ω}\right)}{|}^2\·S_{w1}\left(\mathrm{\ω}\right)d\mathrm{\ω}}---\left(10\right)$

wherein,is the variance of the second-order wind power fluctuations,for the variance of the system frequency fluctuations caused by second-order wind power fluctuations, S_w1(ω) power spectral density for second order wind power fluctuations;

the power grid secondary frequency modulation capability considering wind power uncertainty fluctuation, namely, under the action of secondary frequency modulation, the ratio of the clock-level wind power fluctuation variance to the power grid frequency fluctuation variance in a certain period of time is represented as follows:

the variance of minute-level wind power fluctuation is also obtainedAnd variance of system frequency fluctuation caused by minute-scale wind power fluctuationThe frequency domain analytic expression of the power grid secondary frequency modulation capability considering the wind power uncertainty fluctuation is obtained through derivation and is as follows:

$D_{SFRA}=\frac{{\mathrm{\σ}}_{pw2}^2}{{\mathrm{\σ}}_{fA2}^2}=\frac{\frac{1}{2\mathrm{\π}}\underset{-\mathrm{\∞}}{\overset{\mathrm{\∞}}{\∫}}{S}_{w2}\left(\mathrm{\ω}\right)d\mathrm{\ω}}{\frac{1}{2\mathrm{\π}}\underset{-\mathrm{\∞}}{\overset{\mathrm{\∞}}{\∫}}|\frac{1}{T_{a\mathrm{\Σ}A}j\mathrm{\ω}+{\mathrm{\β}}_A+B_A\·\left(\frac{K_A}{j\mathrm{\ω}}\right)\·G_A\left(j\mathrm{\ω}\right)}{|}^2\·S_{w2}\left(\mathrm{\ω}\right)d\mathrm{\ω}}---\left(12\right)$

wherein,is the variance of the minute-scale wind power fluctuations,is the variance of the system frequency fluctuation, S_w2(ω) power spectral density for minute-scale wind power fluctuations;

step three: and quantitatively analyzing and calculating the primary and secondary frequency modulation capacities of the regional power grid under different conditions based on the frequency modulation capacity expression.

2. The method for configuring the regional power grid thermal power frequency modulation unit with wind power uncertainty fluctuation in consideration of claim 1, wherein the establishment of the regional power grid model containing wind power for frequency modulation analysis in the second step is realized by using Matlab/Simulink software.

3. The method for configuring the thermal power frequency modulation unit of the regional power grid considering wind power uncertainty fluctuation according to claim 1, wherein the third step is based on a frequency modulation capability expression, and quantitatively analyzes and calculates the primary and secondary frequency modulation capabilities of the regional power grid under different conditions:

step 1: keeping the proportion of the thermal power primary frequency modulation unit unchanged, and calculating the primary frequency modulation capability of the system under different difference modulation coefficients; keeping the difference adjustment coefficient of each unit unchanged, and calculating the primary frequency modulation capability of the system in different thermal power primary frequency modulation unit proportions; according to the calculation result, drawing a table with the proportion of the thermal power primary frequency modulation unit as an abscissa and the difference adjustment coefficient as an ordinate, and counting the influence rules of the thermal power primary frequency modulation unit with different proportions and different difference adjustment coefficients on the primary frequency modulation capacity from data in the table;

step 2: keeping the proportion of the thermal power secondary frequency modulation unit unchanged, and calculating the secondary frequency modulation capability of the system under different integrator gains; keeping the gain of integrators of all units unchanged, and calculating the secondary frequency modulation capability of the system in different thermal power secondary frequency modulation unit proportions; according to the calculation result, drawing a table with the proportion of the thermal power secondary frequency modulation unit as an abscissa and the gain of the integrator as an ordinate, and counting the influence rules of the thermal power secondary frequency modulation unit with different proportions and the gain of different integrators on the secondary frequency modulation capacity from data in the table;

and step 3: according to the quantitative analysis results of the step 1 and the step 2, the thermal power generating unit configuration method capable of effectively inhibiting system frequency fluctuation caused by uncertainty fluctuation of the wind power plant power is provided.

4. The method for configuring the thermal power frequency modulation unit of the regional power grid considering wind power uncertainty fluctuation according to claim 3, wherein the step 3 is implemented by providing the thermal power unit configuration method capable of effectively suppressing the system frequency fluctuation caused by the wind power plant power uncertainty fluctuation according to the quantitative analysis results of the steps 1 and 2, and comprises the following steps:

step 3.1.1: if the difference adjustment coefficient of each unit in the area A_iARemains unchanged, D_PFRAValue of K₁，K₁Is a constant; when the proportion of the primary frequency modulation unit is increased from p% to 2 p%, D_PFRAIncrease the value to 2K₁(ii) a That is, if the second-order fluctuation quantity of wind power output at a certain moment is increased from P to k₁·P，k₁Is a constant, maintains the frequency stable at the level when the wind power fluctuation is not increased, and increases the proportion of the primary frequency modulation unit from p percent to k₁·p％；

Step 3.1.2: if the gain K of the A-region integrator_AInvariable, D_SFRAValue of K₂，K₂Is a constant of the hypothesis; when the proportion of the secondary frequency modulation unit is increased from p% to 2 p%, D_SFRAIncrease the value to 2K₂(ii) a That is, if the wind power output minute-level fluctuation quantity at a certain moment is increased from P to k₂P, maintaining the frequency stable at the level when the wind power fluctuation is not increased, increasing the proportion of the primary frequency modulation unit from P% to k₂·p％。

5. The method for configuring the thermal power frequency modulation unit of the regional power grid considering wind power uncertainty fluctuation according to claim 3, wherein the step 3 is implemented by providing the thermal power unit configuration method capable of effectively suppressing the system frequency fluctuation caused by the wind power plant power uncertainty fluctuation according to the quantitative analysis results of the steps 1 and 2, and comprises the following steps:

step 3.2.1: if the proportion of the unit participating in the primary frequency modulation in the area A is kept unchanged, the unequal rate of each unit is adjusted_iA，D_PFRAValue and_iAhas the following approximate relationship between:

$D_{PFRA}=\frac{1}{10\·{\mathrm{\δ}}_{iA}}---\left(13\right)$

that is, if the second-order fluctuation quantity of wind power output at a certain moment is increased from P to k₁·P，k₁To maintain the frequency stable at a level where the amount of wind power fluctuation is not increased, as a hypothetical constant, will_iAIs reduced to k₁·_iA；

Step 3.2.2: if the gain K of the A-region integrator_AInvariable, D_SFRAValue of K₂，K₂Is a constant of the hypothesis; when the proportion of the secondary frequency modulation unit is increased from p% to 2 p%, D_SFRAIncrease the value to 2K₂(ii) a That is, if the wind power output minute-level fluctuation quantity at a certain moment is increased from P to k₂P, maintaining the frequency stable at the level when the wind power fluctuation is not increased, increasing the proportion of the primary frequency modulation unit from P% to k₂·p％。