CN104201700A - Regional power grid thermal power frequency modulation unit configuration method considering wind power uncertainty fluctuation - Google Patents
Regional power grid thermal power frequency modulation unit configuration method considering wind power uncertainty fluctuation Download PDFInfo
- Publication number
- CN104201700A CN104201700A CN201410486165.4A CN201410486165A CN104201700A CN 104201700 A CN104201700 A CN 104201700A CN 201410486165 A CN201410486165 A CN 201410486165A CN 104201700 A CN104201700 A CN 104201700A
- Authority
- CN
- China
- Prior art keywords
- mrow
- msub
- wind power
- power
- frequency modulation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 55
- 230000014509 gene expression Effects 0.000 claims abstract description 41
- 238000004458 analytical method Methods 0.000 claims abstract description 25
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 22
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 11
- 230000003595 spectral effect Effects 0.000 claims description 32
- 238000005070 sampling Methods 0.000 claims description 28
- 238000004445 quantitative analysis Methods 0.000 claims description 13
- 238000009795 derivation Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 11
- 230000001360 synchronised effect Effects 0.000 claims description 8
- 230000002401 inhibitory effect Effects 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 claims description 4
- 239000003205 fragrance Substances 0.000 claims description 4
- 238000010248 power generation Methods 0.000 claims description 3
- 230000006641 stabilisation Effects 0.000 abstract description 2
- 238000011105 stabilization Methods 0.000 abstract description 2
- 230000010354 integration Effects 0.000 abstract 1
- 230000033228 biological regulation Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000001052 transient effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000007430 reference method Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Landscapes
- Wind Motors (AREA)
- Control Of Eletrric Generators (AREA)
Abstract
The invention relates to a wind power considered thermal power frequency modulation unit configuration method, in particular to a regional power grid thermal power frequency modulation unit configuration method considering wind power uncertainty fluctuation, in order to solve the problem of influence caused on system frequency stabilization after large-scale wind power integration. The method includes: performing decomposition and restructuring of a wind power time sequence by taking a Mallat wavelet decomposition and restructuring algorithm as a tool, establishing an instantaneous model of second-scale and minute-scale wind power plant power uncertainty fluctuation on the basis of a wavelet multi-scale analysis method, giving a time-domain instantaneous expression prior to establishing a wind power regional power grid model used for frequency modulation analysis, giving an expression for representing primary and secondary frequency modulation capacities of the system based on the frequency modulation analysis, and quantitatively analyzing and computing the primary and secondary frequency modulation capacities of the system under different conditions on the basis of the frequency modulation capacity expression. By the method, system frequency fluctuation caused by wind power fluctuation can be reduced, and system frequency stability is maintained. The method is applicable to wind power considered thermal power frequency modulation unit configuration.
Description
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 ensuredmIs exactly 15min or n.2mGreater than 15min and closest to 15min, if n.2mWithin 3min of 15min, the average hourly wind power of the mth layer is in the order of hour, and n.2mIf 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, σmIs 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, σsThe 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 regionf(s), frequency deviation χ of each regionf(s) as a feedback signal, implementing primary and secondary regulation of respective region frequencies through primary and secondary frequency modulation channels; in the figure, αiIs the power generation share coefficient, delta, of the ith unitiIs the difference coefficient R of the ith thermal power generating unitiPower distribution coefficient for participating in secondary frequency modulation units, BA、BBIs a frequency deviation coefficient, K, of each regionA、KBAdding secondary frequency modulation integrators for each region respectivelyBenefiting; gi(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, Tss is the time constant of the hydraulic servo motor, and T is takenss＝0.2s；Representing the volumetric dynamic characteristics, T, of the steam turbine0s is the volume time constant of the high pressure cylinder; t isa∑The equivalent rotor time constant is expressed, synchronous generators are arranged except for a fan in the system, all the synchronous generators are equivalent to one synchronous generator, and the equivalent rotor time constant can be obtained by multiplying the power share coefficient by the respective rotor time constant; beta is aA、βBThe equivalent friction coefficients of the region A and the region B, respectively, and Ta∑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 XNL(s)、χWind(s) and χPtie(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, <math>
<mrow>
<msub>
<mi>G</mi>
<mi>A</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mi>Σ</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>M</mi>
<mn>1</mn>
</mrow>
</munderover>
<mfrac>
<msub>
<mi>α</mi>
<mi>iA</mi>
</msub>
<msub>
<mi>δ</mi>
<mi>iA</mi>
</msub>
</mfrac>
<msub>
<mi>G</mi>
<mi>iA</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
</math>
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 Sy(ω)＝Sx(ω)|H(jω)|2，Sy(ω) represents the power spectral density of the output, Sx(ω) represents the input power spectral density;
taking into account the deviation of the variable concernedMean value of 0, and the variance of system frequency fluctuation caused by second-order wind power fluctuation can be obtained by substituting formula (5) into formula (6)
Wherein S isw1(ω) power spectral density for second order wind power fluctuations;
can be obtained by PSD time-frequency conversion computer algorithms(i) The power spectral density of (d) in the frequency domain can be approximated as:
wherein M isFFT(.) is a fast Fourier transform, N is a sample length, ω issFor 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, Sw1(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, Sw2(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 delta of each unit in the area A isiARemains unchanged, DPFRAValue of K1，K1Is a constant; when the proportion of the primary frequency modulation unit is increased from p% to 2 p%, DPFRAIncrease 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 integratorAInvariable, DSFRAValue of K2，K2Is a constant of the hypothesis; when the proportion of the secondary frequency modulation unit is increased from p% to 2 p%, DSFRAIncrease 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 delta of each unit is adjustediA，DPFRAValue and deltaiAHas 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, δ is a hypothetical constantiAIs reduced to k1·δiA；
Step 3.2.2: if the gain K of the A-region integratorAInvariable, DSFRAValue of K2，K2Is a constant of the hypothesis; when the proportion of the secondary frequency modulation unit is increased from p% to 2 p%, DSFRAIncrease 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 ensuredmIs exactly 15min or n.2mGreater than 15min and closest15min, if n.2mWithin 3min of 15min, the average hourly wind power of the mth layer is in the order of hour, and n.2mIf 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, σmIs 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, σsThe 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 contains a primary frequency modulation channel and a secondary frequency modulation channel, and the output of the system is the frequency of each current regionRate deviation χf(s), frequency deviation χ of each regionf(s) as a feedback signal, implementing primary and secondary regulation of respective region frequencies through primary and secondary frequency modulation channels; in the figure, αiIs the power generation share coefficient, delta, of the ith unitiIs the difference coefficient R of the ith thermal power generating unitiPower distribution coefficient for participating in secondary frequency modulation units, BA、BBIs a frequency deviation coefficient, K, of each regionA、KBRespectively gaining the secondary frequency modulation integrators in each region; gi(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, Tss is the time constant of the hydraulic servo motor, and T is takenss＝0.2s；Representing the volumetric dynamic characteristics, T, of the steam turbine0s is the volume time constant of the high pressure cylinder; t isa∑Representing equivalent rotor time constant, in which the system is de-airingAll synchronous generators are equivalent to one synchronous generator outside the machine, and the equivalent rotor time constant can be obtained by multiplying the power share coefficient by the respective rotor time constant; beta is aA、βBThe equivalent friction coefficients of the region A and the region B, respectively, and Ta∑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 XNL(s)、χWind(s) and χPtie(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, <math>
<mrow>
<msub>
<mi>G</mi>
<mi>A</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mi>Σ</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>M</mi>
<mn>1</mn>
</mrow>
</munderover>
<mfrac>
<msub>
<mi>α</mi>
<mi>iA</mi>
</msub>
<msub>
<mi>δ</mi>
<mi>iA</mi>
</msub>
</mfrac>
<msub>
<mi>G</mi>
<mi>iA</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
</math>
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 Sy(ω)＝Sx(ω)|H(jω)|2，Sy(ω) represents the power spectral density of the output, Sx(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 isw1(ω) power spectral density for second order wind power fluctuations;
can be obtained by PSD time-frequency conversion computer algorithms(i) The power spectral density of (d) in the frequency domain can be approximated as:
wherein M isFFT(.) is a fast Fourier transform, N is a sample length, ω issFor 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, Sw1(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, Sw2(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 delta of each unit in the area A isiARemains unchanged, DPFRAValue of K1，K1Is a constant; when the proportion of the primary frequency modulation unit is increased from p% to 2 p%, DPFRAIncrease 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 to maintain the frequency stable in the wind power fluctuationIncreasing the proportion of the primary frequency modulation unit from p% to k1·p％；
Step 3.1.2: if the gain K of the A-region integratorAInvariable, DSFRAValue of K2，K2Is a constant of the hypothesis; when the proportion of the secondary frequency modulation unit is increased from p% to 2 p%, DSFRAIncrease 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 the proportion of the unit participating in the primary frequency modulation in the area A is kept unchanged, the unequal rate delta of each unit is adjustediA，DPFRAValue and deltaiAHas 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，k1Is a constant of an assumptionMaintaining the frequency stable at a level where wind power fluctuations are not increased, will beiAIs reduced to k1·δiA；
Step 3.2.2: if the gain K of the A-region integratorAInvariable, DSFRAValue of K2，K2Is a constant of the hypothesis; when the proportion of the secondary frequency modulation unit is increased from p% to 2 p%, DSFRAIncrease 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, <math>
<mrow>
<msub>
<mi>G</mi>
<mi>A</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mi>Σ</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>M</mi>
<mn>1</mn>
</mrow>
</munderover>
<mfrac>
<msub>
<mi>α</mi>
<mi>iA</mi>
</msub>
<msub>
<mi>δ</mi>
<mi>iA</mi>
</msub>
</mfrac>
<msub>
<mi>G</mi>
<mi>iA</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
</mrow>
</math>
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 Sy(ω)＝Sx(ω)|H(jω)|2，Sy(ω) Representing the power spectral density, S, of the outputx(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 isw1(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 fluctuations(i) The power spectral density of (d) in the frequency domain can be approximated as:
wherein M isFFT(.) is a fast Fourier transform, N is a sample length, ω issFor 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, Sw1(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, Sw2(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 parametersPFCA
Wherein A is the primary frequency modulation unit percentage, and B is the primary frequency modulation capability D of the area APFRAAnd C is the unequal rate of each unit in the area A.
TABLE 2 Secondary FM Capacity of System under different parameters DSFRA
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 ASFRAAnd C is the integrator gain K of the region AAThe value of (a).
DPFCAAnd DSFRAThe calculation of (A) can characterize the current frequency regulation capability of the system with respect to wind power fluctuations, DPFCAAnd DSFRAThe 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, 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 ensuredmIs exactly 15min or n.2mGreater than 15min and closest to 15min, if n.2mWithin 3min of 15min, the average hourly wind power of the mth layer is in the order of hour, and n.2mIf 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, σmIs 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, σsThe standard deviation of second-level fluctuation of the output power of the wind power plant;
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, 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 regionf(s)，αiIs the power generation share coefficient, delta, of the ith unitiIs the difference coefficient R of the ith thermal power generating unitiPower distribution coefficient for participating in secondary frequency modulation units, BA、BBIs a frequency deviation coefficient, K, of each regionA、KBRespectively gaining the secondary frequency modulation integrators in each region; gi(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, Tss is the time constant of the hydraulic servo motor, and T is takenss＝0.2s；Representing the volumetric dynamic characteristics, T, of the steam turbine0s is the volume time constant of the high pressure cylinder; t isa∑The equivalent rotor time constant is expressed, synchronous generators are arranged except for a fan in the system, all the synchronous generators are equivalent to one synchronous generator, and the equivalent rotor time constant can be obtained by multiplying the power share coefficient by the respective rotor time constant; beta is aA、βBThe equivalent friction coefficients of the region A and the region B, respectively, and Ta∑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 XNL(s)、χWind(s) and χPtie(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:
assuming that the load in the area A has no fluctuation, the secondary frequency modulation channel is disconnected, namely only the primary frequency modulation effect is considered, and the frequency domain analytic expression of the grid frequency variation and the second-level wind power fluctuation can be calculated as follows:
wherein, <math>
<mrow>
<msub>
<mi>G</mi>
<mi>A</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mi>Σ</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>M</mi>
<mn>1</mn>
</mrow>
</munderover>
<mfrac>
<msub>
<mi>α</mi>
<mi>iA</mi>
</msub>
<msub>
<mi>δ</mi>
<mi>iA</mi>
</msub>
</mfrac>
<msub>
<mi>G</mi>
<mi>iA</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
</math>
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 Sy(ω)＝Sx(ω)|H(jω)|2，Sy(ω) represents the power spectral density of the output, Sx(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 isw1(ω) power spectral density for second order wind power fluctuations;
can be obtained by PSD time-frequency conversion computer algorithms(i) The power spectral density of (d) in the frequency domain can be approximated as:
wherein M isFFT(.) is a fast Fourier transform, N is a sample length, ω issFor 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, Sw1(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 system frequency fluctuations caused by minute-scale wind power fluctuationsVariance (variance)The 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, Sw2(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 local power grid thermal power frequency modulation unit with wind power uncertainty fluctuation in consideration of claim 1, wherein the establishment of the wind power-containing local power grid model for frequency modulation analysis in the third step is realized by using Matlab/Simulink software.
3. The method for configuring the regional power grid thermal power frequency modulation unit considering wind power uncertainty fluctuation according to claim 1, wherein the third step is based on a frequency modulation capability expression quantitative analysis and calculation system, and the implementation process of the primary and secondary frequency modulation capabilities 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.
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 delta of each unit in the area A isiARemains unchanged, DPFRAValue of K1，K1Is a constant; when the proportion of the primary frequency modulation unit is increased from p% to 2 p%, DPFRAIncrease 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 integratorAInvariable, DSFRAValue of K2，K2Is a constant of the hypothesis; when the proportion of the secondary frequency modulation unit is increased from p% to 2 p%, DSFRAIncrease 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 delta of each unit is adjustediA，DPFRAValue and deltaiAHas 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 the amount of wind power fluctuation is not increased, δ is a constant assumediAIs reduced to k1·δiA；
Step 3.2.2: if the gain K of the A-region integratorAInvariable, DSFRAValue of K2，K2Is a constant of the hypothesis; when the proportion of the secondary frequency modulation unit is increased from p% to 2 p%, DSFRAIncrease 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％。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410486165.4A CN104201700B (en) | 2014-09-22 | 2014-09-22 | Meter and the regional power grid thermoelectricity frequency modulation crew qiting method of the uncertain fluctuation of wind-powered electricity generation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410486165.4A CN104201700B (en) | 2014-09-22 | 2014-09-22 | Meter and the regional power grid thermoelectricity frequency modulation crew qiting method of the uncertain fluctuation of wind-powered electricity generation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104201700A true CN104201700A (en) | 2014-12-10 |
| CN104201700B CN104201700B (en) | 2017-06-20 |
Family
ID=52086953
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410486165.4A Expired - Fee Related CN104201700B (en) | 2014-09-22 | 2014-09-22 | Meter and the regional power grid thermoelectricity frequency modulation crew qiting method of the uncertain fluctuation of wind-powered electricity generation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104201700B (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104462839A (en) * | 2014-12-19 | 2015-03-25 | 哈尔滨工业大学 | Wind electricity uncertainty estimation method based on wind power fluctuation strength instant model |
| CN104868469A (en) * | 2015-05-29 | 2015-08-26 | 中国电力科学研究院 | Starting optimization method of thermal power generating set |
| CN105303056A (en) * | 2015-11-16 | 2016-02-03 | 哈尔滨工业大学 | Method for depicting real-time change rate of wind speeds |
| CN105914787A (en) * | 2016-05-25 | 2016-08-31 | 哈尔滨工业大学 | Method of quantitatively characterizing wind power uncertainty based on power instantaneous relative variation speed |
| CN107947200A (en) * | 2017-12-04 | 2018-04-20 | 长沙理工大学 | Method for determining frequency deviation coefficient of system containing wind power |
| CN108053097A (en) * | 2017-11-23 | 2018-05-18 | 上海电力学院 | The frequency-domain index test and evaluation method of primary frequency modulation performance |
| CN108808699A (en) * | 2018-07-10 | 2018-11-13 | 华北电力大学（保定） | A kind of dual quadrant frequency-response analysis method suitable for bidirectional energy-storage equipment |
| CN109995087A (en) * | 2017-12-29 | 2019-07-09 | 北京天诚同创电气有限公司 | The frequency modulation control method, apparatus and frequency modulation system in power station |
| CN111313466A (en) * | 2020-03-09 | 2020-06-19 | 国网山东省电力公司电力科学研究院 | Sending-end power grid AGC optimization regulation and control method and system based on wind power priority regulation |
| CN113011002A (en) * | 2021-02-22 | 2021-06-22 | 北京凌阳伟业科技有限公司 | Wind energy permeability limit evaluation method, system and device based on power spectral density |
| CN113364813A (en) * | 2021-08-09 | 2021-09-07 | 新风光电子科技股份有限公司 | Compression transmission method and system for rail transit energy feedback data |
| CN113839416A (en) * | 2021-09-13 | 2021-12-24 | 国网湖南省电力有限公司 | Wind power maximum access proportion determining method and device based on frequency response model |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102594244A (en) * | 2012-02-20 | 2012-07-18 | 江苏省电力试验研究院有限公司 | Joint control method of primary frequency modulation for doubly-fed wind power generation set |
| CN102820656A (en) * | 2012-08-25 | 2012-12-12 | 华北电力大学（保定） | Method for jointly scheduling power generation load by using wind power generation unit and thermal power generation unit |
| CN103715720A (en) * | 2014-01-07 | 2014-04-09 | 哈尔滨工业大学 | High-permeability wind power primary and secondary backup coordination control method |
| CN104037772A (en) * | 2014-06-12 | 2014-09-10 | 国家电网公司 | Interconnected power grid automatic gain control (AGC) frequency modulation unified controller and control method |
-
2014
- 2014-09-22 CN CN201410486165.4A patent/CN104201700B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102594244A (en) * | 2012-02-20 | 2012-07-18 | 江苏省电力试验研究院有限公司 | Joint control method of primary frequency modulation for doubly-fed wind power generation set |
| CN102820656A (en) * | 2012-08-25 | 2012-12-12 | 华北电力大学（保定） | Method for jointly scheduling power generation load by using wind power generation unit and thermal power generation unit |
| CN103715720A (en) * | 2014-01-07 | 2014-04-09 | 哈尔滨工业大学 | High-permeability wind power primary and secondary backup coordination control method |
| CN104037772A (en) * | 2014-06-12 | 2014-09-10 | 国家电网公司 | Interconnected power grid automatic gain control (AGC) frequency modulation unified controller and control method |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104462839A (en) * | 2014-12-19 | 2015-03-25 | 哈尔滨工业大学 | Wind electricity uncertainty estimation method based on wind power fluctuation strength instant model |
| CN104868469A (en) * | 2015-05-29 | 2015-08-26 | 中国电力科学研究院 | Starting optimization method of thermal power generating set |
| CN104868469B (en) * | 2015-05-29 | 2017-11-03 | 中国电力科学研究院 | A kind of fired power generating unit start optimization method |
| CN105303056A (en) * | 2015-11-16 | 2016-02-03 | 哈尔滨工业大学 | Method for depicting real-time change rate of wind speeds |
| CN105303056B (en) * | 2015-11-16 | 2017-12-01 | 哈尔滨工业大学 | A kind of wind speed real-time change speed depicting method |
| CN105914787A (en) * | 2016-05-25 | 2016-08-31 | 哈尔滨工业大学 | Method of quantitatively characterizing wind power uncertainty based on power instantaneous relative variation speed |
| CN108053097A (en) * | 2017-11-23 | 2018-05-18 | 上海电力学院 | The frequency-domain index test and evaluation method of primary frequency modulation performance |
| CN107947200A (en) * | 2017-12-04 | 2018-04-20 | 长沙理工大学 | Method for determining frequency deviation coefficient of system containing wind power |
| CN109995087B (en) * | 2017-12-29 | 2021-03-09 | 北京天诚同创电气有限公司 | Frequency modulation control method and device for power station and frequency modulation system |
| CN109995087A (en) * | 2017-12-29 | 2019-07-09 | 北京天诚同创电气有限公司 | The frequency modulation control method, apparatus and frequency modulation system in power station |
| CN108808699A (en) * | 2018-07-10 | 2018-11-13 | 华北电力大学（保定） | A kind of dual quadrant frequency-response analysis method suitable for bidirectional energy-storage equipment |
| CN111313466A (en) * | 2020-03-09 | 2020-06-19 | 国网山东省电力公司电力科学研究院 | Sending-end power grid AGC optimization regulation and control method and system based on wind power priority regulation |
| CN113011002A (en) * | 2021-02-22 | 2021-06-22 | 北京凌阳伟业科技有限公司 | Wind energy permeability limit evaluation method, system and device based on power spectral density |
| CN113364813A (en) * | 2021-08-09 | 2021-09-07 | 新风光电子科技股份有限公司 | Compression transmission method and system for rail transit energy feedback data |
| CN113839416A (en) * | 2021-09-13 | 2021-12-24 | 国网湖南省电力有限公司 | Wind power maximum access proportion determining method and device based on frequency response model |
| CN113839416B (en) * | 2021-09-13 | 2023-10-27 | 国网湖南省电力有限公司 | Wind power maximum access proportion determining method and device based on frequency response model |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104201700B (en) | 2017-06-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104201700B (en) | Meter and the regional power grid thermoelectricity frequency modulation crew qiting method of the uncertain fluctuation of wind-powered electricity generation | |
| CN104485665B (en) | Meter and the dynamic probability trend computational methods of forecasting wind speed error temporal correlation | |
| CN102562469B (en) | Short-term wind driven generator output power predicting method based on correction algorithm | |
| Shao et al. | Short‐term wind speed forecasting using wavelet transformation and AdaBoosting neural networks in Yunnan wind farm | |
| CN102055188A (en) | Ultra-short term wind power forecasting method based on time series method | |
| Jin et al. | Equivalent modeling of wind energy conversion considering overall effect of pitch angle controllers in wind farm | |
| CN106875033A (en) | A kind of wind-powered electricity generation cluster power forecasting method based on dynamic self-adapting | |
| Jin et al. | Method for assessing grid frequency deviation due to wind power fluctuation based on “time-frequency transformation” | |
| CN104810826A (en) | Bidirectional iteration parallel probability load flow calculation method combining Latin hypercube sampling | |
| Carlin et al. | The probability distribution of wind power from a dispersed array of wind turbine generators | |
| CN104462861A (en) | Reservoir regulation decision-making method based on reservoir regulation rule synthesis | |
| Guo et al. | A stochastic-process-based method for assessing frequency regulation ability of power systems with wind power fluctuations | |
| CN105356462A (en) | Wind farm harmonic evaluation prediction method | |
| CN107203827A (en) | A kind of wind turbine forecasting wind speed optimization method based on multiscale analysis | |
| CN105634018A (en) | Random response surface method and interior point method based wind-power-plant-contained random optimal power flow solving method | |
| CN113991651A (en) | Power grid static voltage stability margin probability prediction method considering new energy uncertainty | |
| Yang et al. | Impact of forecasting error characteristics on battery sizing in hybrid power systems | |
| CN103678940B (en) | Fluctuations in wind speed uncertainty estimation method based on effective turbulence intensity instantaneous model | |
| CN109149566B (en) | Modeling method of simulation model for predicting lowest frequency point under high-power deficiency | |
| Miao et al. | Fluctuation feature extraction of wind power | |
| Yang et al. | Ensemble singular vectors and their use as additive inflation in EnKF | |
| Shi et al. | Short term wind power forecasting using Hilbert-Huang Transform and artificial neural network | |
| CN105610199A (en) | Method and device for determining wind-solar ratio considering grid constraints | |
| Olauson et al. | Simulating intra‐hourly wind power fluctuations on a power system level | |
| CN103543332A (en) | Power harmonic prediction method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20170620 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |