CN102738828A - Method for stabilizing uncertainty of large-scale wind power grid-connected power fluctuation by utilizing integrated combined power generation unit - Google Patents

Abstract

The invention discloses a method for stabilizing uncertainty of large-scale wind power grid-connected power fluctuation by utilizing an integrated combined power generation unit, relates to a method for stabilizing uncertainty of large-scale wind power grid-connected power fluctuation, and aims to solve the problem that the large-scale wind power grid-connected electric field is low in power fluctuation stabilizing capacity. The method comprises the following steps of: decomposing the large-scale wind power grid-connected power fluctuation into superposition of a predictable component and an uncertain component; performing boundary estimation on the uncertain component in the step 1 by utilizing the integrated combined power generation unit, optimally matching the traditional power supply with the wind power; acquiring the frequency spectrum of the uncertain component, analyzing the frequency spectrum, and dividing the uncertain component into an ultrahigh frequency part, a high frequency part, an intermediate frequency part and a low frequency part; respectively tracking and stabilizing the four parts by employing an ultrahigh frequency tracking and stabilizing unit, a high frequency tracking and stabilizing unit, an intermediate frequency tracking and stabilizing unit and a low frequency tracking and stabilizing unit; and stabilizing the uncertainty of the large-scale wind power grid-connected power fluctuation. The method is suitable for stabilizing the uncertainty of the large-scale wind power grid-connected power fluctuation.

Description

Utilize integrated cogeneration unit to stabilize probabilistic method of scale wind-electricity integration power fluctuation

Technical field

The present invention relates to a kind of uncertain method that presses down scale wind-electricity integration power fluctuation.

Background technology

Face fossil energy human common difficulties such as exhaustion, environmental pollution day by day, new forms of energy electric power safeties such as scale wind energy efficiently develop, and are inevitable choice and great strategic measure that China realizes sustainable development.And along with the reaching its maturity of wind generating technology, being incorporated into the power networks of scale wind-powered electricity generation just becomes one of most critical issue that current urgent need solves.In conjunction with the particularly thorny national conditions of China's power supply architecture property, hope can utilize integrated cogeneration unit that the fluctuation of wind power is carried out omnibearingly comprehensively stabilizing, and solves the key technical problem of wind-electricity integration, realizes the safe and efficient utilization to the energy.

Though China's wind-resources is abundanter, wind has characteristics such as randomness, strong fluctuation and uncertainty, makes the active power of output of wind-powered electricity generation unit exist uncertain.At present, the single-machine capacity of wind turbine generator has developed into the MW class level, but current wind power prediction technology still is difficult to satisfy demands such as efficient operation of power system security or scheduling; When being incorporated into the power networks; Because the uncertainty of wind power can cause very difficulty of day balance of electric power and ener and power supply arrangement, the traffic control of electrical network faces huge test; And along with the increasing substantially of wind-powered electricity generation installed capacity, scale new forms of energy electric power is dissolved the problem and the contradiction that face will be more outstanding.The important restraining factors that large-scale wind power is incorporated into the power networks are that electrical network can be the peak modulation capacity that wind-powered electricity generation provides; Current; The scale wind-powered electricity generation is dissolved becomes the great realistic problem that China's electric power system faces, and for example wind-powered electricity generation online purchase electric weight was 222.54 hundred million kilowatt hours to June in 2010 1; Not purchasing electric weight is 27.76 hundred million kilowatt hours, and loss wind energy ratio is 11.1%.The traditional generator unit of the current main utilization for example way of participation peak regulation such as fired power generating unit is stabilized the uncertainty of wind power, the peak regulation method of promptly usually said " paddy is mended in peak clipping ".But; Because the peak modulation capacity of different peak regulations unit such as fired power generating unit is different; And single one or several generator units that utilize are stabilized the uncertainty of wind power since current China to be still with the thermoelectricity generating be main, and other the energy output proportion in electrical network of generator unit that can participate in peak regulation is less; Therefore, in actual mechanical process, tend to occur some problems.With the data instance of statistics at the end of Year 2008, the average vacancy of Inner Mongolia Power Grid peak regulation electric power reaches 1GW, in servicely is faced with the situation that electrical network is rationed the power supply, and directly has influence on the arrangement of day balance of electric power and ener and power system operating mode; Simultaneously, during owing to the fired power generating unit peak regulation, be in low load operation, economic effect is relatively poor.Therefore, large tracts of land adopts the fired power generating unit peak regulation not only resource to be caused waste, and effect is also obvious inadequately.

Summary of the invention

The present invention stabilizes the problem of scarce capacity in order to solve extensive grid connected wind power field power fluctuation, thereby a kind of probabilistic method of utilizing integrated cogeneration unit to stabilize scale wind-electricity integration power fluctuation is provided.

Utilize integrated cogeneration unit to stabilize probabilistic method of scale wind-electricity integration power fluctuation, it is realized by following steps:

Step 1, scale wind-electricity integration power fluctuation is decomposed into the stack that can forecast component and uncertain component;

Step 2, utilize integrated cogeneration unit that the uncertain component in the step 1 is carried out the boundary to estimate, realize the Optimum Matching of conventional power source and wind-powered electricity generation;

The frequency spectrum of uncertain component in step 3, the obtaining step two, and this frequency spectrum analyzed, uncertain component is divided into hyperfrequency, high frequency, intermediate frequency, four parts of low frequency;

Step 4, the hyperfrequency that step 3 is obtained partly adopt the hyperfrequency in the integrated cogeneration unit to follow the tracks of to stabilize the unit to follow the tracks of to stabilize;

The HFS that step 3 is obtained adopts the high frequency in the integrated cogeneration unit to follow the tracks of to stabilize the unit to follow the tracks of to stabilize;

The intermediate-frequency section that step 3 is obtained adopts the intermediate frequency in the integrated cogeneration unit to follow the tracks of to stabilize the unit to follow the tracks of to stabilize;

The low frequency part that step 3 is obtained adopts the low frequency in the integrated cogeneration unit to follow the tracks of to stabilize the unit to follow the tracks of to stabilize;

Thereby realize stabilizing the uncertainty of scale wind-electricity integration power fluctuation.

Hyperfrequency in the integrated cogeneration unit is followed the tracks of and is stabilized unit employing flywheel energy storage unit realization.

HFS in the integrated cogeneration unit adopts pumped storage or Gas Turbine Generating Units to realize.

Intermediate-frequency section in the integrated cogeneration unit adopts the method for the response speed of intake valve in the current steam turbine unit of adjustment to realize.

Low frequency part in the integrated cogeneration unit adopts the thermal power generation unit to realize.

In the step 1 scale wind-electricity integration power fluctuation being decomposed into the stack that can forecast component and uncertain component is to adopt the method based on SVMs to realize, is specially:

Steps A, with loss function:

$\frac{1}{2}{\left|\right|w\left|\right|}^2+C\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\ξ}}_i+{\mathrm{\ξ}}_i^{*})$

Be revised as:

$\frac{1}{2}{\left|\right|w\left|\right|}^2+C[\mathrm{v\ϵ}+\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\ξ}}_i+{\mathrm{\ξ}}_i^{*})]$

The constraints of following formula is:

In the formula: ε is the variable in the loss function; C>0, the parameter of the balance between decision empiric risk and the regular part; V is the constant between 0～1; W is the vector that overall coefficient is formed; B is a real number, and x is an input value, and y is an output valve, and n is a positive integer;

Step B, selected positive number v, C and kernel function, Φ is a mapping function, kernel function K (x _i, x _j)=Φ (x _i) Φ (x _j); Wherein the present invention selects gaussian kernel function

The dual problem of amended loss function among step C, structure and the solution procedure A obtains optimal solution:

α=(α ₁, α ₁ ^*, α ₂, α ₂ ^*, ∧, α _n, α _n ^*) ^T, α wherein _n,

Be Lagrange multiplier;

Step D, the optimal solution structure decision function that obtains according to step C:

$f\left(x\right)=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)K(x_i,x)+b$

In the formula: ask the method for b value to be by the method for undetermined coefficients:

$b=\frac{1}{2}[y_j+y_k-(\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)K(x_i,x_j)+\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)K(x_i,x_k))]$

Obtain; Wherein k is the integer between 1 to n; J is the integer between 1 to n; And k ≠ j;

Step e, according to formula:

${\mathrm{\ϵ}}^{*}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)K(x_i,x_j)+b-y_j$

Or ${\mathrm{\ϵ}}^{*}=y_k-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)K(x_i,x_k)-b$

Carry out Estimating Confidence Interval, and generate boundary's drawing for estimate; The predicted value curve is and can forecasts component among the figure, and in confidential interval, near the range of indeterminacy this curve is uncertain component; Thereby realize scale wind-electricity integration power fluctuation is decomposed into the stack that can forecast component and uncertain component.

In the step 3 this frequency spectrum is analyzed, it is to adopt wavelet analysis method to realize that uncertain component is divided into hyperfrequency, high frequency, intermediate frequency, four parts of low frequency.

Beneficial effect: method of the present invention is divided into high frequency, intermediate frequency, low frequency three parts with wind power uncertainty in the scale wind field; Adopting integrated cogeneration unit to adopt the unit of corresponding follow-up control to follow the tracks of to the fluctuation in the different frequency scope stabilizes; Not only can make electrical network respond the frequency change of wind-powered electricity generation, the rotation stand-by heat that minimizing is operated in low load condition fast like this; And improved wind-powered electricity generation and penetrated power limit; Satisfy the power market transaction needs more, and be convenient to arrange unit maintenance and maintenance.

Description of drawings

Fig. 1 is that sketch map is formed in the electric power system of traditional energy; Fig. 2 is the composition sketch map of new forms of energy electric power system; Fig. 3 is the classification sketch map of wind power fluctuation; Fig. 4 is the principle schematic of the uncertain integrated cogeneration of wind power unit; Fig. 5 is wind farm grid-connected wiring schematic diagram; Fig. 6 is a wind power fluctuation frequency division spectrum diagram; Fig. 7 is the spectrum diagram of low-frequency range; Fig. 8 is the spectrum diagram of Mid Frequency; Fig. 9 is the spectrum diagram of high band; Figure 10 is the spectrum diagram of hyper band; Figure 11 is actual wind power fluctuation spectrum diagram; Figure 12 is the schematic flow sheet of Mallat decomposition algorithm.

Embodiment

Embodiment one, utilize integrated cogeneration unit to stabilize probabilistic method of scale wind-electricity integration power fluctuation, it is realized by following steps:

Step 1, scale wind-electricity integration power fluctuation is decomposed into the stack that can forecast component and uncertain component;

Step 2, utilize integrated cogeneration unit that the uncertain component in the step 1 is carried out the boundary to estimate, realize the Optimum Matching of conventional power source and wind-powered electricity generation;

The frequency spectrum of uncertain component in step 3, the obtaining step two, and this frequency spectrum analyzed, uncertain component is divided into hyperfrequency, high frequency, intermediate frequency, four parts of low frequency;

Step 4, the hyperfrequency that step 3 is obtained partly adopt the hyperfrequency in the integrated cogeneration unit to follow the tracks of to stabilize the unit to follow the tracks of to stabilize;

The HFS that step 3 is obtained adopts the high frequency in the integrated cogeneration unit to follow the tracks of to stabilize the unit to follow the tracks of to stabilize;

The intermediate-frequency section that step 3 is obtained adopts the intermediate frequency in the integrated cogeneration unit to follow the tracks of to stabilize the unit to follow the tracks of to stabilize;

The low frequency part that step 3 is obtained adopts the low frequency in the integrated cogeneration unit to follow the tracks of to stabilize the unit to follow the tracks of to stabilize;

Thereby realize stabilizing the uncertainty of scale wind-electricity integration power fluctuation.

Hyperfrequency in the integrated cogeneration unit is followed the tracks of and is stabilized unit employing flywheel energy storage unit realization.

HFS in the integrated cogeneration unit adopts pumped storage or Gas Turbine Generating Units to realize.

Intermediate-frequency section in the integrated cogeneration unit adopts the method for the response speed of intake valve in the current steam turbine unit of adjustment to realize.

Low frequency part in the integrated cogeneration unit adopts the thermal power generation unit to realize.

In the step 1 scale wind-electricity integration power fluctuation being decomposed into the stack that can forecast component and uncertain component is to adopt the method based on SVMs to realize, is specially:

Steps A, with loss function:

$\frac{1}{2}{\left|\right|w\left|\right|}^2+C\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\ξ}}_i+{\mathrm{\ξ}}_i^{*})$

Be revised as:

$\frac{1}{2}{\left|\right|w\left|\right|}^2+C[\mathrm{v\ϵ}+\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\ξ}}_i+{\mathrm{\ξ}}_i^{*})]$

The constraints of following formula is:

In the formula: ε is the variable in the loss function; C>0, the parameter of the balance between decision empiric risk and the regular part; V is the constant between 0～1; W is the vector that overall coefficient is formed; B is a real number, and x is an input value, and y is an output valve, and n is a positive integer;

Step B, selected positive number v, C and kernel function, Φ is a mapping function, kernel function K (x _i, x _j)=Φ (x _i) Φ (x _j); Wherein the present invention selects gaussian kernel function

The dual problem of amended loss function among step C, structure and the solution procedure A obtains optimal solution:

α=(α ₁, α ₁ ^*, α ₂, α ₂ ^*, ∧, α _n, α _n ^*) ^T, α wherein _n,

Be Lagrange multiplier;

Step D, the optimal solution structure decision function that obtains according to step C:

$f\left(x\right)=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)K(x_i,x)+b$

In the formula: ask the method for b value to be by the method for undetermined coefficients:

$b=\frac{1}{2}[y_j+y_k-(\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)K(x_i,x_j)+\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)K(x_i,x_k))]$

Obtain; Wherein k is the integer between 1 to n; J is the integer between 1 to n; And k ≠ j;

Step e, according to formula:

${\mathrm{\ϵ}}^{*}=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)K(x_i,x_j)+b-y_j$

Or ${\mathrm{\ϵ}}^{*}=y_k-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)K(x_i,x_k)-b$

Carry out Estimating Confidence Interval, and generate boundary's drawing for estimate; The predicted value curve is and can forecasts component among the figure, and in confidential interval, near the range of indeterminacy this curve is uncertain component; Thereby realize scale wind-electricity integration power fluctuation is decomposed into the stack that can forecast component and uncertain component.

In the step 3 this frequency spectrum is analyzed, it is to adopt wavelet analysis method to realize that uncertain component is divided into hyperfrequency, high frequency, intermediate frequency, four parts of low frequency.

The boundary's drawing for estimate that provides can be shown in accompanying drawing 3, and predicted value is its desired value, and this curve is the certainty part, and near the range of indeterminacy desired value is its uncertain part, is the part that needs integrated cogeneration unit to stabilize.Shown in accompanying drawing 3, because the amplitude of uncertain component is significantly less than total power fluctuation amplitude, so this method can be stabilized the problem of scarce capacity a kind of new approaches are provided for solving power fluctuation in the large-scale wind power field.

Principle: shown in accompanying drawing 1, the fluctuation power supply of mains side is less in the electric power system of traditional energy, mainly is that the load center side wave is moving bigger, therefore can keep grid balance through the power output of control mains side in the former electrical network; And current greatly developing owing to new forms of energy; New forms of energy such as wind-powered electricity generation are also increasing to the influence of electrical network; Existing problem is to have certain fluctuation in the mains side power output in the current electrical network shown in accompanying drawing 2; And still there is fluctuation in load side, therefore goes to stabilize the power output of fluctuation to the power supply that how to utilize fluctuation, keeps this key issue of grid balance; We have proposed to utilize integrated cogeneration unit to stabilize wind power fluctuation uncertainty in the scale wind field, and then are expected to solve the influence to electrical network when being incorporated into the power networks such as other fluctuation type power supply such as photoelectricity.

Stabilize the scarce capacity problem to power fluctuation in the large-scale wind power field, the thought that adopts classification to solve, shown in accompanying drawing 3, the fluctuation that wind-powered electricity generation is loaded is decomposed into the stack that can forecast component and uncertain component (prediction error).Utilize existing power system call pattern to stabilize predictable wave component by intrasystem peak modulation capacity; Uncertain component (prediction error) is stabilized in the integrated power generation unit that utilizes conventional power source and new forms of energy power supply to constitute; Because the amplitude of uncertain component is significantly less than total power fluctuation amplitude, thereby this method is expected to stabilize the problem of scarce capacity a kind of new approaches are provided for solving power fluctuation in the large-scale wind power field.

In the integrated power generation unit; At first, to the boundary that carries out of the uncertain component of power fluctuation estimating, realized the Optimum Matching problem of conventional power source and wind-powered electricity generation; On the basis that guarantees enough control abilities, realized minimum traditional energy configuration; The wind power of not only having realized stabilizing fast and effectively in the scale wind energy turbine set is uncertain, thereby and reduced system hot standby usefulness to a certain extent, improved economic benefit; Once more; Shown in accompanying drawing 4; Angle from the uncertain component spectrum analysis of wind-powered electricity generation; The thought of utilizing frequency-division section is divided into hyperfrequency, high frequency, intermediate frequency, low frequency four parts with the uncertain component of wind power, and to demand performance index requests such as traditional energy dynamic response bandwidth, utilizes integrated cogeneration unit to adopt the unit of corresponding follow-up control to follow the tracks of to the fluctuation in the different frequency scope and stabilize.

Frequency division way among the present invention adopts multiple dimensioned dividing method:

The present invention relates to utilize multiple dimensioned thought of time that the uncertain component of wind power is divided into hyperfrequency, high frequency, intermediate frequency, low frequency four parts.The main theory that adopts wavelet analysis among the present invention; Frequency division is carried out in fluctuation in the different frequency domains in the scale wind energy turbine set handle, and then utilize integrated cogeneration unit to adopt the unit of corresponding follow-up control to follow the tracks of and stabilize to the fluctuation in the different frequency scope.

The main theory development of wavelet analysis is from Fourier transform, and Short Time Fourier Transform arrives the process of wavelet analysis again.A breakthrough achievement of wavelet transform is fast algorithm-Mallat algorithm that S.Malalt proposed on the basis at multiresolution analysis in 1989.Provide Mallat basic idea and some formula below.

Suppose multiresolution analysis

In

Be orthonormal, corresponding wavelet basis function does

Because

Constituted L ²(R) one group of orthonormal basis, thereby to appointing the function f=L that gives ²(R) all can use

Analyze.Because always only have finite resolution for a certain specific signal; So can suppose

is the integer confirmed, and by

$\stackrel{\‾}{\underset{j\∈Z}{Y}{V}_j}=L^{2}R---\left(1\right)$

Therefore have:

Wherein:

Know by multiresolution analysis:

$V_J=W_{J-1}\⊕V_{J-1}=\mathrm{\Λ}=W_{J-1}\⊕W_{J-2}\⊕\mathrm{\Λ}\⊕W_{J-M}\⊕V_{J-M}---\left(4\right)$

So f (x) can be expressed as again:

Wherein:

d _j,k=(f(x),Ψ _j，k) （6）

Following formula is called the wavelet series of f (x) and launches expression.If note:

$g_j\left(x\right)=\underset{\mathrm{k\ϵ\ϵ}}{\mathrm{\Σ}}{d}_{j,k}{\mathrm{\Ψ}}_{j,k}\∈W_J---\left(7\right)$

Then formula (5) can be written as again:

$f\left(x\right)=\underset{J-M\&le;j<J}{\mathrm{\&Sigma;}}{g}_j+f_{J-M}---\left(9\right)$

F (x) is represented in stack with f (x) differentiates the projection function on the layer in difference, and along with the increase f of j _j(x) more and more near f (x), promptly have

$f\left(x\right)=\underset{j\&RightArrow;\&infin;}{\overset{\mathrm{limt}}{}}{f}_j\left(x\right)---\left(10\right)$

And the decomposition algorithm of merit mallat through to concerning between each layer decomposition coefficient research and constructing.

By two yardstick equations

Can get:

Again because:

d _j，k=(f(x),Ψ _j，k) （16）

The following relationship formula is arranged between the coefficient thus:

$c_{j,k}=\underset{n}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{h}}_{n-2k}{c}_{j+1,n}---\left(17\right)$

$d_{j,k}=\underset{n}{\mathrm{\&Sigma;}}{\stackrel{\&OverBar;}{g}}_{n-2k}{c}_{j+1,n}---\left(18\right)$

This is by { c _{J+1, k}} _{K ∈ z}Calculate { c _{J, k}} _{K ∈ z}{ d _{J, k}} _{K ∈ z}Algorithm be called the mallat decomposition algorithm.Utilize this decomposition algorithm can be at an easy rate by the { c among the formula ⑵ _{J, k}} _{K ∈ z}Calculate each different small echo expansion coefficient { d that differentiate on the layer among the formula ⑸ _{J, k}} _{K ∈ z}(j=J-1, J-2 ..., J-M) and the scaling function expansion coefficient { c in " slightly " yardstick subspace _{J-M, k}} _{K ∈ z}

The process of Mallat decomposition algorithm can be used shown in Figure 12; Be to utilize method of wavelet analysis; With the uncertain part of certain actual wind-powered electricity generation factory atmosphere power fluctuation, be divided into four part hyperfrequencies as shown in the figure, high frequency, intermediate frequency and low frequency four parts, the frequency spectrum of acquisition such as Fig. 6 are to shown in Figure 11.

Claims (7)

1. utilize integrated cogeneration unit to stabilize probabilistic method of scale wind-electricity integration power fluctuation, it is characterized in that: it is realized by following steps:

Step 1, scale wind-electricity integration power fluctuation is decomposed into the stack that can forecast component and uncertain component;

Step 2, utilize integrated cogeneration unit that the uncertain component in the step 1 is carried out the boundary to estimate, realize the Optimum Matching of conventional power source and wind-powered electricity generation;

The frequency spectrum of uncertain component in step 3, the obtaining step two, and this frequency spectrum analyzed, uncertain component is divided into hyperfrequency, high frequency, intermediate frequency, four parts of low frequency;

Step 4, the hyperfrequency that step 3 is obtained partly adopt the hyperfrequency in the integrated cogeneration unit to follow the tracks of to stabilize the unit to follow the tracks of to stabilize;

The HFS that step 3 is obtained adopts the high frequency in the integrated cogeneration unit to follow the tracks of to stabilize the unit to follow the tracks of to stabilize;

The intermediate-frequency section that step 3 is obtained adopts the intermediate frequency in the integrated cogeneration unit to follow the tracks of to stabilize the unit to follow the tracks of to stabilize;

The low frequency part that step 3 is obtained adopts the low frequency in the integrated cogeneration unit to follow the tracks of to stabilize the unit to follow the tracks of to stabilize;

Thereby realize stabilizing the uncertainty of scale wind-electricity integration power fluctuation.

2. probabilistic method of utilizing integrated cogeneration unit to stabilize scale wind-electricity integration power fluctuation according to claim 1 is characterized in that unit employing flywheel energy storage unit realization is stabilized in the hyperfrequency tracking in the integrated cogeneration unit.

3. probabilistic method of utilizing integrated cogeneration unit to stabilize scale wind-electricity integration power fluctuation according to claim 1 is characterized in that the HFS in the integrated cogeneration unit adopts pumped storage or Gas Turbine Generating Units to realize.

4. probabilistic method of utilizing integrated cogeneration unit to stabilize scale wind-electricity integration power fluctuation according to claim 1 is characterized in that the intermediate-frequency section in the integrated cogeneration unit adopts the method for the response speed of intake valve in the current steam turbine unit of adjustment to realize.

5. probabilistic method of utilizing integrated cogeneration unit to stabilize scale wind-electricity integration power fluctuation according to claim 1 is characterized in that the low frequency part in the integrated cogeneration unit adopts the thermal power generation unit to realize.

6. probabilistic method of utilizing integrated cogeneration unit to stabilize scale wind-electricity integration power fluctuation according to claim 1; It is characterized in that in the step 1 scale wind-electricity integration power fluctuation being decomposed into the stack that can forecast component and uncertain component is to adopt the method based on SVMs to realize, is specially:

Steps A, with loss function:

$\frac{1}{2}{\left|\right|w\left|\right|}^2+C\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({\mathrm{\&xi;}}_i+{\mathrm{\&xi;}}_i^{*})$

Be revised as:

$\frac{1}{2}{\left|\right|w\left|\right|}^2+C[\mathrm{v\&epsiv;}+\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({\mathrm{\&xi;}}_i+{\mathrm{\&xi;}}_i^{*})]$

The constraints of following formula is:

In the formula: ε is the variable in the loss function; C>0, the parameter of the balance between decision empiric risk and the regular part; V is the constant between 0～1; W is the vector that overall coefficient is formed; B is a real number, and x is an input value, and y is an output valve, and n is a positive integer;

Step B, selected positive number v, C and kernel function, Φ is a mapping function, kernel function is selected gaussian kernel function for use $k(x_i,x_j)=e^{-{|x_i-x_j|}^2/2{\mathrm{\&sigma;}}^2};$

The dual problem of amended loss function among step C, structure and the solution procedure A obtains optimal solution:

α=(α ₁, α ₁ ^*, α ₂, α ₂ ^*, ∧, α _n, α _n ^*) ^T, α wherein _n,

Be Lagrange multiplier;

Step D, the optimal solution structure decision function that obtains according to step C:

$f\left(x\right)=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({\mathrm{\&alpha;}}_i^{*}-{\mathrm{\&alpha;}}_i)K(x_i,x)+b$

In the formula: ask the method for b value to be by the method for undetermined coefficients:

$b=\frac{1}{2}[y_j+y_k-(\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({\mathrm{\&alpha;}}_i^{*}-{\mathrm{\&alpha;}}_i)K(x_i,x_j)+\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({\mathrm{\&alpha;}}_i^{*}-{\mathrm{\&alpha;}}_i)K(x_i,x_k))]$

Obtain; Wherein k is the integer between 1 to n; J is the integer between 1 to n; And k ≠ j;

Step e, according to formula:

${\mathrm{\&epsiv;}}^{*}=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({\mathrm{\&alpha;}}_i^{*}-{\mathrm{\&alpha;}}_i)K(x_i,x_j)+b-y_j$

Or ${\mathrm{\&epsiv;}}^{*}=y_k-\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({\mathrm{\&alpha;}}_i^{*}-{\mathrm{\&alpha;}}_i)K(x_i,x_k)-b$

Carry out Estimating Confidence Interval, and generate boundary's drawing for estimate; The predicted value curve is and can forecasts component among the figure, and in confidential interval, near the range of indeterminacy this curve is uncertain component; Thereby realize scale wind-electricity integration power fluctuation is decomposed into the stack that can forecast component and uncertain component.

7. probabilistic method of utilizing integrated cogeneration unit to stabilize scale wind-electricity integration power fluctuation according to claim 1; It is characterized in that in the step 3 this frequency spectrum being analyzed, it is to adopt wavelet analysis method to realize that uncertain component is divided into hyperfrequency, high frequency, intermediate frequency, four parts of low frequency.

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