CN103296701A - Active power control method in wind power plant - Google Patents

Abstract

The invention provides an active power control method in a wind power plant. The active power control method in the wind power plant comprises the steps of computing the active power prediction errors of all predicted points in the wind power plant, establishing a wind power plant active power prediction error distribution function, establishing a wind power plant active power prediction error distribution module, a wind power plant active power prediction confidence degree module and a wind power plant active power control module in sequence, optimizing the wind power plant active power control module, obtaining an optimization confidence weight of all unit sets, and controlling the active power of the wind power plant. The active power control method in the wind power plant is based on historical data analysis. By means of the active power control method in the wind power plant, the power-outputting error of the wind power plant is reduced, the reliability and accuracy of the wind power plant are guaranteed, and a powerful guarantee is supplied to the wind power plant in the grid-connected operation.

Description

A kind of wind energy turbine set active power control method

Technical field

The invention belongs to power system planning and running technology field, be specifically related to a kind of wind energy turbine set active power control method.

Background technology

According to country's " new forms of energy industry development planning " draft, the year two thousand twenty China wind-powered electricity generation total installation of generating capacity will reach 1.5 hundred million kilowatts, be 5 times of " regenerative resource midium or long term development plan " target of issue in 2007.According to planning, China will be in Gansu, 7 ten million multikilowatt wind-powered electricity generation bases are made in Xinjiang, Hebei, Jilin, the Inner Mongol, six provinces and regions, Jiangsu.Jiuquan ten million multikilowatt wind-powered electricity generation construction of base planning total installation of generating capacity is 3,565 ten thousand kilowatts; Hami is planned 2,000 ten thousand kilowatts; 5,000 ten thousand kilowatts of Inner Mongol planning constructions wherein cover 2,000 ten thousand kilowatts in west, cover 3,000 ten thousand kilowatts in east; Hebei planning is built together in coastal and northern territory and is established 1,000 ten thousand kilowatts; 1,000 ten thousand kilowatts of Jiangsu planning constructions, wherein the coastal waters is 7,000,000 kilowatts; The Technique in Western Jilin Province area mainly in cities such as loose source and Baicheng, is planned for the year two thousand twenty and reaches 2,300 ten thousand kilowatts.By the end of the year 2009, China's wind-powered electricity generation installed capacity has reached 2,500 ten thousand kilowatts, leaps to second in the whole world.

China's wind power generation development is swift and violent, but faces the multiple challenge of fault in the primary stage.Wind-resources exists fluctuation and intermittence, so wind power generation is a kind of insecure forms of electricity generation.The wind-powered electricity generation resource is by including Forecasting Methodologies such as ultrashort phase prediction, short-term forecast in the power system dispatching system effectively, can reduce the wind-powered electricity generation fluctuation to a certain extent to the influence of power system dispatching, but analyze existing Forecasting Methodology, what big metering method obtained all is the power prediction sequence of determining, only provides one group of wind power forecasting sequence of determining.

Therefore, there is certain error in the result who obtains according to existing wind-powered electricity generation Forecasting Methodology.If can analyze the distribution probability and the probability that obtain the wind power predicated error predicts the outcome, then can improve the precision of wind-powered electricity generation scheduling, reduce the system cost increase that the wind-powered electricity generation predicated error is brought.

The ultrashort phase prediction of wind energy turbine set can be predicted the wind power in the following 4h, because all there is certain error in existing Forecasting Methodology, if ultrashort phase predicted power is directly applied to scheduling, and can be to the negative effect of electric power system economy.

Summary of the invention

In order to overcome above-mentioned the deficiencies in the prior art, the invention provides a kind of wind energy turbine set active power control method, based on historical data analysis, can reduce the output of wind electric field error, reliability and the accuracy of wind energy turbine set have been guaranteed, for wind farm grid-connected operation provides powerful guarantee.

In order to realize the foregoing invention purpose, the present invention takes following technical scheme:

A kind of wind energy turbine set active power control method is provided, said method comprising the steps of:

Step 1: calculate each future position active power predicated error of wind energy turbine set, and set up wind energy turbine set active power predicated error distribution function;

Step 2: based on wind energy turbine set active power predicated error distribution function, set up wind energy turbine set active power predicated error distributed model;

Step 3: based on wind energy turbine set active power predicated error distributed model, set up wind energy turbine set active power forecast confidence model;

Step 4: comprehensive wind energy turbine set active power predicated error distributed model and wind energy turbine set active power forecast confidence model, set up wind energy turbine set active power control model;

Step 5: described wind energy turbine set active power control model is optimized, and the optimization confidence weighting that obtains each machine group is heavy, and then control wind energy turbine set active power.

Described step 1 may further comprise the steps:

Step 1-1: the historical predicted power data of input wind energy turbine set and historical actual power data, select wind energy turbine set rated power to calculate each future position active power predicated error of wind energy turbine set as fiducial value, have:

$e_i=\frac{P_{\mathrm{predict}\_i}-P_{\mathrm{actual}\_i}}{P_{\mathrm{farm}}}---\left(1\right)$

Wherein: e _iActive power predicated error for wind energy turbine set i time point; P _{Predict_i}Be wind energy turbine set i time point predicted power; P _{Actual_i}Be wind energy turbine set i time point actual power; P _FarmBe the specified installed capacity of wind energy turbine set;

Step 1-2: judge that by hypothesis testing in the mathematical statistics which kind of wind energy turbine set active power predicated error distribute for, and set up wind energy turbine set active power predicated error distribution function.

Described step 1-2 may further comprise the steps:

Step 1-2-1: whether false wind electric field active power predicated error is normal distribution, check this hypothesis to set up:

Utilize maximum-likelihood method estimation error average and variance, calculate interval frequency and the statistic test value in each interval If satisfy test value

In acceptance region, determine that then wind energy turbine set active power predicated error is normal distribution;

Step 1-2-2: set up wind energy turbine set active power predicated error distribution function;

Wind energy turbine set active power predicated error distribution function is described as:

$f\left(e_i\right)=\frac{1}{\mathrm{\σ}\sqrt{2\mathrm{\π}}}{e}^{-\frac{{(e_i-\mathrm{\μ})}^2}{{2\mathrm{\σ}}^2}}---\left(2\right)$

Wherein: μ is the wind energy turbine set anticipation error, and σ is the standard deviation of active power predicated error;

Then at time point t place, the active power predicated error desired value E of wind energy turbine set _i(t) be:

E _i(t)＝e(t) （3）

Wherein: t is current time, and e (t) is t wind energy turbine set active power predicated error constantly.

In the described step 2, wind energy turbine set active power predicated error Normal Distribution is normal distribution, calculate active power predicated error average and the variance that can obtain wind energy turbine set by historical data, because each future position of wind energy turbine set disperses, active power predicated error according to each time point of wind energy turbine set active power predicated error distribution function calculating wind energy turbine set obtains wind energy turbine set active power predicated error distributed model thus.

In the described step 3, for wind energy turbine set active power predicated error, select confidence level, and then calculate the wind energy turbine set expectation confidential interval under this confidence level, be expressed as:

P{e _min＜e＜e _max}≥1-α （4）

Wherein: e _MinBe confidential interval lower limit, e _MaxBe the confidential interval upper limit, 1-α is confidence level, (e _Min, e _Max) be confidential interval.

In the described step 4, for the wind energy turbine set that comprises N typhoon power generator group, according to the running status of each unit in the wind energy turbine set wind-powered electricity generation unit is divided into the M group;

Choosing of comprehensive wind energy turbine set active power predicated error distribution and confidence level, confidential interval set up wind energy turbine set active power control model, and target function is:

${\min f}_i=|\underset{j=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\ξ}}_{\mathrm{ij}}+\frac{{\mathrm{\λ}}_{\mathrm{ij}}}{2})P_{\mathrm{group}\_\mathrm{ij}}-P_{\mathrm{predict}\_i}\·E_i|---\left(5\right)$

Wherein: f _iBe i time point wind energy turbine set desired output power and historical error distribution expectation difference, ξ _IjIt is the weight coefficient of j group wind-powered electricity generation group of planes i time point; λ _IjIt is the fluctuation weight coefficient of j group wind-powered electricity generation group of planes i time point; P _{Group_ij}It is the prediction generated output of j group wind-powered electricity generation group of planes time point; P _{Predict_i}It is the wind energy turbine set predicted power of i time point; E _iActive power predicated error desired value for each time point of wind energy turbine set.

The statistical history data, be divided in one day n interval, calculate the active power predicated error desired value of each time point of wind energy turbine set, thereby obtain each interval error e _iDistribution;

The active power predicated error desired value E that integration obtains each time point of wind energy turbine set is asked in each interval _i, have

$E_i={\∫}_{t_{i-1}}^{t_i}{e}_i\mathrm{dt}---\left(6\right)$

Ask for f _iDuring minimum value, variable bound is divided into generated output constraint and weight coefficient constraint;

Generated output is constrained to:

$\left\{\begin{array}{c}\underset{j=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\ξ}}_{\mathrm{ij}}+{\mathrm{\λ}}_{\mathrm{ij}})P_{\mathrm{group}\_\mathrm{ij}}=P_{\mathrm{predict}\_i}\×(1+e_{+})\\ \underset{j=1}{\overset{M}{\mathrm{\Σ}}}{\mathrm{\ξ}}_{\mathrm{ij}}{P}_{\mathrm{group}\_\mathrm{ij}}=P_{\mathrm{predict}\_i}\×(1-e_{-})\\ ({\mathrm{\ξ}}_{\mathrm{ij}}+{\mathrm{\λ}}_{\mathrm{ij}})P_{\mathrm{group}\_\mathrm{ij}}\≤\underset{j=1}{\overset{M}{\mathrm{\Σ}}}{Q}_{\mathrm{ij}}{P}_{\mathrm{gen}}\end{array}\right.,i=\mathrm{1,2,3}...,N;j=\mathrm{1,2},...,M---\left(7\right)$

Wherein: e ₊Be the predicted power confidence upper limit; e _-Be the predicted power confidence lower limit; Q _IjIt is j group of planes i unit number constantly; P _GenBe wind-powered electricity generation unit rated power;

The gross power of each wind-powered electricity generation machine group is represented on the equality constraint equal sign left side in the formula (7), the power bound of equal sign the right expression wind energy turbine set; The gross power of each wind-powered electricity generation machine group is represented on the inequality constraints equal sign left side, the power upper limit of each machine group of equal sign the right expression;

Weight coefficient is constrained to:

0≤ξ _ij＜1，i=1,2,3...,N;j=1,2,...,M（8）

0≤λ _ij＜1，i=1,2,3...,N;j=1,2,...,M（9）。

In the described step 5, according to determined target function and variable bound, described wind energy turbine set active power control model is optimized, the optimization confidence weighting that obtains each machine group is heavy, and then control wind energy turbine set active power;

Optimizing process is expressed as Mathematical Modeling, has:

$\left.\begin{array}{c}s.t.\min f(u,x)\\ h(u,x)=0\\ g(u,x)\≥0\end{array}\right\}---\left(10\right)$

Wherein: u and x are state variable, and state variable x comprises the weight coefficient of each machine group; F is wind energy turbine set desired output power and historical error distribution expectation difference, and h and g are respectively equality constraint function and inequality constraints function.

Compared with prior art, beneficial effect of the present invention is:

(1) machine group predicted power weight reassignment method in the wind energy turbine set has been proposed, overcome the scheduling error that predicated error causes in the conventional method, introducing by historical data analysis and weight coefficient, seek suitable mathematical optimization target function, and then set up and optimize model, for wind farm grid-connected control provides suitable method;

(2) the present invention has considered various factors in the wind energy turbine set, and gained active power control scheme has more accurately rationally reduced wind farm grid-connected loss, and method is simple to operation, is convenient to promote.

(3) based on historical data analysis, can reduce the output of wind electric field error, guaranteed reliability and the accuracy of wind energy turbine set, for wind farm grid-connected operation provides powerful guarantee.

Description of drawings

Fig. 1 is wind energy turbine set active power control method flow chart.

Embodiment

Below in conjunction with accompanying drawing the present invention is described in further detail.

As Fig. 1, consider that the wind energy turbine set active power optimal control method of confidence level may further comprise the steps:

(1) historical predicted power data and the actual power data of input wind energy turbine set are calculated wind energy turbine set active power predicated error distribution function;

(2) the wind energy turbine set predicated error is analyzed, selected suitable confidence level, calculate wind energy turbine set predicted power confidential interval;

(3) according to the realtime power data of the wind-powered electricity generation machine group that collects the wind-powered electricity generation unit is hived off;

(4) utilize the active set algorithm optimization to seek the optimum confidence level weight of each machine group;

(5) based on the confidence level weight that calculates wind energy turbine set active power is optimized control.

Below be an example of calculation shows of the inventive method, based on the ultrashort phase prediction data of Gansu wind energy turbine set, realize that each machine group power weight reallocation is regulated in the wind energy turbine set.The wind-powered electricity generation unit that to comprise 15 single-machine capacities in the wind energy turbine set be 3MW, unit is all controlled, and temporal resolution is 30 minutes, after each group of planes is received dispatch command, can transfer to the scheduling value before next time point.And according to analyzing needs, the configuration scheduling instruction.Difference such as the table 1 of wind energy turbine set power shortage before and after optimizing:

Table 1

Time t/h	Difference power/MW
		0.5	0.7138
1	0.7138
		1.5	1.9201
2	1.3843
		2.5	2.0061
3	1.7158
		3.5	1.7506
4	1.6988
		6.5	2.1053
7	1.4473
		7.5	1.2804
8	0.5268
		11	0.6836

11.5	0.8016
		12	0.3652
22	0.3316
		22.5	0.5528
23	1.8566
		23.5	3.0329
24	2.4334

As shown in Table 1, after optimizing by said method, during greater than scheduling power, the largest optimization amount is about 2MW at the wind energy turbine set generated output; During power, maximum can reduce about 3MW power shortage to the wind energy turbine set generated output less than scheduling.Therefore for this wind energy turbine set, this active power optimal control method has important investment value, and example has also been verified the feasibility of method proposed by the invention simultaneously, is the method very effectively of finding the solution wind energy turbine set active power control problem.

Should be noted that at last: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit, although with reference to above-described embodiment the present invention is had been described in detail, those of ordinary skill in the field are to be understood that: still can make amendment or be equal to replacement the specific embodiment of the present invention, and do not break away from any modification of spirit and scope of the invention or be equal to replacement, it all should be encompassed in the middle of the claim scope of the present invention.

Claims (8)

1. wind energy turbine set active power control method is characterized in that: said method comprising the steps of:

Step 1: calculate each future position active power predicated error of wind energy turbine set, and set up wind energy turbine set active power predicated error distribution function;

Step 2: based on wind energy turbine set active power predicated error distribution function, set up wind energy turbine set active power predicated error distributed model;

Step 3: based on wind energy turbine set active power predicated error distributed model, set up wind energy turbine set active power forecast confidence model;

Step 4: comprehensive wind energy turbine set active power predicated error distributed model and wind energy turbine set active power forecast confidence model, set up wind energy turbine set active power control model;

Step 5: described wind energy turbine set active power control model is optimized, and the optimization confidence weighting that obtains each machine group is heavy, and then control wind energy turbine set active power.

2. wind energy turbine set active power control method according to claim 1, it is characterized in that: described step 1 may further comprise the steps:

Step 1-1: the historical predicted power data of input wind energy turbine set and historical actual power data, select wind energy turbine set rated power to calculate each future position active power predicated error of wind energy turbine set as fiducial value, have:

$e_i=\frac{P_{\mathrm{predict}\_i}-P_{\mathrm{actual}\_i}}{P_{\mathrm{farm}}}---\left(1\right)$

Wherein: e _iActive power predicated error for wind energy turbine set i time point; P _{Predict_i}Be wind energy turbine set i time point predicted power; P _{Actual_i}Be wind energy turbine set i time point actual power; P _FarmBe the specified installed capacity of wind energy turbine set;

Step 1-2: judge that by hypothesis testing in the mathematical statistics which kind of wind energy turbine set active power predicated error distribute for, and set up wind energy turbine set active power predicated error distribution function.

3. wind energy turbine set active power control method according to claim 2, it is characterized in that: described step 1-2 may further comprise the steps:

Step 1-2-1: whether false wind electric field active power predicated error is normal distribution, check this hypothesis to set up:

Utilize maximum-likelihood method estimation error average and variance, calculate interval frequency and the statistic test value in each interval

If satisfy test value

In acceptance region, determine that then wind energy turbine set active power predicated error is normal distribution;

Step 1-2-2: set up wind energy turbine set active power predicated error distribution function;

Wind energy turbine set active power predicated error distribution function is described as:

$f\left(e_i\right)=\frac{1}{\mathrm{\σ}\sqrt{2\mathrm{\π}}}{e}^{-\frac{{(e_i-\mathrm{\μ})}^2}{{2\mathrm{\σ}}^2}}---\left(2\right)$

Wherein: μ is the wind energy turbine set anticipation error, and σ is the standard deviation of active power predicated error;

Then at time point t place, the active power predicated error desired value E of wind energy turbine set _i(t) be:

E _i(t)＝e(t) （3）

Wherein: t is current time, and e (t) is t wind energy turbine set active power predicated error constantly.

4. wind energy turbine set active power control method according to claim 1, it is characterized in that: in the described step 2, wind energy turbine set active power predicated error Normal Distribution is normal distribution, calculate active power predicated error average and the variance that can obtain wind energy turbine set by historical data, because each future position of wind energy turbine set disperses, active power predicated error according to each time point of wind energy turbine set active power predicated error distribution function calculating wind energy turbine set obtains wind energy turbine set active power predicated error distributed model thus.

5. wind energy turbine set active power control method according to claim 1 is characterized in that: in the described step 3, for wind energy turbine set active power predicated error, select confidence level, and then calculate the wind energy turbine set expectation confidential interval under this confidence level, be expressed as:

P{e _min＜e＜e _max}≥1-α （4）

Wherein: e _MinBe confidential interval lower limit, e _MaxBe the confidential interval upper limit, 1-α is confidence level, (e _Min, e _Max) be confidential interval.

6. wind energy turbine set active power control method according to claim 1 is characterized in that: in the described step 4, for the wind energy turbine set that comprises N typhoon power generator group, according to the running status of each unit in the wind energy turbine set wind-powered electricity generation unit is divided into the M group;

Choosing of comprehensive wind energy turbine set active power predicated error distribution and confidence level, confidential interval set up wind energy turbine set active power control model, and target function is:

${\min f}_i=|\underset{j=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\ξ}}_{\mathrm{ij}}+\frac{{\mathrm{\λ}}_{\mathrm{ij}}}{2})P_{\mathrm{group}\_\mathrm{ij}}-P_{\mathrm{predict}\_i}\·E_i|---\left(5\right)$

Wherein: f _iBe i time point wind energy turbine set desired output power and historical error distribution expectation difference, ξ _IjIt is the weight coefficient of j group wind-powered electricity generation group of planes i time point; λ _IjIt is the fluctuation weight coefficient of j group wind-powered electricity generation group of planes i time point; P _{Group_ij}It is the prediction generated output of j group wind-powered electricity generation group of planes time point; P _{Predict_i}It is the wind energy turbine set predicted power of i time point; E _iActive power predicated error desired value for each time point of wind energy turbine set.

7. wind energy turbine set active power control method according to claim 6 is characterized in that: the statistical history data, be divided in one day n interval, calculate the active power predicated error desired value of each time point of wind energy turbine set, thereby obtain each interval error e _iDistribution;

The active power predicated error desired value E that integration obtains each time point of wind energy turbine set is asked in each interval _i, have

$E_i={\∫}_{t_{i-1}}^{t_i}{e}_i\mathrm{dt}---\left(6\right)$

Ask for f _iDuring minimum value, variable bound is divided into generated output constraint and weight coefficient constraint;

Generated output is constrained to:

$\left\{\begin{array}{c}\underset{j=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\ξ}}_{\mathrm{ij}}+{\mathrm{\λ}}_{\mathrm{ij}})P_{\mathrm{group}\_\mathrm{ij}}=P_{\mathrm{predict}\_i}\×(1+e_{+})\\ \underset{j=1}{\overset{M}{\mathrm{\Σ}}}{\mathrm{\ξ}}_{\mathrm{ij}}{P}_{\mathrm{group}\_\mathrm{ij}}=P_{\mathrm{predict}\_i}\×(1-e_{-})\\ ({\mathrm{\ξ}}_{\mathrm{ij}}+{\mathrm{\λ}}_{\mathrm{ij}})P_{\mathrm{group}\_\mathrm{ij}}\≤\underset{j=1}{\overset{M}{\mathrm{\Σ}}}{Q}_{\mathrm{ij}}{P}_{\mathrm{gen}}\end{array}\right.,i=\mathrm{1,2,3}...,N;j=\mathrm{1,2},...,M---\left(7\right)$

Wherein: e ₊Be the predicted power confidence upper limit; e _-Be the predicted power confidence lower limit; Q _IjIt is j group of planes i unit number constantly; P _GenBe wind-powered electricity generation unit rated power;

The gross power of each wind-powered electricity generation machine group is represented on the equality constraint equal sign left side in the formula (7), the power bound of equal sign the right expression wind energy turbine set; The gross power of each wind-powered electricity generation machine group is represented on the inequality constraints equal sign left side, the power upper limit of each machine group of equal sign the right expression;

Weight coefficient is constrained to:

0≤ξ _ij＜1，i=1,2,3...,N;j=1,2,...,M（8）

0≤λ _ij＜1，i=1,2,3...,N;j=1,2,...,M（9）。

8. wind energy turbine set active power control method according to claim 1, it is characterized in that: in the described step 5, according to determined target function and variable bound, described wind energy turbine set active power control model is optimized, the optimization confidence weighting that obtains each machine group is heavy, and then control wind energy turbine set active power;

Optimizing process is expressed as Mathematical Modeling, has:

$\left.\begin{array}{c}s.t.\min f(u,x)\\ h(u,x)=0\\ g(u,x)\≥0\end{array}\right\}---\left(10\right)$

Wherein: u and x are state variable, and state variable x comprises the weight coefficient of each machine group; F is wind energy turbine set desired output power and historical error distribution expectation difference, and h and g are respectively equality constraint function and inequality constraints function.

Images