CN102570509B - Base-point power off-set setting method for BLR-type AGC unit - Google Patents

Abstract

The invention provides a base-point power off-set setting method for a BLR-type AGC (Automatic Gain Control) unit. The method comprises the following steps: counting load prediction errors in the peak period to form load prediction error probability distribution; counting wind-power prediction errors to form prediction error probability distribution; and setting the base-point power of the BLR-type AGC unit at the Tau-1 time interval when the load peak period is at the Tau-1-th time interval according to the related load prediction error probability distribution information and the wind-power prediction error probability distribution information till the whole peak time interval finishes. The invention reasonably reflects the offset characteristic of probability distribution when the load and the wind-power prediction errors in the peak period are superposed, facilitates providing reference for coordination between non-AGC units and the BLR-type AGC unit in the pre-scheduling period and the on-line scheduling period, reduces power shortage frequency, and improves the frequency quality; complicated computation is not contained, and practicability is realized; and only software is required for implementation without the need of hardware, and the economical cost is lower.

Description

A kind of BLR type AGC unit basic point power bias setting method

(1) technical field

The present invention relates to the electrical network AGC control technology under large-scale wind power access background, is exactly BLR type AGC unit basic point power bias setting method in a kind of high wind-powered electricity generation permeability electrical network specifically.

(2) background technology

The load peak load operation period is the very crucial period of system active balance and frequency adjustment.Along with improving constantly of electrical network wind-powered electricity generation permeability, intermittence, fluctuation and the anti-peak regulation characteristic of wind-powered electricity generation aggravated peak regulation and the frequency modulation pressure of this period sometimes, is embodied in AGC unit pondage deficiency, CPS index and Region control deviation (ACE) deterioration, operation risk increase etc.Under large-scale wind power access background, it is nervous that the adjustable resource of electrical network level of each scheduling time is tending towards, and the research of BLR type AGC unit basic point power setting mode has seemed very important.In high wind-powered electricity generation permeability electrical network, what in system, have that regulating power unit follows the tracks of is system net load curve (load checking electrical power).Although single Power Output for Wind Power Field is intermittent comparatively obvious with unsteadiness feature, but in real system, large-scale wind power field group is owing to there being wide area space-time complementarity, overall power output shows trend stability within longer a period of time, i.e. in significant period of time, show as the rising of gross power output tendency, decline or basicly stable.Wherein, if peak period, wind power showed as tendency decline, can make peak period net load curve more more precipitous than former load curve, thereby may climb kurtosis gesture by severe exacerbation electrical network.Be found that by actual count, the actual value of loading peak period has greater probability higher than its predicted value.Occurring this situation, is to be load forecasting method problem because some period load of peak period there will be spike, another major reason on the one hand.Load Forecasting algorithm is conventionally to pursue training sample set forecast deviation root mean square minimum as target, and because peak load speedup is often greater than training set sample speedup, " stagnant increasing " just easily appears in the forecast result obtaining according to training set sample.This peak load period in actual electric network has embodiment also time, as peak period frequency to continue on the low side, BLR type AGC unit easily locked at pondage upper limit position etc.

Common BLR type AGC unit basic point power setting mode, comprises the various ways such as AUTO, SCHE, BASE, AVER, CECO, PROP and LDFC at present.In relatively more conventional front four kinds of modes, the basic point power of AUTO mode is current exerting oneself; The basic point power of SCHE mode obtains from planned value curve, and automatically inputs in AGC database; The basic point power of BASE mode is artificial set-point; The basic point power of AVER is got the mean value that regulates bound.Without wind-powered electricity generation or wind-powered electricity generation permeability hour, traditional setting method relatively easily ensures that in most cases BLR type AGC unit all has certain up-down adjustment nargin to electrical network.But along with improving constantly of wind-powered electricity generation permeability, unit regulates nargin constantly to reduce, load forecasts that with wind-powered electricity generation the inclined to one side characteristic issues that has of the probability distribution after the two stack of deviation will expose.In the situation that the total pondage of BLR type AGC unit is limited, by traditional basic point power setting method, easily cause system frequency and/or interconnection power index to continue to worsen.Therefore, under large-scale wind power access background, the basic point performance number of the BLR type of rationally adjusting AGC machine, the Probability Characteristics after making it by the two stack of load and wind-powered electricity generation realizes bias-adjusted, system is reduced to power shortage, holding frequency and interconnection power stability peak period useful.

(3) summary of the invention

The object of the present invention is to provide BLR type AGC unit basic point power bias setting method in a kind of high wind-powered electricity generation permeability electrical network.

The object of the present invention is achieved like this: it comprises the following steps:

Step 1: peak period Load Forecasting error is added up, form the Load Forecasting probability of error and distribute;

Choose similar day peak sample of N load, obtaining on-line scheduling time stage load sample number is N _peak, in this on-line scheduling period, the maximum overgauge ε that loads of ultrashort period _{(+), i}with maximum minus deviation ε _{(-), i}for:

$\left\{\begin{array}{c}{\mathrm{\ϵ}}_{(+),i}=\max \{0,\max \{D^{({\mathrm{\τ}}_i,{\mathrm{\τ}}_j)}-D^{{\mathrm{\τ}}_i,f}j=1~\mathrm{end}\left\}\right\}\\ {\mathrm{\ϵ}}_{(-),i}=\min \{0,\min \{D^{({\mathrm{\τ}}_i,{\mathrm{\τ}}_j)}-D^{{\mathrm{\τ}}_i,f},j=1~\mathrm{end}\left\}\right\}\end{array}\right.---\left(1\right)$

In formula:

be in i on-line scheduling period, a j ultrashort period load actual value;

it is the Load Forecasting value of i on-line scheduling period; End represents ultrashort period sum in online dispatching cycle;

ε _{(+), i}and ε _{(-), i}be the deviation of ultrashort period load actual value and Load Forecasting value, can reflect the load variations characteristic of ultrashort period; By N _peakthe ε of individual period _{(+), i}and ε _{(-), i}be classified as sample set G, G will comprise 2N _peakindividual sample; Set after confidence level m, can obtain the maximum overgauge ε of Load Forecasting corresponding to this confidence level _{load, (+)}with maximum minus deviation ε _{load, (-)}; ε _{load, (+)}and ε _{load, (-)}using the probabilistic forecasting reference as peak load operation on same day period Load Forecasting deviation; M can determine according to the maximum power deviation that peak period, on-line scheduling time stage can bear;

Step 2: wind-powered electricity generation prediction error is added up, formed prediction error probability distribution;

Consider that wind-powered electricity generation prediction error probability distribution is relevant with wind power predicted value, wind-powered electricity generation prediction error probability distribution adopts to forecast performance number

for the prediction error statistical method of statistical condition; Other statistic processes is identical with peak period Load Forecasting probability of error distribution statistical method; Wind-powered electricity generation forecast deviation and peak load forecast deviation non-correlation, if therefore N day interior sample deficiency, can extend measurement period; Obtain after wind-powered electricity generation prediction error probability distribution, set confidence level m, obtain wind-powered electricity generation corresponding to this confidence level and forecast maximum overgauge ε _{wind, (+)}with maximum minus deviation ε _{wind, (-)}; ε _{wind, (+)}and ε _{wind, (-)}using the probabilistic forecasting reference as peak load operation on same day period wind-powered electricity generation forecast deviation;

Step 3: at load peak time τ _i-1when period, according to relevant Load Forecasting probability of error distributed intelligence, and wind-powered electricity generation prediction error probability distribution information, to τ _ithe BLR type AGC unit basic point power of period is adjusted, until finish whole peak period; Step is as follows:

A), at τ _i-1when period, in the time that starting, peak load just from peak load forecast deviation Sample Storehouse, obtains ε _{load, (+)}and ε _{load, (-)}, the two is definite value in the whole peak load operation period;

B), according to online scheduling slot τ in peak period _iwind power predicted value

mate wind-powered electricity generation prediction error probability distribution Sample Storehouse information, obtain the ε of respective conditions _{wind, (+)}and ε _{wind, (-)};

C), calculate τ _ithe positive and negative parital coefficient of period net load:

$\left\{\begin{array}{c}{\mathrm{\σ}}_{(+)}=\frac{{\mathrm{\ϵ}}_{\mathrm{load},(+)}-{\mathrm{\ϵ}}_{\mathrm{wind},(-)}}{{\mathrm{\ϵ}}_{\mathrm{load},(+)}-{\mathrm{\ϵ}}_{\mathrm{wind},(-)}-({\mathrm{\ϵ}}_{\mathrm{load},(-)}-{\mathrm{\ϵ}}_{\mathrm{wind},(+)})}\\ {\mathrm{\σ}}_{(-)}=\frac{-({\mathrm{\ϵ}}_{\mathrm{load},(-)}-{\mathrm{\ϵ}}_{\mathrm{wind},(+)})}{{\mathrm{\ϵ}}_{\mathrm{load},(+)}-{\mathrm{\ϵ}}_{\mathrm{wind},(-)}-({\mathrm{\ϵ}}_{\mathrm{load},(-)}-{\mathrm{\ϵ}}_{\mathrm{wind},(+)})}\end{array}\right.---\left(2\right)$

D), the τ that adjusts _iperiod all BLR types AGC unit basic point power:

$P_{\mathrm{BLR}}^{{\mathrm{\τ}}_i}={\mathrm{\σ}}_{(-)}\underset{k\∈G_{\mathrm{BLR}}}{\mathrm{\Σ}}{P}_k^{\max }+{\mathrm{\σ}}_{(+)}\underset{k\∈G_{\mathrm{BLR}}}{\mathrm{\Σ}}{P}_k^{\min }---\left(3\right)$

In formula:

τ _iperiod BLR type AGC unit basic point performance number;

it is respectively k platform BLR type AGC unit variable capacity bound.

Beneficial effect of the present invention is as follows:

(1) being applicable to BLR type AGC unit basic point power in high wind-powered electricity generation permeability electrical network adjusts, rationally reflect the inclined to one side characteristic of having of probability distribution after the two stack of peak period load and wind-powered electricity generation prediction error, be of value within pre-scheduling cycle and on-line scheduling cycle and provide reference to non-AGC unit and BLR type AGC unit cooperative, reduce power shortage number of times, improve frequency quality;

(2) tuning process, not containing complicated calculations, has practicality;

(3) this method only needs software to realize, and without increasing hardware, Financial cost is lower.

(4) brief description of the drawings

Fig. 1 is control flow chart of the present invention;

Fig. 2 is that under confidence degree, the maximum positive and negative deviation of Load Forecasting calculates schematic diagram;

Fig. 3 is the small sample statistics figure of net load forecast deviation.

(5) embodiment

Below in conjunction with accompanying drawing, the invention will be further described for example.

Embodiment 1: in conjunction with Fig. 1, the present invention is a kind of BLR type AGC unit basic point power bias setting method, and step is as follows:

Step 1: peak period Load Forecasting error is added up, form the Load Forecasting probability of error and distribute;

Choose similar day peak sample of N load, obtaining on-line scheduling time stage load sample number is N _peak, in this on-line scheduling period, the maximum overgauge ε that loads of ultrashort period _{(+), i}with maximum minus deviation ε _{(-), i}for:

$\left\{\begin{array}{c}{\mathrm{\ϵ}}_{(+),i}=\max \{0,\max \{D^{({\mathrm{\τ}}_i,{\mathrm{\τ}}_j)}-D^{{\mathrm{\τ}}_i,f}j=1~\mathrm{end}\left\}\right\}\\ {\mathrm{\ϵ}}_{(-),i}=\min \{0,\min \{D^{({\mathrm{\τ}}_i,{\mathrm{\τ}}_j)}-D^{{\mathrm{\τ}}_i,f},j=1~\mathrm{end}\left\}\right\}\end{array}\right.---\left(1\right)$

In formula:

be in i on-line scheduling period, a j ultrashort period load actual value;

it is the Load Forecasting value of i on-line scheduling period; End represents ultrashort period sum in online dispatching cycle;

ε _{(+), i}and ε _{(-), i}be the deviation of ultrashort period load actual value and Load Forecasting value, can reflect the load variations characteristic of ultrashort period; By N _peakthe ε of individual period _{(+), i}and ε _{(-), i}be classified as sample set G, G will comprise 2N _peakindividual sample; Set after confidence level m, can obtain the maximum overgauge ε of Load Forecasting corresponding to this confidence level _{load, (+)}with maximum minus deviation ε _{load, (-)}; ε _{load, (+)}and ε _{load, (-)}using the probabilistic forecasting reference as peak load operation on same day period Load Forecasting deviation; M can be according to peak

The maximum power deviation that period on-line scheduling time stage can bear is determined;

Step 2: wind-powered electricity generation prediction error is added up, formed prediction error probability distribution;

Consider that wind-powered electricity generation prediction error probability distribution is relevant with wind power predicted value, wind-powered electricity generation prediction error probability distribution adopts to forecast performance number

for the prediction error statistical method of statistical condition; Other statistic processes is identical with peak period Load Forecasting probability of error distribution statistical method; Wind-powered electricity generation forecast deviation and peak load forecast deviation non-correlation, if therefore N day interior sample deficiency, can extend measurement period; Obtain after wind-powered electricity generation prediction error probability distribution, set confidence level m, obtain wind-powered electricity generation corresponding to this confidence level and forecast maximum overgauge ε _{wind, (+)}with maximum minus deviation ε _{wind, (-)}; ε _{wind, (+)}and ε _{wind, (-)}using the probabilistic forecasting reference as peak load operation on same day period wind-powered electricity generation forecast deviation;

Step 3: at load peak time τ _i-1when period, according to relevant Load Forecasting probability of error distributed intelligence, and wind-powered electricity generation prediction error probability distribution information, to τ _ithe BLR type AGC unit basic point power of period is adjusted, until finish whole peak period; Step is as follows:

A), at τ _i-1when period, in the time that starting, peak load just from peak load forecast deviation Sample Storehouse, obtains ε _{load, (+)}and ε _{loaa, (-)}, the two is definite value in the whole peak load operation period;

B), according to online scheduling slot τ in peak period _iwind power predicted value

mate wind-powered electricity generation prediction error probability distribution Sample Storehouse information, obtain the ε of respective conditions _{wind, (+)}and ε _{wind, (-)};

C), calculate τ _ithe positive and negative parital coefficient of period net load:

$\left\{\begin{array}{c}{\mathrm{\σ}}_{(+)}=\frac{{\mathrm{\ϵ}}_{\mathrm{load},(+)}-{\mathrm{\ϵ}}_{\mathrm{wind},(-)}}{{\mathrm{\ϵ}}_{\mathrm{load},(+)}-{\mathrm{\ϵ}}_{\mathrm{wind},(-)}-({\mathrm{\ϵ}}_{\mathrm{load},(-)}-{\mathrm{\ϵ}}_{\mathrm{wind},(+)})}\\ {\mathrm{\σ}}_{(-)}=\frac{-({\mathrm{\ϵ}}_{\mathrm{load},(-)}-{\mathrm{\ϵ}}_{\mathrm{wind},(+)})}{{\mathrm{\ϵ}}_{\mathrm{load},(+)}-{\mathrm{\ϵ}}_{\mathrm{wind},(-)}-({\mathrm{\ϵ}}_{\mathrm{load},(-)}-{\mathrm{\ϵ}}_{\mathrm{wind},(+)})}\end{array}\right.---\left(2\right)$

D), the τ that adjusts _iperiod all BLR types AGC unit basic point power:

$P_{\mathrm{BLR}}^{{\mathrm{\τ}}_i}={\mathrm{\σ}}_{(-)}\underset{k\∈G_{\mathrm{BLR}}}{\mathrm{\Σ}}{P}_k^{\max }+{\mathrm{\σ}}_{(+)}\underset{k\∈G_{\mathrm{BLR}}}{\mathrm{\Σ}}{P}_k^{\min }---\left(3\right)$

In formula: τ _iperiod BLR type AGC unit basic point performance number;

it is respectively k platform BLR type AGC unit variable capacity bound.

Embodiment 2: in conjunction with Fig. 1, the present invention relates to the basic point power bias setting method of BLR type AGC unit in a kind of high wind-powered electricity generation permeability electrical network, can effectively reduce peak period grid power vacancy event frequency, improve system frequency quality.As shown in Figure 1, grid company is added up the Load Forecasting error of similar day peak period on-line scheduling time stage, distributes according to the Load Forecasting probability of error, obtains the maximum positive and negative deviation ε of load under confidence degree _{load, (+)}and ε _{load, (-)}.Grid company is according to the wind-powered electricity generation predicated error of operation day each on-line scheduling period in past, establishment wind-powered electricity generation prediction error probability distribution Sample Storehouse, this Sample Storehouse comprises multiple condition prediction error probability distribution, these condition prediction error probability distribution, all obtain by statistics taking wind power predicted value as condition.The Load Forecasting probability of error distributes and wind-powered electricity generation prediction error probability distribution Sample Storehouse, all passes in time regular update.At the Japan-China τ of operation _i-1the individual on-line scheduling period, according to next on-line scheduling period τ _iwind power predicted value coupling wind-powered electricity generation forecasts deviation Sample Storehouse information, obtains the ε of respective conditions _{wind, (+)}and ε _{wind, (-)}.According to ε _{load, (+)}and ε _{load, (-)}information, ε _{wind, (+)}and ε _{wind, (-)}information, and the power bound of each BLR type AGC unit, adjust in real time and obtain τ _iindividual on-line scheduling period BLR type AGC unit entirety basic point performance number is

according to

to τ _iin the individual on-line scheduling period, the generation schedule of BLO type AGC unit and non-AGC unit is adjusted.

Embodiment 3: in conjunction with Fig. 2, Fig. 3, get certain province's actual electric network year load data of the north this paper control strategy is analyzed.This electrical network has 23 non-AGC units, 6 BLO type AGC units and 6 BLR type AGC units; Wind-powered electricity generation total installation of generating capacity is 1260MW; Natural frequency characteristic coefficient-1384MW/Hz, frequency allowed band 49.95Hz～50.05Hz; This electrical network is within 10 pre-scheduling periods of the 8:00 to 10:30 on July 31, and wind power continues to be reduced to 286.9MW from 531.8MW, and load continues to rise to 7763.3MW from 6609.6MW; BLR type AGC unit parameter, as table 1, respectively has 3 BLR, 1 type unit and BLR 2 type units in electrical network.

Table 1BLR type AGC unit parameter

Load measurement period is five operations day.Be 0.9 o'clock in confidence level, obtain ε according to Fig. 2 _{load, (+)}and ε _{load, (-)}be respectively 95.1MW and-55.1MW.Consider the large sample reasonability that ensures wind-powered electricity generation statistics, choose the wind power data of two weeks and add up, can form the wind-powered electricity generation prediction error Sample Storehouse taking different wind-powered electricity generation predicted power as condition.Calculate σ corresponding to different wind-powered electricity generation predicted power by formula (2) ₍₊₎and σ _(-)value is as table 2.

The positive and negative parital coefficient of net load that the different wind-powered electricity generation predicted values of table 2 are corresponding

From table 2, ε under different condition probability _{wind, (+)}and ε _{wind, (-)}there is difference, but due to ε _{load, (+)}and ε _{load, (-)}numerical value is larger, and therefore the interval corresponding positive and negative parital coefficient of net load of different wind-powered electricity generations forecast power is more approaching, less on the impact of adjusting of BLR type AGC unit basic point power.For this paper electrical network, desirable σ ₍₊₎be 0.6, σ _(-)be 0.4 as mean value replace, further simplified real-time online tuning process.

The pattern (AVER pattern) that BLR type AGC unit basic point performance number is adjusted by adjustable capacity mid point is called Pat1; The pattern that BLR type AGC unit basic point performance number is adjusted by the inventive method is called Pat2.To Pat1 and Pat2, the positive negative regulator surplus mean value of BLR type AGC unit within 150 ultrashort periods of 1min compares, the j of an i on-line scheduling period ultrashort period, the deviation delta P of system net load actual value and predicted value _{i, j}for

$\mathrm{\Δ}{P}_{i,j}=(D^{({\mathrm{\τ}}_i,u_j)}-W^{({\mathrm{\τ}}_i,u_j)})-(D^{{\mathrm{\τ}}_i,f}-W^{{\mathrm{\τ}}_i,f})---\left(4\right)$

If Δ P _{i, j}; For positive quantity is N ₊, for negative quantity is N _-, with positive negative regulator surplus mean value

with

weigh the adjustable surplus of BLR type AGC unit,

with

be defined as:

$\left\{\begin{array}{c}\stackrel{\&OverBar;}{R_{+}}=\frac{1}{N_{+}}\underset{i,j}{\mathrm{\&Sigma;}}(\underset{j\&Element;G_{\mathrm{BLR}}}{\mathrm{\&Sigma;}}{P}_k^{\max }-\underset{j\&Element;G_{\mathrm{BLR}}}{\mathrm{\&Sigma;}}{P}_k^{\mathrm{plan}}-{\mathrm{\&Delta;P}}_{i,j}),{\mathrm{\&Delta;P}}_{i,j}>0\\ \stackrel{\&OverBar;}{R_{-}}=\frac{1}{N_{-}}\underset{i,j}{\mathrm{\&Sigma;}}(\underset{j\&Element;G_{\mathrm{BLR}}}{\mathrm{\&Sigma;}}{P}_k^{\min }-\underset{j\&Element;G_{\mathrm{BLR}}}{\mathrm{\&Sigma;}}{P}_k^{\mathrm{plan}}-{\mathrm{\&Delta;P}}_{i,j}),{\mathrm{\&Delta;P}}_{i,j}<0\end{array}\right.---\left(5\right)$

150 Δ P of 10 pre-scheduling periods _{i, j}small sample statistics is shown in Fig. 3, Pat1 and Pat2's

with

the results are shown in Table 3.

The positive negative regulator surplus average of table 3

From Fig. 3 and table 3, Δ P _{i, j}its value of small sample statistical result showed for positive amplitude and probability all larger, but in Pat1, BLR type AGC unit negative regulator surplus average is greater than and just regulates surplus average, negative regulator surplus exists wastes, this is obviously irrational.Pat2 basic point power has been realized offset joint, has stayed adjusting surplus in the positively biased direction of the large probability of net load, amplitude more.Even if coordinate like this deficiency with BLO type AGC unit at non-AGC unit of some period, BLR type AGC unit still can leave part and just regulate surplus to tackle deviation outside the plan.Because BLR type AGC unit control in whole Peak climbing is a Dynamic Regulating Process, stay more and just regulating surplus to be also conducive to eliminate in time the frequency departure producing after a upper period primary frequency modulation, avoid frequency departure to pass in time continuous accumulation and expand, realized the reasonable distribution of BLR type AGC unit adjusting resource.

Although the present invention only analyzes single electrical network, but for interconnected network situation, the adjustable resource that rationally has each electrical network of inclined to one side distribution BLR type AGC unit, is conducive to each electrical network and eliminates in time self power deviation, is also useful to the raising of whole interconnected network CPS index.

Claims (1)

1. a peak period BLR type AGC unit basic point power bias setting method in high wind-powered electricity generation permeability electrical network, is characterized in that it comprises the following steps:

Step 1: peak period Load Forecasting error is added up, form the Load Forecasting probability of error and distribute;

Choose similar day peak sample of N load, obtaining on-line scheduling time stage load sample number is N _peak, in this on-line scheduling period, the maximum overgauge ε that loads of ultrashort period _{(+), i}with maximum minus deviation ε _{(-), i}for:

In formula:

be in i on-line scheduling period, a j ultrashort period load actual value;

it is the Load Forecasting value of i on-line scheduling period; End represents ultrashort period sum in online dispatching cycle;

ε _{(+), i}and ε _{(-), i}be the deviation of ultrashort period load actual value and Load Forecasting value, can reflect the load variations characteristic of ultrashort period; By N _peakthe ε of individual period _{(+), i}and ε _{(-), i}be classified as sample set G, G will comprise 2N _peakindividual sample; Set after confidence level m, can obtain the maximum overgauge ε of Load Forecasting corresponding to this confidence level _{load, (+)}with maximum minus deviation ε _{load, (-)}; ε _{load, (+)}and ε _{load, (-)}using the probabilistic forecasting reference as peak load operation on same day period Load Forecasting deviation; M determines according to the maximum power deviation that peak period, on-line scheduling time stage can bear;

Step 2: wind-powered electricity generation prediction error is added up, formed prediction error probability distribution;

Consider that wind-powered electricity generation prediction error probability distribution is relevant with wind power predicted value, wind-powered electricity generation prediction error probability distribution adopts to forecast performance number

for the prediction error statistical method of statistical condition; Other statistic processes is identical with peak period Load Forecasting probability of error distribution statistical method; Wind-powered electricity generation forecast deviation and peak load forecast deviation non-correlation, if therefore sample deficiency in N day, prolongation measurement period; Obtain after wind-powered electricity generation prediction error probability distribution, set confidence level m, obtain wind-powered electricity generation corresponding to this confidence level and forecast maximum overgauge ε _{wind, (+)}with maximum minus deviation ε _{wind, (-)}; ε _{wind, (+)}and ε _{wind, (-)}using the probabilistic forecasting reference as peak load operation on same day period wind-powered electricity generation forecast deviation;

Step 3: at load peak time τ _i-1when period, according to relevant Load Forecasting probability of error distributed intelligence, and wind-powered electricity generation prediction error probability distribution information, to τ _ithe BLR type AGC unit basic point power of period is adjusted, until finish whole peak period; Step is as follows:

A), at τ _i-1when period, in the time that starting, peak load just from peak load forecast deviation Sample Storehouse, obtains ε _{load, (+)}and ε _{load, (-)}, the two is definite value in the whole peak load operation period;

B), according to online scheduling slot τ in peak period _iwind power predicted value mate wind-powered electricity generation prediction error probability distribution Sample Storehouse information, obtain the ε of respective conditions _{wind, (+)}and ε _{wind, (-)};

C), calculate τ _ithe positive and negative parital coefficient of period net load:

D), the τ that adjusts _iperiod all BLR types AGC unit basic point power:

In formula:

τ _iperiod BLR type AGC unit basic point performance number;

it is respectively k platform BLR type AGC unit variable capacity bound.

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