CN104182910A - Correlation-associated wind power output scene construction method - Google Patents
Correlation-associated wind power output scene construction method Download PDFInfo
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- CN104182910A CN104182910A CN201410420997.6A CN201410420997A CN104182910A CN 104182910 A CN104182910 A CN 104182910A CN 201410420997 A CN201410420997 A CN 201410420997A CN 104182910 A CN104182910 A CN 104182910A
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- 239000011159 matrix material Substances 0.000 claims abstract description 71
- 238000005070 sampling Methods 0.000 claims abstract description 30
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- 230000005684 electric field Effects 0.000 claims description 19
- 238000006243 chemical reaction Methods 0.000 claims description 6
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention provides a correlation-associated wind power output scene construction method. The method comprises the following steps: firstly, according to a historical output data statics result of a wind power field, extracting a representative wind power output edge probability distribution function and an output correlation index matrix among the wind power fields; calculating a representative sequence matrix in advance by adopting an off-line mode; and when a wind power output scene needs to be constructed, Latin hypercube sampling is carried out according to a wind power probability prediction result, a sampling matrix is sorted by directly adopting the sequence matrix which is calculated in advance to quickly construct the wind power output scene with correlation, so that the on-line calculation time of scene construction is greatly shortened to meet the practical requirements of engineering.
Description
Technical field
The present invention relates to the construction method of a kind of generation of electricity by new energy and access technology, specifically relate to a kind of wind-powered electricity generation that relates to correlativity scene construction method of exerting oneself.
Background technology
Be subject to the wind-powered electricity generation of the impact of the multiple natural causes such as weather, height above sea level, landform and temperature, prediction difficulty is larger, causes wind power prediction to have larger error.Along with the increase of wind-electricity integration capacity, its predicated error is also increasing on the impact of system, in the time carrying out wind-powered electricity generation Optimized Operation, wind power need to be predicted the outcome and is considered as stochastic variable.Meanwhile, be subject to the impact of wind-resources correlativity in region, between these stochastic variables, also there is very strong correlativity, must consider, so that result of calculation is realistic.But in the wind-powered electricity generation Optimized Operation of the correlativity of considering to exert oneself calculates, the wind energy turbine set quantity of calculating due to need is many, and need the time discontinuity surface analyzed many at every turn, cause computation process very consuming time.
Summary of the invention
For the deficiencies in the prior art, the object of this invention is to provide a kind of wind-powered electricity generation that relates to correlativity scene construction method of exerting oneself, the method improves the wind-powered electricity generation Optimized Operation computing velocity of considering correlativity, , utilize the feature of Latin hypercube stratified sampling, calculate in advance the sequential matrix for sorting, and be applied to consider that the wind-powered electricity generation of correlativity exerts oneself in scene Fast Construction, sequential matrix for different time section is calculated to be reduced in advance and calculate once, greatly reduce the scene structure time, thereby wind-powered electricity generation Optimized Operation computing velocity and the accuracy requirement of considering correlativity are effectively met.
The object of the invention is to adopt following technical proposals to realize:
The invention provides a kind of wind-powered electricity generation of considering correlativity scene building method of exerting oneself, its improvements are, described method comprises the steps:
(1) input historical data, recovers the wind-powered electricity generation of not rationing the power supply in the situation historical data of exerting oneself;
(2) determine wind-powered electricity generation exert oneself correlation matrix and marginal probability density distribution function;
(3) adopt Nataf conversion, determine sequential matrix;
(4) input wind power predicts the outcome;
(5) determine the wind-powered electricity generation sampling matrix of exerting oneself;
(6) determine and consider the wind-powered electricity generation of the correlativity scene of exerting oneself;
(7) judge whether wind-powered electricity generation Optimized Operation finishes.
Further, described step (1) comprising: input each wind energy turbine set wind-powered electricity generation historical data of exerting oneself in section at one time, the situation if interior wind energy turbine set of this period is rationed the power supply, according to ration the power supply record and wind farm power prediction historical data, the data of rationing the power supply are recovered, obtained wind-powered electricity generation in the situation of the not rationing the power supply historical data of exerting oneself; Historical data all comprises in the past period (in the past period as one hour, one day or a week) wind-powered electricity generation actual value of exerting oneself.
Further, in described step (2): marginal probability distribution is determined according to wind-powered electricity generation probabilistic forecasting result, i.e. given input quantity;
Suppose n output of wind electric field X
1, X
2..., X
ncorrelation matrix be C
x,
In formula,
for correlation matrix is C
xin vector;
with
be respectively output of wind electric field X
iand X
jstandard deviation, cov (X
i, X
j) be output of wind electric field X
iand X
jcovariance, i, j=1,2 ... n.
Further, described step (3) comprising: according to the correlation matrix of exerting oneself of the wind-powered electricity generation between the marginal probability density distribution function of each output of wind electric field and wind energy turbine set, adopt Nataf conversion, calculate the sequential matrix L for the sequence of correlativity scene
nN,
Wherein, the quantity that n is wind energy turbine set, what N was Latin Hypercube Sampling counts, sequential matrix L
nNin every a line be a rank results of integer 1 to N, represent the sorting position of a wind energy turbine set Latin Hypercube Sampling result.
Further, in described step (4), in the time carrying out wind-powered electricity generation Optimized Operation, input the wind farm power prediction result of period to be assessed.
Further, in described step (5), according to each wind farm power prediction result, adopt Latin Hypercube Sampling mode, sample to exerting oneself the future of each wind energy turbine set, obtain the wind-powered electricity generation sampling matrix X that exerts oneself
nN; The wind-powered electricity generation sampling matrix X that exerts oneself
nNrepresent by following expression formula:
Further, in described step (6), adopt the wind-powered electricity generation sequential matrix L that exerts oneself
nNto the wind-powered electricity generation sampling matrix X that exerts oneself
nNsort, obtain and consider the following wind-powered electricity generation of the correlativity scene S that exerts oneself
nN; Sequential matrix L
nNrepresent by following expression formula:
If sequential matrix:
Use sequential matrix L
nNto formula 3) the wind-powered electricity generation sampling matrix X that exerts oneself
nNsort, can obtain scene matrix S
nN:
Further, in described step (7), if wind-powered electricity generation Optimized Operation does not finish, return to (4) step, construct other periods and consider the wind-powered electricity generation of the correlativitys scene of exerting oneself; If wind-powered electricity generation Optimized Operation finishes, exit.
Further, the calculating that described step (1)～carry out for offline mode (3), the calculating that described step (4)～carry out for online mode (7).
Compared with the prior art, the beneficial effect that the present invention reaches is:
1, a kind of wind-powered electricity generation that relates to correlativity provided by the invention scene construction method of exerting oneself, the method is utilized the feature of Latin hypercube stratified sampling, calculate in advance the sequential matrix for sorting, and be applied to consider that the wind-powered electricity generation of correlativity exerts oneself in scene Fast Construction, sequential matrix for different time section is calculated to be reduced in advance and calculate once, greatly reduce the scene structure time of considering correlativity, improved the wind-powered electricity generation Optimized Operation speed of considering correlativity.Thereby wind-powered electricity generation Optimized Operation computing velocity and the accuracy requirement of considering correlativity are effectively met.
2, the method only need be inputted the marginal distribution of stochastic variable and the correlation matrix between them, and probability distribution is had no requirement, and does not also need the joint probability density function between stochastic variable, thereby is more conducive to engineering application.
Brief description of the drawings
Fig. 1 is the exert oneself process flow diagram of scene construction method of the wind-powered electricity generation that relates to correlativity provided by the invention;
Fig. 2 (a) is Taonan provided by the invention output of wind electric field probability distribution situation;
Fig. 2 (b) is wash one's face provided by the invention north output of wind electric field probability distribution situation;
Fig. 2 (c) is prince's output of wind electric field probability distribution situation provided by the invention;
Fig. 3 is that the wind-powered electricity generation provided by the invention scene of exerting oneself always adds with reality and always adds result comparison diagram;
Fig. 4 is that the scene of employing different order matrix construction provided by the invention always adds result comparison diagram.
Embodiment
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in further detail.
The invention provides a kind of wind-powered electricity generation that relates to correlativity scene construction method of exerting oneself, the method is first according to the wind energy turbine set history data statistics result of exerting oneself, extract the representative wind-powered electricity generation correlation matrix of exerting oneself between marginal probability distribution function and wind energy turbine set of exerting oneself, adopt again the mode of off-line, calculate in advance representative sequential matrix.Carrying out wind-powered electricity generation at needs exerts oneself scene when structure, carry out Latin Hypercube Sampling according to wind power probabilistic forecasting result, and directly adopt the sequential matrix having calculated in advance to sort to sampling matrix, the wind-powered electricity generation that Fast Construction has the correlativity scene of exerting oneself, significantly reduce the online computing time of scene structure, to meet the needs of engineering reality.Its process flow diagram as shown in Figure 1, comprises the steps:
(1) input each wind energy turbine set wind-powered electricity generation historical data of exerting oneself in section at one time, the situation if interior wind energy turbine set of this period is rationed the power supply, according to ration the power supply record and wind farm power prediction historical data, the data of rationing the power supply are recovered, obtained wind-powered electricity generation in the situation of the not rationing the power supply historical data of exerting oneself; Historical data all comprises in the past period (in the past period as one hour, one day or a week) wind-powered electricity generation actual value of exerting oneself.
(2), according to the wind-powered electricity generation historical data of exerting oneself, determine the wind-powered electricity generation correlation matrix of exerting oneself; According to the wind-powered electricity generation of the each wind energy turbine set historical data of exerting oneself, determine marginal probability density distribution function; Marginal probability distribution is determined according to wind-powered electricity generation probabilistic forecasting result, i.e. given input quantity;
Suppose n output of wind electric field X
1, X
2..., X
ncorrelation matrix be C
x,
In formula,
for correlation matrix is C
xin vector;
with
be respectively output of wind electric field X
iand X
jstandard deviation, cov (X
i, X
j) be output of wind electric field X
iand X
jcovariance, i, j=1,2 ... n.
(3) adopt Nataf conversion, determine sequential matrix: according to the correlation matrix of exerting oneself of the wind-powered electricity generation between the probability density function of each output of wind electric field and wind energy turbine set, adopt Nataf conversion, calculate the sequential matrix L for the sequence of correlativity scene
nN,
Wherein, the quantity that n is wind energy turbine set, what N was Latin Hypercube Sampling counts, sequential matrix L
nNin every a line be a rank results of integer 1 to N, represent the sorting position of a wind energy turbine set Latin Hypercube Sampling result.
(4), in the time carrying out wind-powered electricity generation Optimized Operation, input the wind farm power prediction data of period to be assessed;
(5) according to each wind farm power prediction data, adopt Latin Hypercube Sampling method, sample to exerting oneself the future of each wind energy turbine set, obtain the wind-powered electricity generation sampling matrix X that exerts oneself
nN;
The wind-powered electricity generation sampling matrix X that exerts oneself
nNrepresent by following expression formula:
(6) adopt the wind-powered electricity generation sequential matrix L that exerts oneself
nNto the wind-powered electricity generation sampling matrix X that exerts oneself
nNsort, obtain and consider the following wind-powered electricity generation of the correlativity scene S that exerts oneself
nN; Sequential matrix L
nNrepresent by following expression formula:
If sequential matrix:
Use sequential matrix L
nNto formula 3) the wind-powered electricity generation sampling matrix X that exerts oneself
nNsort, can obtain scene matrix S
nN:
(7) judge whether wind-powered electricity generation Optimized Operation finishes: if wind-powered electricity generation Optimized Operation does not finish, return to (4) step, construct other periods and consider the wind-powered electricity generation of the correlativitys scene of exerting oneself; If wind-powered electricity generation Optimized Operation finishes, exit.
The calculating that step (1)～carry out for offline mode (3), the calculating that described step (4)～carry out for online mode (7).
The present invention is not limited only to structure and considers the wind-powered electricity generation of the correlativity scene of exerting oneself, and protection domain also comprises in all objects with correlativity and randomness feature such as sun power, load.
Embodiment
Taking Jilin Province, domestic three wind energy turbine set actual operating data are example in the present invention, to carry and consider that the wind-powered electricity generation of the correlativity scene building method of exerting oneself verifies.Three wind energy turbine set are respectively Taonan wind energy turbine set and the wash one's face north wind electric field of Baicheng Prefecture, and prince's wind energy turbine set in area, Songyuan City.The time range of wind energy turbine set history data is that data time resolution is 15min from Dec 31,1 day to 2010 January in 2010.The history data of each wind energy turbine set is carried out to matching with Weibull distribution, and corresponding parameter is as shown in table 1, and the result of matching is as Fig. 2 (a), (b) with (c).
Table 1 wind energy turbine set information and Weibull Distribution parameter
From Fig. 2 (a), (b) and (c), there is certain error though adopt Weibull distribution to carry out matching to the history data of wind energy turbine set, substantially can characterize the probability distribution situation that wind-powered electricity generation is exerted oneself.Therefore, the marginal probability distribution of each output of wind electric field can be similar to and regard parameter Weibull distribution as shown in table 1 as.
Because data volume is larger, the joint probability distribution function between three wind energy turbine set is difficult to calculate.And correlation matrix between wind energy turbine set can directly calculate acquisition according to formula (1).
In formula,
with
be respectively stochastic variable X
iand X
jstandard deviation, cov (X
i, X
j) be stochastic variable X
iand X
jcovariance.
Table 2 has shown distance and the correlativity situation between these three wind energy turbine set.Can find out, along with reducing of distance between wind energy turbine set, the correlativity of exerting oneself between wind energy turbine set is increasing.
Distance and correlativity situation between table 2 wind energy turbine set
The marginal probability distribution (as shown in table 1) of exerting oneself according to three wind energy turbine set history and correlation matrix (as shown in table 2), adopt said method, and the scene of exerting oneself of these three wind energy turbine set is constructed.Under the condition that is 1000 in LHS sampling scale, utilize the wind-powered electricity generation of the constructing scene of exerting oneself to exert oneself and always add calculating three wind energy turbine set, result of calculation as shown in Figure 3.In figure, solid line represents to utilize and has the result that the scene of exerting oneself of correlativity calculates, and dotted line represents to think result of calculation when three output of wind electric field are separate.For contrasting, in figure, histogram has represented the probability statistics result of these three actual gross capabilities of wind energy turbine set,
As seen from Figure 3, in the time that wind-powered electricity generation is analyzed, if ignore the correlativity between wind energy turbine set, will cause result of calculation to occur larger error.Therefore,, in the time processing the relevant issues of wind-powered electricity generation, must consider the impact of its correlativity, so that result of calculation is realistic.And the consideration correlativity wind-powered electricity generation that adopts institute of the present invention extracting method structure is exerted oneself scene while calculating, gained wind energy turbine set always adds the probability distribution of exerting oneself can Efficient Characterization reality always add the probability distribution situation of exerting oneself, thereby has proved the correctness of institute of the present invention extracting method.
In addition, according to the output of wind electric field marginal probability distribution of multiple hypothesis, sequential matrix is calculated, and apply gained sequential matrix the wind-powered electricity generation scene of exerting oneself is carried out to Fast Construction, table 3 has shown the correlativity evaluation index of the scene of constructing, and Fig. 4 has shown that the employing scene of constructing always adds the result of calculating.
Table 3 adopts different marginal distribution computation sequence matrixes to carry out the correlativity evaluation index of scene structure
Can find out, when the Weibull distribution with different parameters, normal distribution or while being evenly distributed the marginal probability distribution as stochastic variable, the wind-powered electricity generation that adopts the sequential matrix that calculates the to construct scene correlativity evaluation index of exerting oneself is all very little, and adopt these scenes to carry out wind-powered electricity generation and always add while calculating, acquired results is all very approaching.This explanation, adopts different marginal distribution to carry out the calculating of sequential matrix, and the structure impact on final correlativity scene is also little.Therefore,, in the time carrying out the Optimized Operation of wind-powered electricity generation, can directly adopt in advance the sequential matrix calculating to carry out the exert oneself Fast Construction of scene of wind-powered electricity generation, to meet the requirement of computing velocity.
Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit, although the present invention is had been described in detail with reference to above-described embodiment, those of ordinary skill in the field are to be understood that: still can modify or be equal to replacement the specific embodiment of the present invention, and do not depart from any amendment of spirit and scope of the invention or be equal to replacement, it all should be encompassed in the middle of claim scope of the present invention.
Claims (9)
1. the wind-powered electricity generation that the relates to correlativity scene construction method of exerting oneself, is characterized in that, described method comprises the steps:
(1) input historical data, recovers the wind-powered electricity generation of not rationing the power supply in the situation historical data of exerting oneself;
(2) determine wind-powered electricity generation exert oneself correlation matrix and marginal probability density distribution function;
(3) adopt Nataf conversion, determine sequential matrix;
(4) input wind power predicts the outcome;
(5) determine the wind-powered electricity generation sampling matrix of exerting oneself;
(6) wind-powered electricity generation of determining the correlativity that the relates to scene of exerting oneself;
(7) judge whether wind-powered electricity generation Optimized Operation finishes.
2. the wind-powered electricity generation as claimed in claim 1 scene construction method of exerting oneself, it is characterized in that, described step (1) comprising: input each wind energy turbine set wind-powered electricity generation historical data of exerting oneself in section at one time, the situation if interior wind energy turbine set of this period is rationed the power supply, according to ration the power supply record and wind farm power prediction historical data, the data of rationing the power supply are recovered, obtained wind-powered electricity generation in the situation of the not rationing the power supply historical data of exerting oneself; Historical data all comprises in the past period the wind-powered electricity generation actual value of exerting oneself.
3. the wind-powered electricity generation as claimed in claim 1 scene construction method of exerting oneself, is characterized in that, in described step (2): determine marginal probability distribution according to wind-powered electricity generation probabilistic forecasting result, i.e. given input quantity;
Suppose n output of wind electric field X
1, X
2..., X
ncorrelation matrix be C
x,
In formula,
for correlation matrix is C
xin vector;
with
be respectively output of wind electric field X
iand X
jstandard deviation, cov (X
i, X
j) be output of wind electric field X
iand X
jcovariance, i, j=1,2 ... n.
4. the wind-powered electricity generation as claimed in claim 1 scene construction method of exerting oneself, it is characterized in that, described step (3) comprising: according to the correlation matrix of exerting oneself of the wind-powered electricity generation between the marginal probability density distribution function of each output of wind electric field and wind energy turbine set, adopt Nataf conversion, calculate the sequential matrix L for the sequence of correlativity scene
nN,
Wherein, the quantity that n is wind energy turbine set, what N was Latin Hypercube Sampling counts, sequential matrix L
nNin every a line be a rank results of integer 1 to N, represent the sorting position of a wind energy turbine set Latin Hypercube Sampling result.
5. the wind-powered electricity generation as claimed in claim 1 scene construction method of exerting oneself, is characterized in that, in described step (4), in the time carrying out wind-powered electricity generation Optimized Operation, inputs the wind farm power prediction result of period to be assessed.
6. the wind-powered electricity generation as claimed in claim 1 scene construction method of exerting oneself, is characterized in that, in described step (5), according to each wind farm power prediction result, use Latin Hypercube Sampling mode, sample to exerting oneself the future of each wind energy turbine set, obtain the wind-powered electricity generation sampling matrix X that exerts oneself
nN; The wind-powered electricity generation sampling matrix X that exerts oneself
nNrepresent by following expression formula:
7. the wind-powered electricity generation as claimed in claim 1 scene construction method of exerting oneself, is characterized in that, in described step (6), with the wind-powered electricity generation sequential matrix L that exerts oneself
nNto the wind-powered electricity generation sampling matrix X that exerts oneself
nNsort, obtain and consider the following wind-powered electricity generation of the correlativity scene S that exerts oneself
nN; Sequential matrix L
nNrepresent by following expression formula:
If sequential matrix:
Use sequential matrix L
nNto formula 3) the wind-powered electricity generation sampling matrix X that exerts oneself
nNsort, can obtain scene matrix S
nN:
8. the wind-powered electricity generation as claimed in claim 1 scene construction method of exerting oneself, is characterized in that, in described step (7), if wind-powered electricity generation Optimized Operation does not finish, returns to (4) step, constructs other periods and considers the wind-powered electricity generation of the correlativitys scene of exerting oneself; If wind-powered electricity generation Optimized Operation finishes, exit.
9. the wind-powered electricity generation as claimed in claim 1 scene construction method of exerting oneself, it is characterized in that, the calculating that described step (1)～carry out for offline mode (3), the calculating that described step (4)～carry out for online mode (7).
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104899797A (en) * | 2015-06-29 | 2015-09-09 | 广西大学 | Method for reducing several wind power field contribution scenes |
| CN106532762A (en) * | 2015-09-14 | 2017-03-22 | 南京理工大学 | DC probabilistic load flow method for classification processing of load and wind speed correlation |
| CN107239863A (en) * | 2017-04-12 | 2017-10-10 | 广东电网有限责任公司电力调度控制中心 | The robust Unit Combination method of power system security constraints |
| CN108805388A (en) * | 2018-04-09 | 2018-11-13 | 中国电力科学研究院有限公司 | A kind of determination method and apparatus of non-coming year Load Time Series scene |
| CN111340330A (en) * | 2020-02-06 | 2020-06-26 | 国家电网公司华中分部 | Synchronous back-substitution reduction method based on correlation-quantity-shape three-dimensional distance |
-
2014
- 2014-08-25 CN CN201410420997.6A patent/CN104182910A/en active Pending
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| 潘雄等: "风速相关性对最优潮流的影响", 《电力系统自动化》 * |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104899797A (en) * | 2015-06-29 | 2015-09-09 | 广西大学 | Method for reducing several wind power field contribution scenes |
| CN104899797B (en) * | 2015-06-29 | 2018-04-24 | 广西大学 | A kind of method for cutting down multiple output of wind electric field scenes |
| CN106532762A (en) * | 2015-09-14 | 2017-03-22 | 南京理工大学 | DC probabilistic load flow method for classification processing of load and wind speed correlation |
| CN106532762B (en) * | 2015-09-14 | 2018-12-14 | 南京理工大学 | A kind of direct current Probabilistic Load Flow method of classification processing load and wind speed correlation |
| CN107239863A (en) * | 2017-04-12 | 2017-10-10 | 广东电网有限责任公司电力调度控制中心 | The robust Unit Combination method of power system security constraints |
| CN107239863B (en) * | 2017-04-12 | 2020-07-14 | 广东电网有限责任公司电力调度控制中心 | Robust unit combination method for power grid safety constraint |
| CN108805388A (en) * | 2018-04-09 | 2018-11-13 | 中国电力科学研究院有限公司 | A kind of determination method and apparatus of non-coming year Load Time Series scene |
| CN111340330A (en) * | 2020-02-06 | 2020-06-26 | 国家电网公司华中分部 | Synchronous back-substitution reduction method based on correlation-quantity-shape three-dimensional distance |
| CN111340330B (en) * | 2020-02-06 | 2022-07-19 | 国家电网公司华中分部 | Synchronous back-substitution reduction method based on correlation-quantity-shape three-dimensional distance |
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Application publication date: 20141203 |