CN110008491A - A kind of wind light mutual complementing power generation power output digital simulation method based on improvement probabilistic model - Google Patents
A kind of wind light mutual complementing power generation power output digital simulation method based on improvement probabilistic model Download PDFInfo
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- CN110008491A CN110008491A CN201811425162.4A CN201811425162A CN110008491A CN 110008491 A CN110008491 A CN 110008491A CN 201811425162 A CN201811425162 A CN 201811425162A CN 110008491 A CN110008491 A CN 110008491A
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
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- G—PHYSICS
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Abstract
The invention discloses a kind of based on the wind light mutual complementing power generation power output digital simulation method for improving probabilistic model, consider the correlation between areal wind speed and intensity of solar radiation variation, independent honourable probabilistic model is improved, the amplitude item of wind speed probabilistic model under scene round the clock is corrected, and corrects wind speed data distribution probability under rainy weather scene；It emulates to obtain the long-range meteorological data of research area, institute as time scale using hour using Monte Carlo method, and constructs wind and solar hybrid generating system model and convert out force data for meteorological data.
Description
Technical field
The present invention relates to field of new energy generation more particularly to a kind of wind light mutual complementing power generation based on improvement probabilistic model to go out
Force data analogy method.
Background technique
Wind and solar hybrid generating system takes full advantage of the complementarity between wind-powered electricity generation and photovoltaic power output, has stabilized the wave of aggregate power
Dynamic property reduces and climbs spare and energy storage device dependence to power grid, improves the performance driving economy and power grid pair of electricity generation system
The receiving ability of renewable energy.In the planning process of wind and solar hybrid generating system, need to wind-powered electricity generation and photovoltaic power generation apparatus
For a long time go out force data simulated.Wind-powered electricity generation and photovoltaic power generation power output digital simulation can be divided into based on long history data and be based on
Two methods of probabilistic model.In actual engineer application, due to the limit for the problems such as measuring cost, trade secret and national security
System, the long-term more difficult acquisition of honourable metric data, the generated output simulation based on long history data are relatively difficult to achieve.And mutually compared with
Under, the parameter of honourable probabilistic model is generally easier to obtain from open source data, therefore, the generated output mould based on probabilistic model
Intend application value and meaning with higher in practical projects.
Currently, the research for wind light mutual complementing power generation power output digital simulation problem is enriched and is goed deep into not enough, it is existing to grind
Independent honourable probabilistic model is mostly used to carry out the force data that goes out of wind-powered electricity generation in complementary power generation system and photovoltaic in studying carefully and designing greatly
Simulation, and correlation, complementarity between wind light generation power output are had ignored, the accuracy of analogue data is reduced, acquisition is caused
Program results have conservative, reduce the economic benefit of complementary power generation system investment planning.
Summary of the invention
The object of the invention is to solve the above problems, provide it is a kind of based on improve probabilistic model wind light mutual complementing power generation go out
Force data analogy method considers the correlation between areal wind speed and intensity of solar radiation variation, to independent honourable probability
Model improves, and corrects the amplitude item of wind speed probabilistic model under scene round the clock, and corrects air speed data under rainy weather scene
Distribution probability；It emulates to obtain the long-range meteorological data of research area, institute as time scale using hour using Monte Carlo method, and
It constructs wind and solar hybrid generating system model and converts out force data for meteorological data.
To achieve the above object, concrete scheme of the invention is as follows:
The invention discloses a kind of based on the wind light mutual complementing power generation power output digital simulation method for improving probabilistic model, comprising:
(1) relevant parameter and statistic are obtained from open source data；
(2) independent wind speed and intensity of solar radiation model are initialized by time span of the moon；
(3) according to the diurnal variation law of wind speed, the mean value based on daytime, night wind speed repairs the amplitude of wind speed probabilistic model
Just；
(4) correlation changed under rainy weather according to scene, is modified the distribution probability of wind speed；
(5) it emulates to obtain the long-range meteorological data of research area, institute as time scale using hour using Monte Carlo method；
(6) meteorological data is converted out force data by building wind and solar hybrid generating system model.
Further, in the step (1), open source data include the free number that the websites such as State Statistics Bureau, NASA provide
According to.Relevant parameter and statistic include that the parameter of independent wind speed and intensity of solar radiation model, wind speed mean value, daytime wind speed are equal
Value, night wind speed mean value, overcast and rainy hourage, the intensity of solar radiation value under rainy weather, the air speed value under rainy weather.
Further, in the step (2), independent wind speed probabilistic model are as follows:
Wherein, v is wind speed instantaneous value；aiAnd biThe form parameter and scale parameter of respectively i-th month Wind speed model；I=
1,2 ..., 12.
Further, in the step (2), independent intensity of solar radiation probabilistic model are as follows:
Wherein, HhiFor the intensity of solar radiation instantaneous value at i-th month h moment；HmhFor i-th month h moment intensity of solar radiation
Maximum value；H=1,2 ..., 24；I=1,2 ..., 12.
Further, in the step (3), general air speed value is smaller when the diurnal variation law of wind speed is daytime, when night
Air speed value is generally large.
Further, in the step (3), the amplitude correction of wind speed probabilistic model specifically:
Wherein, fwdi(v) and fwni(v) the wind speed profile probability in daytime and night after correcting is respectively indicated；U is the total of wind speed
Body mean value；udFor daytime wind speed mean value；unFor night wind speed mean value.
Further, in the step (4), the correlation that changes under rainy weather of scene be under rainy weather scene too
Positive radiation intensity value generally reduces, and air speed value generally increases and is greater than its mean value.
Further, it in the step (4), is carried out by distribution probability of the reference value to wind speed of sun radiation intensity data
The probability that air speed value under rainy weather is greater than its a reference value is modified in amendment are as follows:
Wherein, nrFor overcast and rainy hour sum；nrlIt is greater than the hourage of its mean value for air speed value under rainy weather.
Further, in the step (5), the intensity of solar radiation value in first analog study area is needed, and with sun spoke
Penetrating intensity value is that reference value judges rainy weather, and then simulates to air speed value.When intensity of solar radiation analogue value H is less than it
When maximum value under rainy weather, that is, judge that current hour for overcast and rainy hour, and the probability that air speed value is greater than its a reference value is repaired
It is changed to Pwd。
Further, in the step (6), wind and solar hybrid generating system model specifically:
Po=(1-dr)Pwb+drPpv
Wherein, vwFor the air speed value of simulation；PwFor ideally Wind turbines generated output；PrFor the volume of Wind turbines
Determine power, kW；vs、vpAnd vrRespectively cut wind speed, cut-out wind speed and rated wind speed；PwdFor wind-powered electricity generation portion, wind light mutual complementing power generation system
The power output divided；NwFor the installation number of wind power generating set；ηwFor the generating efficiency of wind power generating set；PpvFor wind light mutual complementing hair
The power output of electricity system photovoltaic part；H is the intensity of solar radiation value of simulation；ApFor the mounting area of photovoltaic battery array, m2；ηpFor
The comprehensive electric generating efficiency of photovoltaic power generation；PoFor the general power of wind and solar hybrid generating system；drFor the allocation ratio of wind light mutual complementing.
The invention has the advantages that:
It is contributed digital simulation method the present invention provides a kind of based on the wind light mutual complementing power generation for improving probabilistic model, and it is existing
Technology is compared, the invention has the benefit that
1) present invention improves over independent honourable meteorologic models, it is contemplated that related between wind speed and intensity of solar radiation variation
Property, the accuracy of the analogue data improved, advance with higher；
2) present invention needs not rely upon the long history measured data of higher cost, and required relevant parameter and statistic can
It is relatively easily obtained from open source data, reduces application cost, improve economy；
3) present invention is applied to can reduce conservative caused by conventional method when wind and solar hybrid generating system planning, improves
The investment repayment and economic benefit of complementary power generation system planning.
4) the not special application conditions of the present invention, it is versatile, go out force data suitable for each department wind light mutual complementing power generation
Simulation, can be widely applied in the planning and designing of wind and solar hybrid generating system engineering, have promotional value and meaning；
5) principle of the invention is clear, easy to operate, low to performing environment and maintenance requirement, is adapted to practical implementation.
Detailed description of the invention
Fig. 1 is that wind light mutual complementing power generation power output digital simulation method of the present invention executes flow diagram；
Fig. 2 is the 10 years wind power output analogue data schematic diagrames in research area in embodiment；
Fig. 3 is the 10 years photovoltaics in research area power output analogue data schematic diagram in embodiment；
Fig. 4 is the 10 years wind light mutual complementing power generations in research area power output analogue data schematic diagram in embodiment.
Specific embodiment
The present invention will be further described with embodiment with reference to the accompanying drawing:
As shown in Figure 1, the invention discloses a kind of based on the wind light mutual complementing power generation power output digital simulation for improving probabilistic model
Method, comprising:
(1) relevant parameter and statistic are obtained from open source data；
(2) independent wind speed and intensity of solar radiation model are initialized by time span of the moon；
(3) according to the diurnal variation law of wind speed, the mean value based on daytime, night wind speed repairs the amplitude of wind speed probabilistic model
Just；
(4) correlation changed under rainy weather according to scene, is modified the distribution probability of wind speed；
(5) it emulates to obtain the long-range meteorological data of research area, institute as time scale using hour using Monte Carlo method；
(6) meteorological data is converted out force data by building wind and solar hybrid generating system model.
In the step (1), open source data include the free data that the websites such as State Statistics Bureau, NASA provide.Correlation ginseng
Several and statistic includes the parameter of independent wind speed and intensity of solar radiation model, wind speed mean value, daytime wind speed mean value, night wind speed
Mean value, overcast and rainy hourage, the intensity of solar radiation value under rainy weather, the air speed value under rainy weather.
In the step (2), independent wind speed probabilistic model are as follows:
Wherein, v is wind speed instantaneous value；aiAnd biThe form parameter and scale parameter of respectively i-th month Wind speed model；I=
1,2 ..., 12.
In the step (2), independent intensity of solar radiation probabilistic model are as follows:
Wherein, HhiFor the intensity of solar radiation instantaneous value at i-th month h moment；HmhFor i-th month h moment intensity of solar radiation
Maximum value；H=1,2 ..., 24；I=1,2 ..., 12.
In the step (3), general air speed value is smaller when the diurnal variation law of wind speed is daytime, and air speed value is general when night
It is larger.
In the step (3), the amplitude correction of wind speed probabilistic model specifically:
Wherein, fwdi(v) and fwni(v) the wind speed profile probability in daytime and night after correcting is respectively indicated；U is the total of wind speed
Body mean value；udFor daytime wind speed mean value；unFor night wind speed mean value.
In the step (4), the correlation that scene changes under rainy weather is that solar radiation is strong under rainy weather scene
Angle value generally reduces, and air speed value generally increases and is greater than its mean value.
In the step (4), it is modified by distribution probability of the reference value to wind speed of sun radiation intensity data, it will be negative
The probability that air speed value is greater than its a reference value under rainy day gas is modified are as follows:
Wherein, nrFor overcast and rainy hour sum；nrlIt is greater than the hourage of its mean value for air speed value under rainy weather.
In the step (5), the intensity of solar radiation value in first analog study area is needed, and is with sun radiation intensity value
Reference value judges rainy weather, and then simulates to air speed value.When intensity of solar radiation analogue value H is less than under its rainy weather
Maximum value when, that is, judge that the probability that air speed value is greater than its a reference value for overcast and rainy hour, and is revised as P by current hourwd。
In the step (6), wind and solar hybrid generating system model specifically:
Po=(1-dr)Pwb+drPpv
Wherein, vwFor the air speed value of simulation；PwFor ideally Wind turbines generated output；PrFor the volume of Wind turbines
Determine power, kW；vs、vpAnd vrRespectively cut wind speed, cut-out wind speed and rated wind speed；PwdFor wind-powered electricity generation portion, wind light mutual complementing power generation system
The power output divided；NwFor the installation number of wind power generating set；ηwFor the generating efficiency of wind power generating set；PpvFor wind light mutual complementing hair
The power output of electricity system photovoltaic part；H is the intensity of solar radiation value of simulation；ApFor the mounting area of photovoltaic battery array, m2；ηpFor
The comprehensive electric generating efficiency of photovoltaic power generation；PoFor the general power of wind and solar hybrid generating system；drFor the allocation ratio of wind light mutual complementing.
Embodiment
The present invention using the wind and solar hybrid generating system of research area, Shandong Province plan engineering as embodiment, the research
The power output analogue data difference of Qu Shinian wind-powered electricity generation, photovoltaic and complementary power generation system is as shown in Figure 2, Figure 3 and Figure 4, in conjunction in attached drawing
Related data, it is apparent that, present invention improves over independent honourable meteorologic models, it is contemplated that wind speed and solar radiation are strong
Correlation between degree variation, the accuracy of the analogue data improved, advance with higher；The present invention is applied to wind
Light complementary power generation system can reduce conservative caused by conventional method when planning, the investment for improving complementary power generation system planning is returned
Report and economic benefit.
Although above-mentioned be described and verify to a specific embodiment of the invention and validity in conjunction with attached drawing, not
Limiting the scope of the invention, those skilled in the art should understand that, based on the technical solutions of the present invention, this
Field technical staff does not need to make the creative labor the various modifications or changes that can be made still in protection scope of the present invention
Within.
Claims (7)
1. a kind of based on the wind light mutual complementing power generation power output digital simulation method for improving probabilistic model characterized by comprising
(1) relevant parameter and statistic are obtained from open source data；
(2) independent wind speed and intensity of solar radiation model are initialized by time span of the moon；
(3) according to the diurnal variation law of wind speed, the mean value based on daytime, night wind speed is modified the amplitude of wind speed probabilistic model；
(4) correlation changed under rainy weather according to scene, is modified the distribution probability of wind speed；
(5) it emulates to obtain the long-range meteorological data of research area, institute as time scale using hour using Monte Carlo method；
(6) meteorological data is converted out force data by building wind and solar hybrid generating system model.
2. as described in claim 1 based on the wind light mutual complementing power generation power output digital simulation method for improving probabilistic model, feature
It is, in the step (1), open source data include the free data of State Statistics Bureau, the offer of the website NASA；Relevant parameter and system
Metering include the parameter of independent wind speed and intensity of solar radiation model, wind speed mean value, daytime wind speed mean value, night wind speed mean value,
The air speed value under intensity of solar radiation value, rainy weather under overcast and rainy hourage, rainy weather.
3. as described in claim 1 based on the wind light mutual complementing power generation power output digital simulation method for improving probabilistic model, feature
It is, in the step (2), independent wind speed probabilistic model are as follows:
Wherein, v is wind speed instantaneous value；aiAnd biThe form parameter and scale parameter of respectively i-th month Wind speed model；I=1,
2 ..., 12；
Independent intensity of solar radiation probabilistic model are as follows:
Wherein, HhiFor the intensity of solar radiation instantaneous value at i-th month h moment；HmhFor the maximum of i-th month h moment intensity of solar radiation
Value；H=1,2 ..., 24；I=1,2 ..., 12.
4. as described in claim 1 based on the wind light mutual complementing power generation power output digital simulation method for improving probabilistic model, feature
It is, in the step (3), air speed value is smaller when the diurnal variation law of wind speed is daytime, and air speed value is larger when night；
The amplitude correction of wind speed probabilistic model specifically:
Wherein, fwdi(v) and fwni(v) the wind speed profile probability in daytime and night after correcting is respectively indicated；U is the overall equal of wind speed
Value；udFor daytime wind speed mean value；unFor night wind speed mean value.
5. as described in claim 1 based on the wind light mutual complementing power generation power output digital simulation method for improving probabilistic model, feature
It is, in the step (4), the correlation that scene changes under rainy weather is intensity of solar radiation value under rainy weather scene
It reduces, and air speed value increases and is greater than its mean value；
It is modified by distribution probability of the reference value to wind speed of sun radiation intensity data, air speed value under rainy weather is greater than
The probability of its a reference value is modified are as follows:
Wherein, nrFor overcast and rainy hour sum；nrlIt is greater than the hourage of its mean value for air speed value under rainy weather.
6. as described in claim 1 based on the wind light mutual complementing power generation power output digital simulation method for improving probabilistic model, feature
It is, in the step (5), needs the intensity of solar radiation value in first analog study area, and be ginseng with sun radiation intensity value
It examines value and judges rainy weather, and then air speed value is simulated, when intensity of solar radiation analogue value H is less than under its rainy weather
When maximum value, that is, judge that the probability that air speed value is greater than its a reference value for overcast and rainy hour, and is revised as P by current hourwd。
7. as described in claim 1 based on the wind light mutual complementing power generation power output digital simulation method for improving probabilistic model, feature
It is, in the step (6), wind and solar hybrid generating system model specifically:
Po=(1-dr)Pwb+drPpv
Wherein, vwFor the air speed value of simulation；PwFor ideally Wind turbines generated output；PrFor the specified function of Wind turbines
Rate, kW；vs、vpAnd vrRespectively cut wind speed, cut-out wind speed and rated wind speed；PwdFor wind light mutual complementing power generation system wind-powered electricity generation part
Power output；NwFor the installation number of wind power generating set；ηwFor the generating efficiency of wind power generating set；PpvFor wind light mutual complementing power generation system
The power output of photovoltaic part；H is the intensity of solar radiation value of simulation；ApFor the mounting area of photovoltaic battery array, m2；ηpFor photovoltaic
The comprehensive electric generating efficiency of power generation；PoFor the general power of wind and solar hybrid generating system；drFor the allocation ratio of wind light mutual complementing.
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