CN108321840A - The grid-connected logout selection method contributed based on photo-voltaic power generation station fining - Google Patents
The grid-connected logout selection method contributed based on photo-voltaic power generation station fining Download PDFInfo
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Abstract
The invention discloses a kind of grid-connected logout selection methods contributed based on photo-voltaic power generation station fining, include the following steps：Selected pre-selection grid-connected photovoltaic power generation station, obtains photo-voltaic power generation station cluster, and the history for obtaining all photo-voltaic power generation stations in photo-voltaic power generation station company-data and photo-voltaic power generation station cluster goes out force data；The daily output characteristic index of photo-voltaic power generation station is chosen, and calculates separately all daily output characteristic index values of single photo-voltaic power generation station；Photo-voltaic power generation station daily output characteristic index is divided, sub-period output characteristic index value is obtained；The weight for setting all characteristic index values obtains each photo-voltaic power generation station and logout impact coefficient, according to simultaneously logout impact coefficient, is carried out to grid-connected and logout photo-voltaic power generation station preselected；According to final photo-voltaic power generation station cluster, current photo-voltaic power generation station cluster output characteristic index value is calculated, current electric grid is assessed, obtains final grid-connected and logout selection result.Advantageous effect：Former power grid is more stablized.Select good reliability.
Description
Technical field
The present invention relates to technical field of photovoltaic power generation, it is specifically a kind of based on photo-voltaic power generation station fining contribute and
Net logout selection method.
Background technology
In short supply with global fossil energy, cleaning, solar energy renewable, that reserves are big have obtained quick development.
But since solar energy has the characteristics that intermittent, randomness and fluctuation, the cymomotive force (CMF) of photovoltaic generation output power must give big rule
Mould power station concentration is grid-connected to bring difficulty.By carrying out scientific description to photovoltaic generation power curve, photovoltaic generation power curve is：
According to different moments sampled point, the moment output power value of acquisition, the output power curve drawn.Dispatching of power netwoks personnel can be with
Photovoltaic power producing characteristics rule is relatively accurately grasped, goes out fluctuation to stabilizing photovoltaic, reducing grid-connected difficulty has definite meaning.Cause
This, analysis and the output characteristics for grasping photovoltaic generation are to solve the problems, such as that photo-voltaic power generation station concentrates grid-connected basis and premise.
As a large amount of photo-voltaic power generation stations put into operation, the data analysis work that photo-voltaic power generation station is contributed also gradually is carried out,
But research not yet forms the power producing characteristics description system of a set of science.Existing literature is mainly retouched from single output of power station property quantification
It states and photovoltaic output is studied with two angles of cluster photo-voltaic power generation station specificity analysis.It is retouched in single output of power station property quantification
Aspect is stated, some documents propose that average cymomotive force, reversed fluctuation three indexs of number and advanced wave dynamic density come to single
The output fluctuation of photo-voltaic power generation station is described, although proposing out the quantization method of fluctuation, the calculating to fluctuation
It is still inaccurate.The prior art also indicate that photo-voltaic power generation station can by the number of solar panel in photo-voltaic power generation station with point
Scattered factor quantifies the wave characteristic in power station, but result of study be only applicable in each solar panel of photo-voltaic power generation station size, point
Under conditions of cloth direction and space interval all same, it is difficult to promote.It is existing in terms of photo-voltaic power generation station cluster polymerization property research
Literature research indicates the expansion with photo-voltaic power generation station cluster scale, and the output of cluster entirety will be gradually smooth-out, still
Lack the quantitative description to cluster output of power station smoothing effect.The existing photo-voltaic power generation station output description indexes researched and proposed are more
Generally, the thinking of wind power output characteristic description has been continued to use in most of research, does not consider the exclusive power producing characteristics of photovoltaic, therefore need
It is conducted further research.In addition, the research of photo-voltaic power generation station cluster polymerization power producing characteristics still rests on qualitative analysis layer
Face lacks quantitative study.
In the prior art, all it is random selection when selecting the power station of grid-connected and logout.When grid-connected, if grid-connected
The fluctuation of power station generated output it is big, then being incorporated to will cause to impact power grid, and network voltage is caused to fluctuate.When logout,
If the power generation network exited is relatively stable processing power station, the stability of former power grid can be reduced.Grid-connected logout at any time may be used
To occur, then in the prior art, power station processing also rests on the stage as unit of number of days, cannot meet grid-connected logout need
It asks.
Invention content
In view of the above-mentioned problems, the present invention provides a kind of grid-connected logout selecting partys contributed based on photo-voltaic power generation station fining
Method polymerize power producing characteristics with cluster from single photo-voltaic power generation station output feature extraction and analyzes output of two levels to photovoltaic generation
Characteristic is studied.In single power station level, feature extraction is carried out to its power curve, further carrying out fining to part retouches
It states, finally obtains the selection of grid-connected logout photo-voltaic power generation station, reduce impact of the grid-connected logout to power grid, keep power grid power supply more steady
It is fixed.
In order to achieve the above objectives, the specific technical solution that the present invention uses is as follows：
A kind of grid-connected logout selection method contributed based on photo-voltaic power generation station fining, key technology are to include following
Step：
S1：Selected pre-selection grid-connected photovoltaic power generation station, obtains photo-voltaic power generation station cluster, and obtain photo-voltaic power generation station company-data
Go out force data with the history of all photo-voltaic power generation stations in photo-voltaic power generation station cluster；
S2：The daily output characteristic index of photo-voltaic power generation station is chosen, and calculates separately all sunrise of single photo-voltaic power generation station
Power characteristic index value；
S3：Photo-voltaic power generation station daily output characteristic index is divided, sub-period output characteristic index value is obtained；
S4：The weight for setting all characteristic index values obtains each photo-voltaic power generation station and logout impact coefficient, according to and move back
Net impact coefficient, it is preselected to the progress of grid-connected and logout photo-voltaic power generation station, obtain final photo-voltaic power generation station cluster；
S5：The final photo-voltaic power generation station cluster obtained according to step S4 calculates current photo-voltaic power generation station cluster output feature
Index value assesses current electric grid, obtains final grid-connected and logout selection result.
By the above method, single photovoltaic plant is object with region photovoltaic generation cluster, is gone out respectively for single website
Force curve carries out feature extraction, studies its evaluating characteristics index.It realizes fining analysis, improves analysis precision.Select grid-connected light
When lying prostrate power station, the simultaneously smaller photo-voltaic power generation station of logout impact coefficient is selected.When selecting logout photo-voltaic power generation station, selects and move back
The larger photo-voltaic power generation station of net impact coefficient.Improve grid stability.It is directed to region photovoltaic cluster polymerization property on this basis
It is analyzed, the quantizating index of research cluster polymerization smoothing effect, and discloses the mechanism of production of smoothing effect, pass through real data
Relationship between survey region power station quantity and regional diameter and smoothing effect is totally contributed to power grid and is assessed, owned
Correlation between photo-voltaic power generation station.
Further, photo-voltaic power generation station company-data in step sl includes at least photo-voltaic power generation station number, region
Manage range, the relative distance of cluster regions diameter and all photo-voltaic power generation stations；
The history of the photo-voltaic power generation station goes out the photovoltaic output number that force data is x days before selecting the grid-connected or logout moment
According to；It is described go out force data include at least photovoltaic generation output power sampled data and photovoltaic generation power curve.
It further describes, the daily output feature of photo-voltaic power generation station includes at least in step S2：Per day output index, day
It contributes and is distributed degree of bias index and per day stability bandwidth index；
The per day output indexCalculation formula be：
Wherein, n sampled point numbers between daytime；PtFor the output power sequence of photo-voltaic power generation station, t=1,2,3 ... M；PbaseFor
The installed capacity of photo-voltaic power generation station；
The per day output that per day output index describes photovoltaic plant is horizontal, and the index is related to weather pattern,
There are relatively big differences for the size that photovoltaic is contributed under different weather patterns.
The calculation formula of daily output distribution degree of bias index is：
Wherein,Indicate that daily output is distributed degree of bias index；The apparent energy that day sends out；
Daily output is distributed the deflection that degree of bias index characterization photovoltaic plant works as daily output distribution, the influences such as no cloud cover
Photovoltaic generation power curve, relative to normal distribution, distribution kurtosis of contributing is biased to bigger numerical direction, and the degree of bias is negative value,
With gradually increasing for the effect of blocking, the fluctuation of curve entirety gradually increases, and photovoltaic output distribution is inclined to low output power direction
It moves, degree of bias value gradually increases.
The calculation formula of the per day stability bandwidth index σ is：
The photovoltaic generation power curve fluctuation opposite with intrinsic fluctuation direction is set as effectively fluctuation；Wherein, photovoltaic is contributed
The whole parabolically shape of intrinsic fluctuation；N is the number effectively fluctuated；Δ Pi is effective fluctuation width that ith effectively fluctuates
Value, effective undulate quantity are equal to the absolute of the minimum point nearest maximum point power difference adjacent thereto effectively fluctuated every time
Value.
By per day stability bandwidth index, further overcome photovoltaic output itself present parabola shaped wave characteristic and
It is influenced by cloud cover etc., improves intrinsic wave characteristic and random fluctuation characteristic resolution ratio.To photo-voltaic power generation station
The specific descriptions of undulate quantity propose that the concept of effective stability bandwidth is described come the fluctuating level contributed to photovoltaic.
Per day effective stability bandwidth is the important feature for characterizing photovoltaic plant when daily output fluctuation situation, reflects weather indirectly
The situation of change of state.The value of this feature is smaller, and being averaged on the same day that expression photovoltaic plant is contributed, fluctuation is smaller, and state of weather is got over
Stablize.
It further describes, photo-voltaic power generation station daily output characteristic index includes sub-period average output index in step s3
With sub-period stability bandwidth index；It is that entirety is divided into Y period with day, any one period in Y period is son
Period；The sub-period stability bandwidth index includes the mean value specification and sub-period stability bandwidth maximum index of sub-period stability bandwidth.
It further describes, the calculation formula of the sub-period average output index is：
The period average output indexCalculation formula is：
Wherein, myFor the sampled point number of y-th of sub-period；Y=1,2,3 ... Y, m1+m2+m3+…+mY=n, PbaseFor light
Lie prostrate the installed capacity in power station；N sampled point numbers between daytime, PtFor the output power sequence of photo-voltaic power generation station；Period averages out
Power index can determine the rough amplitude size of power curve at times, help to realize and contribute more to photovoltaic under more state of weather
Add the scientific description of precision.
If the effective stability bandwidth sequence S of sub-periodyFor：
Wherein, yl is effective fluctuation number of y-th of sub-period；Δ P is the effective undulate quantity effectively fluctuated,It is
Effective undulate quantity when y sub-period yl secondary undulation；
The mean value specification of the sub-period stability bandwidthCalculation formula is：
The sub-period stability bandwidth maximum indexCalculation formula is：
If it is higher that certain period average wave moves rate, illustrate to be influenced by Changes in weather such as cloud layer movements in this period photovoltaic plant
It is whole larger.Sub-period fluctuation can be described utmostly, this feature value is bigger, illustrates the wave that the period photovoltaic is contributed
It is dynamic bigger to the impact of power grid, large effect can be generated to power grid.
In step s 4 and the calculation formula of logout impact coefficient α is：
Wherein, λ1+λ2+λ3+λ4+λ5+λ6, λ1, λ2, λ3, λ4, λ5, λ6Value range be 0~1.λ1, λ2, λ3, λ4, λ5, λ6
For weight coefficient；
For the per day output index of the daily output feature of photo-voltaic power generation station；Indicate the sunrise of photo-voltaic power generation station
The daily output of power feature is distributed degree of bias index；σ is that the daily output characteristic day of photo-voltaic power generation station is averaged stability bandwidth index；For light
Lie prostrate the sub-period average output index of power station daily output characteristic index；For photo-voltaic power generation station daily output characteristic index
The mean value specification of sub-period stability bandwidth；For the sub-period stability bandwidth maximum index of photo-voltaic power generation station daily output characteristic index.
By setting weight coefficient, the ratio that each variable impacts power grid is obtained.Finally obtained and logout impact system
Number α.When needing grid-connected or logout, by simultaneously logout impact coefficient α, to be selected, when grid-connected, former power grid is rushed in reduction
It hits.After logout selection, former power grid is made more to stablize.
It further describes, photo-voltaic power generation station cluster output characteristic index includes cluster smoothing effect coefficient in step S5
ξclusterWith photo-voltaic power generation station output inconsistency COEFFICIENT K；
The cluster smoothing effect coefficient ξclusterCalculation formula is：
Wherein,Indicate effective stability bandwidth of first of photo-voltaic power generation station in cluster；
σclusterIndicate effective stability bandwidth of cluster gross capability；
M indicates the number of photo-voltaic power generation station in photo-voltaic power generation station cluster；
If ξclusterMore than 1, illustrate that photovoltaic plant cluster output has smooth effect relative to each single photo-voltaic power generation station,
And ξclusterIt is bigger, illustrate that the smooth effect of power swing is more notable.
The calculation formula of photo-voltaic power generation station output inconsistency COEFFICIENT K is：
Wherein, P1,P2Indicate the output sequence of two different photo-voltaic power generation stations；
fi(P1,P2) expression formula be：
fi(P1,P2) indicate the two photovoltaic generation power curve trend between (i-1)-th sampled point and ith sample point
Inconsistency.
Inconsistency COEFFICIENT K indicates that the sampling interval number that trend is inconsistent on two power curves accounts for total linear spacing number
Ratio is one and is more than 0 number for being less than 1, due to the inherently fluctuation trend having the same of two power curves, not for the fluctuation
Consistency coefficient is 0, which only reflects the influence of random fluctuation, therefore can be used to explain the smooth effect of power station cluster polymerization
It answers, K values are bigger to illustrate that the smoothing effect of the two photovoltaic plant clusters polymerization is better.
Decline afterwards since the general trend of photovoltaic generation power curve first rises, two complete complementaries are not present
Power curve, i.e., the inconsistency coefficients of arbitrary two power curves can not possibly reach 1.It is calculated by mass data, when not
Consistency coefficient is close to being just believed that the correlation between them is poor when 0.5, complementarity is preferably.It, can be with by above-mentioned design
Obtain the correlation of all photo-voltaic power generation stations and inconsistent coefficient in final power grid.
It further describes, carrying out assessment to current electric grid in step s 5 includes：
The photo-voltaic power generation station output inconsistency COEFFICIENT K of photo-voltaic power generation station two-by-two is calculated, and obtains the photo-voltaic power generation station and goes out
The matrix relationship of power inconsistency COEFFICIENT K and the relative distance of two photo-voltaic power generation stations；
Cluster regions diameter and the relationship of cluster smoothing effect coefficient are fitted using multinomial, which closes
It is that formula is：ξcluster=-3.717d2+4.685d+0.4053；Wherein d indicates cluster regions diameter；
Cluster smoothing effect coefficient, cluster regions diameter and power station quantity are fitted using multinomial；This is second quasi-
Closing relational expression is：Wherein d indicates cluster regions diameter；Num indicates the region
Power station quantity.Beneficial effects of the present invention：By historical data, the characteristic value of each photo-voltaic power generation station is calculated, makes and logout is selected
It selects reliable.When grid-connected selection, former power grid is impacted small.When logout selects, former power grid is made more to stablize.Select good reliability.And
By calculating current photo-voltaic power generation station cluster output characteristic index value, current electric grid is assessed.
Description of the drawings
Fig. 1 is the selection method flow chart of the present invention；
Fig. 2 is the per day power curve figure under the conditions of different weather；
Fig. 3 is that daily output is distributed degree of bias comparison diagram under different fluctuation situations；
Fig. 4 is power curve fluctuation situation and distribution degree of bias correspondence schematic diagram of contributing；
Fig. 5 is photo-voltaic power generation station output effectively fluctuation schematic diagram；
Fig. 6 is photovoltaic generation cluster polymerization property schematic diagram；
Fig. 7 is two power station E, C, 1 sunrise force curve March in 2015；
Fig. 8 is five photovoltaic plant power curve figures；
Fig. 9 is cluster regions diameter and smoothing effect Coefficient Fitting curve graph；
Figure 10 is smoothing effect coefficient and cluster regions diameter, central-station number matched curve figure；
Specific implementation mode
Specific embodiment and working principle of the present invention will be described in further detail below in conjunction with the accompanying drawings.
A kind of grid-connected logout selection method contributed based on photo-voltaic power generation station fining, can be seen that, including following in conjunction with Fig. 1
Step：
S1：Selected pre-selection grid-connected photovoltaic power generation station, obtains photo-voltaic power generation station cluster, and obtain photo-voltaic power generation station company-data
Go out force data with the history of all photo-voltaic power generation stations in photo-voltaic power generation station cluster；
S2：The daily output characteristic index of photo-voltaic power generation station is chosen, and calculates separately all sunrise of single photo-voltaic power generation station
Power characteristic index value；
S3：Photo-voltaic power generation station daily output characteristic index is divided, sub-period output characteristic index value is obtained；
S4：The weight for setting all characteristic index values obtains each photo-voltaic power generation station and logout impact coefficient, according to and move back
Net impact coefficient carries out grid-connected and logout photo-voltaic power generation station preselected；
S5：The final photo-voltaic power generation station cluster obtained according to step S4 calculates current photo-voltaic power generation station cluster output feature
Index value assesses current electric grid, obtains final grid-connected and logout selection result.
In the present embodiment, photo-voltaic power generation station company-data in step sl includes photo-voltaic power generation station number, region
Manage the relative distance of range and all photo-voltaic power generation stations；
The history of the photo-voltaic power generation station goes out the photovoltaic output number that force data is x days before selecting the grid-connected or logout moment
According to；It is described go out force data include at least photovoltaic generation output power sampled data and photovoltaic generation power curve.
In the present embodiment, historical data is the data acquired in March, 2016 in March, 2015, and sampling time interval is
15min。
The daily output feature of photo-voltaic power generation station includes in step s 2：Per day output index, the daily output distribution degree of bias refer to
Mark and per day stability bandwidth index；
In the present embodiment, per day output indexCalculation formula be：
Wherein, n sampled point numbers between daytime；PtFor the output power sequence of photo-voltaic power generation station, t=1,2,3 ... M；PbaseFor
The installed capacity of photo-voltaic power generation station；
The per day output that per day output index describes photovoltaic plant is horizontal, and the index is related to weather pattern,
There are relatively big differences for the size that photovoltaic is contributed under different weather patterns.As shown in Fig. 2, cloudy day, sleety weather cloud cover are made
With relatively strong, photovoltaic plant is contributed horizontal not high, and per day contribute is only 0.236 and 0.049, Clear-Sky Surface solar irradiance compared with
Height is contributed horizontal higher, and per day output reaches 0.436.
Daily output distribution degree of bias index calculation formula be：
Wherein,Indicate that daily output is distributed degree of bias index；The apparent energy that day sends out；
Daily output is distributed the deflection that degree of bias index characterization photovoltaic plant works as daily output distribution, the influences such as no cloud cover
Photovoltaic generation power curve, relative to normal distribution, distribution kurtosis of contributing is biased to bigger numerical direction, and the degree of bias is negative value,
With gradually increasing for the effect of blocking, the fluctuation of curve entirety gradually increases, and photovoltaic output distribution is inclined to low output power direction
It moves, degree of bias value gradually increases.
From figure 3, it can be seen that curve a, b output smoothing in first subgraph, the degree of bias is respectively -0.42 and -0.39, and the
It is violent to go out fluctuation in two subgraphs, the more above-mentioned numerical value of curve c, d degree of bias value has a larger increase, reaches 0.46 and 0.64.
From fig. 4, it can be seen that the degree of bias of photovoltaic output distribution curve, which is characterization power curve, integrally fluctuates severe degree
Statistical nature, it is strong that curve goes out fluctuation Shaoxing opera, and distribution degree of bias value of contributing is bigger, power curve fluctuation and distribution degree of bias value of contributing
Correspondence.
From fig. 5, it can be seen that power curve fluctuation opposite with intrinsic fluctuation direction in photovoltaic generation power curve is to have
The absolute value of effect fluctuation, the minimum point nearest maximum point power difference adjacent thereto effectively fluctuated every time is the secondary undulation
Effective undulate quantity.
The calculation formula of the per day stability bandwidth index σ is：
The photovoltaic generation power curve fluctuation opposite with intrinsic fluctuation direction is set as effectively fluctuation；N is effectively fluctuated
Number；ΔPiFor ith fluctuation the effective undulate quantity effectively fluctuated, effective undulate quantity be equal to every time effectively fluctuate it is minimum
The absolute value of value point nearest maximum point power difference adjacent thereto.
By per day stability bandwidth index, further overcome photovoltaic output itself present parabola shaped wave characteristic and
It is influenced by cloud cover etc., improves intrinsic wave characteristic and random fluctuation characteristic resolution ratio.To photo-voltaic power generation station
The specific descriptions of undulate quantity propose that the concept of effective stability bandwidth is described come the fluctuating level contributed to photovoltaic.It is per day to have
Effect stability bandwidth is the important feature for characterizing photovoltaic plant when daily output fluctuation situation, reflects the situation of change of state of weather indirectly.
The value of this feature is smaller, and being averaged on the same day that expression photovoltaic plant is contributed, fluctuation is smaller, and state of weather is more stable.
Photo-voltaic power generation station daily output characteristic index includes sub-period average output index and sub-period fluctuation in step s3
Rate index；Wherein, it is that entirety is divided into Y period with day, any period in Y period is sub-period；Institute
State the mean value specification and sub-period stability bandwidth maximum index that sub-period stability bandwidth index includes sub-period stability bandwidth.
In the present embodiment, 2 sub-periods, respectively above-mentioned sub-period and sub-period in afternoon are provided with.
The calculation formula of the sub-period average output index is：
Morning sub-period average output indexCalculation formula is：
Morning sub-period average output indexCalculation formula is：
Wherein, m1For the sampled point number of morning sub-period；m2For the sampled point number of sub-period in afternoon
In the present embodiment, when y=1, morning sub-period is indicated；When y=2, sub-period in afternoon is indicated；
m1+m2=n, PbaseFor the installed capacity of photo-voltaic power generation station；N sampled point numbers between daytime, PtFor photo-voltaic power generation station
Output power sequence；
If the effective stability bandwidth sequence S of morning sub-period1For：
If the effective stability bandwidth sequence S of morning sub-period2For：
Wherein, 1l is effective fluctuation number of morning sub-period；2l is effective fluctuation number of morning sub-period；Δ P is
The effective undulate quantity effectively fluctuated,For the morning sub-period l secondary undulation when effective undulate quantity；For sub-period in afternoon l times
Effective undulate quantity when fluctuation；
The then mean value specification of morning sub-period stability bandwidthCalculation formula is：
The then mean value specification of sub-period in afternoon stability bandwidthCalculation formula is：
Morning sub-period stability bandwidth maximum indexCalculation formula is：
Morning sub-period stability bandwidth maximum indexCalculation formula is：
In step s 4 and the calculation formula of logout impact coefficient α is：
In step s 4 and the calculation formula of logout impact coefficient α is：
Wherein, λ1+λ2+λ3+λ4+λ5+λ6=, 1 λ1, λ2, λ3, λ4, λ5, λ6Value range be 0~1.λ1, λ2, λ3, λ4, λ5,
λ6For weight coefficient.
In the present embodiment, λ1=0, λ2=0, λ3=0, λ4=0, λ5=0.85, λ6=0.15.
From fig. 6, it can be seen that photovoltaic generation contribute inherently fluctuated by it be affected, power curve substantially presents " singly
Peak " is distributed, after smoothing effect is substantially the polymerization of region photovoltaic plant cluster, in cluster each power station random fluctuation it is smooth
Effect.Therefore, with the enhancing of smoothing effect, cluster power producing characteristics can form more smooth power curve, advantageously reduce
Single output of power station fluctuation influence caused by power grid, but curve still keeps intrinsic " unimodal " waveform to be basically unchanged.
Photo-voltaic power generation station cluster output characteristic index includes cluster smoothing effect coefficient ξ in step S4clusterIt is sent out with photovoltaic
Output of power station inconsistency COEFFICIENT K；
The cluster smoothing effect coefficient ξclusterCalculation formula is：
Wherein,Indicate effective stability bandwidth of first of photo-voltaic power generation station in cluster；
σclusterIndicate effective stability bandwidth of cluster gross capability；
M indicates the number of photo-voltaic power generation station in photo-voltaic power generation station cluster；
The calculation formula of photo-voltaic power generation station output inconsistency COEFFICIENT K is：
Wherein, P1,P2Indicate the output sequence of two different photo-voltaic power generation stations；
fi(P1,P2) expression formula be：
fi(P1,P2) indicate the two photovoltaic generation power curve trend between (i-1)-th sampled point and ith sample point
Inconsistency.As shown in figure 8, f when power curve trend is inconsistent both between two sampled pointsi(P1,P2) 1 is taken, on the contrary take 0.
Inconsistency COEFFICIENT K indicates that the sampling interval number that trend is inconsistent on two power curves accounts for the ratio of total linear spacing number
Value is that a number more than 0 less than 1 is differed due to the inherently fluctuation trend having the same of two power curves for the fluctuation
It is 0 to cause property coefficient, which only reflects the influence of random fluctuation, therefore can be used to explain the smoothing effect of power station cluster polymerization, K
Value is bigger to illustrate that the smoothing effect of the two photovoltaic plant clusters polymerization is better.
Decline afterwards since the general trend of photovoltaic generation power curve first rises, two complete complementaries are not present
Power curve, in other words the inconsistency coefficient of arbitrary two power curves can not possibly reach 1.It is calculated by mass data,
Just it is believed that the correlation between them is poor when inconsistency coefficient is close to 0.5, it is complementary preferable.
Carrying out assessment to current electric grid in step s 5 includes：
The photo-voltaic power generation station output inconsistency COEFFICIENT K of photo-voltaic power generation station two-by-two is calculated, and obtains the photo-voltaic power generation station and goes out
The matrix relationship of power inconsistency COEFFICIENT K and the relative distance of two photo-voltaic power generation stations；
Cluster regions diameter and the relationship of cluster smoothing effect coefficient are fitted using multinomial, which closes
It is that formula is：ξcluster=-3.717d2+4.685d+0.4053；Wherein d indicates cluster regions diameter；
Cluster smoothing effect coefficient, cluster regions diameter and power station quantity are fitted using multinomial；This is second quasi-
Closing relational expression is：Wherein d indicates cluster regions diameter；Num indicates the region
Power station quantity.The entirety extracted for the verification present invention, local feature to the description effect of practical photovoltaic generation power curve, and
Application effect of the photovoltaic generation cluster polymerization property analysis theories in measured data.To it is aforementioned it is theoretical using Matlab softwares into
Row simulation calculation.In the present embodiment, the measured data that used data are Yunnan somewhere five photovoltaic plants of A~E is calculated.
The acquisition time section of data is emulated as in March, 2015 in March, 2016, sampling time interval 15min.
Example calculation analysis is carried out with the output characteristic curve in two power station Yunnan somewhere E, C.From figure 7 it can be seen that point
Not Wei two power station E, C on March 1st, 2015 output power curve.
From figure 7 it can be seen that same day whole day output in the power stations E has fluctuation, fluctuation distribution more average.The power stations C are in the morning
Period contribute it is steady, but afternoon since the changes photovoltaic generation power curve of state of weather is with more violent fluctuation, such as
It is clear to cloudy.It is described with two curves of feature pair extracted herein, index result of calculation such as table 1.
1 liang of power curve index result of calculation of table
Two power station average outputs are essentially identical as can be seen from Table 1, and per day two curve of stability bandwidth numerical response is contributed
Fluctuation total amount is close, but can be seen that the degree of fluctuation of the power stations C curve is slightly violent from the degree of bias for distribution of contributing.Due to having no out
Existing sleet, which clears up, waits weather, and E output of power station curve periods morning and afternoon are more symmetrical, and day part average output is sufficiently close to, the power stations C
Morning hours average output is slightly above afternoon.It can be seen that from sub-period stability bandwidth mean value and maximum value, the power stations E whole day has few
Cloud cover is measured, morning and afternoon has fluctuation, fluctuation distribution more uniform.
The power stations C morning hours line smoothing is steady, apparent fluctuation does not occur, relatively low average stability bandwidth is only 0.008, wave
Dynamic rate maximum is also only 0.01, and afternoon hours are since the variation of state of weather is so that more violent output occur in the power stations C
Fluctuation, stability bandwidth mean value are 0.133, and maximum fluctuation rate has reached 0.174, and the big ups and downs of the output of power station at this time can be to power grid
It affects greatly.Two curve global shapes are similar, and global feature is more close, but from the point of view of the result of calculation of local feature,
Influence of the two to power grid differs greatly, and should be managed according to different output situations.
Therefore when carrying out preselected to the more similar power stations E of global feature and the power stations C, according to simultaneously logout impact coefficient α
It can be calculated：
The power stations E morning sub-period and logout impact coefficient：α=0.85 × 0.048+0.15 × 0.08=0.0528
The power stations E sub-period in afternoon and logout impact coefficient：α=0.85 × 0.083+0.15 × 0.105=0.0863
The power stations C morning sub-period and logout impact coefficient：α=0.85 × 0.008+0.15 × 0.010=0.0083
The power stations C sub-period in afternoon and logout impact coefficient：α=0.85 × 0.133+0.15 × 0.174=0.13915
By above-mentioned calculating, in morning sub-period, when to select grid-connected photo-voltaic power generation station, power grid is impacted in selection
The small power stations C reduce the impact to power grid.When photo-voltaic power generation station to select logout, select to impact big E electricity to power grid
It stands；After the logout of the power stations E, former power grid is more stablized.
In the afternoon when sub-period, when to select grid-connected photo-voltaic power generation station, selection impacts power grid in the small power stations E, drops
The low impact to power grid.To select logout photo-voltaic power generation station when, selection the big power stations C, the power stations C logout are impacted to power grid
Afterwards, former power grid is more stablized.
The smoothing effect that photovoltaic is contributed influences space point mainly caused by the meteorological resources difference in spatial distribution
There are two leading factor for cloth effect：The number and regional geography range of photovoltaic plant, are indicated with regional diameter in the present embodiment.
The inconsistency coefficient between smoothing effect coefficient and each power station by calculating power station cluster completes the amount to smoothing effect
Change analysis.
In the present embodiment, overall calculation point is carried out with the power stations A, the power stations C, the power stations D in the power stations E, the power stations C and Yunnan
Analysis.
ABCDE is stated as PVF1~PVF5 respectively for sake of convenience.Relative distance between each photovoltaic plant is as shown in table 2
Relative distance (km) between 2 each photovoltaic plant of table
It is research object, 5 photovoltaic electrics to choose above-mentioned 5 photovoltaic plants active power output time series on March 19th, 2015
Output of standing variation is as shown in Figure 8.
For the correlation that quantitative measurement difference photovoltaic plant is contributed, the trend between calculating separately this 5 photovoltaic plants two-by-two
Inconsistency coefficient, result of calculation are as shown in table 3.
Inconsistency coefficient matrix between 3 photovoltaic plant of table
At a distance of 5.72km relatively closely, is only separated by, the trend inconsistency coefficient of the two is 0.16 in the geographical locations PVF1 and PVF2,
Certain complementarity is showed, but it is complementary not high；The distance between PVF1 and PVF3 increase to 28.67km, both
Trend inconsistency coefficient be 0.30 have higher complementarity；Inconsistency coefficient matrix is observed it is found that its maximum value goes out
Now between PVF3 and PVF5, both is at a distance of 82.41km, therefore regional diameter is to influence photovoltaic to put down as the above analysis
An important factor for sliding effect.
Based on PVF1, PVF2~5 are gradually added into, calculate separately the smoothing effect coefficient under different photovoltaic clusters
ξcluster, result of calculation is as shown in table 4.In table：PVF1~2 indicates PVF1 and PVF2；PVF1~3 indicate PVF1, PVF2 and
PVF3；PVF1~4 indicates that PVF1, PVF2, PVF3 and PVF4, PVF1~5 indicate the photovoltaic plant collection of 5 photovoltaic plants composition
Group.As can be seen that increasing with photovoltaic plant number, smoothing effect coefficient shows gradual increased trend, illustrate with
The expansion of photovoltaic range, the addition of different zones photovoltaic plant, overall output smoothing effect are embodied.
4 smoothing effect analysis of Influential Factors of table
| Photovoltaic power station group | Power station number | Regional diameter/km | ξcluster |
| PVF1 | 1 | 0 | 1.000 |
| PVF1~2 | 2 | 5.72 | 1.175 |
| PVF1~3 | 3 | 28.67 | 1.362 |
| PVF1~4 | 4 | 33.57 | 1.495 |
| PVF1~5 | 5 | 82.41 | 1.513 |
With five photovoltaic plants data instance on March 2nd, 2015, quantitative analysis regional diameter, central-station number are to light
Overhead utility cluster polymerize the influence of power producing characteristics.To the smoothing effect of the photovoltaic plant cluster of different zones diameter corresponding thereto
Coefficient carries out data analysis, and wherein smoothing effect coefficient is the mean value under each regional diameter numerical value.Using polynomial fitting method
The relationship of the two is fitted.Since the smoothing effect that photovoltaic is contributed mainly is caused by the meteorological resources difference in geographical distribution
, and the area coverage of cluster regions can directly react the size of the difference of geographical distribution in cluster, therefore, the present embodiment is chosen
The regional diameter quadratic expression of cluster overlay area can be reacted to be fitted with smoothing effect coefficient.
Fitting expression is：ξcluster=-3.717d2+4.685d+0.4053；
D indicates cluster regions diameter in formula.The error sum of squares (SSE) that fitting expression is fitted data is 0.1195,
R2Coefficient is 0.9034, illustrates that expression formula has preferable fitting effect.
It can be seen that from Fig. 9 curves when cluster regions diameter is smaller, the coefficient of inconsistency coefficient is smaller between power station,
Smoothing effect is weaker.With the expansion of cluster regions diameter, smoothing effect coefficient also gradually increases, and preferable space is presented in cluster
Smooth effect, but after distance reaches 60km, smoothing effect will appear saturated phenomenon, or even have a declining tendency.It can in conjunction with table 2
To find out, the power stations the E power stations distance A, D and E are within 60km, then when sub-period is grid-connected in the afternoon, then the power stations E may be selected.
On the basis of fitted area diameter and smoothing effect Relationship of Coefficients, analyzed area power station quantity and regional diameter pair
The joint effect of smoothing effect.Fitting of a polynomial is still taken, to the pass of smoothing effect coefficient and regional diameter and power station quantity
System is fitted.
Fitting expression is：
D indicates cluster regions diameter in formula；Num indicates central-station quantity.The error that fitting expression is fitted data
Quadratic sum (SSE) is 0.037, R2Coefficient is 0.97, illustrates that expression formula can preferably react smoothing effect coefficient and two and influence
Relationship between factor.
The increase with central-station number and cluster regions diameter, smoothing effect are can be seen that from Figure 10 fitting result figures
Gradually increase, and smoothing effect influenced by cluster regions diameter it is more strong.In conclusion cluster regions diameter and region electricity
The two impact factors of quantity of standing have stronger explanation to act on smoothing effect, and the fitting result of triadic relation can be used for photovoltaic electric
It stands grid-connected and logout selection, integrally going out fluctuation to stabilizing region photovoltaic plant has certain reference significance.
It in conjunction with Fig. 9 and Figure 10, can obtain, in the present embodiment, when selecting grid-connected power station, when power station quantity one
Periodically, photo-voltaic power generation station of the chosen distance close to 60km as possible.When then sub-period is grid-connected in the afternoon, then the power stations E may be selected.Afternoon
When logout, selection exceeds the power stations C of 60km with the power stations E, then identical as the pre-selection of step S4.
It should be pointed out that it is limitation of the present invention that above description, which is not, the present invention is also not limited to the example above,
What those skilled in the art were made in the essential scope of the present invention changes, is modified, adds or replaces, and also answers
It belongs to the scope of protection of the present invention.
Claims (8)
1. a kind of grid-connected logout selection method contributed based on photo-voltaic power generation station fining, it is characterised in that include the following steps：
S1：Selected pre-selection grid-connected photovoltaic power generation station, obtains photo-voltaic power generation station cluster, and obtain photo-voltaic power generation station company-data and light
The history of all photo-voltaic power generation stations goes out force data in volt power station cluster；
S2：The daily output characteristic index of photo-voltaic power generation station is chosen, and all daily outputs for calculating separately single photo-voltaic power generation station are special
Levy index value；
S3：Photo-voltaic power generation station daily output characteristic index is divided, sub-period output characteristic index value is obtained；
S4：The weight for setting all characteristic index values obtains each photo-voltaic power generation station and logout impact coefficient, is rushed according to simultaneously logout
Coefficient is hit, it is preselected to the progress of grid-connected and logout photo-voltaic power generation station, obtain final photo-voltaic power generation station cluster；
S5：The final photo-voltaic power generation station cluster obtained according to step S4 calculates current photo-voltaic power generation station cluster output characteristic index
Value, assesses current electric grid, obtains final grid-connected and logout selection result.
2. the grid-connected logout selection method according to claim 1 contributed based on photo-voltaic power generation station fining, feature are existed
Photo-voltaic power generation station number, regional geography range, cluster regions are included at least in photo-voltaic power generation station company-data in step sl
The relative distance of diameter and all photo-voltaic power generation stations；
The history of the photo-voltaic power generation station goes out the photovoltaic that force data is x days before selecting the grid-connected or logout moment and goes out force data；Institute
It states out force data and includes at least photovoltaic generation output power sampled data and photovoltaic generation power curve.
3. the grid-connected logout selection method according to claim 2 contributed based on photo-voltaic power generation station fining, feature are existed
It is included at least in the daily output feature of photo-voltaic power generation station in step s 2：Per day output index, daily output are distributed degree of bias index
With per day stability bandwidth index；
The per day output indexCalculation formula be：
Wherein, n sampled point numbers between daytime；PtFor the output power sequence of photo-voltaic power generation station, t=1,2,3 ... M；PbaseFor photovoltaic
The installed capacity in power station；
The calculation formula of daily output distribution degree of bias index is：
Wherein,Indicate that daily output is distributed degree of bias index；The apparent energy that day sends out；
The calculation formula of the per day stability bandwidth index σ is：
The photovoltaic generation power curve fluctuation opposite with intrinsic fluctuation direction is set as effectively fluctuation；Wherein, day photovoltaic is contributed
The intrinsic whole parabolically shape of fluctuation；N is the number effectively fluctuated；ΔPiIt, should for effective undulate quantity that ith effectively fluctuates
Effective undulate quantity is equal to the absolute value of the minimum point nearest maximum point power difference adjacent thereto effectively fluctuated every time.
4. the grid-connected logout selection method according to claim 1 or 3 contributed based on photo-voltaic power generation station fining, feature
It is that photo-voltaic power generation station daily output characteristic index includes that sub-period average output index and sub-period stability bandwidth refer in step s3
Mark；
Wherein, it is that entirety is divided into Y period with day, any one period in Y period is sub-period；
The sub-period stability bandwidth index includes the mean value specification and sub-period stability bandwidth maximum index of sub-period stability bandwidth.
5. the grid-connected logout selection method according to claim 4 contributed based on photo-voltaic power generation station fining, feature are existed
It is in the calculation formula of the sub-period average output index：
The sub-period average output indexCalculation formula is：
Wherein, myFor the sampled point number of y-th of sub-period；Y=1,2,3 ... Y, m1+m2+m3+…+mY=n, PbaseIt is sent out for photovoltaic
The installed capacity in power station；N sampled point numbers between daytime, PtFor the output power sequence of photo-voltaic power generation station；
If the effective stability bandwidth sequence S of sub-periodyFor：
Wherein, yl is effective fluctuation number of y-th of sub-period；Δ P is the effective undulate quantity effectively fluctuated,For y-th of son
Effective undulate quantity when period yl secondary undulation；
The mean value specification of the sub-period stability bandwidthCalculation formula is：
The sub-period stability bandwidth maximum indexCalculation formula is：
6. the grid-connected logout selection method according to claim 1 contributed based on photo-voltaic power generation station fining, feature are existed
In simultaneously the calculation formula of logout impact coefficient α is in step s 4：
Wherein, λ1+λ2+λ3+λ4+λ5+λ6=1, λ1, λ2, λ3, λ4, λ5, λ6Value range be 0~1；
For the per day output index of the daily output feature of photo-voltaic power generation station；Indicate that the daily output of photo-voltaic power generation station is special
The daily output of sign is distributed degree of bias index；σ is that the daily output characteristic day of photo-voltaic power generation station is averaged stability bandwidth index；It is sent out for photovoltaic
The sub-period average output index of power station daily output characteristic index；For the sub-period of photo-voltaic power generation station daily output characteristic index
The mean value specification of stability bandwidth；For the sub-period stability bandwidth maximum index of photo-voltaic power generation station daily output characteristic index.
7. the grid-connected logout selection method contributed based on photo-voltaic power generation station fining according to claim 1 or 6, feature
It is in step S5 that photo-voltaic power generation station cluster output characteristic index includes cluster smoothing effect coefficient ξclusterAnd photo-voltaic power generation station
Output inconsistency COEFFICIENT K；
The cluster smoothing effect coefficient ξclusterCalculation formula is：
Wherein,Indicate effective stability bandwidth of first of photo-voltaic power generation station in cluster；
σclusterIndicate effective stability bandwidth of cluster gross capability；
M indicates the number of photo-voltaic power generation station in photo-voltaic power generation station cluster；
The calculation formula of photo-voltaic power generation station output inconsistency COEFFICIENT K is：
Wherein, P1,P2Indicate the output sequence of two different photo-voltaic power generation stations；
fi(P1,P2) expression formula be：
fi(P1,P2) indicate that two photovoltaic generation power curve trend differ between (i-1)-th sampled point and ith sample point
Cause property.
8. the grid-connected logout selection method according to claim 7 contributed based on photo-voltaic power generation station fining, feature are existed
Include in carrying out assessment to current electric grid in step s 5：
The photo-voltaic power generation station output inconsistency COEFFICIENT K of photo-voltaic power generation station two-by-two is calculated, and obtains the photo-voltaic power generation station and contributes not
The matrix relationship of consistency coefficient K and the relative distance of two photo-voltaic power generation stations；
Cluster regions diameter and the relationship of cluster smoothing effect coefficient are fitted using multinomial, the first fit correlation formula
For：ξcluster=-3.717d2+4.685d+0.4053；Wherein d indicates cluster regions diameter；
Cluster smoothing effect coefficient, cluster regions diameter and power station quantity are fitted using multinomial；Second fitting is closed
It is that formula is：Wherein d indicates cluster regions diameter；Num indicates the region
Power station quantity.
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