CN107103411A - Based on the markovian simulation wind power time series generation method of improvement - Google Patents
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Abstract
One kind is based on improving markovian simulation wind power time series generation method, is characterized in, including：History wind power time series is divided into different pieces of information fragment by different months and period, the state-transition matrix of each fragment is calculated；The probability distribution of history wind power undulate quantity is fitted, generation meets the set of the random number of the distribution；Zero moment value before history wind power average value is started as simulation wind power sequence, in the multiple state-transition matrixes for the improvement Markov chain model having calculated that, month and period according to residing for simulation wind power previous moment, determine corresponding state-transition matrix, and generate accumulated state transfer matrix, draw current time wind power status, i.e., it is interval where it；Extract in undulate quantity, and the previous moment wind power value that is added to as current time wind power value, similarly obtain subsequent time wind power value, the wind power time series of data amount check needed for generation.
Description
Technical field
It is that one kind is based on improving Ma Er the present invention relates to the wind power time series simulation field in wind-electricity integration planning
The simulation wind power time series generation method of section's husband's chain.
Background technology
The randomness of wind-power electricity generation causes power system stability, safe and reliable operation face after wind-electricity integration with uncertain
Face huge challenge, improve the accuracy of wind power simulated series, have in the field such as Power System Planning and safety evaluation
Significance.
Simulation generation wind power time series refers to based on history wind power time series, generates multiple in system
The new sequence matched in meter feature with history wind power time series.
The method of generation wind power time series can be divided into two classes：Wind speed method and wind power method.If with wind speed time sequence
Row generate wind power time series, due to the power output of wind power plant as input quantity according to wind speed-Power Conversion Model
Also influenceed by other factorses such as landform, temperature, it is difficult accurate, therefore wind speed method generation to make wind speed-Power Conversion Model
There is relatively large deviation than history wind power time series in wind power time series.Wind power method is directly to utilize history wind
Electrical power time series is simulated, it is to avoid the deviation further resulted in by wind speed-Power Conversion Model；Using horse
Er Kefu chain models and the wind power time series generated using wind power method, the wind power time generated with other models
Sequence is compared, in probability density distribution with being performed better than on autocorrelation, however, the wind power time series side of prior art
The original Markov chain model of method does not account for the time domain specification of wind power itself, and its analog result need to be improved.Cause
This need to set up one consideration wind power characteristic based on markovian wind power time series models based on improvement
Markovian simulation wind power time series generation method.So far, have no relevant based on improvement Markov Chain
Simulation wind power time series generation method document report and practical application.
The content of the invention
Simple and practical it is an object of the present invention to provide one kind is scientific and reasonable, calculating speed is fast, and precision is higher, simulates effect
More preferably based on the markovian simulation wind power time series generation method of improvement.
The object of the invention is realized the technical scheme adopted is that a kind of based on the markovian simulation wind power of improvement
Time series generation method, it is characterised in that it comprises the following steps：
1) data are classified
Need to consider wind power based on markovian simulation wind power time series generation method is improved
Seasonal Characteristics, day characteristic and wave characteristic, therefore calculate state-transition matrix before need to classify to historical data, divide
Class principle is as follows：
1. Seasonal Characteristics are considered
The Seasonal Characteristics of wind power are mainly shown as that the size of different month power outputs among 1 year has differences, and are
In the wind power time series of generation Seasonal Characteristics are embodied, when need to be by history wind power that time span is 1 year
Between sequence be divided into 12 fragments, represented with λ, λ=1,2 ..., 12, each moon is fragment；
2. day characteristic is considered
The day characteristic of wind power is mainly shown as that due to sunshine different periods wind power is big among one day
It is small to have differences, and the day characteristic in different month is general also different, need to be by the data of history wind power time series every day
It is divided into four parts, each section corresponding time is respectively 00:00-06:00,06:00-12:00,12:00-18:00,18:00-
24:00, four periods are represented with θ, θ=θ1, θ2, θ3, θ4；
History wind power time series is represented with Ω, then the history wind power time sequence in month λ in period θ
List is shown as Ωλ.θ, wherein λ=1,2 ..., 12；θ=θ1, θ2, θ3, θ4；
2) wind power status number is determined
According to the interval size of history wind power, the interval of wind power is equally divided into S part, S is wind-powered electricity generation
Power rating number；
3) wind power state-transition matrix is calculated
In the single order Markov Chain P={ P of wind power1, P2..., Pt..., PNIn, PtFor the simulation wind power time
Element in sequence, wherein t=1,2 ..., N, N is simulates the data amount check of wind power time series, and wind power value is located
In state space E, E={ E1, E2..., Ei..., ES, EiFor i-th of state of wind power, i=1,2 ..., S, S is wind
Electrical power state total number, the performance number at simulation wind power time series each moment can be only in one of state, wind
The process of electrical power from current time state shift subsequent time state is referred to as state transfer, and it has in state migration procedure
S-1 kinds direction is to turn to other states, and it is to turn to itself to have a kind of direction, therefore each state has S kind shift directions；
The definition of state transition probability is to be in state E in the wind power value of tiOn the premise of, the t+1 moment
Wind power value is in state EjProbability, be denoted as fi.j, it can be calculated with formula (1),
fi.j=g (Pt+1∈Ej|Pt∈Ei) (1)
In formula, PtAnd Pt+1The respectively wind power value of t and t+1 moment, wherein t=1,2 ..., N, N is simulation
The data amount check of wind power time series, EiWith EjRespectively i-th of the state and j-th of state of wind power, i=1,
2 ..., S；J=1,2 ..., S；S is wind power state total number；G (|) is conditional transition probability；
State transition probability matrix F is made up of state transition probability, is S rank square formations, each row element such as shown in formula (2)
Sum is 1；
In formula, fi.jIt is wind power from state EiIt is transferred to state EjState transition probability, i=1,2 ..., S；J=1,
2 ..., S；S is wind power state total number；
In history wind power time series Ωλ.θIn, wind power is from state EiIt is transferred to state EjState transfer it is general
Rate is denoted as fi.j.λ.θ, calculated with formula (3),
In formula, ki.j.λ.θFor history wind power time series Ωλ.θIn, wind power is from state EiIt is transferred to state Ej's
Frequency is shifted, the frequency shifted by a step, i=1,2 ..., S are only calculated here；J=1,2 ..., S, S are wind power state
Total number；
Correspondence wind power time series Ωλ.θState transition probability matrix Fλ.θExpression formula see formula (4),
In formula, fi.j.λ.θFor history wind power time series Ωλ.θMiddle wind power is from state EiIt is transferred to state Ej's
State transition probability, i=1,2 ..., S；J=1,2 ..., S；S is wind power state total number, λ=1,2 ..., 12；θ=
θ1, θ2, θ3, θ4；
4) it is fitted undulate quantity
The fluctuation of wind power refers to that the wind power value at later moment in time and current time has differences, here using one
Jump component portrays undulate quantity, and its expression formula is shown in formula (5),
Δ P=Pt+1-Pt (5)
Wherein, Δ P represents the undulate quantity of wind-powered electricity generation wind power；Pt+1And PtRespectively in wind power time series during t+1
Quarter and the wind power value of t, t=1,2 ..., N, N is the data amount check of simulation wind power time series；
The probability-distribution function of wind power undulate quantity is fitted using t location-scale distribution functions, it is expressed
Formula is shown in formula (6),
In formula：Г calculation formula isμ is location parameter；б is scale parameter；ν is form parameter；
History wind power undulate quantity is fitted using formula (6), μ is obtained, б, ν value, then generation is met in formula (6)
The set of the undulate quantity of parameters obtained；
5) simulation generation wind power time series
The step of simulation generation wind power time series, is as follows：
1. the border of data used is determined
Current time is denoted as t, t ∈ { 1,2 ..., N }, and N is simulates the data amount check of wind power time series, when current
Die sinking intends wind power status and is denoted as Ei, i ∈ { 1,2 ..., S }, S is wind power state total number, history wind power
Average value is denoted as simulating the zero moment value before wind power time series starts, and current time wind power size is denoted as Pt, Pt∈
(PEi.min, PEi.max), wherein, PEi.min、PEi.maxRespectively simulation wind power state EiCorresponding wind power is interval most
Small value and maximum, are denoted as λ in month residing for current time wind powert, λt∈ { 1,2 ..., 12 }, current time wind power institute
Place's period is denoted as θt, θt∈{θ1, θ2, θ3, θ4, t ∈ { 1,2 ..., N }, N is the data amount check of simulation wind power time series,
Equally distributed random number ε, and ε ∈ (0,1) are obeyed in generation one；
2. accumulated state transfer matrix is calculated
According to known λt、θt, corresponding state-transition matrix is determined, and then calculate corresponding accumulated state transfer
Matrix Qλt.θt, calculation formula is shown in formula (7),
In formula：Q is accumulated state transfer matrix, Qλt.θtFor month λtMiddle period θtCorresponding accumulated state transfer matrix,
fi.δ.tIt is t wind power from state EiIt is transferred to state EδState transition probability, qi.j.tIt is t wind power from shape
State EiIt is transferred to state EjAccumulative transition probability, δ=1,2 ..., j；I=1,2 ..., S；J=1,2 ..., S；S is wind power
State total number, t ∈ { 1,2 ..., N }, N is the data amount check of simulation wind power time series；
3. subsequent time wind power state is determined
Obtain after accumulated state transfer matrix, further determine that present day analog wind power PtSubsequent time simulation wind-powered electricity generation
Power Pt+1State in which Ej, Ej∈{E1, E2..., ES, when 0<ε<qi.1.tWhen, j=1, qi.1.tFor the wind power of t
From state EiIt is transferred to state E1Accumulative transition probability；Work as qi.m.t<ε<qi.m+1.tWhen, j=m+1, qi.m.tFor the wind-powered electricity generation of t
Power is from state EiIt is transferred to state EmAccumulative transition probability, qi.m+1.tFor t wind power from state EiIt is transferred to shape
State Em+1Accumulative transition probability, EmAnd Em+1Respectively m-th of the state and the m+1 state of wind power, m ∈ 1,2 ...,
S-1 }, S is wind power state total number, and t ∈ { 1,2 ..., N }, N is the data amount check of simulation wind power time series；
4. undulate quantity superposition generation subsequent time wind power value is extracted
Present day analog wind power P is determinedtSubsequent time simulation wind power Pt+1Status EjAfterwards, further really
Fix a period of time die sinking and intend wind power Pt+1Numerical value, Pt+1∈(PEj.min, PEj.max), PEj.min、PEj.maxRespectively state EjIt is right
The minimum value and maximum in the wind power interval answered, ripple is randomly selected in the fluctuation value set of the parameter of coincidence formula (6)
Momentum γt, γ is the set of wind power undulating value, γ ∈ (γmin, γmax), γtThe wind power fluctuation extracted for t
Amount, γt∈(γmin, γmax), γmin, γmaxRespectively wind power fluctuates the minimum value and maximum of value set；
In present day analog wind power PtOn the basis of, the wind power undulate quantity γ that superposition current time is extractedt, obtain
Subsequent time simulation wind power Pt+1, i.e. Pt+1=Pt+γt, Pt+1∈(Pt+γmin, Pt+γmax), work as Pt+1In state EjTake
When in the range of value, by Pt+1As the value of simulation wind power subsequent time, wind power undulate quantity γ is otherwise extracted againt, and
Again be added to PtOn, then judged, t ∈ { 1,2 ..., N }, N is the data amount check of simulation wind power time series；
5. judge to calculate and whether terminate
Work as t+1<During N, 2. the sub-step for returning to 5) simulation generation wind power time series, continues to calculate subsequent time mould
Intend wind power value；As t+1=N, calculating process terminates, the simulation wind power time series of length needed for just generating,
Wherein t=1,2 ..., N, N are the data amount check of simulation wind power time series.
The present invention based on improve it is markovian simulation wind power time series generation method, gone through making full use of
On the basis of the history wind power time domain specification of itself, to being carried out based on markovian wind power time series models
Improve, simulate the wind power time series of generation and generated with history wind power time series and based on Markov Chain simulation
Wind power time series compare, show more preferably in terms of probability density distribution and autocorrelation.Closed with methodological science
Reason, simple and practical, calculating speed is fast, the advantages of precision is higher.
Brief description of the drawings
Fig. 1 is the present invention based on the flow for improving markovian simulation wind power time series generation method
Figure；
Fig. 2 be history wind power time series, based on Markov chain model generate wind power time series and
Curve based on the wind power time series for improving Markov chain model generation；
Fig. 3 be history wind power time series, based on Markov chain model generate wind power time series and
Probability distribution comparison diagram based on the wind power time series for improving Markov chain model generation；
Fig. 4 is the auto-correlation coefficient figure of history wind power time series；
Fig. 5 is the auto-correlation coefficient figure of the wind power time series generated based on Markov chain model；
Fig. 6 is the auto-correlation coefficient figure based on the wind power time series for improving Markov chain model generation.
Embodiment
Reference picture 1, it is of the invention based on the markovian simulation wind power time series generation method of improvement, including
Following steps：
1) data are classified
Need to consider the Seasonal Characteristics, day characteristic and wave characteristic of wind power, therefore in the state of calculating transfer square
Need to classify to historical data before battle array, principle of classification is as follows：
1. Seasonal Characteristics are considered
The Seasonal Characteristics of wind power are mainly shown as that the size of different month power outputs among 1 year has differences, and are
In the wind power time series of generation Seasonal Characteristics are embodied, when need to be by history wind power that time span is 1 year
Between sequence be divided into 12 fragments, represented with λ, λ=1,2 ..., 12, each moon is fragment；
2. day characteristic is considered
The day characteristic of wind power is mainly shown as that due to sunshine different periods wind power is big among one day
It is small to have differences, and the day characteristic in different month is general also different, need to be by the data of history wind power time series every day
It is divided into four parts, each section corresponding time is respectively 00:00-06:00,06:00-12:00,12:00-18:00,18:00-
24:00, four periods are represented with θ, θ=θ1, θ2, θ3, θ4；
History wind power time series is represented with Ω, then the history wind power time sequence in month λ in period θ
List is shown as Ωλ.θ, wherein λ=1,2 ..., 12；θ=θ1, θ2, θ3, θ4；
2) wind power status number is determined
According to the interval size of history wind power, the interval of wind power is equally divided into S part, S is wind-powered electricity generation
Power rating number；
3) wind power state-transition matrix is calculated
In the single order Markov Chain P={ P of wind power1, P2..., Pt..., PNIn, PtFor the simulation wind power time
Element in sequence, wherein t=1,2 ..., N, N is simulates the data amount check of wind power time series, and wind power value is located
In state space E, E={ E1, E2..., Ei..., ES, EiFor i-th of state of wind power, i=1,2 ..., S, S is wind
Electrical power state total number, the performance number at simulation wind power time series each moment can be only in one of state, wind
The process of electrical power from current time state shift subsequent time state is referred to as state transfer, and it has in state migration procedure
S-1 kinds direction is to turn to other states, and it is to turn to itself to have a kind of direction, therefore each state has S kind shift directions；
The definition of state transition probability is to be in state E in the wind power value of tiOn the premise of, the t+1 moment
Wind power value is in state EjProbability, be denoted as fi.j, it can be calculated with formula (1),
fi.j=g (Pt+1∈Ej|Pt∈Ei) (1)
In formula, PtAnd Pt+1The respectively wind power value of t and t+1 moment, wherein t=1,2 ..., N, N is simulation
The data amount check of wind power time series, EiWith EjRespectively i-th of the state and j-th of state of wind power, i=1,
2 ..., S；J=1,2 ..., S；S is wind power state total number；G (|) is conditional transition probability；
State transition probability matrix F is made up of state transition probability, is S rank square formations, each row element such as shown in formula (2)
Sum is 1；
In formula, fi.jIt is wind power from state EiIt is transferred to state EjState transition probability, i=1,2 ..., S；J=1,
2 ..., S；S is wind power state total number；
In history wind power time series Ωλ.θIn, wind power is from state EiIt is transferred to state EjState transfer it is general
Rate is denoted as fi.j.λ.θ, calculated with formula (3),
In formula, ki.j.λ.θFor history wind power time series Ωλ.θIn, wind power is from state EiIt is transferred to state Ej's
Frequency is shifted, the frequency shifted by a step, i=1,2 ..., S are only calculated here；J=1,2 ..., S, S are wind power state
Total number；
Correspondence wind power time series Ωλ.θState transition probability matrix Fλ.θExpression formula see formula (4),
In formula, fi.j.λ.θFor history wind power time series Ωλ.θMiddle wind power is from state EiIt is transferred to state Ej's
State transition probability, i=1,2 ..., S；J=1,2 ..., S；S is wind power state total number, λ=1,2 ..., 12；θ=
θ1, θ2, θ3, θ4；
4) it is fitted undulate quantity
The fluctuation of wind power refers to that the wind power value at later moment in time and current time has differences, here using one
Jump component portrays undulate quantity, and its expression formula is shown in formula (5),
Δ P=Pt+1-Pt (5)
Wherein, Δ P represents the undulate quantity of wind-powered electricity generation wind power；Pt+1And PtRespectively in wind power time series during t+1
Quarter and the wind power value of t, t=1,2 ..., N, N is the data amount check of simulation wind power time series；
The probability-distribution function of wind power undulate quantity is fitted using t location-scale distribution functions, it is expressed
Formula is shown in formula (6),
In formula：Г calculation formula isμ is location parameter；б is scale parameter；ν is form parameter；
History wind power undulate quantity is fitted using formula (6), μ is obtained, б, ν value, then generation is met in formula (6)
The set of the undulate quantity of parameters obtained；
5) simulation generation wind power time series
The step of simulation generation wind power time series, is as follows：
1. the border of data used is determined
Current time is denoted as t, t ∈ { 1,2 ..., N }, and N is simulates the data amount check of wind power time series, when current
Die sinking intends wind power status and is denoted as Ei, i ∈ { 1,2 ..., S }, S is wind power state total number, history wind power
Average value is denoted as simulating the zero moment value before wind power time series starts, and current time wind power size is denoted as Pt, Pt∈
(PEi.min, PEi.max), wherein, PEi.min、PEi.maxRespectively simulation wind power state EiCorresponding wind power is interval most
Small value and maximum, are denoted as λ in month residing for current time wind powert, λt∈ { 1,2 ..., 12 }, current time wind power institute
Place's period is denoted as θt, θt∈{θ1, θ2, θ3, θ4, t ∈ { 1,2 ..., N }, N is the data amount check of simulation wind power time series,
Equally distributed random number ε, and ε ∈ (0,1) are obeyed in generation one；
2. accumulated state transfer matrix is calculated
According to known λt、θt, corresponding state-transition matrix is determined, and then calculate corresponding accumulated state transfer
Matrix Qλt.θt, calculation formula is shown in formula (7),
In formula：Q is accumulated state transfer matrix, Qλt.θtFor month λtMiddle period θtCorresponding accumulated state transfer matrix,
fi.δ.tIt is t wind power from state EiIt is transferred to state EδState transition probability, qi.j.tIt is t wind power from shape
State EiIt is transferred to state EjAccumulative transition probability, δ=1,2 ..., j；I=1,2 ..., S；J=1,2 ..., S；S is wind power
State total number, t ∈ { 1,2 ..., N }, N is the data amount check of simulation wind power time series；
3. subsequent time wind power state is determined
Obtain after accumulated state transfer matrix, further determine that present day analog wind power PtSubsequent time simulation wind-powered electricity generation
Power Pt+1State in which Ej, Ej∈{E1, E2..., ES, when 0<ε<qi.1.tWhen, j=1, qi.1.tFor the wind power of t
From state EiIt is transferred to state E1Accumulative transition probability；Work as qi.m.t<ε<qi.m+1.tWhen, j=m+1, qi.m.tFor the wind-powered electricity generation of t
Power is from state EiIt is transferred to state EmAccumulative transition probability, qi.m+1.tFor t wind power from state EiIt is transferred to shape
State Em+1Accumulative transition probability, EmAnd Em+1Respectively m-th of the state and the m+1 state of wind power, m ∈ 1,2 ...,
S-1 }, S is wind power state total number, and t ∈ { 1,2 ..., N }, N is the data amount check of simulation wind power time series；
4. undulate quantity superposition generation subsequent time wind power value is extracted
Present day analog wind power P is determinedtSubsequent time simulation wind power Pt+1Status EjAfterwards, further really
Fix a period of time die sinking and intend wind power Pt+1Numerical value, Pt+1∈(PEj.min, PEj.max), PEj.min、PEj.maxRespectively state EjIt is right
The minimum value and maximum in the wind power interval answered, ripple is randomly selected in the fluctuation value set of the parameter of coincidence formula (6)
Momentum γt, γ is the set of wind power undulating value, γ ∈ (γmin, γmax), γtThe wind power fluctuation extracted for t
Amount, γt∈(γmin, γmax), γmin, γmaxRespectively wind power fluctuates the minimum value and maximum of value set；
In present day analog wind power PtOn the basis of, the wind power undulate quantity γ that superposition current time is extractedt, obtain
Subsequent time simulation wind power Pt+1, i.e. Pt+1=Pt+γt, Pt+1∈(Pt+γmin, Pt+γmax), work as Pt+1In state EjTake
When in the range of value, by Pt+1As the value of simulation wind power subsequent time, wind power undulate quantity γ is otherwise extracted againt, and
Again be added to PtOn, then judged, t ∈ { 1,2 ..., N }, N is the data amount check of simulation wind power time series；
5. judge to calculate and whether terminate
Work as t+1<During N, 2. the sub-step for returning to 5) simulation generation wind power time series, continues to calculate subsequent time mould
Intend wind power value；As t+1=N, calculating process terminates, the simulation wind power time series of length needed for just generating,
Wherein t=1,2 ..., N, N are the data amount check of simulation wind power time series.
One kind of embodiment is based on improving markovian simulation wind power time series generation method, including following
Step：
1. using the wind power data in the Northwest's wind power plant a certain year as history wind power data, by history wind
The interval of electrical power is equally divided into 40 states, wherein i-th of state corresponding wind power interval for [(i-1), i] ×
2MW, i=1,2 ..., 40, history wind power average value is denoted as simulating the zero moment value before wind power time series starts；
2. by history wind power time series according to the section into different with Time segments division of different months, and formula is utilized
(3), formula (4) calculates the corresponding state-transition matrix of each class data；
3. the undulate quantity of history wind power time series is calculated using formula (5), is fitted using formula (6),
Generation meets the set of the undulate quantity of parameters obtained in formula (6)；
4. in the multiple state-transition matrixes for the improvement Markov chain model having calculated that, according to simulation wind power
Month and period residing for current time, corresponding state-transition matrix is determined, and accumulated state turn is calculated using formula (7)
Probability matrix is moved, show that subsequent time simulates wind power state in which, and then determine subsequent time simulation wind power institute
Interval；
5. a value is randomly selected in the set of the wind power undulate quantity of generation as superposition undulate quantity, is added to and works as
In the wind power value at preceding moment, and the wind power value after superposition is judged whether in the range of subsequent time wind power,
It is the wind power value for then continuing to calculate the remaining moment, undulate quantity is otherwise extracted again；
6. when the data amount check for untill subsequent time, simulating obtained wind power time series is required wind power
During the data amount check of time series, stop calculating, otherwise, make after moment value plus 1, return to sub-step 4.；
Because the time interval of history wind power time series is 5 minutes, therefore the simulation wind power time of generation
Sequence is also the data every 5 minutes, and Fig. 2 is the wind power part-time sequence of generation, in order to clear, intuitively right
Than only providing the wind power time series of first 750 minutes；
7. the wind power time series of Markov chain model and the generation of original Markov chain model will be improved by not
With 12 classes are divided into month, respectively calculate per class average value, standard deviation and maximum, and each statistical parameter absolute error and
Relative error, and contrasted with each parameter value of original wind power time series, as a result as shown in table 1 to table 3：
The wind power average value in the different months of table 1 compares
The different month wind power standard deviations of table 2 compare
The different month wind power maximums of table 3 compare
As can be seen from Table 1, the average value for improving the wind power time series of Markov chain model generation is closer
In the average value of original wind power time series, and the situation of change of each month wind power average value can be embodied, and
In the wind power time series that original Markov chain model is generated, the wind power average value of every month does not have too big
Change, can not embody the Seasonal Characteristics of wind power, it can thus be concluded that, improve Markov chain model and consider wind power season
After characteristic, day characteristic and wave characteristic, its wind power time series generated has higher precision.Table 2, table 3 and table 1
Analysis result it is similar.
6) probability density function (PDF, probability density function) and auto-correlation coefficient (ACF,
The comparison of (autocorrelation function)
The wind power time series and history of Markov chain model and the generation of original Markov chain model will be improved
The PDF of wind power time series is contrasted, as shown in figure 3, can be drawn from Fig. 3 contrast, improves Markov Chain mould
The wind power sequence of type generation preferably remains the probability density characteristicses of history wind power time series, and original Ma Er
The wind power sequence of section's husband's chain model generation differs greatly in probability distribution compared with history wind power time series, no
The probability density characteristicses of historical data can be retained.
Original wind power time series, the wind power time series of original Markov chain model generation are with improving horse
The wind power time series of Er Kefu chain models generation, the ACF under different lag times is as shown in Figures 4 to 6.By than
Relatively as can be seen that the wind power time series for improving Markov Chain generation substantially completely remains the history wind power time
The autocorrelation of sequence, the ACF and history wind power sequence phase of the wind power time series of original Markov Chain generation
More larger than differing, this is due to that the wind power characteristic that different months shows of the wind power in 1 year is different, and one
Characteristic of different periods is also different among it, and day characteristic and Seasonal Characteristics are introduced due to improving Markov chain model, therefore raw
Into wind power time series remain good autocorrelation.
The particular embodiment of the present invention is made that detailed explanation to present disclosure, but does not limit to the present embodiment,
Any obvious change that those skilled in the art are done according to the enlightenment of the present invention, belongs to rights protection of the present invention
Scope.
Claims (1)
1. one kind is based on the markovian simulation wind power time series generation method of improvement, it is characterised in that it includes
Following steps：
1) data are classified
Need to consider the season of wind power based on markovian simulation wind power time series generation method is improved
Characteristic, day characteristic and wave characteristic are saved, therefore needs to classify to historical data before state-transition matrix is calculated, classification is former
It is then as follows：
1. Seasonal Characteristics are considered
The Seasonal Characteristics of wind power are mainly shown as among 1 year that the size of different month power outputs has differences, in order to
Seasonal Characteristics are embodied in the wind power time series of generation, need to be the history wind power time sequence of 1 year by time span
Row are divided into 12 fragments, are represented with λ, λ=1,2 ..., 12, and each moon is a fragment；
2. day characteristic is considered
The day characteristic of wind power is mainly shown as that due to sunshine the size of different periods wind power is deposited among one day
In difference, and the day characteristic in different months is general also different, need to be divided into the data of history wind power time series every day
Four parts, each section corresponding time is respectively 00:00-06:00,06:00-12:00,12:00-18:00,18:00-24:
00, four periods are represented with θ, θ=θ1, θ2, θ3, θ4；
History wind power time series is represented with Ω, then the history wind power time series table in month λ in period θ
It is shown as Ωλ.θ, wherein λ=1,2 ..., 12；θ=θ1, θ2, θ3, θ4；
2) wind power status number is determined
According to the interval size of history wind power, the interval of wind power is equally divided into S part, S is wind power
Status number；
3) wind power state-transition matrix is calculated
In the single order Markov Chain P={ P of wind power1, P2..., Pt..., PNIn, PtFor simulation wind power time series
In element, wherein t=1,2 ..., N, N is the data amount check of simulation wind power time series, and wind power value is in shape
In state space E, E={ E1, E2..., Ei..., ES, EiFor i-th of state of wind power, i=1,2 ..., S, S is wind-powered electricity generation work(
Rate state total number, the performance number at simulation wind power time series each moment can be only in one of state, wind-powered electricity generation work(
The process of rate from current time state shift subsequent time state is referred to as state transfer, and it has S-1 kinds in state migration procedure
Direction is to turn to other states, and it is to turn to itself to have a kind of direction, therefore each state has S kind shift directions；
The definition of state transition probability is to be in state E in the wind power value of tiOn the premise of, the wind-powered electricity generation work(at t+1 moment
Rate value is in state EjProbability, be denoted as fi.j, it can be calculated with formula (1),
fi.j=g (Pt+1∈Ej|Pt∈Ei) (1)
In formula, PtAnd Pt+1The respectively wind power value of t and t+1 moment, wherein t=1,2 ..., N, N is simulation wind-powered electricity generation work(
The data amount check of rate time series, EiWith EjRespectively i-th of the state and j-th of state of wind power, i=1,2 ..., S；j
=1,2 ..., S；S is wind power state total number；G (|) is conditional transition probability；
State transition probability matrix F is made up of state transition probability, is S rank square formations, each row element sum such as shown in formula (2)
It is 1；
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Make fi.j.λ.θ, calculated with formula (3),
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In formula, fi.j.λ.θFor history wind power time series Ωλ.θMiddle wind power is from state EiIt is transferred to state EjState
Transition probability, i=1,2 ..., S；J=1,2 ..., S；S is wind power state total number, λ=1,2 ..., 12；θ=θ1, θ2,
θ3, θ4；
4) it is fitted undulate quantity
The fluctuation of wind power refers to that the wind power value at later moment in time and current time has differences, here using a jump
Component portrays undulate quantity, and its expression formula is shown in formula (5),
Δ P=Pt+1-Pt (5)
Wherein, Δ P represents the undulate quantity of wind-powered electricity generation wind power；Pt+1And PtRespectively in wind power time series the t+1 moment and
The wind power value of t, t=1,2 ..., N, N is the data amount check of simulation wind power time series；
The probability-distribution function of wind power undulate quantity is fitted using t location-scale distribution functions, its expression formula is shown in
Formula (6),
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In formula：Г calculation formula isμ is location parameter；б is scale parameter；ν is form parameter；
History wind power undulate quantity is fitted using formula (6), μ is obtained, б, ν value, then generation meets gained in formula (6)
The set of the undulate quantity of parameter；
5) simulation generation wind power time series
The step of simulation generation wind power time series, is as follows：
1. the border of data used is determined
Current time is denoted as t, t ∈ { 1,2 ..., N }, and N is the data amount check of simulation wind power time series, current time mould
Intend wind power status and be denoted as Ei, i ∈ { 1,2 ..., S }, S is wind power state total number, and history wind power is averaged
Value is denoted as simulating the zero moment value before wind power time series starts, and current time wind power size is denoted as Pt, Pt∈
(PEi.min, PEi.max), wherein, PEi.min、PEi.maxRespectively simulation wind power state EiCorresponding wind power is interval most
Small value and maximum, are denoted as λ in month residing for current time wind powert, λt∈ { 1,2 ..., 12 }, current time wind power institute
Place's period is denoted as θt, θt∈{θ1, θ2, θ3, θ4, t ∈ { 1,2 ..., N }, N is the data amount check of simulation wind power time series,
Equally distributed random number ε, and ε ∈ (0,1) are obeyed in generation one；
2. accumulated state transfer matrix is calculated
According to known λt、θt, corresponding state-transition matrix is determined, and then calculate corresponding accumulated state transfer matrix
Qλt.θt, calculation formula is shown in formula (7),
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</mtd>
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</mtd>
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<mtd>
<msub>
<mi>q</mi>
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</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>q</mi>
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</mtd>
</mtr>
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</mfenced>
<mrow>
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<mtr>
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<mi>&Sigma;</mi>
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<mi>f</mi>
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<mn>1.</mn>
<mi>&delta;</mi>
<mo>.</mo>
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</mrow>
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<mtd>
<mo>...</mo>
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<mi>&Sigma;</mi>
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<mi>&Sigma;</mi>
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</msub>
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</mtd>
</mtr>
<mtr>
<mtd>
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<mtr>
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<mi>&Sigma;</mi>
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<msub>
<mi>f</mi>
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<mi>i</mi>
<mo>.</mo>
<mi>&delta;</mi>
<mo>.</mo>
<mi>t</mi>
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<mtd>
<mo>...</mo>
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<mi>f</mi>
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<mi>i</mi>
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</mrow>
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<mi>&Sigma;</mi>
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</mrow>
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<msub>
<mi>f</mi>
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<mi>i</mi>
<mo>.</mo>
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</msub>
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</mtd>
</mtr>
<mtr>
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<mi>&Sigma;</mi>
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<mo>.</mo>
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</mrow>
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</msub>
</mrow>
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<mtr>
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<mi>&Sigma;</mi>
<mrow>
<mi>&delta;</mi>
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<mn>1</mn>
</mrow>
<mn>1</mn>
</munderover>
<msub>
<mi>f</mi>
<mrow>
<mi>S</mi>
<mo>.</mo>
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<mi>t</mi>
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<mtd>
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<mi>&Sigma;</mi>
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<mi>f</mi>
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</mrow>
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<mi>&Sigma;</mi>
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<msub>
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In formula：Q is accumulated state transfer matrix, Qλt.θtFor month λtMiddle period θtCorresponding accumulated state transfer matrix, fi.δ.t
It is t wind power from state EiIt is transferred to state EδState transition probability, qi.j.tIt is t wind power from state Ei
It is transferred to state EjAccumulative transition probability, δ=1,2 ..., j；I=1,2 ..., S；J=1,2 ..., S；S is wind power shape
State total number, t ∈ { 1,2 ..., N }, N is the data amount check of simulation wind power time series；
3. subsequent time wind power state is determined
Obtain after accumulated state transfer matrix, further determine that present day analog wind power PtSubsequent time simulation wind power
Pt+1State in which Ej, Ej∈{E1, E2..., ES, when 0<ε<qi.1.tWhen, j=1, qi.1.tFor t wind power from shape
State EiIt is transferred to state E1Accumulative transition probability；Work as qi.m.t<ε<qi.m+1.tWhen, j=m+1, qi.m.tFor the wind power of t
From state EiIt is transferred to state EmAccumulative transition probability, qi.m+1.tFor t wind power from state EiIt is transferred to state
Em+1Accumulative transition probability, EmAnd Em+1Respectively m-th of the state and the m+1 state of wind power, m ∈ { 1,2 ..., S-
1 }, S is wind power state total number, and t ∈ { 1,2 ..., N }, N is the data amount check of simulation wind power time series；
4. undulate quantity superposition generation subsequent time wind power value is extracted
Present day analog wind power P is determinedtSubsequent time simulation wind power Pt+1Status EjAfterwards, further determine that down
Wind power P is intended in a period of time die sinkingt+1Numerical value, Pt+1∈(PEj.min, PEj.max), PEj.min、PEj.maxRespectively state EjIt is corresponding
Wind power interval minimum value and maximum, undulate quantity is randomly selected in the fluctuation value set of the parameter of coincidence formula (6)
γt, γ is the set of wind power undulating value, γ ∈ (γmin, γmax), γtThe wind power undulate quantity extracted for t,
γt∈(γmin, γmax), γmin, γmaxRespectively wind power fluctuates the minimum value and maximum of value set；
In present day analog wind power PtOn the basis of, the wind power undulate quantity γ that superposition current time is extractedt, obtain next
Moment simulation wind power Pt+1, i.e. Pt+1=Pt+γt, Pt+1∈(Pt+γmin, Pt+γmax), work as Pt+1In state EjValue model
When enclosing interior, by Pt+1As the value of simulation wind power subsequent time, wind power undulate quantity γ is otherwise extracted againt, and again
Be added to PtOn, then judged, t ∈ { 1,2 ..., N }, N is the data amount check of simulation wind power time series；
5. judge to calculate and whether terminate
Work as t+1<During N, 2. the sub-step for returning to 5) simulation generation wind power time series, continues to calculate subsequent time simulation wind
Electrical power value；As t+1=N, calculating process terminates, the simulation wind power time series of length, wherein t needed for just generating
=1,2 ..., N, N are the data amount check of simulation wind power time series.
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| CN109558905A (en) * | 2018-11-23 | 2019-04-02 | 北京信息科技大学 | A kind of data classification method of the energy consumption perception based on K-means |
| CN110222361A (en) * | 2019-04-23 | 2019-09-10 | 中国电力科学研究院有限公司 | A kind of Power Output for Wind Power Field analogy method and device |
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| CN111611741A (en) * | 2020-06-02 | 2020-09-01 | 国网山东省电力公司电力科学研究院 | Time sequence wind speed simulation method and system based on finite state Markov sequence |
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| CN113449471A (en) * | 2021-06-25 | 2021-09-28 | 东北电力大学 | Wind power output simulation generation method for continuously improving MC (multi-channel) by utilizing AP (access point) clustering-skipping |
| CN113656953A (en) * | 2021-08-09 | 2021-11-16 | 南京理工大学 | Wind power sequence modeling method based on state number optimal decision model |
| CN113656953B (en) * | 2021-08-09 | 2024-04-09 | 南京理工大学 | Wind power sequence modeling method based on state number optimal decision model |
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