CN107437149A - The determination method and system that a kind of photovoltaic plant is contributed - Google Patents
The determination method and system that a kind of photovoltaic plant is contributed Download PDFInfo
- Publication number
- CN107437149A CN107437149A CN201710664905.2A CN201710664905A CN107437149A CN 107437149 A CN107437149 A CN 107437149A CN 201710664905 A CN201710664905 A CN 201710664905A CN 107437149 A CN107437149 A CN 107437149A
- Authority
- CN
- China
- Prior art keywords
- output
- photovoltaic power
- power station
- component
- moment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 241000039077 Copula Species 0.000 claims abstract description 23
- 238000005315 distribution function Methods 0.000 claims description 28
- 230000007704 transition Effects 0.000 claims description 24
- 238000009826 distribution Methods 0.000 claims description 14
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000002194 synthesizing effect Effects 0.000 claims description 7
- 238000009827 uniform distribution Methods 0.000 claims description 7
- 230000002123 temporal effect Effects 0.000 abstract description 4
- 230000015572 biosynthetic process Effects 0.000 abstract 1
- 238000003786 synthesis reaction Methods 0.000 abstract 1
- 241000196324 Embryophyta Species 0.000 description 19
- 230000005855 radiation Effects 0.000 description 11
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 244000089409 Erythrina poeppigiana Species 0.000 description 1
- 235000009776 Rathbunia alamosensis Nutrition 0.000 description 1
- 101100313471 Streptomyces sp getA gene Proteins 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000005307 time correlation function Methods 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Photovoltaic Devices (AREA)
Abstract
The invention discloses the determination method and system that a kind of photovoltaic plant is contributed.This method includes：The temporal correlation model of the randomness component of multiple photovoltaic plants outputs is determined based on Copula functions；According to the temporal correlation model, the randomness component that the single photovoltaic plant is contributed is determined；Obtain the certainty component that the single photovoltaic plant is contributed；The certainty component synthesis that the randomness component that the photovoltaic plant is contributed and the photovoltaic plant are contributed, obtains the photovoltaic plant and contributes.The determination method and system that photovoltaic plant provided by the invention is contributed consider the temporal correlation between multiple photovoltaic plants outputs, can accurately determine the output of each photovoltaic plant.
Description
Technical Field
The invention relates to the field of photovoltaic power generation, in particular to a method and a system for determining output of a photovoltaic power station.
Background
Photovoltaic power generation has received wide attention from people because of its advantages such as wide distribution, convenient to use, no pollution, and is the most rapidly developing renewable energy in recent years. However, the output of the photovoltaic power station is influenced by environmental factors such as irradiation intensity, temperature, weather conditions and the like, and the output of the photovoltaic power station has high randomness and intermittency. When the photovoltaic power stations are operated in a large-scale grid-connected mode, output of different power stations has certain correlation, and the randomness and the correlation of the output of the photovoltaic power stations can greatly affect the safe and reliable operation of a power grid. Therefore, the randomness and the correlation of the photovoltaic power are considered in the modeling process of the photovoltaic power station output sequence, and the method has important significance.
At present, modeling methods for photovoltaic power station output mainly include two types: in the first category, an irradiation intensity model is firstly researched, and then a photovoltaic power station output model is established based on the conversion relation between the irradiation intensity and the photovoltaic power. And in the second category, the output sequence of the photovoltaic power station is directly formed on the basis of the actually measured data of the photovoltaic power station. In the first method, the requirement on the precision of the conversion function of the irradiation intensity and the photovoltaic power is high, and different power stations have different conversion functions due to different structures, so that the method is difficult to apply in practice; the second method has high requirements on measured data, and requires years or even decades of data as a basis. In summary, the above two methods have certain disadvantages, and there are still disadvantages in describing the spatial correlation between the outputs of multiple photovoltaic power stations and the temporal correlation before and after the photovoltaic power of a single power station.
Disclosure of Invention
The invention aims to provide a method and a system for determining the output of a photovoltaic power station, which take the time-space correlation among the outputs of a plurality of photovoltaic power stations into consideration and can accurately determine the output of each photovoltaic power station.
In order to achieve the purpose, the invention provides the following scheme:
a method of determining photovoltaic power plant output, the method comprising:
determining a space-time correlation model of stochastic components of the photovoltaic power station output based on a Copula function;
determining a randomness component of the output of the single photovoltaic power station according to the space-time correlation model;
obtaining a deterministic component of the output of a single photovoltaic power station;
and synthesizing the stochastic component of the output of the photovoltaic power station and the deterministic component of the output of the photovoltaic power station to obtain the output of the photovoltaic power station.
Optionally, the determining a spatio-temporal correlation model of stochastic components of the photovoltaic power plant output based on a Copula function specifically includes:
determining a model of the spatio-temporal correlation of the stochastic components of the two photovoltaic plant outputs by means of a Copula function C (·,. theta.)
Whereinas a function of the transition probability distribution of the photovoltaic plant a,as a function of the transition probability distribution of the photovoltaic plant B,respectively are the random components of the output of the photovoltaic power station A at the t-1 moment and the t moment,respectively are the random components of the output of the photovoltaic power station B at the t-1 moment and the t moment,the correlation of the random components of the output of the photovoltaic power station A at the t-1 moment and the t moment is shown,a probability distribution function representing the stochastic component of the photovoltaic plant a contribution at time t-1,showing the correlation of the random components of the output of the photovoltaic power station B at the t-1 moment and the t moment,probability distribution function representing the stochastic component of the photovoltaic plant B output at time t-1, α, β, [ theta ]A、θBAnd theta is a coefficient.
Optionally, the coefficients α, β, θA、θBThe determination method of theta comprises the following steps:
acquiring historical data of random components of the output force of the photovoltaic power station;
substituting the historical data into the space-time correlation model, and calculating to obtain coefficients α, β and thetaA、θBAnd theta.
Optionally, determining a stochastic component of the photovoltaic power plant output according to the space-time correlation model specifically includes:
respectively acquiring multiple physical quantitiesAnd physical quantitiesWherein,w1t、w2ttwo random variables which are independent of each other and obey uniform distribution on (0,1), t is 1, … n;
according to the formulaCalculating the random component of the output force of the photovoltaic power station A at the moment t, wherein,transition probability distribution function of random component of output of photovoltaic power station A at t momentThe inverse function of (a) is,
according to the formulaCalculating the random component of the output of the photovoltaic power station B at the moment t, wherein,transition probability distribution function of random component of output of photovoltaic power station B at t momentThe inverse function of (a) is,
optionally, the obtaining of the deterministic component of the photovoltaic power plant output specifically includes:
according to the formulaCalculating a deterministic component P of the photovoltaic power plant outputc,tWherein, IstcAs standard irradiation intensity, ItIs the maximum value of the irradiation intensity without any occlusion, PstcIs the output of the photovoltaic power station under the standard condition.
Optionally, the synthesizing the stochastic component of the photovoltaic power station output and the deterministic component of the photovoltaic power station output to obtain the photovoltaic power station output specifically includes:
according to formula Pt＝Pc,t-ηt·Pc,tCalculating the output P of the photovoltaic power stationtWherein P isc,tFor a deterministic component of the photovoltaic plant output, ηtAnd outputting a random component of the photovoltaic power station.
The invention also provides a system for determining the output of the photovoltaic power station, which comprises the following steps:
the space-time correlation model determining unit is used for determining a space-time correlation model of stochastic components of the photovoltaic power station output based on a Copula function;
the randomness component determining unit is used for determining the randomness component of the output of the single photovoltaic power station according to the space-time correlation model;
the deterministic component acquisition unit is used for acquiring a deterministic component of the output of a single photovoltaic power station;
and the photovoltaic power station output determining unit is used for synthesizing the randomness component of the photovoltaic power station output and the certainty component of the photovoltaic power station output to obtain the photovoltaic power station output.
Alternatively to this, the first and second parts may,
the space-time correlation model determining unit specifically includes:
a space-time correlation model determining subunit, configured to determine a space-time correlation model of stochastic components of the two photovoltaic power plant outputs using a Copula function C (·;. theta)
Whereinas a function of the transition probability distribution of the photovoltaic plant a,as a function of the transition probability distribution of the photovoltaic plant B,respectively are the random components of the output of the photovoltaic power station A at the t-1 moment and the t moment,respectively are the random components of the output of the photovoltaic power station B at the t-1 moment and the t moment,the correlation of the random components of the output of the photovoltaic power station A at the t-1 moment and the t moment is shown,a probability distribution function representing the stochastic component of the photovoltaic plant a contribution at time t-1,showing the correlation of the random components of the output of the photovoltaic power station B at the t-1 moment and the t moment,probability distribution function representing the stochastic component of the photovoltaic plant B output at time t-1, α, β, [ theta ]A、θBTheta is a coefficient;
the coefficient determining subunit is used for acquiring historical data of the random component of the output force of the photovoltaic power station, substituting the historical data into the space-time correlation model, and calculating to obtain coefficients α, β and thetaA、θBAnd theta.
Optionally, the randomness component determining unit specifically includes:
a random variable acquiring subunit for acquiring a plurality of physical quantities respectivelyAnd physical quantitiesWherein,w1t、w2ttwo random variables which are independent of each other and obey uniform distribution on (0,1), t is 1, … n;
a first randomness component calculation subunit for calculating a first randomness component according to a formulaCalculating the random component of the output force of the photovoltaic power station A at the moment t, wherein,transition probability distribution function of random component of output of photovoltaic power station A at t momentThe inverse function of (a) is,
a second randomness component calculation subunit for calculating a second randomness component according to the formulaCalculating the random component of the output of the photovoltaic power station B at the moment t, wherein,for photovoltaic power station B at time tOf the random component of the outputThe inverse function of (a) is,
optionally, the deterministic component obtaining unit specifically includes:
a deterministic component acquisition subunit for obtaining a deterministic component based on a formulaCalculating a deterministic component P of the photovoltaic power plant outputc,tWherein, IstcAs standard irradiation intensity, ItIs the maximum value of the irradiation intensity without any occlusion, PstcThe output of the photovoltaic power station is under the standard condition;
the photovoltaic power station output determining unit specifically comprises:
a photovoltaic power station output determination subunit for determining the output of the photovoltaic power station according to the formula Pt＝Pc,t-ηt·Pc,tCalculating the output P of the photovoltaic power stationtWherein, ηtAnd outputting a random component of the photovoltaic power station.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the method and the system for determining the output of the photovoltaic power station provided by the invention utilize Copula function to establish a space-time correlation model of random components of the output of a plurality of photovoltaic power stations, the model not only contains the spatial correlation among the output random components of the photovoltaic power station, but also contains the front-back time correlation of the output random components of a single photovoltaic power station, according to the space-time correlation model provided by the invention, the randomness component of the output of a single photovoltaic power station can be obtained, because the random component of the photovoltaic power station output is obtained by the space-time correlation model, the random component of the photovoltaic power station output takes the correlation between the photovoltaic power stations and the randomness of the photovoltaic power station output into consideration, and further, the output of the photovoltaic power station synthesized according to the random component and the deterministic component of the output of the photovoltaic power station is more accurate and conforms to the actual situation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a method for determining photovoltaic power plant output according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a photovoltaic power station output determination system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for determining the output of a photovoltaic power station, which take the time-space correlation among the outputs of a plurality of photovoltaic power stations into consideration and can accurately determine the output of each photovoltaic power station.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flowchart of a method for determining photovoltaic power plant output according to an embodiment of the present invention, and as shown in fig. 1, the method for determining photovoltaic power plant output provided by the present invention specifically includes the following steps:
step 101: determining a space-time correlation model of stochastic components of the photovoltaic power station output based on a Copula function;
step 102: determining a randomness component of the output of the single photovoltaic power station according to the space-time correlation model;
step 103: obtaining a deterministic component of the output of a single photovoltaic power station;
step 104: and synthesizing the stochastic component of the output of the photovoltaic power station and the deterministic component of the output of the photovoltaic power station to obtain the output of the photovoltaic power station.
Wherein, step 101 specifically includes: determining a model of the spatio-temporal correlation of the stochastic components of the two photovoltaic plant outputs by means of a Copula function C (·,. theta.)
Whereinas a function of the transition probability distribution of the photovoltaic plant a,as a function of the transition probability distribution of the photovoltaic plant B,respectively are the random components of the output of the photovoltaic power station A at the t-1 moment and the t moment,respectively are the random components of the output of the photovoltaic power station B at the t-1 moment and the t moment,the correlation of the random components of the output of the photovoltaic power station A at the t-1 moment and the t moment is shown,a probability distribution function representing the stochastic component of the photovoltaic plant a contribution at time t-1,showing the correlation of the random components of the output of the photovoltaic power station B at the t-1 moment and the t moment,probability distribution function representing the stochastic component of the photovoltaic plant B output at time t-1, α, β, [ theta ]A、θBTheta is a coefficient α, β, thetaA、θBThe determining method of theta comprises the steps of obtaining historical data of random components of output force of the photovoltaic power station, substituting the historical data into the space-time correlation model, and calculating to obtain coefficients α, β and thetaA、θBAnd theta.
Step 102 specifically includes:
respectively acquiring multiple physical quantitiesAnd physical quantitiesWherein,w1t、w2tis two mutually independentRandom variables that are upright and obey a uniform distribution over (0,1), t 1, … n;
according to the formulaCalculating the random component of the output force of the photovoltaic power station A at the moment t, wherein,transition probability distribution function of random component of output of photovoltaic power station A at t momentThe inverse function of (a) is,
according to the formulaCalculating the random component of the output of the photovoltaic power station B at the moment t, wherein,transfer probability distribution function of stochastic component of output of photovoltaic power station B at time tThe inverse function of (a) is,
step 103 specifically comprises:
according to the formulaCalculating a deterministic component P of the photovoltaic power plant outputc,tWherein, IstcAs standard irradiation intensity, ItIs the maximum value of the irradiation intensity without any occlusion, PstcIs the output of the photovoltaic power station under the standard condition.
Step 104 specifically includes:
according to formula Pt＝Pc,t-ηt·Pc,tCalculating the output P of the photovoltaic power stationtWherein P isc,tFor a deterministic component of the photovoltaic plant output, ηtAnd outputting a random component of the photovoltaic power station.
The method for determining the output of the photovoltaic power station comprises the steps that a Copula function is utilized to establish a space-time correlation model of random components of the output of a plurality of photovoltaic power stations, the model comprises space correlation among the random components of the output of the photovoltaic power stations and front-back time correlation of the random components of the output of a single photovoltaic power station, the random components of the output of the single photovoltaic power station can be obtained according to the space-time correlation model, the random components of the output of the photovoltaic power station are obtained through the space-time correlation model, the correlation among the photovoltaic power stations and the randomness of the output of the photovoltaic power stations are considered in the random components of the output of the photovoltaic power station, and the output of the photovoltaic power station synthesized according to the random components of the output of the photovoltaic power station and the deterministic components is more accurate and more consistent with the actual situation.
As another embodiment of the invention, two power stations of a certain photovoltaic base in China are taken as an example for analysis, the installed capacities of the power station A, B are respectively 30MW and 20MW, the adopted data are measured data from 5 months in 2013 to 4 months in 2014, and the sampling interval is 15 min. The inclination angle of the solar panel of the power station A, B was 39 degrees, and the latitudes at which the power station was located were 36.18 degrees and 35.21 degrees, respectively. The method for determining the output of the photovoltaic power station comprises the following specific steps:
firstly, modeling is carried out on the time-space correlation of the output randomness components of the photovoltaic power stations based on a Copula function. The specific steps are as follows:
stochastic component time correlation modeling:
H(ηt-1,ηt)＝C(F(ηt-1；α),F(ηt；α)；θ)
wherein, F (η)tα) and F (η)t-1α) a unitary probability distribution function representing the stochastic components of photovoltaic power at two preceding and succeeding time points C (·;. theta) is related to F (η)tα) and F (η)t-1α) continuous Copula function H (η)t-1,ηt) Denotes F (η)tα) and F (η)t-1α).
Stochastic component spatial correlation modeling:
the probability distribution function and the density function of the random component time sequence transition with the joint probability distribution of H are respectively as follows:
f(ηt|ηt-Δt；α,θ)＝c(F(ηt-1；α),F(ηt；α)；θ)·f(ηt；α)
suppose that the randomness components of two photovoltaic power stations A, B in a certain area are η respectivelyAAnd ηBThen the corresponding transition probability distribution function is respectively
Using Copula function C*(·,·；θ*) To describe the variableAndthe joint probability distribution function of (a):
wherein α, θA,θB,θ*Are all undetermined parameters.The method not only comprises the spatial correlation between the random components of the outputs of the power stations A and B, but also comprises the time correlation before and after the random component of the output of a single power station.
In the space-time correlation model, the method for determining the undetermined coefficient comprises the following steps:
acquiring the output of the photovoltaic power station at the t moment:
in the formula, PstcIs under standard conditions (irradiation intensity I)stc＝1000W/m2The ambient temperature Tstc is 25 deg.C, the output power of the power station, αTRepresenting the temperature coefficient of the panel; i isr,tThe measured value of the irradiation intensity at the time t is represented; t istRepresenting the ambient temperature at time t. The output of the photovoltaic power station is mainly determined by the irradiation intensity and the ambient temperature, and the irradiation intensity is also related to factors such as cloud cover shielding and weather conditions.
The acquisition of the deterministic component of photovoltaic power is defined as follows:
in the formula ItRepresents the maximum value of the irradiation intensity without any occlusion; pc,tThe method is characterized by comprising the following steps of representing a deterministic component of the output of a photovoltaic power station, wherein the value is only related to the geographical position, the altitude and the time of the power station without considering a series of factors such as cloud cover shielding, weather conditions and the like.
Wherein, ItThe calculation method is specifically as follows:
according to the formulaCalculating the radiation intensity in the earth's atmosphere, wherein I0Representing the amount of solar radiation in the earth's atmosphere; s0Representing the constant of radiation intensity, i.e. the amount of radiation entering the atmosphere per unit area, S0≈1367W/m2(ii) a N represents the number of the day over the year.
According to formula Ib＝I0τbsina calculates the amount of direct solar radiation, wherein,sina＝sinφsin+cosφcoscosw，Ibrepresenting the direct solar radiation intensity; tau isbTransparency representing the intensity of direct solar radiation; mhRepresenting the mass of the atmosphere, in relation to the altitude at which the power station is located; a represents the solar altitude at the site of the power station; phi represents the latitude of the place; representing the declination angle of the sun; w represents the time angle of the sun, relative to the time of day,
according to the formulaCalculating the intensity of the solar scattered radiation, whereind＝0.271-0.274τb，IdRepresenting the scattered radiation intensity; tau isdRepresenting the sunTransparency of scattered radiation intensity; the value of k is related to the atmospheric mass, and the specific range is as follows:
according to formula I, under the condition of not considering a series of factors such as cloud layer shielding and weather conditionst＝Ib+IdAnd calculating the total solar radiation intensity.
Calculating a photovoltaic power randomness component according to the actual output and the deterministic output of the photovoltaic power station:
in the formula, ηtThe difference value of the actual output and the deterministic output of the photovoltaic power station is shown, and the value is called a randomness component because the influence of randomness factors such as cloud cover, weather conditions, temperature changes and the like is considered.
According to the formula and the historical data, historical data of the randomness components of the photovoltaic power station output can be calculated, the historical data of the randomness components of the photovoltaic power station output are brought into an expression of a space-time correlation model of the randomness components of the photovoltaic power station output, and undetermined coefficients in the space-time correlation model can be solved. And furthermore, a space-time correlation model of the output randomness components of the photovoltaic power station is determined.
After a space-time correlation model of the output randomness component of the photovoltaic power station is obtained, the output randomness component of the photovoltaic power station is determined according to the model:
the analog sampling generates two random variables w that are independent of each other and obey a uniform distribution over (0,1)1,w2；
Photovoltaic power station output randomness component space-time correlation model based on establishmentC*(·,·；θ*) Let us order
vA＝w1
In the formula,
repeating the above two steps n times to obtain vectorsN observations of (a), which satisfy Copula function C*(·,·；θ*)；
To the sequenceThe following steps are carried out:
getA first value as a sequence; for a given time correlation modelAnd (3) recursive calculation:
in the formula,
and then sequentially calculating:
sequences can be obtainedIs F (.;) α and satisfies a model C (;) of the time dependence functionA)。
For sequenceAnd obtainingThe same procedure is carried out by only adding C (·,. theta.),. thetaA) Change to C (·,; thetaB) Can obtain a sequenceIts distribution function is G (-) β and satisfies the time correlation function model C (-) thetaB)。
In thatAccording to the formulaAnd calculating the deterministic component of the output of the photovoltaic power station, and synthesizing the deterministic component and the stochastic component through the following formula to obtain the output sequences of the photovoltaic power stations A and B.
The method for determining the output of the photovoltaic power station comprises the steps that a Copula function is utilized to establish a space-time correlation model of random components of the output of a plurality of photovoltaic power stations, the model comprises space correlation among the random components of the output of the photovoltaic power stations and front-back time correlation of the random components of the output of a single photovoltaic power station, the random components of the output of the single photovoltaic power station can be obtained according to the space-time correlation model, the random components of the output of the photovoltaic power station are obtained through the space-time correlation model, the correlation among the photovoltaic power stations and the randomness of the output of the photovoltaic power stations are considered in the random components of the output of the photovoltaic power station, and the output of the photovoltaic power station synthesized according to the random components of the output of the photovoltaic power station and the deterministic components is more accurate and more consistent with the actual situation.
Fig. 2 is a schematic structural diagram of a system for determining photovoltaic power plant output according to an embodiment of the present invention, and as shown in fig. 2, the system for determining photovoltaic power plant output according to the present invention includes:
a space-time correlation model determining unit 201, configured to determine a space-time correlation model of stochastic components of the photovoltaic power plant output based on Copula function;
a stochastic component determining unit 202, configured to determine a stochastic component of the output of a single photovoltaic power station according to the space-time correlation model;
a deterministic component obtaining unit 203, configured to obtain a deterministic component of the output of a single photovoltaic power station;
the photovoltaic power station output determining unit 204 is configured to synthesize the stochastic component of the photovoltaic power station output and the deterministic component of the photovoltaic power station output to obtain the photovoltaic power station output.
The spatio-temporal correlation model determining unit 201 specifically includes:
a space-time correlation model determining subunit, configured to determine a space-time correlation model of stochastic components of the two photovoltaic power plant outputs using a Copula function C (·;. theta)
Whereinas a function of the transition probability distribution of the photovoltaic plant a,as a function of the transition probability distribution of the photovoltaic plant B,respectively are the random components of the output of the photovoltaic power station A at the t-1 moment and the t moment,respectively are the random components of the output of the photovoltaic power station B at the t-1 moment and the t moment,the correlation of the random components of the output of the photovoltaic power station A at the t-1 moment and the t moment is shown,a probability distribution function representing the stochastic component of the photovoltaic plant a contribution at time t-1,showing the correlation of the random components of the output of the photovoltaic power station B at the t-1 moment and the t moment,representing the randomness of the output of the photovoltaic power station B at the moment t-1Probability distribution function of components, α, β, θA、θBTheta is a coefficient;
the coefficient determining subunit is used for acquiring historical data of the random component of the output force of the photovoltaic power station, substituting the historical data into the space-time correlation model, and calculating to obtain coefficients α, β and thetaA、θBAnd theta.
The randomness component determining unit 202 specifically includes:
a random variable acquiring subunit for acquiring a plurality of physical quantities respectivelyAnd physical quantitiesWherein,w1t、w2ttwo random variables which are independent of each other and obey uniform distribution on (0,1), t is 1, … n;
a first randomness component calculation subunit for calculating a first randomness component according to a formulaCalculating the random component of the output force of the photovoltaic power station A at the moment t, wherein,transition probability distribution function of random component of output of photovoltaic power station A at t momentThe inverse function of (a) is,
a second randomness component calculation subunit for calculating a second randomness component according to the formulaCalculating the random component of the output of the photovoltaic power station B at the moment t, wherein,transition probability distribution function of random component of output of photovoltaic power station B at t momentThe inverse function of (a) is,
the deterministic component obtaining unit 203 specifically includes:
a deterministic component acquisition subunit for obtaining a deterministic component based on a formulaCalculating a deterministic component P of the photovoltaic power plant outputc,tWherein, IstcAs standard irradiation intensity, ItIs the maximum value of the irradiation intensity without any occlusion, PstcThe output of the photovoltaic power station is under the standard condition;
the photovoltaic power station output determining unit 204 specifically includes:
a photovoltaic power station output determination subunit for determining the output of the photovoltaic power station according to the formula Pt＝Pc,t-ηt·Pc,tCalculating the output P of the photovoltaic power stationt。
The photovoltaic power station output determining system provided by the invention utilizes a Copula function to establish a time-space correlation model of random components of the output of a plurality of photovoltaic power stations, the model comprises space correlation between the random components of the output of the photovoltaic power stations and front-back time correlation of the random component of the output of a single photovoltaic power station.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A method of determining photovoltaic power plant output, the method comprising:
determining a space-time correlation model of stochastic components of the photovoltaic power station output based on a Copula function;
determining a randomness component of the output of the single photovoltaic power station according to the space-time correlation model;
obtaining a deterministic component of the output of a single photovoltaic power station;
and synthesizing the stochastic component of the output of the photovoltaic power station and the deterministic component of the output of the photovoltaic power station to obtain the output of the photovoltaic power station.
2. The method of claim 1, wherein the determining the spatio-temporal correlation model of the stochastic components of the photovoltaic plant contribution based on the Copula function comprises:
determining a model of the spatio-temporal correlation of the stochastic components of the two photovoltaic plant outputs by means of a Copula function C (·,. theta.)
Wherein,as a function of the transition probability distribution of the photovoltaic plant a,as a function of the transition probability distribution of the photovoltaic plant B,respectively are the random components of the output of the photovoltaic power station A at the t-1 moment and the t moment,respectively are the random components of the output of the photovoltaic power station B at the t-1 moment and the t moment,the correlation of the random components of the output of the photovoltaic power station A at the t-1 moment and the t moment is shown,a probability distribution function representing the stochastic component of the photovoltaic plant a contribution at time t-1,showing the correlation of the random components of the output of the photovoltaic power station B at the t-1 moment and the t moment,probability distribution function representing the stochastic component of the photovoltaic plant B output at time t-1, α, β, [ theta ]A、θBAnd theta is a coefficient.
3. The method of determining photovoltaic power plant output of claim 2 wherein said coefficients α, β, θA、θBThe determination method of theta comprises the following steps:
acquiring historical data of random components of the output force of the photovoltaic power station;
substituting the historical data into the space-time correlation model, and calculating to obtain coefficients α, β and thetaA、θBAnd theta.
4. The method of claim 1, wherein determining the stochastic component of the photovoltaic power plant output based on the spatio-temporal correlation model comprises:
respectively acquiring multiple physical quantitiesAnd physical quantitiesWherein,w1t、w2ttwo random variables which are independent of each other and obey uniform distribution on (0,1), t is 1, … n;
according to the formulaCalculating the random component of the output force of the photovoltaic power station A at the moment t, wherein,transition probability distribution function of random component of output of photovoltaic power station A at t momentThe inverse function of (a) is,
according to the formulaCalculating the random component of the output of the photovoltaic power station B at the moment t, wherein,transition probability distribution function of random component of output of photovoltaic power station B at t momentThe inverse function of (a) is,
5. the method of claim 1, wherein the obtaining the deterministic component of the photovoltaic power plant output comprises:
according to the formulaCalculating a deterministic component P of the photovoltaic power plant outputc,tWherein, IstcAs standard irradiation intensity, ItIs the maximum value of the irradiation intensity without any occlusion, PstcIs the output of the photovoltaic power station under the standard condition.
6. The method of claim 1, wherein the step of combining the stochastic component of the photovoltaic plant output with the deterministic component of the photovoltaic plant output to obtain the photovoltaic plant output comprises:
according to formula Pt＝Pc,t-ηt·Pc,tCalculating the output P of the photovoltaic power stationtWherein P isc,tFor a deterministic component of the photovoltaic plant output, ηtAnd outputting a random component of the photovoltaic power station.
7. A system for determining photovoltaic power plant contribution, the system comprising:
the space-time correlation model determining unit is used for determining a space-time correlation model of stochastic components of the photovoltaic power station output based on a Copula function;
the randomness component determining unit is used for determining the randomness component of the output of the single photovoltaic power station according to the space-time correlation model;
the deterministic component acquisition unit is used for acquiring a deterministic component of the output of a single photovoltaic power station;
and the photovoltaic power station output determining unit is used for synthesizing the randomness component of the photovoltaic power station output and the certainty component of the photovoltaic power station output to obtain the photovoltaic power station output.
8. The photovoltaic power plant contribution determination system of claim 7, wherein,
the space-time correlation model determining unit specifically includes:
a space-time correlation model determining subunit, configured to determine a space-time correlation model of stochastic components of the two photovoltaic power plant outputs using a Copula function C (·;. theta)
Wherein,as a function of the transition probability distribution of the photovoltaic plant a,as a function of the transition probability distribution of the photovoltaic plant B,respectively are the random components of the output of the photovoltaic power station A at the t-1 moment and the t moment,respectively are the random components of the output of the photovoltaic power station B at the t-1 moment and the t moment,the correlation of the random components of the output of the photovoltaic power station A at the t-1 moment and the t moment is shown,a probability distribution function representing the stochastic component of the photovoltaic plant a contribution at time t-1,showing the correlation of the random components of the output of the photovoltaic power station B at the t-1 moment and the t moment,probability distribution function representing the stochastic component of the photovoltaic plant B output at time t-1, α, β, [ theta ]A、θBTheta is a coefficient;
the coefficient determining subunit is used for acquiring historical data of the random component of the output force of the photovoltaic power station, substituting the historical data into the space-time correlation model, and calculating to obtain coefficients α, β and thetaA、θBAnd theta.
9. The system for determining photovoltaic power plant output according to claim 7, wherein the stochastic component determination unit comprises:
a random variable acquiring subunit for acquiring a plurality of physical quantities respectivelyAnd physical quantitiesWherein,w1t、w2ttwo random variables which are independent of each other and obey uniform distribution on (0,1), t is 1, … n;
a first randomness component calculation subunit for calculating a first randomness component according to a formulaCalculating the random component of the output force of the photovoltaic power station A at the moment t, wherein,transition probability distribution function of random component of output of photovoltaic power station A at t momentThe inverse function of (a) is,
a second randomness component calculation subunit for calculating a second randomness component according to the formulaCalculating the random component of the output of the photovoltaic power station B at the moment t, wherein,transition probability distribution function of random component of output of photovoltaic power station B at t momentThe inverse function of (a) is,
10. the system for determining photovoltaic power plant output according to claim 7, wherein said deterministic component obtaining unit comprises:
a deterministic component acquisition subunit for obtaining a deterministic component based on a formulaCalculating a deterministic component P of the photovoltaic power plant outputc,tWherein, IstcAs standard irradiation intensity, ItIs the maximum value of the irradiation intensity without any occlusion, PstcThe output of the photovoltaic power station is under the standard condition;
the photovoltaic power station output determining unit specifically comprises:
a photovoltaic power station output determination subunit for determining the output of the photovoltaic power station according to the formula Pt＝Pc,t-ηt·Pc,tCalculating the output P of the photovoltaic power stationtWherein, ηtAnd outputting a random component of the photovoltaic power station.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710664905.2A CN107437149B (en) | 2017-08-07 | 2017-08-07 | Method and system for determining output of photovoltaic power station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710664905.2A CN107437149B (en) | 2017-08-07 | 2017-08-07 | Method and system for determining output of photovoltaic power station |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107437149A true CN107437149A (en) | 2017-12-05 |
| CN107437149B CN107437149B (en) | 2020-11-24 |
Family
ID=60460995
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710664905.2A Active CN107437149B (en) | 2017-08-07 | 2017-08-07 | Method and system for determining output of photovoltaic power station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107437149B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107918920A (en) * | 2017-12-13 | 2018-04-17 | 上海交通大学 | The output correlation analysis method of more photovoltaic plants |
| CN109919353A (en) * | 2019-01-11 | 2019-06-21 | 杭州电子科技大学 | A kind of distributed photovoltaic prediction technique of the ARIMA model based on spatial coherence |
| CN110084430A (en) * | 2019-04-29 | 2019-08-02 | 国网上海市电力公司 | A method of considering space-time characterisation design distributed photovoltaic power output prediction model |
| CN110188964A (en) * | 2019-06-06 | 2019-08-30 | 河北工业大学 | A kind of photovoltaic power generation output forecasting method based on correlation |
| CN113487100A (en) * | 2021-07-19 | 2021-10-08 | 国网浙江省电力有限公司电力科学研究院 | Global accurate prediction method and system for photovoltaic power generation output |
| CN114648251A (en) * | 2022-04-19 | 2022-06-21 | 湖南大学 | Method and system for building space-time correlation model of distributed photovoltaic power station |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140214722A1 (en) * | 2013-01-25 | 2014-07-31 | The Research Foundation Of The State University Of New York | Real time evaluation of financial returns based on nearly elliptical models |
| CN104091077A (en) * | 2014-07-11 | 2014-10-08 | 东南大学 | Combined force output simulation method of wind power plant and photovoltaic power station |
| CN104933315A (en) * | 2015-06-30 | 2015-09-23 | 中国电力科学研究院 | Photovoltaic power generation output joint probability distributed modeling method |
| CN106529700A (en) * | 2016-10-20 | 2017-03-22 | 华北电力大学（保定） | Space-time correlation based construction method and system of wind power plant prediction model |
| CN106548256A (en) * | 2016-12-05 | 2017-03-29 | 西南石油大学 | A kind of method and system of wind energy turbine set space-time dynamic correlation modeling |
-
2017
- 2017-08-07 CN CN201710664905.2A patent/CN107437149B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140214722A1 (en) * | 2013-01-25 | 2014-07-31 | The Research Foundation Of The State University Of New York | Real time evaluation of financial returns based on nearly elliptical models |
| CN104091077A (en) * | 2014-07-11 | 2014-10-08 | 东南大学 | Combined force output simulation method of wind power plant and photovoltaic power station |
| CN104933315A (en) * | 2015-06-30 | 2015-09-23 | 中国电力科学研究院 | Photovoltaic power generation output joint probability distributed modeling method |
| CN106529700A (en) * | 2016-10-20 | 2017-03-22 | 华北电力大学（保定） | Space-time correlation based construction method and system of wind power plant prediction model |
| CN106548256A (en) * | 2016-12-05 | 2017-03-29 | 西南石油大学 | A kind of method and system of wind energy turbine set space-time dynamic correlation modeling |
Non-Patent Citations (2)
| Title |
|---|
| FARANAK GOLESTANEH等: "Generation and Evaluation of Space-Time Trajectories of Photovoltaic Power", 《APPLIED ENERGY》 * |
| 夏泠风等: "考虑光伏电站时空相关性的光伏出力序列生成方法", 《中国电机工程学报》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107918920A (en) * | 2017-12-13 | 2018-04-17 | 上海交通大学 | The output correlation analysis method of more photovoltaic plants |
| CN109919353A (en) * | 2019-01-11 | 2019-06-21 | 杭州电子科技大学 | A kind of distributed photovoltaic prediction technique of the ARIMA model based on spatial coherence |
| CN109919353B (en) * | 2019-01-11 | 2022-07-05 | 杭州电子科技大学 | Distributed photovoltaic prediction method of ARIMA model based on spatial correlation |
| CN110084430A (en) * | 2019-04-29 | 2019-08-02 | 国网上海市电力公司 | A method of considering space-time characterisation design distributed photovoltaic power output prediction model |
| CN110188964A (en) * | 2019-06-06 | 2019-08-30 | 河北工业大学 | A kind of photovoltaic power generation output forecasting method based on correlation |
| CN110188964B (en) * | 2019-06-06 | 2021-03-16 | 河北工业大学 | Photovoltaic output prediction method based on correlation |
| CN113487100A (en) * | 2021-07-19 | 2021-10-08 | 国网浙江省电力有限公司电力科学研究院 | Global accurate prediction method and system for photovoltaic power generation output |
| CN113487100B (en) * | 2021-07-19 | 2023-08-15 | 国网浙江省电力有限公司电力科学研究院 | Global accurate prediction method and system for photovoltaic power generation output |
| CN114648251A (en) * | 2022-04-19 | 2022-06-21 | 湖南大学 | Method and system for building space-time correlation model of distributed photovoltaic power station |
| CN114648251B (en) * | 2022-04-19 | 2024-08-06 | 湖南大学 | Space-time correlation model construction method and system for distributed photovoltaic power station |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107437149B (en) | 2020-11-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107437149B (en) | Method and system for determining output of photovoltaic power station | |
| Agoua et al. | Short-term spatio-temporal forecasting of photovoltaic power production | |
| Pfenninger et al. | Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data | |
| Lermusiaux | Data Assimilation via Error Subspace Statistical Estimation.: Part II: Middle Atlantic Bight Shelfbreak Front Simulations and ESSE Validation | |
| Huld et al. | A new solar radiation database for estimating PV performance in Europe and Africa | |
| Dahmani et al. | Estimation of 5-min time-step data of tilted solar global irradiation using ANN (Artificial Neural Network) model | |
| Reikard et al. | Forecasting ground-level irradiance over short horizons: Time series, meteorological, and time-varying parameter models | |
| Nuño et al. | On the simulation of aggregated solar PV forecast errors | |
| Almonacid et al. | Estimating the maximum power of a high concentrator photovoltaic (HCPV) module using an artificial neural network | |
| CN105512760B (en) | The calculation method and system of built photovoltaic power station power generation amount neural network based | |
| Anderson et al. | Intercomparison of physical models and observations of the ionosphere | |
| CN105005937B (en) | A kind of photovoltaic plant power output timing simulation method based on clearness index | |
| CN112200377A (en) | Photovoltaic medium-long term power generation capacity forecasting method and device based on SARIMAX model | |
| Piliougine et al. | Modelling photovoltaic modules with neural networks using angle of incidence and clearness index | |
| Santos-Martin et al. | SoL–A PV generation model for grid integration analysis in distribution networks | |
| Dokur | Swarm decomposition technique based hybrid model for very short-term solar PV power generation forecast | |
| Mao et al. | Effects of atmospheric aerosol on the direct normal irradiance on the earth's surface | |
| Blanc et al. | On the effective solar zenith and azimuth angles to use with measurements of hourly irradiation | |
| Yue et al. | Development of a middle and low latitude theoretical ionospheric model and an observation system data assimilation experiment | |
| Seo et al. | Assimilation of sea surface temperature in the Northwest Pacific Ocean and its marginal seas using the ensemble Kalman filter | |
| Carreno et al. | SoDa: An irradiance-based synthetic solar data generation tool | |
| CN106649943B (en) | A kind of evaluation method of building integrated photovoltaic system inclined-plane total radiation | |
| Mannucci et al. | Scientific challenges in thermosphere-ionosphere forecasting–conclusions from the October 2014 NASA JPL community workshop | |
| Graabak et al. | Developing a wind and solar power data model for Europe with high spatial-temporal resolution | |
| Huang et al. | Nonparametric temporal downscaling of GHI clear-sky indices using Gaussian copula |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |