CN114336637B - Power distribution network evidence theory power flow calculation method considering wind and light output correlation - Google Patents

Power distribution network evidence theory power flow calculation method considering wind and light output correlation Download PDF

Info

Publication number
CN114336637B
CN114336637B CN202210023423.XA CN202210023423A CN114336637B CN 114336637 B CN114336637 B CN 114336637B CN 202210023423 A CN202210023423 A CN 202210023423A CN 114336637 B CN114336637 B CN 114336637B
Authority
CN
China
Prior art keywords
output
wind
power
result
focal element
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.)
Active
Application number
CN202210023423.XA
Other languages
Chinese (zh)
Other versions
CN114336637A (en
Inventor
吴红斌
仇茹嘉
王旻洋
徐斌
王小明
盛金马
胡斌
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd
Hefei University of Technology
Economic and Technological Research Institute of State Grid Anhui Electric Power Co Ltd
Original Assignee
Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd
Hefei University of Technology
Economic and Technological Research Institute of State Grid Anhui Electric Power Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd, Hefei University of Technology, Economic and Technological Research Institute of State Grid Anhui Electric Power Co Ltd filed Critical Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd
Priority to CN202210023423.XA priority Critical patent/CN114336637B/en
Publication of CN114336637A publication Critical patent/CN114336637A/en
Application granted granted Critical
Publication of CN114336637B publication Critical patent/CN114336637B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

Landscapes

  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a power distribution network evidence theory power flow calculation method considering wind-solar output correlation, which comprises the following steps of: 1. establishing an evidence structure model of distributed power supply output and giving weight to an evidence focal element by using a fuzzy judgment matrix method; 2. based on the correlation coefficient among the distributed power supplies, calculating the focal element correction coefficients of all the joint focal elements by using an improved ellipsoid theory method; 3. and modifying the basic probability distribution value of the joint focal element, and using a probability box for obtaining a load flow result after complex affine load flow calculation. The invention describes the correlation among the uncertain quantities in the evidence theory by using an improved ellipsoid theory, and establishes the uncertain power flow calculation method of the power distribution network by comprehensively considering the subjective and objective uncertainties of the uncertain quantities, so that the uncertainty and the correlation of the distributed power output in the power grid planning can be described, and the state of the power grid can be reflected more comprehensively, accurately and quickly.

Description

Power distribution network evidence theory power flow calculation method considering wind and light output correlation
Technical Field
The invention relates to uncertainty load flow calculation of a power distribution network of a power system, in particular to a power distribution network evidence theory load flow calculation method considering wind and light output correlation.
Background
The load flow calculation is one of the most widely, fundamentally and importantly applied electrical operations in an electric power system, and is the basis of power grid planning and scheduling. However, as distributed power sources generated by renewable energy sources are largely incorporated into power distribution networks, uncertainty in their output power can present new challenges to power flow calculations. Especially, photovoltaic power generation and wind power generation are greatly influenced by external environment, and output power is intermittent and uncontrollable essentially. Researchers often use the probability density distribution of the distributed power source for analysis, but in practice, the output power of the distributed power source cannot be strictly fitted into a function, and the analysis result may deviate from reality according to the classical probability theory.
In contrast, some scholars consider the defects of the classical probability analysis method and analyze the relevant problems of the uncertainty factors of the power system by adopting theoretical methods such as a blind number theory, an evidence theory and a probability box. However, the application of the theory embodying the subjectivity of the model in the power system is in a starting stage at home and abroad, and the existing theory has some defects in the application level of the power system: on the one hand, these theories still have some disadvantages in terms of application: partial methods only analyze the expected value and variance of the result from the perspective of classical probability theory, and do not well embody the advantages of the methods compared with the traditional methods; the construction of a power output model of the distributed power supply lacks a theoretical basis, and an analysis result is not strict enough and lacks practicability. On the other hand, the theoretical methods comprehensively considering subjective and objective uncertainties are usually based on interval models, and the correlation between uncertain quantities is difficult to consider in the traditional methods. Certain correlation exists between photovoltaic power generation and wind power generation, and if influence of the correlation on load flow calculation is neglected, result errors can be brought.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides the power distribution network evidence theory trend calculation method considering the wind-solar output correlation, so that the subjective uncertainty and the objective uncertainty of the distributed power supply can be effectively analyzed, and the correlation of the output power can be reflected, and the state of the power grid can be reflected more comprehensively, accurately and quickly.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention relates to a power distribution network evidence theory power flow calculation method considering wind-solar output correlation, wherein a wind power generator and a photovoltaic power station are connected to a power distribution network; the method is characterized by comprising the following steps:
step one, establishing an evidence structure body output model of the distributed power supply:
step 1.1, dividing N output intervals according to the output range of the wind driven generator, and recording the N output intervals as
Figure BDA0003463528330000011
Figure BDA0003463528330000012
The ith small interval representing the output force of the fan, i belongs to [1,N ]](ii) a And is
Figure BDA0003463528330000013
Wherein it is present>
Figure BDA0003463528330000014
The maximum output of the wind driven generator;
step 1.2, counting the output of the wind driven generator to obtain the ith cell
Figure BDA0003463528330000021
For the jth cell
Figure BDA0003463528330000022
Degree of likelihood->
Figure BDA0003463528330000023
And the jth small interval->
Figure BDA0003463528330000024
For the ith small interval>
Figure BDA0003463528330000025
Degree of likelihood->
Figure BDA0003463528330000026
Step 1.3, degree of probability
Figure BDA0003463528330000027
And &>
Figure BDA0003463528330000028
Comparing, and processing the comparison result according to the scale method of 0.1-0.9 to obtain a fuzzy judgment value a ij ，a ij ∈[0,1](ii) a Thereby constructing a fuzzy judgment matrix A = [ a ] ij ] N×N Wherein a is ij Elements representing the ith row and jth column;
step 1.4, constructing an element R of the ith row and the jth column in a fuzzy consistent judgment matrix R by using the formula (1) ij ：
Figure BDA0003463528330000029
Step 1.5, calculating the reliability of each output interval of the distributed power supply by using the formula (2):
Figure BDA00034635283300000210
in formula (2): omega i For the ith cell of fan output
Figure BDA00034635283300000211
The reliability of (2); r ik Representing the elements of the ith row and the kth column in the fuzzy consistent judgment matrix R; thereby obtaining the output P of the fan W Evidence structural body of (a)>
Figure BDA00034635283300000212
Step 1.6, dividing M output intervals according to the output range of the photovoltaic generator in the same way, and obtaining the photovoltaic output P according to the steps from step 1.1 to step 1.5 S Of the witness structure
Figure BDA00034635283300000213
Wherein it is present>
Figure BDA00034635283300000214
Indicating the ith cell, xi, of the photovoltaic output l Is the credibility of the first cell, is epsilon [1,M]；
Step two, solving a focal element correction coefficient based on the wind-solar output correlation:
step 2.1, obtaining the wind-light output combined focal element
Figure BDA00034635283300000215
Wherein +>
Figure BDA00034635283300000216
An nth combined focal unit acting as wind and light output and->
Figure BDA00034635283300000217
m n The total number N of the combined focal elements is assigned to the basic probability distribution of the nth combined focal element for wind and light output m ＝MN；
Step 2.2, initializing i =1,l =1, and obtaining a mean value E (P) of fan output by using statistical data based on sample data of wind and light output W ) Sum variance D (P) W ) Average value of photovoltaic output E (P) S ) Sum variance D (P) S ) And calculating a correlation coefficient rho and covariance Cov (P) of wind and light output S ,P W ) (ii) a The covariance matrix C is thus constructed using equation (3):
Figure BDA00034635283300000218
step 2.3, let n = (i-1) M + l, set
Figure BDA00034635283300000219
And &>
Figure BDA00034635283300000220
For the ith cell of the fan output->
Figure BDA00034635283300000221
Left and right boundaries of the interval (4), and>
Figure BDA00034635283300000222
and &>
Figure BDA00034635283300000223
For the first small interval of photovoltaic output->
Figure BDA00034635283300000224
Let X be a coordinate value; setting the focal element correction coefficient of the nth combined focal element to be omega n Let the basic probability distribution m of the nth joint focal element of wind and light output n ＝ω i ξ l ；
Step 2.4, setting coordinate value X of center c ＝[E(P S ),E(P W )]And verifying whether the coordinate value X is in the ellipse by using the formula (4):
(X-X c )C -1 (X-X c ) T 1 (4) or less in the formula (4): (X-X) c ) T Is a matrix (X-X) c ) Transposed matrix of C -1 Is an inverse matrix of the covariance matrix C;
when X is respectively
Figure BDA0003463528330000031
If all the equations (4) are satisfied, then make Ω n =1, directly skip step 2.6, if none is true, make Ω n =0, directly skipping step 2.6, otherwise, defining the number of iterations as Z, initializing Z =1, and defining an ellipsoid proportion coefficient Z of a focal element at the Z-th iteration z =0, the maximum number of iterations is defined as z m And step 2.5 is executed;
step 2.5, calculating a focal element correction coefficient when the focal element intersects with the ellipsoid:
step 2.5.1, in the ith cell of fan output
Figure BDA0003463528330000032
Decimating the random number of the z-th iteration inside->
Figure BDA0003463528330000033
In the first small interval of photovoltaic output->
Figure BDA0003463528330000034
Random number/s internally extracted for the z-th iteration>
Figure BDA0003463528330000035
Make->
Figure BDA0003463528330000036
Judging that z is less than or equal to z m If yes, execute step 2.5.2, otherwise, make Ω n ＝Z z Skipping to step 2.6;
step 2.5.2, judging whether the formula (4) is all true, if true, making Z z ＝Z z-1 +1/z m And step 2.6 is performed, otherwise, Z is z-1 Assigned to Z z (ii) a Assigning z +1 to z, and skipping the step 2.5.1 to execute in sequence;
step 2.6, judging whether all focal element correction coefficients are obtained:
step 2.6.1, judging whether l is greater than or equal to M, and if so, assigning l =1, i +1 to i; otherwise, assigning l +1 to l;
step 2.6.2, judging whether i is greater than N, if so, indicating that all focal element correction coefficients omega are obtained n And executing the step three, otherwise, jumping to the step 2.3 for sequential execution;
step three, combining the load flow calculation of the focal unit:
step 3.1, initialize n =1, order
Figure BDA0003463528330000037
Wherein gamma is the total basic probability distribution value before correction;
step 3.2, calculating a basic probability distribution correction value m 'of the combined focal element by utilizing the formula (5)' n ：
m′ n ＝Ω n m n /Γ (5)
Step 3.3, judging that N is less than N m If yes, assigning n +1 to n, and skipping to the step 3.2 for sequential execution, otherwise, executing the step 3.4;
step 3.4, adopting a complex affine-based power flow algorithm to pair the combined focal elements
Figure BDA0003463528330000038
Respectively carrying out load flow calculation; the corresponding focus unit which obtains the tide result f is marked as ^ or ^>
Figure BDA0003463528330000039
Wherein it is present>
Figure BDA00034635283300000310
For the wind and light output interval to be combined focus unit>
Figure BDA0003463528330000041
The jiao Yuan of the seasonal fashion results, and->
Figure BDA0003463528330000042
Figure BDA0003463528330000043
And &>
Figure BDA0003463528330000044
Respectively is a combined focal unit>
Figure BDA0003463528330000045
Left and right boundaries of the time-tidal current result interval, n belongs to [1,N ] m ]The power flow result f comprises a node voltage amplitude value, a node voltage phase angle or a branch current;
step 3.4, obtaining a probability box form of the trend result according to an evidence theory:
step 3.4.1, the focal element of each trend result
Figure BDA0003463528330000046
According to +>
Figure BDA0003463528330000047
The ascending order of the tide is sorted, and the sorted tide result is obtained by the focus cell>
Figure BDA0003463528330000048
Wherein n' is the reordered focal number, and>
Figure BDA0003463528330000049
and &>
Figure BDA00034635283300000410
Respectively is the left and right boundaries of the section of the nth tide result focus cell after sorting, and is/are selected>
Figure BDA00034635283300000411
Distributing the basic probability of the n' th sequenced power flow result focal element; n' is from [1,N m ]；
Obtaining a simulated function curve PCPD (f) generated according to the principle of evidence theory by using the formula (6):
Figure BDA00034635283300000412
in formula (6): f represents the tidal current result;
step 3.4.2, focusing each tidal current result on a focal element
Figure BDA00034635283300000413
According to>
Figure BDA00034635283300000414
The ascending order of the tidal current is sequenced in sequence, and the sequenced tidal current result is obtained by the focus cell->
Figure BDA00034635283300000415
Wherein n' is the reordered focal number, is greater than or equal to>
Figure BDA00034635283300000416
And &>
Figure BDA00034635283300000417
Is the left and right boundaries of the section of the nth tide result focus cell after sorting respectively, and is/are judged>
Figure BDA00034635283300000418
The n 'belongs to the 1,N basic probability distribution of the n' th ordered tidal current result focal element m ]；
Obtaining a trust function curve BCPD (f) generated according to the principle of evidence theory by using the formula (7):
Figure BDA00034635283300000419
the simulation function curve PCPD (f) and the trust function curve BCPD (f) form a probability box of the power flow result f.
Compared with the prior art, the invention has the beneficial effects that:
1. aiming at the uncertainty of the output power of the distributed power supply, an evidence structure model of the output power of the distributed power supply is established by using a fuzzy judgment matrix method, so that the evidence structure model can better reflect various uncertainties of information, and the uncertain mathematical model can more accurately describe uncertain information and reflect the state of a power grid when initial data is insufficient;
2. aiming at the condition that a probability distribution function of accurate and objective output power is difficult to obtain when statistical information and modeling reliability of a distributed power supply are insufficient, the output range is divided into a plurality of intervals, and a complex affine load flow method is adopted to calculate each joint focus element, so that the influence of subjective uncertainty introduced during probability distribution function estimation is reduced, and a load flow result is accurately and quickly obtained;
3. the method utilizes the improved ellipsoid theory, so that the evidence theory can better analyze and consider the uncertainty of the correlation, the concept of the focal element correction coefficient is provided, the defect that the basic probability distribution value of partial focal elements is higher when the focal elements and the ellipsoid intersect in the traditional ellipsoid theory is reduced, and the basic probability distribution correction value of the combined focal element is closer to the actual condition;
4. the invention utilizes the evidence theory to establish the trust function and the simulation function of the trend result, and embodies the uncertainty of the trend result more comprehensively in the form of a probability box, thereby further quantifying the objectivity and subjectivity brought by the distributed power supply model compared with the classical probability analysis method.
Drawings
Fig. 1 is a general flow chart of a power distribution network evidence theory power flow calculation method considering wind-solar output correlation.
Detailed Description
In the embodiment, a wind power generator and a photovoltaic power station are connected to a power distribution network, the method for calculating the evidence theoretical power flow of the power distribution network considering the wind-solar output correlation is characterized in that the decision is converted from qualitative to quantitative by means of a fuzzy judgment matrix method, and basic probability distribution is given to an evidence structure model of distributed power output. An improved ellipsoid theory method is provided, a basic probability distribution value of a combined focal element is corrected based on wind-solar output correlation, and a load flow result interval value of the combined focal element is calculated by using a complex affine load flow calculation method. And (4) obtaining a simulation function curve and a trust function curve of the calculation result by an evidence theory, and obtaining a probability box form of the load flow result. Specifically, as shown in fig. 1, the method comprises the following steps:
step one, establishing an evidence structure body output model of the distributed power supply:
the evidence structure model containing the uncertain factors of the power distribution network of the distributed power supply, which is determined according to the fuzzy matrix judgment method, can reflect the uncertainty of information more accurately and objectively, improve the reliability of the initial basic probability distribution value of the focus element when the initial data is insufficient, and effectively reflect various uncertain information such as randomness, grayness, ambiguity and the like. An evidence structure model of uncertain quantity of the traditional evidence theory is similar to an interval model, and the basic probability distribution value of the method can reflect uncertainty better.
Step 1.1, dividing N output intervals according to the output range of the wind driven generator, and recording the N output intervals as
Figure BDA0003463528330000051
Figure BDA0003463528330000052
The ith small interval representing the output force of the fan, i belongs to [1,N ]](ii) a And is provided with
Figure BDA0003463528330000053
Wherein it is present>
Figure BDA0003463528330000054
The maximum output of the wind driven generator;
step 1.2, counting the output of the wind driven generator to obtain the ith cell
Figure BDA0003463528330000055
For the jth cell
Figure BDA0003463528330000056
Degree of likelihood->
Figure BDA0003463528330000057
And the jth small interval->
Figure BDA0003463528330000058
For the ith small interval>
Figure BDA0003463528330000059
Degree of likelihood->
Figure BDA00034635283300000510
Step 1.3, degree of probability
Figure BDA00034635283300000511
And &>
Figure BDA00034635283300000512
Comparing, and processing the comparison result according to the scale method of 0.1-0.9 to obtain a fuzzy judgment value a ij ，a ij ∈[0,1](ii) a Thereby constructing a fuzzy judgment matrix A = [ a ] ij ] N×N Wherein a is ij Elements representing ith row and jth column;
step 1.4, constructing an element R of the ith row and the jth column in a fuzzy consistent judgment matrix R by using the formula (1) ij ：
Figure BDA0003463528330000061
Step 1.5, calculating the reliability of each output interval of the distributed power supply by using the formula (2):
Figure BDA0003463528330000062
in formula (2): omega i For ith cell of fan output
Figure BDA0003463528330000063
The reliability of (2); r ik Representing the elements of the ith row and the kth column in the fuzzy consistent judgment matrix R; thereby obtaining the output P of the fan W Evidence structure of (a)>
Figure BDA0003463528330000064
Step 1.6, the sameDividing M output intervals according to the output range of the photovoltaic generator, and obtaining the photovoltaic output P according to the steps from step 1.1 to step 1.5 S Of (2) a witness structure
Figure BDA0003463528330000065
Wherein it is present>
Figure BDA0003463528330000066
Indicating the ith cell, xi, of the photovoltaic output l As the confidence level of the first cell, l is the [1,M ]]；
Step two, solving a focal element correction coefficient based on the wind-solar output correlation:
the traditional processing method of the relevance of the evidence theory comprises a copula function method and an ellipsoid theory method, wherein the copula function method needs to calculate two uncertain copula functions, the requirements on the quantity and accuracy of initial data are high, and the calculation is complex. The basic trust distribution value of the focal element intersected with the ellipsoid theory is exaggerated based on the ellipsoid theory method, a large number of cells are needed to ensure the accuracy of the result, and the fuzzy matrix judgment method has fewer intervals, which causes larger errors. Therefore, the accuracy of the ellipsoid theory can be improved by setting the correction coefficient parameters of the focal elements. In addition, the ellipsoid theory can only calculate the problem of two-dimensional correlation, and when the distributed power source evidence structure body model comprises various distributed power source evidence structure bodies, the dimensionality of the ellipsoid model can be improved, namely the order of a matrix in a formula is increased.
Step 2.1, obtaining the wind-light output combined focal element
Figure BDA0003463528330000067
Wherein +>
Figure BDA0003463528330000068
Is the nth combined focal unit of wind-solar power and->
Figure BDA0003463528330000069
m n The total number N of the combined focal elements is assigned to the basic probability distribution of the nth combined focal element for wind and light output m ＝MN；
Step 2.2, initializing i =1,l =1, and obtaining a mean value E (P) of fan output by using statistical data based on sample data of wind and light output W ) Sum variance D (P) W ) Average value of photovoltaic output E (P) S ) Sum variance D (P) S ) And calculating a correlation coefficient rho and covariance Cov (P) of wind and light output S ,P W ) (ii) a The covariance matrix C is thus constructed using equation (3):
Figure BDA00034635283300000610
step 2.3, let n = (i-1) M + l, set
Figure BDA00034635283300000611
And &>
Figure BDA00034635283300000612
For the ith cell of the fan output->
Figure BDA00034635283300000613
Left and right boundaries of the interval (4), and>
Figure BDA00034635283300000614
and &>
Figure BDA00034635283300000615
For the first small interval of photovoltaic output->
Figure BDA00034635283300000616
Let X be a coordinate value; setting the focal element correction coefficient of the nth combined focal element to be omega n Let the basic probability distribution m of the nth joint focal element of wind and light output n ＝ω i ξ l ；
Step 2.4, setting coordinate value X of center c ＝[E(P S ),E(P W )]And verifying whether the coordinate value X is in the ellipse by using the formula (4):
(X-X c )C -1 (X-X c ) T 1 (4) or less in the formula (4): (X-X) c ) T Is a matrix (X-X) c ) Transposed matrix of C -1 Is an inverse matrix of the covariance matrix C;
when X is respectively
Figure BDA0003463528330000071
If the two formulas (4) are all satisfied, then make Ω n =1, directly skip step 2.6, if none is true, make Ω n And =0, directly jumping to step 2.6, otherwise, defining the number of iterations as Z, initializing Z =1, and defining an ellipsoid proportion coefficient Z of a focal element at the Z-th iteration z =0, the maximum number of iterations is defined as z m And executing the step 2.5;
step 2.5, calculating a focal element correction coefficient when the focal element intersects with the ellipsoid:
step 2.5.1, in the ith cell of fan output
Figure BDA0003463528330000072
Random number/s internally extracted for the z-th iteration>
Figure BDA0003463528330000073
In the first small interval of photovoltaic output->
Figure BDA0003463528330000074
Decimating the random number of the z-th iteration inside->
Figure BDA0003463528330000075
Make->
Figure BDA0003463528330000076
Judging that z is less than or equal to z m If yes, execute step 2.5.2, otherwise, let Ω n ＝Z z Skipping to step 2.6;
step 2.5.2, judging whether the formula (4) is all true, if true, making Z z ＝Z z-1 +1/z m And step 2.6 is performed, otherwise, Z is z-1 Is assigned to Z z (ii) a Assigning z +1 to z, and skipping the step 2.5.1 to execute in sequence;
step 2.6, judging whether all focal element correction coefficients are obtained:
step 2.6.1, judging whether l is greater than or equal to M, and if so, assigning l =1, i +1 to i; otherwise, assigning l +1 to l;
step 2.6.2, judging whether i is more than N, if so, showing that all the focal element correction coefficients omega are obtained n And executing the step three, otherwise, jumping to the step 2.3 to execute in sequence;
step three, combining the load flow calculation of the focus elements:
step 3.1, initialize n =1, order
Figure BDA0003463528330000077
Wherein gamma is the total basic probability distribution value before correction;
step 3.2, calculating a basic probability distribution correction value m 'of the combined focal element by utilizing the formula (5)' n ：
m′ n ＝Ω n m n /Γ (5)
Step 3.3, judging that N is less than N m If yes, assigning n +1 to n, and skipping to the step 3.2 for sequential execution, otherwise, executing the step 3.4;
step 3.4, adopting a complex affine-based power flow algorithm to pair the combined focal elements
Figure BDA0003463528330000078
Respectively carrying out load flow calculation; the corresponding focus unit which obtains the tide result f is marked as ^ or ^>
Figure BDA0003463528330000079
Wherein it is present>
Figure BDA00034635283300000710
For the wind and light output interval being combined focal unit>
Figure BDA0003463528330000081
The jiao Yuan of the seasonal fashion results, and->
Figure BDA0003463528330000082
Figure BDA0003463528330000083
And &>
Figure BDA0003463528330000084
Respectively is a combined focal unit>
Figure BDA0003463528330000085
Left and right boundaries of the time-tidal current result interval, n belongs to [1,N ] m ]The power flow result f comprises a node voltage amplitude value, a node voltage phase angle or a branch current;
and 3.4, obtaining a probability box form of the trend result according to an evidence theory:
according to the evidence theory, a plausible function curve and a trust function curve of the trend result are formed to form a probability box of the trend result, and the maximum value and the minimum value of the cumulative probability of different trend result values can be given based on the subjectivity of the current evidence structure model. The area between the belief function curve and the probabilistic function curve represents subjective uncertainty due to limitations of researchers analyzing the acquired raw data.
Step 3.4.1, the focal element of each trend result
Figure BDA0003463528330000086
According to>
Figure BDA0003463528330000087
The ascending order of the tide is sorted, and the sorted tide result is obtained by the focus cell>
Figure BDA0003463528330000088
Wherein n' is the reordered coke bin number, is greater than or equal to>
Figure BDA0003463528330000089
And &>
Figure BDA00034635283300000810
Are respectively the n 'th after sorting'Left and right boundaries of the interval of the focus unit of each trend result->
Figure BDA00034635283300000811
Distributing the basic probability of the n' th sequenced power flow result focal element; n' is from [1,N m ]；
Obtaining a simulated function curve PCPD (f) generated according to the principle of evidence theory by using the formula (6):
Figure BDA00034635283300000812
in formula (6): f represents the tidal current result;
step 3.4.2, focusing each tidal current result on a focal element
Figure BDA00034635283300000813
According to>
Figure BDA00034635283300000814
The ascending order of the tidal current is sequenced in sequence, and the sequenced tidal current result is obtained by the focus cell->
Figure BDA00034635283300000815
Wherein n' is the reordered focal number, is greater than or equal to>
Figure BDA00034635283300000816
And &>
Figure BDA00034635283300000817
The left and right boundaries of the section of the nth tidal current result focal element after sorting are respectively combined>
Figure BDA00034635283300000818
The n 'epsilon [1,N ] is the basic probability distribution of the n' th power flow result focal element after the sorting m ]；
Obtaining a trust function curve BCPD (f) generated according to the principle of evidence theory by using the formula (7):
Figure BDA00034635283300000819
and (4) simulating a function curve PCPD (f) and a trust function curve BCPD (f) and forming a probability box of the power flow result f.

Claims (1)

1. A power distribution network evidence theory power flow calculation method considering wind-solar output correlation is disclosed, wherein a wind power generator and a photovoltaic power station are connected to a power distribution network; the method is characterized by comprising the following steps of:
step one, establishing an evidence structure body output model of the distributed power supply:
step 1.1, dividing N output intervals according to the output range of the wind driven generator, and recording the N output intervals as N output intervals
Figure QLYQS_1
Figure QLYQS_2
The ith small interval representing the output force of the fan, i belongs to [1,N ]](ii) a And->
Figure QLYQS_3
Wherein it is present>
Figure QLYQS_4
The maximum output of the wind driven generator;
step 1.2, counting the output of the wind driven generator to obtain the ith cell
Figure QLYQS_5
For the jth small interval->
Figure QLYQS_6
Degree of likelihood->
Figure QLYQS_7
And the jth small interval->
Figure QLYQS_8
For the ith cell interval>
Figure QLYQS_9
Degree of likelihood->
Figure QLYQS_10
Step 1.3, degree of probability
Figure QLYQS_11
And &>
Figure QLYQS_12
Comparing, and processing the comparison result according to the scale method of 0.1-0.9 to obtain a fuzzy judgment value a ij ，a ij ∈[0,1](ii) a Thereby constructing a fuzzy judgment matrix A = [ a = ij ] N×N Wherein a is ij Elements representing the ith row and jth column;
step 1.4, constructing the element R of the ith row and the jth column in the fuzzy consistent judgment matrix R by using the formula (1) ij ：
Figure QLYQS_13
Step 1.5, calculating the reliability of each output interval of the distributed power supply by using the formula (2):
Figure QLYQS_14
in the formula (2): omega i For ith cell of fan output
Figure QLYQS_15
The reliability of (2); r is ik Representing the elements of the ith row and the kth column in the fuzzy consistent judgment matrix R; thereby obtaining the fan output P W Evidence structural body of (a)>
Figure QLYQS_16
Step 1.6, similarly, dividing M output intervals according to the output range of the photovoltaic generator, and obtaining photovoltaic output P according to the steps from step 1.1 to step 1.5 S Of (2) a witness structure
Figure QLYQS_17
Wherein +>
Figure QLYQS_18
Indicates the ith cell interval, xi, of photovoltaic output l Is the credibility of the first cell, is epsilon [1,M]；
Step two, calculating a focal element correction coefficient based on the wind-solar output correlation:
step 2.1, obtaining the wind-light output combined focal element
Figure QLYQS_19
Wherein +>
Figure QLYQS_20
Is the nth combined focal unit of wind-solar power and->
Figure QLYQS_21
m n The basic probability distribution of the nth combined focal element for wind-solar output ensures that the total number of the combined focal elements is N m ＝MN；
Step 2.2, initializing i =1,l =1, and obtaining a mean value E (P) of fan output by using statistical data based on sample data of wind and light output W ) Sum variance D (P) W ) Mean value of photovoltaic contribution E (P) S ) Sum variance D (P) S ) And calculating a correlation coefficient rho and covariance Cov (P) of wind and light output S ,P W ) (ii) a The covariance matrix C is thus constructed using equation (3):
Figure QLYQS_22
step 2.3, let n = (i-1) M + l, set
Figure QLYQS_23
And &>
Figure QLYQS_24
For the ith cell of the fan output->
Figure QLYQS_25
Left and right boundaries of the interval (4), and>
Figure QLYQS_26
and &>
Figure QLYQS_27
For the first small interval of photovoltaic output->
Figure QLYQS_28
Let X be a coordinate value; setting the focal element correction coefficient of the nth combined focal element to be omega n Let the basic probability distribution m of the nth joint focal element of wind and light output n ＝ω i ξ l ；
Step 2.4, setting coordinate value X of center c ＝[E(P S ),E(P W )]And verifying whether the coordinate value X is in the ellipse by using the formula (4):
(X-X c )C -1 (X-X c ) T 1 (4) or less in the formula (4): (X-X) c ) T Is a matrix (X-X) c ) Transposed matrix of C -1 Is an inverse matrix of the covariance matrix C;
when X is respectively
Figure QLYQS_29
Then, whether the equations (4) are all true is determined in sequence, if all, then let Ω n =1, directly skip step 2.6, if none is true, make Ω n And =0, directly jumping to step 2.6, otherwise, defining the number of iterations as Z, initializing Z =1, and defining an ellipsoid proportion coefficient Z of a focal element at the Z-th iteration z =0, the maximum number of iterations is defined as z m And executing the step 2.5;
step 2.5, calculating a focal element correction coefficient when the focal element intersects with the ellipsoid:
step 2.5.1, in the ith cell of fan output
Figure QLYQS_30
Decimating the random number of the z-th iteration inside->
Figure QLYQS_31
In the first small interval of photovoltaic output->
Figure QLYQS_32
Decimating the random number of the z-th iteration inside->
Figure QLYQS_33
Make->
Figure QLYQS_34
Judging that z is less than or equal to z m If yes, execute step 2.5.2, otherwise, let Ω n ＝Z z Skipping to step 2.6;
step 2.5.2, judging whether the formulas (4) are all true, if true, making Z z ＝Z z-1 +1/z m And step 2.6 is performed, otherwise, Z is z-1 Is assigned to Z z (ii) a Assigning z +1 to z, and skipping the step 2.5.1 to execute in sequence;
step 2.6, judging whether all focal element correction coefficients are obtained:
step 2.6.1, judging whether l is greater than or equal to M, and if so, assigning l =1, i +1 to i; otherwise, assigning l +1 to l;
step 2.6.2, judging whether i is greater than N, if so, indicating that all focal element correction coefficients omega are obtained n And executing the step three, otherwise, jumping to the step 2.3 for sequential execution;
step three, combining the load flow calculation of the focal unit:
step 3.1, initialize n =1, order
Figure QLYQS_35
Wherein the gamma is a repairA positive total base probability distribution value;
step 3.2, calculating a basic probability distribution correction value m of the combined focal element by using the formula (5) n ′：
m′ n ＝Ω n m n /Γ (5)
Step 3.3, judging that N is less than N m If yes, assigning n +1 to n, and skipping to the step 3.2 for sequential execution, otherwise, executing the step 3.4;
step 3.4, adopting a complex affine-based power flow algorithm to pair the combined focal elements
Figure QLYQS_37
Respectively carrying out load flow calculation; the corresponding focus unit which obtains the tide result f is marked as ^ or ^>
Figure QLYQS_39
Wherein +>
Figure QLYQS_42
For the wind and light output interval to be combined focus unit>
Figure QLYQS_38
The jiao Yuan of the seasonal fashion results, and->
Figure QLYQS_40
Figure QLYQS_41
And &>
Figure QLYQS_43
Respectively in the wind and light output interval of combined focal unit>
Figure QLYQS_36
Left and right boundaries of the time-tidal current result interval, n belongs to [1,N ] m ]The power flow result f comprises a node voltage amplitude value, a node voltage phase angle or a branch current;
and 3.4, obtaining a probability box form of the trend result according to an evidence theory:
step 3.4.1, the focal element of each trend result
Figure QLYQS_44
According to>
Figure QLYQS_45
The ascending order of the tide is sorted, and the sorted tide result is obtained by the focus cell>
Figure QLYQS_46
Wherein n' is the reordered coke bin number, is greater than or equal to>
Figure QLYQS_47
And &>
Figure QLYQS_48
Respectively is the left and right boundaries of the section of the nth tide result focus cell after sorting, and is/are selected>
Figure QLYQS_49
Distributing the basic probability of the n' th ordered power flow result focal element; n' is from [1,N m ]；
Obtaining a simulated function curve PCPD (f) generated according to the principle of evidence theory by using the formula (6):
Figure QLYQS_50
in formula (6): f represents the power flow result;
step 3.4.2, focusing each tidal current result
Figure QLYQS_51
According to +>
Figure QLYQS_52
The ascending order of the tidal current is sequenced in sequence, and the sequenced tidal current result is obtained by the focus cell->
Figure QLYQS_53
Wherein n "is the reordered focal number, and>
Figure QLYQS_54
and &>
Figure QLYQS_55
Is the left and right boundaries of the section of the nth tide result focus cell after sorting respectively, and is/are judged>
Figure QLYQS_56
The n 'belongs to the 1,N basic probability distribution of the n' th ordered tidal current result focal element m ]；
Obtaining a trust function curve BCPD (f) generated according to the principle of evidence theory by using the formula (7):
Figure QLYQS_57
and (4) simulating a function curve PCPD (f) and a trust function curve BCPD (f) and forming a probability box of the power flow result f.
CN202210023423.XA 2022-01-10 2022-01-10 Power distribution network evidence theory power flow calculation method considering wind and light output correlation Active CN114336637B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210023423.XA CN114336637B (en) 2022-01-10 2022-01-10 Power distribution network evidence theory power flow calculation method considering wind and light output correlation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210023423.XA CN114336637B (en) 2022-01-10 2022-01-10 Power distribution network evidence theory power flow calculation method considering wind and light output correlation

Publications (2)

Publication Number Publication Date
CN114336637A CN114336637A (en) 2022-04-12
CN114336637B true CN114336637B (en) 2023-04-07

Family

ID=81026011

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210023423.XA Active CN114336637B (en) 2022-01-10 2022-01-10 Power distribution network evidence theory power flow calculation method considering wind and light output correlation

Country Status (1)

Country Link
CN (1) CN114336637B (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015192613A1 (en) * 2014-06-20 2015-12-23 国家电网公司 Power system control method and device
CN105743093A (en) * 2016-04-13 2016-07-06 合肥工业大学 Power distribution network power flow calculation method considering wind power and photovoltaic power output uncertainty
CN106022480A (en) * 2016-05-13 2016-10-12 北京工业大学 Robot functional module granularity division evaluating method based on D-S evidence theory
CN112736927A (en) * 2020-12-28 2021-04-30 昆明理工大学 Wind power plant uncertainty trend modeling method based on probability box theory

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015192613A1 (en) * 2014-06-20 2015-12-23 国家电网公司 Power system control method and device
CN105743093A (en) * 2016-04-13 2016-07-06 合肥工业大学 Power distribution network power flow calculation method considering wind power and photovoltaic power output uncertainty
CN106022480A (en) * 2016-05-13 2016-10-12 北京工业大学 Robot functional module granularity division evaluating method based on D-S evidence theory
CN112736927A (en) * 2020-12-28 2021-04-30 昆明理工大学 Wind power plant uncertainty trend modeling method based on probability box theory

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
苏瑜 等.数据信息不完备下基于证据理论的可靠性分析.《四川建筑科学研究》.2020,第46卷(第05期),第8-16页. *
鲍海波 等.考虑风电不确定性的概率区间潮流模型与算法.《中国电机工程学报》.2017,第37卷(第19期),第5633-5642+5840页. *

Also Published As

Publication number Publication date
CN114336637A (en) 2022-04-12

Similar Documents

Publication Publication Date Title
CN109117951B (en) BP neural network-based probability load flow online calculation method
Roy et al. A comparative performance analysis of ANN algorithms for MPPT energy harvesting in solar PV system
Ferlito et al. Predictive models for building's energy consumption: An Artificial Neural Network (ANN) approach
CN111027732B (en) Method and system for generating multi-wind power plant output scene
Abdel-Nasser et al. A novel smart grid state estimation method based on neural networks
CN111144644B (en) Short-term wind speed prediction method based on variation variance Gaussian process regression
Zhao et al. DeepOPF+: A deep neural network approach for DC optimal power flow for ensuring feasibility
CN109948920B (en) Electric power market settlement data risk processing method based on evidence theory
Li et al. Calibration of dynamic building energy models with multiple responses using Bayesian inference and linear regression models
CN114818345A (en) Method and system for predicting residual life of photovoltaic module
CN112418495A (en) Building energy consumption prediction method based on longicorn stigma optimization algorithm and neural network
Lu et al. Neural network interpretability for forecasting of aggregated renewable generation
Yasin et al. Prediction of solar irradiance using grey Wolf optimizer least square support vector machine
CN115759445A (en) Machine learning and cloud model-based classified flood random forecasting method
CN115329669A (en) Power system transient stability evaluation method based on deep Bayes active learning
CN111105005A (en) Wind power prediction method
Sulaiman et al. Partial evolutionary ANN for output prediction of a grid-connected photovoltaic system
CN114336637B (en) Power distribution network evidence theory power flow calculation method considering wind and light output correlation
Tómasson et al. Multi-area generation adequacy and capacity credit in power system analysis
CN117335425A (en) Tidal current calculation method based on GA-BP neural network
Yundra et al. Hybrid Model Combined Fuzzy Multi-Objective Decision Making with Feed Forward Neural Network (F-MODM-FFNN) For Very Short-Term Load Forecasting Based on Weather Data.
CN115310727A (en) Building cooling, heating and power load prediction method and system based on transfer learning
CN112701679B (en) Active power distribution network short-term reliability evaluation method based on model-data hybrid drive
CN115456286A (en) Short-term photovoltaic power prediction method
CN114861555A (en) Regional comprehensive energy system short-term load prediction method based on Copula theory

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