CN113346489A - New energy space coupling modeling evaluation method and system - Google Patents

New energy space coupling modeling evaluation method and system Download PDF

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CN113346489A
CN113346489A CN202110644478.8A CN202110644478A CN113346489A CN 113346489 A CN113346489 A CN 113346489A CN 202110644478 A CN202110644478 A CN 202110644478A CN 113346489 A CN113346489 A CN 113346489A
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photovoltaic
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wind
wind power
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CN113346489B (en
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梁燕
王尧
郎青勇
王建学
李锴英
刘红丽
王皑
李旭霞
胡迎迎
荆永明
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Xian Jiaotong University
Economic and Technological Research Institute of State Grid Shanxi Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Shanxi Electric Power Co Ltd
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Abstract

The invention discloses a new energy space coupling modeling evaluation method and system, which fully consider the geographical space coupling of new energy and carry out quantitative analysis; for partial areas, not enough wind power stations and photovoltaic stations are built, partial analysis is completed by utilizing wind speed and illumination intensity data, and a basis is provided for initial construction; the natural complementarity of the wind power output and the photovoltaic output is fully considered, and quantitative analysis is carried out; the method aims at minimizing the impact of new energy fluctuation on a power grid, provides the optimal wind power and photovoltaic installed ratio, and provides a basis for a power system to fully utilize the complementarity of wind energy and solar energy, make up the defects of a single wind power and photovoltaic power generation system, and increase the stability, the continuity and the reliability of power supply.

Description

New energy space coupling modeling evaluation method and system
Technical Field
The invention belongs to the technical field of planning and operation evaluation in an electric power system, and particularly relates to a new energy space coupling modeling evaluation method and system.
Background
In recent years, due to the rapid development of new energy, large-scale new energy power generation is connected into a power system, on one hand, the power supply and carbon emission pressure of the power system is reduced, on the other hand, the randomness of system operation is increased, and great challenges are brought to the safe and reliable operation of the power system. The wind speed and the illumination intensity have obvious coupling in the geographical space distribution, so that the output of the wind power photovoltaic also has obvious spatial coupling. With the continuous increase of the permeability of new energy, the coupling degree of the power generation side of the power system is also continuously improved, and the impact on the power system when the output of the new energy fluctuates is aggravated. Therefore, the method is particularly important for analyzing the spatial coupling of wind power output and photovoltaic output, and the rationality of the analysis method determines the reference value of the evaluation result.
In the planning and subsequent operation processes of a power grid, the evaluation of the geospatial coupling of new energy is of great importance. The condition that the wind power photovoltaic output is suddenly increased or reduced in a large range can be analyzed by evaluating the geospatial coupling of wind power generation and photovoltaic power generation, and early warning is provided for the operation of a power system; the complementary property of wind energy and solar energy can be utilized to make up the defect of a single wind power and photovoltaic power generation system, so that the stability, the continuity and the reliability of power supply are ensured, and better social benefit and economic benefit are obtained. In the conventional evaluation method used at present, the geospatial coupling of the new energy is not comprehensively considered, and vital information in the new energy is omitted when the output characteristics of the new energy are analyzed, so that unnecessary cost is brought to the planning and construction of the power system, and unnecessary risks are brought to the safe and stable operation of the power system.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a new energy space coupling modeling evaluation method and system aiming at the defects in the prior art, guide the evaluation of the geospatial coupling of wind power generation and photovoltaic power generation in the operation evaluation of a power system, and analyze the possible situations that the wind power photovoltaic output is increased or reduced suddenly and in a large range; the complementary property of wind energy and solar energy is utilized to make up the defect of a single wind power and photovoltaic power generation system, an effective suggestion is provided for new energy investment planning, and the stability, the continuity and the reliability of power supply are ensured.
A new energy space coupling modeling evaluation method comprises the following steps:
s1, acquiring wind speed v, illumination intensity S and wind power output P of a corresponding area for at least one yearwPhotovoltaic output PsAnd installed wind power capacity S of corresponding regionwAnd installed photovoltaic capacity Ss；
S2, calculating a wind speed correlation matrix rho of the region by using the data acquired in the step S1vAnd illumination intensity correlation matrix ρsCarrying out space coupling evaluation analysis on the wind speed and the illumination intensity to obtain the coupling condition of the wind speed and the illumination intensity in the region;
s3, if no wind power/photovoltaic actual output data exist, utilizing the wind speed correlation matrix rho obtained in the step S2vAnd illumination intensity correlation matrix ρsConstructing a simulated wind speed vector H obeying normal distribution and corresponding correlation matrixvAnd the simulated illumination vector HsAccording to the simulated wind velocity vector HvAnd the simulated illumination vector HsCalculating to obtain a time sequence P of the simulated wind power outputi wAnd simulating a time series P of photovoltaic outputsi s；
S4, wind power/photovoltaic actual output data or the time sequence P of the simulated wind power output obtained in the step S3 are utilizedi wAnd simulating a time series P of photovoltaic outputsi sConstructing a correlation matrix rho of wind power outputPwCorrelation matrix p with photovoltaic outputPsAnd for the constructed correlation matrix rhoPw and ρPsCarrying out space coupling evaluation analysis to obtain the coupling condition of wind power output and photovoltaic output of the region; s5, wind power/photovoltaic actual output data or the time sequence P of the simulated wind power output obtained in the step S3 are utilizedi wAnd simulating a time series P of photovoltaic outputsi sConstruction of new energy output complementarity index
Figure RE-GDA0003196076670000021
Evaluating the complementarity between wind and light in the region, and analyzing the wind-light cooperative development prospect of the corresponding region;
s6, obtaining the new energy output complementation index according to the step S5
Figure RE-GDA0003196076670000022
Determining the optimal wind power-photovoltaic installed ratio in a region by taking the minimum wind power-photovoltaic combined output variance as a target
Figure RE-GDA0003196076670000023
And a proportion suggestion of corresponding building machines is provided for the wind and light collaborative development in the region.
Specifically, in step S2, the wind speed correlation matrix ρvComprises the following steps:
Figure RE-GDA0003196076670000031
wherein ,
Figure RE-GDA0003196076670000032
for wind velocity time sequence vector v of region iiWind velocity time sequence vector v with region jjA correlation coefficient between;
illumination intensity correlation coefficient matrix rhosComprises the following steps:
Figure RE-GDA0003196076670000033
wherein ,
Figure RE-GDA0003196076670000034
is the correlation coefficient between the wind speed of region i and the wind speed of region j.
Specifically, in step S3, the time sequence P of wind power output is simulatedi wAnd simulating a time series P of photovoltaic outputsi sComprises the following steps:
Figure RE-GDA0003196076670000035
Figure RE-GDA0003196076670000036
wherein ,viThe simulated wind speed of the area i; v. ofi,inCutting in wind speed for a fan of a region i; v. ofi,outCutting out wind speed for a fan in a region i; v. ofi,rRated wind speed of a fan in a region i;
Figure RE-GDA0003196076670000037
rated power of fan for region i, siThe simulated illumination intensity of the area i; etaiPhotovoltaic efficiency for region i;
Figure RE-GDA0003196076670000038
for the photovoltaic power rating of region i
Further, a simulated wind speed time sequence vector H obeying a normal distribution and a corresponding correlation matrixvAnd the simulated illumination timing vector Hs；
Figure RE-GDA0003196076670000039
wherein ,Gv and GsIs an n-dimensional independent standard normal distribution vector, Bv and BsIs a mean vector, Lv and LsIs a lower triangular matrix.
Specifically, in step S4, the correlation matrix ρ of the wind power outputPwComprises the following steps:
Figure RE-GDA0003196076670000041
wherein ,
Figure RE-GDA0003196076670000042
wind power output time sequence vector P for region ii vWind power output time sequence vector of region j
Figure RE-GDA0003196076670000043
Coefficient of correlation between
Correlation matrix rho of photovoltaic outputPsComprises the following steps:
Figure RE-GDA0003196076670000044
wherein ,
Figure RE-GDA0003196076670000045
is the correlation coefficient between the wind power output of the area i and the wind power output of the area j,
Figure RE-GDA0003196076670000046
is the correlation coefficient between the photovoltaic contribution of region i and the photovoltaic contribution of region j.
Specifically, in step S5, the wind-solar complementarity index
Figure RE-GDA0003196076670000047
Comprises the following steps:
Figure RE-GDA0003196076670000048
wherein ,
Figure RE-GDA0003196076670000049
and the correlation between the wind power output and the photovoltaic output in each area is obtained.
Further, the correlation between the wind power output and the photovoltaic output in each region
Figure RE-GDA00031960766700000410
Comprises the following steps:
Figure RE-GDA00031960766700000411
where E () is the mean function, Pi sIs a photovoltaic output timing vector, Pi wIs a wind power output time sequence vector,
Figure RE-GDA00031960766700000412
for the photovoltaic output variance, the output power of the photovoltaic,
Figure RE-GDA00031960766700000413
and the variance of the wind power output is obtained.
Specifically, in step S6, the wind power/photovoltaic output is normalized, and the normalized wind power output and photovoltaic output are split into PΙ，PΙΙ，PrThree completely independent components, PΙThe wind power output is a component irrelevant to the photovoltaic output in the wind power output; pΙΙThe photovoltaic output is a component irrelevant to the wind power output; prThe photovoltaic output and the wind power output are related components; a/b is a function of control forceCalculating the wind-solar combined output variance, minimizing the wind-solar combined output variance, and finally obtaining the optimal wind power-photovoltaic installed ratio according to the relation between the output and the installed ratio
Figure RE-GDA00031960766700000414
Further, the optimal wind power-photovoltaic installation ratio
Figure RE-GDA0003196076670000051
The method specifically comprises the following steps:
Figure RE-GDA0003196076670000052
wherein ,RPw/PsIs the ratio of wind power-photovoltaic output power,
Figure RE-GDA0003196076670000053
for installed capacity of wind, E (P)i w) For average wind power output, E (P)i s) In order to average the photovoltaic output,
Figure RE-GDA0003196076670000054
is the photovoltaic installed capacity.
Another technical solution of the present invention is a new energy space coupling modeling and evaluating system, including:
the data module acquires the wind speed v, the illumination intensity s and the wind power output P of the corresponding area for at least one yearwPhotovoltaic output PsAnd installed wind power capacity S of corresponding regionwAnd installed photovoltaic capacity Ss；
A coupling module for calculating a wind speed correlation matrix rho between the regions by using the data acquired by the data modulevAnd illumination intensity correlation coefficient matrix ρsCarrying out space coupling evaluation analysis on the wind speed and the illumination intensity to obtain the coupling condition of the wind speed and the illumination intensity in the region;
a vector module, if no wind power/photovoltaic actual output data exists, a wind speed correlation matrix rho obtained by using the coupling modulevAnd illumination intensity correlation matrix ρsConstructing a simulated wind speed vector H obeying normal distribution and corresponding correlation matrixvAnd the simulated illumination vector HsAccording to the simulated wind velocity vector HvAnd the simulated illumination vector HsCalculating to obtain a time sequence P of the simulated wind power outputi wAnd simulating a time series P of photovoltaic outputsi s；
The simulation analysis module is used for simulating the time sequence P of the wind power output by utilizing the wind power/photovoltaic actual output data or the vector modulei wAnd simulating a time series P of photovoltaic outputsi sConstructing a correlation matrix rho of wind power outputPwCorrelation matrix p with photovoltaic outputPsAnd for the constructed correlation matrix rhoPw and ρPsEvaluating and analyzing to obtain the coupling condition of wind power output and photovoltaic output of the region;
a complementary module for simulating the wind power output time sequence P obtained by the wind power/photovoltaic actual output data or the vector modulei wAnd simulating a time series P of photovoltaic outputsi sConstruction of new energy output complementarity index
Figure RE-GDA0003196076670000055
Evaluating the complementarity between wind and light in the region, and analyzing the wind-light cooperative development prospect of the corresponding region;
the evaluation module is used for obtaining the new energy output complementarity index according to the complementation module
Figure RE-GDA0003196076670000056
Determining the optimal wind power-photovoltaic installed ratio in a region by taking the minimum wind power-photovoltaic combined output variance as a target
Figure RE-GDA0003196076670000061
And a proportion suggestion of corresponding building machines is provided for the wind and light collaborative development in the region.
Compared with the prior art, the invention has at least the following beneficial effects:
the new energy space coupling modeling evaluation method can perform quantitative analysis and depiction on the specific geographical space coupling of wind speed, illumination intensity, wind power output and photovoltaic output. Through analyzing the obtained wind speed and illumination intensity data, the correlation between the wind speed and the illumination intensity in the region is obtained, and then the simulated wind speed and the illumination intensity capable of keeping the spatial coupling are constructed. The space coupling of wind power and photovoltaic in a region is obtained by analyzing the obtained wind power output and photovoltaic output data, so that the construction and capacity configuration of a new energy power station are optimized, and if no new energy output data available for use in the region exists, the wind power photovoltaic output can be deduced and simulated by adopting simulated wind speed and illumination intensity; the wind power photovoltaic output data is analyzed, wind and light complementarity in a region can be obtained, the optimal wind power-photovoltaic installation ratio of the region is further obtained through analysis, the total wind power photovoltaic output waves are reduced through reasonable configuration, and wind and light are cooperatively developed. Compared with the traditional evaluation method, the evaluation method disclosed by the invention has the advantage that the influence of the geospatial coupling on the power grid can be better reflected, so that a more practical guidance is provided for power grid planning and operation evaluation in the power system compared with the traditional evaluation method.
Further, a space coupling evaluation method related to wind speed and illumination intensity is constructed, the correlation between wind speed and illumination intensity between regions is analyzed, the stronger the correlation is, the closer the climates in the regions are, and the higher the output correlation between the wind power station and the photovoltaic power station is; it is worth noting that this index can still be used when the region has not yet established enough wind and photovoltaic stations.
Furthermore, the deduced simulated wind speed vector and the simulated illumination vector obeying the normal distribution and the corresponding correlation matrix can better simulate the sudden change of the wind speed and the illumination intensity; and providing more practical guidance for planning site selection and capacity configuration of the new energy station in the area.
Furthermore, the wind speed vector and the illumination vector are simulated, so that sudden changes of wind speed and illumination intensity can be better simulated.
Furthermore, a calculation method for simulating wind power output and photovoltaic output is calculated according to the deduced simulated wind speed vector and simulated illumination vector, the new energy output is simulated for the part of the area without the new energy station, detailed and reliable data are provided for the analysis of the new energy output of the area, and more practical guidance is provided for planning site selection and capacity configuration of the new energy station of the area.
Furthermore, a space coupling evaluation method of wind power output and photovoltaic output is constructed, the correlation of the wind power output between regions and the correlation of the photovoltaic output between the regions are analyzed, and the coupling characteristic of the wind power output and the coupling characteristic of the photovoltaic output are better reflected. In general, the correlation of the output of new energy between a group of adjacent areas is relatively large, and the large-scale construction of the wind power/photovoltaic power station in the group of areas increases the impact of the output fluctuation of the new energy on the power system, thereby affecting the safe and stable operation of the power system, and the occurrence of the phenomenon needs to be avoided as much as possible.
Further, through the correlation of wind power output and photovoltaic output in each region
Figure RE-GDA0003196076670000071
The coupling characteristic of wind power output and the coupling characteristic of photovoltaic output are better reflected.
Furthermore, a spatial coupling evaluation method for wind-light complementation in the region is constructed, the complementarity of wind power output and photovoltaic output in the region is analyzed, the stronger the complementarity of the wind power photovoltaic output in the region is, the stronger the capability of reducing the wind power photovoltaic total output fluctuation through reasonable configuration is, and the better the wind-light synergistic development prospect is.
Furthermore, the wind-solar hybrid power generation method is combined with wind-solar complementary analysis, the wind-solar hybrid power generation method aims at minimizing the wind-solar combined output variance, deduces and determines the optimal wind-solar installation ratio in the region, and provides suggestions of corresponding installation and installation ratio for wind-solar collaborative development in the region.
In conclusion, the method can perform quantitative analysis and characterization aiming at the specific geographic space coupling of wind speed, illumination intensity, wind power output and photovoltaic output, and further provide corresponding suggestions. The evaluation method can better reflect the influence of the geographic spatial coupling on the power system, thereby providing more practical guidance for power grid planning and operation evaluation in the power system.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic diagram of an optimal wind power photovoltaic installed ratio point of the region 1.
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 some, not all, embodiments of the present invention. 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.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Various structural schematics according to the disclosed embodiments of the invention are shown in the drawings. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, according to actual needs.
The invention provides a new energy space coupling modeling evaluation method, which is characterized in that on the basis that the existing wind power photovoltaic installed capacity does not change, the geographical space coupling of wind power generation and photovoltaic power generation is evaluated, and the condition that the wind power photovoltaic output is increased or reduced suddenly and in a large range is analyzed, so that the influence of the new energy output coupling on a power system is reflected more truly, and early warning is provided for the operation of the power system; the complementary property of wind energy and solar energy can be utilized to make up the defect of a single wind power and photovoltaic power generation system, so that the stability, the continuity and the reliability of power supply are ensured, and better social benefit and economic benefit are obtained. According to the invention, on the basis that the existing wind power photovoltaic installed capacity is not changed, an evaluation method considering the wind power photovoltaic geographic space coupling is constructed, and the influence of the new energy output coupling on the power system is reflected more truly.
Referring to fig. 1, the new energy spatial coupling modeling evaluation method of the present invention fully considers the geospatial coupling of new energy and performs quantitative analysis on the new energy spatial coupling; for partial areas, enough wind power stations and photovoltaic stations are not established, and when the conventional method cannot analyze the wind power stations and the photovoltaic stations, partial analysis is completed by utilizing wind speed and illumination intensity data, so that a basis is provided for initial construction; the natural complementarity of the wind power output and the photovoltaic output is fully considered, and quantitative analysis is carried out; the method aims at minimizing the impact of new energy fluctuation on a power grid, provides the optimal wind power and photovoltaic installed ratio, and provides a basis for a power system to fully utilize the complementarity of wind energy and solar energy, make up the defects of a single wind power and photovoltaic power generation system, and increase the stability, the continuity and the reliability of power supply. The method comprises the following specific steps:
s1, acquiring wind speed v and illumination intensity data S of each area for at least one year from a meteorological station of each area; acquiring wind power output P of each region for at least one year from power system operation departmentswAnd photovoltaic output PsAnd installed wind power capacity S of corresponding regionwAnd installed photovoltaic capacity Ss；
S2, constructing a space coupling evaluation method related to wind speed and illumination intensity;
s201, calculating a wind speed correlation matrix rho of the region by using the data acquired in the step S1vAnd illumination intensity correlation coefficient matrix ρs(ii) a Wind speed correlation matrix ρvComprises the following steps:
Figure RE-GDA0003196076670000091
wherein ,
Figure RE-GDA0003196076670000092
for wind velocity time sequence vector v of region iiWind velocity time sequence vector v with region jjThe correlation coefficient between the two is specifically as follows:
Figure RE-GDA0003196076670000093
wherein ,
Figure RE-GDA0003196076670000094
for wind velocity time sequence vector v of region iiStandard deviation of (d); e (x) is the mean of the variable x.
Illumination intensity correlation coefficient matrix rhosComprises the following steps:
Figure RE-GDA0003196076670000101
wherein ,
Figure RE-GDA0003196076670000102
time sequence vector s of wind speed for region iiWind velocity time sequence vector s with region jjThe correlation coefficient between the two is specifically as follows:
Figure RE-GDA0003196076670000103
wherein ,
Figure RE-GDA0003196076670000104
time sequence vector s of wind speed for region iiStandard deviation of (d); e (x) is the mean of the variable x.
S202, carrying out space coupling evaluation analysis on wind speed and illumination intensity
The diagonal elements of the wind speed/illumination intensity correlation matrix are all 1, namely the diagonal elements are completely linearly correlated with the matrix, and the data
Figure RE-GDA0003196076670000105
In [ -1,1 [)]In between, a larger absolute value indicates a stronger correlation, a positive value indicates a linear positive correlation, and a negative value indicates a linear negative correlation. The correlation degree of the weather conditions of the regions is reflected, the stronger the correlation is, the closer the climate is, and the higher the output correlation of the wind power station and the photovoltaic power station is.
S3, and combining the wind speed correlation matrix rho obtained in the step S2vAnd illumination intensity correlation coefficient matrix ρsConstructing a simulated wind speed vector H obeying normal distribution and corresponding correlation matrixvAnd the simulated illumination vector HsIf no wind power photovoltaic output data exists, further simulating a wind speed vector HvAnd the simulated illumination vector HsCalculating to obtain a time sequence P of the simulated wind power outputi wAnd simulating a time series P of photovoltaic outputsi s；
S301, constructing a covariance matrix Cv；
Deriving a covariance matrix C between the wind speed distributions from the correlation matrixvAnd the light intensityDifference matrix Cs：
Figure RE-GDA0003196076670000106
Figure RE-GDA0003196076670000111
S302, the covariance matrix C constructed in the step S301v and CsCholesky decomposition is carried out to obtain a lower triangular matrix Lv and Ls；
Figure RE-GDA0003196076670000112
S303, constructing a mean vector Bv and Bs；
Figure RE-GDA0003196076670000113
S304, constructing an n-dimensional independent standard normal distribution vector Gv and Gs(mean 0, variance 1);
s305, constructing a simulated wind speed time sequence vector H obeying normal distribution and corresponding correlation matrixvAnd the simulated illumination timing vector Hs；
Figure RE-GDA0003196076670000114
S306, constructing a simulated wind power output time sequence vector Pi wAnd simulating photovoltaic output timing sequence vector Pi s；
Simulating wind power output data Pi wComprises the following steps:
Figure RE-GDA0003196076670000115
wherein ,viFor simulated wind speed of region i, i.e. simulated wind speed time sequence vector HvThe elements of (1); v. ofi,inCutting in wind speed for a fan of a region i; v. ofi,outCutting out wind speed for a fan in a region i; v. ofi,rRated wind speed of a fan in a region i;
Figure RE-GDA0003196076670000116
the rated power of the fan in the area i.
Simulated photovoltaic output data Pi sComprises the following steps:
Figure RE-GDA0003196076670000121
qi in all, siFor simulated illumination intensity of region i, i.e. simulated illumination intensity time sequence vector HsThe elements of (1); etaiPhotovoltaic efficiency for region i;
Figure RE-GDA0003196076670000122
is the photovoltaic rated power of the region i.
S4, calculating and analyzing the correlation between the new energy output regions;
s401, constructing a correlation matrix rho of wind power outputPwCorrelation matrix p with photovoltaic outputPs；
Figure RE-GDA0003196076670000123
Figure RE-GDA0003196076670000124
wherein ,
Figure RE-GDA0003196076670000125
wind power output time sequence vector P for region ii vWind power output time sequence vector of region j
Figure RE-GDA0003196076670000126
The correlation coefficient between the two components is calculated,
Figure RE-GDA0003196076670000127
photovoltaic output timing vector P for region ii sPhotovoltaic output timing sequence vector with region j
Figure RE-GDA0003196076670000128
The correlation coefficient between the two is specifically as follows:
Figure RE-GDA0003196076670000129
wherein ,
Figure RE-GDA00031960766700001210
wind power output time sequence vector P for region ii wThe standard deviation of (a) is determined,
Figure RE-GDA00031960766700001211
photovoltaic output timing vector P for region ii sStandard deviation of (2).
S402, evaluating and analyzing the constructed correlation matrix;
diagonal elements of the wind power/photovoltaic output correlation matrix are all 1, namely the diagonal elements are completely linearly correlated with the diagonal elements, and data are obtained
Figure RE-GDA00031960766700001212
In [ -1,1 [)]In between, a larger absolute value indicates a stronger correlation, a positive value indicates a linear positive correlation, and a negative value indicates a linear negative correlation.
In general, the correlation between a group of adjacent areas is large, and the impact of the power system caused by the output fluctuation of new energy can be increased by constructing the wind power/photovoltaic power station in the group of areas in a large range, so that the safe and stable operation of the power system is influenced.
S5, calculating a new energy output complementarity index according to the step S3 or the existing new energy output time sequence vector, and evaluating and analyzing the complementarity between wind and light in the region;
s501, calculating the correlation between wind power output and photovoltaic output in each region
Figure RE-GDA0003196076670000131
Figure RE-GDA0003196076670000132
S502, defining wind-solar complementarity index
Figure RE-GDA0003196076670000133
Figure RE-GDA0003196076670000134
S503, analysis of complementarity evaluation
Index of wind-solar complementarity
Figure RE-GDA0003196076670000135
Takes on a value of [ -1,1]Meanwhile, the larger the value is, the stronger the complementarity of the wind power photovoltaic output is, the stronger the capability of reducing the wind power photovoltaic total output fluctuation through reasonable configuration is, and the better the wind and light collaborative development prospect is.
S6, according to the complementarity index, determining the optimal installed wind-photovoltaic ratio in the region by taking the minimum wind-photovoltaic combined output variance as a target
Figure RE-GDA0003196076670000136
And provides suggestions of corresponding building machine proportion for the wind and light collaborative development in the region.
S601, wind power/photovoltaic output normalization;
Figure RE-GDA0003196076670000137
outputting the normalized wind powerForce and photovoltaic output split into PΙ，PΙΙ，PrThree completely independent components are split in the following way:
Figure RE-GDA0003196076670000138
wherein E (x) is the mathematical expectation of x; pΙFor the component of wind power output irrelevant to photovoltaic output, let E (P)Ι)＝1p.u.； PΙΙFor the component of photovoltaic output irrelevant to wind power output, let E (P)ΙΙ)＝1p.u.；PrThe photovoltaic output and the wind power output are related components; and a/b is a variable for controlling the output.
S602, calculating a combined output variance;
as defined by the correlation coefficient:
Figure RE-GDA0003196076670000141
where Var (x) is the variance of x.
Average total force was 1 p.u.:
Figure RE-GDA0003196076670000142
wherein m is a parameter for controlling wind-solar output, and ensures E (P)sum) 1p.u. Wind power photovoltaic output ratio RPw/PsFrom m, we derive:
Figure RE-GDA0003196076670000143
Figure RE-GDA0003196076670000144
due to PΙ，PΙΙ，PrIndependently of each other, therefore:
Figure RE-GDA0003196076670000145
s603, calculating the optimal wind power-photovoltaic installation ratio
Figure RE-GDA0003196076670000146
If the variance of the combined wind and solar output is to be minimized, the combined wind and solar output is obtained
Figure RE-GDA0003196076670000147
Figure RE-GDA0003196076670000148
Figure RE-GDA0003196076670000149
Obtaining the optimal wind power-photovoltaic installation ratio through the relationship between output power and installation power:
Figure RE-GDA0003196076670000151
wherein ,RPw/PsIs the ratio of the wind power photovoltaic output to the power,
Figure RE-GDA0003196076670000152
for installed capacity of wind, E (P)i w) For wind-power average output, E (P)i s) In order to average the photovoltaic output,
Figure RE-GDA0003196076670000153
is the photovoltaic installed capacity.
In another embodiment of the present invention, a new energy spatial coupling modeling and evaluating system is provided, which can be used to implement the new energy spatial coupling modeling and evaluating method described above, and specifically, the new energy spatial coupling modeling and evaluating system includes a data module, a coupling module, a vector module, a simulation analysis module, a complementation module, and an evaluation module.
The data module acquires the wind speed v, the illumination intensity s and the wind power output P of a corresponding area for at least one yearwPhotovoltaic output PsAnd installed wind power capacity S of corresponding regionwAnd installed photovoltaic capacity Ss；
A coupling module for calculating a wind speed correlation matrix rho between the regions by using the data acquired by the data modulevAnd illumination intensity correlation coefficient matrix ρsCarrying out space coupling evaluation analysis on the wind speed and the illumination intensity to obtain the coupling condition of the wind speed and the illumination intensity in the region;
a vector module, if no wind power/photovoltaic actual output data exists, a wind speed correlation matrix rho obtained by using the coupling modulevAnd illumination intensity correlation matrix ρsConstructing a simulated wind speed vector H obeying normal distribution and corresponding correlation matrixvAnd the simulated illumination vector HsAccording to the simulated wind velocity vector HvAnd the simulated illumination vector HsCalculating to obtain a time sequence P of the simulated wind power outputi wAnd simulating a time series P of photovoltaic outputsi s；
The simulation analysis module is used for simulating the time sequence P of the wind power output by utilizing the wind power/photovoltaic actual output data or the vector modulei wAnd simulating a time series P of photovoltaic outputsi sConstructing a correlation matrix rho of wind power outputPwCorrelation matrix p with photovoltaic outputPsAnd for the constructed correlation matrix rhoPw and ρPsEvaluating and analyzing to obtain the coupling condition of wind power output and photovoltaic output of the region;
a complementary module for simulating the wind power output time sequence P obtained by the wind power/photovoltaic actual output data or the vector modulei wAnd simulating a time series P of photovoltaic outputsi sConstruction of new energy output complementarity index
Figure RE-GDA0003196076670000154
Evaluating the complementarity between wind and light in the region, and analyzing the wind-light cooperative development prospect of the corresponding region;
the evaluation module is used for obtaining the new energy output complementarity index according to the complementation module
Figure RE-GDA0003196076670000161
Determining the optimal wind power-photovoltaic installed ratio in a region by taking the minimum wind power-photovoltaic combined output variance as a target
Figure RE-GDA0003196076670000162
And a proportion suggestion of corresponding building machines is provided for the wind and light collaborative development in the region.
In yet another embodiment of the present invention, a terminal device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor provided by the embodiment of the invention can be used for the operation of a new energy space coupling modeling evaluation method, and comprises the following steps:
acquiring the wind speed v, the illumination intensity s and the wind power output P of the corresponding area for at least one yearwPhotovoltaic output PsAnd installed wind power capacity S of corresponding regionwAnd installed photovoltaic capacity Ss(ii) a Calculating a wind speed correlation matrix ρ between regions using the datavAnd illumination intensity correlation matrix ρsAnd to wind speed and light intensityPerforming space coupling evaluation analysis to obtain the coupling condition of wind speed and illumination intensity in the region; if no wind power/photovoltaic actual output data exists, utilizing a wind speed correlation matrix rhovAnd illumination intensity correlation matrix ρsConstructing a simulated wind speed vector H obeying normal distribution and corresponding correlation matrixvAnd the simulated illumination vector HsAccording to the simulated wind velocity vector HvAnd the simulated illumination vector HsCalculating to obtain a time sequence P of the simulated wind power outputi wAnd simulating a time series P of photovoltaic outputsi s(ii) a Time sequence P for simulating wind power output or wind power actual output datai wAnd simulating a time series P of photovoltaic outputsi sConstructing a correlation matrix rho of wind power outputPwCorrelation matrix p with photovoltaic outputPsAnd for the constructed correlation matrix rhoPw and ρPsCarrying out space coupling evaluation analysis to obtain the coupling condition of wind power output and photovoltaic output of the region; time sequence P for simulating wind power output or wind power actual output datai wAnd simulating a time series P of photovoltaic outputsi sConstruction of new energy output complementarity index
Figure RE-GDA0003196076670000171
Evaluating the complementarity between wind and light in the region, and analyzing the wind-light cooperative development prospect of the corresponding region; according to the new energy output complementation index
Figure RE-GDA0003196076670000172
Determining the optimal wind power-photovoltaic installed ratio in a region by taking the minimum wind power-photovoltaic combined output variance as a target
Figure RE-GDA0003196076670000173
And a proportion suggestion of corresponding building machines is provided for the wind and light collaborative development in the region.
In still another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a terminal device and is used for storing programs and data. It is understood that the computer readable storage medium herein may include a built-in storage medium in the terminal device, and may also include an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory.
One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the new energy space coupling modeling and evaluating method in the above embodiments; one or more instructions in the computer-readable storage medium are loaded by the processor and perform the steps of:
acquiring the wind speed v, the illumination intensity s and the wind power output P of the corresponding area for at least one yearwPhotovoltaic output PsAnd installed wind power capacity S of corresponding regionwAnd installed photovoltaic capacity Ss(ii) a Calculating a wind speed correlation matrix ρ between regions using the datavAnd illumination intensity correlation matrix ρsCarrying out space coupling evaluation analysis on the wind speed and the illumination intensity to obtain the coupling condition of the wind speed and the illumination intensity in the region; if no wind power/photovoltaic actual output data exists, utilizing a wind speed correlation matrix rhovAnd illumination intensity correlation matrix ρsConstructing a simulated wind speed vector H obeying normal distribution and corresponding correlation matrixvAnd the simulated illumination vector HsAccording to the simulated wind velocity vector HvAnd the simulated illumination vector HsCalculating to obtain a time sequence P of the simulated wind power outputi wAnd simulating a time series P of photovoltaic outputsi s(ii) a Utilizing wind power/photovoltaic actual output data orTime sequence P for simulating wind power outputi wAnd simulating a time series P of photovoltaic outputsi sConstructing a correlation matrix rho of wind power outputPwCorrelation matrix p with photovoltaic outputPsAnd for the constructed correlation matrix rhoPw and ρPsCarrying out space coupling evaluation analysis to obtain the coupling condition of wind power output and photovoltaic output of the region; time sequence P for simulating wind power output or wind power actual output datai wAnd simulating a time series P of photovoltaic outputsi sConstruction of new energy output complementarity index
Figure RE-GDA0003196076670000181
Evaluating the complementarity between wind and light in the region, and analyzing the wind-light cooperative development prospect of the corresponding region; according to the new energy output complementation index
Figure RE-GDA0003196076670000182
Determining the optimal wind power-photovoltaic installed ratio in a region by taking the minimum wind power-photovoltaic combined output variance as a target
Figure RE-GDA0003196076670000183
And a proportion suggestion of corresponding building machines is provided for the wind and light collaborative development in the region.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. 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.
In order to further demonstrate the benefits of the method, the evaluation method is adopted to evaluate the output coupling of new energy in 3 adjacent areas containing large-scale wind power/photovoltaic power stations in a certain actual provincial power system in China.
Through calculation: the correlation matrix of the wind power output and the photovoltaic output is as follows:
TABLE 1 wind power output correlation matrix
Figure RE-GDA0003196076670000184
TABLE 2 photovoltaic output correlation matrix
Figure RE-GDA0003196076670000191
Tables 1 and 2 show the wind power and photovoltaic correlation coefficients for three adjacent regions. In the table, the opposite angles of the correlation coefficient table are all 1, that is, the wind power and photovoltaic coefficients of each region have the maximum correlation with the correlation.
As can be seen from table 1: in the aspect of wind power output, the wind power correlations of the three regions are extremely high and cannot be ignored, wherein the wind power output correlations between the region 1 and the region 3 are strongest.
As can be seen from table 2: in the aspect of photovoltaic output, the wind power correlation of the three regions is extremely high and cannot be ignored, wherein the photovoltaic output correlation between the region 1 and the region 2 is strongest; it is worth noting that the relevance of photovoltaics is significantly greater than that of wind power. These conclusions reflect the high geospatial coupling of the wind/photovoltaic output of the area, the effect of which is not negligible.
The wind-solar complementarity, the optimal wind-solar output ratio and the optimal wind-solar installation ratio of the three regions are calculated according to the formula (11) and the formula (22), and are shown in the table 3.
TABLE 3 wind-solar complementarity, output ratio and optimum installation ratio in three regions
Figure RE-GDA0003196076670000192
From table 3, it is seen that the wind power photovoltaic output complementarity of region 1 is strong; meanwhile, the power system configures the capacities of the wind power and the photovoltaic units according to the optimal wind power photovoltaic output ratio and the installed ratio of the corresponding areas in table 3.
When the installed ratio of the wind power photovoltaic in the area 1 is 2.43, as shown in fig. 1, the output variance is the smallest when the average total output is 10MW, and the conclusion in table 3 is verified.
In summary, the new energy space coupling modeling evaluation method and system provided by the invention are used for evaluating the geographic space coupling of wind power generation and photovoltaic power generation in the operation evaluation of the power system, and analyzing the possible situation that the wind power photovoltaic output is suddenly increased or reduced in a large range at the same time; the complementary property of wind energy and solar energy is utilized to make up the defect of a single wind power and photovoltaic power generation system, an effective suggestion is provided for new energy investment planning, and the stability, the continuity and the reliability of power supply are ensured.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (10)

1. A new energy space coupling modeling evaluation method is characterized by comprising the following steps:
s1, acquiring wind speed v, illumination intensity S and wind power output P of a corresponding area for at least one yearwPhotovoltaic output PsAnd installed wind power capacity S of corresponding regionwAnd installed photovoltaic capacity Ss；
S2, calculating a wind speed correlation matrix rho of the region by using the data acquired in the step S1vAnd illumination intensity correlation matrix ρsAnd carrying out space coupling evaluation analysis on the wind speed and the illumination intensity to obtain the coupling condition of the wind speed and the illumination intensity in the region；
S3, if no wind power/photovoltaic actual output data exist, utilizing the wind speed correlation matrix rho obtained in the step S2vAnd illumination intensity correlation matrix ρsConstructing a simulated wind speed vector H obeying normal distribution and corresponding correlation matrixvAnd the simulated illumination vector HsAccording to the simulated wind velocity vector HvAnd the simulated illumination vector HsCalculating to obtain a time sequence P of the simulated wind power outputi wAnd simulating a time series P of photovoltaic outputsi s；
S4, wind power/photovoltaic actual output data or the time sequence P of the simulated wind power output obtained in the step S3 are utilizedi wAnd simulating a time series P of photovoltaic outputsi sConstructing a correlation matrix rho of wind power outputPwCorrelation matrix p with photovoltaic outputPsAnd for the constructed correlation matrix rhoPw and ρPsCarrying out space coupling evaluation analysis to obtain the coupling condition of wind power output and photovoltaic output of the region; s5, wind power/photovoltaic actual output data or the time sequence P of the simulated wind power output obtained in the step S3 are utilizedi wAnd simulating a time series P of photovoltaic outputsi sConstruction of new energy output complementarity index
Figure FDA0003108570190000011
Evaluating the complementarity between wind and light in the region, and analyzing the wind-light cooperative development prospect of the corresponding region;
s6, obtaining the new energy output complementation index according to the step S5
Figure FDA0003108570190000012
Determining the optimal wind power-photovoltaic installed ratio in a region by taking the minimum wind power-photovoltaic combined output variance as a target
Figure FDA0003108570190000013
And a proportion suggestion of corresponding building machines is provided for the wind and light collaborative development in the region.
2. The method of claim 1, wherein the wind speed correlation matrix ρ is obtained in step S2vComprises the following steps:
Figure FDA0003108570190000021
wherein ,
Figure FDA0003108570190000022
for wind velocity time sequence vector v of region iiWind velocity time sequence vector v with region jjA correlation coefficient between;
illumination intensity correlation coefficient matrix rhosComprises the following steps:
Figure FDA0003108570190000023
wherein ,
Figure FDA0003108570190000024
is the correlation coefficient between the wind speed of region i and the wind speed of region j.
3. The method according to claim 1, wherein in step S3, the time series P of wind power output is simulatedi wAnd simulating a time series P of photovoltaic outputsi sComprises the following steps:
Figure FDA0003108570190000025
Figure FDA0003108570190000026
wherein ,viThe simulated wind speed of the area i; v. ofi,inCutting in wind speed for a fan of a region i;vi,outcutting out wind speed for a fan in a region i; v. ofi,rRated wind speed of a fan in a region i;
Figure FDA0003108570190000027
rated power of fan for region i, siThe simulated illumination intensity of the area i; etaiPhotovoltaic efficiency for region i;
Figure FDA0003108570190000028
is the photovoltaic rated power of the region i.
4. The method of claim 3, wherein the simulated wind speed timing vector H obeys a normal distribution and corresponding correlation matrixvAnd the simulated illumination timing vector Hs；
Figure FDA0003108570190000029
wherein ,Gv and GsIs an n-dimensional independent standard normal distribution vector, Bv and BsIs a mean vector, Lv and LsIs a lower triangular matrix.
5. The method of claim 1, wherein in step S4, the correlation matrix p of the wind power output isPwComprises the following steps:
Figure FDA0003108570190000031
wherein ,
Figure FDA0003108570190000032
wind power output time sequence vector P for region ii vWind power output time sequence vector of region j
Figure FDA0003108570190000033
Coefficient of correlation between
Correlation matrix rho of photovoltaic outputPsComprises the following steps:
Figure FDA0003108570190000034
wherein ,
Figure FDA0003108570190000035
is the correlation coefficient between the wind power output of the area i and the wind power output of the area j,
Figure FDA0003108570190000036
is the correlation coefficient between the photovoltaic contribution of region i and the photovoltaic contribution of region j.
6. The method according to claim 1, wherein in step S5, the wind-solar complementarity index
Figure FDA0003108570190000037
Comprises the following steps:
Figure FDA0003108570190000038
wherein ,
Figure FDA0003108570190000039
and the correlation between the wind power output and the photovoltaic output in each area is obtained.
7. The method of claim 6, wherein the correlation of wind power output to photovoltaic output within each region is determined by comparing the wind power output to the photovoltaic output
Figure FDA00031085701900000310
Comprises the following steps:
Figure FDA00031085701900000311
where E () is the mean function, Pi sIs a photovoltaic output timing vector, Pi wIs a wind power output time sequence vector,
Figure FDA00031085701900000312
for the photovoltaic output variance, the output power of the photovoltaic,
Figure FDA00031085701900000313
and the variance of the wind power output is obtained.
8. The method according to claim 1, wherein in step S6, the wind/photovoltaic output is normalized, and the normalized wind and photovoltaic outputs are split into PI，PII，PrThree completely independent components, PIThe wind power output is a component irrelevant to the photovoltaic output in the wind power output; pIIThe photovoltaic output is a component irrelevant to the wind power output; prThe photovoltaic output and the wind power output are related components; a/b is a variable for controlling output, the wind-solar combined output variance is calculated and minimized, and finally the optimal wind power-photovoltaic installed ratio is obtained according to the relation between the output and the installed ratio
Figure FDA0003108570190000041
9. The method of claim 8, wherein an optimal wind-to-photovoltaic installed ratio
Figure FDA0003108570190000042
The method specifically comprises the following steps:
Figure FDA0003108570190000043
wherein ,RPw/PsIs the ratio of wind power-photovoltaic output power,
Figure FDA0003108570190000044
for installed capacity of wind, E (P)i w) For average wind power output, E (P)i s) In order to average the photovoltaic output,
Figure FDA0003108570190000045
is the photovoltaic installed capacity.
10. A new energy space coupling modeling evaluation system is characterized by comprising:
the data module acquires the wind speed v, the illumination intensity s and the wind power output P of the corresponding area for at least one yearwPhotovoltaic output PsAnd installed wind power capacity S of corresponding regionwAnd installed photovoltaic capacity Ss；
A coupling module for calculating a wind speed correlation matrix rho between the regions by using the data acquired by the data modulevAnd illumination intensity correlation coefficient matrix ρsCarrying out space coupling evaluation analysis on the wind speed and the illumination intensity to obtain the coupling condition of the wind speed and the illumination intensity in the region;
a vector module, if no wind power/photovoltaic actual output data exists, a wind speed correlation matrix rho obtained by using the coupling modulevAnd illumination intensity correlation matrix ρsConstructing a simulated wind speed vector H obeying normal distribution and corresponding correlation matrixvAnd the simulated illumination vector HsAccording to the simulated wind velocity vector HvAnd the simulated illumination vector HsCalculating to obtain a time sequence P of the simulated wind power outputi wAnd simulating a time series P of photovoltaic outputsi s；
The simulation analysis module is used for simulating the time sequence P of the wind power output by utilizing the wind power/photovoltaic actual output data or the vector modulei wAnd simulating a time series P of photovoltaic outputsi sConstructing a correlation matrix rho of wind power outputPwAnd photovoltaicCorrelation matrix ρ of the outputPsAnd for the constructed correlation matrix rhoPw and ρPsEvaluating and analyzing to obtain the coupling condition of wind power output and photovoltaic output of the region;
a complementary module for simulating the wind power output time sequence P obtained by the wind power/photovoltaic actual output data or the vector modulei wAnd simulating a time series P of photovoltaic outputsi sConstruction of new energy output complementarity index
Figure FDA0003108570190000051
Evaluating the complementarity between wind and light in the region, and analyzing the wind-light cooperative development prospect of the corresponding region;
the evaluation module is used for obtaining the new energy output complementarity index according to the complementation module
Figure FDA0003108570190000052
Determining the optimal wind power-photovoltaic installed ratio in a region by taking the minimum wind power-photovoltaic combined output variance as a target
Figure FDA0003108570190000053
And a proportion suggestion of corresponding building machines is provided for the wind and light collaborative development in the region.
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