CN115173465A - Wind, light, water, fire and storage integrated coupling mechanism analysis method based on Copula theory - Google Patents
Wind, light, water, fire and storage integrated coupling mechanism analysis method based on Copula theory Download PDFInfo
- Publication number
- CN115173465A CN115173465A CN202210708142.8A CN202210708142A CN115173465A CN 115173465 A CN115173465 A CN 115173465A CN 202210708142 A CN202210708142 A CN 202210708142A CN 115173465 A CN115173465 A CN 115173465A
- Authority
- CN
- China
- Prior art keywords
- wind
- representing
- fire
- power
- water
- 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.)
- Pending
Links
- 238000003860 storage Methods 0.000 title claims abstract description 82
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 70
- 241000039077 Copula Species 0.000 title claims abstract description 68
- 238000004458 analytical method Methods 0.000 title claims abstract description 27
- 230000008878 coupling Effects 0.000 title claims abstract description 27
- 238000010168 coupling process Methods 0.000 title claims abstract description 27
- 238000005859 coupling reaction Methods 0.000 title claims abstract description 27
- 230000007246 mechanism Effects 0.000 title claims abstract description 27
- 230000000295 complement effect Effects 0.000 claims abstract description 36
- 230000009467 reduction Effects 0.000 claims abstract description 14
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 9
- 238000000513 principal component analysis Methods 0.000 claims abstract description 8
- 238000012360 testing method Methods 0.000 claims abstract description 6
- 230000006870 function Effects 0.000 claims description 69
- 239000011159 matrix material Substances 0.000 claims description 38
- 238000000034 method Methods 0.000 claims description 25
- 239000002245 particle Substances 0.000 claims description 24
- 230000002776 aggregation Effects 0.000 claims description 20
- 238000004364 calculation method Methods 0.000 claims description 15
- 238000005070 sampling Methods 0.000 claims description 15
- 238000011161 development Methods 0.000 claims description 11
- 239000000203 mixture Substances 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 11
- 238000010606 normalization Methods 0.000 claims description 10
- 238000005315 distribution function Methods 0.000 claims description 9
- 238000004220 aggregation Methods 0.000 claims description 8
- 230000008859 change Effects 0.000 claims description 6
- 238000005086 pumping Methods 0.000 claims description 6
- 239000002994 raw material Substances 0.000 claims description 5
- 238000012847 principal component analysis method Methods 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- PMGQWSIVQFOFOQ-YKVZVUFRSA-N clemastine fumarate Chemical compound OC(=O)\C=C\C(O)=O.CN1CCC[C@@H]1CCO[C@@](C)(C=1C=CC(Cl)=CC=1)C1=CC=CC=C1 PMGQWSIVQFOFOQ-YKVZVUFRSA-N 0.000 claims description 3
- 230000005611 electricity Effects 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000009434 installation Methods 0.000 claims description 3
- 238000003064 k means clustering Methods 0.000 claims description 3
- 230000001105 regulatory effect Effects 0.000 claims description 3
- 239000000758 substrate Substances 0.000 claims description 3
- 239000000654 additive Substances 0.000 claims description 2
- 230000000996 additive effect Effects 0.000 claims description 2
- 238000009499 grossing Methods 0.000 claims description 2
- 230000010354 integration Effects 0.000 abstract description 2
- 238000005457 optimization Methods 0.000 description 16
- 238000010248 power generation Methods 0.000 description 6
- 238000004146 energy storage Methods 0.000 description 4
- 230000013016 learning Effects 0.000 description 4
- 230000001186 cumulative effect Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000009326 social learning Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Wind Motors (AREA)
Abstract
The invention discloses a Copula theory-based wind, light, water, fire and storage integrated coupling mechanism analysis method, which comprises the following steps: collecting historical output data, clustering and reducing a scene set based on principal component analysis dimension reduction and a K-means algorithm, and obtaining a typical scene to obtain all combined output scenes between wind power plants and photovoltaic power plants which meet correlation tests in the same area of a power grid in a single scheduling period; comparing the fit degree of the Copula function to be selected with the empirical Copula function, selecting a Copula function of a proper type, and determining a relevant parameter lambda, a main wind power field and a main photovoltaic power station; extracting output fluctuation characteristics of the main power station by using historical output data, and reconstructing a combined output scene into a main wind power field and main photovoltaic power station continuous output scene set; and obtaining the optimal proportion of integration of wind, light, water, fire and storage. The wind, light, water and fire storage and output complementary characteristics are fully utilized, the operation efficiency of the regional power grid is effectively improved, and the clean energy utilization rate of the regional power grid is stably improved.
Description
Technical Field
The invention relates to the field of power supply coupling mechanism analysis of a power system, in particular to a wind, light, water, fire and storage integrated coupling mechanism analysis method based on a Copula theory.
Background
At present, the main consumption body of energy in all countries of the world still is fossil energy, and the non-renewable energy still is the main energy consumed in production and life in all countries of the world, and the problem still cannot be avoided in the development of all countries of the world. In the long-term development, the reserves of the traditional fossil energy resources are at risk due to the development needs and excessive mining of various countries, and the traditional fossil energy resources are gradually exhausted. Aiming at the challenge brought by the energy crisis, various countries around the world adopt various coping means to solve the influence caused by the exhaustion of fossil energy, and more clean and renewable energy sources gradually replace the traditional fossil energy sources to become the main direction of energy development of the countries around the world. In the face of the development of 'wind, light, water and fire storage integration', the existing research at home and abroad mostly focuses on the operation characteristics of wind, light, water and other independent power generation systems or aspects of modeling simulation, optimal configuration, control strategies and the like of wind, light, wind and water and other two energy complementary power generation systems, a series of research results are formed, and the research on the active power output characteristics of the wind, light, water and fire storage multi-energy complementary power generation system is relatively less.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the technical problem solved by the invention is as follows: the operating efficiency of the regional power grid is low, and the utilization rate of clean energy is low.
In order to solve the technical problems, the invention provides the following technical scheme: acquiring historical output data of each wind power plant and each photovoltaic power station of a regional power grid, clustering and reducing a scene set based on principal component analysis dimension reduction and a K-means algorithm, and acquiring a typical scene to obtain combined output scenes between all wind power plants and photovoltaic power stations which meet correlation tests in the same region of the regional power grid in a single scheduling period; comparing the fit degree of the Copula function to be selected with the empirical Copula function, selecting a Copula function of a proper type, and determining a relevant parameter lambda and a main wind power field and a main photovoltaic power station of different areas of a regional power grid; extracting output fluctuation characteristics of a main power station by using the historical output data, and reconstructing the combined output scene into a main wind power field and main photovoltaic power station continuous output scene set; and obtaining the wind, light, water and fire storage integrated optimal ratio with the maximum complementarity index and the minimum difference value between the output curve and the load curve of the wind, light, water and fire storage complementary system based on the peak regulation complementary characteristic between wind power, photovoltaic power, hydropower, thermal power and the historical output data of the pumping and storage year in the complementary index and peak regulation capacity ratio description regional power grid.
As a preferable scheme of the Copula theory-based wind, light, water, fire and storage integrated coupling mechanism analysis method, the method comprises the following steps: the historical output data of the wind power plant and the photovoltaic power plant is a matrix S with a sampling interval of T, a sampling number of N and a number of sampling objects of M, wherein the matrix S comprises,
wherein, V M,N Representing the Nth historical output data of the Mth wind power plant and the photovoltaic power station,
and representing the output data matrix of the M power stations sampled at the Nth time.
As a preferable scheme of the Copula theory-based wind, light, water, fire and storage integrated coupling mechanism analysis method, the method comprises the following steps: the step of principal component analysis dimensionality reduction comprises,
carrying out normalization processing on historical output data of the wind power plant and the photovoltaic power station to compress the historical output data between [0,1 ];
wherein v is i Representing the initial value of the historical output data of the ith wind power plant or photovoltaic power plant,
representing the historical output data average value of the ith wind power plant or photovoltaic power station;
representing the average value, s, of the normalized historical output data of the ith wind power plant or photovoltaic power station i Representing the standard deviation of historical output data of the ith wind power plant or photovoltaic power station;
performing a covariance matrix calculation on the normalized historical contribution data includes,
wherein cov (S) represents the covariance of the matrix S,
representing the covariance of the Nth sampled M power station output data and the Nth sampled M power station output data;
calculating eigenvectors and eigenvalues of the covariance matrix, and sorting the eigenvectors from highest to highest according to the order of the eigenvalues to obtain principal components sorted according to importance;
cov(S)-λE＝0
wherein, lambda represents the eigenvalue of covariance matrix, E represents unit eigenvector; arranging the unit eigenvectors into a matrix according to the sequence of the eigenvalues from big to small to obtain a conversion matrix P, and calculating a principal component matrix according to PX;
after the principal component analysis method is used for reducing the dimension, the dimension of the matrix S is reduced to S 1 Comprises the steps of (a) preparing a substrate,
wherein S is 1 Representing the output data of the M power stations after dimensionality reduction,
n representing the Mth plant 1 The main components of the composition are as follows,
n representing M power stations 1 And (4) a main component.
As a preferable scheme of the Copula theory-based wind, light, water, fire and storage integrated coupling mechanism analysis method, the method comprises the following steps: the K-means clustering method comprises the steps of,
determining cluster number based on elbow method, based on p and m i Obtaining a sum of squared errors SSE, said sum of squared errors SSE comprising,
wherein p represents all output data of each wind power plant or photovoltaic power station after dimensionality reduction, and m i Representing the aggregation centers of each class;
based on p ij And p ik Calculating Euclidean distance from each output data to the aggregation center
Wherein,
representing all the output sets of the ith wind farm and photovoltaic plant,
representing the initial set of clustering centers, p, for the ith wind farm and photovoltaic plant ij Representing the jth output, p, of the ith wind or photovoltaic plant ik Representing the output of the kth initial clustering center of the ith wind power plant or photovoltaic power plant;
will be provided with
Belonging to the cluster corresponding to the aggregation center with the minimum Euclidean distance, and aiming at the aggregation center u in each cluster im The updating is carried out, and the updating is carried out,
wherein, N m Represents the number of output data, u, contained in the mth cluster im Representing the aggregate center of the ith wind farm and the mth cluster of photovoltaic plants, p il Representing the ith output in the ith wind power plant and the mth cluster of the photovoltaic power plant;
iteration is carried out in a circulating mode until the average error criterion function is converged, clustering is finished, and S is added 1 Clustering into S 2 ，
Wherein N is 2 The number of contribution data after clustering is represented,
representing the Nth wind power plant or photovoltaic power plant after clustering 2 The historical force-out data is used as a basis,
nth of M power stations after representing clustering 2 An output data matrix;
output sample space S of wind power plants and photovoltaic power stations in various regions by utilizing clustering results 1 Is divided into S 1 ’、 S 2 ’、…、S R ' determining the frequency of each cluster center in the sample space as the corresponding scene probability P ar And obtaining a typical output scene, and determining a main output power station for a clustering center sample set in the typical output scene based on a Copula function.
As a preferable scheme of the Copula theory-based wind, light, water, fire and storage integrated coupling mechanism analysis method, the method comprises the following steps: the determination of the appropriate type of Copula function and associated parameters includes,
determining an edge distribution function in a typical scene of a wind power plant and a photovoltaic power station;
edge distribution function under typical scene of wind power plant and photovoltaic power station is obtained by adopting kernel density function
Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
wherein N is 2 Representing the number of the clustered output data, h represents the smoothing K h The kernel function is represented by a function of a kernel,
representing the output of the ith wind farm or photovoltaic power plant
The sample of (a);
taking the minimum sum of Euclidean distances of the to-be-selected Copula function and the empirical Copula function at each sample point as a selection standard, wherein the empirical Copula function
Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
wherein x and y represent setting parameters,
and
respectively representEdge distribution function of the I-th wind farm or photovoltaic power plant output and the I + 1-th wind farm or photovoltaic power plant output, I [ ·]Representing an indicative function;
if it is
Then
On the contrary, the first step is to take the reverse,
as a preferable scheme of the Copula theory-based wind, light, water, fire and storage integrated coupling mechanism analysis method, the method comprises the following steps: also comprises a step of adding a new type of additive,
the euclidean distance calculation includes the calculation of,
wherein,
represents the Euclidean distance between the Copula function to be selected and the empirical Copula function,
represents the Copula function to be selected,
represents an empirical Copula function;
and calculating Kendall rank correlation coefficients according to the samples, and calculating a Copula function parameter lambda to be selected.
As a preferable scheme of the Copula theory-based wind, light, water, fire and storage integrated coupling mechanism analysis method, the method comprises the following steps: the extraction of the main power station output fluctuation characteristics comprises,
marking out a local maximum value point and a local minimum value point of output data in a typical scene of a leading power station;
connecting the maximum value points through cubic spline interpolation to form an upper envelope line, connecting the minimum value points to form a lower envelope line, and solving the mean value m of the upper envelope line and the lower envelope line 1 ；
Subtracting the mean value m from the input signal 1 To obtain an intermediate signal h 1 ，
h 1 ＝X(t)-m 1
Wherein X (t) represents an input signal, m 1 Represents the mean of the upper and lower envelopes, h 1 Represents an intermediate signal;
standard deviation SD k The calculation of (a) includes that,
wherein h is k (t) eigenmode function, h, for the kth iteration k-1 (t) a witness model function representing the k-1 th iteration;
when the standard deviation is less than epsilon, extracting corresponding h 1 As eigenmode function, i.e. extracting the force fluctuation characteristic, when the standard deviation is larger than epsilon, using m 1 As a new original signal;
and (5) circularly iterating until the standard deviation requirement is met.
As a preferable scheme of the Copula theory-based wind, light, water, fire and storage integrated coupling mechanism analysis method, the method comprises the following steps: the obtaining of the optimal mixture ratio comprises the following steps,
carrying out normalization processing on wind power, photovoltaic, hydroelectric power, thermal power and historical output data of a pumped storage year in the regional power grid;
establishing a target function with the maximum complementarity index and the minimum difference value between the wind-light-water-fire storage complementary system output curve and the load curve by utilizing the wind-light-water-fire storage output complementary characteristic;
establishing peak regulation capacity of the regional power grid according to the peak regulation capacity of flexible peak regulation resources such as a thermal power generating unit capable of deeply regulating peaks, a watershed type hydropower station, a pumped storage power station and the like in the regional power grid;
determining the highest installed proportion of the wind, light, water and fire storage integrated optimal proportion model according to the peak regulation capacity and the peak regulation capacity ratio of the regional power grid, optimizing the proportion of the wind, light, water and fire storage integrated optimal proportion model based on an improved particle swarm algorithm, and obtaining the optimal proportion with the maximum complementarity index and the minimum difference value between the output curve and the load curve of the wind, light, water and fire storage complementary system.
As a preferable scheme of the Copula theory-based wind, light, water, fire and storage integrated coupling mechanism analysis method, the method comprises the following steps: the indicator of complementarity comprises the index of complementarity,
wherein, delta i k Representing the variation of the generating power of the ith energy system calendar history, n representing the total sampling points in the time scale of the year, I C Representing a complementary coefficient;
the calculation of the peak shaver capacity ratio lambda comprises,
wherein E is reg Indicating the peak shaving capacity, E, required after complementation of various energy sources regi The peak shaving capacity required by the ith single power supply is represented;
the establishment of the objective function with the maximum complementarity index and the minimum difference between the output curve and the load curve of the wind, light, water, fire and storage complementation system comprises the following steps of,
min(I T -I C )
wherein,α k and alpha k L Respectively is the change rate of the generated power and the change rate of the load after the wind, light, water, fire and storage complementation after the normalization processing, I T Represents the difference between the complementary generated power and the load power of multiple energy sources, delta i k Representing the variation of the generating power of the ith energy system calendar, n representing the total sampling points in the time scale of year, I C Representing a complementary coefficient;
the establishing of the peak shaving capacity of the regional power grid comprises,
wherein, P up，max Represents the maximum value of the peak shaving capacity, P, of the regional power grid G,upmax Representing the maximum uptake capacity, P, of thermal power H,upmax Represents the maximum upturn capacity, P, of the water and electricity P,upmax Indicating the maximum capacity of the pumped reservoir, P down，max Represents the maximum value of peak-load capacity, P, of regional power grid G,downmax Representing the maximum uptake capacity, P, of thermal power P,downmax Representing the maximum upward capacity, P, of the pumping cut，max Indicating the maximum allowable water, wind, and light reject amount.
As a preferable scheme of the Copula theory-based wind, light, water, fire and storage integrated coupling mechanism analysis method, the method comprises the following steps: also comprises the following steps of (1) preparing,
the constraints of the objective function comprise maximum installed capacity constraint, clean energy installed lower limit constraint and load balance constraint;
the maximum installed capacity constraints include the number of available,
N j,max ≤N j ≤N j,max
wherein N is j,max Represents the maximum development upper limit, N, of various types of resources j,min Indicating the currently available installed capacity, N j The installed capacity of the jth unit is represented;
the clean energy installation lower limit constraints include,
wherein N is NEW Representing the number of clean energy plants, N G Representing the number of stations in the system, alpha min Represents the minimum installed ratio, alpha, of clean energy according to the regional power grid energy development plan max Is calculated by the ratio of peak shaving capacity to peak shaving capacity;
the load balancing constraints include the number of load balancing constraints,
wherein, P j Represents the peak shaving capacity, delta L, of the power station j in a typical scene z And the load peak-valley difference under the typical scene of the regional power grid is shown.
The invention has the beneficial effects that: the wind, light, water and fire storage optimal capacity ratio is obtained by taking the maximum complementarity index and the minimum difference value between the output curve and the load curve of the wind, light, water and fire storage complementary system as targets and taking the peak regulation capacity of the system and the wind, light, water and fire storage power generation proportion as constraints, the wind, light, water and fire storage output complementary characteristic is fully utilized, the operation efficiency of a regional power grid is effectively improved, and the clean energy utilization rate of the regional power grid is stably improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor. Wherein:
fig. 1 is a basic flow diagram of a wind, light, water, fire and storage integrated coupling mechanism analysis method based on Copula theory according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Also in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, which are only for convenience of description and simplification of description, but do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected" and "connected" in the present invention are to be construed broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1, for an embodiment of the present invention, a wind, light, water, fire and storage integrated coupling mechanism analysis method based on Copula theory is provided, including:
s1: historical output data of each wind power plant and each photovoltaic power station of the regional power grid are collected, a reduced scene set is clustered and obtained based on principal component analysis dimension reduction and a K-means algorithm, and a typical scene is obtained, so that all combined output scenes between the wind power plants and the photovoltaic power stations which meet correlation tests in the same region of the regional power grid in a single scheduling period are obtained. It should be noted that:
the historical output data refers to historical output energy of the wind power plant and the photovoltaic power station in unit time, and is also called historical output power of the wind power plant and the photovoltaic power station.
The historical output data of the wind power plant and the photovoltaic power plant is a matrix S with a sampling interval of T, a sampling number of N and a number of sampling objects of M, wherein the matrix S comprises,
wherein, V M,N Representing the Nth historical output data of the Mth wind power plant and the photovoltaic power station,
and representing the output data matrix of the M power stations sampled at the Nth time.
The step of principal component analysis dimensionality reduction comprises,
(1) normalization
Carrying out normalization processing on historical output data of the wind power plant and the photovoltaic power station to compress the historical output data between [0,1 ];
wherein v is i Representing the initial value of the historical output data of the ith wind power plant or photovoltaic power plant,
representing the historical output data average value of the ith wind power plant or photovoltaic power station;
representing the average value, s, of the normalized historical output data of the ith wind power plant or photovoltaic power station i Representing the standard deviation of the historical output data of the ith wind power plant or photovoltaic power plant;
(2) calculating covariance
Performing a covariance matrix calculation on the normalized historical contribution data includes,
wherein cov (S) represents the covariance of the matrix S,
representing the covariance of the Nth sampled M power station output data and the Nth sampled M power station output data;
(3) computing eigenvectors and eigenvalues of a covariance matrix to identify principal components
Calculating an eigenvector and an eigenvalue of a covariance matrix, wherein the eigenvector of the covariance matrix is actually the direction (or the information) with the largest variance, and sorting the eigenvector from the highest to the highest according to the order of the eigenvalue to obtain principal components sorted according to importance;
cov(S)-λE＝0
wherein, λ represents the eigenvalue of the covariance matrix, and E represents the unit eigenvector; arranging the unit eigenvectors into a matrix according to the sequence of the eigenvalues from big to small to obtain a conversion matrix P, and calculating a principal component matrix according to PX;
(4) obtaining the main component
Calculating variance contribution rate and variance cumulative contribution rate by using the characteristic value, and taking the principal component with the variance cumulative contribution rate exceeding 85% -95% in the conventional principal component analysis, so the invention takes the top N with the variance cumulative contribution rate exceeding 85% 1 And (4) a main component.
After the principal component analysis method is used for reducing the dimension, the dimension of the matrix S is reduced to S 1 Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
wherein S is 1 Representing the M power station output data after dimensionality reduction,
n representing the Mth plant 1 The main components of the composition are as follows,
nth representing M power stations 1 A main component.
The steps of the K-means clustering method comprise,
(1) index quantization
After the principal component analysis method is used for dimensionality reduction, the output data of the wind power plant and the photovoltaic power station are normalized, and therefore quantification processing is not carried out.
(2) Determining a clustering number K
Determining the number of clusters based on the elbow method, based on p and m i Obtaining a sum of squared errors SSE, the sum of squared errors SSE including,
wherein p represents all output data of each wind power plant or photovoltaic power station after dimensionality reduction, and m i Represents the aggregation of each classA center; along with the increase of K, the aggregation degree of each aggregation cluster is gradually increased, the sum of squared errors is gradually reduced, when the K reaches the real aggregation number, the return of the aggregation degree is sharply reduced, the maximum value of the decrease of the corresponding SSE is similar to the elbow of a SSE-K relation graph, and the corresponding number can be determined as the size of the aggregation number K.
(3) Calculating the Euclidean distance from each output data to the aggregation center
Based on p ij And p ik Calculating Euclidean distance from each output data to the aggregation center
Wherein,
representing all the output sets of the ith wind farm and photovoltaic plant,
representing the initial set of clustering centers, p, for the ith wind farm and photovoltaic plant ij Representing the jth output, p, of the ith wind or photovoltaic plant ik Representing the output of the kth initial clustering center of the ith wind power plant or photovoltaic power station;
will be provided with
Belonging to the cluster corresponding to the aggregation center with the minimum Euclidean distance, and aiming at the aggregation center u in each cluster im The updating is carried out, and the updating is carried out,
wherein N is m Represents the number of output data, u, contained in the mth cluster im Representing the ith wind farmAnd the aggregation center, p, of the mth cluster of a photovoltaic power plant il Representing the ith output in the ith wind power plant and the mth cluster of the photovoltaic power station;
and (4) circularly iterating (1) to (3) until the average error criterion function is converged, finishing clustering and dividing S 1 Clustering into S 2 ，
Wherein N is 2 Representing the number of the clustered contribution data,
representing the Nth wind power plant or photovoltaic power plant after clustering 2 The historical force-out data is used as a basis,
n-th representing M power stations after clustering 2 An output data matrix;
utilizing clustering results to obtain output sample space S of wind power plants and photovoltaic power stations in various regions 1 Is divided into S 1 ’、S 2 ’、…、S R ' determining the frequency of each cluster center in the sample space as the corresponding scene probability P ar And obtaining a typical output scene, and determining a main output power station for a clustering center sample set in the typical output scene based on a Copula function.
S2: and comparing the fit degree of the Copula function to be selected with the empirical Copula function, selecting a Copula function of a proper type, and determining a relevant parameter lambda and the main wind power field and the main photovoltaic power station of different areas of the regional power grid. It should be noted that:
the determination of the appropriate type of Copula function and associated parameters includes,
(1) determining an edge distribution function in a typical scene of a wind power plant and a photovoltaic power station;
edge distribution function under typical scene of wind power plant and photovoltaic power station is obtained by adopting kernel density function
Comprises the steps of (a) preparing a substrate,
wherein, N 2 Representing the number of clustered output data, h represents the smoothed K h The kernel function is represented by a function of a kernel,
representing the output of the ith wind farm or photovoltaic power plant
The sample of (a);
taking the minimum sum of Euclidean distances of the Copula function to be selected and the empirical Copula function at each sample point as a selection standard, performing the empirical Copula function,
wherein x and y represent setting parameters,
and
respectively representing the edge distribution functions of the output of the ith wind farm or photovoltaic power station and the output of the (I + 1) th wind farm or photovoltaic power station, I [ ·]Representing an indicative function;
if it is
Then
On the contrary, the method can be used for carrying out the following steps,
also comprises the following steps of (1) preparing,
the euclidean distance calculation includes the calculation of,
wherein,
representing the Euclidean distance between the Copula function to be selected and the empirical Copula function,
represents the Copula function to be selected,
represents an empirical Copula function;
and calculating Kendall rank correlation coefficients according to the samples, and calculating a Copula function parameter lambda to be selected.
S3: and extracting output fluctuation characteristics of the main guide power station by using the historical output data, and reconstructing the combined output scene into a main wind power field and main guide photovoltaic power station continuous output scene set. It should be noted that:
(1) the extraction of the output fluctuation characteristics of the main power station comprises the steps of,
(2) marking a local maximum value point and a local minimum value point of output data in a typical scene of a leading power station;
(3) connecting maximum value points through cubic spline interpolation to form an upper envelope curve, connecting minimum value points to form a lower envelope curve, and solving the mean value m of the upper envelope curve and the lower envelope curve 1 ；
(4) Subtracting mean m from input signal 1 To obtain an intermediate signal h 1 ，
h 1 ＝X(t)-m 1
Wherein X (t) represents an input signal, m 1 Represents an upper envelope and a lower envelopeMean value of (d), h 1 Represents an intermediate signal;
(5) standard deviation SD k The calculation of (a) includes that,
wherein h is k (t) an eigenmode function of the kth iteration, h k-1 (t) a witness model function representing the k-1 th iteration;
when the standard deviation is less than epsilon, extracting corresponding h 1 As eigenmode function, i.e. extracting the force fluctuation characteristic, when the standard deviation is larger than epsilon, in m 1 As a new original signal;
and (5) circularly iterating the steps (1) to (4) until the standard deviation requirement is met.
S4: and obtaining the wind, light, water and fire storage integrated optimal ratio with the maximum complementarity index and the minimum difference value between the output curve and the load curve of the wind, light, water and fire storage complementary system based on the peak regulation complementary characteristic between wind power, photovoltaic power, hydropower, thermal power and the historical output data of the pumping and storage year in the complementary index and peak regulation capacity ratio description regional power grid. It should be noted that:
the obtaining of the optimal mixture ratio comprises the following steps,
carrying out normalization processing on wind power, photovoltaic, hydroelectric power, thermal power and historical output data of a pumped storage year in a regional power grid;
establishing a target function with the maximum complementarity index and the minimum difference value between the wind-light-water-fire storage complementary system output curve and the load curve by utilizing the wind-light-water-fire storage output complementary characteristic;
establishing peak regulation capacity of the regional power grid according to the peak regulation capacity of flexible peak regulation resources such as thermal power generating units capable of deeply regulating peaks, watershed hydropower stations, pumped storage power stations and the like in the regional power grid;
determining the highest wind-light-water-fire-storage integrated optimal matching model according to the peak regulation capacity and the peak regulation capacity ratio of the regional power grid, establishing a wind-light-water-fire-storage integrated optimal matching model, and optimizing the proportion of a wind-light-water-fire-storage integrated optimal matching model based on an improved particle swarm optimization algorithm to obtain the optimal matching with the maximum complementarity index and the minimum difference value between the output curve and the load curve of a wind-light-water-fire-storage complementary system.
The indicators of complementarity include,
wherein, delta i k Representing the variation of the generating power of the ith energy system calendar, n representing the total sampling points in the time scale of year, I C Representing a complementary coefficient;
the calculation of the peak shaver capacity ratio lambda comprises,
wherein, E reg Indicating the peak shaving capacity, E, required after complementation of various energy sources regi The peak shaving capacity required by the ith single power supply is represented;
the establishment of the objective function with the maximum complementarity index and the minimum difference between the output curve and the load curve of the wind, light, water, fire and storage complementation system comprises the following steps of,
min(I T -I C )
wherein alpha is k And alpha k L Respectively the change rate of the generated power and the change rate of the load after the wind, light, water, fire and storage complementation after the normalization processing, I T Representing the difference, delta, between the complementary generated power and the load power of the various sources i k Representing the variation of the generating power of the ith energy system calendar, n representing the total sampling points in the time scale of year, I C Representing a complementary coefficient;
the establishment of the peak shaving capacity of the regional power grid includes,
wherein, P up，max Represents the maximum value of the peak shaving capacity, P, of the regional power grid G,upmax Representing the maximum uptake capacity, P, of thermal power H,upmax Represents the maximum upward capacity, P, of the water and electricity P,upmax Representing the maximum upward capacity, P, of the pumping down，max Represents the maximum value of peak-load capacity, P, of regional power grid G,downmax Representing the maximum capacity of thermal power, P P,downmax Representing the maximum upward capacity, P, of the pumping cut，max Indicating the maximum allowable water, wind and light throws.
Also comprises the following steps of (1) preparing,
the constraints of the objective function comprise maximum installed capacity constraint, clean energy installed lower limit constraint and load balance constraint;
the maximum installed capacity constraints include the number of,
N j,max ≤N j ≤N j,max
wherein N is j,max Represents the maximum development upper limit, N, of various types of resources j,min Indicating that there is currently installed capacity,
N j the installed capacity of the jth unit is represented;
the lower limit constraints of the clean energy installation include,
wherein N is NEW Representing the number of clean energy plants, N G Representing the number of stations in the system, alpha min Represents the minimum installed ratio, alpha, of clean energy according to the regional power grid energy development plan max Is calculated by the ratio of peak shaving capacity to peak shaving capacity;
the load balancing constraints include the number of load balancing constraints,
wherein, P j Represents the peak shaving capacity, delta L, of the power station j in a typical scene z And the load peak-valley difference under the typical scene of the regional power grid is represented.
The method for solving the wind, light, water, fire and storage integrated optimal proportion based on the improved particle swarm comprises the following specific steps:
(1) particle and parameter initialization
Firstly, determining a wind, light, water, fire and storage integrated optimal matching optimization variable dimension D, namely a space dimension of particles, and setting a population size N;
next, a lower limit value L of the search area is set d Upper limit value U d And an initial position for setting the maximum velocity v of each particle max Minimum velocity v min And an initial speed;
finally, an initial learning factor c is set 1 、c 2 And an initial inertial weight ω; here, in order to make the algorithm have better convergence results, the learning factor and the inertia weight need to be improved.
Improvement of inertial weight ω:
wherein, ω is max 、ω min Respectively representing initial and final inertia weight values, T representing the number of iterations at the current moment, T being the total number of iterations,
the method has the advantages that a large inertia weight is selected at the initial stage of optimization, a small inertia weight is selected at the later stage of optimization, and the speed and the precision of the algorithm are effectively improved;
learning factor c 1 ,c 2 The improvement is as follows:
wherein, c 1f 、c 1e Respectively represent c 1 Initial and end values of c 2f 、c 2e Respectively represent c 2 The initial value and the end value of (1);
at the initial stage of optimization, c 1 Taking a large value, c 2 The minimum value is taken, and the proportion occupied by self experience is larger, so that global optimization is facilitated; at the end of the optimization, c 2 Taking a large value, c 1 The minimum value is taken, and the proportion occupied by social experience is larger, so that local optimization is facilitated.
(2) And (5) evaluating the particle fitness. The fitness function is the objective function. Based on the evaluation of the quality of the particles, in the optimizing process, the speed and the position of the particles need to meet the constraint:
wherein L is d 、U d Respectively representing the lower limit and the upper limit of the corresponding search space, v min 、v max Lower and upper limit values, v, respectively, representing the search speed i Representing the set of velocities, v, of each particle iD Denotes the velocity of the D-th particle, X i Representing the set of positions, X, of each particle iD Indicating the position of the D-th particle; when the speed or position of the particle exceeds the constraint condition, processing according to the boundary condition;
in addition, the optimal position of the current population needs to be selected in a comparison mode in the iteration process, and the optimal position comprises a local optimal solution p i And the global optimal solution p g ：
Wherein p is iD Represents the value of the particle D when the local optimal solution pi, p g T Representing the global optimal solution for the T-th iteration.
(3) And judging whether the particle is updated or not according to the convergence condition. The convergence conditions are as follows:
wherein,
representing the fitness value corresponding to the global optimal particle of the t iteration, wherein epsilon represents convergence precision; if the convergence condition is met, the global optimal particle is the optimal solution of the optimization variables; if not, continuing to the step (4).
(4) And updating the information such as the speed, the position and the like according to the particle evaluation result, wherein the specific updating process is as follows:
wherein,
respectively representing the corresponding learning factors, r, of the t-th iteration 1 ,r 2 E (0, 1) represents a random number, ω t A weight representing a maintenance historical speed; the speed updating formula relates to three parts of historical experience, prior cognition and social learning, wherein,
the inertia motion of the self-body is reflected,
the influence of the memory value of the particle on the speed at the next moment is expressed, which is equivalent to the thought of the particle;
the method is related to the global optimal particles, and social learning and information exchange among the particles are embodied.
The wind, light, water and fire storage optimal capacity ratio is obtained by taking the maximum complementarity index and the minimum difference value between the output curve and the load curve of the wind, light, water and fire storage complementary system as targets and taking the peak load regulation capacity of the system and the wind, light, water and fire storage power generation ratio as constraints, the wind, light, water and fire storage output complementary characteristic is fully utilized, the operation efficiency of a regional power grid is effectively improved, and the clean energy utilization rate of the regional power grid is stably improved.
Example 2
The embodiment is a second embodiment of the invention, and is different from the first embodiment in that a verification test of a wind, light, water, fire and storage integrated coupling mechanism analysis method based on Copula theory is provided, and a scientific demonstration means is used for comparing test results to verify the real effect of the method in order to verify the technical effect adopted in the method.
In this embodiment, a certain system performs power supply matching, and compares the scene 1 before optimization and the scene 2 before optimization with the optimization ratio provided by the present invention, so as to obtain the annual wind and water abandoning light abandoning rate after optimization, and the energy storage capacity, the power utilization rate, and the like are shown in table 1.
Table 1: and (5) optimizing a proportioning table.
| Pre-optimization scenario 1 | Pre-optimization scenario 2 | Optimized proportion | |
| Wind, light, water and fire storage and distribution ratio | 0.1:0.1:0.25:0.35:0.2 | 0.25:0.2:0.2:0.25:0.1 | 0.15:0.2:0.25:0.35:0.05 |
| Light-abandoning rate of annual wind-abandoning water-abandoning | 1.3％ | 11.2％ | 3.1％ |
| Energy storage capacity utilization rate | 42.6％ | 96.7％ | 92.3％ |
| Utilization ratio of energy storage power | 51.2％ | 97.2％ | 91.2％ |
| Rate of load rejection | 2.6％ | 4.3％ | 2.1％ |
| Deep peak regulation utilization rate of thermal power | 71.2％ | 89％ | 93.1％ |
| Utilization rate of hydroelectric peak regulation capacity | 66.4％ | 88.1％ | 95.5％ |
Compared with scenes 1 and 2 before optimization, the optimized proportion provided by the invention effectively reduces the annual wind and water abandoning light rate to 3.1 percent and effectively improves the system operation efficiency, respectively effectively improves the energy storage capacity and power utilization rate, the thermal power deep peak regulation utilization rate and the water and power peak regulation capacity utilization rate to 92.3 percent, 91.2 percent, 93.1 percent and 95.5 percent, aims at minimizing the difference between the complementary index maximum and the wind-light-water-fire complementary system output curve and the load curve, and takes the system peak regulation capacity and the water-fire-storage power generation ratio as constraints to obtain the optimal proportion of the wind-light-water-fire-storage wind-light-fire-storage capacity, fully utilizes the wind-light-fire-water-fire-storage power complementation characteristic, effectively improves the operation efficiency of a regional power grid, and step-improves the clean energy utilization rate of the regional power grid.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. A wind, light, water, fire and storage integrated coupling mechanism analysis method based on a Copula theory is characterized by comprising the following steps:
acquiring historical output data of each wind power plant and each photovoltaic power station of a regional power grid, clustering and reducing a scene set based on principal component analysis dimension reduction and a K-means algorithm, and acquiring a typical scene to obtain all combined output scenes between the wind power plants and the photovoltaic power stations which meet correlation test in the same region of the regional power grid in a single scheduling period;
comparing the fit degree of the Copula function to be selected with the empirical Copula function, selecting a Copula function of a proper type, and determining a relevant parameter lambda and a main wind power field and a main photovoltaic power station of different areas of a regional power grid;
extracting output fluctuation characteristics of a main power station by using the historical output data, and reconstructing the combined output scene into a main wind power field and main photovoltaic power station continuous output scene set;
and obtaining the wind, light, water and fire storage integrated optimal ratio with the maximum complementarity index and the minimum difference value between the output curve and the load curve of the wind, light, water and fire storage complementary system based on the peak regulation complementary characteristic between wind power, photovoltaic power, hydropower, thermal power and the historical output data of the pumping and storage year in the complementary index and peak regulation capacity ratio description regional power grid.
2. The Copula theory-based wind, light, water, fire and storage integrated coupling mechanism analysis method as claimed in claim 1, characterized in that: the historical output data of the wind power plant and the photovoltaic power station is a matrix S with a sampling interval of T, a sampling number of N and a sampling object number of M, the matrix S comprises,
wherein, V M,N Representing the Nth historical output data of the Mth wind power plant and the photovoltaic power station,
and representing the output data matrix of the N-th sampled M power stations.
3. The wind, light, water, fire and storage integrated coupling mechanism analysis method based on the Copula theory as claimed in claim 2, characterized in that: the step of principal component analysis dimension reduction comprises,
carrying out normalization processing on historical output data of the wind power plant and the photovoltaic power station, and compressing the historical output data to be between [0,1 ];
wherein v is i Representing the ith wind farm orThe historical output data of the photovoltaic power station is initially taken,
representing the historical output data average value of the ith wind power plant or photovoltaic power station;
representing the average value, s, of the normalized historical output data of the ith wind power plant or photovoltaic power station i Representing the standard deviation of the historical output data of the ith wind power plant or photovoltaic power plant;
performing covariance matrix calculations on the normalized historical contribution data includes,
wherein cov (S) represents the covariance of the matrix S,
representing the covariance of the Nth sampled M power station output data and the Nth sampled M power station output data;
calculating the eigenvectors and eigenvalues of the covariance matrix, and sorting the eigenvectors from highest to highest according to the order of the eigenvalues to obtain principal components sorted according to importance;
cov(S)-λE＝0
wherein, lambda represents the eigenvalue of covariance matrix, E represents unit eigenvector; arranging the unit eigenvectors into a matrix according to the sequence of the eigenvalues from big to small to obtain a conversion matrix P, and calculating a principal component matrix according to PX;
after the principal component analysis method is used for reducing the dimension, the dimension of the matrix S is reduced to S 1 Comprises the steps of (a) preparing a substrate,
wherein S is 1 Representing the M power station output data after dimensionality reduction,
n representing the Mth plant 1 The main components of the composition are as follows,
nth representing M power stations 1 And (4) a main component.
4. The Copula theory-based wind, light, water, fire and storage integrated coupling mechanism analysis method of claim 3, characterized in that: the K-means clustering method comprises the steps of,
determining cluster number based on elbow method, based on p and m i Obtaining a sum of squared errors SSE, said sum of squared errors SSE comprising,
wherein p represents all output data of each wind power plant or photovoltaic power station after dimensionality reduction, and m i Representing the aggregation centers of each class;
based on p ij And p ik Calculating Euclidean distance from each output data to the aggregation center
Wherein,
representing all the output sets of the ith wind farm and photovoltaic plant,
representing the initial set of cluster centers, p, for the ith wind farm and photovoltaic plant ij Representing the jth output, p, of the ith wind or photovoltaic plant ik Representing the output of the kth initial clustering center of the ith wind power plant or photovoltaic power station;
will be provided with
Belonging to the cluster corresponding to the aggregation center with the minimum Euclidean distance, and aiming at the aggregation center u in each cluster im The updating is carried out, and the updating is carried out,
wherein N is m Represents the number of output data, u, contained in the mth cluster im Representing the aggregation center, p, of the ith wind farm and the mth cluster of photovoltaic plants il Representing the ith output in the ith wind power plant and the mth cluster of the photovoltaic power plant;
iteration is carried out in a circulating mode until the average error criterion function is converged, clustering is finished, and S is added 1 Clustering into S 2 ，
Wherein N is 2 Representing the number of the clustered contribution data,
representing the Nth wind power plant or photovoltaic power plant after clustering 2 The historical force-out data is used as a basis,
n-th representing M power stations after clustering 2 A matrix of output data;
output sample space S of wind power plants and photovoltaic power stations in various regions by utilizing clustering results 1 Is divided into S 1 ’、S 2 ’、…、S R ' determining the frequency of each cluster center in the sample space as the corresponding scene probability P ar And obtaining a typical output scene, and determining a leading output power station for a clustering center sample set in the typical output scene based on a Copula function.
5. The Copula theory-based wind, light, water, fire and storage integrated coupling mechanism analysis method of claim 4, wherein the method comprises the following steps: the determination of the appropriate type of Copula function and associated parameters includes,
determining an edge distribution function in a typical scene of a wind power plant and a photovoltaic power station;
edge distribution function under typical scene of wind power plant and photovoltaic power station is obtained by adopting kernel density function
Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
wherein N is 2 Representing the number of the clustered output data, h represents the smoothing K h The function of the kernel is represented by a function,
representing the output of the ith wind farm or photovoltaic power plant
The sample of (1);
taking the minimum sum of Euclidean distances of the to-be-selected Copula function and the empirical Copula function at each sample point as a selection standard, wherein the empirical Copula function
Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
wherein x and y represent setting parameters,
and
respectively representing the edge distribution functions of the output of the ith wind power plant or photovoltaic power plant and the output of the (I + 1) th wind power plant or photovoltaic power plant, I [ ·]Representing an indicative function;
if it is
Then
On the contrary, the first step is to take the reverse,
6. the wind, light, water, fire and storage integrated coupling mechanism analysis method based on the Copula theory as claimed in claim 5, wherein: also comprises the following steps of (1) preparing,
the euclidean distance calculation includes the calculation of,
wherein,
representing the Euclidean distance between the Copula function to be selected and the empirical Copula function,
represents the Copula function to be selected,
represents an empirical Copula function;
and calculating Kendall rank correlation coefficients according to the samples, and calculating a Copula function parameter lambda to be selected.
7. The wind, light, water, fire and storage integrated coupling mechanism analysis method based on the Copula theory is characterized in that: the extraction of the main power station output fluctuation characteristics comprises,
marking out a local maximum value point and a local minimum value point of output data in a typical scene of a leading power station;
connecting the maximum value points through cubic spline interpolation to form an upper envelope line, connecting the minimum value points to form a lower envelope line, and solving the mean value m of the upper envelope line and the lower envelope line 1 ；
Subtracting the mean value m from the input signal 1 To obtain an intermediate signal h 1 ，
h 1 ＝X(t)-m 1
Wherein X (t) represents an input signal, m 1 Represents the mean of the upper and lower envelopes, h 1 Represents an intermediate signal;
standard deviation SD k The calculation of (a) includes that,
wherein h is k (t) an eigenmode function of the kth iteration, h k-1 (t) a witness model function representing the k-1 th iteration;
when the standard deviation is less than epsilon, extracting corresponding h 1 As eigenmode function, i.e. extracting the force fluctuation characteristic, when the standard deviation is larger than epsilon, using m 1 As a new original signal;
and (5) circularly iterating until the standard deviation requirement is met.
8. The wind, light, water, fire and storage integrated coupling mechanism analysis method based on the Copula theory as claimed in claim 7, wherein: the obtaining of the optimal mixture ratio comprises the following steps,
carrying out normalization processing on wind power, photovoltaic, hydroelectric power, thermal power and historical output data of a pumped storage year in the regional power grid;
establishing a target function with the maximum complementarity index and the minimum difference value between the wind-light-water-fire storage complementary system output curve and the load curve by utilizing the wind-light-water-fire storage output complementary characteristic;
establishing peak regulation capacity of the regional power grid according to the peak regulation capacity of flexible peak regulation resources such as a thermal power generating unit capable of deeply regulating peaks, a watershed type hydropower station, a pumped storage power station and the like in the regional power grid;
determining the highest installed proportion of the wind, light, water and fire storage integrated optimal proportion model according to the peak regulation capacity and the peak regulation capacity ratio of the regional power grid, optimizing the proportion of the wind, light, water and fire storage integrated optimal proportion model based on an improved particle swarm algorithm, and obtaining the optimal proportion with the maximum complementarity index and the minimum difference value between the output curve and the load curve of the wind, light, water and fire storage complementary system.
9. The Copula theory-based wind, light, water, fire and storage integrated coupling mechanism analysis method as claimed in claim 8, characterized in that: the indicator of complementarity comprises the index of complementarity,
wherein, delta i k Representing the variation of the generating power of the ith energy system calendar history, n representing the total sampling points in the time scale of the year, I C Representing a complementary coefficient;
the calculation of the peak shaver capacity ratio lambda comprises,
wherein E is reg Indicating the need for complementation of multiple energy sourcesPeak shaving capacity of, E regi Indicating the peak shaving capacity required by the ith single power supply;
the establishment of the objective function with the maximum complementarity index and the minimum difference between the output curve and the load curve of the wind, light, water, fire and storage complementation system comprises the following steps of,
min(I T -I C )
wherein alpha is k And alpha k L Respectively the change rate of the generated power and the change rate of the load after the wind, light, water, fire and storage complementation after the normalization processing, I T Represents the difference between the complementary generated power and the load power of multiple energy sources, delta i k Representing the variation of the generating power of the ith energy system calendar, n representing the total sampling points in the time scale of year, I C Representing a complementary coefficient;
the establishing of the peak shaving capacity of the regional power grid comprises,
wherein, P up，max Represents the maximum value of the peak shaving capacity, P, of the regional power grid G,upmax Representing the maximum capacity of thermal power, P H,upmax Represents the maximum upward capacity, P, of the water and electricity P,upmax Indicating the maximum capacity of the pumped reservoir, P down，max Represents the maximum value of the peak-load-reduction capacity, P, of the regional power grid G,downmax Representing the maximum capacity of thermal power, P P,downmax Indicating the maximum capacity of the pumped reservoir, P cut，max Indicating the maximum allowable water, wind, and light reject amount.
10. The Copula theory-based wind, light, water, fire and storage integrated coupling mechanism analysis method as claimed in claim 9, characterized in that: also comprises a step of adding a new type of additive,
the constraints of the objective function comprise maximum installed capacity constraint, clean energy installed lower limit constraint and load balance constraint;
the maximum installed capacity constraint may include,
N j,max ≤N j ≤N j,max
wherein, N j,max Represents the maximum development upper limit, N, of various types of resources j,min Indicating the currently available installed capacity, N j The installed capacity of the jth unit is represented;
the clean energy installation lower limit constraints include,
wherein, N NEW Representing the number of clean energy plants, N G Representing the number of stations in the system, alpha min Represents the minimum installed ratio, alpha, of clean energy according to the regional power grid energy development plan max Is calculated by the ratio of peak shaving capacity to peak shaving capacity;
the load balancing constraints include the number of load balancing constraints,
wherein, P j Represents the peak shaving capacity, delta L, of the power station j in a typical scene z And the load peak-valley difference under the typical scene of the regional power grid is shown.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210708142.8A CN115173465A (en) | 2022-06-21 | 2022-06-21 | Wind, light, water, fire and storage integrated coupling mechanism analysis method based on Copula theory |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210708142.8A CN115173465A (en) | 2022-06-21 | 2022-06-21 | Wind, light, water, fire and storage integrated coupling mechanism analysis method based on Copula theory |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115173465A true CN115173465A (en) | 2022-10-11 |
Family
ID=83488098
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210708142.8A Pending CN115173465A (en) | 2022-06-21 | 2022-06-21 | Wind, light, water, fire and storage integrated coupling mechanism analysis method based on Copula theory |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115173465A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115796721A (en) * | 2023-02-09 | 2023-03-14 | 国网山西省电力公司营销服务中心 | Intelligent sensing method and system for operation state of power distribution network with high-proportion new energy access |
| CN116316898A (en) * | 2023-04-10 | 2023-06-23 | 大连理工大学 | Space-time coordination method, system, equipment and medium for water-wind-solar multi-energy complementary system |
| CN117081161A (en) * | 2023-10-17 | 2023-11-17 | 长江三峡集团实业发展(北京)有限公司 | Method, device, computer equipment and medium for determining water-light complementary optimization target |
| CN117312899A (en) * | 2023-11-30 | 2023-12-29 | 国网浙江省电力有限公司 | Photovoltaic output typical scene generation method, system and storage medium |
| CN118100318A (en) * | 2024-04-23 | 2024-05-28 | 国网安徽省电力有限公司经济技术研究院 | New energy-based electric power safety supply protection method and system |
| CN118229030A (en) * | 2024-05-20 | 2024-06-21 | 国网山东省电力公司东营市河口区供电公司 | Multi-type new energy station joint output curve fitting method, system and device |
-
2022
- 2022-06-21 CN CN202210708142.8A patent/CN115173465A/en active Pending
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115796721A (en) * | 2023-02-09 | 2023-03-14 | 国网山西省电力公司营销服务中心 | Intelligent sensing method and system for operation state of power distribution network with high-proportion new energy access |
| CN115796721B (en) * | 2023-02-09 | 2023-05-16 | 国网山西省电力公司营销服务中心 | Intelligent sensing method and system for power distribution network operation state of high-proportion new energy access |
| CN116316898A (en) * | 2023-04-10 | 2023-06-23 | 大连理工大学 | Space-time coordination method, system, equipment and medium for water-wind-solar multi-energy complementary system |
| CN116316898B (en) * | 2023-04-10 | 2023-10-27 | 大连理工大学 | Space-time coordination method, system, equipment and medium for water-wind-solar multi-energy complementary system |
| CN117081161A (en) * | 2023-10-17 | 2023-11-17 | 长江三峡集团实业发展(北京)有限公司 | Method, device, computer equipment and medium for determining water-light complementary optimization target |
| CN117081161B (en) * | 2023-10-17 | 2024-01-26 | 长江三峡集团实业发展(北京)有限公司 | Method, device, computer equipment and medium for determining water-light complementary optimization target |
| CN117312899A (en) * | 2023-11-30 | 2023-12-29 | 国网浙江省电力有限公司 | Photovoltaic output typical scene generation method, system and storage medium |
| CN118100318A (en) * | 2024-04-23 | 2024-05-28 | 国网安徽省电力有限公司经济技术研究院 | New energy-based electric power safety supply protection method and system |
| CN118100318B (en) * | 2024-04-23 | 2024-07-30 | 国网安徽省电力有限公司经济技术研究院 | New energy-based electric power safety supply protection method and system |
| CN118229030A (en) * | 2024-05-20 | 2024-06-21 | 国网山东省电力公司东营市河口区供电公司 | Multi-type new energy station joint output curve fitting method, system and device |
| CN118229030B (en) * | 2024-05-20 | 2024-08-06 | 国网山东省电力公司东营市河口区供电公司 | Multi-type new energy station joint output curve fitting method, system and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115173465A (en) | Wind, light, water, fire and storage integrated coupling mechanism analysis method based on Copula theory | |
| CN108022001B (en) | Short-term load probability density prediction method based on PCA (principal component analysis) and quantile regression forest | |
| WO2021142900A1 (en) | Index-linkage-analysis-based multi-objective optimization method for peak regulation scheduling of power system | |
| CN110111003A (en) | A kind of new energy typical scene construction method based on improvement FCM clustering algorithm | |
| CN108376262A (en) | A kind of analysis model construction method of wind power output typical characteristics | |
| CN112186761B (en) | Wind power scene generation method and system based on probability distribution | |
| CN103955521B (en) | Cluster classification method for wind power plant | |
| CN111612244B (en) | QRA-LSTM-based method for predicting nonparametric probability of photovoltaic power before day | |
| CN111626473A (en) | Two-stage photovoltaic power prediction method considering error correction | |
| CN107528350A (en) | A kind of wind power output typical scene generation method for adapting to long -- term generation expansion planning | |
| CN107730399B (en) | Theoretical line loss evaluation method based on wind power generation characteristic curve | |
| CN112330045A (en) | Power transmission network line loss evaluation and reduction method based on K-medoids clustering analysis method | |
| Luo et al. | Extreme scenario extraction of a grid with large scale wind power integration by combined entropy-weighted clustering method | |
| CN105184672A (en) | Evaluation method for open, fair and impartial dispatching power generation schedule | |
| CN108694475B (en) | Short-time-scale photovoltaic cell power generation capacity prediction method based on hybrid model | |
| CN113708418A (en) | Micro-grid optimization scheduling method | |
| CN117786441A (en) | Multi-scene photovoltaic user electricity consumption behavior analysis method based on improved K-means clustering algorithm | |
| CN107832928B (en) | Equivalent comprehensive line loss evaluation method based on wind power generation characteristic curve | |
| CN112668764B (en) | Offshore wind farm energy storage system optimization configuration method based on cloud model and FCM algorithm | |
| CN111553398B (en) | Wind power scene uncertain continuous interval obtaining method based on multidimensional normal distribution | |
| CN113469420A (en) | Electric power energy structure evaluation optimization method for multi-element power supply system | |
| Maochun et al. | Typical scenario construction for fast assessment of renewable energy absorption capacity | |
| Su et al. | Cluster division and optimal scheduling of offshore wind power with energy storage based on fast unfolding algorithm | |
| CN116340798A (en) | Typical working condition generation method and device for PEM (PEM) electrolytic stack in wind power hydrogen production system | |
| Xu et al. | Wind Power Scenarios Analysis Method Based on Prediction Box and Improved k-means Algorithm |
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 |