CN119448196A

CN119448196A - A source-load dynamic modeling method, system, terminal and storage medium suitable for active distribution network morphology evolution analysis

Info

Publication number: CN119448196A
Application number: CN202411315941.4A
Authority: CN
Inventors: 杨慢慢; 陈建; 尹兆磊; 丁然; 刘震宇; 陈晨; 李润鑫; 韩宇; 王勇; 高明亮; 张媛一; 杨明爽; 张婉明; 王新浩; 石少通; 张柏杨; 赵磊; 邢明华; 李雪; 曹颖
Original assignee: Chengde Power Supply Co of State Grid Jibei Electric Power Co Ltd
Current assignee: Chengde Power Supply Co of State Grid Jibei Electric Power Co Ltd
Priority date: 2024-09-20
Filing date: 2024-09-20
Publication date: 2025-02-14

Abstract

The present invention belongs to the technical field of digital simulation of electric power system, and specifically provides a source-load dynamic modeling method, system, terminal and storage medium suitable for active distribution network morphological evolution analysis, including calculating the correlation between the output of each station and the load according to the Spearman rank correlation coefficient, and selecting the Copula function to estimate the tail correlation coefficient, and quantify the dependence between the source and the load under extreme weather or load conditions; based on the preprocessed data, a source-load characteristic database is established; according to the source-load characteristic database, in combination with the topological structure and operation rules of the active distribution network, a source-load dynamic model is constructed; the present invention not only considers the linear relationship between the output of the new energy station and the load, but more importantly, it can accurately characterize the nonlinear and asymmetric dependence that may exist between them, and provides an important reference for the stable operation of the electric power system under extreme conditions.

Description

Source load dynamic modeling method, system, terminal and storage medium suitable for morphological evolution analysis of active power distribution network

Technical Field

The invention belongs to the technical field of digital simulation of power systems, and particularly relates to a source load dynamic modeling method, a system, a terminal and a storage medium suitable for morphological evolution analysis of an active power distribution network.

Background

With the rapid development of global energy structure transformation and distributed generation technology, active distribution networks have become an important component of future power systems. The new energy stations such as wind power, photovoltaic and the like are connected into the power distribution network in a large scale, the output characteristics of the new energy stations are obviously influenced by natural factors such as weather, seasons and the like, and the new energy stations show stronger uncertainty and fluctuation. Meanwhile, the power consumption load also shows a diversified trend, particularly under the peak period and extreme weather conditions, the load demand changes sharply, and higher requirements are put on the stable operation of the power distribution network.

The traditional modeling method of the electric power system is mainly based on steady-state analysis and a deterministic model, and is difficult to accurately reflect dynamic change characteristics of the output and the load of the new energy station and complex interaction relations between the output and the load. In particular, in the face of extreme weather and sudden load changes, conventional models often fail to provide effective predictions and countermeasures, resulting in serious impacts on the safety and economy of operation of the power system. In addition, the traditional correlation analysis method such as Pearson correlation coefficient mainly focuses on linear relation, and nonlinear and asymmetric dependence relation possibly existing between the output and the load of the new energy station cannot be accurately distinguished. This dependence is particularly pronounced especially in extreme weather or load conditions, but the prior art often fails to adequately account for. Meanwhile, the existing modeling method of the power system is mostly based on steady state assumption, and dynamic characteristics of the output and the load change of the new energy station along with time are difficult to reflect. Meanwhile, a dynamic modeling method for comprehensively considering the topology structure and the operation rule of the active power distribution network is lacking, and system behaviors in different operation states cannot be accurately predicted and dealt with.

Disclosure of Invention

Aiming at the defects in the prior art, the invention provides a source load dynamic modeling method, a system, a terminal and a storage medium suitable for the morphological evolution analysis of an active power distribution network so as to solve the technical problems.

In a first aspect, the present invention provides a method for dynamically modeling source load suitable for morphological evolution analysis of an active power distribution network, including:

collecting real-time and historical output data from each new energy station, and synchronously collecting corresponding load data;

Cleaning, screening and normalizing the acquired data to eliminate noise and abnormal values;

Calculating the correlation between the output and the load of each station according to the Spearman rank correlation coefficient, selecting a Copula function, estimating a tail correlation coefficient, and quantifying the correlation between source loads under extreme weather or load conditions;

Analyzing dynamic characteristics of output and load change of the new energy station along with time based on the preprocessed data, and establishing a source-load characteristic database;

According to a source load characteristic database, combining the topological structure and the operation rule of an active power distribution network, constructing a source load dynamic model, wherein the model can reflect the dynamic change characteristics of the output and the load of an energy station in different operation states;

Different testing scenes are constructed according to the rank correlation coefficient and the tail correlation coefficient, the established source load dynamic model is verified, and the source load dynamic model is optimized and adjusted according to the verification result.

Further improvement of the technical scheme is that the correlation between the output and the load of each station is calculated according to the Spearman rank correlation coefficient, and the specific steps comprise:

Respectively sequencing the output data and the load data of the new energy station, and calculating the grade difference between the output data and the load data after sequencing;

And calculating a Spearman rank correlation coefficient according to the grade difference, and calculating the correlation between the output and the load of each station according to the Spearman rank correlation coefficient.

Further improvement of the technical scheme is that the formula for calculating the Spearman rank correlation coefficient according to the rank difference is as follows:

Wherein ρ is Spearman rank correlation coefficient, n is the number of pairs of observations, d _i is the level difference of each pair of observations, ρ ranges from-1 to 1, the closer ρ is to 1, the stronger the positive correlation of the two is, the closer ρ is to-1, the stronger the negative correlation of the two is, and ρ is to 0, indicating no correlation.

Further improvement of the technical scheme is that a Copula function is selected to estimate the tail correlation coefficient and quantify the dependence between source charges under extreme weather or load conditions, and the method specifically comprises the following steps:

Selecting a corresponding Copula function according to the distribution characteristics of the output and load data of the new energy station, wherein the Copula function does not comprise Gumbel Copula, clayton Copula, frank Copula and Gaussian Copula;

Estimating a parameter θ of the Copula function using a maximum likelihood estimation method, a moment estimation method, or a rank-based estimation method;

and calculating a tail correlation coefficient according to the parameter theta of the Copula function and a corresponding formula, wherein the range of the tail correlation coefficient is between-1 and 1, positive values represent positive dependencies, negative values represent negative dependencies, and larger absolute values of the tail correlation coefficient represent stronger dependencies.

Further improvement of the technical scheme is that based on the preprocessed data, dynamic characteristics of output and load change of the new energy station along with time are analyzed, and a source-load characteristic database is built, and the method specifically comprises the following steps:

The method comprises the steps of identifying long-term operation trend and seasonal fluctuation of output and load of a new energy station by using a time sequence analysis method, and analyzing change trend and law in different time periods;

Analyzing fluctuation characteristics of the output and the load of the new energy station, and quantifying the fluctuation by using standard deviation or variation coefficient indexes;

And recording the preprocessed fluctuation characteristics and the analysis results corresponding to the preprocessed fluctuation characteristics into a database, and adding a time tag and a type tag for the fluctuation characteristics.

According to the technical scheme, a source load dynamic model is constructed according to a source load characteristic database and by combining the topological structure and the operation rule of an active power distribution network, and the method comprises the following specific steps:

constructing a network topology graph according to the connection relation among nodes, lines, power supplies and loads of the active power distribution network;

according to the operation rule of the active power distribution network, defining constraint conditions and response mechanisms of the power grid in different operation states;

according to the analysis result of the source load characteristic database, a time sequence model is selected to describe the dynamic change characteristic of the source load;

Selecting a state variable according to the dynamic characteristics of the source load;

Establishing a dynamic equation of a state variable based on a network topological graph of the active power distribution network and a power grid constraint condition;

The dynamic behavior of the source load at different time scales is described according to a dynamic equation in combination with a time series model.

According to the technical scheme, different test scenes are constructed according to rank correlation coefficients and tail correlation coefficients, the established source load dynamic model is verified, and the source load dynamic model is optimized and adjusted according to verification results, and the method comprises the following specific steps:

Simultaneously, utilizing extreme weather data and data in a load peak period to construct a test data set with high tail dependency and low tail dependency;

inputting each test data set into a source load dynamic model to obtain predicted new energy station output and a load value corresponding to the new energy station output;

Using rank correlation coefficient to evaluate the sequencing consistency between the prediction result and the test data, and using tail correlation coefficient to evaluate the prediction accuracy of the source load dynamic model under extreme conditions;

and adjusting and optimizing the model parameters according to the evaluation results, wherein the evaluation indexes comprise, but are not limited to, prediction accuracy, error rate and stability.

In a second aspect, the present invention provides a source load dynamic modeling system suitable for morphological evolution analysis of an active power distribution network, including:

The data collection module is used for collecting real-time and historical output data from each new energy station and synchronously collecting corresponding load data;

the data preprocessing module is used for cleaning, screening and normalizing the acquired data so as to eliminate noise and abnormal values;

The correlation analysis module is used for calculating the correlation between the output and the load of each station according to the Spearman rank correlation coefficient, selecting a Copula function, estimating a tail correlation coefficient and quantifying the correlation between the source load under extreme weather or load conditions;

The database building module is used for analyzing the dynamic characteristics of the output and the load change of the new energy station along with time based on the preprocessed data and building a source load characteristic database;

The source load dynamic model construction module is used for constructing a source load dynamic model according to a source load characteristic database and combining the topological structure and the operation rule of the active power distribution network, and the model can reflect the dynamic change characteristics of the output and the load of the energy station under different operation states;

and the verification optimization module is used for constructing different test scenes according to the rank correlation coefficient and the tail correlation coefficient, verifying the established source load dynamic model and optimizing and adjusting the source load dynamic model according to the verification result.

In a third aspect, the present invention provides a terminal comprising:

a processor, a memory, wherein,

The memory is used for storing a computer program,

The processor is configured to call and run the computer program from the memory, so that the terminal performs the method of the terminal as described above.

In a fourth aspect, the present invention provides a computer storage medium having instructions stored therein which, when run on a computer, cause the computer to perform the method of the above aspects.

The invention has the beneficial effects that:

the data quality and the processing efficiency are improved, namely output data and load data are collected from a plurality of new energy stations in real time and synchronously, and data preprocessing is performed, so that noise and abnormal values in the data are effectively eliminated, the data quality is remarkably improved, and a solid foundation is laid for subsequent modeling analysis.

The method for accurately describing the nonlinear dependence relationship of the source load adopts a method of combining the Spearman rank correlation coefficient and the Copula function, not only considers the linear relationship between the output and the load of the new energy station, but also can accurately describe the nonlinear and asymmetric dependence relationship possibly existing between the new energy station and the load. Particularly, under extreme weather or load conditions, the dependence between source charges is further quantified by estimating the tail correlation coefficient, and an important reference is provided for the stable operation of the power system under extreme conditions.

And establishing a dynamic characteristic database, supporting fine modeling, namely carrying out deep analysis on dynamic characteristics of the output and the load change of the new energy station along with time based on the preprocessed high-quality data, and establishing a source load characteristic database. The database not only records the basic characteristics of the source load, but also covers the dynamic change rule of the source load under different time scales and different weather conditions, and provides powerful support for constructing a refined source load dynamic model.

The invention combines the topological structure and the operation rule of the active power distribution network to construct a source load dynamic model capable of reflecting the dynamic change characteristics of the output and the load of the energy station in different operation states. The model not only considers the uncertainty and fluctuation of the output of the new energy station, but also considers the diversified trend of the load, and realizes the comprehensive description and accurate prediction of the complex dynamic behavior of the power system.

And the adaptability and the optimization capability of the model are enhanced, namely the established source load dynamic model is fully verified by constructing diversified test scenes and utilizing rank correlation coefficients and tail correlation coefficients as the basis. And according to the verification result, the model is continuously optimized and adjusted, so that the accuracy and the reliability of the model under different running conditions are ensured. The continuous optimization mechanism enables the model to be better suitable for complex changes in actual operation of the power system, and improves the overall operation efficiency and safety of the power system.

In addition, the invention has reliable design principle, simple structure and very wide application prospect.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.

FIG. 1 is a schematic flow chart of a method of one embodiment of the invention.

FIG. 2 is a schematic block diagram of a system of one embodiment of the present invention.

Fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the present invention.

210 Is a data collection module, 220 is a data preprocessing module, 230 is a correlation analysis module, 240 is a database building module, 250 is a source load dynamic model building module, and 260 is a verification optimization module.

Detailed Description

In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

The source load dynamic modeling method suitable for the morphological evolution analysis of the active power distribution network provided by the embodiment of the invention is executed by computer equipment, and correspondingly, a source load dynamic modeling system suitable for the morphological evolution analysis of the active power distribution network is operated in the computer equipment.

FIG. 1 is a schematic flow chart of a method of one embodiment of the invention. The execution subject of fig. 1 may be a source load dynamic modeling system suitable for morphological evolution analysis of an active power distribution network. The order of the steps in the flow chart may be changed and some may be omitted according to different needs.

As shown in fig. 1, the method includes:

step 110, collecting real-time and historical output data from each new energy station, and synchronously collecting corresponding load data;

Step 120, cleaning, screening and normalizing the collected data to eliminate noise and abnormal values;

step 130, calculating the correlation between the output and the load of each station according to the Spearman rank correlation coefficient, selecting a Copula function, estimating a tail correlation coefficient, and quantifying the correlation between the source load under extreme weather or load conditions;

Step 140, analyzing dynamic characteristics of output and load change of the new energy station along with time based on the preprocessed data, and establishing a source load characteristic database;

Step 150, constructing a source load dynamic model according to a source load characteristic database and combining a topological structure and an operation rule of an active power distribution network, wherein the model can reflect dynamic change characteristics of output and load of an energy station in different operation states;

And 160, constructing different test scenes according to the rank correlation coefficient and the tail correlation coefficient, verifying the established source load dynamic model, and optimizing and adjusting the source load dynamic model according to the verification result.

In order to facilitate understanding of the invention, the principle of the source load dynamic modeling method suitable for the form evolution analysis of the active power distribution network is used, and the source load dynamic modeling process suitable for the form evolution analysis of the active power distribution network is combined in the embodiment to further describe the source load dynamic modeling method suitable for the form evolution analysis of the active power distribution network.

Specifically, the correlation between the output and the load of each station is calculated according to the Spearman rank correlation coefficient (Spearman rank correlation coefficient: the correlation is described by using the product moment correlation coefficient for data which is not subjected to normal distribution, original data grade data, one-side opening data and data with unknown overall distribution type), and the specific steps comprise:

S1311, respectively sequencing output and load data of a new energy station, and calculating the level difference between the output and load data after sequencing;

S1312, calculating a Spearman rank correlation coefficient according to the level difference, and calculating the correlation between the output and the load of each station according to the Spearman rank correlation coefficient.

And carrying out ascending order on the output data of each new energy station, and recording the ordered positions (i.e. grades) of each data point. Similarly, load data at the same time as the output data is also sorted in ascending order, and the rank is recorded. For each pair of output data and load data, the difference in their respective ranked levels is calculated. If the rank of the output data is x _i and the rank of the load data is y _i, the rank is d _i＝x_i-y_i.

Wherein, the formula for calculating the Spearman rank correlation coefficient according to the level difference is as follows:

For example, there are two new energy stations (station A and station B), each with an hourly output and corresponding load data for a day as shown in Table 1:

。

For an out force of 100MW and a load of 500MW, assuming a level difference of d ₁ =level (out force) -level (load) =2-2=0, the level differences at other time points are similarly calculated. Thereafter, a rank correlation coefficient ρ is calculated.

Further, a Copula function is selected to estimate a tail correlation coefficient and quantify the dependence between source charges under extreme weather or load conditions, and the specific steps include:

S1321, selecting a corresponding Copula function according to the distribution characteristics of the new energy station output and the load data, wherein the Copula function does not include but is not limited to Gumbel Copula (used in the case of strong upper tail dependency), clayton Copula (used in the case of strong upper tail dependency), frank Copula (used in the case of strong lower tail dependency) and Gaussian Copula (although the Gaussian Copula is not good at capturing tail dependency, the Copula can be modified by transformation or used in combination with other Copula);

S1322, estimating a parameter θ of the Copula function using a maximum likelihood estimation method, a moment estimation method, or a rank-based estimation method;

S1323, calculating a tail correlation coefficient according to the parameter theta of the Copula function and a corresponding formula, wherein the range of the tail correlation coefficient is between-1 and 1, positive values represent positive dependencies, negative values represent negative dependencies, and larger absolute values of the tail correlation coefficient represent stronger dependencies.

For example, for a power system comprising a wind farm and a photovoltaic power plant, the dependence between the output and load of these new energy sites under extreme weather conditions is evaluated, the distribution characteristics of the wind farm output and load data are analyzed, and significant dependence of both at extremely high values (i.e. the upper tail) is found, and therefore gummel Copula is selected as a function describing this dependence. And estimating the parameter theta of the Gumbel Copula by using a maximum likelihood estimation method, solving the optimal value of the parameter theta by using a numerical optimization algorithm, and then calculating the upper tail correlation coefficient according to the parameter theta of the Gumbel Copula and a corresponding formula. The cumulative distribution function of Gumbel Copula is:

Where the values of u and v marginal distribution functions, θ, are parameters of Gumbel Copula, control the strength of the dependency.

The calculation formula of the upper tail correlation coefficient is as follows:

where λ _U is the upper mantissa, a function of the parameter θ, increasing with increasing θ.

In addition, based on the preprocessed data, the dynamic characteristics of the output and the load of the new energy station changing along with time are analyzed, and a source-load characteristic database is built, and the method specifically comprises the following steps:

S141, recognizing long-term operation trend and seasonal fluctuation of the output and the load of the new energy station by using a time sequence analysis method, and analyzing the change trend and rule in different time periods;

S142, analyzing fluctuation characteristics of the output and the load of the new energy station, and quantifying the fluctuation by using standard deviation or variation coefficient indexes;

S143, recording the preprocessed volatility characteristics and the analysis results corresponding to the volatility characteristics into a database, and adding time tags and type tags for the volatility characteristics.

In addition, according to the source load characteristic database, combining the topological structure and the operation rule of the active power distribution network, constructing a source load dynamic model, which comprises the following specific steps:

S151, constructing a network topology graph according to the connection relation among nodes, lines, power supplies and loads of the active power distribution network;

S152, defining constraint conditions and response mechanisms of the power grid in different operation states according to operation rules of the active power distribution network;

s153, according to analysis results of the source load characteristic database, selecting a time sequence model to describe dynamic change characteristics of the source load;

S154, selecting a state variable according to the dynamic characteristics of the source charge;

S155, establishing a dynamic equation of a state variable based on a network topological graph of the active power distribution network and a power grid constraint condition;

and S156, describing the dynamic behavior of the source load under different time scales according to a dynamic equation and in combination with a time sequence model.

Finally, constructing different test scenes according to the rank correlation coefficient and the tail correlation coefficient, verifying the established source load dynamic model, and optimizing and adjusting the source load dynamic model according to the verification result, wherein the method comprises the following specific steps of:

s161, generating a plurality of test data sets with different rank correlation coefficients by adjusting noise levels of historical photovoltaic power generation and load data; meanwhile, using extreme weather data and data of load peak period to construct a test data set of high tail dependency and low tail dependency;

S162, inputting each test data set into a source load dynamic model to obtain predicted new energy station output and a load value corresponding to the new energy station output;

s163, evaluating the sequencing consistency between the prediction result and the test data by using a rank correlation coefficient, and evaluating the prediction accuracy of the source load dynamic model under extreme conditions by using a tail correlation coefficient;

and S164, adjusting and optimizing model parameters according to the evaluation results, wherein the evaluation indexes comprise, but are not limited to, prediction accuracy, error rate and stability.

In some embodiments, the source load dynamic modeling system 200 suitable for the morphological evolution analysis of the active power distribution network may include a plurality of functional modules composed of computer program segments. The computer program of each program segment in the source load dynamic modeling system 200 for the morphology evolution analysis of the active power distribution network may be stored in a memory of a computer device and executed by at least one processor to perform (see fig. 1 for details) the functions of the source load dynamic modeling for the morphology evolution analysis of the active power distribution network.

In this embodiment, the source load dynamic modeling system 200 suitable for the morphological evolution analysis of the active power distribution network may be divided into a plurality of functional modules according to the functions performed by the system, as shown in fig. 2. The functional modules may include a data collection module 210, a data preprocessing module 220, a correlation analysis module 230, a database building module 240, a source load dynamic model building module 250, and a verification optimization module 260. The module referred to in the present invention refers to a series of computer program segments capable of being executed by at least one processor and of performing a fixed function, stored in a memory. In the present embodiment, the functions of the respective modules will be described in detail in the following embodiments.

The system comprises a data collection module, a data preprocessing module, a correlation analysis module, a verification optimization module and a verification optimization module, wherein the data collection module is used for collecting real-time and historical output data from each new energy station and synchronously collecting corresponding load data, the data preprocessing module is used for cleaning, screening and normalizing the collected data to eliminate noise and abnormal values, the correlation analysis module is used for calculating the correlation between the output and the load of each station according to a Spearman rank correlation coefficient and selecting a Copula function to estimate a tail correlation coefficient and quantify the dependence between source loads under extreme weather or load conditions, the database establishment module is used for analyzing the dynamic characteristics of the new energy station and the load changing along with time based on the preprocessed data to establish a source load characteristic database, the source load dynamic model establishment module is used for establishing a source load dynamic model according to the source load characteristic database and combining the topological structure and the operation rules of an active power distribution network, and the verification optimization module is used for establishing different test scenes according to the rank correlation coefficient and the tail correlation coefficient to verify the established source dynamic model, and the source load optimization result is adjusted according to the rank.

Fig. 3 is a schematic structural diagram of a terminal 300 according to an embodiment of the present invention, where the terminal 300 may be used to execute the source load dynamic modeling method applicable to the active power distribution network morphological evolution analysis according to the embodiment of the present invention.

The terminal 300 may include a processor 310, a memory 320, and a communication module 330. The components may communicate via one or more buses, and it will be appreciated by those skilled in the art that the configuration of the server as shown in the drawings is not limiting of the invention, as it may be a bus-like structure, a star-like structure, or include more or fewer components than shown, or may be a combination of certain components or a different arrangement of components.

The memory 320 may be used to store instructions for execution by the processor 310, and the memory 320 may be implemented by any type of volatile or non-volatile memory terminal or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. The execution of the instructions in memory 320, when executed by processor 310, enables terminal 300 to perform some or all of the steps in the method embodiments described below.

The processor 310 is a control center of the storage terminal, connects various parts of the entire electronic terminal using various interfaces and lines, and performs various functions of the electronic terminal and/or processes data by running or executing software programs and/or modules stored in the memory 320, and invoking data stored in the memory. The processor may be comprised of an integrated circuit (INTEGRATED CIRCUIT, simply referred to as an IC), for example, a single packaged IC, or may be comprised of multiple packaged ICs connected to one another for the same function or for different functions. For example, the processor 310 may include only a central processing unit (Central Processing Unit, CPU for short). In the embodiment of the invention, the CPU can be a single operation core or can comprise multiple operation cores.

And a communication module 330, configured to establish a communication channel, so that the storage terminal can communicate with other terminals. Receiving user data sent by other terminals or sending the user data to other terminals.

The present invention also provides a computer storage medium in which a program may be stored, which program may include some or all of the steps in the embodiments provided by the present invention when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random-access memory (random access memory RAM), or the like.

It will be apparent to those skilled in the art that the techniques of embodiments of the present invention may be implemented in software plus a necessary general purpose hardware platform. Based on such understanding, the technical solution in the embodiments of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium such as a U-disc, a mobile hard disc, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, etc. various media capable of storing program codes, including several instructions for causing a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the method described in the embodiments of the present invention.

The same or similar parts between the various embodiments in this specification are referred to each other. In particular, for the terminal embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference should be made to the description in the method embodiment for relevant points.

In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with respect to each other may be through some interface, indirect coupling or communication connection of systems or modules, electrical, mechanical, or other form.

The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.

Although the present invention has been described in detail by way of preferred embodiments with reference to the accompanying drawings, the present invention is not limited thereto. Various equivalent modifications and substitutions may be made in the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and it is intended that all such modifications and substitutions be within the scope of the present invention/be within the scope of the present invention as defined by the appended claims.

Claims

1. The utility model provides a source load dynamic modeling method suitable for active distribution network morphological evolution analysis, which is characterized by comprising the following steps:

2. The method for modeling source load dynamics suitable for analysis of morphology evolution of an active power distribution network according to claim 1, wherein the step of calculating correlation between output and load of each station according to Spearman rank correlation coefficient comprises the following specific steps:

3. The method for modeling source load dynamics suitable for morphological evolution analysis of an active power distribution network according to claim 2, wherein the formula for calculating Spearman rank correlation coefficient according to the level difference is:

4. The method for modeling source load dynamics suitable for morphological evolution analysis of an active power distribution network according to claim 1, wherein the method for modeling source load dynamics suitable for morphological evolution analysis of an active power distribution network according to claim 1 is characterized by selecting a Copula function, estimating tail correlation coefficients, and quantifying the dependencies between source loads under extreme weather or load conditions, and comprises the following specific steps:

selecting a corresponding Copula function according to the distribution characteristics of the output and load data of the new energy station, wherein the Copula function does not comprise GumbelCopula, clayton Copula, frank Copula and Gaussian Copula;

5. The method for modeling source load dynamics suitable for analysis of morphological evolution of active power distribution network according to claim 1, wherein the method for modeling source load dynamics based on the preprocessed data analyzes dynamic characteristics of output and load change of new energy station along with time, and creates a source load characteristic database, and the specific steps include:

6. The method for modeling source load dynamics suitable for morphological evolution analysis of an active power distribution network according to claim 1, wherein the method for modeling source load dynamics is characterized by combining a topological structure and an operation rule of the active power distribution network according to a source load characteristic database, and comprises the following specific steps:

7. The method for modeling source load dynamics suitable for morphological evolution analysis of an active power distribution network according to claim 1, wherein different test scenes are constructed according to rank correlation coefficients and tail correlation coefficients, the constructed source load dynamics model is verified, and the source load dynamics model is optimized and adjusted according to verification results, and the method comprises the following specific steps:

8. A source load dynamic modeling system suitable for active power distribution network morphological evolution analysis, comprising:

9. A terminal, comprising:

A processor;

A memory for storing execution instructions of the processor;

Wherein the processor is configured to perform the method of any of claims 1-7.

10. A computer readable storage medium storing a computer program, which when executed by a processor implements the method of any one of claims 1-7.