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

Abstract

The invention belongs to the technical field of digital simulation of a power system, and particularly provides a source load dynamic modeling method and system suitable for morphological evolution analysis of an active power distribution network, a terminal and a storage medium, and the method comprises the steps: calculating the correlation between the output of each station and the load according to a Spearman rank correlation coefficient, selecting a Copula function, estimating a tail correlation coefficient, and calculating the output of each station according to the Spearman rank correlation coefficient; the dependency between source loads under the extreme weather or load condition is quantified; establishing a source load characteristic database based on the preprocessed data; constructing a source load dynamic model according to the source load characteristic database in combination with a topological structure and an operation rule of the active power distribution network; according to the method, the linear relation between the output and the load of the new energy field station is considered, more importantly, the possible nonlinear and asymmetric dependent relation between the output and the load can be accurately described, and an important reference is provided for stable operation of a power system under extreme conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of digital simulation of electric power systems, and in particular relates to a source-load dynamic modeling method, system, terminal and storage medium suitable for active distribution network morphology evolution analysis. Background Art

[0002] With the transformation of the global energy structure and the rapid development of distributed power generation technology, active distribution networks have become an important part of the future power system. New energy sites such as wind power and photovoltaic power are connected to the distribution network on a large scale. Their output characteristics are significantly affected by natural factors such as weather and seasons, showing strong uncertainty and volatility. At the same time, the power load also shows a trend of diversification, especially during peak hours and extreme weather conditions, the load demand changes dramatically, which puts higher requirements on the stable operation of the distribution network.

[0003] Traditional power system modeling methods are mainly based on steady-state analysis and deterministic models, which are difficult to accurately reflect the dynamic characteristics of the output and load of new energy stations, as well as the complex interaction between them. Especially in the face of extreme weather and sudden load changes, traditional models often cannot provide effective predictions and countermeasures, resulting in serious impacts on the safety and economy of power system operation. In addition, traditional correlation analysis methods such as the Pearson correlation coefficient mainly focus on linear relationships and cannot accurately describe the nonlinear and asymmetric dependencies that may exist between the output and load of new energy stations. Especially under extreme weather or load conditions, this dependency is particularly significant, but existing technologies often fail to fully consider it. At the same time, existing power system modeling methods are mostly based on steady-state assumptions, which are difficult to reflect the dynamic characteristics of the output and load of new energy stations over time. At the same time, there is a lack of dynamic modeling methods that comprehensively consider the topological structure and operating rules of the active distribution network, and it is impossible to accurately predict and respond to system behavior under different operating conditions. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a source-load dynamic modeling method, system, terminal and storage medium suitable for active distribution network morphology evolution analysis to solve the above-mentioned technical problems.

[0005] In a first aspect, the present invention provides a source-load dynamic modeling method suitable for active distribution network morphology evolution analysis, comprising:

[0006] Collect real-time and historical output data from each renewable energy station, and simultaneously collect corresponding load data;

[0007] Clean, filter and normalize the collected data to eliminate noise and outliers;

[0008] The correlation between the output and load of each station is calculated according to the Spearman rank correlation coefficient, and the copula function is used to estimate the tail correlation coefficient to quantify the dependence between source and load under extreme weather or load conditions;

[0009] Based on the pre-processed data, the dynamic characteristics of the output and load of the new energy stations over time are analyzed to establish a source-load characteristics database;

[0010] According to the source-load characteristic database, combined with the topological structure and operation rules of the active distribution network, a source-load dynamic model is constructed, which can reflect the dynamic change characteristics of energy station output and load under different operating conditions;

[0011] Different test scenarios 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 results.

[0012] A further improvement of the technical solution is to calculate the correlation between the output and load of each station according to the Spearman rank correlation coefficient, and the specific steps include:

[0013] Sort the output and load data of new energy stations respectively, and calculate the grade difference between the two after sorting;

[0014] The Spearman rank correlation coefficient is calculated based on the grade difference, and the correlation between the output and load of each station is calculated according to the Spearman rank correlation coefficient.

[0015] A further improvement of the technical solution is that the formula for calculating the Spearman rank correlation coefficient based on the rank difference is:

[0016]

[0017] where ρ is the Spearman rank correlation coefficient, n is the number of paired observations, and d i is the rank difference of each pair of observations; ρ ranges from -1 to 1. The closer ρ is to 1, the stronger the positive correlation between the two is; the closer ρ is to -1, the stronger the negative correlation between the two is; and ρ is close to 0, indicating no correlation.

[0018] A further improvement of the technical solution is to use a Copula function to estimate the tail correlation coefficient and quantify the dependence between the source and the load under extreme weather or load conditions. The specific steps include:

[0019] According to the distribution characteristics of the output and load data of the new energy station, a corresponding Copula function is selected. The Copula function includes but is not limited to Gumbel Copula, Clayton Copula, Frank Copula and Gaussian Copula;

[0020] Estimate the parameter θ of the Copula function using maximum likelihood estimation, moment estimation, or rank-based estimation;

[0021] The tail correlation coefficient is calculated according to the parameter θ of the Copula function and the corresponding formula; the value range of the tail correlation coefficient is between -1 and 1, a positive value indicates positive dependence, a negative value indicates negative dependence, and the larger the absolute value of the tail correlation coefficient, the stronger the dependence.

[0022] Further improvements to this technical solution include analyzing the dynamic characteristics of the output and load of the new energy station over time based on the pre-processed data and establishing a source-load characteristics database, the specific steps of which include:

[0023] Use time series analysis methods to identify the long-term operating trends and seasonal fluctuations of the output and load of new energy stations, and analyze the changing trends and patterns in different time periods;

[0024] Analyze the volatility characteristics of the output and load of new energy stations, and quantify the volatility using standard deviation or coefficient of variation indicators;

[0025] The preprocessed volatility features and their corresponding analysis results are entered into the database, and time labels and type labels are added to the volatility features.

[0026] A further improvement of the technical solution is to construct a source-load dynamic model based on the source-load characteristic database and in combination with the topological structure and operation rules of the active distribution network. The specific steps include:

[0027] Construct a network topology diagram based on the connection relationship between nodes, lines, power sources and loads of the active distribution network;

[0028] According to the operation rules of the active distribution network, the constraints and response mechanisms of the power grid under different operating conditions are clarified;

[0029] According to the analysis results of the source load characteristic database, a time series model is selected to describe the dynamic change characteristics of the source load;

[0030] Select state variables according to the dynamic characteristics of the source load;

[0031] Establish dynamic equations of state variables based on the network topology of the active distribution network and grid constraints;

[0032] The dynamic behavior of source load at different time scales is described based on the dynamic equation combined with the time series model.

[0033] Further improvements of the technical solution include constructing different test scenarios 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 results. The specific steps include:

[0034] By adjusting the noise level of historical photovoltaic power generation and load data, multiple test data sets with different rank correlation coefficients are generated. At the same time, extreme weather data and load peak data are used to construct test data sets with high tail dependence and low tail dependence.

[0035] Input each test data set into the source-load dynamic model to obtain the predicted output of the new energy station and its corresponding load value;

[0036] The rank correlation coefficient was used to evaluate the ranking consistency between the prediction results and the test data, and the tail correlation coefficient was used to evaluate the prediction accuracy of the source-load dynamic model under extreme conditions;

[0037] The model parameters are adjusted and optimized based on the evaluation results. The evaluation indicators include but are not limited to prediction accuracy, error rate and stability.

[0038] In a second aspect, the present invention provides a source-load dynamic modeling system suitable for active distribution network morphology evolution analysis, comprising:

[0039] The data collection module is used to collect real-time and historical output data from various renewable energy stations, and simultaneously collect the corresponding load data;

[0040] Data preprocessing module, used to clean, filter and normalize the collected data to eliminate noise and outliers;

[0041] The correlation analysis module is used to calculate the correlation between the output and load of each station according to the Spearman rank correlation coefficient, and use the Copula function to estimate the tail correlation coefficient to quantify the dependence between source and load under extreme weather or load conditions;

[0042] The database establishment module is used to analyze the dynamic characteristics of the output and load of new energy stations over time based on the pre-processed data and establish a source-load characteristics database;

[0043] The source-load dynamic model building module is used to build a source-load dynamic model based on the source-load characteristic database, combined with the topological structure and operation rules of the active distribution network. The model can reflect the dynamic change characteristics of the energy station output and load under different operating conditions;

[0044] The verification and optimization module is used to construct different test scenarios according to the rank correlation coefficient and the tail correlation coefficient, verify the established source-load dynamic model, and optimize and adjust the source-load dynamic model according to the verification results.

[0045] In a third aspect, the present invention provides a terminal, comprising:

[0046] processor, memory, wherein:

[0047] The memory is used to store computer programs.

[0048] The processor is used to call and run the computer program from the memory, so that the terminal executes the above-mentioned terminal method.

[0049] In a fourth aspect, the present invention provides a computer storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the methods described in the above aspects.

[0050] The beneficial effects of the present invention are:

[0051] Improve data quality and processing efficiency: By collecting output and load data from multiple new energy sites in real time and synchronously and performing data preprocessing, noise and outliers in the data are effectively eliminated, data quality is significantly improved, and a solid foundation is laid for subsequent modeling and analysis.

[0052] Accurately characterize the nonlinear dependence between source and load: The method combining Spearman rank correlation coefficient and Copula function not only considers the linear relationship between the output of new energy stations and load, but more importantly, it can accurately characterize the possible nonlinear and asymmetric dependence between them. Especially under extreme weather or load conditions, by estimating the tail correlation coefficient, the dependence between source and load is further quantified, providing an important reference for the stable operation of the power system under extreme conditions.

[0053] Establish a dynamic characteristics database to support refined modeling: Based on the pre-processed high-quality data, the dynamic characteristics of the output and load of new energy stations over time are deeply analyzed, and a source-load characteristics database is established. This database not only records the basic characteristics of the source and load, but also covers their dynamic change laws under different time scales and different weather conditions, providing strong support for the construction of a refined source-load dynamic model.

[0054] Constructing a dynamic model that comprehensively considers the topological structure and operating rules: This invention combines the topological structure and operating rules of the active distribution network to construct a source-load dynamic model that can reflect the dynamic change characteristics of energy station output and load under different operating conditions. This model not only takes into account the uncertainty and volatility of the output of new energy stations, but also takes into account the diversified trend of loads, achieving a comprehensive description and accurate prediction of the complex dynamic behavior of the power system.

[0055] Enhance model adaptability and optimization capabilities: By building a variety of test scenarios, the rank correlation coefficient and tail correlation coefficient are used as the basis to fully verify the established source-load dynamic model. Based on the verification results, the model is continuously optimized and adjusted to ensure the accuracy and reliability of the model under different operating conditions. This continuous optimization mechanism enables the model to better adapt to the complex changes in the actual operation of the power system and improve the overall operating efficiency and safety of the power system.

[0056] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.

[0059] Figure 2 is a schematic block diagram of a system according to an embodiment of the present invention.

[0060] Figure 3 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention.

[0061] 210 is a data collection module, 220 is a data preprocessing module, 230 is a correlation analysis module, 240 is a database establishment module, 250 is a source-load dynamic model construction module, and 260 is a verification and optimization module. DETAILED DESCRIPTION

[0062] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0064] The source-load dynamic modeling method applicable to active distribution network morphology evolution analysis provided in the embodiment of the present invention is executed by a computer device, and accordingly, the source-load dynamic modeling system applicable to active distribution network morphology evolution analysis runs in the computer device.

[0065] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1 The execution subject may be a source-load dynamic modeling system suitable for active distribution network morphology evolution analysis. According to different requirements, the order of the steps in the flow chart may be changed, and some may be omitted.

[0066] like Figure 1 As shown, the method includes:

[0067] Step 110, collecting real-time and historical output data from each new energy station, and synchronously collecting corresponding load data;

[0068] Step 120, cleaning, screening and normalizing the collected data to eliminate noise and outliers;

[0069] Step 130, calculating the correlation between the output and load of each station according to the Spearman rank correlation coefficient, and using the Copula function to estimate the tail correlation coefficient to quantify the dependence between the source and the load under extreme weather or load conditions;

[0070] Step 140, based on the preprocessed data, analyzing the dynamic characteristics of the output and load of the new energy station over time, and establishing a source-load characteristics database;

[0071] Step 150, constructing a source-load dynamic model based on the source-load characteristic database and in combination with the topological structure and operation rules of the active distribution network, the model can reflect the dynamic change characteristics of the energy station output and load under different operating conditions;

[0072] Step 160, construct different test scenarios according to the rank correlation coefficient and the tail correlation coefficient, verify the established source-load dynamic model, and optimize and adjust the source-load dynamic model according to the verification result.

[0073] To facilitate understanding of the present invention, the source-load dynamic modeling method for active distribution network morphology evolution analysis provided by the present invention is further described below based on the principle of the source-load dynamic modeling method for active distribution network morphology evolution analysis of the present invention, combined with the source-load dynamic modeling process for active distribution network morphology evolution analysis in the embodiments.

[0074] Specifically, the correlation between the output and load of each station is calculated according to the Spearman rank correlation coefficient (Spearman rank correlation coefficient: for data that do not conform to normal distribution, original data grade data, one-side opening data, and data with unknown overall distribution type, the product-moment correlation coefficient is not suitable for describing the correlation). The specific steps include:

[0075] S1311, sorting the output and load data of the new energy stations respectively, and calculating the level difference between the two after sorting;

[0076] S1312. Calculate the Spearman rank correlation coefficient based on the grade difference, and calculate the correlation between the output and load of each station according to the Spearman rank correlation coefficient.

[0077] The output data of each new energy station is sorted in ascending order, and the position (i.e., rank) of each data point after sorting is recorded. Similarly, the 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 between their ranks after their respective sorting is calculated. If the rank of the output data is x i , the level of load data is y i , then the grade difference d i =x i -y i .

[0078] Among them, the formula for calculating the Spearman rank correlation coefficient based on the rank difference is:

[0079]

[0080] where ρ is the Spearman rank correlation coefficient, n is the number of paired observations, and d i is the rank difference of each pair of observations; ρ ranges from -1 to 1. The closer ρ is to 1, the stronger the positive correlation between the two is; the closer ρ is to -1, the stronger the negative correlation between the two is; and ρ is close to 0, indicating no correlation.

[0081] For example, there are two new energy stations (station A and station B), and the output and corresponding load data of each station per hour on a certain day are shown in Table 1:

[0082] .

[0084] For an output of 100 MW and a load of 500 MW, assuming that the level difference is d1 = level (output) - level (load) = 2 - 2 = 0, the level differences at other time points are calculated similarly. Then, the rank correlation coefficient ρ is calculated.

[0085] Furthermore, the Copula function is used to estimate the tail correlation coefficient and quantify the dependence between sources and loads under extreme weather or load conditions. The specific steps include:

[0086] S1321. According to the distribution characteristics of the output and load data of the new energy station, select the corresponding Copula function. The Copula function includes but is not limited to Gumbel Copula (used in the case of strong upper tail dependence), Clayton Copula (used in the case of strong upper tail dependence), Frank Copula (used in the case of strong lower tail dependence) and Gaussian Copula (although Gaussian Copula itself is not good at capturing tail dependence, it can be improved by transformation or combination with other Copulas);

[0087] S1322, using maximum likelihood estimation method, moment estimation method or rank-based estimation method to estimate the parameter θ of the Copula function;

[0088] S1323. Calculate the tail correlation coefficient according to the parameter θ of the Copula function and the corresponding formula; the value range of the tail correlation coefficient is between -1 and 1, a positive value indicates positive dependence, and a negative value indicates negative dependence. The larger the absolute value of the tail correlation coefficient is, the stronger the dependence is.

[0089] For example, for a power system that includes wind farms and photovoltaic power stations, the dependence between the output and load of these new energy stations under extreme weather conditions is evaluated, and the distribution characteristics of wind farm output and load data are analyzed. It is found that the two have significant dependence at extremely high values ​​(i.e., the upper tail). Therefore, Gumbel Copula is selected as a function to describe this dependence. The maximum likelihood estimation method is used to estimate the parameter θ of Gumbel Copula, and the numerical optimization algorithm is used to solve the optimal value of the parameter θ. Then, the upper tail correlation coefficient is calculated based on the parameter θ of Gumbel Copula and the corresponding formula. The cumulative distribution function of Gumbel Copula is:

[0090]

[0091] where u and v are the values ​​of the marginal distribution functions and θ is the parameter of the Gumbel Copula, controlling the strength of the dependence.

[0092] The calculation formula for the upper tail correlation coefficient is:

[0093]

[0094] Among them, λ U is the upper tail correlation coefficient, which is a function of parameter θ and increases as θ increases.

[0095] In addition, based on the pre-processed data, the dynamic characteristics of the output and load of the new energy station over time are analyzed to establish a source-load characteristics database. The specific steps include:

[0096] S141. Use time series analysis methods to identify the long-term operating trends and seasonal fluctuations of the output and load of new energy stations, and analyze the changing trends and patterns in different time periods;

[0097] S142. Analyze the volatility characteristics of the output and load of new energy stations, and quantify the volatility using standard deviation or coefficient of variation indicators;

[0098] S143. Enter the preprocessed volatility features and their corresponding analysis results into a database, and add time tags and type tags to the volatility features.

[0099] In addition, according to the source-load characteristic database, combined with the topological structure and operation rules of the active distribution network, a source-load dynamic model is constructed. The specific steps include:

[0100] S151, constructing a network topology diagram according to the connection relationship between nodes, lines, power sources and loads of the active distribution network;

[0101] S152. According to the operation rules of the active distribution network, clarify the constraints and response mechanisms of the power grid under different operating conditions;

[0102] S153, according to the analysis result of the source load characteristic database, selecting a time series model to describe the dynamic change characteristics of the source load;

[0103] S154, selecting a state variable according to the dynamic characteristics of the source load;

[0104] S155, establishing dynamic equations of state variables based on a network topology diagram of the active distribution network and grid constraints;

[0105] S156. Based on the dynamic equation and combined with the time series model, the dynamic behavior of the source load at different time scales is described.

[0106] Finally, different test scenarios are constructed according to the rank correlation coefficient and the tail correlation coefficient to verify the established source-load dynamic model. The source-load dynamic model is optimized and adjusted according to the verification results. The specific steps include:

[0107] S161. By adjusting the noise level of historical photovoltaic power generation and load data, multiple test data sets with different rank correlation coefficients are generated; at the same time, extreme weather data and load peak data are used to construct test data sets with high tail dependence and low tail dependence;

[0108] S162, inputting each test data set into the source-load dynamic model to obtain the predicted output of the new energy station and its corresponding load value;

[0109] S163, using the rank correlation coefficient to evaluate the ranking consistency between the prediction results and the test data, and using the tail correlation coefficient to evaluate the prediction accuracy of the source-load dynamic model under extreme conditions;

[0110] S164. Adjust and optimize the model parameters according to the evaluation results. The evaluation indicators include but are not limited to prediction accuracy, error rate and stability.

[0111] In some embodiments, the source-load dynamic modeling system 200 for active distribution network morphology evolution analysis 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 active distribution network morphology evolution analysis may be stored in a memory of a computer device and executed by at least one processor to execute (see Figure 1 Description) The function of source-load dynamic modeling is applicable to the analysis of the evolution of active distribution network morphology.

[0112] In this embodiment, the source-load dynamic modeling system 200 suitable for active distribution network morphology evolution analysis can be divided into multiple functional modules according to the functions it performs, such as Figure 2 As shown. The functional modules may include: a data collection module 210, a data preprocessing module 220, a correlation analysis module 230, a database establishment module 240, a source-load dynamic model construction module 250, and a verification optimization module 260. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0113] The data collection module is used to collect real-time and historical output data from each new energy station, and simultaneously collect the corresponding load data; the data preprocessing module is used to clean, filter and normalize the collected data to eliminate noise and outliers; the correlation analysis module is used to calculate the correlation between the output and load of each station according to the Spearman rank correlation coefficient, and select the Copula function to estimate the tail correlation coefficient to quantify the dependence between the source and load under extreme weather or load conditions; the database establishment module is used to analyze the dynamic characteristics of the output and load of the new energy station over time based on the preprocessed data, and establish a source-load characteristic database; the source-load dynamic model construction module is used to construct a source-load dynamic model based on the source-load characteristic database, combined with the topological structure and operation rules of the active distribution network, which can reflect the dynamic change characteristics of the output and load of the energy station under different operating conditions; the verification and optimization module is used to construct different test scenarios based on the rank correlation coefficient and the tail correlation coefficient, verify the established source-load dynamic model, and optimize and adjust the source-load dynamic model according to the verification results.

[0114] Figure 3 A schematic diagram of the structure of a terminal 300 provided in an embodiment of the present invention is provided. The terminal 300 can be used to execute the source-load dynamic modeling method suitable for active distribution network morphology evolution analysis provided in an embodiment of the present invention.

[0115] The terminal 300 may include: a processor 310, a memory 320 and a communication module 330. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0116] The memory 320 can be used to store the execution instructions of the processor 310, and the memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a 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. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can perform some or all of the steps in the following method embodiments.

[0117] The processor 310 is the control center of the storage terminal, and uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of a plurality of packaged ICs with the same or different functions. For example, the processor 310 can include only a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0118] The communication module 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals or send user data to other terminals.

[0119] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0120] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention.

[0121] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0122] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, which can be electrical, mechanical or other forms.

[0123] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple 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.

[0124] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0125] Although the present invention has been described in detail with reference to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention.

Claims

1. A source-load dynamic modeling method suitable for active distribution network morphology evolution analysis, characterized in that: include: Collect real-time and historical output data from each renewable energy station, and simultaneously collect corresponding load data; Clean, filter and normalize the collected data to eliminate noise and outliers; The correlation between the output and load of each station is calculated according to the Spearman rank correlation coefficient, and the copula function is used to estimate the tail correlation coefficient to quantify the dependence between source and load under extreme weather or load conditions; Based on the pre-processed data, the dynamic characteristics of the output and load of the new energy stations over time are analyzed to establish a source-load characteristics database; According to the source-load characteristic database, combined with the topological structure and operation rules of the active distribution network, a source-load dynamic model is constructed, which can reflect the dynamic change characteristics of energy station output and load under different operating conditions; Different test scenarios 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 results.

2. The source-load dynamic modeling method for active distribution network morphology evolution analysis according to claim 1 is characterized in that: The correlation between the output and load of each station is calculated according to the Spearman rank correlation coefficient. The specific steps include: Sort the output and load data of new energy stations respectively, and calculate the grade difference between the two after sorting; The Spearman rank correlation coefficient is calculated based on the grade difference, and the correlation between the output and load of each station is calculated according to the Spearman rank correlation coefficient.

3. The source-load dynamic modeling method suitable for active distribution network morphology evolution analysis according to claim 2 is characterized in that: The formula for calculating the Spearman rank correlation coefficient based on the rank difference is: where ρ is the Spearman rank correlation coefficient, n is the number of paired observations, and d i is the rank difference of each pair of observations; ρ ranges from -1 to 1. The closer ρ is to 1, the stronger the positive correlation between the two is; the closer ρ is to -1, the stronger the negative correlation between the two is; and ρ is close to 0, indicating no correlation.

4. The source-load dynamic modeling method for active distribution network morphology evolution analysis according to claim 1 is characterized in that: The Copula function is used to estimate the tail correlation coefficient and quantify the dependence between sources and loads under extreme weather or load conditions. The specific steps include: According to the distribution characteristics of the output and load data of the new energy station, a corresponding Copula function is selected. The Copula function includes but is not limited to GumbelCopula, Clayton Copula, Frank Copula and Gaussian Copula; Estimate the parameter θ of the Copula function using maximum likelihood estimation, moment estimation, or rank-based estimation; The tail correlation coefficient is calculated according to the parameter θ of the Copula function and the corresponding formula; the value range of the tail correlation coefficient is between -1 and 1, a positive value indicates positive dependence, a negative value indicates negative dependence, and the larger the absolute value of the tail correlation coefficient, the stronger the dependence.

5. The source-load dynamic modeling method suitable for active distribution network morphology evolution analysis according to claim 1 is characterized in that: Based on the pre-processed data, the dynamic characteristics of the output and load of the new energy station over time are analyzed to establish a source-load characteristics database. The specific steps include: Use time series analysis methods to identify the long-term operating trends and seasonal fluctuations of the output and load of new energy stations, and analyze the changing trends and patterns in different time periods; Analyze the volatility characteristics of the output and load of new energy stations, and quantify the volatility using standard deviation or coefficient of variation indicators; The preprocessed volatility features and their corresponding analysis results are entered into the database, and time labels and type labels are added to the volatility features.

6. The source-load dynamic modeling method for active distribution network morphology evolution analysis according to claim 1 is characterized in that: According to the source-load characteristic database, combined with the topological structure and operation rules of the active distribution network, a source-load dynamic model is constructed. The specific steps include: Construct a network topology diagram based on the connection relationship between nodes, lines, power sources and loads of the active distribution network; According to the operation rules of the active distribution network, the constraints and response mechanisms of the power grid under different operating conditions are clarified; According to the analysis results of the source load characteristic database, a time series model is selected to describe the dynamic change characteristics of the source load; Select state variables according to the dynamic characteristics of the source load; Establish dynamic equations of state variables based on the network topology of the active distribution network and grid constraints; The dynamic behavior of source load at different time scales is described based on the dynamic equation combined with the time series model.

7. The source-load dynamic modeling method for active distribution network morphology evolution analysis according to claim 1 is characterized in that: Different test scenarios are constructed according to the rank correlation coefficient and the tail correlation coefficient, and the established source-load dynamic model is verified. The source-load dynamic model is optimized and adjusted according to the verification results. The specific steps include: By adjusting the noise level of historical photovoltaic power generation and load data, multiple test data sets with different rank correlation coefficients are generated. At the same time, extreme weather data and load peak data are used to construct test data sets with high tail dependence and low tail dependence. Input each test data set into the source-load dynamic model to obtain the predicted output of the new energy station and its corresponding load value; The rank correlation coefficient was used to evaluate the ranking consistency between the prediction results and the test data, and the tail correlation coefficient was used to evaluate the prediction accuracy of the source-load dynamic model under extreme conditions; The model parameters are adjusted and optimized based on the evaluation results. The evaluation indicators include but are not limited to prediction accuracy, error rate and stability.

8. A source-load dynamic modeling system suitable for active distribution network morphology evolution analysis, characterized in that: include: The data collection module is used to collect real-time and historical output data from various renewable energy stations, and simultaneously collect the corresponding load data; Data preprocessing module, used to clean, filter and normalize the collected data to eliminate noise and outliers; The correlation analysis module is used to calculate the correlation between the output and load of each station according to the Spearman rank correlation coefficient, and use the Copula function to estimate the tail correlation coefficient to quantify the dependence between source and load under extreme weather or load conditions; The database establishment module is used to analyze the dynamic characteristics of the output and load of new energy stations over time based on the pre-processed data and establish a source-load characteristics database; The source-load dynamic model building module is used to build a source-load dynamic model based on the source-load characteristic database, combined with the topological structure and operation rules of the active distribution network. The model can reflect the dynamic change characteristics of the energy station output and load under different operating conditions; The verification and optimization module is used to construct different test scenarios according to the rank correlation coefficient and the tail correlation coefficient, verify the established source-load dynamic model, and optimize and adjust the source-load dynamic model according to the verification results.

9. A terminal, characterized in that: include: processor; A memory for storing execution instructions of the processor; The processor is configured to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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