CN117933569A

CN117933569A - Power distribution network flexibility scoring method and device considering high-proportion new energy access

Info

Publication number: CN117933569A
Application number: CN202410237376.8A
Authority: CN
Inventors: 李岩; 刘志文; 邵冲; 洪跃佳; 董楠; 苏步芸; 袁康龙; 梁宇; 刘展志; 吴煜文
Original assignee: Energy Development Research Institute of China Southern Power Grid Co Ltd
Current assignee: Energy Development Research Institute of China Southern Power Grid Co Ltd
Priority date: 2024-03-01
Filing date: 2024-03-01
Publication date: 2024-04-26

Abstract

The application relates to a power distribution network flexibility scoring method and device considering high-proportion new energy access. The method comprises the following steps: constructing power supply side index information, power grid side index information and load side index information according to the power grid operation characteristic information corresponding to the target power grid; inputting the power supply side index information, the power grid side index information and the load side index information into a subjective and objective weighting model to obtain flexibility weight information of the power distribution network; inputting the power supply side index information, the power grid side index information and the load side index information into a flexibility grading model to obtain flexibility index grading information of the power distribution network; the power distribution network flexibility weight information and the power distribution network flexibility index scoring information are fused to obtain power distribution network flexibility scoring information; the power distribution network flexibility grading information is used for grading the power distribution network flexibility of the target power grid. The method can provide effective guidance for the rapid development of new energy, and is suitable for the development requirement of a novel power system, thereby improving the safety and stability of the power grid system.

Description

Power distribution network flexibility scoring method and device considering high-proportion new energy access

Technical Field

The application relates to the technical field of smart grids, in particular to a power distribution network flexibility scoring method, a power distribution network flexibility scoring device, computer equipment, a storage medium and a computer program product which consider high-proportion new energy access.

Background

With the development of computer technology, smart grid technology has emerged, which is based on an integrated, high-speed two-way communication network, and by applying advanced sensing and measurement technologies, advanced equipment technologies, advanced control methods, and advanced decision support system technologies, the goals of reliability, safety, economy, high efficiency, environmental friendliness, and safe use of the power grid are achieved, wherein a flexible power distribution manner can allow access to various different power generation forms, start up of the power market, and optimized and efficient operation of assets.

In the traditional technology, the flexibility of the power distribution network mainly solves the problems of load flow out-of-limit and electric energy quality caused by load fluctuation, and under the development background of new energy, large-scale distributed new energy is connected into the power distribution network, the power distribution network is changed into an active network from a passive network, and the real-time electric power balance difficulty of the power distribution network side is obviously increased due to the characteristics of randomness, fluctuation and intermittence of new energy output, so that the safety and the stability of a power grid system are poor.

Disclosure of Invention

Based on this, there is a need to provide a power distribution network flexibility scoring method, apparatus, computer device, computer readable storage medium and computer program product, which can improve the safety and stability of the power grid system and consider high-proportion new energy access.

In a first aspect, the application provides a power distribution network flexibility scoring method considering high-proportion new energy access. The method comprises the following steps:

constructing power source side index information, power grid side index information and load side index information corresponding to a target power grid according to power grid operation characteristic information corresponding to the target power grid; the power grid side index information comprises voltage grading grades;

Inputting the power supply side index information, the power grid side index information and the load side index information into a subjective and objective weighting model corresponding to the target power grid to obtain power distribution network flexibility weight information of the target power grid;

Inputting the power supply side index information, the power grid side index information and the load side index information into a flexibility scoring model corresponding to the target power grid to obtain power distribution network flexibility index scoring information of the target power grid;

Fusing the power distribution network flexibility weight information and the power distribution network flexibility index scoring information to obtain power distribution network flexibility scoring information of the target power grid; the power distribution network flexibility grading information is used for grading the power distribution network flexibility grading grade of the target power grid.

In a second aspect, the application further provides a power distribution network flexibility scoring device considering high-proportion new energy access. The device comprises:

The index information acquisition module is used for constructing power supply side index information, power grid side index information and load side index information corresponding to a target power grid according to power grid operation characteristic information corresponding to the target power grid; the power grid side index information comprises voltage grading grades;

the weight information obtaining module is used for inputting the power supply side index information, the power grid side index information and the load side index information into a subjective and objective weighting model corresponding to the target power grid to obtain the flexibility weight information of the power distribution network of the target power grid;

The weight information obtaining module is further used for inputting the power source side index information, the power grid side index information and the load side index information into a flexibility grading model corresponding to the target power grid to obtain power distribution network flexibility index grading information of the target power grid;

the scoring information obtaining module is used for fusing the flexibility weight information of the power distribution network and the flexibility index scoring information of the power distribution network to obtain the flexibility scoring information of the power distribution network of the target power grid; the power distribution network flexibility grading information is used for grading the power distribution network flexibility grading grade of the target power grid.

In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:

In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:

In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:

According to the power distribution network flexibility scoring method, device, computer equipment, storage medium and computer program product, which consider high-proportion new energy access, power source side index information, power grid side index information and load side index information corresponding to a target power grid are constructed according to the power grid operation characteristic information corresponding to the target power grid; the power grid side index information comprises voltage grading grades; inputting the power supply side index information, the power grid side index information and the load side index information into a subjective and objective weighting model corresponding to a target power grid to obtain power distribution network flexibility weight information of the target power grid; inputting the power source side index information, the power grid side index information and the load side index information into a flexibility scoring model corresponding to a target power grid to obtain power distribution network flexibility index scoring information of the target power grid; the power distribution network flexibility weight information and the power distribution network flexibility index scoring information are fused to obtain power distribution network flexibility scoring information of a target power grid; the power distribution network flexibility grading information is used for grading the power distribution network flexibility of the target power grid.

And constructing index information of a power supply side, a power grid side and a load side by analyzing the power grid operation characteristics of the target power grid, and inputting the index information into a subjective and objective weighting model and a flexibility scoring model to respectively obtain flexibility weight information and flexibility index scoring information of the power distribution network. And finally, obtaining the flexibility scores of the power distribution network of the target power grid by fusing the two information, wherein the flexibility scores are used for dividing different flexibility score grades. The influence of high-proportion new energy and voltage level can be fully considered, a scientific and practical calculation method is provided for power distribution network flexibility evaluation, effective guidance is provided for the rapid development of new energy, and the method is more suitable for the development requirement of a novel power system, so that the safety and stability of the power grid system are improved.

Drawings

FIG. 1 is an application environment diagram of a power distribution network flexibility scoring method that takes into account high-proportion new energy access in one embodiment;

FIG. 2 is a flow chart of a method for scoring flexibility of a power distribution network in consideration of high-proportion new energy access in one embodiment;

FIG. 3 is a flow chart of a method for obtaining flexibility weight information of a power distribution network in one embodiment;

FIG. 4 is a flowchart of a method for obtaining objective weight information according to one embodiment;

fig. 5 is a flowchart of a method for obtaining flexibility weight information of a power distribution network in another embodiment;

fig. 6 is a flowchart of a method for obtaining flexibility weight information of a power distribution network according to another embodiment;

FIG. 7 is a flowchart of a method for obtaining flexibility index score information of a power distribution network according to an embodiment;

FIG. 8 is a flow chart of a method of constructing a matrix of scoring remorse-like values in one embodiment;

FIG. 9 is a flowchart of a method for constructing a score information utility value matrix in one embodiment;

FIG. 10 is a block diagram illustrating a power distribution network flexibility scoring apparatus that allows for high-proportion new energy access in one embodiment;

FIG. 11 is an internal block diagram of a computer device in one embodiment.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

The power distribution network flexibility scoring method considering high-proportion new energy access provided by the embodiment of the application can be applied to an application environment shown in figure 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The server 140 acquires power grid operation characteristic information from the terminal 102, and constructs power source side index information, power grid side index information and load side index information corresponding to the target power grid according to the power grid operation characteristic information corresponding to the target power grid; the power grid side index information comprises voltage grading grades; inputting the power supply side index information, the power grid side index information and the load side index information into a subjective and objective weighting model corresponding to a target power grid to obtain power distribution network flexibility weight information of the target power grid; inputting the power source side index information, the power grid side index information and the load side index information into a flexibility scoring model corresponding to a target power grid to obtain power distribution network flexibility index scoring information of the target power grid; the power distribution network flexibility weight information and the power distribution network flexibility index scoring information are fused to obtain power distribution network flexibility scoring information of a target power grid; the power distribution network flexibility grading information is used for grading the power distribution network flexibility of the target power grid. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.

In one embodiment, as shown in fig. 2, a power distribution network flexibility scoring method considering high-proportion new energy access is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:

Step 202, constructing power source side index information, power grid side index information and load side index information corresponding to a target power grid according to power grid operation characteristic information corresponding to the target power grid.

The target power grid can be a power grid with the power distribution flexibility of new energy access to be considered.

The power grid operation characteristic information can be parameter information in the power grid operation process and inherent information of power grid equipment. For example: waste power, load factor, etc.

The power source side index information may be index information reflecting the power source side, for example: new energy consumption rate, new energy fluctuation rate, flexible power proportion and the like.

The grid-side index information may be index information reflecting the grid side, for example: the distribution transformer maximum internet average load rate, the line maximum average load rate, the N-1 pass rate, the line transfer rate, the voltage deviation rate and the like, and meanwhile, the voltage grade influence is considered when calculating the index information of the power grid side.

The load side index information may be index information reflecting the load side, for example: payload fluctuation rate, demand response peak clipping capability information, demand response valley filling capability information, and the like. The three indexes all adopt optimal assignment based on game theory when calculating weights

Specifically, the server 104 obtains, from the terminal 102, grid operation characteristic information corresponding to the target grid. According to the power grid operation characteristic information corresponding to the target power grid, power source side index information (new energy consumption rate, new energy fluctuation rate and flexible power specific gravity) corresponding to the target power grid is constructed, according to the power grid operation characteristic information corresponding to the target power grid, power grid side index information (distribution transformer maximum upper net average load rate, distribution transformer maximum lower net average load rate, line maximum average load rate, N-1 passing rate, line transfer rate and voltage deviation rate) corresponding to the target power grid is constructed, and according to the power grid operation characteristic information corresponding to the target power grid, load side index information (net load fluctuation rate, demand response peak clipping capacity information and demand response valley filling capacity information) corresponding to the target power grid is constructed.

The new energy consumption rate is used for evaluating the consumption capacity of the stored new energy, and the calculation formula is as follows:

the new energy fluctuation rate is used for evaluating the regulation requirement of the new energy, and the calculation formula is as follows:

wherein, flexible power installation duty ratio is used for evaluating the adjustable ability of power, and the calculation formula is as follows:

in the formula, the flexibility power includes distributed gas electricity, adjustable little water and electricity, small-size drawing and accumulating, novel energy storage etc..

The distribution transformer maximum internet average load rate is used for evaluating the adequacy of the main transformer internet capacity under the condition of large new energy of the power distribution network, and the calculation formula is as follows:

where N represents the number of voltage level transitions.

The distribution transformer maximum off-grid average load rate is used for evaluating the adequacy of the main transformer off-grid capacity under the conditions of large load and small new energy output of the power distribution network, and the calculation formula is as follows:

The maximum average load rate of the line is used for evaluating the adequacy of the transmission capacity of the line under the condition of large load or large new energy of the power distribution network,

The N-1 passing rate is used for evaluating the flexible adaptability of the power distribution network to N-1 faults, and the calculation formula is as follows:

the line rotatable supply rate is used for evaluating the flexible adaptability of the power distribution network to the condition of the occurrence of a transformer substation outlet switch fault or planned outage, and the calculation formula is as follows:

The voltage deviation rate is used for evaluating the flexible adaptability of the power distribution network to the electric energy quality problem caused by new energy power fluctuation, and the calculation formula is as follows:

And calculating the voltage class weight and the power grid side flexibility index. Because the lower the voltage class is, the more the number of devices is, the smaller the single capacity of the devices is, and in order to balance the number of devices and the influence of the capacity of each voltage class, after calculating the power grid side index according to the voltage class, the capacity weights are multiplied respectively Obtaining a final result, wherein the calculation formula is as follows:

In the formula, j represents the voltage grade of the power distribution network, eta represents the final calculated value of the index, eta _j represents the calculated value of the index of the voltage grade, omega _j represents the weight value corresponding to the voltage grade j, and S _j represents the total capacity of the j voltage grade grading transformer.

The net load fluctuation rate reflects the regulation requirement of the load, and if the net load fluctuation rate is higher, the requirement on the flexibility of the power distribution network is higher.

The peak clipping capability for demand response is used for evaluating the adjustment capability of the demand side resource to the peak load of the power grid, and the calculation method is as follows:

The demand response valley filling capability is used for evaluating the adjustment capability of the demand side resource to the payload valley, and the calculation method is as follows:

And 204, inputting the power source side index information, the power grid side index information and the load side index information into a subjective and objective weighting model corresponding to the target power grid to obtain the flexibility weight information of the power distribution network of the target power grid.

The subjective and objective weighting model may be an algorithm model capable of combining a subjective weighting method (maximum fixed point method) and an objective weighting method (CRITIC method).

The flexibility weight information of the power distribution network can be weight obtained by calculating optimal weight based on game theory through a subjective and objective weighting model.

Specifically, all relevant index information from the power source side index information, the grid side index information, and the load side index information are summarized. The power supply side index information, the power grid side index information and the load side index information are further input into a subjective and objective weighting model corresponding to the target power grid, the calculation of the maximum fixed point method and the CRITIC method can be related to the subjective and objective weighting model, the power distribution network flexibility weight information of the target power grid is obtained by combining the optimal weight calculation based on the game theory, and the weight information can reflect the importance or contribution degree of each index of the power supply side, the power grid side and the load side in the target power grid.

And 206, inputting the power source side index information, the power grid side index information and the load side index information into a flexibility grading model corresponding to the target power grid to obtain the flexibility index grading information of the power distribution network of the target power grid.

The flexibility scoring model may be an algorithm model that may calculate the power distribution network flexibility of the target power grid.

The power distribution network flexibility index scoring information may be an output result calculated through a flexibility scoring model.

Specifically, the power source side index information, the grid side index information, and the load side index information are input into a specific mathematical model, which is a flexibility scoring model. The core idea of the flexible scoring model is to take into account the consequences that different choices may have in the decision process and introduce a potential "remorse" in the evaluation. In the calculation of the flexible scoring model, "remorse" may represent a potential dissatisfaction or frustration that may occur after some trade-off decision is made between the power source side, the grid side, and the load side. The flexibility scoring model will take these potential remorse factors into account and assign power, grid and load sides to subsequently calculated second power distribution network flexibility weights. These weight information reflect the contribution of various aspects to the overall flexibility of the target grid under the regret theory framework. Finally, the flexibility index scoring information of the power distribution network of the target power grid can be obtained through calculation of the flexibility scoring model.

And step 208, fusing the flexibility weight information of the power distribution network and the flexibility index scoring information of the power distribution network to obtain the flexibility scoring information of the power distribution network of the target power grid.

The power distribution network flexibility scoring information may be an amount of power distribution network flexibility of the target power grid, and is used for dividing the power distribution network flexibility scoring grade of the target power grid.

Specifically, the power distribution network flexibility weight information and the power distribution network flexibility index scoring information are fused, wherein the fusion comprises weighted average or other suitable mathematical operation. The purpose of fusion is to comprehensively consider the contribution of the flexibility weight information of the power distribution network and the flexibility index scoring information of the power distribution network so as to obtain more comprehensive and balanced flexibility scoring information of the power distribution network. This process may involve adjustment of weights to ensure that the contributions of the different models are properly reflected in the final score. Finally, obtaining the power distribution network flexibility scoring information of the target power grid through fusion operation, wherein the mathematical expression of the power distribution network flexibility scoring information is as follows.

The flexibility scoring information of the power distribution network integrates the weight consideration of the subjective and objective weighting model and the regret theory model, provides a more comprehensive and comprehensive power grid flexibility measurement, and finally calculates the flexibility scoring information of the power distribution network to be a numerical value between [0 and 10], and the larger the flexibility scoring information of the power distribution network is, the better the flexibility of the power distribution network is, and the stronger the bearing capacity of new energy sources is.

In the power distribution network flexibility scoring considering high-proportion new energy access, the power source side index information, the power grid side index information and the load side index information corresponding to the target power grid are constructed according to the power grid operation characteristic information corresponding to the target power grid; inputting the power supply side index information, the power grid side index information and the load side index information into a subjective and objective weighting model corresponding to a target power grid to obtain power distribution network flexibility weight information of the target power grid; inputting the power source side index information, the power grid side index information and the load side index information into a flexibility scoring model corresponding to a target power grid to obtain power distribution network flexibility index scoring information of the target power grid; the power distribution network flexibility weight information and the power distribution network flexibility index scoring information are fused to obtain power distribution network flexibility scoring information of a target power grid; the power distribution network flexibility grading information is used for grading the power distribution network flexibility of the target power grid.

In one embodiment, as shown in fig. 3, the step of inputting the power source side index information, the power grid side index information and the load side index information into the subjective and objective weighting model corresponding to the target power grid to obtain the flexibility weight information of the power distribution network of the target power grid includes:

step 302, inputting the power source side index information, the power grid side index information and the load side index information to a maximum fixed point determining layer to obtain subjective weight information.

The maximum fixed point determining layer can be one of calculation layers of the subjective and objective weighting model, and the calculation is performed by using a maximum fixed point method.

The subjective weight information may be a calculated value obtained after the calculation of the maximum fixed point determination layer.

Specifically, the importance of each of the power source side index information, the grid side index information, and the load side index information is ranked, and the ratio r _j of the importance of the adjacent indexes, j=1, 2, … …, n-1, is given on the basis of the ranking. And calculating subjective weight information omega _an of the nth index by carrying out continuous multiplication on the ratio of the importance degrees, wherein the formula is as follows: . Calculating subjective weight information of the rest n-1 indexes, 。

Step 304, the power source side index information, the power grid side index information and the load side index information are input to an objective weighting layer to obtain objective weight information.

The objective weighting layer may be one of the calculation layers of the subjective and objective weighting model, and uses CRITIC method to calculate.

The objective weight information may be a calculated value obtained by CRITIC method calculation.

Specifically, the power source side index information, the grid side index information, and the load side index information are input to the CRITIC method and processed. The CRITIC method establishes a hierarchical structure through pairwise comparison of different indexes, and then determines the relative weights among the indexes in the power source side index information, the power grid side index information and the load side index information through calculation. In combination with the optimal weight calculation based on game theory, each index obtains relative advantages in the overall hierarchical structure. And finally, objective weight information is obtained.

And step 306, obtaining the flexibility weight information of the power distribution network according to the subjective weight information and the objective weight information.

Specifically, based on the game theory, subjective weight information and objective weight information are regarded as two game parties, and the optimal solution is selected through game candidates to obtain the flexibility weight information of the power distribution network.

In this embodiment, the subjective and objective weight values are obtained by inputting the index information of the power source side, the power grid side and the load side to the processing layers of different levels. The subjective weight is obtained by the maximum fixed point determination layer, and the objective weight is obtained by the objective weighting layer. Finally, the flexibility weight information of the power distribution network is obtained by integrating subjective and objective weight information, so that the operation and distribution of a power system are optimized, and the overall efficiency of the system is improved.

In one embodiment, as shown in fig. 4, the power source side index information, the grid side index information and the load side index information are input to an objective weighting layer to obtain objective weight information, which includes:

Step 402, calculating index information standard deviation and index information correlation coefficient among each index information in the power source side index information, the power grid side index information and the load side index information.

The standard deviation of the index information may be a standard deviation of the index information.

Wherein, the index information correlation coefficient may be a correlation coefficient between different indexes.

Specifically, the standard deviation of the index information is calculated according to each index information of the power source side index information, the power grid side index information and the load side index information, and the correlation coefficient between different indexes is calculated according to each index information of the power source side index information, the power grid side index information and the load side index information. The calculation formula of the index information standard deviation and the index information correlation coefficient is as follows.

Wherein S _l、S_j is the standard deviation of the first index and the j index respectively; The average value of the j-th column value in the index matrix after dimensionless treatment; A correlation coefficient between the first index and the j index; The first and j columns of the standardized matrix X, respectively.

Step 404, obtaining the numerical value of each index information according to the standard deviation of each index information and the correlation coefficient of each index information.

The index information amount value may be the size of the information amount contained in the index.

Specifically, for each index information, according to the standard deviation of each index information and the correlation coefficient of each index information, the numerical value of each index information quantity is obtained, and the calculation formula of the numerical value of the index information quantity is as follows.

In the method, in the process of the invention,The information amount contained in the j-th index.

And step 406, obtaining objective weight information according to the index information quantity values.

Specifically, any index information quantity value is taken as a dividend, the sum of the index information quantity values is taken as a divisor, and objective weight information is obtained after division. The mathematical expression of the objective weight information is as follows.

In the present embodiment, the numerical value of the amount of each index information is obtained by calculating the standard deviation and the correlation coefficient therebetween. The process is helpful to quantify the difference and the association degree of the index information, thereby providing basis for determining the objective weight information. By considering the change range and the interrelationship of the indexes, the system can evaluate the importance of each index more accurately, and provides more scientific basis for the management and optimization of the power system, thereby improving the stability and efficiency of the system.

In one embodiment, as shown in fig. 5, obtaining the flexibility weight information of the power distribution network according to the subjective weight information and the objective weight information includes:

step 502, calculating a subjective comprehensive weight combination coefficient and an objective comprehensive weight combination coefficient according to the subjective weight information and the objective weight information.

The subjective comprehensive weight combination coefficient can be only the difference of the distance between the comprehensive weight information of the power grid and the subjective weight information.

And the objective comprehensive weight combination coefficient is used for combining the distance difference between the power grid comprehensive weight information and the objective weight information.

Specifically, after subjective weight information ω _a and objective weight information ω _b are obtained, an optimal weight combination coefficient is calculated based on a game theory, and assuming that a subjective weight information ω _a comprehensive weight combination coefficient is k ₁, an objective weight information ω _b comprehensive weight combination coefficient is k ₂, and a power grid comprehensive weight information ω is:

based on the game theory, the subjective weight information and the objective weight information are regarded as the two game parties, and then the optimal combination weight is the comprehensive weight of the two game parties in the balance state. The subjective weight information and the comprehensive weight information of the power grid of the game two parties under the balance state are satisfied as Wherein n is the index number, and ω _an is the objective weight information of the nth index.

Based on the game theory, the subjective weight information and the objective weight information are regarded as the two game parties, and then the optimal combination weight is the comprehensive weight of the two game parties in the balance state. The objective weight information and the comprehensive weight information of the power grid of the two game parties in the balance state are satisfied asWherein n is the index number, and ω _bn is the objective weight information of the nth index. The two game parties should meet the minimum and the dispersion between the main and auxiliary weight information and the comprehensive weight information of the power grid in the balance state. The minimum dispersion of ω from ω _a and ω _b is targeted.

And step 504, determining the flexibility weight information of the power distribution network according to the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient.

And the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient are subjected to linear summation to obtain the flexibility weight information of the power distribution network. Wherein, the flexibility weight information of the distribution network is as follows

In this embodiment, comprehensive weight information of the power grid is obtained by integrating subjective weight information and objective weight information. Further, the determination of the flexibility weight information of the system is realized by calculating the subjective comprehensive weight combination coefficient between the comprehensive weight and the subjective weight of the power grid and the objective comprehensive weight combination coefficient between the subjective comprehensive weight and the objective weight. The method is helpful for more accurately evaluating the overall performance of the power grid, and provides a finer weight adjustment means by balancing subjective and objective factors, so that the operation and management of the power system are optimized.

In one embodiment, as shown in fig. 6, determining the power distribution network flexibility weight information from the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient includes:

step 602, inputting the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient to a weight game theory model to obtain an optimized subjective comprehensive weight combination coefficient and an optimized objective comprehensive weight combination coefficient.

The optimized subjective comprehensive weight combination coefficient may be a coefficient of the amount of the optimized subjective weight information.

The optimized objective comprehensive weight combination coefficient may be a coefficient for adjusting the amount of objective weight information after optimization.

Specifically, specific values of the first weight combination coefficient and the second weight combination coefficient are not given, and are subsequently found by simultaneous equations. Assume that the comprehensive weight combination coefficient of the subjective weight information ω _a is k ₁ and the comprehensive weight combination coefficient of the objective weight adjustment ω _b is k ₂. Further obtaining the optimized subjective comprehensive weight combination coefficient through the calculation of the weight game theory modelOptimizing objective comprehensive weight combination coefficients。

Step 604, determining the flexibility weight information of the power distribution network according to the optimized subjective comprehensive weight combination coefficient and the optimized objective comprehensive weight combination coefficient.

Specifically, if the sum of the optimized subjective comprehensive weight combination coefficient and the optimized objective comprehensive weight combination coefficient meets the preset coefficient threshold, the linear sum is calculated according to the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient, so that the flexibility weight information of the power distribution network is obtained, and the mathematical expression is as follows.

From the differential principle, the minimum function conditions can be obtained as:

Obtaining the supplement AndThe analytical solution of (2) is:

normalized combining coefficient:

Then:

In this embodiment, the adjustment of the sum of the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient is achieved by ensuring that the sum of the two coefficients meets the preset coefficient threshold. The process aims at optimizing the flexibility weight information of the first power distribution network, and ensures that the system is adjusted to provide more accurate and controllable flexibility weight adjustment on the premise of conforming to expected coefficients by reasonably balancing subjective and objective factors, so that the overall performance of the power system is optimized.

In one embodiment, as shown in fig. 7, the power source side index information, the grid side index information, and the load side index information are input to a flexibility scoring model corresponding to a target grid, to obtain flexibility scoring information of a power distribution network of the target grid, including:

Step 702, a scoring information utility value matrix is constructed according to the scoring information of the power source side index information, the scoring information of the power grid side index information and the scoring information of the load side index information.

The scoring information utility value matrix may be a matrix in the scoring information recommendation system application, where there are two types of elements, one is a user, and the other is an item (tem). The user will love certain items and this preference information must be combed out of the data. The data itself may be represented as a utility matrix (utlity matrix) with each (user, item) element value of the matrix representing the current user's preference for the current item. Assuming that the matrix is sparse, i.e. most elements are unknown, the unknowns mean that we have no knowledge of the current user's preference for the current item. For example: an example of a utility matrix represents the result of a user's rating (1-5 level, 5 highest) of a movie. The blank indicates that the current user has no score for the current movie.

Specifically, the scoring information of the power source side index information, the scoring information of the grid side index information and the scoring information of the load side index information are integrated into one matrix as an initial scoring matrix. In order for this matrix to reflect the relative weights and relationships between the various indices, it is therefore necessary to construct an ideal matrix of scoring information, i.e., the expected value of each index in an ideal case. The ideal matrix serves as a reference point to optimize the initial scoring matrix. By applying some optimization techniques, including mathematical models or algorithms, the optimization process is to optimize the initial scoring matrix according to the scoring information ideal matrix, and finally a scoring information utility value matrix which is more in line with expectations is obtained.

And step 704, constructing a grading regret-nugget value matrix according to the correlation coefficient of each index information.

The scoring regret-gladness matrix can be obtained by calculating index information by a regret decision method, a so-called Savich method or an regret method.

Specifically, a scoring regret-nuance matrix is constructed according to the correlation coefficient of each index information. Wherein, the mathematical expression of the scoring regret-happiness value matrix R is as follows.

The regret-nugget value matrix R is constructed as follows:

Where β is a remorse avoidance coefficient, and the larger β is, the larger the remorse avoidance degree is, and here β is 0.013.

And step 706, adding the scoring information utility value matrix and the scoring remorse-happy value matrix to obtain the scoring information of the flexibility index of the power distribution network.

Specifically, adding the scoring information utility value matrix and the scoring remorse-happy value matrix to obtain the scoring information of the flexibility index of the power distribution network. The mathematical expression of the power distribution network flexibility index scoring information D is as follows.

In this embodiment, a scoring information utility value matrix is constructed by using scoring information of index information of a power source side, a power grid side and a load side, a scoring remorse-happy value matrix is established based on a correlation coefficient of the index information, the two matrices are added, and the system obtains flexibility weight information of a second power distribution network. The method combines the scoring information and the regret value, is beneficial to more comprehensively evaluating the influence of each index on the system performance, thereby providing more comprehensive and accurate weight information and optimizing the operation and distribution of the power system.

In one embodiment, as shown in fig. 8, constructing a scoring regret-nuance matrix according to the correlation coefficient of each index information, including:

step 802, optimizing each index information correlation coefficient according to the remorse avoidance coefficient to obtain each optimized index information correlation coefficient;

wherein the remorse avoidance coefficient can be the remorse avoidance coefficient of the grading remorse-gladness value matrix

The optimization index information correlation coefficient may be an index information correlation coefficient optimized by a regret avoidance coefficient.

Specifically, according to the index information correlation coefficients, optimizing the index information correlation coefficients to obtain optimized index information correlation coefficients. Wherein the expression is:

Step 804, constructing a scoring remorse-happy value matrix according to the correlation coefficient of each optimization index information.

Specifically, a scoring remorse-happy value matrix is constructed according to the correlation coefficient of each optimization index information. Wherein, the mathematical expression of the scoring regret-happiness value matrix R is as follows.

The regret-nugget value matrix R is constructed as follows:

In this embodiment, by using the regret-gladness value matrix, not only the correlation between the indexes can be comprehensively considered, but also the relationships can be accurately adjusted according to the regret avoidance coefficients, so as to provide more targeted decision support for the decision maker. This helps to reduce the risk of decision making in a complex decision making environment, improves the accuracy of decision making, and provides a more reliable guide for system operation and planning.

In one embodiment, as shown in fig. 9, constructing a scoring information utility value matrix according to the scoring information of the power source side index information, the scoring information of the grid side index information, and the scoring information of the load side index information includes:

Step 902, an initial scoring matrix is constructed according to the scoring information of the power source side index information, the scoring information of the power grid side index information and the scoring information of the load side index information.

The initial scoring matrix may be a matrix constructed according to data obtained by scoring each scoring index information.

Specifically, a distribution network flexibility evaluation model is built based on the remorse theory, m target objects are adopted to score n index information according to a scoring mechanism, and an initial scoring matrix Q is built.

Wherein q _nm is the score of the mth target object to the nth index information.

And 904, constructing a scoring information ideal matrix corresponding to the initial scoring matrix.

The scoring information ideal matrix can be a scoring matrix with ideal flexibility for the target power grid.

Specifically, the scoring information ideal matrix B is constructed from the initial scoring matrix Q, and the mathematical expression is as follows.

In the formula, b is an ideal grading value of index information, and if the network allocation is expected to have better flexibility, b can be assigned as 10. To reduce the remorse, the poorer the flexibility score is generally set.

Step 906, optimizing the initial scoring matrix according to the scoring information ideal matrix to obtain a scoring information utility value matrix.

Specifically, the initial scoring matrix is optimized according to the scoring information ideal matrix, and a scoring information utility value matrix can be obtained, wherein a mathematical expression H of the scoring information utility value matrix is as follows.

In the formula, α is an aversion coefficient, and the smaller α is, the greater the aversion degree is, that is, the less reliable the target object is scored, and thus α is 0.9.

In this embodiment, according to the scoring information of the index information of the power source side, the power grid side and the load side, an initial scoring matrix is formed, a scoring information ideal matrix corresponding to the initial scoring matrix is established, an idealized scoring distribution is represented, and the scoring information ideal matrix is used for optimizing the initial scoring matrix so that the initial scoring matrix is closer to an ideal state. The process is helpful for improving the accuracy of index evaluation of the power system, thereby providing finer and effective guidance for optimizing the system performance.

It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

Based on the same inventive concept, the embodiment of the application also provides a power distribution network flexibility scoring device considering high-proportion new energy access, which is used for realizing the power distribution network flexibility scoring method considering high-proportion new energy access. The implementation scheme of the device for solving the problem is similar to the implementation scheme recorded in the method, so the specific limitation in the embodiment of the power distribution network flexibility scoring device for considering high-proportion new energy access provided below can be referred to the limitation of the power distribution network flexibility scoring method for considering high-proportion new energy access hereinabove, and the description is omitted here.

In one embodiment, as shown in fig. 10, there is provided a power distribution network flexibility scoring apparatus considering high-proportion new energy access, including: an index information obtaining module 1002, a weight information obtaining module 1004, and a score information obtaining module 1006, configured to:

The index information obtaining module 1002 is configured to construct power source side index information, power grid side index information and load side index information corresponding to the target power grid according to power grid operation characteristic information corresponding to the target power grid;

The weight information obtaining module 1004 is configured to input power source side index information, grid side index information, and load side index information to a subjective and objective weighting model corresponding to a target grid, so as to obtain flexibility weight information of a power distribution network of the target grid;

the weight information obtaining module 1004 is further configured to input power source side index information, grid side index information, and load side index information to a flexibility scoring model corresponding to the target grid, to obtain flexibility index scoring information of the power distribution network of the target grid;

The scoring information obtaining module 1006 is configured to fuse the power distribution network flexibility weight information and the power distribution network flexibility index scoring information to obtain power distribution network flexibility scoring information of the target power grid; the power distribution network flexibility grading information is used for grading the power distribution network flexibility of the target power grid.

In one embodiment, the weight information obtaining module 1004 is configured to input power source side index information, grid side index information, and load side index information to a maximum fixed point determining layer to obtain subjective weight information; inputting the power supply side index information, the power grid side index information and the load side index information into an objective weighting layer to obtain objective weight information; and obtaining the flexibility weight information of the power distribution network according to the subjective weight information and the objective weight information.

In one embodiment, the weight information obtaining module 1004 is configured to calculate a subjective comprehensive weight combination coefficient and an objective comprehensive weight combination coefficient according to subjective weight information and objective weight information; and determining the flexibility weight information of the power distribution network according to the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient.

In one embodiment, the weight information obtaining module 1004 is configured to input the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient to the weight game theory model to obtain an optimized subjective comprehensive weight combination coefficient and an optimized objective comprehensive weight combination coefficient; and determining the flexibility weight information of the power distribution network according to the optimized subjective comprehensive weight combination coefficient and the optimized objective comprehensive weight combination coefficient.

In one embodiment, the weight information obtaining module 1004 is configured to calculate an index information standard deviation and an index information correlation coefficient between each index information in the power source side index information, the power grid side index information and the load side index information; obtaining the numerical value of each index information according to the standard deviation of each index information and the correlation coefficient of each index information; and obtaining objective weight information according to the numerical value of each index information.

In one embodiment, the weight information obtaining module 1004 is configured to construct a scoring information utility value matrix according to the scoring information of the power source side index information, the scoring information of the power grid side index information, and the scoring information of the load side index information; constructing a scoring regret-gladness value matrix according to the correlation coefficient of each index information; and adding the scoring information utility value matrix and the scoring remorse-happy value matrix to obtain the scoring information of the flexibility index of the power distribution network.

In one embodiment, the weight information obtaining module 1004 is configured to optimize each index information correlation coefficient according to the remorse avoidance coefficient to obtain each optimized index information correlation coefficient; and constructing a grading regret-gladness value matrix according to the correlation coefficient of each optimization index information.

In one embodiment, the weight information obtaining module 1004 is configured to construct an initial scoring matrix according to the scoring information of the power source side index information, the scoring information of the grid side index information, and the scoring information of the load side index information; constructing a scoring information ideal matrix corresponding to the initial scoring matrix; and optimizing the initial scoring matrix according to the scoring information ideal matrix to obtain a scoring information utility value matrix.

In one embodiment, the scoring information obtaining module 1006 is configured to determine power source side indicator information according to a power distribution network flexibility scoring level of the target power grid; and generating power distribution network flexibility adjustment information according to the index information of the power supply side.

The modules in the power distribution network flexibility scoring device considering high-proportion new energy access can be all or partially realized by software, hardware and combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 11. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing server data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements a power distribution network flexibility scoring method that takes into account high proportion of new energy access.

It will be appreciated by those skilled in the art that the structure shown in FIG. 11 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.

In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.

In one embodiment, a computer-readable storage medium is provided, storing a computer program which, when executed by a processor, implements the steps of the method embodiments described above.

In one embodiment, a computer program product or computer program is provided that includes computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-described method embodiments.

The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.

Claims

1. A power distribution network flexibility scoring method considering high-proportion new energy access, the method comprising:

2. The method of claim 1, wherein the subjective and objective weighting model comprises a maximum fixed point determination layer and an objective weighting layer; the step of inputting the power source side index information, the power grid side index information and the load side index information to a subjective and objective weighting model corresponding to the target power grid to obtain the flexibility weight information of the power distribution network of the target power grid comprises the following steps:

inputting the power supply side index information, the power grid side index information and the load side index information to a maximum fixed point determining layer to obtain subjective weight information;

Inputting the power supply side index information, the power grid side index information and the load side index information into an objective weighting layer to obtain objective weight information;

and obtaining the flexibility weight information of the power distribution network according to the subjective weight information and the objective weight information.

3. The method according to claim 2, wherein the inputting the power source side index information, the grid side index information, and the load side index information to an objective weighting layer, obtaining objective weight information, includes:

calculating index information standard deviation and index information correlation coefficients among the power supply side index information, the power grid side index information and the load side index information;

obtaining the numerical value of each index information according to the standard deviation of each index information and the correlation coefficient of each index information;

and obtaining the objective weight information according to the index information quantity values.

4. The method according to claim 2, wherein the obtaining the power distribution network flexibility weight information according to the subjective weight information and the objective weight information includes:

According to the subjective weight information and the objective weight information, calculating a subjective comprehensive weight combination coefficient and an objective comprehensive weight combination coefficient;

And determining the flexibility weight information of the power distribution network according to the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient.

5. The method of claim 4, wherein said determining the power distribution network flexibility weight information from the subjective comprehensive weight combination coefficients and the objective comprehensive weight combination coefficients comprises:

Inputting the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient into a weight game theory model to obtain an optimized subjective comprehensive weight combination coefficient and an optimized objective comprehensive weight combination coefficient;

And determining the flexibility weight information of the power distribution network according to the optimized subjective comprehensive weight combination coefficient and the optimized objective comprehensive weight combination coefficient.

6. The method according to claim 5, wherein the inputting the power source side index information, the grid side index information, and the load side index information into the flexibility scoring model corresponding to the target grid to obtain the power distribution network flexibility index scoring information of the target grid includes:

Constructing a scoring information utility value matrix according to the scoring information of the power source side index information, the scoring information of the power grid side index information and the scoring information of the load side index information;

constructing a grading regret-happy value matrix according to the index information correlation coefficients;

and adding the scoring information utility value matrix and the scoring remorse-gladness value matrix to obtain the power distribution network flexibility index scoring information.

7. The method of claim 6, wherein constructing a scoring regret-nux matrix based on each of the index information correlation coefficients comprises:

optimizing each index information correlation coefficient according to the remorse avoidance coefficient to obtain each optimized index information correlation coefficient;

And constructing the scoring remorse-gladness value matrix according to the correlation coefficient of each piece of optimization index information.

8. The method of claim 6, wherein constructing a scoring information utility value matrix based on the scoring information of the power source side index information, the scoring information of the grid side index information, and the scoring information of the load side index information, comprises:

constructing an initial scoring matrix according to the scoring information of the power source side index information, the scoring information of the power grid side index information and the scoring information of the load side index information;

constructing a scoring information ideal matrix corresponding to the initial scoring matrix;

And optimizing the initial scoring matrix according to the scoring information ideal matrix to obtain the scoring information utility value matrix.

9. A power distribution network flexibility scoring apparatus that considers high proportion of new energy access, the apparatus comprising:

10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 8 when the computer program is executed.