Distribution network flexibility scoring method and device considering high proportion of new energy access

Abstract

The present application relates to a distribution network flexibility scoring method and device considering a high proportion of new energy access. The method includes: constructing power supply side indicator information, power grid side indicator information and load side indicator information according to the power grid operation characteristic information corresponding to the target power grid; inputting the power supply side indicator information, power grid side indicator information and load side indicator information into the subjective and objective weighting model to obtain the distribution network flexibility weight information; inputting the power supply side indicator information, power grid side indicator information and load side indicator information into the flexibility scoring model to obtain the distribution network flexibility indicator scoring information; integrating the distribution network flexibility weight information and the distribution network flexibility indicator scoring information to obtain the distribution network flexibility scoring information; the distribution network flexibility scoring information is used to divide the distribution network flexibility scoring level of the target power grid. It can provide effective guidance for the rapid development of new energy, is suitable for the development needs of new power systems, and thus improves the safety and stability of the power grid system.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a distribution network flexibility scoring method, apparatus, computer equipment, storage medium and computer program product that takes into account a high proportion of new energy access. Background Art

[0002] With the development of computer technology, smart grid technology has emerged. This technology is based on an integrated, high-speed two-way communication network. Through the application of advanced sensing and measurement technology, advanced equipment technology, advanced control methods and advanced decision support system technology, it achieves the goals of reliable, safe, economical, efficient, environmentally friendly and safe use of the power grid. The flexible distribution method can allow the access of various different forms of power generation, the start-up of the power market and the optimization and efficient operation of assets.

[0003] In traditional technology, the flexibility of the distribution network is mainly used to deal with power flow over-limit and power quality problems caused by load fluctuations. However, in the context of the development of new energy, large-scale distributed new energy will be connected to the distribution network, and the distribution network will change from a passive network to an active network. In addition, due to the randomness, volatility and intermittent characteristics of new energy output, the difficulty of real-time power balance on the distribution network side will increase significantly, resulting in poor safety and stability of the power grid system. Summary of the invention

[0004] Based on this, it is necessary to provide a distribution network flexibility scoring method, device, computer equipment, computer-readable storage medium and computer program product that can improve the safety and stability of the power grid system and take into account a high proportion of new energy access in order to address the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a distribution network flexibility scoring method considering a high proportion of new energy access. The method comprises:

[0006] According to the grid operation characteristic information corresponding to the target grid, construct the power supply side index information, grid side index information and load side index information corresponding to the target grid; the grid side index information includes voltage classification level;

[0007] Inputting the power supply side indicator information, the grid side indicator information and the load side indicator information into the subjective and objective weighting model corresponding to the target grid to obtain the distribution network flexibility weight information of the target grid;

[0008] Inputting the power supply 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 distribution network flexibility index scoring information of the target grid;

[0009] The distribution network flexibility weight information and the distribution network flexibility index scoring information are integrated to obtain the distribution network flexibility scoring information of the target power grid; the distribution network flexibility scoring information is used to divide the distribution network flexibility scoring level of the target power grid.

[0010] In a second aspect, the present application also provides a distribution network flexibility scoring device considering a high proportion of new energy access. The device comprises:

[0011] An index information acquisition module, used to construct power supply side index information, grid side index information and load side index information corresponding to the target grid according to grid operation characteristic information corresponding to the target grid; the grid side index information includes voltage classification level;

[0012] A weight information obtaining module, used to input the power supply side indicator information, the grid side indicator information and the load side indicator information into the subjective and objective weighting model corresponding to the target grid, to obtain the distribution network flexibility weight information of the target grid;

[0013] The weight information obtaining module is also used to input the power supply 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 distribution network flexibility index scoring information of the target grid;

[0014] The scoring information obtaining module is used to integrate the distribution network flexibility weight information and the distribution network flexibility index scoring information to obtain the distribution network flexibility scoring information of the target power grid; the distribution network flexibility scoring information is used to divide the distribution network flexibility scoring level of the target power grid.

[0015] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0016] According to the grid operation characteristic information corresponding to the target grid, construct the power supply side index information, grid side index information and load side index information corresponding to the target grid; the grid side index information includes voltage classification level;

[0017] Inputting the power supply side indicator information, the grid side indicator information and the load side indicator information into the subjective and objective weighting model corresponding to the target grid to obtain the distribution network flexibility weight information of the target grid;

[0018] Inputting the power supply 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 distribution network flexibility index scoring information of the target grid;

[0019] The distribution network flexibility weight information and the distribution network flexibility index scoring information are integrated to obtain the distribution network flexibility scoring information of the target power grid; the distribution network flexibility scoring information is used to divide the distribution network flexibility scoring level of the target power grid.

[0020] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0021] According to the grid operation characteristic information corresponding to the target grid, construct the power supply side index information, grid side index information and load side index information corresponding to the target grid; the grid side index information includes voltage classification level;

[0022] Inputting the power supply side indicator information, the grid side indicator information and the load side indicator information into the subjective and objective weighting model corresponding to the target grid to obtain the distribution network flexibility weight information of the target grid;

[0023] Inputting the power supply 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 distribution network flexibility index scoring information of the target grid;

[0024] The distribution network flexibility weight information and the distribution network flexibility index scoring information are integrated to obtain the distribution network flexibility scoring information of the target power grid; the distribution network flexibility scoring information is used to divide the distribution network flexibility scoring level of the target power grid.

[0025] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0026] According to the grid operation characteristic information corresponding to the target grid, construct the power supply side index information, grid side index information and load side index information corresponding to the target grid; the grid side index information includes voltage classification level;

[0027] Inputting the power supply side indicator information, the grid side indicator information and the load side indicator information into the subjective and objective weighting model corresponding to the target grid to obtain the distribution network flexibility weight information of the target grid;

[0028] Inputting the power supply 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 distribution network flexibility index scoring information of the target grid;

[0029] The distribution network flexibility weight information and the distribution network flexibility index scoring information are integrated to obtain the distribution network flexibility scoring information of the target power grid; the distribution network flexibility scoring information is used to divide the distribution network flexibility scoring level of the target power grid.

[0030] The above-mentioned distribution network flexibility scoring method, device, computer equipment, storage medium and computer program product considering a high proportion of new energy access constructs the power supply side indicator information, grid side indicator information and load side indicator information corresponding to the target power grid according to the grid operation characteristic information corresponding to the target power grid; the grid side indicator information includes voltage classification level; the power supply side indicator information, grid side indicator information and load side indicator information are input into the subjective and objective weighting model corresponding to the target power grid to obtain the distribution network flexibility weight information of the target power grid; the power supply side indicator information, grid side indicator information and load side indicator information are input into the flexibility scoring model corresponding to the target power grid to obtain the distribution network flexibility indicator scoring information of the target power grid; the distribution network flexibility weight information and the distribution network flexibility indicator scoring information are integrated to obtain the distribution network flexibility scoring information of the target power grid; the distribution network flexibility scoring information is used to divide the distribution network flexibility scoring level of the target power grid.

[0031] By analyzing the grid operation characteristics of the target power grid, constructing the index information of the power supply side, grid side and load side, and inputting the index information into the subjective and objective weighting model and the flexibility scoring model, the distribution network flexibility weight information and flexibility index scoring information are obtained respectively. Finally, by integrating these two information, the distribution network flexibility score of the target power grid is obtained, which is used to divide different flexibility scoring levels. It can fully consider the impact of high proportion of new energy and voltage level, provide a scientific and practical calculation method for distribution network flexibility evaluation, provide effective guidance for the rapid development of new energy, and be more suitable for the development needs of new power systems, thereby improving the safety and stability of the power grid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 An application environment diagram of a distribution network flexibility scoring method considering a high proportion of new energy access in one embodiment;

[0033] Figure 2 A schematic flow chart of a distribution network flexibility scoring method considering a high proportion of new energy access in one embodiment;

[0034] Figure 3 A schematic diagram of a flow chart of a method for obtaining distribution network flexibility weight information in one embodiment;

[0035] Figure 4 A schematic diagram of a flow chart of a method for obtaining objective weight information in one embodiment;

[0036] Figure 5 A schematic flow chart of a method for obtaining distribution network flexibility weight information in another embodiment;

[0037] Figure 6 A flowchart of a method for obtaining distribution network flexibility weight information in another embodiment;

[0038] Figure 7 A schematic diagram of a flow chart of a method for obtaining distribution network flexibility index scoring information in one embodiment;

[0039] Figure 8 A schematic diagram of a process for constructing a scoring regret-happiness value matrix in one embodiment;

[0040] Fig. 9 A schematic diagram of a process for constructing a rating information utility value matrix in one embodiment;

[0041] Fig.10 It is a structural block diagram of a distribution network flexibility scoring device considering a high proportion of new energy access in one embodiment;

[0042] Fig.11 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] The present application provides a distribution network flexibility scoring method considering a high proportion of new energy access, which can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 140 obtains the power grid operation characteristic information from the terminal 102, and constructs the power supply side indicator information, the power grid side indicator information and the load side indicator 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 indicator information includes the voltage classification level; the power supply side indicator information, the power grid side indicator information and the load side indicator information are input into the subjective and objective weighting model corresponding to the target power grid to obtain the distribution network flexibility weight information of the target power grid; the power supply side indicator information, the power grid side indicator information and the load side indicator information are input into the flexibility scoring model corresponding to the target power grid to obtain the distribution network flexibility indicator scoring information of the target power grid; the distribution network flexibility weight information and the distribution network flexibility indicator scoring information are integrated to obtain the distribution network flexibility scoring information of the target power grid; the distribution network flexibility scoring information is used to divide the distribution network flexibility scoring level of the target power grid. The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0045] In one embodiment, Figure 2 As shown in the figure, a distribution network flexibility scoring method considering a high proportion of renewable energy access is provided. Figure 1 The server in the example is used to illustrate the following steps:

[0046] Step 202: construct power supply side index information, grid side index information and load side index information corresponding to the target grid according to grid operation characteristic information corresponding to the target grid.

[0047] Among them, the target power grid can be a power grid that needs to consider the distribution flexibility of new energy access.

[0048] The grid operation characteristic information may be parameter information during grid operation and inherent information of grid equipment, such as power abandonment, load rate, etc.

[0049] Among them, the power supply side indicator information may be indicator information reflecting the power supply side, such as: new energy consumption rate, new energy volatility, proportion of flexible power sources, etc.

[0050] Among them, the grid side indicator information may be indicator information reflecting the grid side, such as: the maximum average on-grid load rate of the distribution transformer, the maximum average off-grid load rate of the distribution transformer, the maximum average load rate of the line, the N-1 pass rate, the line transfer rate, the voltage deviation rate, etc. At the same time, the influence of the voltage level is considered when calculating the grid side indicator information.

[0051] The load-side index information may be index information reflecting the load side, such as net load fluctuation rate, demand response peak shaving capability information, demand response valley filling capability information, etc. The optimal assignment based on game theory is used in calculating the weights of the three indicators.

[0052] Specifically, the server 104 obtains the grid operation characteristic information corresponding to the target grid from the terminal 102. According to the grid operation characteristic information corresponding to the target grid, the power supply side indicator information corresponding to the target grid (new energy consumption rate, new energy fluctuation rate, flexible power supply proportion) is constructed, according to the grid operation characteristic information corresponding to the target grid, the grid side indicator information corresponding to the target grid (distribution transformer maximum average load rate on the grid, distribution transformer maximum average load rate off the grid, line maximum average load rate, N-1 pass rate, line transfer rate, voltage deviation rate) is constructed, according to the grid operation characteristic information corresponding to the target grid, the load side indicator information corresponding to the target grid (net load fluctuation rate, demand response peak shaving capability information, demand response valley filling capability information) is constructed.

[0053] Among them, the new energy consumption rate is used to evaluate the consumption capacity of existing new energy, and the calculation formula is as follows:

[0054]

[0055] Among them, the new energy volatility is used to evaluate the regulation demand of new energy, and the calculation formula is as follows:

[0056]

[0057] Among them, the proportion of flexible power supply installed is used to evaluate the adjustable ability of the power supply. The calculation formula is as follows:

[0058]

[0059] In the formula, flexible power sources include distributed gas power, adjustable small hydropower, small pumped storage, new energy storage, etc.

[0060] The maximum average load rate of the distribution transformer is used to evaluate the adequacy of the main transformer capacity when the distribution network is powered by renewable energy. The calculation formula is as follows:

[0061]

[0062] In the formula, N represents the number of distribution transformers of this voltage level.

[0063] The maximum average load rate of the distribution transformer is used to evaluate the adequacy of the main transformer's off-grid capacity when the distribution network is under heavy load and the output of new energy is small. The calculation formula is as follows:

[0064]

[0065] The maximum average load rate of the line is used to evaluate the adequacy of the line transmission capacity when the distribution network is under heavy load or when new energy is generated.

[0066]

[0067] The N-1 pass rate is used to evaluate the flexible adaptability of the distribution network to N-1 faults. The calculation formula is as follows:

[0068]

[0069] The line transfer rate is used to evaluate the flexible adaptability of the distribution network to the situation of substation outgoing line switch failure or planned outage. The calculation formula is as follows:

[0070]

[0071] The voltage deviation rate is used to evaluate the flexible adaptability of the distribution network to power quality problems caused by renewable energy power fluctuations. The calculation formula is as follows:

[0072]

[0073] Calculate the voltage level weight and grid-side flexibility index. Since the lower the voltage level, the more equipment there is and the smaller the single capacity of the equipment, in order to balance the impact of the number and capacity of equipment at each voltage level, after calculating the grid-side index by voltage level, multiply it by the capacity weight respectively. The final result is calculated as follows:

[0074]

[0075] In the formula, j represents the voltage level of the distribution network, η represents the final calculated value of the indicator, and η j Indicates the calculated value of the voltage level, ω j Table 1. Weight value corresponding to voltage level j, S j Represents the total capacity of distribution transformers at voltage level j.

[0076] The net load fluctuation rate reflects the load regulation demand. The higher the net load fluctuation rate, the higher the requirement for distribution network flexibility.

[0077]

[0078] The peak shaving capacity for demand response is used to evaluate the regulation capability of demand-side resources to cope with the peak load of the power grid. The calculation method is as follows:

[0079]

[0080] Demand response valley filling capacity is used to evaluate the regulation capacity of demand-side resources to cope with net load valleys. The calculation method is as follows:

[0081]

[0082] Step 204, inputting the power supply side index information, the grid side index information and the load side index information into the subjective and objective weighting model corresponding to the target grid to obtain the distribution network flexibility weight information of the target grid.

[0083] Among them, the subjective and objective weighting model can be an algorithm model that can realize the combination of subjective weighting method (maximum fixed point method) and objective weighting method (CRITIC method).

[0084] Among them, the distribution network flexibility weight information can be the weight obtained by calculating the optimal weight based on game theory through a subjective and objective weighting model.

[0085] Specifically, all relevant indicator information from the power supply side indicator information, the grid side indicator information and the load side indicator information are summarized. The power supply side indicator information, the grid side indicator information and the load side indicator information are further input into the subjective and objective weighting model corresponding to the target grid. The subjective and objective weighting model may involve the calculation of the maximum fixed point method and the CRITIC method, and the distribution network flexibility weight information of the target grid is obtained by combining the optimal weight calculation based on game theory. This weight information may reflect the importance or contribution of each indicator on the power supply side, the grid side and the load side in the target grid.

[0086] Step 206, input the power supply 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 distribution network flexibility index scoring information of the target grid.

[0087] Among them, the flexibility scoring model can be an algorithm model that can calculate the distribution network flexibility of the target power grid.

[0088] Among them, the distribution network flexibility index scoring information can be the output result calculated by the flexibility scoring model.

[0089] Specifically, the power supply side indicator information, the grid side indicator information and the load side indicator information are input into a specific mathematical model, which is a flexibility scoring model. The core idea of ​​the flexibility scoring model is to consider the possible consequences of different choices in the decision-making process and introduce potential "regret" in the evaluation. In the calculation of the flexibility scoring model, "regret" may mean potential dissatisfaction or disappointment that may arise after making a certain trade-off decision between the power supply side, the grid side and the load side. The flexibility scoring model will take these potential regret factors into account and assign them to the second distribution network flexibility weights calculated subsequently for the power supply side, the grid side and the load side. These weight information reflects the contribution of various aspects to the overall flexibility of the target power grid under the framework of regret theory. Finally, by calculating the flexibility scoring model, the distribution network flexibility indicator scoring information of the target power grid can be obtained.

[0090] Step 208, integrating the distribution network flexibility weight information and the distribution network flexibility index scoring information to obtain the distribution network flexibility scoring information of the target power grid.

[0091] Among them, the distribution network flexibility scoring information can be the amount of distribution network flexibility of the target power grid, which is used to divide the distribution network flexibility scoring level of the target power grid.

[0092] Specifically, the distribution network flexibility weight information and the distribution network flexibility index scoring information are subjected to a fusion operation, including weighted average or other appropriate mathematical operations. The purpose of the fusion is to comprehensively consider the contributions of the distribution network flexibility weight information and the distribution network flexibility index scoring information to obtain a more comprehensive and balanced distribution network flexibility scoring information. This process may involve the adjustment of weights to ensure that the contributions of different models are properly reflected in the final score. Finally, through the fusion operation, the distribution network flexibility scoring information of the target power grid is obtained, where the mathematical expression of the distribution network flexibility scoring information is as follows.

[0093]

[0094] The distribution network flexibility score information integrates the weight considerations of the subjective and objective empowerment model and the regret theory model, providing a more comprehensive and integrated measure of grid flexibility. The distribution network flexibility score information finally calculated is a value between [0 10]. The larger the distribution network flexibility score information, the better the distribution network flexibility and the stronger the carrying capacity of new energy.

[0095] In the above-mentioned distribution network flexibility scoring considering a high proportion of new energy access, the power supply side indicator information, grid side indicator information and load side indicator information corresponding to the target power grid are constructed according to the grid operation characteristic information corresponding to the target power grid; the power supply side indicator information, grid side indicator information and load side indicator information are input into the subjective and objective weighting model corresponding to the target power grid to obtain the distribution network flexibility weight information of the target power grid; the power supply side indicator information, grid side indicator information and load side indicator information are input into the flexibility scoring model corresponding to the target power grid to obtain the distribution network flexibility index scoring information of the target power grid; the distribution network flexibility weight information and the distribution network flexibility index scoring information are integrated to obtain the distribution network flexibility scoring information of the target power grid; the distribution network flexibility scoring information is used to divide the distribution network flexibility scoring level of the target power grid.

[0096] By analyzing the grid operation characteristics of the target power grid, constructing the index information of the power supply side, grid side and load side, and inputting the index information into the subjective and objective weighting model and the flexibility scoring model, the distribution network flexibility weight information and flexibility index scoring information are obtained respectively. Finally, by integrating these two information, the distribution network flexibility score of the target power grid is obtained, which is used to divide different flexibility scoring levels. It can fully consider the impact of high proportion of new energy and voltage level, provide a scientific and practical calculation method for distribution network flexibility evaluation, provide effective guidance for the rapid development of new energy, and be more suitable for the development needs of new power systems, thereby improving the safety and stability of the power grid system.

[0097] In one embodiment, Figure 3 As shown, the power supply side indicator information, the grid side indicator information and the load side indicator information are input into the subjective and objective weighting model corresponding to the target grid to obtain the distribution network flexibility weight information of the target grid, including:

[0098] Step 302: input the power supply side index information, the grid side index information and the load side index information into the maximum fixed point determination layer to obtain subjective weight information.

[0099] Among them, the maximum fixed point determination layer can be one of the calculation layers of the subjective and objective weighted model, and the maximum fixed point method is used for calculation.

[0100] The subjective weight information may be a calculated value obtained after the maximum fixed point determination layer is calculated.

[0101] Specifically, the importance of each indicator information in the power supply side indicator information, the grid side indicator information and the load side indicator information is ranked, and on this basis, the importance ratio r of adjacent indicators is given. j , j = 1, 2, ..., n-1. By multiplying the ratios of each importance, the subjective weight information ω of the nth indicator is calculated.an , the formula is: . Calculate the subjective weight information of the remaining n-1 indicators, .

[0102] Step 304: input the power supply side indicator information, the grid side indicator information and the load side indicator information into the objective weighting layer to obtain objective weight information.

[0103] The objective weighting layer may be one of the calculation layers of the subjective and objective weighting model, and is calculated using the CRITIC method.

[0104] The objective weight information may be a calculated value obtained by the CRITIC method.

[0105] Specifically, the power supply side indicator information, the grid side indicator information, and the load side indicator information are input into the CRITIC method for processing. The CRITIC method establishes a hierarchical structure by comparing different indicators in pairs, and then determines the relative weights between the indicators in the power supply side indicator information, the grid side indicator information, and the load side indicator information by calculation. Combined with the optimal weight calculation based on game theory, each indicator obtains a relative advantage in the overall hierarchical structure. Finally, objective weight information is obtained.

[0106] Step 306, obtaining distribution network flexibility weight information based on the subjective weight information and the objective weight information.

[0107] Specifically, based on game theory, the subjective weight information and the objective weight information are regarded as the two parties in the game, and the optimal solution is obtained through the game candidates to obtain the distribution network flexibility weight information.

[0108] In this embodiment, the subjective and objective weights are obtained by inputting the index information of the power supply side, the grid side and the load side into the processing layers at different levels. The subjective weight is obtained through the maximum fixed point determination layer, while the objective weight is obtained through the objective weighting layer. Finally, by combining the subjective and objective weight information, the distribution network flexibility weight information is obtained, thereby optimizing the operation and distribution of the power system and improving the overall efficiency of the system.

[0109] In one embodiment, Figure 4 As shown, the power supply side indicator information, the grid side indicator information and the load side indicator information are input into the objective weighting layer to obtain objective weight information, including:

[0110] Step 402, calculating the index information standard deviation and the index information correlation coefficient between each index information in the power supply side index information, the grid side index information and the load side index information.

[0111] Among them, the indicator information standard deviation can be the standard deviation of the indicator information.

[0112] The indicator information correlation coefficient may be a correlation coefficient between different indicators.

[0113] Specifically, according to each indicator information in the power supply side indicator information, the grid side indicator information and the load side indicator information, the standard deviation of the indicator information is calculated, and according to each indicator information in the power supply side indicator information, the grid side indicator information and the load side indicator information, the correlation coefficient between different indicators is calculated. The calculation formulas of the indicator information standard deviation and the indicator information correlation coefficient are as follows.

[0114]

[0115]

[0116]

[0117] In the formula, S l , S j are the standard deviations of the lth and jth indicators respectively; is the average value of the jth column in the indicator matrix after dimensionless processing; is the correlation coefficient between the lth indicator and the jth indicator; are the lth and jth columns of the normalized matrix X, respectively.

[0118] Step 404, obtaining the value of each indicator information amount according to the standard deviation of each indicator information and the correlation coefficient of each indicator information.

[0119] The indicator information value may be the amount of information contained in the indicator.

[0120] Specifically, for each indicator information, the value of each indicator information amount is obtained according to the standard deviation of each indicator information and the correlation coefficient of each indicator information. The calculation formula of the indicator information amount value is as follows.

[0121]

[0122] In the formula, is the amount of information contained in the jth indicator.

[0123] Step 406, obtaining objective weight information according to the information value of each indicator.

[0124] Specifically, any indicator information value is used as the dividend, and the sum of the indicator information values ​​is used as the divisor, and the objective weight information is obtained after division. The mathematical expression of the objective weight information is as follows.

[0125]

[0126] In this embodiment, the quantitative values ​​of each indicator information are obtained by calculating the standard deviation and correlation coefficient between them. This process helps to quantify the differences and correlations of the indicator information, thereby providing a basis for determining the objective weight information. By considering the range of variation and mutual relationship of the indicators, the system can more accurately evaluate the importance of each indicator, providing a more scientific basis for the management and optimization of the power system, thereby improving the stability and efficiency of the system.

[0127] In one embodiment, Figure 5 As shown in FIG, based on the subjective weight information and the objective weight information, the distribution network flexibility weight information is obtained, including:

[0128] Step 502: Calculate a subjective comprehensive weight combination coefficient and an objective comprehensive weight combination coefficient according to the subjective weight information and the objective weight information.

[0129] The subjective comprehensive weight combination coefficient may be the distance difference between the comprehensive weight information of the power grid and the subjective weight information.

[0130] Among them, the objective comprehensive weight combination coefficient is the difference between the distance between the comprehensive weight information of the power grid and the objective weight information.

[0131] Specifically, obtain the subjective weight information ω a and objective weight information ω b After that, the optimal weight combination coefficient is calculated based on game theory, assuming that the subjective weight information ω a The comprehensive weight combination coefficient is k1, and the objective weight information ω b The comprehensive weight combination coefficient is k2, and the comprehensive weight information of the power grid ω is:

[0132] ω= k 1 ω a + k 2 ω b =[ ω 1 , ω 2 ,⋯, ω n ]

[0133] Based on game theory, if we regard subjective weight information and objective weight information as the two parties in the game, then the optimal combination weight is the comprehensive weight in the equilibrium state reached by the two parties in the game. The subjective weight information and the comprehensive weight information of the power grid should be satisfied in the equilibrium state. ω a =[ ω a1 , ω a2 ,⋯, ω an ] , where n is the index number, ω an is the objective weight information of the nth indicator.

[0134] Based on game theory, if we regard subjective weight information and objective weight information as the two parties in the game, then the optimal combination weight is the comprehensive weight in the equilibrium state reached by the two parties in the game. The objective weight information and the comprehensive weight information of the power grid should be satisfied in the equilibrium state. ω b =[ ω b1 , ω b2 ,⋯, ω bn ] , where n is the index number, ω bn is the objective weight information of the nth indicator. In the equilibrium state, the two parties in the game should satisfy the minimum deviation between the subject and object weight information and the comprehensive weight information of the power grid. a and b The goal is to minimize the deviation.

[0135] Step 504, determining the distribution network flexibility weight information according to the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient.

[0136] Specifically, the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient are linearly summed to obtain the distribution network flexibility weight information. Among them, the distribution network flexibility weight information is

[0137] In this embodiment, the comprehensive weight information of the power grid is obtained by integrating the subjective weight and objective weight information. Furthermore, by calculating the subjective comprehensive weight combination coefficient between the comprehensive weight of the power grid and the subjective weight and the objective comprehensive weight combination coefficient between the comprehensive weight of the power grid and the objective weight, the flexibility weight information of the system is determined. This method helps to more accurately evaluate the overall performance of the power grid, and by weighing subjective and objective factors, it provides a more sophisticated means of weight adjustment, thereby optimizing the operation and management of the power system.

[0138] In one embodiment, Figure 6 As shown, the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient are combined to determine the distribution network flexibility weight information, including:

[0139] Step 602, input the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient into the weight game theory model to obtain the optimized subjective comprehensive weight combination coefficient and the optimized objective comprehensive weight combination coefficient.

[0140] The optimized subjective comprehensive weight combination coefficient may be a coefficient of the amount of the optimized subjective weight information.

[0141] The optimized objective comprehensive weight combination coefficient may be a coefficient for adjusting the amount of objective weight information after optimization.

[0142] Specifically, no specific values ​​are assigned to the first weight combination coefficient and the second weight combination coefficient, which are subsequently obtained by simultaneous equations. Assume that the subjective weight information ω a The comprehensive weight combination coefficient is k1 and the objective weight value ω is adjusted b The comprehensive weight combination coefficient is k2. Further through the calculation of the weighted game theory model, the optimal subjective comprehensive weight combination coefficient is obtained. And optimize the objective comprehensive weight combination coefficient .

[0143] Step 604, determining the distribution network flexibility weight information based on the optimized subjective comprehensive weight combination coefficient and the optimized objective comprehensive weight combination coefficient.

[0144] 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 based on the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient to obtain the distribution network flexibility weight information, and the mathematical expression is as follows.

[0145]

[0146]

[0147] According to the differential principle, the condition for minimizing the function is:

[0148]

[0149] Seek supplement and The analytical solution is:

[0150]

[0151] Standardized combination coefficient:

[0152]

[0153] but:

[0154]

[0155] In this embodiment, by ensuring that the sum of the two coefficients meets the preset coefficient threshold, the sum of the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient is adjusted. This process aims to optimize the flexibility weight information of the first distribution network, and ensure that the adjustment of the system provides more accurate and controllable flexibility weight adjustment under the premise of meeting the expected coefficient by reasonably weighing subjective and objective factors, thereby optimizing the overall performance of the power system.

[0156] In one embodiment, Figure 7 As shown, the power supply side index information, the grid side index information and the load side index information are input into the flexibility scoring model corresponding to the target grid to obtain the distribution network flexibility index scoring information of the target grid, including:

[0157] Step 702: construct a scoring information utility value matrix based on the scoring information of the power supply side indicator information, the scoring information of the grid side indicator information, and the scoring information of the load side indicator information.

[0158] Among them, the rating information utility value matrix can be a matrix in the application of the rating information recommendation system, in which there are two types of elements, one is user (user) and the other is item (term). Users will like certain items, and this preference information must be sorted out from the data. The data itself can be expressed as a utility matrix (utlity matrix), and the element value corresponding to each (user, item) in the matrix represents the current user's preference for the current item. The preference value comes from an ordered set. Assume that the matrix is ​​sparse, that is, most of the elements are unknown, and unknown means that we are not clear about the current user's preference information for the current item. For example: the example of the utility matrix, the matrix represents the user's rating of the movie (1-5 levels, 5 is the highest). Blank means that the current user has no rating for the current movie.

[0159] Specifically, the scoring information of the power supply side indicator information, the scoring information of the grid side indicator information, and the scoring information of the load side indicator information are integrated into a matrix as the initial scoring matrix. In order for this matrix to reflect the relative weights and relationships between the various indicators, it is necessary to construct an ideal scoring information matrix, that is, the expected value of each indicator under ideal conditions. The ideal matrix is ​​used 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 ideal scoring information matrix, and finally obtain a scoring information utility value matrix that is more in line with expectations.

[0160] Step 704: construct a scoring regret-happiness value matrix based on the correlation coefficient of each indicator information.

[0161] The score regret-delight value matrix may be a matrix obtained by calculating the indicator information using the regret value decision method, also known as the Savage method or the regret method.

[0162] Specifically, according to the correlation coefficient of each indicator information, a score regret-delight value matrix is ​​constructed. The mathematical expression of the score regret-delight value matrix R is as follows.

[0163] The regret-happiness value matrix R is constructed as follows:

[0164]

[0165]

[0166] In the formula, β is the regret avoidance coefficient. The larger β is, the greater the degree of regret avoidance is. Here β is set to 0.013.

[0167] Step 706, adding the scoring information utility value matrix and the scoring regret-delight value matrix to obtain the distribution network flexibility index scoring information.

[0168] Specifically, the scoring information utility value matrix and the scoring regret-delight value matrix are added together to obtain the distribution network flexibility index scoring information. The mathematical expression of the distribution network flexibility index scoring information D is as follows.

[0169]

[0170] In this embodiment, the scoring information utility value matrix is ​​constructed by using the scoring information of the indicator information on the power supply side, the grid side and the load side, and the scoring regret-delight value matrix is ​​established based on the correlation coefficient of the indicator information. By adding these two matrices, the system obtains the flexibility weight information of the second distribution network. This method combines the scoring information and the regret-delight value, which helps to more comprehensively evaluate the impact of each indicator on the system performance, thereby providing more comprehensive and accurate weight information and optimizing the operation and distribution of the power system.

[0171] In one embodiment, Figure 8 As shown in the figure, based on the information correlation coefficient of each indicator, a scoring regret-happiness value matrix is ​​constructed, including:

[0172] Step 802, optimizing the correlation coefficient of each indicator information according to the regret avoidance coefficient to obtain the correlation coefficient of each optimized indicator information;

[0173] Among them, the regret avoidance coefficient can be the regret avoidance coefficient of the score regret-delight value matrix

[0174] The optimized indicator information correlation coefficient may be the indicator information correlation coefficient optimized by the regret avoidance coefficient.

[0175] Specifically, according to the correlation coefficients of each indicator information, the correlation coefficients of each indicator information are optimized to obtain the correlation coefficients of each optimized indicator information. The expression is:

[0176]

[0177] In the formula, β is the regret avoidance coefficient. The larger β is, the greater the degree of regret avoidance is. Here β is set to 0.013.

[0178] Step 804: construct a scoring regret-happiness value matrix based on the correlation coefficients of the optimization indicator information.

[0179] Specifically, according to the information correlation coefficient of each optimization indicator, a score regret-delight value matrix is ​​constructed. The mathematical expression of the score regret-delight value matrix R is as follows.

[0180] The regret-happiness value matrix R is constructed as follows:

[0181]

[0182]

[0183] In the formula, β is the regret avoidance coefficient. The larger β is, the greater the degree of regret avoidance is. Here β is set to 0.013.

[0184] In this embodiment, by using the regret-delight value matrix, not only can the correlation between the indicators be comprehensively considered, but also these relationships can be accurately adjusted according to the regret avoidance coefficient, providing more targeted decision support for decision makers. This helps to reduce the risk of decision-making in a complex decision-making environment, improve the accuracy of decision-making, and provide more reliable guidance for system operation and planning.

[0185] In one embodiment, Fig. 9 As shown, according to the scoring information of the power supply side indicator information, the scoring information of the grid side indicator information and the scoring information of the load side indicator information, a scoring information utility value matrix is ​​constructed, including:

[0186] Step 902: construct an initial scoring matrix based on the scoring information of the power supply side indicator information, the scoring information of the grid side indicator information, and the scoring information of the load side indicator information.

[0187] The initial scoring matrix may be a matrix constructed based on data obtained after scoring each scoring indicator information.

[0188] Specifically, a distribution network flexibility evaluation model is established based on the regret theory, and the initial scoring matrix Q is established by using m target objects to score n indicator information according to the scoring mechanism.

[0189]

[0190] Among them, q nm The score of the mth target object for the nth indicator information.

[0191] Step 904: construct an ideal scoring information matrix corresponding to the initial scoring matrix.

[0192] The ideal scoring information matrix may be a scoring matrix in which the target power grid has ideal flexibility.

[0193] Specifically, the ideal scoring information matrix B is constructed according to the initial scoring matrix Q, and the mathematical expression is as follows.

[0194] B=[ b 1 , b 2 , ⋯ , b n ]

[0195] In the formula, b is the ideal score value of the indicator information. If the distribution network is expected to have good flexibility, b can be assigned a value of 10. In order to reduce the degree of regret, the worse the flexibility, the lower the score.

[0196] Step 906, optimizing the initial rating matrix according to the ideal rating information matrix to obtain a rating information utility value matrix.

[0197] Specifically, the initial rating matrix is ​​optimized according to the ideal rating information matrix to obtain a rating information utility value matrix, wherein the mathematical expression H of the rating information utility value matrix is ​​as follows.

[0198]

[0199] In the formula, α is the disgust coefficient. The smaller α is, the greater the disgust is, that is, the less trust is placed in the target object's rating. Therefore, α is set to 0.9.

[0200] In this embodiment, an initial scoring matrix is ​​formed based on the scoring information of the indicator information on the power supply side, the grid side, and the load side, and an ideal scoring information matrix corresponding to the initial scoring matrix is ​​established to represent the idealized scoring distribution, and the initial scoring matrix is ​​optimized using the ideal scoring information matrix to make it closer to the ideal state. This process helps to improve the accuracy of the power system's indicator evaluation, thereby providing more sophisticated and effective guidance for the optimization of system performance.

[0201] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0202] Based on the same inventive concept, the embodiment of the present application also provides a distribution network flexibility scoring device that takes into account a high proportion of new energy access, which is used to implement the distribution network flexibility scoring method that takes into account a high proportion of new energy access involved in the above-mentioned distribution network. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the distribution network flexibility scoring device that takes into account a high proportion of new energy access provided below can be referred to the above limitations on a distribution network flexibility scoring method that takes into account a high proportion of new energy access, and will not be repeated here.

[0203] In one embodiment, Fig.10 As shown, a distribution network flexibility scoring device considering a high proportion of new energy access is provided, including: an indicator information acquisition module 1002, a weight information acquisition module 1004 and a scoring information acquisition module 1006, which are used for:

[0204] The index information acquisition module 1002 is used to construct the power supply side index information, the grid side index information and the load side index information corresponding to the target grid according to the grid operation characteristic information corresponding to the target grid;

[0205] The weight information obtaining module 1004 is used to input the power supply side index information, the grid side index information and the load side index information into the subjective and objective weighting model corresponding to the target grid to obtain the distribution network flexibility weight information of the target grid;

[0206] The weight information obtaining module 1004 is also used to input the power supply 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 distribution network flexibility index scoring information of the target grid;

[0207] The scoring information obtaining module 1006 is used to integrate the distribution network flexibility weight information and the distribution network flexibility index scoring information to obtain the distribution network flexibility scoring information of the target power grid; the distribution network flexibility scoring information is used to divide the distribution network flexibility scoring level of the target power grid.

[0208] In one embodiment, the weight information obtaining module 1004 is used to input the power supply side indicator information, the grid side indicator information and the load side indicator information into the maximum fixed point determination layer to obtain subjective weight information; input the power supply side indicator information, the grid side indicator information and the load side indicator information into the objective weighting layer to obtain objective weight information; and obtain the distribution network flexibility weight information based on the subjective weight information and the objective weight information.

[0209] In one embodiment, the weight information obtaining module 1004 is used to calculate the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient based on the subjective weight information and the objective weight information; and determine the distribution network flexibility weight information based on the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient.

[0210] In one embodiment, the weight information obtaining module 1004 is used to input the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient into the weight game theory model to obtain the optimized subjective comprehensive weight combination coefficient and the optimized objective comprehensive weight combination coefficient; according to the optimized subjective comprehensive weight combination coefficient and the optimized objective comprehensive weight combination coefficient, the distribution network flexibility weight information is determined.

[0211] In one embodiment, the weight information obtaining module 1004 is used to calculate the indicator information standard deviation and the indicator information correlation coefficient between each indicator information in the power supply side indicator information, the grid side indicator information and the load side indicator information; according to the standard deviation of each indicator information and the correlation coefficient of each indicator information, the value of each indicator information amount is obtained; according to the value of each indicator information amount, the objective weight information is obtained.

[0212] In one embodiment, the weight information obtaining module 1004 is used to construct a scoring information utility value matrix based on the scoring information of the power supply side indicator information, the scoring information of the grid side indicator information, and the scoring information of the load side indicator information; construct a scoring regret-delight value matrix based on the correlation coefficient of each indicator information; add the scoring information utility value matrix and the scoring regret-delight value matrix to obtain the distribution network flexibility indicator scoring information.

[0213] In one embodiment, the weight information obtaining module 1004 is used to optimize the correlation coefficients of each indicator information according to the regret avoidance coefficient to obtain the correlation coefficients of each optimized indicator information; and to construct a scoring regret-delight value matrix according to the correlation coefficients of each optimized indicator information.

[0214] In one embodiment, the weight information obtaining module 1004 is used to construct an initial scoring matrix based on the scoring information of the power supply side indicator information, the scoring information of the grid side indicator information, and the scoring information of the load side indicator information; construct an ideal scoring information matrix corresponding to the initial scoring matrix; and optimize the initial scoring matrix based on the ideal scoring information matrix to obtain a scoring information utility value matrix.

[0215] In one embodiment, the scoring information obtaining module 1006 is used to determine the power supply side indicator information according to the distribution network flexibility scoring level of the target power grid; and generate the distribution network flexibility adjustment information according to the power supply side indicator information.

[0216] Each module in the above-mentioned distribution network flexibility scoring device considering a high proportion of new energy access can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0217] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig.11 As shown. The computer device includes a processor, a memory and a network interface connected via a system bus. The processor of the computer device is used 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store server data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a distribution network flexibility scoring method considering a high proportion of new energy access is implemented.

[0218] Those skilled in the art will understand that Fig.11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0219] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0220] In one embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0221] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a 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-mentioned method embodiments.

[0222] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0223] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0224] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0225] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A distribution network flexibility scoring method considering a high proportion of new energy access, characterized in that: The method comprises: According to the grid operation characteristic information corresponding to the target grid, construct the power supply side indicator information, grid side indicator information and load side indicator information corresponding to the target grid; the power supply side indicator information includes the new energy consumption rate, the new energy fluctuation rate and the proportion of flexible power sources; the grid side indicator information includes the voltage level, the maximum average load rate of the distribution transformer on the grid, the maximum average load rate of the distribution transformer off the grid, the maximum average load rate of the line, the N-1 pass rate, the line transfer rate, and the voltage deviation rate; the load side indicator information includes the net load fluctuation rate, the demand response peak shaving capability, and the demand response valley filling capability; The calculation formula for the new energy consumption rate is: ; The calculation formula of the new energy volatility is: ; The calculation formula for the proportion of the flexible power supply is: The calculation formula of the maximum average load rate of the distribution transformer is: , M represents the number of distribution transformers of the voltage level; the calculation formula for the maximum average load rate of the distribution transformer is: ; The calculation formula for the maximum average load rate of the line is: ; The calculation formula of the N-1 pass rate is: ; The calculation formula of the line transfer rate is: ; The calculation formula of the voltage deviation rate is: ; The calculation formula of the net load fluctuation rate is: The calculation formula of the demand response peak shaving capability is: The calculation formula of the demand response valley filling capacity is: ; The calculation formula of the grid-side index information is: , j represents the voltage level of the distribution network, η represents the final calculated value of the grid-side index information, ηj represents the index calculation value of voltage level j, ωj represents the weight value corresponding to voltage level j, and Sj represents the total capacity of distribution transformers at voltage level j; The power supply side indicator information, the grid side indicator information and the load side indicator information are input into the subjective and objective weighting model corresponding to the target grid to obtain the distribution network flexibility weight information of the target grid, specifically including: inputting the power supply side indicator information, the grid side indicator information and the load side indicator information into the subjective weighting layer of the subjective and objective weighting model to obtain subjective weight information; the subjective weighting layer uses the maximum fixed point method to calculate the subjective weight information; inputting the power supply side indicator information, the grid side indicator information and the load side indicator information into the objective weighting layer of the subjective and objective weighting model to obtain objective weight information; the objective weighting layer uses the CRITIC method to calculate the objective weight information; according to the subjective weight information and the objective weight information, the distribution network flexibility weight information is obtained; Inputting the power supply 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 distribution network flexibility index scoring information of the target grid; The distribution network flexibility weight information and the distribution network flexibility index scoring information are integrated to obtain the distribution network flexibility scoring information of the target power grid; the distribution network flexibility scoring information is used to divide the distribution network flexibility scoring level of the target power grid.

2. The method according to claim 1, characterized in that The step of inputting the power supply side indicator information, the grid side indicator information and the load side indicator information into the objective weighting layer to obtain objective weight information includes: Calculate the index information standard deviation and the index information correlation coefficient between each index information in the power supply side index information, the grid side index information and the load side index information; According to the standard deviation of each indicator information and the correlation coefficient of each indicator information, the value of each indicator information amount is obtained; The objective weight information is obtained according to the information amount value of each indicator.

3. The method according to claim 1, characterized in that The obtaining the distribution network flexibility weight information according to the subjective weight information and the objective weight information includes: 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 distribution network flexibility weight information is determined according to the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient.

4. The method according to claim 3, characterized in that The determining the distribution network flexibility weight information according to the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient includes: Inputting the subjective comprehensive weight combination coefficient and the objective comprehensive weight combination coefficient into a weighted game theory model to obtain an optimized subjective comprehensive weight combination coefficient and an optimized objective comprehensive weight combination coefficient; The distribution network flexibility weight information is determined according to the optimized subjective comprehensive weight combination coefficient and the optimized objective comprehensive weight combination coefficient.

5. The method according to claim 4, characterized in that The step of inputting the power supply side indicator information, the grid side indicator information and the load side indicator information into the flexibility scoring model corresponding to the target grid to obtain the distribution network flexibility indicator scoring information of the target grid includes: Constructing a scoring information utility value matrix according to the scoring information of the power supply side indicator information, the scoring information of the grid side indicator information, and the scoring information of the load side indicator information; According to the correlation coefficient of each indicator information, a scoring regret-happiness value matrix is ​​constructed; The scoring information utility value matrix and the scoring regret-delight value matrix are added together to obtain the distribution network flexibility index scoring information.

6. The method according to claim 5, characterized in that The step of constructing a scoring regret-happiness value matrix according to the correlation coefficient of each indicator information includes: According to the regret avoidance coefficient, the information correlation coefficient of each indicator is optimized to obtain the information correlation coefficient of each optimized indicator; The scoring regret-delight value matrix is ​​constructed according to the correlation coefficient of each optimization indicator information.

7. The method according to claim 5, characterized in that The constructing a scoring information utility value matrix according to the scoring information of the power supply side indicator information, the scoring information of the grid side indicator information and the scoring information of the load side indicator information comprises: Constructing an initial scoring matrix according to the scoring information of the power supply side indicator information, the scoring information of the grid side indicator information, and the scoring information of the load side indicator information; Constructing an ideal matrix of scoring information corresponding to the initial scoring matrix; The initial scoring matrix is ​​optimized according to the ideal scoring information matrix to obtain the scoring information utility value matrix.

8. A distribution network flexibility scoring device considering a high proportion of new energy access, characterized in that: The device comprises: The indicator information acquisition module is used to construct the power supply side indicator information, grid side indicator information and load side indicator information corresponding to the target power grid according to the grid operation characteristic information corresponding to the target power grid; the power supply side indicator information includes the new energy consumption rate, the new energy fluctuation rate and the proportion of flexible power sources; the grid side indicator information includes the voltage level, the maximum average load rate of the distribution transformer on the grid, the maximum average load rate of the distribution transformer off the grid, the maximum average load rate of the line, the N-1 pass rate, the line transfer rate, and the voltage deviation rate; the load side indicator information includes the net load fluctuation rate, the demand response peak shaving capability, and the demand response valley filling capability; wherein, the calculation formula of the new energy consumption rate is: ; The calculation formula of the new energy volatility is: ; The calculation formula for the proportion of the flexible power supply is: The calculation formula of the maximum average load rate of the distribution transformer is: , M represents the number of distribution transformers of the voltage level; the calculation formula for the maximum average load rate of the distribution transformer is: ; The calculation formula for the maximum average load rate of the line is: ; The calculation formula of the N-1 pass rate is: ; The calculation formula of the line transfer rate is: ; The calculation formula of the voltage deviation rate is: ; The calculation formula of the net load fluctuation rate is: The calculation formula of the demand response peak shaving capability is: The calculation formula of the demand response valley filling capacity is: ; Wherein, the calculation formula of the grid side indicator information is: , j represents the voltage level of the distribution network, η represents the final calculated value of the grid-side index information, ηj represents the index calculation value of voltage level j, ωj represents the weight value corresponding to voltage level j, and Sj represents the total capacity of distribution transformers at voltage level j; The weight information obtaining module is used to input the power supply side indicator information, the grid side indicator information and the load side indicator information into the subjective and objective weighting model corresponding to the target grid to obtain the distribution network flexibility weight information of the target grid, specifically including: inputting the power supply side indicator information, the grid side indicator information and the load side indicator information into the subjective weighting layer of the subjective and objective weighting model to obtain subjective weight information; the subjective weighting layer uses the maximum fixed point method to calculate the subjective weight information; inputting the power supply side indicator information, the grid side indicator information and the load side indicator information into the objective weighting layer of the subjective and objective weighting model to obtain objective weight information; the objective weighting layer uses the CRITIC method to calculate the objective weight information; according to the subjective weight information and the objective weight information, the distribution network flexibility weight information is obtained; The weight information obtaining module is also used to input the power supply 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 distribution network flexibility index scoring information of the target grid; The scoring information obtaining module is used to integrate the distribution network flexibility weight information and the distribution network flexibility index scoring information to obtain the distribution network flexibility scoring information of the target power grid; the distribution network flexibility scoring information is used to divide the distribution network flexibility scoring level of the target power grid.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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