Intelligent power distribution network deep decarburization potential evaluation model construction method

Abstract

The invention discloses an intelligent power distribution network deep decarburization potential evaluation model construction method, and belongs to the field of power distribution network deep decarburization potential evaluation. According to the method, the problem that the current carbon reduction management situation cannot be fully reflected in the prior art is solved, the nonlinear relation between carbon emission and low-carbon technical factors of the intelligent power distribution network can be researched by establishing the multi-factor STIRPAT model, the EKC inverted-U-shaped characteristics of the carbon emission and the technical factors are analyzed, and the carbon reduction management situation is analyzed. Technical factors are decomposed into technical progress factors of a'source-network-load-storage 'link of the intelligent power distribution network, a correlation model of the technical progress and energy, power grid, energy consumption and energy storage links of the intelligent power distribution network can be established, a power distribution network low-carbon index system and an evaluation method are constructed, and according to historical data of carbon emissions and related factors in previous years, a power distribution network low-carbon index evaluation method is established. A multiple regression model equation can be obtained through a multiple regression analysis method, relevant parameters of key driving factors influencing power low-carbon development in the model are set on the basis of analysis of a prediction scene, and the carbon emission can be calculated.

Description

Technical Field

[0001] The present invention relates to the technical field of deep decarbonization potential assessment for distribution networks, and in particular to a method for constructing a deep decarbonization potential assessment model for smart distribution networks. Background Art

[0002] Compared with the traditional distribution network, the new distribution network has more complex components, network topology, and operation mode, which leads to more factors affecting the low-carbonization of the new distribution network, which are intertwined and complex. It will be difficult to adapt to the development requirements of the new power system in the future by relying solely on the indicator system and evaluation method of the traditional distribution network. The core goal of the new distribution network is to achieve low carbonization. The construction of a deep decarbonization evaluation model for the intelligent distribution network can not only fully reflect the current status of carbon reduction management of the distribution network, but also provide a basis for scientific evaluation of benefits. However, relying solely on the traditional power supply side and grid side control methods is not enough to meet the needs of large-scale grid connection and consumption of new energy; therefore, it does not meet the existing needs. For this reason, we propose a method for constructing a deep decarbonization potential evaluation model for the intelligent distribution network. Summary of the invention

[0003] The purpose of the present invention is to provide a method for constructing a deep decarbonization potential assessment model for a smart distribution network. By establishing a multi-factor STIRPAT model, the nonlinear relationship between carbon emissions and low-carbon technical factors of smart distribution networks can be studied, and the EKC "inverted U-shaped" characteristics of carbon emissions and technical factors can be analyzed. By decomposing technical factors into technical progress factors in the "source-grid-load-storage" link of the smart distribution network, a correlation model between technical progress and the energy, grid, energy consumption and energy storage links of the smart distribution network can be established, and a low-carbon index system and evaluation method suitable for a new distribution network can be constructed. Based on the historical data of carbon emissions and related factors in previous years, a multiple regression analysis method can be used to obtain a multiple regression model equation. Based on the analysis of the forecast scenario, the relevant parameters of the key driving factors affecting the low-carbon development of electricity in the model are set, and the carbon emissions can be calculated, thereby solving the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: A method for constructing a deep decarbonization potential assessment model for a smart distribution network comprises the following steps: The STIRPAT model is established to analyze the nonlinear relationship between carbon emissions and low-carbon technology factors of smart distribution networks, and the carbon emissions and technology factors show the EKC "inverted U-shaped" characteristics; Decompose the technical factors into the technical progress factors of the "source-grid-load-storage" link of the smart distribution network, analyze the impact of the technical progress of the smart distribution network on the changes in carbon emissions in the energy, grid, energy consumption and energy storage sides, and establish a correlation model between technical progress and the energy, grid, energy consumption and energy storage links of the smart distribution network; Construct a low-carbon index system and evaluation method suitable for the new distribution network, and quantitatively evaluate the low-carbon level of the new distribution network. Based on the historical data of carbon emissions and related factors in previous years, the functional relationship between carbon emissions and multiple driving factors is fitted through multivariate regression analysis to obtain the multivariate regression model equation. Based on the analysis of forecast scenarios, relevant parameters of key driving factors affecting low-carbon development of electricity in the model are set, and considering the impact of each driving factor on carbon emissions, relevant parameters and changes are set to calculate carbon emissions.

[0005] Furthermore, the calculation formula of the STIRPAT model is as follows:

[0006] Where: a , b , c , d are all parameters to be estimated, representing residuals; e represents the random error term; The STIRPAT model is flexible, and the STIRPAT model considering multiple factors is established, and the expression is as follows:

[0007] Where: C t Representative t Annual carbon emissions; A t The per capita economy of the resident population is the gross regional product divided by the resident population; D t To express the urbanization rate, the urban population is divided by the resident population; H t The intensity of the tertiary industry is expressed by the ratio of the total output value of the tertiary industry to the regional GDP; M t It represents the technological progress factor, which is the low-carbon comprehensive evaluation value of the smart distribution network. Considering the differences in the impact of various factors, c , d , f , g Respectively represent B t , A t , D t , H t as well as M t The elasticity coefficient of changes in driving factors is used to measure the different degrees of impact of various factors on low-carbon development.

[0008] Furthermore, a correlation model between technological progress and the energy, power grid, energy consumption and energy storage links of the smart distribution network is established, as shown in the following formula:

[0009] Where: E t , N t , U t , S t Respectively represent the energy, power grid, energy consumption and energy storage links. t The development level of low-carbon technology in yes are other influencing variables.

[0010] Furthermore, in order to determine the nonlinear relationship between carbon emissions and low-carbon technology factors of smart distribution networks, and to verify and analyze whether carbon emissions and technology factors present the EKC "inverted U-shaped" characteristics, the quadratic term of technological progress factors is added, and in order to facilitate the calculation of carbon emission prediction values, some parameters are logarithmized, and the carbon emission prediction model after conversion is as shown in the following formula:

[0011] Where: h represent M t Elasticity coefficient of the quadratic term of the driving factor.

[0012] Furthermore, a low-carbon index system and evaluation method suitable for the new distribution network is constructed, and the low-carbon level of the new distribution network is quantitatively evaluated, specifically: Selection of low-carbon evaluation indicators: Select evaluation indicators covering the multi-level influencing factors of "source-grid-load-storage" to comprehensively evaluate the comprehensive level of regional low-carbon development and construct a low-carbon development comprehensive evaluation indicator system. The indicator system is a hierarchical structure with a total of three layers: The comprehensive level of low-carbon development is taken as the first layer of the indicator system, namely the target layer; According to the driving factors associated with carbon emissions decomposed by the multi-factor driving force model, the macro driving factors on the source side, grid side, load side and energy storage side are used as the second layer of the indicator system, namely the criterion layer; Select specific indicators from the indicators closely related to their indicators under each criterion layer as the third layer of the indicator system, namely the indicator layer; Calculation of the combined weights of low-carbon assessment indicators: including subjective weighting of indicators based on AHP, objective weighting of indicators based on anti-entropy weight method, and calculation of combined weights based on AHP-anti-entropy weight method; Among them, based on the subjective weighting of indicators in AHP, the evaluation index system of low-carbon operation of smart distribution network is divided into three levels to form a hierarchical structure model; Among them, the combined weight calculation based on the AHP-anti-entropy weight method uses the hierarchical analysis method and the entropy value method to subjectively and objectively weight each indicator, and combines the two weights to obtain a more reasonable indicator weight. In order to make the indicator weight more accurate and scientific, the following formula is used to calculate the combined weight of each evaluation indicator v i :

[0013] In the formula: integer coefficient , α is 0.7; Calculation of the low-carbon comprehensive evaluation value of the distribution network: Find the data of various indicators in previous years, build an evaluation indicator matrix, calculate the entropy value and objective weight of each indicator, and then combine the established hierarchical model to establish judgment matrices for the criterion layer to the target layer, and the indicator layer to the criterion layer. After normalizing the eigenvalues ​​of each judgment matrix, the subjective weight of each indicator to the target layer is obtained.

[0014] Furthermore, the subjective weighting of the indicators based on AHP adopts the hierarchical analysis method to divide the evaluation indicator system of the low-carbon operation of the smart distribution network into three levels to form a hierarchical structure model. The top layer is the target layer, the second layer is the criterion layer, and the third layer is the indicator layer. The subjective weights of the criterion layer to the target layer and the indicator layer to the criterion layer are determined respectively. The steps for calculating the weights of indicators at each level are as follows: Construct judgment matrix and consistency check: Assume that m evaluation indicators, l evaluation objects, and the indicator data is c ij ( i =1, 2, … m ; j =1, 2, … l ), within the same level of the hierarchical model, the indicators are compared pairwise according to the importance scaling method, and a judgment matrix is ​​formed based on the relative importance of the indicators. ; Where: m is the number of evaluation indicators; a ij It indicates the relative importance of the evaluation indicators. a ij >0, a ij =1 / a ij , a ii =1; The judgment matrix is ​​affected by the subjective judgment of the decision maker and has errors with objective facts. It needs to be checked for consistency. The consistency ratio CR is defined as: CR=CI / RI Where: CI is called the consistency index, , is the judgment matrix The maximum eigenvalue of RI; RI is defined as the average random consistency index, RI is based on the number of evaluation indicators m and change; Determine the subjective weight of the indicator: The geometric mean method and normalization method are used to calculate the subjective weight of the evaluation index, as shown in the following formula: .

[0015] Furthermore, the objective weighting of indicators based on the anti-entropy weight method is to use the anti-entropy weight method to objectively weight the indicator layer, and define an anti-entropy as:

[0016] Where: ,and ; The characteristics of anti-entropy are opposite to those of entropy, that is, the greater the disorder of an indicator, the greater the anti-entropy value, and thus the greater the weight. The steps for determining the objective weight of an indicator by the anti-entropy weight method are as follows: Constructing the evaluation index matrix: In order to eliminate the differences in magnitude and type between data, some influencing factors are standardized. The specific normalization results are shown in the following formula: Normalization of benefit indicators:

[0017] Normalization of cost-based indicators:

[0018] Normalization of intermediate indicators:

[0019] The evaluation index matrix is ​​constructed as follows:

[0020] Calculate the anti-entropy of each indicator as shown in the following formula:

[0021] Determine the objective weight of the indicator: Normalize the anti-entropy to obtain the anti-entropy weight of each evaluation indicator, that is, the objective weight, as shown in the following formula:

[0022] Furthermore, based on the scenario prediction method of multiple regression analysis, the regional carbon emissions were used as the independent variable. y , taking the multiple correlation factors of low-carbon development of smart distribution network as five independent variables, and based on the historical data of carbon emissions and related factors in previous years, the functional relationship between carbon emissions and multiple driving factors is fitted through multiple regression analysis method, and then the specific multiple regression model equation is obtained, as shown in the following formula:

[0023] Where: β 0 , β 1 ,… β 4 are the 5 unknown coefficients to be analyzed; yes is an unobservable random variable, namely the error term.

[0024] Furthermore, based on the analysis of the forecast scenarios, the relevant parameters of the key driving factors affecting the low-carbon development of electricity in the model are set, and the technical factors are decomposed into the four links of "source-grid-load-storage" of the smart distribution network. The influence of each driving factor on carbon emissions is considered, and the relevant parameters and changes are set. The specific calculation is shown in the following formula: ; ; ; ; Where: E 0 , N 0 , U 0 , S 0 Respectively represent the index values ​​of the driving factors of energy, power grid, energy consumption and energy storage in the base year. t The situation in the year is compared with that in the base year; γ1, γ2, γ3, and γ3 are the degrees of progress in energy, power grid, energy consumption, and energy storage, respectively.

[0025] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention constructs a low-carbon index system and evaluation method suitable for the new distribution network, and quantitatively evaluates the low-carbon level of the new distribution network. Based on the historical data of carbon emissions and related factors in previous years, the functional relationship between carbon emissions and multiple driving factors can be fitted through multiple regression analysis methods, thereby obtaining a specific multiple regression model equation.

[0026] 2. The present invention establishes a STIRPAT model that takes into account economic, technological and social factors, so that the nonlinear relationship between carbon emissions and low-carbon technical factors of smart distribution networks can be studied, and then the carbon emissions and technical factors present an EKC "inverted U-shaped" characteristic. At the same time, the quadratic term of the low-carbon technical factors of smart distribution networks is added.

[0027] 3. The present invention decomposes technical factors into technical progress factors of the "source-grid-load-storage" link of the smart distribution network, which can study the impact of technical progress on the energy, grid, energy consumption and energy storage side of the smart distribution network on the change of carbon emissions, thereby establishing a correlation model between technical progress and the energy, grid, energy consumption and energy storage links of the smart distribution network.

[0028] 4. Based on the analysis of forecast scenarios, the present invention sets relevant parameters of key driving factors affecting the low-carbon development of electricity in the model, considers the degree of influence of each driving factor on carbon emissions, takes these key driving factors as the basic basis for the low-carbon development scenario analysis of the smart distribution network, sets relevant parameters and changes, and calculates carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of low-carbon operation analysis of smart distribution network based on AHP-anti-entropy weight method of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] In order to solve the problem that the existing technology is not enough to meet the needs of large-scale grid connection and consumption of new energy by simply relying on the traditional power supply side and grid side control methods, and the technical problem that the indicator system and evaluation method of the traditional distribution network will be difficult to adapt to the development requirements of the future new power system, please refer to Figure 1 , this embodiment provides the following technical solutions: The method for constructing a model for evaluating the deep decarbonization potential of smart distribution networks includes the following steps: The STIRPAT model is established to analyze the nonlinear relationship between carbon emissions and low-carbon technology factors of smart distribution networks, and the carbon emissions and technology factors show the EKC "inverted U-shaped" characteristics; Decompose the technical factors into the technical progress factors of the "source-grid-load-storage" link of the smart distribution network, analyze the impact of the technical progress of the smart distribution network on the changes in carbon emissions in the energy, grid, energy consumption and energy storage sides, and establish a correlation model between technical progress and the energy, grid, energy consumption and energy storage links of the smart distribution network; Construct a low-carbon index system and evaluation method suitable for the new distribution network, and quantitatively evaluate the low-carbon level of the new distribution network. Based on the historical data of carbon emissions and related factors in previous years, the functional relationship between carbon emissions and multiple driving factors is fitted through multivariate regression analysis to obtain the multivariate regression model equation. Based on the analysis of the forecast scenarios, the relevant parameters of the key driving factors affecting the low-carbon development of electricity in the model are set. The degree of influence of each driving factor on carbon emissions is considered. The key driving factors are used as the basic basis for the analysis of the low-carbon development scenario of the smart distribution network. The relevant parameters and changes are set to calculate carbon emissions.

[0032] The calculation formula of STIRPAT model is as follows:

[0033] Where: a , b , c , d are all parameters to be estimated, representing residuals; e represents the random error term; The STIRPAT model is flexible, and the STIRPAT model considering multiple factors is established, and the expression is as follows:

[0034] Where: C t Representative t Annual carbon emissions; A t The per capita economy of the resident population is the gross regional product divided by the resident population; D t To express the urbanization rate, the urban population is divided by the resident population; H t The intensity of the tertiary industry is expressed by the ratio of the total output value of the tertiary industry to the regional GDP; M t It represents the technological progress factor, which is the low-carbon comprehensive evaluation value of the smart distribution network. Considering the differences in the impact of various factors, c , d , f , g Respectively represent B t , At , D t , H t as well as M t The elasticity coefficient of changes in driving factors is used to measure the different degrees of impact of various factors on low-carbon development.

[0035] A correlation model between technological progress and the energy, power grid, energy consumption and energy storage links of smart distribution networks is established, as shown in the following formula:

[0036] Where: E t , N t , U t , S t Respectively represent the energy, power grid, energy consumption and energy storage links. t The development level of low-carbon technology in yes are other influencing variables.

[0037] In order to determine the nonlinear relationship between carbon emissions and low-carbon technology factors of smart distribution networks, verify and analyze whether carbon emissions and technical factors present the EKC "inverted U-shaped" characteristics, add the quadratic term of technological progress factors, and take the logarithm of some parameters to facilitate the calculation of carbon emission prediction values. The carbon emission prediction model after conversion is shown in the following formula:

[0038] Where: h represent M t Elasticity coefficient of the quadratic term of the driving factor.

[0039] It should be noted that the traditional method is to analyze the impact of human factors on the environment through the IPAT model, and its calculation formula is: , where: I Indicates the degree of impact, P Represents population factors, A Represents socioeconomic factors, T It represents technical factors. However, due to the limitation of IPAT model, it can only be used to study the linear relationship between population factors and the environment. Therefore, a nonlinear model, namely STIRPAT model, was developed on this basis.

[0040] Specifically, the STIRPAT model is flexible and takes into account economic, technological, and social factors. It can establish a STIRPAT model that takes into account economic, technological, and social factors. Through the STIRPAT model, the nonlinear relationship between carbon emissions and low-carbon technology factors of smart distribution networks can be studied, and the carbon emissions and technology factors can be analyzed to show the EKC "inverted U-shaped" characteristics. At the same time, the technological progress factor is jointly affected by the source, network, load, and energy storage factors of the smart distribution network. Its driving mechanism for carbon emissions is different, and its effects are also different, which may promote carbon emissions reduction or increase carbon emissions. There is a coupling relationship between the multiple driving factors of technological progress factors. In the initial stage, with the continuous improvement of the technical level, the construction of low-carbon facilities in smart distribution networks requires a lot of energy, and the process of obtaining and utilizing energy is often accompanied by a lot of carbon emissions. However, in the middle and late stages, with the advancement of smart distribution network technology and the increasing maturity of application, carbon emissions will gradually decrease. Therefore, in order to study the nonlinear relationship between carbon emissions and technological progress factors of smart distribution networks, and verify and analyze whether carbon emissions and technological progress factors show the EKC "inverted U-shaped" characteristics, the quadratic term of technological progress factors can be added.

[0041] Construct a low-carbon index system and evaluation method suitable for the new distribution network, and quantitatively evaluate the low-carbon level of the new distribution network, specifically: Selection of low-carbon evaluation indicators: Select evaluation indicators covering the multi-level influencing factors of "source-grid-load-storage" to comprehensively evaluate the comprehensive level of regional low-carbon development and construct a low-carbon development comprehensive evaluation indicator system. The indicator system is a hierarchical structure with a total of three layers: The comprehensive level of low-carbon development is taken as the first layer of the indicator system, namely the target layer; According to the driving factors associated with carbon emissions decomposed by the multi-factor driving force model, the macro driving factors on the source side, grid side, load side and energy storage side are used as the second layer of the indicator system, namely the criterion layer; Select specific indicators from the indicators closely related to their indicators under each criterion layer as the third layer of the indicator system, namely the indicator layer; Calculation of the combined weights of low-carbon evaluation indicators: including subjective weighting of indicators based on AHP, objective weighting of indicators based on anti-entropy weight method, and combined weight calculation based on AHP-anti-entropy weight method. Among them, subjective weighting of indicators based on AHP divides the evaluation indicator system of low-carbon operation of smart distribution network into three levels to form a hierarchical structure model. Calculation of the low-carbon comprehensive evaluation value of the distribution network: Find the data of various indicators in previous years, build an evaluation indicator matrix, calculate the entropy value and objective weight of each indicator, and then combine the established hierarchical model to establish the judgment matrix of the criterion layer to the target layer, and the indicator layer to the criterion layer. After normalizing the eigenvalues ​​of each judgment matrix, the subjective weight of each indicator to the target layer can be obtained. The combined weight of each evaluation index can be obtained, and the annual low-carbon comprehensive evaluation value of the smart distribution network can be obtained by multiplying the evaluation index matrix by the combined weight.

[0042] Specifically, the core goal of the smart distribution network is to achieve low carbonization. Simply relying on the traditional power supply side control method is not enough to meet the needs of large-scale grid connection and consumption of new energy. The smart distribution network uses load-side management and new energy storage technology to build a "source-grid-load-storage" architecture for the coordinated consumption of new energy. This method enables the smart distribution network to adapt to the continuous growth and utilization of large-scale and high-proportion new energy. Its low-carbon connotation can be described from four aspects: power supply side, grid side, load side and energy storage side.

[0043] Evaluation indicators covering the multi-level influencing factors of "source-grid-load-storage" are selected to comprehensively evaluate the comprehensive level of regional low-carbon development and construct a low-carbon development comprehensive evaluation index system. The index system is a hierarchical structure with a total of three layers. The comprehensive level of low-carbon development is taken as the first layer of the index system, namely the target layer; according to the driving factors related to carbon emissions obtained by decomposing the multi-factor driving force model, the macro-driving factors such as the source side, grid side, load side and energy storage side are taken as the second layer of the index system, namely the criterion layer; specific indicators are selected from the indicators closely related to their indicators under each criterion layer as the third layer of the index system, namely the indicator layer. The comprehensive evaluation index system of low-carbon development potential is shown in Table 1 below.

[0044]

[0045] As shown in Table 1, potential assessment indicators involve multi-level and multi-dimensional factors. The many indicators of the indicator layer listed in Table 1 correspond to different criterion layer indicators, that is, different assessment dimensions of low-carbon potential. There are differences in the types of different indicators. For example, the proportion of thermal power generation is a cost-based indicator, that is, the smaller the indicator value, the better; the new energy consumption rate is a benefit-based indicator, that is, the larger the indicator value, the better; the capacity load ratio is an intermediate indicator, that is, the closer the indicator value is to a specific value, the better.

[0046] Based on the subjective weighting of AHP indicators, the hierarchical analysis method is used to divide the evaluation index system of low-carbon operation of smart distribution network into three levels to form a hierarchical structure model. The top layer is the target layer, the second layer is the criterion layer, and the third layer is the indicator layer. The subjective weights of the criterion layer to the target layer and the indicator layer to the criterion layer are determined respectively. The steps for calculating the weights of indicators at each level are as follows: Construct judgment matrix and consistency check: Assume that m evaluation indicators, l evaluation objects, and the indicator data is c ij ( i =1, 2, … m ; j =1, 2, …l ), within the same level of the hierarchical model, the indicators are compared pairwise according to the importance scaling method, and a judgment matrix is ​​formed based on the relative importance of the indicators. ; Where: m is the number of evaluation indicators; a ij It indicates the relative importance of the evaluation indicators. a ij >0, a ij =1 / a ij , a ii =1; The judgment matrix is ​​affected by the subjective judgment of the decision maker and has errors with objective facts. It needs to be checked for consistency. The consistency ratio CR is defined as: CR=CI / RI Where: CI is called the consistency index, , is the judgment matrix The maximum eigenvalue of RI; RI is defined as the average random consistency index, RI is based on the number of evaluation indicators m The RI values ​​are shown in Table 2 below:

[0047] As shown in Table 2, when the consistency ratio CR is less than 0.1, the judgment matrix A The consistency is acceptable, otherwise the judgment matrix should be modified accordingly to make it meet the consistency.

[0048] Determine the subjective weight of the indicator: The geometric mean method and normalization method are used to calculate the subjective weight of the evaluation index, as shown in the following formula:

[0049] Specifically, in the comprehensive evaluation method, the design of weights has a key influence on the evaluation results, and the analytic hierarchy process is a commonly used decision-making method that combines qualitative and quantitative analysis. It mainly uses the experience of decision makers to quantitatively evaluate indicators. By dividing the evaluation index system of low-carbon operation of smart distribution networks into three levels, a hierarchical model is formed. The top layer is the target layer (low-carbon operation of smart distribution networks), the second layer is the criterion layer (corresponding to the first-level indicators), and the third layer is the indicator layer (corresponding to the second-level indicators). The subjective weights of the criterion layer to the target layer and the indicator layer to the criterion layer are determined respectively, so as to calculate the weights of indicators at all levels.

[0050] Objective weighting of indicators based on anti-entropy weight method. The anti-entropy weight method is used to objectively weight the indicator layer, and an anti-entropy is defined as:

[0051] Where: ,and ; The characteristics of anti-entropy are opposite to those of entropy, that is, the greater the disorder of an indicator, the greater the anti-entropy value, and thus the greater the weight. The steps for determining the objective weight of an indicator by the anti-entropy weight method are as follows: Constructing the evaluation index matrix: In order to eliminate the differences in magnitude and type between data, some influencing factors are standardized. As shown in Table 1, the potential evaluation indicators can be divided into benefit type, cost type and intermediate type. The specific normalization results are shown in the following formula: Normalization of benefit indicators:

[0052] Normalization of cost-based indicators:

[0053] Normalization of intermediate indicators:

[0054] The evaluation index matrix is ​​constructed as follows:

[0055] Calculate the anti-entropy of each indicator as shown in the following formula:

[0056] Determine the objective weight of the indicator: Normalize the anti-entropy to obtain the anti-entropy weight of each evaluation indicator, that is, the objective weight, as shown in the following formula:

[0057] Specifically, entropy is a measure of the degree of disorder in a system. The principle of the entropy weight method is: the greater the degree of disorder of an indicator, the smaller the entropy value, and the greater its influence on the evaluation results of the evaluation index system, so the greater the weight. However, the entropy weight method is prone to extreme weights caused by excessive sensitivity to the degree of disorder of indicators in the weighting process. To avoid this situation, the anti-entropy weight method is used to objectively weight the indicator layer.

[0058] Based on the combined weight calculation of AHP-anti-entropy weight method, each indicator is weighted subjectively and objectively using the analytic hierarchy process and the entropy value method, and the two weights are combined to obtain a more reasonable indicator weight. In order to make the indicator weight more accurate and scientific, the combined weight of each evaluation indicator is calculated using the following formula v i :

[0059] In the formula: integer coefficient In the present invention, α is taken as 0.7.

[0060] Specifically, the AHP-anti-entropy weight method refers to the use of the analytic hierarchy process and the entropy value method to subjectively and objectively weight each indicator, and combine the two weights to obtain a more reasonable indicator weight. This method can not only weaken the influence of some subjective factors on the analytic hierarchy process weighting, but also weaken the bias problem of objective weighting caused by the large difference in original data information. The above formula is used to calculate the combined weight of each evaluation indicator. v i , which can make the indicator weights more accurate and scientific.

[0061] The scenario prediction method based on multiple regression analysis takes the regional carbon emissions as the independent variable y , taking the multiple correlation factors of low-carbon development of smart distribution network as five independent variables, and based on the historical data of carbon emissions and related factors in previous years, the functional relationship between carbon emissions and multiple driving factors is fitted through multiple regression analysis method, and then the specific multiple regression model equation is obtained, as shown in the following formula:

[0062] Where: β 0 , β 1 ,… β 4 are the 5 unknown coefficients to be analyzed; yes is an unobservable random variable, namely the error term.

[0063] Specifically, multiple regression analysis takes the research object as the dependent variable, analyzes multiple independent variables associated with it, and then fits the correlation function between the independent variables and the dependent variable based on the actual data of each variable obtained by statistics to establish a multiple regression equation.

[0064] Based on the analysis of the forecast scenario, the relevant parameters of the key driving factors affecting the low-carbon development of electricity in the model are set. The technical factors are decomposed into four links of "source-grid-load-storage" of the smart distribution network. Considering the impact of each driving factor on carbon emissions, the key driving factors are used as the basic basis for the scenario analysis of the low-carbon development of the smart distribution network, and the relevant parameters and changes are set. The specific calculation is shown in the following formula: ; ; ; ; Where: E 0 , N 0 , U 0 , S 0 Respectively represent the index values ​​of the driving factors of energy, power grid, energy consumption and energy storage in the base year. t The situation in the year is compared with that in the base year; γ1, γ2, γ3, and γ3 are the degrees of progress in energy, power grid, energy consumption, and energy storage, respectively.

[0065] Specifically, the above formula describes the carbon emission prediction model. t The key driving factors of the year and the factors of the base year are analyzed. By analyzing historical data, the changing trends of various technical links of the power system in the past are summarized, and the trend of technological progress in the next few years is speculated. In this embodiment, 2023 is taken as the base year, and one year is used as the time scale to analyze the degree of technological development of each link in different development stages. Based on this, the trend of technological level changes in energy, power grid, energy consumption, and energy storage links is predicted and analyzed, and the change rate of each factor at different stages is obtained. After obtaining the degree of technological progress in the energy, power grid, energy consumption, and energy storage links of the new power system, the technical factors are calculated and finally substituted into the formula In this way, carbon emissions can be calculated, and future scenarios can be set, the values ​​of technical factors can be changed, and then scenario comparisons can be carried out.

[0066] Working principle: First, the STIRPAT model considering economic, technological and social factors is established to study the nonlinear relationship between carbon emissions and low-carbon technology factors of smart distribution networks, and analyze the EKC "inverted U-shaped" characteristics of carbon emissions and technology factors. Secondly, the technical factors are decomposed into the technological progress factors of the "source-grid-load-storage" link of the smart distribution network, which can study the impact of technological progress on carbon emissions changes in the energy, grid, energy consumption and energy storage side of the smart distribution network, so as to establish a correlation model between technological progress and the energy, grid, energy consumption and energy storage links of the smart distribution network. Then, a new The low-carbon index system and evaluation method of the new distribution network are proposed, and the low-carbon level of the new distribution network is quantitatively evaluated. Through the multiple regression analysis method, the functional relationship between carbon emissions and multiple driving factors can be fitted, so as to obtain the specific multiple regression model equation. Finally, based on the analysis of the forecast scenario, the relevant parameters of the key driving factors affecting the low-carbon development of electricity in the model are set. It is necessary to consider the degree of influence of each driving factor on carbon emissions, so as to use these key driving factors as the basic basis for the low-carbon development scenario analysis of the smart distribution network, set the relevant parameters and changes, and calculate the carbon emissions.

[0067] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0068] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for constructing a model for evaluating the deep decarbonization potential of a smart distribution network, characterized in that: The following steps are involved: The STIRPAT model is established to analyze the nonlinear relationship between carbon emissions and low-carbon technology factors of smart distribution networks, and the carbon emissions and technology factors show the EKC "inverted U-shaped" characteristics; Decompose the technical factors into the technical progress factors of the "source-grid-load-storage" link of the smart distribution network, analyze the impact of the technical progress of the smart distribution network on the changes in carbon emissions in the energy, grid, energy consumption and energy storage sides, and establish a correlation model between technical progress and the energy, grid, energy consumption and energy storage links of the smart distribution network; Construct a low-carbon index system and evaluation method suitable for the new distribution network, and quantitatively evaluate the low-carbon level of the new distribution network. Based on the historical data of carbon emissions and related factors in previous years, the functional relationship between carbon emissions and multiple driving factors is fitted through multivariate regression analysis to obtain the multivariate regression model equation. Based on the analysis of forecast scenarios, relevant parameters of key driving factors affecting low-carbon development of electricity in the model are set, and considering the impact of each driving factor on carbon emissions, relevant parameters and changes are set to calculate carbon emissions.

2. The method for constructing a deep decarbonization potential assessment model for a smart distribution network according to claim 1, characterized in that: The calculation formula of the STIRPAT model is as follows: Where: a , b , c , d are all parameters to be estimated, representing residuals; e represents the random error term; The STIRPAT model is flexible, and the STIRPAT model considering multiple factors is established, and the expression is as follows: Where: C t Representative t Annual carbon emissions; A t The per capita economy of the resident population is the gross regional product divided by the resident population; D t To express the urbanization rate, the urban population is divided by the resident population; H t The intensity of the tertiary industry is expressed by the ratio of the total output value of the tertiary industry to the regional GDP; M t It represents the technological progress factor, which is the low-carbon comprehensive evaluation value of the smart distribution network. Considering the differences in the impact of various factors, c , d , f , g Respectively represent B t , A t , D t , H t as well as M t The elasticity coefficient of changes in driving factors is used to measure the different degrees of impact of various factors on low-carbon development.

3. The method for constructing a deep decarbonization potential assessment model for a smart distribution network according to claim 1, characterized in that: A correlation model between technological progress and the energy, power grid, energy consumption and energy storage links of smart distribution networks is established, as shown in the following formula: Where: E t , N t , U t , S t Respectively represent the energy, power grid, energy consumption and energy storage links. t The development level of low-carbon technology in ɛ are other influencing variables.

4. The method for constructing a deep decarbonization potential assessment model for a smart distribution network according to claims 1-2, characterized in that: In order to determine the nonlinear relationship between carbon emissions and low-carbon technology factors of smart distribution networks, verify and analyze whether carbon emissions and technical factors present the EKC "inverted U-shaped" characteristics, add the quadratic term of technological progress factors, and take the logarithm of some parameters to facilitate the calculation of carbon emission prediction values. The carbon emission prediction model after conversion is shown in the following formula: Where: h represent M t Elasticity coefficient of the quadratic term of the driving factor.

5. The method for constructing a deep decarbonization potential assessment model for a smart distribution network according to claim 1, characterized in that: Construct a low-carbon index system and evaluation method suitable for the new distribution network, and quantitatively evaluate the low-carbon level of the new distribution network, specifically: Selection of low-carbon evaluation indicators: Select evaluation indicators covering the multi-level influencing factors of "source-grid-load-storage" to comprehensively evaluate the comprehensive level of regional low-carbon development and construct a low-carbon development comprehensive evaluation indicator system. The indicator system is a hierarchical structure with a total of three layers: The comprehensive level of low-carbon development is taken as the first layer of the indicator system, namely the target layer; According to the driving factors associated with carbon emissions decomposed by the multi-factor driving force model, the macro driving factors on the source side, grid side, load side and energy storage side are used as the second layer of the indicator system, namely the criterion layer; Select specific indicators from the indicators closely related to their indicators under each criterion layer as the third layer of the indicator system, namely the indicator layer; Calculation of the combined weights of low-carbon assessment indicators: including subjective weighting of indicators based on AHP, objective weighting of indicators based on anti-entropy weight method, and calculation of combined weights based on AHP-anti-entropy weight method; Among them, based on the subjective weighting of indicators in AHP, the evaluation index system of low-carbon operation of smart distribution network is divided into three levels to form a hierarchical structure model; Among them, the combined weight calculation based on the AHP-anti-entropy weight method uses the hierarchical analysis method and the entropy value method to subjectively and objectively weight each indicator, and combines the two weights to obtain a more reasonable indicator weight. In order to make the indicator weight more accurate and scientific, the following formula is used to calculate the combined weight of each evaluation indicator v i : In the formula: integer coefficient , α is 0.7; Calculation of the low-carbon comprehensive evaluation value of the distribution network: Find the data of various indicators in previous years, build an evaluation indicator matrix, calculate the entropy value and objective weight of each indicator, and then combine the established hierarchical model to establish judgment matrices for the criterion layer to the target layer, and the indicator layer to the criterion layer. After normalizing the eigenvalues ​​of each judgment matrix, the subjective weight of each indicator to the target layer is obtained.

6. The method for constructing a deep decarbonization potential assessment model for a smart distribution network according to claim 5, characterized in that: The subjective weighting of indicators based on AHP adopts the hierarchical analysis method to divide the evaluation indicator system of low-carbon operation of smart distribution network into three levels to form a hierarchical structure model. The top layer is the target layer, the second layer is the criterion layer, and the third layer is the indicator layer. The subjective weights of the criterion layer to the target layer and the indicator layer to the criterion layer are determined respectively. The steps for calculating the weights of indicators at each level are as follows: Construct judgment matrix and consistency check: Assume that m evaluation indicators, l evaluation objects, and the indicator data is c ij ( i =1, 2, … m ; j =1, 2, … l ), within the same level of the hierarchical model, the indicators are compared pairwise according to the importance scaling method, and a judgment matrix is ​​formed based on the relative importance of the indicators. ; Where: m is the number of evaluation indicators; a ij It indicates the relative importance of the evaluation indicators. a ij >0, a ij =1 / a ij , a ii =1; The judgment matrix is ​​affected by the subjective judgment of the decision maker and has errors with objective facts. It needs to be checked for consistency. The consistency ratio CR is defined as: CR=CI / RI Where: CI is called the consistency index, , is the judgment matrix The maximum eigenvalue of RI; RI is defined as the average random consistency index, RI is based on the number of evaluation indicators m and change; Determine the subjective weight of the indicator: The geometric mean method and normalization method are used to calculate the subjective weight of the evaluation index, as shown in the following formula:

7. The method for constructing a deep decarbonization potential assessment model for a smart distribution network according to claim 5, characterized in that: The objective weighting of indicators based on the anti-entropy weight method is to use the anti-entropy weight method to objectively weight the indicator layer, and define an anti-entropy as: Where: ,and ; The characteristics of anti-entropy are opposite to those of entropy, that is, the greater the disorder of an indicator, the greater the anti-entropy value, and thus the greater the weight. The steps for determining the objective weight of an indicator by the anti-entropy weight method are as follows: Constructing the evaluation index matrix: In order to eliminate the differences in magnitude and type between data, some influencing factors are standardized. The specific normalization results are shown in the following formula: Normalization of benefit indicators: ； Normalization of cost-based indicators: ； Normalization of intermediate indicators: ； The evaluation index matrix is ​​constructed as follows: ； Calculate the anti-entropy of each indicator as shown in the following formula: ； Determine the objective weight of the indicator: Normalize the anti-entropy to obtain the anti-entropy weight of each evaluation indicator, that is, the objective weight, as shown in the following formula:

8. The method for constructing a deep decarbonization potential assessment model for a smart distribution network according to claim 1, characterized in that: The scenario prediction method based on multiple regression analysis takes the regional carbon emissions as the independent variable y , taking the multiple correlation factors of low-carbon development of smart distribution network as five independent variables, and based on the historical data of carbon emissions and related factors in previous years, the functional relationship between carbon emissions and multiple driving factors is fitted through multiple regression analysis method, and then the specific multiple regression model equation is obtained, as shown in the following formula: Where: β 0. β 1. … β 4 are the 5 unknown coefficients to be analyzed; ɛ is an unobservable random variable, namely the error term.

9. The method for constructing a deep decarbonization potential assessment model for a smart distribution network according to claim 1, characterized in that: Based on the analysis of the forecast scenario, the relevant parameters of the key driving factors affecting the low-carbon development of electricity in the model are set, and the technical factors are decomposed into the four links of "source-grid-load-storage" of the smart distribution network. The impact of each driving factor on carbon emissions is considered, and the relevant parameters and changes are set. The specific calculation is shown in the following formula: ； ； ； ； Where: E 0. N 0. U 0. S 0 represents the index values ​​of the driving factors of energy, power grid, energy consumption and energy storage in the base year respectively. t The situation in the year is compared with that in the base year; γ1, γ2, γ3, and γ3 are the degrees of progress in energy, power grid, energy consumption, and energy storage, respectively.

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