Carbon emission factor calculation method considering electricity utilization differentiation

Abstract

The invention relates to a carbon emission factor calculation method considering power utilization differentiation. The method comprises the following steps: collecting real-time power flow data of a power system and power supply composition information of each node; constructing an electricity and carbon behavior comprehensive evaluation system, and calculating an electricity and carbon behavior comprehensive evaluation index of each node based on the real-time power flow data of the power system and the power supply composition information of each node; performing forward and reverse normalization processing on the comprehensive evaluation index of the electrical and carbon behaviors of each node, and performing preliminary weighting on the comprehensive evaluation index of the electrical and carbon behaviors of each node after the forward and reverse normalization processing through an entropy method; performing secondary empowerment on the comprehensive evaluation indexes of the electricity and carbon behaviors of the nodes subjected to preliminary empowerment through an ideal solution approaching sorting method improved based on an objective empowerment method; and calculating a clean energy emission reduction index, and calculating a carbon emission factor of each node based on the secondary weighting result and the clean energy emission reduction index.

Description

Technical Field

[0001] The present invention relates to a method for calculating a carbon emission factor taking into account differences in electricity consumption, and belongs to the technical field of calculating carbon emission factors of electricity. Background Art

[0002] It is of great significance to formulate a reasonable carbon emission factor calculation system and guide the upstream and downstream of the power system to coordinate emission reduction. At present, the mainstream at home and abroad adopts the carbon emission flow theory to calculate the carbon emission factor. Although the carbon flow analysis theory has undergone years of development and improvement, there is still room for improvement, which is mainly reflected in the following four aspects: 1. In carbon flow analysis theory, the low-carbon attribute of clean energy is usually represented by a carbon emission intensity index of 0, which cannot reflect the magnitude of its carbon-saving effect.

[0003] 2. The purpose of reasonable carbon responsibility allocation is to effectively mobilize users' carbon reduction potential. The existing carbon emission flow theory does not take into account user behavior when calculating carbon emission factors. How to incorporate users' electricity usage habits and timing patterns into the calculation of carbon emission factors and further achieve reasonable allocation of carbon emission responsibilities will effectively mobilize users' willingness to reduce carbon emissions.

[0004] 3. The carbon emission factor calculation method based on carbon emission flow theory has the characteristic of "proximity principle", that is, according to the tidal flow results, nodes close to low-emission power generation units are naturally low-carbon potential, while nodes close to high-emission power generation units are at high carbon potential, resulting in the location of clean energy power stations to obtain more significant low-carbon benefits, and lack of fairness for loads at unfavorable nodes.

[0005] 4. The output volatility of new energy units will force thermal power units to adjust their output levels, resulting in reduced operating efficiency and increased coal consumption, which will generate additional carbon emissions and cause the unit carbon emission factor to be higher. How to clarify the respective carbon emission reduction responsibilities for additional carbon emissions is of great significance for achieving low-carbon operation of the power system in the transition stage.

[0006] In summary, it is urgent to take into account the principle of fairness, share the low-carbon benefits of clean energy power sources, and study the fairness calculation method of carbon emission factors that consider users' electricity-carbon behavior. Summary of the invention

[0007] In order to solve the above problems existing in the prior art, the present invention proposes a method for calculating carbon emission factors taking into account the differentiation of electricity consumption.

[0008] The technical solution of the present invention is as follows: The present invention proposes a method for calculating a carbon emission factor taking into account the differentiation of electricity consumption, comprising the following steps: Collect real-time power flow data of the power system and power supply composition information of each node; Construct a comprehensive evaluation system for electricity-carbon behavior, and calculate the comprehensive evaluation index of electricity-carbon behavior of each node based on the real-time power flow data of the power system and the power supply composition information of each node. The comprehensive evaluation index of electricity-carbon behavior includes electricity fluctuation index, new energy interactivity index, renewable energy penetration index, node marginal carbon intensity index and peak-valley load factor index; After the comprehensive evaluation index of the electric-carbon behavior of each node is normalized in both positive and negative directions, the entropy method is used to preliminarily assign weights to the comprehensive evaluation index of the electric-carbon behavior of each node after the normalization in both positive and negative directions. The comprehensive evaluation index of the electric-carbon behavior of each node that was initially weighted was re-weighted by using the improved approximate ideal solution sorting method based on the objective weighting method. Calculate the clean energy emission reduction index, and calculate the carbon emission factor of each node based on the secondary weighting results and the clean energy emission reduction index.

[0009] As a preferred embodiment of the present invention, the calculation formula of the power consumption volatility index is: ; in: Indicates the fluctuation index of electricity consumption; Indicates the total number of users in the node; Indicates the power consumption cycle; Indicates user exist The power consumption at each moment; Indicates user During the power cycle The average power within.

[0010] As a preferred embodiment of the present invention, the calculation formula of the new energy interactivity index is: ; ; in: It represents the interactivity index of new energy; Indicates the number of new energy units coupled to the current node; Indicates The power generation of new energy units; Indicates the similarity coefficient between the new energy unit and the power generation unit; Indicates power consumption cycle The total power generation of the generator sets coupled to the current node; It represents the Euclidean distance between the normalized curve of renewable energy generation in the power system and the normalized curve of power consumption at the previous node; represents the adjustment coefficient; Represents a natural constant.

[0011] As a preferred embodiment of the present invention, the calculation formula of the renewable energy penetration index is: ; in: represents the renewable energy penetration index; Indicates the number of renewable energy units in the power system; Indicates New energy units as nodes The amount of electricity provided; Representation Node power consumption.

[0012] As a preferred embodiment of the present invention, the calculation formula of the node marginal carbon intensity index is: ; in: It represents the marginal carbon intensity index of the node; Represents the carbon emissions of the power system.

[0013] As a preferred embodiment of the present invention, the calculation formula of the peak-valley load factor index is: ; in: It indicates the peak and valley load factor index; express Time Node Power consumption; Indicates the load factor.

[0014] As a preferred embodiment of the present invention, the specific steps of preliminarily weighting the comprehensive evaluation index of the electric-carbon behavior of each node after the forward and reverse normalization processing are as follows: The comprehensive evaluation indicators of the electric-carbon behavior of all nodes are combined into an original data matrix and subjected to forward and reverse normalization processing, specifically: The positive indicators are processed as follows: ; in: Represents the first The node Item indicator value; Indicates The node Item indicator value; Indicates the total number of nodes; The negative indicators are processed as follows: ; Calculate the proportion of each node under each indicator, as shown in the following formula: ; in: Indicates Under the indicator The proportion of nodes; Calculate the entropy value of each indicator as shown in the following formula: ; in: Indicates The entropy value of each indicator; ; Calculate the coefficient of variation for each indicator , as shown in the following formula: ; Based on the difference coefficient of each indicator, the comprehensive evaluation index of the electric carbon behavior of each node is preliminarily weighted, as shown in the following formula: ; in: Indicates the total number of indicators; Indicates The initial weights of the indicators; Substitute the initial weighted values ​​into the original data matrix to obtain the initial weighted data matrix.

[0015] As a preferred embodiment of the present invention, the electric-carbon behavior comprehensive evaluation index of each node preliminarily weighted is secondary weighted by using the improved approximate ideal solution sorting method based on the objective weighting method. The specific steps are as follows: The standard deviation of each indicator is calculated based on the preliminary weighted data matrix, as shown in the following formula: ; in: Indicates The standard deviation of the indicators; Represents the first The node Item indicator value; Represents the first The mean of the index values; For any two indicators and ,satisfy ,and , use the Pearson correlation coefficient to calculate the correlation coefficient between indicators, as shown in the following formula: ; in: Indicators and The correlation coefficient of Represents the first The node Item indicator value; Represents the first The mean of the index values; The information content of each indicator is calculated based on the objective weighting method, as shown in the following formula: ; in: Indicates The amount of information of each indicator; The information weight of each indicator is calculated based on the information content of each indicator, as shown in the following formula: ; in: Indicates The information weight of the index value; Construct a weighted standardized decision matrix. The specific steps are: The preliminary weighted data matrix is ​​standardized as shown in the following formula: ; in: Represents the first The node Item indicator value; Construct a weighted standardized decision matrix, as shown below: ; ; in: represents the weighted normalized decision matrix; represents the weighted standardized decision matrix The node Item indicator value; Determine the positive ideal solution and negative ideal solution for each indicator, as shown in the following formula: ; ; in: Indicates Positive ideal solution of the index; Indicates Negative ideal solution of the index; Calculate the distance between each node and the positive ideal solution and the negative ideal solution, as shown in the following formula: ; ; in: Indicates The distance between each node and the positive ideal solution; Indicates The distance between each node and the negative ideal solution; The relative proximity of each node is calculated based on the distance between each node and the positive ideal solution and the negative ideal solution, as shown in the following formula: ; in: Indicates The relative proximity of nodes; Calculate the relative proximity weight of each node and calculate the low-carbon benefit allocation weight of each node, as shown in the following formula: ; ; in: Indicates The relative proximity weight of each node; Indicates The low-carbon benefit allocation weight of each node is the secondary weighted value.

[0016] As a preferred embodiment of the present invention, the steps for calculating the carbon emission factor of each node are: Calculate the clean energy emission reduction index as shown in the following formula: ; in: It represents the clean energy emission reduction index; Indicates the equivalent carbon emission factor of a thermal power plant corresponding to the power generation of a clean energy power plant; It represents the electricity generated by clean energy power plants; The carbon emission responsibility of each node is calculated based on the clean energy emission reduction index, as shown in the following formula: ; in: Representation Node of power generation; Representation Node carbon emission responsibility; It indicates the proportion of clean energy emission reduction that can be allocated to the system; The carbon emission factor of each node is calculated based on the carbon emission responsibility of each node under the clean energy emission reduction index, as shown in the following formula:

[0017] in: Representation Node carbon emission factor.

[0018] The present invention has the following beneficial effects: 1. The present invention can effectively eliminate the unfairness caused by the node location, making the carbon emission responsibility sharing of loads at different locations more reasonable, ensuring the fairness of economic benefits on the load side, and achieving more efficient energy resource allocation; 2. The present invention can provide a reference for the allocation of additional carbon emission responsibilities caused by peak load regulation of thermal power units due to the uncertainty of user electricity consumption and the volatility of new energy output, more accurately reflect the impact of user electricity consumption on the environment, and promote the improvement of energy-saving awareness on the user side.

[0019] 3. The present invention can incorporate the user's electricity usage habits and timing patterns into the calculation of carbon emission factors, and further realize the sharing of carbon emission responsibilities, which will effectively mobilize users' willingness to reduce carbon emissions and promote coordinated emission reduction in the upstream and downstream of the power system.

[0020] 4. The present invention can fully reflect the carbon-saving effect of new energy units in power generation. The carbon saving amount can be converted into potential economic value, which can effectively mobilize the enthusiasm for regional new energy installation investment and help guide the energy industry to develop in a low-carbon direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of the method of the present invention; Figure 2 This is a power supply topology diagram of a typical regional power system in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] 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.

[0023] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0024] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0025] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0026] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0027] Embodiment 1: See also Figure 1 , a method for calculating carbon emission factors taking into account the differentiation of electricity consumption, comprising the following steps: Collect real-time power flow data of the power system and power supply composition information of each node; Construct a comprehensive evaluation system for electricity-carbon behavior, and calculate the comprehensive evaluation index of electricity-carbon behavior of each node based on the real-time power flow data of the power system and the power supply composition information of each node. The comprehensive evaluation index of electricity-carbon behavior includes electricity fluctuation index, new energy interactivity index, renewable energy penetration index, node marginal carbon intensity index and peak-valley load factor index; After the comprehensive evaluation index of the electric-carbon behavior of each node is normalized in both positive and negative directions, the entropy method is used to preliminarily assign weights to the comprehensive evaluation index of the electric-carbon behavior of each node after the normalization in both positive and negative directions. The comprehensive evaluation index of the electric-carbon behavior of each node that was initially weighted was re-weighted by using the improved approximate ideal solution sorting method based on the objective weighting method. Calculate the clean energy emission reduction index, and calculate the carbon emission factor of each node based on the secondary weighting results and the clean energy emission reduction index.

[0028] See also Figure 2 In this embodiment, a typical regional power system is taken as an example. The region is composed of a 500kv substation, a 220kv substation, a 110kv substation, a power supply plant, and a park user, where each park represents a node; Collect the time-series electricity information of the area, including branch power flow distribution matrix, unit injection distribution matrix, load distribution matrix, node active flux matrix and generator unit carbon emission intensity vector; The carbon potential information of each node is calculated based on the time-series electricity information of the area, and the indirect carbon emissions of each node are further calculated. The power supply composition of each node is analyzed through the indirect carbon emissions of each node.

[0029] As a preferred implementation of this embodiment, the calculation formula of the power consumption volatility index is: ; in: Indicates the fluctuation index of electricity consumption; Indicates the total number of users in the node; Indicates the power consumption cycle; Indicates user exist The power consumption at each moment; Indicates user During the power cycle The average power within The greater the volatility of electricity consumption, the greater the dispersion of the park's electricity consumption around its mean, and the greater the fluctuations in the electricity consumption curve.

[0030] As a preferred implementation of this embodiment, the calculation formula of the new energy interactivity index is: ; ; in: It represents the interactivity index of new energy; Indicates the number of new energy units coupled to the current node; Indicates The power generation of new energy units; Represents the similarity coefficient between the new energy unit and the generator unit, ; Indicates power consumption cycle The total power generation of the generator sets coupled to the current node; It represents the Euclidean distance between the normalized curve of the 24-hour renewable energy generation of the power system and the normalized curve of the power consumption of the previous node; represents the adjustment coefficient; represents a natural constant; The larger the similarity coefficient, the more the park's electricity consumption curve conforms to the characteristics of new energy output, and the greater its contribution to new energy consumption.

[0031] As a preferred implementation of this embodiment, the calculation formula of the renewable energy penetration index is: ; in: represents the renewable energy penetration index; Indicates the number of renewable energy units in the power system; Indicates New energy units as nodes The amount of electricity provided; Representation Node Power consumption; The greater the permeability, the greater the amount of new energy power supplied to the park, and the higher the carbon efficiency it obtains.

[0032] As a preferred implementation of this embodiment, the calculation formula of the node marginal carbon intensity index is: ; in: It represents the marginal carbon intensity index of the node; represents the carbon emissions of the power system; The greater the marginal carbon intensity, the greater the impact of the park's own electricity consumption behavior on the carbon emissions of the entire system.

[0033] As a preferred implementation of this embodiment, the calculation formula of the peak-valley load factor index is: ; in: It indicates the peak and valley load factor index; express Time Node Power consumption; It represents the load factor, and its value varies according to different time periods, as shown in the following formula; ; like If the value is positive and high, it means that the peak and valley time periods of the node user are highly consistent with the peak and valley time periods of the power grid, and the user is a peak-facing user. If the value is negative, it means that its peak and valley periods are exactly opposite to those of the power grid, and it is a peak avoidance user; for continuous users, their load basically remains near the average value. The value can be positive or negative, but its value is generally very small.

[0034] As a preferred implementation of this embodiment, the specific steps of preliminarily weighting the comprehensive evaluation index of the electric-carbon behavior of each node after the forward and reverse normalization processing are as follows: The comprehensive evaluation indicators of the electric-carbon behavior of all nodes are combined into an original data matrix and subjected to forward and reverse normalization processing, specifically: For positive indicators (the larger the better), the following processing is performed: ; in: Represents the first The node Item indicator value; Indicates The node Item indicator value; Indicates the total number of nodes; For negative indicators (the smaller the better), the following processing is performed: ; Calculate the proportion of each node under each indicator, as shown in the following formula: ; in: Indicates Under the indicator The proportion of nodes; Calculate the entropy value of each indicator as shown in the following formula: ; in: Indicates The entropy value of each indicator; ; Calculate the coefficient of variation for each indicator , as shown in the following formula: ; Based on the difference coefficient of each indicator, the comprehensive evaluation index of the electric carbon behavior of each node is preliminarily weighted, as shown in the following formula: ; in: Indicates the total number of indicators; Indicates The initial weights of the indicators; Substitute the initial weighted values ​​into the original data matrix to obtain the initial weighted data matrix.

[0035] As a preferred implementation of this embodiment, the electric-carbon behavior comprehensive evaluation index of each node that is initially weighted is secondary weighted by using an improved approximate ideal solution sorting method based on an objective weighting method. The specific steps are as follows: The standard deviation of each indicator is calculated based on the preliminary weighted data matrix, as shown in the following formula: ; in: Indicates The standard deviation of the indicators; Represents the first The node Item indicator value; Represents the first The mean of the index values; For any two indicators and ,satisfy ,and , use the Pearson correlation coefficient to calculate the correlation coefficient between indicators, as shown in the following formula: ; in: Indicators and The correlation coefficient of Represents the first The node Item indicator value; Represents the first The mean of the index values; The information content of each indicator is calculated based on the objective weighting method, as shown in the following formula: ; in: Indicates The amount of information of each indicator; The information weight of each indicator is calculated based on the information content of each indicator, as shown in the following formula: ; in: Indicates The information weight of the index value; Construct a weighted standardized decision matrix. The specific steps are: The preliminary weighted data matrix is ​​standardized as shown in the following formula: ; in: Represents the first The node Item indicator value; Construct a weighted standardized decision matrix, as shown below: ; ; in: represents the weighted normalized decision matrix; represents the weighted standardized decision matrix The node Item indicator value; Determine the positive ideal solution (the vector of the maximum value of each indicator in all samples) and the negative ideal solution (the vector of the maximum value of each indicator in all samples) of each indicator, as shown in the following formula: ; ; in: Indicates Positive ideal solution of the index; Indicates Negative ideal solution of the index; Calculate the distance between each node and the positive ideal solution and the negative ideal solution, as shown in the following formula: ; ; in: Indicates The distance between each node and the positive ideal solution; Indicates The distance between each node and the negative ideal solution; The relative proximity of each node is calculated based on the distance between each node and the positive ideal solution and the negative ideal solution, as shown in the following formula: ; in: Indicates The relative proximity of nodes; Calculate the relative proximity weight of each node and calculate the low-carbon benefit allocation weight of each node, as shown in the following formula: ; ; in: Indicates The relative proximity weight of each node; Indicates The low-carbon benefit allocation weight of each node is the secondary weighted value.

[0036] As a preferred implementation of this embodiment, the steps for calculating the carbon emission factor of each node are: Calculate the clean energy emission reduction index as shown in the following formula: ; in: It represents the clean energy emission reduction index; Indicates the equivalent carbon emission factor of a thermal power plant corresponding to the power generation of a clean energy power plant; It represents the electricity generated by clean energy power plants; The carbon emission responsibility of each node is calculated based on the clean energy emission reduction index, as shown in the following formula: ; in: Representation Node of power generation; Representation Node carbon emission responsibility. It indicates the percentage of clean energy emission reduction that can be allocated to the system. Its value is determined by local power operators and ranges from (0,1).

[0037] The carbon emission factor of each node is calculated based on the carbon emission responsibility of each node under the clean energy emission reduction index, as shown in the following formula:

[0038] in: Representation Node carbon emission factor.

[0039] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0040] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0041] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0042] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0043] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for calculating carbon emission factors taking into account the differentiation of electricity consumption, characterized in that: The following steps are involved: Collect real-time power flow data of the power system and power supply composition information of each node; Construct a comprehensive evaluation system for electricity-carbon behavior, and calculate the comprehensive evaluation index of electricity-carbon behavior of each node based on the real-time power flow data of the power system and the power supply composition information of each node. The comprehensive evaluation index of electricity-carbon behavior includes electricity fluctuation index, new energy interactivity index, renewable energy penetration index, node marginal carbon intensity index and peak-valley load factor index; After the comprehensive evaluation index of the electric-carbon behavior of each node is normalized in both positive and negative directions, the entropy method is used to preliminarily assign weights to the comprehensive evaluation index of the electric-carbon behavior of each node after the normalization in both positive and negative directions. The comprehensive evaluation index of the electric-carbon behavior of each node that was initially weighted was re-weighted by using the improved approximate ideal solution sorting method based on the objective weighting method. Calculate the clean energy emission reduction index, and calculate the carbon emission factor of each node based on the secondary weighting results and the clean energy emission reduction index.

2. The method for calculating carbon emission factors taking into account electricity consumption differentiation according to claim 1 is characterized in that: The calculation formula of the power consumption volatility index is: ； in: Indicates the fluctuation index of electricity consumption; Indicates the total number of users in the node; Indicates the power consumption cycle; Indicates user exist The power consumption at each moment; Indicates user During the power cycle The average power within.

3. The method for calculating carbon emission factors taking into account electricity consumption differentiation according to claim 1 is characterized in that: The calculation formula of the new energy interactivity index is: ； ； in: It represents the interactivity index of new energy; Indicates the number of new energy units coupled to the current node; Indicates The power generation of new energy units; Indicates the similarity coefficient between the new energy unit and the power generation unit; Indicates power consumption cycle The total power generation of the generator sets coupled to the current node; It represents the Euclidean distance between the normalized curve of renewable energy generation in the power system and the normalized curve of power consumption at the previous node; represents the adjustment coefficient; Represents a natural constant.

4. The method for calculating carbon emission factors taking into account electricity consumption differentiation according to claim 1 is characterized in that: The calculation formula of the renewable energy penetration index is: ； in: represents the renewable energy penetration index; Indicates the number of renewable energy units in the power system; Indicates New energy units as nodes The amount of electricity provided; Representation Node power consumption.

5. A method for calculating carbon emission factors taking into account electricity consumption differentiation according to claim 4, characterized in that: The calculation formula of the node marginal carbon intensity index is: ； in: It represents the marginal carbon intensity index of the node; represents the carbon emissions of the power system; Indicates the proportion of clean energy emission reduction that can be allocated to the system.

6. A method for calculating carbon emission factors taking into account electricity consumption differentiation according to claim 1, characterized in that: The calculation formula of the peak-valley load factor index is: ； in: It indicates the peak and valley load factor index; express Time Node Power consumption; Indicates the load factor.

7. The method for calculating carbon emission factors taking into account electricity consumption differentiation according to claim 1, characterized in that: The specific steps for preliminarily weighting the comprehensive evaluation index of the electric-carbon behavior of each node after positive and negative normalization are as follows: The comprehensive evaluation indicators of the electric-carbon behavior of all nodes are combined into an original data matrix and subjected to forward and reverse normalization processing, specifically: The positive indicators are processed as follows: ； in: Represents the first The node Item indicator value; Indicates The node Item indicator value; Indicates the total number of nodes; The negative indicators are processed as follows: ； Calculate the proportion of each node under each indicator, as shown in the following formula: ； in: Indicates Under the indicator The proportion of nodes; Calculate the entropy value of each indicator as shown in the following formula: ； in: Indicates The entropy value of each indicator; ; Calculate the coefficient of variation for each indicator , as shown in the following formula: ； Based on the difference coefficient of each indicator, the comprehensive evaluation index of the electric carbon behavior of each node is preliminarily weighted, as shown in the following formula: ； in: Indicates the total number of indicators; Indicates The initial weighting values ​​of the indicators; Substitute the initial weighted values ​​into the original data matrix to obtain the initial weighted data matrix.

8. A method for calculating carbon emission factors taking into account electricity consumption differentiation according to claim 7, characterized in that: The improved approximate ideal solution sorting method based on the objective weighting method is used to perform secondary weighting on the comprehensive evaluation index of the electric-carbon behavior of each node that is initially weighted. The specific steps are as follows: The standard deviation of each indicator is calculated based on the preliminary weighted data matrix, as shown in the following formula: ； in: Indicates The standard deviation of the indicators; Represents the first The node Item indicator value; Represents the first The mean of the index values; For any two indicators and ,satisfy ,and , use the Pearson correlation coefficient to calculate the correlation coefficient between indicators, as shown in the following formula: ； in: Indicator and The correlation coefficient of Represents the first The node Item indicator value; Represents the first The mean of the index values; The information content of each indicator is calculated based on the objective weighting method, as shown in the following formula: ； in: Indicates The amount of information of each indicator; The information weight of each indicator is calculated based on the information content of each indicator, as shown in the following formula: ； in: Indicates The information weight of the index value; Construct a weighted standardized decision matrix. The specific steps are: The preliminary weighted data matrix is ​​standardized as shown in the following formula: ； in: Represents the first The node Item indicator value; Construct a weighted standardized decision matrix, as shown below: ； ； in: represents the weighted normalized decision matrix; represents the weighted standardized decision matrix The node Item indicator value; Determine the positive ideal solution and negative ideal solution for each indicator, as shown in the following formula: ； ； in: Indicates Positive ideal solution of the index; Indicates Negative ideal solution of the index; Calculate the distance between each node and the positive ideal solution and the negative ideal solution, as shown in the following formula: ； ； in: Indicates The distance between each node and the positive ideal solution; Indicates The distance between each node and the negative ideal solution; The relative proximity of each node is calculated based on the distance between each node and the positive ideal solution and the negative ideal solution, as shown in the following formula: ； in: Indicates The relative proximity of nodes; Calculate the relative proximity weight of each node and calculate the low-carbon benefit allocation weight of each node, as shown in the following formula: ； ； in: Indicates The relative proximity weight of each node; Indicates The low-carbon benefit allocation weight of each node is the secondary weighted value.

9. A method for calculating carbon emission factors taking into account electricity consumption differentiation according to claim 8, characterized in that: The calculation steps of the carbon emission factor of each node are as follows: Calculate the clean energy emission reduction index as shown in the following formula: ； in: It represents the clean energy emission reduction index; Indicates the equivalent carbon emission factor of a thermal power plant corresponding to the power generation of a clean energy power plant; It represents the electricity generated by clean energy power plants; The carbon emission responsibility of each node is calculated based on the clean energy emission reduction index, as shown in the following formula: ； in: Representation Node of power generation; Representation Node carbon emission responsibility; It indicates the proportion of clean energy emission reduction that can be allocated to the system; The carbon emission factor of each node is calculated based on the carbon emission responsibility of each node under the clean energy emission reduction index, as shown in the following formula: ； in: Representation Node carbon emission factor.

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