A calculation method of carbon emission factor considering electricity consumption differences

Abstract

The present invention relates to a calculation method of carbon emission factors considering electricity consumption differences, comprising the following steps: collecting real-time power flow data of the power system and power supply composition information of each node; constructing a comprehensive evaluation system for electricity-carbon behavior, and calculating 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; after performing positive and negative normalization processing on the comprehensive evaluation indexes of electricity-carbon behavior of each node, initially weighting the comprehensive evaluation indexes of electricity-carbon behavior of each node after positive and negative normalization processing by using the entropy value method; performing secondary weighting on the comprehensive evaluation indexes of electricity-carbon behavior of each node with initial weighting by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) improved based on the objective weighting method; calculating the clean energy emission reduction index, and calculating the 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 calculation method for carbon emission factors considering electricity consumption differences, belonging to the technical field of power carbon emission factor calculation. Background Art

[0002] Developing a reasonable carbon emission factor calculation system to guide the collaborative emission reduction of the upstream and downstream of the power system is of great significance. Currently, the mainstream at home and abroad uses the carbon emission flow theory to calculate carbon emission factors. Although the carbon flow analysis theory has experienced years of development and improvement, there is still room for improvement, mainly reflected in the following four aspects:

[0003] 1. In the carbon flow analysis theory, the low-carbon attribute of clean energy is usually represented by a carbon emission intensity index with a value of 0, which cannot reflect the magnitude of its carbon-saving effect.

[0004] 2. The purpose of reasonable carbon responsibility sharing is to effectively mobilize the carbon reduction potential of users. The existing carbon emission flow theory does not consider user behavior in the calculation of carbon emission factors. How to incorporate users' electricity consumption habits and time series patterns into the calculation of carbon emission factors and further achieve reasonable sharing of carbon emission responsibilities will effectively mobilize users' carbon reduction willingness.

[0005] 3. The carbon emission factor calculation method based on the carbon emission flow theory has the characteristic of the "proximity principle", that is, according to the result of power flow, the nodes close to low-emission generating units inherently have a low carbon potential, while the nodes close to high-emission generating units are in a high carbon potential, resulting in more significant low-carbon benefits for the locations of clean energy power stations, lacking fairness for the loads at disadvantageous nodes.

[0006] 4. The output volatility of new energy units will cause thermal power units to be forced to adjust their output levels, resulting in a decline in the operating efficiency of thermal power units and an increase in the coal consumption rate, generating additional carbon emissions, leading to a relatively high unit carbon emission factor of the units. How to clarify their respective carbon emission reduction responsibilities for the additional carbon emissions is of great significance for achieving low-carbon operation of the power system in the transition stage.

[0007] In summary, it is urgent to consider the fairness principle, share the low-carbon benefits for clean energy power sources, and study a fairness calculation method for carbon emission factors considering users' electricity-carbon behavior. Summary of the Invention

[0008] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes a calculation method for carbon emission factors considering electricity consumption differences.

[0009] The technical solution of the present invention is as follows:

[0010] The present invention proposes a calculation method for carbon emission factors considering electricity consumption differences, including the following steps:

[0011] Collect real-time power flow data of the power system and the power supply composition information of each node;

[0012] 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 an electricity consumption volatility index, a new energy interaction index, a renewable energy penetration index, a node marginal carbon intensity index, and a peak-valley load factor index;

[0013] After performing forward and reverse normalization processing on the comprehensive evaluation indexes of electricity-carbon behavior of each node, initially assign weights to the forward and reverse normalized comprehensive evaluation indexes of electricity-carbon behavior of each node by using the entropy method;

[0014] Perform secondary weighting on the initially weighted comprehensive evaluation indexes of electricity-carbon behavior of each node by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) improved based on the objective weighting method;

[0015] Calculate the clean energy emission reduction index, and calculate the carbon emission factor of each node based on the secondary weighting result and the clean energy emission reduction index.

[0016] As a preferred embodiment of the present invention, the calculation formula of the electricity consumption volatility index is:

[0017] ;

[0018] Where: represents the electricity consumption volatility index; represents the total number of users within the node; represents the electricity consumption cycle; represents user at the electricity consumption power at the moment; represents user during the electricity consumption cycle the average power within.

[0019] As a preferred embodiment of the present invention, the calculation formula of the new energy interaction index is:

[0020] ;

[0021] ;

[0022] Where: represents the new energy interaction index; represents the number of new energy units coupled to the current node; represents the th power generation of the new energy unit; Indicates the similarity coefficient between the new energy unit and the generator set; Indicates the electricity consumption cycle Total power generation of the generator sets coupled to the current node within; Indicates the Euclidean distance between the normalized curve of new energy power generation in the power system and the normalized curve of electricity consumption at the previous node; Indicates the adjustment coefficient; Indicates the natural constant.

[0023] As a preferred embodiment of the present invention, the calculation formula of the renewable energy penetration index is:

[0024] ;

[0025] Where: Indicates the renewable energy penetration index; Indicates the number of new energy units in the power system; Indicates the th new energy unit provides the power supply for the node ; Indicates the node Power consumption.

[0026] As a preferred embodiment of the present invention, the calculation formula of the node marginal carbon intensity index is:

[0027] ;

[0028] Where: Indicates the node marginal carbon intensity index; Indicates the carbon emissions of the power system.

[0029] As a preferred embodiment of the present invention, the calculation formula of the peak-valley load coefficient index is:

[0030] ;

[0031] Where: Indicates the peak-valley load coefficient index; Indicates The power consumption of the node at the moment; Indicates the load coefficient.

[0032] As a preferred embodiment of the present invention, the specific steps for initially weighting the comprehensive evaluation index of the electro-carbon behavior of each node after forward and reverse normalization processing are:

[0033] Form the original data matrix of the comprehensive evaluation index of the electro-carbon behavior of all nodes and perform forward and reverse normalization processing, specifically:

[0034] The following processing is performed on positive indicators:

[0035] ;

[0036] Among them: represents the value of the th item of the th node after positive and negative normalization processing; represents the value of the th item of the th node; represents the total number of nodes;

[0037] The following processing is performed on negative indicators:

[0038] ;

[0039] Calculate the proportion of each node under each indicator, as shown in the following formula:

[0040] ;

[0041] Among them: represents the proportion of the th item of the th node;

[0042] Calculate the entropy value of each indicator, as shown in the following formula:

[0043] ;

[0044] Among them: represents the entropy value of the th item of the indicator; ;

[0045] Calculate the difference coefficient of each indicator, as shown in the following formula:

[0046] ;

[0047] Based on the difference coefficient of each indicator, conduct a preliminary weighting of the comprehensive evaluation indicators of the electro-carbon behavior of each node, as shown in the following formula:

[0048] ;

[0049] Among them: represents the total number of indicators; represents the preliminary weighting value of the th item of the indicator;

[0050] Substitute the preliminary weighting value into the original data matrix to obtain the preliminary weighted data matrix.

[0051] As a preferred embodiment of the present invention, the comprehensive evaluation index of the electro-carbon behavior of each preliminarily weighted node is re-weighted by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) improved based on the objective weighting method. The specific steps are as follows:

[0052] Calculate the standard deviation of each index based on the preliminarily weighted data matrix, as shown in the following formula:

[0053] ;

[0054] Where: represents the standard deviation of the th index; represents the value of the th node and the th index in the preliminarily weighted data matrix; represents the mean value of the th index value in the preliminarily weighted data matrix;

[0055] For any two indices and , satisfying , and , calculate the correlation coefficient between the indices using the Pearson correlation coefficient, as shown in the following formula:

[0056] ;

[0057] Where: represents the correlation coefficient between the indices and ; represents the value of the th node and the th index in the preliminarily weighted data matrix; represents the mean value of the th index value in the preliminarily weighted data matrix;

[0058] Calculate the information content of each index based on the objective weighting method, as shown in the following formula:

[0059] ;

[0060] Where: represents the information content of the th index;

[0061] Calculate the information content weight of each index based on the information content of each index, as shown in the following formula:

[0062] ;

[0063] Where: represents the The information weight of the item index value;

[0064] Construct a weighted standardized decision matrix. The specific steps are as follows:

[0065] Perform standardization processing on the preliminary weighted data matrix as shown in the following formula:

[0066] ;

[0067] Where: Represents the value of the th item index of the th node in the preliminary weighted data matrix after standardization processing;

[0068] Construct a weighted standardized decision matrix as shown in the following formula:

[0069] ;

[0070] ;

[0071] Where: Represents the weighted standardized decision matrix; Represents the value of the th item index of the th node in the weighted standardized decision matrix;

[0072] Determine the positive ideal solution and negative ideal solution for each item index as shown in the following formula:

[0073] ;

[0074] ;

[0075] Where: Represents the positive ideal solution of the th item index; Represents the negative ideal solution of the th item index;

[0076] Calculate the distance between each node and the positive ideal solution and the negative ideal solution as shown in the following formula:

[0077] ;

[0078] ;

[0079] Where: Represents the distance between the th node and the positive ideal solution; Represents the distance between the th node and the negative ideal solution;

[0080] Calculate the relative closeness of each node based on the distance between each node and the positive ideal solution and the negative ideal solution, as shown in the following formula:

[0081] ;

[0082] Where: represents the relative closeness of the th node;

[0083] Calculate the relative closeness weight of each node and calculate the low-carbon benefit sharing weight of each node, as shown in the following formula:

[0084] ;

[0085] ;

[0086] Where: represents the relative closeness weight of the th node; represents the low-carbon benefit sharing weight of the th node, that is, the secondary weighting value.

[0087] As a preferred embodiment of the present invention, the calculation steps of the carbon emission factor of each node are:

[0088] Calculate the clean energy emission reduction index, as shown in the following formula:

[0089] ;

[0090] Where: represents the clean energy emission reduction index; represents the equivalent carbon emission factor of the thermal power plant corresponding to the power generation of the clean energy power plant; represents the power generation of the clean energy power plant;

[0091] Calculate the carbon emission responsibility of each node based on the clean energy emission reduction index, as shown in the following formula:

[0092] ;

[0093] Where: represents the power generation of node ; represents the carbon emission responsibility of node ; represents the allocable proportion of the clean energy emission reduction system;

[0094] Calculate the carbon emission factor of each node based on the carbon emission responsibility of each node under the clean energy emission reduction index, as shown in the following formula:

[0095]

[0096] Wherein: represents the carbon emission factor of the node .

[0097] The present invention has the following beneficial effects:

[0098] 1. The present invention can effectively eliminate the unfair phenomenon caused by the node position, making the load at different positions more reasonable in the carbon emission responsibility sharing, ensuring the fairness of the economic benefits on the load side, and realizing a more efficient energy resource allocation;

[0099] 2. The present invention can provide a reference for the problem of sharing the additional carbon emission responsibility caused by the peak regulation of thermal power units due to the uncertainty of user electricity consumption and the volatility of new energy output, more accurately reflecting the impact of user electricity consumption behavior on the environment, and promoting the improvement of the energy-saving awareness on the user side.

[0100] 3. The present invention can incorporate the electricity consumption habits and time sequence laws of users into the calculation of carbon emission factors, and further realize the sharing of carbon emission responsibilities, which will effectively mobilize the willingness of users to reduce carbon and promote the collaborative carbon emission reduction between the upstream and downstream of the power system.

[0101] 4. The present invention can fully reflect the size of the carbon emission reduction effect of new energy units in power generation, and the carbon emission reduction amount can be converted into potential economic value, which can effectively mobilize the enthusiasm of new energy installation investment in the region and help guide the energy industry to develop in the direction of low carbon. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figure 1 is a flowchart of the method of the present invention;

[0103] Figure 2 is a power supply topology diagram of a typical regional power system in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0104] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0105] It should be understood that the step numbers used in the text are only for convenient description and do not limit the execution order of the steps.

[0106] It should be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0107] The terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0108] The term "and / or" refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0109] Embodiment 1:

[0110] See Figure 1 , a carbon emission factor calculation method considering electricity consumption differentiation, comprising the following steps:

[0111] Collect real-time power flow data of the power system and power supply composition information of each node;

[0112] 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 an electricity consumption volatility index, a new energy interaction index, a renewable energy penetration index, a node marginal carbon intensity index, and a peak-valley load coefficient index;

[0113] After performing forward and reverse normalization processing on the comprehensive evaluation indexes of electricity-carbon behavior of each node, initially weight the comprehensive evaluation indexes of electricity-carbon behavior of each node after forward and reverse normalization processing by the entropy value method;

[0114] Perform secondary weighting on the comprehensively evaluated indexes of electricity-carbon behavior of each node with initial weighting by the technique for order preference by similarity to an ideal solution (TOPSIS) improved based on the objective weighting method;

[0115] Calculate the clean energy emission reduction index, and calculate the carbon emission factor of each node based on the secondary weighting result and the clean energy emission reduction index.

[0116] See Figure 2 , in this embodiment, taking the power system of a typical area as an example, the area consists of a 500 kV substation, a 220 kV substation, a 110 kV substation, a power supply power plant, and park users. Among them, each park represents a node;

[0117] Collect the time-series power consumption information of this area, including the branch power flow distribution matrix, the unit injection distribution matrix, the load distribution matrix, the node active power flux matrix, and the carbon emission intensity vector of the generator set.

[0118] Calculate the carbon potential information of each node based on the time-series power consumption information of this area, and further calculate the indirect carbon emissions of each node, and analyze its power supply composition through the indirect carbon emissions of each node.

[0119] As a preferred implementation manner of this embodiment, the calculation formula of the power consumption volatility index is:

[0120] ;

[0121] Where: represents the power consumption volatility index; represents the total number of users in the node; represents the power consumption cycle; represents the user at time of the power consumption; represents the user during the power consumption cycle average power within;

[0122] The greater the power consumption volatility indicates that the discrete degree of the park's electricity around its mean value is greater, and the fluctuation of the electricity curve is larger.

[0123] As a preferred implementation manner of this embodiment, the calculation formula of the new energy interaction index is:

[0124] ;

[0125] ;

[0126] Where: represents the new energy interaction index; represents the number of new energy units coupled to the current node; represents the power generation of the th new energy unit; represents the similarity coefficient between the new energy unit and the generator set, ; represents the total power generation of the generator sets coupled to the current node during the power consumption cycle ; represents the Euclidean distance between the normalized curve of the new energy power generation in the power system in 24 hours and the normalized curve of the power consumption of the previous node; represents the adjustment coefficient; represents the natural constant;

[0127] The larger the similarity coefficient is, the more in line the park's electricity consumption curve is with the new energy output characteristics, and the greater its contribution to new energy consumption.

[0128] As a preferred implementation manner of this embodiment, the calculation formula of the renewable energy penetration index is:

[0129] ;

[0130] Where: represents the renewable energy penetration index; represents the number of new energy units in the power system; represents the th new energy unit's power supply provided for the node ; represents the power consumption of the node ;

[0131] The greater the penetration, the greater the new energy power supply obtained by the park, and the higher the electricity-carbon benefit it obtains itself.

[0132] As a preferred implementation manner of this embodiment, the calculation formula of the node marginal carbon intensity index is:

[0133] ;

[0134] Where: represents the node marginal carbon intensity index; represents the carbon emissions of the power system;

[0135] 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.

[0136] As a preferred implementation manner of this embodiment, the calculation formula of the peak-valley load factor index is:

[0137] ;

[0138] Where: represents the peak-valley load factor index; represents the power consumption of the node at the moment; the load factor, and its value is different according to different time periods, as shown in the following formula;

[0139] ;

[0140] If is a positive number and relatively high, it means that the peak-valley period of the user at this node is highly consistent with the peak-valley period of the power grid, and it is a peak-loading user; if A negative value indicates that the peak-valley period of the user is exactly opposite to that of the power grid, and the user is a peak-avoiding type; 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.

[0141] As a preferred implementation manner of this embodiment, the specific steps for initially weighting the comprehensive evaluation index of the electro-carbon behavior of each node after positive and negative normalization are as follows:

[0142] Form the comprehensive evaluation index of the electro-carbon behavior of all nodes into an original data matrix and perform positive and negative normalization processing, specifically:

[0143] For positive indicators (indicators that are better when larger), the following processing is performed:

[0144] ;

[0145] Where: represents the value of the th item of the th node after positive and negative normalization; represents the value of the th item of the th node; represents the total number of nodes;

[0146] For negative indicators (indicators that are better when smaller), the following processing is performed:

[0147] ;

[0148] Calculate the proportion of each node under each indicator, as shown in the following formula:

[0149] ;

[0150] Where: represents the proportion of the th node under the th indicator;

[0151] Calculate the entropy value of each indicator, as shown in the following formula:

[0152] ;

[0153] Where: represents the entropy value of the th indicator; ;

[0154] Calculate the difference coefficient of each indicator, as shown in the following formula:

[0155] ;

[0156] The comprehensive evaluation index of the electro-carbon behavior of each node is initially weighted based on the coefficient of variation of each index, as shown in the following formula:

[0157] ;

[0158] Where: represents the total number of indicators; represents the initial weighting value of the th indicator;

[0159] Substitute the initial weighting value into the original data matrix to obtain the initially weighted data matrix.

[0160] As the preferred implementation method of this embodiment, the comprehensive evaluation index of the electro-carbon behavior of each initially weighted node is re-weighted by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) improved based on the objective weighting method. The specific steps are as follows:

[0161] Calculate the standard deviation of each indicator based on the initially weighted data matrix, as shown in the following formula:

[0162] ;

[0163] Where: represents the standard deviation of the th indicator; represents the value of the th node and the th indicator in the initially weighted data matrix; represents the mean value of the th indicator value in the initially weighted data matrix;

[0164] For any two indicators and , satisfying , and , calculate the correlation coefficient between the indicators using the Pearson correlation coefficient, as shown in the following formula:

[0165] ;

[0166] Where: represents the correlation coefficient between the indicators and ; represents the value of the th node and the th indicator in the initially weighted data matrix; represents the mean value of the th indicator value in the initially weighted data matrix;

[0167] Calculate the information content of each index based on the objective weighting method, as shown in the following formula:

[0168] ;

[0169] Among them: represents the information content of the th index;

[0170] Calculate the information content weight of each index based on the information content of each index, as shown in the following formula:

[0171] ;

[0172] Among them: represents the information content weight of the th index value;

[0173] Construct a weighted standardized decision matrix. The specific steps are as follows:

[0174] Perform standardization processing on the preliminary weighted data matrix, as shown in the following formula:

[0175] ;

[0176] Among them: represents the th node of the preliminary weighted data matrix after standardization processing th index value;

[0177] Construct a weighted standardized decision matrix, as shown in the following formula:

[0178] ;

[0179] ;

[0180] Among them: represents the weighted standardized decision matrix; represents the th node of the weighted standardized decision matrix th index value;

[0181] Determine the positive ideal solution (the vector composed of the maximum value of each index in all samples) and the negative ideal solution (the vector composed of the minimum value of each index in all samples) of each index, as shown in the following formula:

[0182] ;

[0183] ;

[0184] Among them: represents the The positive ideal solution of each index; Denote the negative ideal solution of each index;

[0185] Calculate the distance between each node and the positive ideal solution as well as the negative ideal solution, as shown in the following formula:

[0186] ;

[0187] ;

[0188] Where: Denote the distance between the nth node and the positive ideal solution; Denote the distance between the nth node and the negative ideal solution;

[0189] Calculate the relative closeness of each node based on the distance between each node and the positive ideal solution as well as the negative ideal solution, as shown in the following formula:

[0190] ;

[0191] Where: Denote the relative closeness of the nth node;

[0192] Calculate the relative closeness weight of each node and calculate the low-carbon benefit sharing weight of each node, as shown in the following formula:

[0193] ;

[0194] ;

[0195] Where: Denote the relative closeness weight of the nth node; Denote the low-carbon benefit sharing weight of the nth node, that is, the secondary weighting value.

[0196] As a preferred implementation manner of this embodiment, the calculation steps of the carbon emission factor of each node are:

[0197] Calculate the clean energy emission reduction index, as shown in the following formula:

[0198] ;

[0199] Where: Denote the clean energy emission reduction index; Denote the equivalent carbon emission factor of the thermal power plant corresponding to the power generation of the clean energy power plant; Denote the power generation of the clean energy power plant;

[0200] Calculate the carbon emission responsibility of each node based on the clean energy emission reduction index, as shown in the following formula:

[0201] ;

[0202] Where: represents the power generation of node ; represents the carbon emission responsibility of node . represents the allocable proportion of the clean energy emission reduction system, and its value is determined by local power operators, ranging from (0, 1).

[0203] Calculate the carbon emission factor of each node based on the carbon emission responsibility of each node under the clean energy emission reduction index, as shown in the following formula:

[0204]

[0205] Where: represents the carbon emission factor of node .

[0206] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent the situation where A exists alone, A and B exist simultaneously, or B exists alone. Where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0207] Those of ordinary skill in the art can realize that the units and algorithm steps described in the embodiments disclosed herein can be implemented by a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0208] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0209] In several embodiments provided by 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, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0210] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally 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 positive and negative normalization. The specific steps 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 preliminary weighted values ​​into the original data matrix to obtain the preliminary weighted data matrix; 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 result and the clean energy emission reduction index. 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.

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 In 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 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.

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