Urban power distribution system carbon energy cooperative comprehensive benefit evaluation method oriented to deep decarburization

Abstract

The invention relates to a deep decarburization-oriented urban power distribution system carbon energy cooperative comprehensive benefit evaluation method. The method comprises the steps of selecting main influence factors influencing the comprehensive energy efficiency of an urban power distribution system; building a carbon energy collaborative evaluation index system of the urban power distribution system; calculating each index value and a comprehensive score in the urban power distribution system carbon energy collaborative evaluation index system; and selecting an area with a relatively low comprehensive score to carry out carbon reduction project construction. According to the method, existing related standard specifications are deeply analyzed, the Pareto rule is applied, influence factors and influence indexes are screened, and completeness and balance of an index system are ensured; according to the method, objective differences between economic development conditions of different regions and a power distribution network structure and openness and advancement of an index system are considered, so that the method adapts to the development trend of an evaluated object.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission analysis of power systems, and in particular to a method for evaluating the comprehensive benefits of carbon-energy synergy in urban power distribution systems oriented to deep decarbonization. Background Art

[0002] Deep decarbonization is an important measure to address global climate change and promote sustainable development. With the continuous increase in global carbon emissions, the problem of climate change is becoming increasingly serious. As the main source of carbon emissions, the low-carbon transformation of cities is of great significance to achieving global carbon reduction goals. The comprehensive energy efficiency of urban power distribution systems is an important indicator to measure the energy utilization efficiency and energy management level of urban power distribution systems, which is directly related to the city's energy consumption, environmental protection and economic development. Constructing a scientific and reasonable comprehensive benefit evaluation index system and evaluation method for urban power distribution systems can significantly reduce carbon emissions and effectively promote low-carbon transformation.

[0003] The comprehensive energy efficiency of urban power distribution system refers to the energy utilization efficiency and management level of the power distribution system in the process of transmitting, distributing and using electric energy. At present, many scholars have studied the evaluation of low-carbon power grid from qualitative and quantitative perspectives. However, since the academic community has not yet formed a unified definition of "low-carbon power grid" and the low-carbon power grid involves a wide range of factors, the evaluation of low-carbon power grid has problems such as unclear orientation of indicator system setting and insufficient adaptability of evaluation model. Therefore, constructing a low-carbon power grid benefit evaluation indicator system and evaluation model, and quantitatively evaluating the low-carbonization level of power grid enterprises, has a strong guiding significance for the low-carbon development of power grids under the background of new power systems. Summary of the invention

[0004] In order to solve the problems of unclear index system setting and insufficient adaptability of evaluation models in low-carbon power grid evaluation, the purpose of the present invention is to provide a carbon-energy synergistic comprehensive benefit evaluation method for urban distribution systems oriented towards deep decarbonization, which ensures the integrity and balance of the index system by screening influencing factors and influencing indicators and adapts to the development trend of the evaluated object.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for evaluating the comprehensive benefits of carbon-energy synergy in urban power distribution systems for deep decarbonization, the method comprising the following steps in order:

[0006] (1) Analyze the factors affecting the comprehensive benefits of electricity-carbon synergy and select the main factors affecting the comprehensive energy efficiency of urban power distribution systems, including economic efficiency, low-carbon development and pollution control;

[0007] (2) Based on the main factors affecting the comprehensive energy efficiency of urban power distribution systems, a carbon-energy synergy evaluation index system for urban power distribution systems is established;

[0008] (3) Based on the carbon-energy synergy evaluation index system for urban power distribution systems, use the calculation formulas for each index, collect the data required for the calculation, and calculate each index value and comprehensive score in the carbon-energy synergy evaluation index system for urban power distribution systems;

[0009] (4) Select regions with lower comprehensive scores to build carbon reduction projects, sort the regions from low to high according to their comprehensive scores, and invest funds in regional project construction in order of comprehensive scores from low to high.

[0010] The step (1) specifically refers to: screening out all influencing factors that affect the comprehensive energy efficiency of the urban power distribution system, wherein all influencing factors include energy structure, consumption pattern, economic efficiency, low-carbon development, pollution control, intelligence, environmental friendliness and policy support, applying the Pareto principle, conducting a correlation analysis between the influencing factors and the degree of influence, sorting the influence proportions from high to low according to the data, accumulating them in sequence, and selecting factors whose sum of influence proportions is equal to or close to 80% as the main influencing factors, wherein the main influencing factors include three aspects: economic efficiency, low-carbon development and pollution control.

[0011] The step (2) specifically comprises the following steps in order:

[0012] (2a) Determine the evaluation method: use the analytic hierarchy process to determine the subjective weight, use the entropy weight method to determine the objective weight, apply game theory to calculate and optimize the indicator weight, build the indicator membership function, and use the fuzzy evaluation matrix to combine multiple evaluation indicators;

[0013] (2b) Construct an evaluation index system for carbon-energy synergy in urban power distribution systems: take the main influencing factors as the first-level indicators, and use the hierarchical analysis method to mine the lower-level indicators. The economic efficiency factor includes three indicators, namely, the system's new unit power input efficiency, unit cost-effectiveness contribution, and the reduction rate of carbon dioxide emissions per 10,000 yuan of output value; the low-carbon development factor includes five indicators, namely, the power demand response capability index, the comprehensive line loss rate, the utilization rate of new energy, the proportion of clean energy power, and the activity of the power market; the pollution control factor includes three indicators, namely, the sulfur hexafluoride gas recovery rate, the carbon emissions per kilowatt-hour, and the change rate of total carbon emissions;

[0014] (2c) Construct the judgment matrix M: Based on the influencing factors, let the index of a certain layer be F = [f1, f2, ..., fn], and compare f i With f j The importance of the impact on a certain indicator of the upper layer, expressed as f ij =f i / f j Indicates that m ij The value of is represented by numbers from 1 to 9 and their reciprocals, that is, the scale in AHP; if f i With f jIf the importance is equal, then m ij =1; if f i Slightly more important than f j , then m ij =3; if f i Importance is greater than f j , then m ij =5; if f i The importance is far greater than f j , then m ij =7; if f i The importance is far greater than f j , then m ij =9; if f j Slightly more important than f i , then m ij =2; if f j Importance is greater than f i , then m ij =4; if f j The importance is far greater than f i , then m ij =6; if f j The importance is far greater than f i , then m ij =8;

[0015] Finally, the judgment matrix M is formed, and the definition of the elements in the judgment matrix M is as follows:

[0016]

[0017] Where: m ij >0; j = 1, 2, ..., n; i = 1, 2, ..., n, where n is the number of indicators included in the urban distribution system carbon energy synergy evaluation index system;

[0018] (2d) Hierarchical sorting: Calculate the maximum eigenvalue λ of the judgment matrix M max , the eigenvector ξ corresponding to the judgment matrix M, and then normalize the eigenvector to obtain the ranking weight of the importance of each evaluation indicator at the same level relative to a certain indicator in the upper level, and the total hierarchical ranking weight of each indicator at the bottom level for the overall goal; the total hierarchical ranking weight of each indicator at the bottom level for the overall goal is the weight value of the indicator in the upper level * the weight value of the indicator in the corresponding indicators at the same level.

[0019] The step (3) specifically comprises the following steps in order:

[0020] (3a) Benchmark value area division: Due to the objective differences in economic development and distribution network structure in different regions, in order to make the evaluation results reasonable and comparable, the evaluation objects should be on the same baseline. The baseline is divided into four categories according to load density: I, II, III, and IV. Category I areas are densely populated areas in urban areas and areas with concentrated administrative, economic, commercial, and transportation areas, with a load density greater than 10MW / km 2 ; Category II areas are remote suburban areas, including only the district government seat, economic development zone, and industrial park, with a load density of 5MW / km 2 and 10MW / km 2 Category III areas are county towns and relatively concentrated industrial and populated townships and towns, or relatively concentrated industrial and populated townships and towns in remote suburbs, with a load density of 1MW / km 2 and 5MW / km 2 Category IV areas are mainly agricultural areas, with a load density of less than 1MW / km 2 ;

[0021] (3b) Calculate the indicator status value: the ratio of the original value of the indicator to the corresponding reference value is the indicator status value;

[0022] If the indicator is a positive indicator, the positive indicator state value S pos，i for:

[0023]

[0024] Where: S org，i is the original value of the i-th evaluation index; S base，i is the benchmark value of the ith evaluation indicator;

[0025] If the indicator is a reverse indicator, the reverse indicator status value S neg，i for:

[0026]

[0027] (3c) Calculate the comprehensive score: Assume that the total ranking weight of the lowest level indicators for the overall goal calculated by the hierarchical analysis method is W = [w1, w2, ..., wn], and the status value of each indicator is S = [s1, s2, ..., sn] T , then the final comprehensive score Y is:

[0028]

[0029] In the formula, w i is the total ranking weight of a certain indicator for the overall goal, S i is the state value of a certain indicator, and n is the number of indicators included in the carbon-energy synergy evaluation index system of urban distribution system.

[0030] In step (3), the formula for calculating each indicator specifically refers to:

[0031] For economic efficiency indicators:

[0032] The investment benefit of the system's new unit electricity consumption = annual new electricity consumption / annual main new investment in the system × 100%;

[0033] Among them, the main new investment in the annual system = investment in new energy access supporting power grid + investment in new energy supporting energy storage + power grid investment;

[0034] Investment in supporting power grid for new energy access = length of supporting transmission line for new energy × average unit cost of line + capacity of supporting substation for new energy × average unit cost of substation;

[0035] Investment in energy storage for new energy = installed capacity of energy storage for new energy × unit cost of energy storage;

[0036] Unit cost-effectiveness contribution = total regional electricity cost / regional GDP × 100%;

[0037] Decline rate of carbon dioxide emissions per 10,000 yuan of output value = [carbon dioxide emissions per 10,000 yuan of output value in the base period - carbon dioxide emissions per 10,000 yuan of output value in the evaluation period] / carbon dioxide emissions per 10,000 yuan of output value in the base period × 100%;

[0038] For low-carbon development indicators:

[0039] Power demand response capability index = power demand response load / maximum load × 100%;

[0040] Comprehensive line loss rate = (power supply - power sales) / power supply × 100%;

[0041] New energy utilization rate = new energy power generation / (new energy power generation + new energy abandoned power);

[0042] Clean energy electricity proportion = clean energy power generation in the operating area during the evaluation period / total power generation in the operating area during the evaluation period;

[0043] Electricity market activity = market-based electricity trading share × 0.4 + green electricity trading share × 0.6;

[0044] For pollution control indicators:

[0045] Sulfur hexafluoride gas recovery rate = recovered sulfur hexafluoride gas mass / sulfur hexafluoride gas mass in the gas chamber before recovery × 100%;

[0046] Carbon emissions per kilowatt-hour = (local thermal power generation + net external thermal power generation) × thermal power carbon emission factor / total social electricity consumption;

[0047] Change rate of total carbon emissions = [carbon dioxide emissions in the evaluation period - carbon dioxide emissions in the base period] / carbon dioxide emissions in the base period × 100%.

[0048] The step (4) specifically refers to: according to the comprehensive evaluation score and the scores of each sub-item, finding the indicators that affect the improvement effect and sorting them from high to low; proposing one or more solutions for the indicators that need to be improved; comparing the economic efficiency of projects with the same effect, sorting them according to economic advantages and disadvantages, and selecting the solution with the best economy for the same effect or the better output for the same input as the solution.

[0049] It can be seen from the above technical scheme that the beneficial effects of the present invention are: First, from the perspective of low-carbon development, from multiple aspects and angles such as economic efficiency, low-carbon development and pollution control, the present invention deeply analyzes the existing relevant standards and specifications, applies the Pareto principle, and screens influencing factors and influencing indicators to ensure the integrity and balance of the indicator system; Second, the present invention takes into account the objective differences in regional economic development conditions and distribution network structures in different regions, as well as the openness and advancement of the indicator system, in order to adapt to the development trend of the evaluated object and build an urban distribution system carbon energy synergy comprehensive benefit evaluation indicator system for deep decarbonization; Third, by evaluating the energy efficiency indicators of distribution systems in different regions, it is helpful to explore the weak links in regional distribution systems, evaluate the cost-effectiveness of different emission reduction measures, determine the most cost-effective emission reduction strategy, provide data support for governments and decision makers, and help formulate and implement emission reduction policies. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flow chart of the method of the present invention;

[0051] Figure 2 It is a structural diagram of the comprehensive benefit evaluation index system of the power distribution system in the present invention. DETAILED DESCRIPTION

[0052] like Figure 1 As shown, a method for evaluating the comprehensive benefits of carbon-energy synergy in urban power distribution systems for deep decarbonization is provided, and the method comprises the following steps in order:

[0053] (1) Analyze the factors affecting the comprehensive benefits of electricity-carbon synergy and select the main factors affecting the comprehensive energy efficiency of urban power distribution systems, including economic efficiency, low-carbon development and pollution control;

[0054] (2) Based on the main factors affecting the comprehensive energy efficiency of urban power distribution systems, a carbon-energy synergy evaluation index system for urban power distribution systems is established;

[0055] (3) Based on the carbon-energy synergy evaluation index system for urban power distribution systems, use the calculation formulas for each index, collect the data required for the calculation, and calculate each index value and comprehensive score in the carbon-energy synergy evaluation index system for urban power distribution systems;

[0056] (4) Select regions with lower comprehensive scores to build carbon reduction projects, sort the regions from low to high according to their comprehensive scores, and invest funds in regional project construction in order of comprehensive scores from low to high.

[0057] The step (1) specifically refers to: screening out all influencing factors that affect the comprehensive energy efficiency of the urban power distribution system, wherein all influencing factors include energy structure, consumption pattern, economic efficiency, low-carbon development, pollution control, intelligence, environmental friendliness and policy support, applying the Pareto principle, conducting a correlation analysis between the influencing factors and the degree of influence, sorting the influence proportions from high to low according to the data, accumulating them in sequence, and selecting factors whose sum of influence proportions is equal to or close to 80% as the main influencing factors, wherein the main influencing factors include three aspects: economic efficiency, low-carbon development and pollution control.

[0058] The step (2) specifically comprises the following steps in order:

[0059] (2a) Determine the evaluation method: use the analytic hierarchy process to determine the subjective weight, use the entropy weight method to determine the objective weight, apply game theory to calculate and optimize the indicator weight, build the indicator membership function, and use the fuzzy evaluation matrix to combine multiple evaluation indicators;

[0060] (2b) Figure 2 As shown in the figure, a carbon-energy synergy evaluation index system for urban power distribution systems is constructed: the main influencing factors are taken as the first-level indicators, and the hierarchical analysis method is used to mine the lower-level indicators. The economic efficiency factor includes three indicators, namely, the input benefit of the system's new unit electricity consumption, the unit cost-effectiveness contribution, and the reduction rate of carbon dioxide emissions per ten thousand yuan of output value; the low-carbon development factor includes five indicators, namely, the power demand response capability index, the comprehensive line loss rate, the utilization rate of new energy, the proportion of clean energy electricity, and the activity of the power market; the pollution control factor includes three indicators, namely, the sulfur hexafluoride gas recovery rate, the carbon emissions per kilowatt-hour, and the change rate of the total carbon emissions;

[0061] (2c) Construct the judgment matrix M: Based on the influencing factors, let the index of a certain layer be F = [f1, f2, ..., fn], and compare f i With f j The importance of the impact on a certain indicator of the upper layer, expressed as f ij =f i / f j Indicates that m ij The value of is represented by numbers from 1 to 9 and their reciprocals, that is, the scale in AHP; if f i With fj If the importance is equal, then m ij =1; if f i Slightly more important than f j , then m ij =3; if f i Importance is greater than f j , then m ij =5; if f i The importance is far greater than f j , then m ij =7; if f i The importance is far greater than f j , then m ij =9; if f j Slightly more important than f i , then m ij =2; if f j Importance is greater than f i , then m ij =4; if f j The importance is far greater than f i , then m ij =6; if f j The importance is far greater than f i , then m ij =8;

[0062] Finally, the judgment matrix M is formed, and the definition of the elements in the judgment matrix M is as follows:

[0063]

[0064] Where: m ij >0; j = 1, 2, ..., n; i = 1, 2, ..., n, where n is the number of indicators included in the urban distribution system carbon energy synergy evaluation index system;

[0065] (2d) Hierarchical sorting: Calculate the maximum eigenvalue λ of the judgment matrix M max , the eigenvector ξ corresponding to the judgment matrix M, and then normalize the eigenvector to obtain the ranking weight of the importance of each evaluation indicator at the same level relative to a certain indicator in the upper level, and the total hierarchical ranking weight of each indicator at the bottom level for the overall goal; the total hierarchical ranking weight of each indicator at the bottom level for the overall goal is the weight value of the indicator in the upper level * the weight value of the indicator in the corresponding indicators at the same level.

[0066] The step (3) specifically comprises the following steps in order:

[0067] (3a) Benchmark value area division: Due to the objective differences in economic development and distribution network structure in different regions, in order to make the evaluation results reasonable and comparable, the evaluation objects should be on the same baseline. The baseline is divided into four categories according to load density: I, II, III, and IV. Category I areas are densely populated areas in urban areas and areas with concentrated administrative, economic, commercial, and transportation areas, with a load density greater than 10MW / km 2 ; Category II areas are remote suburban areas, including only the district government seat, economic development zone, and industrial park, with a load density of 5MW / km 2 and 10MW / km 2 Category III areas are county towns and relatively concentrated industrial and populated townships and towns, or relatively concentrated industrial and populated townships and towns in remote suburbs, with a load density of 1MW / km 2 and 5MW / km 2 Category IV areas are mainly agricultural areas, with a load density of less than 1MW / km 2 ;

[0068] (3b) Calculate the indicator status value: the ratio of the original value of the indicator to the corresponding reference value is the indicator status value;

[0069] If the indicator is a positive indicator, the positive indicator state value S pos，i for:

[0070]

[0071] Where: S org，i is the original value of the i-th evaluation index; S base，i is the benchmark value of the ith evaluation indicator;

[0072] If the indicator is a reverse indicator, the reverse indicator status value S neg，i for:

[0073]

[0074] (3c) Calculate the comprehensive score: Assume that the total ranking weight of the lowest level indicators for the overall goal calculated by the hierarchical analysis method is W = [w1, w2, ..., wn], and the status value of each indicator is S = [s1, s2, ..., sn] T , then the final comprehensive score Y is:

[0075]

[0076] In the formula, w i is the total ranking weight of a certain indicator for the overall goal, S i is the state value of a certain indicator, and n is the number of indicators included in the carbon-energy synergy evaluation index system of urban distribution system.

[0077] In step (3), the formula for calculating each indicator specifically refers to:

[0078] For economic efficiency indicators:

[0079] The investment benefit of the system's new unit electricity consumption = annual new electricity consumption / annual main new investment in the system × 100%;

[0080] Among them, the main new investment in the annual system = investment in new energy access supporting power grid + investment in new energy supporting energy storage + power grid investment;

[0081] Investment in supporting power grid for new energy access = length of supporting transmission line for new energy × average unit cost of line + capacity of supporting substation for new energy × average unit cost of substation;

[0082] Investment in energy storage for new energy = installed capacity of energy storage for new energy × unit cost of energy storage;

[0083] Unit cost-effectiveness contribution = total regional electricity cost / regional GDP × 100%;

[0084] The reduction rate of carbon dioxide emissions per 10,000 yuan of output value = [carbon dioxide emissions per 10,000 yuan of output value in the base period - carbon dioxide emissions per 10,000 yuan of output value in the evaluation period] / carbon dioxide emissions per 10,000 yuan of output value in the base period × 100%; the base period refers to the present;

[0085] For low-carbon development indicators:

[0086] Power demand response capability index = power demand response load / maximum load × 100%;

[0087] Comprehensive line loss rate = (power supply - power sales) / power supply × 100%;

[0088] New energy utilization rate = new energy power generation / (new energy power generation + new energy abandoned power);

[0089] Clean energy electricity proportion = clean energy power generation in the operating area during the evaluation period / total power generation in the operating area during the evaluation period; the evaluation period refers to the time period after the measures are adopted;

[0090] Electricity market activity = market-based electricity trading share × 0.4 + green electricity trading share × 0.6;

[0091] For pollution control indicators:

[0092] Sulfur hexafluoride gas recovery rate = recovered sulfur hexafluoride gas mass / sulfur hexafluoride gas mass in the gas chamber before recovery × 100%;

[0093] Carbon emissions per kilowatt-hour = (local thermal power generation + net external thermal power generation) × thermal power carbon emission factor / total social electricity consumption;

[0094] Change rate of total carbon emissions = [carbon dioxide emissions in the evaluation period - carbon dioxide emissions in the base period] / carbon dioxide emissions in the base period × 100%.

[0095] The step (4) specifically refers to: according to the comprehensive evaluation score and the scores of each sub-item, finding the indicators that affect the improvement effect and sorting them from high to low; proposing one or more solutions for the indicators that need to be improved; comparing the economic efficiency of projects with the same effect, sorting them according to economic advantages and disadvantages, and selecting the solution with the best economy for the same effect or the better output for the same input as the solution.

[0096] To sum up, from the perspective of low-carbon development, from the perspective of economic efficiency, low-carbon development and pollution control, the present invention deeply analyzes the existing relevant standards and specifications, applies the Pareto principle, and screens influencing factors and influencing indicators to ensure the integrity and balance of the index system; the present invention takes into account the objective differences in regional economic development conditions and distribution network structures in different regions, as well as the openness and advancement of the index system, in order to adapt to the development trend of the evaluated object and build an urban distribution system carbon energy synergy comprehensive benefit evaluation index system for deep decarbonization; by evaluating the energy efficiency indicators of distribution systems in different regions, it is helpful to explore the weak links in regional distribution systems, evaluate the cost-effectiveness of different emission reduction measures, determine the most cost-effective emission reduction strategy, provide data support for governments and decision makers, and help formulate and implement emission reduction policies.

Claims

1. A method for evaluating the comprehensive benefits of carbon-energy synergy in urban power distribution systems for deep decarbonization, characterized by: The method comprises the following steps in order: (1) Analyze the factors affecting the comprehensive benefits of electricity-carbon synergy and select the main factors affecting the comprehensive energy efficiency of urban power distribution systems, including economic efficiency, low-carbon development and pollution control; (2) Based on the main factors affecting the comprehensive energy efficiency of urban power distribution systems, a carbon-energy synergy evaluation index system for urban power distribution systems is established; (3) Based on the carbon-energy synergy evaluation index system for urban power distribution systems, using the calculation formulas for each index, collecting the data required for the calculation, and calculating each index value and comprehensive score in the carbon-energy synergy evaluation index system for urban power distribution systems; (4) Select regions with lower comprehensive scores to build carbon reduction projects, sort the regions from low to high according to their comprehensive scores, and invest funds in regional project construction in order of comprehensive scores from low to high.

2. The method for evaluating the carbon-energy synergy comprehensive benefits of urban power distribution systems for deep decarbonization according to claim 1 is characterized by: The step (1) specifically refers to: screening out all influencing factors that affect the comprehensive energy efficiency of the urban power distribution system, wherein all influencing factors include energy structure, consumption pattern, economic efficiency, low-carbon development, pollution control, intelligence, environmental friendliness and policy support, applying the Pareto principle, conducting a correlation analysis between the influencing factors and the degree of influence, sorting the influence proportions from high to low according to the data, accumulating them in sequence, and selecting factors whose sum of influence proportions is equal to or close to 80% as the main influencing factors, wherein the main influencing factors include three aspects: economic efficiency, low-carbon development and pollution control.

3. The method for evaluating the carbon-energy synergy comprehensive benefits of urban power distribution systems for deep decarbonization according to claim 1 is characterized by: The step (2) specifically comprises the following steps in order: (2a) Determine the evaluation method: use the analytic hierarchy process to determine the subjective weight, use the entropy weight method to determine the objective weight, apply game theory to calculate and optimize the indicator weight, build the indicator membership function, and use the fuzzy evaluation matrix to combine multiple evaluation indicators; (2b) Construct an evaluation index system for carbon-energy synergy in urban power distribution systems: take the main influencing factors as the first-level indicators, and use the hierarchical analysis method to mine the lower-level indicators. The economic efficiency factor includes three indicators, namely, the system's new unit power input efficiency, unit cost-effectiveness contribution, and the reduction rate of carbon dioxide emissions per 10,000 yuan of output value; the low-carbon development factor includes five indicators, namely, the power demand response capability index, the comprehensive line loss rate, the utilization rate of new energy, the proportion of clean energy power, and the activity of the power market; the pollution control factor includes three indicators, namely, the sulfur hexafluoride gas recovery rate, the carbon emissions per kilowatt-hour, and the change rate of total carbon emissions; (2c) Construct the judgment matrix M: Based on the influencing factors, let the index of a certain layer be F = [f1, f2, ..., fn], and compare f i With f j The importance of the impact on a certain indicator of the upper layer, expressed as f ij =f i / f j Indicates that m ij The value of is represented by numbers from 1 to 9 and their reciprocals, that is, the scale in AHP; if f i With f j If the importance is equal, then m ij =1; if f i Slightly more important than f j , then m ij =3; if f i Importance is greater than f j , then m ij =5; if f i The importance is far greater than f j , then m ij =7; if f i The importance is far greater than f j , then m ij =9; if f j Slightly more important than f i , then m ij =2; if f j Importance is greater than f i , then m ij =4; if f j The importance is far greater than f i , then m ij =6; if f j The importance is far greater than f i , then m ij =8; Finally, the judgment matrix M is formed, and the definition of the elements in the judgment matrix M is as follows: Where: m ij >0; j = 1, 2, ..., n; i = 1, 2, ..., n, where n is the number of indicators included in the urban distribution system carbon energy synergy evaluation index system; (2d) Hierarchical ranking: Calculate the maximum eigenvalue λmax of the judgment matrix M and the eigenvector ξ corresponding to the judgment matrix M, and then normalize the eigenvector to obtain the ranking weight of the importance of each evaluation indicator at the same level relative to an indicator at the upper level, and the hierarchical total ranking weight of each indicator at the bottom level for the overall goal; the hierarchical total ranking weight of each indicator at the bottom level for the overall goal is the weight of the indicator in the upper level * the weight of the indicator in the corresponding indicators at the same level.

4. The method for evaluating the carbon-energy synergy comprehensive benefits of urban power distribution systems for deep decarbonization according to claim 1 is characterized by: The step (3) specifically comprises the following steps in order: (3a) Benchmark value area division: Due to the objective differences in economic development and distribution network structure in different regions, in order to make the evaluation results reasonable and comparable, the evaluation objects should be on the same baseline. The baseline is divided into four categories according to load density: I, II, III, and IV. Category I areas are densely populated areas in urban areas and areas with concentrated administrative, economic, commercial, and transportation areas, with a load density greater than 10MW / km 2 ; Category II areas are remote suburban areas, including only the district government seat, economic development zone, and industrial park, with a load density of 5MW / km 2 and 10MW / km 2 Category III areas are county towns and relatively concentrated industrial and populated townships and towns, or relatively concentrated industrial and populated townships and towns in remote suburbs, with a load density of 1MW / km 2 and 5MW / km 2 Category IV areas are mainly agricultural areas, with a load density of less than 1MW / km 2 ; (3b) Calculate the indicator status value: the ratio of the original value of the indicator to the corresponding reference value is the indicator status value; If the indicator is a positive indicator, the positive indicator state value S pos，i for: Where: S org，i is the original value of the i-th evaluation index; S base，i is the benchmark value of the ith evaluation indicator; If the indicator is a reverse indicator, the reverse indicator status value S neg，i for: (3c) Calculate the comprehensive score: Assume that the total ranking weight of the lowest level indicators for the overall goal calculated by the hierarchical analysis method is W = [w1, w2, ..., wn], and the status value of each indicator is S = [s1, s2, ..., sn] T , then the final comprehensive score Y is: In the formula, w i is the total ranking weight of a certain indicator for the overall goal, S i is the state value of a certain indicator, and n is the number of indicators included in the carbon-energy synergy evaluation index system of urban distribution system.

5. The method for evaluating the comprehensive benefits of carbon-energy synergy in urban power distribution systems for deep decarbonization according to claim 1 is characterized by: In step (3), the formula for calculating each indicator specifically refers to: For economic efficiency indicators: The investment benefit of the system's new unit electricity consumption = annual new electricity consumption / annual main new investment in the system × 100%; Among them, the main new investment in the annual system = investment in new energy access supporting power grid + investment in new energy supporting energy storage + power grid investment; Investment in supporting power grid for new energy access = length of supporting transmission line for new energy × average unit cost of line + capacity of supporting substation for new energy × average unit cost of substation; Investment in energy storage for new energy = installed capacity of energy storage for new energy × unit cost of energy storage; Unit cost-effectiveness contribution = total regional electricity cost / regional GDP × 100%; Decline rate of carbon dioxide emissions per 10,000 yuan of output value = [carbon dioxide emissions per 10,000 yuan of output value in the base period - carbon dioxide emissions per 10,000 yuan of output value in the evaluation period] / carbon dioxide emissions per 10,000 yuan of output value in the base period × 100%; For low-carbon development indicators: Power demand response capability index = power demand response load / maximum load × 100%; Comprehensive line loss rate = (power supply - power sales) / power supply × 100%; New energy utilization rate = new energy power generation / (new energy power generation + new energy abandoned power); Clean energy electricity proportion = clean energy power generation in the operating area during the evaluation period / total power generation in the operating area during the evaluation period; Electricity market activity = market-based electricity trading share × 0.4 + green electricity trading share × 0.6; For pollution control indicators: Sulfur hexafluoride gas recovery rate = recovered sulfur hexafluoride gas mass / sulfur hexafluoride gas mass in the gas chamber before recovery × 100%; Carbon emissions per kilowatt-hour = (local thermal power generation + net external thermal power generation) × thermal power carbon emission factor / total social electricity consumption; Change rate of total carbon emissions = [carbon dioxide emissions in the evaluation period - carbon dioxide emissions in the base period] / carbon dioxide emissions in the base period × 100%.

6. The method for evaluating the carbon-energy synergy comprehensive benefits of urban power distribution systems for deep decarbonization according to claim 1 is characterized by: The step (4) specifically refers to: according to the comprehensive evaluation score and the scores of each sub-item, finding the indicators that affect the improvement effect and sorting them from high to low; proposing one or more solutions for the indicators that need to be improved; comparing the economic efficiency of projects with the same effect, sorting them according to economic advantages and disadvantages, and selecting the solution with the best economy for the same effect or the better output for the same input as the solution.

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