Carbon emission assessment method and equipment for rural integrated energy system considering driving factors

Abstract

The present invention considers the driving factors of the rural comprehensive energy system carbon emission assessment method and equipment, considers the multiple factors that affect the carbon emissions of the comprehensive energy system, establishes a multi-dimensional low-carbon comprehensive assessment index system, and constructs a scientific low-carbon development assessment method. First, the construction process of the comprehensive assessment index system is clarified; then the external driving factors of carbon emissions at multiple levels such as the environment, economy, society and technology are analyzed; the evolution mechanism and development factors of energy, power grid and energy use are analyzed, and the internal driving factors of the new rural comprehensive energy system at the low-carbon technology level are explored; the internal and external driving factors are integrated to construct a multi-dimensional low-carbon comprehensive assessment index system. Finally, based on the positive and negative bull's eye improved gray target model, a low-carbon development potential assessment method is proposed to achieve quantitative analysis of the assessment indicators, thereby exploring the development potential of each factor.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy systems, and in particular to a carbon emission assessment method and device for a rural integrated energy system taking driving factors into consideration. Background Art

[0002] The implementation of the "dual carbon" goal is accelerating, rural energy issues are becoming increasingly prominent, and the situation of transformation and upgrading is urgent. Building a rural comprehensive energy supply system with multi-energy complementarity and coordinated development can reasonably and efficiently utilize the abundant renewable clean energy in rural areas, which is of great significance to promoting the rural revitalization strategy and promoting the sustainable development of rural energy.

[0003] At present, the low-carbon evolution of rural integrated energy systems faces many challenges in energy production, transmission, and use. It takes a long time, has many uncertain factors, and has a complex evolution process. Therefore, it is necessary to study and analyze the practical path of integrated energy in low-carbon transformation. Analyze the impact mechanism of carbon emissions, extract the technical characteristics of each link of the new rural integrated energy system and the low-carbon "internal" driving factors, consider the "external" driving factors at the macro level such as policy guidance and social and economic levels, build a scientific low-carbon development evaluation model, and tap the potential for low-carbon development. It plays an important role in the low-carbon development of rural integrated energy systems. Summary of the invention

[0004] The present invention proposes a rural integrated energy system carbon emission assessment method that takes driving factors into consideration, which can solve the technical defects of the above-mentioned background technology.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] The carbon emission assessment method of rural integrated energy system considering driving factors includes the following steps:

[0007] S100. Clarify the process of building a comprehensive evaluation indicator system;

[0008] S200, based on S100, analyzes the external driving factors of carbon emissions at multiple levels such as environment, economy, society and technology; explores the internal driving factors of the new rural integrated energy system at the low-carbon technology level; integrates the internal and external driving factors to build a multi-dimensional low-carbon comprehensive evaluation index system;

[0009] S300, based on S100 and S200, establishes a positive and negative bullseye improved grey target model, proposes a low-carbon development potential assessment method, and realizes quantitative analysis of assessment indicators.

[0010] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0011] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0012] Compared with the prior art, the present invention has the following beneficial effects:

[0013] The present invention considers the multiple influencing factors of carbon emissions in the comprehensive energy system and constructs a scientific low-carbon development evaluation method. It analyzes the external driving factors of carbon emissions at multiple levels such as environment, economy, society and technology, and analyzes the internal driving factors of carbon emissions in energy, power grid and energy consumption. It integrates the internal and external driving factors to construct a multi-dimensional low-carbon comprehensive evaluation index system. Compared with the traditional evaluation method, the evaluation index system is more scientific and the evaluation effect is more realistic. The traditional gray target model adopts a single bull's eye mode, which only considers the positive ideal scenario. The quantitative results can only reflect the single tendency of the evaluation object and the bull's eye, which is easy to cause the lack of evaluation information. The positive and negative bull's eyes are used to improve the gray target model for effect evaluation. TOPSIS can compare the gap between the index values ​​of the scheme to be evaluated and the positive and negative ideal solutions, and judge the approximation of all schemes to be evaluated to the positive and negative ideal solutions, so as to evaluate and rank the pros and cons of the evaluation schemes. By using the idea of ​​selecting the most ideal positive and negative solutions from TOPSIS, an improved grey target model based on positive and negative bull's-eyes is constructed. The positive target scenario and the reverse reference scenario are considered as positive and negative bull's-eyes, and the deviation between the regional status and its positive and negative bull's-eyes is quantitatively analyzed. This can achieve quantitative analysis of evaluation indicators, and the decision-making evaluation effect is more valuable for reference. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a flow chart of the method of the present invention;

[0015] Figure 2 The process of building a comprehensive evaluation indicator system;

[0016] Figure 3 Schematic diagram of the low-carbon development potential assessment method;

[0017] Figure 4 Schematic diagram of improved grey target space and relative bull's-eye degree. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0019] like Figure 1As shown, the carbon emission assessment method of the rural integrated energy system considering driving factors described in this embodiment includes the following steps:

[0020] S100. Clarify the process of building a comprehensive evaluation indicator system;

[0021] S200, based on S100, analyzes the external driving factors of carbon emissions at multiple levels such as environment, economy, society and technology; explores the internal driving factors of the new rural integrated energy system at the low-carbon technology level; integrates the internal and external driving factors to build a multi-dimensional low-carbon comprehensive evaluation index system;

[0022] S300, based on S100 and S200, establishes a positive and negative bullseye improved grey target model, proposes a low-carbon development potential assessment method, and realizes quantitative analysis of assessment indicators;

[0023] The following is a detailed description based on specific applications:

[0024] A rural area in Anhui was selected as the research object, and 4 areas were selected, namely s1-s4, which are diversified industrial rural areas, leisure and tourism rural areas, livable rural areas and traditional planting rural areas. In addition, two areas were selected for comparison to facilitate the analysis of the results. S5 is a national low-carbon pilot area, located in the Yangtze River Delta Ecological Green Integrated Development Demonstration Zone. The economic operation of the whole region is stable and improving, structural adjustment continues to deepen, emerging kinetic energy is accelerating the transformation, quality and efficiency are steadily improving, and comprehensive strength ranks among the top of similar regions in the country, with a high level of low-carbon development; S6 is a national economic and technological development zone, a high-tech industrial development zone, and has an outstanding level of economic development.

[0025] Step 1: Clarify the process of building a comprehensive evaluation indicator system;

[0026] Low-carbon comprehensive assessment needs to consider many indicators that affect regional low-carbon development. It is a complex system decision-making problem for global and holistic assessment of multiple assessment areas. The construction process of the comprehensive assessment indicator system is as follows: Figure 2 shown.

[0027] First, by decomposing the multiple related factors of carbon emissions, the influencing mechanism of carbon emissions is deeply analyzed, and the external driving factors of low-carbon development are clarified; secondly, the low-carbon technology of the integrated energy system and the carbon reduction effect of the technology are analyzed, and the internal development factors of the rural integrated energy system are deeply analyzed from the three aspects of energy, transmission and energy use, and the key indicators of the low-carbon form evolution of the integrated energy system are refined and summarized; on this basis, the internal and external multi-perspective influencing factors are comprehensively considered to construct a comprehensive evaluation index system for low-carbon development. After analyzing the key factors affecting low-carbon development and establishing an evaluation index system, it is necessary to select a scientific and reasonable evaluation method to quantify each indicator, so as to analyze the low-carbon development level of different evaluation areas and realize the comprehensive evaluation and mining of low-carbon development potential in various dimensions.

[0028] Step 2: Analyze the external driving factors of carbon emissions at multiple levels, such as environment, economy, society and technology; explore the internal driving factors of the new rural integrated energy system at the low-carbon technology level; integrate internal and external driving factors to build a multi-dimensional low-carbon comprehensive evaluation indicator system.

[0029] Step 2.1 Selection of external driving factor indicators;

[0030] In the process of low-carbon development of the integrated energy system, environmental constraints, economic development, and social level are external driving factors with a reverse driving effect. The corresponding criterion layer indicators are: environmental pressure indicators, economic development indicators, and social scale indicators. The corresponding key evaluation indicators are selected as the indicator layer of the corresponding level to construct a hierarchical structure.

[0031] (1) Environmental pressure indicators

[0032] Under the environmental constraint criteria layer, carbon dioxide emissions and carbon reduction indicators are selected. Carbon dioxide emissions are the key judgment indicator for whether the dual carbon goals are achieved or not, reflecting the current low-carbon level of the region. The smaller the carbon emissions, the better the low-carbon development of the region. Carbon reduction is the difference between the current year's carbon emissions and the previous year, reflecting the trend of low-carbon development in the region. The larger the value, the better the carbon reduction trend.

[0033] Considering the influencing factors under the environmental pressure criterion layer, the indicators closely related to them form the indicator layer of the next level of the criterion layer, and then the second-level hierarchical structure can be constructed, I1 = [I 11 , I 12 ]. Among them, I1 represents the environmental pressure index, which is a criterion level index, and I 11 , I 12 Represents the indicator layer of the next level under this criterion layer, including carbon dioxide emissions and carbon emission reductions.

[0034] (2) Economic Development Indicators

[0035] Considering the environmental constraint of carbon emissions, the sustainable development of the economy is the key driving force for the low-carbon development of comprehensive energy, and the per capita GDP is selected to measure the economic development status. The higher the per capita GDP, the more developed the regional economy, the higher the emphasis on low-carbon development, and the better the resource input, the higher the level of low-carbon development. Considering the influencing factors under the economic development criterion layer, the indicators closely related to it form the indicator layer of the next level of the criterion layer, and the second-level hierarchical structure can be constructed, I2 = [I 21 ]. Among them, I2 represents the criterion level indicator of economic development index, I 21 The indicator layer representing the next level below this criterion layer includes GDP per capita.

[0036] (3) Social scale indicators

[0037] The expansion of social scale will inevitably increase carbon emissions accordingly. The number of permanent residents in a region can affect regional energy consumption, thus having a greater impact on carbon emissions. Therefore, the number of permanent residents is selected as an indicator to characterize social scale. The larger the number of permanent residents, the higher the carbon emission level.

[0038] Considering the influencing factors under the social scale criterion layer, the indicators closely related to it form the indicator layer of the next level of the criterion layer, and then the second-level hierarchical structure can be constructed, I3 = [I 31 ]. Among them, I3 represents the social scale index, which is a criterion level index, and I 31 The indicator layer representing the next level below this criterion layer includes the number of permanent residents.

[0039] Step 2.2 Selection of indicators of internal driving factors;

[0040] In the context of the development of low-carbon power technology, the evolution mechanism and development factors of low-carbon development of new rural integrated energy systems are analyzed. The carbon reduction mechanism of various low-carbon technologies for energy, power grid and energy consumption in rural integrated energy systems can be summarized as clean substitution on the energy side, loss reduction and efficiency improvement on the power grid side, and terminal electrification on the energy consumption side. The specific technologies and their carbon reduction mechanisms are shown in Table 1.

[0041] Table 1 Low-carbon electricity technologies

[0042]

[0043] Focusing on the macro-driving factor of technological level, combined with the low-carbon development elements of the new rural comprehensive energy system, this paper studies the key low-carbon technologies in the "source-grid-load" and other links of the new rural comprehensive energy system, analyzes the technical carbon reduction principles of the energy, power grid and energy use links, and analyzes the internal development factors of the low-carbon evolution of the new rural comprehensive energy system based on the effects of these technologies on low-carbon development, so as to select specific indicators for low-carbon development in each technical link.

[0044] (1) Energy structure indicators

[0045] Considering the technical indicators corresponding to low-carbon elements, select indicators under the energy structure criterion layer. The application objects of low-carbon technology in the energy sector include fossil energy such as coal, oil, and natural gas, as well as clean energy such as hydropower, wind power, and photovoltaics. Select indicators such as the proportion of coal consumption, the proportion of thermal power generation, and the proportion of clean energy installed capacity to evaluate the degree of low-carbon development on the energy side. Among them, the smaller the proportion of coal consumption and thermal power generation, the better the low-carbon development; the larger the proportion of clean energy installed capacity, the higher the degree of clean substitution on the energy side, and the higher the level of low-carbon development.

[0046] Table 2 Energy link evaluation index system

[0047]

[0048] Considering the multi-dimensional influencing factors under the energy structure criterion layer, the indicators closely related to them form the indicator layer of the next level of the criterion layer, and then the second-level hierarchical structure can be constructed, which consists of 4 indicators and forms the energy structure indicator set I4 = [I 41 , I 42 , I 43 , I 44 By calculating I 41 ,I 42 ,I 43 ,I 44 The data of indicators in four dimensions can reflect the development level of the region in these aspects. By integrating multiple dimensions, the potential I4 at the regional energy structure level can be obtained.

[0049] (2) Power grid construction indicators

[0050] Considering the technical indicators corresponding to low-carbon elements, the indicators under the grid construction criteria layer are selected. The large-scale access of new energy sources has added difficulties to the stability of the system power supply and power transmission and distribution. The transmission and distribution network and substation are important components of the power grid. Reducing network losses and improving power transmission capacity and transmission efficiency are the key points of the development of low-carbon technology in the power grid. Therefore, comprehensive network loss rate, capacity-load ratio, main transformer load rate and other indicators are selected to reflect the advantages and disadvantages of the planning and construction of the power grid in low-carbon development. Refer to the standards specified in the guidelines. The better the indicator value, the better the regional low-carbon development.

[0051] Table 3 Evaluation indicators of power grid links

[0052]

[0053] Considering the multi-dimensional influencing factors under the grid construction criterion layer, the indicators closely related to them form the indicator layer of the next level of the criterion layer; the second-level hierarchical structure can be constructed, which consists of three indicators to form the grid construction indicator set I5 = [I 51, I 52 , I 53 By calculating I 51 , I 52 , I 53 The data of indicators in three dimensions can reflect the development level of the region in these aspects. By integrating multiple dimensions, the potential I5 of regional power grid construction can be obtained.

[0054] (3) Energy efficiency index

[0055] Considering the technical indicators corresponding to low-carbon elements, the indicators under the energy efficiency criterion layer are selected. As the substitution of electric energy in the new rural comprehensive energy system is further deepened, the proportion of terminal energy consumption of electric energy is selected to reflect the level of electrification. The indicator is the ratio of terminal energy consumption to total regional energy consumption. The larger the value, the higher the degree of electrification on the energy side and the better the low-carbon development. On the other hand, through various low-carbon power technologies in energy consumption links, the multi-energy coupling utilization on the energy side can be realized, and controllable load resources can be effectively organized. Indicators such as comprehensive energy utilization rate, load control ratio, and clean energy consumption rate are selected to characterize the efficiency level of the new rural comprehensive energy system for various energy sources. The larger the indicator value, the higher the energy efficiency and the higher the degree of low-carbon development.

[0056] Table 4 Evaluation indicators of energy consumption

[0057]

[0058] Considering the multi-dimensional influencing factors under the energy efficiency criterion layer, the indicators closely related to them form the indicator layer of the next level of the criterion layer, and then the second-level hierarchical structure can be constructed, which consists of 4 indicators and forms the energy efficiency indicator set I6 = [I 61 , I 62 , I 63 , I 64 By calculating I 61 ,I 62 ,I 63 ,I 64 The data of indicators in four dimensions can reflect the development level of the region in these aspects. By integrating multiple dimensions, the potential I6 of regional energy efficiency can be obtained.

[0059] Step 2.3 Construct a low-carbon comprehensive evaluation index system

[0060] In order to comprehensively evaluate the comprehensive level of regional low-carbon development, a comprehensive evaluation index system for low-carbon development is constructed. The index system is a hierarchical structure with a total of three layers. The comprehensive level of low-carbon development is taken as the first layer of the index system, namely the target layer; according to the driving factors associated with carbon emissions obtained by decomposing the multi-factor driving force model, macro-driving factors such as energy structure factors, technological level factors, economic development factors, and population size factors are taken as the second layer of the index system, namely the criterion layer; specific indicators are selected from the indicators closely related to their indicators under each criterion layer as the third layer of the index system, namely the indicator layer. The comprehensive evaluation index system for low-carbon development potential is shown in Table 5.

[0061] Potential assessment indicators involve multi-level and multi-dimensional factors. The many indicators listed in the indicator layer correspond to different criterion layer indicators, that is, different assessment dimensions of low-carbon potential, and their units and dimensions are also different. For example, the number of permanent residents is in 10,000 people; the unit of carbon dioxide emissions is 10,000 tons. There are also differences in the types of different indicators. For example, the proportion of coal consumption is a cost-based indicator, that is, the smaller the indicator value, the better; the proportion of clean energy installed capacity is a benefit-based indicator, that is, the larger the indicator value, the better; the capacity load ratio is an intermediate indicator, that is, the closer the indicator value is to a specific value, the better.

[0062] The constructed evaluation index system is used to comprehensively evaluate the low-carbon development level of region i. The evaluation problem can be modeled as a mathematical expression, as shown in formula (1):

[0063]

[0064] It can be seen from the formula that for m evaluation areas, the number of indicators that need to be calculated reaches (m×15). These complex evaluation indicators with large differences in dimensions and types make the indicator calculation process cumbersome and complicated, leading to the complexity of the comprehensive evaluation problem.

[0065] Table 5 Comprehensive evaluation index system for low-carbon development

[0066]

[0067]

[0068] The comprehensive evaluation index system of low-carbon development involves multi-level and multi-dimensional evaluation indicators. Since the evaluation indicators of each dimension of the indicator layer have different attributes, and the importance of each criterion layer indicator to low-carbon development is different for different regions or different development stages, there are differences in their impact. Therefore, each indicator has a different impact on the evaluation results of the comprehensive level of low-carbon development. In order to objectively evaluate the potential of each dimension of low-carbon development, it is necessary to adopt a reasonable decision-making method to measure the relative importance of each evaluation indicator, and then determine the indicator weight.

[0069] Step 3 improves the grey target model based on the positive and negative bull's-eyes, proposes a low-carbon development potential assessment method, and realizes quantitative analysis of the assessment indicators.

[0070] The multidimensional evaluation index set constructed by the multidimensional factors affecting low-carbon development is regarded as a vector space. The index set composed of n indicators constitutes an n-dimensional evaluation space I = [I1, I2, ..., I n ]. Assume that there are m regions to be evaluated with different resource endowments, and the different regions constitute the set to be evaluated S = [s1, s2, ..., s m ], a ij For i in I j The indicator values ​​in the dimension can be used to construct a low-carbon development assessment space A that includes all the areas to be assessed. S , the matrix is ​​expressed as formula (2)

[0071]

[0072] In the formula, the i-th row represents s i The state vector a in the evaluation space i =[a i1 , a i2 , ..., a in ], a1, a2, ..., a m are m state points in the space, which are distributed in different positions of the gray target space, reflecting the development differences of different assessment areas. Therefore, the problem of evaluating the potential for regional power low-carbon development is essentially a positioning description of the geometric position of the system development state in the multidimensional space of low-carbon development.

[0073] The grey target model can integrate multi-layer decision information and is applicable to complex system decision problems. Its technical method is to find the data closest to the target value among all the decision options to be decided to construct a standard model, namely the bull's eye. The options and the standard model together constitute the grey target. The options are ranked and decision analysis is performed by calculating the distance between the bull's eye and the target. Therefore, the grey target model is used to quantitatively evaluate the low-carbon development potential of multiple regions, and a low-carbon development potential evaluation method is proposed, such as Figure 3 shown.

[0074] As can be seen from the figure, the main idea of ​​the low-carbon development potential assessment is: according to the development requirements of the "dual carbon" goal for the low-carbon transformation of regional power grids, the expected value of indicators is set, and the gap between the current value and the target value of each indicator in the low-carbon potential assessment indicator system is calculated to clarify the low-carbon potential of each dimension; further, according to the hierarchical relationship of the evaluation indicator system, the low-carbon potential of the previous level is calculated in turn; the multi-dimensional indicator layer indicators are comprehensively compared to compare the distance between the target layer indicator value of the area to be assessed and the indicator value in the dual carbon scenario, and the degree of proximity of all areas to be assessed to the target scenario is judged, so as to clarify the low-carbon development space of each region and realize the quantitative assessment of the low-carbon development potential of the region.

[0075] The traditional grey target model adopts a single bullseye mode, which only considers the positive ideal scenario. The quantitative results can only reflect the single tendency of the evaluation object and the bullseye, which easily leads to the lack of evaluation information. TOPSIS can compare the gap between the index values ​​of the scheme to be evaluated and the positive and negative ideal solutions, judge the approximation of all schemes to be evaluated to the positive and negative ideal solutions, and thus evaluate and rank the advantages and disadvantages of the evaluation schemes. Using the idea of ​​selecting the most positive and negative ideal solutions by TOPSIS, an improved grey target model based on positive and negative bullseyes is constructed, which comprehensively considers the positive target scenario and the reverse reference scenario as the positive and negative bullseyes, and quantitatively analyzes the deviation between the current status of the region and its positive and negative bullseyes.

[0076] The grey target model is used to evaluate and analyze the low-carbon development potential of regional power grids, and the positive target of the grey target model is set, that is, the positive target scenario of regional low-carbon development. Scenario construction involves the influence of multi-dimensional factors, and the development target values ​​of the region in each dimensional indicator constitute the positive target scenario of the region, that is, the positive target a + , the area is from the current situation a i Gradually towards the bull's eye + Development has a positive trend.

[0077] The grey target model is improved by using the TOPSIS idea. On the basis of setting the positive target, the negative target is added for comparative analysis. i The reverse reference scenario, the negative bullseye vector a - The region gradually deviates from the negative target during the low-carbon development process. - , considering that the negative bull's-eye can show the opposite trend of the region and the reverse reference scenario, the positive and negative bull's-eye can measure the difference in the status quo of multiple assessment regions. The specific expression of the positive and negative bull's-eye is shown in formula (3).

[0078]

[0079] In the formula, the indicators of each dimension in the positive bull's-eye vector take the most ideal value a+j, that is, the target value of each indicator when the region achieves carbon peak and carbon neutrality, which is used as the positive target scenario for regional low-carbon potential assessment; the indicators of each dimension in the negative bull's-eye vector take the worst value aj of regional development, that is, the worst case of regional low-carbon development, as the reverse reference scenario, and compare and analyze with the current development status to determine whether the regional low-carbon development is deviating from the reference scenario.

[0080] Due to the different dimensions of the indicators, it is necessary to perform gray-target transformation on the spatial axis, that is, T(a ij )=b ij , the specific formula is as formula (4):

[0081]

[0082] In the formula, M=max{|a+jaj|}, Ψ B , C , F They represent cost-based indicator set, benefit-based indicator set and fixed indicator set respectively. Among them, the meaning of cost-based indicator is that the smaller the indicator value is, the closer it is to the positive target scenario; the meaning of benefit-based indicator is that the larger the indicator value is, the closer it is to the positive target scenario; the meaning of fixed indicator is that the indicator value tends to a certain optimal value, the closer it is to the positive target scenario.

[0083] For the low-carbon development assessment space A including all the areas to be assessed S Change the gray target and get B S =[b ij ] m×n At the same time, the positive and negative bullseye vectors are gray-targeted and b is obtained. + =[b+j] 1×n 、b - =[bj] 1×n .

[0084] The gray target model is improved based on the positive and negative bullseyes, so that the model can simultaneously reflect the positive and negative trends between the current status of regional low-carbon development and the regional positive target scenario and the reverse reference scenario. The improved gray target model B based on the positive and negative bullseyes is shown in formula (5):

[0085]

[0086] Where, the first to the mth rows represent the regions s1, s2, ..., s after the gray target transformation. m The standard multi-dimensional indicator vector reflects the multi-dimensional low-carbon status of each assessment area; the last two rows represent the standard positive and negative bullseye vectors of regional low-carbon development. The model standardizes the indicators of each dimension so that the indicators of different dimensions can be compared and calculated with each other, and can realize the quantitative analysis of the regional low-carbon development level by combining multi-dimensional indicators.

[0087] Definition b i With b + 、b - The Euclidean space distances are respectively the positive bull's-eye distance d+i and the negative bull's-eye distance di, and the expressions are shown in formula (6):

[0088]

[0089] In the formula, |b ij -b+j| represents the distance between the current status and target value of each dimension indicator in the current year, which is reflected in the indicator I j The size of the development space in the dimension. The larger the value, the better the area is in indicator I. j The development in this area is further away from the dual carbon goals; ij -bj| represents the distance between the current status of each dimension indicator and the worst historical value, which is reflected in the indicator I j The lower the carbon level of the region, the higher the value. j The better the development.

[0090] According to the hierarchical structure of the evaluation index system, the distance between the indicators of the criterion layer and the target layer is calculated step by step, and the low-carbon potential of each layer can be analyzed in turn. i –b + ||、||b i –b - || reflects the comprehensive level of low-carbon development in the target layer indicator region. When d+i is smaller and di is larger, it indicates that the regional low-carbon status is closer to the target scenario and farther from the reverse reference scenario, that is, the regional low-carbon development space is larger. The positive and negative center distances d+i and di can reflect the i With positive and negative bullseye b + 、b - The positive and negative trends of the region can be analyzed quantitatively, and the deviation between the regional status and its positive and negative targets can be analyzed quantitatively.

[0091] The endowment coefficient is used to correct the positive and negative target distances obtained in formula (6) to obtain the weighted positive target distance D+i and the weighted negative target distance Di, which are expressed as follows:

[0092]

[0093] by Figure 4 The three-dimensional space I 3 For example, the evaluation indicators I1, I2, I n is the spatial coordinate axis. The red dots in the figure represent the positive and negative bullseyes. iDistributed in this three-dimensional gray target space, the black dotted cube represents the gray target space B with improved positive and negative bull's-eyes, and the orange dotted line represents the improved gray target space WB that further considers endowment differences.

[0094] As can be seen from the figure, by calculating the weighted target distance D+i, Di, the regional state point Wb of the endowment coefficient is measured i With Wb + 、Wb - In the improved grey target space after W correction, the positive bull's eye b + and the state point b representing the evaluation area i The spatial positions of the two have shifted, and the original bull's-eye distance has limitations. The weighted bull's-eye distance D+i, Di can more accurately measure the gap between the two, that is, the current level of low-carbon development in the assessment area Wb i With the target scenario Wb + The gap value of regional low-carbon development status Wb i Compared with the positive target scenario Wb + It has a positive trend, and compared with the reverse reference scenario Wb - The comprehensive analysis of the low-carbon development path needs to combine the directional analysis of the two and comprehensively consider b i With positive bullseye + The closeness of the negative bullseye b - The deviation degree is combined with D+i and Di to define the relative bull's-eye degree. The specific calculation formula is as follows:

[0095]

[0096] From the formula, we can see that C i The value range is [0,1], and the closer to 1, the higher the Wb i The closer you get to the bullseye, + , with negative bullseye Wb - The farther the distance, the better its low-carbon development status.

[0097] Analysis of calculation results:

[0098] According to the power grid planning report, the proportion of clean energy installed capacity, the proportion of flexible power supply, the load rate of main transformers, the capacity load ratio and the proportion of electric energy terminal consumption are selected to form a low-carbon development indicator set I = [I1, I2, I3, I4, I5]. The relevant data of areas s1-s6 in the report are statistically calculated to obtain A S as follows.

[0099]

[0100] To the positive bullseye vector a + With negative bullseye vector a - The expression is as follows:

[0101]

[0102] Considering the feasibility of calculation, the negative target value of the capacity ratio indicator is replaced by a reasonable larger value to ensure the integrity of the gray target space. The indicator is transformed into a gray target, and the low-carbon development status and positive and negative targets of the evaluation area are quantified into a standard gray target model B. The standard space vector B of the evaluation area is S With standard positive and negative bullseye b + 、b - The specific matrix expression is as follows:

[0103]

[0104] The endowment coefficient matrix is ​​calculated based on the AHP-improved entropy method combined with the weighting method, and the subjective weight matrix is ​​obtained according to AHP:

[0105] μ=[0.087 0.173 0.206 0.206 0.328] T

[0106] According to the improved entropy method, the objective weight matrix is ​​obtained:

[0107] λ=[0.203 0.212 0.193 0.188 0.206] T

[0108] Taking expert experience and data information into consideration, δ = 0.6 is selected. Based on the combined weights, the endowment coefficient matrix is ​​obtained:

[0109] ω=[0.133 0.189 0.2 0.199 0.279] T

[0110] Comparing the indicator weights obtained by AHP, improved entropy method and combined weighting method, the weights calculated by the combined weighting method are less biased than those calculated by the separate AHP and entropy weight methods, and can more scientifically describe the bias of the region in various elements of low-carbon development.

[0111] Calculate D+i, Di and relative bull's-eye degree C of each area according to the model i , the results are shown in Table 6.

[0112] Table 6 Calculation results of relative bull's-eye degree in different regions

[0113]

[0114] As can be seen from Table 6, the weighted positive bull's-eye distance D+i of s5 is the smallest, which is 0.0855. The positive trend of area s5 with the positive target scenario is stronger. The relative bull's-eye degree of s5 is 0.8186, which is the largest in the evaluation area, indicating that the current situation of s5 is most inclined to the positive target scenario of this area, and is at the optimal level compared with other evaluation areas. On the other hand, the weighted negative bull's-eye distance Di of s4 is the largest, indicating that its deviation from the negative bull's-eye is not high, and its reverse trend with the reverse reference scenario is strong, indicating that there is still a large gap between this area and the positive target scenario, and it is more inclined to the reverse reference scenario. The relative bull's-eye degree quantified value of s4 is 0.3616, indicating that the current situation of s4 is at the lowest level compared with other evaluation areas.

[0115] In order to find the key factors affecting low-carbon development in different regions, the relative bullseye of a single indicator can be used to analyze the gap between different indicators and the target value. After evaluating and analyzing the relative bullseye of the six evaluation areas, such as s1-s6, the area s4 with the smallest relative bullseye and the area s5 with the largest relative bullseye were selected for further analysis to calculate their relative bullseye values ​​in I j The relative bull's eye degree C ij , see Table 7.

[0116] Table 7 Calculation results of relative bullseye of multi-dimensional indicators

[0117]

[0118] For the s4 area, the relative bullseyes of the I1 and I2 indicators are 0.0228 and 0, respectively, indicating that there is a large gap between these two indicators and the target scenario. The proportion of clean energy installed capacity and the proportion of flexible power sources in this area need to be developed.

[0119] In summary, the carbon emission assessment method of a rural integrated energy system taking driving factors into consideration in the embodiment of the present invention constructs a comprehensive assessment index system to analyze the external driving factors of carbon emissions at multiple levels, such as environment, economy, society and technology; explores the internal driving factors of the new rural integrated energy system at the low-carbon technology level; and integrates the internal and external driving factors to construct a multi-dimensional low-carbon comprehensive assessment index system. A positive and negative bullseye improved gray target model is established, and a low-carbon development potential assessment method is proposed to achieve quantitative analysis of the assessment indicators.

[0120] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0121] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0122] It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above method.

[0123] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0124] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0125] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0127] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A carbon emission assessment method for rural integrated energy system considering driving factors, characterized in that: The following steps are involved: S100. Clarify the process of building a comprehensive evaluation indicator system; S200, based on S100, analyzes the external driving factors of carbon emissions at multiple levels: environmental, economic, social and technical; Explore the internal driving factors of the new rural integrated energy system at the low-carbon technology level; Integrate internal and external driving factors to build a multi-dimensional low-carbon comprehensive evaluation indicator system; S300, based on S100 and S200, establish a positive and negative bull's-eye improved grey target model, propose a low-carbon development potential assessment method, and achieve quantitative analysis of assessment indicators.

2. The rural integrated energy system carbon emission assessment method considering driving factors according to claim 1 is characterized by: The S100 specifically includes: First, by decomposing the multiple related factors of carbon emissions, we conduct an in-depth analysis of the influencing mechanism of carbon emissions and clarify the external driving factors of low-carbon development; secondly, we analyze the low-carbon technologies of the integrated energy system and the carbon reduction effects of the technologies, and deeply analyze the internal development factors of the rural integrated energy system from the three aspects of energy, transmission and energy use, and extract and summarize the key indicators of the evolution of the low-carbon form of the integrated energy system; on this basis, we comprehensively consider the internal and external multi-perspective influencing factors and construct a comprehensive evaluation index system for low-carbon development; after analyzing the key factors affecting low-carbon development and establishing an evaluation index system, we set an evaluation method and quantify each indicator, so as to analyze the low-carbon development level of different evaluation areas and realize the comprehensive evaluation and exploration of the low-carbon development potential in various dimensions.

3. The rural integrated energy system carbon emission assessment method considering driving factors according to claim 1 is characterized by: The S200 specifically includes: S201. Selection of indicators for external driving factors (1) Environmental pressure indicators Considering the influencing factors under the environmental pressure criterion layer, the indicators closely related to them form the indicator layer of the next level of the criterion layer, and then the second-level hierarchical structure can be constructed, I1 = [I 11 , I 12 ], where I1 represents the environmental pressure index, which is a criterion level index, and I 11 , I 12 Represents the indicator layer of the next level under this criterion layer, including carbon dioxide emissions and carbon emission reduction; (2) Economic Development Indicators Considering the influencing factors under the economic development criterion layer, the indicators closely related to them form the indicator layer of the next level of the criterion layer, and then the second-level hierarchical structure can be constructed, I2 = [I 21 ], where I2 represents the economic development index, which is a criterion level index, and I 21 The indicator layer representing the next level below this criterion layer includes GDP per capita; (3) Social scale indicators Considering the influencing factors under the social scale criterion layer, the indicators closely related to it form the indicator layer of the next level of the criterion layer, and then the second-level hierarchical structure can be constructed, I3 = [I 31 ]; I3 represents the social scale index, which is a criterion level index, and I 31 The indicator layer representing the next level below this criterion layer includes the number of permanent residents; S202. Selection of indicators for internal driving factors (1) Energy structure indicators Table 2 Energy link evaluation index system Considering the multi-dimensional influencing factors under the energy structure criterion layer, the indicators closely related to them form the indicator layer of the next level of the criterion layer, and then the second-level hierarchical structure can be constructed, which consists of 4 indicators and forms the energy structure indicator set I4 = [I 41 , I 42 , I 43 , I 44 ]; by calculating I 41 ,I 42 ,I 43 ,I 44 The data of the four dimensional indicators can reflect the development level of the region in these aspects. The potential I4 at the regional energy structure level can be obtained by integrating multiple dimensions; (2) Power grid construction indicators Table 3 Evaluation indicators of power grid links Considering the multi-dimensional influencing factors under the grid construction criterion layer, the indicators closely related to them form the indicator layer of the next level of the criterion layer; the second-level hierarchical structure can be constructed, which consists of three indicators to form the grid construction indicator set I5 = [I 51 , I 52 , I 53 ]; by calculating I 51 , I 52 , I 53 The data of the three-dimensional indicators can reflect the development level of the region in these aspects. The potential I5 of the regional power grid construction can be obtained by integrating multiple dimensions; (3) Energy efficiency index Table 4 Evaluation indicators of energy consumption Considering the multi-dimensional influencing factors under the energy efficiency criterion layer, the indicators closely related to them form the indicator layer of the next level of the criterion layer, and then the second-level hierarchical structure can be constructed, which consists of 4 indicators and forms the energy efficiency indicator set I6 = [I 61 , I 62 , I 63 , I 64 ], by calculating I 61 ,I 62 ,I 63 ,I 64 The data of the four dimensional indicators reflect the development level of the region in these aspects. The potential of regional energy efficiency can be obtained by integrating multiple dimensions. S203. Construct a low-carbon comprehensive evaluation index system In order to comprehensively evaluate the comprehensive level of regional low-carbon development, a comprehensive evaluation index system for low-carbon development is constructed. The index system is a hierarchical structure, which is divided into three layers in total. The comprehensive level of low-carbon development is taken as the first layer of the index system, namely the target layer. According to the driving factors associated with carbon emissions obtained by decomposing the multi-factor driving force model, macro-driving factors such as energy structure factors, technological level factors, economic development factors and population size factors are taken as the second layer of the index system, namely the criterion layer. Specific indicators are selected from the indicators closely related to their indicators under each criterion layer as the third layer of the index system, namely the indicator layer. The comprehensive evaluation index system for low-carbon development potential is shown in Table 5. The constructed evaluation index system is used to comprehensively evaluate the low-carbon development level of region i. The evaluation problem is modeled as a mathematical expression, as shown in formula (1): It can be seen from the formula that for m evaluation areas, the number of indicators that need to be calculated reaches m×15; Table 5 Comprehensive evaluation index system for low-carbon development 4. The rural integrated energy system carbon emission assessment method considering driving factors according to claim 1 is characterized by: The S300 specifically includes: The multidimensional evaluation index set constructed by the multidimensional factors affecting low-carbon development is regarded as a vector space. The index set composed of n indicators constitutes an n-dimensional evaluation space I = [I1, I2, ..., I n ]; Assume that there are m regions to be evaluated with different resource endowments, and the different regions constitute the set to be evaluated S = [s1, s2, ..., s m ], a ij For i in I j The indicator values ​​in the dimension can be used to construct a low-carbon development assessment space A that includes all the areas to be assessed. S , the matrix is ​​expressed as formula (2) In the formula, the i-th row represents s i The state vector a in the evaluation space i =[a i1 , a i2 , ..., a in ], a1, a2, ..., a m are m state points in the space, which are distributed in different positions of the gray target space, reflecting the development differences of different evaluation areas; The grey target model is used to quantitatively evaluate the low-carbon development potential of multiple regions, and a low-carbon development potential evaluation method is proposed. According to the development requirements of the "dual carbon" goal for the low-carbon transformation of regional power grids, the expected values ​​of indicators are set, and the gap between the current value and the target value of each indicator in the low-carbon potential evaluation index system is calculated to clarify the low-carbon potential of each dimension; further, according to the hierarchical relationship of the evaluation index system, the low-carbon potential of the previous level is calculated in turn; the multi-dimensional indicator layer indicators are comprehensively compared, and the distance between the target layer indicator value of the area to be evaluated and the indicator value in the dual carbon scenario is compared to judge the degree of proximity between all areas to be evaluated and the target scenario, so as to clarify the low-carbon development space of each region and realize the quantitative evaluation of the low-carbon development potential of the region.

5. The rural integrated energy system carbon emission assessment method considering driving factors according to claim 4 is characterized by: Step S300 also includes using the grey target model to evaluate and analyze the low-carbon development potential of the regional power grid, setting the positive target of the grey target model, that is, the positive target scenario of regional low-carbon development. The scenario construction involves the influence of multi-dimensional factors. The development target values ​​of the region in each dimensional indicator constitute the positive target scenario of the region, that is, the positive target a. + , the area is from the current situation a i Gradually towards the bull's eye + Development, there is a positive trend; The grey target model is improved by using the TOPSIS idea. On the basis of setting the positive target, the negative target is added for comparative analysis. i The reverse reference scenario, the negative bull's eye vector a - , the region gradually deviates from the negative target a in the process of low-carbon development - , considering that the negative bull's-eye can represent the opposite trend of the region and the reverse reference scenario, the differences in the status quo of multiple assessment regions can be measured based on the positive and negative bull's-eyes; the specific expressions of the positive and negative bull's-eyes are as shown in formula (3); In the formula, the indicators of each dimension in the positive bull's-eye vector take the most ideal value a+j, that is, the target value of each indicator when the region achieves carbon peak and carbon neutrality, as the positive target scenario for regional low-carbon potential assessment; the indicators of each dimension in the negative bull's-eye vector take the worst value aj of regional development, that is, the worst case of regional low-carbon development, as the reverse reference scenario, and compare and analyze with the current development status to see whether the regional low-carbon development is deviating from the reference scenario; Due to the different dimensions of the indicators, the gray target transformation is performed on the spatial axis, that is, T(a ij )=b ij , the specific formula is as formula (4): In the formula, M=max{|a+jaj|}, Ψ B , C , F They represent the cost-type indicator set, the benefit-type indicator set, and the fixed-type indicator set respectively; among them, the cost-type indicator means that the smaller the indicator value, the closer it is to the positive target scenario; the benefit-type indicator means that the larger the indicator value, the closer it is to the positive target scenario; the fixed-type indicator means that the indicator value tends to a certain optimal value, the closer it is to the positive target scenario; For the low-carbon development assessment space A that includes all the areas to be assessed S Change the gray target and get B S =[b ij ] m×n At the same time, the positive and negative bullseye vectors are gray-targeted and b is obtained. + =[b+j] 1×n , b - =[bj] 1×n ; The grey target model is improved based on the positive and negative bullseyes, so that the model can simultaneously reflect the positive and negative trends between the regional low-carbon development status and the regional positive target scenario and the reverse reference scenario; the improved grey target model B is established based on the positive and negative bullseyes as shown in formula (5): Where, the first to the mth rows represent the regions s1, s2, ..., s after the gray target transformation. m The standard multi-dimensional indicator vector reflects the multi-dimensional low-carbon status of each assessment area; the last two rows represent the standard positive and negative bullseye vectors of regional low-carbon development; the model standardizes the indicators of each dimension so that the indicators of different dimensions can be compared and calculated with each other, and the multi-dimensional indicators can be combined to achieve quantitative analysis of the regional low-carbon development level; Definition b i With b + , b - The Euclidean space distances are respectively the positive bull's-eye distance d+i and the negative bull's-eye distance di, and the expressions are shown in formula (6): In the formula, |b ij -b+j| represents the distance between the current status and target value of each dimension indicator in the current year, which is reflected in the indicator I j The size of the development space in the dimension. The larger the value, the better the area is in indicator I. j The development in this area is further away from the dual carbon goals; ij -bj| indicates the distance between the current status of each dimension indicator and the worst historical value, which is reflected in the indicator I j The lower the carbon level of the region, the higher the value. j The better the development situation; According to the hierarchical structure of the evaluation index system, the distance between the indicators of the criterion layer and the target layer is calculated step by step, and the low-carbon potential of each layer can be analyzed in turn; || b i –b + ||、||b i –b - ||, which reflects the comprehensive level of low-carbon development in the target layer indicator region. When d+i is smaller and di is larger, it indicates that the regional low-carbon status is closer to the target scenario and farther from the reverse reference scenario, that is, the regional low-carbon development space is larger; the positive and negative target center distances d+i and di can reflect b i With positive and negative bullseye b + , b - The positive and negative trends of the region can also be quantified to analyze the deviation between the regional status and its positive and negative targets; The endowment coefficient is used to correct the positive and negative target distances obtained in formula (6) to obtain the weighted positive target distance D+i and the weighted negative target distance Di, which are expressed as follows: By calculating the weighted target distance D+i, Di, the regional state point Wb of the endowment coefficient is measured i With Wb + 、Wb - The gap between them; in the improved gray target space after W correction, the positive bull's eye b + and the state point b representing the evaluation area i The spatial positions of the two have shifted, and the original bull's-eye distance has limitations. The weighted bull's-eye distance D+i, Di can more accurately measure the gap between the two, that is, the current level of low-carbon development in the assessment area Wb i With the target scenario Wb + The gap value; Regional low-carbon development status Wb i Compared with the positive target scenario Wb + It has a positive trend, and compared with the reverse reference scenario Wb - The comprehensive analysis of the low-carbon development path needs to combine the directional analysis of the two and comprehensively consider b i With positive bullseye + The closeness of the negative bullseye b - The deviation degree is combined with D+i and Di to define the relative bull's-eye degree. The specific calculation formula is as follows: From the formula, we can see that C i The value range is [0,1], and the closer to 1, the higher the Wb i The closer you get to the bullseye, + , with negative bullseye Wb - The farther the distance, the better its low-carbon development status.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 5.

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