CN117455264A - Rural typical application scene carbon emission reduction comprehensive value evaluation method - Google Patents

Abstract

The invention belongs to the technical field of carbon emission reduction value evaluation, and particularly relates to a rural typical application scene carbon emission reduction comprehensive value evaluation method; s1, establishing a comprehensive value measuring and calculating model; s2, establishing a comprehensive value weighting model; s3, determining flexible resources and constructing a typical application scene of the flexible resources; the step S1 includes: constructing a flexible resource comprehensive value evaluation index system from three aspects of economic value, environmental value and safety value; the step S2 includes: s21, establishing a subjective weighting model based on an analytic hierarchy process; s22, establishing an objective weighting model based on an entropy weighting method; s23, establishing a TOPSIS-based rural carbon emission reduction value measurement model; according to the invention, the demand response load and the electric automobile are selected for technical and economic characteristic analysis, four typical scenes of green agriculture, clean heating, green traffic and reference low carbon and a virtual power plant participation mode are provided, and the carbon emission reduction comprehensive value of the rural typical application scene based on TOPSIS can be effectively evaluated.

Description

Rural typical application scene carbon emission reduction comprehensive value evaluation method

Technical Field

The invention belongs to the technical field of carbon emission reduction value evaluation, and particularly relates to a comprehensive carbon emission reduction value evaluation method for rural typical application scenes.

Background

The low-carbon rural area is to promote low-carbon development in rural areas, reduce greenhouse gas emission, improve resource utilization efficiency and promote sustainable development of rural economy, society and environment; the core targets of the low-carbon rural area are to realize the cleanness of rural energy sources, low carbonization of agricultural production, low carbonization of rural life and low carbonization of rural traffic; fully applying rural flexible resources is beneficial to the power grid service of low-carbon rural areas, improves the rural electrification level and reduces carbon emission; the renewable energy sources, the energy source storage, the intelligent power grid and other technical means are fully utilized in rural areas, so that the efficient utilization and low-carbon emission of the energy sources are realized, and the concept and practice of sustainable development in rural areas are promoted; the method for measuring and calculating the low-carbon rural carbon emission reduction value in rural typical application scenes is lacking in the prior art, and is not beneficial to the development of low-carbon rural areas.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provides a comprehensive value evaluation method for carbon emission reduction of rural typical application scenes.

The purpose of the invention is realized in the following way: a rural typical application scene carbon emission reduction comprehensive value evaluation method comprises the following steps:

s1, establishing a comprehensive value measuring and calculating model;

s2, establishing a comprehensive value weighting model;

s3, determining flexible resources and constructing a typical application scene of the flexible resources.

Further, the step S1 of establishing a comprehensive value measurement model includes:

constructing a flexible resource comprehensive value evaluation index system from three aspects of economic value, environmental value and safety value;

the economic value comprises three indexes of business income, direct cost and profit;

the environmental value comprises three indexes of carbon emission reduction, clean energy substitution rate and flexible resource utilization level;

the safety value comprises three indexes of adjustable potential, response time and power supply reliability.

Further, the step S2 of establishing the comprehensive value weighting model includes:

s21, establishing a subjective weighting model based on an analytic hierarchy process;

s22, establishing an objective weighting model based on an entropy weighting method;

s23, establishing a TOPSIS-based rural carbon emission reduction value measurement model.

Further, the step S21 of establishing a subjective weighting model based on the analytic hierarchy process includes:

s211, establishing a hierarchical structure model;

s212, constructing a judgment matrix;

starting from level 2 of the hierarchical structure model, constructing a judgment matrix by a pairwise comparison method for the same-level factors of each factor subordinate to (or affecting) the previous level until the last level;

s213, consistency test is carried out

Calculating the maximum characteristic root and the corresponding characteristic vector of each judgment matrix, and carrying out consistency test by using a consistency index, a random consistency index and a consistency proportion; the consistency test steps are as follows:

(1) Calculating consistency test index

Wherein: lambda (lambda) _max Representing the maximum feature root of the judgment matrix;

(2) Searching a corresponding average random consistency index RI;

(3) Calculation of the coherence proportion CR

CR＝CI/RI (1-3)

When CR <0.1, the consistency of the judgment matrix is considered acceptable; when CR is greater than 0.1, the judgment matrix is appropriately corrected;

s214, calculating weight vector

The weight vector is calculated by adopting a summation method, and the specific process is as follows:

(1) The data of each column is summed up,obtaining a sum value vector B _j ＝[b ₁ ,b ₂ ,...,b _m ]；

(2) Calculating normalized vector C _ij WhereinThe method can obtain:

(3) Calculating a weight vector ω _i The calculation formula is as follows:

further, the step S22 of establishing an objective weighting model based on the entropy weighting method includes:

(1) Constructing matrix P from raw data of index _mn The method comprises the steps of carrying out a first treatment on the surface of the Wherein P is _mn A numerical value representing an nth index of the mth region;

(2) The original data is standardized to obtain a standardized matrix P' _mn The standardized process of extremely large indexes such as business income and extremely small indexes such as annual operation and maintenance cost is as follows:

(3) Calculating the entropy value e of the nth index _n The method comprises the following steps:

(4) Calculating the difference coefficient g of the nth index _i ：

g _i ＝1-e _n (1-11)

(5) Calculating the weight w of the nth index _j ：

(6) Comprehensive weight calculation is carried out based on the least square principle;

the comprehensive weights are designed by adopting the idea of combined weighting, and the scaling factor of the weights is determined by applying the least square optimization idea:

w _n ＝αw _n,i +(1-α)w _n,j (1-13)

wherein, alpha represents a weight coefficient; w (w) _n,i Subjective weight representing the nth index; w (w) _n,j Objective weight representing the nth index; f represents the minimum target value of the sum of the complex weight solution variances.

Further, the step S23 of establishing a calculation model of the rural carbon emission reduction value based on TOPSIS includes:

(1) Obtaining an original matrix P according to the data of the evaluation index _mn Then subtracting the minimum index from the maximum value to realize forward direction, and finally carrying out normalization treatment to obtain a change matrix P' _mn ；

(2) Using the weights ω obtained previously _j Weighting the normalized data to form a weighted normalized matrix;

V＝(ω _j P _ij ) _mn (1-16)

(3) Definition of the Positive idealized scheme V ⁺ And negative ideality scheme V ^- ；

Wherein: j (J) ₁ Representing a set of benefit indicators, J ₂ A set of representation cost indicators;

(4) Calculating Euclidean distance

Let scheme i (i=1, 2,., n) be the distance from the positive ideal schemeDistance to negative ideal scheme ofThen

(5) Calculating relative closeness

Scheme i (i=1, 2,., n) to ideal scheme:

and calculating the TOPSIS evaluation value of each scheme by using the formula, and sorting and optimizing the evaluation objects according to the evaluation values.

The invention has the beneficial effects that: the invention relates to a rural typical application scene carbon emission reduction comprehensive value assessment method, which comprises the following steps of S1, establishing a comprehensive value measuring and calculating model; s2, establishing a comprehensive value weighting model; s3, determining flexible resources and constructing a typical application scene of the flexible resources; the step S1 of establishing the comprehensive value measuring and calculating model comprises the following steps: constructing a flexible resource comprehensive value evaluation index system from three aspects of economic value, environmental value and safety value; the economic value comprises three indexes of business income, direct cost and profit; the environmental value comprises three indexes of carbon emission reduction, clean energy substitution rate and flexible resource utilization level; the safety value comprises three indexes of adjustable potential, response time and power supply reliability; the step S2 of establishing the comprehensive value weighting model comprises the following steps: s21, establishing a subjective weighting model based on an analytic hierarchy process; s22, establishing an objective weighting model based on an entropy weighting method; s23, establishing a TOPSIS-based rural carbon emission reduction value measurement model; according to the rural typical application scene carbon emission reduction comprehensive value evaluation method, the demand response load and the electric automobile are selected for technical and economic characteristic analysis, and the rural low-carbon energy consumption elements are combined to provide four typical scenes of green agriculture, clean heating, green traffic and reference low-carbon and a virtual power plant participation mode, so that the TOPSIS-based rural typical application scene carbon emission reduction comprehensive value can be effectively evaluated.

Drawings

FIG. 1 is a schematic diagram of a virtual power plant scenario engagement.

FIG. 2 is a schematic diagram of the evaluation results of green agricultural production business models.

Fig. 3 is a schematic diagram of the evaluation results of the green traffic business model.

Fig. 4 is a schematic diagram of evaluation results of a clean heating business mode.

Fig. 5 is a schematic diagram of the results of the benchmark low-carbon business model evaluation.

Detailed Description

The invention is further described below with reference to the accompanying drawings.

In order to better understand the technical solution of the present invention, the following detailed description is given by way of specific examples.

It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

The following description of the technical solutions in the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The invention discloses a rural typical application scene carbon emission reduction comprehensive value evaluation method, which comprises the following steps of:

s1, establishing a comprehensive value measuring and calculating model;

s2, establishing a comprehensive value weighting model;

s3, determining flexible resources and constructing a typical application scene of the flexible resources.

Further, the step S1 of establishing a comprehensive value measurement model includes:

the construction of the flexible resource value evaluation model in rural typical application scenes is to evaluate the effect that flexible resources can provide for rural areas and power grids. And constructing a flexible resource comprehensive value evaluation index system from three aspects of economic value, environmental value and safety value. The constructed comprehensive value evaluation index system of the flexible resources under the rural typical application scene is shown in table 1;

table 1 Flexible resource comprehensive value evaluation index System

The economic value index reflects the capacity and the profit level of the power grid for acquiring profits by applying flexible resources in rural typical scenes.

The economic value comprises three indexes of business income, direct cost and profit;

(1) Revenue of business

By applying flexible resources in rural typical scenarios, the total revenue is achieved by means of production, sales or service provision in a unit time.

(2) Direct cost of

Direct costs refer to the sum of the costs directly applied to the production process, such as major materials, worker wages, equipment depreciation, etc., when flexible resources are applied in rural typical scenarios.

(3) Profit margin

The profit margin refers to the remainder of the revenue after deducting the cost consumption and tax. The calculation formula is as follows: enterprise profit = business income-cost-tax and additional.

The environmental value index reflects the contribution degree of the application flexibility resources to the aspects of carbon emission reduction and electric energy substitution in rural typical scenes and the utilization degree of the local flexibility resources.

The environmental value comprises three indexes of carbon emission reduction, clean energy substitution rate and flexible resource utilization level;

(1) Carbon emission reduction

Reflecting the carbon dioxide emission reduction of rural areas after application of rural flexible resources. The calculation formula is as follows:

1 degree electricity saving = 0.997 kg CO2 reduction = 0.272 kg "carbon reduction;

saving 1 kg of standard coal = reduced emissions 2.493 kg of co2 = reduced emissions 0.68 kg of "carbon";

1 kg of raw coal is saved = 1.781 kg of emission reduction is reduced, and 0.486 kg of carbon is reduced.

(2) Clean energy substitution rate

The using substitution rate of clean energy to fossil energy after flexible resources are applied is calculated as follows:

clean energy substitution rate = total annual clean energy substituted fossil energy/total annual fossil energy consumption.

(3) Flexible resource utilization level

And evaluating the utilization degree of the application flexibility resource process in rural typical scenes.

The calculation formula is as follows: flexible resource utilization level = flexible resource utilization total/local total of all flexible resources.

The safety value index reflects the feasibility of the application flexible resource in rural typical scenes and the stability of the power grid operation in the application process.

The safety value comprises three indexes of adjustable potential, response time and power supply reliability.

(1) Potential adjustment

Refers to the maximum capacity limit of each of two flexible resources, namely demand side response and virtual power plant.

(2) Response time

Refers to the time t from the corresponding instruction received by the dispatching mechanism to the completion of the output of the demand side response and virtual power plant flexible resource.

(3) Reliability of power supply

The power supply reliability is the capability of the power system to continuously supply electric energy after flexible resources are applied in rural typical scenes, and is one of important assessment indexes of electric energy quality, and the average power failure time of users in 2020 is 13.72 hours/household according to related files. The power supply reliability index of the invention is expressed by the average power failure time of users,

the calculation formula is as follows:

wherein R represents the power supply reliability after flexible resources are applied in rural typical scenes; h is a _s Representing the average power failure time of a user after flexible resources are applied in a rural typical scene; h represents the sum of the annual hours of the current year.

The weight is one of the most important parts in the quantitative evaluation model, the accuracy and the credibility of the evaluation can be improved by the combined application of the multi-criterion weight calculation method, and an evaluation method combining subjective and objective is selected.

Further, the step S2 of establishing the comprehensive value weighting model includes:

s21, establishing a subjective weighting model based on an analytic hierarchy process;

s22, establishing an objective weighting model based on an entropy weighting method;

s23, establishing a TOPSIS-based rural carbon emission reduction value measurement model.

The analytic hierarchy process integrates qualitative and quantitative analysis, and is favorable for analyzing complex large systems with multiple targets, multiple factors and multiple criteria; further, the step S21 of establishing a subjective weighting model based on the analytic hierarchy process includes:

s211, establishing a hierarchical structure model;

the hierarchical model corresponds to an evaluation index system for each attribute management, so that the hierarchical model is not independently built.

S212, constructing a judgment matrix;

starting from level 2 of the hierarchical structure model, constructing a judgment matrix by a pairwise comparison method for the same-level factors of each factor subordinate to (or affecting) the previous level until the last level;

s213, consistency test is carried out

Calculating the maximum characteristic root and the corresponding characteristic vector of each judgment matrix, and carrying out consistency test by using a consistency index, a random consistency index and a consistency proportion; the consistency test steps are as follows:

(1) Calculating consistency test index

Wherein: lambda (lambda) _max Representing the maximum feature root of the judgment matrix;

(2) Searching a corresponding average random consistency index RI;

the following table gives the average random uniformity index for the 1-3 order decision matrix:

table 2 RI value distribution

(3) Calculation of the coherence proportion CR

CR＝CI/RI (1-3)

When CR <0.1, the consistency of the judgment matrix is considered acceptable; when CR is greater than 0.1, the judgment matrix is appropriately corrected;

s214, calculating weight vector

The weight vector is calculated by adopting a summation method, and the specific process is as follows:

(1) The data of each column is summed up,obtaining a sum value vector B _j ＝[b ₁ ,b ₂ ,...,b _m ]；

(2) Calculating normalized vector C _ij WhereinThe method can obtain:

(3) Calculating a weight vector ω _i The calculation formula is as follows:

the entropy weighting method belongs to an objective weighting method and has the advantages that the value and the weight of the data are defined completely from the discrete degree of the data; further, the step S22 of establishing an objective weighting model based on the entropy weighting method includes:

(1) Constructing matrix P from raw data of index _mn The method comprises the steps of carrying out a first treatment on the surface of the Wherein P is _mn A numerical value representing an nth index of the mth region;

(2) The original data is standardized to obtain a standardized matrix P' _mn The standardized process of extremely large indexes such as business income and extremely small indexes such as annual operation and maintenance cost is as follows:

(3) Calculating the entropy value e of the nth index _n The method comprises the following steps:

(4) Calculating the difference coefficient g of the nth index _i ：

g _i ＝1-e _n (1-11)

(5) Calculating the weight w of the nth index _j ：

(6) Comprehensive weight calculation is carried out based on the least square principle;

the comprehensive weights are designed by adopting the idea of combined weighting, and the scaling factor of the weights is determined by applying the least square optimization idea:

w _n ＝αw _n,i +(1-α)w _n,j (1-13)

wherein, alpha represents a weight coefficient; w (w) _n,i Subjective weight representing the nth index; w (w) _n,j Objective weight representing the nth index; f represents the minimum target value of the sum of the complex weight solution variances.

Because objective data of various indexes among the rural power grid services of the same type are relatively strong in comparability, a superior-inferior solution distance (TOPSIS) method is selected when the rural power grid service value is quantized; further, the step S23 of establishing a calculation model of the rural carbon emission reduction value based on TOPSIS includes:

(1) Obtaining an original matrix P according to the data of the evaluation index _mn Then subtracting the minimum indicator from the maximum valueRealizing forward direction, and finally carrying out normalization processing to obtain a change matrix P _m ′ _n ；

(2) Using the weights ω obtained previously _j Weighting the normalized data to form a weighted normalized matrix;

V＝(ω _j P _ij ) _mn (1-16)

(3) Definition of the Positive idealized scheme V ⁺ And negative ideality scheme V ^- ；

Wherein: j (J) ₁ Representing a set of benefit indicators, J ₂ A set of representation cost indicators;

(4) Calculating Euclidean distance

Let scheme i (i=1, 2,., n) be the distance from the positive ideal schemeDistance to negative ideal scheme ofThen

(5) Calculating relative closeness

Scheme i (i=1, 2,., n) to ideal scheme:

and calculating the TOPSIS evaluation value of each scheme by using the formula, and sorting and optimizing the evaluation objects according to the evaluation values.

The novel flexible resource refers to a power resource which can increase considerable elasticity and flexibility for system service in a power system; the invention selects the demand response load and the electric automobile to analyze the technical and economic characteristics, and provides economic and technical characteristic data for resource comparison selection in typical application scenes

The demand side response refers to the power users on the demand side of power grids such as industry, commerce, residents and the like, and after receiving the fund compensation policy or market price signal of the induced load reduction sent by the government or the power supply party, the inherent power utilization mode is changed so as to reduce or shift the power utilization load of a certain period to respond to the power supply, thereby ensuring the stability of the power grid and inhibiting the short-term behavior of the rising of the power price.

The electric automobile load aggregation has the characteristics of large scale, variability, distribution, controllability, sustainability and the like, and the self load curve can be adjusted by controlling the self charge and discharge time. The electric automobile can be regarded as movable small-sized energy storage, and the electric automobile has the dual characteristics of a power supply and a load, and can form good complementation with the operation of a power grid through orderly organization and management.

The virtual power plant aggregate demand response load and the electric automobile are applied to rural typical scenes, and rural flexible resources are effectively developed. When the virtual power plants are optimized and aggregated, the scene characteristics and the complementary characteristics of the resource output are considered, and the virtual power plants and rural residents sign energy management contracts, so that the demand response cooperation alliance is achieved, the effective utilization of rural flexible resources is realized, and meanwhile, the residential and civil energy cost is reduced.

By combining rural low-carbon energy consumption factors, four typical scenes of green agriculture, clean heating, green traffic and reference low-carbon are constructed.

Green agricultural scene

The scene is oriented to rural agricultural production, the energy consumption type is single, the electric load is usually taken as the main, rural flexible resources are aggregated through the virtual power plant, so that the electricity consumption of the agricultural production is met, the carbon emission is reduced, and the cost of a power grid is reduced. The green agricultural scene develops rural clean energy resources by aggregating rural flexible resources, and coordinates to meet agricultural energy loads, including crop production energy, such as sowing, irrigation, harvesting and the like, and breeding industry energy, such as illumination, water pumping, indoor heating and the like.

Clean heating scene

The scene is oriented to modern rural resident heating. The virtual power plant provides clean heating load for rural residents by aggregating rural flexible resources. The electric heating is utilized to reduce carbon emission while meeting resident heating demands, the clean heating scene utilizes rural flexible resources aggregated by virtual power plants, and the electric load demands of modern rural industrial heating are coordinated to be met, so that the rural heating problem in northern areas is solved, and certain benefits can be obtained by supplying power or heating to the outside.

Green traffic scene

The scene is oriented to rural low-carbon traffic modes. The green traffic scene utilizes the virtual power plant to aggregate idle loads of electric vehicles by perfecting rural electric vehicle charging system equipment such as charging piles, charging stations and the like. Because electric vehicle charging station and electric pile are used energy time period nature stronger, the day is more with energy, night is few, introduces energy storage equipment, through the energy storage, satisfies the energy demand, on the other hand, through introducing energy conversion equipment, absorbs clean energy and abandons the energy to realize the energy collaborative conversion of system.

Benchmark low carbon scenes

The scene is a centralized form of all typical scenes and is oriented to rural novel village communities. The reference low-carbon scene is used for aggregating rural flexible resources through the virtual power plant, and the cold, heat and electric load demands of village users are coordinated and met. Farmers and virtual power plants achieve a demand response alliance, and electric energy balance and peak staggering power utilization are achieved; aiming at self-built energy utilization equipment of farmers, a low-carbon energy utilization mode is embodied in 'electric energy replacement', a virtual power plant can increase the utilization rate of rural resources by aggregating and dispersing resources, and residents can obtain benefits by selling the resources.

Participation mode

In the four typical scenes constructed above, the virtual power plant can respond to rural flexible resources such as loads and electric vehicles by aggregating demands, as shown in fig. 1, so that the adjustment capability and system flexibility of the power system are improved, and the interaction between the power grid and a user is enhanced. After the virtual power plant gathers rural flexible resources, power is supplied to rural residents in four scenes, an energy utilization solution scheme is provided, user equipment maintenance and design energy utilization transfer package is carried out, and benefits are obtained; rural residents respond to the cooperation alliance through the signing and selling demands, low-price electric energy is obtained from the virtual electric factories, agricultural production is met, and carbon emission is reduced. In the process, the two parties aggregate resources through the virtual power plant, so that the resource utilization rate is improved, the electric energy use cost is reduced, the carbon emission is reduced, and the double-carbon policy is responded.

The invention will be further illustrated by the following specific examples.

The invention selects the national standard of the national statistical office, academic literature, industry standard, the Shuobo paper and the data of related website data, and performs example analysis on the TOPSIS-based rural typical application scene comprehensive value evaluation model.

1. Multi-class index weight calculation

And layering the rural power grid business value influence factors according to an analytic hierarchy process, wherein the rural power grid business value influence factors can be divided into a target layer, a criterion layer and a scheme layer.

(1) Subjective weight calculation

Constructing judgment matrixes according to the evaluation results of the experts in the rural power field, and carrying out consistency test on each judgment matrix.

TABLE 3 criterion layer determination matrix under target layer

The consistency result of the judgment matrix of the criterion layer below the target layer is 0<0.1. The eigenvectors are calculated from the judgment matrix of the scheme layer (C1-C3) below the criterion layer (economic value B1), as shown in the following table:

table 4 judgment matrix of scheme layer under criterion layer B1

The consistency result of the judgment matrix is 0.046<0.1 criterion layer (social value B2) and the judgment matrix of the scheme layer (C4-C5) calculates the feature vector, as shown in the following table:

TABLE 5 judgment matrix for scheme layer under criterion layer B2

The consistency test result of the judgment matrix is 0<0.1. The judgment matrix of the scheme layer (C6-C8) below the criterion layer (strategic value B3) calculates feature vectors as follows:

TABLE 6 judgment matrix for scheme layer under criterion layer B3

The consistency test result of the judgment matrix is 0<0.1. The judgment matrix of the scheme layer (C9-C10) below the criterion layer (environmental value B4) calculates feature vectors as follows:

TABLE 7 judgment matrix for scheme layer under criterion layer B4

The consistency test result of the judgment matrix is 0<0.1. And sequentially calculating weights of other scheme layers relative to the criterion layer, wherein the calculation results are shown in the following table.

Table 8 subjective weighting of influencing factors

(2) Objective weight calculation

The objective weights of the various value evaluation indexes are determined by using an entropy weight method, as shown in the following table.

TABLE 9 influence factor information entropy and objective weight

(3) Comprehensive weight

And determining the optimal distribution coefficient of the subjective weight and the objective weight through a simultaneous least square optimization function, thereby determining the comprehensive weight of the evaluation index, as shown in the table below.

Table 10 comprehensive weighting of influencing factors of energy Internet business investment value

2. Example analysis

The distances between the average value of corresponding indexes of the standard low-carbon commercial mode, the green agricultural production commercial mode, the clean heating commercial mode and the green traffic commercial mode and the positive ideal scheme and the negative ideal scheme are calculated by using the method, and the result is as follows:

table 11 positive and negative ideal schemes for four scenarios

Table 12 four kinds of scene good-bad solution distances and evaluation results

As can be seen from the table, the green agricultural production business model has the best comprehensive value of carbon emission reduction, the standard low-carbon business model is inferior, and the clean heating business model and the green traffic business model are sequential. The green agricultural production business model has the best comprehensive value, and is close to rural production and living scenes, so that investment and production are better than other scenes. The worst green traffic business mode is that the rural areas are wide and thin, the application area is small, the electric automobile cannot meet the existing travel demands of rural residents, the electric automobile is kept in a small quantity, rural infrastructure is behind, the use cost and the endurance time of the electric automobile are increased, the green traffic business mode is difficult to develop in the rural areas, and the comprehensive value is low.

(1) Analysis of evaluation results of commercial models of green agricultural production as shown in FIG. 2

Comprehensive score: 74.52

And (3) comprehensive evaluation: the current commercial mode of green agricultural production has good environmental value and strategic value, and the economic value and the environmental value of the commercial mode are required to be further developed. The overall score is higher, and the comprehensive value is good.

Economic value: in 2021, the green agricultural production business project has incomes of 2400 ten thousand yuan, achieves profits of 94.30 ten thousand yuan, and reduces carbon dioxide emission by 1154.78 tons.

Social value: the mode of the green agricultural production industry is close to the actual scene of rural production and living, the national double-carbon strategy and rural industry strategy are responded actively, the problems and the demands of rural residents in production and management are solved, and the method has good social value.

Environmental value: green agricultural production business projects have fewer environmental impact factors during business operations and therefore contribute relatively limited in terms of environmental benefits.

Strategic value: the green agricultural production business project has wide market space, and meanwhile, few competitors exist in the current market, so that the green agricultural production business project has high strategic value.

(2) Analysis of green traffic business model evaluation results as shown in FIG. 3

Comprehensive score: 67.18

And (3) comprehensive evaluation: the green traffic business mode represented by the electric automobile charging service has the advantages of long development time and relatively early development, but the electric automobile service development profit mode is still in a growing stage in rural areas. In the whole, the electric automobile charging service has better economic value and environmental value, the strategic value and the social value of the electric automobile charging service also have a certain improvement space, and the comprehensive score is good in performance.

Economic value: in 2021, a certain green traffic business realizes business income of 635.15 ten thousand yuan, profits of 382.49 ten thousand yuan, charging facilities subsidize 0.03 yuan/kWh, annual subsidize 58.48 ten thousand yuan, and carbon dioxide emission of 11726.34 tons is reduced.

Social value: the company mainly plays an demonstration and advance role in the electric automobile industry, builds a batch of charging facilities by early investment according to the principle of 'station pile advance and moderate advance', lays a foundation for the development of the whole society electric automobile industry, and has good social benefit.

Environmental value: the electric automobile charging service embodies the property of electric power as clean energy, and makes great contribution in the aspects of reducing primary energy consumption, carbon emission and the like.

Strategic value: the electric automobile charging business has better performance on the market scale and the market share, but the strategic value of the electric automobile charging business is still to be further improved due to the insufficient research and development investment scale.

(3) Clean heating business model evaluation result analysis as shown in FIG. 4

Comprehensive score: 63.46

And (3) comprehensive evaluation: the clean heating business mode is shorter than other business development time at present, and the whole industry is not mature enough. In the whole, the clean heating business mode has better social value and environmental value, the strategic value and the economic value of the clean heating business mode also have a certain improvement space, and the comprehensive score is good.

Economic value: in 2021, a certain clean heating commercial project realizes business income of 210.5 ten thousand yuan, realizes profit of 123.27 ten thousand yuan, reduces carbon dioxide emission of 1075.84 tons, and annual subsidy income of 0.01 yuan and annual subsidy income of 18.95 ten thousand yuan.

Social value: the social value of the clean heating business mode is mainly reflected in the improvement of the infrastructure perfection level, and more infrastructure support is needed, so that the clean heating business mode has higher social value.

Environmental value: the clean heating business mode has great contribution in promoting clean energy consumption, reducing primary energy consumption, carbon emission and the like, and has higher environmental value

Strategic value: the clean heating business mode has room for improvement in the market scale, market share and research and development investment scale, and the strategic value is to be improved.

(4) Analysis of the baseline low-carbon business model evaluation results, as shown in FIG. 5

Comprehensive score: 52.15

And (3) comprehensive evaluation: the environmental value of the current reference low-carbon business mode is outstanding, and the economic value, the social value and the strategic value are required to be further improved compared with the environmental value, and the comprehensive score is relatively low.

Economic value: in 2021, a certain standard low-carbon commercial project realizes that the business income is 60.45 ten thousand yuan, the profit is 35.40 ten thousand yuan, and the carbon emission is reduced by 433.50 tons. But at the present stage, the operation mode of the scene service is relatively single, the added value of the product is lower, and the market share is not high.

Social value: the standard low-carbon business mode can effectively drive the development of clean energy industry and upstream and downstream industries thereof, and promote employment growth, improve social welfare level, organically coordinate different energy supply systems, improve the utilization rate of the infrastructure of the social energy supply system and optimize the utilization of various energy sources.

Environmental value: the standard low-carbon business mode actively responds to the national call, can make a certain contribution in the aspects of reducing the emission of atmospheric pollutants, saving loss, reducing carbon emission, improving the energy utilization efficiency, saving water resources, saving fuel and the like, and has remarkable environmental benefit.

Strategic value: the benchmark low-carbon business model has further room for improvement over the other two businesses in terms of market size and market share.

Conclusion(s)

In order to solve the problem of economic comparison of flexible resources in various rural areas at present, the invention mainly develops comprehensive evaluation and research of carbon emission reduction in the flexible resource aggregation rural areas. Firstly, combining rural energy utilization characteristics, providing a value evaluation index of a flexible resource providing service, respectively assigning subjective weights and objective weights based on a hierarchical analysis method and an entropy weight method, and based on a TOPSIS rural carbon emission reduction value measuring and calculating model; and constructing a typical application scene of rural flexible resources in a rural power grid, and finally comprehensively evaluating the carbon emission reduction comprehensive value of different types of flexible resources by combining rural area examples and related evaluation indexes. According to the evaluation result, the green agricultural production commercial mode has the best comprehensive value of carbon emission reduction and the green traffic commercial mode has the lowest evaluation.

In summary, the invention relates to a rural typical application scene carbon emission reduction comprehensive value assessment method, which comprises the following steps of S1, establishing a comprehensive value measurement model; s2, establishing a comprehensive value weighting model; s3, determining flexible resources and constructing a typical application scene of the flexible resources; the step S1 of establishing the comprehensive value measuring and calculating model comprises the following steps: constructing a flexible resource comprehensive value evaluation index system from three aspects of economic value, environmental value and safety value; the economic value comprises three indexes of business income, direct cost and profit; the environmental value comprises three indexes of carbon emission reduction, clean energy substitution rate and flexible resource utilization level; the safety value comprises three indexes of adjustable potential, response time and power supply reliability; the step S2 of establishing the comprehensive value weighting model comprises the following steps: s21, establishing a subjective weighting model based on an analytic hierarchy process; s22, establishing an objective weighting model based on an entropy weighting method; s23, establishing a TOPSIS-based rural carbon emission reduction value measurement model; according to the rural typical application scene carbon emission reduction comprehensive value evaluation method, the demand response load and the electric automobile are selected for technical and economic characteristic analysis, and the rural low-carbon energy consumption elements are combined to provide four typical scenes of green agriculture, clean heating, green traffic and reference low-carbon and a virtual power plant participation mode, so that the TOPSIS-based rural typical application scene carbon emission reduction comprehensive value can be effectively evaluated.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. The foregoing embodiments are only for illustrating the present invention, wherein the structures, connection modes, manufacturing processes, etc. of the components may be changed, and all equivalent changes and modifications performed on the basis of the technical solutions of the present invention should not be excluded from the protection scope of the present invention.

The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims.

Claims (6)

1. A rural typical application scene carbon emission reduction comprehensive value evaluation method is characterized by comprising the following steps:

s1, establishing a comprehensive value measuring and calculating model;

s2, establishing a comprehensive value weighting model;

s3, determining flexible resources and constructing a typical application scene of the flexible resources.

2. The method for evaluating the comprehensive value of carbon emission reduction in rural typical application scenarios according to claim 1, wherein the step S1 of establishing the comprehensive value measuring and calculating model comprises the following steps:

constructing a flexible resource comprehensive value evaluation index system from three aspects of economic value, environmental value and safety value;

the economic value comprises three indexes of business income, direct cost and profit;

the environmental value comprises three indexes of carbon emission reduction, clean energy substitution rate and flexible resource utilization level;

the safety value comprises three indexes of adjustable potential, response time and power supply reliability.

3. The method for evaluating carbon emission reduction comprehensive value of rural typical application scenarios according to claim 1, wherein the step S2 of establishing a comprehensive value weighting model comprises:

s21, establishing a subjective weighting model based on an analytic hierarchy process;

s22, establishing an objective weighting model based on an entropy weighting method;

s23, establishing a TOPSIS-based rural carbon emission reduction value measurement model.

4. The method for evaluating carbon emission reduction comprehensive value of rural typical application scenarios according to claim 3, wherein the step S21 of establishing a subjective weighting model based on a hierarchical analysis method comprises:

s211, establishing a hierarchical structure model;

s212, constructing a judgment matrix;

starting from level 2 of the hierarchical structure model, constructing a judgment matrix by a pairwise comparison method for the same-level factors of each factor subordinate to (or affecting) the previous level until the last level;

s213, consistency test is carried out

Calculating the maximum characteristic root and the corresponding characteristic vector of each judgment matrix, and carrying out consistency test by using a consistency index, a random consistency index and a consistency proportion; the consistency test steps are as follows:

(1) Calculating consistency test index

Wherein: lambda (lambda) _max Representing the maximum feature root of the judgment matrix;

(2) Searching a corresponding average random consistency index RI;

(3) Calculation of the coherence proportion CR

CR＝CI/RI (1-3)

When CR <0.1, the consistency of the judgment matrix is considered acceptable; when CR is greater than 0.1, the judgment matrix is appropriately corrected;

s214, calculating weight vector

The weight vector is calculated by adopting a summation method, and the specific process is as follows:

(1) The data of each column is summed up,obtaining a sum value vector B _j ＝[b ₁ ,b ₂ ,...,b _m ]；

(2) Calculating normalized vector C _ij WhereinThe method can obtain:

(3) Calculating a weight vector ω _i The calculation formula is as follows:

5. the method for evaluating carbon emission reduction comprehensive value of rural typical application scenarios according to claim 3, wherein the step S22 of establishing an objective weighting model based on an entropy weighting method comprises:

(1) Constructing matrix P from raw data of index _mn The method comprises the steps of carrying out a first treatment on the surface of the Wherein P is _mn A numerical value representing an nth index of the mth region;

(2) The original data is standardized to obtain a standardized matrix P' _mn Wherein the business income and the like are extremely large indexes and annual operationThe standardization process of the extremely small indexes such as dimension cost is as follows:

(3) Calculating the entropy value e of the nth index _n The method comprises the following steps:

(4) Calculating the difference coefficient g of the nth index _i ：

g _i ＝1-e _n (1-11)

(5) Calculating the weight w of the nth index _j ：

(6) Comprehensive weight calculation is carried out based on the least square principle;

the comprehensive weights are designed by adopting the idea of combined weighting, and the scaling factor of the weights is determined by applying the least square optimization idea:

w _n ＝αw _n,i +(1-α)w _n,j (1-13)

wherein, alpha represents a weight coefficient; w (w) _n,i Subjective weight representing the nth index; w (w) _n,j Objective weight representing the nth index; f represents the minimum target value of the sum of the complex weight solution variances.

6. The method for evaluating the comprehensive carbon emission reduction value of the rural typical application scenario as claimed in claim 3, wherein the step S23 of establishing the TOPSIS-based rural carbon emission reduction value measuring and calculating model comprises the following steps:

(1) Obtaining an original matrix P according to the data of the evaluation index _mn Then subtracting the minimum index from the maximum value to realize forward direction, and finally carrying out normalization treatment to obtain a change matrix P' _mn ；

(2) Using the weights ω obtained previously _j Weighting the normalized data to form a weighted normalized matrix;

V＝(ω _j P _ij ) _mn (1-16)

(3) Definition of the Positive idealized scheme V ⁺ And negative ideality scheme V ^- ；

Wherein: j (J) ₁ Representing a set of benefit indicators, J ₂ A set of representation cost indicators;

(4) Calculating Euclidean distance

Let scheme i (i=1, 2,., n) be the distance from the positive ideal schemeDistance to negative ideal scheme S _i ^- Then

(5) Calculating relative closeness

Scheme i (i=1, 2,., n) to ideal scheme:

and calculating the TOPSIS evaluation value of each scheme by using the formula, and sorting and optimizing the evaluation objects according to the evaluation values.