CN115034618A - Community comprehensive energy system benefit evaluation method based on fuzzy evaluation - Google Patents

Abstract

The invention relates to a community comprehensive energy system benefit evaluation method based on fuzzy evaluation, which is characterized by comprising the following steps: based on the physical architecture of the comprehensive energy system and the benefit influence factors thereof, an evaluation index system comprising four dimensions of technology, economy, environment and society is constructed, and the comprehensive benefits of the system are comprehensively and reasonably reflected; providing a combined weighting method to determine index weight, wherein the subjective weight of the index is determined by combining a triangular fuzzy number and an analytic hierarchy process, the objective weight is determined by an entropy weight method, and the combined weight is obtained by combining a linear weighting method and the subjective and objective weights; a fuzzy comprehensive evaluation method is provided for evaluating the benefit of the comprehensive energy system, so that the problem of evaluation index ambiguity is solved; the method is scientific and reasonable, has high accuracy and feasibility, and has important theoretical significance and practical value in aspects of supporting project optimization decision, guaranteeing project construction benefit and the like.

Description

Community comprehensive energy system benefit evaluation method based on fuzzy evaluation

Technical Field

The invention relates to the technical field of comprehensive energy, in particular to a community comprehensive energy system benefit evaluation method based on fuzzy evaluation.

Background

Comprehensive utilization and coordinated configuration of energy are development directions in the field of energy, and have important significance on energy conservation and emission reduction. Under the research and promotion of new Energy and Energy cascade utilization technology, the concept of Integrated Energy System (IES) is developed and obtained. The comprehensive energy system is a comprehensive energy system for uniformly planning and scheduling various types of energy, integrates various types of resources by utilizing an advanced informatization technology, realizes cooperative management among various links, improves the utilization efficiency of the energy, and promotes the sustainable development of the energy. The construction of Integrated Energy Systems (IES) is currently an effective measure to solve the problems of energy shortage and environmental pollution. How to realize the comprehensive utilization and coordination configuration of energy and ensure the maximization of the comprehensive benefits of the system is the primary goal of building IES. Therefore, the utility evaluation work of the IES needs to be carried out, the comprehensive benefits are accurately evaluated, a reliable basis is provided for the subsequent construction, and the utility model is an important feedback link for promoting the IES construction. Compared with the traditional energy distribution and supply system, the comprehensive energy system can realize scientific scheduling among various energy sources and cascade utilization of the energy sources, and has the characteristics of high dimensionality and strong nonlinearity. Therefore, the benefit evaluation system of the comprehensive energy system needs to be designed and improved, and a reasonable evaluation index system and a proper evaluation method which are researched can provide theoretical basis and guidance for the sustainable development of the comprehensive energy project.

Therefore, the benefit evaluation of the comprehensive energy system can be interpreted as a complex comprehensive evaluation problem. The current benefit evaluation problem for integrated energy systems involves a number of aspects. Firstly, the existing index system is lack of systematicness, the correlation among all energy sources is not considered too much, most of the existing indexes aim at single energy sources or links, and the overall discussion of the energy source system is lacked. Secondly, the index weight determination should consider both subjective and objective aspects, because the opinion and the real data of the decision maker have a great influence on the evaluation result. The objective evaluation is weighting of the index based on real data, and results contrary to objective laws may occur. Finally, the existing evaluation method mainly focuses on the equipment level for analyzing the interior of the energy system, and does not realize the unified analysis of the energy supply subsystem. Uncertainty exists in the index during evaluation, so that the qualitative index cannot be solved; and the problem of complexity of calculation during evaluation exists, and the problem of information loss in the evaluation process exists.

Disclosure of Invention

The invention is based on the idea that an evaluation index system comprising four dimensions of technology, economy, environment and society is constructed based on the physical architecture of the IES and the benefit influence factors thereof so as to comprehensively and reasonably reflect the comprehensive benefit of the IES. Secondly, determining the subjective weight of the index by combining a triangular fuzzy number and an analytic hierarchy process, wherein the triangular fuzzy number brings uncertainty of a decision maker in an evaluation process, so that the calculation efficiency is improved; an entropy weight method is adopted to determine objective weight, so that the influence of subjective factors on the weight is avoided; and a linear weighting method is used to obtain a combined weight by combining subjective and objective weights, so that the problem of inaccurate weight calculation caused by mutual influence among indexes is solved. And finally, a fuzzy comprehensive evaluation method is provided, multi-criterion benefit evaluation of the community comprehensive energy system is carried out, a benefit evaluation grade and a score value of a planning scheme of the community comprehensive energy system are solved, qualitative to quantitative conversion of indexes is achieved, and the problem that the indexes cannot be solved due to uncertainty in evaluation is solved.

The technical scheme adopted for realizing the aim of the invention is as follows: a community comprehensive energy system benefit evaluation method based on fuzzy evaluation is characterized by comprising the following steps:

1) constructing a benefit evaluation index system of a community comprehensive energy system, wherein the benefit index system comprises 3 layers; the upper layer is a target layer, represents the comprehensive benefit of the community comprehensive energy system and consists of secondary indexes of four dimensions; the middle layer is a secondary index and comprises secondary indexes of four dimensions of economic benefit, technical benefit, environmental benefit and social benefit, the secondary indexes of the upper layer target layer and the middle layer do not have specific numerical information, and only have weight values and final evaluation values which are only used for reflecting the condition of the system in different dimensions; the bottom layer is a plurality of corresponding three-level indexes under each two-level index, and reflects the condition of the system in specific attributes;

the economic benefit indexes comprise three levels of indexes:

(a) initial investment cost

Initial investment cost G _t The purchase fee of each device of the system is calculated by the following formula:

G _t ＝∑H _n G _a (1)

in the formula G _t For initial investment, G _a The unit is the unit price of the equipment and ten thousand yuan; h _n The number of equipment is the number of units;

(b) cost of system operation

The system operation cost comprises operation maintenance cost and external energy purchase cost, and is shown as formula (2):

G _S ＝C _U +D _e (2)

in the formula G _S For the total cost, C _U For operating maintenance costs, D _e A cost for external energy purchase;

(c) service life of equipment

The service life of the equipment refers to the time process from the beginning of use to the elimination of all the machine equipment purchased in the project due to the fact that the technology is over, the unit is year, and the service life of each equipment can be obtained by referring to the information provided by national standards and manufacturers;

(d) payback period

The investment recovery period T refers to the time required for the project to recover the investment cost through operation, and the calculation formula is shown as (3):

wherein the net present value represents the difference between the net cash inflow and the net cash outflow, and the calculation formula is shown as (4):

wherein CI is the net inflow of cash, CO is the net outflow of cash, i is the discount rate, n is the operation age of the project, and t is the operation time;

the technical benefit indexes comprise three levels of indexes:

(a) coefficient of energy conversion efficiency

The operating efficiency of the system energy link is one of the important indexes reflecting the technical level, and because the energy quality coefficients of different energy sources are different, the energy conversion efficiency coefficient can be obtained according to the energy quality coefficients, as shown in formula (5):

wherein eta is energy conversion efficiency; h and lambda are the total amount of the ith energy consumed by the system and the energy quality coefficient of the ith energy respectively; q _C 、Q _h E is the cold consumption amount, the heat consumption amount and the output electric quantity of the system respectively;

(b) permeability of renewable energy

The renewable energy permeability is defined as the ratio of the consumption of renewable energy to the consumption of all energy of the system, as shown in equation (6):

in the formula, theta is the permeability of renewable energy; q. q.s _p Energy produced for renewable energy source P in kWh; k is a radical of _i Total energy utilized by the target system in kWh;

(c) comprehensive energy utilization efficiency

The comprehensive energy utilization efficiency refers to the ratio of the total output quantity of the system to the total input quantity, wherein the output energy of the system comprises electricity, cold and heat, and the input quantity comprises the supply, cooling and heating quantities, as shown in the formula (7):

wherein eta is the comprehensive energy utilization efficiency, P _i The ith energy output by the system is in kWh; e _i The ith energy input by the system is in kWh;

(d) utilization rate of Primary Energy (PER)

Primary energy means directly utilizable energy, which means the ratio of the output energy of the system to the amount of primary energy input, as shown in equation (8):

in the formula Q _C ,Q _H ,Q _E Cold, heat, electric energy, Q, output for the system _i The input quantity of primary energy;

thirdly, the environmental benefit indexes comprise three levels of indexes:

the environmental pollution caused by energy consumption is caused by gases discharged by the combustion of fossil resources, SO ₂ For sulfur oxides, and PM _X For dust, NO _X Expressed as nitrogen oxides, CO ₂ Represents carbon dioxide; therefore, PM _X 、SO ₂ 、CO ₂ The NOx emission is a three-level index of the environmental benefit, and the calculation formula is shown as (9):

in the formula E _i Q is the heat and refrigerating capacity output by the system, and P is the output electric quantity; eta _e ,η _h ,η _c The power generation efficiency, the heating efficiency and the refrigeration efficiency are improved; alpha is an emission factor;

fourthly, the environmental benefit indexes comprise three levels of indexes:

the social benefit refers to the influence of project construction and operation on social development and is an indirect benefit; the social benefit indexes are qualitative indexes and comprise compatibility with national policies, social acceptance, space occupation and convenience in maintenance;

2) the index weight calculation method based on combined weighting comprises four parts of index standardization processing, subjective weight calculation, objective weight calculation and combined weight calculation:

firstly, standardizing indexes, wherein the indexes are divided into qualitative indexes and quantitative indexes, the indexes have different magnitude and dimension and cannot be used for mutual comparison, data needs to be standardized, and the data is reformed into same dimension values which can be directly summed;

(a) standardization of qualitative index

The qualitative indexes are divided into five grades from A to E, wherein A represents excellent, C represents medium, E represents poor, the rating score is 0 to 1, and the grades are divided into 5 parts; then, the value of the qualitative index is processed by a quantitative index normalization method, as shown in equations (10) and (11);

(b) standardization of quantitative indicators

Quantitative indicators can be expressed as numerical values, but since dimensions and units of data sequences are different from each other, a standardization process is required, and evaluation indicators are classified into two types: "Large expected response" and "Small expected response";

for a "large expected response", the target value of the original sequence has the feature of "maximum best", and the original sequence is normalized, as shown in equation (10):

for a "small expected response", the target value of the original sequence has the feature of "minimum best", the original sequence is normalized, as shown in equation (11):

where i is 1,2, …, n, j is 1,2, …, m, m is the number of options, n is the number of desired indices; e.g. of the type _ij Is the ith index value of the jth option;

is the minimum of the ith index among all j options;

is the maximum value of the ith index in all j options; b _ij Is e _ij A normalized value of (d);

a subjective weight calculation method based on TFN-AHP, wherein an Analytic Hierarchy Process (AHP) is a decision analysis method for determining subjective weight; the Triangular Fuzzy Number (TFN) is a numerical interval of the form (x, y, z), a in the decision matrix in TFN _ij The importance scale value of (a) is an integer of 1-9; comparing two elements, 1 means that the two have the same importance degree; 9 represents that the former is extremely important than the latter, the number is 2-8 represents an intermediate value, and the steps of the TFN-AHP method are as follows:

(a) constructing fuzzy judgment matrix

Comparing the ith index with the jth index, and using a as the relative importance _ij Meaning that if n elements are compared, a decision matrix A is formed, where a _ij ＝1/a _ji ，a _ii When 1, the decision matrix a is defined as shown in equation (12):

middle triangular fuzzy number a _ij I.e. the average relative importance value, expressed as (x) _ij ，y _ij ，z _ij )，y _ij Greater than x _ij But less than z _ij (ii) a Accordingly a _ji Is a _ij Value of the symmetrical position, by a _ij Transformed and expressed as (1/z) _ij ，1/y _ij ，1/x _ij ). For a _ij If there are H experts to evaluate, a _ij Expressed as shown in equation (13):

wherein the content of the first and second substances,

is the triangular fuzzy number of the H-th expert, H is 1,2, …, H;

(b) calculating the weight of the index

Calculating fuzzy weight G of ith element _i As shown in equation (14):

G ₁ greater than G ₂ Degree of likelihood V, as shown in equation (15); the probability degree that one TFN is greater than k TFNs is shown in equation (16):

in the formula

Is G ₁ Is a membership function of (l) is G ₁ And G ₂ The ordinate of the highest intersection point between them;

V(G≥G ₁ ,G ₂ …,G _k )＝minV(G≥G _i ),i＝1,2,…,k (16)

finally, based on the above formula, obtain the non-fuzzy weight d (C) _i ) As shown in formula (17):

in the formula C _i Representing the ith secondary index or tertiary index; i and k are integers between 1 and n, but are not equal to each other;

standardizing the non-fuzzy weight to obtain C _i Standard of (2)Change the weight w _i As shown in equation (18):

where i is 1,2, …, n, and w is the secondary index _i Is calculated under the target layer; and for the three-level index, w _i Calculating under a corresponding secondary standard;

(c) calculating the geometric mean of the elements

The fuzzy judgment matrix A is defuzzified to obtain a non-fuzzy judgment matrix A', as shown in formula (19):

after normalizing the row vectors of the matrix a', the geometric mean value ω of each row element is obtained as shown in equations (20) and (21):

in the formula

Is a weight vector, ω is a geometric mean;

(d) matrix consistency check

Defining the consistency index as CI, as shown in equations (22) and (23):

where n is the order of the matrix, λ _max Is the maximum characteristic root, A' is a non-fuzzy judgment matrix;

defining a random checking coefficient as CR, as shown in formula (24); when CR is less than or equal to 0.1, A meets the consistency test; otherwise, modifying the parameter of A and verifying again;

CR＝CI/RI (24)

in the formula, RI is an average random consistency index, the value range of the order n is 1-9, and the value of RI corresponding to n is 0, 0, 0.58, 0.9, 1.12, 1.24, 1.32, 1.41 and 1.45 from small to large;

and thirdly, an objective weight calculation method based on an entropy weight method is an objective method based on data evaluation, and index weight is determined according to the information entropy provided by the index. If the entropy value is smaller, the index variation degree is larger, the weight is larger, otherwise, the weight is smaller;

(a) determining an evaluation coefficient matrix B as shown in equation (25):

(b) normalized evaluation coefficient matrix

Normalizing the evaluation coefficient matrix B to obtain a matrix P, as shown in formula (26):

(c) calculating the information entropy e of the jth index _j As shown in equations (27) and (28):

(d) calculating the difference coefficient g of the j index _i As shown in equation (29):

g _j ＝1-e _j (29)

(e) the weight ω is calculated as shown in equation (30):

calculating the index combination weight, and determining the index combination weight by using a linear weighting method according to the result of the subjective weight and the objective weight obtained previously; setting the subjective index weight w _i ＝[w ₁ ,w ₂ ,w ₃ ,…,w _n ]Objective index weight w _j ＝[w ₁ ,w ₂ ,w ₃ ,…,w _n ]Then combining weights w _y As shown in formula (31):

w _y ＝λw _i ·(1-λ)w _j (i＝1,2,…,n；j＝1,2,…,n) (31)

in order to reduce the subjectivity of the weight and make the combined weight more reasonable, the subjective and objective weight proportion is set as 1: 1, namely lambda is 0.5, and the parameter lambda meets the condition that lambda is more than or equal to 0 and less than or equal to 1;

3) the comprehensive energy system benefit evaluation based on the fuzzy comprehensive evaluation method solves the problem of difficult processing of fuzzy factors by defining the grade of membership degree, and ensures that the result is more real and objective by eliminating the selective deviation caused by the subjective experience of people;

firstly, determining an index set U and an evaluation set V

Dividing the constructed index set U according to the index system hierarchy, namely: u ═ U ₁ ,U ₂ ,…,U _i In which secondary index set

i＝1,2,…,N,U _i In is k _i An index; determining an evaluation set V-V according to task requirements ₁ ,v ₂ ,…,v _n N is the number of evaluation grades;

② constructing fuzzy judgment matrix

H experts are provided to participate in evaluation, and a secondary index set U is set _i Scoring by rating of evaluation set V, V ═ V ₁ ,v ₂ ,v ₃ ,v ₄ ,v ₅ Get statistics of each index { good, general, poor, bad }

Frequency belonging to each evaluation set is

Solving the membership value corresponding to each index to construct U _i Fuzzy decision matrix of

Third, primary fuzzy comprehensive evaluation

Each set of secondary indexes U _i All have fuzzy judgment matrixes F corresponding to the fuzzy judgment matrixes _i Determining an index set U by a weight calculation method _i Weight w of _i Then, a single-level evaluation is performed, as shown in equation (32):

B _i ＝w _i F _i (32)

fourthly, multi-stage fuzzy comprehensive evaluation

The weight subset corresponding to the index set U is w ═ w ₁ ,w ₂ ,…,w _i And f, judging the fuzzy judgment vector B of the U, as shown in the formula (33):

calculating comprehensive evaluation value

Calculating a comprehensive evaluation value P according to the fuzzy evaluation vector B and the evaluation set V, as shown in formula (34):

P＝VB (34)。

the beneficial effects of the community comprehensive energy system benefit evaluation method based on fuzzy evaluation are as follows:

firstly, based on the physical architecture of the comprehensive energy system and factors influencing the benefits of the comprehensive energy system, an evaluation index system comprising four dimensions of economy, technology, environment and society is constructed, and the comprehensive benefits of the system are comprehensively and reasonably reflected. Secondly, the subjective weight of the index is determined by adopting a TFN-AHP method, the objective weight is determined by an entropy weight method, and the combined weight is obtained by combining the subjective weight and the objective weight, so that the influence of subjective factors on the weight is effectively avoided, and the complexity of calculation is reduced. And finally, a fuzzy comprehensive evaluation method is used for evaluating the benefit of the community comprehensive energy system, so that the problem of evaluation index ambiguity can be effectively solved. The method is scientific and reasonable, and has strong practicability.

Drawings

FIG. 1 is a block diagram of a benefit evaluation method of a community comprehensive energy system based on fuzzy evaluation according to the invention;

FIG. 2 is a flowchart of index weight calculation;

FIG. 3 shows evaluation index combination weights;

FIG. 4 is a graph comparing the results of the index weights;

FIG. 5 is a flow chart of the evaluation of the fuzzy comprehensive evaluation method;

fig. 6 is a diagram of the comprehensive benefit evaluation result of the system scheme.

Detailed Description

The method for evaluating the benefit of the community comprehensive energy system based on fuzzy evaluation is described in detail below by using the accompanying drawings and embodiments.

The invention discloses a community comprehensive energy system benefit evaluation method based on fuzzy evaluation, which comprises the following steps of:

referring to fig. 1, 1) constructing a community comprehensive energy system benefit evaluation index system, wherein the benefit index system comprises 3 layers; the upper layer is a target layer, represents the comprehensive benefit of the community comprehensive energy system and consists of secondary indexes of four dimensions; the middle layer is a second-level index and comprises two-level indexes with four dimensions of economic benefit, technical benefit, environmental benefit and social benefit, the upper layer target layer and the middle layer second-level index have no specific numerical information, and only have weight values and final evaluation values and are only used for reflecting the conditions of the system in different dimensions; the bottom layer is a plurality of corresponding three-level indexes under each two-level index, and reflects the condition of the system in specific attributes.

The economic benefit indexes in the second-level indexes comprise the following third-level indexes:

(a) initial investment cost

Initial investment cost G _t The purchase fee of each device of the system is calculated by the following formula:

G _t ＝∑H _n G _a (1)

in the formula G _t For initial investment, G _a The unit is the unit price of the equipment and ten thousand yuan; h _n The number of equipment is unit table;

(b) cost of system operation

The system operation cost comprises operation maintenance cost and external energy purchase cost, and is shown as formula (2):

G _S ＝C _U +D _e (2)

in the formula G _S For the total cost, C _U For operating maintenance costs, D _e External energy purchase costs;

(c) service life of equipment

The service life of the equipment refers to the time process from the beginning of use to the elimination of all the machine equipment purchased in the project due to the fact that the technology is over, the unit is year, and the service life of each equipment can be obtained by referring to the information provided by national standards and manufacturers;

(d) period of investment recovery

The investment recovery period T refers to the time required for the project to recover the investment cost through operation, and the calculation formula is shown as (3):

wherein the net present value represents the difference between the net cash inflow and the net cash outflow, and the calculation formula is shown as (4):

wherein CI is the net inflow of cash, CO is the net outflow of cash, i is the discount rate, n is the operation age of project, and t is the operation time.

And the technical benefit indexes in the secondary indexes comprise three indexes:

(a) coefficient of energy conversion efficiency

The operating efficiency of the system energy link is one of the important indexes reflecting the technical level, and because the energy quality coefficients of different energy sources are different, the energy conversion efficiency coefficient can be obtained according to the energy quality coefficients, as shown in formula (5):

wherein eta is energy conversion efficiency; h and lambda are the total amount of the ith energy consumed by the system and the energy quality coefficient of the ith energy respectively; q _C 、Q _h E is the cold consumption amount, the heat consumption amount and the output electric quantity of the system respectively;

(b) permeability of renewable energy

The renewable energy permeability is defined as the ratio of the consumption of renewable energy to the consumption of all energy of the system, as shown in equation (6):

in the formula, theta is the permeability of renewable energy; q. q.s _p Energy produced for renewable energy source P in kWh; k is a radical of _i Total energy utilized by the target system in kWh;

(c) comprehensive energy utilization efficiency

The comprehensive energy utilization efficiency refers to the ratio of the total output quantity of the system to the total input quantity, wherein the output energy of the system comprises electricity, cold and heat, and the input quantity comprises the power supply, cold supply and heat supply, and is shown in the formula (7):

wherein eta is the comprehensive energy utilization efficiency, P _i The ith energy output by the system is in kWh; e _i The ith energy input by the system is in kWh;

(d) utilization rate of Primary Energy (PER)

Primary energy means directly utilizable energy, which means the ratio of the output energy of the system to the amount of primary energy input, as shown in equation (8):

in the formula Q _C ,Q _H ,Q _E Cold, heat, electric energy, Q, output for the system _i Is the input of primary energy.

Third level indexes contained in the environmental benefit indexes in the second level indexes are as follows:

the environmental pollution caused by energy consumption is caused by gases discharged by the combustion of fossil resources, SO ₂ For sulfur oxides, and PM _X For dust, NO _X Expressed as nitrogen oxides, CO ₂ Represents carbon dioxide; therefore, PM _X 、SO ₂ 、CO ₂ The NOx emission is a three-level index of the environmental benefit, and the calculation formula is shown as (9):

in the formula E _i Q is the heat and refrigerating capacity output by the system, and P is the output electric quantity; eta _e ,η _h ,η _c The power generation efficiency, the heating efficiency and the refrigeration efficiency are achieved; alpha is an emission factorAnd (4) adding the active ingredients.

Fourthly, the environmental benefit indexes in the secondary indexes comprise three indexes:

the social benefit refers to the influence of project construction and operation on social development and is an indirect benefit; the social benefit indexes are qualitative indexes and comprise compatibility with national policies, social acceptance, space occupation and convenience in maintenance.

Referring to fig. 2-4, 2) the method for calculating the index weight based on combination weighting comprises four parts of index standardization, subjective weight calculation, objective weight calculation and combination weight calculation:

firstly, performing index standardization, wherein the indexes are divided into qualitative indexes and quantitative indexes, the magnitude and dimension of the indexes are different from each other and cannot be used for mutual comparison, and data needs to be subjected to standardization processing and are reformed into same-dimension numerical values which can be directly summed;

(a) standardization of qualitative index

The qualitative indexes are divided into five grades from A to E, wherein A represents excellent, C represents medium, E represents poor, the rating score is 0 to 1, and the grades are divided into 5 parts; then, the value of the qualitative index is processed by a quantitative index standardization method, as shown in equations (10) and (11);

(b) standardization of quantitative index

Quantitative indicators can be expressed as numerical values, but since dimensions and units of data sequences are different from each other, a standardization process is required, and evaluation indicators are classified into two types: "Large expected response" and "Small expected response";

for a "large expected response", the target value of the original sequence has the feature of "maximum best", and the original sequence is normalized, as shown in equation (10):

for a "small expected response", the target value of the original sequence has the feature of "minimum best", the original sequence is normalized, as shown in equation (11):

where i is 1,2, …, n, j is 1,2, …, m, m is the number of options, n is the number of desired indices; e.g. of a cylinder _ij Is the ith index value of the jth option;

is the minimum of the ith index among all j options;

is the maximum of the ith index among all j options; b _ij Is e _ij Is measured.

A subjective weight calculation method based on TFN-AHP, wherein an Analytic Hierarchy Process (AHP) is a decision analysis method for determining subjective weight; the Triangular Fuzzy Number (TFN) is a numerical interval of the form (x, y, z), a in the decision matrix in TFN _ij The importance scale value of (a) is an integer of 1-9; comparing two elements, 1 means that the two have the same importance degree; 9 represents that the former is extremely important than the latter, the number is 2-8 represents an intermediate value, and the steps of the TFN-AHP method are as follows:

(a) constructing fuzzy judgment matrix

Comparing the ith index with the jth index, and using a as the relative importance _ij Meaning that if n elements are compared, a decision matrix A is formed, where a _ij ＝1/a _ji ，a _ii The decision matrix a is defined as shown in equation (12) when 1:

middle triangular fuzzy number a _ij I.e. the average relative importance value, expressed as (x) _ij ，y _ij ，z _ij )，y _ij Greater than x _ij But less than z _ij (ii) a Accordingly a _ji Is a _ij Value of the symmetrical position, by a _ij Transformed and expressed as (1/z) _ij ，1/y _ij ，1/x _ij ). For a _ij If there are H experts to evaluate, a _ij The expression is shown in formula (13):

wherein the content of the first and second substances,

is the triangular fuzzy number of the H-th expert, H is 1,2, …, H;

(b) calculating the weight of the index

Calculating fuzzy weight G of ith element _i As shown in equation (14):

G ₁ greater than G ₂ The degree of likelihood V as shown in formula (15); the probability degree that one TFN is greater than k TFNs is shown in equation (16):

in the formula

Is G ₁ Is a membership function of (l) is G ₁ And G ₂ The ordinate of the highest intersection point between them;

V(G≥G ₁ ,G ₂ …,G _k )＝minV(G≥G _i ),i＝1,2,…,k (16)

finally, based on the above formula, obtain the non-fuzzy weight d (C) _i ) As shown in formula (17):

in the formula C _i Representing the ith secondary index or the tertiary index; i and k are integers between 1 and n, but are not equal to each other;

standardizing the non-fuzzy weight to obtain C _i Normalized weight w of _i As shown in equation (18):

where i is 1,2, …, n, and w is the secondary index _i Is calculated under the target layer; and for the three-level index, w _i Calculating under a corresponding secondary standard;

(c) calculating the geometric mean of the elements

The fuzzy judgment matrix A is defuzzified to obtain a non-fuzzy judgment matrix A', as shown in formula (19):

after normalizing the row vectors of the matrix a', the geometric mean value ω of each row element is obtained as shown in equations (20) and (21):

in the formula

Is a weight vector, ω is a geometric mean;

(d) matrix consistency check

Defining the consistency index as CI, as shown in equations (22) and (23):

where n is the order of the matrix, λ _max Is the maximum characteristic root, A' is a non-fuzzy judgment matrix;

defining a random checking coefficient as CR, as shown in formula (24); when CR is less than or equal to 0.1, A meets the consistency test; otherwise, modifying the parameter of A and verifying again;

CR＝CI/RI (24)

in the formula, RI is an average random consistency index, the value range of the order n is 1-9, and the value of RI corresponds to the value of n and is 0, 0, 0.58, 0.9, 1.12, 1.24, 1.32, 1.41 and 1.45 from small to large.

And thirdly, an objective weight calculation method based on an entropy weight method is an objective method based on data evaluation, and index weight is determined according to the information entropy provided by the index. If the entropy value is smaller, the index variation degree is larger, the weight is larger, otherwise, the weight is smaller;

(a) determining an evaluation coefficient matrix B as shown in equation (25):

(b) normalized evaluation coefficient matrix

Normalizing the evaluation coefficient matrix B to obtain a matrix P, as shown in formula (26):

(c) calculating the information entropy e of the jth index _j As shown in equations (27) and (2)8) Shown in the figure:

(d) calculating the difference coefficient g of the j index _i As shown in equation (29):

g _j ＝1-e _j (29)

(e) the weight ω is calculated as shown in equation (30):

calculating the index combination weight, and determining the index combination weight by using a linear weighting method according to the result of the subjective weight and the objective weight obtained previously; setting the subjective index weight w _i ＝[w ₁ ,w ₂ ,w ₃ ,…,w _n ]Objective index weight w _j ＝[w ₁ ,w ₂ ,w ₃ ,…,w _n ]Then combining weights w _y As shown in formula (31):

w _y ＝λw _i ·(1-λ)w _j (i＝1,2,…,n；j＝1,2,…,n) (31)

in order to reduce the subjectivity of the weight and make the combined weight more reasonable, the subjective and objective weight proportion is set as 1: 1, i.e. λ is 0.5, the parameter λ satisfies 0 ≦ λ ≦ 1.

Referring to fig. 5 and 6, 3) comprehensive energy system benefit evaluation based on a fuzzy comprehensive evaluation method, by defining the level of membership, solving a difficult-to-handle fuzzy factor, and by eliminating a selective deviation caused by subjective experience of a person, making a result more real and objective;

firstly, determining an index set U and an evaluation set V

The constructed index set U is expressed byDividing according to the index system hierarchy, namely: u ═ U ₁ ,U ₂ ,…,U _i In which secondary index set

i＝1,2,…,N,U _i In is k _i An index; determining an evaluation set V-V according to task requirements ₁ ,v ₂ ,…,v _n N is the number of evaluation grades;

② constructing fuzzy judgment matrix

H experts are provided to participate in evaluation, and a secondary index set U is set _i Scoring by rating of evaluation set V, V ═ V ₁ ,v ₂ ,v ₃ ,v ₄ ,v ₅ Get the statistics of each index { good, better, general, worse, bad })

The frequency belonging to each evaluation set is

Solving the membership value corresponding to each index to construct U _i Fuzzy decision matrix of

Third, primary fuzzy comprehensive evaluation

Each set of secondary indexes U _i All have fuzzy judgment matrixes F corresponding to the fuzzy judgment matrixes _i Determining an index set U by a weight calculation method _i Weight w of _i Then, a single-level evaluation is performed, as shown in equation (32):

B _i ＝w _i F _i (32)

fourthly, multi-stage fuzzy comprehensive evaluation

The weight subset corresponding to the index set U is w ═ w ₁ ,w ₂ ,…,w _i And f, judging the fuzzy judgment vector B of the U, as shown in the formula (33):

calculating comprehensive evaluation value

Calculating a comprehensive evaluation value P according to the fuzzy evaluation vector B and the evaluation set V, as shown in formula (34):

P＝VB (34)。

in conclusion, the community comprehensive energy system benefit evaluation method based on fuzzy evaluation provides a scientific and reasonable solution for benefit evaluation work of the comprehensive energy system. The effectiveness and the feasibility of the community comprehensive energy system benefit evaluation method based on fuzzy evaluation are verified.

The computer program according to the present invention is programmed according to the fields of electrical engineering, automation, informatization and computer processing technology, and is well known to those skilled in the art.

The embodiments of the present invention are provided for further illustration, are not exhaustive, and do not limit the scope of the claims, and those skilled in the art will be able to conceive other substantially equivalent alternatives without inventive step in light of the teachings of the embodiments of the present invention, which are within the scope of the present invention.

Claims (1)

1. A community comprehensive energy system benefit evaluation method based on fuzzy evaluation is characterized by comprising the following steps:

1) constructing a benefit evaluation index system of a community comprehensive energy system, wherein the benefit index system comprises 3 layers; the upper layer is a target layer, represents the comprehensive benefit of the community comprehensive energy system and consists of secondary indexes of four dimensions; the middle layer is a second-level index and comprises two-level indexes with four dimensions of economic benefit, technical benefit, environmental benefit and social benefit, the upper layer target layer and the middle layer second-level index have no specific numerical information, and only have weight values and final evaluation values and are only used for reflecting the conditions of the system in different dimensions; the bottom layer is a plurality of corresponding three-level indexes under each two-level index, and reflects the condition of the system in specific attributes;

the economic benefit indexes comprise three levels of indexes:

(a) initial investment cost

Initial investment cost G _t The purchase fee of each device of the system is calculated by the following formula:

G _t ＝∑H _n G _a (1)

in the formula G _t For initial investment, G _a The unit is the unit price of the equipment and ten thousand yuan; h _n The number of equipment is unit table;

(b) cost of system operation

The system operation cost comprises operation maintenance cost and external energy purchase cost, and is shown as formula (2):

G _S ＝C _U +D _e (2)

in the formula G _S For the total cost, C _U For operating maintenance costs, D _e External energy purchase costs;

(c) service life of equipment

The service life of the equipment refers to the time process from the beginning of use to the elimination of all the machine equipment purchased in the project due to the fact that the technology is over, the unit is year, and the service life of each equipment can be obtained by referring to the information provided by national standards and manufacturers;

(d) period of investment recovery

The investment recovery period T refers to the time required for the project to recover the investment cost through operation, and the calculation formula is shown as (3):

wherein the net present value represents the difference between the net cash inflow and the net cash outflow, and the calculation formula is shown as (4):

wherein CI is the net inflow of cash, CO is the net outflow of cash, i is the discount rate, n is the operation age of the project, and t is the operation time;

the technical benefit indexes comprise three levels of indexes:

(a) coefficient of energy conversion efficiency

The operating efficiency of the system energy link is one of the important indexes reflecting the technical level, and because the energy quality coefficients of different energy sources are different, the energy conversion efficiency coefficient can be obtained according to the energy quality coefficients, as shown in formula (5):

wherein eta is energy conversion efficiency; h and lambda are respectively the total amount of the ith energy consumed by the system and the energy quality coefficient of the ith energy; q _C 、Q _h E is the cold consumption amount, the heat consumption amount and the output electric quantity of the system respectively;

(b) permeability of renewable energy

The renewable energy permeability is defined as the ratio of the consumption of renewable energy to the consumption of all energy of the system, as shown in equation (6):

in the formula, theta is the permeability of renewable energy; q. q.s _p Energy produced for renewable energy source P in kWh; k is a radical of _i Total energy utilized by the target system in kWh;

(c) comprehensive energy utilization efficiency

The comprehensive energy utilization efficiency refers to the ratio of the total output quantity of the system to the total input quantity, wherein the output energy of the system comprises electricity, cold and heat, and the input quantity comprises the supply, cooling and heating quantities, as shown in the formula (7):

wherein eta is the comprehensive energy utilization efficiency, P _i The ith energy output by the system is in kWh; e _i The ith energy input by the system is in kWh;

(d) utilization rate of Primary Energy (PER)

Primary energy means directly utilizable energy, which means the ratio of the output energy of the system to the amount of primary energy input, as shown in equation (8):

in the formula Q _C ,Q _H ,Q _E Cold, heat, electric energy, Q, output for the system _i The input quantity of primary energy;

thirdly, the environmental benefit indexes comprise three levels of indexes:

the environmental pollution caused by energy consumption is caused by gases discharged by the combustion of fossil resources, SO ₂ For sulfur oxides, and PM _X For dust, NO _X Expressed as nitrogen oxides, CO ₂ Represents carbon dioxide; therefore, PM _X 、SO ₂ 、CO ₂ The NOx emission is a three-level index of the environmental benefit, and the calculation formula is shown as (9):

in the formula E _i Q is the heat and refrigeration output by the system, and P is the output electric quantity; eta _e ,η _h ,η _c The power generation efficiency, the heating efficiency and the refrigeration efficiency are achieved; alpha is an emission factor;

and fourthly, the environmental benefit indexes comprise three levels of indexes:

the social benefit refers to the influence of project construction and operation on social development and is an indirect benefit; the social benefit indexes are qualitative indexes and comprise compatibility with national policies, social acceptance, space occupation and convenience in maintenance;

2) the index weight calculation method based on combined weighting comprises four parts of index standardization processing, subjective weight calculation, objective weight calculation and combined weight calculation:

firstly, standardizing indexes, wherein the indexes are divided into qualitative indexes and quantitative indexes, the indexes have different magnitude and dimension and cannot be used for mutual comparison, data needs to be standardized, and the data is reformed into same dimension values which can be directly summed;

(a) standardization of qualitative index

The qualitative indexes are divided into five grades from A to E, wherein A represents excellent, C represents medium, E represents poor, the rating score is 0 to 1, and the grades are divided into 5 parts; then, the value of the qualitative index is processed by a quantitative index normalization method, as shown in equations (10) and (11);

(b) standardization of quantitative index

Quantitative indicators can be expressed as numerical values, but since dimensions and units of data sequences are different from each other, a standardization process is required, and evaluation indicators are classified into two types: "Large expected response" and "Small expected response";

for a "large expected response", the target value of the original sequence has the feature of "maximum best", and the original sequence is normalized, as shown in equation (10):

for a "small expected response", the target value of the raw sequence has the characteristic of "minimum best", and the raw sequence is normalized as shown in equation (11):

where i is 1,2, …, n, j is 1,2, …, m, m is the number of options, n is the number of desired indices; e.g. of a cylinder _ij Is the firstThe ith index value of j options;

is the minimum of the ith index among all j options;

is the maximum value of the ith index in all j options; b is a mixture of _ij Is e _ij A normalized value of (a);

a subjective weight calculation method based on TFN-AHP, wherein an Analytic Hierarchy Process (AHP) is a decision analysis method for determining subjective weight; the Triangular Fuzzy Number (TFN) is a numerical interval of the form (x, y, z), a in the decision matrix in TFN _ij The importance scale value of (a) is an integer of 1-9; comparing two elements, 1 means that the two have the same importance degree; 9 represents that the former is extremely important than the latter, the number is 2-8 represents an intermediate value, and the steps of the TFN-AHP method are as follows:

(a) constructing fuzzy judgment matrix

The ith index and the jth index are compared, and the relative importance degree is expressed as a _ij Meaning that if n elements are compared, a decision matrix A is formed, where a _ij ＝1/a _ji ，a _ii The decision matrix a is defined as shown in equation (12) when 1:

middle triangular fuzzy number a _ij I.e. the average relative importance value, expressed as (x) _ij ，y _ij ，z _ij )，y _ij Greater than x _ij But less than z _ij (ii) a Accordingly a _ji Is a _ij Value of the symmetrical position, by a _ij Transformed and expressed as (1/z) _ij ，1/y _ij ，1/x _ij ). For a _ij If there are H experts to evaluate, a _ij The expression is shown in formula (13):

wherein the content of the first and second substances,

is the triangular fuzzy number of the H-th expert, H is 1,2, …, H;

(b) calculating the weight of the index

Calculating fuzzy weight G of ith element _i As shown in equation (14):

G ₁ greater than G ₂ The degree of likelihood V as shown in formula (15); the degree of probability that one TFN is greater than k TFNs is shown in equation (16):

in the formula

Is G ₁ Is a membership function of (l) is G ₁ And G ₂ The ordinate of the highest intersection point between them;

V(G≥G ₁ ,G ₂ …,G _k )＝minV(G≥G _i ),i＝1,2,…,k (16)

finally, based on the above formula, obtain the non-fuzzy weight d (C) _i ) As shown in formula (17):

in the formula C _i Representing the ith secondary index or the tertiary index; i and k are 1 ton, but not equal to each other;

standardizing the non-fuzzy weight to obtain C _i Normalized weight w of _i As shown in equation (18):

where i is 1,2, …, n, and w is the secondary index _i Is calculated under the target layer; and for the three-level index, w _i Calculating under a corresponding secondary standard;

(c) calculating the geometric mean of the elements

The fuzzy judgment matrix A is defuzzified to obtain a non-fuzzy judgment matrix A', as shown in formula (19):

after normalizing the row vectors of the matrix a', the geometric mean value ω of each row element is obtained as shown in equations (20) and (21):

in the formula

Is a weight vector, ω is a geometric mean;

(d) matrix consistency check

Defining the consistency index as CI, as shown in equations (22) and (23):

where n is the order of the matrix, λ _max Is the maximum characteristic root, A' is a non-fuzzy judgment matrix;

defining a random checking coefficient as CR, as shown in formula (24); when CR is less than or equal to 0.1, A meets the consistency test; otherwise, modifying the parameter of A and verifying again;

CR＝CI/RI (24)

in the formula, RI is an average random consistency index, the value range of the order n is 1-9, and the value of RI corresponds to the value of n and is 0, 0, 0.58, 0.9, 1.12, 1.24, 1.32, 1.41 and 1.45 from small to large;

and thirdly, an objective weight calculation method based on an entropy weight method is an objective method based on data evaluation, and index weight is determined according to the information entropy provided by the index. If the entropy value is smaller, the index variation degree is larger, the weight is larger, otherwise, the weight is smaller;

(a) determining an evaluation coefficient matrix B as shown in equation (25):

(b) normalized evaluation coefficient matrix

Normalizing the evaluation coefficient matrix B to obtain a matrix P, as shown in formula (26):

(c) calculating the information entropy e of the jth index _j As shown in equations (27) and (28):

(d) calculating the difference coefficient g of the j index _i As shown in equation (29):

g _j ＝1-e _j (29)

(e) the weight ω is calculated as shown in equation (30):

calculating the index combination weight, and determining the index combination weight by using a linear weighting method according to the result of the subjective weight and the objective weight obtained previously; setting the subjective index weight w _i ＝[w ₁ ,w ₂ ,w ₃ ,…,w _n ]Objective index weight w _j ＝[w ₁ ,w ₂ ,w ₃ ,…,w _n ]Then combining weights w _y As shown in formula (31):

w _y ＝λw _i ·(1-λ)w _j (i＝1,2,…,n；j＝1,2,…,n) (31)

in order to reduce the subjectivity of the weight and make the combined weight more reasonable, the subjective and objective weight proportion is set as 1: 1, namely lambda is 0.5, and the parameter lambda satisfies 0 ≦ lambda ≦ 1;

3) the comprehensive energy system benefit evaluation based on the fuzzy comprehensive evaluation method solves the problem of difficult processing of fuzzy factors by defining the grade of membership degree, and ensures that the result is more real and objective by eliminating the selective deviation caused by the subjective experience of people;

firstly, determining an index set U and an evaluation set V

Dividing the constructed index set U according to the index system hierarchy, namely: u ═ U ₁ ,U ₂ ,…,U _i In which secondary index set

U _i In is k _i An index; determining an evaluation set V-V according to task requirements ₁ ,v ₂ ,…,v _n N is the number of evaluation grades;

② constructing fuzzy judgment matrix

H experts are provided to participate in evaluation, and a secondary index set U is set _i Scoring by rating of evaluation set V, V ═ V ₁ ,v ₂ ,v ₃ ,v ₄ ,v ₅ Get the statistics of each index { good, better, general, worse, bad })

The frequency belonging to each evaluation set is

Solving the membership value corresponding to each index to construct U _i Fuzzy decision matrix of

Third, primary fuzzy comprehensive evaluation

Each set of secondary indexes U _i All have fuzzy judgment matrixes F corresponding to the fuzzy judgment matrixes _i Determining an index set U by a weight calculation method _i Weight w of _i Then, a single-level evaluation is performed as shown in equation (32):

B _i ＝w _i F _i (32)

four multi-stage fuzzy comprehensive evaluation

The weight subset corresponding to the index set U is w ═ w ₁ ,w ₂ ,…,w _i And f, judging the fuzzy judgment vector B of the U, as shown in the formula (33):

calculating comprehensive evaluation value

Calculating a comprehensive evaluation value P according to the fuzzy evaluation vector B and the evaluation set V, as shown in formula (34):

P＝VB (34)。

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