CN109615262A - An evaluation method of energy internet development index based on fuzzy analytic hierarchy process - Google Patents

Abstract

The invention discloses a method for evaluating the development index of energy internet based on fuzzy hierarchy analysis. The energy Internet development index evaluation system is constructed from the perspective, and the energy Internet development index evaluation index set is established; then, the fuzzy analytic hierarchy process is used to establish the development index evaluation index weighting optimization model; then, the particle swarm optimization algorithm is used to solve the evaluation index weighting optimization model , the weight of the evaluation index is given; finally, according to the score of each index data and the index weight, the evaluation result of the energy Internet development index is calculated; the energy Internet development index evaluation index adopted by the method of the present invention is comprehensive, the evaluation method is easy to understand, and has strong The operability ensures the rationality of the evaluation of the Energy Internet Development Index.

Description

An evaluation method of energy internet development index based on fuzzy analytic hierarchy process

technical field

The invention relates to the technical field of energy internet, in particular to an energy internet development index evaluation method based on fuzzy hierarchy analysis.

Background technique

In recent years, the Energy Internet is promoting profound changes in energy production and utilization, driving the rapid transformation and upgrading of energy and economy. Energy Internet is a complex system deeply coupled by energy flow and information flow. It comprehensively utilizes clean energy such as wind energy and solar energy, as well as resources such as electric vehicles and energy storage, and promotes large-scale and efficient consumption of clean energy through the interconnection of multiple energy flows. An effective way to reduce energy consumption and improve energy efficiency. At present, my country already has the conditions for the construction and production of the energy Internet, but there is still a lack of simple and effective evaluation tools and methods for the development of the energy Internet.

With the development of energy Internet technology, it is urgent to build an energy Internet development index evaluation system from four perspectives: clean energy supply, clean energy consumption and electric energy substitution, energy Internet social and economic benefits, and energy Internet industry development. The level represents the real level of the development of the energy Internet; using the decision-making evaluation theory and method, the evaluation method of the energy Internet development index is given. The traditional analytic hierarchy process cannot describe the vague understanding of the importance of evaluation indicators, and it is difficult to provide reference for policy formulation, planning and construction, and investment consultation of energy Internet.

SUMMARY OF THE INVENTION

In view of the above problems, the present invention provides a method for evaluating the development index of the energy Internet based on the fuzzy analysis hierarchy process, which is used to comprehensively evaluate the development status of the energy Internet and provide a reference for the construction of the energy Internet. The technical solution is as follows:

An energy Internet development index evaluation method based on fuzzy AHP, comprising the following steps:

Step 1: Construct the energy Internet development index evaluation index set, collect the data of each index and give its score;

Step 2: Use the fuzzy analytic hierarchy process to establish the development index evaluation index weighting optimization model;

Step 3: Use the particle swarm optimization algorithm to solve the evaluation index weighting optimization model, and give the weight of the evaluation index;

Step 4: Calculate the evaluation result of the Energy Internet Development Index according to the score of each indicator data and the indicator weight.

According to the development characteristics of the Energy Internet, the energy Internet development index evaluation system is constructed from four perspectives: clean energy supply, clean energy consumption and electric energy substitution, energy Internet social and economic benefits, and energy Internet industry development (ie: clean energy supply, clean energy Energy consumption and electric energy substitution, energy Internet social and economic benefits, and energy Internet industry development are the first-level evaluation indicators). The second-level evaluation indicators included in each first-level evaluation index are:

a. Clean energy supply

1) Installed capacity of clean energy power generation. This indicator is the sum of the rated power of wind power, photovoltaic power and other clean energy generating units;

2) Clean heating rate. This indicator represents the proportion of heating by coal-to-electricity, coal-to-gas, geothermal and other clean methods;

3) Abandoned wind rate. This indicator reflects the number of wind turbines that stop generating electricity due to insufficient electricity demand or insufficient grid capacity;

4) Light rejection rate. This indicator reflects the number of photovoltaics that stop generating electricity due to insufficient electricity demand or insufficient grid capacity;

b. Clean energy consumption and electric energy substitution

1) The proportion of clean energy consumption. This indicator reflects the proportion of clean energy consumption in total energy consumption;

2) Clean energy delivery capacity. This indicator reflects the level of energy delivered by the clean energy power generation base to the load center;

3) The proportion of electric vehicle ownership. This indicator represents the proportion of vehicles powered by on-board power supply in all vehicles;

4) Energy storage capacity. This indicator represents the ability of the energy storage system to store energy in the area;

c. Indicators related to growth ability

1) The duration of the power outage. This indicator refers to the total power outage time caused by faults or maintenance in the area;

2) Carbon dioxide emissions have decreased. This indicator reflects the reduction of carbon dioxide emissions due to the development of the Energy Internet;

3) Return on energy investment. This indicator reflects the ratio of the total annual net income of the Energy Internet to the total investment;

d. Indicators related to social benefits

1) The degree of rationalization of the industrial structure. This indicator depicts the degree of conformity between the energy Internet industry structure and social and economic development;

2) The number of registered energy companies. This indicator reflects the number of energy-related companies spawned during the development of the Energy Internet;

3) The penetration rate of smart energy equipment. This indicator represents the usage of smart devices or instruments in the Energy Internet.

On the basis of constructing the evaluation index set of the energy Internet development index, the index data of the evaluation area is collected, and the percentage value is used to score each index data, and the energy Internet development model to be evaluated is recorded as E _o (o=1,2 ,…,O), where the p-th evaluation indicator is C _p (p=1,2,…,P), where O is the total number of energy Internet development models, P is the total number of evaluation indicators, and x _op represents the o-th energy The value of the p-th evaluation index of the Internet development model; s _op is the score of the index value x _op , s _op ∈ [0,100].

Further, step 2 is specifically:

(1) Express the uncertain comparison judgment as a triangular fuzzy number:

Using fuzzy set theory, the uncertain comparison judgment is expressed as a triangular fuzzy number to represent the relative importance of fuzzy. In a given universe U, for any χ∈U, a triangular fuzzy set has a triangular fuzzy membership Correspondingly, its expression is as follows:

In the formula, l, m, u represent the minimum possible value, the most likely value and the maximum possible value describing the fuzzy event, respectively, Represents a fuzzy number, denoted as (l,m,u);

(2) Establish a fuzzy analytic hierarchy process model:

a. Constructing a decision-making hierarchy: Similar to the traditional AHP, the first step is to decompose the decision-making problem into a hierarchical structure, that is, the first-level evaluation index layer and the second-level evaluation index layer in step 1;

b. Generating a pairwise fuzzy comparison matrix: for a priority problem with n elements, the first-level index n=4; the second-level index n=3 or 4, where the pairwise comparison judgment is determined by the fuzzy triangular number Representation, on this basis, construct the regular fuzzy reciprocal comparison matrix:

c. Consistency check and priority derivation: This step checks the consistency and derives the priority according to the pairwise comparison matrix, if then the regular fuzzy comparison matrix is consistent, where i,j,k=1,2,...,n, means fuzzy multiplication, ≈ means fuzzy equals; once the matrices are compared pairwise Pass the consistency check, that is, use the traditional AHP method to calculate the fuzzy priority Then, use the pairwise comparison matrix to obtain the local priority weight vector (w ₁ ,w ₂ ,...,w _n ) ^T ;

d. Aggregation of global priorities, that is, the determination of the final weight value: using the weighted sum method, the local priority weights obtained at different levels of the decision-making hierarchy are summarized into a comprehensive global priority, that is, the final weight value (W ₁ , W _{2 )} ,…,W _p ,…,W _P ) ^T ;

(3) Establish a fuzzy optimization model:

The elements of the judgment matrix are composed of fuzzy triangular numbers represented by pairwise comparison ratios where i,j=1,2,...,n; furthermore, assuming that when i≠j l _ij < m _ij <u _ij , if i=j, then Therefore, the matrices are compared pairwise by regular fuzzy numbers The derived weight value vector (w ₁ ,w ₂ ,…,w _n ) ^T must satisfy the fuzzy inequalities:

In the formula, w _i > 0, w _j > 0, i≠j, Indicates fuzzy less than or equal to;

In order to measure the satisfaction of different ratios to the above bilateral inequality, the new membership function is defined as:

In the formula, i≠j, the value of μ _ij ( _wi /w _j ) can be greater than 1, and decreases linearly in the interval (0,m _ij ] and increases linearly in the interval [m _ij ,∞); μ _ij The smaller the value of ( _wi /w _j ), the more acceptable the value of w _i /w _j is;

In order to determine the weight value vector (w ₁ ,w ₂ ,...,w _n ) ^T , all the exact ratios of w _i /w _j should satisfy n(n-1)/2 fuzzy comparison judgments, that is, w _i /w _j should Satisfy: in, Thus, the minimization model of μ _ij ( _wi /w _j ) can be used to solve the weight value vector (w ₁ ,w ₂ ,…,w _n ) ^T as follows:

The above formula needs to satisfy:

In the formula, i≠j, δ is the Heaviside function:

Further, step 3 is specifically:

The minimization model in step 2 is a constrained nonlinear optimization model. Let χ _i = μ _ij ( _wi /w _j ), i,j=1,2,...,n, then minJ(w ₁ ,w ₂ , ...,w _n ) The optimization model is in the form of

Therefore, the particle swarm optimization algorithm can be applied to solve the weight value vector (w ₁ ,w ₂ ,...,w _n ) ^T . First, the minJ(w ₁ ,w ₂ ,...,w _n ) optimization problem is described as:

Then, the following steps are used to solve:

a) Set the control parameters and the number of iterations t=1;

b) initialize the position χ _i and velocity vi of particle _i ;

c) update the position p _i of each particle;

d) Evaluate the fitness function f(χ ₁ ,χ ₂ ,...,χ _n ) of each particle;

e) Update the individual best position p _id (t) and the group best position p _gd (t) of each particle;

f) If f(χ ₁ ,χ ₂ ,...,χ _n )<p _gd (t), output the best position (global solution);

g) Otherwise, update the number of iterations, t=t+1, and repeat steps c-f.

Further, step 4 is specifically:

From the weights (W ₁ , W ₂ ,...,W _p ,...,W _P ) ^T of each index and the index value score s _op , the weighted score of the index is obtained:

q _op = W _j s _op (o=1,2,...,O; p=1,2,...,P)

Finally, the index evaluation value of each energy Internet development model is obtained:

The beneficial effects of the present invention are as follows: the present invention comprehensively considers factors such as clean energy supply, clean energy consumption and electric energy replacement, energy Internet social and economic benefits, and energy Internet industry development in the development of energy Internet, and provides an energy Internet development index evaluation method , builds a systematic and comprehensive development index evaluation index set, which can more comprehensively reflect the development characteristics and laws of the energy Internet; at the same time, compared with the traditional analytic hierarchy process, the triangular fuzzy number, fuzzy analytic hierarchy process and particle swarm optimization algorithm are used to give The weight of the evaluation index, the ambiguity of the relative importance of the index that can be better considered, is closer to the cognition of experts, and has the advantage of being easy to understand.

Description of drawings

FIG. 1 is a flow chart of the present invention.

Figure 2 shows the core structure of the Energy Internet Development Index Evaluation System.

Detailed ways

The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

As shown in Figure 1, the present invention proposes an energy Internet development index evaluation method based on optimized fuzzy AHP, and the specific steps are as follows:

Step 1: Construct the evaluation index set of the energy Internet development index, collect the data of each index and give its score

According to the development characteristics of the Energy Internet, the energy Internet development index evaluation system is constructed from four perspectives: clean energy supply, clean energy consumption and electric energy substitution, energy Internet social and economic benefits, and energy Internet industry development (ie: clean energy supply, clean energy Energy consumption and electric energy substitution, energy Internet social and economic benefits, and energy Internet industry development are the first-level evaluation indicators). The second-level evaluation indicators included in each first-level evaluation index are:

a. Clean energy supply

1) Installed capacity of clean energy power generation. This indicator is the sum of the rated power of wind power, photovoltaic power and other clean energy generating units;

2) Clean heating rate. This indicator represents the proportion of heating by coal-to-electricity, coal-to-gas, geothermal and other clean methods;

3) Abandoned wind rate. This indicator reflects the number of wind turbines that stop generating electricity due to insufficient electricity demand or insufficient grid capacity;

4) Light rejection rate. This indicator reflects the number of photovoltaics that stop generating electricity due to insufficient electricity demand or insufficient grid capacity;

b. Clean energy consumption and electric energy substitution

1) The proportion of clean energy consumption. This indicator reflects the proportion of clean energy consumption in total energy consumption;

2) Clean energy delivery capacity. This indicator reflects the level of energy delivered by the clean energy power generation base to the load center;

3) The proportion of electric vehicle ownership. This indicator represents the proportion of vehicles powered by on-board power supply in all vehicles;

4) Energy storage capacity. This indicator represents the ability of the energy storage system to store energy in the area;

c. Indicators related to growth ability

1) The duration of the power outage. This indicator refers to the total power outage time caused by faults or maintenance in the area;

2) Carbon dioxide emissions have decreased. This indicator reflects the reduction of carbon dioxide emissions due to the development of the Energy Internet;

3) Return on energy investment. This indicator reflects the ratio of the total annual net income of the Energy Internet to the total investment;

d. Indicators related to social benefits

1) The degree of rationalization of the industrial structure. This indicator depicts the degree of conformity between the energy Internet industry structure and social and economic development;

2) The number of registered energy companies. This indicator reflects the number of energy-related companies spawned during the development of the Energy Internet;

3) The penetration rate of smart energy equipment. This indicator represents the usage of smart devices or instruments in the Energy Internet.

On the basis of constructing the evaluation index set of the energy Internet development index, the index data of the evaluation area is collected, and the percentage value is used to score each index data, and the energy Internet development model to be evaluated is recorded as E _o (o=1,2 ,…,O), where the p-th evaluation indicator is C _p (p=1,2,…,P), where O is the total number of energy Internet development models, P is the total number of evaluation indicators, and x _op represents the o-th energy The value of the p-th evaluation index of the Internet development model; s _op is the score of the index value x _op , s _op ∈ [0,100].

Step 2: Use Fuzzy Analytic Hierarchy Process to establish a development index evaluation index weighting optimization model

2.1 Express the uncertain comparison judgment as a triangular fuzzy number:

Using fuzzy set theory, the uncertain comparison judgment is expressed as a triangular fuzzy number to represent the relative importance of fuzzy. In a given universe U, for any χ∈U, a triangular fuzzy set has a triangular fuzzy membership Correspondingly, its expression is as follows:

In the formula, l, m, u represent the minimum possible value, the most likely value and the maximum possible value describing the fuzzy event, respectively, Represents a fuzzy number, denoted as (l,m,u);

2.2 Establish a fuzzy AHP model:

a. Constructing a decision-making hierarchy: Similar to the traditional AHP, the first step is to decompose the decision-making problem into a hierarchical structure, that is, the first-level evaluation index layer and the second-level evaluation index layer in step 1;

b. Generating a pairwise fuzzy comparison matrix: for a priority problem with n elements, the first-level index n=4; the second-level index n=3 or 4, where the pairwise comparison judgment is determined by the fuzzy triangular number Representation, on this basis, construct the regular fuzzy reciprocal comparison matrix:

c. Consistency check and priority derivation: This step checks the consistency and derives the priority according to the pairwise comparison matrix, if then the regular fuzzy comparison matrix is consistent, where i,j,k=1,2,...,n, means fuzzy multiplication, ≈ means fuzzy equals; once the matrices are compared pairwise Pass the consistency check, that is, use the traditional AHP method to calculate the fuzzy priority Then, use the pairwise comparison matrix to obtain the local priority weight vector (w ₁ ,w ₂ ,...,w _n ) ^T ;

d. Aggregation of global priorities, that is, the determination of the final weight value: using the weighted sum method, the local priority weights obtained at different levels of the decision-making hierarchy are summarized into a comprehensive global priority, that is, the final weight value (W ₁ , W _{2 )} ,…,W _p ,…,W _P ) ^T ;

2.3 Establish a fuzzy optimization model:

The elements of the judgment matrix are composed of fuzzy triangular numbers represented by pairwise comparison ratios where i,j=1,2,...,n; furthermore, assuming that when i≠j l _ij < m _ij <u _ij , if i=j, then Therefore, the matrices are compared pairwise by regular fuzzy numbers The derived weight value vector (w ₁ ,w ₂ ,…,w _n ) ^T must satisfy the fuzzy inequalities:

In the formula, w _i > 0, w _j > 0, i≠j, Indicates fuzzy less than or equal to;

In order to measure the satisfaction of different ratios to the above bilateral inequality, the new membership function is defined as:

In the formula, i≠j, the value of μ _ij ( _wi /w _j ) can be greater than 1, and decreases linearly in the interval (0,m _ij ] and increases linearly in the interval [m _ij ,∞); μ _ij The smaller the value of ( _wi /w _j ), the more acceptable the value of w _i /w _j is;

In order to determine the weight value vector (w ₁ ,w ₂ ,...,w _n ) ^T , all the exact ratios of w _i /w _j should satisfy n(n-1)/2 fuzzy comparison judgments, that is, w _i /w _j should Satisfy: in, Thus, the minimization model of μ _ij ( _wi /w _j ) can be used to solve the weight value vector (w ₁ ,w ₂ ,…,w _n ) ^T as follows:

The above formula needs to satisfy:

In the formula, i≠j, δ is the Heaviside function:

Step 3: Use the particle swarm optimization algorithm to solve the evaluation index weighting optimization model, and give the weight of the evaluation index

The minimization model in step 2 is a constrained nonlinear optimization model. Let χ _i = μ _ij ( _wi /w _j ), i,j=1,2,...,n, then minJ(w ₁ ,w ₂ , ...,w _n ) The optimization model is in the form of

Therefore, the particle swarm optimization algorithm can be applied to solve the weight value vector (w ₁ ,w ₂ ,...,w _n ) ^T . First, the minJ(w ₁ ,w ₂ ,...,w _n ) optimization problem is described as:

Then, the following steps are used to solve:

h) Set the control parameters and the number of iterations t=1;

i) initialize the position χ _i and velocity vi of particle _i ;

j) update the position p _i of each particle;

k) evaluating the fitness function f(χ ₁ ,χ ₂ ,...,χ _n ) of each particle;

l) Update the individual best position p _id (t) and the group best position p _gd (t) of each particle;

m) If f(χ ₁ ,χ ₂ ,...,χ _n )<p _gd (t), output the best position (global solution);

n) Otherwise, update the number of iterations, t=t+1, and repeat steps c～f.

Step 4: Calculate the evaluation results of the Energy Internet Development Index according to the scores and weights of each indicator data

From the weights (W ₁ , W ₂ ,...,W _p ,...,W _P ) ^T of each index and the index value score s _op , the weighted score of the index is obtained:

q _op = W _j s _op (o=1,2,...,O; p=1,2,...,P)

Finally, the index evaluation value of each energy Internet development model is obtained:

Example

Collect the data of a city's energy system in 2017, and simulate the city's energy Internet development index evaluation indicators in 2020 and 2030 according to the "13th Five-Year" development plan and medium and long-term plan in the national energy field to form an urban energy Internet. The development index evaluation index data is shown in Table 1; the evaluation index of the urban energy Internet development index is scored, as shown in Table 2.

Table 1 Evaluation Indicators of Urban Energy Interconnection Development Index (2017, 2020 and 2030)

Table 2 Evaluation Index Scores of Urban Energy Interconnection Development Index (2017, 2020 and 2030)

In the present invention, the fuzzy evaluation criteria and fuzzy scores used in the fuzzy AHP are shown in Table 3.

Table 3 Fuzzy evaluation criteria and fuzzy scores in fuzzy AHP

In the table x=2,3,9&y,z=1,2,...,9&y<z.

The relative importance of the first-level indicators is fuzzy judged (denoted as A1), and the corresponding fuzzy scores are given, as shown in Table 4, and then the weight of the first-level indicators is calculated by the optimized fuzzy AHP.

Table 4 Fuzzy scoring of first-level indicators

The obtained first-level indicator weight is w _A1 =(0.4706,0.2154,0.2154,0.0986) ^T .

Fuzzy evaluation is carried out on the relative importance of the second-level indicators under each first-level index (respectively denoted as B1-B4), and the corresponding fuzzy scores are given, as shown in Tables 5-8, and then calculated by the optimized fuzzy AHP. The weight of the secondary indicator.

Table 5 Fuzzy score of secondary indicator B1

The obtained indicator weight is w _B1 =(0.5282,0.2469,0.1124,0.1124) ^T .

Table 6 Fuzzy score of secondary indicator B2

The obtained indicator weight is w _B2 =(0.5282,0.2469,0.1124,0.1124) ^T .

Table 7 Fuzzy score of secondary indicator B3

The obtained indicator weight is w _B3 =(0.5981, 0.2752, 0.1267) ^T .

Table 8 Fuzzy score of secondary indicator B4

The obtained indicator weight is w _B4 =(0.5981, 0.2752, 0.1267) ^T .

Based on the above results, the weight results obtained by optimizing the fuzzy AHP are listed in Table 9.

Table 9 Weights of Evaluation Indicators for Urban Energy Interconnection Development Index (2017, 2020 and 2030)

According to the weight of each indicator and the corresponding indicator value score, the index evaluation value of each energy Internet development model is obtained, as shown in Table 10.

Table 10 Evaluation Values of Urban Energy Interconnection Development Index (2017, 2020 and 2030)

The invention proposes an evaluation system for the development of the energy Internet from the perspectives of clean energy supply, clean energy consumption and electric energy substitution, energy Internet social and economic benefits, and energy Internet industry development, etc., and provides an evaluation calculation method to ensure the development of the energy Internet. Systematic and practical index assessment.

The method of the present invention is expected to generate an "Energy Internet Development Index Evaluation System" (the core structure of which is shown in Figure 2), to scientifically evaluate the development level of the Energy Internet, find out the weak links that affect the development index, and accordingly propose the construction and development of the Energy Internet. The production strategy specifies the energy links that need to be broken through and established urgently to achieve energy conservation and consumption reduction, enhance system flexibility, generate greater economic benefits, and provide important assistance for the sustainable development of the Energy Internet.

Claims (6)

1. An energy Internet development index assessment method based on fuzzy hierarchical analysis is characterized by comprising the following steps:

step 1: constructing an energy Internet development index evaluation index set, collecting each index data and giving a score;

step 2: establishing a development index evaluation index weighting optimization model by adopting a fuzzy analytic hierarchy process;

and step 3: solving an evaluation index weighting optimization model by adopting a particle swarm optimization algorithm, and giving the weight of an evaluation index;

and 4, step 4: and calculating an energy Internet development index evaluation result according to the index data scores and the index weights.

2. The method according to claim 1, wherein the energy internet development index evaluation indexes comprise 4 primary evaluation indexes, namely clean energy supply, clean energy consumption and electric energy replacement, energy internet social and economic benefits, and energy internet industrial development into the primary evaluation indexes; each primary evaluation index comprises a plurality of secondary evaluation indexes;

the clean energy supply comprises 4 secondary evaluation indicators: clean energy power generation installed capacity, clean heating rate, wind abandoning rate and light abandoning rate;

the clean energy consumption and electric energy substitution comprises 4 secondary evaluation indexes: the consumption ratio of clean energy, the delivery capacity of clean energy, the holding capacity ratio of the electric automobile and the energy storage capacity;

the social and economic benefits of the energy Internet comprise 3 secondary evaluation indexes: the power failure duration, the reduction of carbon dioxide emission and the energy investment yield are prolonged;

the energy Internet industry development comprises 3 secondary evaluation indexes: the rationalization degree of an industrial structure, the registration quantity of energy enterprises and the popularization rate of intelligent energy equipment;

the total number of the secondary evaluation indexes is 14, namely the total number of the evaluation indexes of the invention is 14.

3. The method for evaluating the energy internet development index based on the fuzzy hierarchy analysis as claimed in claim 1, wherein the step 1 is:

on the basis of constructing an energy Internet development index evaluation index set, acquiring index data of an evaluation area, grading each index data by adopting a percentage value, and recording an energy Internet development mode to be evaluated as E_o(O ═ 1,2, …, O), where the p-th evaluation index is C_p(P ═ 1,2, …, P) where O is the energy internet growth pattern in generalNumber, P is the total number of evaluation indexes, x_opThe value of the p-th evaluation index representing the o-th energy internet development mode is obtained; s_opTo the index value x_opScore of, s_op∈[0,100]。

4. The method for evaluating the energy internet development index based on the fuzzy hierarchy analysis as claimed in claim 1, wherein the step 2 is:

(1) the uncertain comparison judgment is expressed as a triangular fuzzy number:

and expressing the uncertain comparison judgment as a triangular fuzzy number by adopting a fuzzy set theory to represent the relative importance of the fuzzy, wherein on a given domain U, for any x ∈ U, one triangular fuzzy set has a triangular fuzzy membershipCorrespondingly, the expression is as follows:

where l, m, u represent the minimum possible value, the most likely value and the maximum possible value, respectively, describing the ambiguity event,represents the fuzzy number, and is marked as (l, m, u);

(2) establishing a fuzzy hierarchical analysis model:

a. constructing a decision hierarchy: similar to the traditional analytic hierarchy process, firstly, a decision problem is decomposed into hierarchical structures, namely a first-level evaluation index layer and a second-level evaluation index layer in step 1;

b. generating a pair-wise fuzzy comparison matrix: for a priority problem with n elements, wherein a first-level index n is 4; two-level index n being 3 or 4, wherein the pair-wise comparison is judged by fuzzy trigonometric numberAnd expressing that a regular fuzzy reciprocal comparison matrix is constructed on the basis of the method:

c. consistency check and priority derivation: this step checks for consistency and derives a priority based on the pairwise comparison matrix ifThen the regular fuzzy comparison matrixAre identical, wherein i, j, k is 1,2, …, n,representing fuzzy multiplication, with ≈ representing fuzzy equal to; once paired comparison matrixBy consistency check, i.e. using conventional hierarchical analysis methods to calculate fuzzy prioritiesThen, a local priority weight vector (w) is obtained using the pairwise comparison matrix₁,w₂,…,w_n)^T；

d. Global priority aggregation, i.e. determination of the final weight value: the local priority weights obtained at different levels of the decision level are summarized into a comprehensive global priority by adopting a weighting sum method, namely a final weight value (W)₁,W₂,…,W_p,…,W_P)^T；

(3) Establishing a fuzzy optimization model:

the elements of the decision matrix are determined by fuzzy trigonometric numbersThe pair-wise comparison ratios expressed, wherein i, j ═ 1, 2.., n; further, assume that l is when i ≠ j_ij＜m_ij＜u_ijIf i equals j, thenThus, the comparison matrix is paired by a regular fuzzy numberDerived weight value vector (w)₁,w₂,…,w_n)^TThe fuzzy inequality must be satisfied:

in the formula, w_i＞0,w_j＞0,i≠j，Representing a blur less than or equal to;

to measure the satisfaction of different ratios with the above-mentioned bilateral inequality, a new membership function is defined as:

in the formula, i is not equal to j,may be greater than 1 and in the interval (0, m)_ij]Upper linear decrease in the interval [ m_ijInfinity) linear increase;smaller is said to be w_i/w_jThe more acceptable the value;

to determine a weight value vector (w)₁,w₂,…,w_n)^TAll of w_i/w_jShould be precisely proportionedSatisfies n (n-1)/2 fuzzy comparison judgments, i.e. w_i/w_jIt should satisfy:wherein,thus, μ_ij（w_i/w_j) Can be used to solve the weight value vector (w)₁,w₂,…,w_n)^TAs shown in the following formula:

the above formula needs to satisfy:

where i ≠ j, δ is the Heaviside function:

5. the method for evaluating the energy internet development index based on the fuzzy hierarchy analysis as claimed in claim 1, wherein the step 3 is:

the minimization model in the step 2 is a constraint nonlinear optimization model, let χ_i＝μ_ij(w_i/w_j) I, J equals 1,2, …, n, then min J (w)₁,w₂,...,w_n) The optimization model is as follows

Therefore, a particle swarm optimization algorithm can be applied to solve the weight value vector (w)₁,w₂,…,w_n)^TFirst, mix min J (w)₁,w₂,...,w_n) The optimization problem is characterized in that:

further, the following steps are adopted for solving:

a) setting a control parameter and the iteration number t as 1;

b) initializing the position χ of the particle i_iAnd velocity v_i；

c) Updating the position p of each particle_i；

d) Evaluating a fitness function f (χ) for each particle₁,χ₂,...,χ_n)；

e) Updating the individual optimal position p of each particle_id(t) and population optimum position p_gd(t)；

f) If f (χ)₁,χ₂,...,χ_n)＜p_gd(t), then output the best position (global solution);

g) otherwise, updating the iteration number, and repeating the steps c-f, wherein t is t + 1.

6. The method for evaluating the energy internet development index based on the fuzzy hierarchy analysis as claimed in claim 1, wherein the step 4 is:

by the weight (W) of each index₁,W₂,…,W_p,…,W_P)^TAnd the index value score s_opObtaining the weighted score of the index:

q_op＝W_js_op(o＝1,2,…,O；p＝1,2,…,P)

finally, index evaluation values of the energy Internet development modes are obtained: