CN109615262A - A kind of energy internet development Index Assessment method based on Fuzzy Level Analytic Approach - Google Patents
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Abstract
The energy internet development Index Assessment method based on Fuzzy Level Analytic Approach that the invention discloses a kind of, method particularly includes: first, develop 4 angles with electric energy substitution, energy internet social and economic effects, energy Internet industry from clean energy resource supply, clean energy resource consumption and construct energy internet development Index Assessment system, establishes energy internet development Index Assessment index set；Then, development index evaluation index is established using Fuzzy AHP and assigns power Optimized model；In turn, power Optimized model is assigned using particle swarm optimization algorithm evaluation index, provides the weight of evaluation index；Finally, calculating energy internet development Index Assessment result according to the scoring of each achievement data and index weights；The method of the present invention use energy internet development Index Assessment index system it is comprehensive, appraisal procedure it can be readily appreciated that and have stronger operability, ensure that the reasonability of energy internet development Index Assessment.
Description
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 hierarchical analysis.
Background
In recent years, the energy internet is promoting deep revolution of energy production and utilization, driving energy and rapid transformation and upgrade of economy. The energy internet is a complex system deeply coupled by energy flow and information flow, comprehensively utilizes clean energy such as wind energy and solar energy and resources such as electric vehicles and energy storage, promotes large-scale efficient consumption of the clean energy through multi-energy flow interconnection and intercommunication, and is an effective way for reducing energy consumption and improving energy utilization efficiency. At present, China has already possessed energy Internet construction and production conditions, but still lacks the simple and easy, effective energy Internet development assessment tool and method.
With the development of the energy internet technology, an energy internet development index evaluation system needs to be constructed at 4 degrees from clean energy supply, clean energy consumption and electric energy substitution, energy internet social and economic benefits and energy internet industry development, and the true level of energy internet development is represented from multiple visual angles and multiple levels; and (4) providing an energy internet development index evaluation method by adopting a decision evaluation theory and method. The traditional analytic hierarchy process cannot depict the fuzzy understanding of the importance degree of the evaluation index, and hardly provides reference for policy making, planning construction and investment consultation of the energy Internet.
Disclosure of Invention
Aiming at the problems, the invention provides an energy internet development index evaluation method based on fuzzy hierarchical analysis, which is used for comprehensively evaluating the development condition of an energy internet and providing reference for the construction of the energy internet. The technical scheme is as follows:
an energy Internet development index assessment method based on fuzzy hierarchical analysis comprises 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.
According to the development characteristics of the energy Internet, an energy Internet development index evaluation system is constructed from 4 aspects of clean energy supply, clean energy consumption and electric energy substitution, energy Internet social and economic benefits and energy Internet industry development (namely, the clean energy supply, the clean energy consumption and electric energy substitution, the energy Internet social and economic benefits and the energy Internet industry development are used as primary evaluation indexes). The secondary evaluation indexes contained in each primary evaluation index are as follows:
a. clean energy supply
1) Clean energy is used for generating installed capacity. The index is the sum of rated power of clean energy generator sets such as wind power generation, photovoltaic power generation and the like;
2) and (4) cleaning the heating rate. The index represents the proportion of heating in clean modes of changing coal into electricity, changing coal into gas, heating by geothermy and the like;
3) air abandon rate. The index reflects the amount of power generation stop of the fan caused by insufficient power demand or insufficient power grid receiving capacity;
4) light rejection rate. The index reflects the amount of photovoltaic power generation stopping caused by insufficient power demand or insufficient grid receiving capacity;
b. clean energy consumption and electric energy replacement
1) The consumption ratio of clean energy. The index reflects the proportion of the clean energy consumption to the total energy consumption;
2) clean energy delivery capacity. The index reflects the level of energy transmitted to the load center by the clean energy power generation base;
3) the holding capacity of the electric automobile is proportional. The index represents the proportion of the automobile taking the vehicle-mounted power supply as power in all automobiles;
4) energy storage capacity. The index represents the capacity of the energy storage system for storing energy in the area;
c. growth performance related index
1) The power outage is long. The index refers to the total power failure time caused by faults or overhaul and other reasons in the area where the index is located;
2) the carbon dioxide emission is reduced. The index reflects the reduced carbon dioxide emission due to the development of energy Internet;
3) energy investment yield. The index reflects the ratio of annual net income sum to investment sum of the energy Internet;
d. social benefit related index
1) The rationalization of the industrial structure. The index characterizes the coincidence degree of the industrial structure of the energy Internet and the social and economic development;
2) and (4) registering quantity of energy enterprises. The index reflects the number of energy related enterprises which are urged to grow in the energy internet development process;
3) intelligent energy equipment popularity. The index represents the usage amount of intelligent equipment or instruments in the energy Internet.
In constructing the energy interconnectionOn the basis of the network development index evaluation index set, index data of an evaluation area are collected, the index data are scored by adopting percentage values, and an energy internet development mode to be evaluated is recorded as Eo(O ═ 1,2, …, O), where the p-th evaluation index is Cp(P ═ 1,2, …, P), where O is the total number of energy internet development patterns, P is the total number of assessment indicators, xopThe value of the p-th evaluation index representing the o-th energy internet development mode is obtained; sopTo the index value xopScore of, sop∈[0,100]。
Further, step 2 specifically comprises:
(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. is generated intoFor the 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,…,wn)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,…,Wp,…,WP)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 ≠ jij＜mij＜uijIf i equals j, thenThus, the comparison matrix is paired by a regular fuzzy numberDerived weight value vector (w)1,w2,…,wn)TThe fuzzy inequality must be satisfied:
in the formula, wi＞0,wj＞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:
where i ≠ j, μij(wi/wj) May be greater than 1 and in the interval (0, m)ij]Upper linear decrease in the interval [ mijInfinity) linear increase; mu.sij(wi/wj) Smaller is said to be wi/wjThe more acceptable the value；
To determine a weight value vector (w)1,w2,…,wn)TAll of wi/wjShould satisfy n (n-1)/2 fuzzy comparison judgments, i.e., wi/wjIt should satisfy:wherein,thus, μij(wi/wj) Can be used to solve the weight value vector (w)1,w2,…,wn)TAs shown in the following formula:
the above formula needs to satisfy:
where i ≠ j, δ is the Heaviside function:
further, step 3 specifically comprises:
the minimization model in the step 2 is a constraint nonlinear optimization model, let χi＝μij(wi/wj) I, j is 1,2, …, n, then minJ (w)1,w2,...,wn) The optimization model is as follows
Therefore, a particle swarm optimization algorithm can be applied to solve the weight value vector (w)1,w2,…,wn)TFirst, minJ (w)1,w2,...,wn) 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 iiAnd velocity vi；
c) Updating the position p of each particlei；
d) Evaluating a fitness function f (χ) for each particle1,χ2,...,χn)；
e) Updating the individual optimal position p of each particleid(t) and population optimum position pgd(t)；
f) If f (χ)1,χ2,…,χn)＜pgd(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.
Further, step 4 specifically includes:
by the weight (W) of each index1,W2,…,Wp,…,WP)TAnd the index value score sopObtaining the weighted score of the index:
qop＝Wjsop(o＝1,2,…,O；p＝1,2,…,P)
finally, index evaluation values of the energy Internet development modes are obtained:
the invention has the beneficial effects that: the invention comprehensively considers factors such as clean energy supply, clean energy consumption and electric energy substitution, social and economic benefits of the energy Internet, energy Internet industry development and the like in the energy Internet development, provides an energy Internet development index evaluation method, constructs a comprehensive development index evaluation index set of the system, and can comprehensively reflect the development characteristics and rules of the energy Internet; meanwhile, compared with the traditional analytic hierarchy process, the weight of the evaluation index is given by adopting a triangular fuzzy number, fuzzy analytic hierarchy process and a particle swarm optimization algorithm, the fuzziness of the relative importance of the index can be better considered, the method is closer to the cognition of experts, and the method has the advantage of easy understanding.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a core structure of an energy internet development index evaluation system.
Detailed Description
The invention is described in further detail below with reference to the figures and specific embodiments.
As shown in fig. 1, the invention provides an energy internet development index evaluation method based on optimization fuzzy hierarchical analysis, which comprises the following specific steps:
step 1: establishing an energy Internet development index evaluation index set, collecting data of each index and giving scores of the data
According to the development characteristics of the energy Internet, an energy Internet development index evaluation system is constructed from 4 aspects of clean energy supply, clean energy consumption and electric energy substitution, energy Internet social and economic benefits and energy Internet industry development (namely, the clean energy supply, the clean energy consumption and electric energy substitution, the energy Internet social and economic benefits and the energy Internet industry development are used as primary evaluation indexes). The secondary evaluation indexes contained in each primary evaluation index are as follows:
a. clean energy supply
1) Clean energy is used for generating installed capacity. The index is the sum of rated power of clean energy generator sets such as wind power generation, photovoltaic power generation and the like;
2) and (4) cleaning the heating rate. The index represents the proportion of heating in clean modes of changing coal into electricity, changing coal into gas, heating by geothermy and the like;
3) air abandon rate. The index reflects the amount of power generation stop of the fan caused by insufficient power demand or insufficient power grid receiving capacity;
4) light rejection rate. The index reflects the amount of photovoltaic power generation stopping caused by insufficient power demand or insufficient grid receiving capacity;
b. clean energy consumption and electric energy replacement
1) The consumption ratio of clean energy. The index reflects the proportion of the clean energy consumption to the total energy consumption;
2) clean energy delivery capacity. The index reflects the level of energy transmitted to the load center by the clean energy power generation base;
3) the holding capacity of the electric automobile is proportional. The index represents the proportion of the automobile taking the vehicle-mounted power supply as power in all automobiles;
4) energy storage capacity. The index represents the capacity of the energy storage system for storing energy in the area;
c. growth performance related index
1) The power outage is long. The index refers to the total power failure time caused by faults or overhaul and other reasons in the area where the index is located;
2) the carbon dioxide emission is reduced. The index reflects the reduced carbon dioxide emission due to the development of energy Internet;
3) energy investment yield. The index reflects the ratio of annual net income sum to investment sum of the energy Internet;
d. social benefit related index
1) The rationalization of the industrial structure. The index characterizes the coincidence degree of the industrial structure of the energy Internet and the social and economic development;
2) and (4) registering quantity of energy enterprises. The index reflects the number of energy related enterprises which are urged to grow in the energy internet development process;
3) intelligent energy equipment popularity. The index represents the usage amount of intelligent equipment or instruments in the energy Internet.
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 Eo(O ═ 1,2, …, O), where the p-th evaluation index is Cp(P ═ 1,2, …, P), where O is the total number of energy internet development patterns, P is the total number of assessment indicators, xopThe value of the p-th evaluation index representing the o-th energy internet development mode is obtained; sopTo the index value xopScore of, sop∈[0,100]。
Step 2: method for establishing development index evaluation index empowerment optimization model by adopting fuzzy analytic hierarchy process
2.1 express the uncertain comparison determination as a triangular blur 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);
2.2 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 matrixIs aWherein, 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,…,wn)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,…,Wp,…,WP)T；
2.3, 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 ≠ jij＜mij＜uijIf i equals j, thenThus, the comparison matrix is paired by a regular fuzzy numberDerived weight value vector (w)1,w2,…,wn)TThe fuzzy inequality must be satisfied:
in the formula, wi＞0,wj＞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:
where i ≠ j, μij(wi/wj) May be greater than 1 and in the interval (0, m)ij]Upper linear decrease in the interval [ mijInfinity) linear increase; mu.sij(wi/wj) Smaller is said to be wi/wjThe more acceptable the value;
to determine a weight value vector (w)1,w2,…,wn)TAll of wi/wjShould satisfy n (n-1)/2 fuzzy comparison judgments, i.e., wi/wjIt should satisfy:wherein,thus, μij(wi/wj) Can be used to solve the weight value vector (w)1,w2,…,wn)TAs shown in the following formula:
the above formula needs to satisfy:
where i ≠ j, δ is the Heaviside function:
and step 3: solving the evaluation index weighted optimization model by adopting a particle swarm optimization algorithm to give the weight of the evaluation index
The minimization model in the step 2 is a constraint nonlinear optimization model, let χi＝μij(wi/wj) I, j is 1,2, …, n, then minJ (w)1,w2,...,wn) The optimization model is as follows
Therefore, a particle swarm optimization algorithm can be applied to solve the weight value vector (w)1,w2,…,wn)TFirst, minJ (w)1,w2,...,wn) The optimization problem is characterized in that:
further, the following steps are adopted for solving:
h) setting a control parameter and the iteration number t as 1;
i) initializing the position χ of the particle iiAnd velocity vi；
j) Updating the position p of each particlei；
k) Evaluation ofFitness function f (χ) of each particle1,χ2,...,χn)；
l) updating the individual optimal position p of each particleid(t) and population optimum position pgd(t)；
m) if f (χ)1,χ2,...,χn)＜pgd(t), then output the best position (global solution);
n) otherwise, updating the iteration number, t being t +1, and repeating steps c-f.
And 4, step 4: calculating the energy Internet development index evaluation result according to the grading and the weight of each index data
By the weight (W) of each index1,W2,…,Wp,…,WP)TAnd the index value score sopObtaining the weighted score of the index:
qop＝Wjsop(o＝1,2,…,O；p＝1,2,…,P)
finally, index evaluation values of the energy Internet development modes are obtained:
examples
Acquiring data of an urban energy system in 2017, and performing simulation calculation on energy Internet development index evaluation indexes in 2020 and 2030 of the city according to thirteen-five development plans and medium-long term plans in the national energy field to form urban energy Internet development index evaluation index data as shown in Table 1; and scoring the urban energy Internet development index evaluation indexes as shown in Table 2.
Table 1 urban energy internet development index evaluation index (2017, 2020 and 2030 years)
Table 2 city energy internet development index assessment index score (2017, 2020 and 2030 years)
In the present invention, the fuzzy evaluation criteria and fuzzy scores used in the fuzzy hierarchical analysis are shown in table 3.
TABLE 3 fuzzy criterion and fuzzy score in fuzzy hierarchy analysis
In the table, x is 2,3,9& y, z is 1,2, …,9& y < z.
Fuzzy evaluation is carried out on the relative importance of the primary index (denoted as A1), a corresponding fuzzy score is given, as shown in Table 4, and then the weight of the primary index is calculated by adopting an optimized fuzzy analytic hierarchy process.
TABLE 4 fuzzy scores for the first-order indices
The obtained first-level index weight is wA1＝(0.4706,0.2154,0.2154,0.0986)T。
Fuzzy evaluation is carried out on the relative importance of the secondary indexes under each primary index (respectively marked as B1-B4), corresponding fuzzy scores are given out, and the weights of the secondary indexes are calculated by adopting an optimized fuzzy analytic hierarchy process as shown in tables 5-8.
TABLE 5 fuzzy Scoring of the second-level index B1
The obtained index weight is wB1＝(0.5282,0.2469,0.1124,0.1124)T。
TABLE 6 fuzzy scores for the second-level index B2
The obtained index weight is wB2＝(0.5282,0.2469,0.1124,0.1124)T。
TABLE 7 fuzzy scores for the secondary index B3
The obtained index weight is wB3＝(0.5981,0.2752,0.1267)T。
TABLE 8 fuzzy scores for the second-level index B4
The obtained index weight is wB4＝(0.5981,0.2752,0.1267)T。
Combining the above results, the weight results obtained by the optimized fuzzy hierarchy analysis are listed in table 9.
Table 9 evaluation index weight of city energy internet development index (2017, 2020 and 2030 years)
And obtaining index evaluation values of the energy Internet development modes according to the weights of the indexes and corresponding index value scores, as shown in a table 10.
TABLE 10 evaluation of urban energy Internet development index (2017, 2020 and 2030 years)
The invention provides an energy Internet development evaluation system and an evaluation calculation method from the viewpoints of clean energy supply, clean energy consumption and electric energy substitution, energy Internet social and economic benefits, energy Internet industrial development and the like so as to ensure the systematicness and practicability of energy Internet development index evaluation.
The method of the invention is expected to generate an energy internet development index evaluation system (the core structure of which is shown in figure 2), so as to scientifically evaluate the development level of the energy internet, find out weak links influencing the development index, and accordingly propose energy internet construction and production strategies, indicate energy links to be broken through and established urgently, so as to realize energy conservation and consumption reduction, enhance system flexibility, generate greater economic benefit and provide important assistance for the continuous 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 Eo(O ═ 1,2, …, O), where the p-th evaluation index is Cp(P ═ 1,2, …, P) where O is the energy internet growth pattern in generalNumber, P is the total number of evaluation indexes, xopThe value of the p-th evaluation index representing the o-th energy internet development mode is obtained; sopTo the index value xopScore of, sop∈[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,…,wn)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,…,Wp,…,WP)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 ≠ jij＜mij＜uijIf i equals j, thenThus, the comparison matrix is paired by a regular fuzzy numberDerived weight value vector (w)1,w2,…,wn)TThe fuzzy inequality must be satisfied:
in the formula, wi＞0,wj＞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 [ mijInfinity) linear increase;smaller is said to be wi/wjThe more acceptable the value;
to determine a weight value vector (w)1,w2,…,wn)TAll of wi/wjShould be precisely proportionedSatisfies n (n-1)/2 fuzzy comparison judgments, i.e. wi/wjIt should satisfy:wherein,thus, μij（wi/wj) Can be used to solve the weight value vector (w)1,w2,…,wn)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 χi＝μij(wi/wj) I, J equals 1,2, …, n, then min J (w)1,w2,...,wn) The optimization model is as follows
Therefore, a particle swarm optimization algorithm can be applied to solve the weight value vector (w)1,w2,…,wn)TFirst, mix min J (w)1,w2,...,wn) 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 iiAnd velocity vi；
c) Updating the position p of each particlei；
d) Evaluating a fitness function f (χ) for each particle1,χ2,...,χn)；
e) Updating the individual optimal position p of each particleid(t) and population optimum position pgd(t)；
f) If f (χ)1,χ2,...,χn)＜pgd(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,…,Wp,…,WP)TAnd the index value score sopObtaining the weighted score of the index:
qop＝Wjsop(o＝1,2,…,O；p＝1,2,…,P)
finally, index evaluation values of the energy Internet development modes are obtained:
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