CN115564495A - Comprehensive evaluation method suitable for site selection of new energy power station - Google Patents
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Abstract
The invention relates to a comprehensive evaluation method suitable for site selection of a new energy power station, which comprises the following steps: 1) Determining a judgment matrix R; 2) Checking and adjusting the consistency of the judgment matrix R; 3) Calculating a hierarchical single ordering weight vector; 4) Calculating a hierarchical total sorting weight vector; 5) And calculating to obtain a comprehensive evaluation value of the site selection scheme. According to the method, a grey correlation analysis method and a cosine sorting method are combined, distance information and angle information are comprehensively considered, each scheme can be more accurately evaluated, when a new energy power station building address is selected, multi-standard analysis decision is carried out on a target position, the reasonability of corresponding weights of various indexes is guaranteed, scientific and reasonable suggestions are provided for a decision maker, and expected effects are obtained.
Description
Technical Field
The invention belongs to the technical field of power management, and particularly relates to a comprehensive evaluation method suitable for site selection of a new energy power station.
Background
With the increasing complexity of social and economic evaluation problems, the multi-index comprehensive evaluation method is widely applied, and various comprehensive evaluation methods are developed.
The existing evaluation methods are different in various aspects, such as the presentation mode of weight and index, the aggregation mode of evaluation information, and the like. These differences lead to inconsistent evaluation conclusions for the various methods.
At present, a plurality of inspection methods and standards aiming at an index system are provided at home and abroad to provide help for an evaluation demander to select an evaluation method.
The weight determines the emphasis difference of each index of the comprehensive evaluation, and is the essence for measuring the accuracy and the reasonability of the comprehensive evaluation. Some studies have attempted to analyze the applicability of the evaluation method from a weight point of view. There are documents that propose the concept of natural weight, considering the natural weight factors presented in the linear evaluation method; the scholars also think that the natural weight problem can be eliminated because the relative evaluation has high occurrence rate in practice. Some researchers believe that the monotonicity of the weights approaching the Ideal point ranking method (TOPSIS) is poor, and the accuracy is limited to be used for equal weight evaluation. In addition, the scholars have proposed a concept of simulation weight, and used it for checking and selecting an evaluation method. The simulation weight theory considers that in linear evaluation, the weight is clear, and the meaning and the weight of each index are clear, but in nonlinear evaluation, the weight is fuzzy, and the weight of each index needs to be calculated by design, research and simulation. In the prior art, a multiple choice regression analysis method is studied to simulate the index weight of a nonlinear evaluation method, and the regression coefficient is normalized to be the simulation weight. The method can convert the nonlinear relation between the index value and the evaluation value into a linear relation, and has the problems of low estimation precision, negative part of index regression coefficients and the like, so that a method for simulating the nonlinear evaluation by adopting an artificial neural network is proposed by some scholars. The method can increase the complexity and fineness of the nonlinear relation between the index value and the evaluation value, and also changes the original evaluation structure. Whether the structural change of the simulation can guarantee the matching degree of the simulation weight and the actual importance or not is not provided by the existing research, and a clear theoretical basis is not provided for further demonstration.
The Analytic Hierarchy Process (AHP) is used as a subjective weighting method and is most widely applied to the aspect of considering the index weight problem, the AHP utilizes expert judgment to construct a judgment index matrix, judges whether the judgment of the expert is correct or not through logic consistency, and finally gives each index weight coefficient. However, the multi-attribute comprehensive evaluation index system is often composed of a large number of indexes, and the complexity of mutual judgment of every two indexes by experts is increased due to the excessive number of indexes, so that the problem of increased consistency judgment workload caused by logic disorder is easily caused.
The present invention has been made in view of the above circumstances.
Disclosure of Invention
Aiming at the defects of the analytic hierarchy process in the prior art, the invention introduces a new method, namely the fuzzy analytic hierarchy process, in the macro site selection of the new energy power station, the method solves the problem that the consistency judgment standard lacks scientific basis, and effectively makes up the defects of the analytic hierarchy process in consistency check and adjustment.
The specific technical scheme of the invention is as follows: a comprehensive evaluation method suitable for site selection of a new energy power station comprises the following steps:
1) Determining a judgment matrix R;
2) Checking and adjusting the consistency of the judgment matrix R;
3) Calculating a hierarchical single ordering weight vector;
4) Calculating a hierarchical total sorting weight vector;
5) And calculating to obtain a comprehensive evaluation value of the site selection scheme.
Further, the method also comprises a step 6) of carrying out quality ranking on the comprehensive evaluation of the candidate new energy resource areas, and selecting the new energy resource area with the highest comprehensive evaluation value from the candidate new energy resource areas.
Further, in step 1), an importance scale of 0.1-0.9 is adopted to construct a judgment matrix R; element R in the decision matrix R ij Is represented by r i Ratio r j Degree of importance, r ij The larger the value, the more indicated that i Ratio r j The more important is r therein i And r j Is a criterion or indicator.
Further, in step 1), any row or column of the judgment matrix R can reflect the judgment of relative importance of each element by an expert.
Further, in step 2), when the constructed judgment matrix R does not have consistency, the following theorem is applied for transformation:
theorem fuzzy complementary matrix R = (R) ij ) nxn The essential condition for the fuzzy consistent matrix is that the difference between corresponding elements of any appointed line and other lines is a constant.
Further, the step of adjusting the judgment matrix R to be the fuzzy consistent matrix is as follows:
(1) determining an element which is obtained by comparing the importance of the element with the importance of other elements and has a positive judgment;
(2) subtracting the corresponding elements of the second row from the elements of the first row of the judgment matrix R, and if the obtained n difference numbers are constants, adjusting the elements of the second row is not needed; otherwise, the second row of elements is adjusted until the difference between the first row of elements and the corresponding elements of the second row is a constant;
(3) subtracting the corresponding element of the third row from the element of the first row of the judgment matrix R, and if the obtained n difference numbers are constants, not adjusting the element of the third row; otherwise, adjusting the elements in the third row until the difference between the first row elements and the corresponding elements in the third row is a constant;
the above steps are continued until the difference between the first row element minus the corresponding element of the nth row is constant.
Further, in step 3), the rule layer is set to have n rules, and the weights thereof are respectively
Wherein the ith criterion a i (i =1, 2.. Multidot., n) includes s indices, each being b 1 ,b 2 ,...,b s Respectively having a weight of w i1 ,w i2 ,...,w is The criterion hierarchical single-ordering weight is expressed by the formula (2-1):
in the formula: alpha is the criterion a i And criterion a j Difference in degree of importance
The index factor of (a) is more than or equal to (n-1)/2;
the index layer hierarchical single ordering is expressed by equation (2-2):
in the formula: beta is an index b i And an index b j Difference in degree of importance (w) ii -w jj ) The index factor of beta is more than or equal to (s-1)/2, and the smaller beta is, the more the expert pays more attention to the difference of the importance degree among the indexes;
the hierarchical total rank weight vector for all indices is represented by equation (2-3):
W＝(w 1 B ,w 2 B ,...,w m B )(2-3)。
further, in step 4), the hierarchical structure is set to have m indexes, each index is b 1 ,b 2 ,...,b m They have a single ordering weight of w for criterion A hierarchy i1 ,w i2 ,...,w im (ii) a If b is j Is not a criterion of i Lower corresponding index, then w ji =0; the index layer B and the total hierarchical ranking weight of each index are expressed by the formula (2-4):
further, in step 5), each index value is calculated for a specific charging station access scheme, and a final comprehensive evaluation value is obtained by summing up the weighted vector corresponding to each index and each index value.
The invention has the advantages that: the method combines a gray correlation analysis method and a cosine ordering method, comprehensively considers distance and angle information, can evaluate each scheme more accurately, carries out multi-standard analysis decision on a target position when selecting a new energy power station building address, ensures the reasonability of corresponding weight of each index, and provides scientific and reasonable suggestions for a decision maker to obtain expected effects.
Drawings
Fig. 1 is a structural diagram of an evaluation index system framework adopted in an embodiment of a comprehensive evaluation method suitable for new energy power station site selection according to the present invention.
Fig. 2 is a flowchart of an algorithm adopted in an embodiment of the comprehensive evaluation method suitable for site selection of a new energy power station according to the present invention.
Detailed Description
The comprehensive evaluation method suitable for site selection of the new energy power station is further described below with reference to the attached figures 1-2.
In order to enable the evaluation index system to fully reflect the actual condition and feasibility of the transformer substation, the invention takes the relevant factors of site selection of the power station as starting points from the aspects of safety, economy, environment and operation of the power station on a regional power grid, selects indexes of relevant standards, and constructs an evaluation index system suitable for site selection of the power station according to the construction principle of the evaluation index system and the characteristics and purposes of the site selection of the power station, as shown in figure 1. As can be seen from fig. 1, the constructed index system is composed of a target layer, a criterion layer and an index layer, the target layer is a new energy power station site selection comprehensive evaluation index system, and the criterion layer includes four aspects of weather, power grid, society and geology and respectively corresponds to the index layer: 1. annual average wind speed, wind power density, annual average utilization hours, annual average radiant quantity, annual sunshine hours; 2. distance to a transformer substation, capacity margin, average voltage deviation, network loss rate and average load rate; 3. the radiation influence of electric appliances, the construction noise influence and the energy conservation and emission reduction benefits; 4. landform factor, land utilization factor, slope direction, hydrogeological condition. Each index can obtain a corresponding numerical value through a quantization model.
As shown in fig. 2, the comprehensive evaluation method for site selection of a new energy power station provided by the present invention is an algorithm flowchart of an embodiment, the new energy power station is a wind energy power station, the comprehensive evaluation method is a fuzzy analytic hierarchy process, and the method includes the following important steps:
1) Determining a judgment matrix;
2) Checking and adjusting the consistency of the judgment matrix;
3) Calculating a hierarchical single ordering weight vector;
4) Calculating a hierarchical total sorting weight vector;
5) And calculating to obtain a comprehensive evaluation value of the address selection scheme.
In addition, the method also comprises the step 6) of carrying out quality ranking on the comprehensive evaluation of the candidate new energy resource areas and selecting the new energy resource area with the highest comprehensive evaluation value.
Further, the checking and adjusting the consistency of the judgment matrix in the step 2) includes two operations of checking the consistency of the judgment matrix and adjusting the consistency of the judgment matrix.
The comprehensive evaluation method relates to actual data processing, namely processing corresponding data (such as meteorological data, geological data and the like) of each index, and comprises two parts of determining sample set data, calculating and constructing a fuzzy evaluation matrix. Calculating each index value aiming at a specific charging station access scheme, and solving the membership degree of each index according to the corresponding interval of the calculated value so as to obtain a fuzzy evaluation matrix; and multiplying the comprehensive weight matrix and the fuzzy evaluation matrix, and summing to obtain a fuzzy comprehensive evaluation score, namely a comprehensive value of the fuzzy evaluation.
The following is a detailed description of the important steps.
1) Determining a decision matrix
Let the judgment matrix be R, the element R in R ij Representing a criterion (or index) r i Ratio criterion (or indicator) r j Degree of importance, r ij The larger, r i Just to r j The more important, the significance scale of 0.1-0.9 is used to construct the decision matrix, as shown in Table 1.
TABLE 1 significance Scale of significance table
Definition 1 sets matrix R = (R) ij ) nxn If the following conditions are met: r is not less than 0 ij 1 ≦, (i, j =1, 2.. Times, n), then R is said to be the blur matrix.
Definition 2 if fuzzy matrix R = (R) ij ) nxn Satisfies the following conditions: r is ij +r ji =1, (i, j =1, 2.. Multidot., n), the fuzzy matrix R is said to be a fuzzy complementary matrix.
Through analysis, the constructed judgment matrix is a fuzzy complementary matrix and has the following properties: (i) r is a radical of hydrogen ij ＝0.5，i，j＝1，2，…,n；(ii)r ij ＝1-r ji I, j =1,2, \ 8230;, n. According to the above properties, r is usually filled in first ii And a part of =0.5, then judging and filling the upper triangle or the lower triangle of the judgment matrix, and finally filling the rest parts according to the properties of the judgment matrix.
2) Checking and adjusting consistency of decision matrices
Although any row or column of the judgment matrix completely reflects one judgment of relative importance of each element by a certain expert, different experts can subconsciously form the row or column of the judgment matrix according to habits of the experts, and the combination mode also participates in expressing subjective judgment of the experts. The expert's habits are summarized into 3 behavioral hypotheses:
the first assumption holds that a certain row (column) in the decision matrix reflects the expert's decision on the relative importance between the elements.
The second assumption holds that a certain row (column) in the decision matrix can comprehensively reflect the importance of one element relative to the total number of elements.
The third assumption is that all rows (columns) in the decision matrix collectively reflect the expert's decision on the relative importance of elements, and even if some values deviate from the consistency requirement, it is a reasonable result of mutual correction between the values.
Each hypothesis corresponds to a different combination of matrix rows or columns, and thus determines a different matrix adjustment method.
The present invention uses a first assumption that a certain row (column) in the decision matrix is the most reflective of the relative importance of the expert between the elements. Thus, when the constructed judgment matrix does not have consistency, the following theorem can be applied to transform the judgment matrix.
Theorem fuzzy complementary matrix R = (R) ij ) nxn The essential condition of the fuzzy consistent matrix is that the difference between corresponding elements of any appointed line and other lines is a certain constant.
The specific steps of adjusting the judgment matrix are as follows:
(1) determining an element with judgment of validity obtained by comparing importance of other elements, without loss of generality, and determining that the decision maker considers that the element has judgment r 11 ,r 12 ,...,r 1n The comparison is reliable.
(2) The corresponding element of the second row is subtracted from the element of the first row of R, and if the obtained n differences are constants, the element of the second row does not need to be adjusted. Otherwise, the second row element is adjusted until the difference between the first row element minus the corresponding element of the second row is constant.
(3) The corresponding element of the third row is subtracted from the element of the first row of R, and if the obtained n differences are constant, the element of the third row does not need to be adjusted. Otherwise, the element in the third row is adjusted until the difference between the first row element and the corresponding element in the third row is a constant.
The above steps are continued until the difference between the first row element minus the corresponding element of the nth row is constant.
3) Calculating a hierarchical single sort weight
The rule layer has n rules, and the weights of the rules are respectively
Wherein the ith criterion a i (i =1, 2.. N.) includes s indices, each b 1 ,b 2 ,...,b s Respectively having a weight of w i1 ,w i2 ,...,w is . According to the above assumptions, the criterion level single ordering weight can be expressed as:
in the formula: alpha is the criterion a i And criterion a j Difference in degree of importance
The smaller the alpha is, the more the expert pays more attention to the difference of the importance degree among the criteria. To highlight the difference between the criteria, in practice it is generally assumed that α = (n-1)/2.
The index layer hierarchical single ordering may be expressed as:
in the formula: beta is an index b i And an index b j Difference in degree of importance (w) ii -w jj ) The smaller beta is, the more important the expert pays attention to the difference of the importance degree among the indexes. In order to highlight the difference between the indices, β = (s-1)/2 is generally adopted in practical application.
Thus, the total hierarchical ranking weight vector for all the indicators is:
W＝(w 1 B ,w 2 B ,...,w m B ) (2-3)
4) Calculating a hierarchical total ordering weight
The total hierarchical ordering appears different according to the single hierarchical ordering. Additionally, a hierarchical structure is set to have m indexes, respectively b 1 ,b 2 ,...,b m They have a single ordering weight of w for criterion A i1 ,w i2 ,...,w im (if b) j Is not a criterion i Lower corresponding index, then w ji = 0). Then, according to the above hierarchical single ordering formula, the index layer B and the hierarchical total ordering weight formula of each index are:
and calculating each index value aiming at the specific charging station access scheme, multiplying the weight vector corresponding to each index by each index value, and summing to obtain a final comprehensive evaluation value, namely obtaining the comprehensive evaluation value of the site selection scheme.
It should be noted that, in the present invention, the new energy includes energy sources such as wind energy and solar energy, which are different from the conventional thermal power, hydroelectric power, and nuclear power, and the theory of the associated empowerment method related to the present invention is as follows.
1. Order relation analysis method
For subjective weighting, analytic Hierarchy Process (AHP) is not suitable for a multi-index evaluation system. In order to avoid the problems of logic and workload caused by the number of indexes, the invention adopts a sequence relation analysis method to subjectively weight the indexes.
The m system schemes and the n evaluation indexes can form m multiplied by n evaluation index values a mn The formed index matrix G = [ x = ij ] m×n (i =1,2, \8230;, m; j =1,2, \8230; n), i.e.:
the sequence relation analysis method mainly compares every two adjacent indexes of the same layer with each other through experience and cognition of experts in the related energy field, sorts the relative importance degree of the indexes of each layer, and determines the subjective weight of each index through the relative importance among the indexes. The sequence relation analysis method mainly comprises the following steps:
a) Judging the importance of the indexes and determining the order relation
Inviting related experts to sort the relative importance of the first-level indexes if the index x i Ratio index x j Is higher than the relative importance of (2), then is marked as x i >x j . Sequentially ordering each level index as x according to relative importance i >x j >…>x t >x n (i, j, t =1,2, \ 8230;, n), secondary index was carried out for importance in the same mannerAnd (6) sorting.
b) After determining the relative importance degree of the adjacent indexes and determining the relative importance relation ranking of the indexes of each layer, the expert needs to determine the relative importance degree of each adjacent index. Wherein, the index x k With adjacent index x k-1 Relative degree of importance R k Can be expressed as the formula (1.2), further on the relative degree of importance R k And (4) taking values.
Sum of relative importance values of the same hierarchy
When the judgment result is positive, the judged accumulative importance degree is larger than the extreme importance degree, which indicates that the abnormality is judged to occur artificially, and the correction coefficient mu is needed to correct the relative importance degree value R k And (6) correcting.
R k ′＝μ·R k (0.4)
c) Calculating subjective weight values
According to the relative importance degree of the evaluation index, the index subjective weight is calculated as follows:
ω′ k-1 ＝Rk·ω′ k (0.6)
2. fuzzy entropy weight method
Entropy is an important concept in physical science and thermodynamics, generally representing the state of a substance, and is introduced into information theory by Shennong to measure the uncertainty of information. The smaller the information entropy is, the larger the information amount is, and the smaller the uncertainty is; and the larger the entropy of the information, the larger the tableThe smaller the amount of information, the greater the uncertainty. Therefore, the size of the entropy value can judge the uncertainty and randomness of the system scheme and can also represent the dispersion degree of the index. If a random variable set X = { X =isdefined 1 ,x 2 …,x n The probability distribution corresponding to its variable can be represented as P (x = x) i )＝p(x i ) Then the definition of entropy can be expressed as:
the entropy weight method is generated by determining the weight of the index according to the size of the information of the index by using the characteristic of the entropy, wherein the larger the weight of the index is, the more the provided information indicating the index is, the greater the importance degree is, and the more important the role played in the evaluation is. When all p (x) i ) When the phases are equal, H (X) takes the maximum value, namely the concept of the maximum entropy principle.
The entropy weight resisting method is an objective weighting method improved based on the entropy weight method, the characteristics of the entropy weight resisting value and the entropy weight value are opposite, namely the larger the entropy weight resisting value is, the larger the difference between the indications is, and the larger the weight coefficient is. There are two main definitions of the entropy resistance, expressed as follows:
in the formula: p is a radical of formula ij Normalized index value, 0 ≦ p ij Less than or equal to 1 and
definition of: if p is ij If not =0, then
If p is ij =1, then
The objective weights for each index are calculated as follows:
because the index system has the problem of coexistence of uncertainty indexes and certainty indexes, the uncertainty indexes expressed by triangular fuzzy numbers cannot be directly applied to the entropy weight inversion method. In the precision research based on the triangular fuzzy number, documents adopt a relative preference relationship to carry out precision sequencing on the triangular fuzzy number. Therefore, the invention is mainly based on the thought of relative preference relationship, and adopts the relevant preference distance between each index and a positive ideal point and a negative ideal point to refine the triangular fuzzy number, thereby providing an improved entropy weight resisting method.
Assume a set of triangular fuzzy numbers
Wherein the positive ideal point is the maximum value of the upper limit, the median and the lower limit of the corresponding triangular fuzzy number set, namely
The negative ideal point is the minimum value of the upper limit, the median and the lower limit of the corresponding triangular fuzzy number set, namely
Triangular fuzzy number
The operator of the relative preference relationship with the positive and negative ideal points is:
in the formula:
if it is
Then represents A + And positive ideal point A - Is 0, i.e. the degree of deviation
The closer to 0.5,A i The closer to the positive ideal point A + . If it is
Then represents A i And a negative ideal point A - Is 0, i.e. the degree of deviation
The closer to 0.5,A is i The closer to the negative ideal point A - . Therefore, the meanings of the relative preference operators between the triangular fuzzy numbers and the positive and negative ideal points are completely opposite, so that in order to avoid the difference of results caused by the fact that the triangular fuzzy numbers are respectively refined from the positive and negative ideal points, and the greater the triangular fuzzy numbers are required to be normalized with quantitative index values, the more optimal the triangular fuzzy numbers are, the more optimal the meanings are, the relative preference operators between the triangular fuzzy numbers and the positive and negative ideal points are unified through the relative preference distance, as shown in the following formula:
the relative preference distances to the positive and negative ideal points are normalized, respectively, as shown in the following equation:
combining the triangular fuzzy number with the anti-entropy weight method through the relative preference distance between the triangular fuzzy number and the positive and negative ideal points to form a fuzzy anti-entropy weight method, wherein the anti-entropy value of the qualitative index is shown as the following formula:
in the formula: p is a radical of ij Value of
Or
3. Linear combined empowerment
The combinatorial weighting method is generally divided into two main flows, namely a linear weighting method and a product synthesis method. The invention mainly adopts a linear weighting method to combine the subjective weight and the objective weight of the index through a linear relation. Weighting each index in the multi-attribute mixed index system based on a sequence relation analysis method and a fuzzy entropy weight resisting method, wherein the obtained subjective weight vector and objective weight vector are as follows:
ω′＝(ω′ 1 ,ω′ 2 …,ω′ j ) (0.21)
ω″＝(ω″ 1 ,ω″ 2 …,ω″ j ) (0.22)
solving the combining weights by means of linear combination, the combining weights can be expressed as:
wherein λ is 1 、λ 2 Are linear combination coefficients.
The sum of the combination weights of the indices should be 1, as shown in the following formula:
solving for combining weight omega using linear weighted summation j The comprehensive evaluation value V of the ith scenario i Comprises the following steps:
and constructing a positive ideal scheme, namely selecting the elements of each behavior 1 from the normalized index matrix to construct the positive ideal scheme. Thus, the generalized distance of solution i from the ideal solution is:
in order to determine the appropriate linear combination coefficients, a minimum weighted generalized distance model of all solutions and the ideal solution needs to be constructed first, that is:
secondly, adopting the Jaynes maximum entropy principle to eliminate the uncertainty of the linear combination coefficient, and constructing a maximum information entropy model, namely:
by using a linear weighted optimization method, the following single-target optimization is constructed:
where ρ is a trade-off coefficient for two optimization objectives, 0<ρ<1. The single-target optimization problem is solved by constructing a Lagrange function, and then the linear combination coefficient lambda is obtained 1 、λ 2 The calculation is as follows:
4. optimal combination weighting based on moment estimation theory
The weight of the evaluation index is the key for improving the grey correlation analysis method. Whether the weight is reasonable or not directly influences the scientific reasonability of the optimization result. In order to reflect subjectivity and objectivity of decision making in evaluation, it is necessary to integrate subjective and objective weights of the above-described indices.
The method comprises the following steps that y subjective weighting methods are used for weighting evaluation indexes, and a subjective weighting set of the evaluation indexes is as follows:
after the decision matrix is normalized, weighting is carried out on the evaluation indexes by using various g-y objective weighting methods, and the obtained objective weighting set is as follows:
suppose that the comprehensive weight vector of the evaluation index is [ w 1 ,w 2 ...,w n ]. For subjective weight values, w is greater than or equal to 0 j J is more than or equal to 1,1 and less than or equal to n, if the number of decision makers tends to be large, the law of large number statistics shows that the result of the weight vector integral judgment is close to the integral vector [ w ≤ w 1 ,w 2 ...,w n ](ii) a The results obtained by the different algorithms are repeatable for the target weight values. Thus, from a statistical point of view, it can be considered as a sample extracted from the population to estimate the integrated weight vector w 1 ,w 2 ...,w n ]。
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention, and it will be obvious to those skilled in the art that other variations or modifications may be made on the basis of the above description, and all embodiments may not be exhaustive, and all obvious variations or modifications may be included within the scope of the present invention.
Claims (9)
1. A comprehensive evaluation method suitable for site selection of a new energy power station is characterized by comprising the following steps:
1) Determining a judgment matrix R;
2) Checking and adjusting the consistency of the judgment matrix R;
3) Calculating a hierarchical single ordering weight vector;
4) Calculating a hierarchical total sorting weight vector;
5) And calculating to obtain a comprehensive evaluation value of the site selection scheme.
2. The comprehensive evaluation method suitable for site selection of a new energy power station as claimed in claim 1, further comprising the step 6) of ranking the comprehensive evaluations of the candidate new energy resource areas to select the new energy resource area with the highest comprehensive evaluation value.
3. The comprehensive evaluation method suitable for site selection of new energy power stations of claim 1, wherein in step 1), the judgment matrix R is constructed by using an importance scale of 0.1-0.9; element R in the decision matrix R ij Is represented by r i Ratio r j Degree of importance, r ij The larger the value, indicates r i Ratio r j The more important is r therein i And r j Is a criterion or indicator.
4. The comprehensive evaluation method suitable for site selection of the new energy power station as claimed in claim 1, wherein in the step 1), any row or column of the judgment matrix R can reflect the judgment of relative importance of each element by experts.
5. The comprehensive evaluation method for locating new energy power stations according to claim 1, wherein in step 2), when the constructed judgment matrix R does not have consistency, the following theorem is applied for transformation:
theorem fuzzy complementary matrix R = (R) ij ) nxn The essential condition for the fuzzy consistent matrix is that the difference between corresponding elements of any appointed line and other lines is a constant.
6. The comprehensive evaluation method suitable for site selection of the new energy power station as claimed in claim 5, wherein the step of adjusting the judgment matrix R to be a fuzzy consistent matrix is as follows:
(1) determining an element with a positive judgment obtained by comparing the importance of the element with the importance of other elements;
(2) subtracting the corresponding elements of the second row from the elements of the first row of the judgment matrix R, and if the obtained n difference numbers are constants, not adjusting the elements of the second row; otherwise, the second row of elements is adjusted until the difference between the first row of elements and the corresponding elements of the second row is a constant;
(3) subtracting the corresponding element of the third row from the element of the first row of the judgment matrix R, and if the obtained n difference numbers are constant, not adjusting the element of the third row; otherwise, adjusting the elements in the third row until the difference between the elements in the first row and the corresponding elements in the third row is a constant;
the above steps are continued until the difference between the first row element minus the corresponding element of the nth row is constant.
7. The comprehensive evaluation method for locating new energy power stations according to claim 1, wherein in step 3), n criteria are set in the criteria layer, and the weights of the n criteria are respectively
Wherein the ith criterion a i (i =1, 2.. N.) includes s indices, each b 1 ,b 2 ,...,b s Respectively having a weight of w i1 ,w i2 ,...,w is The criterion hierarchical single-ordering weight is expressed by the formula (2-1):
in the formula: alpha is the criterion a i And criterion a j Difference in degree of importance
The index factor of (a) is more than or equal to (n-1)/2;
the index level hierarchical single ordering is represented by equation (2-2):
in the formula: beta is an index b i And an index b j Difference in degree of importance (w) ii -w jj ) The index factor of beta is more than or equal to (s-1)/2, and the smaller beta is, the more the expert pays more attention to the difference of the importance degree among the indexes;
the hierarchical total rank weight vector for all indices is represented by equation (2-3):
W＝(w 1 B ,w 2 B ,...,w m B ) (2-3)。
8. the comprehensive evaluation method suitable for site selection of a new energy power station as claimed in claim 1, wherein in step 4), a hierarchical structure is set to have m indexes, respectively b 1 ,b 2 ,...,b m They have a single ordering weight of w for criterion A level i1 ,w i2 ,...,w im (ii) a If b is j Is not a criterion i Lower corresponding index, then w ji =0; the index layer B and the total hierarchical ranking weight of each index are expressed by the formula (2-4):
9. the comprehensive evaluation method suitable for site selection of new energy power stations as claimed in claim 1, wherein in step 5), each index value is calculated for a specific charging station access scheme, and a final comprehensive evaluation value is obtained by multiplying each index value by a weight vector corresponding to each index and then summing the result.
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