CN113139701B - Regional energy health evaluation method based on analytic hierarchy process - Google Patents
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Abstract
The method is characterized in that a complex problem is decomposed into various component factors, the component factors are layered according to a dominant relationship, a clear hierarchical structure is built, on the same hierarchy, the relative importance of the factors in the hierarchy is determined in a pairwise comparison mode, judgment standards are quantized by using relative scales, a corresponding judgment matrix is built layer by layer, and finally the weight coefficient of each factor is determined by solving a normalized feature vector corresponding to the maximum feature value of the judgment matrix.
Description
Technical Field
The invention relates to the field of evaluation analysis methods, in particular to an intra-area energy health evaluation method based on an analytic hierarchy process.
Background
The application relates to an regional energy health evaluation method based on an analytic hierarchy process, which establishes a reasonable health evaluation system through deep analysis of a park comprehensive energy system. And grading each subsystem of the park comprehensive energy by adopting an analytic hierarchy process to realize the health evaluation of each subsystem, comprehensively evaluating the whole comprehensive energy system, and realizing the optimization of the intelligent energy system based on the comprehensive evaluation.
The intelligent energy system is a system which is based on energy industry infrastructure and based on an information physical system to realize digitization, automation, informatization, interactivity and intellectualization in each energy field, further realizes comprehensive integration of the whole energy network and can realize optimization decision and wide area coordination of energy on the basis of various primary and secondary energy development achievements in a certain area.
The comprehensive intelligent energy system aims at integrating various energy supply types such as coal, gas, oil, wind, light, biomass and the like in a certain area, planning, scheduling, cooperating and complementing are well carried out, various energy requirements such as electricity, heat, cold, steam, gas and the like of different users are optimally met according to local conditions, finally, the comprehensive improvement of the energy utilization efficiency is realized, the carbon dioxide emission is reduced, the environmental burden is reduced to the maximum extent, and the goal of carbon peak and carbon neutralization is positively promoted to be realized.
The comprehensive intelligent energy system energy management business needs to be realized by means of an informatization platform so as to realize the automation of energy management, and the standardization and systemization of push energy management. The energy management system should mainly include: energy input, energy conversion, energy distribution and transmission, energy use, energy consumption condition analysis, system health evaluation and the like. The health evaluation of the energy system is particularly important, and the running state of the energy system can be rapidly and effectively judged through the analysis of the health evaluation score of the system, and an optimization target and a scheme are provided.
In the prior art, the health degree of an energy system is evaluated, and most of the health degree of energy utilization equipment is evaluated. By adopting the method for scoring the running state of the running equipment, the health diagnosis of the system (equipment) is realized.
The invention patent CN202011431422.6 establishes a system optimization objective function by acquiring comprehensive energy system parameters, takes the sum of the running cost divided by the health degree of energy utilization equipment as a system control target, and establishes a source load balance constraint according to the total yield of the energy utilization equipment and the energy demand; and solving by using an energy optimization model to obtain an optimized yield control target of each energy utilization device. The energy utilization optimization method of the invention relates to the health degree of equipment, but a reasonable and effective equipment health degree evaluation method is not provided. The health of the energy usage device is obtained by other prior art techniques, or from other modules.
Aiming at the defects of the prior art, the regional energy health evaluation method based on the analytic hierarchy process establishes a health evaluation system of each subsystem in the park comprehensive energy system by the analytic hierarchy process, and establishes an evaluation system of the park comprehensive energy system based on the evaluation result of each subsystem to carry out overall evaluation on the whole comprehensive energy system.
Disclosure of Invention
The embodiment of the invention provides an intra-area energy health evaluation method based on an analytic hierarchy process. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
The embodiment of the invention provides an intra-area energy health evaluation method based on an analytic hierarchy process, which comprises the following steps:
s1: establishing a hierarchical structure model: dividing a hierarchical structure model into different levels by analyzing various factors of energy sources in a certain area, wherein one level is one level, each level is provided with a plurality of subsystems, and each level subsystem is provided with one or a plurality of evaluation indexes; determining the evaluation index of each level to complete the establishment of the hierarchical structure diagram/model,
s2: constructing a judgment matrix: quantitatively describing the importance of the comparison of every two evaluation indexes in the same hierarchy according to the hierarchical structure model to obtain the relative importance of each evaluation index, thereby constructing a judgment matrix; the numerical value in the judgment matrix is determined according to experience, data and research of evaluation analysts;
s3: calculating the relative weight of each evaluation index: according to the judgment matrix, carrying out normalization processing on the feature vector to obtain the weight of each evaluation index;
s5: scoring the health of energy sources within the certain region: according to the hierarchical analysis method, the evaluation index and the weight of the hierarchy of the hierarchical structure model are determined, and the overall score of the health degree of the energy system in a certain area is calculated, namely the health degree scoring of the energy system in the certain area is completed.
In some embodiments, preferably, the S2: constructing a judgment matrix, quantitatively describing the importance of the comparison of the evaluation indexes in the same layer according to the hierarchical structure model, and obtaining the relative importance of each evaluation index, thereby constructing the judgment matrix, and specifically comprising the following steps:
quantitatively describing the importance of the comparison of the evaluation indexes in the same layer, and constructing a judgment matrix A:
wherein,
a: judgment matrix
i: evaluation index i
j: evaluation index j
The matrix a satisfies the following condition:
α ij >0,α ii ＝1。
in some embodiments, preferably, S3: calculating the relative weight of each evaluation index: according to the judgment matrix A, carrying out normalization processing on the feature vector to obtain the weight of each evaluation index; the specific method comprises the following steps:
according to the judging matrix A, the maximum eigenvalue lambda max and eigenvector W are calculated,
and carrying out normalization processing on the feature vector W to obtain the weight of each evaluation index, wherein the weight is as follows:
wherein w= (W 1 ，w 2 ，w 3 …，w n )，
And satisfies the following formula:
AW＝λ max W。
in some embodiments, preferably, at said S3: calculating the relative weight of each evaluation index: according to the judgment matrix, carrying out normalization processing on the feature vector to obtain the weight of each evaluation index; thereafter, the method further comprises the following steps:
s4: consistency test: checking the reliability and consistency of the judgment matrix;
if the consistency is within the range of consistency test, accepting;
and if the judgment matrix is not in the range of the consistency test, correcting the judgment matrix until the judgment matrix is in the range of the consistency test.
In some embodiments, the first and second embodiments, preferably,
calculating a consistency index CI:
CI＝(λ max -n)/(n-1)
wherein:
CI represents a consistency index;
λmax represents the maximum eigenvalue;
n represents the order of the judgment matrix;
CI reflects the gap that the current judgment matrix achieves consistency:
the larger the CI value, the worse the consistency; the nearer the CI value is to 0, the closer the judgment matrix A is to complete consistency;
n is the order of the judgment matrix, namely the number of indexes participating in pairwise comparison, and the larger the value of n is, the larger the index CI deviating from the complete consistency due to subjective judgment is; the smaller the value of n, the smaller the deviation caused by subjective factors.
In some embodiments, it is preferred to find and calculate the corresponding average random uniformity index RI,
calculating a consistency ratio CR:
if CR is less than 0.1, the judgment matrix is considered to have satisfactory consistency and is accepted;
if CR is more than or equal to 0.1, the judgment matrix should be corrected until the consistency check condition is met.
In some embodiments, preferably, the S5: scoring the health of energy sources within the certain region: according to the analytic hierarchy process, determining the evaluation index and weight of each level of the hierarchical structure model, and after consistency test, finishing scoring the health degree of the energy source in a certain area, wherein the specific method comprises the following steps:
s51: calculating health scores of subsystems of all layers;
s52: calculating the overall score of the energy system health evaluation in a certain area;
s53: and classifying and prompting the overall score.
In some embodiments, preferably, the step S51: calculating health evaluation scores of subsystems of all layers; the specific calculation formula is as follows:
wherein:
Q s a health evaluation score indicating a subsystem of a certain layer;
i represents an evaluation index i;
k represents the total number of evaluation indexes of the subsystems of a certain layer;
P s,i a score representing an evaluation index i of subsystem s;
W s,i the normalized weight of the evaluation index i of subsystem s is represented.
In some embodiments, preferably, the S52: the method for calculating the overall health score of the energy system in a certain area comprises the following steps of:
calculating the overall score of the energy system health evaluation in a certain area according to the calculated health evaluation scores of the subsystems of each layer and the weights occupied by the subsystems of each layer,
the calculation formula is as follows:
wherein:
q represents the overall score of the regional energy system health evaluation;
s represents the total number of subsystems;
Q s a health assessment score representing subsystem s;
w s representing the normalized weights of subsystem s.
In some embodiments, preferably, the step S53: classifying the overall score and prompting:
by dividing the scores by 100, the scores were all rated at 4:
scores below 60 suggest "severe anomaly";
the score is between 60 and 80 (without 80) to prompt 'focus' attention;
the score is between 80 and 90 points (without 90 points) to indicate "good";
the score is between 90 and 100 points to indicate "healthy".
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
the invention provides an regional energy health evaluation method based on an analytic hierarchy process, which aims at a park comprehensive energy system with coexisting multiple energy forms, utilizes the analytic hierarchy process to evaluate and analyze the health of a subsystem and the comprehensive energy system, comprehensively evaluates and analyzes the running state of the park comprehensive energy system through the above forms, and provides an analysis basis and a guiding basis for park energy use safety, energy use economy and subsequent energy saving optimization.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flowchart illustrating a method for in-area energy health assessment based on a hierarchical analysis method, according to an exemplary embodiment;
FIG. 2 is a flowchart illustrating an intra-regional energy health assessment method optimized based on a hierarchical analysis method, according to an exemplary embodiment;
FIG. 3 is a schematic diagram of a hierarchical model showing an intra-area energy health assessment method based on a hierarchical analysis method according to an exemplary embodiment.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in, or substituted for, those of others. The scope of embodiments of the invention encompasses the full ambit of the claims, as well as all available equivalents of the claims. Embodiments may be referred to herein, individually or collectively, by the term "invention" merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed. Various embodiments are described herein in a progressive manner, each embodiment focusing on differences from other embodiments, and identical and similar parts between the various embodiments are sufficient to be seen with each other. The structures, products and the like disclosed in the embodiments correspond to the parts disclosed in the embodiments, so that the description is relatively simple, and the relevant parts refer to the description of the method parts.
The invention is further described below with reference to the accompanying drawings and examples:
as shown in fig. 1, a method for evaluating the health of energy in a region based on an analytic hierarchy process,
s1: establishing a hierarchical structure model: dividing a hierarchical structure model into different levels by analyzing various factors of energy sources in a certain area, wherein one level is one level, each level is provided with a plurality of subsystems, and each level subsystem is provided with one or a plurality of evaluation indexes; determining the evaluation index of each level to complete the establishment of the hierarchical structure diagram/model,
s2: constructing a judgment matrix: quantitatively describing the importance of the comparison of every two evaluation indexes in the same hierarchy according to the hierarchical structure model to obtain the relative importance of each evaluation index, thereby constructing a judgment matrix; the numerical value in the judgment matrix is determined according to experience, data and research of evaluation analysts;
s3: calculating the relative weight of each evaluation index: according to the judgment matrix, carrying out normalization processing on the feature vector to obtain the weight of each evaluation index;
s5: scoring the health of energy sources within the certain region: according to the hierarchical analysis method, the evaluation index and the weight of the hierarchy of the hierarchical structure model are determined, and the overall score of the health degree of the energy system in a certain area is calculated, namely the health degree scoring of the energy system in the certain area is completed.
Wherein,
s2: constructing a judgment matrix, quantitatively describing the importance of the comparison of the evaluation indexes in the same layer according to the hierarchical structure model, and obtaining the relative importance of each evaluation index, thereby constructing the judgment matrix, and specifically comprising the following steps:
quantitatively describing the importance of the comparison of the evaluation indexes in the same layer, and constructing a judgment matrix A:
wherein,
a: judgment matrix
i: evaluation index i
j: evaluation index i
The matrix a satisfies the following condition:
α ij >0,α ii ＝1。
wherein,
s3: calculating the relative weight of each evaluation index: according to the judgment matrix A, carrying out normalization processing on the feature vector to obtain the weight of each evaluation index; the specific method comprises the following steps:
according to the judging matrix A, the maximum eigenvalue lambda max and eigenvector W are calculated,
and carrying out normalization processing on the feature vector W to obtain the weight of each evaluation index, wherein the weight is as follows:
wherein w= (W 1 ，w 2 ，w 3 …，w n )，
And satisfies the following formula:
AW＝λ max W。
in some embodiments, as shown in fig. 2, between the steps S3 and S5, the following steps are further included:
s4: consistency test: checking the reliability and consistency of the judgment matrix;
if the consistency is within the range of consistency test, accepting;
and if the judgment matrix is not in the range of the consistency test, correcting the judgment matrix until the judgment matrix is in the range of the consistency test.
According to the above scheme, further, a consistency index CI is calculated:
CI＝(λ max -n)/(n-1)
wherein:
CI represents a consistency index;
λmax represents the maximum eigenvalue;
n represents the order of the judgment matrix;
CI reflects the gap that the current judgment matrix achieves consistency:
the larger the CI value, the worse the consistency; the nearer the CI value is to 0, the closer the judgment matrix A is to complete consistency;
n is the order of the judgment matrix, namely the number of indexes participating in pairwise comparison, and the larger the value of n is, the larger the index CI deviating from the complete consistency due to subjective judgment is; the smaller the value of n, the smaller the deviation caused by subjective factors.
Searching and calculating corresponding average random consistency index RI, calculating the average random consistency index obtained 1000 times through 1-11 th order positive-negative matrix,
calculating a consistency ratio CR:
if CR is less than 0.1, the judgment matrix is considered to have satisfactory consistency and is accepted;
if CR is more than or equal to 0.1, the judgment matrix should be corrected until the consistency check condition is met;
the average random consistency index is a calculated fixed value, is only related to the order of the matrix, and only the average random consistency index value of 1-11 orders is given here, so that the average random consistency index is convenient to find in practical application.
Wherein,
s5: scoring the health of energy sources within the certain region: according to the analytic hierarchy process, determining the evaluation index and weight of each level of the hierarchical structure model, and after consistency test, finishing scoring the health degree of the energy source in a certain area, wherein the specific method comprises the following steps:
s51: calculating health scores of subsystems of all layers;
s52: calculating the overall score of the health degree of the energy system in a certain area;
s53: and classifying and prompting the overall score.
Wherein,
s51: calculating health evaluation scores of subsystems of all layers; the specific calculation formula is as follows:
wherein:
Q s a health evaluation score indicating a subsystem of a certain layer;
i represents an evaluation index i;
k represents the total number of evaluation indexes of the subsystems of a certain layer;
P s,i a score representing an evaluation index i of subsystem s;
W s,i the normalized weight of the evaluation index i of subsystem s is represented.
Wherein,
s52: the overall score of the energy system health evaluation in a certain area is calculated, and the specific calculation method is as follows:
calculating the overall score of the energy system health evaluation in a certain area according to the calculated health evaluation scores of the subsystems of each layer and the weights occupied by the subsystems of each layer,
the calculation formula is as follows:
wherein:
q represents the overall score of the regional energy system health evaluation;
s represents the total number of subsystems;
Q s a health assessment score representing subsystem s;
w s representing the normalized weights of subsystem s.
Wherein,
s53: classifying the overall score and prompting:
by dividing the scores by 100, the scores were all rated at 4:
scores below 60 suggest "severe anomaly";
the score is between 60 and 80 (without 80) to prompt 'focus' attention;
the score is between 80 and 90 points (without 90 points) to indicate "good";
the score is between 90 and 100 points to indicate "healthy".
The area or area mentioned in the application may be, in some embodiments, an integrated energy system in some industrial parks, which generally includes a heating system, a power supply system, a cooling system, a water supply system, a gas supply system, and so on, and a core problem of health evaluation on the integrated energy system is how to formulate a scientific and reasonable evaluation index system, which affects the accuracy of the final evaluation result. According to the characteristics of each system and the operation targets, the secondary evaluation indexes and the related sub-level indexes are determined through detailed analysis and demonstration, and the hierarchical structure model is built by detailed table 1.
Table 1 comprehensive energy System health evaluation-hierarchical model
Under the hierarchical structure model shown in the table, the thought of the comprehensive energy system health evaluation is as follows: the first step, calculating the health degree score of each subsystem according to each evaluation index of each subsystem and the weight occupied by each subsystem; and secondly, according to the health degree score of each subsystem and the weight corresponding to each subsystem, making an overall score of the health degree of the whole comprehensive energy system, and making a prompt of health degree evaluation.
The basic idea of the analytic hierarchy process is to decompose a complex problem into constituent factors and to layer the constituent factors according to a dominant relationship, thereby establishing a clear hierarchical model of the hierarchy. On the same layer, determining the relative importance of each factor in the hierarchy in a pairwise comparison mode, quantifying the judgment standard by using a relative scale, establishing a corresponding judgment matrix layer by layer, and finally determining the weight coefficient of each factor by solving the normalized feature vector corresponding to the maximum feature value of the judgment matrix.
The method mainly comprises the following steps:
1. establishing a hierarchical model
The hierarchical structure model is built by dividing various factors in the complex problem into mutually related ordered hierarchies to make the factors physicochemical and hierarchical. And determining evaluation indexes of each level, and establishing a hierarchical evaluation structure chart as shown in figure 3.
2. Constructing a judgment matrix
The hierarchical model reflects the relationship between the factors, but the specific gravity of the evaluation index in each index layer in the target measurement is not necessarily the same. Therefore, the importance of the comparison of the factors in the same layer is quantitatively described according to experience and subjective judgment of a certain objective reality, and the relative importance of a single evaluation index is obtained, so that a judgment matrix is constructed. The numerical values in the judgment matrix are determined by repeated study mainly according to experience, data of evaluation analysts and the like. Each element in the matrix reflects the importance of that layer element relative to each evaluation index of the previous layer.
In order to convert the comparison problem of the complex features into simple two-factor comparison, the embodiment performs two-by-two comparison between the two factors to obtain a quantized judgment matrix, determines the scale rule of the two-factor comparison, and introduces the data scale index and meaning, as shown in table 2.
Table 2 scale rules for two factor comparison
| Data scale a ij | Meaning of |
| 1 | The factor i is as important as the factor j |
| 3 | The i factor is slightly more important than the j factor |
| 5 | The factor i is more important than the factor j |
| 7 | The factor i is more important than the factor j |
| 9 | The factor i is very important than the factor j |
| 2，4，6，8 | Is the scale value corresponding to the intermediate state between the above judgments |
| Reciprocal count | If the j factor is compared with the i factor, a judgment value of a is obtained ji ＝1/a ij |
According to the scale rule of the two factor comparison shown in the table, a pair-wise discrimination judgment matrix can be constructed as follows:
from the above discriminant rule, it can be seen that matrix a satisfies the following condition:
α ij >0,α ii ＝1
3. calculating the relative weight of each evaluation index
According to the judgment matrix A, the maximum eigenvalue lambda max and the eigenvector W are calculated. W is the corresponding normalized eigenvector, w= (W 1 ，w 2 ，w 3 …，w n ) The following formula is satisfied.
AW＝λ max W
And carrying out normalization processing on the feature vectors to obtain the weight of each evaluation index.
4. Consistency checking of hierarchy
Since the comparison values in the judgment matrix are determined by the expert according to subjective factors, logic errors like "A-to-B important, B-to-C important, and C-to-A important" may occur, and thus consistency check is required to evaluate the reliability of the matrix. The consistency test steps are as follows:
first, a consistency index CI is calculated:
CI＝(λ max -n)/(-1)
wherein:
CI represents a consistency index;
λmax represents the maximum eigenvalue;
n represents the order of the judgment matrix;
CI reflects the gap that the current judgment matrix achieves consistency:
the larger the CI value, the worse the consistency; the closer the CI value is to 0, the closer the decision matrix is to full consistency. N in the formula is the order of the judgment matrix, namely the number of indexes participating in pairwise comparison. The larger the value of n, the larger the index CI will deviate from perfect consistency due to subjective judgment; the smaller the value of n, the smaller the deviation caused by subjective factors.
Then, the corresponding average random uniformity index RI is found. The average random consistency index is a calculated fixed value, is only related to the order of the matrix, and only the average random consistency index value of 1-11 orders is given here, so that the average random consistency index is convenient to find in practical application. The average random consistency index obtained by calculating the 1-11 th order positive-negative matrix for 1000 times is shown in Table 3 in detail.
TABLE 3 average random uniformity index
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
| RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | 1.51 |
Finally, the consistency ratio CR is calculated:
if CR is less than 0.1, the judgment matrix is considered to have satisfactory consistency and is acceptable;
if CR is more than or equal to 0.1, the judgment matrix should be properly corrected until the consistency check condition is met.
5. Calculating health scores for subsystems
And after the weight of each evaluation index is calculated according to the analytic hierarchy process, substituting the corresponding weight coefficient into a subsystem health degree calculation formula to respectively calculate the comprehensive evaluation score of each subsystem. And comparing the health evaluation score of the subsystem with the alarm area score section to make corresponding alarm prompt information.
The calculation formula is as follows:
wherein:
Q s a health evaluation score representing a subsystem;
k represents the total number of subsystem evaluation indexes;
P s，i indicating the subsystem i index evaluation score;
w s，i representing the normalized weight of the i index.
6. Integrated score for health evaluation of park comprehensive energy system
And calculating the overall score of the health degree evaluation obtained by the park comprehensive energy system according to the health degree evaluation calculation formula of the comprehensive energy system and the weight of each subsystem, and giving corresponding prompt or alarm information according to the overall score condition of the health degree evaluation.
The calculation formula is as follows:
wherein:
q represents the overall score of the health evaluation of the park comprehensive energy system;
s represents the total number of subsystems;
Q s a health assessment score representing subsystem s;
w s representing the normalized weights of subsystem s.
7. System health score evaluation prompt
In the invention, the overall score of the health evaluation of the subsystem and the comprehensive energy system is divided according to 100 minutes. The sub-systems and the comprehensive energy system health degree are classified into 4 grades according to the result of the overall score of the sub-systems and the comprehensive energy system health degree evaluation:
scores below 60 suggest "severe anomaly";
the score is between 60 and 80 (without 80) to prompt 'focus' attention;
the score is between 80 and 90 points (without 90 points) to indicate "good";
the score is between 90 and 100 points to indicate "healthy".
Example 1
The regional energy health evaluation method based on the analytic hierarchy process comprises the following specific implementation steps:
1. constructing a judgment matrix
Aiming at the hierarchical structure model of the park comprehensive energy system, the basic principle and method steps of the hierarchical analysis method are applied, and the health degree weight of the park comprehensive energy system is calculated. The subsystems A1, A2, A3, A4 and A5 respectively evaluate the indexes to form a judgment matrix A for the park layer, and the numerical values in the judgment matrix are determined by repeated researches mainly according to data and experience of evaluation analysts.
The value of each evaluation index in the matrix A reflects the analysis and judgment of the importance degree difference of the related factors compared by the evaluation personnel.
According to the method, the judgment matrix formed by each factor of the performance index layer corresponding to each evaluation index condition of the subsystem layer can be obtained.
/>
The values of the evaluation indexes in the judgment matrixes A1-A5 also reflect the analysis and judgment of the importance degree difference of the related factors compared by the evaluation staff.
2. Calculating a maximum feature vector
And respectively calculating approximate solutions of the maximum eigenvectors of the judgment matrix A and the judgment matrices A1-A5:
w A ＝(0.489，0.233，0.086，0.140，0.051) T
w A1 ＝(0.572，0.109，0.209，0.109) T
w A2 ＝(0.539，0.297，0.164) T
w A3 ＝(0.572，0.109，0.209，0.109) T
w A4 ＝(0.539，0.297，0.164) T
w A5 ＝(0.539，0.297，0.164) T
3. consistency check
And respectively carrying out consistency test on each judgment matrix, wherein the random consistency ratio CR value of each judgment matrix is as follows:
CR A ＝0.017＜0.1
CR A1 ＝0.001＜0.1
CR A2 ＝0.004＜0.1
CR A3 ＝0.001＜0.1
CR A4 ＝0.004＜0.1
CR A5 ＝0.004＜0.1
the above results can demonstrate that the comparison judgment matrices A and A1-A5 all pass the consistency test and have acceptable consistency.
4. Calculating health evaluation scores of all subsystems
According to the formula:
the scores of the subsystems are calculated as follows:
Q s1 ＝0.572×88+0.109×90+0.209×85+0.109×92＝87.94
Q s2 ＝0.539×85+0.297×90+0.164×87＝86.81
Q s3 ＝0.572×90+0.109×86+0.209×85+0.109×84＝87.78
Q s4 ＝0.539×75+0.297×80+0.164×74＝76.32
Q s5 ＝0.539×82+0.297×80+0.164×78＝80.75
5. calculating overall score of comprehensive energy system health evaluation
Q s5 ＝0.489×87.94+0.233×86.81+0.086×87.78+0.140×76.32+0.051×80.75＝85.58
6. Prompt for overall score of system health evaluation
The integrated energy system health degree evaluation score is 85.58 and is 80-90, so the system health degree evaluation prompt is good.
The regional energy health evaluation method based on the analytic hierarchy process is characterized in that a health evaluation system of each subsystem in a park comprehensive energy system is established through the analytic hierarchy process, and an evaluation system of the park comprehensive energy system is established based on the evaluation results of each subsystem, so that the whole comprehensive energy system is evaluated.
It is to be understood that the invention is not limited to the arrangements and instrumentality shown in the drawings and described above, and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (6)
1. A regional energy health evaluation method based on an analytic hierarchy process is characterized in that,
s1: establishing a hierarchical structure model: dividing a hierarchical structure model by analyzing various factors of energy sources in a certain area, namely dividing the hierarchical structure model into different grades, wherein one layer is one grade, each layer is provided with a plurality of subsystems, and each subsystem of each layer is provided with one or a plurality of evaluation indexes; determining evaluation indexes of each level to finish building a hierarchical structure diagram/model;
s2: constructing a judgment matrix: quantitatively describing the importance of the comparison of every two evaluation indexes in the same hierarchy according to the hierarchical structure model to obtain the relative importance of each evaluation index, thereby constructing a judgment matrix; the numerical value in the judgment matrix is determined according to experience, data and research of evaluation analysts;
s3: calculating the relative weight of each evaluation index: according to the judgment matrix, carrying out normalization processing on the feature vector to obtain the weight of each evaluation index;
s5: scoring the health of energy sources within the certain region: determining an evaluation index and a weight of the hierarchy of the hierarchical structure model according to the method of S1-S3, and calculating an overall score of the health degree of the energy system in a certain area, namely finishing the grading of the health degree of the energy system in the certain area;
the S2: constructing a judgment matrix: according to the hierarchical structure model, quantitatively describing the importance of the comparison of every two evaluation indexes in the same hierarchy to obtain the relative importance of each evaluation index, thereby constructing a judgment matrix, and specifically comprising the following steps:
quantitatively describing the importance of the comparison of the evaluation indexes in the same layer, and constructing a judgment matrix:
wherein,
a: judgment matrix
i: evaluation index i
j: evaluation index j
The matrix a satisfies the following condition:
；
the evaluation index i and the evaluation index j are compared in pairs to obtain a quantized judgment matrix, a scale rule of the two-factor comparison is determined, a data scale index and a meaning are introduced, and the scale rule of the two-factor comparison is as follows:
s3: calculating the relative weight of each evaluation index: according to the judgment matrix, carrying out normalization processing on the feature vector to obtain the weight of each evaluation index; the specific method comprises the following steps:
according to the judging matrix A, the maximum eigenvalue lambda max and eigenvector W are calculated,
and carrying out normalization processing on the feature vector W to obtain the weight of each evaluation index, wherein the weight is as follows:
wherein,，
and satisfies the following formula:
；
at the S3: calculating the relative weight of each evaluation index: according to the judgment matrix, carrying out normalization processing on the feature vector to obtain the weight of each evaluation index; thereafter, the method further comprises the following steps:
s4: consistency test: checking the reliability and consistency of the judgment matrix;
if the consistency is within the range of consistency test, accepting;
if the judgment matrix is not in the consistency check range, correcting the judgment matrix until the judgment matrix is in the consistency check range;
calculating a consistency index CI:
wherein:
CI represents a consistency index;
λmax represents the maximum eigenvalue;
n represents the order of the judgment matrix;
CI reflects the gap that the current judgment matrix achieves consistency;
the larger the CI value, the worse the consistency; the nearer the CI value is to 0, the closer the judgment matrix A is to complete consistency;
n is the order of the judgment matrix, namely the number of indexes participating in pairwise comparison, and the larger the value of n is, the larger the index CI deviating from the complete consistency due to subjective judgment is; the smaller the value of n, the smaller the deviation caused by subjective factors.
2. The method for evaluating the intra-area energy health based on the analytic hierarchy process of claim 1, wherein the corresponding average random uniformity index RI is found and calculated,
calculating a consistency ratio CR:
if CR is less than 0.1, the judgment matrix is considered to have satisfactory consistency and is accepted;
if CR is more than or equal to 0.1, the judgment matrix should be corrected until the consistency check condition is met.
3. The method for evaluating the intra-area energy health based on the analytic hierarchy process of claim 1, wherein S5: scoring the health of energy sources within the certain region: according to the analytic hierarchy process, determining the evaluation index and weight of each level of the hierarchical structure model, and after consistency test, finishing scoring the health degree of the energy source in a certain area, wherein the specific method comprises the following steps:
s51: calculating health scores of subsystems of all layers;
s52: calculating the overall score of the energy system health evaluation in a certain area;
s53: and classifying and prompting the overall score.
4. The method for evaluating regional energy health based on analytic hierarchy process of claim 3, wherein S51: calculating health evaluation scores of subsystems of all layers; the specific calculation formula is as follows:
wherein:
Q s a health evaluation score indicating a subsystem of a certain layer;
i represents an evaluation index i;
k represents the total number of evaluation indexes of the subsystems of a certain layer;
P s , i a score representing an evaluation index i of subsystem s;
W s , i the normalized weight of the evaluation index i of subsystem s is represented.
5. The method for evaluating regional energy health based on analytic hierarchy process of claim 4, wherein S52: the method for calculating the overall health score of the energy system in a certain area comprises the following steps of:
calculating the overall score of the energy system health evaluation in a certain area according to the calculated health evaluation scores of the subsystems of each layer and the weights occupied by the subsystems of each layer,
the calculation formula is as follows:
wherein:
q represents the overall score of the regional energy system health evaluation;
s represents the total number of subsystems;
Q s a health assessment score representing subsystem s;
w s representing the normalized weights of subsystem s.
6. The method for evaluating regional energy health based on analytic hierarchy process of claim 5, wherein S53: classifying the overall score and prompting:
by dividing the scores by 100, the scores were all rated at 4:
scores below 60 suggest "severe anomaly";
the score is between 60 and 80 (without 80) to prompt 'focus' attention;
the score is between 80 and 90 points (without 90 points) to indicate "good";
the score is between 90 and 100 points to indicate "healthy".
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