CN114548641A - Power distribution network elasticity index evaluation method considering multi-energy coordination - Google Patents
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Abstract
The invention discloses a distribution network elasticity index evaluation method considering multi-energy coordination, which comprises the following steps: s1: selecting a multi-dimensional elasticity evaluation index; s2: performing relevance analysis on the evaluation indexes to obtain a judgment matrix, and obtaining a subjective weight vector based on a network analysis method; s3: obtaining an objective weight vector based on an entropy weight method; s4: obtaining a comprehensive weight vector according to the subjective weight vector and the objective weight vector; s5: calculating to obtain a fuzzy relation matrix; s6: obtaining a fuzzy comprehensive evaluation result; s7: and defuzzifying the fuzzy comprehensive evaluation result to obtain a gravity center value. On the basis of analyzing elastic influence factors, a set of multi-dimensional elastic evaluation indexes is selected to quantify the influence of multi-energy coordination, an elastic evaluation index system based on a network hierarchical structure is established, the correlation among all indexes is reflected, subjective and objective factors are combined, and an optimal weight model is established, so that the comprehensive weight of the indexes is more scientific, and the accuracy and the practicability of the elastic evaluation of the power distribution network are improved.
Description
Technical Field
The invention relates to the technical field of distribution network elasticity evaluation, in particular to a distribution network elasticity index evaluation method considering multi-energy coordination.
Background
The distribution network is located the end of electric power system, directly links to each other with the user, is the key of guarantee power supply. It should not only provide continuous power supply under normal operating conditions, but also maintain the necessary functionality in the event of a disturbance. In this context, the concept of distribution network resiliency is introduced to reflect the ability of the distribution network to minimize blackout margins under disturbances, reduce load loss during faults, guarantee as much as possible the supply of critical loads, and quickly recover blackout loads. The calculation of the existing elasticity index is mainly divided into two types, wherein the first type is dependent on the occurrence probability of a fault event, and weather information and the fault rate are integrated into the elasticity evaluation of the power distribution network; and the second type utilizes the system performance loss quantity to reflect elasticity according to different running and control targets of the power distribution network. One of the two types of elasticity indexes is to convert elasticity into a probability problem according to historical data such as fault probability and the like; and the other type is that according to the simulation power supply recovery process, the accumulated quantity of the system loss performance from the disturbance to the recovery process is calculated. The first type of evaluation method is based on an average value, the influence of a small probability event cannot be described, and the existing research basically aims at a certain specific event and does not have universality; in the second type of evaluation, the integration method relates to the whole process of different stages after the fault, but because the real-time variability of the power grid is not considered, the calculation of the performance missing area has the ideality.
Disclosure of Invention
The invention aims to solve the problems that the influence of small-probability events cannot be described and the method has ideality in the prior art, and provides the power distribution network elasticity index evaluation method considering multi-energy coordination.
In order to achieve the purpose, the invention adopts the following technical scheme:
a power distribution network elasticity index evaluation method considering multi-energy coordination comprises the following steps: s1: selecting a multi-dimensional elasticity evaluation index; s2: performing relevance analysis on the evaluation indexes to obtain a judgment matrix, and calculating subjective weight according to the judgment matrix and based on a network analysis method to obtain a subjective weight vector; s3: calculating objective weight according to the evaluation index and based on an entropy weight method to obtain an objective weight vector; s4: calculating comprehensive weight according to the subjective weight vector and the objective weight vector to obtain a comprehensive weight vector; s5: calculating the fuzzy membership degree of the evaluation index to obtain a fuzzy relation matrix; s6: obtaining a fuzzy comprehensive evaluation result according to the comprehensive weight vector and the fuzzy relation matrix; s7: defuzzifying the fuzzy comprehensive evaluation result to obtain a gravity center value, and finishing index evaluation corresponding to the evaluation grade. Aiming at the problems of the two existing elastic evaluation methods, the elastic evaluation index system of the power distribution network and the corresponding comprehensive evaluation method considering the multiple energy coordination are provided, and the power distribution network can be supplied with power under extreme interference, so that the recovery capability is positively influenced. According to the method, on the basis of analyzing the elasticity influence factors, a set of multi-dimensional elasticity evaluation indexes is selected to quantify the influence of multi-energy coordination. Secondly, an elasticity evaluation index system based on a network hierarchical structure is established, and the correlation among indexes is reflected. And finally, combining subjective and objective factors to establish an optimal weight model, so that the comprehensive weight of the index is more scientific, and the accuracy and the practicability of the elasticity evaluation of the power distribution network are improved by the method.
In a preferred embodiment of the present invention, the elasticity evaluation index in S1 includes a primary index and a secondary index under the primary index. The elastic index system provided by the invention can well quantify the influence of multi-energy coordination on the elasticity of the power distribution network, comprehensively considers the complex correlation among multi-dimensional indexes, and reduces the influence of the redundancy of the evaluation indexes on the elasticity evaluation result of the power distribution network.
As a preferred embodiment of the present invention, the S2 specifically is: performing relevance analysis on the secondary indexes under the primary indexes to obtain a judgment matrix, calculating eigenvectors corresponding to the maximum eigenvalues of the judgment matrix, namely the priority vectors of the secondary indexes, obtaining priority vectors of all indexes in the evaluation indexes, synthesizing the priority matrix, combining the priority vectors into an initial super matrix, performing relevance analysis on the primary indexes to obtain the judgment matrix, calculating eigenvectors corresponding to the maximum eigenvalues of the judgment matrix, namely the priority vectors of the primary indexes, obtaining priority vectors of all indexes in the evaluation indexes, synthesizing a weight matrix, weighting the initial super matrix by using the weight matrix to obtain a weighted super matrix, and calculating eigenvectors corresponding to the maximum eigenvalues of the weighted super matrix, namely subjective weight vectors.
As a preferred embodiment of the present invention, the S3 specifically is: the entropy of each index under different scenes is calculated, the entropy weight is calculated according to the entropy of the indexes, and the entropy weights of the indexes are combined to obtain an objective weight vector.
As a preferred embodiment of the present invention, the S4 specifically is: and establishing an optimal weight combination model according to the subjective weight vector and the objective weight vector, solving the optimal weight combination model by adopting a Lagrange multiplier method to obtain the comprehensive weight of each index, and combining the comprehensive weights of each index to obtain the comprehensive weight vector. The optimal weighting method provided by the invention establishes an optimal weight combination model, can relieve the excessive dependence of elastic evaluation on preference and data of an evaluator, and enables the comprehensive weight of the index to be more scientific.
As a preferred embodiment of the present invention, the S5 specifically is: dividing the evaluation result into five evaluation grades, calculating the membership of the secondary indexes to the five evaluation grades, forming a membership subset for each secondary index, and combining the membership subsets of each index into a fuzzy relation matrix.
As a preferred embodiment of the present invention, the S6 specifically is: and integrating the comprehensive weight vector and the fuzzy relation matrix to obtain a fuzzy comprehensive evaluation result vector. The fuzzy comprehensive evaluation method provided by the invention obtains the fuzzy comprehensive evaluation result, provides visual evaluation for the elasticity of the power distribution network, and is beneficial to finding the weak point of the elasticity performance.
As a preferred embodiment of the present invention, the S7 specifically is: and obtaining the gravity center value of the index according to a gravity center value calculation formula of the index, and finishing index evaluation according to the gravity center value and the evaluation grade.
Therefore, the invention has the following beneficial effects: on the basis of analyzing elastic influence factors, a set of multi-dimensional elastic evaluation indexes is selected to quantify the influence of multi-energy coordination, an elastic evaluation index system based on a network hierarchical structure is established, the correlation among all indexes is reflected, subjective and objective factors are combined, an optimal weight model is established, the comprehensive weight of the indexes is more scientific, and the accuracy and the practicability of the elastic evaluation of the power distribution network are improved through the method; the elastic index system provided by the invention can well quantify the influence of multi-energy coordination on the elasticity of the power distribution network, comprehensively considers the complex correlation among multi-dimensional indexes, and reduces the influence of the redundancy of the evaluation indexes on the elasticity evaluation result of the power distribution network; the optimal weighting method provided by the invention establishes an optimal weight combination model, so that the excessive dependence of elastic evaluation on preference and data of an evaluator can be relieved, and the comprehensive weight of the index is more scientific; the fuzzy comprehensive evaluation method provided by the invention obtains a fuzzy comprehensive evaluation result, provides visual evaluation for the elasticity of the power distribution network, and is beneficial to finding the weak point of the elasticity performance.
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FIG. 1 is a flow chart of a method of the present invention;
figure 2 is a schematic diagram of a node power distribution network architecture of an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following detailed description and accompanying drawings.
As shown in fig. 1, a method for evaluating distribution network elasticity indexes considering multi-energy coordination.
S1: and selecting a multi-dimensional elasticity evaluation index, and establishing a multi-dimensional elasticity evaluation index system. The system parameters, the running state, the response capability and the power supply reliability are selected as first-level indexes of elastic evaluation, the indexes can reflect the elastic characteristics of the power distribution network from different aspects, and the first-level indexes are subdivided into second-level indexes.
The system parameter index reflects the performance of the multi-energy network and the coupling equipment, and is the basis for measuring the running state of the system. The indexes comprise source network load indexes which influence the anti-interference capacity of the power distribution network, such as external power supply capacity, network strength, load distribution and the like, and coupling equipment parameter indexes which reflect multi-energy coordination are also considered, and specific secondary indexes are shown in table 1. All the metrics can be obtained or converted directly from the configuration data of the actual system.
TABLE 1 Secondary indices of System parameters
The system running state index reflects the adaptability of the system to extreme interference during the combined running of the multi-energy network, and is the basis for measuring the response capability of the system. The index set comprises indexes describing the running state and the running efficiency of the power distribution network and also comprises indexes reflecting the margin of power supply resources, and specific secondary indexes are shown in a table 2. The indexes correspond to the coupling operation state of the multi-energy network and can be obtained through multi-energy load flow calculation of an actual system.
TABLE 2 Secondary indices of System operating State
The system response capability index can reflect the response capability of the system to the pole-end interference, and comprises an index describing island power supply, the output of the coupling equipment and the regulation capability of the coupling equipment, and specific secondary indexes are shown in table 3. These indicators correspond to the support of the supply by means of a multi-energy coordination, which can be derived from the change in the state of the system after extreme disturbances.
TABLE 3 Secondary indices of System responsiveness
The basic goal of resiliency research is to improve the reliability of power supply under extreme disturbances. The power supply reliability index is the direct recovery performance of the power distribution network, and comprises indexes reflecting interference destructive results and load power supply capacity during a fault, and specific secondary indexes are shown in a table 4. These indicators can be obtained by counting the load loss and blackout time of the distribution network during the whole restoration process.
TABLE 4 Secondary index of Power supply reliability
S2: and analyzing the relevance of the indexes, and calculating subjective weight.
And integrating the correlation among the indexes into an evaluation system by using a network structure by adopting a network analysis method. Firstly, the indexes needing decision weight are systematically analyzed, and are divided into a plurality of different index sets according to first and second indexes, and the independence, possible mutual dependency and feedback of each index set level are judged. The method can be scored by experts or performed empiricallyAnd (4) judging (subjective factor). From the perspective of a system operation mechanism, progressive influence relations exist among the four primary index sets. For example, distributed power generation permeability I13Will affect the available power capacity margin I in the index set of the system operation status23. The power capacity margin will then have an impact on the proportion of islanding power in the system responsiveness index set. The actual power output will finally influence the power supply capability through multi-energy coordination, and is directly reflected on the power supply reliability index set.
In addition, from the perspective of the multi-energy coupling characteristic, the indexes included in the same primary index set are also correlated. For example, in a system operating condition index set, the distribution line load rate may affect the distribution network loss rate. Based on this, the correlation between the indexes can be given according to logic analysis and experience, the strong correlation corresponds to 1, the no correlation corresponds to 0, and a judgment matrix can be formed.
Each secondary index is regarded as an individual, all the secondary indexes are compared in pairs to obtain the correlation, and the distribution line strength I is measured according to the indexes11And index set System parameters I1For example, the following decision matrix may be formed:
wherein,
the upper corner mark represents a first-level index, namely index set system parameter I1Lower corner mark represents specific second-level index distribution line strength I under index set11Thus judging the matrix
From a secondary index I11And index set I1Formed of wherein c14Represents a secondary index I11And index set I1Second level index of14So that the judgment matrices are all symmetric matrices; the above-mentioned 4 first-class indexes are 4 indexesAnd the index sets have 4 secondary indexes in each index set, so that 64 judgment matrixes can be formed in total.
Second level index I11And index set I1Index set I2Index set I3Index set I4Respectively forming 4 judgment matrixes, calculating the eigenvector corresponding to the maximum eigenvalue of each judgment matrix, namely the priority vector, to obtain 4 priority vectors, and synthesizing the 4 priority vectors into a priority matrix W11By analogy, each secondary index and index set I1Index set I2Index set I3Index set I4A priority matrix can be formed, 16 priority matrices are formed in total, and the priority matrices are used as sub-matrices of the initial super-matrix W to synthesize the initial super-matrix W.
Each index set is regarded as an individual, the influence among all index sets is compared in pairs, the priority vectors are calculated according to the method for obtaining the priority vectors, the priority vectors are synthesized into a weight matrix A, the initial hypermatrix W is weighted by using the weight matrix A, and the weighted hypermatrix W can be obtained
Wherein
Wherein a isijBeing elements of a weight matrix A, WijFor the priority matrix, i is 1, 2, 3, 4, and j is 1, 2, 3, 4.
Weighted supermatrix
The numerical value of each row reflects the relative importance of the corresponding index in the whole evaluation system, and a weighted hypermatrix is calculated
The feature vector corresponding to the maximum feature value of the index is obtained to obtain a subjective weight vector [ gamma ] of the index1，γ2，……γY]TY is a selected secondary indexAnd (4) the number.
S3: the objective weights are calculated based on an entropy weight method.
And calculating objective weight based on an entropy weight method, and determining the information quantity provided by an index under different evaluation scenes by measuring the uncertainty of information, wherein the larger the information quantity is, the larger the index weight is. The method comprises the following steps:
assume that there are X evaluation scenarios and Y evaluation indices. Then, the evaluation index matrix is formed as follows:
wherein, the element IyxThe value of the y-th evaluation index in the x-th evaluation scenario is represented, and normalized data of the index is used here to eliminate the influence of different dimensions. By the formula
The entropy of the y-th evaluation index is calculated. Wherein
If p isyxIs equal to 0, then pyxlnpyxWill be set to 0.
Entropy weight τ of the y indexyBy the formula
And calculating to obtain an objective weight vector of the index through an entropy weight method: [ tau ] to1，τ2，……τY]T。
S4: and establishing an optimal weight combination model, obtaining comprehensive weight by combining subjective and objective factors, and deducing the comprehensive weight with the minimum subjective and objective weight deviation of each index based on a weighted least square criterion so as to minimize the weighted square sum of the deviation of the comprehensive weight and the subjective and objective weight. The problem that a network analysis method depends too much on the preference of an evaluator and an entropy weight method depends too much on data is effectively solved. Therefore, the influence of preference of evaluators can be relieved while the advantages of subjective and objective weights are kept, excessive dependence on data is made up, and the comprehensive weight of the index is more reasonable. The method comprises the following specific steps:
for the y index in the elastic evaluation index system, the subjective weight gamma is obtainedyAnd objective weight τyIts subjective and objective weighting coefficients are respectively alphay＝γy/(γy+τy) And betay＝τy/(γy+τy) Establishing an optimal weight combination model aiming at minimizing the weighted square sum of the deviation of the comprehensive weight and the subjective and objective weights, wherein the optimal model is as follows:
solving the optimization model by adopting a Lagrange multiplier method to obtain the comprehensive weight omega of each indexyForm a weight vector [ omega ]1，ω2，…ωy…ωY]T. F is the objective function, here a least squares construction, to finally find a set of ωyMinimizing the objective function; s.t. is a constraint representing ωyTo satisfy the condition, 1. must not be negative, 2. all combining weights add up to 1.
S5: and calculating fuzzy membership degree, and quantizing the performances of all indexes to obtain a fuzzy relation matrix. The evaluation of the grid elasticity is classified into five classes, i.e., V ═ V1，v2，v3，v4，v5}. On the basis, a comprehensive elasticity evaluation result is obtained through fuzzy relation transformation, the membership degree of each index to the evaluation grade is calculated according to the index value and the membership function, and the membership degree R of the yth index to the evaluation grade Vy＝{ry1，ry2，ry3，ry4，ry5Wherein each r isvIs in the range of [0,1]And their derivativesAnd is 1, rvThe larger the degree of membership. The method for calculating the membership degree is as follows:
assume the value I of the y indexyAt [ u1, u5]Within the range, will be [ u1, u5 ]]Dividing equally to obtain u1、u2、u3、u4、u5Obtaining fuzzy subsets R of all indexesySynthesizing a fuzzy relation matrix R
S6: for the weight vector [ omega ]1，ω2，…ωy…ωY]TAnd integrating with the fuzzy relation matrix R to obtain a fuzzy comprehensive evaluation result vector.
S7: and defuzzification is carried out on the evaluation result, and the gravity center value directly reflects the performance of each index and the elasticity of the power distribution network.
Converting the evaluation level to a value between 0 and 1, e.g. v1Corresponds to 1.0, v2Corresponding to 0.6, etc., the quantified evaluation level
Then index IyValue of center of gravity gyCan be calculated as:
the specific embodiment is as follows:
1) index selection
As shown in fig. 2, a modified IEEE 33 node distribution network is selected for analysis, and wind power, photovoltaic, energy storage, and interruptible loads are respectively connected to nodes 12,19,23, and 29.
Changing the operation mode of the system, generating a typical fault scene based on the component vulnerability and Monte Carlo simulation, and acquiring and calculating the proposed elasticity evaluation index value by using the original data obtained by simulation, wherein the result is shown in Table 5: (for simplicity of the process, only one secondary indicator is extracted from under each primary indicator)
TABLE 5 elasticity index values
| Index (I) | Index value |
| I11 | 0.6721 |
| I12 | 0.9234 |
| I13 | 0.2461 |
| I14 | 0.0584 |
2) Index correlation analysis
The correlation of the indexes is analyzed by adopting a network analysis method, and the correlation analysis result of each index is formed by combining the network structure and the historical experience used by the method, which is shown in table 6.
TABLE 6 index correlation results
| I11 | I12 | I13 | I14 | |
| I11 | 1 | 0.8 | 0.2 | 0.5 |
| I12 | 0.8 | 1 | 0.6 | 0.7 |
| I13 | 0.2 | 0.6 | 1 | 0.3 |
| I14 | 0.5 | 0.7 | 0.3 | 1 |
(3) Index weight calculation
Subjective weight, objective weight and comprehensive weight of each index are calculated based on a network analysis method, an entropy weight method and an optimal weight combination method respectively, and weight results are shown in a table 7. It can be seen that the comprehensive weight method provided by the invention can effectively overcome the defects of the subjective or objective weight method. For example, distribution line strength index I11In relation to a plurality of indices in the evaluation system, it is generally judged to be relatively important in the pair comparison. Thus, I obtained by network analysis11The subjective weight is too large and is obviously affected by subjective judgment. Objective weight of the synthetic entropy weight method, I11The comprehensive weight of the method is obviously reduced, and the weight result is more reasonable.
TABLE 7 elasticity index weights
| Index (I) | Subjective weighting | Objective weight | Composite weight |
| I11 | 0.0722 | 0.0255 | 0.0569 |
| I12 | 0.0501 | 0.0309 | 0.0396 |
| I13 | 0.0188 | 0.0221 | 0.0175 |
| I14 | 0.0208 | 0.0094 | 0.0141 |
4) Comprehensive evaluation of distribution network elasticity
And carrying out distribution network elasticity evaluation based on a fuzzy comprehensive evaluation method. First, all the index pairs { v } in the evaluation system are calculated separately1，v2，v3，v4，v5Membership of these five evaluation levels. And then, deriving the gravity center value of the index from the fuzzy relation matrix for directly judging the elasticity of the power distribution network.
The detailed evaluation results of the comprehensive reflection of the elasticity performance of the power distribution network are shown in table 8. Finally, the vector of the comprehensive evaluation result of the elasticity of the power distribution network obtained through index weighting operation is [0.38, 0.15, 0.14, 0.16, 0.17], the gravity center value of the overall elasticity performance is 0.69, and a large space is still left for elasticity improvement.
TABLE 8 index membership and center of gravity values
| Index/grade | v1 | v2 | v3 | v4 | v5 | Center of gravity value |
| I11 | 0.16 | 0.29 | 0.39 | 0.17 | 0.00 | 0.69 |
| I12 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| I13 | 0.22 | 0.24 | 0.36 | 0.15 | 0.04 | 0.69 |
| I14 | 0.44 | 0.11 | 0.07 | 0.21 | 0.16 | 0.69 |
The invention belongs to the technical field of distribution network elasticity evaluation, and aims to solve the problems that most of the existing methods only carry out unilateral evaluation on distribution network elasticity, and the correlation among multiple indexes is not well researched. Secondly, the influence of the coordinated operation of various energy sources on the elasticity of the distributed network is not fully quantified. In addition, the weighting method of a plurality of evaluation indexes is not scientific enough, and the elastic performance is difficult to present intuitively.
The above description is only for the specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto, and any changes or substitutions that are not thought of through the inventive work should be covered within the protection scope of the present invention.
Claims (8)
1. A distribution network elasticity index evaluation method considering multi-energy coordination is characterized by comprising the following steps:
s1: selecting a multi-dimensional elasticity evaluation index;
s2: performing relevance analysis on the evaluation indexes to obtain a judgment matrix, and calculating subjective weight according to the judgment matrix and based on a network analysis method to obtain a subjective weight vector;
s3: calculating objective weight according to the evaluation index and based on an entropy weight method to obtain an objective weight vector;
s4: calculating comprehensive weight according to the subjective weight vector and the objective weight vector to obtain a comprehensive weight vector;
s5: calculating the fuzzy membership degree of the evaluation index to obtain a fuzzy relation matrix;
s6: obtaining a fuzzy comprehensive evaluation result according to the comprehensive weight vector and the fuzzy relation matrix;
s7: defuzzifying the fuzzy comprehensive evaluation result to obtain a gravity center value, and finishing index evaluation corresponding to the evaluation grade.
2. The method as claimed in claim 1, wherein the elasticity evaluation index of the distribution network considering coordination of multiple energy sources in the step S1 includes a primary index and a secondary index under the primary index.
3. The method according to claim 2, wherein the step S2 is as follows: performing relevance analysis on the secondary indexes under the primary indexes to obtain a judgment matrix, calculating eigenvectors corresponding to the maximum eigenvalues of the judgment matrix, namely the priority vectors of the secondary indexes, obtaining priority vectors of all indexes in the evaluation indexes, synthesizing the priority matrix, combining the priority vectors into an initial super matrix, performing relevance analysis on the primary indexes to obtain the judgment matrix, calculating eigenvectors corresponding to the maximum eigenvalues of the judgment matrix, namely the priority vectors of the primary indexes, obtaining priority vectors of all indexes in the evaluation indexes, synthesizing a weight matrix, weighting the initial super matrix by using the weight matrix to obtain a weighted super matrix, and calculating eigenvectors corresponding to the maximum eigenvalues of the weighted super matrix, namely subjective weight vectors.
4. The method as claimed in claim 1, wherein the step S3 is as follows: the entropy of each index under different scenes is calculated, the entropy weight is calculated according to the entropy of the indexes, and the entropy weights of the indexes are combined to obtain an objective weight vector.
5. The method as claimed in claim 1, wherein the step S4 is as follows: and establishing an optimal weight combination model according to the subjective weight vector and the objective weight vector, solving the optimal weight combination model by adopting a Lagrange multiplier method to obtain the comprehensive weight of each index, and combining the comprehensive weights of each index to obtain the comprehensive weight vector.
6. The method according to claim 2, wherein the step S5 is as follows: dividing the evaluation result into five evaluation grades, calculating the membership of the secondary indexes to the five evaluation grades, forming a membership subset for each secondary index, and combining the membership subsets of each index into a fuzzy relation matrix.
7. The method as claimed in claim 1, wherein the step S6 is as follows: and integrating the comprehensive weight vector and the fuzzy relation matrix to obtain a fuzzy comprehensive evaluation result vector.
8. The method as claimed in claim 1, wherein the step S7 is as follows: and obtaining the gravity center value of the index according to a gravity center value calculation formula of the index, and finishing index evaluation according to the gravity center value and the evaluation grade.
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| CN116775439B (en) * | 2023-08-17 | 2023-11-14 | 北京万界数据科技有限责任公司 | AI cloud computing resource pool evaluation system |
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