CN110472822A - A kind of intelligent distribution network Reliability Evaluation system and method - Google Patents

A kind of intelligent distribution network Reliability Evaluation system and method Download PDF

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CN110472822A
CN110472822A CN201910602918.6A CN201910602918A CN110472822A CN 110472822 A CN110472822 A CN 110472822A CN 201910602918 A CN201910602918 A CN 201910602918A CN 110472822 A CN110472822 A CN 110472822A
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赵俊浩
吴杰康
张文杰
毛颖卓
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Guangdong University of Technology
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Abstract

The present invention relates to a kind of intelligent distribution network Reliability Evaluation system and method, the system comprises: power system of data acquisition module, data processing module, Reliability Evaluation module；It the described method comprises the following steps: S1: determining the intelligent distribution network Reliability Evaluation factor；S2: factor value matrix is established；S3: the assessment entropy coefficient of each factor is calculated using improvement entropy assessment；S4: the assessment interests coefficient of each factor is calculated；S5: comprehensive assessment coefficient is calculated；S6: it according to comprehensive assessment coefficient, adds up again after the numerical value of the corresponding factor is standardized, after being multiplied with corresponding metewand and obtains the power supply reliability scoring of intelligent distribution network.The present invention can more objectively and accurately assess the power supply reliability of intelligent distribution network, for realizing to the assay of intelligent distribution network power supply reliability, provide guidance and help for the planning, construction, transformation of power distribution network and be of great significance.

Description

System and method for evaluating power supply reliability of intelligent power distribution network
Technical Field
The invention relates to the technical field of power systems and automation thereof, in particular to a system and a method for evaluating power supply reliability of an intelligent power distribution network.
Background
In recent years, the research and construction of smart grids has risen globally. As an important component of the smart power grid, the intellectualization of the power distribution network becomes a new trend of the future power grid development, and plays a significant role in realizing the overall goal of smart power grid construction. And with the transformation and upgrading of the traditional power distribution network to the intelligent power distribution network, factors considered by reliability evaluation are changed. For the power supply reliability evaluation of the intelligent power distribution network, besides the factors considered in the traditional power supply reliability evaluation of the power distribution network, the power supply reliability evaluation of the intelligent power distribution network also needs to be considered according to the characteristics of the intelligent power distribution network. Therefore, a scientific, reasonable and comprehensive evaluation factor system and method need to be constructed to analyze and evaluate the power supply reliability of the intelligent power distribution network and provide guidance and help for planning, construction and transformation of the power distribution network.
At present, a great deal of research work is done on the intelligent power distribution network by a plurality of scholars at home and abroad, but the research on the power supply reliability of the intelligent power distribution network is relatively less. For example, in the entropy weight method, the traditional entropy value calculation entropy weight calculation formula, when the entropy value is in a certain interval, the small difference between the entropy values may cause the change of the entropy weight by multiple times, which is inconsistent with the information transmitted by the entropy value, and in the analytic hierarchy process, the traditional method scores evaluation objects through a plurality of judgment main bodies, and then simply takes the scored mean value to construct a judgment matrix to calculate the weight of each level of factor; however, when the same preference exists between the judgment subjects or the judgment subjects are biased to a certain evaluation factor, more floors can be won by increasing the weight of the factor, so that the fairness of the factor weight calculation is lost.
Disclosure of Invention
The invention provides a system and a method for evaluating the power supply reliability of an intelligent power distribution network, aiming at overcoming the defect that the method for evaluating the power supply reliability of the intelligent power distribution network in the prior art is not objective and accurate enough.
The system comprises: the system comprises a power system data acquisition module, a data processing module and a power supply reliability evaluation module;
the data processing module is respectively connected with the power supply reliability evaluation module and the power system data acquisition module;
the power system data acquisition module is responsible for acquiring related data of a power system and transmitting the acquired data to the data processing module; the data collected by the power system data collection module comprise: the method comprises the steps that the output of a distributed power supply of the microgrid system, the discharge power of a storage battery, the load size, the power failure load size, the number of users, the accumulated times of power failure of the users, the power failure duration time of the users and the total time of power failure of the users are calculated;
the data processing module is responsible for data processing and comprises calculation of power supply reliability evaluation factors of the intelligent power distribution network, calculation of an improved entropy weight method, calculation of evaluation benefit coefficients and calculation of comprehensive evaluation coefficients; the data processing module is also responsible for transmitting the comprehensive evaluation coefficient to the power supply reliability evaluation module;
and the power supply reliability evaluation module receives the data result processed by the data processing module and calculates and evaluates the power supply reliability of the intelligent power distribution network.
The method is applied to the power supply reliability evaluation system of the intelligent power distribution network, and comprises the following steps:
s1: determining a power supply reliability evaluation factor of the intelligent power distribution network;
s2: according to the power supply reliability evaluation factors of the intelligent power distribution network, the factors with higher reliability are processed respectively when the numerical value is larger, and the factors with higher reliability are processed respectively when the numerical value is smaller; after dimensionless factor values are obtained, a factor value matrix is established;
s3: calculating to obtain an evaluation entropy coefficient of each factor by using an improved entropy weight method according to the factor numerical matrix obtained in the step S2;
s4: calculating benefit preference degrees among the intelligent power distribution networks, preventing the objective evaluation of the factors from being influenced by the same benefit or preference relationship among the intelligent power distribution networks, updating an evaluation matrix according to the benefit preference degrees, and then calculating evaluation benefit coefficients of the factors;
s5: calculating a comprehensive evaluation coefficient according to the two evaluation coefficients obtained in the step S3 and the step S4;
s6: and according to the comprehensive evaluation coefficient obtained in the step S5, normalizing the numerical value of the corresponding factor, multiplying the numerical value by the corresponding evaluation coefficient, and accumulating to obtain the power supply reliability score of the intelligent power distribution network.
Preferably, S1 includes the steps of:
s1.1: collecting related data by using a data collector of the power system: the method comprises the steps that the output of a distributed power supply of the microgrid system, the discharge power of a storage battery, the load size, the power failure load size, the number of users, the accumulated times of power failure of the users, the power failure duration time of the users and the total time of power failure of the users are calculated; defining the adequacy alpha of the microgrid system and the load transfer degree beta;
in the formula, ωiThe probability of forming a micro-grid system i after a fault occurs; n is the number of possible micro-grid system scenes; alpha is alphaiThe method comprises the steps of (1) providing a microgrid system adequacy of a microgrid system i; n is a radical ofiThe number of all possible combinations of the operating states in the microgrid i is set; alpha is alphai,jThe probability of the j operation state of the microgrid i is obtained;andthe output of the distributed power supply, the discharge power of the storage battery and the load under the j operation mode of the microgrid i are respectively the magnitude;
wherein M is the number of possible N-1 faults; omega'iThe probability of occurrence of the ith N-1 fault; beta is aiThe load transfer degree of the ith N-1 fault; pLIs the sum of the loads of the system before the fault;is the limit transmission power of line j; n isiThe residual lines which can normally run in the distribution network are the lines when the ith N-1 fault occurs;in the ith N-1 fault, the power transmitted by the line j is required to ensure that the system load is cut off to the minimum.
S1.2: combine the reliability factor of traditional distribution network: load point factors and system factors, so as to determine power supply reliability evaluation factors of the intelligent power distribution network: a microgrid system adequacy alpha; load transfer degree β; the average failure rate p of the load points; the annual average power failure time U of a load point; the average power failure duration time r of each fault of the load point; average power failure frequency SAIFI of the system; average power failure frequency CAIFI of a user; the average power failure duration SAIDI of the system; average power failure duration CAIDI of a user; average power supply availability ASAI; the total electric quantity of the system is insufficient ENS; the average power shortage factor AENS of the system.
Preferably, the processing of the factor with higher reliability in S2 with larger value and the factor with higher reliability in S2 with smaller value is:
(1) for factors with higher numerical values and higher reliability, the process is:
in the formula, aijThe value of the dimensionless factor is processed; x is the number ofijIs a factor value before processing;
(2) for factors with smaller values and higher reliability, the process is:
in the formula, aijThe value of the dimensionless factor is processed; x is the number ofijIs the factor value before processing.
Preferably, the matrix of factor values in S2 is:
in the formula, aijA dimensionless number, i ═ 1, 2.·, n, representing the ith evaluation factor for the ith evaluation subject; j is 1, 2.. the m, n is the number of the evaluation objects, and m is the number of the evaluation factors.
Preferably, S3 includes the steps of:
s3.1: according to the elements in the factor numerical matrix, calculating the entropy value of each evaluation factor, wherein the calculation formula is as follows:
in the formula, when aikWhen equal to 0, aik ln aik＝0；aikRepresents a dimensionless number of the ith evaluation object at the kth evaluation factor, and m is the number of the evaluation factors.
S3.2: on the basis of obtaining the entropy value, obtaining the entropy coefficient of each factor according to the following improved entropy weight calculation formula:
in the formula, λ1jAn estimated entropy coefficient for the jth estimation factor; hkEntropy of the kth evaluation factor; m is the number of evaluation factors; hlEntropy for the l-th assessment factor;
s3.3: the evaluation entropy coefficient vector λ of the evaluation factor can be obtained from step S3.21＝[λ11,λ12,…,λ1m]。
Preferably, S4 includes the steps of:
s4.1: calculating the membership degree of the corresponding factor to the reliability of the intelligent power distribution network according to the factor numerical matrix obtained in the step S2, and substituting the factor with higher reliability into the ring function when the numerical value is smaller; for larger values, factors with higher reliability are substituted into the ring-down function. And constructing an evaluation matrix Q of the intelligent power distribution network:
in the formula, qijThe membership degree value of a factor j in the intelligent power distribution network i is 1, 2. j is 1,2,. said, m; m is the number of evaluation factors;
s4.2: calculating benefit preference coefficients among the intelligent power distribution networks and benefit preference degrees of the intelligent power distribution network individuals, and if the benefit preference degrees are smaller, proving that the benefits or the preferences of the intelligent power distribution network are the same as those of other intelligent power distribution networks, and the evaluation of the factors is more unfair; the calculation formula of the interest preference degree is as follows:
in the formula, muabFor the interest preference q between the intelligent power distribution network a and the intelligent power distribution network bak，qbkRespectively taking the numerical values of a factor k in the intelligent power distribution network a and the intelligent power distribution network b; mu.saThe interest preference of an individual of the intelligent power distribution network a is given; a 1,2,. and x; 1,2, x; mu.saiThe preference degree of interest between the intelligent power distribution network a and the intelligent power distribution network i is 1, 2.. times, x;
s4.3: calculating the evaluation ratio of each intelligent power distribution network:
in the formula etaaThe evaluation proportion of the intelligent power distribution network a is obtained; a 1, 2.
S4.4: according to the membership value of the evaluation factor in each intelligent power distribution network, a judgment matrix D is constructedx：
Wherein the content of the first and second substances,the relative importance of the ith factor and the jth factor in the intelligent power distribution network y is shown;
s4.5: obtaining benefit coefficient vectors W of all intelligent power distribution networks by using analytic hierarchy processi＝[Wi1,Wi2,…,Wim]，WimEvaluating benefit coefficients of the mth evaluation factor in the intelligent power distribution network i;
s4.6: finally, calculating to obtain an evaluation benefit coefficient vector lambda of the evaluation factor2＝[λ21,λ22,…,λ2m]Wherein, in the step (A),ηifor the evaluation of the intelligent distribution network i, WijAnd evaluating benefit coefficients of the factors j of the intelligent power distribution network i.
Preferably, the coefficient vector λ ═ λ is comprehensively evaluated in S51,λ2,…,λm]Wherein λ ism＝0.5λ1m+0.5λ2m，λmFor the comprehensive evaluation of the coefficient vector, λ1mFor the estimated entropy coefficient of the mth estimation factor, λ2mThe evaluation interest coefficient for the mth evaluation factor.
Preferably, in S6, the calculation formula of the power supply reliability score of the intelligent power distribution network is as follows:
U＝I1λ1+I2λ2+…+Ikλk+…+I12λ12
in the formula IkIs a normalized value of the k-th factor, λkAnd U is an evaluation coefficient corresponding to the kth factor, wherein k is 1,2, 12, and the higher the score is, the higher the power supply reliability of the intelligent power distribution network is proved to be.
The basic principle of the method is that firstly, the concept of the adequacy and the load transfer degree of the micro-grid system is provided according to the characteristics of the intelligent power distribution network, and a power supply reliability evaluation factor system of the intelligent power distribution network is established by combining the traditional power supply reliability factor of the power distribution network. Meanwhile, aiming at the limitations existing in the entropy weight method and the analytic hierarchy process, the improved entropy weight method and the improved analytic hierarchy process are provided, and the two methods are applied to the power supply reliability evaluation of the intelligent power distribution network.
The method not only provides an assessment factor system for the power supply reliability of the intelligent power distribution network, but also solves the problem that an entropy weight method and an analytic hierarchy process have limitations.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that: the method not only selects various factors to evaluate the power supply reliability of the intelligent power distribution network, but also overcomes the defects of the traditional weight determination method by an entropy weight method and an Analytic Hierarchy Process (AHP), can evaluate the power supply reliability of the intelligent power distribution network more objectively and accurately, and has important significance for realizing the analysis and evaluation of the power supply reliability of the intelligent power distribution network and providing guidance and help for planning, construction and reconstruction of the power distribution network.
Drawings
Fig. 1 is a flowchart of a method for evaluating power supply reliability of an intelligent power distribution network.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1:
the embodiment provides a system for evaluating power supply reliability of an intelligent power distribution network, which comprises: the system comprises a power system data acquisition module, a data processing module and a power supply reliability evaluation module;
the data processing module is respectively connected with the power supply reliability evaluation module and the power system data acquisition module;
the power system data acquisition module is responsible for acquiring related data of a power system and transmitting the acquired data to the data processing module; the data collected by the power system data collection module comprise: the method comprises the steps that the output of a distributed power supply of the microgrid system, the discharge power of a storage battery, the load size, the power failure load size, the number of users, the accumulated times of power failure of the users, the power failure duration time of the users and the total time of power failure of the users are calculated;
the data processing module is responsible for data processing and comprises calculation of power supply reliability evaluation factors of the intelligent power distribution network, calculation of an improved entropy weight method, calculation of evaluation benefit coefficients and calculation of comprehensive evaluation coefficients; the data processing module is also responsible for transmitting the comprehensive evaluation coefficient to the power supply reliability evaluation module;
and the power supply reliability evaluation module receives the data result processed by the data processing module and calculates and evaluates the power supply reliability of the intelligent power distribution network.
Example 2:
the embodiment provides a method for evaluating the power supply reliability of an intelligent power distribution network, which is applied to the system in the embodiment 1; as shown in fig. 1, the method comprises the steps of:
s1: determining a power supply reliability evaluation factor of the intelligent power distribution network;
s2: according to the power supply reliability evaluation factors of the intelligent power distribution network, the factors with higher reliability are processed respectively when the numerical value is larger, and the factors with higher reliability are processed respectively when the numerical value is smaller; after dimensionless factor values are obtained, a factor value matrix is established;
s3: calculating to obtain an evaluation entropy coefficient of each factor by using an improved entropy weight method according to the factor numerical matrix obtained in the step S2;
s4: firstly, calculating benefit preference degrees among the intelligent power distribution networks, preventing the objective evaluation of the factors from being influenced by the same benefit or preference relationship among the intelligent power distribution networks, updating an evaluation matrix according to the benefit preference degrees, and then calculating evaluation benefit coefficients of all the factors;
s5: calculating a comprehensive evaluation coefficient according to the two evaluation coefficients obtained in the step S3 and the step S4;
s6: and according to the comprehensive evaluation coefficient obtained in the step S5, normalizing the numerical value of the corresponding factor, multiplying the numerical value by the corresponding evaluation coefficient, and accumulating to obtain the power supply reliability score of the intelligent power distribution network.
S1 in fig. 1 describes a method for determining the evaluation factor of the power supply reliability of the smart distribution grid.
Because in the intelligent power distribution network, after the trouble takes place, can isolate the trouble fast, carry out the load and transfer and supply and can form little electric wire netting even and guarantee user's normal power supply. Therefore, the data acquisition device of the power system is used for acquiring related data: the method comprises the steps that the output of a distributed power supply of the microgrid system, the discharge power of a storage battery, the load size, the power failure load size, the number of users, the accumulated times of power failure of the users, the power failure duration time of the users and the total time of power failure of the users are calculated; and defining two concepts of the microgrid system adequacy alpha and the load transfer degree beta:
in the formula, alphaiThe method comprises the steps of (1) providing a microgrid system adequacy of a microgrid system i; n is a radical ofiThe number of all possible combinations of the operating states in the microgrid i is set; alpha is alphai,jThe probability of the j operation state of the microgrid i is obtained;anddistributed under the j operation modes of the microgrid i respectivelyPower supply output, discharge power of the storage battery and the size of the load.
In the formula, ωiThe probability of forming a micro-grid system i after a fault occurs; and N is the number of possible formed micro-grid system scenes.
In the formula, betaiThe load transfer degree of the ith N-1 fault; pLIs the sum of the loads of the system before the fault;is the limit transmission power of line j; n isiThe residual lines which can normally run in the distribution network are the lines when the ith N-1 fault occurs;in the ith N-1 fault, the power transmitted by the line j is required to ensure that the system load is cut off to the minimum.
From this, the load transfer degree β of the entire system:
wherein M is the number of possible N-1 faults; omega'iIs the probability of occurrence of the ith N-1 fault.
Then, combining the reliability factors of the traditional power distribution network: load point factors and system factors, so as to determine power supply reliability evaluation factors of the intelligent power distribution network: a microgrid system adequacy alpha; load transfer degree β; the average failure rate p of the load points; the annual average power failure time U of a load point; the average power failure duration time r of each fault of the load point; average power failure frequency SAIFI of the system; average power failure frequency CAIFI of a user; the average power failure duration SAIDI of the system; average power failure duration CAIDI of a user; average power supply availability ASAI; the total electric quantity of the system is insufficient ENS; the average power shortage factor AENS of the system.
Step S2 in fig. 1 describes a method of building a factor value matrix.
And evaluating the factors according to the power supply reliability of the intelligent power distribution network, and respectively processing the factors with higher reliability when the numerical value is larger and the factors with higher reliability when the numerical value is smaller.
For factors with higher values and higher reliability, the process is as follows:
in the formula, aijThe value of the dimensionless factor is processed; x is the number ofijIs the factor value before processing.
For factors with smaller values and higher reliability, the following is processed:
in the formula, aijThe value of the dimensionless factor is processed; x is the number ofijIs the factor value before processing.
After dimensionless factor values are obtained, a factor value matrix A is established
In the formula, aijA dimensionless number, i ═ 1, 2.·, n, representing the ith evaluation factor for the ith evaluation subject; j is 1, 2.. the m, n is the number of the evaluation objects, and m is the number of the evaluation factors.
S3 in fig. 1 describes a method of calculating objective weights using the modified entropy weight method.
For the factor value matrix obtained in step S2, the estimated entropy coefficients of the respective factors are calculated by using the improved entropy weight method.
S3.1: according to the elements in the factor numerical matrix, calculating the entropy value of each evaluation factor, wherein the calculation formula is as follows:
in the formula, when aikWhen equal to 0, aik ln aik＝0；aikRepresents a dimensionless number of the ith evaluation object at the kth evaluation factor, and m is the number of the evaluation factors.
S3.2: on the basis of obtaining the entropy value, obtaining the entropy coefficient of each factor according to the following improved entropy weight calculation formula:
in the formula, λ1jAn estimated entropy coefficient for the jth estimation factor; hkEntropy of the kth evaluation factor; m is the number of evaluation factors; hlEntropy for the l-th assessment factor;
s3.3: the evaluation entropy coefficient vector λ of the evaluation factor can be obtained from step S3.21＝[λ11,λ12,…,λ1m]。
Step S4 in fig. 1 describes a method for calculating the estimated benefit coefficient using the modified AHP method.
S4.1: calculating the membership degree of the corresponding factor to the reliability of the intelligent power distribution network according to the factor numerical matrix obtained in the step S2, and substituting the factor with higher reliability into the ring function when the numerical value is smaller; for larger values, factors with higher reliability are substituted into the ring-down function. And constructing an evaluation matrix Q of the intelligent power distribution network:
in the formula, qijThe membership degree value of a factor j in the intelligent power distribution network i is 1, 2. j is 1,2,. said, m; m is the number of evaluation factors;
s4.2: calculating benefit preference coefficients among the intelligent power distribution networks and benefit preference degrees of the intelligent power distribution network individuals, and if the benefit preference degrees are smaller, proving that the benefits or the preferences of the intelligent power distribution network are the same as those of other intelligent power distribution networks, and the evaluation of the factors is more unfair; the calculation formula of the interest preference degree is as follows:
in the formula, muabFor the interest preference q between the intelligent power distribution network a and the intelligent power distribution network bak，qbkRespectively taking the numerical values of a factor k in the intelligent power distribution network a and the intelligent power distribution network b; mu.saThe interest preference of an individual of the intelligent power distribution network a is given; a 1,2,. and x; 1,2, x; mu.saiThe preference degree of interest between the intelligent power distribution network a and the intelligent power distribution network i is 1, 2.. times, x;
s4.3: calculating the evaluation ratio of each intelligent power distribution network:
in the formula etaaThe evaluation proportion of the intelligent power distribution network a is obtained; a 1, 2.
S4.4: according to the membership value of the evaluation factor in each intelligent power distribution network, a 1-9 proportion scaling method is utilized to construct a judgment matrix DxThe relative importance of the evaluation factors of each level is qualitatively described and quantitatively expressed by accurate numbers.
The 1-9 proportional scaling method is that the number 1 in the judgment matrix represents that two elements have the same importance on a certain attribute; the number 9 indicates that the former is extremely important compared to the latter; the middle numbers indicate the meaning and so on, and the values of the individual scales indicate the following table:
judgment matrix DxThe expression of (a) is:
wherein,the relative importance of the ith factor and the jth factor in the intelligent power distribution network y is shown;
s4.5: obtaining benefit coefficient vectors W of all intelligent power distribution networks by using analytic hierarchy processi＝[Wi1,Wi2,…,Wim]，WimEvaluating benefit coefficients of the mth evaluation factor in the intelligent power distribution network i;
s4.6: finally, calculating to obtain an evaluation benefit coefficient vector lambda of the evaluation factor2＝[λ21,λ22,…,λ2m]Wherein, in the step (A),ηifor the evaluation of the intelligent distribution network i, WijAnd evaluating benefit coefficients of the factors j of the intelligent power distribution network i.
S5 in fig. 1 describes a method of calculating the comprehensive evaluation coefficient.
Substituting the two estimation coefficient vectors obtained in steps S3 and S4 into the following formula to obtain a comprehensive estimation coefficient vector λ ═ λ [ [ λ ] ]1,λ2,…,λm]Wherein λ ism＝0.5λ1m+0.5λ2m，λmFor comprehensive evaluation of the vector, λ1mFor the estimated entropy coefficient of the mth estimation factor, λ2mThe evaluation interest coefficient for the mth evaluation factor.
S6 in fig. 1 describes a method for calculating the reliability of power supply of the smart distribution grid.
According to the comprehensive evaluation coefficient obtained in the step S5, after the numerical value of the corresponding factor is normalized, the numerical value is multiplied by the corresponding evaluation coefficient and accumulated to obtain the power supply reliability score of the smart distribution network, and the formula is as follows:
U＝I1λ1+I2λ2+…+Ikλk+…+I12λ12
in the formula IkIs a normalized value of the k-th factor, λkAnd U is an evaluation coefficient corresponding to the kth factor, wherein k is 1,2, 12, and the higher the score is, the higher the power supply reliability of the intelligent power distribution network is proved to be.
The terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (9)

1. A system for evaluating power supply reliability of a smart distribution network is characterized by comprising: the system comprises a power system data acquisition module, a data processing module and a power supply reliability evaluation module;
the data processing module is respectively connected with the power supply reliability evaluation module and the power system data acquisition module;
the power system data acquisition module is responsible for acquiring related data of a power system and transmitting the acquired data to the data processing module; the data collected by the power system data collection module comprise: the method comprises the steps that the output of a distributed power supply of the microgrid system, the discharge power of a storage battery, the load size, the power failure load size, the number of users, the accumulated times of power failure of the users, the power failure duration time of the users and the total time of power failure of the users are calculated;
the data processing module is responsible for data processing and comprises calculation of power supply reliability evaluation factors of the intelligent power distribution network, calculation of an improved entropy weight method, calculation of evaluation benefit coefficients and calculation of comprehensive evaluation coefficients; the data processing module is also responsible for transmitting the comprehensive evaluation coefficient to the power supply reliability evaluation module;
and the power supply reliability evaluation module receives the data result processed by the data processing module and calculates and evaluates the power supply reliability of the intelligent power distribution network.
2. An evaluation method applied to the intelligent power distribution network power supply reliability evaluation system of claim 1, wherein the method comprises the following steps:
s1: determining a power supply reliability evaluation factor of the intelligent power distribution network;
s2: according to the power supply reliability evaluation factors of the intelligent power distribution network, the factors with higher reliability are processed respectively when the numerical value is larger, and the factors with higher reliability are processed respectively when the numerical value is smaller; after dimensionless factor values are obtained, a factor value matrix is established;
s3: calculating to obtain an evaluation entropy coefficient of each factor by using an improved entropy weight method according to the factor numerical matrix obtained in the step S2;
s4: calculating benefit preference degrees among the intelligent power distribution networks, preventing the objective evaluation of the factors from being influenced by the same benefit or preference relationship among the intelligent power distribution networks, updating an evaluation matrix according to the benefit preference degrees, and then calculating evaluation benefit coefficients of the factors;
s5: calculating a comprehensive evaluation coefficient according to the two evaluation coefficients obtained in the step S3 and the step S4;
s6: and according to the comprehensive evaluation coefficient obtained in the step S5, normalizing the numerical value of the corresponding factor, multiplying the numerical value by the corresponding evaluation coefficient, and accumulating to obtain the power supply reliability score of the intelligent power distribution network.
3. The method for evaluating the power supply reliability of the intelligent power distribution network according to claim 2, wherein the step S1 comprises the following steps:
s1.1: collecting related data by using a data collector of the power system: the method comprises the steps that the output of a distributed power supply of the microgrid system, the discharge power of a storage battery, the load size, the power failure load size, the number of users, the accumulated times of power failure of the users, the power failure duration time of the users and the total time of power failure of the users are calculated; defining the adequacy alpha of the microgrid system and the load transfer degree beta;
in the formula,ωiThe probability of forming a micro-grid system i after a fault occurs; n is the number of possible micro-grid system scenes; alpha is alphaiThe method comprises the steps of (1) providing a microgrid system adequacy of a microgrid system i; n is a radical ofiThe number of all possible combinations of the operating states in the microgrid i is set; alpha is alphai,jThe probability of the j operation state of the microgrid i is obtained;andthe output of the distributed power supply, the discharge power of the storage battery and the load under the j operation mode of the microgrid i are respectively the magnitude;
wherein M is the number of possible N-1 faults; omegai' is the probability of occurrence of the ith N-1 fault; beta is aiThe load transfer degree of the ith N-1 fault; pLIs the sum of the loads of the system before the fault;being line jA limit transmission power; n isiThe residual lines which can normally run in the distribution network are the lines when the ith N-1 fault occurs;in the ith N-1 fault, the power transmitted by the line j is needed to ensure that the load removal amount of the system is minimum;
s1.2: the reliability evaluation method combined with the traditional power distribution network comprises the following steps: load point factors and system factors, so as to determine an evaluation factor of the power supply reliability of the intelligent power distribution network: a microgrid system adequacy alpha; load transfer degree β; the average failure rate p of the load points; the annual average power failure time U of a load point; the average power failure duration time r of each fault of the load point; average power failure frequency SAIFI of the system; average power failure frequency CAIFI of a user; the average power failure duration SAIDI of the system; average power failure duration CAIDI of a user; average power supply availability ASAI; the total electric quantity of the system is insufficient ENS; the average power shortage factor AENS of the system.
4. The method for evaluating the power supply reliability of the intelligent power distribution network according to claim 2, wherein the processing of the factors with higher reliability in the case that the numerical value is larger and the factors with higher reliability in the case that the numerical value is smaller in S2 is as follows:
(1) for factors with higher numerical values and higher reliability, the process is:
in the formula,aijThe value of the dimensionless factor is processed; x is the number ofijIs a factor value before processing;
(2) for factors with smaller values and higher reliability, the process is:
in the formula,aijThe value of the dimensionless factor is processed; x is the number ofijIs the factor value before processing.
5. The method for evaluating the power supply reliability of the intelligent power distribution network according to claim 4, wherein the factor value matrix in S2 is:
in the formula,aijA dimensionless number, i ═ 1, 2.·, n, representing the ith evaluation factor for the ith evaluation subject; j is 1, 2.. the m, n is the number of the evaluation objects, and m is the number of the evaluation factors.
6. The method for evaluating the power supply reliability of the intelligent power distribution network according to claim 5, wherein the step S3 comprises the following steps:
s3.1: according to the elements in the factor numerical matrix, calculating the entropy value of each evaluation factor, wherein the calculation formula is as follows:
in the formula, when aikWhen equal to 0, aik ln aik＝0；aikA dimensionless number representing the ith evaluation object at the kth evaluation factor, m being the number of evaluation factors;
s3.2: on the basis of obtaining the entropy value, obtaining the entropy coefficient of each factor according to the following improved entropy weight calculation formula:
in the formula,λ1jAn estimated entropy coefficient for the jth estimation factor; hkEntropy of the kth evaluation factor; m is the number of evaluation factors; hlEntropy for the l-th assessment factor;
s3.3: the evaluation entropy coefficient vector λ of the evaluation factor can be obtained from step S3.21＝[λ11,λ12,…,λ1m]。
7. The method for evaluating the power supply reliability of the intelligent power distribution network according to claim 6, wherein the step S4 comprises the following steps:
s4.1: calculating the membership degree of the corresponding factor to the reliability of the intelligent power distribution network according to the factor numerical matrix obtained in the step S2, and substituting the factor with higher reliability into the ring function when the numerical value is smaller; substituting factors with higher reliability into the ring-down function when the numerical value is larger; and constructing an evaluation matrix Q of the intelligent power distribution network:
in the formula,qijThe membership degree value of a factor j in the intelligent power distribution network i is 1, 2. j is 1,2,. said, m; m is the number of evaluation factors;
s4.2: calculating benefit preference coefficients among the intelligent power distribution networks and benefit preference degrees of the intelligent power distribution network individuals, and if the benefit preference degrees are smaller, proving that the benefits or the preferences of the intelligent power distribution network are the same as those of other intelligent power distribution networks, and the evaluation of the factors is more unfair; the calculation formula of the interest preference degree is as follows:
in the formula,μabFor the interest preference q between the intelligent power distribution network a and the intelligent power distribution network bak，qbkRespectively taking the numerical values of a factor k in the intelligent power distribution network a and the intelligent power distribution network b; mu.saThe interest preference of an individual of the intelligent power distribution network a is given; a 1,2,. and x; 1,2, x; mu.saiThe preference degree of interest between the intelligent power distribution network a and the intelligent power distribution network i is 1, 2.. times, x;
s4.3: calculating the evaluation ratio of each intelligent power distribution network:
in the formula,ηaThe evaluation proportion of the intelligent power distribution network a is obtained; a 1,2,. and x;
s4.4: according to the membership value of the evaluation factor in each intelligent power distribution network, a judgment matrix D is constructedx；
wherein ,the relative importance of the ith factor and the jth factor in the intelligent power distribution network y is shown;
s4.5: obtaining benefit coefficient vectors W of all intelligent power distribution networks by using analytic hierarchy processi＝[Wi1,Wi2,…,Wim]，WimEvaluating benefit coefficients of the mth evaluation factor in the intelligent power distribution network i;
s4.6: finally, calculating to obtain an evaluation benefit coefficient vector lambda of the evaluation factor2＝[λ21,λ22,…,λ2m], wherein ,ηifor the evaluation of the intelligent distribution network i, WijAnd evaluating benefit coefficients of the factors j of the intelligent power distribution network i.
8. The method for evaluating reliability of power supply to the intelligent power distribution network according to any one of claims 2 to 7, wherein a coefficient vector λ ═ λ is comprehensively evaluated in S51,λ2,…,λm], wherein λm＝0.5λ1m+0.5λ2m，λmFor the comprehensive evaluation of the coefficient vector, λ1mFor the estimated entropy coefficient of the mth estimation factor, λ2mFor the mth evaluation factorAnd evaluating the benefit coefficient.
9. The method for evaluating the power supply reliability of the intelligent power distribution network according to claim 2, wherein the power supply reliability score of the intelligent power distribution network in the step S6 is calculated according to the formula:
U＝I1λ1+I2λ2+…+Ikλk+…+I12λ12
in the formula,IkIs a normalized value of the k-th factor, λkAnd U is an evaluation coefficient corresponding to the kth factor, wherein k is 1,2, 12, and the higher the score is, the higher the power supply reliability of the intelligent power distribution network is proved to be.
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