CN110472822B - Intelligent power distribution network power supply reliability evaluation system and method - Google Patents

Intelligent power distribution network power supply reliability evaluation system and method Download PDF

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CN110472822B
CN110472822B CN201910602918.6A CN201910602918A CN110472822B CN 110472822 B CN110472822 B CN 110472822B CN 201910602918 A CN201910602918 A CN 201910602918A CN 110472822 B CN110472822 B CN 110472822B
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赵俊浩
吴杰康
张文杰
毛颖卓
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Abstract

The invention relates to a power supply reliability evaluation system and method for an intelligent power distribution network, wherein the system comprises the following components: the power system comprises a power system data acquisition module, a data processing module and a power supply reliability evaluation module; the method comprises the following steps: s1: determining a power supply reliability evaluation factor of the intelligent power distribution network; s2: establishing a factor numerical matrix; s3: calculating to obtain an estimated entropy coefficient of each factor by using an improved entropy weight method; s4: calculating the evaluation benefit coefficient of each factor; s5: calculating a comprehensive evaluation coefficient; s6: and according to the comprehensive evaluation coefficients, carrying out standardization processing on the values of the corresponding factors, multiplying the values by the corresponding evaluation coefficients, and accumulating to obtain the power supply reliability score of the intelligent power distribution network. The intelligent power distribution network power supply reliability evaluation method based on the intelligent power distribution network power supply analysis can evaluate the power supply reliability of the intelligent power distribution network more objectively and accurately, and has important significance in achieving analysis and evaluation of the power supply reliability of the intelligent power distribution network and providing guidance and assistance for planning, construction and transformation of the power distribution network.

Description

Intelligent power distribution network power supply reliability evaluation system and method
Technical Field
The invention relates to the technical field of power systems and automation thereof, in particular to a power supply reliability evaluation system and method for an intelligent power distribution network.
Background
In recent years, research and construction of smart grids has been raised globally. As an important component of the smart power grid, the intellectualization of the power distribution network has become a new trend of power grid development in the future, and plays a role in realizing the overall objective of smart power grid construction. And along with the transformation and upgrading of the traditional power distribution network to the intelligent power distribution network, factors considered in reliability evaluation are changed. For the power supply reliability evaluation of the intelligent power distribution network, besides factors considered in the power supply reliability evaluation of the traditional power distribution network, the characteristics of the intelligent power distribution network are considered. Therefore, a set of scientific, reasonable and comprehensive evaluation factor system and method are needed to be constructed, so that analysis and evaluation of power supply reliability of the intelligent power distribution network are realized, and guidance and assistance are provided 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 domestic and foreign scholars, but the research on the power supply reliability of the intelligent power distribution network is relatively less. While the conventional power grid evaluation weight determination methods mainly comprise an entropy weight method, an Analytic Hierarchy Process (AHP) and the like, but the methods have limitations, such as the entropy weight method, the traditional entropy value calculation entropy weight calculation method, when the entropy value is in a certain interval, the tiny difference between the entropy value and the entropy value can cause the change of the entropy weight times, which is inconsistent with the information transmitted by the entropy value, and the analytic hierarchy process, the traditional method scores an evaluation object through a plurality of judgment subjects, and then simply takes the average value of the scores to construct a judgment matrix to calculate the weight of each level of factors; however, when the same preference exists between the judgment subjects or the judgment subjects bias a certain evaluation factor, more floors are strived for by increasing the weight of the factor, so that the calculation of the factor weight is out of fairness.
Disclosure of Invention
The invention provides a system and a method for evaluating the power supply reliability of an intelligent power distribution network, which are used for overcoming the defect that the power supply reliability evaluation method of the intelligent power distribution network in the prior art is not objective and accurate enough.
The system comprises: the power 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 the power system and transmitting the acquired data to the data processing module; the data collected by the data collection module of the power system comprises: the micro-grid system distributes power output, discharge power of a storage battery, load size, power failure load size, the number of users, the accumulated number of times of power failure of the users, duration of power failure of the users and total time of no power failure;
the data processing module is responsible for data processing, including calculation of an intelligent power distribution network power supply reliability evaluation factor, calculation of an improved entropy weight method, calculation of an evaluation benefit coefficient and calculation of a comprehensive evaluation coefficient; 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 intelligent power distribution network power supply reliability evaluation system, and comprises the following 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, respectively processing factors with higher reliability and factors with lower reliability for larger values; after obtaining dimensionless factor values, establishing a factor value matrix;
s3: calculating to obtain an estimated 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 degree between intelligent power distribution networks, preventing the same benefit or preference relation between the intelligent power distribution networks from influencing objective evaluation of factors, updating an evaluation matrix according to the benefit preference degree, 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 (3) according to the comprehensive evaluation coefficient obtained in the step (S5), carrying out standardization processing on 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 comprises the steps of:
s1.1: the method comprises the steps of collecting relevant data by using a power system data collector: the micro-grid system distributes power output, discharge power of a storage battery, load size, power failure load size, the number of users, the accumulated number of times of power failure of the users, duration of power failure of the users and total time of no power failure; and define the adequacy alpha of the micro-grid system and the load transfer beta;
Figure BDA0002119879180000021
Figure BDA0002119879180000031
in the formula ,ωi Forming probability of the micro grid system i after the fault occurs; n is the number of micro grid system scenarios that may be formed; alpha i The method is sufficient for the micro-grid system of the micro-grid system i; n (N) i The number of all combinations of possible operating states in the micro-grid i; alpha i,j Probability of j-th operation state of micro-grid i;
Figure BDA0002119879180000032
and />
Figure BDA0002119879180000033
The power output of the distributed power supply, the discharge power of the storage battery and the load of the storage battery in the j-th operation mode of the micro-grid i are respectively;
Figure BDA0002119879180000034
Figure BDA0002119879180000035
wherein M is the number of N-1 faults that may occur; omega' i Is the probability of occurrence of the ith N-1 fault; beta i Load transfer for the ith N-1 fault; p (P) L The sum of the loads of the system before failure;
Figure BDA0002119879180000036
the limit transmission power for line j; n is n i The power distribution network is a circuit which can normally run and is left when the ith N-1 fault occurs; />
Figure BDA0002119879180000037
In order to ensure that the power transmitted by line j is required in the ith N-1 fault to ensure that the system load shedding is minimal.
S1.2: combining the reliability factors of the traditional power distribution network: load point factors and system factors so as to determine intelligent power distribution network power supply reliability assessment factors: the adequacy alpha of the micro-grid system; load transfer degree β; load point average failure rate p; average power failure time U of load point year; average power failure duration r of each fault of the load point; average power failure frequency SAIFI of the system; user average power failure frequency CAIFI; average power outage duration SAIDI of the system; user average power outage duration CAIDI; average power availability ASAI; the total electric quantity deficiency ENS of the system; the system average power shortage factor AENS.
Preferably, the processing of the factor of higher reliability for a larger value and the factor of higher reliability for a smaller value in S2 is:
(1) For factors of higher reliability for larger values, the process is:
Figure BDA0002119879180000038
in the formula ,aij The processed dimensionless factor value; x is x ij Is the factor value before treatment;
(2) For factors of higher reliability for smaller values, the process is:
Figure BDA0002119879180000041
in the formula ,aij The processed dimensionless factor value; x is x ij Is the factor value before treatment.
Preferably, the factor value matrix in S2 is:
Figure BDA0002119879180000042
in the formula ,aij A dimensionless number representing the j-th evaluation factor for the i-th evaluation subject, i=1, 2,..n; j=1, 2,..m, n is the number of evaluation subjects and m is the number of evaluation factors.
Preferably, S3 comprises 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:
Figure BDA0002119879180000043
wherein, when a ik When=0, a ik ln a ik ＝0；a ik And (3) representing the dimensionless number of the ith evaluation object at the kth evaluation factor, wherein m is the number of the evaluation factors.
S3.2: on the basis of obtaining the entropy value, the entropy coefficient of each factor is obtained according to the following improved entropy weight calculation formula:
Figure BDA0002119879180000044
in the formula ,λ1j An estimated entropy coefficient for the j-th estimation factor; h k Entropy for the kth evaluation factor; m is the number of evaluation factors; h l Entropy value of the first evaluation factor;
s3.3: from step S3.2 an estimated entropy coefficient vector lambda of the estimation factor can be obtained 1 ＝[λ 11 ,λ 12 ,…,λ 1m ]。
Preferably, S4 comprises the steps of:
s4.1: calculating the membership degree of the corresponding factor to the reliability of the intelligent power distribution network by the factor numerical matrix obtained in the step S2, and substituting the factor with higher reliability into the upper-class function when the numerical value is smaller; for larger values, factors of higher reliability are substituted into the ring-down function. And thus constructing an evaluation matrix Q of the intelligent power distribution network:
Figure BDA0002119879180000051
/>
in the formula ,qij The membership value of the factor j in the intelligent distribution network i, i=1, 2, n; j=1, 2, m; m is the number of the evaluation factorsAn order;
s4.2: calculating benefit preference coefficients among the intelligent power distribution networks and benefit preference degrees of individuals of the intelligent power distribution networks, and if the benefit preference degrees are smaller, proving that the benefits or preference of the intelligent power distribution networks are the same as those of other intelligent power distribution networks, and evaluating factors of the intelligent power distribution networks are less fair; the calculation formula of the benefit preference is as follows:
Figure BDA0002119879180000052
Figure BDA0002119879180000053
in the formula ,μab For benefit preference q between intelligent distribution network a and intelligent distribution network b ak ，q bk The values of the factors k in the intelligent power distribution network a and the intelligent power distribution network b are respectively; mu (mu) a Benefit preference degree for the individual intelligent power distribution network a; a=1, 2,; b=1, 2,; mu (mu) ai I=1, 2, x, is a benefit preference between the smart distribution network a and the smart distribution network i;
s4.3: calculating the evaluation duty ratio of each intelligent power distribution network:
Figure BDA0002119879180000054
in the formula ,ηa The evaluation duty ratio of the intelligent power distribution network a; a=1, 2,..x.
S4.4: constructing a judgment matrix D according to membership values of evaluation factors in each intelligent power distribution network x ：
Figure BDA0002119879180000055
wherein ,
Figure BDA0002119879180000056
is the ith factor in the intelligent power distribution network yThe relative importance of the child and the jth factor;
s4.5: obtaining benefit coefficient vector W of each intelligent power distribution network by using analytic hierarchy process i ＝[W i1 ,W i2 ,…,W im ]，W im The method comprises the steps of (1) evaluating benefit coefficients for an mth evaluation factor in the intelligent power distribution network i;
s4.6: finally, calculating the estimated benefit coefficient vector lambda of the estimated factors 2 ＝[λ 21 ,λ 22 ,…,λ 2m], wherein ,
Figure BDA0002119879180000061
η i for evaluating the duty ratio, W of the intelligent power distribution network i ij And evaluating benefit coefficients for the factors j of the intelligent power distribution network i.
Preferably, the complex evaluation coefficient vector λ= [ λ ] in S5 1 ,λ 2 ,…,λ m], wherein λm ＝0.5λ 1m +0.5λ 2m ，λ m To comprehensively evaluate coefficient vectors, lambda 1m An estimated entropy coefficient, lambda, for the mth estimation factor 2m And evaluating the benefit 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:
U＝I 1 λ 1 +I 2 λ 2 +…+I k λ k +…+I 12 λ 12
in the formula ,Ik Is the normalized value of the kth factor, lambda k And for the evaluation coefficient corresponding to the kth factor, k=1, 2, 12, and u is a power supply reliability score of the intelligent power distribution network, wherein the higher the score is, the higher the power supply reliability of the intelligent power distribution network is proved.
The basic principle of the invention is that firstly, aiming at the characteristics of the intelligent power distribution network, the concepts of the adequacy and the load transfer degree of the micro-grid system are provided, and a power supply reliability evaluation factor system of the intelligent power distribution network is established by combining the traditional power supply reliability factors of the power distribution network. Meanwhile, with respect to limitations in the entropy weight method and the analytic hierarchy process, an improved entropy weight method and an improved analytic hierarchy process are provided, and the two methods are applied to power supply reliability evaluation of the intelligent power distribution network.
The method not only provides an intelligent power distribution network power supply reliability evaluation factor system, but also solves the problem that the entropy weight method and the analytic hierarchy process have limitations.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that: the method selects various factors to evaluate the power supply reliability of the intelligent power distribution network, overcomes the defects of the traditional entropy weight method and the weight determination method by the 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 assistance for planning, construction and transformation of the power distribution network.
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Fig. 1 is a flowchart of a power supply reliability evaluation method for an intelligent power distribution network.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
for the purpose of better illustrating the embodiments, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the actual product dimensions;
it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
Example 1:
the embodiment provides a smart distribution network power supply reliability evaluation system, which comprises: the power 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 the power system and transmitting the acquired data to the data processing module; the data collected by the data collection module of the power system comprises: the micro-grid system distributes power output, discharge power of a storage battery, load size, power failure load size, the number of users, the accumulated number of times of power failure of the users, duration of power failure of the users and total time of no power failure;
the data processing module is responsible for data processing, including calculation of an intelligent power distribution network power supply reliability evaluation factor, calculation of an improved entropy weight method, calculation of an evaluation benefit coefficient and calculation of a comprehensive evaluation coefficient; 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 power supply reliability evaluation method 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, respectively processing factors with higher reliability and factors with lower reliability for larger values; after obtaining dimensionless factor values, establishing a factor value matrix;
s3: calculating to obtain an estimated 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 degree between intelligent power distribution networks, preventing the same benefit or preference relation between the intelligent power distribution networks from influencing objective evaluation of factors, updating an evaluation matrix according to the benefit preference degree, 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 (3) according to the comprehensive evaluation coefficient obtained in the step (S5), carrying out standardization processing on 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 a smart distribution network power supply reliability assessment factor.
In the intelligent power distribution network, after the fault occurs, the fault can be quickly isolated, load transfer can be performed, and even a micro-grid can be formed to ensure normal power supply of a user. The relevant data are collected by the power system data collector firstly: the micro-grid system distributes power output, discharge power of a storage battery, load size, power failure load size, the number of users, the accumulated number of times of power failure of the users, duration of power failure of the users and total time of no power failure; and defines two concepts of the adequacy alpha and the load transfer beta of the micro-grid system:
Figure BDA0002119879180000081
in the formula ,αi The method is sufficient for the micro-grid system of the micro-grid system i; n (N) i The number of all combinations of possible operating states in the micro-grid i; alpha i,j Probability of j-th operation state of micro-grid i;
Figure BDA0002119879180000082
and />
Figure BDA0002119879180000083
The power output of the distributed power supply, the discharge power of the storage battery and the load of the storage battery in the j-th operation mode of the micro-grid i are respectively.
Figure BDA0002119879180000084
in the formula ,ωi Forming probability of the micro grid system i after the fault occurs; n is the number of micro grid system scenarios that may be formed.
Figure BDA0002119879180000085
in the formula ,βi Load transfer for the ith N-1 fault; p (P) L The sum of the loads of the system before failure;
Figure BDA0002119879180000086
the limit transmission power for line j; n is n i The power distribution network is a circuit which can normally run and is left when the ith N-1 fault occurs; />
Figure BDA0002119879180000087
In order to ensure that the power transmitted by line j is required in the ith N-1 fault to ensure that the system load shedding is minimal.
The load transfer degree beta of the whole system can be obtained by the method:
Figure BDA0002119879180000088
wherein M is the number of N-1 faults that may occur; omega' i Is the probability of occurrence of the ith N-1 fault.
And then combining the reliability factors of the traditional power distribution network: load point factors and system factors so as to determine intelligent power distribution network power supply reliability assessment factors: the adequacy alpha of the micro-grid system; load transfer degree β; load point average failure rate p; average power failure time U of load point year; average power failure duration r of each fault of the load point; average power failure frequency SAIFI of the system; user average power failure frequency CAIFI; average power outage duration SAIDI of the system; user average power outage duration CAIDI; average power availability ASAI; the total electric quantity deficiency ENS of the system; the system average power shortage factor AENS.
Step S2 in fig. 1 describes a method of creating a matrix of factor values.
And according to the power supply reliability evaluation factors of the intelligent power distribution network, respectively processing the factors with higher reliability and the factors with lower reliability for the larger values.
For factors of higher reliability for larger values, the process is as follows:
Figure BDA0002119879180000091
in the formula ,aij The processed dimensionless factor value; x is x ij Is the factor value before treatment.
For factors of higher reliability with smaller values, the process is as follows:
Figure BDA0002119879180000092
in the formula ,aij The processed dimensionless factor value; x is x ij Is the factor value before treatment.
After obtaining dimensionless factor values, a factor value matrix A is established
Figure BDA0002119879180000093
in the formula ,aij A dimensionless number representing the j-th evaluation factor for the i-th evaluation subject, i=1, 2,..n; j=1, 2,..m, n is the number of evaluation subjects and m is the number of evaluation factors.
S3 in fig. 1 describes a method of calculating an objective weight using the improved entropy weight method.
And (3) calculating the factor numerical matrix obtained in the step (S2) by using an improved entropy weight method to obtain the estimated entropy coefficient of each factor.
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:
Figure BDA0002119879180000094
wherein, when a ik When=0, a ik ln a ik ＝0；a ik And (3) representing the dimensionless number of the ith evaluation object at the kth evaluation factor, wherein m is the number of the evaluation factors.
S3.2: on the basis of obtaining the entropy value, the entropy coefficient of each factor is obtained according to the following improved entropy weight calculation formula:
Figure BDA0002119879180000101
in the formula ,λ1j An estimated entropy coefficient for the j-th estimation factor; h k Entropy for the kth evaluation factor; m is the number of evaluation factors; h l Entropy value of the first evaluation factor;
s3.3: from step S3.2 an estimated entropy coefficient vector lambda of the estimation factor can be obtained 1 ＝[λ 11 ,λ 12 ,…,λ 1m ]。
Step S4 in fig. 1 describes a method of calculating an evaluation 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 by the factor numerical matrix obtained in the step S2, and substituting the factor with higher reliability into the upper-class function when the numerical value is smaller; for larger values, factors of higher reliability are substituted into the ring-down function. And thus constructing an evaluation matrix Q of the intelligent power distribution network:
Figure BDA0002119879180000102
in the formula ,qij The membership value of the factor j in the intelligent distribution network i, i=1, 2, n; j=1, 2, 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 individuals of the intelligent power distribution networks, and if the benefit preference degrees are smaller, proving that the benefits or preference of the intelligent power distribution networks are the same as those of other intelligent power distribution networks, and evaluating factors of the intelligent power distribution networks are less fair; the calculation formula of the benefit preference is as follows:
Figure BDA0002119879180000103
Figure BDA0002119879180000104
in the formula ,μab For benefit preference q between intelligent distribution network a and intelligent distribution network b ak ，q bk The values of the factors k in the intelligent power distribution network a and the intelligent power distribution network b are respectively; mu (mu) a Benefit preference degree for the individual intelligent power distribution network a; a=1, 2,; b=1, 2,; mu (mu) ai I=1, 2, x, is a benefit preference between the smart distribution network a and the smart distribution network i;
s4.3: calculating the evaluation duty ratio of each intelligent power distribution network:
Figure BDA0002119879180000111
in the formula ,ηa The evaluation duty ratio of the intelligent power distribution network a; a=1, 2,..x.
S4.4: constructing a judgment matrix D by using a 1-9 scale method according to membership value of evaluation factors in each intelligent power distribution network x The relative importance of the assessment factors of each level is described qualitatively and quantitatively expressed by accurate numbers.
The 1-9 scale method is that the number 1 in the judgment matrix indicates that two elements have the same importance to a certain attribute; the numeral 9 indicates a comparison of two elements, the former being extremely important than the latter; the intermediate numbers represent the meanings and so on, and the meanings of the individual scale values are as follows:
Figure BDA0002119879180000112
judgment matrix D x The expression of (2) is:
Figure BDA0002119879180000113
wherein ,
Figure BDA0002119879180000114
the relative importance of the ith factor and the jth factor in the intelligent power distribution network y;
s4.5: obtaining benefit coefficient vector W of each intelligent power distribution network by using analytic hierarchy process i ＝[W i1 ,W i2 ,…,W im ]，W im The method comprises the steps of (1) evaluating benefit coefficients for an mth evaluation factor in the intelligent power distribution network i;
s4.6: finally, calculating the estimated benefit coefficient vector lambda of the estimated factors 2 ＝[λ 21 ,λ 22 ,…,λ 2m], wherein ,
Figure BDA0002119879180000115
η i for evaluating the duty ratio, W of the intelligent power distribution network i ij And evaluating benefit coefficients for 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 kinds of evaluation coefficient vectors obtained in the step S3 and the step S4 into the following formula to obtain a comprehensive evaluation coefficient vector λ= [ λ ] 1 ,λ 2 ,…,λ m], wherein λm ＝0.5λ 1m +0.5λ 2m ，λ m To comprehensively evaluate the vector lambda 1m An estimated entropy coefficient, lambda, for the mth estimation factor 2m And evaluating the benefit coefficient for the mth evaluation factor.
S6 in fig. 1 describes a method for calculating the power supply reliability of the smart distribution network.
And (3) according to the comprehensive evaluation coefficient obtained in the step (S5), carrying out standardization processing on the numerical value of the corresponding factor, and multiplying and accumulating the numerical value with the corresponding evaluation coefficient to obtain the power supply reliability score of the intelligent power distribution network, wherein the formula is as follows:
U＝I 1 λ 1 +I 2 λ 2 +…+I k λ k +…+I 12 λ 12
in the formula ,Ik Is the normalized value of the kth factor, lambda k For the evaluation coefficient corresponding to the kth factor, k=1, 2, 12,and U is the power supply reliability score of the intelligent power distribution network, and the higher the score is, the higher the power supply reliability of the intelligent power distribution network is proved.
The terms describing the positional relationship in the drawings are merely illustrative, and are not to be construed as limiting the present patent;
it is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.

Claims (8)

1. A smart distribution network power supply reliability assessment system, the system comprising: the power 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 the power system and transmitting the acquired data to the data processing module; the data collected by the data collection module of the power system comprises: the micro-grid system distributes power output, discharge power of a storage battery, load size, power failure load size, the number of users, the accumulated number of times of power failure of the users, duration of power failure of the users and total time of no power failure;
the data processing module is responsible for data processing, including calculation of an intelligent power distribution network power supply reliability evaluation factor, calculation of an improved entropy weight method, calculation of an evaluation benefit coefficient and calculation of a comprehensive evaluation coefficient; the data processing module is also responsible for transmitting the comprehensive evaluation coefficient to the power supply reliability evaluation module;
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 data processing module calculates a power supply reliability evaluation factor of the intelligent power distribution network through the following steps:
s1.1: the method comprises the steps of collecting relevant data by using a power system data collector: the micro-grid system distributes power output, discharge power of a storage battery, load size, power failure load size, the number of users, the accumulated number of times of power failure of the users, duration of power failure of the users and total time of no power failure; and define the adequacy alpha of the micro-grid system and the load transfer beta;
Figure QLYQS_1
Figure QLYQS_2
in the formula ,ωi Forming probability of the micro grid system i after the fault occurs; n is the number of micro grid system scenarios that may be formed; alpha i The method is sufficient for the micro-grid system of the micro-grid system i; n (N) i The number of all combinations of possible operating states in the micro-grid i; alpha i,j Probability of j-th operation state of micro-grid i;
Figure QLYQS_3
and />
Figure QLYQS_4
The power output of the distributed power supply, the discharge power of the storage battery and the load of the storage battery in the j-th operation mode of the micro-grid i are respectively;
Figure QLYQS_5
Figure QLYQS_6
wherein M is the number of N-1 faults that may occur;ω’ i is the probability of occurrence of the ith N-1 fault; beta i Load transfer for the ith N-1 fault; p (P) L The sum of the loads of the system before failure;
Figure QLYQS_7
the limit transmission power for line j; n is n i The power distribution network is a circuit which can normally run and is left when the ith N-1 fault occurs; />
Figure QLYQS_8
In order to ensure that the power transmitted by a line j is required when the system load cut-off amount is minimum in the ith N-1 fault;
s1.2: the reliability evaluation method of the traditional power distribution network is combined: load point factors and system factors so as to determine intelligent power distribution network power supply reliability assessment factors: the adequacy alpha of the micro-grid system; load transfer degree β; load point average failure rate p; average power failure time U of load point year; average power failure duration r of each fault of the load point; average power failure frequency SAIFI of the system; user average power failure frequency CAIFI; average power outage duration SAIDI of the system; user average power outage duration CAIDI; average power availability ASAI; the total electric quantity deficiency ENS of the system; the system average power shortage factor AENS.
2. A smart distribution network power supply reliability assessment method applied to the smart distribution network power supply reliability assessment system of claim 1, the method comprising 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, respectively processing factors with higher reliability and factors with lower reliability for larger values; after obtaining dimensionless factor values, establishing a factor value matrix;
s3: calculating to obtain an estimated 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 degree between intelligent power distribution networks, preventing the same benefit or preference relation between the intelligent power distribution networks from influencing objective evaluation of factors, updating an evaluation matrix according to the benefit preference degree, 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: according to the comprehensive evaluation coefficients obtained in the step S5, carrying out standardization processing on the numerical values of the corresponding factors, multiplying the numerical values with the corresponding evaluation coefficients, and accumulating to obtain the power supply reliability scores of the intelligent power distribution network;
s1 comprises the following steps:
s1.1: the method comprises the steps of collecting relevant data by using a power system data collector: the micro-grid system distributes power output, discharge power of a storage battery, load size, power failure load size, the number of users, the accumulated number of times of power failure of the users, duration of power failure of the users and total time of no power failure; and define the adequacy alpha of the micro-grid system and the load transfer beta;
Figure QLYQS_9
Figure QLYQS_10
in the formula ,ωi Forming probability of the micro grid system i after the fault occurs; n is the number of micro grid system scenarios that may be formed; alpha i The method is sufficient for the micro-grid system of the micro-grid system i; n (N) i The number of all combinations of possible operating states in the micro-grid i; alpha i,j Probability of j-th operation state of micro-grid i;
Figure QLYQS_11
and />
Figure QLYQS_12
Distributed power output and power storage under j-th operation mode of micro-grid i respectivelyThe discharge power of the cell and the size of the load;
Figure QLYQS_13
Figure QLYQS_14
wherein M is the number of N-1 faults that may occur; omega' i Is the probability of occurrence of the ith N-1 fault; beta i Load transfer for the ith N-1 fault; p (P) L The sum of the loads of the system before failure;
Figure QLYQS_15
the limit transmission power for line j; n is n i The power distribution network is a circuit which can normally run and is left when the ith N-1 fault occurs; />
Figure QLYQS_16
In order to ensure that the power transmitted by a line j is required when the system load cut-off amount is minimum in the ith N-1 fault;
s1.2: the reliability evaluation method of the traditional power distribution network is combined: load point factors and system factors so as to determine intelligent power distribution network power supply reliability assessment factors: the adequacy alpha of the micro-grid system; load transfer degree β; load point average failure rate p; average power failure time U of load point year; average power failure duration r of each fault of the load point; average power failure frequency SAIFI of the system; user average power failure frequency CAIFI; average power outage duration SAIDI of the system; user average power outage duration CAIDI; average power availability ASAI; the total electric quantity deficiency ENS of the system; the system average power shortage factor AENS.
3. The power supply reliability evaluation method of the smart distribution network according to claim 2, wherein the processing of the factor with higher reliability and the factor with lower reliability in S2 is:
(1) For factors of higher reliability for larger values, the process is:
Figure QLYQS_17
in the formula ,aij The processed dimensionless factor value; x is x ij Is the factor value before treatment;
(2) For factors of higher reliability for smaller values, the process is:
Figure QLYQS_18
in the formula ,aij The processed dimensionless factor value; x is x ij Is the factor value before treatment.
4. The intelligent power distribution network power supply reliability evaluation method according to claim 3, wherein the factor numerical matrix in S2 is:
Figure QLYQS_19
in the formula ,aij A dimensionless number representing the j-th evaluation factor for the i-th evaluation subject, i=1, 2,..n; j=1, 2,..m, n is the number of evaluation subjects and m is the number of evaluation factors.
5. The smart distribution network power supply reliability assessment method according to claim 4, wherein S3 comprises 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:
Figure QLYQS_20
wherein, when a ik When=0, a ik lna ik ＝0；a ik A 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, the entropy coefficient of each factor is obtained according to the following improved entropy weight calculation formula:
Figure QLYQS_21
in the formula ,λ1j An estimated entropy coefficient for the j-th estimation factor; h k Entropy for the kth evaluation factor; m is the number of evaluation factors; h l Entropy value of the first evaluation factor;
s3.3: from step S3.2 an estimated entropy coefficient vector lambda of the estimation factor can be obtained 1 ＝[λ 11 ,λ 12 ,…,λ 1m ]。
6. The smart distribution network power supply reliability assessment method according to claim 5, wherein S4 comprises the steps of:
s4.1: calculating the membership degree of the corresponding factor to the reliability of the intelligent power distribution network by the factor numerical matrix obtained in the step S2, and substituting the factor with higher reliability into the upper-class function when the numerical value is smaller; for larger values, factors with higher reliability are substituted into the ring-down function; and thus constructing an evaluation matrix Q of the intelligent power distribution network:
Figure QLYQS_22
in the formula ,qij The membership value of the factor j in the intelligent distribution network i, i=1, 2, n; j=1, 2, 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 individuals of the intelligent power distribution networks, and if the benefit preference degrees are smaller, proving that the benefits or preference of the intelligent power distribution networks are the same as those of other intelligent power distribution networks, and evaluating factors of the intelligent power distribution networks are less fair; the calculation formula of the benefit preference is as follows:
Figure QLYQS_23
Figure QLYQS_24
in the formula ,μab For benefit preference q between intelligent distribution network a and intelligent distribution network b ak ，q bk The values of the factors k in the intelligent power distribution network a and the intelligent power distribution network b are respectively; mu (mu) a Benefit preference degree for the individual intelligent power distribution network a; a=1, 2,; b=1, 2,; mu (mu) ai I=1, 2, x, is a benefit preference between the smart distribution network a and the smart distribution network i;
s4.3: calculating the evaluation duty ratio of each intelligent power distribution network:
Figure QLYQS_25
in the formula ,ηa The evaluation duty ratio of the intelligent power distribution network a; a=1, 2,;
s4.4: constructing a judgment matrix D according to membership values of evaluation factors in each intelligent power distribution network x ；
Figure QLYQS_26
wherein ,
Figure QLYQS_27
the relative importance of the ith factor and the jth factor in the intelligent power distribution network y;
s4.5: obtaining benefit coefficient vector W of each intelligent power distribution network by using analytic hierarchy process i ＝[W i1 ,W i2 ,…,W im ]，W im Evaluation benefit coefficient for mth evaluation factor in intelligent power distribution network i；
S4.6: finally, calculating the estimated benefit coefficient vector lambda of the estimated factors 2 ＝[λ 21 ,λ 22 ,…,λ 2m], wherein ,
Figure QLYQS_28
η i for evaluating the duty ratio, W of the intelligent power distribution network i ij And evaluating benefit coefficients for the factors j of the intelligent power distribution network i.
7. The method for evaluating power supply reliability of intelligent power distribution network according to any one of claims 2 to 6, wherein the comprehensive evaluation coefficient vector λ= [ λ ] in S5 1 ,λ 2 ,…,λ m], wherein λm ＝0.5λ 1m +0.5λ 2m ，λ m To comprehensively evaluate coefficient vectors, lambda 1m An estimated entropy coefficient, lambda, for the mth estimation factor 2m And evaluating the benefit coefficient for the mth evaluation factor.
8. The intelligent power distribution network power supply reliability evaluation method according to claim 2, wherein the calculation formula of the power supply reliability score of the intelligent power distribution network in S6 is:
U＝I 1 λ 1 +I 2 λ 2 +…+I k λ k +…+I 12 λ 12
in the formula ,Ik Is the normalized value of the kth factor, lambda k And for the evaluation coefficient corresponding to the kth factor, k=1, 2, 12, and u is a power supply reliability score of the intelligent power distribution network, wherein the higher the score is, the higher the power supply reliability of the intelligent power distribution network is proved.
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