CN107591840B - Regional multi-microgrid system reliability evaluation method considering random correlation - Google Patents
Regional multi-microgrid system reliability evaluation method considering random correlation Download PDFInfo
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Abstract
The invention discloses a reliability evaluation method of a regional multi-microgrid system considering random correlation, which comprises the following steps: step 1, analyzing random correlation among micro-grid group operation variables; step 2, establishing a reliability model of the micro-grid group for the wind speed and the load in the micro-grid group; step 3, sampling the micro-grid group operation variables; step 4, judging the convergence condition of the reliability index of the microgrid group; step 5, a power supply strategy in the reliability evaluation of the micro-grid group is formulated; and 6, calculating the reliability of the microgrid group system. The invention considers the random correlation among the operation variables among each micro-grid in the micro-grid group; the micro-grid group reliability evaluation model is applied to a micro-grid group reliability evaluation algorithm so as to provide guidance for high-reliability power supply of the micro-grid group, not only provides guidance for coordinated operation and scheduling of the micro-grid group, but also ensures high-reliability power supply of the micro-grid group.
Description
Technical Field
The invention relates to the technical field of power system reliability evaluation, in particular to a method for evaluating the reliability of a regional multi-microgrid system by considering random correlation.
Background
In recent years, with the development of micro-grid technology, several micro-grid projects are built in succession from place to place. In industrial parks and other occasions, a microgrid group consisting of a plurality of microgrids has become one of the trends in the field of microgrid development.
A microgrid group is a more complex system composed of a plurality of microgrids, and has different characteristics compared with the microgrid, such as mutual energy coordination and supporting action among the microgrids in the microgrid group system. However, microgrid groups have more complex characteristics of power supply reliability than a single microgrid.
Therefore, an evaluation scheme capable of evaluating the power supply reliability of the microgrid system is needed to be found, and guidance is provided for coordinated operation scheduling of the microgrid group.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for evaluating the reliability of a regional multi-microgrid system by considering random correlation, which can provide guidance for coordinated operation and scheduling of a microgrid group and ensure high-reliability power supply of the microgrid group.
The technical scheme adopted for solving the technical problems is as follows:
the method for evaluating the reliability of the regional multi-microgrid system with the random correlation taken into consideration provided by the embodiment of the invention comprises the following steps:
step 1, analyzing random correlation among micro-grid group operation variables;
step 2, establishing a reliability model of the micro-grid group for the wind speed and the load in the micro-grid group;
step 3, sampling the micro-grid group operation variables;
step 4, judging the convergence condition of the reliability index of the microgrid group;
step 5, a power supply strategy in the reliability evaluation of the micro-grid group is formulated;
and 6, calculating the reliability of the microgrid group system.
As a possible implementation manner of this embodiment, in step 1, for n variables x
iThe random correlation among the variables is analyzed by adopting a hypothesis test method, and the specific process comprises the following steps:
step 11, define hypothesis H
0And alternative hypothesis H
1Suppose H
0For n variables x
iThere is no correlation between them, i.e. the correlation coefficient ρ is 0; alternative hypothesis H
1For n variables x
iCorrelation between the two signals, that is, the correlation coefficient ρ ≠ 0, and the correlation index α ═ 1;
step 12, calculating statistic gamma between variables
sAnd obtaining its criticality by summing and looking up the tableStatistics
Step 13, determining whether hypothesis H can be accepted
0: if it is not
Then the assumption H is accepted
0Else reject hypothesis H
0Accept alternative hypothesis H
1。
As a possible implementation manner of this embodiment, in step 2, a Vine copula function is used to establish a reliability model of a microgrid group for wind speed and load in the microgrid group, and the specific process includes the following steps:
step 21, respectively obtaining n-dimensional variable data x
1，x
2，x
3，…，x
nAnd the cumulative probability density function of (2) and are respectively denoted as f
i(x
i) And F
i(x
i)；
Step 22, respectively obtaining variable data x
1，x
2，x
3，…，x
nThe coefficient of Spearsman correlation between them and is marked as rho
ij；
Step 23, for the correlation coefficient rho
ijPerforming relevance sorting according to the size relation;
step 24, based on the sequencing result of the correlation coefficient, modeling is carried out on the variable according to the modeling rule of the Vine copula function, and the n-dimensional variable x is obtained
1，x
2，x
3，…，x
nIs used to represent the random correlation function of (1).
As a possible implementation manner of this embodiment, the expression of the random correlation function includes:
expressions of the probability density function and the cumulative probability density function of the wind speed in the micro-grid group are respectively as follows:
f(v)＝(k/c)·(v/c)
k-1·exp(-(v/c)
k) (1)
F(v)＝1-exp(-(v/c)
k) (2)
in the formula: v is the wind speed, and k and c are the shape parameter and the scale parameter respectively;
the expressions of the probability density function and the cumulative probability density function of the load in the microgrid group are respectively as follows:
in the formula: l is the load, μ and σ are the mathematical expectation and mean square error, respectively, of the gaussian distribution;
the basic structure of the n-dimensional Vine copula function is as follows:
in the formula: x is the number of
iIs the ith variable;
is a variable x
n-1、x
nCopula function of (v-
jIs divided by a variable x
jThe outer set of variables.
As a possible implementation manner of this embodiment, in step 3, the process of sampling the microgrid group operation variables includes the following steps:
step 31, in the interval [0,1]]Generating uniformly distributed random numbers w
1、w
2、w
3；
Step 32, let x
1＝w
1(ii) a Will w
2Substituting into formula x
2＝f
-1(w
2,x
1,ρ
1) To obtain x
2；
Step 33, converting w
2、w
3Substituting into formula x
3＝f
-1[f
-1(w
3,x
2,ρ
3),x
1,p
2]To obtain x
3；
Step 34, (x)
1，x
2，x
3) Random numbers to meet the requirements;
as a possible implementation manner of this embodiment, in step 3, for the magnitude of the illumination intensity, the atmospheric transparency coefficient k is sampled
tAnd further obtaining a sampling value of the illumination intensity:
ρ
1are respectively a variable x
1And x
2The correlation coefficient between; rho
3Is a variable x
2And x
3The correlation coefficient between; k. k is a radical of
tuRespectively representing the atmospheric transparency coefficient to be sampled and the maximum value of the atmospheric transparency coefficient; c is the shape coefficient and scale coefficient of the weibull distribution.
As a possible implementation manner of this embodiment, in step 4, the convergence of the index of the amount of power supply lacking is used to determine the convergence of the reliability index of the piconet group, and the determination is as follows:
β, E (x), ε are the convergence parameter, the mean of the variable x, and the convergence criterion, respectively.
As a possible implementation manner of this embodiment, in step 5, the power supply policy in the microgrid group reliability evaluation includes:
firstly, meeting the power supply requirement of key loads in the microgrid, and calculating the residual power supply capacity of the microgrid;
if the microgrid has the residual power supply capacity, the power supply requirement of critical loads in other microgrids is considered to be met;
the power supply requirement of non-critical loads in the micro-grid is met again;
and finally, the power supply requirements of non-critical loads in other micro-networks are met.
As a possible implementation manner of this embodiment, in step 6, the process of calculating the reliability of the microgrid group system includes the following steps:
step 61, inputting historical meteorological data, element reliability parameters, microgrid group configuration data, a power distribution network structure where the microgrid group is located and other calculation data;
step 62, calculating Spearsman correlation parameters of the wind speed historical data and the load historical data, and establishing a corresponding Vine Copula function; establishing a probability distribution function of atmospheric transparency data obtained from historical illumination intensity data;
step 63, sampling the running state of the microgrid group, and sampling the running states of distributed power supplies and loads in the microgrid group based on the established Vine Copula function model;
step 64, determining an energy distribution strategy in the microgrid group, and calculating the internal tangent load of each microgrid;
step 65, calculating a reliability index of the power supply of the micro-grid group, and judging the convergence of the index; if the index reaches the convergence criterion, executing the next step; otherwise, executing step 63;
and step 66, outputting the calculation result.
The technical scheme of the embodiment of the invention has the following beneficial effects:
the method for evaluating the reliability of the regional multi-microgrid system considering the random correlation in the technical scheme of the embodiment of the invention mainly comprises the following steps: analyzing the random correlation among the micro-grid group operation variables; establishing a reliability model of the micro-grid group for the wind speed and the load in the micro-grid group; sampling the micro-grid group operation variables; judging the convergence condition of the reliability index of the microgrid group; a power supply strategy in the reliability evaluation of the micro-grid group is formulated; and calculating the reliability of the microgrid group system. According to the technical scheme of the embodiment of the invention, based on the advantages of the Vine Copula function in describing the random correlation among a plurality of variables, corresponding models are respectively established for the wind speed data of the fan installation position in each micro-grid in the micro-grid group and the load data in each micro-grid, and a micro-grid group reliability evaluation algorithm is established based on the Vine Copula function; random correlation among operation variables among the micro-grids in the micro-grid group is considered; the micro-grid group reliability evaluation model is applied to a micro-grid group reliability evaluation algorithm so as to provide guidance for high-reliability power supply of the micro-grid group, not only provides guidance for coordinated operation and scheduling of the micro-grid group, but also ensures high-reliability power supply of the micro-grid group.
Compared with the prior art, the technical scheme of the embodiment of the invention has the following characteristics:
1) a random correlation model of wind speed and load data of the positions of the micro-grids in the micro-grid group based on the Vine copula function is established, and the established model better reflects the actual operation condition of fans in the micro-grid group;
2) a reliability evaluation algorithm of the micro-grid group system considering random correlation is established, and the evaluation algorithm considers the difference of the load in the micro-grid group on the power supply reliability requirement. By modeling the energy distribution strategy in the microgrid group, the power supply potential of the microgrid group is fully excavated, and a foundation is laid for making a corresponding operation strategy and optimizing a decision.
Drawings
Fig. 1 is a flowchart illustrating a method for reliability assessment of an area multi-piconet system taking into account random correlation according to an exemplary embodiment;
fig. 2 is a flow chart illustrating a method for calculating reliability of a piconet system according to an example embodiment;
fig. 3 is a system block diagram of a piconet group according to an example embodiment.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
Distributed power generation units such as fans and photovoltaic units are often configured in the micro-grid, and each micro-grid in the micro-grid group is often close to the geographical position. Influenced by a geographical microenvironment, the wind speeds of the positions of the fans in the micro-grids are often different, and certain differences are shown. Therefore, a relevant model needs to be established according to the operation characteristics of the fans in the microgrid group to better describe the operation characteristics of the positions of the fans in the microgrid group. Photovoltaic modules in the microgrid are important power generation units. The output of photovoltaic in the microgrid is influenced by the illumination intensity variable, the illumination intensity variable is mainly influenced by the latitude degree, and the influence of the geographical microenvironment on the illumination intensity is small. Therefore, an illumination intensity variable model can be established for each photovoltaic in the microgrid group.
In a microgrid group system, the load composition of each microgrid is often different. The change rule of each type of load shows certain uniqueness. Meanwhile, the change characteristics of various loads have certain similarity under the influence of production and living habits of people. Therefore, certain random correlation exists among the change characteristics of the loads in each microgrid in the microgrid group.
The random correlation characteristics of the wind speed and the load of the position of the fan in the micro-grid group can affect the running state of the micro-grid group, and further affect the power supply reliability of the micro-grid group. For this reason, consideration is needed in the microgrid group reliability evaluation. In mathematical theory, a random correlation model based on a Vine copula function is usually adopted to describe the random correlation among a plurality of variables. The method introduces the Vine copula function into the modeling and evaluation of the reliability of the micro-grid group, so as to evaluate the operation reliability of the micro-grid group better.
Fig. 1 is a flowchart illustrating a method for reliability assessment of an area multi-piconet system considering random correlation according to an exemplary embodiment. As shown in fig. 1, a method for evaluating reliability of an area multi-piconet system in consideration of random correlation according to an embodiment of the present invention may include the following steps:
step 1, analyzing random correlation among micro-grid group operation variables;
step 2, establishing a reliability model of the micro-grid group for the wind speed and the load in the micro-grid group;
step 3, sampling the micro-grid group operation variables;
step 4, judging the convergence condition of the reliability index of the microgrid group;
step 5, a power supply strategy in the reliability evaluation of the micro-grid group is formulated;
and 6, calculating the reliability of the microgrid group system.
In one possible implementation, since for n variables x i, random correlations between variables are typically analyzed using a hypothesis testing method. Therefore, in step 1, the specific process of analyzing the random correlation between variables by using the hypothesis testing method includes the following steps:
step 11, define hypothesis H
0And alternative hypothesis H
1Suppose H
0For n variables x
iThere is no correlation between them, i.e. the correlation coefficient ρ is 0; alternative hypothesis H
1For n variables x
iCorrelation between the two signals, that is, the correlation coefficient ρ ≠ 0, and the correlation index α ═ 1;
step 12, calculating statistic gamma between variables
sAnd obtaining its critical statistic by summing and looking up table
Step 13, determining whether hypothesis H can be accepted
0: if it is not
Then the assumption H is accepted
0Else reject hypothesis H
0Accept alternative hypothesis H
1。
In a possible implementation manner, in step 2, a Vine copula function is adopted to establish a reliability model of the microgrid group for the wind speed and the load in the microgrid group, and the specific process includes the following steps:
step 21, respectively obtaining n-dimensional variable data x
1，x
2，x
3，…，x
nAnd the cumulative probability density function of (2) and are respectively denoted as f
i(x
i) And F
i(x
i)；
Step 22, respectively obtaining variable data x
1，x
2，x
3，…，x
nThe coefficient of Spearsman correlation between them and is marked as rho
ij；
Step 23, for the correlation coefficient rho
ijPerforming relevance sorting according to the size relation;
step 24, based on the sequencing result of the correlation coefficient, modeling is carried out on the variable according to the modeling rule of the Vine copula function, and the n-dimensional variable x is obtained
1，x
2，x
3，…，x
nIs used to represent the random correlation function of (1).
In the modeling of the random correlation of wind speed and load in the microgrid based on the Vine copula function, the probability distribution and the cumulative probability distribution of the variables are processed according to a certain rule, and a combined probability density function capable of describing the random correlation of a plurality of variables is obtained.
In one possible implementation, the expression of the random correlation function includes:
expressions of the probability density function and the cumulative probability density function of the wind speed in the micro-grid group are respectively as follows:
f(v)＝(k/c)·(v/c)
k-1·exp(-(v/c)
k) (1)
F(v)＝1-exp(-(v/c)
k) (2)
in the formula: v is the wind speed, and k and c are the shape parameter and the scale parameter respectively;
the expressions of the probability density function and the cumulative probability density function of the load in the microgrid group are respectively as follows:
in the formula: l is the load, μ and σ are the mathematical expectation and mean square error, respectively, of the gaussian distribution;
the basic structure of the n-dimensional Vine copula function is as follows:
in the formula: x is the number of
iIs the ith variable;
is a variable x
n-1、x
nCopula function of v
-jIs divided by a variable x
jThe outer set of variables.
In a possible implementation manner, in step 3, the process of sampling the microgrid group operation variables includes the following steps:
step 31, generating uniformly distributed random numbers w1, w2 and w3 in the interval [0,1 ];
step 32, let x1 be w 1; substituting w2 into formula x
2＝f
-1(w
2,x
1,ρ
1) To yield x 2;
step 33, substituting w2, w3 into formula x
3＝f
-1[f
-1(w
3,x
2,ρ
3),x
1,p
2]To yield x 3;
step 34, (x1, x2, x3) is a random number that meets the requirement;
in the reliability evaluation of the microgrid group system, the operation condition of the distributed power supply in each microgrid group and the size data of the load need to be sampled. The output data of the distributed power supply comprises the wind speed and the illumination intensity, and the load data is the load in each microgrid. For the wind speed data and the load data, the wind speed data and the load data can be obtained by sampling the established Vine copula function.
In a possible implementation manner, in step 3, for the magnitude of the illumination intensity, by sampling the atmospheric transparency coefficient kt, a sampled value of the illumination intensity is obtained:
ρ
1are respectively a variable x
1And x
2The correlation coefficient between;ρ
3is a variable x
2And x
3The correlation coefficient between; k. k is a radical of
tuRespectively representing the atmospheric transparency coefficient to be sampled and the maximum value of the atmospheric transparency coefficient; c is the shape coefficient and scale coefficient of the weibull distribution.
In a possible implementation manner, in the micro-grid group reliability evaluation, the operation state of the micro-grid group is obtained by sampling the operation states of distributed power supplies and loads in the micro-grid group, so that the power supply reliability index of the micro-grid group is calculated, and the convergence criterion is the basis for determining the accuracy of the calculation result of the reliability index of the micro-grid group.
In step 4, the convergence of the index of the power shortage amount is used to determine the convergence of the reliability index of the microgrid group, and the determination is as follows:
β, E (x), ε are the convergence parameter, the mean of the variable x, and the convergence criterion, respectively.
In the microgrid group reliability evaluation, the convergence condition of the microgrid group reliability index is judged by adopting the convergence of ENS (energy in absence) power supply index.
In a possible implementation manner, in a microgrid group reliability evaluation algorithm, in view of the difference of importance of loads in a microgrid group, an energy distribution strategy problem in an islanding state of the microgrid group needs to be considered. In an island operation state, the micro-grid group firstly meets the power supply requirement of the critical load, and the power supply requirement of the non-critical load is considered. In step 5, the power supply strategy in the microgrid group reliability evaluation includes:
firstly, meeting the power supply requirement of key loads in the microgrid, and calculating the residual power supply capacity of the microgrid;
if the microgrid has the residual power supply capacity, the power supply requirement of critical loads in other microgrids is considered to be met;
the power supply requirement of non-critical loads in the micro-grid is met again;
and finally, the power supply requirements of non-critical loads in other micro-networks are met.
In one possible implementation manner, as shown in fig. 2, in step 6, the process of calculating the reliability of the microgrid group system includes the following steps:
step 61, inputting historical meteorological data, element reliability parameters, microgrid group configuration data, a power distribution network structure where the microgrid group is located and other calculation data;
step 62, calculating Spearsman correlation parameters of the wind speed historical data and the load historical data, and establishing a corresponding Vine Copula function; establishing a probability distribution function of atmospheric transparency data obtained from historical illumination intensity data;
step 63, sampling the running state of the microgrid group, and sampling the running states of distributed power supplies and loads in the microgrid group based on the established Vine Copula function model;
step 64, determining an energy distribution strategy in the microgrid group, and calculating the internal tangent load of each microgrid;
step 65, calculating a reliability index of the power supply of the micro-grid group, and judging the convergence of the index; if the index reaches the convergence criterion, executing the next step; otherwise, executing step 63;
and step 66, outputting the calculation result.
In this embodiment, based on the advantage of the Vine Copula function in describing the random correlation among a plurality of variables, corresponding models are respectively established for the wind speed data of the installation position of the fan in each microgrid in the microgrid group and the load data in each microgrid, and a microgrid group reliability evaluation algorithm is established based on the Vine Copula function; random correlation among operation variables among the micro-grids in the micro-grid group is considered; the micro-grid group reliability evaluation model is applied to a micro-grid group reliability evaluation algorithm so as to provide guidance for high-reliability power supply of the micro-grid group, not only provides guidance for coordinated operation and scheduling of the micro-grid group, but also ensures high-reliability power supply of the micro-grid group.
The technical scheme of the invention is described by combining a reliability evaluation calculation example of the microgrid system.
According to the energy distribution strategy and the operation characteristics of the microgrid group system, the power supply reliability of the microgrid group system is calculated without considering the dynamic process of microgrid control, and fig. 3 is a system structure diagram of the microgrid group. Table 1 shows the reliability parameters of the corresponding devices and other related parameters, table 2 shows the configuration data of the distributed power supplies in each microgrid of the microgrid group system, and table 3 shows the load data of each microgrid in the microgrid group.
Table 1: device reliability parameters
Table 2: distributed power supply configuration data of each microgrid in microgrid group system
Table 3: load data of micro-grid group system
And a power supply reliability evaluation result of the key load of the microgrid system is shown in table 4. The parenthetical numbers in the table are reliability assessment results obtained without taking into account random correlation between the microarrays. As can be seen from table 4, the reliability evaluation result obtained by considering the reliability of the power supply of the critical load of the microgrid after the random correlation has a certain difference compared with the conventional model. In addition, as can be seen from the results in table 4, due to the fact that the distributed power supply configuration data in each microgrid is different, the reliability of critical load power supply in each microgrid is not completely the same. For example, the piconets 4,5 have higher power supply reliability compared with the piconets 1,2, 3.
Table 4: micro-grid group system key load power supply reliability parameter table
Table 5 shows the estimation result of the general load power supply reliability of the microgrid group system. The parenthetical numbers in the table are reliability assessment results obtained without taking into account random correlation between the microarrays.
Table 5: evaluation result of general load power supply reliability of micro-grid group system
The method is not only suitable for the random correlation model of the output and load of the distributed power supply in the micro-grid group, but also suitable for modeling other variables with random correlation, and only needs to correspondingly adjust the corresponding system structure diagram and the equivalent system structure diagram.
The foregoing is only a preferred embodiment of the present invention, and it will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the principle of the invention, and such modifications and improvements are also considered to be within the scope of the invention.
Claims (2)
1. A reliability evaluation method of a regional multi-microgrid system considering random correlation is characterized by comprising the following steps:
step 1, analyzing random correlation among micro-grid group operation variables;
step 2, establishing a reliability model of the micro-grid group for the wind speed and the load in the micro-grid group;
step 3, sampling the micro-grid group operation variables;
step 4, judging the convergence condition of the reliability index of the microgrid group;
step 5, a power supply strategy in the reliability evaluation of the micro-grid group is formulated;
step 6, calculating the reliability of the micro-grid group system;
in step 1, x is calculated for n variables
iThe random correlation among the variables is analyzed by adopting a hypothesis test method, and the specific process comprises the following steps:
step 11, define hypothesis H
0And alternative hypothesis H
1Suppose H
0For n variables x
iThere is no correlation between them, i.e. the correlation coefficient ρ is 0; alternative hypothesis H
1For n variables x
iCorrelation between the two signals, that is, the correlation coefficient ρ ≠ 0, and the correlation index α ═ 1;
step 12, calculating variablesStatistic gamma between quantities
sAnd obtaining its critical statistic by looking up table
Step 13, determining whether hypothesis H can be accepted
0: if it is not
Then the assumption H is accepted
0Else reject hypothesis H
0Accept alternative hypothesis H
1；
In the step 2, a Vine copula function is adopted to establish a reliability model of the micro-grid group for the wind speed and the load in the micro-grid group;
in step 3, the process of sampling the microgrid group operation variables includes the following steps:
step 31, in the interval [0,1]]Generating uniformly distributed random numbers w
1、w
2、w
3；
Step 32, let x
1＝w
1(ii) a Will w
2Substituting into formula x
2＝f
-1(w
2,x
1,ρ
1) To obtain x
2；
Step 33, converting w
2、w
3Substituting into formula x
3＝f
-1[f
-1(w
3,x
2,ρ
3),x
1,ρ
2]To obtain x
3；
Step 34, (x)
1，x
2，x
3) Random numbers to meet the requirements;
in step 3, for the intensity of the illumination, the atmospheric transparency coefficient k is sampled
tAnd further obtaining a sampling value of the illumination intensity:
ρ
1is a variable x
1And x
2The correlation coefficient between; rho
3Is a variable x
2And x
3The correlation coefficient between; k. k is a radical of
tuRespectively representing the atmospheric transparency coefficient to be sampled and the maximum value of the atmospheric transparency coefficient; c is the shape coefficient of Weibull distribution, and lambda is the scale coefficient;
in step 4, the convergence of the index of the power shortage amount is used to determine the convergence of the reliability index of the microgrid group, and the determination is as follows:
β, E (x), epsilon are convergence parameter, mean value of variable x and convergence standard respectively;
in step 5, the power supply strategy in the microgrid group reliability evaluation includes:
firstly, meeting the power supply requirement of key loads in the microgrid, and calculating the residual power supply capacity of the microgrid;
if the microgrid has the residual power supply capacity, the power supply requirement of critical loads in other microgrids is considered to be met;
the power supply requirement of non-critical loads in the micro-grid is met again;
finally, the power supply requirements of non-critical loads in other micro-networks are met;
in step 6, the process of calculating the reliability of the microgrid group system includes the following steps:
step 61, inputting historical meteorological data, element reliability parameters, microgrid group configuration data and power distribution network structure calculation data of the microgrid group;
step 62, calculating Spearsman correlation parameters of the wind speed historical data and the load historical data, and establishing a corresponding Vine Copula function; establishing a probability distribution function of atmospheric transparency data obtained from historical illumination intensity data;
step 63, sampling the running state of the microgrid group, and sampling the running states of distributed power supplies and loads in the microgrid group based on the established Vine Copula function model;
step 64, determining an energy distribution strategy in the microgrid group, and calculating the internal tangent load of each microgrid;
step 65, calculating a reliability index of the power supply of the micro-grid group, and judging the convergence of the index; if the index reaches the convergence criterion, executing the next step; otherwise, executing step 63;
and step 66, outputting the calculation result.
2. The method for evaluating the reliability of the regional multi-microgrid system based on the consideration of the random correlation as claimed in claim 1, wherein in the step 2, a Vine copula function is adopted to establish a reliability model of the microgrid group for the wind speed and the load in the microgrid group, and the specific process comprises the following steps:
step 21, respectively obtaining n-dimensional variable data x
1,x
2,x
3,…,x
nAnd the cumulative probability density function of (2) and are respectively denoted as f
i(x
i) And F
i(x
i)；
Step 22, respectively obtaining variable data x
1,x
2,x
3,…,x
nThe coefficient of Spearsman correlation between them and is marked as rho
ij；
Step 23, for the correlation coefficient rho
ijPerforming relevance sorting according to the size relation;
step 24, based on the sequencing result of the correlation coefficient, modeling is carried out on the variable according to the modeling rule of the Vine copula function, and the n-dimensional variable x is obtained
1，x
2，x
3，…，x
nThe random correlation function expression of (1); the expression of the random correlation function comprises:
expressions of the probability density function and the cumulative probability density function of the wind speed in the micro-grid group are respectively as follows:
f(v)＝(k/c)·(v/c)
k-1·exp(-(v/c)
k) (1)
F(v)＝1-exp(-(v/c)
k) (2)
in the formula: v is the wind speed, and k and c are the shape parameter and the scale parameter respectively;
the expressions of the probability density function and the cumulative probability density function of the load in the microgrid group are respectively as follows:
in the formula: l is the load, μ and σ are the mathematical expectation and mean square error, respectively, of the gaussian distribution;
the basic structure of the n-dimensional Vine copula function is as follows:
in the formula: x is the number of
iIs the ith variable;
is a variable x
n-1、x
nCopula function of v
-jIs divided by a variable x
jThe outer set of variables.
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