CN118505039A - Distribution line vulnerability assessment method considering wind-light-load time sequence fluctuation characteristics - Google Patents

Abstract

A distribution line vulnerability assessment method considering the time series fluctuation characteristics of wind and solar load is proposed. Based on the random output model of distributed energy, its output characteristics are obtained in combination with its environmental parameters; line vulnerability assessment indicators are established; according to the improved power grid structure and the output of distributed energy in each period, the values of various vulnerability indicators of the line in each period are calculated in combination with load demand, and the values of various vulnerability indicators are normalized; the objective weight and subjective weight are obtained by using the improved CRITIC method and the analytic hierarchy process, and the comprehensive weight of various vulnerability indicators is obtained by combining game theory; based on the line vulnerability assessment indicators and the comprehensive weight of various vulnerability indicators, the VIKOR method is used to obtain the vulnerability evaluation results of each line, and the Gini coefficient _Gt is used to represent the uniform distribution degree of system line vulnerability, and the real vulnerability of the system in each period is obtained. This method obtains the real vulnerability of the system based on the distribution characteristics of system vulnerability and its comprehensive vulnerability for different periods.

Description

A distribution line vulnerability assessment method considering the temporal fluctuation characteristics of wind and solar loads

Technical Field

The present invention relates to the technical field of distribution network fault probability assessment, and in particular to a distribution line vulnerability assessment method that takes into account the time series fluctuation characteristics of wind and solar loads.

Background Art

The development of distribution network is an important part of the power system field, which involves the distribution, transmission and management of electricity. With the growth of population, accelerated urbanization and economic development, the demand for electricity is growing. In order to meet the growing demand for electricity, the distribution network needs to be continuously expanded and upgraded to ensure reliable power supply. With the introduction and development of the concept of active distribution network, the structure and operation mode of the distribution network are becoming increasingly complex, and the probability of distribution network failure is also on the rise. It is particularly important to accurately assess its vulnerability.

In the existing research on the vulnerability of power grid lines, most of the research results are based on the transmission network as the research object, so the vulnerability assessment of the transmission network is relatively mature. Due to the large difference between the distribution network and the transmission network in structural design, the distribution network has the structural characteristics of closed-loop design and open-loop operation. This also leads to the fact that many results of the transmission network research cannot be directly applied to the distribution network. In addition, the current research on the vulnerability of the distribution network has insufficient consideration of the scenario after the access of distributed energy resources, and does not consider the manifestation of the vulnerability after the formation of the island, which leads to inaccurate vulnerability assessment results of the distribution network.

The existing technologies related to distribution network vulnerability assessment mainly include:

In the technical solution disclosed in the Chinese patent "A comprehensive vulnerability analysis method and system for distribution networks based on game theory" (application number: 202310635453.0), the vulnerability of distribution network lines after distributed energy is connected to the distribution network is analyzed based on the structure and operating status of the distribution network.

In the technical solution disclosed in the Chinese patent "A method for assessing the vulnerability of a multi-energy distribution network" (application number: 202211301176.1), the output control parameters of the distributed energy output model are obtained, and the bus voltage and whether it is overloaded are analyzed to determine the risk level of the system.

The technical solution disclosed in the Chinese patent "Distribution Network Vulnerability Identification Method Taking into Account Bilateral Uncertainty" (Application Number: 202211200301.X) discloses a distribution network vulnerability assessment method that takes into account the simultaneous access of new energy and electric vehicles. By analyzing its inherent topological structure and flow state, the safety and risk resistance of each line are obtained.

The above existing technical contents have made certain contributions in the field of vulnerability assessment of distribution networks containing distributed energy. However, when constructing line vulnerability assessment indicators for distribution networks containing distributed energy, their characteristics are not taken into account, and the vulnerability of the lines is not considered from the load end; the impact of source load fluctuations on the weights is not considered when processing subjective and objective weights; when analyzing system vulnerability, the distribution characteristics of vulnerability are not taken into account, resulting in inaccurate results.

Summary of the invention

In response to the lack of vulnerability assessment of distribution network lines containing wind and solar power, the present invention proposes a distribution line vulnerability assessment method that takes into account the time-series fluctuation characteristics of wind and solar power loads. This method obtains the true vulnerability of the system based on the distribution characteristics of system vulnerability and its comprehensive vulnerability for different time periods.

The technical solution adopted by the present invention is:

A method for evaluating the vulnerability of distribution lines considering the time series fluctuation characteristics of wind and solar loads comprises the following steps:

Step 1: Based on the random output model of distributed energy, its output characteristics are obtained in combination with its environmental parameters;

Step 2: Considering the grid structure, operating status and fault impact, establish line vulnerability assessment indicators;

Step 3: According to the improved grid structure and the output of distributed energy in each period, combined with the load demand, calculate the vulnerability index values of the line in each period, and normalize the vulnerability index values;

Step 4: Based on the values of each vulnerability index in each period obtained in step 3, the objective weight and subjective weight are obtained using the improved CRITIC method and the analytic hierarchy process, and then the comprehensive weight of each vulnerability index is obtained by combining game theory;

Step 5: Based on the line vulnerability assessment index established in step 2 and combined with the comprehensive weights of each vulnerability index obtained in step 4, the VIKOR method is used to obtain the vulnerability assessment results of each line;

Step 6: Based on the vulnerability evaluation results of each line obtained in step 5, the Gini coefficient _Gt is used to represent the uniform distribution of the system line vulnerability, and the actual vulnerability of the system in each time period is obtained.

In step 1, the distributed energy DG includes wind energy and solar energy, and the random output model of the distributed energy includes:

1.1: Random output model of wind turbine generator:

The relationship between the output _Pw of a wind turbine and the wind speed v is a complex nonlinear function, which can be approximated by a piecewise function, as shown below:

Where P _w (v) is the output power of the wind turbine; v is the actual wind speed in the environment where the wind turbine is located; v _ci is the cut-in wind speed; v _N is the rated wind speed; v _co is the cut-out wind speed; _Pa is the rated output power of the wind turbine.

1.2: Random output model of photovoltaic power generation:

The relationship between the photovoltaic array output P _pv and the light intensity s is expressed by a piecewise function as follows:

Where P _P (s) is the output power of the photovoltaic array; P _c is the rated output power of the photovoltaic array; s _NI is the light intensity corresponding to the rated output power of the photovoltaic generator set; s _N is the rated light intensity; s is the actual light intensity of the environment in which the photovoltaic array is located.

In step 2, the line vulnerability assessment indicators include:

Improve line intermediate index, improve line degree index, line voltage stability index, line fault loss index; 2.1: Improve line intermediate index:

The improved line betweenness truly reflects the utilization degree of the "power-load" node pair on the line. The larger the value, the more important the line is. The calculation formula of the improved line betweenness is as follows:

In the formula, B _Lk is the improved line number; _SN is the system benchmark capacity; _Sa is the rated capacity or actual output of the power node; _Sb is the peak power or actual load of the load node; _Iab (i) represents the current formed on the i-th branch after the unit current source is injected between nodes a and b. G is the set of generators; F is the set of load nodes.

2.2: Improve line degree indicators:

The network is composed of nodes and edges. The importance of a line is definitely affected by its head and end nodes. The degree of the nodes connected to the line is used to characterize the difference in line degree. The improved line degree calculation formula is as follows:

Where D _Lk is the improved line degree; D _pi and D _pj are the improved node degree values calculated at the beginning and end of the line respectively;

D _i is the initial degree of node i; E is the set of all nodes directly connected to node i; D _j is the initial degree value of point j in set E; is the mean degree of all nodes.

μ _i is the global efficiency coefficient, n is the number of system nodes;

_Zij represents the shortest path length between nodes i and j, and the line weight is its impedance value, so _Zij reflects the line distance by taking the impedance value; J is the set of all nodes except node i.

2.3: Line voltage stability indicators:

For the voltage stability analysis of distribution networks with branches, the voltage stability index based on the existence of power flow solutions can be used:

Wherein, _SLk is the line voltage stability, and the value of _SLK is between 0 and 1. The larger _{the SLk} value is, the worse the voltage stability of the branch is, and the greater the vulnerability of the branch is; _Pj and _Qj are the active power and reactive power injected into node j respectively; _Xij and _Rij are the reactance and resistance values of line k respectively; _Ui is the voltage of node i.

2.4: Line fault loss indicators:

The economic loss caused by line failure is defined as fault loss E _Lk , which includes the load loss caused by the DG output in the island being less than the load demand, or the loss arbitrage caused by the abandonment _of wind or solar power due to line failure;

Wherein, _ELk is the line fault loss; r represents the set of load nodes lost due to line k fault; _lj is the node load loss caused by line fault; _δj is the unit power loss cost of node j. According to the difference in load types, its unit power loss cost can be divided into three categories: residential electricity, industrial electricity, and commercial electricity; _PDG is the DG output in the island; g is the set of load nodes in the island; α is the arbitrage loss coefficient; R(x) is the judgment function. If the DG output is greater than the load demand in the island, R(x) is 1, otherwise it is zero.

In step 3, the improved grid structure is shown in FIG1 , where node 25 in the standard IEEE 33 node system is connected to the photovoltaic generator set, and nodes 30 and 32 are connected to the wind generator set. The distributed energy output in each time period includes:

(1): Fan output:

Where P _w (v) is the output power of the wind turbine; v is the actual wind speed in the environment where the wind turbine is located; v _ci is the cut-in wind speed; v _N is the rated wind speed; v _co is the cut-out wind speed; _Pa is the rated output power of the wind turbine.

(2): Photovoltaic output:

Where P _P (s) is the output power of the photovoltaic array; P _c is the rated output power of the photovoltaic array; s _NI is the light intensity corresponding to the rated output power of the photovoltaic generator set; s _N is the rated light intensity.

The vulnerability index values of the line in each time period are calculated in combination with the load demand.

In step 3, in order to avoid the impact of dimension, the values of various vulnerability indicators are normalized, as shown in the following formula:

In the formula, is the normalized value of the vulnerability index; _mij is the true value of the line index; _maxmij is the maximum true value of the vulnerability index in a day.

In step 4, the steps of improving the CRITIC method to obtain the weight are as follows:

(1): Assuming that the total number of evaluation objects is n and the number of indicators involved in the evaluation is k, the decision matrix T can be constructed;

Where _tij represents the value of the jth indicator of evaluation object i; n is the number of evaluation objects; k is the number of indicators involved in the evaluation.

(2): Calculate the standard deviation of each indicator and the correlation coefficient between indicators:

In the formula, σ _j is the standard deviation of the jth indicator; is the arithmetic mean of the jth index; is the correlation coefficient between the i-th indicator and the j-th indicator; Ti and Tj are the i-th column and j-th column of the decision matrix T respectively; cov(T _i ,T _j ) represents the covariance between Ti and T _j .

(3): Determine the amount of information for each indicator:

Where _Dj is the amount of information contained in the jth indicator.

(4): Determine the objective weight ω ₁ :

In the formula, ω _1i is the objective weight of the i-th indicator; D _j is the information content of the j-th indicator; D _i is the information content of the i-th indicator; k is the number of indicators.

The step 4 comprises the following steps:

S401, using the improved CRITIC method to solve the impact of the change of indicators over time on the objective weighting of indicators. Indicators that are not affected by wind and solar load fluctuations do not need to perform this step. The specific steps include:

The vulnerability index values of each line in each time period obtained in step 3 need to be analyzed for objective weights to address the impact caused by time period fluctuations. The decision matrix M is formed based on the values of the index in different time periods:

Where, _mij is the indicator value of node i in the jth time period; n is the number of lines; and t is the number of time periods.

According to the decision matrix M, the contribution of each time period to the objectively weighted decision matrix can be improved by using CRITIC to form the time period contribution vector U:

U=(u ₁ , u ₂ , u ₃ ,…, u _t );

In the formula, _{ui is} the contribution of the indicator to the formation of the objective judgment matrix in the i-th period.

Where, _ui is the contribution of the indicator in the i-th period to the formation of the objective judgment matrix; Dj _is the amount of information in the j-th period; and t is the number of periods.

In the formula, σ _j is the standard deviation of the indicator in the jth period; is the correlation coefficient between the i-th period and the j-th period.

Where n is the number of lines; is the arithmetic mean of the index in the jth period; is the index value of the jth time period of the ith line.

Where Mi and Mj are the i-th and j-th columns of the decision matrix M respectively; cov(M _i ,M _j ) represents the covariance between Mi and Mj; σ _j is the standard deviation of the j-th period;

S402, the objective evaluation matrix B can be obtained from the time period contribution vector of each indicator obtained in step 3:

_Bj ＝MiU ^T

Among them, _Bj is the j-th column element of the objective evaluation matrix B; M is the decision matrix composed of indicator values in different time periods; ^UT is the transpose of the time period contribution vector.

Where _bij represents the size of the jth index of the ith line; n represents the number of lines; and k represents the number of indicators.

Applying the improved CRITIC method to the decision matrix B again can obtain the objective weights of each vulnerability indicator. The specific steps include:

1): Obtain the standard deviation of each indicator and the correlation coefficient between indicators:

In the formula, σ _j is the standard deviation of the jth indicator; is the arithmetic mean of the jth index; is the correlation coefficient between the i-th indicator and the j-th indicator; Bi and Bj are the i-th column and j-th column of the decision matrix B respectively; cov(B _i ,B _j ) represents the covariance between Bi and B _j .

2): Obtain indicator information D:

In the formula, σ _j is the standard deviation of the jth indicator; is the correlation coefficient between the ith indicator and the jth indicator.

3): Get the objective weight vector ω ₁ of the indicator:

In the formula, ω _1i is the objective weight of the ith indicator; D _i is the information content of the ith indicator; k is the number of indicators; D _j is the information content of the jth indicator.

S403, based on the collected expert opinions, specifically:

The improved betweenness only describes the degree of utilization of the line in the power transmission process, and the amount and importance of the information reflected are lower than the other indicators; the improved line degree indicator is only related to the fixed topological structure of the distribution network, which not only reflects the local characteristics of the structure, but also reflects the global nature; the voltage stability indicator is related to the line's own resistance and reactance, and is also affected by voltage and injected power; the fault loss is defined by the economic loss caused by the loss of load or wind and solar abandonment. When part of the power grid forms an island due to line faults, this part of the loss is greatly affected by the load demand and DG output at that time. In summary, the improved line betweenness indicator is the least important, and its importance is between equally important and slightly important compared to the other three indicators, and the importance of the other three indicators remains consistent.

The collected expert opinions were transformed from qualitative to quantitative using the 1-9 scale method, and the judgment basis of the 1-9 scale method is shown in Table 1.

Table 1 1-9 degree scale method judgment basis

Relative importance significance 1 Both indicators are equally important 3 Row elements are slightly more important than column elements 5 Row elements are significantly more important than column elements 7 Row elements are much more important than column elements 9 Row elements are completely more important than column elements 2n(n＝1,2,3,4) The importance is between 2n-1 and 2n+1

The judgment matrix Y formed by quantifying expert opinions is:

In the formula, if indicator 1 is slightly more important than indicator 2, then _y12 should be 3, _y21 should be 1/3, and all diagonal elements should be 1.

The subjective weight vector ω ₂ can be obtained from the judgment matrix as follows:

Where n is the number of indicators; y _ij is the importance of element i relative to element j; y _kj is the importance of element k relative to element j.

S404, based on the obtained subjective and objective weights of various indicators, combined with the game theory, the goal is to minimize the difference between the subjective and objective weights, that is, the following formula must be satisfied:

Where ω ₁ is the objective weight vector; ω ₂ is the objective weight vector; is the transpose of the objective weight vector; is the transpose of the subjective weight vector; a ₁ and a ₂ are the objective and subjective preliminary combination coefficients respectively.

From the obtained preliminary combination coefficient, we can get the subjective and objective optimal combination coefficient a′:

Where a _i ′ is the combination coefficient of the i-th weighting method; a _k represents the preliminary combination coefficient; and k represents the total number of weighting methods.

Thus, the comprehensive weight vector W of each indicator is obtained:

Where W is the comprehensive weight vector of indicators; α ₁ ′ is the objective weighted combination coefficient; a ₂ ′ is the subjective weighted combination coefficient; They are the transpose of the subjective weight vector and the transpose of the objective weight vector respectively.

In step 5, the VIKOR method is used to obtain the benefit ratio Q _Lk of each line, and the steps are as follows:

S5.1: Obtain the positive and negative ideal solutions Z ⁺ , Z ^- of all solutions:

Where n is the total number of lines; m is the number of vulnerability indicators; _bij represents the size of the jth indicator of the ith line.

S5.2: Calculate the distance ratio of each line to the positive ideal solution and the negative ideal solution:

In the formula, S _i is the group utility value; R _i is the individual regret value; W _j is the comprehensive weight corresponding to the jth indicator; Z ⁺ and Z ^- are the positive and negative ideal solutions of all routes, respectively.

S5.3: Get the benefit ratio Q _Lk of each line:

Where, Q _Lki is the benefit ratio of route i; S _i is the group utility value of route i; R _i is the individual regret value of route i; v is the decision mechanism coefficient.

According to the benefit ratio of each line, the smaller the benefit ratio, the higher the line vulnerability and the higher the ranking, the line vulnerability can be ranked. For example, the benefit ratio of line 1 in each indicator is 0.3 using the VIKOR method, while the benefit ratio of line 2 is 0.5. Then, according to the ranking basis of the VIKOR method, the vulnerability of line 1 is ranked before line 2, that is, the vulnerability of line 1 is greater than that of line 2.

In step 6, the distribution characteristics of vulnerability are expressed according to the benefit ratio Q _Lk of each line obtained in step 5 and the Gini coefficient G. During the calculation process, the line and the line benefit ratio Q _Lk correspond to the population and the population income Gini coefficient respectively. The calculation formula is as follows:

Where Gi _is the Gini coefficient describing the uniformity of vulnerability distribution in the i-th period; n is the number of groups after the lines are equally divided; _{Si is} the ratio of the accumulated line benefit ratio from the i-th group to the total line benefit ratio.

The decision matrix C is constructed by summing up the indicators in each period:

In the formula, _cij is the sum of the jth index of the system in the i-th time period; t is the number of time periods; k represents the number of indicators.

Through the obtained comprehensive weights, combined with the VIKOR method, the benefit ratio Q of the system in each period can be obtained, including:

①: Get the positive and negative ideal solutions Z ⁺ and Z ^- for all time periods:

Where t is the total number of time periods; m is the number of vulnerability indicators; _bij represents the size of the jth indicator in the i-th time period.

②: Calculate the distance ratio to the positive ideal solution and the negative ideal solution in each time period:

In the formula, S _i is the group utility value in the i-th period; R _i is the individual regret value in the i-th period; W _j is the comprehensive weight corresponding to the j-th indicator; Z ⁺ and Z ^- are the positive and negative ideal solutions of all routes, respectively.

③: Get the profit ratio Q of the system in each period:

In the formula, _Qi is the benefit ratio of the system at time i; _Si is the group utility value of the system in the i-th period; _Ri is the individual regret value of the system in the i-th period; v is the decision mechanism coefficient.

The system benefit ratio Q is processed positively and recorded as the system comprehensive vulnerability RV:

RV _i = 1-Q _i

Where _RVi is the comprehensive vulnerability of the system in the i-th period; Qi _is the benefit ratio of the system in the i-th period.

According to the comprehensive vulnerability of the system and the Gini coefficient G, the real vulnerability of the system V can be obtained:

V _i =(G _i +RV _i )/2

Where _Vi is the real vulnerability of the system in the i-th period; Gi _is the Gini coefficient describing the uniformity of the vulnerability distribution in the i-th period; _RVi is the comprehensive vulnerability of the system in the i-th period.

The present invention provides a distribution line vulnerability assessment method considering the time series fluctuation characteristics of wind and solar loads, and the technical effects are as follows:

1) Step 1 of the present invention uses a mathematical model of wind and solar power output to abstract the relationship between the output of wind turbines and photovoltaic arrays and wind speed and light into a piecewise function. Based on the mathematical model combined with wind and solar information, the real-time output of wind and solar power can be obtained.

2) Step 2 of the present invention takes into account various factors of the line for the distribution network with multiple types of DG access, and proposes vulnerability indicators such as improved line intermediate number, improved line degree, line voltage stability, and fault loss based on the structure, operating status, and fault loss of the power grid, which effectively solves the problem of single traditional indicators and can accurately reflect the vulnerability of the distribution network line containing DG.

3) In calculating each vulnerability index in step 3 of the present invention, the source-load volatility is taken into account, that is, the DG output and load demand change over time. This can reflect the size of the vulnerability index under different DG output and load demand scenarios, which is more in line with the actual grid operation scenario.

4) Step 4 of the present invention uses the improved CRITIC method twice to obtain the objective weights for the objective weights. First, the improved CRITIC method is used to obtain the time period contribution vector. The index values that constitute the objective judgment matrix obtained in this way not only consider the information of each time period itself, but also consider the interaction of information of other time periods. Based on the obtained objective judgment matrix, the improved CRITIC method is used again to obtain the objective weights of each indicator. The objective weights obtained by correcting the source-load fluctuations are more accurate and reliable. The comprehensive weights obtained by integrating game theory avoid subjective preferences and make the weighting more reasonable.

5) Step 5 of the present invention uses the VIKOR method to rank the vulnerability of the lines. This method has the characteristics of maximizing the group benefit value and minimizing the individual regret value, making it superior to the TOPSIS method commonly used to solve multi-criteria decision-making problems.

6) In step 6 of the present invention, when analyzing the vulnerability of the entire distribution network, not only the overall size of the line vulnerability is considered, but also the distribution characteristics of the vulnerability of the distribution network line are reflected by using the Gini coefficient. The real vulnerability of the system in different time periods is obtained by combining the overall and distribution characteristics, which more truly and accurately reflects the system vulnerability status.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 is the improved IEEE33 node system structure diagram.

FIG2 is a flow chart of line vulnerability assessment according to the present invention.

Figure 3 shows the fluctuations in the output of each distributed energy source and load demand.

Figure 4 is a normalized value diagram of the line improvement degree index.

Figure 5 is a graph showing the normalized values of the improved line betweenness in each time period.

Figure 6 is a graph showing the normalized values of line voltage stability indicators at different time periods.

Figure 7 is a graph showing the normalized values of line fault vulnerability indicators in each time period.

Figure 8 is the Lorenz curve corresponding to the vulnerability distribution in some time periods.

DETAILED DESCRIPTION

A distribution line vulnerability assessment method that comprehensively considers factors such as distribution network structure, state, faults, and source-load fluctuation characteristics is proposed. The method includes three parts: First, the distributed energy output model and load fluctuation model are used to obtain the daily fluctuation characteristics of DG output and load demand. From the three aspects of grid structure, operating state and fault impact, a distribution network vulnerability evaluation index suitable for DG is proposed. According to the daily fluctuation characteristics of wind and solar load, the values of each index of the line in each time period are calculated. For the index with the same time series fluctuation characteristics, the time period contribution vector is calculated according to the improved CRITIC method to determine the value of the index when objectively weighted. According to the objective decision matrix obtained, the objective weight is calculated by the improved CRITIC method, and the subjective weight is obtained by the hierarchical analysis method. Then, the game theory is used to cross-integrate the subjective and objective weights to obtain the comprehensive weight. According to the obtained comprehensive weight and the normalized value of each index, combined with the VIKOR evaluation method, the vulnerability evaluation results of the line in each time period are obtained. Finally, the real vulnerability of the system is obtained by using the Gini coefficient and the comprehensive vulnerability.

The specific steps are shown in Figure 2, including:

S1: Based on the random output model of distributed energy, its output characteristics in 24 periods are obtained in combination with its environmental parameters.

S2: According to the characteristics of distribution networks with multiple types of DGs, appropriate line vulnerability assessment indicators are established from three aspects: grid structure, operating status and fault impact.

S3: According to the improved grid structure and DG output in each time period, combined with the load demand, the size of various indicators of the line in each time period is calculated, and each indicator is normalized.

S4: Based on the obtained index values of each period, the improved CRITIC method is used to solve the problem of index values changing over time, determine the elements of the decision matrix, and use the improved CRITIC method again to obtain the objective weight of each index. The hierarchical analysis method is used to obtain the subjective weight of each index, and finally the game theory is used to obtain the comprehensive weight of each index.

S5: Using the VIKOR method and combining the comprehensive weight values of various indicators, the final evaluation results of the vulnerability of each line are obtained.

S6: Use the Gini coefficient to characterize the uniform distribution of system line vulnerability, and combine it with the system's comprehensive vulnerability to obtain the real vulnerability of the system in each period.

In step S1, distributed energy sources such as wind and light are connected to the IEEE33 node system, thereby obtaining an improved IEEE33 node system. The capacities of the wind turbines are 700kW and 300kW respectively, the cut-in wind speed is 4m/s, the rated wind speed is 14m/s, and the cut-out wind speed is 24m/s; the photovoltaic array capacity is 400kW, and the rated light intensity is 303.1W/m ² . The distributed energy involved includes wind energy and solar energy. The random output model of distributed energy includes:

S101, random output model of wind turbines, wind speed follows a two-parameter Weibull distribution, and its probability density function is:

Where c is the scale parameter of the Weibull distribution, which reflects the regional wind speed distribution characteristics; k is the shape parameter of the Weibull distribution, which reflects the average level of regional wind speed; and v is the actual wind speed in the region.

The relationship between the output _Pw of a wind turbine and the wind speed v is a complex nonlinear function, which can be approximated by a piecewise function, as shown below:

Where P _w (v) is the output power of the wind turbine; v is the actual wind speed in the environment where the wind turbine is located; v _ci is the cut-in wind speed; v _N is the rated wind speed; v _co is the cut-out wind speed; _Pa is the rated output power of the wind turbine.

S102, the random output model of photovoltaic power generation, the Beta distribution is often used to describe the light intensity, and its probability density function is:

Where α and β are the shape parameters of Beta distribution; s is the real-time illumination intensity of the region; s _max is the peak illumination intensity of the region.

The relationship between the photovoltaic array output P _pv and the light intensity s can be expressed by a piecewise function as follows:

Where P _P (s) is the output power of the photovoltaic array; P _c is the rated output power of the photovoltaic array; s _NI is the light intensity corresponding to the rated output power of the photovoltaic generator set; s _N is the rated light intensity.

The wind-solar output curve and load demand curve can be obtained through the wind-solar output model and load demand, as shown in Figure 3.

In step S2, the structure, operation status and fault impact of the power grid are considered to construct its structural vulnerability index, which specifically includes: improving the line betweenness index, line degree index, line voltage stability index and line fault loss index;

S201, Improved Line Betweenness: The improved line betweenness truly reflects the utilization of the "power-load" node pair on the line. The larger the value, the more important the line. The calculation formula for the improved line betweenness is as follows:

In the formula, B _Lk is the improved line number; _SN is the system benchmark capacity; _Sa is the rated capacity or actual output of the power node; _Sb is the peak power or actual load of the load node; _Iab (i) represents the current formed on the i-th branch after the unit current source is injected between nodes a and b. G is the set of generators; F is the set of load nodes.

S202, Improved line degree: The network is composed of nodes and edges. The importance of a line is definitely affected by its head and end nodes. The difference in line degree is represented by the degree of the nodes connected to the line. The calculation formula for improved line degree is as follows:

Where D _Lk is the improved line degree; D _pi and D _pj are the improved node degree values calculated at the beginning and end of the line respectively;

D _i is the initial degree of node i; E is the set of all nodes directly connected to node i; D _j is the initial degree value of point j in set E; is the mean degree of all nodes.

μ _i is the global efficiency coefficient, n is the number of system nodes; _Zij represents the shortest path length between nodes i and j. In the distribution network, _Zij reflects the line distance by taking the impedance value; v is the set of all nodes except node i.

S203, Line voltage stability index: For the voltage stability analysis of the distribution network with branches, the voltage stability index based on the power flow solution can be used.

In the formula, S _Lk is the line voltage stability, the value of S _LK is between 0 and 1, the larger _the S _Lk value, the worse the voltage stability of the branch and the greater the vulnerability of the branch; P _j and Q _j are the active power and reactive power injected into node j respectively; _Xij and _Rij are the reactance and resistance values of line k respectively; _Ui is the voltage of node i.

S204, Line Fault Loss Indicator: The economic loss caused by line fault is defined as fault loss E _Lk . Fault loss includes load loss caused by DG output being less than load demand in the isolated island or loss arbitrage caused by wind or solar abandonment due to line fault.

In the formula, E _Lk is the line fault loss; r represents the set of load nodes lost due to line k fault; l _j is the node load loss caused by line fault; δ _j is the unit power loss cost of node j. According to the difference in load type, the unit power loss cost can be divided into three categories: residential electricity, industrial electricity, and commercial electricity; P _DG is the DG output in the island; g is the set of load nodes in the island; α is the arbitrage loss coefficient; R(x) is the judgment function. If the DG output is greater than the load demand in the island, R(x) is 1, otherwise it is zero.

In step S3, using the source-load fluctuation curve in step S1 and the index model constructed in step S2, the values of each vulnerability index in each time period can be obtained, including the normalized value of the improved line intermediate number in each time period, the normalized value of the improved line degree, the normalized value of the line voltage stability index in each time period, and the normalized value of the line fault loss index in each time period, as shown in Figures 4 to 7 for details.

In step S4, the objective weight and subjective weight are obtained by using the improved CRITIC method and the analytic hierarchy process, and then the comprehensive weight of each vulnerability indicator is obtained by combining the game theory, which specifically includes the following steps:

S401: Use the improved CRITIC method to solve the impact of the change of indicators over time on the objective weighting of indicators. Indicators that are not affected by wind and solar load fluctuations do not need to undergo this step. The specific steps include:

From step S3, the vulnerability index values of each line in each time period can be obtained. For objective weight analysis, the impact caused by time period fluctuations needs to be resolved, and a decision matrix M is formed according to the values of the index in different time periods:

In order to avoid the impact of inconsistent dimensions on subsequent weight assignment, the values in the decision matrix M are normalized:

In the formula, is the normalized value of the vulnerability index; _mij is the true value of the line index; _maxmij is the maximum true value of the vulnerability index in a day.

According to the decision matrix M, the contribution of each time period to the objectively weighted decision matrix can be improved by CRITIC to form the time period contribution vector U:

U＝(u ₁ ,u ₂ ,u ₃ ,…,u _t )

In the formula, _{ui is} the contribution of the indicator to the formation of the objective judgment matrix in the i-th period.

Where, _ui is the contribution of the indicator in the i-th period to the formation of the objective judgment matrix; Dj _is the information content of the j-th period; t is the number of periods;

In the formula, σ _j is the standard deviation of the indicator in the jth period; is the correlation coefficient between the i-th period and the j-th period.

Where n is the number of lines; is the arithmetic mean of the index in the jth period; is the index value of the jth time period of the ith line.

Where _Mi and _Mj are the i-th and j-th columns of the decision matrix M respectively; cov( _Mi , _Mj ) represents the covariance between _Mi and _Mj ; _σj is the standard deviation of the j-th period.

S402, the objective evaluation matrix B can be obtained from the time period contribution vector of each indicator obtained in step 3:

_Bj ＝MiU ^T

Among them, _Bj is the j-th column element of the objective evaluation matrix B; M is the decision matrix; ^UT is the transpose of the time period contribution vector.

Where _bij represents the size of the jth index of the ith line; n represents the number of lines; and k represents the number of indicators.

The decision matrix B is used again with the improved CRITIC method to obtain the objective weights of each vulnerability indicator. The specific steps include:

(1) Obtain the standard deviation σ of each indicator and the correlation coefficient between indicators

In the formula, σ _j is the standard deviation of the jth indicator; is the arithmetic mean of the jth index; is the correlation coefficient between the i-th indicator and the j-th indicator; _Bi and _Bj are the i-th column and j-th column of the decision matrix B respectively; cov( _Bi , _Bj ) represents the covariance between Bi and _Bj .

(2) Obtaining the index information D:

In the formula, σ _j is the standard deviation of the jth indicator; is the correlation coefficient between the ith indicator and the jth indicator.

(3) Obtain the objective weight vector ω ₁ of the indicator:

In the formula, ω _1i is the objective weight of the ith indicator; D _i is the information content of the ith indicator; k is the number of indicators; D _j is the information content of the jth indicator.

S403, based on the collected expert opinions, specifically:

The improved betweenness only describes the degree of utilization of the line in the power transmission process, and the amount and importance of the information reflected are lower than the other indicators; the improved line degree indicator is only related to the fixed topological structure of the distribution network, which not only reflects the local characteristics of the structure, but also reflects the global nature; the voltage stability indicator is related to the line's own resistance and reactance, and is also affected by voltage and injected power; the fault loss is defined by the economic loss caused by the loss of load or wind and solar abandonment. When part of the power grid forms an island due to line faults, this part of the loss is greatly affected by the load demand and DG output at that time. In summary, the improved line betweenness indicator is the least important, and its importance is between equally important and slightly important compared to the other three indicators, and the importance of the other three indicators remains consistent.

The collected expert opinions are transformed from qualitative to quantitative using the 1-9 scale method, and the expert opinions are quantified to form a judgment matrix Y:

The subjective weight vector ω ₂ can be obtained from the judgment matrix Y as follows:

Where ω _2i is the subjective weight of the i-th indicator; n is the number of indicators; y _ij is the importance of element i relative to element j; y _kj is the importance of element k relative to element j.

S404, based on the obtained subjective and objective weights of various indicators, combined with the game theory, the goal is to minimize the difference between the subjective and objective weights, that is, the following formula must be satisfied:

Where ω ₁ is the objective weight vector; ω ₂ is the objective weight vector; is the transpose of the objective weight vector; is the transpose of the subjective weight vector; a ₁ and a ₂ are the objective and subjective preliminary combination coefficients respectively.

From the obtained preliminary combination coefficient, the subjective and objective optimal combination coefficient a′ can be obtained:

Where a _i ′ is the combination coefficient of the i-th weighting method; a _k represents the initial combination coefficient; and k represents the number of weighting methods.

Thus, the comprehensive weight vector W of each indicator is obtained:

In the formula, α ₁ ′ is the objective weighted combination coefficient; a ₂ ′ is the subjective weighted combination coefficient; They are the transpose of the subjective weight vector and the transpose of the objective weight vector respectively.

The subjective, objective and comprehensive weights of the indicators such as improved line betweenness, improved line degree, line voltage stability and fault loss obtained in step S4 are shown in Table 2.

Table 2 Weight values of each indicator

index Subjective weight Objective weight Comprehensive weight B _L 0.1429 0.2739 0.1945 _LqCy 0.2857 0.2146 0.2577 S _L 0.2857 0.3130 0.2965 E _L 0.2857 0.1985 0.2513

Step S5 is based on the index values in step S2 and combined with the comprehensive weight W of the subjective and objective cross-integration of the indexes obtained in step S4, and the VIKOR method is used to obtain the benefit ratio Q _Lk of each route. The calculation steps of Q _Lk are as follows:

(1) Obtain the positive and negative ideal solutions Z ⁺ and Z ^- of all solutions:

Where n is the total number of lines; m is the number of vulnerability indicators; _bij represents the size of the jth indicator of the ith line.

(2) Calculate the distance ratio of each line to the positive ideal solution and the negative ideal solution:

In the formula, S _i is the group utility value; R _i is the individual regret value; W _j is the comprehensive weight corresponding to the jth indicator; Z ⁺ and Z ^- are the positive and negative ideal solutions of all routes, respectively.

(3) Obtain the profit ratio Q _Lk of each route:

Where, Q _Lki is the benefit ratio of route i; S _i is the group utility value of route i; R _i is the individual regret value of route i; v is the decision mechanism coefficient.

According to the obtained benefit ratio Q _Lk of each line, the smaller the benefit ratio, the higher the line vulnerability and the higher the ranking, the line vulnerability evaluation results in each period can be obtained, as shown in Table 3.

Table 3 Ranking of top ten line vulnerabilities in each period

Time period/h Line comprehensive vulnerability ranking Time period/h Line comprehensive vulnerability ranking 1 5,27,28,29,2,8,26,23,9,6 13 2,5,3,4,6,7,1,9,8,27 2 27,5,28,2,29,26,23,8,25,9 14 2,5,3,4,6,7,9,8,1,12 3 27,28,29,5,2,26,25,3,23,8 15 2,5,3,4,6,7,8,9,12,1 4 5,27,28,2,29,6,8,23,12,9 16 2,5,3,4,6,27,8,9,7,28 5 5,27,28,29,2,6,8,25,23,12 17 2,5,3,4,6,7,1,9,8,23 6 5,27,28,29,2,6,26,25,8,7 18 2,5,3,4,6,7,1,9,23,8 7 5,27,28,2,29,6,8,7,23,9 19 2,5,3,4,6,1,7,23,9,8 8 5,2,27,8,28,3,6,7,9,12 20 2,5,3,6,4,1,7,22,23,9 9 5,2,27,8,28,6,9,7,3,12 twenty one 2,5,3,4,6,1,7,23,9,22 10 2,5,3,4,6,8,27,9,7,28 twenty two 2,5,3,4,6,1,7,9,23,8 11 2,5,3,4,6,8,7,9,27,12 twenty three 5,2,6,7,27,9,8,23,28,29 12 2,5,3,4,6,7,8,9,12,1 twenty four 5,2,6,27,9,8,7,23,28,12

In step S6, according to the vulnerability evaluation results of each line obtained in step S5, the distribution characteristics of the system vulnerability at this moment can be obtained, and the Gini coefficient G is used to express the uniformity of the distribution. The Lorenz curve corresponding to the Gini coefficient in some time periods is shown in Figure 8. The Gini coefficient calculation formula is as follows:

Where Gi _is the Gini coefficient describing the uniformity of vulnerability distribution in the i-th period; n is the number of groups after the lines are equally divided; _{Si is} the ratio of the accumulated line benefit ratio from the i-th group to the total line benefit ratio.

The decision matrix C is constructed by summing up the indicators in each period:

Through the obtained comprehensive weights, combined with the VIKOR method, the benefit ratio Q of the system in each period can be obtained. For the convenience of the subsequent, the benefit ratio Q of the system is positively processed and recorded as the system comprehensive vulnerability RV _i :

RV _i = 1-Q _i

Where _RVi is the comprehensive vulnerability of the system in the i-th period; Qi _is the benefit ratio of the system in the i-th period.

According to the comprehensive vulnerability of the system and the Gini coefficient G, the real vulnerability of the system V can be obtained:

V _i =(G _i +RV _i )/2

In the formula, V _i is the real vulnerability of the system in the i-th period; G _i is the Gini coefficient describing the uniformity of the vulnerability distribution in the i-th period; RV _i is the comprehensive vulnerability of the system in the i-th period. The ranking results of the real vulnerability V _t of the system in each period are shown in Table 4.

Table 4 Line vulnerability ranking in each period

Sorting Time period/h V _t Sorting Time period/h V _t 1 20 0.6948 13 twenty four 0.3839 2 twenty one 0.6432 14 1 0.3642 3 19 0.6334 15 11 0.3584 4 twenty two 0.5990 16 2 0.3536 5 18 0.5468 17 10 0.3431 6 17 0.5369 18 9 0.2705 7 13 0.5175 19 8 0.2652 8 14 0.4827 20 3 0.2507 9 twenty three 0.4772 twenty one 7 0.2220 10 16 0.4531 twenty two 6 0.2160 11 12 0.3971 twenty three 5 0.1808 12 15 0.3934 twenty four 4 0.1805

Based on the obtained real vulnerability of the system, the time periods with greater vulnerability of the distribution network can be accurately grasped, which can provide a basis for daily inspections of the distribution network.

The present invention proposes a method for assessing the vulnerability of distribution network lines for access to multiple types of distributed energy sources. The vulnerability analysis scenario is closer to the actual scenario. A more comprehensive consideration is taken into account in the construction of vulnerability indicators, avoiding the problem of inaccurate assessment results caused by a single indicator. The subjective and objective weights are cross-fused in the indicator weight assignment link, avoiding the generation of subjective preferences. When analyzing the real vulnerability of the distribution network, the overall vulnerability is combined with the distribution characteristics of the vulnerability, which can better reflect the real situation of the vulnerability of the distribution network.

Claims (10)

1. A method for evaluating the vulnerability of distribution lines considering the temporal fluctuation characteristics of wind and solar loads, characterized by comprising the following steps: Step 1: Based on the random output model of distributed energy, its output characteristics are obtained in combination with its environmental parameters; Step 2: Considering the grid structure, operating status and fault impact, establish line vulnerability assessment indicators; Step 3: According to the improved grid structure and the output of distributed energy in each period, combined with the load demand, calculate the vulnerability index values of the line in each period, and normalize the vulnerability index values; Step 4: Based on the values of each vulnerability index in each period obtained in step 3, the objective weight and subjective weight are obtained using the improved CRITIC method and the analytic hierarchy process, and then the comprehensive weight of each vulnerability index is obtained by combining game theory; Step 5: Based on the line vulnerability assessment index established in step 2 and combined with the comprehensive weights of each vulnerability index obtained in step 4, the VIKOR method is used to obtain the vulnerability assessment results of each line; Step 6: Based on the vulnerability evaluation results of each line obtained in step 5, the Gini coefficient _Gt is used to represent the uniform distribution of the system line vulnerability, and the actual vulnerability of the system in each time period is obtained. 2. According to claim 1, a method for evaluating the vulnerability of distribution lines considering the time series fluctuation characteristics of wind and solar loads is characterized in that: in step 1, the distributed energy DG includes wind energy and solar energy, and the random output model of the distributed energy includes: 1.1: Random output model of wind turbine generator: The relationship between the wind turbine output _Pw and wind speed v is as follows: Where P _w (v) is the output power of the wind turbine; v is the actual wind speed of the environment where the wind turbine is located; v _ci is the cut-in wind speed; v _N is the rated wind speed; v _co is the cut-out wind speed; Pa _is the rated output power of the wind turbine; 1.2: Random output model of photovoltaic power generation: The relationship between the photovoltaic array output P _pv and the light intensity s is expressed by a piecewise function as follows: Where P _P (s) is the output power of the photovoltaic array; P _c is the rated output power of the photovoltaic array; s _NI is the light intensity corresponding to the rated output power of the photovoltaic generator set; s _N is the rated light intensity; s is the actual light intensity of the environment in which the photovoltaic array is located. 3. According to claim 1, a method for evaluating the vulnerability of a distribution line taking into account the time series fluctuation characteristics of wind and solar loads, characterized in that: in step 2, the line vulnerability evaluation index includes: Improved line intermediate index, improved line degree index, line voltage stability index, line fault loss index; 2.1: Improve the line betweenness index: The improved line betweenness reflects the utilization degree of the "power-load" node pair on the line. The larger the value, the more important the line is. The calculation formula of the improved line betweenness is as follows: Wherein, B _Lk is the improved line intermediate number; _SN is the system benchmark capacity; _Sa is the rated capacity or actual output of the power node; _Sb is the peak power or actual load of the load node; _Iab (i) represents the current formed on the i-th branch after the unit current source is injected between nodes a and b; G is the set of generator sets; F is the set of load nodes; 2.2: Improve line degree indicators: The difference of line degree is characterized by the degree of nodes connected to the line; the improved line degree calculation formula is as follows: Where D _Lk is the improved line degree; D _pi and D _pj are the improved node degree values calculated at the beginning and end of the line respectively; D _i is the initial degree of node i; E is the set of all nodes directly connected to node i; D _j is the initial degree value of point j in set E; is the mean degree of all nodes; μ _i is the global efficiency coefficient, n is the number of system nodes; _Zij represents the shortest path length between nodes i and j, and the line weight is its impedance value, so _Zij reflects the line distance by taking the impedance value; J is the set of all nodes except node i; 2.3: Line voltage stability indicators: Adopt the voltage stability index based on the existence of power flow solution: Where, S _Lk is the line voltage stability, and the value of S _LK is between 0 and 1. The larger the S _Lk value is, the worse the voltage stability of the branch is, and the greater the vulnerability of the branch is; P _j and Q _j are the active power and reactive power injected into node j respectively; _Xij and _Rij are the reactance and resistance values of line k respectively; U _i is the voltage of node i; 2.4: Line fault loss indicators: The economic loss caused by line failure is defined as failure loss E _Lk ; Wherein, _ELk is the line fault loss; r represents the set of load nodes lost due to line k fault; _lj is the node load loss caused by line fault; _δj is the unit electricity loss cost of node j. According to the difference in load types, its unit electricity loss cost can be divided into three categories: residential electricity, industrial electricity, and commercial electricity; _PDG is the DG output in the island; g is the set of load nodes in the island; α is the arbitrage loss coefficient; R(x) is the judgment function. If the DG output is greater than the load demand in the island, R(x) is 1, otherwise it is zero. 4. According to claim 1, a method for evaluating the vulnerability of distribution lines considering the time series fluctuation characteristics of wind and solar loads is characterized in that: in step 3, in the improved power grid structure, the output of distributed energy in each time period includes: (1): Fan output: Where P _w (v) is the output power of the wind turbine; v is the actual wind speed of the environment where the wind turbine is located; v _ci is the cut-in wind speed; v _N is the rated wind speed; v _co is the cut-out wind speed; Pa _is the rated output power of the wind turbine; (2): Photovoltaic output: Where, P _P (s) is the output power of the photovoltaic array; P _c is the rated output power of the photovoltaic array; s _NI is the light intensity corresponding to the rated output power of the photovoltaic generator set; s _N is the rated light intensity; The vulnerability index values of the line in each time period are calculated in combination with the load demand. 5. According to claim 4, a method for evaluating the vulnerability of distribution lines taking into account the time series fluctuation characteristics of wind and solar loads is characterized in that: in step 3, each vulnerability index value is normalized, as shown in the following formula: In the formula, is the normalized value of the vulnerability index; _mij is the true value of the line index; _maxmij is the maximum true value of the vulnerability index in a day. 6. According to claim 1, a method for evaluating the vulnerability of distribution lines considering the time series fluctuation characteristics of wind and solar loads, characterized in that: in step 4, the step of improving the CRITIC method to obtain the weight is as follows: (1): Assuming that the total number of evaluation objects is n and the number of indicators involved in the evaluation is k, the decision matrix T can be constructed; In the formula, t _ij represents the value of the jth indicator of evaluation object i; n is the number of evaluation objects; k is the number of indicators involved in the evaluation; (2): Calculate the standard deviation of each indicator and the correlation coefficient between indicators: In the formula, σ _j is the standard deviation of the jth indicator; is the arithmetic mean of the jth index; is the correlation coefficient between the i-th indicator and the j-th indicator; Ti and Tj are the i-th column and j-th column of the decision matrix T respectively; cov(T _i ,T _j ) represents the covariance between Ti and T _j ; (3): Determine the amount of information for each indicator: In the formula, _Dj is the amount of information contained in the jth indicator; (4): Determine the objective weight ω ₁ : In the formula, ω _1i is the objective weight of the i-th indicator; D _j is the information content of the j-th indicator; D _i is the information content of the i-th indicator; k is the number of indicators. 7. According to claim 6, a method for evaluating the vulnerability of distribution lines considering the time series fluctuation characteristics of wind and solar loads, characterized in that: step 4 comprises the following steps: S401, the vulnerability index values of each line in each time period obtained in step 3 are analyzed for objective weights to solve the impact caused by time period fluctuations, and a decision matrix M is formed according to the values of the index in different time periods: Where, m _ij is the index value of node i in the jth time period; n is the number of lines; t is the number of time periods; According to the decision matrix M, the improved CRITIC is used to calculate the contribution of each time period to the objectively weighted decision matrix to form the time period contribution vector U: U=(u ₁ , u ₂ , u ₃ ,…, u _t ); In the formula, _ui is the contribution of the indicator to the formation of the objective judgment matrix in the i-th period; Where, _ui is the contribution of the indicator in the i-th period to the formation of the objective judgment matrix; Dj _is the amount of information in the j-th period; t is the number of periods; In the formula, σ _j is the standard deviation of the indicator in the jth period; is the correlation coefficient between the i-th period and the j-th period; In the formula, n is the number of lines; is the arithmetic mean of the index in the jth period; is the index value of the jth period of the ith line; Where _Mi and _Mj are the i-th and j-th columns of the decision matrix M respectively; cov( _Mi , _Mj ) represents the covariance between _Mi and _Mj ; _σj is the standard deviation of the j-th period; S402, obtain the objective evaluation matrix B of each indicator from the time period contribution vector obtained in step 3: _Bj ＝MiU ^T Among them, _Bj is the j-th column element of the objective evaluation matrix B; M is the decision matrix composed of the index values of different time periods; U ^T is the transpose of the time period contribution vector; Where _bij represents the size of the jth index of the ith line; n represents the number of lines; and k represents the number of indicators. 8. According to claim 7, a method for evaluating the vulnerability of distribution lines taking into account the time series fluctuation characteristics of wind and solar loads is characterized in that: the decision matrix B is again applied with the improved CRITIC method to obtain the objective weight of each vulnerability index, and the specific steps include: 1): Obtain the standard deviation of each indicator and the correlation coefficient between indicators: In the formula, σ _j is the standard deviation of the jth indicator; is the arithmetic mean of the jth index; is the correlation coefficient between the i-th indicator and the j-th indicator; _Bi and _Bj are the i-th column and j-th column of the decision matrix B respectively; cov( _Bi , _Bj ) represents the covariance between _Bi and _Bj ; 2): Obtain indicator information D: In the formula, σ _j is the standard deviation of the jth indicator; is the correlation coefficient between the i-th indicator and the j-th indicator; 3): Get the objective weight vector ω ₁ of the indicator: In the formula, ω _1i is the objective weight of the ith indicator; D _i is the information content of the ith indicator; k is the number of indicators; D _j is the information content of the jth indicator; 4): Use the 1-9 scale method to transform the collected expert opinions from qualitative to quantitative, and quantify the expert opinions to form a judgment matrix Y: In the formula, if indicator 1 is slightly more important than indicator 2, then y ₁₂ should be 3, y ₂₁ should be 1/3, and all diagonal elements should be 1; The subjective weight vector ω ₂ can be obtained from the judgment matrix as follows: Where n is the number of indicators; y _ij is the importance of element i relative to element j; y _kj is the importance of element k relative to element j; 5): Based on the obtained subjective and objective weights of various indicators, combined with the game theory, the goal is to minimize the difference between the subjective and objective weights, that is, the following formula must be satisfied: Where ω ₁ is the objective weight vector; ω ₂ is the objective weight vector; is the transpose of the objective weight vector; is the transpose of the subjective weight vector; a ₁ and a ₂ are the objective and subjective preliminary combination coefficients respectively; From the obtained preliminary combination coefficient, we can get the subjective and objective optimal combination coefficient a′: In the formula, a _i ′ is the combination coefficient of the i-th weighting method; a _k represents the preliminary combination coefficient; k represents the total number of weighting methods; Thus, the comprehensive weight vector W of each indicator is obtained: Where W is the comprehensive weight vector of indicators; α ₁ ′ is the objective weighted combination coefficient; a ₂ ′ is the subjective weighted combination coefficient; They are the transpose of the subjective weight vector and the transpose of the objective weight vector respectively. 9. According to claim 1, a method for evaluating the vulnerability of distribution lines considering the time series fluctuation characteristics of wind and solar loads, characterized in that: in step 5, the benefit ratio Q _Lk of each line is obtained by using the VIKOR method, and the steps are as follows: S5.1: Obtain the positive and negative ideal solutions Z ⁺ , Z ^- of all solutions: Where n is the total number of lines; m is the number of vulnerability indicators; _bij represents the size of the jth indicator of the ith line; S5.2: Calculate the distance ratio of each line to the positive ideal solution and the negative ideal solution: In the formula, S _i is the group utility value; R _i is the individual regret value; W _j is the comprehensive weight corresponding to the jth indicator; Z ⁺ and Z ^- are the positive and negative ideal solutions of all routes respectively; S5.3: Get the benefit ratio Q _Lk of each line: Where, Q _Lki is the benefit ratio of route i; S _i is the group utility value of route i; R _i is the individual regret value of route i; v is the decision mechanism coefficient; Based on the obtained benefit ratios of each line, the line vulnerability can be ranked according to the characteristic that the smaller the benefit ratio, the higher the line vulnerability and the higher the ranking. 10. A method for evaluating the vulnerability of distribution lines considering the time series fluctuation characteristics of wind and solar loads according to claim 9, characterized in that: in said step 6, according to the benefit ratio Q _Lk of each line obtained in step 5, the distribution characteristics of the vulnerability are expressed in combination with the Gini coefficient G. During the calculation process, the line and the line benefit ratio Q _Lk correspond to the population and the population income Gini coefficient respectively. The calculation formula is as follows: In the formula, Gi _is the Gini coefficient describing the uniformity of vulnerability distribution in the i-th period; n is the number of groups after the lines are evenly divided; _Si is the ratio of the accumulated line benefit ratio from the i-th group to the total line benefit ratio; The decision matrix C is constructed by summing up the indicators in each period: In the formula, c _ij is the sum of the jth index of the system in the i-th period; t is the number of periods; k represents the number of indicators; Through the obtained comprehensive weights, combined with the VIKOR method, the benefit ratio Q of the system in each period can be obtained, including: In the formula, _Qi is the benefit ratio of the system at time i; _Si is the group utility value of the system in the i-th period; _Ri is the individual regret value of the system in the i-th period; v is the decision mechanism coefficient; The system benefit ratio Q is processed positively and recorded as the system comprehensive vulnerability RV: RV _i = 1-Q _i Where RV _i is the comprehensive vulnerability of the system in the i-th period; Q _i is the benefit ratio of the system in the i-th period; According to the comprehensive vulnerability of the system and the Gini coefficient G, the real vulnerability of the system V can be obtained: V _i =(G _i +RV _i )/2 Where, Vi _is the real vulnerability of the system in the i-th period; Gi _is the Gini coefficient describing the uniformity of the vulnerability distribution in the i-th period; RV _i is the comprehensive vulnerability of the system in the i-th period.