Electric power system elasticity evaluation method and system facing extreme weather

Abstract

The invention belongs to the technical field of power system elasticity evaluation, and particularly relates to a power system elasticity evaluation method and system oriented to extreme weather. The electric power system elasticity evaluation method comprises the steps of determining m evaluation objects, wherein the evaluation objects are electric power system operation conditions in different time periods of extreme weather; introducing evaluation indexes from multiple dimensions; respectively calculating n evaluation indexes of each evaluation object to form an evaluation index matrix; and calculating the comprehensive weight of each evaluation index through an analytic hierarchy process-entropy weight method, and calculating the score of each dimension evaluation index of each evaluation object. According to the evaluation method disclosed by the invention, the safety, the availability and the economical efficiency of the power system can be comprehensively evaluated more accurately.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system resilience evaluation, and specifically relates to a power system resilience evaluation method and system for extreme weather conditions. Background Art

[0002] As global climate change intensifies, frequent extreme weather disasters have posed a serious threat to social and economic activities around the world. Especially for power systems, how to maintain stable operation under extreme weather conditions is an important issue of concern to the global power industry and all sectors of society. In the new situation where power grids are becoming increasingly complex and security threats are increasing, it is imperative to build a resilient power grid that cannot be broken, destroyed, or damaged.

[0003] At present, the power system resilience assessment methods only focus on one aspect of power system resilience, such as power supply reliability, and obtain the resilience evaluation value by calculating the index value and weight value of a certain dimension. However, with the continuous access of a high proportion of new energy, it has a profound impact on the power supply reliability, economy, and system availability of the power system. Designing a multi-dimensional resilience evaluation index system and comprehensively assessing the resilience of the power system before and after extreme weather is very important for quantifying the resilience effect of the power system, exploring the weak elastic units, and thus improving the resilience of the power system. Summary of the invention

[0004] In response to the defects of the prior art, the present invention provides a multi-dimensional evaluation method and system for the resilience of power systems under extreme weather conditions, which can more accurately and comprehensively evaluate the safety, availability and economy of power systems.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a multi-dimensional evaluation method for power system resilience under extreme weather conditions, comprising the following steps:

[0007] Determine m evaluation objects, which are the operation of the power system during different periods of extreme weather;

[0008] Introducing evaluation indicators from multiple dimensions;

[0009] Calculate the n evaluation indicators of each evaluation object respectively to form an evaluation indicator matrix (x ij ) m*n ;

[0010] The comprehensive weight of each evaluation index is calculated through the hierarchical analysis-entropy weight method, and the score of each dimension evaluation index of each evaluation object is calculated.

[0011] Preferably, the evaluation indicators introduced from multiple dimensions include: evaluation indicators of safe operation dimension, system availability dimension and comprehensive economic dimension.

[0012] Preferably, the evaluation index of the safe operation dimension includes system load loss rate, line load rate, transformer load rate, short-circuit current level index, node voltage level index, and N-1 pass rate, wherein:

[0013] The system load loss rate calculation formula is:

[0014]

[0015] In the formula, LOLP is the system load loss rate, LC is the system load loss amount, and T is the total simulation time;

[0016] The line load rate calculation formula is:

[0017]

[0018] In the formula, x L is the line load factor, i=1,2,...,n L , n L is the number of transformers, α L,i is the transformer importance correction factor, η pl,i is the transformer load loss rate;

[0019] The transformer load factor calculation formula is:

[0020]

[0021] In the formula, x t is the transformer load factor, S real Indicates the actual transmission capacity, S rated Indicates the rated capacity of the transformer;

[0022] The short-circuit current level indicator calculation formula is:

[0023]

[0024] Where i = 1, 2, ..., n sc , x I is the short-circuit current level indicator, n sc is the number of short-circuit faults, I i,sc is the short-circuit current value under the i-th short-circuit fault, I i,sc.lim It is the minimum breaking capacity of the circuit breaker connected to the short-circuit busbar;

[0025] The calculation formula of node voltage level index is:

[0026]

[0027] Where i = 1, 2, ..., n v ,λ U is the node voltage level indicator, n v is the number of busbars, α vi is the bus voltage importance correction factor, η v,i is the bus load loss rate;

[0028] The formula for calculating the N-1 pass rate is:

[0029]

[0030] Where η is the N-1 pass rate, is the number of successes, n N-1 is the total number of times.

[0031] Preferably, the evaluation indicators of the system availability dimension include average power supply reliability, average power outage time of users, number of power supply circuits, spinning reserve, and transmission line utilization, among which:

[0032] The calculation formula for average power supply reliability is:

[0033]

[0034] Where η r is the average power supply reliability rate, t CAIDI The average power outage time for users;

[0035] The formula for calculating the average power outage time of users is:

[0036]

[0037] Where, t CAIDI is the average power outage time for users, N i is the number of users in the i-th category, T i The power outage time for the i-th category of users;

[0038] The formula for calculating the number of power supply circuits is:

[0039]

[0040] In the formula, S M is the number of power supply circuits, S i is the number of lines connected in the i-th transformer, and N is the total number of transformers;

[0041] The calculation formula for spinning reserve is:

[0042]

[0043] In the formula, x eFor spinning reserve, P SR,lim As its minimum value, P SR It is active spinning reserve capacity;

[0044] The formula for calculating the utilization rate of transmission lines is:

[0045]

[0046] Where, L 2 is the transmission line utilization rate, P l,t is the transmission power of transmission line l at time t; P l max is the maximum transmission capacity of transmission line l.

[0047] Preferably, the evaluation indicators of the comprehensive economic dimension include transmission congestion costs, system network losses, operating costs, and power system risk costs, among which:

[0048] The calculation formula for transmission congestion cost is:

[0049]

[0050] Where, L 1 Costs incurred for transmission congestion, is the penalty cost for over-limit of transmission line l; is the over-limit power of transmission line l at time t;

[0051] The system network loss calculation formula is:

[0052] P loss =∑P G -∑P D

[0053] Where P loss is the system network loss, P G is the power generation capacity, P D is the load capacity;

[0054] The operation cost calculation formula is:

[0055]

[0056] In the formula, F c is the operation cost, N um is the number of times the line is opened and closed, f i The operating cost for each opening and closing;

[0057] The calculation formula of power system risk cost is:

[0058] RC=C l ×LC

[0059] Where, RC power system risk cost, C l is the output value of electricity per kilowatt-hour, and LC is the load capacity.

[0060] Preferably, the n evaluation indicators of each evaluation object are calculated respectively to form an evaluation indicator matrix (x ij ) m*n ,include:

[0061] The m evaluation objects are grouped into a set B = {B 1 ,B 2 ,...,B m}, for the i-th evaluation object B i , n evaluation indicators constitute the indicator set x i ={x i1 ,x i2 ,…,x in}, the index set of m evaluation objects can form an evaluation index matrix (x ij ) m*n , x ij Indicates the value corresponding to the jth evaluation indicator of the i-th evaluation object.

[0062] Preferably, the comprehensive weight of each evaluation index is calculated by the hierarchical analysis-entropy weight method, including:

[0063] Calculate the subjective weight value of each evaluation index in m evaluation objects through hierarchical analysis method

[0064] The entropy weight w of each evaluation indicator in the m evaluation objects is calculated by entropy weight processing j As an objective weight;

[0065] The weighted sum of subjective and objective weights is used to obtain the comprehensive weight, and the calculation formula is:

[0066]

[0067] In the formula, p 1 、p 2 Take 0.5 for each.

[0068] Preferably, the subjective weight value of each evaluation index in the m evaluation objects is calculated by the hierarchical analysis method. include:

[0069] 1) Decompose the problem of determining subjective weight into target layer, criterion layer and measure layer, and establish a hierarchical structural model;

[0070] 2) Let n factors be compared X = (x 1 ,x 2 ...x n) on a certain dimension Z, and adopt the method of pairwise comparison of paired factors to establish a pairwise comparison matrix to construct a judgment matrix A ij :

[0071] A ij =(a ij ) m*n

[0072] Among them: a ij Represents x i and x j The ratio of the effect size on Z;

[0073] 3) The judgment matrix A ij The elements of are normalized according to each column vector:

[0074]

[0075] Sum the rows of a normalized matrix:

[0076]

[0077] Normalize again:

[0078]

[0079] ω=[ω 1 ,ω 2 ,...,ω N ] T That is the judgment matrix A ij The eigenvector of

[0080] According to Aω=λω, calculate the characteristic root:

[0081]

[0082] λ is the judgment matrix A ij The largest characteristic root of

[0083] 4) Use consistency ratio C R Check the consistency of the judgment matrix:

[0084]

[0085] Among them, C I is the consistency index, R I is a random consistency index, λ represents the judgment matrix A ij The largest characteristic root of , N represents the number of characteristic roots;

[0086] 5) Calculate the subjective weight value of the target layer for different operating conditions

[0087]

[0088] Among them, ω n The feature vector representing the nth operation situation, It represents the score given by the jth expert in the nth operation.

[0089] Preferably, the entropy weight w of each evaluation indicator in the m evaluation objects is calculated by entropy weight processing. j As objective weights, they include:

[0090] 1) Normalize each evaluation index to form a decision matrix (x′ ij ) m*n ,in,

[0091] The calculation formula for the normalized processing of the positive indicator is:

[0092]

[0093] In the formula, x′ positive,ij is the normalized positive indicator; x positive,j is the positive indicator in the jth column;

[0094] The calculation formula for the normalized processing of the reverse indicator is:

[0095]

[0096] In the formula, x′ negative,ij is the reverse indicator after normalization; x negative,j is the reverse indicator in the jth column;

[0097] 2) Calculate the decision matrix (x′ ij ) m*n Each normalized evaluation index x′ ij The characteristic weight M ij :

[0098]

[0099] Calculate the entropy value E j :

[0100]

[0101] Determine the index entropy weight w j :

[0102]

[0103] In a second aspect, the present invention provides a power system resilience assessment system for extreme weather conditions, comprising:

[0104] An evaluation object determination module is used to determine m evaluation objects, where the evaluation objects are the operation conditions of the power system during different periods of extreme weather;

[0105] Evaluation index determination module, used to introduce evaluation indexes from multiple dimensions;

[0106] The evaluation index calculation module is used to calculate the n evaluation indexes of each evaluation object to form an evaluation index matrix (x ij ) m*n ;

[0107] The weight calculation module is used to calculate the comprehensive weight of each evaluation indicator through the hierarchical analysis-entropy weight method, and calculate the score of the evaluation indicator of each dimension of each evaluation object.

[0108] Positive and beneficial effects of the present invention

[0109] 1. The present invention selects the operation of the power system under extreme weather as the evaluation object, and evaluates the elasticity of the power system in the three periods before, during and after the extreme weather. After determining the evaluation index, the values ​​of each evaluation index of each object are calculated, and the comprehensive weight of each index is calculated by the analytic hierarchy process-entropy weight processing method. The comprehensive weight and the normalized index value of each index are weighted and calculated to obtain the scores of various evaluation indicators of each evaluation object. In the present invention, the analytic hierarchy process-entropy weight method is used for multiple evaluation indicators of multiple evaluation objects, and the score sequence of each index is determined by expert scoring. The subjective weight value of each index is obtained by the analytic hierarchy process for the score sequence. The entropy value is used to judge the degree of dispersion of a certain indicator. The smaller the information entropy value, the greater the degree of dispersion of the indicator, the greater the influence of the indicator on the comprehensive evaluation, and the greater the weight; conversely, if the information entropy value of a certain indicator is larger, the smaller the degree of dispersion of the indicator, the smaller the influence of the indicator in the comprehensive evaluation, and the smaller the weight. Finally, the least squares method is used to weight the subjective and objective weights to obtain the final weight value, so as to achieve an objective and comprehensive evaluation of the elasticity of the power system.

[0110] 2. In the evaluation indicators of system safety operation dimension, system availability dimension, and comprehensive economic dimension, not only reverse indicators, such as cost indicators, but also positive indicators, such as N-1 pass rate, are introduced. Compared with the traditional method of only introducing reverse indicators, the indicator types of the present invention are more comprehensive, which is conducive to the comprehensive evaluation of the elasticity of the new power system. For positive indicators and reverse indicators, the present invention adopts different normalization processing methods, and then performs weight calculation based on the normalized evaluation indicators to determine the weight of each normalized evaluation indicator, thereby obtaining the score of each evaluation indicator, and more accurately determining the influence of different evaluation indicators in the overall evaluation, so that the final score of each evaluation indicator is more objective. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] Figure 1 A flowchart of the steps of the power system resilience assessment method;

[0112] Figure 2 This is a diagram of the power system resilience evaluation index system under extreme weather conditions. DETAILED DESCRIPTION

[0113] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0114] A method for evaluating the resilience of power systems under extreme weather conditions, see Figure 1 , including the following steps:

[0115] Step S1: Determine m evaluation objects, which are the operation conditions of the power system in three time periods before, during and after the extreme weather, m=3. Comprehensively evaluate the elasticity of the power system in each time period, and obtain the weak links of the power system elasticity through the scores of the elasticity evaluation indicators in different time periods, which has guiding significance for improving the elasticity of the power system.

[0116] Step S2: Introducing evaluation indicators from multiple dimensions;

[0117] In one embodiment, the dimensions of the evaluation system include safe operation dimension, system availability dimension, and comprehensive economic dimension. The evaluation indicators of the safe operation dimension include system load loss rate, line load rate, transformer load rate, short-circuit current level index, node voltage level index, and N-1 pass rate; the evaluation indicators of the system availability dimension include average power supply reliability rate, average power outage time for users, number of power supply circuits, rotating reserve, and transmission line utilization rate; the evaluation indicators of the comprehensive economic dimension include transmission congestion cost, system network loss, operating cost, and power system risk cost.

[0118] The evaluation indicators are divided into positive indicators and negative indicators. The larger the positive indicator, the better the resilience of the power system; the larger the negative indicator, the weaker the resilience of the power system.

[0119] In one embodiment, if Figure 2As shown, the present invention sets evaluation indicators from the three dimensions of the evaluation system, including the safe operation dimension, the system availability dimension, and the comprehensive economic dimension. Each dimension includes one or more types of evaluation indicators, and a total of n evaluation indicators are set. Among these evaluation indicators, at least part is a positive indicator, and the other part is a negative indicator. The positive indicator is an indicator of the "bigger the better" type, and the negative indicator is an indicator of the "smaller the better" type. The various indicators under each dimension are described below.

[0120] Safe operation dimension indicators include system load loss rate, line load rate, transformer load rate, short-circuit current level index, node voltage level index, and N-1 pass rate.

[0121] In the process of power system development, while meeting the power supply demand, the safety and reliability of power supply is guaranteed and the benefits of safe operation are achieved. The power system safety evaluation index system established by the present invention can discover the unsafe factors in the power grid dispatching system and evaluate the overall safety status of the power grid dispatching system. It can be used to prevent and eliminate the occurrence of major accidents, strengthen the management level of power grid dispatching system equipment and the overall level of various professions, and ensure the safe and stable operation of the power grid.

[0122] The system load loss rate calculation formula is:

[0123]

[0124] In the formula, LOLP is the system load loss rate, LC is the system load loss amount, and T is the total simulation time;

[0125] The line load rate calculation formula is:

[0126]

[0127] In the formula, x L is the line load factor, i=1,2,...,n L , n L is the number of transformers, α L,i is the transformer importance correction factor, η pl,i is the transformer load loss rate;

[0128] The transformer load factor calculation formula is:

[0129]

[0130] In the formula, x t is the transformer load factor, S real Indicates the actual transmission capacity, S rated Indicates the rated capacity of the transformer;

[0131] The short-circuit current level indicator calculation formula is:

[0132]

[0133] Where i = 1, 2, ..., n sc , x I is the short-circuit current level indicator, n sc is the number of short-circuit faults, I i,sc is the short-circuit current value under the i-th short-circuit fault, I i,sc.lim It is the minimum breaking capacity of the circuit breaker connected to the short-circuit busbar;

[0134] The calculation formula of node voltage level index is:

[0135]

[0136] Where i = 1, 2, ..., n v ,λ U is the node voltage level indicator, n v is the number of busbars, α vi is the bus voltage importance correction factor, η v,i is the bus load loss rate;

[0137] The formula for calculating the N-1 pass rate is:

[0138]

[0139] Where η is the N-1 pass rate, is the number of successes, n N-1 is the total number of times.

[0140] The dimensions of system availability include average power supply reliability, average power outage time for users, number of power supply circuits, spinning reserve, and transmission line utilization.

[0141] By evaluating the availability of the power system under extreme weather conditions, we can identify the vulnerable links in the system, take preventive measures, improve the system's disaster resistance, and ensure the continuity and reliability of power supply under extreme conditions. This evaluation process helps power companies and relevant departments to prevent and respond to challenges brought by extreme weather in advance, and ensure that the power system can still operate stably under various adverse conditions. Among them:

[0142] The calculation formula for average power supply reliability is:

[0143]

[0144] Where η r is the average power supply reliability rate, t CAIDI The average power outage time for users;

[0145] The formula for calculating the average power outage time of users is:

[0146]

[0147] Where, t CAIDI is the average power outage time for users, N i is the number of users in the i-th category, T i The power outage time for the i-th category of users;

[0148] The formula for calculating the number of power supply circuits is:

[0149]

[0150] In the formula, S M is the number of power supply circuits, S i is the number of lines connected in the i-th transformer, and N is the total number of transformers;

[0151] The calculation formula for spinning reserve is:

[0152]

[0153] In the formula, x e For spinning reserve; P SR,lim As its minimum limit, take 2% of the maximum load of the system; P SR It is active spinning reserve capacity;

[0154] The formula for calculating the utilization rate of transmission lines is:

[0155]

[0156] Where, L 2 is the transmission line utilization rate, P l,t is the transmission power of transmission line l at time t; P l max is the maximum transmission capacity of transmission line l.

[0157] The evaluation indicators of the comprehensive economic dimension include transmission congestion costs, system network losses, operating costs, and power system risk costs.

[0158] The economic evaluation of the power system improves the disaster resistance and recovery efficiency of the power grid in extreme weather conditions through scientific and reasonable economic analysis and management, reduces economic losses, optimizes investment decisions, ensures power supply reliability, improves user satisfaction, and supports policy formulation, ultimately achieving sustainable development of the power system. Among them:

[0159] The calculation formula for transmission congestion cost is:

[0160]

[0161] Where, L 1Costs incurred for transmission congestion, is the penalty cost for over-limit of transmission line l; is the over-limit power of transmission line l at time t;

[0162] The system network loss calculation formula is:

[0163] P loss =vP G -∑P D (13)

[0164] Where P loss is the system network loss; P G is the power generation capacity; P D is the load capacity;

[0165] The operation cost calculation formula is:

[0166]

[0167] In the formula, F c is the operation cost, N um is the number of times the line is opened and closed; f i The operating cost for each opening and closing is related to the cost and service life of the switch.

[0168] The calculation formula of power system risk cost is:

[0169] RC=C l ×LC (15)

[0170] Where, RC power system risk cost, C l is the output value of electricity, LC is the load loss;

[0171] Step S3: Calculate n evaluation indicators for each evaluation object, n≥2, to form an evaluation indicator matrix (x ij ) m*n , the steps are as follows:

[0172] The m evaluation objects are grouped into a set B = {B 1 ,B 2 ,...,B m}, for the i-th evaluation object B i , n evaluation indicators constitute the indicator set x i ={x i1 ,x i2 ,…,x in}, the index set of m evaluation objects can form an evaluation index matrix (x ij ) m*n , x ij Indicates the value corresponding to the jth evaluation index of the i-th evaluation object;

[0173] Step S4: Calculate the comprehensive weight of each indicator in the m evaluation objects by using the hierarchical analysis method-entropy weight method, and use the comprehensive weight of the evaluation indicator as the weight to perform weighted calculation on the normalized evaluation indicator of each evaluation object and then sum them up to obtain the score of each evaluation indicator of the corresponding evaluation object.

[0174] In one embodiment, the process of calculating the subjective weight includes:

[0175] Step S410: Decompose the decision problem into three levels and establish a hierarchical structure model, where:

[0176] The highest level: There is only one element in the level, namely the target index, which is the predetermined goal of the analysis problem, so it is also called the target level;

[0177] Middle layer: also called the criteria layer, this layer contains the intermediate links involved in achieving the goal. It can be composed of several levels, including the criteria and sub-criteria that need to be considered;

[0178] The bottom layer: also called the measure layer or plan layer, this level includes various measures and decision-making plans that can be selected to achieve the goal;

[0179] Step S411: construct a judgment matrix;

[0180] Suppose we want to compare n factors X=(x 1 ,x 2 ...x n ) on a factor Z. To provide more reliable data, we compare the factors pairwise to establish a pairwise comparison matrix. That is, we take two factors x each time. i and x j , with a ij Represents x i and x j The ratio of the impact on Z, all comparison results are expressed in matrix A ij =(a ij ) m*n , called A ij is the judgment matrix between ZX. It is easy to see that if x i With x j The ratio of the impact on Z is a ij , then x j With x i The ratio of the impact on Z should be Use numbers 1 to 9 and their reciprocals as scales to determine a ij The following table 1 lists the meanings of the scales from 1 to 9;

[0181] Meaning of scales in Tables 11 to 9

[0182] <![CDATA[Scale a ij > meaning 1 Factor i and factor j have the same impact 3 Factor i has a slightly stronger influence than factor j 5 Factor i has a stronger influence than factor j 7 Factor i has a significantly stronger influence than factor j 9 Factor i has an absolute stronger influence than factor j 2,4,6,8 The ratio of the impact of factor i and factor j is between the above two adjacent levels.

[0183] Step S412: solving the judgment matrix eigenvector;

[0184] For the constructed judgment matrix A ij Solve the characteristic roots and eigenvectors, when the judgment matrix A ij When the consistency check is satisfied, the feature vector can be used as the weight vector in the above steps, that is, the subjective weight, and the steps are as follows:

[0185] The judgment matrix A ij The elements of are normalized according to each column vector:

[0186]

[0187] Sum the rows of a normalized matrix:

[0188]

[0189] Normalize again:

[0190]

[0191] ω=[ω 1 ,ω 2 ,...,ω N ] T It is the eigenvector of the judgment matrix A.

[0192] According to Aω=λω, calculate the characteristic root:

[0193]

[0194] λ is the maximum eigenvalue of the judgment matrix A.

[0195] Step S413: judging matrix consistency check;

[0196] The pairwise comparison matrix is ​​usually not a consistency matrix, but in order to use its eigenvector corresponding to the largest eigenvalue as the weight vector of the compared factor, its inconsistency should be within the allowable range, and the consistency ratio C R The calculation formula is as follows:

[0197]

[0198] Where: C I is the consistency index, R I is a random consistency index, λ represents the judgment matrix A ij The largest characteristic root of , N represents the number of characteristic roots;

[0199] C I=0 when judgment matrix A ij is the consistency matrix, C R <0.1, indicating the judgment matrix A ij Good consistency, C I The larger the judgment matrix A ij The more serious the inconsistency;

[0200] Since the sum of the N characteristic roots of the judgment matrix A is exactly equal to N, C I It is equivalent to the absolute value of the average value of the remaining N-1 characteristic roots except λ, and calculates R I The process is: for a fixed N, randomly construct a positive reciprocal matrix A′, whose element a′ ij Randomly select values ​​from 1 to 9, 1 / 1 to 1 / 9, and calculate C of A′ I ;

[0201] Step S414: Calculate the subjective weight values ​​of different operating conditions on the target layer

[0202]

[0203] Among them, ω n The feature vector representing the nth operation situation, It represents the score given by the jth expert in the nth operation.

[0204] In one embodiment, the process of calculating the objective weight includes:

[0205] Step S420: Normalize each evaluation index to form a decision matrix (x′ ij ) m*n , x′ ij is the evaluation index x ij Normalized evaluation index of

[0206] N-1 pass rate, average power supply reliability rate, number of power supply circuits, rotating reserve, and transmission line utilization rate are positive indicators; system load loss rate, line load rate, transformer load rate, short-circuit current level index, node voltage level index, user average power outage time, transmission congestion cost, system network loss, operating cost, and power system risk cost are negative indicators.

[0207] The dimension is used as the first-level indicator, and the evaluation indicator under the same dimension is used as the second-level indicator. Since the dimensions of each second-level indicator are different, or even dimensionless, the magnitude of the directly calculated indicators varies greatly, making it difficult to make an intuitive comparison. Therefore, the calculation results of each indicator need to be dimensionless.

[0208] The calculation formula for the normalized processing of the positive indicator is:

[0209]

[0210] In the formula, x′ positive,ij is the normalized positive indicator; x positive,j is the positive index in the jth column; max{x positive,j} represents the evaluation index matrix (x ij ) m*n The maximum value of the positive indicator in the jth column; min{x positive,j} represents the evaluation index matrix (x ij ) m*n The minimum value of the positive indicator in the jth column;

[0211] The calculation formula for the normalized processing of the reverse indicator is:

[0212]

[0213] In the formula, x′ negative,ij is the reverse indicator after normalization; x negative,j is the reverse index in the jth column; max{x negative,j} represents the evaluation index matrix (x ij ) m*n The maximum value of the reverse indicator in the jth column; min{x negative,ij} represents the evaluation index matrix (x ij ) m*n The minimum value of the reverse indicator in the jth column;

[0214] Through normalization, the original evaluation index matrix (x ij ) m*n Convert to decision matrix (x′ ij ) m*n .

[0215] Step S421: Calculate the decision matrix (x′ ij ) m*n Each normalized evaluation index x′ ij The characteristic weight M ij , the calculation formula is as follows:

[0216]

[0217] Calculate the entropy value E of the jth evaluation index j , the calculation formula is:

[0218]

[0219] Determine the entropy weight W of the jth evaluation index j , the calculation formula is:

[0220]

[0221] Step S430: Determine the comprehensive weight value of the jth evaluation index, and the calculation formula is:

[0222]

[0223] Among them, p 1 、p 2 Take 0.5 for each.

[0224] Step S440: performing weighted calculation on the normalized evaluation indicators under the same dimension of the same evaluation object according to the comprehensive weight of the evaluation indicators and then summing them up to obtain the scores of each dimension of the corresponding evaluation object;

[0225] After obtaining the comprehensive weight of each evaluation indicator, the score of each type of evaluation indicator for each evaluation object can be calculated. Specifically, after obtaining the score of each secondary evaluation indicator, the score of the primary evaluation indicator (evaluation dimension) can be further calculated. The score is the sum of the weighted calculation of the normalized evaluation indicators under the same dimension of the same evaluation object with the comprehensive weight of the evaluation indicator as the weight to obtain the score of each dimension of the corresponding evaluation object.

[0226] Taking the dimension of system safety operation as an example, the first-level rating indicator is the dimension of system safety operation, and the second-level evaluation indicators are system load loss rate, line load rate, transformer load rate, short-circuit current level indicator, node voltage level indicator, and N-1 pass rate. After calculating the comprehensive weights of various second-level indicators, the score of system load loss rate = normalized value of system load loss rate * comprehensive weight; the score of line load rate = normalized value of line load rate * comprehensive weight; the score of transformer load rate = normalized value of transformer load rate * comprehensive weight; the score of short-circuit current level indicator = normalized value of short-circuit current level indicator * comprehensive weight; the score of node voltage level indicator = normalized value of node voltage level indicator * comprehensive weight; the score of N-1 pass rate = normalized value of N-1 pass rate * comprehensive weight. The score of system safety operation dimension = the score of system load loss rate + the score of line load rate + the score of transformer load rate + the score of short-circuit current level indicator + the score of node voltage level indicator + the score of N-1 pass rate, and the scores of other evaluation indicators are similar.

[0227] It should be noted that the sequence of steps in the above embodiments is only for illustrative purposes, but is not limited thereto. In other embodiments, the sequence of steps may be reasonably adjusted to ensure that each step can be executed smoothly.

Claims

1. A method for evaluating the resilience of a power system under extreme weather conditions, characterized in that: include: Determine m evaluation objects, which are the operation of the power system during different periods of extreme weather; Introducing evaluation indicators from multiple dimensions; Calculate the n evaluation indicators of each evaluation object respectively to form an evaluation indicator matrix (x ij ) m*n ; The comprehensive weight of each evaluation index is calculated through the hierarchical analysis-entropy weight method, and the score of each dimension evaluation index of each evaluation object is calculated.

2. The method for evaluating the resilience of a power system under extreme weather conditions according to claim 1 is characterized in that: The evaluation indicators are introduced from multiple dimensions, including evaluation indicators of safe operation dimension, system availability dimension and comprehensive economic dimension.

3. The method for evaluating the resilience of a power system under extreme weather conditions according to claim 2 is characterized in that: The evaluation indicators of the safety operation dimension include system load loss rate, line load rate, transformer load rate, short-circuit current level index, node voltage level index, and N-1 pass rate, among which: The system load loss rate calculation formula is: In the formula, LOLP is the system load loss rate, LC is the system load loss amount, and T is the total simulation time; The line load rate calculation formula is: In the formula, x L is the line load factor, i=1,2,...,n L , n L is the number of transformers, α L,i is the transformer importance correction factor, η pl,i is the transformer load loss rate; The transformer load factor calculation formula is: In the formula, x t is the transformer load factor, S real Indicates the actual transmission capacity, S rated Indicates the rated capacity of the transformer; The short-circuit current level indicator calculation formula is: Where i = 1, 2, ..., n sc , x I is the short-circuit current level indicator, n sc is the number of short-circuit faults, I i,sc is the short-circuit current value under the i-th short-circuit fault, I i,sc.lim It is the minimum breaking capacity of the circuit breaker connected to the short-circuit busbar; The calculation formula of node voltage level index is: Where i = 1, 2, ..., n v ,λ U is the node voltage level indicator, n v is the number of busbars, α vi is the bus voltage importance correction factor, η v,i is the bus load loss rate; The formula for calculating the N-1 pass rate is: Where η is the N-1 pass rate, is the number of successes, n N-1 is the total number of times.

4. The method for evaluating the resilience of a power system under extreme weather conditions according to claim 2 is characterized in that: The evaluation indicators of the system availability dimension include average power supply reliability, average power outage time for users, number of power supply circuits, spinning reserve, and transmission line utilization, among which: The calculation formula for average power supply reliability is: Where η r is the average power supply reliability rate, t CAIDI The average power outage time for users; The formula for calculating the average power outage time of users is: Where, t CAIDI is the average power outage time for users, N i is the number of users in the i-th category, T i The power outage time for the i-th category of users; The formula for calculating the number of power supply circuits is: In the formula, S M is the number of power supply circuits, S i is the number of lines connected in the i-th transformer, and N is the total number of transformers; The calculation formula for spinning reserve is: In the formula, x e For spinning reserve, P SR,lim As its minimum value, P SR It is active spinning reserve capacity; The formula for calculating the utilization rate of transmission lines is: Where L2 is the utilization rate of the transmission line, P l,t is the transmission power of transmission line l at time t, P l max is the maximum transmission capacity of transmission line l.

5. The method for evaluating the resilience of a power system under extreme weather conditions according to claim 2 is characterized in that: The evaluation indicators of the comprehensive economic dimension include transmission congestion costs, system network losses, operating costs, and power system risk costs, among which: The calculation formula for transmission congestion cost is: Where L1 is the cost of transmission congestion, is the penalty cost for over-limit of transmission line l; is the over-limit power of transmission line l at time t; The system network loss calculation formula is: P loss =∑P G -∑P D Where P loss is the system network loss, P G is the power generation capacity, P D is the load capacity; The operation cost calculation formula is: In the formula, F c is the operation cost, N um is the number of times the line is opened and closed; f i The operating cost for each opening and closing; The calculation formula of power system risk cost is: RC=C l ×LC Where, RC power system risk cost, C l is the output value of electricity per kilowatt-hour, and LC is the load capacity.

6. The method for evaluating the resilience of a power system under extreme weather conditions according to claim 1 is characterized in that: The n evaluation indicators of each evaluation object are calculated respectively to form an evaluation indicator matrix (x ij ) m*n ,include: The m evaluation objects are grouped into a set B = {B1, B2, ..., B m }, for the i-th evaluation object B i , n evaluation indicators constitute the indicator set x i ={x i1 ,x i2 ,…,x in }, the index set of m evaluation objects can form an evaluation index matrix (x ij ) m*n , x ij Indicates the value corresponding to the jth evaluation indicator of the i-th evaluation object.

7. The method for evaluating the resilience of a power system under extreme weather conditions according to claim 6 is characterized in that: The comprehensive weight of each evaluation index is calculated by the hierarchical analysis-entropy weight method, including: Calculate the subjective weight value of each evaluation index in m evaluation objects through hierarchical analysis method The entropy weight w of each evaluation indicator in the m evaluation objects is calculated by entropy weight processing j As an objective weight; The weighted sum of subjective and objective weights is used to obtain the comprehensive weight, and the calculation formula is: In the formula, p1 and p2 are each 0.

5.

8. The method for evaluating the resilience of a power system under extreme weather conditions according to claim 7 is characterized in that: The subjective weight value of each evaluation index in the m evaluation objects is calculated by the hierarchical analysis method. include: 1) Decompose the problem of determining subjective weight into target layer, criterion layer and measure layer, and establish a hierarchical structural model; 2) Let n factors be compared X = (x1, x2...x n ) on a certain dimension Z, and adopt the method of pairwise comparison of paired factors to establish a pairwise comparison matrix to construct a judgment matrix A ij : A ij =(a ij ) m*n Among them: a ij Represents x i and x j The ratio of the effect size on Z; 3) The judgment matrix A ij The elements of are normalized according to each column vector: Sum the rows of a normalized matrix: Normalize again: ω=[ω1,ω2,...,ω N ] T That is the judgment matrix A ij The eigenvector of According to Aω=λω, calculate the characteristic root: λ is the judgment matrix A ij The largest characteristic root of 4) Use consistency ratio C R Check the consistency of the judgment matrix: Where: C I is the consistency index, R I is a random consistency index, λ represents the judgment matrix A ij The largest characteristic root of , N represents the number of characteristic roots; 5) Calculate the subjective weight value of the target layer for different operating conditions Among them, ω n The feature vector representing the nth operation situation, It represents the score given by the jth expert in the nth operation.

9. The method for evaluating the resilience of a power system under extreme weather conditions according to claim 8 is characterized in that: The entropy weight wj of each evaluation index in the m evaluation objects is calculated as the objective weight by the entropy weight processing method, including: 1) Normalize each evaluation index to form a decision matrix (x i ' j ) m*n ,in, The calculation formula for the normalized processing of the positive indicator is: In the formula, x' positive,ij is the normalized positive indicator; x positive,j is the positive index in the jth column; max{x positive,j } represents the evaluation index matrix (x ij ) m*n The maximum value of the positive indicator in the jth column; min{x positive,j } represents the evaluation index matrix (x ij ) m*n The minimum value of the positive indicator in the jth column; The calculation formula for the normalized processing of the reverse indicator is: In the formula, x' negative,ij is the reverse indicator after normalization; x negative,j is the reverse index in the jth column; max{x negative,j } represents the evaluation index matrix (x ij ) m*n The maximum value of the reverse indicator in the jth column; min{x negative,ij } represents the evaluation index matrix (x ij ) m*n The minimum value of the reverse indicator in the jth column; 2) Calculate the decision matrix (x′ ij ) m*n Each normalized evaluation index x′ ij The characteristic weight M ij : Calculate the entropy value E j : Determine the index entropy weight w j :

10. A power system resilience evaluation system for extreme weather conditions, characterized in that: include: An evaluation object determination module is used to determine m evaluation objects, where the evaluation objects are the operation conditions of the power system during different periods of extreme weather; Evaluation index determination module, used to introduce evaluation indexes from multiple dimensions; The evaluation index calculation module is used to calculate the n evaluation indexes of each evaluation object to form an evaluation index matrix (x ij ) m*n ; The weight calculation module is used to calculate the comprehensive weight of each evaluation indicator through the hierarchical analysis-entropy weight method, and calculate the score of the evaluation indicator of each dimension of each evaluation object.

Images