CN117895496A - Novel elasticity evaluation method and system for power system and electronic equipment - Google Patents

Novel elasticity evaluation method and system for power system and electronic equipment Download PDF

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CN117895496A
CN117895496A CN202410037093.9A CN202410037093A CN117895496A CN 117895496 A CN117895496 A CN 117895496A CN 202410037093 A CN202410037093 A CN 202410037093A CN 117895496 A CN117895496 A CN 117895496A
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index
determining
weight
power grid
specific
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郑惠萍
程雪婷
刘新元
李鸣镝
潘捷
薛志伟
张秀丽
暴悦爽
刘文旭
张心悦
张旭
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State Grid Electric Power Research Institute Of Sepc
North China Electric Power University
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North China Electric Power University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention discloses a novel elasticity evaluation method and system of a power system and electronic equipment, and relates to the technical field of power grid elasticity evaluation. The method comprises the following steps: building a plurality of power grid structures, and respectively simulating and applying the current disaster type to each power grid structure to obtain the elasticity data of each power grid structure; determining the game combination weight of each specific index by utilizing a game theory principle based on an elastic index evaluation system and elastic data of a plurality of power grid structures; and according to the game combination weight, determining the elastic capability value of each power grid structure under the current disaster type by using a VIKOR model. The invention utilizes the game theory principle and the VIKOR model to complete the elasticity evaluation of the novel electric power system, and can improve the rationality of the elasticity evaluation of the novel electric power system, thereby improving the rationality of the structure planning of the electric power system and the formulation of the emergency response plan.

Description

Novel elasticity evaluation method and system for power system and electronic equipment
Technical Field
The invention relates to the technical field of power grid elasticity evaluation, in particular to an elasticity evaluation method, an elasticity evaluation system and electronic equipment of a novel power system.
Background
In recent years, with global warming and climate change, serious natural disasters cause a plurality of blackout accidents worldwide, and the capability of the power system for coping with the serious natural disasters is insufficient. Meanwhile, along with the propulsion of a novel electric power system, the permeability of renewable energy sources in the electric power system is gradually increased, and compared with a traditional electric power system, the grid connection of a large amount of new energy sources enables the operation of a power grid to be green and environment-friendly and sustainable development, and meanwhile, the uncertainty of the output of the power grid brings great influence to the safe and stable operation and economic dispatch of the power grid. The traditional elasticity evaluation method of the electric power system cannot meet the requirement of carrying out elasticity evaluation on a novel electric power system suffering from natural disasters, each key link of power generation, power transmission and the like in the novel electric power system is closely related to weather and climate, and a power transmission line of a power grid is highly sensitive to disastrous weather. In order to reduce the influence of faults of the power system, an elasticity assessment method which is more suitable for the current novel power system is required, and references can be provided for power system structural planning and emergency response planning.
Disclosure of Invention
The invention aims to provide a novel elastic assessment method, a novel elastic assessment system and electronic equipment for a novel electric power system, which can improve the rationality of elastic assessment of the novel electric power system, and further improve the rationality of structure planning and emergency response plan formulation of the electric power system.
In order to achieve the above object, the present invention provides the following solutions:
a method of elasticity assessment for a novel electrical power system, comprising:
Constructing an elasticity index evaluation system of a novel power system; the elasticity index evaluation system comprises: the system comprises a target layer, a comprehensive index layer and a specific index layer; the target layer is the elastic capability of the novel power system; the comprehensive index layer comprises a plurality of comprehensive indexes influencing the target layer; the specific index layer comprises a plurality of specific indexes influencing each comprehensive index; the intersection of a plurality of specific indexes corresponding to different comprehensive indexes is an empty set;
Building a plurality of power grid structures; the new energy duty ratio and the new energy station access position of different power grid structures are different;
determining the disaster type with highest occurrence probability as the current disaster type from the historical disaster data of the area to be evaluated;
applying the current disaster type to each power grid structure simulation to obtain the elasticity data of each power grid structure; the elastic data of any power grid structure is simulated output data of each specific index in the process from the current disaster type to the normal recovery of the power grid structure;
determining the game combination weight of each specific index by utilizing a game theory principle based on the elastic index evaluation system and the elastic data of a plurality of power grid structures;
And determining the elastic capability value of each power grid structure under the current disaster type by utilizing a VIKOR model according to the game combination weight.
Optionally, determining, based on the elasticity index evaluation system and the elasticity data of the plurality of grid structures, a game combining weight of each specific index by using a game theory principle includes:
determining the sub-weight of each comprehensive index and the sub-weight of each specific index by using an analytic hierarchy process based on the elastic index evaluation system;
determining any specific index as the current specific index;
Determining the product of the sub-weight of the current specific index and the sub-weight of the corresponding comprehensive index as the total subjective weight of the current specific index;
traversing all the specific indexes to obtain the total subjective weight of each specific index;
determining a first objective weight value of each specific index by utilizing an entropy weight method based on elastic data of a plurality of power grid structures;
determining a second objective weight value of each specific index by using a modified CRITIC method;
and determining the game combination weight of each specific index by utilizing a game theory principle based on the total subjective weight, the first objective weight value and the second objective weight value of each specific index.
Optionally, determining the first objective weight value of each specific index by using an entropy weight method based on the elastic data of the plurality of grid structures includes:
based on elastic data of a plurality of power grid structures, constructing an evaluation index system matrix X= [ X ij]m×n; wherein x ij is an element of the ith row and the jth column in the evaluation index system matrix; m is the number of the power grid structures; n is the number of specific indexes;
Normalizing the evaluation index system matrix to obtain a standard matrix Y= [ Y ij]m×n;yij ] which is an element of the ith row and the j columns in the standard matrix;
Determining the entropy value of each specific index by using a formula and a formula/> based on the standard matrix; e j is the entropy value of the j-th specific index; p ij is the element duty ratio of the ith row and j columns in the standard matrix;
determining a first objective weight value for each particular indicator using formula based on entropy values of the plurality of particular indicators; omega j is the first objective weight value for the j-th specific index.
Optionally, determining the second objective weight value for each specific indicator using the improvement CRITIC method includes:
Based on the evaluation index system matrix, determining the average value of each specific index by using a formula ; wherein,/> is the mean of the j-th specific index;
Determining the standard deviation of each specific index by using a formula based on the evaluation index system matrix; wherein, sigma j is the standard deviation of the j-th specific index;
Determining the ratio of standard deviation to mean value corresponding to the same specific index as an index variation coefficient corresponding to the specific index;
Determining the pearson correlation coefficient between any two specific indexes;
Determining an independence coefficient of each specific index by using a formula according to a plurality of pearson correlation coefficients; h j is the independence coefficient of the j-th specific index; r ij is the pearson correlation coefficient between the i-th specific index and the j-th specific index;
Determining the product of the coefficient of variation of the index corresponding to the same specific index and the independence coefficient as the comprehensive coefficient of the corresponding specific index;
Determining a second objective weight value of each specific index by using a formula according to a plurality of comprehensive coefficients; omega l is the second objective weight value of the j-th specific index; q j is the integral coefficient of the j-th specific index.
Optionally, determining the game combining weight of each specific index based on the total subjective weight, the first objective weight value and the second objective weight value of each specific index using the principle of game theory includes:
constructing a subjective weight vector based on the total subjective weight of each specific index;
Constructing a first objective weight vector based on the first objective weight value of each specific indicator;
constructing a second objective weight vector based on the second objective weight value of each specific index;
Constructing a game combined weight equation based on the subjective weight vector, the first objective weight vector and the second objective weight vector; the game combination weight equation is , wherein omega Group of is a game combination weight vector; alpha 1 is a combined weight coefficient vector of the subjective weight vector omega 1; α 2 is the combined weight coefficient vector of the first customer weight vector ω 2; α 3 is the combined weight coefficient vector of the second objective weight vector ω 3; the superscript T denotes a transpose;
Constructing an objective function; the objective function is i=min||ω Group of -ωi||2 (i=1, 2, 3); wherein I is an objective function; the 2 represents the 2-norm;
Based on matrix differential properties and the objective function, solving an optimal combined weight coefficient vector corresponding to each weight vector; the weight vectors include the subjective weight vector, the first objective weight vector, and the second objective weight vector;
Substituting a plurality of optimal combination weight coefficient vectors into a game combination weight equation to determine a game combination weight vector; the gaming combining weight vector includes a gaming combining weight for each individual indicator.
Optionally, according to the game combination weight, determining the elastic capability value of each power grid structure under the current disaster type by using a VIKOR model includes:
determining a positive ideal solution of the standard matrix using formula ; wherein,/> is the positive ideal solution corresponding to the j-th specific index;
Determining a negative ideal solution of the standard matrix by using a formula ; the/> is the negative ideal solution corresponding to the j-th specific index;
Determining an overall satisfaction of each grid structure using formula based on the standard matrix, the plurality of positive ideal solutions, and the plurality of negative ideal solutions; s i is the overall satisfaction of the ith grid structure; omega Group of j is the game combination weight of the j-th specific index;
Determining an individual deviation value for each grid structure using formula based on the standard matrix, the plurality of positive ideal solutions, and the plurality of negative ideal solutions; individual deviation values for the ith grid structure of R i;
Determining a decision index value of each power grid structure by utilizing a formula according to a plurality of overall satisfaction degrees and a plurality of individual deviation values; wherein Q i is a decision index value of the ith power grid structure; v is a decision coefficient;
Determining a maximum decision index value;
and determining the difference value of the decision index value and the maximum decision index value of each power grid structure as the elastic capability value of the corresponding power grid structure under the current disaster type.
Optionally, after determining the elastic capability value of each grid structure under the current disaster type by using the VIKOR model according to the game combination weight, the method further includes:
Updating the current disaster type, and returning to the step of simulating and applying the current disaster type to each power grid structure to obtain elastic data of each power grid structure until traversing all disaster types of which the occurrence probability of the region to be evaluated is greater than a probability threshold value to obtain elastic capability values of each power grid structure under different disaster types;
Updating the plurality of power grid structures, and returning to the step of determining the disaster type with the highest occurrence probability as the current disaster type from the historical disaster data of the area to be evaluated until the iteration times reach the preset number to obtain the elastic capability values of the plurality of power grid structures under different disaster types;
And carrying out power system structure planning and emergency response planning on the region to be evaluated based on the elastic capability values of the power grid structures under the current disaster type.
A novel elasticity assessment system of an electrical power system, comprising:
The elastic index evaluation system construction module is used for constructing an elastic index evaluation system of the novel power system; the elasticity index evaluation system comprises: the system comprises a target layer, a comprehensive index layer and a specific index layer; the target layer is the elastic capability of the novel power system; the comprehensive index layer comprises a plurality of comprehensive indexes influencing the target layer; the specific index layer comprises a plurality of specific indexes influencing each comprehensive index; the intersection of a plurality of specific indexes corresponding to different comprehensive indexes is an empty set;
The power grid structure building module is used for building a plurality of power grid structures; the new energy duty ratio and the new energy station access position of different power grid structures are different;
The current disaster type determining module is used for determining the disaster type with the highest occurrence probability as the current disaster type from the historical disaster data of the area to be evaluated;
The current disaster simulation module is used for simulating and applying the current disaster type to each power grid structure to obtain the elastic data of each power grid structure; the elastic data of any power grid structure is simulated output data of each specific index in the process from the current disaster type to the normal recovery of the power grid structure;
The game combination weight determining module is used for determining the game combination weight of each specific index by utilizing a game theory principle based on the elasticity index evaluation system and the elasticity data of the plurality of power grid structures;
and the elastic capability value determining module is used for determining the elastic capability value of each power grid structure under the current disaster type by utilizing a VIKOR model according to the game combination weight.
An electronic device comprising a memory for storing a computer program and a processor running the computer program to cause the electronic device to perform the elasticity assessment method of a novel power system.
Optionally, the memory is a readable storage medium.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
According to the elasticity evaluation method, the system and the electronic equipment of the novel power system, which are provided by the invention, the novel power system suffering from natural disasters is subjected to elasticity evaluation by applying the multi-attribute decision model, and the influence of the new energy duty ratio and the access position of the new energy station in the power system on the elasticity evaluation is considered: constructing a novel electric power system elasticity index evaluation system comprising three aspects of element failure rate, system loss condition and system defense-recovery performance; constructing power grid structures with different new energy duty ratios and access positions of new energy stations, analyzing historical disaster data of an evaluation area, and determining disasters with highest occurrence probability in the evaluation area; simulating the disaster with highest occurrence probability in the evaluation area, and recording the numerical values of various indexes of the power grids with different structures in the process from disaster suffering to normal recovery; based on elasticity evaluation data obtained by different power grid structures, weighting each index by adopting a combined weighting method of game theory ideas; based on the weighted grid elasticity assessment index, a multi-criterion compromise ordering method (VlseKriterijumska Optimizacija I Kompromisno Resenje, VIKOR) is applied to perform grid elasticity assessment. The invention realizes the elastic evaluation of the novel power system suffered from natural disasters, considers the influence of different new energy duty ratios and new energy station access positions on the elastic evaluation of the power system, can embody the elastic characteristics of the novel power system better than other methods, and can provide references for the structural planning and emergency response plans of the power system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for evaluating elasticity of a novel power system in embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of a method for evaluating elasticity of the novel power system in embodiment 1 of the present invention;
FIG. 3 is a schematic diagram of the elasticity index evaluation in example 1 of the present invention;
fig. 4 is a first exemplary diagram of a power grid structure in embodiment 1 of the present invention;
fig. 5 is a second exemplary diagram of a power grid structure in embodiment 1 of the present invention;
Fig. 6 is a third exemplary diagram of a power grid structure in embodiment 1 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a novel elastic assessment method, a novel elastic assessment system and electronic equipment for a novel electric power system, which can improve the rationality of elastic assessment of the novel electric power system, and further improve the rationality of structure planning and emergency response plan formulation of the electric power system.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
As shown in fig. 1, the embodiment provides a method for evaluating elasticity of a novel electric power system, including:
Step 101: and constructing a novel elasticity index evaluation system of the power system. As shown in fig. 3, the elasticity index evaluation system includes: the system comprises a target layer, a comprehensive index layer and a specific index layer. The target layer is the elastic capability of the novel power system. The composite index layer includes a plurality of composite indexes that affect the target layer. The specific index layer includes a plurality of specific indexes affecting each comprehensive index. The intersection of a plurality of specific indexes corresponding to different comprehensive indexes is an empty set.
Step 102: a plurality of power grid structures are built. The new energy duty ratio and the new energy station access position of different power grid structures are different; examples of grid structure parts are shown in fig. 4-6.
Step 103: and determining the disaster type with highest occurrence probability as the current disaster type from the historical disaster data of the region to be evaluated.
Step 104: and respectively applying the current disaster type to each power grid structure simulation to obtain the elasticity data of each power grid structure. The elastic data of any power grid structure is simulated output data of each specific index in the process from the current disaster type to the normal recovery of the power grid structure.
Step 105: based on the elasticity index evaluation system and the elasticity data of the plurality of power grid structures, the game theory principle is utilized to determine the game combination weight of each specific index.
Step 106: and according to the game combination weight, determining the elastic capability value of each power grid structure under the current disaster type by using a VIKOR model.
Step 105 includes:
Step 105-1: based on the elastic index evaluation system, determining the sub-weight of each comprehensive index and the sub-weight of each specific index by using a analytic hierarchy process.
Step 105-2: and determining any specific index as the current specific index.
Step 105-3: and determining the product of the sub-weight of the current specific index and the sub-weight of the corresponding comprehensive index as the total subjective weight of the current specific index.
Step 105-4: and traversing all the specific indexes to obtain the total subjective weight of each specific index.
Step 105-5: based on the elasticity data of the plurality of power grid structures, a first objective weight value of each specific index is determined by utilizing an entropy weight method.
Step 105-6: a second objective weight value for each particular indicator is determined using the modified CRITIC method.
Step 105-7: and determining the game combination weight of each specific index by utilizing a game theory principle based on the total subjective weight, the first objective weight value and the second objective weight value of each specific index.
Step 105-5, comprising:
Step 105-5-1: and constructing an evaluation index system matrix X= [ X ij]m×n ] based on the elastic data of the plurality of power grid structures. Wherein x ij is an element of the ith row and the jth column in the evaluation index system matrix. m is the number of grid structures. n is the number of specific indicators.
Step 105-5-2: and carrying out normalization processing on the evaluation index system matrix to obtain a standard matrix Y= [ Y ij]m×n.yij ] which is the element of the ith row and j columns in the standard matrix.
Step 105-5-3: based on the standard matrix, the entropy value of each specific index is determined using formula and formula/> . e j is the entropy value of the j-th specific index. p ij is the element duty ratio of the ith row and j columns in the standard matrix.
Step 105-5-4: based on the entropy values of the plurality of specific indicators, a first objective weight value for each specific indicator is determined using formula . Omega j is the first objective weight value for the j-th specific index.
Step 105-6, comprising:
step 105-6-1: based on the evaluation index system matrix, a mean value of each specific index is determined using formula . Wherein,/> is the mean of the j-th specific index.
Step 105-6-2: based on the evaluation index system matrix, the standard deviation of each specific index is determined using formula . Wherein σ j is the standard deviation of the j-th specific index.
Step 105-6-3: and determining the ratio of the standard deviation to the mean value corresponding to the same specific index as the index variation coefficient corresponding to the specific index.
Step 105-6-4: and determining the pearson correlation coefficient between any two specific indexes.
Step 105-6-5: the independence coefficient for each particular index is determined from the plurality of pearson correlation coefficients using formula . h j is the independence coefficient of the j-th specific index. r ij is the pearson correlation coefficient between the i-th specific index and the j-th specific index.
Step 105-6-6: and determining the product of the index variation coefficient and the independence coefficient corresponding to the same specific index as the comprehensive coefficient corresponding to the specific index.
Step 105-6-7: a second objective weight value for each particular indicator is determined using formula based on the plurality of composite coefficients. Omega l is the second objective weight value of the j-th specific index. q j is the integral coefficient of the j-th specific index.
Step 105-7, comprising:
Step 105-7-1: and constructing a subjective weight vector based on the total subjective weight of each specific index.
Step 105-7-2: a first objective weight vector is constructed based on the first objective weight value for each particular indicator.
Step 105-7-3: a second objective weight vector is constructed based on the second objective weight value for each particular indicator.
Step 105-7-4: and constructing a game combined weight equation based on the subjective weight vector, the first objective weight vector and the second objective weight vector. The game combining weight equation is , wherein ω Group of is a game combining weight vector. α 1 is the combined weight coefficient vector of the subjective weight vector ω 1. α 2 is the combined weight coefficient vector of the first customer weight vector ω 2. α 3 is the combined weight coefficient vector of the second objective weight vector ω 3. The superscript T denotes a transpose.
Step 105-7-5: and constructing an objective function. The objective function is i=min||ω Group of -ωi||2 (i=1, 2, 3). Wherein I is an objective function. And 2 represents the 2-norm.
Step 105-7-6: and solving the optimal combined weight coefficient vector corresponding to each weight vector based on the matrix differential property and the objective function. The weight vectors include a subjective weight vector, a first objective weight vector, and a second objective weight vector.
Step 105-7-7: substituting the optimal combination weight coefficient vectors into the game combination weight equation to determine the game combination weight vector. The bet combining weight vector includes a bet combining weight for each of the individual metrics.
Step 106, including:
Step 106-1: the normal-ideal solution of the standard matrix is determined using equation . Wherein,/> is the positive ideal solution corresponding to the j-th specific index.
Step 106-2: the negative ideal solution of the standard matrix is determined using equation . And/> is the negative ideal solution corresponding to the j-th specific index.
Step 106-3: based on the standard matrix, the plurality of positive ideal solutions, and the plurality of negative ideal solutions, an overall satisfaction of each grid structure is determined using formula . S i is the overall satisfaction of the ith grid structure. Omega Group of j is the game combining weight for the j-th specific index.
Step 106-4: based on the standard matrix, the plurality of positive ideal solutions, and the plurality of negative ideal solutions, an individual deviation value for each grid structure is determined using formula . Individual deviation values for the ith grid structure of R i.
Step 106-5: and determining a decision index value of each power grid structure by using a formula according to the plurality of overall satisfaction degrees and the plurality of individual deviation values. Wherein, Q i is the decision index value of the ith grid structure. v is a decision coefficient.
Step 106-6: a maximum decision index value is determined.
Step 106-7: and determining the difference value of the decision index value and the maximum decision index value of each power grid structure as the elastic capability value of the corresponding power grid structure under the current disaster type.
After step 106, further includes:
Step 107: and updating the current disaster type, and returning to the step 104 until traversing all disaster types of which the occurrence probability of the region to be evaluated is greater than a probability threshold value, so as to obtain the elastic capability value of each power grid structure under different disaster types.
Step 108: and updating the plurality of power grid structures, and returning to the step 103 until the iteration times reach the preset number, so as to obtain the elastic capability values of the plurality of power grid structures under different disaster types.
Step 109: and carrying out power system structure planning and emergency response planning on the region to be evaluated based on the elastic capability values of the power grid structures under the current disaster type.
Next, a specific description is given of an elasticity evaluation method of a novel electric power system provided in this embodiment, as shown in fig. 2, where this embodiment includes:
s1, constructing a novel power system elasticity index evaluation system.
Specifically, a novel power system elasticity index evaluation system comprising three aspects of element failure rate, system loss condition, system defense and recovery performance is constructed. The novel electric power system elastic index evaluation system is divided into 3 layers from top to bottom, namely a target layer, a comprehensive index layer and a specific index layer. The target layer is novel power system elasticity. The comprehensive index layer comprises element failure rate, system loss condition, system defense and recovery performance. The specific index layer comprises alternating current line fault rate, direct current line fault rate, wind turbine generator system fault rate, photovoltaic turbine generator system fault rate, total power supply quantity shortage, maximum power supply quantity shortage, time for derating operation of the system after disaster, time for recovering the system from the beginning to recover normal, and time for derating operation of the system.
The index evaluation system comprises a positive index and a negative index, and the higher the value of the positive index is, the higher the elasticity of the novel power system is. The larger the value of the negative index, the lower the elasticity of the novel power system. The element failure rate, the system loss condition, the system defense and recovery performance, the alternating current line failure rate, the direct current line failure rate, the wind turbine generator failure rate, the photovoltaic generator failure rate, the total power supply quantity deficiency, the maximum power supply quantity deficiency, the time of derating operation of the system from disaster to disaster, the time of recovering the system from the beginning to the recovery of the normal state and the time of derating operation of the system are negative indexes.
S2, building power grid structures with different new energy duty ratios and new energy station access positions, analyzing and evaluating historical disaster data of the area, and determining the disaster with the highest occurrence probability.
Specifically, referring to fig. 3, fig. 4 and fig. 5, a power grid structure with different new energy duty ratios and new energy station access positions is built, and the number of new energy stations is set to be 4. And configuring wind power units, photovoltaic units and energy storage equipment with different proportions for each new energy station according to the wind power intensity and the solar radiation intensity of the access position of the new energy station. And analyzing historical disaster data of the evaluation area, and determining the disaster type with the highest occurrence probability of the power grid of the evaluation area.
And S3, simulating the disaster with the highest occurrence probability in the evaluation area, and recording the numerical values of various indexes of the power grids with different structures in the process from disaster suffering to normal recovery.
Specifically, the disaster with the highest occurrence probability in the evaluation area is simulated, and the numerical values of various indexes of the power grids with different structures in the process from disaster attack to normal recovery are recorded. To avoid accidents, under each power system structure, the simulated disasters occur 30 times, and the numerical values of the indexes are averaged. And obtaining relevant index data of the power system from disaster attack to restoration under the condition that the high probability disaster occurs in the evaluation area.
And S4, weighting each index by adopting a game theory combined weighting method based on elasticity evaluation data obtained by different power grid structures.
Specifically, the idea of game theory is utilized, and the subjective weighting method and the objective weighting method are combined through the combined weighting method, so that the weight reflects the subjective judgment of an evaluator and the influence of objective data. The subjective weighting method adopts an analytic hierarchy process, and the objective weighting method adopts an entropy weighting method and an improvement CRITIC method.
And comparing indexes belonging to the same category in each layer in the index evaluation system in pairs to form a judgment matrix. Assuming that n indexes belonging to the same category are X 1,X2,...,Xn, the judgment matrix C is an n-order square matrix, as shown in the following formula.
Wherein c ij represents the importance value of the index X i compared with X j, and is determined by using a 9-level scale, as shown in Table 1.
Table 19 level scale schematic table
Scale value Meaning of
1 X i is as important as X j
3 X i is slightly more important than X j
5 X i is significantly more important than X j
7 X i is of great importance than X j
9 X i is extremely important than X j
2，4，6，8 Importance is between 1,3,5,7,9
Reciprocal of 1 to 9 X i is more important than X j, and c ij＝1/cji
The judgment matrix designs a plurality of indexes in a pairwise comparison mode, and contradiction situations possibly occur, so that the consistency of the judgment matrix is poor. Thus, a consistency index CI is introduced:
In the formula, lambda max is the maximum eigenvalue of the judgment matrix, and n is the index number, namely the matrix order.
Further, a consistency ratio CR is calculated:
Wherein RI is the average random uniformity index, and the value of RI is shown in Table 2. If CR is less than 0.1, the consistency check is passed, otherwise, the judgment matrix is readjusted.
TABLE 2 average random uniformity index schematic Table
n RI n RI
3 0.58 7 1.32
4 0.9 8 1.41
5 1.12 9 1.45
6 1.24
And calculating the weight of the index according to the judgment matrix. Firstly, calculating the average value of each row of the judgment matrix, and defining an arithmetic average value as follows:
The average value of each row is normalized to the weight, namely:
The method is used for calculating the weights of the specific index layer and the comprehensive index layer, and the total subjective weight omega s of the obtained index is as follows:
ωs＝ωⅠωⅡ。
wherein ω Ⅰ is the weight of the specific index layer relative to the comprehensive index layer, and ω Ⅱ is the weight of the comprehensive index layer relative to the target layer.
The steps of using the entropy weight method are as follows:
an evaluation index system matrix X= [ X ij]m×n ] is constructed by m power grid structures to be evaluated and n indexes, and in order to eliminate the influence of different dimensions, a min-max method is adopted to normalize the matrix to obtain a standard matrix Y= [ Y ij]m×n, wherein the calculation formula is as follows:
Forward index normalization formula:
Wherein maxx j and minx j are the maximum value and the minimum value of the j index in sequence.
Negative index normalization formula:
The entropy value of the j-th evaluation index is calculated as follows:
and calculating objective weight values of the indexes, wherein the objective weight values are shown in the following formula:
using the modified CRITIC method, the contrast intensity was calculated as follows:
Wherein v j is an index variation coefficient, sigma j is a standard deviation of an index, and is a mean value of the index.
The independence coefficient is calculated as follows:
where h j is an independent coefficient, and r ij is a pearson correlation coefficient between indexes.
According to the variation coefficient and the independent coefficient of each evaluation index, the larger the value of the comprehensive coefficient q j.qj is constructed to represent the larger the information content contained in the index, the larger weight should be given, as follows:
qj＝vj·hj。
Each index weight is calculated as follows:
determining a combination weight coefficient by using a game theory, setting a subjective weight vector as omega 1, and setting objective weight vectors as omega 2 and omega 3, wherein the linear combination of the subjective and objective combination weights omega is as follows:
Where α 1、α2、α3 is a combining weight coefficient.
To determine the optimal combining weight coefficients, the 2-norm of the difference between ω and ω 1、ω2、ω3 is minimized, namely:
min||ω-ωi||2(i＝1，2，3)。
According to the matrix differential property, the optimal weight coefficient is calculated as follows:
After the corresponding alpha value is solved, normalization processing is carried out on the alpha value:
Finally, the game combination weights of the indexes are obtained as follows:
s5, based on the weighted power grid elasticity evaluation index, performing power grid elasticity evaluation by using a VIKOR model.
Specifically, based on the weighted power grid elasticity evaluation index, a VIKOR model is built to evaluate the power grid of the novel power system, and the power grids of different new energy duty ratios and new energy station access positions are recorded, so that the elasticity of the novel power system is high and low under the condition of encountering natural disasters.
The positive ideal solution and the negative ideal solution/> are determined as follows:
/>
The overall satisfaction S i and the individual deviation value R i of each power grid structure to be evaluated are obtained, and the two formulas are respectively shown as follows:
Wherein ω j is the weight of the j-th index.
And solving a compromise decision index value Q i of each power grid structure to be evaluated, wherein the compromise decision index value Q i is shown in the following formula:
In the formula, v E [0,1] is a decision coefficient, and generally takes a value of 0.5.
And (5) sequencing the comprehensive elasticity scores based on the compromise decision index values. The closer the compromise decision index value is to 1, the lower the elasticity level of the power grid structure to be evaluated is; otherwise, the closer the compromise decision index value is to 0, the higher the elasticity level of the power grid structure to be evaluated. For convenient observation, the decision index value in the doubling is reversely processed to obtain an elasticity score, and the higher the elasticity score is, the higher the elasticity level of the power grid structure to be evaluated is, and the method is as shown in the following formula:
Li＝Qmax-Qi(i＝1,2,...,m)。
Where L i is the elasticity score of the ith grid structure to be evaluated.
According to the embodiment, the elasticity evaluation method for the novel power system suffering from natural disasters is realized, the elasticity evaluation method for the novel power system is based on the improved CRITIC combined weighting-VIKOR model, the probability of occurrence of regional disasters, the different new energy duty ratios and the influence of the access positions of the new energy stations on the elasticity evaluation of the power system are considered, the elasticity characteristics of the novel power system can be reflected more than other methods, and references can be provided for the structural planning and emergency response plans of the power system.
Example 2
In order to perform the method corresponding to the above embodiment 1 to achieve the corresponding functions and technical effects, a novel elasticity evaluation system of an electric power system is provided below, including:
the elastic index evaluation system construction module is used for constructing an elastic index evaluation system of the novel power system. The elasticity index evaluation system comprises: the system comprises a target layer, a comprehensive index layer and a specific index layer. The target layer is the elastic capability of the novel power system. The composite index layer includes a plurality of composite indexes that affect the target layer. The specific index layer includes a plurality of specific indexes affecting each comprehensive index. The intersection of a plurality of specific indexes corresponding to different comprehensive indexes is an empty set.
And the power grid structure building module is used for building a plurality of power grid structures. The new energy duty ratio and the new energy station access position of different power grid structures are different.
The current disaster type determining module is used for determining the disaster type with the highest occurrence probability as the current disaster type from the historical disaster data of the area to be evaluated.
And the current disaster simulation module is used for respectively applying the current disaster type to each power grid structure simulation to obtain the elastic data of each power grid structure. The elastic data of any power grid structure is simulated output data of each specific index in the process from the current disaster type to the normal recovery of the power grid structure.
And the game combination weight determining module is used for determining the game combination weight of each specific index by utilizing a game theory principle based on the elasticity index evaluation system and the elasticity data of the plurality of power grid structures.
And the elastic capability value determining module is used for determining the elastic capability value of each power grid structure under the current disaster type by utilizing the VIKOR model according to the game combination weight.
Example 3
The embodiment provides an electronic device, including a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to execute the computer program to cause the electronic device to execute the elasticity assessment method of the novel power system described in embodiment 1.
Wherein the memory is a readable storage medium.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.

Claims (10)

1. A method for evaluating elasticity of a novel electric power system, comprising:
Constructing an elasticity index evaluation system of a novel power system; the elasticity index evaluation system comprises: the system comprises a target layer, a comprehensive index layer and a specific index layer; the target layer is the elastic capability of the novel power system; the comprehensive index layer comprises a plurality of comprehensive indexes influencing the target layer; the specific index layer comprises a plurality of specific indexes influencing each comprehensive index; the intersection of a plurality of specific indexes corresponding to different comprehensive indexes is an empty set;
Building a plurality of power grid structures; the new energy duty ratio and the new energy station access position of different power grid structures are different;
determining the disaster type with highest occurrence probability as the current disaster type from the historical disaster data of the area to be evaluated;
applying the current disaster type to each power grid structure simulation to obtain the elasticity data of each power grid structure; the elastic data of any power grid structure is simulated output data of each specific index in the process from the current disaster type to the normal recovery of the power grid structure;
determining the game combination weight of each specific index by utilizing a game theory principle based on the elastic index evaluation system and the elastic data of a plurality of power grid structures;
And determining the elastic capability value of each power grid structure under the current disaster type by utilizing a VIKOR model according to the game combination weight.
2. The method for evaluating the elasticity of a novel electric power system according to claim 1, wherein determining the game combining weight of each specific index based on the elasticity index evaluating system and the elasticity data of the plurality of electric network structures by using a game theory principle comprises:
determining the sub-weight of each comprehensive index and the sub-weight of each specific index by using an analytic hierarchy process based on the elastic index evaluation system;
determining any specific index as the current specific index;
Determining the product of the sub-weight of the current specific index and the sub-weight of the corresponding comprehensive index as the total subjective weight of the current specific index;
traversing all the specific indexes to obtain the total subjective weight of each specific index;
determining a first objective weight value of each specific index by utilizing an entropy weight method based on elastic data of a plurality of power grid structures;
determining a second objective weight value of each specific index by using a modified CRITIC method;
and determining the game combination weight of each specific index by utilizing a game theory principle based on the total subjective weight, the first objective weight value and the second objective weight value of each specific index.
3. The method of claim 2, wherein determining the first objective weight value for each specific index based on the elasticity data of the plurality of grid structures using entropy weight method comprises:
based on elastic data of a plurality of power grid structures, constructing an evaluation index system matrix X= [ X ij]m×n; wherein x ij is an element of the ith row and the jth column in the evaluation index system matrix; m is the number of the power grid structures; n is the number of specific indexes;
Normalizing the evaluation index system matrix to obtain a standard matrix Y= [ Y ij]m×n;yij ] which is an element of the ith row and the j columns in the standard matrix;
Determining the entropy value of each specific index by using a formula and a formula/> based on the standard matrix; e j is the entropy value of the j-th specific index; p ij is the element duty ratio of the ith row and j columns in the standard matrix;
Determining a first objective weight value for each particular indicator using formula based on entropy values of the plurality of particular indicators; omega j is the first objective weight value for the j-th specific index.
4. A method of elasticity assessment for a new power system according to claim 3, wherein determining the second objective weight value for each specific indicator using the modified CRITIC method comprises:
Based on the evaluation index system matrix, determining the average value of each specific index by using a formula ; wherein is the mean value of the j-th specific index;
Determining the standard deviation of each specific index by using a formula based on the evaluation index system matrix; wherein, sigma j is the standard deviation of the j-th specific index;
Determining the ratio of standard deviation to mean value corresponding to the same specific index as an index variation coefficient corresponding to the specific index;
Determining the pearson correlation coefficient between any two specific indexes;
Determining an independence coefficient of each specific index by using a formula according to a plurality of pearson correlation coefficients; h j is the independence coefficient of the j-th specific index; r ij is the pearson correlation coefficient between the i-th specific index and the j-th specific index;
Determining the product of the coefficient of variation of the index corresponding to the same specific index and the independence coefficient as the comprehensive coefficient of the corresponding specific index;
Determining a second objective weight value of each specific index by using a formula according to a plurality of comprehensive coefficients; omega l is the second objective weight value of the j-th specific index; q j is the integral coefficient of the j-th specific index.
5. The method of claim 2, wherein determining the gambling combination weight for each specific indicator using gambling theory based on the total subjective weight, the first objective weight value, and the second objective weight value for each specific indicator comprises:
constructing a subjective weight vector based on the total subjective weight of each specific index;
Constructing a first objective weight vector based on the first objective weight value of each specific indicator;
constructing a second objective weight vector based on the second objective weight value of each specific index;
Constructing a game combined weight equation based on the subjective weight vector, the first objective weight vector and the second objective weight vector; the game combination weight equation is , wherein omega Group of is a game combination weight vector; alpha 1 is a combined weight coefficient vector of the subjective weight vector omega 1; α 2 is the combined weight coefficient vector of the first customer weight vector ω 2; α 3 is the combined weight coefficient vector of the second objective weight vector ω 3; the superscript T denotes a transpose;
Constructing an objective function; the objective function is i=min||ω Group of -ωi||2 (i=1, 2, 3); wherein I is an objective function; the 2 represents the 2-norm;
Based on matrix differential properties and the objective function, solving an optimal combined weight coefficient vector corresponding to each weight vector; the weight vectors include the subjective weight vector, the first objective weight vector, and the second objective weight vector;
Substituting a plurality of optimal combination weight coefficient vectors into a game combination weight equation to determine a game combination weight vector; the gaming combining weight vector includes a gaming combining weight for each individual indicator.
6. A method of elasticity assessment of a new power system according to claim 3, wherein determining the elasticity capability value of each grid structure under the current disaster type using the VIKOR model according to the game combination weights comprises:
Determining a positive ideal solution of the standard matrix using formula ; wherein,/> is the positive ideal solution corresponding to the j-th specific index;
Determining a negative ideal solution of the standard matrix by using a formula ; the/> is the negative ideal solution corresponding to the j-th specific index;
Determining an overall satisfaction of each grid structure using formula based on the standard matrix, the plurality of positive ideal solutions, and the plurality of negative ideal solutions; s i is the overall satisfaction of the ith grid structure; omega Group of j is the game combination weight of the j-th specific index;
Determining an individual deviation value for each grid structure using formula based on the standard matrix, the plurality of positive ideal solutions, and the plurality of negative ideal solutions; individual deviation values for the ith grid structure of R i;
Determining a decision index value of each power grid structure by utilizing a formula according to a plurality of overall satisfaction degrees and a plurality of individual deviation values; wherein Q i is a decision index value of the ith power grid structure; v is a decision coefficient;
Determining a maximum decision index value;
and determining the difference value of the decision index value and the maximum decision index value of each power grid structure as the elastic capability value of the corresponding power grid structure under the current disaster type.
7. The method for evaluating the elasticity of a novel electric power system according to claim 1, further comprising, after determining the elasticity capability value of each electric network structure under the current disaster type by using a VIKOR model according to the game combination weights:
Updating the current disaster type, and returning to the step of simulating and applying the current disaster type to each power grid structure to obtain elastic data of each power grid structure until traversing all disaster types of which the occurrence probability of the region to be evaluated is greater than a probability threshold value to obtain elastic capability values of each power grid structure under different disaster types;
Updating the plurality of power grid structures, and returning to the step of determining the disaster type with the highest occurrence probability as the current disaster type from the historical disaster data of the area to be evaluated until the iteration times reach the preset number to obtain the elastic capability values of the plurality of power grid structures under different disaster types;
And carrying out power system structure planning and emergency response planning on the region to be evaluated based on the elastic capability values of the power grid structures under the current disaster type.
8. A novel elasticity assessment system of an electric power system, comprising:
The elastic index evaluation system construction module is used for constructing an elastic index evaluation system of the novel power system; the elasticity index evaluation system comprises: the system comprises a target layer, a comprehensive index layer and a specific index layer; the target layer is the elastic capability of the novel power system; the comprehensive index layer comprises a plurality of comprehensive indexes influencing the target layer; the specific index layer comprises a plurality of specific indexes influencing each comprehensive index; the intersection of a plurality of specific indexes corresponding to different comprehensive indexes is an empty set;
The power grid structure building module is used for building a plurality of power grid structures; the new energy duty ratio and the new energy station access position of different power grid structures are different;
The current disaster type determining module is used for determining the disaster type with the highest occurrence probability as the current disaster type from the historical disaster data of the area to be evaluated;
The current disaster simulation module is used for simulating and applying the current disaster type to each power grid structure to obtain the elastic data of each power grid structure; the elastic data of any power grid structure is simulated output data of each specific index in the process from the current disaster type to the normal recovery of the power grid structure;
The game combination weight determining module is used for determining the game combination weight of each specific index by utilizing a game theory principle based on the elasticity index evaluation system and the elasticity data of the plurality of power grid structures;
and the elastic capability value determining module is used for determining the elastic capability value of each power grid structure under the current disaster type by utilizing a VIKOR model according to the game combination weight.
9. An electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform a method of elasticity assessment of a novel power system according to any one of claims 1 to 7.
10. The electronic device of claim 9, wherein the memory is a readable storage medium.
CN202410037093.9A 2024-01-10 2024-01-10 Novel elasticity evaluation method and system for power system and electronic equipment Pending CN117895496A (en)

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