CN114358619A - Double-layer assessment method and system for elastic power distribution network resilience assessment - Google Patents

Abstract

The invention discloses a double-layer evaluation method and a double-layer evaluation system for elastic power distribution network resilience evaluation, aiming at a planning evaluation layer, obtaining historical disaster information of a power grid, and simulating disaster on the planning layer to generate a disaster scene; according to the operation evaluation level, forecasting disaster scenes in real time according to weather and multi-aspect forecasting information; carrying out disaster scene simulation analysis to obtain a time-interval change curve of the load of the power distribution system, extracting the maximum load loss ratio, the load loss time of the system and the power loss index of the system, continuously simulating a new disaster scene for a planning level until the scene simulation is finished, and then counting the indexes obtained in all scenes to obtain an index expected value; and feeding back the index result in real time for the operation level. The method can be used for evaluating the resilience of the elastic power distribution network on planning and operating levels, and realizes the unification of the two level evaluation methods.

Description

A double-layer evaluation method and system for elastic distribution network resilience evaluation

technical field

The invention belongs to the technical field of power distribution systems, and in particular relates to a double-layer evaluation method and system for evaluating the resilience of an elastic distribution network.

Background technique

In recent years, the frequent occurrence of large-scale power outages in the power system caused by natural disasters such as typhoons, rainstorms, and ice disasters has brought great challenges and threats to the safe and stable operation of the power system. Therefore, there is an urgent need to carry out researches related to the power grid enhancement scheme to resist natural disasters, and it has become a top priority to build a flexible power system with the ability to prevent, resist and quickly recover.

Relevant studies have been carried out extensively at home and abroad, and many evaluation methods and evaluation indicators have been proposed, but the ability to resist and recover power systems against disasters is often evaluated from a single dimension and level, and the evaluation methods are not very versatile. A two-tier assessment method at both the level and the operational level is used to comprehensively measure the resilience level of the grid to address related issues.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to provide a double-layer evaluation method and system for evaluating the resilience of an elastic distribution network, aiming at the deficiencies in the above-mentioned prior art, which can evaluate the recovery of an elastic distribution network from two levels of planning and operation It solves the problem of insufficient versatility of the elastic distribution network resilience evaluation method in the prior art.

The present invention adopts following technical scheme:

A two-layer assessment method for resilience assessment of resilient distribution networks, comprising the following steps:

S1. For the planning evaluation level, obtain the historical disaster information of the power grid, select common disasters for planning-level disaster simulation, and simulate disaster scenarios; for the operation evaluation level, according to meteorological and various forecast information, real-time forecast disaster scenarios;

S2, according to step S1 to simulate the generation of disaster scenarios and real-time prediction of disaster scenarios, carry out simulation analysis of the disaster scenarios, and obtain the variation curve of the load of the power distribution system by time period;

S3, according to the variation curve of the power distribution system load obtained in step S2 by time period, extract the maximum load loss ratio of the power distribution system, the system load loss time and the system power loss index;

S4. According to the maximum loss of load ratio of the power distribution system, the time of system loss of load and the power loss index of the power distribution system extracted in step S3, continue to simulate new disaster scenarios for the planning level, until the scenario simulation ends, and then count the indicators obtained in all scenarios. Obtain the expected value of the indicator; for the operation level, the indicator results are fed back in real time.

Specifically, in step S2, the simulation analysis of the disaster scene is specifically as follows:

S201, using the distribution line failure rate model to obtain the failure rate sequence λ _k of each line of the distribution network per hour;

S202, according to the line failure rate obtained in step S201, use random number sampling to obtain the line failure outage time;

S203, according to the line failure outage time obtained in step S202, carry out the simulation of load transfer during the disaster, and obtain the size and time of the cut load;

S204, according to the result obtained by the load transfer simulation in step S203, carry out the post-disaster load recovery simulation, extract the elasticity index of the distribution network, obtain the repair time of the line according to the distribution function of the repair time _Tr , and obtain the size and time of the recovery load;

S205 , according to the magnitude and time of the cut and restored load obtained by simulation in steps S203 and S204 , obtain a time-period variation curve of the system load.

Further, in step S201, the failure rate sequence _λk of each line of the distribution network per hour is:

Among them, λ _k is the failure rate at time t _k , γ ₁ , γ ₂ , and γ ₃ are fitting coefficients; ν(t _k ) is the wind speed at time t _k , and λ _n is the failure rate of the component under normal conditions.

Further, in step S202, the normal working time of the distribution line is set as T _n , and the probability of T _{n ≤} t is expressed as a fault function. For the initial moment t ₀ of the disaster, all components are new components, so the line works normally. The probability of time T _{n ≤} t ₀ is 0, and F(t ₀ )=0 is obtained, a random number β uniformly distributed in [0,1] is randomly generated, and β=F(t _f ), according to the fault function, the line The fault time t _f , if t _f is greater than the predicted end time of the disaster, it is considered that the line will not trip to the fault during the disaster.

Further, the failure function is:

Among them, t _k ≤t<t _k+1 , C _k is an integral constant, and satisfies C _k =1-F(t _k ); λ _k is the distribution line in the time period [t _k ,t _k+1 ) failure rate.

Further, in step S204, the emergency repair time Tr of each distribution line obeys the exponential distribution of the same parameters, obtains the probability density function f(T _{r )} of the repair time, determines the distribution function of the repair time _Tr , and randomly generates [ 0,1] uniformly distributed random number β, let β=F(T _r ), the repair time of the circuit is obtained as follows:

T _r = -μlnβ

Among them, μ is the expected value of the line repair time.

Specifically, in step S3, the maximum load loss ratio S _r of the power distribution system is:

Among them, P ₀ is the total active load of the distribution network before the disaster; P _min is the minimum active load that the distribution network can supply during the disaster.

Specifically, in step S3, the load loss time S _t of the power distribution system is:

S _t =t _r -t ₀

Among them, _tr represents the moment when all the loads caused by the power outages caused by the disaster are restored; t ₀ represents the initial moment when the power outages of the loads are caused by the disaster of the distribution network.

Specifically, in step S3, the power loss S _e of the power distribution system is:

Among them, P ₀ is the total active load of the distribution network before the disaster; P(t) is the function of the active load supplied by the distribution network with time after the disaster.

Another technical solution of the present invention is a two-layer evaluation system for elastic distribution network resilience evaluation, comprising:

The scenario module, for the planning evaluation level, obtains the historical disaster information of the power grid, selects common disasters for planning-level disaster simulation, and simulates disaster scenarios; for the operation evaluation level, it predicts the disaster scenarios in real time according to meteorological and various forecast information;

The analysis module simulates the generation of disaster scenarios and real-time prediction of disaster scenarios according to the scenario module, conducts simulation analysis of disaster scenarios, and obtains the variation curve of the load of the power distribution system over time;

The index module extracts the maximum load loss ratio of the power distribution system, the system load loss time and the system power loss index according to the change curve of the distribution system load obtained by the analysis module.

The evaluation module continues to simulate new disaster scenarios according to the maximum loss of load ratio of the power distribution system, the time of system loss of load and the power loss of the system index extracted by the index module, until the end of the scenario simulation, and then counts the indicators obtained in all scenarios to obtain the expected value of the indicator. ; Real-time feedback of disaster results at the operational evaluation level.

Compared with the prior art, the present invention at least has the following beneficial effects:

The present invention is a two-layer evaluation method for elastic distribution network resilience evaluation. According to different application scenarios of power system resilience evaluation, the application scenarios of power system resilience evaluation are divided into planning evaluation and operation evaluation, and disasters are carried out on the planning evaluation level. Scenario simulation, predict the disaster scenario for the operation evaluation level, and then carry out the disaster scenario simulation analysis for the two levels of scenarios, obtain the change curve of the distribution system load time by time, and then extract the maximum loss of load ratio of the distribution system and the system loss of load time. Finally, according to the different evaluation levels, the planning evaluation index results are used in the planning of the system to deal with the impact of extreme disaster weather, and the operation evaluation index results are fed back to the operators to provide real-time guidance and reference for the operation decision-makers.

Further, through the simulation analysis of the disaster scenario, the size and timing of the load shedding during the disaster and the size and timing of the post-disaster recovery load can be obtained, so as to obtain the elasticity index under this scenario, which can provide staged results for the final index calculation at the planning level. Provides guidance data for real-time decision-making at the operational level.

Further, by obtaining the hourly failure rate sequence _λk of each line of the distribution network, parameters can be provided for the simulation of subsequent failures and outages.

Further, by assuming that the components are all new components at the fault occurrence time t ₀ , the problem is simplified and the analysis is facilitated.

Further, through the setting of the fault function, the inverse transformation is used to obtain the fault time t _f of the line, and then it is obtained which lines will fail to trip during the disaster, thus causing the load to lose power supply.

Further, by simulating that the line repair time is _Tr , the time of line failure and the magnitude and time of load loss obtained earlier can be combined to obtain the variation curve of the system load by time period.

Further, through the setting of the maximum loss-of-load ratio index S _r of the power distribution system, the resilience of the power distribution network against a certain disaster scenario can be represented, and the power supply capacity of the power distribution network can be reflected in the disaster.

Further, through the setting of the power distribution system loss-of-load time index S _t , the rapid recovery ability of the distribution network for a certain disaster scenario can be characterized, and the recovery ability of the distribution network under disasters can be reflected.

Further, through the setting of the power loss indicator _Se of the power distribution system, the total power loss of the power distribution network in a certain scenario disaster can be represented, and it can be used to estimate the economic loss caused by the power outage.

To sum up, the present invention can be used for elastic distribution network resilience evaluation at planning and operation levels, and realizes the unification of two-level evaluation methods, making the method of the present invention more versatile than the original single-level evaluation method.

The technical solutions of the present invention will be further described in detail below through the accompanying drawings and embodiments.

Description of drawings

Figure 1 shows the dynamic load change process in the distribution network disaster scenario;

Figure 2 is a comparison diagram of two-layer evaluation methods;

Fig. 3 is a schematic flow chart of the double-layer evaluation method;

Figure 4 is a flow chart of disaster scenario simulation analysis;

Figure 5 is a power loss diagram of a system that encounters a disaster for 6 to 13 hours.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

In the description of the present invention, it is to be understood that the terms "comprising" and "comprising" indicate the presence of the described features, integers, steps, operations, elements and/or components, but do not exclude one or more other features, The existence or addition of a whole, step, operation, element, component, and/or a collection thereof.

It should also be understood that the terminology used in the present specification is for the purpose of describing particular embodiments only and is not intended to limit the present invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural unless the context clearly dictates otherwise.

It should further be understood that, as used in this specification and the appended claims, the term "and/or" refers to and including any and all possible combinations of one or more of the associated listed items .

Various structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the accompanying drawings. The figures are not to scale, some details have been exaggerated for clarity, and some details may have been omitted. The shapes of various regions and layers shown in the figures and their relative sizes and positional relationships are only exemplary, and in practice, there may be deviations due to manufacturing tolerances or technical limitations, and those skilled in the art should Regions/layers with different shapes, sizes, relative positions can be additionally designed as desired.

Referring to FIG. 2, the present invention provides a two-layer evaluation method for elastic distribution network resilience evaluation. According to different application scenarios of power system resilience evaluation, the application scenarios of power system resilience evaluation are divided into planning evaluation and operation evaluation , the planning evaluation is an offline evaluation mode, which is a comprehensive elastic evaluation of the system considering extreme events, which can provide a basis for the planning of the system to deal with the impact of extreme disaster weather; the operation evaluation is an online evaluation mode, which considers the operating conditions of the system under specific disaster scenarios. The risk assessment under the system can provide relevant operational decision support when the system encounters disasters.

Both planning evaluation and operation evaluation can quantitatively evaluate the ability of distribution network to resist, absorb and respond to extreme disasters, but they are different in terms of evaluation scenarios, methods, and final application scenarios in the power grid. The assessment at the planning level focuses on reflecting the overall resilience of the system, while the assessment at the operational level focuses on reflecting the real-time risks of the system, and the risks it faces are dynamic.

1) In terms of evaluation scenarios, the planning evaluation uses the simulated disaster scenarios obtained by simulating the disaster process by the simulation method, that is, the disaster type and intensity are obtained through simulation; while the operation evaluation is based on the actual disaster scenarios obtained by meteorological prediction and other methods. .

2) In terms of evaluation methods, planning evaluation needs to simulate not only the uncertainty of the failure rate of system components, but also the uncertainty of disasters; while the operation evaluation only needs to simulate and consider the change characteristics of the failure rate of components under the current strength. Therefore, planning evaluation requires two-level nested Monte Carlo simulation, while operation evaluation only needs to use a single-level Monte Carlo simulation to simulate the risk of the system in a short period of time under the current operating state.

3) In terms of application scenarios, planning evaluation is used to evaluate the comprehensive resilience level of the system for long-term response to disasters, to analyze and compare the effects of different reinforcement strategies for the same system, and to guide the strengthening of the ability to respond to various disasters; operation evaluation can reflect the external environment of the system. The real-time response to disasters provides real-time guidance and advice for power grid operation decision-makers.

It can be seen from the above process that the elasticity assessment at the planning level is not for a specific disaster. If the system is not transformed, it can be considered that the elasticity level of the system will remain unchanged for a certain period of time; the risk assessment at the operational level is for the specific disaster currently faced. Disasters, the evaluation results can reflect the real-time changes of the system's ability to resist disasters, so it has a great relationship with the current disasters, and the indicators may vary greatly under different disasters and different intensities.

Referring to FIG. 3, a double-layer evaluation method for elastic distribution network resilience evaluation of the present invention includes the following steps:

S1. For the planning evaluation level: obtain the historical disaster information of the power grid, analyze the common disaster types in the area, select common disasters for planning-level disaster simulation, and simulate a specific disaster scenario; for the operation evaluation level: according to meteorological and various forecast information, Provide forecasts of impending disaster scenarios;

S2, according to the specific scene obtained in step S1, carry out disaster scene simulation analysis;

Please refer to Figure 4, the simulation analysis of disaster scenarios is as follows:

S201, using the distribution line failure rate model to obtain the failure rate sequence λ _k of each line of the distribution network per hour;

Taking the typhoon disaster as an example, the greater the typhoon wind speed, the greater the line failure rate. We believe that the failure rate within the hourly time scale is a constant, and the k-period failure rate is:

Among them, λ _k is the failure rate at time t _k , the unit is times/(y·km); γ ₁ , γ ₂ , and γ ₃ are the fitting coefficients, which are all constants; ν( _{t k} ) is the Wind speed, in m/s; λ _n is the failure rate of components under normal conditions, in times/(y·km).

S202, sampling the random number to obtain the fault outage time of the line;

It is considered that the line is an irreparable element during the disaster, that is, it is considered that the line has been in a fault state before the end of the disaster. Let the normal working time of the distribution line be T _n , then the probability of T _{n ≤} t is expressed as the fault function:

Among them, C _k is an integral constant, and satisfies C _k =1-F(t _k ); λ _k is the failure rate of the distribution line in the time period [t _k ,t _k+1 ). For the initial time t ₀ when the disaster comes, it can be approximated that all components are intact and are new components, so the probability of the normal working time of the line T _{n ≤} t ₀ is 0, and F(t ₀ )=0 is obtained.

Randomly generate a uniformly distributed random number β in [0,1], let β=F(t _f ), and substitute it into formula (5) to get the fault time t _f of the line. If t _f is greater than the predicted end time of the disaster, it is considered that the line No fault tripping occurs in disasters.

S203. Simulate load transfer during a disaster;

In the disaster, the load of the faulty line can be transferred through the contact line, and the path from the power-lost load to the power source can be found through the path search algorithm. If there is no possible recovery path at the load point, it is considered that the load has lost power and waits for post-disaster recovery. If there is a recovery path, The load is considered to be uninterrupted.

S204. Simulate post-disaster load recovery

After the disaster, the emergency repair team began to repair and restore the line, and simulated the hourly distribution network recovery process. In the simulation, it was considered that the emergency repair time _Tr of each distribution line obeyed the exponential distribution of the same parameters, and the probability density of the repair time was obtained. The function f(T _r ) is expressed as:

Among them, μ is the expected value of the line repair time; _Tr is the repair time.

Further get the distribution function of repair time _Tr

Randomly generate a uniformly distributed random number β in [0,1], let β=F(T _r ), and substitute it into formula (7) to obtain the repair time of the line as follows:

T _r = -μlnβ (8)

S3, according to the variation curve of the power distribution system load by time period obtained in step S2, obtain the system maximum load loss ratio, the system load loss time and the system power loss index;

Please refer to Figure 1. After the disaster of the flexible distribution network, three indicators are selected to measure the elasticity level of the distribution system: the maximum loss of load ratio of the distribution system, the time of loss of load of the distribution system, and the loss of electricity of the distribution system.

1) Maximum loss of load ratio of power distribution system

Among them, P ₀ is the total active load of the distribution network before the disaster; P _min is the minimum active load that the distribution network can supply during the disaster. The system's maximum loss-of-load ratio index S _r characterizes the ability of the distribution network to resist a certain disaster scenario, and reflects the ability of the distribution network to ensure power supply under disasters.

2) Loss of load time of power distribution system

S _t =t _r -t ₀ (2)

Among them, _tr represents the moment when all the loads caused by the power outages caused by the disaster are restored; t ₀ represents the initial moment when the power outages of the loads are caused by the disaster of the distribution network. The system load loss time index S _t represents the rapid recovery ability of the distribution network for a certain disaster scenario, and reflects the recovery ability of the distribution network under the disaster.

3) The power distribution system loses power

Among them, P ₀ is the total active load of the distribution network before the disaster; P(t) is the function of the active load supplied by the distribution network with time after the disaster. The system power loss index S _e represents the total power loss of the distribution network in a certain scenario disaster, and is used to estimate the economic loss caused by the power outage.

S4. Return to step S1 for the evaluation at the planning level, continue to simulate new disaster scenarios until the end of the scenario simulation, and then count the indicators obtained in all scenarios to obtain the expected value of the indicators; for the evaluation at the operation level, the indicator results are fed back in real time Provide real-time guidance and reference for operators and decision-makers.

In yet another embodiment of the present invention, a two-layer evaluation system for evaluating the resilience of an elastic distribution network is provided, and the system can be used to implement the above-mentioned two-layer evaluation method for evaluating the resilience of an elastic distribution network. , the two-layer evaluation system for elastic distribution network resilience evaluation includes a scenario module, an analysis module, an indicator module and an evaluation module.

Among them, the scenario module, for the planning and evaluation level, obtains the historical disaster information of the power grid, selects common disasters for planning-level disaster simulation, and simulates disaster scenarios; for the operation evaluation level, according to meteorological and various forecast information, real-time prediction of disaster scenarios;

The analysis module simulates the generation of disaster scenarios and real-time prediction of disaster scenarios according to the scenario module, conducts simulation analysis of disaster scenarios, and obtains the variation curve of the load of the power distribution system over time;

The index module extracts the maximum load loss ratio of the power distribution system, the system load loss time and the system power loss index according to the change curve of the distribution system load obtained by the analysis module.

The evaluation module continues to simulate new disaster scenarios according to the maximum loss of load ratio of the power distribution system, the time of system loss of load and the power loss of the system index extracted by the index module, until the end of the scenario simulation, and then counts the indicators obtained in all scenarios to obtain the expected value of the indicator. ; Real-time feedback of disaster results at the operational evaluation level.

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all embodiments. The components of the embodiments of the invention generally described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations. Thus, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Example

Please refer to Figure 5. Taking the recovery process of an actual system in a certain region after a simulated disaster as an example, relevant indicators are calculated. Case 1 is the basic case, and cases 2, 3 and 4 are comparative cases with corresponding improvement strategies.

Case 1: Set four 200MW units in A hydropower plant as black-start power supplies, the expected repair time for faulty lines is 45 minutes, and there are three maintenance teams in the area to repair the faulty components.

Case 2: The impact of black-start power supply capacity improvement and layout on power grid restoration. In order to analyze the influence of black-start power supply capacity and layout on power grid restoration, on the basis of Case 1, the black-start power supply capacity was increased and the power supply layout was optimized. 2 of the 300MW units, the rest of the parameters remain unchanged.

Case 3: The impact of pre-disaster maintenance personnel and material optimal deployment on power grid restoration. The repair team and repair materials are deployed in advance to reduce the distance between the repair personnel and the fault point. On the basis of Case 1, the repair completion time of the components is advanced to 30 minutes, and the other parameters remain unchanged.

Case 4: The impact of post-disaster personnel scheduling and component repair sequence optimization on power grid restoration. The coordinated restoration process optimizes the repair sequence of components, and reasonably adjusts the repair completion time of each component on the basis of Case 1 according to the new repair sequence. The rest of the parameters remain unchanged.

Table 1 Calculation results of four case indicators

Compared with Base Case 1, Cases 2 to 4 have a reduction in the proportion of lost load and power loss, and the time for full power restoration is also shortened.

It can be seen that the increase in the capacity of the black-start power supply, the deployment of pre-disaster maintenance personnel, and the coordinated optimization of post-disaster repair-recovery can significantly reduce the time for full system recovery and reduce the power loss of the system during the recovery process.

To sum up, the present invention provides a two-layer evaluation method and system for elastic distribution network resilience evaluation. According to the different application scenarios of power system resilience evaluation, the application scenarios of power system resilience evaluation are divided into planning evaluation and operation evaluation. . The disaster scenario simulation is carried out at the planning evaluation level, and the disaster scenario prediction is carried out at the operation evaluation level. Then, the disaster scenario simulation analysis is carried out for the scenarios at the two levels to obtain the change curve of the distribution system load by time period, and then extract the maximum loss of load of the distribution system. Ratio, system load loss time and system power loss indicators, and finally, according to the different evaluation levels, the results of the planning evaluation indicators are used in the planning of the system to deal with the impact of extreme disaster weather, and the results of the operation evaluation indicators are fed back to the operators. Provides real-time guidance and reference. Through the verification of the results of the test system, it can be seen that the black-start power supply capacity improvement, the deployment of pre-disaster maintenance personnel, and the post-disaster repair-recovery collaborative optimization can significantly reduce the system's overall recovery time and reduce the system power loss during the recovery process. This method can effectively and quantitatively evaluate the elasticity level of the distribution system.

As will be appreciated by those skilled in the art, the embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

The above content is only to illustrate the technical idea of the present invention, and cannot limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention all fall within the scope of the claims of the present invention. within the scope of protection.

Claims (10)

1. a double-layer evaluation method for elastic distribution network resilience evaluation, is characterized in that, comprises the following steps: S1. For the planning evaluation level, obtain the historical disaster information of the power grid, select common disasters for planning-level disaster simulation, and simulate disaster scenarios; for the operation evaluation level, according to meteorological and various forecast information, real-time forecast disaster scenarios; S2, according to step S1 to simulate the generation of disaster scenarios and real-time prediction of disaster scenarios, carry out simulation analysis of the disaster scenarios, and obtain the variation curve of the load of the power distribution system by time period; S3, according to the variation curve of the power distribution system load obtained in step S2 by time period, extract the maximum load loss ratio of the power distribution system, the system load loss time and the system power loss index; S4. According to the maximum loss of load ratio of the power distribution system, the time of system loss of load and the power loss index of the power distribution system extracted in step S3, continue to simulate new disaster scenarios for the planning level, until the scenario simulation ends, and then count the indicators obtained in all scenarios. Obtain the expected value of the indicator; for the operation level, the indicator results are fed back in real time. 2. The double-layer evaluation method for elastic distribution network resilience evaluation according to claim 1, characterized in that, in step S2, the simulation analysis of the disaster scene is specifically: S201, using the distribution line failure rate model to obtain the failure rate sequence λ _k of each line of the distribution network per hour; S202, according to the line failure rate obtained in step S201, use random number sampling to obtain the line failure outage time; S203, according to the line failure outage time obtained in step S202, carry out the simulation of load transfer during the disaster, and obtain the size and time of the cut load; S204, according to the result obtained by the load transfer simulation in step S203, carry out the post-disaster load recovery simulation, extract the elasticity index of the distribution network, obtain the repair time of the line according to the distribution function of the repair time _Tr , and obtain the size and time of the recovery load; S205 , according to the magnitude and time of the load removal and restoration obtained by simulation in steps S203 and S204 , obtain a time-period variation curve of the system load. 3. The double-layer evaluation method for elastic distribution network resilience evaluation according to claim 2, characterized in that, in step S201, the failure rate sequence λ _k of each line of the distribution network per hour is:

Among them, λ _k is the failure rate at time t _k , γ ₁ , γ ₂ , and γ ₃ are fitting coefficients; ν(t _k ) is the wind speed at time t _k , and λ _n is the failure rate of the component under normal conditions. 4. The double-layer evaluation method for elastic distribution network resilience evaluation according to claim 2, characterized in that, in step S202, set the normal working time of the distribution line to be T _n , and set the probability of T _{n ≤} t It is expressed as a fault function. For the initial moment t ₀ of the disaster, all components are new components, so the probability of the normal working time of the line T _{n ≤} t ₀ is 0, and F(t ₀ )=0 is obtained, randomly generating [0, 1] uniformly distributed random number β, let β=F(t _f ), and the fault time t _f of the line is obtained according to the fault function. If t _f is greater than the predicted end time of the disaster, it is considered that the line will not fail to trip during the disaster . 5. The double-layer evaluation method for elastic distribution network resilience evaluation according to claim 4, wherein the fault function is:

Among them, t _k ≤t<t _k+1 , C _k is an integral constant, and satisfies C _k =1-F(t _k ); λ _k is the distribution line in the time period [t _k ,t _k+1 ) failure rate. 6. The double-layer evaluation method for elastic distribution network resilience evaluation according to claim 2, characterized in that, in step S204, the emergency repair time _Tr of each distribution line obeys the exponential distribution of the same parameter, Obtain the probability density function f(T _r ) of the repair time, determine the distribution function of the repair time T _r , and randomly generate a uniformly distributed random number β in [0,1], let β=F(T _r ), and obtain the repair of the line The times are as follows: T _r = -μlnβ Among them, μ is the expected value of the line repair time. 7. The double-layer evaluation method for elastic distribution network resilience evaluation according to claim 1, characterized in that, in step S3, the maximum load loss ratio S _r of the distribution system is:

Among them, P ₀ is the total active load of the distribution network before the disaster; P _min is the minimum active load that the distribution network can supply during the disaster. 8. The double-layer evaluation method for elastic distribution network resilience evaluation according to claim 1, characterized in that, in step S3, the load loss time S _t of the distribution system is: S _t =t _r -t ₀ Among them, _tr represents the moment when all the loads caused by the power outages caused by the disaster are restored; t ₀ represents the initial moment when the power outages of the loads are caused by the disaster of the distribution network. 9. The double-layer evaluation method for elastic distribution network resilience evaluation according to claim 1, characterized in that, in step S3, the power loss S _e of the distribution system is:

Among them, P ₀ is the total active load of the distribution network before the disaster; P(t) is the function of the active load supplied by the distribution network with time after the disaster. 10. A double-layer evaluation system for elastic distribution network resilience evaluation, characterized in that it comprises: The scenario module, for the planning evaluation level, obtains the historical disaster information of the power grid, selects common disasters for planning-level disaster simulation, and simulates disaster scenarios; for the operation evaluation level, according to meteorological and various forecast information, real-time prediction of disaster scenarios; The analysis module simulates the generation of disaster scenarios and real-time prediction of disaster scenarios according to the scenario module, conducts simulation analysis of the disaster scenarios, and obtains the variation curve of the load of the power distribution system over time; The index module extracts the maximum load loss ratio of the power distribution system, the system load loss time and the system power loss index according to the change curve of the distribution system load obtained by the analysis module. The evaluation module continues to simulate new disaster scenarios according to the maximum loss of load ratio of the power distribution system, the time of system loss of load and the power loss of the system index extracted by the index module, until the end of the scenario simulation, and then counts the indicators obtained in all scenarios to obtain the expected value of the indicator. ; Real-time feedback of disaster results at the operational evaluation level.

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