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

Abstract

The application discloses a double-layer evaluation method and system for elastic power distribution network recovery force evaluation, obtains power grid historical disaster information for a planning evaluation level, generates a disaster scene through disaster simulation of the planning level, and realizes real-time prediction of the disaster scene according to meteorological and multi-aspect prediction information for an operation evaluation level. Disaster scene simulation analysis is carried out to obtain a load change curve of the power distribution system at each time interval, and the maximum load loss ratio, system load loss time and system power loss amount of the power distribution system are extracted. New disaster scenes are continuously simulated for the planning level until the simulation of the scenes is finished, and then the index expectation values are obtained by counting the indexes obtained under all the scenes. The index results are fed back in real time for the operation level. The application can be used for elastic power distribution network recovery force evaluation of the planning and operation levels, and realizes unification of the two-level evaluation methods.

Description

Technical Field

[0001] This invention belongs to the field of power distribution system technology, specifically relating to a two-layer assessment method and system for assessing the resilience of resilient power distribution networks. Background Technology

[0002] In recent years, large-scale power outages caused by natural disasters such as typhoons, rainstorms, and ice storms have occurred frequently, posing significant challenges and threats to the safe and stable operation of the power system. Therefore, research on power grid enhancement schemes to withstand natural disasters is urgently needed, and building a resilient power system with preventative, resilient, and rapid recovery capabilities has become a top priority.

[0003] Extensive research has been conducted both domestically and internationally, resulting in numerous assessment methods and indicators. However, these methods often evaluate the power system's resilience and recovery capabilities against disasters from a single dimension and level, lacking versatility. Therefore, this invention proposes a two-tiered assessment method from both the planning and operational levels to comprehensively measure the resilience of the power grid and address related issues. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a two-layer assessment method and system for assessing the resilience of flexible distribution networks, which addresses the shortcomings of the prior art. This method assesses the resilience level of flexible distribution networks from both planning and operation perspectives, thus solving the problem of insufficient universality of existing methods for assessing the resilience of flexible distribution networks.

[0005] The present invention adopts the following technical solution:

[0006] A two-layer assessment method for evaluating the resilience of resilient distribution networks includes the following steps:

[0007] S1. At the planning and assessment level, obtain historical disaster information of the power grid, select common disasters for disaster simulation at the planning level, and simulate disaster scenarios; at the operation assessment level, predict disaster scenarios in real time based on meteorological and other forecast information.

[0008] S2. Based on the disaster scenario simulation and real-time prediction generated in step S1, conduct disaster scenario simulation analysis to obtain the time-period change curve of the power distribution system load.

[0009] S3. Based on the time-period change curve of the power distribution system load obtained in step S2, extract the maximum load loss ratio, system load loss time, and system power loss index of the power distribution system.

[0010] S4. Based on the maximum load loss ratio, system load loss time and system power loss indicators of the power distribution system extracted in step S3, continue to simulate new disaster scenarios at the planning level until the scenario simulation ends, and then statistically analyze the indicators obtained under all scenarios to obtain the expected values ​​of the indicators; at the operation level, the indicator results are fed back in real time.

[0011] Specifically, in step S2, the disaster scenario simulation analysis is performed as follows:

[0012] S201. Obtain the hourly fault rate sequence λ of each line in the distribution network using the power distribution line fault rate model. k ;

[0013] S202. Based on the line fault rate obtained in step S201, use random number sampling to obtain the line fault outage time;

[0014] S203. Based on the line fault outage time obtained in step S202, perform a disaster load transfer simulation to obtain the size and timing of the load shedding;

[0015] S204. Based on the results obtained from the load transfer simulation in step S203, perform a post-disaster load recovery simulation, extract the distribution network resilience index, and determine the recovery time T. r The distribution function yields the line repair time, and the magnitude and timing of the restored load.

[0016] S205. Based on the magnitude and timing of the load cut-off and recovery obtained from the simulation in steps S203 and S204, the system load variation curve for each time period is obtained.

[0017] Furthermore, in step S201, the fault rate sequence λ of each line in the distribution network for each hour is... k for:

[0018]

[0019] Where, λ k For in t k The failure rate at time t, where γ1, γ2, and γ3 are fitting coefficients; ν(t) k ) for t k Wind speed at any given moment, λ n This represents the failure rate of the component under normal conditions.

[0020] Furthermore, in step S202, let the normal operating time of the power distribution line be T. n , will T n The probability ≤ t is represented by the fault function. For the initial moment t0 of the disaster, all components are new components, so the normal operating time of the line is T. nThe probability of ≤t0 is 0, so F(t0) = 0. Randomly generate a uniformly distributed random number β in [0,1], and let β = F(t0). f The fault time t of the line is obtained based on the fault function. f If t f If the time exceeds the predicted end time of the disaster, it is assumed that the line will not trip during the disaster.

[0021] Furthermore, the fault function is:

[0022]

[0023] Among them, t k ≤t <t k+1 C k Let C be the integration constant and satisfy C. k =1-F(t) k );λ k For the power distribution line during the time period [t] k ,t k+1 The failure rate within ).

[0024] Furthermore, in step S204, the emergency repair time T for each power distribution line is... r All follow an exponential distribution with the same parameters, yielding the probability density function f(T) for the repair time. r Determine the repair time T. r The distribution function is used to randomly generate a random number β uniformly distributed in the range [0,1]. Let β = F(T) r The repair time for the line was obtained as follows:

[0025] T r =-μlnβ

[0026] Where μ is the expected value of the line repair time.

[0027] Specifically, in step S3, the maximum load shedding ratio S of the power distribution system r for:

[0028]

[0029] Where P0 is the total active power load of the distribution network before the disaster; P min This is the minimum active load that the distribution network can supply during the disaster.

[0030] Specifically, in step S3, the power distribution system loss-of-load time S t for:

[0031] S t =t r -t0

[0032] Among them, tr t0 indicates the moment when all loads that experienced power outages due to the disaster are restored; t0 indicates the initial moment when a power outage caused by a disaster in the distribution network occurs.

[0033] Specifically, in step S3, the power loss S of the power distribution system e for:

[0034]

[0035] Where P0 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 after the disaster as a function of time.

[0036] Another technical solution of the present invention is a two-layer assessment system for evaluating the resilience of resilient distribution networks, comprising:

[0037] The scenario module, at the planning and assessment level, acquires historical disaster information of the power grid, selects common disasters for planning-level disaster simulation, and simulates disaster scenarios; at the operation assessment level, it predicts disaster scenarios in real time based on meteorological and other forecast information.

[0038] The analysis module simulates and predicts disaster scenarios based on the scenario module, performs disaster scenario simulation analysis, and obtains the time-period change curve of the power distribution system load.

[0039] The indicator module extracts the maximum load loss ratio, system load loss time, and system load loss indicators of the power distribution system based on the time-period change curve of the power distribution system obtained by the analysis module.

[0040] The evaluation module continues to simulate new disaster scenarios based on the maximum load loss ratio, system load loss time, and system power loss indicators extracted by the indicator module until the scenario simulation ends. Then, the indicators obtained from all scenarios are statistically analyzed to obtain the expected values ​​of the indicators. The disaster results are fed back in real time through the operational evaluation level.

[0041] Compared with the prior art, the present invention has at least the following beneficial effects:

[0042] This invention discloses a two-layer assessment method for evaluating the resilience of resilient distribution networks. Based on different application scenarios of power system resilience assessment, the method divides these scenarios into planning assessment and operational assessment. Disaster scenario simulation is performed at the planning assessment level, while disaster scenario prediction is performed at the operational assessment level. Disaster scenario simulation analysis is then conducted at both levels to obtain the time-period variation curves of the distribution system load. The maximum load loss ratio, system load loss time, and system load loss indicators are then extracted. Finally, based on the different assessment levels, the planning assessment indicator results are used for system planning to cope with the impact of extreme weather events, while the operational assessment indicator results are fed back to operators, providing real-time guidance and reference for operational decision-makers.

[0043] Furthermore, through disaster scenario simulation analysis, the magnitude and timing of load shedding during a disaster and the magnitude and timing of load recovery after a disaster can be obtained, thereby obtaining resilience indicators under this scenario. This can provide phased results for the final indicator calculation at the planning level and provide guiding data for real-time decision-making at the operational level.

[0044] Furthermore, by obtaining the hourly fault rate sequence λ of each line in the distribution network... k It can provide parameters for simulating subsequent failure and shutdown times.

[0045] Furthermore, by assuming that all components are new components at the time t0 when the fault occurs, the problem is simplified and easier to analyze.

[0046] Furthermore, by setting the fault function, the fault time t of the line is obtained using inverse transformation. f This allows us to determine which lines will fail and trip during a disaster, resulting in the loss of power to the load.

[0047] Furthermore, by simulating the line repair time as T r In this case, the system load variation curve for each time period can be obtained by combining the previously obtained line fault time and load loss magnitude and time.

[0048] Furthermore, the maximum load shedding ratio index S of the power distribution system is used to further analyze this. r The setting can characterize the resilience of the distribution network to a certain disaster scenario and reflect the power supply guarantee capability of the distribution network under disaster.

[0049] Furthermore, through the power distribution system's load shedding time index S t The settings can characterize the rapid recovery capability of the distribution network in response to a specific disaster scenario, reflecting the resilience of the distribution network under disaster.

[0050] Furthermore, through the power loss index S of the power distribution system eThe setting can characterize the total power loss of the distribution network under a certain scenario disaster, and is used to estimate the economic losses caused by power outages.

[0051] In summary, this invention can be used for resilience assessment of resilient distribution networks at both the planning and operation levels, unifying the assessment methods at both levels and making the method of this invention more versatile than the original single-level assessment methods.

[0052] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0053] Figure 1 This refers to the dynamic load change process under disaster scenarios in the power distribution network.

[0054] Figure 2 A comparison chart of two-layer evaluation methods;

[0055] Figure 3 This is a schematic diagram of the two-layer evaluation method.

[0056] Figure 4 Flowchart for disaster scenario simulation analysis;

[0057] Figure 5 This is a diagram showing the power loss of a real system during a disaster, spanning 6 to 13 hours. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0060] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0061] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0062] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0063] Please see Figure 2 This invention provides a two-layer assessment method for evaluating the resilience of resilient distribution networks. Based on different application scenarios of power system resilience assessment, the application scenarios are divided into planning assessment and operation assessment. Planning assessment is an offline assessment mode, which is a comprehensive system resilience assessment considering extreme events and can provide a basis for planning systems to cope with the impact of extreme weather disasters. Operation assessment is an online assessment mode, which considers the risk assessment of system operation conditions under specific disaster scenarios and can provide relevant operational decision support when the system encounters a disaster.

[0064] Both planning assessment and operational assessment can quantitatively evaluate the distribution network's ability to withstand, absorb, and recover from extreme disasters, but they differ in various aspects, including the assessment scenarios, methods, and the final application scenarios in the power grid. Planning-level assessments focus on reflecting the overall resilience of the system, while operational-level assessments focus on reflecting the real-time risks of the system, which are dynamically changing.

[0065] 1) In terms of assessment scenarios, the planning assessment uses simulation methods to simulate the disaster process and obtain simulated disaster scenarios, that is, the disaster type and intensity are obtained through simulation; while the operational assessment is based on actual disaster scenarios obtained through meteorological forecasts and other methods.

[0066] 2) Regarding the assessment methods, planning assessment needs to simulate and consider both the uncertainty of system component failure rates and the uncertainty of disasters; while operational assessment only needs to simulate and consider the failure rate variation characteristics of components under the current intensity. Therefore, planning assessment requires two nested Monte Carlo simulations, while operational assessment only needs to use a single Monte Carlo simulation to simulate the system's risk in the short term under the current operating state.

[0067] 3) In terms of application scenarios, planning assessment is used to evaluate the overall resilience of the system in the long term to disasters, to analyze and compare the effects of different hardening strategies on the same system, and to guide the strengthening of the ability to cope with various disasters; operation assessment can reflect the real-time response to external disasters of the system and provide real-time guidance and suggestions for power grid operation decision-makers.

[0068] As can be seen from the above process, the resilience assessment at the planning level is not targeted at a specific disaster. If the system is not modified, the resilience level of the system can be considered to remain unchanged within a certain period of time. The risk assessment at the operational level is targeted at the specific disaster currently faced. The assessment results can reflect the real-time changes in the system's ability to resist disasters. Therefore, it is closely related to the disaster currently faced. The indicators may vary greatly under different disasters and intensities.

[0069] Please see Figure 3 This invention provides a two-layer assessment method for evaluating the resilience of resilient distribution networks, comprising the following steps:

[0070] S1. For the planning and assessment level: Obtain historical disaster information of the power grid, analyze common disaster types in the region, select common disasters for planning-level disaster simulation, and simulate a specific disaster scenario; For the operation assessment level: Based on meteorological and multi-faceted forecast information, provide predictions of upcoming disaster scenarios;

[0071] S2. Based on the specific scenarios obtained in step S1, conduct disaster scenario simulation analysis;

[0072] Please see Figure 4 The specific steps for conducting disaster scenario simulation analysis are as follows:

[0073] S201. Obtain the hourly fault rate sequence λ of each line in the distribution network using the power distribution line fault rate model. k ;

[0074] Taking typhoon disasters as an example, the higher the typhoon wind speed, the greater the line failure rate. We assume the failure rate over an hourly timescale is a constant; therefore, the failure rate for time period k is:

[0075]

[0076] Where, λ k For in t k The failure rate at time t is expressed in times per (y·km); γ1, γ2, and γ3 are fitting coefficients, all of which are constants; ν(t) k ) for t k Wind speed at any given time, in m / s; λ n The failure rate of the component under normal conditions is expressed in times per (y·km).

[0077] S202. Random number sampling is used to obtain the fault outage time of the line;

[0078] Assuming the power line is an irreparable component during the disaster, meaning it remains in a faulty state until the disaster ends, and assuming the normal operating time of the distribution line is T. n Then T n The probability of ≤t is expressed as the fault function:

[0079]

[0080] Among them, C k Let C be the integration constant and satisfy C. k =1-F(t) k );λ k For the power distribution line during the time period [t] k ,t k+1 The failure rate within ) . For the initial moment t0 of the disaster, it can be approximated that all components are intact and new, therefore the normal operating time T of the line is . n The probability of ≤t0 is 0, so F(t0) = 0.

[0081] Randomly generate a uniformly distributed random number β within the range [0,1], and let β = F(t) f Substituting into formula (5), we obtain the fault time t of the line. f If t f If the time is greater than the predicted end time of the disaster, it is considered that the line will not trip during the disaster.

[0082] S203, Simulated disaster load transfer;

[0083] During a disaster, the load on a faulty line can be transferred through a connecting line. A path search algorithm is used to find the path from the power-loss load to the power source. If there is no possible recovery path for the load, it is considered that the load has lost power and is waiting for post-disaster restoration. If there is a recovery path, it is considered that the load has not lost power.

[0084] S204, Simulated Post-Disaster Load Recovery

[0085] Following the disaster, repair teams began emergency repairs and restoration of power lines, simulating the hourly restoration process of the power distribution network. The simulation assumed a repair time T for each power distribution line. r All follow an exponential distribution with the same parameters, yielding the probability density function f(T) for the repair time. r ) is represented as:

[0086]

[0087] Where μ is the expected value of the line repair time; T r This refers to the repair time.

[0088] Further obtain the repair time T r Distribution function

[0089]

[0090] Randomly generate a uniformly distributed random number β within the range [0,1], and let β = F(T r Substituting these values ​​into formula (7), the repair time for the line is as follows:

[0091] T r =-μlnβ (8)

[0092] S3. Based on the time-period variation curve of the power distribution system load obtained in step S2, obtain the maximum load loss ratio, system load loss time, and system power loss index.

[0093] Please see Figure 1 In the event of a disaster, the resilience of a resilient distribution network is measured by three indicators: the maximum load loss ratio, the load loss time, and the amount of load loss.

[0094] 1) Maximum load shedding ratio of the power distribution system

[0095]

[0096] Where P0 is the total active power load of the distribution network before the disaster; P min This represents the minimum active load that the distribution network can supply during the disaster. The system's maximum load loss ratio index, S. r It characterizes the resilience of the distribution network to a specific disaster scenario and reflects the power supply guarantee capability of the distribution network under disaster conditions.

[0097] 2) Power distribution system failure time

[0098] S t =t r -t0 (2)

[0099] Among them, t r t0 represents the moment when all loads affected by a disaster-induced power outage are restored; t0 represents the initial moment when a power outage occurs due to a distribution network disaster. System load loss time index S t It characterizes the rapid recovery capability of the distribution network in response to a specific disaster scenario, reflecting the resilience of the distribution network under disaster.

[0100] 3) Power loss in the power distribution system

[0101]

[0102] Where P0 is the total active power load of the distribution network before the disaster; P(t) is the function of the active power load supplied by the distribution network after the disaster as a function of time. System power loss index S e It represents the total power loss of a distribution network under a certain scenario disaster, and is used to estimate the economic losses caused by power outages.

[0103] S4. For the assessment at the planning level, return to step S1 and continue simulating new disaster scenarios until the scenario simulation ends. Then, statistically analyze all the indicators obtained under all scenarios to obtain the expected values ​​of the indicators. For the assessment at the operational level, the indicator results are fed back to the operational personnel in real time to provide real-time guidance and reference for operational decision-makers.

[0104] In another embodiment of the present invention, a two-layer assessment system for evaluating the resilience of a resilient distribution network is provided. This system can be used to implement the above-mentioned two-layer assessment method for evaluating the resilience of a resilient distribution network. Specifically, the two-layer assessment system for evaluating the resilience of a resilient distribution network includes a scenario module, an analysis module, an indicator module, and an assessment module.

[0105] Among them, the scenario module, at the planning and evaluation level, obtains historical disaster information of the power grid, selects common disasters to conduct disaster simulation at the planning level, and simulates disaster scenarios; at the operation evaluation level, it predicts disaster scenarios in real time based on meteorological and other forecast information.

[0106] The analysis module simulates and predicts disaster scenarios based on the scenario module, performs disaster scenario simulation analysis, and obtains the time-period change curve of the power distribution system load.

[0107] The indicator module extracts the maximum load loss ratio, system load loss time, and system load loss indicators of the power distribution system based on the time-period change curve of the power distribution system obtained by the analysis module.

[0108] The evaluation module continues to simulate new disaster scenarios based on the maximum load loss ratio, system load loss time, and system power loss indicators extracted by the indicator module until the scenario simulation ends. Then, the indicators obtained from all scenarios are statistically analyzed to obtain the expected values ​​of the indicators. The disaster results are fed back in real time through the operational evaluation level.

[0109] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0110] Example

[0111] Please see Figure 5 Taking the recovery process of a real system in a certain region after a simulated disaster as an example, relevant indicators were calculated. Case 1 is the basic case, and Cases 2, 3 and 4 are comparative cases with corresponding improvement strategies.

[0112] Case 1: Four 200MW units of Hydropower Plant A are set as black start power sources. The expected repair time for the faulty line is 45 minutes. There are three maintenance teams in the area to repair the faulty components.

[0113] Case 2: The Impact of Black-Start Power Supply Capacity Increase and Layout on Grid Restoration. To analyze the impact of black-start power supply capacity and layout on grid restoration, based on Case 1, the black-start power supply capacity was increased and the power supply layout was optimized. The black-start power supplies were set as four 200MW units of Hydropower Plant A and two of the four 300MW units of Hydropower Plant B, with other parameters remaining unchanged.

[0114] Case 3: The impact of optimized deployment of pre-disaster maintenance personnel and materials on power grid restoration. By deploying repair teams and materials in advance to reduce the distance between maintenance personnel and the fault location, the repair completion time for components was reduced to 30 minutes, building upon Case 1, while other parameters remained unchanged.

[0115] Case 4: The impact of post-disaster personnel deployment and component repair sequence optimization on power grid recovery. The collaborative recovery process optimizes the component repair sequence, adjusting the repair completion time of each component based on Case 1 according to the new repair sequence. All other parameters remain unchanged.

[0116] Table 1 Calculation results of four case indicators

[0117]

[0118]

[0119] Compared to basic Case 1, Cases 2-4 show a reduction in the proportion of lost load and the amount of lost electricity, and the time to restore full power is also shortened.

[0120] It can be seen that increasing the capacity of black-start power supplies, deploying pre-disaster maintenance personnel and materials, and optimizing post-disaster repair-recovery coordination can significantly reduce the time required for full system power restoration and reduce the power loss during the recovery process.

[0121] In summary, this invention presents a two-layer assessment method and system for evaluating the resilience of resilient distribution networks. Based on different application scenarios for power system resilience assessment, the method categorizes these scenarios into planning assessment and operational assessment. Disaster scenario simulation is performed at the planning assessment level, while disaster scenario prediction is performed at the operational assessment level. Furthermore, disaster scenario simulation analysis is conducted for both levels to obtain time-period load variation curves of the distribution system. The maximum load loss ratio, system load loss time, and system power loss indicators are then extracted. Finally, based on the different assessment levels, the planning assessment indicators are used for planning systems to cope with the impact of extreme weather events, while the operational assessment indicators are fed back to operators, providing real-time guidance and reference for operational decision-makers. Verification through the test system shows that improving black-start power supply capacity, pre-disaster maintenance personnel and material deployment, and post-disaster repair-recovery synergy optimization can significantly reduce the time for full system restoration and minimize power loss during the recovery process. This method can effectively and quantitatively assess the resilience level of distribution systems.

[0122] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can 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.) containing computer-usable program code.

[0123] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0126] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A two-layer assessment method for evaluating the resilience of resilient distribution networks, characterized in that, Includes the following steps: S1. At the planning and assessment level, obtain historical disaster information of the power grid, select common disasters for disaster simulation at the planning level, and simulate disaster scenarios; at the operation assessment level, predict disaster scenarios in real time based on meteorological and other forecast information. S2. Based on the disaster scenario simulation and real-time prediction generated in step S1, conduct disaster scenario simulation analysis to obtain the time-period change curve of the power distribution system load. The specific details of the disaster scenario simulation analysis are as follows: S201. Obtain the hourly failure rate sequence of each line in the distribution network using the distribution line failure rate model. Fault rate sequence of each line in the distribution network per hour for: in, In order to be in t k Failure rate at any time These are the fitting coefficients; for t k Wind speed at any moment This represents the failure rate of components under normal conditions. S202. Based on the line fault rate obtained in step S201, the fault outage time of the line is obtained using random number sampling. Let the normal operating time of the distribution line be... T n ,Will T n ≤ t The probability is represented by the failure function, which is the probability of the disaster occurring at the initial moment. t 0. All components are new, therefore the normal operating time of the circuit is [not specified]. T n ≤ t The probability of 0 is 0, therefore... Generate random numbers uniformly distributed within the range [0,1]. ,make The fault time of the line is obtained based on the fault function. t f ,if t f If the time exceeds the predicted end time of the disaster, the line is considered not to trip during the disaster, and the fault function is: in, , Let be the integration constant, and satisfy . ; For power distribution lines during time periods Internal failure rate; S203. Based on the line fault outage time obtained in step S202, perform a disaster load transfer simulation to obtain the size and timing of the load shedding; S204. Based on the results obtained from the load transfer simulation in step S203, conduct a post-disaster load recovery simulation, extract the distribution network resilience index, and determine the recovery time. T r The distribution function yields the line repair time, the magnitude and timing of load restoration, and the emergency repair time for each distribution line. T r All follow an exponential distribution with the same parameters, yielding the probability density function of the repair time. Determine the repair time T r The distribution function is used to randomly generate random numbers uniformly distributed within the range [0,1]. ,make The repair time for the line is as follows: in, This represents the expected time for line repair. S205. Based on the magnitude and timing of the load cut-off and recovery obtained from the simulation in steps S203 and S204, the system load variation curve for each time period is obtained. S3. Based on the time-period variation curve of the distribution system load obtained in step S2, extract the maximum load loss ratio, system load loss time, and system load loss index of the distribution system. for: in, P 0 represents the total active power load of the distribution network before the disaster. P min This represents the minimum active load that the distribution network can supply during the disaster's impact. Power distribution system load loss time for: in, t r This indicates the moment when all power outages caused by the disaster have been restored. t 0 represents the initial moment when a power outage occurs due to a disaster in the power distribution network, and the amount of power lost in the power distribution system. for: in, P 0 represents the total active power load of the distribution network before the disaster. P ( t () is a function of the change in active power load supplied by the distribution network over time after a disaster. S4. Based on the maximum load loss ratio, system load loss time and system power loss indicators of the power distribution system extracted in step S3, continue to simulate new disaster scenarios at the planning level until the scenario simulation ends, and then statistically analyze the indicators obtained under all scenarios to obtain the expected values ​​of the indicators; at the operation level, the indicator results are fed back in real time.

2. A two-layer assessment system for evaluating the resilience of resilient distribution networks, characterized in that, include: The scenario module, at the planning and assessment level, obtains historical disaster information of the power grid, selects common disasters to conduct disaster simulation at the planning level, and simulates disaster scenarios. At the operational assessment level, disaster scenarios are predicted in real time based on meteorological and other forecast information. The analysis module, based on the disaster scenarios simulated and predicted in real time by the scenario module, performs disaster scenario simulation analysis to obtain the time-period change curve of the power distribution system load. Specifically, the disaster scenario simulation analysis includes: The fault rate sequence of each line in the distribution network for each hour is obtained using the distribution line fault rate model. Based on the obtained line fault rate, random number sampling is used to obtain the line fault outage time; based on the obtained line fault outage time, a disaster-related load transfer simulation is performed to obtain the magnitude and timing of the load shedding; based on the results of the load transfer simulation, a post-disaster load recovery simulation is performed to extract the distribution network resilience index, and based on the repair time... T r The distribution function is used to obtain the line repair time, the magnitude and timing of the restored load; based on the magnitude and timing of the disconnected and restored loads obtained from the simulation, the system load variation curve for each time period is obtained, and the fault rate sequence of each line in the distribution network for each hour is obtained. for: in, In order to be in t k Failure rate at any time These are the fitting coefficients; for t k Wind speed at any moment This represents the failure rate of components under normal conditions. Assume the normal operating time of the power distribution line is T n ,Will T n ≤ t The probability is represented by the failure function, which is the probability of the disaster occurring at the initial moment. t 0. All components are new, therefore the normal operating time of the circuit is [not specified]. T n ≤ t The probability of 0 is 0, therefore... Generate random numbers uniformly distributed within the range [0,1]. ,make The fault time of the line is obtained based on the fault function. t f ,if t f If the time exceeds the predicted end time of the disaster, the line is considered not to trip during the disaster, and the fault function is: in, , Let be the integration constant, and satisfy . ; For power distribution lines during time periods Internal failure rate; Emergency repair time for each power distribution line T r All follow an exponential distribution with the same parameters, yielding the probability density function of the repair time. Determine the repair time T r The distribution function is used to randomly generate random numbers uniformly distributed within the range [0,1]. ,make The repair time for the line is as follows: in, This represents the expected time for line repair. The indicator module extracts the maximum load loss ratio, system load loss time, and system load loss indicators from the time-period change curve of the distribution system obtained from the analysis module. for: in, P 0 represents the total active power load of the distribution network before the disaster. P min This represents the minimum active load that the distribution network can supply during the disaster's impact. Power distribution system load loss time for: in, t r This indicates the moment when all power outages caused by the disaster have been restored. t 0 represents the initial moment when a power outage occurs due to a disaster in the power distribution network, and the amount of power lost in the power distribution system. for: in, P 0 represents the total active power load of the distribution network before the disaster. P ( t () is a function of the change in active power load supplied by the distribution network over time after a disaster. The evaluation module continues to simulate new disaster scenarios based on the maximum load loss ratio, system load loss time, and system power loss indicators extracted by the indicator module until the scenario simulation ends. Then, the indicators obtained from all scenarios are statistically analyzed to obtain the expected values ​​of the indicators. The disaster results are fed back in real time through the operational evaluation level.

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