A method and system for improving anti-fragility of urban power grid system

Abstract

This application relates to the field of public safety technology and provides a method and system for enhancing the anti-fragility of urban power grid systems. The method involves constructing a functional resonance analysis model of the urban power grid system based on identified risk factors; determining the variation values ​​of functional modules using Monte Carlo simulation to identify key functional modules in the functional resonance analysis model; decomposing the functional modules to construct an anti-fragility enhancement model for the urban power grid system; and optimizing the anti-fragility schemes by allocating anti-fragility solutions to key nodes in the anti-fragility enhancement model according to different investment ratios. By combining the functional resonance analysis model and system dynamics, a risk assessment based on multi-agent and multi-factor coupling is achieved, establishing a quantitative assessment model for the anti-fragility of urban power grids, improving the dynamic response capability of the power grid to highly random and impactful events, and realizing the transformation of power grid operation from passive security to proactive security.

Description

Technical Field

[0001] The present application relates to the field of public safety technology, and in particular to a method and system for improving the anti-fragility of a city power grid system. Background Art

[0002] At present, urban power grids have a reasonable grid structure, high safety margin and management level, but they cannot avoid large power outages when facing low-probability, highly random and high-impact events such as extreme disasters, showing the vulnerability of urban power grids. The evolution of typical accidents such as urban power grid power outages shows nonlinear coupling and emergence characteristics. The causes of accidents may include multiple dimensions such as customers, equipment and environment. Traditional accident causation theories are mostly linear derivations and cannot conduct coupled analysis of multiple accident causes.

[0003] Therefore, there is an urgent need to provide a technical solution to the above-mentioned deficiencies in the prior art. Summary of the invention

[0004] The purpose of this application is to provide a method and system for improving the anti-fragility of a city power grid system to solve or alleviate the problems existing in the above-mentioned prior art.

[0005] In order to achieve the above objectives, this application provides the following technical solutions:

[0006] The present application provides an anti-fragility improvement method for a city power grid system, comprising: step S101, constructing a functional resonance analysis model of the city power grid system according to the identified risk factors of the city power grid system; step S102, determining the variation values ​​of the functional modules in the functional resonance analysis model when an accident of the city power grid system occurs based on a Monte Carlo simulation method, so as to identify the key functional modules in the functional resonance analysis model; step S103, disassembling the functional modules in the functional resonance analysis model and performing system dynamics mapping, so as to construct an anti-fragility improvement model for the city power grid system; step S104, allocating the anti-fragility scheme to the key nodes corresponding to the key functional modules in the anti-fragility improvement model according to different investment ratios, so as to optimize the anti-fragility scheme of the city power grid system.

[0007] Preferably, in step S101, the functional modules of the urban power grid system are determined based on the pre-collected data files of the urban power grid system; the functional modules are divided according to risk characteristics, and the functional modules are connected according to causal relationships to construct the functional resonance analysis model.

[0008] Preferably, in step S102, in response to an accident occurring in the urban power grid system, the probability distribution of changes in the variability of each functional module in the functional resonance analysis model in the time dimension and the precision dimension is determined; based on the Monte Carlo simulation method, the variation value of the functional module of the urban power grid system where the accident occurred is determined according to the probability distribution of changes in the variability of each functional module in the time dimension and the precision dimension.

[0009] Preferably, the variation value of the functional module of the urban power grid system where the accident occurs is determined according to the variation probability distribution of the variability of each functional module in the time dimension and the precision dimension, including: generating a variation value simulation sample based on the Monte Carlo simulation method according to the variation probability distribution of the variability of each functional module in the time dimension and the precision dimension; wherein the variation value simulation sample contains multiple groups of variation random numbers, each group of variation random numbers includes: The random number of mutation values ​​of the functional modules in the time dimension ; The urban power grid system The random number of mutation values ​​of the functional modules in the precision dimension ; Time variation coefficient , characterizing the first The mutation of the functional module has an impact on the The influence of the functional modules on the time dimension; the accuracy influence coefficient , characterizing the first The mutation of the functional module has an impact on the The impact of each of the functional modules on the accuracy dimension;

[0010] Simulate samples according to the variation value, according to the formula:

[0011]

[0012] Determine the variation value of each functional module; where, The urban power grid system The variation value of the functional module.

[0013] Preferably, the identifying of the key functional module in the functional resonance analysis model includes: in response to a variation value of the functional module being greater than a preset variation threshold, determining that the corresponding functional module is the key functional module.

[0014] Preferably, in step S103, each of the functional modules of the functional resonance analysis model is disassembled respectively, and the obtained submodules and the functional modules are mapped in system dynamics to construct a system dynamics model of the urban power grid system, and an accident consequence node representing an accident occurring in the urban power grid system is added to the system dynamics model;

[0015] An anti-fragility enhancement node representing the anti-fragility measures of the urban power grid system is added to the system dynamics model with the accident consequence node added to obtain an anti-fragility enhancement model of the urban power grid system; wherein one anti-fragility enhancement node is correspondingly set for each of the key functional modules.

[0016] Preferably, the obtained sub-modules and the functional modules are mapped in system dynamics to construct a system dynamics model of the urban power grid system, including: mapping the obtained sub-modules and the functional modules in system dynamics to obtain a causal loop diagram of the urban power grid system; determining the system dynamics equation of each node in the causal loop diagram according to the variation value of each functional module to construct the system dynamics model.

[0017] Preferably, determining the system dynamics equation of each node in the causal loop diagram based on the variation value of each functional module includes: taking the ratio of the variation value of the sub-module to the sum of the variation values ​​of all sub-modules obtained by disassembling the corresponding functional module as the weight of the corresponding sub-module, and constructing the system dynamics equation of the corresponding functional module.

[0018] Preferably, in step S104, the anti-fragility scheme is allocated to the key nodes corresponding to the key functional modules in the anti-fragility improvement model according to different investment ratios to optimize the anti-fragility scheme of the urban power grid system, including: based on the system dynamics model with the accident consequence nodes added, the accident evolution simulation is performed on the urban power grid system to obtain the severity simulation value of the initial accident evolution of the urban power grid system;

[0019] Allocate the anti-fragility scheme to the anti-fragility improvement node corresponding to each key node in the anti-fragility improvement model according to different investment ratios, perform accident evolution simulation on the urban power grid system again, and obtain a severity simulation value of the accident evolution of the urban power grid system again; wherein the key nodes in the anti-fragility improvement model correspond one-to-one to the key functional modules in the functional resonance analysis model;

[0020] The optimal investment ratio of the anti-fragility scheme is determined based on the comparison result of the severity simulation value of the second accident evolution of the urban power grid system and the severity simulation value of the first accident evolution of the urban power grid system.

[0021] The embodiment of the present application also provides an anti-fragility improvement system for a city power grid system, comprising: a functional resonance analysis simulation construction unit, configured to construct a functional resonance analysis model of the city power grid system according to the identified risk factors of the city power grid system;

[0022] A function module identification unit is configured to determine the variation value of the function module when the urban power grid system accident occurs based on the Monte Carlo simulation method, so as to identify the key function modules in the functional resonance analysis model;

[0023] An anti-fragility enhancement model construction unit is configured to disassemble and map the functional modules in the functional resonance analysis model to construct an anti-fragility enhancement model for the urban power grid system;

[0024] The anti-fragility scheme optimization unit is configured to allocate the anti-fragility scheme to the key nodes corresponding to the key functional modules in the anti-fragility improvement model according to different investment ratios, so as to optimize the anti-fragility scheme of the urban power grid system.

[0025] Beneficial effects:

[0026] In the anti-fragility improvement method for the urban power grid system provided in the embodiment of the present application, first, a functional resonance analysis model of the urban power grid system is constructed according to the identified risk factors of the urban power grid system; then, based on the Monte Carlo simulation method, the variation values ​​of the functional modules in the functional resonance analysis model when an accident of the urban power grid system occurs are determined to identify the key functional modules in the functional resonance analysis model; then, the functional modules in the functional resonance analysis model are disassembled to construct an anti-fragility improvement model for the urban power grid system; the anti-fragility scheme is allocated to the key nodes corresponding to the key functional modules in the anti-fragility improvement model according to different investment ratios to optimize the anti-fragility scheme of the urban power grid system.

[0027] Therefore, in view of the complex coupling and dynamic evolution characteristics of each module in the operation of urban power grid, the functional resonance analysis model is used to analyze and identify risks of urban power grid operation. On this basis, the anti-fragility schemes with different input ratios are dynamically evolved in combination with system dynamics, and the effects of different response mechanisms on the recovery of urban power grid are quantified.

[0028] With the help of the functional resonance analysis model, risk assessment based on multi-subject and multi-factor coupling is realized. From the disassembly of functional modules and their sub-modules, causal relationships and node setting references are effectively provided for system dynamics, and a quantitative anti-fragility assessment model for urban power grids is established. Through different input ratios, the dynamic response capability of the power grid in the face of strong random and high-impact events is improved, so as to timely and accurately formulate emergency management "anti-fragility" strategies, optimize emergency management resource allocation and personnel scheduling, enable the power grid to benefit from shocks, and continuously promote the transformation of power grid operations from passive safety to active safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. Among them:

[0030] Figure 1 A schematic diagram of a flow chart of a method for improving anti-fragility of a city power grid system according to some embodiments of the present application;

[0031] Figure 2 A schematic diagram of a functional resonance analysis model of a city power grid system provided according to some embodiments of the present application;

[0032] Figure 3 A schematic diagram of system dynamics of a city power grid system provided according to some embodiments of the present application;

[0033] Figure 4 It is a simulation value of severity of the urban power grid system at different input ratios provided according to some embodiments of the present application;

[0034] Figure 5 A schematic diagram of the structure of an urban power grid system anti-fragility enhancement system provided according to some embodiments of the present application. DETAILED DESCRIPTION

[0035] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. Each example is provided by way of explanation of the present application and does not limit the present application. In fact, it will be clear to those skilled in the art that modifications and variations may be made in the present application without departing from the scope or spirit of the present application. For example, a feature shown or described as a part of an embodiment may be used in another embodiment to produce yet another embodiment. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the embodiments of the present invention should belong to the scope of protection of the embodiments of the present invention.

[0036] The Functional Resonance Analysis Method (FRAM) is an effective method for analyzing the evolution process of accidents and identifying the causal mechanisms of accidents. It has been widely used in shipping, oil and gas, medicine, transportation and other fields. However, the existing FRAM model usually quantifies the output variability of each function based on the judgment of experts. It still relies on the subjective opinions of experts and is greatly affected by human factors, making it impossible to achieve quantitative analysis.

[0037] At present, the evolution of accidents during the operation of urban power grids presents nonlinear coupling and emergence characteristics. The causes of accidents may include multiple dimensions such as customers, equipment, and environment. However, most traditional accident causation theories are linear derivations and cannot perform coupled analysis of multiple accident causes.

[0038] Based on this, this application proposes a method for improving the anti-fragility of urban power grid systems, such as Figures 1 to 4 As shown, the anti-fragility enhancement method includes:

[0039] Step S101: construct a functional resonance analysis model of the urban power grid system according to the identified risk factors of the urban power grid system.

[0040] Based on the collected data files of the urban power grid system, the causes of accidents are sorted out from the aspects of man-machine-environment-management based on the "4M theory", the potential risks of the urban power grid are analyzed, the coupling relationship between the functional modules during the operation of the urban power grid is determined, and the vulnerability factors of the urban power grid system are identified. Specifically, based on the pre-collected data files of the urban power grid system, the functional modules of the urban power grid system are determined, including: power generation module, transmission module, distribution module and power consumption module.

[0041] After determining that the functional modules represent different links involved in the vulnerability of the urban power grid system, the functional modules are divided according to risk characteristics. Specifically, the six aspects of input (I), output (O), time (T), control (C), prerequisite (P) and resources (R) are used to describe the conditions required for the normal operation of the functional modules, that is, the vulnerability factors contained in the functional modules are determined with the help of "input, output, time, control, prerequisite and resources". As shown in Table 1:

[0042] Table 1 Operation conditions of functional modules

[0043] Function No. enter Output Prerequisites resource time control F1 power workers Personnel system investment Comprehensive quality of personnel Personnel's technical and professional level Personnel's safety awareness level Personnel's physical and mental state Eligible Persons - Power management department of power grid enterprises F2 Power Management Department Management system investment Power industry personnel Power equipment Power grid enterprises Organizational structure building Emergency plan command and coordination capabilities Unit Organizational Structure - Safety Supervision Department F3 Power Equipment Equipment system investment Grid customers Equipment maintenance frequency Equipment performance Power grid module equipment - Monitoring system F4 Grid Operation Environment New energy investment Power grid enterprise power equipment Proportion of new energy investment Renewable Resources - Natural Environment

[0044] Among them, the power generation module refers to the use of power production equipment to convert various primary energy or other forms of energy into electrical energy. The "preconditions" of the power generation module include the power system's absorption capacity, resources include primary energy and renewable energy, "control" includes the relevant departments that manage energy and power systems, and "time" includes working days and holidays because working days and holidays will affect power generation. The power generation system needs to deliver electricity to each user end through transmission lines, so the output of the power generation system is connected to the transmission system functional module.

[0045] The transmission module refers to the transmission link of electric energy, which connects power plants and users that are far apart, so that the development and utilization of electric energy transcend geographical limitations. The prerequisites of the transmission module include the normal operation of transmission equipment, resources including towers and low-voltage lines, medium-voltage lines, and high-voltage lines, control including power system maintenance and dispatching departments, and time including working days and holidays as the generation module. However, the transportation of electric energy to the user end through the transmission line requires the use of a substation to convert the electric energy into the voltage level required by the user end. Therefore, the output of the transmission module points to the input of the substation module.

[0046] The power distribution module refers to the process in which a certain device changes the voltage from a low level to a high level (boost) or from a high level to a low level (step-down). Therefore, the prerequisites for the power transformation module include qualified power quality, continuity and reliability of power supply. The power distribution link involves two parts: distribution lines and substations. Therefore, substations and switch stations are used as resources, and time refers to working days and holidays. The power distribution module will deliver the voltage to the user after conversion, so the output of the power distribution module is connected to the input of the power consumption module.

[0047] The power consumption module is the process of consuming electric energy through electrical appliances, and is the last node in the power link. The premise for the normal operation of the power consumption module is that the distribution station operates normally and the supply and demand are balanced. The resources of the power consumption module mainly include new energy clients and traditional clients, and the time refers to weekdays and holidays.

[0048] Finally, based on the description and sorting of the functional modules, the functional modules are connected according to the causal relationship between them to construct a functional resonance analysis model of the urban power grid system, such as Figure 2 shown.

[0049] Step S102: Based on the Monte Carlo simulation method, the variation values ​​of the functional modules in the functional resonance analysis model when the urban power grid system accident occurs are determined to identify the key functional modules in the functional resonance analysis model.

[0050] When an accident occurs in the urban power grid, the functional module is defined to mutate. The variability of the functional module mainly includes two aspects: on the one hand, the variation caused by the change of the functional module itself, and on the other hand, the variation caused by the upstream functional module to the downstream functional module. Whether it is the variation of the functional module itself, or the variation of the downstream functional module caused by the influence of the upstream functional module, it includes the variation of the accuracy dimension and the time dimension. Among them, the time dimension includes four dimensional indicators: on time, too early, too late and not happening, and the accuracy dimension includes four dimensional indicators: accurate, acceptable, inaccurate and completely wrong.

[0051] By determining the variation value of the functional module, the change of the functional module when the urban power grid system accident occurs is quantified. First, when an accident occurs in the urban power grid system, the probability distribution of the variation of each functional module in the functional resonance analysis model in the time dimension and the precision dimension is determined. Specifically, by statistically analyzing the historical data of the urban power grid system accident from the time dimension and the precision dimension, the variation times of each functional module when the urban power grid system accident occurs are determined from the time dimension and the precision dimension respectively, and the ratio of the variation times of the functional module when the accident occurs to the total number of accidents in the urban power grid system is used as the probability distribution of the variation of each functional module in the time dimension and the precision dimension.

[0052] Then, based on the Monte Carlo simulation method, according to the probability distribution of the variability of each functional module in the time dimension and the precision dimension, the variation value of the functional module of the urban power grid system where the accident occurred is determined. Specifically, according to the probability distribution of the variability of each functional module in the time dimension and the precision dimension, based on the Monte Carlo simulation method, a variation value simulation sample is generated.

[0053] In a specific example, according to the probability distribution of the variability of the functional module in the time dimension and the accuracy dimension, the mean and standard deviation of the probability distribution of the time "too late" and the accuracy "acceptable" are obtained; then, a random function (for example: "=NORMINV(RAND(), mean, standard deviation)") is used to randomly generate 1000 groups through the Monte Carlo simulation method. The mutation value simulation sample contains multiple sets of mutation random numbers. Each group of mutation random numbers includes: The random number of mutation values ​​of the functional modules in the time dimension ; Urban power grid system The random number of mutation values ​​of the function modules in the precision dimension ; Time variation coefficient , characterizing the urban power grid system The mutation of the functional module has an impact on The influence of each functional module on the time dimension; the accuracy influence coefficient , characterizing the urban power grid system The mutation of the functional module has an impact on The impact of each functional module on the accuracy dimension. All are positive integers.

[0054] After generating the simulated sample of mutation value, according to the formula:

[0055]

[0056] Determine the variation value of each functional module; where, The first The variation value of a functional module.

[0057] In another specific example, the changes that may occur in the time dimension and the precision dimension of the power generation module, the power transmission module, the power distribution module, and the power consumption module are first analyzed. The changes include the changes that may occur in the functional module itself and the changes that may occur in the upstream module to the downstream module, as shown in Table 2. Table 2 is as follows:

[0058] Table 2 Variation of functional modules

[0059] Serial number Module Name Source of change Changes downstream F1 Power generation module The output of new energy is uncertain and the anti-interference performance of new energy grid connection is poor Transient overvoltage F2 Power transmission module The DC commutation fails due to tight coupling between the sending and receiving ends Large-scale disconnection, short circuit, tripping and oscillation of new energy F3 Power distribution module DC lockout Poses a serious threat to the transient frequency safety of the AC system at the transmitting and receiving ends F4 Power consumption module New load access Power bidirectional reverse flow

[0060] Then, the relevant accidents in the table that occurred in the power grid system in the past five years were counted in the power generation, transmission, transformation, and distribution links, and the distribution probability of changes in the time dimension and accuracy dimension was obtained, as shown in Table 3. Table 3 is as follows:

[0061] Table 3 Change probability distribution

[0062]

[0063] Here, take the coupling change of the upstream functional module F3 to the downstream functional module F4 as an example. If the output time of the F3 power distribution module is "on time", it will have no effect on the F4 power consumption module. At this time, the time variation coefficient 1; if the output time of the power distribution module is "too late", it will affect the normal power consumption time of the power consumption end, causing the power consumption module to be amplified. At this time, the time variation coefficient is 2.

[0064] Finally, the variation values ​​of each functional module based on Monte Carlo simulation are as described in Table 4. Table 4 is as follows:

[0065] Table 4 Variation values ​​of functional modules

[0066]

[0067] After obtaining the variation value of each functional module, the variation value of the obtained functional module is compared with the preset variation threshold. When the variation value of the functional module is greater than the preset variation threshold, the urban power grid system has functional resonance, and the coupling is identified as a vulnerable functional module, that is, the corresponding functional module is determined to be a key functional module. Then, corresponding anti-fragility improvement measures are formulated for the vulnerable functional modules (key functional modules) that are prone to resonance, thereby improving the recovery capability of the urban power grid system to cope with emergencies.

[0068] In the above specific example, the preset mutation threshold is 12. After the Monte Carlo module, it is found that the power generation module and the power consumption module are more fragile and prone to accidents. Considering that the power consumption structure may change due to fluctuations in power generation energy, the subsequent system dynamics model sets anti-fragility improvement measures for the power generation module and the power consumption module.

[0069] Step S103: disassemble the functional modules in the functional resonance analysis model and perform system dynamics mapping to construct an anti-fragility enhancement model for the urban power grid system.

[0070] In this application, a risk assessment based on multi-subject and multi-factor coupling is realized with the help of a functional resonance analysis model. By disassembling functional modules and their sub-modules, a causal relationship and node setting reference are effectively provided for system dynamics, and a quantitative assessment model for anti-fragility of urban power grids is established.

[0071] First, each functional module of the functional resonance analysis model is disassembled separately, and the obtained submodules and functional modules are mapped in the system dynamics to construct the system dynamics model of the urban power grid system. That is, the functional modules of the functional resonance analysis module are transformed into the main nodes in the system dynamics model, and the submodules contained in each functional module are decomposed into more detailed subnodes of the system dynamics model (usually, these subnodes are included in the "preconditions" of each functional module).

[0072] Specifically, the obtained submodules and functional modules are mapped in the system dynamics to obtain the causal loop diagram of the urban power grid system. That is to say, after the functional modules are decomposed into system dynamics variables, the connection mode of each variable in the system dynamics is determined according to the functional resonance analysis module, and the part of the functional module is decomposed from the output variable to the precondition variable. And according to the variation value of each functional module, the system dynamics equation of each node in the causal loop diagram is determined to construct the system dynamics model.

[0073] Among them, the ratio of the variation value of the submodule to the sum of the variation values ​​of all submodules obtained by disassembling the corresponding functional module is used as the weight of the corresponding submodule, and the system dynamics equation of the corresponding functional module is constructed, as shown in Table 5. Table 5 is as follows:

[0074] Table 5 System dynamics equations of functional modules

[0075] Serial number Module Name System dynamics equations F1 Power generation module 0.2*new energy is normal+0.1*traditional energy is normal+0.3*power generation equipment is intact+0.25*primary energy+0.15*renewable energy F2 Power transmission module 0.1*Normal power generation equipment + 0.2*Ratio of high-voltage transmission lines + 0.15*Normal transmission lines + 0.3*Ratio of ultra-high voltage transmission lines + 0.25*Ratio of ultra-high voltage transmission lines F3 Power distribution module 0.25*reliable power supply+0.1*qualified power quality+0.05*substation status+0.2*switch station status+0.4*overhead line status F4 Power consumption module 0.15*normal operation of distribution station+0.05*supply and demand balance+0.35*new energy user end+0.45*traditional user end

[0076] Then, based on the constructed system dynamics model of the urban power grid system, an accident consequence node is added to characterize the accident when the urban power grid system occurs. Specifically, the accident consequence nodes include: accident economic loss nodes and accident economic loss reduction rate nodes. Since the accident economic loss reduction rate will change dynamically over time after the anti-fragility scheme is optimized, the accident economic loss is taken as the stock and the accident economic loss reduction rate is taken as the rate variable. Here, the accident economic loss node is identified in an integral form according to the definition of the system dynamics stock, and the accident economic loss reduction rate node is directly connected to the nodes of the power generation module, the transmission module, the distribution module and the power consumption module. The system dynamics equations of the accident consequence nodes are shown in Table 6. Table 6 is as follows:

[0077] Table 6 System dynamics equations of accident consequence nodes

[0078]

[0079] The consequences of an accident include the actual impact and damage after the accident, including the actual impact or results on relevant personnel, economy, society, etc., which can directly reflect the degree of vulnerability of the system; through the economic losses caused by the accident, the degree of vulnerability and anti-fragility of the system can be displayed with specific figures, and the consequences of the accident can be intuitively quantified for tracking and comparison.

[0080] Finally, an anti-fragility improvement node that represents the anti-fragility measures of the urban power grid system is added to the system dynamics model with the accident consequence node, and the anti-fragility improvement model of the urban power grid system is obtained. Among them, an anti-fragility improvement node is set for each key functional module. Then, by formulating corresponding anti-fragility improvement measures for the vulnerable functional modules (key functional modules) that are prone to resonance, the recovery capacity of the urban power grid system to cope with emergencies is improved.

[0081] Step S104: Allocate the anti-fragility scheme to the key nodes corresponding to the key functional modules in the anti-fragility improvement model according to different investment ratios, so as to optimize the anti-fragility scheme of the urban power grid system.

[0082] In this application, the dynamic response capability of the power grid to strong random high-impact events is improved through different investment ratios, so as to formulate emergency management "anti-fragility" strategies in a timely and accurate manner, optimize emergency management resource allocation and personnel scheduling, enable the power grid to benefit from the impact, and continuously promote the transformation of power grid operations from passive safety to active safety. For example, by allocating different investment plans for personnel, management, equipment, and new energy systems, the dynamic evolution of anti-fragility improvement of urban power grids can be achieved.

[0083] Firstly, based on the system dynamics model with accident consequence nodes added, the accident evolution of the urban power grid system is simulated to obtain the severity simulation value of the initial accident evolution of the urban power grid system; then, the anti-fragility scheme is allocated to the anti-fragility enhancement node corresponding to each key node in the anti-fragility enhancement model according to different investment ratios, and the accident evolution of the urban power grid system is simulated again to obtain the severity simulation value of the secondary accident evolution of the urban power grid system; among them, the key nodes in the anti-fragility enhancement model correspond one to one with the key functional modules in the functional resonance analysis model; finally, according to the comparison results of the severity simulation value of the secondary accident evolution of the urban power grid system and the severity simulation value of the initial accident evolution of the urban power grid system, the optimal investment ratio of the anti-fragility scheme is determined.

[0084] In the constructed anti-fragility improvement model, by changing different input ratios and comparing the severity simulation values ​​of the urban power grid system accident evolution based on the loss data of the anti-fragility improvement model nodes, the optimal investment ratio of the anti-fragility solution is determined to achieve dynamic prediction of the anti-fragility improvement capability.

[0085] Therefore, in view of the complex coupling and dynamic evolution characteristics of each module in the operation of urban power grid, the functional resonance analysis model is used to analyze and identify risks of urban power grid operation. On this basis, the anti-fragility schemes with different input ratios are dynamically evolved in combination with system dynamics, and the effects of different response mechanisms on the recovery of urban power grid are quantified.

[0086] With the help of the functional resonance analysis model, risk assessment based on multi-subject and multi-factor coupling is realized. From the disassembly of functional modules and their sub-modules, causal relationships and node setting references are effectively provided for system dynamics, and a quantitative anti-fragility assessment model for urban power grids is established. Through different input ratios, the dynamic response capability of the power grid in the face of strong random high-impact events is improved, so as to timely and accurately formulate emergency management "anti-fragility" strategies, optimize emergency management resource allocation and personnel scheduling, enable the power grid to benefit from shocks, and continuously promote the transformation of power grid operations from passive safety to active safety.

[0087] like Figure 5 As shown, the embodiment of the present application also provides an urban power grid system anti-fragility improvement system, including:

[0088] A functional resonance analysis simulation construction unit 501 is configured to construct a functional resonance analysis model of the urban power grid system according to the identified risk factors of the urban power grid system;

[0089] The function module identification unit 502 is configured to determine the variation value of the function module when an urban power grid system accident occurs based on the Monte Carlo simulation method, so as to identify the key function modules in the functional resonance analysis model;

[0090] The anti-fragility enhancement model construction unit 503 is configured to disassemble and map the functional modules in the functional resonance analysis model to construct an anti-fragility enhancement model of the urban power grid system;

[0091] The anti-fragility scheme optimization unit 504 is configured to allocate the anti-fragility scheme to the key nodes corresponding to the key functional modules in the anti-fragility improvement model according to different investment ratios, so as to optimize the anti-fragility scheme of the urban power grid system.

[0092] The urban power grid system anti-fragility improvement system provided in the embodiment of the present application can implement the steps and processes of the urban power grid system anti-fragility improvement method of any of the above-mentioned embodiments and achieve the same technical effects, which will not be repeated here one by one.

[0093] In the present invention, the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0094] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for improving the anti-fragility of a city power grid system, characterized in that: include: Step S101: constructing a functional resonance analysis model of the urban power grid system according to the identified risk factors of the urban power grid system; wherein, according to the pre-collected data file of the urban power grid system, the functional modules of the urban power grid system are determined, and the functional modules are divided according to the risk characteristics, and the functional modules are connected according to the cause-effect relationship to construct the functional resonance analysis model; Step S102: Based on the Monte Carlo simulation method, the variation value of the functional module in the functional resonance analysis model when the urban power grid system accident occurs is determined to identify the key functional modules in the functional resonance analysis model; wherein, in response to the accident of the urban power grid system, the probability distribution of the change of the variability of each functional module in the functional resonance analysis model in the time dimension and the accuracy dimension is determined, and based on the Monte Carlo simulation method, the variation value of the functional module of the urban power grid system where the accident occurs is determined; Step S103, disassemble each of the functional modules of the functional resonance analysis model respectively, and map the obtained submodules and the functional modules in system dynamics to construct a system dynamics model of the urban power grid system; and add an accident consequence node representing the accident of the urban power grid system to the system dynamics model, and add an anti-fragility enhancement node representing the anti-fragility measures of the urban power grid system to the system dynamics model with the accident consequence node, to obtain an anti-fragility enhancement model of the urban power grid system; wherein, one anti-fragility enhancement node is correspondingly set for each of the key functional modules; Step S104: Based on the system dynamics model with the accident consequence node added, the accident evolution simulation is performed on the urban power grid system to obtain a severity simulation value of the initial accident evolution of the urban power grid system; Allocate the anti-fragility scheme to the anti-fragility improvement node corresponding to each key node in the anti-fragility improvement model according to different investment ratios, perform accident evolution simulation on the urban power grid system again, and obtain the severity simulation value of the accident evolution of the urban power grid system again; wherein the key nodes in the anti-fragility improvement model correspond one-to-one to the key functional modules in the functional resonance analysis model; The optimal investment ratio of the anti-fragility scheme is determined based on the comparison result of the severity simulation value of the second accident evolution of the urban power grid system and the severity simulation value of the first accident evolution of the urban power grid system.

2. The method for improving the anti-fragility of urban power grid system according to claim 1, characterized in that: The step of determining the variation value of the functional module of the urban power grid system where an accident occurs includes: According to the probability distribution of the variability of each functional module in the time dimension and the precision dimension, a variation value simulation sample is generated based on the Monte Carlo simulation method; wherein the variation value simulation sample contains multiple groups of variation random numbers, each group of variation random numbers includes: The random number of mutation values ​​of the functional modules in the time dimension ; The urban power grid system The random number of mutation values ​​of the functional modules in the precision dimension ; Time variation coefficient , characterizing the first The mutation of the functional module has an effect on the The influence of the functional modules on the time dimension; the accuracy influence coefficient , characterizing the first The mutation of the functional module has an effect on the The impact of each of the functional modules on the accuracy dimension; Simulate samples according to the variation value, according to the formula: ； Determine the variation value of each functional module; where, The urban power grid system The variation value of the functional module.

3. The method for improving the anti-fragility of urban power grid system according to claim 2, characterized in that: The identifying of key functional modules in the functional resonance analysis model includes: In response to the variation value of the functional module being greater than a preset variation threshold, the corresponding functional module is determined to be the key functional module.

4. The method for improving the anti-fragility of urban power grid system according to claim 1, characterized in that: The obtained submodules and the functional modules are mapped in system dynamics to construct a system dynamics model of the urban power grid system, including: Mapping the obtained submodules and the functional modules in system dynamics to obtain a causal loop diagram of the urban power grid system; According to the variation value of each functional module, the system dynamics equation of each node in the causal loop diagram is determined to construct the system dynamics model.

5. The method for improving the anti-fragility of urban power grid system according to claim 4, characterized in that: Determining the system dynamics equation of each node in the causal loop diagram according to the variation value of each functional module includes: The ratio of the variation value of the submodule to the sum of the variation values ​​of all submodules obtained by disassembling the corresponding functional module is used as the weight of the corresponding submodule, and the system dynamics equation of the corresponding functional module is constructed.

6. An urban power grid system anti-fragility improvement system, characterized in that: include: A functional resonance analysis simulation construction unit is configured to construct a functional resonance analysis model of the urban power grid system according to the identified risk factors of the urban power grid system; wherein, according to the pre-collected data file of the urban power grid system, the functional modules of the urban power grid system are determined, and the functional modules are divided according to the risk characteristics, and the functional modules are connected according to the cause-effect relationship to construct the functional resonance analysis model; The function module identification unit is configured to determine the variation value of the function module when the urban power grid system accident occurs based on the Monte Carlo simulation method, so as to identify the key function modules in the functional resonance analysis model; wherein, in response to the accident of the urban power grid system, the change probability distribution of the variability of each function module in the functional resonance analysis model in the time dimension and the accuracy dimension is determined, and based on the Monte Carlo simulation method, the variation value of the function module of the urban power grid system where the accident occurs is determined; The anti-fragility enhancement model construction unit is configured to disassemble each of the functional modules of the functional resonance analysis model respectively, and map the obtained sub-modules and the functional modules in the system dynamics to construct the system dynamics model of the urban power grid system; and add an accident consequence node representing the accident of the urban power grid system to the system dynamics model, and add an anti-fragility enhancement node representing the anti-fragility measures of the urban power grid system to the system dynamics model with the accident consequence node, so as to obtain the anti-fragility enhancement model of the urban power grid system; wherein, one anti-fragility enhancement node is correspondingly set for each of the key functional modules; An anti-fragility scheme optimization unit is configured to simulate the accident evolution of the urban power grid system based on a system dynamics model in which the accident consequence node is added, and obtain a severity simulation value of the initial accident evolution of the urban power grid system; Allocate the anti-fragility scheme to the anti-fragility improvement node corresponding to each key node in the anti-fragility improvement model according to different investment ratios, perform accident evolution simulation on the urban power grid system again, and obtain the severity simulation value of the accident evolution of the urban power grid system again; wherein the key nodes in the anti-fragility improvement model correspond one-to-one to the key functional modules in the functional resonance analysis model; The optimal investment ratio of the anti-fragility scheme is determined based on the comparison result of the severity simulation value of the second accident evolution of the urban power grid system and the severity simulation value of the first accident evolution of the urban power grid system.

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