Natural disaster early warning method, system and equipment for integrated energy system and medium

Abstract

The invention belongs to the technical field of disaster early warning, and discloses a natural disaster early warning method, system, equipment and medium for an integrated energy system, and the method comprises the steps: analyzing a frequent item set and an association mode between a catastrophe scene and a data set of the integrated energy system through a big data association analysis method; constructing a spatial distribution network of catastrophe types according to an analysis result, screening coupling nodes with frequent occurrence of disaster-inducing factors and vulnerability of the integrated energy system based on a spatial angle, and performing monitoring and early warning according to the coupling nodes; and establishing an index system based on a monitorable index and a measurable index of the operation of the integrated energy system, and analyzing an optimal risk prevention and control strategy in different risk environments according to the index system. The system operation efficiency can be improved, and the local power network can be better served.

Description

Technical Field

[0001] The present invention relates to the field of disaster early warning technology, and in particular to a natural disaster early warning method, system, equipment and medium for an integrated energy system. Background Art

[0002] In recent years, power outages and power grid failures caused by natural disasters have occurred frequently. Domestic and foreign scholars have conducted extensive research on emergency management of power systems caused by emergencies such as sudden disasters and sudden weather changes. Domestic scholars have conducted extensive research on natural disaster risks and have formed a systematic research system. Based on historical data, some scholars have proposed a fault probability prediction model that takes into account weather, environment, and human factors. The rare event regression model is used to simulate the impact of weather on faults, and the feeder classification and hierarchical clustering methods are used to simulate the impact of geographical environment on faults. Although the principle of the statistical resilience assessment method is relatively simple, the accuracy of this method is difficult to guarantee because there are few historical data on natural disasters themselves, and there may be differences between future natural disaster data and historical data. Some scholars use graph theory methods to obtain indicators such as the number of power supply branches, the number of overlapping branches, and the node centrality, and use Choquet integrals to process the above indicators to obtain the overall resilience level of the system. The sampling simulation method is based on the mechanism of the impact of natural disasters on components, samples possible fault scenarios in disasters, and simulates the performance of the system during and after the disaster through the disaster response model. Some scholars have also faced the problem of elasticity assessment of electric-gas IES. Based on the historical data of extreme disasters, they have generated a Markov state transfer matrix to describe the changes in the state of components during disasters, and then used the Monte Carlo sampling method to assess the elasticity level of electric-gas IES. However, the formation of the Markov state transfer matrix proposed in this document depends on historical data, so the correctness of the assessment results cannot be guaranteed. Some foreign scholars have considered the time-varying load demand and the output of new energy units, established a system response model, and constructed a non-sequential Monte Carlo sampling simulation method.

[0003] Analyzing the impact mechanism of natural disasters on components and establishing a component failure probability model under disasters can reduce the dependence of resilience assessment methods on historical data. At present, most of the research on the impact of natural disasters on the system is concentrated on the power system. In order to analyze the anti-disturbance ability of the power system in the face of various natural disasters, scholars at home and abroad have analyzed the impact of various natural disasters on the power system, including wildfires, strong winds, snow disasters, heavy rains and landslides caused by them. A university studied the tripping mechanism of power transmission lines under wildfire conditions, realized the quantitative estimation of wildfires on transmission lines, and constructed a probability calculation model for wildfire tripping of transmission lines. On the basis of the above, the influence mechanism of icing on transmission lines in freezing disasters was studied, and based on this, an intelligent assessment model for icing risk of transmission lines was proposed. Some scholars have established a model of weather-related line icing and a model of line failure rate under icing load, and established resilience indicators from three perspectives: the severity of the disaster, the ability of the distribution network to recover urgently, and the overall recovery capacity of the system. Foreign scholars have analyzed the impact of typhoons and rainstorms on power equipment. Typhoons can cause disconnection of transmission lines, tower collapse, wind deflection flashover, and foreign objects hanging. The article evaluates the failure rates of the above four types of faults in typhoon weather and integrates them to obtain a comprehensive failure rate. At present, there are relatively few studies on the failure probability of gas grid components under natural disasters. Compared with the overhead lines of the power grid, natural gas pipelines are mostly buried pipelines, which have a certain ability to resist surface natural disasters such as typhoons and blizzards, but they still fail in geological disasters such as earthquakes.

[0004] At the same time, some domestic and foreign scholars have formed empirical pre-processing plans for impending or already occurring power grid disasters based on the power system itself; and based on the above research, they have carried out research on early warning decision support systems, and some power grid companies have built corresponding power emergency management or disaster prevention and mitigation platforms in accordance with relevant requirements. However, as an important energy supply unit, the integrated energy system rarely has reliable emergency protection plans or technologies for emergency situations such as sudden disasters and sudden weather changes.

[0005] Therefore, how to provide a natural disaster early warning method, system, equipment and medium for an integrated energy system is a problem that needs to be solved urgently. Summary of the invention

[0006] The embodiments of the present invention provide a natural disaster early warning method, system, device and medium for an integrated energy system to solve the above-mentioned technical problems in the prior art.

[0007] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0008] According to a first aspect of an embodiment of the present invention, a natural disaster early warning method for an integrated energy system is provided.

[0009] In one embodiment, the comprehensive energy system natural disaster early warning method includes:

[0010] Using big data association analysis, we analyze the frequent item sets and association patterns between the disaster scenarios and data sets of the integrated energy system;

[0011] Based on the analysis results, a spatial distribution network of disaster types is constructed, coupling nodes between frequent disaster-causing factors and the vulnerability of the integrated energy system are screened from a spatial perspective, and monitoring and early warning are carried out based on the coupling nodes;

[0012] An indicator system is established based on the monitorable and measurable indicators of the operation of the integrated energy system, and the optimal risk prevention and control strategies under different risk environments are analyzed based on the indicator system.

[0013] In one embodiment, the use of big data association analysis to analyze frequent item sets and association patterns between the disaster scenario of the integrated energy system and the data set includes:

[0014] Analyze the spatiotemporal distribution of long-term static historical data of disaster-causing factors in the integrated energy system, integrate historical information on the time intervals of disasters in the integrated energy system, form a structured data warehouse, and extract monitoring data features under fault scenarios in integrated energy system disasters;

[0015] The big data association analysis method is used to analyze the frequent item sets and association patterns between the disaster scenarios of the integrated energy system and the data sets, combined with the monitoring values ​​at the time nodes of the fault occurrence.

[0016] In one embodiment, the construction of a spatial distribution network of disaster types based on the analysis results, screening coupling nodes of frequent disaster-causing factors and vulnerability of the integrated energy system based on a spatial perspective, and monitoring and early warning based on the coupling nodes include:

[0017] Based on the statistical analysis results of the spatial occurrence patterns of disaster-causing factors, combined with the disaster-prone environment and historical scenario factors, a spatial distribution network of disaster types is established;

[0018] By analyzing the spatial vulnerable node distribution of the spatial distribution network from the perspective of integrated energy system disasters, a network of integrated energy system disaster vulnerable nodes is formed, and the coupling relationship of the network is analyzed from the perspectives of spatial distribution, physical connectivity, and network structure;

[0019] Based on the spatial perspective, the coupling nodes with frequent disaster-causing factors and that meet the vulnerability requirements of the integrated energy system are screened, and monitoring and early warning are carried out based on the coupling nodes.

[0020] In one embodiment, the calculation formula of the failure rate under typhoon disaster is:

[0021]

[0022] V N =K V V r

[0023]

[0024] In the formula, λ et is the line failure rate under typhoon operation conditions, v is the wind speed at the line research point, V N is the line design wind speed, β is the fragility curve coefficient, K V is the correction factor, V r is the wind speed of the typhoon in the area, T typ To adjust the wind speed distribution parameters, r is the distance from the typhoon center, V max is the maximum wind speed of the typhoon, R max It is the maximum wind speed radius of the typhoon.

[0025] In one embodiment, the failure rate under earthquake disaster includes the failure rate of power distribution lines, the failure rate of natural gas pipelines and the failure rate of heat network pipelines;

[0026] Among them, the calculation formula for the failure rate of distribution lines under earthquake disasters is:

[0027]

[0028] The calculation formula for the failure rate of natural gas pipelines under earthquake disasters is:

[0029]

[0030] The calculation formula of the failure rate of heat network pipelines under earthquake disaster is:

[0031]

[0032] The expression of the failure rate of the integrated energy system under earthquake disaster is:

[0033]

[0034] In the formula, λ e is the failure rate of distribution lines under earthquake disaster, Π is the product operation, P fi is the failure probability of the ith tower, n is the number of towers of the distribution line, λ g is the failure rate of natural gas pipelines under earthquake disasters, R g is the natural gas pipeline earthquake damage rate, L g is the length of the natural gas pipeline, e is the base of the natural logarithm, I MM is the earthquake intensity, C d is the pipe diameter influence coefficient, C g is the site soil influence coefficient, λ h is the failure rate of heat network pipelines under earthquake disaster, R h is the damage rate of heat network pipelines, L h is the pipeline length of the heating network pipeline, λ IES is the failure rate of the comprehensive energy system under earthquake disaster, is the k-order subset of A, A is the set of all components, N is the number of components in the integrated energy system, λ i is the failure rate of each component in the comprehensive energy system, s is An element in , Card(u) is the number of faulty devices included in state u.

[0035] In one embodiment, the indicator system is established based on the monitorable indicators and measurable indicators of the operation of the integrated energy system, and the optimal risk prevention and control strategy under different risk environments is analyzed according to the indicator system, including:

[0036] Establish an indicator system based on the monitorable and measurable indicators of the operation of the integrated energy system, and use the indicator system to evaluate the ability of active prevention and control measures to prevent and control system disaster risks;

[0037] Quantitatively calculate the level and grade of disaster risk reduction after the implementation of various risk prevention and control measures, analyze the prevention and control sensitivity and effect of various active prevention and control measures on various disaster risks, and determine the optimal risk prevention and control strategy under different risk environments.

[0038] In one embodiment, the index system includes a robustness index, a rapidity index, and a redundancy index;

[0039] Among them, the expression of robustness index is:

[0040] R = f i ·L R

[0041] f i =P i ·λ

[0042]

[0043] The expression of the rapidity index is:

[0044] Δt=t3-t1

[0045] The expression of the redundancy index is:

[0046]

[0047] In the formula, R is the robustness index, f i is the frequency of occurrence of fault condition i under disaster, L R is the load loss ratio of the comprehensive energy system, P i is the probability of fault type i occurring under disaster conditions, λ is the failure rate of distribution lines or pipelines, are the load loss of the power subsystem, natural gas subsystem and thermal subsystem when coupled under extreme natural disaster x, are the total loads of the power subsystem, natural gas subsystem and thermal subsystem, respectively; Δt is the time from the beginning of the extreme natural disaster to the system returning to normal; t3 is the time from the beginning of the extreme natural disaster to the system returning to normal; t1 is the time when the extreme natural disaster occurs; ξ k is the redundancy index, N MCS is the total number of fault samples of each order, k is the natural connectivity of the integrated energy system when it is in fault state k, w is the fault number, γ k,w is the natural connectivity, γ k It is a part of the redundancy metric used to evaluate the redundancy of a node or component. s is the total number of nodes in the integrated energy system, N CTG is the fault state order for accurate calculation, is the characteristic root of the adjacency matrix A of the integrated energy system, v i 、v j are the positions of nodes i and j respectively, and E(G) is the branch set of the system graph G.

[0048] In one embodiment, the calculation of the robustness index includes:

[0049] Step 1, input the preset parameters of each subsystem, natural disasters and system fault order in the integrated energy system; Step 2, initialize the fault set order; Step 3, calculate the k-order state set; Step 4, select a k-order system state s from the k-order state set; Step 5, calculate the optimal load reduction amount of the integrated energy electricity / gas / heat subsystem in fault state s; Step 6, determine whether all states in the k-order state set have been analyzed, if so, jump to step 7, if not, return to step 4; Step 7, determine whether the order is equal to the number of components in the integrated energy system, if so, jump to step 8, if not, set k=k+1, and return to step 3; Step 8, calculate the failure rate of each component of the integrated energy electricity / gas / heat system; Step 9, calculate the fault frequency index of the integrated energy system; Step 10, calculate the load loss ratio of the integrated energy system; Step 11, calculate and output the robustness index of the integrated energy system;

[0050] The calculation of the rapidity index includes:

[0051] Step 1, input the time when the natural disaster occurs, the time when the system starts to repair, and the total number of faulty components in the integrated energy system, and count the total number of faulty components in each subsystem and the repair time of each subsystem component; Step 2, determine whether the faulty components of the integrated energy system are all from the same system, if so, go to step 3, otherwise go to step 6; Step 3, initialize the repair order of the faulty components and the total repair time of the system; Step 4, calculate the total repair time of the integrated energy system, Step 5, determine whether the order is equal to the number of components in the integrated energy system, if so, go to step 13, if not, set k=k+1, and return to step 4; Step 6, initialize the total repair time of each subsystem; Step 7, determine whether the faulty components are from two systems, if so, go to step 8, if not, go to step 11; Step 8, calculate the total repair time of the two faulty systems respectively; Step 9, calculate the maximum value of the repair time of the two faulty systems respectively, and go to step 13; Step 11, calculate the total repair time of the three faulty systems respectively; Step 12, calculate the maximum value of the repair time of the three faulty systems respectively; Step 13, calculate and output the total recovery time of the integrated energy system;

[0052] The calculation of redundancy index includes:

[0053] Step 1, input the data of the integrated energy system and the total number of nodes, set the fault state order and the total number of fault samples of each order; Step 2, initialize the fault set order, fault number, and node number; Step 3, create a k-order state set; Step 4, establish the adjacency matrix A after the fault i of the integrated energy system occurs; Step 5, calculate the characteristic root of the adjacency matrix of the integrated energy system at this time; Step 6, determine whether the node number i is equal to the total number of nodes of the integrated energy, if so, go to step 7, if not, let i=i+1, and return to step 4; Step 7, calculate the natural connectivity of the integrated energy system at this time; Step 8: Determine whether the fault number is equal to the total number of fault samples of each order, if so, go to step 9, if not, let w=w+1, and return to step 4; Step 9, determine whether the fault set order is equal to the fault state order, if so, go to step 10, if not, let k=k+1, and return to step 3; Step 10, calculate the mean of the natural connectivity of the system integrated energy system in each order of fault state; Step 11, output the natural connectivity of each order of the integrated energy system.

[0054] According to a second aspect of an embodiment of the present invention, a natural disaster early warning system for an integrated energy system is provided.

[0055] In one embodiment, the comprehensive energy system natural disaster early warning system includes:

[0056] Data association analysis module, used to analyze frequent item sets and association patterns between disaster scenarios and data sets of integrated energy systems using big data association analysis methods;

[0057] The monitoring and early warning module is used to construct a spatial distribution network of disaster types based on the analysis results, screen the coupling nodes of frequent disaster-causing factors and the vulnerability of the integrated energy system from a spatial perspective, and conduct monitoring and early warning based on the coupling nodes;

[0058] The risk prevention and control strategy analysis module is used to establish an indicator system based on the monitorable and measurable indicators of the operation of the integrated energy system, and analyze the optimal risk prevention and control strategy under different risk environments based on the indicator system.

[0059] In one embodiment, when the data association analysis module uses the big data association analysis method to analyze the frequent item sets and association patterns between the disaster scenarios and data sets of the integrated energy system, it analyzes the spatiotemporal distribution patterns of the long-term static historical data of disaster-causing factors of the integrated energy system, integrates the historical information of the time intervals of disaster occurrence in the integrated energy system, forms a structured data warehouse, and extracts the monitoring data characteristics of the fault scenarios in the disasters of the integrated energy system; uses the big data association analysis method, combined with the monitoring values ​​at the time nodes of the fault occurrence, to analyze the frequent item sets and association patterns between the disaster scenarios and data sets of the integrated energy system.

[0060] In one embodiment, the monitoring and early warning module constructs a spatial distribution network of disaster types according to the analysis results, screens the coupling nodes of frequent disaster-causing factors and the vulnerability of the integrated energy system based on a spatial perspective, and performs monitoring and early warning based on the coupling nodes. Based on the statistical analysis results of the spatial occurrence laws of disaster-causing factors, combined with the disaster-prone environment and historical scenario factors, a spatial distribution network of disaster types is established; the spatial vulnerable node distribution of the spatial distribution network is analyzed from the perspective of integrated energy system disasters to form an integrated energy system disaster vulnerable node network, and the coupling relationship of the network is analyzed from the perspectives of spatial distribution, physical connectivity, and network structure; based on a spatial perspective, the coupling nodes of frequent disaster-causing factors and those that meet the vulnerability requirements of the integrated energy system are screened, and monitoring and early warning are performed based on the coupling nodes.

[0061] In one embodiment, the calculation formula of the failure rate under typhoon disaster is:

[0062]

[0063] V N =K V V r

[0064]

[0065] The failure rates under earthquake disasters include the failure rates of power distribution lines, natural gas pipelines, and heat network pipelines;

[0066] Among them, the calculation formula for the failure rate of distribution lines under earthquake disasters is:

[0067]

[0068] The calculation formula for the failure rate of natural gas pipelines under earthquake disasters is:

[0069]

[0070] The calculation formula of the failure rate of heat network pipelines under earthquake disaster is:

[0071]

[0072] The expression of the failure rate of the integrated energy system under earthquake disaster is:

[0073]

[0074] In the formula, λ et is the line failure rate under typhoon operation conditions, v is the wind speed at the line research point, V N is the line design wind speed, β is the fragility curve coefficient, K V is the correction factor, V r is the wind speed of the typhoon in the area, Ttyp To adjust the wind speed distribution parameters, r is the distance from the typhoon center, V max is the maximum wind speed of the typhoon, R max is the maximum wind speed radius of the typhoon, λ e is the failure rate of distribution lines under earthquake disaster, Π is the product operation, P fi is the failure probability of the ith tower, n is the number of towers of the distribution line, λ g is the failure rate of natural gas pipelines under earthquake disasters, R g is the natural gas pipeline earthquake damage rate, L g is the length of the natural gas pipeline, e is the base of the natural logarithm, I MM is the earthquake intensity, C d is the pipe diameter influence coefficient, C g is the site soil influence coefficient, λ h is the failure rate of heat network pipelines under earthquake disaster, R h is the damage rate of heat network pipelines, L h is the length of the heat network pipeline, λ IES is the failure rate of the comprehensive energy system under earthquake disaster, is the k-order subset of A, A is the set of all components, N is the number of components in the integrated energy system, λ i is the failure rate of each component in the comprehensive energy system, s is An element in , Card(u) is the number of faulty devices included in state u.

[0075] In one embodiment, the risk prevention and control strategy analysis module establishes an indicator system based on the monitorable indicators and measurable indicators of the operation of the integrated energy system, and analyzes the optimal risk prevention and control strategy under different risk environments based on the indicator system. The indicator system is established based on the monitorable indicators and measurable indicators of the operation of the integrated energy system, and the indicator system is used to evaluate the ability of active prevention and control measures to prevent and control system disaster risks; quantitatively calculate the level and grade of disaster risk reduction after the execution of each risk prevention and control measure, and analyze the prevention and control sensitivity and effect of each active prevention and control measure on various disaster risks, so as to determine the optimal risk prevention and control strategy under different risk environments.

[0076] In one embodiment, the index system includes a robustness index, a rapidity index, and a redundancy index;

[0077] Among them, the expression of robustness index is:

[0078] R = f i ·L R

[0079] f i =P i ·λ

[0080]

[0081] The expression of the rapidity index is:

[0082] Δt=t3-t1

[0083] The expression of the redundancy index is:

[0084]

[0085] In the formula, R is the robustness index, f i is the frequency of occurrence of fault condition i under disaster, L R is the load loss ratio of the comprehensive energy system, P i is the probability of fault type i occurring under disaster conditions, λ is the failure rate of distribution lines or pipelines, are the load loss of the power subsystem, natural gas subsystem and thermal subsystem when coupled under extreme natural disaster x, are the total loads of the power subsystem, natural gas subsystem and thermal subsystem, respectively; Δt is the time from the beginning of the extreme natural disaster to the system returning to normal; t3 is the time from the beginning of the extreme natural disaster to the system returning to normal; t1 is the time when the extreme natural disaster occurs; ξ k is the redundancy index, N MCS is the total number of fault samples of each order, k is the natural connectivity of the integrated energy system when it is in fault state k, w is the fault number, γ k,w is the natural connectivity, γ k It is a part of the redundancy metric used to evaluate the redundancy of a node or component. s is the total number of nodes in the integrated energy system, N CTG is the fault state order for accurate calculation, is the characteristic root of the adjacency matrix A of the integrated energy system, v i 、v j are the positions of nodes i and j respectively, and E(G) is the set of branches of the system graph G;

[0086] The calculation of robustness indicators includes:

[0087] Step 1, input the preset parameters of each subsystem, natural disasters and system fault order in the integrated energy system; Step 2, initialize the fault set order; Step 3, calculate the k-order state set; Step 4, select a k-order system state s from the k-order state set; Step 5, calculate the optimal load reduction amount of the integrated energy electricity / gas / heat subsystem in fault state s; Step 6, determine whether all states in the k-order state set have been analyzed, if so, jump to step 7, if not, return to step 4; Step 7, determine whether the order is equal to the number of components in the integrated energy system, if so, jump to step 8, if not, set k=k+1, and return to step 3; Step 8, calculate the failure rate of each component of the integrated energy electricity / gas / heat system; Step 9, calculate the fault frequency index of the integrated energy system; Step 10, calculate the load loss ratio of the integrated energy system; Step 11, calculate and output the robustness index of the integrated energy system;

[0088] The calculation of the rapidity index includes:

[0089] Step 1, input the time when the natural disaster occurs, the time when the system starts to repair, and the total number of faulty components in the integrated energy system, and count the total number of faulty components in each subsystem and the repair time of each subsystem component; Step 2, determine whether the faulty components of the integrated energy system are all from the same system, if so, go to step 3, otherwise go to step 6; Step 3, initialize the repair order of the faulty components and the total repair time of the system; Step 4, calculate the total repair time of the integrated energy system, Step 5, determine whether the order is equal to the number of components in the integrated energy system, if so, go to step 13, if not, set k=k+1, and return to step 4; Step 6, initialize the total repair time of each subsystem; Step 7, determine whether the faulty components are from two systems, if so, go to step 8, if not, go to step 11; Step 8, calculate the total repair time of the two faulty systems respectively; Step 9, calculate the maximum value of the repair time of the two faulty systems respectively, and go to step 13; Step 11, calculate the total repair time of the three faulty systems respectively; Step 12, calculate the maximum value of the repair time of the three faulty systems respectively; Step 13, calculate and output the total recovery time of the integrated energy system;

[0090] The calculation of redundancy index includes:

[0091] Step 1, input the data of the integrated energy system and the total number of nodes, set the fault state order and the total number of fault samples of each order; Step 2, initialize the fault set order, fault number, and node number; Step 3, create a k-order state set; Step 4, establish the adjacency matrix A after the fault i of the integrated energy system occurs; Step 5, calculate the characteristic root of the adjacency matrix of the integrated energy system at this time; Step 6, determine whether the node number i is equal to the total number of nodes of the integrated energy, if so, go to step 7, if not, let i=i+1, and return to step 4; Step 7, calculate the natural connectivity of the integrated energy system at this time; Step 8: Determine whether the fault number is equal to the total number of fault samples of each order, if so, go to step 9, if not, let w=w+1, and return to step 4; Step 9, determine whether the fault set order is equal to the fault state order, if so, go to step 10, if not, let k=k+1, and return to step 3; Step 10, calculate the mean of the natural connectivity of the system integrated energy system in each order of fault state; Step 11, output the natural connectivity of each order of the integrated energy system.

[0092] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0093] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0094] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0095] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0096] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0097] The present invention can improve the consumption of renewable energy, smooth the output, and reduce the phenomenon of abandoned solar and wind power on the power generation side; the energy storage power station connected from the grid side can provide power support, play the role of peak shaving and valley filling, and increase system reserve, thereby improving the safety and stability of the power system; for the user side, it can be used for emergency, backup, and smoothing power fluctuations, etc., so as to ensure the quality of electric energy. It can be seen that the fast switching of charging and discharging modes and the flexible and controllable power regulation technical characteristics of the energy storage power station / device itself enable it to provide different types and values ​​of services for grid operation and dispatching. By regulating the system through relevant technologies, the system operation efficiency can be further improved to better serve the local power network.

[0098] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0100] Figure 1 is a flow chart showing a natural disaster early warning method for an integrated energy system according to an exemplary embodiment;

[0101] Figure 2 is a structural block diagram of a natural disaster early warning system for an integrated energy system according to an exemplary embodiment;

[0102] Figure 3 is a schematic diagram of a natural disaster early warning system according to an exemplary embodiment;

[0103] Figure 4 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0104] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0105] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0106] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0107] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0108] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0109] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0110] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0111] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0112] Figure 1An embodiment of a natural disaster early warning method for an integrated energy system of the present invention is shown.

[0113] In this optional embodiment, the comprehensive energy system natural disaster early warning method includes:

[0114] Step S101, using a big data association analysis method to analyze frequent item sets and association patterns between the disaster scenario of the integrated energy system and the data set;

[0115] Step S103: construct a spatial distribution network of disaster types according to the analysis results, screen coupling nodes of frequent disaster-causing factors and vulnerability of the integrated energy system from a spatial perspective, and perform monitoring and early warning according to the coupling nodes;

[0116] Step S105: Establish an indicator system based on the monitorable indicators and measurable indicators of the operation of the integrated energy system, and analyze the optimal risk prevention and control strategies under different risk environments based on the indicator system.

[0117] Figure 2 An embodiment of a natural disaster early warning system for an integrated energy system of the present invention is shown.

[0118] In this optional embodiment, the comprehensive energy system natural disaster early warning system includes:

[0119] The data association analysis module 201 is used to analyze the frequent item sets and association patterns between the disaster scenario of the integrated energy system and the data set by using the big data association analysis method;

[0120] The monitoring and early warning module 203 is used to construct a spatial distribution network of disaster types based on the analysis results, screen the coupling nodes of frequent disaster-causing factors and the vulnerability of the integrated energy system from a spatial perspective, and perform monitoring and early warning based on the coupling nodes;

[0121] The risk prevention and control strategy analysis module 205 is used to establish an indicator system based on the monitorable indicators and measurable indicators of the operation of the integrated energy system, and analyze the optimal risk prevention and control strategy under different risk environments according to the indicator system.

[0122] In order to facilitate understanding of the above technical solutions of the present invention, the above technical solutions of the present invention are further described below from the perspective of architecture and principle, as follows:

[0123] Pre-disaster prevention is one of the most effective ways to enhance the system's resistance and improve its resilience. Line reinforcement is the main means of pre-disaster prevention. Through reinforcement, the probability of line failure in disasters can be reduced, thereby improving the system's performance in disasters. However, blindly reinforcing the line will lead to a waste of resources. Therefore, how to effectively resist disasters with limited reinforcement costs is the focus of pre-disaster prevention strategies. Considering the uncertainty of failures in disasters and post-disaster operation in pre-disaster prevention strategies is the key to solving the above problems. Combined with the natural disaster early warning management and early warning technology of the power grid, considering the location, importance and other characteristics of the source-grid-load-storage integrated energy system in the system energy supply, a high-reliability emergency guarantee system for the source-grid-load-storage integrated energy system in the face of natural disasters and other emergencies is proposed.

[0124] Predicting and evaluating the impact of disasters on the power system includes guiding the allocation of emergency materials, completing system pre-control and other physical prevention measures. However, predictions and evaluations are often inaccurate, which affects the effectiveness of system prevention measures. Therefore, how to analyze the relationship between the uncertainty of prediction and evaluation and the effectiveness of power system prevention measures is the basis for improving the resilience of the power system based on the information level from the perspective of pre-disaster prevention. For natural disasters, the natural disaster early warning management of the source-grid-load-storage integrated energy system has built a four-dimensional model of natural disaster early warning management, including the main body of natural disaster objects, the characteristics of the disaster evolution process, the early warning management method and the early warning information technology, which correspond to the object dimension, evolution dimension, early warning method dimension and early warning information technology dimension respectively.

[0125] (1) Natural disaster early warning methods:

[0126] The specific early warning management methods of natural disaster early warning methods include hidden danger identification, situation prediction, critical warning and disaster countermeasure selection. Natural disaster hidden danger identification is mainly used for early warning identification of disaster systems. In the early stage of natural disasters in the source-grid-load-storage integrated energy system, the hazard factors cannot be directly monitored. Hidden danger identification is the best early warning management method at this time. The natural disaster situation prediction of the source-grid-load-storage integrated energy system is the identification of the development and evolution of natural disasters in the power grid, or the changes in the coupling relationship of various elements. It is one of the main early warning management methods after the mid-term hazard factors of natural disasters in the source-grid-load-storage integrated energy system have been formed. It is an in-depth analysis based on the monitoring of the natural disaster system of the source-grid-load-storage integrated energy system. The critical warning of natural disasters in the source-grid-load-storage integrated energy system refers to the process of warning natural disasters that may threaten the safety of the source-grid-load-storage integrated energy system based on the monitoring or prediction of natural disasters in the source-grid-load-storage integrated energy system, combined with the pre-set early warning evaluation criteria. Critical warning is one of the links of early warning management. A series of work such as monitoring and prediction ultimately serves the early warning. The selection of natural disaster countermeasures for power grids is the process of natural disaster early warning decision-making for power grids. It is the process of selecting the optimal disaster countermeasures based on the natural disaster early warning of the source-grid-load-storage integrated energy system and the development and evolution characteristics of disasters. In this process, it is necessary not only to consider the static information currently perceived, but also to consider various possible scenarios in the future, so as to select the best countermeasures.

[0127] In the face of emergencies such as natural disasters, first of all, it is necessary to study and analyze the temporal and spatial distribution patterns of long-term static historical data of disaster-causing factors (disaster-causing factors include natural disasters, system load imbalance, energy storage system problems, and power market fluctuations) in the source-grid-load-storage integrated energy system, including the collection of disaster-causing factor data; data cleaning and standardization; combining disaster-causing factor data with the geographical location of the power system (such as power plants, substations, load centers, and energy storage equipment locations) to form a geographic information database; spatial and temporal data visualization: using a geographic information system (GIS) to draw a spatial heat map of the occurrence of disasters to see whether disaster events have obvious spatial aggregation. For example, use a heat map to represent the disaster risk level of each region in the power system; statistically analyze the correlation between disaster events and power system failures; combine disaster data with power system failure data to establish a spatiotemporal regression model to analyze the impact of disasters on the power system.

[0128] Then, the historical information such as dispatching information, load data, maintenance data and micro-meteorological data in the disaster time interval of the source-grid-load-storage integrated energy system is integrated, the numerical information related to the meteorological forecast of the meteorological department is obtained, and the relevant data obtained by the Internet of Things monitoring equipment is considered to form a structured data warehouse under the conditions of big data and cloud computing. The characteristics of the monitoring data such as voltage and frequency of the fault scene in the disaster of the source-grid-load-storage integrated energy system are sorted out, and the monitoring values ​​at the time of the fault occurrence are considered to build a big data association analysis algorithm to analyze the frequent item sets (frequent item sets refer to events, states or conditions that often appear together in the system) and association patterns between the disaster scenes of the key source-grid-load-storage integrated energy system and each data set (data set refers to the source-grid-load-storage, that is, the various data collected during the operation of the "generation source, distribution network, load and energy storage system" in the power system). Specifically, it uses association rule mining technology to discover frequent item sets and association patterns between disaster scenes (such as system overload, equipment failure, natural disasters, etc.) and key events based on the time node of the fault occurrence and its monitoring values, providing a basis for the safety, reliability and optimal scheduling of the power system. Using the big data association analysis method, the frequent item sets and association patterns between the disaster scenarios of the source-grid-load-storage system and the data set are analyzed, including the calculation of the failure rate under typhoon disasters and the calculation of the failure rate under earthquake disasters.

[0129] Next, based on the statistical analysis of the spatial occurrence law of disaster-causing factors, considering the disaster-prone environment, historical scenarios (disaster-prone environment and historical scenarios include failure of energy storage equipment, charging / discharging imbalance or insufficient capacity, etc., which may not be effectively adjusted at the peak of power grid load, resulting in increased system pressure. If there is a problem with the scheduling algorithm or control system of the energy storage system, it may also lead to a decrease in energy storage efficiency, thereby affecting the stability of the system. Natural disasters such as typhoons, blizzards, and lightning may cause damage to power grid equipment, power generation facilities and transmission lines) and other factors, a spatial distribution network of disaster types is established. From the perspective of disasters in the integrated energy system, the distribution of system spatial vulnerable nodes is studied to form a disaster vulnerable node network of the source-grid-load-storage integrated energy system. From the perspectives of spatial distribution, physical connectivity, and network structure, the coupling relationship of the network is analyzed, and from the perspective of spatial distribution, physical connectivity, and network structure, coupling nodes with frequent occurrence of disaster-causing factors and strong vulnerability of the source-grid-load-storage integrated energy system are given. Specifically, coupling nodes refer to the key nodes in the system where multiple subsystems (such as power generation, load, energy storage, network, etc.) intersect and influence each other in space. These nodes connect different system functions closely in space or physically, and are therefore high-risk areas for disaster risk or parts of the system with high vulnerability. Coupling nodes usually refer to those points where multiple potential disaster-causing factors (such as weather, load fluctuations, equipment failures, etc.) act simultaneously under certain conditions and may cause disasters, and they are subject to key monitoring and early warning.

[0130] Finally, in terms of monitorable indicators such as voltage, frequency, load loss range, and measurable indicators such as reliability of the source-grid-load-storage integrated energy system, an indicator system is established to evaluate the ability of the above-mentioned active prevention and control measures to prevent and control system disaster risks (i.e., calculate the failure rate under typhoon disasters and the failure rate under earthquake disasters), quantitatively calculate the level and grade of disaster risk reduction after the implementation of each risk prevention and control measure, analyze the prevention and control sensitivity and effect of each active prevention and control measure on various disaster risks, and study and analyze the optimal risk prevention and control strategies under different risk environments, including combining high-reliability emergency guarantee technologies for emergencies of other energy systems such as power grids, in-depth analysis of the possible impact of natural disasters on the source-grid-load-storage integrated energy system, and establishing phased and hierarchical early warning plans for different development stages and degrees of natural disasters to ensure efficient and reliable energy supply of the source-grid-load-storage integrated energy system; at the same time, fully analyze the action mechanism between the power grid and the source-grid-load-storage integrated energy system and the operating characteristics of the global green power source grid-load-storage integrated energy system, consider energy supply reliability and economy, and formulate a response mechanism for the source-grid-load-storage integrated energy system under external effects such as emergency dispatch, virtual peak regulation, and demand response.

[0131] (2) Natural disaster early warning information technology for integrated energy systems of source, grid, load and storage:

[0132] The business application requirements of natural disaster early warning management in the source-grid-load-storage integrated energy system can be roughly divided into three levels: information perception layer, analysis and decision-making layer, and visualization display layer. The corresponding information technology can be summarized into four aspects: intelligent perception technology, intelligent decision-making technology, visualization technology, and information architecture technology:

[0133] Intelligent perception technology is the key implementation technology of the information perception layer of natural disaster early warning management in the source-grid-load-storage integrated energy system, covering various sensor technologies, satellite remote sensing technology and information communication technology; intelligent decision-making technology is the main means of decision-making analysis for natural disaster early warning management in power grids, covering technical means such as multi-agents, big data, data mining and knowledge discovery; the natural disaster coupling system of the source-grid-load-storage integrated energy system has the characteristics of wide spatial range, multiple information levels and complex topological structure, and its visualization process requires data visualization technology and spatial visualization technology as support; information architecture technology is the key technology for the implementation of natural disaster early warning decision support system in power grids.

[0134] The key technologies of the present invention are as follows:

[0135] 1. Calculation method of failure rate under typhoon disaster

[0136] The typhoon model can be described as a circular model in which the wind speed reaches the maximum at the maximum wind speed radius and gradually decreases toward the center and the outer circle. The path and impact range of a typhoon can be described by the improved Rankine model. It is assumed that the wind speed reduction caused by the environmental wind field and land topography at the boundary of the tropical cyclone is not taken into account. The specific model is shown in formula (1):

[0137]

[0138] Where V r is the wind speed of the typhoon in the area, T typ To adjust the wind speed distribution parameters, r is the distance from the typhoon center, V max is the maximum wind speed of the typhoon, R max is the maximum wind speed radius of the typhoon;

[0139] Actual wind speed V N The calculation should be performed according to formula (2), adjusted to the axisymmetric wind speed at the standard altitude of 10m.

[0140] V N =K V V r (2)

[0141] In the formula, K V is the correction factor;

[0142] Under the action of wind, the grid side of the receiving power grid considers the distribution line failure, and the relationship between the distribution line failure rate and the typhoon wind speed is represented by the vulnerability curve. The specific expression of the line failure rate is shown in formula (3):

[0143]

[0144] In the formula, λ et is the line failure rate under typhoon operation conditions, v is the wind speed at the line research point, V N is the design wind speed of the line, and β is the fragility curve coefficient.

[0145] 2. Calculation method of failure rate under earthquake disaster

[0146] Under extreme natural disasters such as typhoons, the main damaged components of the entire source-grid-load-storage integrated energy system are the distribution lines of the power subsystem. Since the natural gas pipelines and thermal pipelines are buried underground, the typhoon has a relatively small impact on them. Under earthquake disasters, the damaged components include not only the distribution lines, but also the entire natural gas pipelines and thermal pipelines, which in turn leads to a sharp increase in the failure rate of the power subsystem distribution lines, natural gas subsystems, and thermal subsystem pipelines of the source-grid-load-storage integrated energy system. Therefore, the resilience of the source-grid-load-storage integrated energy system will be studied in the case of earthquake disasters. Under earthquake disasters, the failure rates of the distribution lines, natural gas pipelines, and thermal pipelines of the source-grid-load-storage integrated energy system will increase, and the specific failure rate model is as follows:

[0147] 1) Power subsystem distribution line failure rate model:

[0148] The failure rate of distribution lines under earthquake disasters is shown in formula (4):

[0149]

[0150] In the formula, λ e is the failure rate of distribution lines under earthquake disasters, ∏ is the product operation, which multiplies each term of the expression given below within a specific range, P fi is the failure probability of the ith tower, n is the number of towers of the distribution line;

[0151] 2) Natural gas subsystem pipeline failure rate model:

[0152] Under earthquake disasters, the pipeline failure rate of the natural gas subsystem is mainly analyzed by the earthquake damage rate. Based on the earthquake intensity, the earthquake damage rate of the natural gas pipeline under earthquake conditions can be obtained as follows:

[0153]

[0154] In the formula, I MM C is the earthquake intensity, which indicates the strength of the earthquake's impact on the ground and buildings on the ground. d is the pipe diameter influence coefficient, C g is the site soil influence coefficient.

[0155] Furthermore, the probability of natural gas pipeline failure under earthquake disaster is:

[0156]

[0157] In the formula, R g is the natural gas pipeline earthquake damage rate, times / km, L g is the length of the natural gas pipeline, and e is the base of the natural logarithm.

[0158] 3) Thermal subsystem pipeline failure rate model:

[0159] Due to the difference in the transmitted materials, the failure rate of the heat network pipeline is mainly analyzed based on the earthquake acceleration VPG compared to the natural gas subsystem. The failure probability of the heat network pipeline under earthquake disaster is:

[0160]

[0161] In the formula, R h is the damage rate of heat network pipelines, times / km, L h is the pipeline length of the heating network pipeline;

[0162] 4) Failure rate model of source-grid-load-storage integrated energy system under earthquake disaster:

[0163] Under earthquake disasters, the source-grid-load-storage integrated energy system may experience severe accidents in which a large number of components fail simultaneously. Therefore, the failure rate model of the source-grid-load-storage integrated energy system in this failure scenario is as follows:

[0164]

[0165] In the formula, is the k-order subset of A, which can be obtained by formula (9), A is the set of all components, N is the number of components in the source-grid-load-storage integrated energy system, λ i is the failure rate of each component in the source-grid-load-storage integrated energy system, which can be calculated by equations (4)-(7), and s is the set An element in , usually a subset, which contains related elements (such as events, states, or components, etc.), multiply each s, calculate the joint metric or probability of these elements, traverse all possible subsets s when summing, and accumulate the products of each subset;

[0166]

[0167] Where Card(u) is the number of faulty devices in state u. When k = 0,

[0168] 3. Quantitative evaluation index method for resilience of integrated energy system with source, grid, load and storage

[0169] The resilience of the source-grid-load-storage integrated energy system has three major attributes: robustness, rapidity, and redundancy. Therefore, the following constructs the resilience evaluation index of the source-grid-load-storage integrated energy system from the three aspects of robustness, rapidity, and redundancy. The three indicators exist in parallel to jointly quantitatively evaluate the resilience of the entire source-grid-load-storage integrated energy system.

[0170] 1) Robustness indicators:

[0171] The smaller the robustness index of the source-grid-load-storage integrated energy system, the better the robustness of the source-grid-load-storage integrated energy system, and the stronger the system's ability to resist extreme natural disasters. In addition, over a period of time, due to the impact of extreme natural disasters, multiple different types of faults may occur simultaneously in the source-grid-load-storage integrated energy system. Therefore, the frequency of occurrence of different faults is also an aspect of measuring the resilience of the source-grid-load-storage integrated energy system. By reducing the frequency of occurrence of various types of faults, the overall resilience of the source-grid-load-storage integrated energy system can also be improved. Therefore, a fault frequency index is added to the robustness index to measure the number of times different faults of the source-grid-load-storage integrated energy system occur per unit time. The smaller the fault frequency index, the smaller the probability of the fault occurring, that is, the stronger the overall resilience of the system. Therefore, the robustness index of the source-grid-load-storage integrated energy system is:

[0172] R = f i ·L R (10)

[0173] In the formula, f i is the frequency of occurrence of fault condition i under disaster, which can be calculated by formula (11), L R is the load loss ratio of the source-grid-load-storage integrated energy system, which can be calculated by formula (12);

[0174] f i =P i ·λ (11)

[0175]

[0176] In the formula, f i is the frequency of failure. According to the Markov method, the frequency of system failure state i can be calculated by the state probability P0 of the system's previous possible state i0 and the failure rate λ of the component; λ is the failure rate of the distribution line or pipeline are the load loss of the power subsystem, natural gas subsystem and thermal subsystem when coupled under extreme natural disaster x, are the total loads of the power subsystem, natural gas subsystem and thermal subsystem respectively;

[0177] The specific steps for calculating the robustness index are as follows:

[0178] Step 1: Input the preset parameters of each subsystem, natural disasters and system fault order in the source-grid-load-storage integrated energy system; Step 2: Initialize the fault set order k=1; Step 3: Create a k-order state set according to formula (9): Step 4, from Select a k-order system state s from the above; Step 5, calculate the optimal load reduction amount of the source-grid-load-storage integrated energy power / gas / heat subsystem in fault state s; Step 6, determine Have all the states in been analyzed? If so, jump to step 7; if not, return to step 4; Step 7, determine whether the order is equal to the number of components of the source-grid-load-storage integrated energy system (i.e., k=N); if so, jump to step 8; if not, set k=k+1 and return to step 3; Step 8, calculate the failure rate λ of each component of the source-grid-load-storage integrated energy electricity / gas / heat system according to equations (4)-(8); Step 9, calculate the failure frequency index f of the source-grid-load-storage integrated energy system according to equation (11); Step 10, calculate the load loss ratio L of the source-grid-load-storage integrated energy system according to equation (12) R ; Step 11, calculate and output the robustness index R of the source-grid-load-storage integrated energy system according to formula (10);

[0179] 2) Rapidity index:

[0180] The rapidity of the source-grid-load-storage integrated energy system reflects how quickly the source-grid-load-storage integrated energy system recovers to the initial energy supply level from the occurrence of an extreme natural disaster. The rapidity index of the source-grid-load-storage integrated energy system of the present invention describes the ability of the entire source-grid-load-storage integrated energy system to quickly restore key energy supply functions and reduce the power outages, gas outages and heat outages caused by natural disasters. The rapidity index is reflected by the time required for the entire system to recover to a normal state from the occurrence of a fault, as shown in formula (13). The smaller the rapidity index, the faster the recovery speed of the entire system, that is, the stronger the overall resilience of the system.

[0181] Δt=t3-t1 (13)

[0182] Where, Δt is the time from the beginning of the extreme natural disaster to the system returning to normal; t3 is the time from the beginning of the extreme natural disaster to the system returning to normal; t1 is the time when the extreme natural disaster occurs;

[0183] The calculation of the rapidity index includes:

[0184] Step 1: Input the time t1 when the natural disaster occurred, the time t3 when the system started to repair, and the total number N of faulty components in the source-grid-load-storage integrated energy system, and count the total number N of faulty components in each subsystem. i and the repair time of each subsystem componentω i , i=1,2,3; Step 2, determine whether the faulty components of the source-grid-load-storage integrated energy system are all from the same system. If so, go to step 3, otherwise go to step 6; Step 3, initialize the repair order of the faulty components k=1, and the total repair time of the system T=0; Step 4, according to the formula T=T+ω iCalculate the total repair time of the source-grid-load-storage integrated energy system. Step 5: determine whether the order is equal to the number of components of the source-grid-load-storage integrated energy system (i.e., k=N). If so, go to step 13; if not, set k=k+1 and return to step 4. Step 6: initialize the total repair time of each subsystem T1=0, T2=0, T3=0. Step 7: determine whether the faulty component comes from two systems. If so, go to step 8; if not, go to step 11. Step 8: calculate the total repair time of faulty systems 1 and 2 according to the following formula: Step 9, calculate the maximum value of the repair time of systems 1 and 2 according to the following formula, and go to step 13, T = max{T1, T2}; Step 11, calculate the total repair time of faulty systems 1, 2 and 3 according to the following formula: Step 12, calculate the maximum value of the repair time of systems 1, 2 and 3 according to the following formula: T = max{T1, T2, T3}; Step 13, calculate and output the total recovery time Δt of the source-grid-load-storage integrated energy system according to the following formula, Δt = T + t2 - t1;

[0185] 3) Redundancy index:

[0186] The redundancy of the source-grid-load-storage integrated energy system reflects a characteristic of enhancing the system's operational reliability under extreme natural disasters by adding system backup components. In the face of extreme natural disasters, the overall ability of the source-grid-load-storage integrated energy system to resist tearing is also an important manifestation of the resilience of the source-grid-load-storage integrated energy system, that is, the ability of the source-grid-load-storage integrated energy system to maintain normal operation without collapse or disassembly under extreme natural disasters. The network's tear resistance is a static indicator that describes the security of the network topology from the perspective of network connectivity. Therefore, natural connectivity is used as a redundancy indicator to measure the anti-destruction capability of the entire source-grid-load-storage integrated energy system under extreme natural disasters, and then evaluate the resilience of the entire system. The specific calculation formula is as follows:

[0187]

[0188] In the formula, γ k It is a part of the redundancy metric and is used to evaluate the redundancy of a node or component. s is the total number of nodes in the source-grid-load-storage integrated energy system, N CTG is the fault state order for accurate calculation, is the characteristic root of the adjacency matrix A of the source-grid-load-storage integrated energy system; the adjacency matrix A can be calculated by formula (15):

[0189]

[0190] Where E(G) is the branch set of the system graph G, v i 、v jare the positions of nodes i and j respectively;

[0191] By taking the average value of the natural connectivity of the system under various fault conditions, the redundancy index of the source-grid-load-storage integrated energy system can be obtained as follows:

[0192]

[0193] Where N MCS is the total number of fault samples of each order, k is the natural connectivity of the system when it is in fault state k, and the number of closed paths in the network is used to measure the redundancy of alternative paths in the network. The more backup paths each node has in the network, the stronger the network's tear resistance is, and the stronger the ability of the entire system to maintain reliable operation in the face of extreme natural disasters.

[0194] The calculation of redundancy index includes:

[0195] Step 1: Input the source-grid-load-storage integrated energy system data and the total number of nodes N s , set the fault state order N for accurate calculation CTG And the total number of fault samples of each order N MCS ; Step 2, initialize the fault set order k = 1, fault number w = 1, node number i = 1; Step 3, create a k-order state set according to formula (9) Step 4, according to formula (15), establish the adjacency matrix A of the source-grid-load-storage integrated energy system after the fault i occurs; Step 5, calculate the characteristic root μ of the adjacency matrix of the source-grid-load-storage integrated energy system at this time; Step 6, determine whether the node number i is equal to the total number of nodes of the source-grid-load-storage integrated energy system (i.e., i=N s ), if yes, go to step 7, if no, set i = i + 1 and return to step 4; Step 7, calculate the natural connectivity γ of the source-grid-load-storage integrated energy system at this time according to formula (14) k,w ; Step 8: Determine whether the fault number is equal to the total number of fault samples of each order (ie w = N MCS ), if yes, go to step 9, if no, set w = w + 1 and return to step 4; step 9, determine whether the fault set order is equal to the fault state order (i.e. k = N CTG ), if yes, go to step 10, if not, set k=k+1 and return to step 3; step 10, calculate the mean of the natural connectivity of the source-grid-load-storage integrated energy system under various fault states according to formula (16); step 11, output the natural connectivity of each order of the source-grid-load-storage integrated energy system.

[0196] In addition, if Figure 3As shown, the present invention also provides a schematic diagram of a natural disaster early warning system, including: a model building module, used to pre-build prediction models corresponding to multiple disaster types and save the prediction models to a model library, the prediction models are used to predict the failure probability of the energy network in a disaster scenario; a model calling module, used to collect disaster prediction data, and call a matching target prediction model from the model library based on the disaster type to which the disaster prediction data belongs; a fault prediction module, used to input the disaster prediction data into the target prediction model to obtain the failure probability of the energy network; an indicator evaluation module, used to calculate the robustness index, rapidity index and redundancy index of the energy network based on the failure probability.

[0197] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0198] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0199] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0200] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0201] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0202] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A natural disaster early warning method for an integrated energy system, characterized in that: include: Using big data association analysis, we analyze the frequent item sets and association patterns between the disaster scenarios and data sets of the integrated energy system; Based on the analysis results, a spatial distribution network of disaster types is constructed, coupling nodes between frequent disaster-causing factors and the vulnerability of the integrated energy system are screened from a spatial perspective, and monitoring and early warning are carried out based on the coupling nodes; An indicator system is established based on the monitorable and measurable indicators of the operation of the integrated energy system, and the optimal risk prevention and control strategies under different risk environments are analyzed based on the indicator system.

2. The natural disaster early warning method for the integrated energy system according to claim 1 is characterized in that: The method of using big data association analysis to analyze frequent item sets and association patterns between disaster scenarios of integrated energy systems and data sets includes: Analyze the spatiotemporal distribution of long-term static historical data of disaster-causing factors in the integrated energy system, integrate historical information on the time intervals of disasters in the integrated energy system, form a structured data warehouse, and extract monitoring data features under fault scenarios in integrated energy system disasters; The big data association analysis method is used to analyze the frequent item sets and association patterns between the disaster scenarios of the integrated energy system and the data sets, combined with the monitoring values ​​at the time nodes of the fault occurrence.

3. The natural disaster early warning method for the integrated energy system according to claim 1 is characterized in that: The spatial distribution network of disaster types is constructed based on the analysis results, coupling nodes of frequent disaster factors and vulnerability of the integrated energy system are screened from a spatial perspective, and monitoring and early warning are performed based on the coupling nodes, including: Based on the statistical analysis results of the spatial occurrence patterns of disaster-causing factors, combined with the disaster-prone environment and historical scenario factors, a spatial distribution network of disaster types is established; By analyzing the spatial vulnerable node distribution of the spatial distribution network from the perspective of integrated energy system disasters, a network of integrated energy system disaster vulnerable nodes is formed, and the coupling relationship of the network is analyzed from the perspectives of spatial distribution, physical connectivity, and network structure; Based on the spatial perspective, the coupling nodes with frequent disaster-causing factors and that meet the vulnerability requirements of the integrated energy system are screened, and monitoring and early warning are carried out based on the coupling nodes.

4. The natural disaster early warning method for the integrated energy system according to claim 1, characterized in that: The calculation formula for the failure rate under typhoon disaster is: V N =K V V r In the formula, λ et is the line failure rate under typhoon operation conditions, v is the wind speed at the line research point, V N is the line design wind speed, β is the fragility curve coefficient, K V is the correction factor, V r is the wind speed of the typhoon in the area, T typ To adjust the wind speed distribution parameters, r is the distance from the typhoon center, V max is the maximum wind speed of the typhoon, R max It is the maximum wind speed radius of the typhoon.

5. The natural disaster early warning method for the integrated energy system according to claim 1 is characterized in that: The failure rates under earthquake disasters include the failure rates of power distribution lines, natural gas pipelines, and heat network pipelines; Among them, the calculation formula for the failure rate of distribution lines under earthquake disasters is: The calculation formula for the failure rate of natural gas pipelines under earthquake disasters is: The calculation formula of the failure rate of heat network pipelines under earthquake disaster is: The expression of the failure rate of the integrated energy system under earthquake disaster is: In the formula, λ e is the failure rate of distribution lines under earthquake disaster, ∏ is the product operation, P fi is the failure probability of the ith tower, n is the number of towers of the distribution line, λ g is the failure rate of natural gas pipelines under earthquake disasters, R g is the natural gas pipeline earthquake damage rate, L g is the length of the natural gas pipeline, e is the base of the natural logarithm, I MM is the earthquake intensity, C d is the pipe diameter influence coefficient, C g is the site soil influence coefficient, λ h is the failure rate of heat network pipelines under earthquake disaster, R h is the damage rate of heat network pipelines, L h is the length of the heat network pipeline, λ IES is the failure rate of the comprehensive energy system under earthquake disaster, is the k-order subset of A, A is the set of all components, N is the number of components in the integrated energy system, λ i is the failure rate of each component in the comprehensive energy system, s is An element in , Card(u) is the number of faulty devices included in state u.

6. The natural disaster early warning method for the integrated energy system according to claim 1, characterized in that: The indicator system is established based on the monitorable indicators and measurable indicators of the operation of the integrated energy system, and the optimal risk prevention and control strategies under different risk environments are analyzed according to the indicator system, including: Establish an indicator system based on the monitorable and measurable indicators of the operation of the integrated energy system, and use the indicator system to evaluate the ability of active prevention and control measures to prevent and control system disaster risks; Quantitatively calculate the level and grade of disaster risk reduction after the implementation of various risk prevention and control measures, analyze the prevention and control sensitivity and effect of various active prevention and control measures on various disaster risks, and determine the optimal risk prevention and control strategy under different risk environments.

7. The natural disaster early warning method for the integrated energy system according to claim 6 is characterized in that: The index system includes robustness index, rapidity index and redundancy index; Among them, the expression of robustness index is: R=f i ·L R f i =P i ·l The expression of the rapidity index is: Δt=t3-t1 The expression of the redundancy index is: In the formula, R is the robustness index, f i is the frequency of occurrence of fault condition i under disaster, L R is the load loss ratio of the comprehensive energy system, P i is the probability of fault type i occurring under disaster conditions, λ is the failure rate of distribution lines or pipelines, are the load loss of the power subsystem, natural gas subsystem and thermal subsystem when coupled under extreme natural disaster x, are the total loads of the power subsystem, natural gas subsystem and thermal subsystem, respectively; Δt is the time from the beginning of the extreme natural disaster to the system returning to normal; t3 is the time from the beginning of the extreme natural disaster to the system returning to normal; t1 is the time when the extreme natural disaster occurs; ξ k is the redundancy index, N MCS is the total number of fault samples of each order, k is the natural connectivity of the integrated energy system when it is in fault state k, w is the fault number, γ k,w is the natural connectivity, γ k It is a part of the redundancy metric used to evaluate the redundancy of a node or component. s is the total number of nodes in the integrated energy system, N CTG is the fault state order for accurate calculation, is the characteristic root of the adjacency matrix A of the integrated energy system, v i 、v j are the positions of nodes i and j respectively, and E(G) is the branch set of the system graph G.

8. The natural disaster early warning method for the integrated energy system according to claim 7 is characterized in that: The calculation of robustness indicators includes: Step 1, input the preset parameters of each subsystem, natural disasters and system fault order in the integrated energy system; Step 2, initialize the fault set order; Step 3, calculate the k-order state set; Step 4, select a k-order system state s from the k-order state set; Step 5, calculate the optimal load reduction amount of the integrated energy electricity / gas / heat subsystem in fault state s; Step 6, determine whether all states in the k-order state set have been analyzed, if so, jump to step 7, if not, return to step 4; Step 7, determine whether the order is equal to the number of components in the integrated energy system, if so, jump to step 8, if not, set k=k+1, and return to step 3; Step 8, calculate the failure rate of each component of the integrated energy electricity / gas / heat system; Step 9, calculate the fault frequency index of the integrated energy system; Step 10, calculate the load loss ratio of the integrated energy system; Step 11, calculate and output the robustness index of the integrated energy system; The calculation of the rapidity index includes: Step 1, input the time when the natural disaster occurs, the time when the system starts to repair, and the total number of faulty components in the integrated energy system, and count the total number of faulty components in each subsystem and the repair time of each subsystem component; Step 2, determine whether the faulty components of the integrated energy system are all from the same system, if so, go to step 3, otherwise go to step 6; Step 3, initialize the repair order of the faulty components and the total repair time of the system; Step 4, calculate the total repair time of the integrated energy system, Step 5, determine whether the order is equal to the number of components in the integrated energy system, if so, go to step 13, if not, set k=k+1, and return to step 4; Step 6, initialize the total repair time of each subsystem; Step 7, determine whether the faulty components are from two systems, if so, go to step 8, if not, go to step 11; Step 8, calculate the total repair time of the two faulty systems respectively; Step 9, calculate the maximum value of the repair time of the two faulty systems respectively, and go to step 13; Step 11, calculate the total repair time of the three faulty systems respectively; Step 12, calculate the maximum value of the repair time of the three faulty systems respectively; Step 13, calculate and output the total recovery time of the integrated energy system; The calculation of redundancy index includes: Step 1, input the data of the integrated energy system and the total number of nodes, set the fault state order and the total number of fault samples of each order; Step 2, initialize the fault set order, fault number, and node number; Step 3, create a k-order state set; Step 4, establish the adjacency matrix A after the fault i of the integrated energy system occurs; Step 5, calculate the characteristic root of the adjacency matrix of the integrated energy system at this time; Step 6, determine whether the node number i is equal to the total number of nodes of the integrated energy, if so, go to step 7, if not, let i=i+1, and return to step 4; Step 7, calculate the natural connectivity of the integrated energy system at this time; Step 8: Determine whether the fault number is equal to the total number of fault samples of each order, if so, go to step 9, if not, let w=w+1, and return to step 4; Step 9, determine whether the fault set order is equal to the fault state order, if so, go to step 10, if not, let k=k+1, and return to step 3; Step 10, calculate the mean of the natural connectivity of the system integrated energy system in each order of fault state; Step 11, output the natural connectivity of each order of the integrated energy system.

9. A natural disaster early warning system for an integrated energy system, characterized in that: include: Data association analysis module, used to analyze frequent item sets and association patterns between disaster scenarios and data sets of integrated energy systems using big data association analysis methods; The monitoring and early warning module is used to construct a spatial distribution network of disaster types based on the analysis results, screen the coupling nodes of frequent disaster-causing factors and the vulnerability of the integrated energy system from a spatial perspective, and conduct monitoring and early warning based on the coupling nodes; The risk prevention and control strategy analysis module is used to establish an indicator system based on the monitorable and measurable indicators of the operation of the integrated energy system, and analyze the optimal risk prevention and control strategy under different risk environments based on the indicator system.

10. The comprehensive energy system natural disaster early warning system according to claim 9, characterized in that: When the data association analysis module uses the big data association analysis method to analyze the frequent item sets and association patterns between the disaster scenarios and data sets of the comprehensive energy system, it analyzes the spatiotemporal distribution laws of the long-term static historical data of disaster-causing factors of the comprehensive energy system, integrates the historical information of the time intervals of disaster occurrence in the comprehensive energy system, forms a structured data warehouse, and extracts the monitoring data characteristics under the fault scenarios in the disasters of the comprehensive energy system; uses the big data association analysis method, combined with the monitoring values ​​of the time nodes when the faults occur, to analyze the frequent item sets and association patterns between the disaster scenarios and data sets of the comprehensive energy system.

11. The comprehensive energy system natural disaster early warning system according to claim 9, characterized in that: The monitoring and early warning module constructs a spatial distribution network of disaster types based on the analysis results, screens the coupling nodes of frequent disaster-causing factors and vulnerability of the integrated energy system from a spatial perspective, and performs monitoring and early warning based on the coupling nodes. Based on the statistical analysis results of the spatial occurrence law of disaster-causing factors, combined with the disaster-prone environment and historical scenario factors, a spatial distribution network of disaster types is established; the spatial vulnerable node distribution of the spatial distribution network is analyzed from the perspective of the disaster of the integrated energy system, a disaster vulnerable node network of the integrated energy system is formed, and the coupling relationship of the network is analyzed from the perspectives of spatial distribution, physical connectivity, and network structure; Based on the spatial perspective, the coupling nodes with frequent disaster-causing factors and that meet the vulnerability requirements of the integrated energy system are screened, and monitoring and early warning are carried out based on the coupling nodes.

12. The comprehensive energy system natural disaster early warning system according to claim 9, characterized in that: The calculation formula for the failure rate under typhoon disaster is: V N =K V V r The failure rates under earthquake disasters include the failure rates of power distribution lines, natural gas pipelines, and heat network pipelines; Among them, the calculation formula for the failure rate of distribution lines under earthquake disasters is: The calculation formula for the failure rate of natural gas pipelines under earthquake disasters is: The calculation formula of the failure rate of heat network pipelines under earthquake disaster is: The expression of the failure rate of the integrated energy system under earthquake disaster is: In the formula, λ et is the line failure rate under typhoon operation conditions, v is the wind speed at the line research point, V N is the line design wind speed, β is the fragility curve coefficient, K V is the correction factor, V r is the wind speed of the typhoon in the area, T typ To adjust the wind speed distribution parameters, r is the distance from the typhoon center, V max is the maximum wind speed of the typhoon, R max is the maximum wind speed radius of the typhoon, λ e is the failure rate of distribution lines under earthquake disaster, Π is the product operation, P fi is the failure probability of the ith tower, n is the number of towers of the distribution line, λ g is the failure rate of natural gas pipelines under earthquake disasters, R g is the natural gas pipeline earthquake damage rate, L g is the length of the natural gas pipeline, e is the base of the natural logarithm, I MM is the earthquake intensity, C d is the pipe diameter influence coefficient, C g is the site soil influence coefficient, λ h is the failure rate of heat network pipelines under earthquake disaster, R h is the damage rate of heat network pipelines, L h is the length of the heat network pipeline, λ IES is the failure rate of the comprehensive energy system under earthquake disaster, is the k-order subset of A, A is the set of all components, N is the number of components in the integrated energy system, λ i is the failure rate of each component in the comprehensive energy system, s is An element in , Card(u) is the number of faulty devices included in state u.

13. The comprehensive energy system natural disaster early warning system according to claim 9, characterized in that: The risk prevention and control strategy analysis module establishes an indicator system based on the monitorable indicators and measurable indicators of the operation of the integrated energy system, and analyzes the optimal risk prevention and control strategy under different risk environments based on the indicator system. The indicator system is established based on the monitorable indicators and measurable indicators of the operation of the integrated energy system, and the indicator system is used to evaluate the ability of active prevention and control measures to prevent and control system disaster risks; quantitatively calculate the level and grade of disaster risk reduction after the implementation of each risk prevention and control measure, and analyze the prevention and control sensitivity and effect of each active prevention and control measure on various disaster risks, so as to determine the optimal risk prevention and control strategy under different risk environments.

14. The comprehensive energy system natural disaster early warning system according to claim 13, characterized in that: The index system includes robustness index, rapidity index and redundancy index; Among them, the expression of robustness index is: R=f i ·L R f i =P i ·l The expression of the rapidity index is: Δt=t3-t1 The expression of the redundancy index is: In the formula, R is the robustness index, f i is the frequency of occurrence of fault condition i under disaster, L R is the load loss ratio of the comprehensive energy system, P i is the probability of fault type i occurring under disaster conditions, λ is the failure rate of distribution lines or pipelines, are the load loss of the power subsystem, natural gas subsystem and thermal subsystem when coupled under extreme natural disaster x, are the total loads of the power subsystem, natural gas subsystem and thermal subsystem, respectively; Δt is the time from the beginning of the extreme natural disaster to the system returning to normal; t3 is the time from the beginning of the extreme natural disaster to the system returning to normal; t1 is the time when the extreme natural disaster occurs; ξ is the redundancy index; N MCS is the total number of fault samples of each order, k is the natural connectivity of the integrated energy system when it is in fault state k, w is the fault number, γ k,w is the natural connectivity, γ k It is a part of the redundancy metric used to evaluate the redundancy of a node or component. s is the total number of nodes in the integrated energy system, N CTG is the fault state order for accurate calculation, is the characteristic root of the adjacency matrix A of the integrated energy system, v i 、v j are the positions of nodes i and j respectively, and E(G) is the set of branches of the system graph G; The calculation of robustness indicators includes: Step 1, input the preset parameters of each subsystem, natural disasters and system fault order in the integrated energy system; Step 2, initialize the fault set order; Step 3, calculate the k-order state set; Step 4, select a k-order system state s from the k-order state set; Step 5, calculate the optimal load reduction amount of the integrated energy electricity / gas / heat subsystem in fault state s; Step 6, determine whether all states in the k-order state set have been analyzed, if so, jump to step 7, if not, return to step 4; Step 7, determine whether the order is equal to the number of components in the integrated energy system, if so, jump to step 8, if not, set k=k+1, and return to step 3; Step 8, calculate the failure rate of each component of the integrated energy electricity / gas / heat system; Step 9, calculate the fault frequency index of the integrated energy system; Step 10, calculate the load loss ratio of the integrated energy system; Step 11, calculate and output the robustness index of the integrated energy system; The calculation of the rapidity index includes: Step 1, input the time when the natural disaster occurs, the time when the system starts to repair, and the total number of faulty components in the integrated energy system, and count the total number of faulty components in each subsystem and the repair time of each subsystem component; Step 2, determine whether the faulty components of the integrated energy system are all from the same system, if so, go to step 3, otherwise go to step 6; Step 3, initialize the repair order of the faulty components and the total repair time of the system; Step 4, calculate the total repair time of the integrated energy system, Step 5, determine whether the order is equal to the number of components in the integrated energy system, if so, go to step 13, if not, set k=k+1, and return to step 4; Step 6, initialize the total repair time of each subsystem; Step 7, determine whether the faulty components are from two systems, if so, go to step 8, if not, go to step 11; Step 8, calculate the total repair time of the two faulty systems respectively; Step 9, calculate the maximum value of the repair time of the two faulty systems respectively, and go to step 13; Step 11, calculate the total repair time of the three faulty systems respectively; Step 12, calculate the maximum value of the repair time of the three faulty systems respectively; Step 13, calculate and output the total recovery time of the integrated energy system; The calculation of redundancy index includes: Step 1, input the data of the integrated energy system and the total number of nodes, set the fault state order and the total number of fault samples of each order; Step 2, initialize the fault set order, fault number, and node number; Step 3, create a k-order state set; Step 4, establish the adjacency matrix A after the fault i of the integrated energy system occurs; Step 5, calculate the characteristic root of the adjacency matrix of the integrated energy system at this time; Step 6, determine whether the node number i is equal to the total number of nodes of the integrated energy, if so, go to step 7, if not, let i=i+1, and return to step 4; Step 7, calculate the natural connectivity of the integrated energy system at this time; Step 8: Determine whether the fault number is equal to the total number of fault samples of each order, if so, go to step 9, if not, let w=w+1, and return to step 4; Step 9, determine whether the fault set order is equal to the fault state order, if so, go to step 10, if not, let k=k+1, and return to step 3; Step 10, calculate the mean of the natural connectivity of the system integrated energy system in each order of fault state; Step 11, output the natural connectivity of each order of the integrated energy system.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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