Electrical coupling element strengthening method and system integrating fragility and fault influence

Abstract

The invention discloses an electrical coupling element strengthening method and system integrating vulnerability and fault influence, and belongs to the technical field of power system safety planning, and the method comprises the steps: generating the fault probability of vulnerable elements of a power system and a natural gas system after different levels of strengthening after an earthquake disaster according to the types of elements influenced by an earthquake; establishing reinforcement strategy constraints of post-disaster elements by adopting earthquake scene-oriented electric-gas coupling system multi-level reinforcement measures; acquiring a fault state of an element in the system after the disaster occurs; establishing a key element identification model of the electricity-gas coupling system, inputting fault states of elements in the system after a disaster occurs into the obtained key element identification model of the electricity-gas coupling system, selecting important elements in advance, and obtaining element first-aid repair priorities considering a large number of fault scenes; and according to the obtained element pre-sorting result and the stochastic programming scene, establishing a stochastic programming model for key element identification, and solving by adopting a step-by-step hedging algorithm to obtain a strengthening scheme to realize strengthening of the electricity-gas coupling system.

Description

Background Art

[0002] The proportion of new energy in the power system is gradually increasing, bringing risks of new energy accommodation and the safe and stable operation of the power system. In addition, global climate change is intensifying, and extreme weather and natural disasters such as typhoons and high temperatures show a new normal of wide occurrence, strong occurrence, frequent occurrence, and concurrent occurrence, posing serious challenges to the power system. The electricity-gas coupling system helps to promote new energy accommodation and achieve efficient energy utilization, and is of great value for supporting the construction of a new power system. At the same time, it can improve the system's adaptability and recovery ability to extreme events such as earthquakes and typhoons through flexible multi-energy conversion, and is an important carrier for enhancing power security.

[0003] In order to ensure the safe operation of the electricity-gas system, it is necessary to fundamentally improve the system's resistance to disturbances. Identifying its key links and strengthening them differentially is an effective means. However, traditional key link identification schemes mainly focus on static topology and operation state analysis. A few studies will consider the recovery and operation scenarios under uncertain faults, but all lack the consideration of the dynamic repair process of post-disaster faults and fail to reflect the complete post-disaster recovery process of the system. The electricity-gas coupling system has a large number of components and complex coupling relationships, and the system operation scenarios are variable. How to improve the calculation efficiency while ensuring the modeling accuracy is also a challenge that needs to be solved urgently.

[0004] Therefore, it is necessary to study new modeling and solution methods for the key component strengthening scheme of the electricity-gas coupling system. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and system for strengthening electrical coupling components that comprehensively consider vulnerability and fault impact in view of the deficiencies in the above-mentioned prior art, so as to solve the technical problem that the planning model relying on decision-making is difficult to integrate the emergency repair scheduling modeling that requires a given set of fault scenarios, and realize the consideration of the complete emergency repair and recovery process. At the same time, it can reduce the scale of the decision variables of the components to be strengthened, and ensure the solution efficiency of the multi-level strengthening model of the electricity-gas coupling system containing a large number of components.

[0006] The present invention adopts the following technical solutions:

[0007] A method for strengthening electrical coupling components that comprehensively considers vulnerability and fault impact, including the following steps:

[0008] S1. According to the disaster classification method for the impact on the electricity-gas coupling system, judge the type of disaster in the event of an earthquake disaster, and obtain the types of components in the electricity-gas coupling system affected by the earthquake;

[0009] S2. Using the component categories affected by the earthquake obtained in step S1, generate the failure probabilities of vulnerable components in the power system and natural gas system after the earthquake with different levels of reinforcement by using the earthquake disaster intensity attenuation model and component failure probability generation method;

[0010] S3. Based on the failure probabilities of vulnerable components in the power system and natural gas system obtained in step S2, adopt multi-level reinforcement measures for the electric-gas coupling system facing the earthquake scenario, and establish the reinforcement strategy constraints for the components after the disaster;

[0011] S4. Based on the reinforcement strategy constraints for the components after the disaster obtained in step S3, generate multi-level reinforcement stochastic programming scenarios, and obtain the failure states of the components in the system after the disaster;

[0012] S5. Establish a key component identification model for the electric-gas coupling system, with the objective function of minimizing the power load shedding cost and gas load shedding cost, and the constraint conditions including the power flow constraint of the distribution network, the radial topology constraint, and the fault impact transfer constraint;

[0013] S6. Input the failure states of the components in the system after the disaster obtained in step S4 into the key component identification model of the electric-gas coupling system obtained in step S5, pre-select important components by using Copeland ranking, and at the same time obtain the repair priorities of the components considering a large number of fault scenarios. The pre-ranking results provide the repair priorities of the components for the optimization of the reinforcement plan;

[0014] S7. Based on the component pre-ranking results obtained in step S6 and the stochastic programming scenarios obtained in step S4, establish a stochastic programming model for key component identification, solve it by using the step-by-step hedging algorithm, and obtain the reinforcement plan to realize the reinforcement of the electric-gas coupling system.

[0015] Preferably, step S1 is specifically:

[0016] Classify the natural disasters that affect the electric-gas coupling system into surface disasters represented by storm disasters and geological disasters represented by earthquake disasters;

[0017] In the power system, both surface disasters and geological disasters affect the power system;

[0018] In the natural gas system, clarify the disaster type and various information of the disaster.

[0019] Preferably, step S2 is specifically:

[0020] Simulate the impact of an earthquake on the system state by combining the earthquake intensity and the failure rate; obtain the peak ground acceleration magnitudes at different locations through an energy propagation model, as well as the failure rate of the gas network pipeline in the electrical coupling system and the peak ground velocity at its location; characterize the vulnerability function of system components in the form of a lognormal vulnerability curve; in the natural gas system, determine the earthquake damage rate of the natural gas transmission pipeline according to the peak ground velocity.

[0021] Preferably, step S3 is specifically as follows:

[0022] Under earthquake disasters, the strengthening method adopted is to upgrade the pole type;

[0023] List in advance the failure situations of components under all strengthening strategies, ζ ij,r,t ζ = 0 indicates that the line (i,j) fails under the r-th strengthening strategy;

[0024] The logical relationship between the available state of components after the disaster and the strengthening strategy is:

[0025]

[0026] Among them, Ω Dis,Pole and Ω Gas,Pipe respectively represent the sets of strengthening types for utility poles and gas transmission pipelines; respectively represent the available states of distribution lines, switches, compressors, and gas transmission pipelines;

[0027] Each component must have and only have one basic or strengthened pole type, and the constraints are as follows:

[0028]

[0029] The number of strengthened utility poles cannot exceed the total strengthening budget, and the constraints are as follows:

[0030]

[0031] Among them, represents the cost required for the (m,n)-th gas transmission pipeline to be strengthened at the r-th level; represents the cost required for the (i,j)-th gas transmission pipeline to be strengthened at the r-th level; respectively represent the maximum budgets for strengthening utility poles and pipelines.

[0032] Preferably, step S4 is specifically as follows:

[0033] S401. Input the earthquake source location, magnitude, and electrical-gas coupling system data, set the initial time to 0, and initialize the parameters of the electrical-gas coupling system;

[0034] S402. According to step S2, calculate the peak ground acceleration and peak ground velocity at component ij, and then calculate the failure probability p of component ij adopting the r-th reinforcement strategy. ij,r ;

[0035] S403. Determine whether the component is repaired at time t. If not, extract a random number r that is uniformly distributed in the [0, 1] space, and obtain the failure state ζ of component ij adopting the r-th reinforcement strategy. ij,r,t ;

[0036] S404. Make a reinforcement decision for the components of the electrical-gas coupling system. According to step S3, obtain the failure state u of component ij. ij,t ;

[0037] S405. Determine whether all components have been traversed. If there are still undetermined component states, return to step S402.

[0038] S406. Determine whether the maximum recovery time is reached at time t. If not, return to step S403. Otherwise, generate a random programming scenario for multi-level reinforcement.

[0039] Preferably, step S5 is specifically as follows:

[0040] The objective function for establishing the identification model of key components in the electrical-gas coupling system is to minimize the power load shedding cost and the gas load shedding cost; use the linearized DistFlow model to construct the operation constraints of the distribution network; construct the radial topology constraints; when a fault occurs in the distribution network line, cut off the power supply to the connected area of the fault point, and the influence of the fault is transmitted along the line with the fault point as the center until the nearest disconnected switch; establish the operation constraints of the natural gas system and the coupling equipment constraints.

[0041] Preferably, the objective function of the identification model of key components in the electrical-gas coupling system is:

[0042]

[0043] where, Ν represents the set of power system nodes; G represents the set of natural gas nodes; T represents the set of recovery times; is the weight of node j; is the weight of node m; P shed,j,t is the active load shed at node j at time t; W shed,m,t is the load shed amount at node m at time t; Δt is the repair time step;

[0044] The power balance constraint for each node is:

[0045]

[0046] Among them, Ν represents the set of power system nodes; L represents the set of power system lines; {DG} represents the set of distributed generator nodes, P ij,t , Q ij,t represents the active power and reactive power flowing through line (i, j) at time t; P L,j,t , Q L,j,t represents the active load and reactive load of power system node j at time t; P DG,j,t , Q DG,j,t represents the active power and reactive power output of DG at node j at time t; and are the minimum and maximum active power limits of DG at bus j; and are the minimum and maximum active power limits of DG at bus j; P shed,j , Q shed,j represents the shed active load and reactive load of the node;

[0047] The voltage relationship constraint between adjacent nodes is:

[0048]

[0049] Among them, U j,t represents the voltage at node j at time t; r ij and x ij represent the resistance and reactance of line (i, j); c ij,t is a 0-1 variable of the state of line (i, j) at time t; M represents a large number;

[0050] The capacity limit constraint of each line is:

[0051]

[0052] Among them, represents the maximum capacity of line (i, j).

[0053] The maximum and minimum voltage constraints of each node are:

[0054]

[0055] Among them, and are the maximum and minimum voltage limits at node j.

[0056] The DG output limit constraint is:

[0057]

[0058] Among them, represents a 0-1 variable indicating whether node j is affected by a fault. When the node is affected by a fault Take 1, otherwise take 0.

[0059] The demand limit constraint of the controllable load is:

[0060]

[0061] Radial topology constraint:

[0062]

[0063] Among them, N node represents the number of nodes; N sub represents the number of substations; γ i,t is a 0-1 variable indicating whether the node is a source node;

[0064] The variable γ i,t is only allowed to take 1 when the line connected to node i is disconnected, and the relevant constraint is:

[0065]

[0066] In each subgraph, the virtual load receives power supply from the virtual power source, and the relevant constraint is:

[0067]

[0068] Among them, {Sub} represents the set of substation nodes; V ij,t represents the virtual power of line (i, j) at time t; W i,t represents the output of the virtual power source at node i;

[0069] If line (i, j) is disconnected, the virtual power cannot flow, satisfying the necessary and sufficient condition 2 of the radial topology of the distribution network, and the relevant constraint is:

[0070]

[0071] The fault impact transfer constraint under N-k faults in the distribution network is as follows:

[0072]

[0073] Among them, ε and M are positive numbers close to 0 and large respectively; y ij1,t , y ij2t are binary variables representing the switch states on the i-side and j-side of line (i, j) at time t respectively; is a binary parameter;

[0074] The operation constraints of the natural gas system are as follows:

[0075]

[0076] Among them, GW represents the set of gas source nodes; G represents the set of natural gas nodes; Z act represents the set of natural gas pipelines with compressors; Z represents the set of natural gas pipelines without compressors. W GW,m,t represents the output of gas source m at time t; W m,min represents the minimum output of gas source m; W m,max represents the maximum output of gas source m; φ mn represents the relationship between gas flow and pressure; W L,m,t represents the gas load of node m at time t; W shed,m,t represents the load shedding of node m at time t; F mn,t represents the flow rate through pipeline (m,n) at time t; π m,t represents the gas pressure of node m at time t; λ mn represents the compression coefficient of the compressor on pipeline (m,n); represents the state of pipeline (m,n) at time t, being 1 indicates that the pipeline is working properly; represents the maximum flow rate of pipeline (m,n); represents the minimum flow rate of pipeline (m,n);

[0077] The coupling device constraints are as follows:

[0078]

[0079] Among them, W DG,m,t represents the natural gas consumption of DG at node m at time t; b j , c j represents the natural gas consumption coefficient of DG; η mn represents the power consumption coefficient of the compressor on pipeline (m,n); P comp,j,t represents the electricity load of the compressor at node j at time t.

[0080] Preferably, step S6 is specifically:

[0081] Use the preprocessing method based on Copeland ranking to pre-select important components, and at the same time obtain the repair priorities of components considering a large number of fault scenarios;

[0082] Adopt the non-sequential Monte Carlo simulation method to sample the states of different components of the electrical coupling system under seismic conditions; the binary variable s j (t) represents the state of component j at time t. If s j (t) = 1, it means that the component can work properly, otherwise s j (t) = 0; extract a random number r κ, the random number is uniformly distributed in the [0,1] space to obtain the operating state s of the line at the start of the repair process after an extreme disaster j (0):

[0083]

[0084] Among them, represents the failure probability of component j in the κth type of component;

[0085] Ensure that all repair teams start from the specified starting positions, and the constraints are:

[0086]

[0087] Among them, Crew represents the set of repair teams; σ represents the category of components to be repaired; represents the time when repair team c arrives at component m; dep σ represents the starting position of the repair team;

[0088] A component can only be repaired after the team arrives, so the relevant constraints are:

[0089]

[0090] Among them, B represents the set of damaged components; y m,c represents whether repair team c arrives at component m; f m,t represents whether component m is repaired at time t;

[0091] The time when the repair team arrives at the component and f m,t The relationship is:

[0092]

[0093] Among them represents the time required for repair team c to repair component m.

[0094] Used to couple f m,t with the component state s m (t) The constraints are:

[0095]

[0096] All repair teams only go to the faulty components once and also leave only once. The relevant constraints are as follows:

[0097]

[0098]

[0099] Among them, {dep} is the set of starting positions of the repair teams; represents x m,n,cA 0-1 variable indicating whether maintenance team c moves from component m to component n; The time it takes for maintenance team c to move from component m to component n;

[0100] The large M method is used to make the routes of maintenance personnel continuous, and the relevant constraints are as follows:

[0101]

[0102] Among them, The time it takes for maintenance team c to move from component m to component n;

[0103] The repair time of each component is calculated by the time when the maintenance personnel arrive at the component and the maintenance duration of the component:

[0104]

[0105] Among them, T m Indicates the completion time of the repair of component m;

[0106] According to the above Monte Carlo sampling method, a large number of fault scenarios can be obtained by sampling the fault scenarios. Solving the above model for each of these fault scenarios to obtain the repair time of each component in each scenario, the repair probability of each component at all times in multiple fault scenarios can be obtained, and finally the cumulative distribution function of the repair time of each component can be formed.

[0107] Preferably, step S7 is specifically:

[0108] Establish a stochastic programming model for key component identification, solve it using the progressive hedging algorithm, and obtain a reinforcement plan to strengthen the electric-gas coupling system. Use a compact expression to represent the proposed model:

[0109]

[0110] s.t. A1x1 ≤ b1

[0111] s.t. A2x2 ≤ b2

[0112]

[0113] Among them, x1, x2 are the first-stage decision variables h Line , h Pipe ; A1, A2 are the coefficient matrices related to x1, x2 respectively, and b1, b2 are the constant vectors related to x1, x2 respectively, is the feasible domain for the discrete variable x to run, including the power flow constraint of the distribution network, the radial topology constraint, and the fault impact transfer constraint. T is the set of restoration times, N is the set of power system nodes, P shed,j,tThe active load shed by node j at time t; Δt is the repair time step.

[0114] In a second aspect, an electrical coupling component strengthening system that comprehensively considers vulnerability and fault impact provided by an embodiment of the present invention includes:

[0115] A category module, which determines the type of disaster in the event of an earthquake disaster according to the disaster classification method for the impact on the electrical-gas coupling system, and obtains the categories of components affected by the earthquake in the electrical-gas coupling system;

[0116] A generation module, which generates the failure probabilities of vulnerable components in the power system and natural gas system after earthquake disasters at different levels of strengthening by using the earthquake disaster intensity attenuation model and the component failure probability generation method based on the obtained categories of components affected by the earthquake;

[0117] A constraint module, which, based on the obtained failure probabilities of vulnerable components in the power system and natural gas system, adopts multi-level strengthening measures for the electrical-gas coupling system facing earthquake scenarios to establish the strengthening strategy constraints for post-disaster components;

[0118] A state module, which, based on the obtained strengthening strategy constraints for post-disaster components, generates multi-level strengthened stochastic programming scenarios and obtains the failure states of components in the system after the disaster;

[0119] A construction module, which establishes a key component identification model for the electrical-gas coupling system, with the objective function being to minimize the power load shedding cost and gas load shedding cost, and the constraint conditions including distribution network power flow constraints, radial topology constraints, and fault impact transmission constraints;

[0120] A sorting module, which inputs the failure states of components in the system after the disaster into the key component identification model of the electrical-gas coupling system, pre-selects important components using Copeland sorting, and at the same time obtains the repair priorities of components considering a large number of fault scenarios. The pre-sorting result provides the repair priorities of components for optimizing the strengthening plan;

[0121] An output module, which, based on the obtained component pre-sorting result and stochastic programming scenario, establishes a stochastic programming model for key component identification, solves it using the progressive hedging algorithm, and obtains a strengthening plan to achieve the strengthening of the electrical-gas coupling system.

[0122] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-mentioned electrical coupling component strengthening method that comprehensively considers vulnerability and fault impact.

[0123] Fourthly, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for strengthening electrical coupling components considering comprehensive vulnerability and fault impact are implemented.

[0124] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for strengthening electrical coupling components considering comprehensive vulnerability and fault impact are implemented.

[0125] Sixthly, an embodiment of the present invention provides an electronic device, including a computer program, and when the computer program is executed by the electronic device, the steps of the above-mentioned method for strengthening electrical coupling components considering comprehensive vulnerability and fault impact are implemented.

[0126] Compared with the prior art, the present invention has at least the following beneficial effects:

[0127] A method for strengthening electrical coupling components considering comprehensive vulnerability and fault impact, adopting an optimization model for strengthening key components of the electrical-gas system considering comprehensive vulnerability and fault impact, can differentially strengthen power transmission towers and gas pipelines during the planning stage to minimize the system load shedding after extreme events; solves the problems that it is difficult to establish a planning model dependent on decision-making and to integrate a repair and dispatch model that requires a given set of fault scenarios through a two-stage decision-making, and reduces the scale of decision variables of components to be strengthened through a two-stage method of pre-sorting - stochastic programming, ensuring the solution efficiency of the model, and providing a scientific reference basis for the strengthening planning of key components of the electrical-gas coupling system in response to extreme events.

[0128] Furthermore, through the classification of disaster types and natures, a structured evaluation framework for disaster impacts is constructed. Its core advantage lies in distinguishing the differential damage mechanisms of different disasters (such as earthquakes, typhoons, etc.) to the electrical-gas coupling system, clarifying the relationship between the physical characteristics of disasters (such as intensity, propagation range) and system vulnerability, providing a scientific basis for subsequent targeted strengthening. By classifying and screening high-risk disaster scenarios, key risk sources can be focused on preferentially, avoiding the waste of resources in the "one-size-fits-all" defense strategy. At the same time, the judgment of disaster nature (such as the chain effect of secondary disasters) helps to identify the risk of cross-system cascading failures, enhance the comprehensiveness of the emergency plan, and lay a foundation for the dynamic adaptation of multi-level strengthening measures.

[0129] Furthermore, by quantifying the mapping relationship between disaster intensity and component failure probability based on the seismic intensity attenuation model, the limitations of traditional static vulnerability assessment are overcome. Its value lies in combining geospatial parameters (epicentral distance, geological conditions) with historical disaster data to dynamically predict the conditional failure probability of power / gas network components under different levels of earthquakes, improving the spatio-temporal accuracy of the probability model. By introducing the "strengthening level" variable, the inhibitory effect of reinforcement measures (such as equipment seismic retrofit) on the failure rate is quantified, realizing the quantitative trade-off between "investment - reliability", and providing data support for optimizing resource allocation. This method is also compatible with the extension of multiple disaster types and supports the modular iterative update of the probability model.

[0130] Furthermore, by establishing multi-level reinforcement strategy constraints, the pre-disaster defense and post-disaster recovery are synergistically optimized. Its innovation lies in constructing a decision variable system that is divided into stages (preventive reinforcement, emergency repair) and intensities (such as from level 1 reinforcement to level 3 reinforcement), realizing the flexible allocation of defense measures under limited resources. Constraint conditions (such as budget limitations, physical reinforcement upper limits) ensure the engineering feasibility of the strategy, while cross-system coupling constraints (such as the mutual supply dependence between the power and gas networks) can block the cross-domain propagation of faults. This method transforms the complex reinforcement problem into a solvable mathematical programming through linear / nonlinear constraint modeling, taking into account both computational efficiency and strategy robustness, and providing a standardized input interface for stochastic scenario generation.

[0131] Furthermore, the stochastic programming scenario generation mechanism effectively solves the modeling problems of disaster uncertainty and failure diversity. Its advantage lies in using Monte Carlo simulation or Latin hypercube sampling to generate a large number of failure scenario sets covering different disaster intensities and component failure combinations, covering "low-probability - high-loss" extreme events, and avoiding strategy biases caused by deterministic optimization. By assigning probability weights, the decision-making weights of high-frequency failure scenarios are highlighted, improving the practicality of the reinforcement plan. In addition, scenario reduction techniques (such as cluster analysis) can compress redundant scenarios and reduce the computational complexity. The failure scenario library output by this step provides real and diverse input data for subsequent identification of key components, supporting the verification of the generalization ability of resilience strategies.

[0132] Furthermore, the key component identification model accurately locates cross-system key nodes through multi-objective optimization and failure propagation constraints. Its core value lies in minimizing the load shedding cost as the goal, quantifying the dual impact of component failures on electricity - gas users, and identifying "high-impact - low-redundancy" bottleneck components through sensitivity analysis. The radial topology constraint ensures the physical feasibility of distribution network reconstruction, while the failure transfer constraint (such as the gas network pressure - power grid power coupling equation) can simulate the cross-domain fault diffusion path and reveal hidden key nodes. Combining the Copeland ranking method, the components are dynamically sorted according to indicators such as failure loss and repair difficulty, forming a reinforcement sequence of "defend the core first, then supplement the periphery". This method takes into account both economy and safety, providing a quantitative decision-making basis for formulating differential defense priorities.

[0133] It can be understood that for the beneficial effects of the above second to sixth aspects, reference can be made to the relevant descriptions in the above first aspect, and details will not be elaborated here.

[0134] To sum up, through the disaster classification and earthquake intensity attenuation model, the present invention realizes the refined assessment of disaster impacts and improves the accuracy of risk prediction; by integrating multi-level reinforcement measures and stochastic programming scenario generation, it strengthens the adaptability of the system to dynamic disasters and enhances the overall resilience of the electric-gas coupling system. The method innovatively establishes a key component identification model, aiming at minimizing the power / gas load shedding cost, taking into account the constraints of fault propagation and operation safety, and balancing economy and reliability; it predicts the priority of component repair through the Copeland ranking algorithm, optimizes the allocation of emergency resources, and significantly shortens the post-disaster recovery time. Through systematic integration of disaster assessment, probability analysis, multi-level reinforcement and dynamic optimization, a closed-loop management framework is formed; it not only ensures the scientific nature of the key component reinforcement strategy, but also covers complex fault conditions through stochastic scenario simulation, improving the anti-destruction ability under extreme events; at the same time, it quantifies the load loss cost, realizes the balance between resilience improvement and economy, and provides efficient decision-making support for the disaster prevention planning and post-disaster rapid recovery of the electric-gas coupling system.

[0135] The technical solutions of the present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0136] To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly introduced below. Obviously, the following described drawings are only some embodiments of the present application, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0137] Figure 1 It is a schematic diagram of the pole type;

[0138] Figure 2 It is a flow chart of stochastic programming scenario generation for multi-level reinforcement;

[0139] Figure 3 It is a subgraph formed during the network reconfiguration process;

[0140] Figure 4 It is a schematic diagram of the reinforcement decision-making process for key components;

[0141] Figure 5 It is a schematic diagram of an example for optimizing and comparing two reinforcement schemes;

[0142] Figure 6 It is a schematic diagram of a 70-node distribution network and a 20-node natural gas network coupling system;

[0143] Figure 7 Schematic diagram of the cumulative distribution function of the repair times for five typical components;

[0144] Figure 8 Schematic diagram of the comparison of the top 10 components before sorting under different simulation times;

[0145] Figure 9 is a schematic diagram of the Copeland sorting result;

[0146] Figure 10 Schematic diagram of the entire process of restoration for a typical scenario;

[0147] Figure 11 Schematic diagram of the comparison of the electrical-gas weighted average load at each moment in multiple scenarios under different reinforcement schemes;

[0148] Figure 12 Schematic diagram of the computer device provided by an embodiment of the present invention;

[0149] Figure 13 Block diagram of an electronic device provided by an embodiment of the present invention according to one embodiment.

[0150] Among them, 60. Computer device; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access storage unit; 6202. Cache storage unit; 6203. Read-only storage unit; 6204. Program / utilities; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed implementation manner

[0151] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0152] Embodiment 1

[0153] An electrical coupling component strengthening method integrating vulnerability and fault impact of the present invention includes the following steps:

[0154] S1. Classify disasters according to their impact on the electrical-gas coupling system, and judge the types and natures of the disasters that occur;

[0155] Natural disasters that affect the electrical-gas coupling system are divided into surface disasters represented by storm disasters and geological disasters represented by earthquake disasters;

[0156] In the power system, most transmission lines adopt the form of overhead lines and are supported by poles and towers. Both surface disasters and geological disasters can cause the poles to fall or the wires to break, so both types of natural disasters have an impact on the power system;

[0157] In the natural gas system, most gas pipelines are buried underground, so surface disasters that only occur on the ground have little impact on them, but geological disasters can cause damage to them. The damage caused by different disasters to the system varies greatly, so it is necessary to clearly distinguish the types of disasters and various information about the disasters.

[0158] S2. Use the seismic hazard intensity attenuation model to obtain the failure probabilities of vulnerable components of the power system and natural gas system strengthened at different levels under the corresponding disaster types;

[0159] Taking the response to seismic disasters as an example for modeling, compared with typhoons and others, earthquakes not only pose a serious threat to the distribution system, but are more likely to cause damage to the natural gas system. Therefore, the model has generality. In order to accurately simulate the restoration process, the impact of earthquakes on the system state is simulated by combining the earthquake intensity and the failure rate;

[0160] S201. In order to accurately simulate the restoration process, the impact of earthquakes on the system state is simulated by combining the earthquake intensity and the failure rate;

[0161] In the electrical coupling system, the failure rates of distribution network components and gas network compressors are related to the peak ground acceleration (PGA) at their locations. The PGA values at different locations are obtained through the energy propagation model; The peak ground acceleration attenuation model obtained from the study of 13 soil layer stations in the 8.0-magnitude Wenchuan earthquake is adopted:

[0162] lgG a = 2.457 + 0.388M - 1.854lg(R + 0.612e 0.457M ) ± 0.236 (1)

[0163] where G a represents PGA, R represents the distance from the earthquake source, and M represents the earthquake magnitude.

[0164] In the electrical coupling system, the failure rate of the gas network pipeline is related to the peak ground velocity (PGV) at its location. The PGV is determined by the 1-second spectral acceleration, as follows:

[0165]

[0166] S A1 ≈ k·PGA (3)

[0167] Among them, S A1 represents the spectral acceleration per second; k is a parameter related to the epicentral distance, terrain, magnitude, etc.

[0168] S202. To simulate the vulnerability of distribution components in earthquake disasters, the vulnerability function of system components is characterized in the form of a lognormal vulnerability curve;

[0169]

[0170] Among them, P[d s |G a represents the probability of reaching or exceeding the damaged state s when the PGA is G a ; represents the probability that the damaged state is s when the PGA is G a ; represents the average value of the peak ground acceleration of the ground motion when reaching the s damage state; represents the logarithmic standard deviation of the peak ground acceleration of the ground motion when reaching the s damage state; φ is the standard cumulative normal distribution function.

[0171] According to the failure rates of components in each damage state, the failure probability of a single component in the distribution network is expressed as:

[0172]

[0173] Among them, Z s represents the damage ratio in the s-th damaged state. The damage ratios of distribution network lines in 4 states are 4%, 12%, 50%, and 80% respectively; l j represents the length of component j affected by the earthquake; l all represents the total length of all lines in the distribution network P j,s represents the failure probability of component j in the s-th damaged state.

[0174] S203. In the natural gas system, the failure model of the compressor is the same as that of the distribution network model. The natural gas pipeline may span multiple seismic intensity zones, and the seismic damage rates of different parts of the pipeline may be different. Therefore, the natural gas pipeline is evenly divided into elemental pipelines with a length of ΔL; when the elemental pipeline is small enough, its position is represented by the midpoint, and the seismic damage rate of the natural gas transmission pipeline is determined by PGV

[0175]

[0176] Among them, a′∈z refers to all elemental pipelines belonging to the natural gas pipeline z; λ a' is the failure probability of the elemental pipeline a′ under the earthquake; R f is the seismic damage rate, and the damage rate R fis the number of damages per 1 km of pipeline; ΔL is the length of the elemental pipeline; p z is the failure probability of pipeline z.

[0177] S3. Propose multi-level reinforcement measures for the electrical-gas coupling system facing earthquake scenarios, and establish constraint conditions such as the logical relationship between the available state of components after disasters and reinforcement strategies.

[0178] For geological disasters represented by earthquake disasters, most of the damage to distribution lines is due to the tilt or collapse of power poles. Most of the power poles in the current distribution system are concrete poles, and some are steel pipe poles. The reinforcement method adopted is to upgrade the pole type. Similarly, the damage of the gas transmission pipeline will lead to the damage of the natural gas system. Similar to the reinforcement of power poles, the reinforcement of gas transmission pipelines also adopts different types of reinforced pipelines. The seismic resistance of ductile pipelines and brittle pipelines is different, and the brittle pipeline is the basic pipeline type. Establish constraint conditions such as the logical relationship between the available state of components after disasters and reinforcement strategies, and the upper limit of reinforcement budget.

[0179] Under earthquake disasters, most of the damage to distribution lines is due to the tilt or collapse of power poles. Most of the power poles in the current distribution system are concrete poles, and some are steel pipe poles. The reinforcement method adopted is to upgrade the pole type.

[0180] According to the requirements of the "Circular Concrete Poles" (GB 4623-2014) specification, concrete poles are divided into ordinary reinforced concrete poles, partially prestressed concrete poles and prestressed concrete poles, and the selected concrete strength grades are not less than C40 and C50 respectively.

[0181] Select three types of pole types, the basic pole type: a non-prestressed concrete pole with a strength of C40;

[0182] The reinforced pole types: a prestressed concrete pole with a strength of C40 and a prestressed concrete pole with a strength of C50. Therefore, as Figure 1 shown, for each line, the decision variable can be used to represent which type of reinforced pole is used for this line. For example, represents that line (i,j) adopts the r-th type of reinforced pole.

[0183] The damage of the gas transmission pipeline leads to the damage of the natural gas system. Similar to the reinforcement of power poles, the reinforcement of gas transmission pipelines also adopts different types of reinforced pipelines. The seismic resistance of ductile pipelines and brittle pipelines is different, and the brittle pipeline is the basic pipeline type. As shown in Table 1, for each pipeline, the decision variable can be used to represent which type of reinforced pipeline is used for the line;

[0184] Table 1 Reinforced Gas Transmission Pipeline Types

[0185]

[0186] Among them, *CI = cast iron, AC = asbestos cement, RCC = reinforced concrete column, DI = Ductile iron , S = iron, PVC = polyethylene.

[0187] For different reinforcement strategies, since the reinforcement strategies will affect the probability of component failure, all the failure situations of components under all reinforcement strategies are listed in advance, that is, ζ ij,r,t = 0 indicates that the line (i,j) will fail when the r-th reinforcement strategy is adopted;

[0188] The logical relationship between the available state of components after the disaster and the reinforcement strategy is:

[0189]

[0190] Among them, Ω Dis,Pole and Ω Gas,Pipe represent the sets of reinforcement types of poles and gas pipelines respectively; represent the available states of distribution lines, switches, compressors, and gas pipelines respectively. If it is 1, it means available.

[0191] Each component must have one and only one basic or reinforced pole type, and the constraints are as follows:

[0192]

[0193] The number of reinforced poles cannot exceed the total reinforcement budget, and the constraints are as follows:

[0194]

[0195] Among them, represents the cost required for the r-th level of reinforcement of the gas pipeline (m,n); represents the cost required for the r-th level of reinforcement of the gas pipeline (i,j); represent the maximum budgets for pole and pipeline reinforcement respectively.

[0196] S4. Generate a multi-level reinforcement stochastic programming scenario and obtain the failure situations of components in the system after the disaster;

[0197] For the multi-level reinforcement stochastic programming scenario, input the earthquake epicenter location, magnitude, and the data of the electrical-gas coupling system, initialize the parameters of the electrical-gas coupling system, the failure probabilities of each component with different reinforcement strategies calculated by step S3, establish the logical relationship between the available state of components after the disaster and the reinforcement strategy, make decisions on the reinforcement of components in the electrical-gas coupling system, obtain the failure states of each component according to the reinforcement decisions, and generate a multi-level reinforcement stochastic programming scenario.

[0198] S401. Input the earthquake source location, magnitude, and data of the electric-gas coupling system, and set the initial time to 0. Initialize the parameters of the electric-gas coupling system;

[0199] S402. According to step S2, calculate the peak ground acceleration PGA and peak ground velocity PGV at component ij during the earthquake, and then calculate the failure probability p of component ij adopting the r-th reinforcement strategy ij,r ;

[0200] S403. Determine whether the component is repaired at time t. If not, draw a random number r, which is uniformly distributed in the [0,1] space, to obtain the failure state ζ of component ij adopting the r-th reinforcement strategy ij,r,t :

[0201]

[0202] S404. Make a reinforcement decision for the components of the electric-gas coupling system. According to step S3, obtain the failure state u of component ij ij,t ;

[0203] S405. Determine whether all components have been traversed. If there are still undetermined component states, return to step S402;

[0204] S406. Determine whether the maximum recovery time is reached at time t. If not, return to step S403. Otherwise, generate a random programming scenario for multi-level reinforcement.

[0205] S5. Establish an identification model for key components of the electric-gas coupling system, with the objective function of minimizing the power load shedding cost and gas load shedding cost, and its constraints including power flow constraints of the distribution network, radial topology constraints, and failure impact transfer constraints;

[0206] S501. Establish an identification model for key components of the electric-gas coupling system. Since earthquake faults can also cause failures in the natural gas system, the objective function is to minimize the power load shedding cost and gas load shedding cost;

[0207] The objective function is to minimize the power load shedding cost and gas load shedding cost:

[0208]

[0209] Among them, Ν represents the set of power system nodes; G represents the set of natural gas nodes; T represents the set of recovery times; is the weight of node j; is the weight of node m; P shed,j,t is the active load shed at node j at time t; W shed,m,t is the load shed at node m at time t, and Δt is the repair time step, assuming Δt is 15 minutes.

[0210] S502. Construct the operation constraints of the distribution network using the linearized DistFlow model;

[0211] The power balance constraints for each node are as follows:

[0212]

[0213] where, Ν represents the set of nodes in the power system; L represents the set of lines in the power system; {DG} represents the set of distributed generator nodes, P ij,t , Q ij,t represent the active power and reactive power flowing through line (i,j) at time t; P L,j,t , Q L,j,t represent the active load and reactive load of power system node j at time t; P DG,j,t , Q DG,j,t represent the active power output and reactive power output of DG at node j at time t; and are the minimum and maximum active power limits of DG at bus j; and are the minimum and maximum active power limits of DG at bus j; P shed,j , Q shed,j represent the shed active load and reactive load of the node;

[0214] If the branch is closed, the voltage difference of the branch is restricted by the power flow. If the branch is open, the voltage difference is arbitrary and the branch flow must be zero. The voltage relationship constraint between adjacent nodes is as follows:

[0215]

[0216] where, U j,t represents the voltage at node j at time t; r ij and x ij represent the resistance and reactance of line (i,j); c ij,t is a 0-1 variable of the state of line (i,j) at time t, 0 means open and 1 means closed; M represents a large number.

[0217] The capacity limit constraint for each line is as follows:

[0218]

[0219] where, represents the maximum capacity of line (i,j).

[0220] The maximum and minimum voltage constraints for each node are as follows:

[0221]

[0222] wherein, and are the maximum and minimum voltage limits at node j.

[0223] The DG output limit constraint is:

[0224]

[0225] wherein, is a 0-1 variable indicating whether node j is affected by a fault. When the node is affected by a fault takes 1, otherwise takes 0.

[0226] The demand limit constraint of the controllable load is:

[0227]

[0228] S503. Constructing the radial topology constraint

[0229] The distribution network usually needs to operate under a radial network structure. The necessary and sufficient conditions for maintaining the radial topology are:

[0230] ① The number of closed lines is equal to the difference between the number of nodes and the number of subgraphs in the topology;

[0231] ② The connectivity of each subgraph is satisfied.

[0232] During the network reconstruction and restoration process after a fault, the subgraphs include the subgraph connected to the substation, the subgraph powered only by DGs, and the passive island subgraphs. As Figure 3 shown, after lines 1-2 and 8-9 are attacked, passive island subgraphs are formed at nodes 2-3 and 8. At this time, the tie line is closed, and nodes 4, 5, 9, and 10 form a subgraph powered by DGs, and the remaining nodes form a subgraph connected to the substation. Through such distribution network reconstruction, flexible reconstruction zoning can be achieved, which not only supports the formation of a microgrid powered by distributed power sources but also supports the formation of load islands.

[0233] The number of closed lines is equal to the number of nodes N node minus 1 (i.e., the number of subgraphs connected to the substation) minus the number of subgraphs powered only by DGs and island subgraphs, which can satisfy the necessary and sufficient condition 1 of the radial topology. The relevant constraint is:

[0234]

[0235] wherein, N node represents the number of nodes; N sub represents the number of substations; γ i,t is a 0-1 variable indicating whether the node is a source node.

[0236] Variable γ i,t It is only allowed to take 1 when the line connected to node i is disconnected. The relevant constraints are:

[0237]

[0238] In each sub - graph, the virtual load can obtain the power supply from the virtual power source, that is, each sub - graph is a connected graph. The relevant constraints are:

[0239]

[0240] Among them, {Sub} represents the set of substation nodes; V ij,t represents the virtual power of line (i, j) at time t; W i,t represents the output of the virtual power source at node i, which is an unrestricted real number. In the virtual network, the substation nodes and the nodes with γ i,t = 1 are regarded as source nodes, and the unrestricted virtual power source output power is set. For other nodes, virtual loads with a load value of 1 are set.

[0241] If line (i, j) is disconnected, the virtual power cannot flow, satisfying the necessary and sufficient condition 2 of the radial topology of the distribution network. The relevant constraints are:

[0242]

[0243] S504. When a fault occurs in the distribution network line, the power supply to the area connected to the fault point should be cut off. The influence of the fault spreads along the line centered on the fault point until the nearest disconnected switch. The task of fault isolation is to limit the influence of the fault to a smaller area as much as possible by disconnecting some switches. The following constraints for the transmission of fault influence under N - k faults in the distribution network are established:

[0244]

[0245]

[0246] Among them, ε and M are positive numbers close to 0 and relatively large respectively; y ij1,t , y ij2t are binary variables representing the switch states on the i - side and j - side of line (i, j) at time t. If there is no switch configured or the switch is in the closed state, it takes 1, otherwise it takes 0; is a binary parameter. If the RCS is configured on the i - side (κ = 1) or j - side (κ = 2) of line (i, j), it takes 1, otherwise it takes 0;

[0247] S505. Establish the operating constraints of the natural gas system.

[0248] Assume that the operation of natural gas is in a steady state, that is, the pressure dynamics of gas flow in the pipeline during operation are not considered, and the gas flow direction in the pipeline is fixed. The following operation constraints of the natural gas system are established:

[0249]

[0250] Among them, GW represents the set of gas source nodes; G represents the set of natural gas nodes; Z act represents the set of natural gas pipelines with compressors; Z represents the set of natural gas pipelines without compressors; W GW,m,t represents the output of gas source m at time t; W m,min represents the minimum output of gas source m; W m,max represents the maximum output of gas source m; φ mn represents the relationship between gas flow and pressure; W L,m,t represents the gas load of node m at time t; W shed,m,t represents the load shedding amount of node m at time t; F mn,t represents the flow rate flowing through pipeline (m,n) at time t; π m,t represents the gas pressure of node m at time t; λ mn represents the compression coefficient of the compressor on pipeline (m,n); represents the state of pipeline (m,n) at time t, being 1 indicates that the pipeline is working properly; represents the maximum flow rate of pipeline (m,n); represents the minimum flow rate of pipeline (m,n);

[0251] S506. Establish the following coupling equipment constraints:

[0252]

[0253] Among them, W DG,m,t represents the natural gas consumption of DG at node m at time t; b j , c j represents the natural gas consumption coefficient of DG; η mn represents the power consumption coefficient of the compressor on pipeline (m,n); P comp,j,t represents the electricity load of the compressor at node j at time t.

[0254] S6. Input the fault states of the components in the system after the disaster obtained in step S4 into the key component identification model of the electrical-gas coupling system obtained in step S5, preselect important components using the preprocessing method based on Copeland ranking, and at the same time obtain the repair priorities of components considering a large number of fault scenarios. At the same time, obtain the repair priorities of components considering a large number of fault scenarios. The pre-ranking results provide the repair priorities of components for the optimization of the reinforcement plan;

[0255] S601. Use the non - chronological Monte Carlo simulation method to sample the states of different components of the electrical coupling system under seismic conditions. The binary variable s j (t) represents the state of component j at time t. If s j (t) = 1, it means the component can work normally; otherwise, s j (t) = 0. Extract a random number r κ , which is uniformly distributed in the [0, 1] space, to obtain the operating state s j (0) of the line at the beginning of the repair process after the extreme disaster:

[0256]

[0257] Among them, represents the failure probability of component j in the κ - type components, which is obtained separately in the previous section. Therefore, the failure state of each component can be obtained to realize the sampling of the failure scenario under seismic conditions;

[0258] S602. To consider the priority of component repair, establish the path constraints of the repair team. After the disaster occurs, carry out the fault repair scheduling for collaborative operation recovery. The present invention considers that components in the electrical and gas systems fail respectively. The power system includes lines, and the gas system includes compressors and gas pipelines. For component failures in different systems, different repair teams are responsible for the repair. At the same time, assume that there is a unified command center for the two systems.

[0259] Ensure that the repair teams all start from the specified departure positions, and the constraints are:

[0260]

[0261] Among them, Crew represents the set of repair teams; σ represents the category of the repaired component; represents the time when repair team c arrives at component m; dep σ represents the departure position of the repair team.

[0262] The component can only be repaired after the team arrives. Therefore, the relevant constraint is:

[0263]

[0264] Among them, B represents the set of damaged components; y m,c represents whether repair team c arrives at component m; f m,t represents whether component m is repaired at time t. If it is, it is equal to 1; otherwise, it is equal to 0.

[0265] The relationship between the time when the repair team arrives at the component and f m,t is:

[0266]

[0267] Among them, represents the time required for repair team c to repair component m.

[0268] For coupling f m,t with component state s m (t) the constraint is:

[0269]

[0270] All repair teams can only go to a faulty component once and can only leave once. The relevant constraints are as follows:

[0271]

[0272] Among them, {dep} represents the set of departure positions of the repair teams; x m,n,c represents a 0-1 variable indicating whether repair team c moves from component m to component n. If it is, it is equal to 1, otherwise it is equal to 0; represents the time consumed for repair team c to move from component m to component n.

[0273] Using the big M method to make the route of the repair personnel continuous, the relevant constraints are as follows:

[0274]

[0275] Among them, represents the time consumed for repair team c to move from component m to component n.

[0276] The repair time of each component is calculated by the time when the repair personnel arrive at the component and the repair duration of the component:

[0277]

[0278] Among them, T m represents the completion time of the repair of component m.

[0279] According to the above Monte Carlo sampling method, sampling the fault scenarios can obtain a large number of fault scenarios. Solving the above model for these fault scenarios, obtaining the repair time of each component in each scenario, the repair probability of each component at all times in multiple fault scenarios can be obtained. Finally, the cumulative distribution function of the repair time of each component can be formed;

[0280] S603. Use the modified Copeland ranking method: update the Copeland score by comparing the repair probabilities of two components at multiple moments. Comparative ranking can be performed using the Copeland ranking method. After comparing all objects, calculate the total score, which is the Copeland score. Finally, sort the Copeland score results of each component to obtain the evaluation result of component importance.

[0281] Among them, the method for determining the components required for scenario s is as follows:

[0282] Objective function: (19)

[0283] Constraint conditions: (20)-(56)

[0284] The Copeland score S of a certain component m m,k is calculated as follows:

[0285]

[0286] Among them, q k (m) represents the k-th percentile of the cumulative distribution function of the repair time of component m; S m,n,k represents the Copeland score after the k-th comparison between component m and n; S m represents the total Copeland score of component m.

[0287] First, perform Ω Copeland pairwise comparisons between component m and all other components to obtain S m,n,k ;

[0288] Then, add the Copeland scores S m,n,k of each pairwise comparison in each group to obtain the final Copeland score S m of component m.

[0289] S7. Based on the pre-sorting result of components obtained in step S6 and the stochastic programming scenario obtained in step S4, establish a stochastic programming model for identifying key components, and solve it using the progressive hedging algorithm to obtain a reinforcement plan to achieve the reinforcement of the electrical-gas coupling system.

[0290] Establish a stochastic programming model for identifying key components, and solve it using the progressive hedging algorithm to obtain a reinforcement plan to achieve the reinforcement of the electrical-gas coupling system.

[0291] The complete form of the stochastic programming model for identifying key components is as follows:

[0292] Objective function: (19)

[0293] Constraint conditions: (10)-(17), (20)-(44)

[0294] This stochastic programming model can be transformed into a mixed-integer linear programming problem. The original problem can be decomposed into several scenario-based sub-problems to reduce the computational complexity.

[0295] To facilitate the solution of the model using the PH algorithm, a compact expression is used to represent the proposed model:

[0296]

[0297] s.t. A1x1 ≤ b1 (61)

[0298] s.t. A2x2 ≤ b2 (62)

[0299]

[0300] where the vectors x1 and x2 are the first-stage decision variables h Line , h Pipe ; A1 and A2 are the coefficient matrices related to x1 and x2 respectively, and b1 and b2 are the constant vectors related to x1 and x2 respectively. is the feasible region for the discrete variable x to run, including the power flow constraints of the distribution network, the radial topology constraints, and the fault impact transfer constraints. T is the set of restoration times, N is the set of power system nodes, and P shed,j,t is the active load shed at node j at time t; Δt is the repair time step; Equation (64) is the objective function expressed in the form of a function related to the decision variables.

[0301] Those skilled in the art of the present invention can understand that various aspects of the present invention can be implemented as a system, a method, or a program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuitry", "module", or "platform" here.

[0302] Embodiment 2

[0303] The present invention provides an electrical coupling component strengthening system integrating vulnerability and fault impact. This system can be used to implement the electrical coupling component strengthening method integrating vulnerability and fault impact. Specifically, the electrical coupling component strengthening system integrating vulnerability and fault impact includes a category module, a generation module, a constraint module, a status module, a construction module, a sorting module, and an output module.

[0304] Among them, the category module determines the type of disaster in the event of an earthquake disaster according to the disaster classification method affecting the electrical-gas coupling system, and obtains the categories of components affected by the earthquake in the electrical-gas coupling system;

[0305] A generation module that generates the failure probabilities of vulnerable components in the power system and natural gas system after earthquake disasters with different levels of reinforcement according to the obtained component categories affected by earthquakes, using the earthquake disaster intensity attenuation model and the component failure probability generation method.

[0306] A constraint module that, based on the obtained failure probabilities of vulnerable components in the power system and natural gas system, adopts multi-level reinforcement measures for the electrical-gas coupled system facing earthquake scenarios to establish the reinforcement strategy constraints for post-disaster components.

[0307] A state module that, based on the obtained reinforcement strategy constraints for post-disaster components, generates random programming scenarios with multi-level reinforcement and obtains the failure states of components in the system after the disaster.

[0308] A construction module that establishes a key component identification model for the electrical-gas coupled system, with the objective function of minimizing the power load shedding cost and gas load shedding cost, and the constraint conditions including distribution network power flow constraints, radial topology constraints, and fault impact transfer constraints.

[0309] A sorting module that inputs the failure states of components in the system after the disaster into the key component identification model of the electrical-gas coupled system, pre-selects important components using Copeland sorting, and simultaneously obtains the repair priorities of components considering a large number of fault scenarios. The pre-sorting results provide the repair priorities of components for optimizing the reinforcement plan.

[0310] An output module that, based on the obtained component pre-sorting results and random programming scenarios, establishes a stochastic programming model for key component identification, solves it using the progressive hedging algorithm, and obtains a reinforcement plan to achieve the reinforcement of the electrical-gas coupled system.

[0311] Embodiment 3

[0312] The present invention provides a terminal device, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Graphics Processing Unit (GPU), Tensor Processing Unit (TPU), Digital Signal Processor (DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function. The processor described in the embodiments of the present invention can be used for the operation of the method for strengthening electrical coupling components considering vulnerability and fault impact, including:

[0313] According to the disaster classification method for the impact on the electrical-gas coupling system, determine the type of disaster caused by the earthquake, and obtain the component categories in the electrical-gas coupling system affected by the earthquake. According to the component categories affected by the earthquake, use the earthquake disaster intensity attenuation model and the component failure probability generation method to generate the failure probabilities of vulnerable components in the power system and natural gas system after different levels of strengthening after the earthquake disaster. Based on the failure probabilities of vulnerable components in the power system and natural gas system, adopt multi-level strengthening measures for the electrical-gas coupling system facing the earthquake scenario, and establish the strengthening strategy constraints for the components after the disaster. Based on the obtained strengthening strategy constraints for the components after the disaster, generate a multi-level strengthened stochastic programming scenario, and obtain the failure states of the components in the system after the disaster. Establish an identification model for key components of the electrical-gas coupling system, with the objective function of minimizing the power load shedding cost and gas load shedding cost, and the constraint conditions including distribution network power flow constraints, radial topology constraints, and fault impact transmission constraints. Input the failure states of the components in the system after the disaster into the obtained identification model for key components of the electrical-gas coupling system, and use Copeland ranking to pre-select important components in advance, and at the same time obtain the repair priorities of the components considering a large number of fault scenarios. The pre-ranking results provide the repair priorities of the components for the optimization of the strengthening plan. According to the obtained pre-ranking results of the components and the stochastic programming scenario, establish a stochastic programming model for key component identification, and use the step-by-step hedging algorithm to solve it to obtain the strengthening plan to achieve the strengthening of the electrical-gas coupling system.

[0314] Please refer to Figure 12 , the terminal device is a computer device. The computer device 60 in this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, it implements the method for strengthening electrical coupling components considering comprehensive vulnerability and fault impact in the embodiment. To avoid repetition, details are not elaborated here. Alternatively, when the computer program 63 is executed by the processor 61, it implements the functions of each model / unit in the system for strengthening electrical coupling components considering comprehensive vulnerability and fault impact in the embodiment. To avoid repetition, details are not elaborated here.

[0315] The computer device 60 can be a computing device such as a desktop computer, a notebook, a palm computer, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art can understand that Figure 12 merely examples of the computer device 60, which do not constitute a limitation on the computer device 60. It may include more or fewer components than those shown in the figure, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, a bus, etc.

[0316] The so-called processor 61 may be a central processing unit (CPU), or may also be other general-purpose processors, a graphics processing unit (GPU), a tensor processing unit (TPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0317] The memory 62 may be an internal storage unit of the computer device 60, such as the hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk equipped on the computer device 60, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.

[0318] Further, the memory 62 may also include both the internal storage unit of the computer device 60 and external storage devices. The memory 62 is used to store computer programs as well as other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0319] Please refer to Figure 13 , the terminal device is the electronic device 600, and the electronic device 600 is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0320] Among them, the storage unit stores program codes, and the program codes can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the method part of this specification. For example, the processing unit 610 can execute steps as shown in Figure 2 .

[0321] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only storage unit (ROM) 6203.

[0322] The storage unit 620 may also include a program / utilities 6204 having a set (at least one) of program modules 6205. Such program modules 6205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0323] The bus 630 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any one of the multiple bus structures.

[0324] The electronic device 600 can also communicate with one or more external devices 700 (such as a keyboard, a pointing device, a Bluetooth device, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (such as a router, a modem). Such communication can be carried out through the input / output interface 650. Moreover, the electronic device 600 can also communicate with one or more networks (such as a local area network, a wide area network, and / or a public network, such as the Internet) through the network adapter 660. The network adapter 660 can communicate with other modules of the electronic device 600 through the bus 630. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0325] Embodiment 4

[0326] The present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. It can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. And, one or more instructions suitable for being loaded and executed by a processor are stored in this storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples of the computer-readable storage medium here include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0327] The computer-readable storage medium also includes a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, radio frequency, etc., or any suitable combination of the above.

[0328] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as an independent software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network or a wide area network, or can be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0329] One or more instructions stored in the computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the method for strengthening an electrical coupling component related to comprehensive vulnerability and fault impact in the above embodiments; the one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0330] According to the disaster classification method for the impact on the electric-gas coupling system, determine the type of disaster occurring in the earthquake disaster, and obtain the categories of components affected by the earthquake in the electric-gas coupling system; according to the categories of components affected by the earthquake, use the earthquake disaster intensity attenuation model and the component failure probability generation method to generate the failure probabilities of vulnerable components in the power system and natural gas system after the earthquake with different levels of enhancement; based on the failure probabilities of vulnerable components in the power system and natural gas system, adopt multi-level enhancement measures for the electric-gas coupling system facing earthquake scenarios, and establish the enhancement strategy constraints for components after the disaster; based on the obtained enhancement strategy constraints for components after the disaster, generate multi-level enhanced stochastic programming scenarios, and obtain the failure states of components in the system after the disaster; establish a key component identification model for the electric-gas coupling system, with the objective function of minimizing the power load shedding cost and gas load shedding cost, and the constraint conditions including distribution network power flow constraints, radial topology constraints, and fault impact transfer constraints; input the obtained failure states of components in the system after the disaster into the obtained key component identification model for the electric-gas coupling system, use the Copeland ranking to pre-select important components in advance, and at the same time obtain the repair priorities of components considering a large number of fault scenarios, and the pre-ranking results provide the repair priorities of components for the optimization of the enhancement plan; according to the obtained pre-ranking results of components and the stochastic programming scenarios, establish a stochastic programming model for key component identification, solve it using the progressive hedging algorithm, and obtain the enhancement plan to achieve the enhancement of the electric-gas coupling system.

[0331] The databases involved in the embodiments provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., and is not limited thereto. The processors involved in the embodiments provided in this application may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and is not limited thereto.

[0332] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0333] After the pre-sorting in step S6 and obtaining multiple stochastic programming scenarios through step S3, the stochastic programming solution is completed using the progressive hedging algorithm, and the effectiveness of the model is verified through numerical examples as follows:

[0334] 1. By using the two-stage method of pre-sorting and stochastic programming, the scale of the decision variables of the components to be strengthened is reduced. The framework of the proposed two-stage decision model is as Figure 4 shown:

[0335] In the first stage, considering the fault impact and optimizing the repair order, the important components are pre-sorted to initially screen the important components from a large number of components, thereby reducing the scale of the decision variables of the optimization problem in the second stage.

[0336] The second stage is a stochastic programming considering multi-level strengthening. Since the stochastic programming on which the decision depends has different fault scenario sets for different combinations of decision variables, it is difficult to establish an optimized repair scheduling model for components that requires a given fault scenario set.

[0337] The present invention approximately uses the importance ranking obtained in the first stage as the actual repair order, thereby bypassing the difficult problem of repair modeling and realizing the simulation of system response and fault repair.

[0338] Figure 5 Fig. shows the improvement effect of considering the repair order on the decision-making effect. Assume that node 3 is connected to an important load, and disasters cause faults in three lines: 1-2, 2-3, and 1-4. To avoid the influence of other factors, assume that the actual value and the randomly generated value of the repair time of each component are both 2h, there is only one repair team in the area, and the strengthening budget only supports strengthening one component.

[0339] Method 1 considers the repair order under limited personnel and preferentially repairs the line connected to the important load after the fault,

[0340] Method 2 randomly generates the repair time of each component. The important load connected to node 3 can only be restored to power after the repairs of both lines 1-2 and 2-3 are completed. However, since Method 2 independently generates the time for each component to complete the repair according to the probability distribution, the recovery time of the important load at node 3 remains unchanged regardless of whether the line to be strengthened is selected as 1-2 or 2-3 in its modeling. Therefore, Method 2 will choose to strengthen component 1-4. Method 1 will choose to strengthen component 1-2 or 2-3 to reduce the power restoration time of the important load, thereby resulting in less loss when dealing with disasters;

[0341] 2. After obtaining multiple stochastic programming scenarios through step S3, for a stochastic mixed-integer problem with |S| scenarios, the Progressive Hedging Algorithm (PH) decomposes the master problem into |S| sub-problems by relaxing the non-anticipated constraints and iteratively penalizing the inconsistency of the decision variables in the first stage.

[0342] The complete form of the proposed model is as follows:

[0343] Objective function: (19)

[0344] Constraints: (10)-(18), (20)-(44)

[0345] This stochastic programming model can be transformed into a mixed-integer linear programming problem. The original problem can be decomposed into several scenario-based sub-problems to reduce the computational complexity.

[0346] To facilitate the solution of the model using the PH algorithm, a compact expression is used to represent the proposed model:

[0347]

[0348] s.t. A1x1 ≤ b1 (61)

[0349] s.t. A2x2 ≤ b2 (62)

[0350]

[0351] where the vectors x1 and x2 are the first-stage decision variables h Line , h Pipe ; A1 and A2 are the coefficient matrices related to x1 and x2 respectively, and b1 and b2 are the constant vectors related to x1 and x2 respectively. is the feasible region for the discrete variable x to run, including the power flow constraint of the distribution network, the radial topology constraint, and the fault impact transfer constraint. T is the set of restoration times, N is the set of power system nodes, and P shed,j,t is the active load shed at node j at time t; Δt is the restoration time step; Equation (64) is the functional expression form of the objective function related to the decision variables.

[0352] Algorithm 1 outlines the implementation of the PH algorithm, as shown in Table 2:

[0353] In Step 1, the iteration coefficient k, the convergence tolerance gap, the multiplier the penalty coefficient ρ are initialized. At the same time, by solving the sub-problems of each scenario, the first-stage decision variables of a single scenario s can be obtained

[0354] In Step 2, the iteration starts;

[0355] In Step 3, according to the probabilities of each scenario, the solutions of the sub-problems are aggregated to obtain the expected value

[0356] In Step 4, the penalty parameter ρ is used to update the multiplier;

[0357] In step 5, the augmented subproblem of the linear terms and and the squared two-norm term of the penalty deviation is re-solved, where is the first-stage decision variable for scenario s in the k-th iteration.

[0358] This process will be repeated until the non-anticipated constraints are satisfied within the convergence tolerance.

[0359] Table 2 Algorithm 1: Progressive Hedging Algorithm

[0360]

[0361] In the presence of integer variables, since some decision variables will oscillate with iterations, the PH algorithm may exhibit cyclic behavior, which hinders the convergence of the algorithm. To break the cycle, the following steps are taken: Each time cyclic behavior is detected, an oscillating variable is assigned to its maximum or minimum value and constrained to a constant value in subsequent iterations. Since only a few variables exhibit such oscillations, this process can ensure convergence with minimal impact on the solution quality;

[0362] 3. Case Study:

[0363] A 70-node distribution network and a 20-node natural gas network coupled system are used as the case for the key component reinforcement method of the electric-gas coupled system proposed in the present invention, and the system topology is as Figure 6 shown. The substation capacity is set to 5 MVA, the system is configured with 4 circuit breakers, 18 RCSs, 5 controllable distributed power sources of 500 kW / 250 kVAr, and the total system load is 4468 kW. The gas source capacity of the natural gas system is set to 2000 Sm3 / h, 3 compressors are configured, and the total gas load of the system is 600 Sm3 / h. The maintenance time of each component of the distribution line is 15 min, and that of the pipeline is 30 min.

[0364] After multiple simulations, the cumulative distribution functions of the repair completion times of 79 distribution network components and 23 natural gas pipelines are obtained. Figure 7 The distribution function of the repair times of five typical distribution network components is shown. It can be seen that the repair completion time of component <59, 61> is always less than that of component <63, 64>. Obviously, component <59, 61> can be considered more important than component <63, 64> because the repair completion time of component <59, 61> is earlier than that of component <63, 64>, and the system will have less loss. However, the relative importance of not all components can be judged so intuitively. For example, it is difficult to determine the importance relationship between components <26, 27> and <39, 40> because their distribution functions intersect. Therefore, the Copeland ranking method needs to be used to rank the importance of these components.

[0365] 300, 600, 900, and 1200 simulations were carried out to obtain the Copeland importance ranking results of the respective distribution network components. The ranking orders of each component obtained from the 300, 600, and 900 simulations were compared with the 1200 - simulation ranking order. As Figure 8 , the top ten components in the ranking results of each simulation were selected for comparison. As the number of simulations increased, the selection results of the top ten components became more and more consistent and finally completely consistent. In addition, as shown in Table 3, as the number of simulations increased, the difference in the ranking order of the top ten components gradually decreased. Therefore, it can be concluded that when the simulation reaches a certain number of times, the simulation results no longer change, and the set of components obtained by pre - sorting can cover all important components. The Copeland ranking results obtained from 900 simulations were taken. Figure 9 shows the Copeland importance ranking of some distribution network components and gas pipelines.

[0366] Table 3 Comparison of Copeland rankings obtained from simulations with different numbers of times

[0367]

[0368] To show the specific restoration process of a single scenario in the Copeland ranking process, a typical scenario was selected to analyze the entire process of distribution network fault isolation, micro - grid reconstruction when a disaster occurs in the electric - gas coupling system, and the repair team optimizing the repair of faulty components and the restoration of the electric - gas coupling system; the entire restoration process is as Figure 10 shown. At time 0, that is, when the disaster occurred, the remote switches and circuit breakers in the distribution network isolated 5 faults and completed the network framework reconstruction through tie - lines and remote switches. In the gas network, 3 nodes supplying energy to distributed units lost natural gas supply. At time 5, since the repair time of the gas pipeline was long, the repair team did not choose to repair pipeline <3,4> first to restore the power supply in area A. At time 8, as the repair team repaired the faulty components, the gas network was restored and the power supply area of the distribution network was expanded. At time 10, the electric - gas coupling system completed the entire restoration process.

[0369] In addition, the damage of pipeline <3,4> caused the distributed power source at distribution network node 35 to be unable to operate normally, so a micro - grid power supply was not formed in area A when the disaster occurred. It can be seen that under earthquake disasters, the natural gas system will be damaged, which in turn affects the power supply safety of the distribution network.

[0370] To compare the advantages of the model proposed in the present invention, 3 schemes were used for analysis:

[0371] 1) Scheme 1: The proposed method was adopted, that is, first pre - select through the Copeland ranking method, simulate the repair order based on the component ranking, and then use stochastic programming to obtain the enhanced decision - making;

[0372] 2) Plan 2: Without considering the repair order under limited personnel and the reduction of the decision-making scale by pre-sorting, generate the time for components to complete repair independently according to the probability distribution, and then obtain the enhanced decision through a stochastic programming model;

[0373] 3) Plan 3: Allocate the enhanced budget to vulnerable components. In the case study, the vulnerable components of the distribution network are lines <61,62>, <39,59>, <62,65>, <63,64>, and the vulnerable components of the natural gas system are pipelines <17,18>, <12,17>.

[0374] The enhanced budgets of Plans 1, 2, and 3 are the same. The enhanced budgets of the distribution system and the natural gas system are set at 960,000 yuan and 255,000 yuan respectively. For the distribution network components, it costs 116,000 yuan to strengthen a section of line with enhanced pole type 1 and 204,422 yuan to strengthen a section of line with enhanced pole type 2. It costs 85,000 yuan to strengthen a section of pipeline in the natural gas system.

[0375] Table 4 Enhanced results of different enhancement plans

[0376]

[0377]

[0378] Table 4 shows the enhanced results and calculation speeds of different enhancement plans. Figure 9 shows the comparison of the restoration effects under the enhancement plans. 400 scenarios are randomly selected to obtain the average load conditions at each moment of the 400 scenarios, and Plans 1, 2, and 3 are compared. It can be seen that Figure 11 compared with the other two plans, the method proposed in the present invention has a significantly higher restoration of the load, and the calculation speed is increased by 53.71% compared with Plan 2.

[0379] In summary, an enhanced method and system for electrical coupling components that comprehensively consider vulnerability and fault impact according to the present invention can improve the ability of the electric-gas coupling system to cope with extreme events by strengthening key components. For extreme disasters that damage both the power system and the natural gas system simultaneously, earthquake fault scenarios are constructed. By pre-sorting and simulating the repair order based on the Copeland method, the problems that it is difficult to establish a planning model dependent on decision-making and the repair scheduling model that needs to integrate a given set of fault scenarios are solved. Through the two-stage method of pre-sorting - stochastic programming, the scale of the decision variables of the components to be enhanced is reduced, ensuring the solution efficiency of the model. The results of different enhancement plans for coping with disasters are shown in the case study, and the effectiveness and rapidity of the proposed model are verified.

[0380] The above content is only to illustrate the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. An electrical coupling component strengthening method considering comprehensive vulnerability and fault impact, characterized in that It includes the following steps: S1. According to the disaster classification method that affects the electrical-gas coupling system, determine the type of disaster caused by the earthquake disaster, and obtain the categories of components in the electrical-gas coupling system affected by the earthquake; S2. Based on the categories of components affected by the earthquake obtained in step S1, use the earthquake disaster intensity attenuation model and the component failure probability generation method to generate the failure probabilities of vulnerable components in the power system and natural gas system after different levels of reinforcement after the earthquake disaster; S3. Based on the failure probabilities of vulnerable components in the power system and natural gas system obtained in step S2, adopt multi-level reinforcement measures for the electrical-gas coupling system facing the earthquake scenario, and establish the reinforcement strategy constraints for the components after the disaster; S4. Based on the reinforcement strategy constraints for the components after the disaster obtained in step S3, generate a multi-level reinforcement stochastic programming scenario, and obtain the failure states of the components in the system after the disaster occurs; S5. Establish a key component identification model for the electrical-gas coupling system, with the objective function of minimizing the power load shedding cost and gas load shedding cost, and the constraint conditions including the distribution network power flow constraint, radial topology constraint, and fault impact transmission constraint; S6. Input the failure states of the components in the system after the disaster obtained in step S4 into the key component identification model of the electrical-gas coupling system obtained in step S5, use the Copeland ranking to pre-select important components in advance, and at the same time obtain the repair priorities of the components considering a large number of fault scenarios. The pre-ranking results provide the repair priorities of the components for the optimization of the reinforcement plan; S7. Based on the component pre-ranking results obtained in step S6 and the stochastic programming scenario obtained in step S4, establish a stochastic programming model for key component identification, solve it using the step-by-step hedging algorithm, and obtain the reinforcement plan to achieve the reinforcement of the electrical-gas coupling system.

2. The method for strengthening an electrical coupling component according to the comprehensive vulnerability and fault impact as claimed in claim 1, wherein Step S1 is specifically as follows: Classify the natural disasters that affect the electrical-gas coupling system into surface disasters represented by storm disasters and geological disasters represented by earthquake disasters; In the power system, both surface disasters and geological disasters affect the power system; In the natural gas system, clarify the type of disaster and all information about the disaster.

3. The method for strengthening an electrical coupling component according to the comprehensive vulnerability and fault impact as claimed in claim 1, wherein Step S2 is specifically as follows: Simulate the impact of the earthquake on the system state by combining the earthquake intensity and failure rate; obtain the peak ground acceleration values at different positions through the energy propagation model, as well as the failure rate of the gas network pipeline in the electrical coupling system and the peak ground velocity at its location; represent the vulnerability function of the system components in the form of a lognormal vulnerability curve; in the natural gas system, determine the seismic damage rate of the natural gas transmission pipeline according to the peak ground velocity.

4. The method for strengthening an electrical coupling component according to the comprehensive vulnerability and fault impact as claimed in claim 1, wherein, Step S3 is specifically as follows: Under the earthquake disaster, the reinforcement method adopted is to upgrade the pole type; List all the failure situations of components under all reinforcement strategies in advance, ζ ij,r,t = 0 indicates that a failure occurs when the line (i, j) adopts the r-th reinforcement strategy; The logical relationship between the available state of the components after the disaster and the reinforcement strategy is as follows: Among them, Ω Dis,Pole and Ω Gas,Pipe respectively represent the set of strengthening types of utility poles and gas pipelines; respectively represent the available states of distribution lines, switches, compressors, and gas pipelines; Each component should have and only have one basic or reinforced pole type, and the constraints are as follows: The number of reinforced poles cannot exceed the total reinforcement budget, and the constraints are as follows: Among them, represents the cost required for the reinforcement of the gas pipeline (m,n) at the r-th level; represents the cost required for the reinforcement of the gas pipeline (i,j) at the r-th level; respectively represent the maximum budgets for pole and pipeline strengthening.

5. The method for strengthening an electrical coupling component according to the comprehensive vulnerability and fault impact as claimed in claim 1, wherein Step S4 is specifically as follows: S401. Input the earthquake epicenter location, magnitude, and electrical-gas coupling system data, set the initial time to 0, and initialize the parameters of the electrical-gas coupling system; S402. According to step S2, calculate the peak ground acceleration and peak ground velocity at component ij, and then calculate the failure probability p of component ij adopting the r-th strengthening strategy ij,r ; S403. Determine whether the component is repaired at time t. If it is not repaired, then extract a random number r, which is uniformly distributed in the [0, 1] space, and obtain the failure state ζ of component ij adopting the r-th reinforcement strategy. ij,r,t ; S404. Make a decision on strengthening the components of the electro - gas coupling system. According to step S3, obtain the fault state u of component ij ij,t ; S405. Determine whether all components have been traversed. If there are still component states undetermined, return to step S402; S406. Determine whether the maximum recovery time is reached at time t. If not, return to step S403; otherwise, generate a randomly planned scenario with multi-level reinforcement.

6. The method for strengthening an electrical coupling component considering comprehensive vulnerability and fault impact according to claim 1, wherein Step S5 is specifically as follows: The objective function for establishing the identification model of key components in the electric-gas coupling system is to minimize the power load shedding cost and the gas load shedding cost; use the linearized DistFlow model to construct the operation constraints of the distribution network; construct the radial topology constraints; when a fault occurs in the distribution network line, cut off the power supply to the area connected to the fault point. The impact of the fault is transmitted along the line centered on the fault point until the nearest disconnected switch. Establish the operation constraints of the natural gas system and the constraints of the coupling equipment.

7. The method for strengthening an electrical coupling component considering comprehensive vulnerability and fault impact according to claim 6, wherein The objective function of the identification model of key components in the electric-gas coupling system is: Among them, is the weight of node j; is the weight of node m; P shed,j,t is the active power load shed by node j at time t; W shed,m,t is the load shedding amount of node m at time t, and Δt is the repair time step; The power balance constraints of each node are: where, Ν represents the set of power system nodes; L represents the set of power system lines; {DG} represents the set of distributed generator nodes, P ij,t , Q ij,t denote the active power and reactive power flowing through line (i, j) at time t; P L,j,t , Q L,j,t denote the active load and reactive load of power system node j at time t; P DG,j,t , Q DG,j,t denote the active power output and reactive power output of DG at node j at time t; and are the minimum and maximum active power limits of DG at bus j; and are the minimum and maximum active power limits of DG at bus j; P shed,j , Q shed,j denote the shed active load and reactive load of the node; The voltage relationship constraints between adjacent nodes are: where U j,t represents the voltage of node j at time t; r ij and x ij represent the resistance and reactance of line (i, j); c ij,t is a 0-1 variable of the state of line (i, j) at time t; M represents a large number; The capacity limit constraints of each line are: Among them, represents the maximum capacity of the line (i, j). The maximum and minimum voltage constraints of each node are: wherein, and are the maximum and minimum voltage limits at node j. The output limit constraints of DG are: Among them, a 0-1 variable indicating whether node j is affected by a fault. When the node is affected by a fault it takes 1, otherwise it takes 0. The demand limit constraints of controllable loads are: Radial topology constraints: Among them, N node represents the number of nodes; N sub represents the number of substations; γ i,t is a 0-1 variable, indicating whether the node is a source node; Variable γ i,t Only allowed to take 1 when the line connected to node i is disconnected, and the relevant constraint is: In each subgraph, the virtual load receives the power supply from the virtual power source, and the relevant constraints are: where {Sub} represents the set of substation nodes; V ij,t represents the virtual power of line (i, j) at time t; W i,t represents the output of the virtual power source at node i; If the line (i, j) is disconnected, the virtual power cannot flow, satisfying the necessary and sufficient condition 2 of the radial topology of the distribution network. The relevant constraints are: The fault impact transmission constraints under the N-k fault of the distribution network are as follows: where ε and M are positive numbers close to 0 and large, respectively; y ij1,t , y ij2t are binary variables representing the switch states of the i-side and j-side of line (i, j) at time period t, respectively; is a binary parameter; The operation constraints of the natural gas system are as follows: Among them, GW represents the set of gas source nodes; G represents the set of natural gas nodes; Z act represents the set of natural gas pipelines with compressors; Z represents the set of natural gas pipelines without compressors. W GW,m,t represents the output of gas source m at time t; W m,min represents the minimum output of gas source m; W m,max represents the maximum output of gas source m; φ mn represents the relationship between gas flow and pressure; W L,m,t represents the gas load of node m at time t; W shed,m,t represents the cut load of node m at time t; F mn,t represents the flow rate through pipeline (m,n) at time t; π m,t represents the gas pressure of node m at time t; λ mn represents the compression coefficient of the compressor on pipeline (m,n); represents the state of pipeline (m,n) at time t, being 1 indicates that the pipeline is working properly; represents the maximum flow rate of pipeline (m,n); represents the minimum flow rate of pipeline (m,n); The constraints of the coupling equipment are as follows: Among them, W DG,m,t represents the natural gas consumption of DG at node m at time t; b j , c j represent the natural gas consumption coefficients of DG; η mn represents the power consumption coefficient of the compressor of pipeline (m, n); P comp,j,t represents the electricity load of the compressor at node j at time t.

8. The method for strengthening an electrical coupling component considering comprehensive vulnerability and fault impact according to claim 7, wherein, Step S6 is specifically as follows: Use the preprocessing method based on Copeland ranking to pre-select important components in advance, and at the same time obtain the repair priorities of components considering a large number of fault scenarios; Use the non-sequential Monte Carlo simulation method to sample the states of different components in the electric-gas coupling system under seismic conditions; Binary variable s j (t) represents the state of component j at time t. If s j (t) = 1, it means the component can work properly; otherwise, s j (t) = 0; extract a random number r κ , which is uniformly distributed in the [0, 1] space, to obtain the operating state s j (0) of the line at the start of the repair process after an extreme disaster: Among them, represents the failure probability of the j-th component among the κ-th type of components; Ensure that all repair teams start from the specified starting positions, and the constraints are: Among them, Crew represents the set of maintenance teams; σ represents the category of maintenance components; represents the time when maintenance team c arrives at component m; dep σ represents the departure location of the maintenance team; Components can only be repaired after the team arrives, so the relevant constraints are: Among them, B represents the set of damaged components; y m,c indicates whether the repair team c has arrived at component m; f m,t indicates whether component m has been repaired at time t; The time when the maintenance team arrives at the component and f m,t has the following relationship: Among them represents the time required for maintenance team c to repair component m. For coupling f m,t with the component state s m (t) the constraint is: All repair teams only go to the faulty components once and also leave only once. The relevant constraints are as follows: Among them, {dep} is the set of starting positions of the maintenance teams; it represents x m,n,c It is a 0-1 variable indicating whether maintenance team c moves from component m to component n; It represents the time consumed for maintenance team c to move from component m to component n; Use the big M method to make the routes of the repair personnel continuous. The relevant constraints are as follows: Among them, represents the time consumed for maintenance team c to move from component m to component n; The repair time of each component is calculated by the time when the repair personnel arrive at the component and the repair duration of the component: Among them, T m represents the moment when component m is repaired and completed; According to the above Monte Carlo sampling method, sample the fault scenarios to obtain a large number of fault scenarios. Solve the above model for these fault scenarios to obtain the repair time of each component in each scenario, and then the repair probability of each component at all times in multiple fault scenarios can be obtained. Finally, the cumulative distribution function of the repair time of each component can be formed.

9. The method for enhancing an electrical coupling component considering comprehensive vulnerability and fault impact according to claim 1, characterized in that Step S7 is specifically as follows: Establish a stochastic programming model for key component identification, solve it using the progressive hedging algorithm, and obtain a reinforcement plan to strengthen the electric-gas coupling system. Use a compact expression to represent the proposed model: s.t. A1x1 ≤ b1 s.t. A2x2 ≤ b2 where x1 and x2 are decision variables in the first stage, h Line , h Pipe ; A1 and A2 are coefficient matrices related to x1 and x2 respectively, and b1 and b2 are constant vectors related to x1 and x2 respectively. is the feasible region for the operation of discrete variable x, including distribution network power flow constraints, radial topology constraints, and fault impact transfer constraints. T is the set of restoration times, N is the set of power system nodes, and P shed,j,t is the active load shed at node j at time t; Δt is the repair time step.

10. An electrical coupling component strengthening system integrating vulnerability and fault impact, characterized in that, Including: The category module judges the type of disaster that occurs in the earthquake disaster according to the disaster classification method that affects the electric-gas coupling system, and obtains the category of components affected by the earthquake in the electric-gas coupling system; A generation module that generates the failure probabilities of vulnerable components in the power system and natural gas system after earthquake disasters with different levels of reinforcement according to the obtained component categories affected by earthquakes, using the earthquake disaster intensity attenuation model and the component failure probability generation method. A constraint module that, based on the obtained failure probabilities of vulnerable components in the power system and natural gas system, adopts multi-level reinforcement measures for the electric-gas coupled system facing earthquake scenarios to establish the reinforcement strategy constraints for components after the disaster. A state module that, based on the obtained reinforcement strategy constraints for components after the disaster, generates random planning scenarios with multi-level reinforcement and obtains the failure states of components in the system after the disaster. A construction module that establishes a key component identification model for the electric-gas coupled system, with the objective function of minimizing the power load shedding cost and gas load shedding cost, and the constraint conditions including the power flow constraint of the distribution network, the radial topology constraint, and the fault impact transmission constraint. A sorting module that inputs the failure states of components in the system after the disaster into the key component identification model of the electric-gas coupled system, pre-selects important components using Copeland sorting, and at the same time obtains the repair priorities of components considering a large number of fault scenarios. The pre-sorting results provide the repair priorities of components for optimizing the reinforcement plan. An output module that, based on the obtained pre-sorting results of components and random planning scenarios, establishes a stochastic programming model for key component identification, solves it using the progressive hedging algorithm, and obtains a reinforcement plan to achieve the reinforcement of the electric-gas coupled system.

Images