Power distribution network disaster prevention evaluation method and device considering energy gas-heat dynamic characteristics, terminal equipment and storage medium

Abstract

The invention discloses a power distribution network disaster prevention evaluation method and device considering energy gas-heat dynamic characteristics, terminal equipment and a storage medium, and belongs to the field of power distribution networks. The method comprises the following steps: constructing a power grid element fault probability model according to typhoon data, power grid element positions and power grid line wind resistance wind speed of a to-be-evaluated power distribution network, and constructing a power distribution network disaster prevention evaluation model and corresponding model constraints based on the model according to topological data, node data, line data, gas source data, heat source data, load data and first-aid repair data; under the constraint of the corresponding model, solving the power distribution network disaster prevention evaluation model by taking the minimum total loss of the multi-energy coupling power distribution network as a target to obtain a power distribution network operation state variable; and calculating a power grid loss expectation, a load loss expectation and a load recovery time expectation according to the power distribution network operation state variable and the total loss of the multi-energy coupling power distribution network, and performing disaster prevention evaluation on the to-be-evaluated power distribution network. According to the invention, the problem of deviation of disaster prevention evaluation in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network, and particularly relates to a power distribution network disaster prevention evaluation method and device considering energy gas and heat dynamic characteristics, a terminal device and a storage medium. BACKGROUND

[0002] Under the situation of global climate change intensifying, the frequent occurrence of extreme climate and extreme disasters presents a significant upward trend in the threat to the safe operation of power distribution networks. In recent years, there have been many large-scale power grid outages caused by natural disasters worldwide, not only directly causing economic losses, but also exposing the system vulnerability of current power distribution networks under extreme weather conditions.

[0003] Current power distribution network disaster prevention capability evaluation mainly focuses on the recovery capability of power load, while ignoring the interaction of multi-energy coupling systems. Even if the influence of gas load and heat load is considered, the dynamic characteristics difference between natural gas systems and heat systems is not considered. Specifically, the pipe storage effect of natural gas systems and the thermal inertia of heat systems result in a significantly slower response time scale than power systems, and this time heterogeneity makes the post-disaster recovery process of multi-energy coupling power distribution networks present nonlinear characteristics. However, most current evaluation models still use static or quasi-static assumptions, which fail to effectively capture the dynamic attenuation and recovery characteristics of gas and heat systems, resulting in deviations between disaster prevention evaluation results and actual disaster scenarios. SUMMARY

[0004] The embodiments of the present application provide a power distribution network disaster prevention evaluation method and device considering energy gas and heat dynamic characteristics, a terminal device and a storage medium, which can effectively solve the problem that the prior art fails to effectively capture the dynamic attenuation and recovery characteristics of gas and heat systems, resulting in deviations between disaster prevention evaluation results and actual disaster scenarios.

[0005] An embodiment of the present application provides a power distribution network disaster prevention evaluation method considering energy gas and heat dynamic characteristics, comprising:

[0006] Obtaining typhoon data, power grid element position, topology data, node data, line data, power grid line wind speed, gas source data, heat source data, load data and repair data in the range to which the power distribution network to be evaluated belongs;

[0007] According to the typhoon data, the power grid element position and the power grid line wind speed, a power grid element failure probability model is constructed;

[0008] Based on the power grid element failure probability model, a power distribution network disaster prevention evaluation model and a model constraint corresponding to the power distribution network disaster prevention evaluation model are constructed according to the topology data, node data, line data, gas source data, heat source data, load data and repair data;

[0009] solving the power distribution network disaster prevention evaluation model under model constraints corresponding to the power distribution network disaster prevention evaluation model, to minimize the total loss of the multi-energy coupled power distribution network, to obtain the power distribution network operation state variable;

[0010] According to the power distribution network operation state variable and the total loss of the multi-energy coupled power distribution network, the power loss expectation, the load loss expectation and the load recovery time expectation are calculated, and the power distribution network to be evaluated is evaluated according to the power loss expectation, the load loss expectation and the load recovery time expectation.

[0011] Further, the typhoon data includes: the typhoon center pressure difference at landing time, the angle between the typhoon moving direction and the north direction, the angle between the coastline and the north direction, the typhoon moving speed and the typhoon center position;

[0012] According to the typhoon data, the power grid element position and the wind speed of the power grid line, a power grid element failure probability model is constructed, including:

[0013] According to the typhoon center position and the power grid element position, the relative position of the typhoon center and the power grid element is calculated;

[0014] According to the angle between the typhoon moving direction and the north direction, the angle between the coastline and the north direction, and the typhoon center pressure difference at landing time, the center pressure difference during typhoon moving is calculated; and according to the center pressure difference during typhoon moving, the maximum wind speed radius during typhoon moving is calculated;

[0015] According to the center pressure difference during typhoon moving, the maximum wind speed radius and the typhoon moving speed, the wind speed of the power grid element position is calculated;

[0016] According to the wind speed of the power grid element position and the wind speed of the power grid line, an initial power grid element failure probability model is constructed;

[0017] Based on the random number generated by Monte Carlo simulation method, the preset dynamic failure probability threshold of the power grid element is compared hour by hour, and when the random number exceeds the preset dynamic failure probability threshold, the power grid element is determined to be in failure state, and a plurality of power distribution network line failure scenarios are obtained;

[0018] Based on the power distribution network line failure scenario, the initial power grid element failure probability model is corrected to obtain the final power grid element failure probability model.

[0019] Further, the model constraints corresponding to the power distribution network disaster prevention evaluation model include: gas load energy supply state constraint, heat load energy supply state constraint, gas load recovery energy supply time constraint, heat load recovery energy supply time constraint, recovery time identification constraint, power distribution network operation constraint, natural gas system operation constraint, heat system operation constraint, coupling element operation constraint, power distribution network topology reconstruction constraint, emergency repair dispatching constraint and energy storage device constraint.

[0020] The power distribution network operation constraints include: power distribution network power balance constraints, power distribution network node voltage constraints, line operation safety constraints, node voltage amplitude upper and lower limit constraints, and load shedding constraints;

[0021] The natural gas system operation constraints include: node flow balance constraints, gas source output constraints, gas load loss constraints, node gas pressure constraints, compressor-containing pipeline flow constraints, and general branch constraints;

[0022] The heat supply system operation constraints include: node power balance constraints, heat source output constraints, heat load loss constraints, heat network pipeline constraints, heat loss constraints, node supply and return heat temperature constraints, node temperature and heat power constraints, mass flow rate continuity constraints, and branch mass flow rate constraints;

[0023] The coupling element operation constraints include: gas turbine constraints, electric compressor constraints, electric gas production equipment constraints, combined heat and power unit constraints, heat pump constraints, and electric boiler constraints;

[0024] The repair emergency dispatching constraints include: repair team dispatching constraints, repair team path constraints, equipment repair constraints, repair team leaving equipment constraints, material carrying constraints, repair time constraints, repair team repairing failed equipment time constraints, repair team repair frequency constraints, and equipment recovery power flow coupling constraints.

[0025] Further, the power distribution network disaster prevention evaluation model is specifically:

[0026]

[0027] Wherein, C all,s is the total loss of the multi-energy coupled power distribution network; P shed,i,t , W shed,j,t , H shed,k,t are the forced loss load of the electric load i, the gas load j, and the heat load k at time t, respectively; N e , N g , N h are the sets of electric nodes e, gas nodes g, and heat nodes h, respectively; Δt is the unit time; T is the post-disaster recovery period; and are the number of users of the gas nodes g and the heat nodes h, respectively; and are the matrices of gas and heat inertia economic compensation, respectively; and are 0-1 variables of the position of the gas load j and the heat load k recovery time in the period, respectively.

[0028] Further, the power distribution network operation state variable comprises: an electrical load recovery time, a gas load recovery time, a thermal load recovery time, and an electrical load loss load amount;

[0029] According to the power distribution network operation state variable and the multi-energy coupling power distribution network total loss, the power grid loss expectation, the load loss expectation, and the load recovery time expectation are calculated, comprising:

[0030] According to the multi-energy coupling power distribution network total loss and the number of power distribution network line fault scenarios, the power grid loss expectation is calculated;

[0031] According to the electrical load loss load amount and the number of power distribution network line fault scenarios, the load loss expectation is calculated;

[0032] According to the electrical load recovery time, the gas load recovery time, the thermal load recovery time, and the number of power distribution network line fault scenarios, the load recovery time expectation is calculated.

[0033] Further, according to the power grid loss expectation, the load loss expectation, and the load recovery time expectation, the disaster prevention evaluation of the to-be-evaluated power distribution network is performed, comprising:

[0034] According to the power grid loss expectation, the load loss expectation, the load recovery time expectation, and a preset weight, a weighted calculation is performed to obtain a power distribution network comprehensive evaluation index value;

[0035] According to the power distribution network comprehensive evaluation index value and a preset evaluation grade table, an evaluation grade of the to-be-evaluated power distribution network is determined;

[0036] According to the evaluation grade, the disaster prevention evaluation of the to-be-evaluated power distribution network is performed.

[0037] As an improvement of the above-mentioned scheme, another embodiment of the present application correspondingly provides a power distribution network disaster prevention evaluation device considering energy gas and heat dynamic characteristics, comprising:

[0038] A data acquisition module is configured to acquire typhoon data, power grid element positions, topology data, node data, line data, power grid line wind-resistant wind speed, gas source data, heat source data, load data, and repair data in a range to which a to-be-evaluated power distribution network belongs;

[0039] A fault probability model construction module is configured to construct a power grid element fault probability model according to the typhoon data, the power grid element positions, and the power grid line wind-resistant wind speed;

[0040] A power distribution network disaster prevention evaluation model construction module is configured to construct a power distribution network disaster prevention evaluation model and model constraints corresponding to the power distribution network disaster prevention evaluation model based on the power grid element fault probability model and according to the topology data, the node data, the line data, the gas source data, the heat source data, the load data, and the repair data;

[0041] The distribution network state variable solving module is used to solve the distribution network disaster prevention assessment model under the model constraints corresponding to the distribution network disaster prevention assessment model, with the goal of minimizing the total loss of the multi-energy coupled distribution network, and obtain the distribution network operation state variables.

[0042] The distribution network disaster prevention assessment module is used to calculate the expected power grid loss, expected load loss, and expected load recovery time based on the distribution network operating status variables and the total loss of multi-energy coupled distribution networks, and to conduct a disaster prevention assessment of the distribution network to be assessed based on the expected power grid loss, expected load loss, and expected load recovery time.

[0043] Furthermore, the disaster prevention assessment model for the power distribution network is specifically as follows:

[0044]

[0045] Among them, C all,s The total loss of a multi-energy coupled distribution network; These are the penalties for unit electrical load loss, gas load loss, and heat load loss after a power outage, respectively; P shed,i,t W shed,j,t H shed,k,t Let N be the forced loss load amounts of electrical load i, gas load j, and heat load k at time t, respectively; e N g N h Let e ​​represent the set of electrical nodes, g represent the set of gaseous nodes, and h represent the set of thermal nodes; Δt represents the unit time; and T represents the post-disaster recovery period. and These represent the number of users at air node g and heat node h, respectively. and These are matrices representing economic compensation for gas and thermal inertia, respectively. and These are 0-1 variables representing the positions of the gas load j and heat load k recovery times within the period, respectively.

[0046] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a power distribution network disaster prevention assessment method considering the dynamic characteristics of energy, gas, and heat as described in the above embodiments.

[0047] Another embodiment of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the power distribution network disaster prevention assessment method considering the dynamic characteristics of energy, gas and heat described in the above embodiment.

[0048] By implementing this invention, at least the following beneficial effects are achieved:

[0049] This invention provides a method, device, terminal equipment, and storage medium for disaster prevention assessment of distribution networks that considers the dynamic characteristics of energy sources such as gas and heat. The method breaks through the traditional assessment's single power perspective by simultaneously collecting power-related data such as gas source data, heat source data, and power grid component location and topology data. It formally incorporates natural gas systems and thermal systems into the scope of distribution network disaster prevention assessment, which can fully cover the interaction of multi-energy coupled systems. It avoids overlooking the impact of gas and heat systems by focusing only on power load recovery, thus solving the problem of assessment one-sidedness. This enables the subsequent construction of distribution network disaster prevention assessment models to cover the interaction between multiple energy sources such as electricity, gas, and heat, and solves the problem of ignoring the interaction of multi-energy coupled systems from the source. Based on the fault probability model of power grid components, a disaster prevention assessment model for distribution networks is constructed according to topology data, node data, line data, gas source data, heat source data, load data, and emergency repair data. The model aims to minimize the total loss of multi-energy coupled distribution networks, rather than using static or quasi-static assumptions. It can initially correlate the dynamic effects of natural gas systems and heating systems. The collaborative integration of gas source data, heat source data, and power system data enables the disaster prevention assessment model to initially correlate the differences in response time scales between gas and heat systems and power systems. This avoids the shortcomings of traditional static models that cannot capture the dynamic characteristics of gas and heat, resulting in nonlinear post-disaster recovery. The disaster prevention assessment model of distribution networks is more in line with the actual operating rules of multi-energy coupled distribution networks. Based on the distribution network operation state variables and the total loss of the multi-energy coupled distribution network, the expected power grid loss, expected load loss, and expected load recovery time are calculated. Since the construction and solution of the distribution network disaster prevention assessment model are based on multi-energy coupled data, the obtained distribution network operation state variables can indirectly reflect the impact of the dynamic characteristics of the gas and heat system on the overall distribution network. Based on this, the expected power grid loss, expected load loss, and expected load recovery time are calculated. The assessment based on multi-dimensional indicators can comprehensively reflect the losses and recovery of electricity, gas, and heat in multiple dimensions, effectively reducing the problem of deviation between the assessment results under traditional static assumptions and actual disaster scenarios. This provides a more accurate and comprehensive basis for the decision on the disaster prevention capability of the distribution network, reduces the deviation of the disaster prevention assessment results, and improves the accuracy of the disaster prevention assessment of the distribution network. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a disaster prevention assessment method for a power distribution network that considers the dynamic characteristics of energy gas and heat, provided by an embodiment of the present invention.

[0051] Figure 2 This is a schematic diagram of the structure of a power distribution network disaster prevention assessment device that considers the dynamic characteristics of energy gas and heat, according to an embodiment of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] See Figure 1 To address the problem that existing technologies fail to effectively capture the dynamic attenuation and recovery characteristics of gas and heat systems, leading to discrepancies between disaster prevention assessment results and actual disaster scenarios, an embodiment of the present invention provides a flowchart illustrating a disaster prevention assessment method for distribution networks that considers the dynamic characteristics of energy gas and heat, including:

[0054] S1. Obtain typhoon data, grid component locations, topology data, node data, line data, grid line wind resistance speed, gas source data, heat source data, load data, and emergency repair data within the scope of the distribution network to be evaluated;

[0055] Specifically, typhoon data refers to key parameters characterizing typhoon characteristics, including the pressure difference at the typhoon center at landfall, the angle between the typhoon's direction of travel and true north, the angle between the coastline and true north, the typhoon's speed, and the typhoon's center location. Power grid component location data represents the coordinates of power grid components, indicating their specific locations. Topology data represents the connections between power grid components and between nodes. Node data refers to the data for power grid nodes, including node voltage, node rated voltage, upper and lower allowable gas pressure limits, and the number of nodes. Gas source data represents core parameters covering the natural gas system, including gas load loss penalties, gas node set, number of gas node users, gas inertia economic compensation matrix, cross-sectional area and inner diameter of natural gas pipelines, pipeline friction coefficient, natural gas pressure, natural gas flow rate benchmark, natural gas temperature, natural gas flow rate, the quotient of natural gas gas constant and molar mass, air specific heat capacity and density, air volume inside buildings, upper and lower limits of gas source output, gas pressure at the beginning and end of pipelines, and data on gas turbines. Heat source data includes key parameters of the thermal system, such as heat load loss penalties, set of heat nodes, number of users at heat nodes, thermal inertia compensation matrix, indoor thermodynamic temperature, supplied heat power, heat loss, building heat dissipation coefficient, ambient temperature, set of heating network pipelines, upper and lower limits of pipeline transported heat power, standard heating temperature of the heating network, regenerative temperature, maximum and minimum allowable heating temperature, heat pump data, combined heat and power unit data, and electric boiler data. Load data includes electrical load loss penalties, set of electrical nodes, active load of grid nodes, active load shedding, active and reactive power output of generators. Emergency repair data refers to parameters supporting post-disaster emergency repairs, including emergency repair team data and emergency repair material data.

[0056] S2. Based on the typhoon data, the location of the power grid components, and the wind resistance speed of the power grid lines, construct a power grid component failure probability model;

[0057] Specifically, the power grid component failure probability model is a mathematical model that describes the relationship between the probability of power grid components (such as lines and towers) failing under the influence of typhoons and the typhoon intensity and the component's disaster resistance capability. It is used to quantify the component failure risk.

[0058] Preferably, the typhoon data includes: the pressure difference at the typhoon center at the time of landfall, the angle between the typhoon's direction of travel and due north, the angle between the coastline and due north, the typhoon's speed, and the typhoon's center location;

[0059] Based on the typhoon data, the location of the power grid components, and the wind resistance speed of the power grid lines, a fault probability model for the power grid components is constructed, including:

[0060] Based on the location of the typhoon center and the location of the power grid components, the relative positions of the typhoon center and the power grid components are calculated.

[0061] The central pressure difference of the typhoon during its journey is calculated based on the angle between the typhoon's direction of travel and due north, the angle between the coastline and due north, and the central pressure difference of the typhoon at the time of landfall; and the radius of the typhoon's maximum wind speed during its journey is calculated based on the central pressure difference of the typhoon.

[0062] The wind speed at the location of the power grid components is calculated based on the central pressure difference, the radius of maximum wind speed, and the speed of the typhoon during its movement.

[0063] Based on the wind speed at the location of power grid components and the wind resistance speed of power grid lines, an initial power grid component failure probability model is constructed.

[0064] Random numbers generated by random simulation based on Monte Carlo simulation method are compared with preset dynamic fault probability thresholds of power grid components hourly. When the random number exceeds the preset dynamic fault probability threshold, the power grid component is determined to be in a fault state, and several distribution network line fault scenarios are obtained.

[0065] The initial power grid component failure probability model is corrected based on the fault scenarios of the distribution network lines to obtain the final power grid component failure probability model.

[0066] Specifically, the typhoon center pressure difference at landfall is the difference (in hPa) between the typhoon's central pressure and the surrounding environmental pressure at the instant of landfall. It is a core indicator of typhoon intensity; the larger the difference, the stronger the typhoon. The angle between the typhoon's direction of travel and true north is the angle between the tangent to the typhoon's center path and geographic true north. It is used to determine the typhoon's impact direction on the assessment area; for example, 30° indicates the typhoon is moving in a northeast-east direction. Monte Carlo simulation is a statistical method based on random sampling. It simulates random events, such as the probability of component failure, by generating pseudo-random numbers uniformly distributed in the interval [0,1]. When the random number exceeds a failure probability threshold, the event is considered to have occurred. The preset dynamic failure probability threshold represents the critical value of component failure probability that varies with the typhoon period, reflecting the difference in failure risk under different typhoon intensities. It is set based on the component's wind resistance characteristics and historical failure data. The distribution network line fault scenario is a set of normal and fault states of each line generated by Monte Carlo simulation. For example, a fault scenario is formed when line 1 is faulty, line 2 is normal, and line 3 is faulty. This scenario is used to cover the uncertainty of line faults under typhoon conditions.

[0067] In a preferred embodiment of this invention, the vulnerability analysis of the power distribution network in a typhoon scenario is limited to key components on the power grid side (including lines, towers, and substation structures). To accurately characterize the time-varying impact mechanism of the typhoon's dynamic evolution on power facilities, the Batts model is used to simulate the continuous changes in key parameters such as the typhoon's center path migration, the spatiotemporal distribution of wind speed, and intensity attenuation. By constructing a spatiotemporal mapping relationship between the disaster intensity field and the power grid's geographical information, a refined modeling of the failure probability of transmission components due to typhoon loads at different time periods is achieved. The Batts typhoon model has a certain degree of simulation accuracy. Compared to other models that require solving complex differential equations, its form is simple and elegant, and its computational complexity is low. Considering the need for large-scale state sampling using Monte Carlo simulation, the Batts model is beneficial for rapid state assessment. Therefore, this embodiment uses the Batts model as follows:

[0068]

[0069]

[0070] In the formula, Δh(t) represents the pressure difference at the center of the typhoon at time t; Δh(t land ) represents the landing time t land The pressure difference at the center of the typhoon; θ and θ are the angles between the typhoon's direction of travel and due north, and between the coastline and due north, respectively. v is the maximum ground wind speed at time t; H This refers to the speed at which the typhoon travels. The maximum wind speed is represented by K, which is the location coefficient and has a value of 6.93. Based on the relative position of the component to the typhoon center, the wind speed v at the component's location can be further obtained. comp (t):

[0071] In the formula, v comp (t) represents the wind speed at the component's location at time t; d(t) represents the relative position of the typhoon center from the component at time t; Let be the radius of the typhoon's maximum wind speed at time t; the formulas for calculating the relative position and the radius of maximum wind speed are as follows:

[0072]

[0073] x H (t)=x H (land)+v H (t)tcosδ,y H (t)=y H (land)+v H (t)tcosδ

[0074]

[0075] In the formula, (x comp ,y comp ) and (x H (t),y H (t) represents the location coordinates of the power grid components and the location coordinates of the typhoon center at time t, respectively; (x) represents the location coordinates of the typhoon center at time t, respectively. H (land),y H (land) represents the landing time t. land The coordinates of the typhoon's center; δ is the angle between the typhoon's path and the coastline.

[0076] Under the influence of typhoon disasters, the vulnerability of power distribution network components exhibits significant spatial differentiation. Overhead feeders, due to their topological extension and high exposed surface area, are more susceptible to chain-like damage from extreme wind loads compared to transformers and locally installed insulators within enclosed substations. This phenomenon stems from three fundamental physical aspects: First, feeder systems traverse complex terrain and micro-meteorological zones, resulting in an exponential amplification of wind speed shear effects. Second, long-distance suspension structures experience aeroelastic resonance under turbulent excitation, inducing dynamic mechanical stresses far exceeding static design limits. Finally, insulator strings only need to maintain local electrical isolation, while feeder conductors must simultaneously bear the dual loads of mechanical tension and current transmission. Engineering disaster records show that feeder fractures account for over 75% of power distribution network faults caused by typhoons, while transformers, protected by casings and gravity-stabilized bases, suffer wind-induced damage rates of less than 5%. Based on this vulnerability gradient distribution characteristic, this embodiment focuses its typhoon damage modeling on dominant damage modes, considering only the damage to power distribution network feeders under typhoon disasters. The mechanical strength of power distribution network lines is strictly constrained by their design wind speed threshold. When the actual wind speed during a typhoon exceeds the line's rated wind resistance, the risk of structural instability increases non-linearly, potentially inducing a chain of faults such as tower buckling, conductor galloping, or insulator breakage, ultimately leading to power outages. Based on this mechanical failure mechanism, and combined with the wind speeds experienced by each line under typhoon conditions obtained from typhoon scenario simulations, a fault probability model for power grid components is established.

[0077] In typhoon disaster scenarios, the operating status of transmission lines can be divided into three characteristic intervals based on the ratio of their wind resistance capacity to typhoon wind speed: when the typhoon wind speed exceeds 200% of the line's designed wind resistance threshold, a deterministic failure will occur; when the typhoon wind speed is lower than the line's rated wind resistance level, the line maintains normal operation; in the critical transition interval (i.e., when the typhoon wind speed is between the line's wind resistance level and twice its wind resistance level), the line failure probability exhibits a non-linear growth characteristic, and its failure risk increases exponentially with increasing wind speed. This failure mechanism can be quantitatively described by constructing a wind speed-failure rate transfer function, where the exponential growth characteristic of the failure probability reflects the accelerated degradation process of the distribution line's structural reliability under wind load.

[0078]

[0079] When R l ≤v comp (t)≤2R l Sometimes,

[0080] In the formula, p l.t R represents the failure rate of line l at time t. l Let l be the wind speed that line l is designed to withstand.

[0081] In a preferred embodiment of the present invention, based on the obtained failure rate of distribution network components, a disaster scenario is generated using Monte Carlo simulation, and failure scenarios that do not meet the requirements are filtered out. Monte Carlo simulation, as an efficient numerical calculation method based on probability and statistics theory, transforms a deterministic mathematical problem into a statistical simulation problem by establishing a probability model and sampling random variables on a large scale, thereby solving the probability distribution or expected value of a complex system. This method is particularly suitable for the analysis of engineering systems with significant uncertainties, and its theoretical basis is the law of large numbers—when the number of independent repeated trials approaches infinity, the frequency of a random event will converge to its theoretical probability. In this embodiment, a failure probability model of power grid components under typhoon disaster is constructed, and then a pseudo-random number sequence uniformly distributed in the interval [0,1] is generated and compared with the dynamic failure probability threshold of the component hourly; when the random number exceeds the preset threshold, the component is determined to enter a failure state, and finally, the statistical sample space of the failure scenario is obtained by setting a set number of independent repeated experiments. This modeling method based on random sampling can effectively overcome the limitations of traditional analytical methods in dealing with complex problems such as multi-factor coupling and nonlinear correlation, and provide a probabilistic scientific basis for assessing the vulnerability of power distribution networks under typhoon disasters.

[0082] During a typhoon, the status of lines in the power distribution network system is represented by 0 and 1 to indicate whether a fault has occurred. The specific discriminant is as follows:

[0083] In the formula, k l,t x is a random number generated by Monte Carlo simulation of line l during the t-th typhoon period; l,t Let be the state variable of line l during the t-th typhoon period. A value of 0 indicates that the line is out of service due to a fault, while a value of 0 indicates that the line is operating normally.

[0084] Assuming the distribution network contains n branches, during the t-th typhoon period, the distribution network line fault scenarios can be represented by matrix X. f,t Represents: X f,t ={x 1,t ,x 2,t ,...,x n,t}, where x n,t Let n be the state variable of the nth route during the tth typhoon period;

[0085] After traversing all typhoon intrusion periods, a sample of fault scenarios throughout the entire typhoon process can be obtained, namely, several power distribution line fault scenarios X. f : In the formula, T comp This represents the total number of periods affected by the typhoon. For the Tth comp Fault scenarios of power distribution lines during typhoon season.

[0086] Based on historical experience data, all generated scenarios in the sample are screened, and obviously unreasonable sampling scenarios are removed. The remaining scenarios are retained as the scenario set for subsequent disaster prevention capability assessment of the power distribution network. A feasibility verification standard based on physical constraints is established. By comparing the statistical characteristics of each scenario parameter (such as extreme typhoon wind speed, duration, and spatial distribution pattern) with historical observation data, abnormal samples that deviate significantly from meteorological laws (such as instantaneous wind speed changes exceeding 30 m / s) or power system physical limits (such as simultaneous line failure rate exceeding the 95th percentile) are eliminated.

[0087] Overhead lines, as the primary carriers of the power system during typhoon disasters, are directly or indirectly affected by strong winds during typhoons, causing power outages such as tower collapse, line breakage, wind-induced flashover, and foreign objects snagging the lines. These faults often cannot be restored by simple reclosing operations, further exacerbating the risk of power system paralysis. Given the significant difficulty in obtaining empirical data on power infrastructure damage during typhoons, current research faces substantial challenges in systematically quantifying the multiple destructive effects of typhoons. This objective constraint has led the academic community to generally focus on structural damage to the distribution network—especially physical damage modes with clear observational characteristics, such as tower collapse and conductor breakage. The Batts model quantifies the spatiotemporal evolution of typhoons, such as the impact of wind speed decay and path movement on components, avoiding the coarseness of traditional simplified relationships between wind speed and failure rate. Monte Carlo simulation generates multiple scenarios, which can reflect the randomness of component failures under typhoons. The corrected model is more in line with engineering reality. The accurate component failure probability is the core input of the power distribution network disaster prevention assessment model, which can reduce the deviation of subsequent loss calculation and recovery strategy.

[0088] S3. Based on the fault probability model of the power grid components, and according to the topology data, node data, line data, gas source data, heat source data, load data, and emergency repair data, construct a power distribution network disaster prevention assessment model and the corresponding model constraints of the power distribution network disaster prevention assessment model.

[0089] Specifically, the distribution network disaster prevention assessment model represents a mathematical model that takes a multi-energy coupled distribution network (interconnection of electric, gas, and heat systems) as the assessment object, integrates component failure probability, multi-energy system operation constraints, and post-disaster recovery strategies, and is used to calculate system losses and recovery effects.

[0090] Preferably, the model constraints corresponding to the disaster prevention assessment model of the distribution network include: gas load energy supply status constraints, heat load energy supply status constraints, gas load recovery energy supply time constraints, heat load recovery energy supply time constraints, recovery time identifier constraints, distribution network operation constraints, natural gas system operation constraints, thermal system operation constraints, coupling element operation constraints, distribution network topology reconfiguration constraints, emergency repair and dispatch constraints, and energy storage equipment constraints.

[0091] The distribution network operation constraints include: distribution network power balance constraints, distribution network node voltage constraints, line operation safety constraints, node voltage amplitude upper and lower limits constraints, and load shedding constraints.

[0092] The natural gas system operation constraints include: node flow balance constraints, gas source output constraints, gas load loss constraints, node gas pressure constraints, pipeline flow constraints including compressors, and general branch constraints.

[0093] The operating constraints of the thermal system include: node power balance constraints, heat source output constraints, heat load loss constraints, heat network pipeline constraints, heat loss constraints, node supply and return heat temperature constraints, node temperature and heat power constraints, mass flow rate continuity constraints, and branch mass flow rate constraints.

[0094] The operating constraints of the coupling elements include: gas turbine constraints, electric compressor constraints, electric gas generator constraints, combined heat and power unit constraints, heat pump constraints, and electric boiler constraints.

[0095] The emergency repair dispatch constraints include: emergency repair team dispatch constraints, emergency repair team route constraints, equipment repair constraints, emergency repair team departure from equipment constraints, material carrying constraints, repair time constraints, emergency repair team repair time constraints, emergency repair team repair frequency constraints, and equipment recovery power flow coupling constraints.

[0096] Preferably, the power distribution network disaster prevention assessment model is as follows:

[0097]

[0098] Among them, C all,s The total loss of a multi-energy coupled distribution network; These are the penalties for unit electrical load loss, gas load loss, and heat load loss after a power outage, respectively; P shed,i,t W shed,j,t H shed,k,t Let N be the forced loss load amounts of electrical load i, gas load j, and heat load k at time t, respectively; e N g N h Let e ​​be the set of electrical nodes, g be the set of gas nodes, and h be the set of thermal nodes. Let i be the electrical load, j be the gas load, and k be the thermal load, which are the types of nodes in the electrical nodes, g be the gas nodes, and h be the thermal nodes. Let Δt be the unit time and T be the post-disaster recovery period. and These represent the number of users at air node g and heat node h, respectively. and These are matrices representing economic compensation for gas and thermal inertia, respectively. and These are 0-1 variables representing the positions of the gas load j and heat load k recovery times within the period, respectively.

[0099] In a preferred embodiment of the present invention, a quantitative modeling method based on fluid dynamics principles is proposed to accurately characterize the dynamic evolution of node state parameters in a natural gas-thermal coupling system. For the dynamic behavior of the natural gas transmission and distribution network, a transient flow control equation considering fluid compressibility, pipeline friction resistance, and the law of mass conservation is first established. The nonlinear partial differential equations are then linearized using characteristic line discretization and Taylor series expansion, leading to the derivation of a second-order ordinary differential equation describing the coupling relationship between gas pressure and mass flow rate at the pipeline terminal nodes.

[0100] In the formula, S g and D g Here, ω represents the cross-sectional area and inner diameter of the natural gas pipeline, respectively; ω is the pipeline friction coefficient; and p(t) is the natural gas pressure. Let p(t) be the first derivative. Let v be the second derivative of p(t). B R is the baseline value for natural gas flow rate. M T is the quotient of the gas constant of natural gas and its molar mass. g Let t be the temperature of the natural gas and f(t) be the flow rate of the natural gas. The equation has a clear physical meaning, and its analytical solution exhibits typical exponential decay characteristics, accurately reflecting the gradual decrease in gas pressure caused by the storage effect after a power outage.

[0101] Based on the law of conservation of energy, this embodiment uses a differential approximation method to construct the temperature change process of the building assembly considering heat loss.

[0102] C a ρ a V b [T f (t+Δt)-T f (t)]=[H use (t)-H loss (t)]Δt

[0103]

[0104] Let Δt approach 0, Revised to:

[0105] H loss (t)=ε loss ·[T f (t)-T en (t)]

[0106] Ignoring the time-varying nature of the building's external temperature, a first-order dynamic equation describes the relationship between the building's indoor temperature and the heating power supplied by the heating network.

[0107]

[0108] In the formula, C a and ρ a These are the specific heat capacity and density of air, respectively, V b T represents the volume of air inside a building. f (t) represents the indoor thermodynamic temperature at time t. For T f The first derivative of (t), H use (t) and H loss (t) represents the supplied heat power and heat loss, respectively, Δt is the time interval, and ε is the heat loss. loss The heat dissipation coefficient of a building is given by its surface area S. b Thermal conductivity λ of the material b Wall thickness d b Calculations show that T en (t) represents the ambient temperature. To linearize the differential component, it can be viewed as a number very close to 0.

[0109] In a preferred embodiment of the present invention, a typhoon disaster causes a power distribution network failure under multi-energy coupling, resulting in the inability of electrically driven compressors, electric gas generators, electric boilers, and heat pumps to supply energy to the gas and heat loads. Based on the ordinary differential equations describing the gas and heat load energy supply levels as state variables, the gas supply pressure at the gas network node and the indoor temperature of the building at the heat network node are inertial parameters, while the flow rate at the end of the natural gas pipeline and the heating power of the heat network are transient parameters. For each gas and heat load whose energy supply is interrupted, by combining the calculated dynamic characteristic parameters of the gas and heat loads with the rate of change of the determining function, as long as the specific values ​​of the transient parameters at the initial moment of the fault and the moment of power restoration are obtained, and substituted into these parameters, two equations describing the decrease of inertial parameters after the energy supply interruption and the recovery of inertial parameters after the energy supply is restored can be obtained. These equations can then be solved to obtain the inertial parameter values ​​at any time within the post-disaster recovery period T for that load point. Based on the solution results of the aforementioned dynamic characteristic parameters, further multi-dimensional evaluation and compensation mechanism design can be carried out. Specifically, by collecting real-time gas supply pressure data from each node of the natural gas network and indoor temperature monitoring values ​​from end-users of the heating system during the recovery period, and combining this with the gas supply pressure contractual clauses specified in industry standards (such as the minimum gas supply pressure provisions in GB 50028-2020 "Code for Design of Urban Gas Supply") and building thermal comfort standards (such as the temperature and humidity range requirements in ASHRAE Standard 55), a graded compensation function model based on deviation is established. This model dynamically compares the actual monitored values ​​of gas pressure / temperature with the contractually agreed values, and uses piecewise linear or nonlinear compensation algorithms to quantify and calculate various economic losses caused by power supply interruptions. By systematically enumerating the compensation amounts at different recovery time points, a clearly defined compensation cost matrix is ​​ultimately constructed. Its row vectors represent different recovery timing schemes, column vectors correspond to the compensation amounts for various load nodes, and matrix elements fully reflect the quantitative relationship between gas and heat dynamic characteristics and economic compensation, and correct the original penalty for economic losses to gas and heat users' production and lives caused by direct power loss. Taking heat users as an example, the specific implementation process is as follows:

[0110] Given the building's relevant parameters and load power curve, but unknown the energy recovery time and current energy consumption. Substitute the heating power and initial indoor temperature at the initial moment of the fault into the following formula:

[0111] The indoor temperature T at different times is obtained by using a unit time Δt as the step size. f,k (t+nΔt), n=0,1,2...T.

[0112] For each time t+nΔt when the power supply for restoration needs to be decided, the temperature inertia variable T f,k (t+nΔt) is the initial value, at which point the thermal energy power H is used. use,k,t (t) is a transient parameter, substituting it into the equation The indoor temperature at each moment during the recovery process was calculated using a unit time Δt as the step size.

[0113] Construct the indoor temperature change matrix T for heat user c f,k :

[0114]

[0115] In the formula, each column represents the indoor temperature of heat user c at the moment of power restoration, with a step size of Δt. The first n elements are the decrease in room temperature before power restoration, and the last T+1-n elements are the increase in room temperature after power restoration.

[0116] Construct the dynamic characteristic economic compensation matrix of hot user c Using a linearization approach, the average temperature of each time period is taken as the temperature of that time period, and compensation is calculated according to the thermal comfort standards.

[0117]

[0118] In the formula, This indicates that when the heat user's power supply is restored at time t2, the temperature T inside the building at time t1 is... f,k (t1) Economic compensation provided in accordance with thermal comfort standards.

[0119] matrix The decision variables and constraints are constructed by incorporating them into the objective function and modifying the economic penalty for thermal forced loss power.

[0120] The specific method for calculating the gas dynamic characteristics is similar to the process described above, and the gas pressure change matrix P of gas network user b can also be obtained. f,j Harmony dynamic characteristics economic compensation matrix Then The objective function is incorporated and the economic penalty for forced gas loss flow is adjusted.

[0121] Specifically, the constraints for the gas load power supply status are as follows:

[0122]

[0123] The constraints for heat load supply status are:

[0124]

[0125] In the formula, W shed,j,t H shed,k,t Let N be the forced loss load amounts of gas load j and heat load k at time t, respectively; e N g N hThese are sets of nodes for the electricity, gas, and heating networks, respectively. and These are 0-1 variables representing the recovery state of gas load and heat load; M and eM represent the maximum and minimum numbers in the big-M method;

[0126] The time constraint for restoring gas load to power supply is:

[0127] The time constraint for restoring energy supply from heat load is:

[0128] In the formula, and These represent the recovery times for gas load and heat load, respectively.

[0129] The recovery time identifier constraint is:

[0130]

[0131] In the formula, and These are 0-1 variables representing the recovery time positions of the gas node and the thermal node, respectively.

[0132] In typhoon disaster scenarios, analysis of the gas and heat dynamic characteristics model based on a multi-energy coupled distribution network shows that although the power outage caused by the disaster will lead to power loss at connected gas and heat load nodes, the pressure and temperature parameters of these load nodes will exhibit a gradual decline rather than an instantaneous drop due to the pipeline storage effect of the natural gas system and the thermal inertia of the thermal system. This dynamic decline process allows users to maintain an acceptable energy consumption state for a certain period of time (usually several hours to tens of hours) before system parameters drop to the minimum operating standard. However, when power is restored, the recovery of system parameters (pressure and temperature) also requires a gradual recovery process due to the limitations of fluid transport dynamics and the physical characteristics of heat conduction processes, and cannot achieve the instantaneous response of the power system. By scientifically quantifying the dynamic characteristics of the gas and heat system, user nodes with strong inertia characteristics can be identified, and priority can be given to ensuring the energy supply of critical loads during the post-disaster recovery phase. This differentiated recovery strategy based on dynamic characteristics can not only effectively extend the available energy window of critical loads, but also optimize resource allocation, prioritizing limited repair resources for nodes that have the greatest impact on the stability and economy of the distribution network, thereby significantly reducing the overall economic losses caused by typhoon disasters to multi-energy coupled distribution networks.

[0133] Specifically, the distribution network operation constraints include: distribution network power balance constraints, distribution network node voltage constraints, line operation safety constraints, node voltage amplitude upper and lower limit constraints, and load shedding constraints.

[0134] The power balance constraints of the distribution network are:

[0135] The voltage constraints at distribution network nodes are:

[0136] The line operation safety constraints are as follows:

[0137] The upper and lower limits of node voltage amplitude are constrained as follows:

[0138] The load shedding constraint is:

[0139] In the formula, B is the set of distribution network nodes, π(j) and δ(j) represent the child and parent node sets of the network nodes (electricity, gas, and heat nodes), respectively, and P ij Q ij Let P be the active power and reactive power flowing through line (i,j), respectively. js and Q js P represents the active power and reactive power output by the grid nodes, respectively. G,j Q G,j P represents the active and reactive power output of the generator. L,j P shed,j These represent the active load and active load shedding at the grid nodes, respectively, Q. L,j Q shed,j These represent the reactive load and reactive load shedding at the grid nodes, respectively; E represents the line set, U... i and U j Represents the voltage at grid node a and grid node b, r ij and x ij Here, U represents the resistance and reactance of the conductor, U0 represents the rated voltage of the node, and c represents the resistance and reactance of the conductor. ij Let (i,j) be a 0-1 variable. A value of 0 indicates that line (i,j) is open, and a value of 1 indicates that line is closed. This indicates the maximum allowable power of the line.

[0140] The natural gas system operation constraints include: node flow balance constraints, gas source output constraints, gas load loss constraints, node gas pressure constraints, pipeline flow constraints including compressors, and general branch constraints.

[0141] The node traffic balancing constraint is: ;

[0142] The gas source output constraint is:

[0143] The gas load loss constraint is:

[0144] The nodal pressure constraint is:

[0145] The flow constraint for the pipeline including the compressor is:

[0146]

[0147] The general branch constraint is:

[0148]

[0149] In the formula, W GW,j,t Provide power for the gas source; W load,j,t Natural gas flow rate required for gas load; W pipe,z,t Let represent the gas flow rate through the natural gas pipeline; Ω(j) and Π(j) represent the sets of downstream and upstream pipelines of the gas node, respectively. and These are the upper and lower limits of the gas source output, respectively. 0-1 variable representing the working status of the gas source, with 1 indicating normal operation; GW represents the set of gas sources. and These represent the upper and lower limits of the allowable gas pressure at the nodes, respectively; for the compressor piping set L g,act Any pipe z, π i,t and π j,t These are the air pressure at the beginning and end of the pipeline, respectively; λ z The boost ratio; The maximum allowable flow rate for the pipeline; This is a 0-1 variable representing the line's operating status, with 1 indicating normal operation; φ z L is the pipeline loss coefficient. g,ina This is a set of general branches.

[0150] The operating constraints of the thermal system include: node power balance constraints, heat source output constraints, heat load loss constraints, heat network pipeline constraints, heat loss constraints, node supply and return heat temperature constraints, node temperature and heat power constraints, mass flow rate continuity constraints, and branch mass flow rate constraints.

[0151] The node power balance constraint is:

[0152] ;

[0153] The heat source output constraint is:

[0154] The heat load loss constraint is:

[0155] The constraints on the heating network pipelines are:

[0156]

[0157] The heat loss constraint is:

[0158] The node supply and return temperature constraints are as follows:

[0159]

[0160] The node temperature and thermal power constraints are: H TS,k,t -H use,k,t =C p M k,t (T s,k,t -T r,k,t );

[0161] The mass flow rate continuity constraint is:

[0162] The branch mass flow rate constraint is:

[0163] In the formula, H TS,k,t Provide power to the heat source; H load,k,t Power required for heat load; H pipe,m,t and ΔH pipe,m,t Ω(k) and Π(k) represent the power and heat loss flowing through the heating network pipes, respectively; Ω(k) and Π(k) represent the sets of downstream and upstream pipes of thermal node k, respectively. and These are the upper and lower limits of the heat source output, respectively. The heat source's operating status is represented by a 0-1 variable, with 1 indicating normal operation; TS represents the set of heat sources; for the set of heating network pipes L... h Any pipe m in the middle, and These represent the upper and lower limits of the heat power transported by the pipeline; T sw and T rw The standard heating temperature and regeneration temperature of the heating network; T en The ambient temperature; m and K m These represent the pipe length and the heat loss coefficient, respectively; S m ρ is the cross-sectional area of ​​the pipe. w and C p These are the density and specific heat capacity of hot water, respectively. The maximum allowable flow velocity in the pipeline; The variable T is a 0-1 value representing the working state of the pipeline, with 1 representing normal operation. s,k,t Let be the heating temperature of thermal node k at time t; and These are the highest and lowest permissible heating temperatures, respectively; T r,k,t H represents the regeneration temperature of hot node k at time t; use,k,tThe actual power consumed by the heat user; M k,t M is the mass flow rate passing through hot node k at time t; pipe,m,t Let m be the mass flow rate through pipe m.

[0164] The operating constraints of the coupling elements include: gas turbine constraints, electric compressor constraints, electric gas generator constraints, combined heat and power unit constraints, heat pump constraints, and electric boiler constraints.

[0165] The constraints for gas turbines are:

[0166] The electric compressor is constrained as follows:

[0167] The constraints of the electro-gas generation equipment are:

[0168] The constraints for combined heat and power (CHP) units are:

[0169]

[0170] The heat pump constraint is:

[0171] The constraints for electric boilers are:

[0172] In the formula, W GT,j,t The equivalent gas load of the gas turbine; a j and b j P represents the natural gas consumption coefficient and no-load consumption coefficient per unit power output of the gas turbine; DG,i,t Let P be the active power output of electric node i; GT be the set of gas turbine nodes; P cps,i,t c is the equivalent electrical power of the compressor; z For compressor parameters; η P2G,i P2G energy conversion efficiency; P P2G,i,t Q is the equivalent electrical power of P2G; LHV The lower calorific value of natural gas; P2G is a collection of P2G nodes for electric gas generation equipment; C m The ratio of heat to electricity; W CHP,j,t For CHP equivalent gas load; η CHP,j η represents the CHP electrical conversion efficiency; CHP is the set of CHP nodes in a combined heat and power unit; η HP,i HP heat production efficiency; P HP,i,t HP is the equivalent electrical power; HP is the set of HP nodes for the heat pump; η EB,i For EB energy conversion efficiency; P EB,i,t EB represents the equivalent electrical power of EB; EB is the set of EB nodes of the electric boiler.

[0173] According to graph theory, a radial topology condition is satisfied when each island in the distribution network is connected and the difference between the number of lines and nodes and the number of islands is equal. Therefore, the distribution network topology reconfiguration constraint is:

[0174]

[0175] In the formula, γ j A binary variable used to determine whether a grid node is a source node; N node c is the number of nodes; ij The line switch status is a 0-1 variable; a value of 1 indicates a closed line. The total number of closed lines equals the number of nodes minus 1 (i.e., the number of subgraphs containing substations {Sub}) and the number of subgraphs formed by load islands; F ij For the virtual power flow of line (i,j), F js For the virtual power flow of line (j,s), take zero when the branch is disconnected; W j Output to the source node;

[0176] A virtual power flow approach is used to ensure the connectivity of the reconstructed distribution network. A virtual network with the same topology as the distribution network is set up, and the connectivity between nodes is determined by virtual power. Each subgraph selects one node as the source node γ. j Other nodes act as load nodes, and the virtual power balance constraint also restricts the virtual power to flow only on closed circuits.

[0177] The emergency repair dispatch constraints include: emergency repair team dispatch constraints, emergency repair team route constraints, equipment repair constraints, emergency repair team leaving equipment constraints, material carrying constraints, repair time constraints, emergency repair team repair time constraints, emergency repair team repair number constraints, and equipment recovery power flow coupling constraints.

[0178] The restrictions on dispatching emergency repair teams are as follows:

[0179] The route constraints for the emergency repair team are:

[0180] Equipment repair constraints are:

[0181]

[0182] The restrictions on the repair team leaving the equipment are as follows:

[0183] The restrictions on carrying supplies are as follows:

[0184] The repair time constraint is:

[0185]

[0186] The time constraint for the repair team to repair the faulty equipment is:

[0187]

[0188] The number of repairs the emergency repair team can perform is limited to:

[0189]

[0190] The power flow coupling constraint for equipment restoration is:

[0191]

[0192] In the formula, the repair team's maintenance route does not involve a turnaround; therefore, a dispatch constraint is designed, x. i,j,c Let y be a 0-1 variable representing the path of repair team c from faulty equipment i1 to j1 in the power grid; EC be the set of all repair teams; N be the set of faulty equipment; and S and R be the starting and ending sets of the repair teams, respectively. The repair team path constraint means that the repair team must start from the starting point and return to the ending point to ensure that the repair path is unidirectional. The equipment repair constraint means that the repair team cannot leave a faulty device and immediately return to that device. i,c Record whether the faulty equipment has been repaired. i,c This is a 0-1 variable; if the equipment has recovered to normal operation when the repair team leaves, its value is 1. Bac i , These represent the materials required to repair the electrical faulty equipment i1 and the total amount of materials carried by the repair team. These represent the repair time of the emergency repair team c at the faulty power grid equipment i1, the travel time from the faulty power grid equipment i1 to the faulty power grid equipment j1, and the time of arrival at the faulty power grid equipment j1, respectively. i,t The variable is 0-1, recording whether the faulty device i has been repaired. If the faulty device i is repaired within the discrete time period t, the value is 1; otherwise, the value is 0. f is the time when the repair team arrives at the faulty equipment i. If the faulty equipment i1 returns to normal after repair, then f i,t The value is 1 in the current time period, and then returns to 0. Therefore, the variable v is introduced. i,t To reflect the real-time status of the device, v i,t The device status is represented by a 0-1 variable, with a value of 1 if repaired; BB is the set of faulty nodes. The line from faulty device i1 to device j1 can participate in operation after repair. ij,t Let BL be a 0-1 variable representing whether a line is in operation or not; if it is in operation, the value is 1. BL is the set of faulty lines. In the system network, if one node on either side of any line ij is in a faulty state, then that line is in a faulty state.

[0193] Constraints for energy storage devices are:

[0194]

[0195] α c +α d ≤1;

[0196]

[0197] In the formula, and It is the charging and discharging power of the battery; and It is the instantaneous maximum charge / discharge capacity; α c and α d It is a binary variable that represents the charging and discharging state; It is in charging state; and These are the upper and lower limits of battery capacity; η c and η d This refers to the battery's charge and discharge efficiency. As a crucial flexibility resource in the power system, the operating characteristics of batteries are strictly limited by multiple constraints. In terms of energy, rated capacity determines its maximum energy support capability; in terms of power, charge and discharge rates affect both response speed and lifespan. The cycle life limitation caused by electrochemical characteristics and the typical energy conversion efficiency of 85%-93% further restrict the practical application of batteries. These interconnected constraints are particularly critical under extreme conditions such as post-disaster recovery of distribution networks and must be considered in refined models established for disaster preparedness assessments.

[0198] The system comprehensively covers the operational constraints of multi-energy coupled systems, especially considering gas and heat dynamics, such as heat loss, pipeline storage, and emergency repair scheduling, to avoid distorting the evaluation results due to the omission of key constraints. Each constraint is based on the radial topology of the power grid and the gas pressure safety limit of the gas grid. The solution results can directly guide the scheduling of emergency repair teams and the charging and discharging of energy storage. The constraints of coupled components, such as gas turbines and electric boilers, clarify the multi-energy conversion relationship, providing a quantitative basis for gas and heat to support power restoration. For example, gas turbines can supplement power supply when the power grid is interrupted.

[0199] S4. Under the model constraints corresponding to the distribution network disaster prevention assessment model, with the goal of minimizing the total loss of the multi-energy coupled distribution network, the distribution network disaster prevention assessment model is solved to obtain the distribution network operation state variables.

[0200] In a preferred embodiment of the present invention, the objective function is to minimize the total loss of the multi-energy coupled distribution network, and the improved particle swarm optimization algorithm (IPSO) is used to solve the problem: first, the particles are initialized, and each particle corresponds to a set of operating state variables, such as the load shedding of the power grid, the flow distribution of the gas grid, and the temperature of the heating grid. Then, the particle positions are iteratively updated, and the solution that minimizes the total loss is found under all constraints, and the operating state variables of the distribution network are output.

[0201] S5. Based on the distribution network operation status variables and the total loss of the multi-energy coupled distribution network, calculate the expected power grid loss, expected load loss, and expected load recovery time, and conduct a disaster prevention assessment of the distribution network to be evaluated based on the expected power grid loss, expected load loss, and expected load recovery time.

[0202] Specifically, the expected power grid loss, expected load loss, and expected load recovery time are all statistical indicators under multiple scenarios. They refer to the average total power grid loss, the average load interruption, and the average time from load interruption to recovery in all fault scenarios, respectively, and are used to comprehensively reflect the disaster prevention capabilities of the distribution network.

[0203] Preferably, the power distribution network operation status variables include: the time of electrical load recovery, the time of gas load recovery, the time of heat load recovery, and the amount of electrical load loss.

[0204] Based on the distribution network operating state variables and the total loss of the multi-energy coupled distribution network, the expected power grid loss, expected load loss, and expected load recovery time are calculated, including:

[0205] Calculate the expected power grid loss based on the total loss of the multi-energy coupled distribution network and the number of fault scenarios in the distribution network lines;

[0206] Calculate the expected load loss based on the amount of electrical load loss and the number of fault scenarios in the distribution network lines;

[0207] The expected load recovery time is calculated based on the timing of electrical load recovery, gas load recovery, heat load recovery, and the number of power distribution network line fault scenarios.

[0208] Specifically, the distribution network operation status variables refer to the key parameters characterizing the operation and recovery status of the distribution network, output after solving the distribution network disaster prevention assessment model. These include the recovery time and interruption loss of electricity, gas, and heat loads. Only electricity load is considered; gas and heat losses are already correlated through constraints and are direct inputs for calculating the three expected indicators. The recovery time of electricity, gas, and heat loads refers to the time it takes for the load to transition from an interrupted state to a normal power supply state.

[0209] In a preferred embodiment of the present invention, the expected power grid loss under a disaster is:

[0210] In the formula, N sThe total number of scenarios selected for disaster prevention capability assessment.

[0211] Expected load loss of distribution network under disaster:

[0212] Expected load recovery time under disaster:

[0213] In the formula, T e,s ,T g,s ,T h,s These represent the recovery times of the electrical, gas, and heat loads under scenario s.

[0214] In the field of disaster prevention capability assessment of distribution networks, constructing a scientific and comprehensive evaluation index system has significant theoretical and practical value. This index system can not only systematically characterize the response characteristics and recovery capabilities of the power system under extreme disaster conditions, but also provide a basis for decision-making regarding the safe operation and maintenance management of distribution networks. Current evaluation index systems have obvious limitations: on the one hand, existing indicators often focus too much on a single dimension (such as considering only equipment damage rate or outage duration), making it difficult to comprehensively reflect the overall performance of the distribution network throughout the entire disaster cycle (prevention-resistance-recovery-adaptation); on the other hand, insufficient research on the correlation and synergy between indicators leads to assessment results that cannot accurately characterize the overall disaster resilience level of the system. These deficiencies in the evaluation system have a dual negative impact: at the operational level, it may mask the true vulnerabilities of the system, affecting the formulation of disaster response strategies; at the economic level, the lack of accurate assessment may lead to misallocation of maintenance resources, increasing unnecessary operating costs. To address these issues, this embodiment innovatively proposes a multi-dimensional collaborative evaluation framework, constructing comprehensive evaluation indicators from three key dimensions: economic loss (reflecting post-disaster repair costs and user power outage losses), load loss (characterizing power supply reliability), and recovery time (reflecting the system's rapid recovery capability). This indicator system has three significant advantages: First, by organically combining economic, technical, and time dimensions, it can more comprehensively assess the system's disaster prevention capabilities; second, the indicator design considers both quantifiability and practicality, facilitating engineering applications; and finally, the evaluation results can directly guide disaster prevention resource allocation and emergency plan optimization. Empirical research shows that this evaluation method can not only accurately identify weak links in the distribution network but also provide a scientific basis for improving system resilience and reducing maintenance costs, possessing significant practical value for ensuring the safe and stable operation of the power system.

[0215] Preferably, a disaster prevention assessment is conducted on the distribution network to be assessed based on the expected power grid loss, expected load loss, and expected load recovery time, including:

[0216] The comprehensive evaluation index value of the distribution network is obtained by weighting the expected power grid loss, expected load loss, expected load recovery time and preset weights.

[0217] The assessment level of the distribution network to be assessed is determined based on the comprehensive assessment index values ​​of the distribution network and the preset assessment level table.

[0218] Disaster prevention assessments are conducted on the power distribution network to be assessed based on the assessment level.

[0219] Specifically, the preset weights represent the weights of indicators set based on load importance, economic impact, and recovery efficiency. These weights are used to convert expected grid losses, expected load losses, and expected load recovery time into a comprehensive score. The total weight is 1. For example, the weight for expected grid losses is 0.3 (economic impact), the weight for expected load losses is 0.4 (load guarantee), and the weight for expected load recovery time is 0.3 (recovery efficiency). These weights are determined by the grid operation and maintenance department in conjunction with policy requirements and user surveys. The comprehensive evaluation index value of the distribution network represents the comprehensive value obtained by multiplying the index score by the preset weights. The range is 0-100 points; a higher score indicates stronger disaster prevention capabilities. For example, a comprehensive index value of 90 points corresponds to excellent, and 60 points corresponds to qualified. The preset evaluation level table refers to a level classification table based on industry standards and regional disaster prevention needs, corresponding the comprehensive evaluation index value to four levels: excellent, good, qualified, and unqualified, as shown in Table 1.

[0220] Table 1

[0221]

[0222]

[0223] The assessment level of the distribution network to be assessed represents the final level obtained by matching the comprehensive assessment index value with the preset assessment level table. It is the core conclusion of the distribution network's disaster prevention capability. For example, a comprehensive index value of 75 points corresponds to a qualified level.

[0224] Based on the resilience assessment results of multi-energy coupled distribution networks under typhoon disaster scenarios, a systematic analysis of various resilience indicators was conducted. By establishing a multi-dimensional assessment matrix, the disaster prevention performance of the distribution network was analyzed from two dimensions: static protection capability and dynamic recovery capability. In the static dimension, structural indicators such as the wind resistance level of key nodes and the wind-resistant reinforcement rate of equipment were examined. In the dynamic dimension, process indicators such as fault isolation speed and power restoration sequence were analyzed. For example, for high-disaster-risk areas (such as coastal substations), priority should be given to implementing wind-resistant reinforcement of equipment and microgrid reconfiguration; for areas with dense critical loads, the black-start power supply configuration and network topology should be optimized. The assessment analysis provides a quantitative basis for formulating differentiated disaster prevention capability enhancement plans, and especially under conditions of limited resources, it can provide decision support for power grid planning departments to determine the priority of disaster prevention upgrades.

[0225] By implementing this embodiment, we break through the traditional single power perspective of assessment. By simultaneously collecting power-related data such as gas source data, heat source data, and power grid component location and topology data, we formally incorporate the natural gas system and heat system into the scope of distribution network disaster prevention assessment. This can fully cover the interaction of multi-energy coupled systems, avoid overlooking the impact of gas and heat systems due to focusing only on power load recovery, solve the problem of assessment one-sidedness, and enable the subsequent construction of distribution network disaster prevention assessment models to cover the interaction between multiple energy sources such as electricity, gas, and heat. This solves the problem of ignoring the interaction of multi-energy coupled systems from the source. Based on the fault probability model of power grid components, a disaster prevention assessment model for distribution networks is constructed according to topology data, node data, line data, gas source data, heat source data, load data, and emergency repair data. The model aims to minimize the total loss of multi-energy coupled distribution networks, rather than using static or quasi-static assumptions. It can initially correlate the dynamic effects of natural gas systems and heating systems. The collaborative integration of gas source data, heat source data, and power system data enables the disaster prevention assessment model to initially correlate the differences in response time scales between gas and heat systems and power systems. This avoids the shortcomings of traditional static models that cannot capture the dynamic characteristics of gas and heat, resulting in nonlinear post-disaster recovery. The disaster prevention assessment model of distribution networks is more in line with the actual operating rules of multi-energy coupled distribution networks. Based on the distribution network operation state variables and the total loss of the multi-energy coupled distribution network, the expected power grid loss, expected load loss, and expected load recovery time are calculated. Since the construction and solution of the distribution network disaster prevention assessment model are based on multi-energy coupled data, the obtained distribution network operation state variables can indirectly reflect the impact of the dynamic characteristics of the gas and heat system on the overall distribution network. Based on this, the expected power grid loss, expected load loss, and expected load recovery time are calculated. The assessment based on multi-dimensional indicators can comprehensively reflect the losses and recovery of electricity, gas, and heat in multiple dimensions, effectively reducing the problem of deviation between the assessment results under traditional static assumptions and actual disaster scenarios. This provides a more accurate and comprehensive basis for the decision on the disaster prevention capability of the distribution network, reduces the deviation of the disaster prevention assessment results, and improves the accuracy of the disaster prevention assessment of the distribution network.

[0226] See Figure 2 This is a schematic diagram of the structure of a power distribution network disaster prevention assessment device considering the dynamic characteristics of energy gas and heat, provided in an embodiment of the present invention, comprising:

[0227] The data acquisition module is used to acquire typhoon data, grid component locations, topology data, node data, line data, grid line wind resistance speed, gas source data, heat source data, load data, and emergency repair data within the scope of the distribution network to be evaluated.

[0228] The failure probability model construction module is used to construct a failure probability model of the power grid components based on the typhoon data, the location of the power grid components, and the wind resistance speed of the power grid lines.

[0229] The power distribution network disaster prevention assessment model construction module is used to construct a power distribution network disaster prevention assessment model and the corresponding model constraints based on the power grid component failure probability model, according to the topology data, node data, line data, gas source data, heat source data, load data and emergency repair data.

[0230] The distribution network state variable solving module is used to solve the distribution network disaster prevention assessment model under the model constraints corresponding to the distribution network disaster prevention assessment model, with the goal of minimizing the total loss of the multi-energy coupled distribution network, and obtain the distribution network operation state variables.

[0231] The distribution network disaster prevention assessment module is used to calculate the expected power grid loss, expected load loss, and expected load recovery time based on the distribution network operating status variables and the total loss of multi-energy coupled distribution networks, and to conduct a disaster prevention assessment of the distribution network to be assessed based on the expected power grid loss, expected load loss, and expected load recovery time.

[0232] Specifically, the disaster prevention assessment model for the power distribution network is as follows:

[0233]

[0234] Among them, C all,s The total loss of a multi-energy coupled distribution network; These are the penalties for unit electrical load loss, gas load loss, and heat load loss after a power outage, respectively; P shed,i,t W shed,j,t H shed,k,t Let N be the forced loss load amounts of electrical load i, gas load j, and heat load k at time t, respectively; e N g N h Let e ​​represent the set of electrical nodes, g represent the set of gaseous nodes, and h represent the set of thermal nodes; Δt represents the unit time; and T represents the post-disaster recovery period. and These represent the number of users at air node g and heat node h, respectively. and These are matrices representing economic compensation for gas and thermal inertia, respectively. and These are 0-1 variables representing the positions of the gas load j and heat load k recovery times within the period, respectively.

[0235] This invention provides a disaster prevention assessment device for power distribution networks that considers the dynamic characteristics of energy sources such as gas and heat. The device acquires typhoon data, power grid component locations, topology data, node data, line data, wind resistance speed of power grid lines, gas source data, heat source data, load data, and emergency repair data within the area of ​​the power distribution network to be assessed, using a data acquisition module. In a fault probability model construction module, a fault probability model for power grid components is constructed based on the typhoon data, the locations of the power grid components, and the wind resistance speed of the power grid lines. In a power distribution network disaster prevention assessment model construction module, based on the fault probability model of the power grid components, and using the topology data, node data, line data, gas source data, heat source data, and other relevant data, a fault probability model for the power grid components is constructed. Load data and emergency repair data are used to construct a distribution network disaster prevention assessment model and its corresponding model constraints. Based on the distribution network state variable solving module, and under the model constraints corresponding to the distribution network disaster prevention assessment model, the model is solved with the objective of minimizing the total loss of the multi-energy coupled distribution network, yielding the distribution network operating state variables. Finally, in the distribution network disaster prevention assessment module, based on the distribution network operating state variables and the total loss of the multi-energy coupled distribution network, the expected grid loss, expected load loss, and expected load recovery time are calculated. Based on these expected values, a disaster prevention assessment is then performed on the distribution network to be assessed.

[0236] Breaking away from the traditional single-electricity perspective of assessment, this method simultaneously collects electricity-related data such as gas source data, heat source data, and grid component location and topology data. It formally incorporates natural gas and heating systems into the scope of distribution network disaster prevention assessment, which can fully cover the interaction of multi-energy coupled systems. This avoids overlooking the impact of gas and heating systems due to focusing only on power load recovery, and solves the problem of assessment one-sidedness. It enables the subsequent construction of distribution network disaster prevention assessment models to cover the interaction between multiple energy sources such as electricity, gas, and heat, thus solving the problem of ignoring the interaction of multi-energy coupled systems from the source. Based on the fault probability model of power grid components, a disaster prevention assessment model for distribution networks is constructed according to topology data, node data, line data, gas source data, heat source data, load data, and emergency repair data. The model aims to minimize the total loss of multi-energy coupled distribution networks, rather than using static or quasi-static assumptions. It can initially correlate the dynamic effects of natural gas systems and heating systems. The collaborative integration of gas source data, heat source data, and power system data enables the disaster prevention assessment model to initially correlate the differences in response time scales between gas and heat systems and power systems. This avoids the shortcomings of traditional static models that cannot capture the dynamic characteristics of gas and heat, resulting in nonlinear post-disaster recovery. The disaster prevention assessment model of distribution networks is more in line with the actual operating rules of multi-energy coupled distribution networks. Based on the distribution network operation state variables and the total loss of the multi-energy coupled distribution network, the expected power grid loss, expected load loss, and expected load recovery time are calculated. Since the construction and solution of the distribution network disaster prevention assessment model are based on multi-energy coupled data, the obtained distribution network operation state variables can indirectly reflect the impact of the dynamic characteristics of the gas and heat system on the overall distribution network. Based on this, the expected power grid loss, expected load loss, and expected load recovery time are calculated. The assessment based on multi-dimensional indicators can comprehensively reflect the losses and recovery of electricity, gas, and heat in multiple dimensions, effectively reducing the problem of deviation between the assessment results under traditional static assumptions and actual disaster scenarios. This provides a more accurate and comprehensive basis for the decision on the disaster prevention capability of the distribution network, reduces the deviation of the disaster prevention assessment results, and improves the accuracy of the disaster prevention assessment of the distribution network.

[0237] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0238] Those skilled in the art will understand that, for convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0239] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a power distribution network disaster prevention assessment method considering the dynamic characteristics of energy, gas, and heat, as described in the above embodiments. The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0240] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting various parts of the terminal device via various interfaces and lines.

[0241] The memory can be used to store the computer program. The processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0242] Another embodiment of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the power distribution network disaster prevention assessment method considering the dynamic characteristics of energy, gas and heat described in the above embodiment.

[0243] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0244] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A disaster prevention assessment method for distribution networks considering the dynamic characteristics of energy gas and heat, characterized in that, include: Acquire typhoon data, grid component locations, topology data, node data, line data, grid line wind resistance speed, gas source data, heat source data, load data, and emergency repair data within the scope of the distribution network to be evaluated; Based on the typhoon data, the location of the power grid components, and the wind resistance speed of the power grid lines, a failure probability model for the power grid components is constructed. Based on the fault probability model of the power grid components, and according to the topology data, node data, line data, gas source data, heat source data, load data, and emergency repair data, a disaster prevention assessment model for the distribution network and the corresponding model constraints are constructed. Under the model constraints corresponding to the distribution network disaster prevention assessment model, with the goal of minimizing the total loss of the multi-energy coupled distribution network, the distribution network disaster prevention assessment model is solved to obtain the distribution network operation state variables; Based on the distribution network operation state variables and the total loss of the multi-energy coupled distribution network, the expected power grid loss, expected load loss, and expected load recovery time are calculated, and a disaster prevention assessment is conducted on the distribution network to be evaluated based on these expected power grid loss, expected load loss, and expected load recovery time.

2. The method for disaster prevention assessment of distribution networks considering the dynamic characteristics of energy gas and heat as described in claim 1, characterized in that, The typhoon data includes: the pressure difference at the typhoon center at the time of landfall, the angle between the typhoon's direction of travel and due north, the angle between the coastline and due north, the typhoon's speed, and the location of the typhoon's center. Based on the typhoon data, the location of the power grid components, and the wind resistance speed of the power grid lines, a fault probability model for the power grid components is constructed, including: Based on the location of the typhoon center and the location of the power grid components, the relative positions of the typhoon center and the power grid components are calculated. The central pressure difference of the typhoon during its journey is calculated based on the angle between the typhoon's direction of travel and due north, the angle between the coastline and due north, and the central pressure difference of the typhoon at the time of landfall; and the radius of the typhoon's maximum wind speed during its journey is calculated based on the central pressure difference of the typhoon. The wind speed at the location of the power grid components is calculated based on the central pressure difference, the radius of maximum wind speed, and the speed of the typhoon during its movement. Based on the wind speed at the location of power grid components and the wind resistance speed of power grid lines, an initial power grid component failure probability model is constructed. Random numbers generated by random simulation based on Monte Carlo simulation method are compared with preset dynamic fault probability thresholds of power grid components hourly. When the random number exceeds the preset dynamic fault probability threshold, the power grid component is determined to be in a fault state, and several distribution network line fault scenarios are obtained. The initial power grid component failure probability model is corrected based on the fault scenarios of the distribution network lines to obtain the final power grid component failure probability model.

3. The method for disaster prevention assessment of distribution networks considering the dynamic characteristics of energy gas and heat as described in claim 2, characterized in that, The model constraints corresponding to the disaster prevention assessment model of the distribution network include: gas load energy supply status constraints, heat load energy supply status constraints, gas load recovery energy supply time constraints, heat load recovery energy supply time constraints, recovery time identifier constraints, distribution network operation constraints, natural gas system operation constraints, thermal system operation constraints, coupling element operation constraints, distribution network topology reconfiguration constraints, emergency repair and dispatch constraints, and energy storage equipment constraints. The distribution network operation constraints include: distribution network power balance constraints, distribution network node voltage constraints, line operation safety constraints, node voltage amplitude upper and lower limits constraints, and load shedding constraints. The natural gas system operation constraints include: node flow balance constraints, gas source output constraints, gas load loss constraints, node gas pressure constraints, pipeline flow constraints including compressors, and general branch constraints. The operating constraints of the thermal system include: node power balance constraints, heat source output constraints, heat load loss constraints, heat network pipeline constraints, heat loss constraints, node supply and return heat temperature constraints, node temperature and heat power constraints, mass flow rate continuity constraints, and branch mass flow rate constraints. The operating constraints of the coupling elements include: gas turbine constraints, electric compressor constraints, electric gas generator constraints, combined heat and power unit constraints, heat pump constraints, and electric boiler constraints. The emergency repair dispatch constraints include: emergency repair team dispatch constraints, emergency repair team route constraints, equipment repair constraints, emergency repair team departure from equipment constraints, material carrying constraints, repair time constraints, emergency repair team repair time constraints, emergency repair team repair frequency constraints, and equipment recovery power flow coupling constraints.

4. The method for disaster prevention assessment of distribution networks considering the dynamic characteristics of energy gas and heat as described in claim 3, characterized in that, The disaster prevention assessment model for the power distribution network is as follows: Among them, C all,s The total loss of a multi-energy coupled distribution network; These are the penalties for unit electrical load loss, gas load loss, and heat load loss after a power outage, respectively; P shed,i,t W shed,j,t H shed,k,t Let N be the forced loss load amounts of electrical load i, gas load j, and heat load k at time t, respectively; e N g N h Let e ​​represent the set of electrical nodes, g represent the set of gaseous nodes, and h represent the set of thermal nodes; Δt represents the unit time; and T represents the post-disaster recovery period. and These represent the number of users at air node g and heat node h, respectively. and These are matrices representing economic compensation for gas and thermal inertia, respectively. and These are 0-1 variables representing the positions of the gas load j and heat load k recovery times within the period, respectively.

5. The method for disaster prevention assessment of distribution networks considering the dynamic characteristics of energy gas and heat as described in claim 4, characterized in that, The power distribution network operation status variables include: the time of power load recovery, the time of gas load recovery, the time of heat load recovery, and the amount of power load loss. Based on the distribution network operating state variables and the total loss of the multi-energy coupled distribution network, the expected power grid loss, expected load loss, and expected load recovery time are calculated, including: Calculate the expected power grid loss based on the total loss of the multi-energy coupled distribution network and the number of fault scenarios in the distribution network lines; Calculate the expected load loss based on the amount of electrical load loss and the number of fault scenarios in the distribution network lines; The expected load recovery time is calculated based on the timing of electrical load recovery, gas load recovery, heat load recovery, and the number of power distribution network line fault scenarios.

6. The method for disaster prevention assessment of distribution networks considering the dynamic characteristics of energy gas and heat as described in claim 5, characterized in that, A disaster prevention assessment is conducted on the distribution network to be assessed based on the expected power grid loss, expected load loss, and expected load recovery time, including: The comprehensive evaluation index value of the distribution network is obtained by weighting the expected power grid loss, expected load loss, expected load recovery time and preset weights. The assessment level of the distribution network to be assessed is determined based on the comprehensive assessment index values ​​of the distribution network and the preset assessment level table. Disaster prevention assessments are conducted on the power distribution network to be assessed based on the assessment level.

7. A disaster assessment device for power distribution networks that considers the dynamic characteristics of energy gas and heat, characterized in that, include: The data acquisition module is used to acquire typhoon data, grid component locations, topology data, node data, line data, grid line wind resistance speed, gas source data, heat source data, load data, and emergency repair data within the scope of the distribution network to be evaluated. The failure probability model construction module is used to construct a failure probability model of the power grid components based on the typhoon data, the location of the power grid components, and the wind resistance speed of the power grid lines. The power distribution network disaster prevention assessment model construction module is used to construct a power distribution network disaster prevention assessment model and the corresponding model constraints based on the power grid component failure probability model, according to the topology data, node data, line data, gas source data, heat source data, load data and emergency repair data. The distribution network state variable solving module is used to solve the distribution network disaster prevention assessment model under the model constraints corresponding to the distribution network disaster prevention assessment model, with the goal of minimizing the total loss of the multi-energy coupled distribution network, and obtain the distribution network operation state variables. The distribution network disaster prevention assessment module is used to calculate the expected power grid loss, expected load loss, and expected load recovery time based on the distribution network operating status variables and the total loss of multi-energy coupled distribution networks, and to conduct a disaster prevention assessment of the distribution network to be assessed based on the expected power grid loss, expected load loss, and expected load recovery time.

8. A power distribution network disaster assessment device considering the dynamic characteristics of energy gas and heat as described in claim 7, characterized in that, The disaster prevention assessment model for the power distribution network is as follows: Among them, C all,s The total loss of a multi-energy coupled distribution network; These are the penalties for unit electrical load loss, gas load loss, and heat load loss after a power outage, respectively; P shed,i,t W shed,j,t H shed,k,t Let N be the forced loss load amounts of electrical load i, gas load j, and heat load k at time t, respectively; e N g N h Let e ​​represent the set of electrical nodes, g represent the set of gaseous nodes, and h represent the set of thermal nodes; Δt represents the unit time; and T represents the post-disaster recovery period. and These represent the number of users at air node g and heat node h, respectively. and These are matrices representing economic compensation for gas and thermal inertia, respectively. and These are 0-1 variables representing the positions of the gas load j and heat load k recovery times within the period, respectively.

9. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a power distribution network disaster prevention assessment method considering the dynamic characteristics of energy, gas, and heat as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform a power distribution network disaster prevention assessment method considering the dynamic characteristics of energy, gas, and heat, as described in any one of claims 1 to 6.

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