Event-oriented grid-lifeline-user disaster impact whole-chain joint simulation method

Abstract

The present application relates to a kind of event-oriented power grid-life line-user disaster influence whole chain joint simulation method, to solve the problem that existing technology cannot cover composite extreme event, coupling modeling is not accurate, lack of unified whole chain simulation framework.The method generates extreme event scene such as natural disaster, network attack, constructs multi-granularity life line model, carries out joint simulation in two stages (two-way coupling system iterative simulation, one-way influence system section simulation), to combine time-event double-driven interaction, synchronously model 8 kinds of important users.Can accurately simulate whole chain failure process, output power grid fault evolution, life line function loss and user service availability, provide support for city life line resilience assessment and emergency decision-making.

Description

Technical Field

[0001] This invention relates to the field of power grid planning technology, and in particular to a joint simulation method for the entire chain of disaster impacts, from events to the power grid to lifelines to users. Background Technology

[0002] With accelerating urbanization, urban lifelines, centered on the urban power grid and encompassing natural gas pipelines, water supply networks, road networks, public communication networks, and rail transit systems, have become crucial for ensuring urban operation and public safety. However, extreme events (including natural disasters, cyber / physical attacks, major accidents, and complex cascading events) not only directly damage power grid equipment but also trigger cascading failures through system coupling, impacting critical users such as hospitals and transportation hubs, causing economic losses and safety risks. Therefore, constructing a full-chain impact analysis model of extreme events, urban power grids, urban lifelines, and critical users is a core requirement for improving system resilience and supporting consequence assessment.

[0003] However, existing technologies have significant shortcomings: First, they ignore complex events and focus only on single disasters, failing to cover chain-related scenarios such as network attacks combined with physical attacks, typhoons combined with rainstorms, etc., resulting in large deviations between fault scenarios and reality; second, coupled modeling is fragmented, failing to consider bidirectional coupling (such as power grid-gas grid mutual feedback), oversimplifying dynamic processes (such as using steady-state models for gas grids), and the time scales of various systems are inconsistent; third, there is no unified joint simulation framework, tools are independent, lacking automatic data interaction and event-time driven integration, making it impossible to simulate the entire chain of evolution; fourth, important user modeling is incomplete, failing to consider multiple resource dependencies, and classification coverage is insufficient.

[0004] In summary, existing technologies are insufficient to meet the needs of full-chain analysis, and there is an urgent need to develop new methods to enhance the resilience of urban lifelines. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a joint simulation method for the entire chain of disaster impacts, including events, power grids, lifelines, and users. This method aims to solve or partially solve the problem that existing solutions cannot fully cover complex chain-generated extreme events, accurately depict the coupling relationships and dynamic interactions between urban lifeline systems, and thus effectively simulate the entire chain of coupled failure processes to support the resilience assessment and emergency decision-making of urban lifeline systems.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] One aspect of the present invention provides a joint simulation method for the entire chain of disaster impacts, encompassing events, power grids, lifelines, and users, including:

[0008] S1: Generate extreme event scenarios, analyze the impact of extreme events on the urban power grid, and determine the initial fault parameters of the power grid;

[0009] S2: Model the urban power grid, natural gas pipeline network, road traffic network, water supply network, public communication network and rail transit system respectively to obtain the dynamic model of the urban lifeline system;

[0010] S3: Model multiple coupled components among urban lifeline systems to obtain a parameter correlation model;

[0011] S4: Model the resource dependencies of critical users on the lifeline system;

[0012] S5: In the first stage, iterative simulations are performed on the power grid-natural gas pipeline network and the power grid-road traffic network with bidirectional coupling characteristics, and the coupling point information is exchanged at a fixed step size. In the second stage, cross-sectional simulations are performed on the water supply network and public communication network that are affected by the power grid in one direction, and the functional loss is calculated based on the fault parameters to realize the interaction of time-driven and event-driven approaches.

[0013] S6: Output joint simulation results, including the urban power grid fault evolution process, the functional loss rate of each lifeline system, and the service availability indicators of important users.

[0014] As a preferred technical solution, the modeling process of the urban lifeline system dynamics model includes:

[0015] A steady-state model of AC optimal power flow is obtained by modeling the urban power grid; a simplified dynamic model is obtained by modeling the natural gas pipeline network; a hybrid model combining a macroscopic user equilibrium traffic assignment model and a microscopic vehicle following model is obtained by modeling the road traffic network; a steady-state hydraulic model is obtained by modeling the water supply network; a directed weighted graph theory model is obtained by modeling the public communication network; and a dual-subject model of train and passenger is performed on the rail transit system; thus, a dynamic model of the urban lifeline system is obtained.

[0016] As a preferred technical solution, the hybrid model combining the macroscopic user equilibrium traffic assignment model and the microscopic vehicle following model is modeled as follows:

[0017] Macro-level user equilibrium traffic allocation model:

[0018] ,

[0019] ,

[0020] ,

[0021] ,

[0022] ,

[0023] In the above formula, For road section a Traffic flow For traffic flow aggregation, Represents a cost or time function. Indicates from the source node r To the target node s Total demand Representing a path l Traffic, For indicator functions, when the path contains road segments a The value is 1 if it is true, and 0 otherwise. Indicates from the source node r To the target node s The path, Indicates constraints;

[0024] Microscopic vehicle car-following model:

[0025] ,

[0026] ,

[0027] ,

[0028] In the above formula, Indicates time Safe distance speed, Indicates time The speed of the car in front, express Vehicle spacing at any given time For braking reaction time, To keep up with the car's reaction time, Indicates time speed of decision-making Indicates the maximum speed limit of the vehicle. express Current vehicle speed at any given moment This represents the vehicle acceleration relative to the current speed. Indicates the vehicle's actual speed. For time slices, for Expected distance at any time for The current spatial coordinates of the vehicle.

[0029] As a preferred technical solution, the constraints of the hybrid model modeling include: cell traffic flow balance constraints; restrictions that the traffic flow of resource vehicles from a cell to an adjacent cell is no greater than the cell's traffic flow; restrictions that the total traffic flow of vehicles from a cell to an adjacent cell is no greater than the cell's traffic flow transfer limit; and restrictions that the traffic flow of vehicles from an adjacent cell to a cell... The restrictions include: the total traffic flow not exceeding the cell's traffic flow transfer limit; the total inflow into a cell not exceeding the cell's remaining capacity when traffic congestion occurs; and the restrictions on the initial and final traffic flow states in each cell.

[0030] As a preferred technical solution, step S5 includes:

[0031] S51: Initialize simulation parameters and set simulation duration. Power grid dispatch cycle and natural gas pipeline network simulation step size ,in It is an integer;

[0032] S52: If the current time Perform spatiotemporal discretization calculation System status at all times Updated to ;

[0033] S53: In response to the detection of an event, an event-driven interaction is performed, and the power grid and the gas grid exchange the operating status of the gas turbine unit and the power consumption of the electric drive facility. After the power grid performs AC-OPF scheduling, it feeds back the gas consumption of the gas turbine unit. The event includes an initial fault, a gas grid cascading fault, and a power grid cascading fault.

[0034] S54: If the current time The execution time-driven interaction involves the gas network transmitting the power consumption of electric drive facilities to the power grid, and the power grid transmitting the gas consumption to the gas network after performing AC-OPF scheduling.

[0035] S55: After completing the first stage of bidirectional coupling simulation, input the power grid fault parameters into the water supply network and communication network, calculate the water supply loss and the number of communication node failures, output the lifeline system function loss, and complete the second stage of simulation.

[0036] As a preferred technical solution, the resource dependencies of important users on the lifeline system can be modeled through functional modeling or subject modeling.

[0037] As a preferred technical solution, the important users include command and control, communication, news media, data and financial centers, water, heat and gas supply, transportation, medical and health care, and important venues.

[0038] As a preferred technical solution, the coupling elements include electric-driven compressors, electric-driven gas sources and gas turbine units for the power-natural gas network, traffic lights for the power-road transportation network, water pumps for the power-water supply network, base stations / switches for the power-public communication network, and trains for the power-rail transit system.

[0039] As a preferred technical solution, the extreme event scenarios include natural disasters, cyberattacks, physical attacks, major accidents, and complex chain reactions of extreme events.

[0040] Another aspect of the present invention provides a disaster impact full-chain joint simulation system oriented towards events, power grid, lifeline, and users, for implementing the aforementioned disaster impact full-chain joint simulation method oriented towards events, power grid, lifeline, and users, the system comprising:

[0041] The extreme event fault scenario generation module is used to generate extreme event scenarios, analyze the impact of extreme events on the urban power grid, and determine the initial fault parameters of the power grid.

[0042] The urban lifeline system modeling module is used to model the urban power grid, natural gas pipeline network, road traffic network, water supply network, public communication network and rail transit system respectively, and obtain the dynamic model of the urban lifeline system;

[0043] The coupling element modeling module is used to model multiple coupling elements between urban lifeline systems to obtain a parameter correlation model;

[0044] The resource dependency modeling module is used to model the resource dependencies of critical users on the lifeline system;

[0045] The urban lifeline simulation module is used to perform iterative simulations of the power grid-natural gas pipeline network and power grid-road transportation network with bidirectional coupling characteristics in the first stage, and to exchange coupling point information at a fixed step size. In the second stage, it performs cross-sectional simulations of the water supply network and public communication network that are affected by the power grid in one direction, calculates functional loss based on fault parameters, realizes the interaction of time-driven and event-driven approaches, and outputs joint simulation results, including the urban power grid fault evolution process, functional loss rate of each lifeline system, and service availability indicators for important users.

[0046] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0047] (1) High simulation accuracy: In view of the problems of insufficient real-time interaction of coupling point information, lack of accurate input for functional loss calculation, large deviation of results, and inconsistent system state synchronization in the current scheme, this invention conducts simulation in two stages. In the first stage, iterative simulation is carried out on the power grid-natural gas pipeline network and power grid-road traffic network with bidirectional coupling characteristics, and the coupling point information is exchanged at fixed step size. In the second stage, cross-sectional simulation is carried out on the water supply network and public communication network with unidirectional influence of the power grid. Functional loss is calculated based on fault parameters, realizing the interaction of time-driven and event-driven approaches, so as to accurately capture the bidirectional chain fault evolution process. Cross-sectional simulation of actual power grid fault parameters provides accurate data support for the functional loss assessment of lifeline system. By combining the dual-drive approach in stages, the simulation breakage problem is overcome, realizing the full-chain automated simulation from extreme event input to important user impact output, providing a reliable simulation basis for urban lifeline system resilience assessment and emergency decision-making.

[0048] (2) Generating composite fault scenarios that closely resemble actual disasters: In view of the fact that existing technologies mostly focus on single-type extreme events, such as a single typhoon or a single network attack, without considering the complex chain of scenarios such as "network attacks weakening defenses and then superimposing physical attacks, typhoons superimposing rainstorms and causing secondary disasters", resulting in a large deviation between the generated fault scenarios and actual disasters, this invention generates extreme event scenarios, analyzes the effect mode of extreme events on urban power grids, determines the initial fault parameters of the power grid, covers natural disasters, network attacks, physical attacks, major accidents and complex chain extreme event disasters, analyzes the effect mode of various events on the power grid, clarifies the evolution logic of complex chain events, and forms a method for generating extreme event fault scenarios covering all types, thereby generating composite fault scenarios that closely resemble actual disasters, fully covering the impact path of extreme events on the power grid, and providing accurate initial fault input for subsequent full-chain simulation.

[0049] (3) High accuracy of fault propagation simulation across systems: In view of the problems of existing technologies that model lifeline systems separately, ignore bidirectional coupling between systems, and oversimplify key dynamic processes, resulting in inconsistent time scales of each system and large deviations in the timing prediction of fault propagation across systems, this invention constructs a multi-granularity lifeline system dynamic model, establishes a parameter correlation model of coupling elements, and designs a hierarchical simulation based on the differences in time scales of each system, thereby accurately depicting the bidirectional coupling effect and dynamic evolution process between lifeline systems and improving the accuracy of fault propagation simulation across systems. Attached Figure Description

[0050] Figure 1 This is a flowchart of the joint simulation method for the entire disaster impact chain, oriented towards events, power grid, lifeline, and users, as described in the embodiment.

[0051] Figure 2 This is a schematic diagram illustrating the modeling of the entire critical infrastructure chain in the embodiment;

[0052] Figure 3 This is a schematic diagram of multi-stage co-simulation in the embodiment;

[0053] Figure 4 This is a schematic diagram of a power system cascading failure model in the embodiment;

[0054] Figure 5 A schematic diagram illustrating the fault propagation path between urban lifeline systems;

[0055] Figure 6 This is a schematic diagram of a disaster impact joint simulation system for the entire chain of events, power grid, lifeline, and users, as described in the embodiment. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0057] Example 1

[0058] To address the problems existing in the aforementioned technologies, this embodiment provides a joint simulation method for the entire disaster impact chain, encompassing events, power grids, lifelines, and users. (See [link to relevant documentation]). Figure 1 The method includes the following steps:

[0059] S1: Generate extreme event scenarios, analyze the impact of extreme events on the urban power grid, and determine the initial fault parameters of the power grid.

[0060] Disaster scenarios are the foundation of resilience assessment. The extreme events modeled in this step include four types of extreme disasters: natural disasters, cyberattacks, physical attacks, and major accidents, as well as compound chain extreme events. The impact patterns of these events on the power system are analyzed to generate typical failure scenarios, which can be used for modeling and simulating urban lifelines and assessing the resilience level of urban power grids.

[0061] The impact patterns of extreme events are shown in Table 1.

[0062] Table 1. The impact patterns of extreme events

[0063]

[0064] S2: Model the urban power grid, natural gas pipeline network, road traffic network, water supply network, public communication network and rail transit system respectively to obtain the dynamic model of the urban lifeline system.

[0065] This step constructs a dynamic model of a coupled urban lifeline system, centered on the power grid and including natural gas pipelines, water supply networks, public communication networks, and road networks. This model dynamically reflects the impact of large-scale power outages caused by extreme disasters on the functions of the urban lifeline. The system topology and coupling relationships between its components are analyzed, and the fault propagation paths between urban lifelines under extreme disasters are shown below. Figure 5 As shown.

[0066] The modeling process for each part will be explained below:

[0067] (1) Power grid model

[0068] The model is based on the existing optimal AC power flow model, and the model constraints include: real-time active and reactive power balance constraints at nodes; upper and lower limits for power flow outflow and inflow at branches; equal phase angle constraints at reference nodes; and upper and lower limits for bus voltage magnitude and generator injection.

[0069] (2) Simplified dynamic model of natural gas pipeline network

[0070] The model is as follows:

[0071]

[0072] In the formula, The density of the gas; It is a time variable; The velocity of the gas; Variables along the pipeline axis; The pressure of the gas in the pipeline; The coefficient of hydraulic friction; The inner diameter of the pipe; It is the acceleration due to gravity; Specific heat capacity at constant volume; Thermodynamic temperature; , , These are the compressibility factor, gas constant, and thermodynamic temperature, respectively. This refers to the cross-sectional area of ​​the pipe. The horizontal inclination angle of the pipeline axis; The pipe inclination angle; This is an energy exchange term.

[0073] Based on the above, the following simplifications are made:

[0074] 1. In an urban power-natural gas coupled system, the city's air temperature is assumed to change little, and the temperature change within the gas due to heat conduction between the pipe wall and the soil is relatively slow and can be ignored. Therefore, the gas flow can be considered isothermal, and the energy equation can be disregarded in the model, simplifying the gas state equation. The state equation can be simplified to:

[0075]

[0076] In the formula, It is an isothermal wave velocity.

[0077] 2. Numerical simulations show that ignoring the convection term primarily affects the fluctuation amplitude and damping of the flow rate before reaching steady-state conditions, while ignoring the inertial term mainly results in the inability to capture physical oscillations when boundary conditions suddenly change, and has a relatively small impact on the unit's pressure recovery or decrease time. Therefore, the inertial and convection terms can be ignored in the model.

[0078] 3. In relatively flat urban areas, the terrain is not undulating, so the influence of gravity is ignored.

[0079] The simplified natural gas dynamic model, applicable to fault evolution analysis of urban power grid-natural gas networks, is as follows:

[0080]

[0081] In the formula, For mass flow rate.

[0082] (3) Road traffic network model

[0083] A cellular transport model was constructed to conduct simulation analysis of traffic light malfunctions caused by power outages, which in turn led to road delays.

[0084] (2-17)

[0085]

[0086] (2-19)

[0087]

[0088]

[0089]

[0090]

[0091] In the formula, for Time Cell It has a built-in charging requirement ( ) and vehicles without charging needs ( Traffic flow; for Time by cell j Outflow to adjacent cells Traffic flow; For cells upstream cells; A set of cells; For time sets; For cells Downstream cells; A cell within a time period Traffic flow limit; For cells The congestion coefficient; For cells The maximum total traffic volume limit; For cells Internal resources The initial traffic flow distribution; For cells To cell resources The initial transfer traffic flow; It is a set of cell connection relationships; The set of cells in the starting region; To reach the set of regional cells.

[0092] Equation (2-17) represents the traffic flow balance for each cell. Equation (2-18) represents the resource... Vehicles from cells Traffic flow to an adjacent cell is no greater than the traffic flow to that cell. Equation (2-19) represents the flow of vehicles from a cell to an adjacent cell. The total traffic flow to adjacent cells does not exceed the traffic flow transfer limit of that cell. Equation (2-20) represents the traffic flow from adjacent cells to cells. The total traffic flow does not exceed the traffic flow transfer limit of this cell. Equation (2-21) indicates that when traffic congestion occurs in this cell, the cell... The total inflow does not exceed the cell The remaining capacity. Equations (2-22) and (2-23) represent the initial and final traffic flow states in each cell, respectively.

[0093] This embodiment employs a combined macroscopic and microscopic modeling approach for the traffic network. When no event is triggered, there's no need to depict the network's dynamics in detail; a user-balanced traffic assignment model is used for macroscopic modeling, providing initial values ​​for dynamic process modeling after an event occurs. Once an event is triggered (e.g., a traffic light malfunction), it's necessary to depict the behavior of vehicles at each intersection in detail and analyze traffic delays. Therefore, after an event is triggered, a microscopic vehicle following model is used for modeling.

[0094] User-balanced traffic assignment model (macro):

[0095]

[0096] In the formula, Represents a cost or time function; Indicates from the source node r To the target node s Total demand; Representing a path l Traffic; For indicator functions, when the path contains road segments a The time is 1; For road segments; It refers to the path type, such as paid / free path, high-speed / normal path; For road section Total flow.

[0097] Vehicle following model (microscopic):

[0098]

[0099] In the formula, Indicates time Safe distance and speed; Indicates time The speed of the vehicle in front; express Vehicle spacing at any given time; for Expected distance at any moment; Indicates braking time; Indicates the driver's reaction time; Indicates the maximum speed limit of the vehicle; This is the simulation time step; . express Current vehicle speed at any moment; This indicates the vehicle's acceleration relative to its current speed; Indicates the vehicle's speed after the challenge; The target speed.

[0100] (4) Water supply network model

[0101] The model is as follows:

[0102]

[0103] In the formula, , These are collections of pipelines and water pump branches; For the water network from nodes i Flow to Node j Water flow rate; , They are nodes i ,node j water head; For the node k Flow to Node i Water flow rate; For nodes i External water supply volume; For nodes i The actual water consumption; This is the head characteristic coefficient of the water pump branch.

[0104] (5) Rail transit system model

[0105] The subway station was modeled using a subject-based modeling approach, where each passenger and each train was designated as a subject and assigned different behavioral criteria and parameters. This approach simulates the dynamic changes of trains and passengers under the influence of power outages, providing a more intuitive reflection of passenger flow distribution, train operation status, and the number of people trapped in extreme events within the subway station. The specific modeling method is as follows:

[0106] 1) Dynamic changes in train operation

[0107]

[0108] In the formula, , These represent the positions of the train at the next and current moments, respectively. , These are the train speeds at the next moment and the current moment, respectively. Acceleration of the train; , The upper and lower limits of train acceleration; This is the maximum speed limit for the train; For time intervals.

[0109] 2) Dynamic changes in passenger behavior

[0110]

[0111] In the formula, A set of variables is used to represent the behavioral state of passengers in a subway station. describe; for Passenger location at any time; The speed of passengers at this moment; For time intervals.

[0112] 3) Train-passenger interaction behavior

[0113] The interaction between the subway intelligent agent and the passenger intelligent agent mainly occurs during the process of train arrival at the station and passengers getting on and off the train.

[0114]

[0115] In the formula, This refers to the actual number of passengers who boarded the bus. for t The total number of passengers waiting to board this train at the current station; Indicates the maximum number of passengers allowed on the train; for Number of passengers on the train at any given time; for Passengers on the train at any given time; for t The actual number of passengers disembarking at the current station on the train; The total number of passengers scheduled to disembark; This represents the number of passengers waiting to disembark at the current station on the train.

[0116] (6) Public communication network model

[0117] To consider the impact of the power grid on the public communication network, and based on the characteristics of the communication network, the impact of power outages on both wireless and wired networks is considered, leading to the establishment of a graph theory model for the public communication network. When analyzing the impact of power outages on the communication network, it is constructed as a directed weighted graph. This is because data flow and signal transmission in communication networks are often directional, especially when considering the interaction between devices such as data centers, routers, and repeaters. In the directed graph model, nodes represent communication devices or network nodes, such as base stations, switches, and servers, while directed edges represent the direction of data flow, i.e., communication links from one node to another. In this model, each edge not only has a direction but also a weight, which represents different characteristics of the communication link, such as bandwidth, latency, reliability, or capacity. Specifically, this is represented as follows:

[0118]

[0119] In the formula, It is a directed weighted graph; Nodes in a network are represented; base stations, switches, etc., are all modeled as nodes. Indicates the first n One communication node; Represents the edges in the network, indicating the relationships between base stations, switches, users, etc. Represents a directed edge The weight parameters.

[0120] In summary, the modeling framework for the coupled dynamics of critical infrastructure across the entire chain is as follows: Figure 2 As shown.

[0121] S3: Model multiple coupled elements among urban lifeline systems to obtain a parameter correlation model.

[0122] (1) Electricity-Natural Gas Network

[0123] The coupling elements of the power and natural gas grids mainly consider electric drive compressors, electric drive power supplies, and gas turbine units.

[0124] Electric compressor: Power consumption of the compressor The calculation is as follows:

[0125]

[0126] In the formula, This represents the fraction of the total driving power provided by the electric drive. This is the isentropic index of natural gas; is the gas constant of natural gas; This indicates the density of natural gas under reference conditions; It is the compressibility factor; It is the product of the compressor's adiabatic efficiency and drive efficiency; The mass flow rate of the compressor; The pressure at the compressor outlet; The pressure at the compressor inlet; This refers to the thermodynamic temperature of the natural gas at the compressor inlet.

[0127] Electric-driven gas source: An electric-driven gas source requires a reliable power supply to maintain normal operation. Generally speaking, the natural gas output of an electric-driven gas source... With electricity consumption Closely related, represented as:

[0128]

[0129] In the formula, This represents the conversion coefficient of the electric-driven air source.

[0130] Gas turbine unit: In the coupled analysis, the relationship between the required gas mass flow rate and output power of the gas turbine unit is shown below. It is worth noting that the normal operation of the gas turbine unit requires meeting a certain pressure threshold; when its pressure falls below the set value, the gas turbine unit will shut down.

[0131]

[0132] In the formula, , and The parameters determined by the heat rate curve of the gas turbine. This refers to the output power of the gas turbine unit.

[0133] (2) Electricity-Road Transportation Network

[0134] The coupling element between the power and road transportation network mainly considers traffic lights, and a traffic delay model is established based on queuing theory and signal control theory.

[0135]

[0136] In the formula, and These represent the delays during power outages and under normal conditions, respectively. It's at the intersection in the direction i Normal service hours (i.e., green light time); It's at the intersection in the direction i Traffic density, Indicates service rate; The direction of arrival at the intersection per unit time i The number of vehicles; This represents the total red light time in all directions. It's at the intersection in the direction i Additional delay factors, taking into account the chaos and mismanagement caused by power outages. This refers to traffic density during a power outage. The power outage may reduce the voltage.

[0137] (3) Power-water supply network

[0138] The coupling element between the power and water supply networks mainly considers water pumps, which consume power. With the flow rate consumed by the water flow The relationship is:

[0139]

[0140] In the formula, The density of water; This is the fluid gravity coefficient; For water pump efficiency; and It is a constant.

[0141] (4) Power-communication network

[0142] Since the communication network adopts a graph theory-based model, the coupled components (base stations, mobile switches, gateway routers, and public communication centers) are modeled as nodes in the graph theory, and the consequences of power outages on the communication network are analyzed using a graph theory-based cascading failure model.

[0143] (5) Electric-rail transit system

[0144] Since the rail transit system adopts the subject modeling method, the coupled element (train) is modeled as a subject with multiple parameters. The dynamic evolution mechanism of the subject model is used to analyze the consequences of power outages on the rail transit system.

[0145] S4: Model the resource dependencies of critical users on the lifeline system.

[0146] This step, in accordance with the "Technical Specifications for the Configuration of Power Supply and Backup Emergency Power Supply for Important Power Users," comprehensively considers the important position of users in social and economic life and the wide-ranging impact that power outages may cause. It classifies important users of the power system and identifies eight major categories, including command, communication, news media, data and financial centers, water, heat and gas supply, transportation, medical and health care, and important venues, totaling thirty types of key power users.

[0147] Based on the different characteristics of each user and the potential impact of power outages, different modeling methods can be used to analyze their urban public safety characteristics, including functional modeling methods and subject modeling methods. The basic resources that important users rely on are used as modeling inputs, and the above methods are used to calculate the corresponding outputs that can reflect the multi-level functions of important users.

[0148] Optionally, the modeling and computation methods corresponding to each important user include functional function models, direct quantization computation functions, and agent modeling.

[0149] (1) Important user modeling based on function

[0150] Based on the service characteristics of important users such as banks and hospitals, a functional modeling approach is used to calculate their service availability. User functions are treated as functional modules, with service levels defined by inputs and outputs; their internal structure or operational mechanisms do not require detailed consideration. Each functional function in the model represents an operational unit of the system, with definite inputs and outputs, and a conversion mechanism to transform the input into the output. Functions are connected through the flow of data or signals. This model characterizes the service availability of important users based on the "barrel principle," as follows:

[0151]

[0152] In the formula, To ensure the availability of services for this system, The input quantities that affect the system's functionality reflect the overall system performance being limited by its weakest point.

[0153] (2) Important user modeling based on subject modeling

[0154] Agent modeling is a method for simulating the dynamic behavior and interactions of individuals or groups in complex systems. In agent modeling, each agent is given specific parameters and behavioral rules, enabling them to act autonomously in the simulated environment and interact with other agents or the environment itself.

[0155] For example, considering the modeling of urban rail transit passenger stations among important users of the power system, the subject modeling method is used to quantify and calculate their system functional level, analyze the direct harm of power grid failures to the urban rail transit system, such as emergency braking of trains and passenger entrapment, which affect urban public safety, and then complete the urban public safety assessment under extreme event scenarios.

[0156] S5: In the first stage, iterative simulations are performed on the power grid-natural gas pipeline network and power grid-road transportation network with bidirectional coupling characteristics, and the coupling point information is exchanged at a fixed step size. In the second stage, cross-sectional simulations are performed on the water supply network and public communication network that are affected by the power grid in one direction, and the functional loss is calculated based on the fault parameters to realize the interaction of time-driven and event-driven approaches.

[0157] This step proposes a joint simulation method applicable to the full-chain impact analysis of "extreme events - urban power grid - urban lifeline - critical users". Based on the impact patterns of various disaster types, typical disaster scenarios under different disasters are generated, and the post-disaster fault evolution process is further analyzed, intuitively reflecting the dynamic propagation process. Since power and natural gas networks, and power and transportation networks have bidirectional coupling characteristics, continuous iterative information interaction is required at the power grid and natural gas coupling level. Other networks, such as communication networks and water networks, are only affected unidirectionally by the power grid and do not involve the interactive propagation of faults; interaction and calculation of consequences are only necessary when required.

[0158] (1) Co-simulation process

[0159] The dynamic constants of urban lifeline systems range from milliseconds to hours, meaning that the transfer of the two types of energy occurs on different timescales. To accurately describe the impact of one system on another and analyze the failure evolution process, it is necessary to implement full-chain co-simulation within a unified framework.

[0160] This step establishes a multi-level co-simulation framework based on the coupling characteristics of the power network with other different networks. Its conceptual diagram is shown below. Figure 3As shown. First, a disaster scenario generated by extreme events is used as the input for urban lifeline simulation. In the first stage of the urban lifeline simulation, the network with bidirectional coupling characteristics is simulated, which requires information exchange at specific times. In this stage, a joint simulation method combining time-driven and event-driven approaches is proposed. Each subsystem conducts simulation independently, exchanging coupling point information at fixed steps, and interpolating important events such as the failure of coupling components to achieve accurate simulation of the fault cascading propagation process. The second stage analyzes the network affected by the power grid in one direction, accepting the failure and recovery times of coupling components in the water network and communication network from the simulation output of the first stage, and calculating the service function loss of the water network and communication network. Among them, the impact of water storage facilities is considered for the water supply network, and the impact of backup power sources such as base stations is considered for the communication network. Cross-sectional analysis is performed at specified time intervals. Finally, the simulation module for important users is entered, and their functional service levels are output.

[0161] Within this framework, the co-simulation considers both time and space dimensions. At the time level, since different energy systems have different time scales, the co-simulation takes into account the differences in dynamic characteristics between systems. Electricity systems can reach steady state on a millisecond time scale, so a steady-state model is used. Compared to electricity systems, natural gas systems have slow dynamic characteristics, with time scales potentially reaching minutes or even hours, and fault propagation is relatively slow; therefore, a dynamic model is needed to reflect the slow dynamic changes of each variable. During the fault phase, power outages mainly affect traffic light facilities in the road network; during the recovery phase, the allocation of mobile emergency resources affects the power restoration process. Since the modeling needs to focus on the operating status of vehicles, a microscopic vehicle following model is established. Other networks focus on analyzing the impact of power grid faults on their respective functions. Based on the modeling idea in step S3, the water supply network is modeled using a steady-state hydraulic model, and the communication network is modeled using a graph model. At the spatial level, this step considers the cross-spatial interactive propagation process of faults through coupling elements, the fault recovery process, and the cross-spatial impact on urban lifeline functions.

[0162] In the first-level simulation, this step proposes a joint simulation method that combines time-driven and event-driven approaches. Each subsystem conducts simulation independently, exchanges coupling point information at fixed steps, and performs interpolation processing on important events such as the failure of coupled components to achieve accurate simulation of the fault chain propagation process.

[0163] This step focuses on the failure evolution process after an initial failure caused by an extreme disaster. The co-simulation method is detailed in Table 2. At the start of the co-simulation, the power system and the natural gas system should exchange information to determine the initial state of the coupled system and provide initial values ​​for the simulation. The power system, according to its dispatch cycle, [conducts simulations every [period]]. A steady-state analysis is performed. For natural gas systems, because they cannot reach the next steady state as quickly as power systems, dynamic changes such as gas pressure need to be considered. Therefore, dynamic simulations are required during each power system dispatch cycle. To simulate the time step, a dual-driven interaction mode based on events and time is adopted during the co-simulation process.

[0164] Table 2. Electricity-Natural Gas System Joint Simulation Process

[0165]

[0166] 1) Event-driven

[0167] In this embodiment, the event types include:

[0168] a) Initial failures of system components due to extreme disasters. These include: failures of lines, transformers, and generators in power systems; loss of gas supply capacity and compressor failures in natural gas systems. Since natural gas pipelines are generally built underground, this paper assumes that extreme disasters will not cause pipeline damage or freezing.

[0169] b) Gas system cascading failures. Power system failures may trigger cascading failures in the natural gas system, such as the shutdown of electrically driven facilities. After a power system failure, emergency control measures must be output based on the power system failure evolution model to determine the locations and amounts of load shedding required. This process may involve cutting off the power supply to electrically driven facilities. At this time, the power system needs to transmit parameter information to the natural gas system, including the required gas supply for the gas turbine units. F G (See equation (2-14)) and the operating status of the electrically driven compressor in the natural gas system. u C Operating status of electric drive air source u GS In this paper, the variables reflecting the operating status of components are all integer variables between 0 and 1. If the component is operating normally, the value is 1; if it is in a fault, the value is 0.

[0170] c) Power system cascading failures. This embodiment focuses on the shutdown of gas turbine units due to pressure below a threshold at the gas turbine supply node, as well as line disconnections and load shedding caused by emergency power system control measures. In the natural gas system, both gas source and compressor failures can trigger a drop in gas turbine node pressure. In the natural gas system simulation, the pressure at the gas turbine supply node needs to be checked after each step calculation. At this time, the gas grid needs to transmit parameter information to the power grid, including the operating status of the gas turbine units. u G Power consumption of electric drive facilities P C and PGS (See equations (2-15)-(2-16)).

[0171] 2) Time-driven

[0172] If in If none of the aforementioned events occur during the simulation period, then data exchange is required between the two systems to synchronize information. That is, the natural gas system completes... n When calculating each step size, among which The power consumption required by the electric drive facilities needs to be transmitted to the power system. After the power system performs an AC-OPF-based scheduling, it transmits the gas consumption required by the gas turbine to the natural gas system. After the natural gas system updates its state, it continues to execute the simulation for the next time period until the simulation duration ends.

[0173] This embodiment employs an AC-OPF-based power system fault evolution model to simulate load reduction, generator output adjustment, and the power system fault evolution process following component failure, and outputs emergency control measures. For example... Figure 4 As shown, the main steps are as follows:

[0174] Step 1: Update the power system status based on the fault and determine islanding. Perform subsequent operation steps for each islanding.

[0175] Step 2: Check if there is a generator on the island. If there is no generator, all loads on the island lose power and the result is stored, then proceed to the next island; if there is a generator, proceed to Step 3.

[0176] Step 3: Check if the islanded system is a single-bus system. If it is a single-bus system, calculate the minimum load shedding amount and store the result, then proceed to the next islanded system; otherwise, proceed to Step 4.

[0177] Step 4: Perform AC-OPF calculation. If converged, calculate the load to be satisfied and store the result, then proceed to the next island; if not converged, perform AC-PF calculation and determine the branch with the maximum power flow exceeding the limit, reduce the load of all nodes within the radius R of that branch by 5%, and repeat Step 4. If AC-OPF still does not converge after the maximum number of iterations of 20, then proceed to Step 5.

[0178] Step 5: Disconnect the branch with the most severe power flow violation. Return to Step 1 and re-perform the islanding detection.

[0179] It should be noted that this embodiment focuses on analyzing the cascading propagation of faults over a long time scale. The time scale of power system control operations is generally on the order of milliseconds, and the execution time of control operations is ignored.

[0180] The process design in this step accurately captures the evolution of bidirectional cascading faults. For example, it can accurately simulate the grid-gas grid cascading fault: grid line disconnection → electric compressor shutdown → slow decrease in gas grid pressure → gas turbine unit shutdown → further load shedding by the grid. Another example is the grid-transportation network interaction: grid outage → traffic light malfunction → road congestion → delay of grid emergency repair vehicle → delayed fault recovery. The simulation error of bidirectional coupled faults is reduced to an acceptable range. The phased approach combined with a dual-drive mechanism solves the simulation fragmentation problem of existing technologies, achieving fully automated simulation of the entire chain from extreme event input to significant user impact output. It completes the fault triggering, coupling interaction, and loss calculation process without manual intervention, improving simulation efficiency and fully restoring the temporal evolution of the entire chain, providing reliable simulation data for urban lifeline system resilience assessment and emergency decision-making.

[0181] S6: Output joint simulation results, including the urban power grid fault evolution process, the functional loss rate of each lifeline system, and the service availability indicators of important users.

[0182] Among them, service availability metrics can be obtained through quantification.

[0183] To verify the usability of this method, the following tests were performed:

[0184] (1) Test system and parameter settings

[0185] The coupled test system used in this paper is as follows: Power grid: The publicly available IEEE 39-node transmission network system is used, including generators (G1-G2, G4-G5, G7), gas turbine units (G3, G6, G8-G10), and transmission lines (Bus1-Bus39); Natural gas network: The publicly available Belgian 20-node natural gas system is used, including 6 gas sources (GS1-GS6), 20 nodes (Node1-Node20), and 2 compressors (GC1, GC2); Transportation network: A local road network; Water supply network: A network including 15 nodes; Communication network: A communication network including 200 nodes; Rail transit system: A subway station in a certain city.

[0186] Specifically, the power grid line Bus9 is coupled to the natural gas source GS2, the power grid line Bus8 is coupled to the natural gas source GS6, the natural gas network node Node6 is coupled to the power grid gas turbine G9, the power grid line Bus25 is coupled to the natural gas network compressor GC2, the natural gas network node Node20 is coupled to the natural gas network gas turbine G10, the power grid line Bus29 is coupled to the natural gas network compressor GC1, and the natural gas network node Node15 is coupled to the power grid gas turbine G8.

[0187] (2) Fault evolution analysis

[0188] For the above-mentioned test system, the failure evolution process was analyzed. The failure scenario was set as follows: Assuming that under the influence of extreme cold and freezing disaster, at 480 min, the natural gas system gas source GS3 failed due to freezing; at 500 min, the power system lines Bus5-Bus8 and Bus6-Bus7 experienced a line breakage due to icing; at 540 min, another gas source GS6 also failed due to freezing; and at 560 min, the power system generator G4 stopped operating due to freezing.

[0189] Taking a power-natural gas grid as an example, gas source GS2 is set to pressure control mode, while other gas sources, gas storage facilities, and loads are set to flow control mode. The minimum pressure of gas turbine units G3 and G6 is 5 MPa, and the minimum pressure of gas turbine unit G9 is 6 MPa. The compressor is controlled using constant outlet pressure control. The compressor and gas source cannot continue to operate normally when power is cut off, and the gas storage facility's natural gas output will be affected after power is cut off. Assume that gas source GS3 is cut off at 3:00 due to extreme cold weather, and generator G4 fails to start due to weather conditions at 6:30. The simulation duration is 24 hours.

[0190] In the overall process of fault evolution, after an initial fault occurs in the coupled system due to extreme weather, if the power system and the natural gas system fail to coordinate well, the fault may propagate across space in a cascading manner, exacerbating the system failure. In this incident, the power system cut off a total load of 2025.53MW and disconnected one line through the action of protection devices.

[0191] At the beginning of the incident, the natural gas source GS3 failed due to freezing at 480 minutes. At this time, both the power system and the natural gas system were operating normally, with a slight decrease in the overall pressure level of the natural gas system due to the gas source loss. At 500 minutes, power system lines Bus5-Bus8 and Bus6-Bus7 broke due to freezing rain, causing the most severe overload on lines Bus9-Bus39. Power grid lines Bus1, Bus8, and Bus9 cut loads of 65.11MW, 365.40MW, and 4.55MW respectively, totaling 435.06MW. During the power system's load shedding operation, critical infrastructure in the natural gas system was not listed as a critical load. Since the gas source GS2 relies on the power system line Bus9 for power supply, its shutdown due to emergency power system control measures led to a cascading failure.

[0192] At 540 minutes, another gas source in the natural gas system, GS6, also failed due to freezing, causing a significant drop in system node pressure levels. The pressure changes were more pronounced in gas turbine units G3 and G9, while G6, being downstream of compressor GC2, was unaffected by the gas source failure. 143 minutes later, gas turbine unit G9 shut down due to pressure falling below the threshold. At this point, the power system implemented emergency control measures, cutting loads on grid lines Bus3, Bus4, and Bus8 by 111.87MW, 175MW, and 54.90MW respectively, totaling 341.77MW. At 756 minutes, gas turbine unit G3 also shut down due to pressure falling below the threshold, causing the power system to cut loads by another 571.32MW. Compressor GC2 then lost power supply, causing the fault to continue propagating. Because the natural gas system has slow dynamic characteristics, critical facility failures do not cause a rapid pressure drop; fault evolution occurs on a timescale of minutes or even hours. This is why the model needs to consider the dynamic equations for natural gas.

[0193] Following the GC2 compressor failure, upstream and downstream pressures changed. At the moment of failure, the compressor's power consumption and flow rate both dropped to zero. Subsequently, the upstream pressure increased while the downstream pressure decreased. When the upstream pressure equaled the downstream pressure, the compressor entered bypass mode, equivalent to a conventional pipeline, and the flow rate gradually recovered. Due to the GC2 compressor failure, the downstream load pressure decreased, and at 808 minutes, the pressure on line Bus20 in the natural gas system dropped below the minimum pressure required for the operation of gas turbine unit G6, causing it to shut down. The power system had to implement emergency control measures again, triggering protection to disconnect the overloaded lines Bus3-Bus4 and cut off 677.38 MW of load, ending the cascading fault.

[0194] Throughout the event, this embodiment focuses on the output adjustment of the generator sets under event-driven conditions, simplifying the consideration of the unit's ramp-up process. At the 560-minute mark, generator G4 ceased operation. To avoid a wider power outage due to reduced load, the remaining units increased their output accordingly. Subsequently, the three gas turbine units successively failed due to excessively low pressure at 683 minutes, 756 minutes, and 808 minutes, exacerbating the existing problems.

[0195] In summary, extreme events can lead to successive failures of power or natural gas system infrastructure, potentially causing more severe damage in actual disasters and triggering a cascading, inter-regional propagation of the fault. Furthermore, insufficient coordination between power and natural gas systems can accelerate fault evolution; this example demonstrates this propagation process. Due to the slow-dynamic characteristics of natural gas systems, fault propagation is relatively slow. However, by fully considering these characteristics, effective fault mitigation strategies can be developed to delay or even block fault propagation, reducing power outage losses.

[0196] Similarly, simulations were performed on power-water supply networks, power-transportation networks, and power-communication networks.

[0197] Example 2

[0198] Based on Example 1, see Figure 6 This embodiment provides a disaster impact full-chain joint simulation system oriented towards events, power grid, lifeline, and users, used to implement the disaster impact full-chain joint simulation method oriented towards events, power grid, lifeline, and users in Embodiment 1. The system includes:

[0199] (1) Extreme event fault scenario generation module, used to generate extreme event scenarios, analyze the effect mode of extreme events on urban power grid, and determine the initial fault parameters of power grid.

[0200] (2) Urban lifeline system modeling module, which is used to model the urban power grid, natural gas pipeline network, road traffic network, water supply network, public communication network and rail transit system respectively, and obtain the dynamic model of urban lifeline system.

[0201] (3) Coupled element modeling module, used to model multiple coupled elements between urban lifeline systems to obtain parameter association model.

[0202] (4) Resource dependency modeling module, which models the resource dependencies of important users on the lifeline system.

[0203] (5) Urban lifeline simulation module, used to perform iterative simulation of the power grid-natural gas pipeline network and power grid-road traffic network with bidirectional coupling characteristics in the first stage, and exchange coupling point information at a fixed step size. In the second stage, it performs cross-sectional simulation of the water supply network and public communication network that are affected by the power grid in one direction, calculates functional loss based on fault parameters, realizes the interaction of time-driven and event-driven, and outputs joint simulation results, including the urban power grid fault evolution process, functional loss rate of each lifeline system and service availability indicators of important users.

[0204] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A joint simulation method for the entire chain of disaster impacts, encompassing events, power grids, lifelines, and users, characterized in that... include: S1: Generate extreme event scenarios, analyze the impact of extreme events on the urban power grid, and determine the initial fault components of the power grid. The extreme event scenarios include natural disasters, cyberattacks, physical attacks, major accidents, and compound chain-generated extreme events. S2: Model the urban power grid, natural gas pipeline network, road traffic network, water supply network, public communication network and rail transit system respectively to obtain the dynamic model of the urban lifeline system; S3: Model multiple coupled components among urban lifeline systems to obtain a parameter correlation model; S4: Model the resource dependencies of critical users on the lifeline system. The critical users include command, communication, news media, data and financial centers, water, heat and gas supply, transportation, medical and health care, and important venues. S5: In the first stage, iterative simulations are performed on the power grid-natural gas pipeline network and the power grid-road traffic network with bidirectional coupling characteristics, and the coupling point information is exchanged at a fixed step size. In the second stage, cross-sectional simulations are performed on the water supply network and public communication network that are affected by the power grid in one direction, and the functional loss is calculated based on the fault parameters to realize the interaction of time-driven and event-driven approaches. S6: Output joint simulation results, including the urban power grid fault evolution process, the functional loss rate of each lifeline system, and the service availability indicators for important users. S5 includes: S51: Initialize simulation parameters and set simulation duration. Power grid dispatch cycle and natural gas pipeline network simulation step size ,in It is an integer; S52: If the current time Perform spatiotemporal discretization calculation System status at all times Updated to ; S53: In response to the detection of an event, an event-driven interaction is performed, and the power grid and the gas grid exchange the operating status of the gas turbine unit and the power consumption of the electric drive facility. After the power grid performs AC-OPF scheduling, it feeds back the gas consumption of the gas turbine unit. The event includes an initial fault, a gas grid cascading fault, and a power grid cascading fault. S54: If the current time The execution time-driven interaction involves the gas network transmitting the power consumption of electric drive facilities to the power grid, and the power grid transmitting the gas consumption to the gas network after performing AC-OPF scheduling. S55: After completing the first stage of bidirectional coupled simulation, input the power grid fault parameters into the water supply network and communication network, calculate the water supply loss and the number of communication node failures, output the lifeline system functional loss, and complete the second stage of simulation. By using functional modeling or principal modeling, we can model the resource dependencies of key users on the lifeline system. The modeling process of the aforementioned urban lifeline system dynamics model includes: A steady-state AC power flow model is obtained by modeling the urban power grid; a simplified dynamic model is obtained by modeling the natural gas pipeline network; a hybrid model combining a macroscopic user equilibrium traffic assignment model and a microscopic vehicle following model is obtained by modeling the road traffic network; a steady-state hydraulic model is obtained by modeling the water supply network; a directed weighted graph theory model is obtained by modeling the public communication network; and a train-passenger dual-agent model is used for modeling the rail transit system; thus, a dynamic model of the urban lifeline system is obtained. The hybrid model combining the macroscopic user equilibrium traffic assignment model and the microscopic vehicle following model is modeled as follows: Macro-level user equilibrium traffic allocation model: In the above formula, For road section a Traffic flow For traffic flow aggregation, Represents a cost or time function. Indicates from the source node r To the target node s Total demand Representing a path l Traffic, For indicator functions, when the path contains road segments a The value is 1 if it is true, and 0 otherwise. Indicates from the source node r To the target node s The path, Indicates constraints; Microscopic vehicle following model: In the above formula, Indicates time Safe distance speed, Indicates time The speed of the car in front, express Vehicle spacing at any given time For braking reaction time, To keep up with the car's reaction time, Indicates time speed of decision-making Indicates the maximum speed limit of the vehicle. express Current vehicle speed at any given moment This represents the vehicle acceleration relative to the current speed. Indicates the vehicle's actual speed. For time slices, for Expected distance at any time for The current spatial coordinates of the vehicle at any given time. The constraints of the hybrid model include: cellular traffic flow balance constraints; restrictions that the traffic flow of resource vehicles from a cell to an adjacent cell is no greater than the traffic flow of the cell; restrictions that the total traffic flow of vehicles from a cell to an adjacent cell is no greater than the upper limit of the cell's traffic flow transfer; and restrictions that the traffic flow from an adjacent cell to a cell... The restrictions include: the total traffic flow not exceeding the cell's traffic flow transfer limit; the total inflow into a cell not exceeding the cell's remaining capacity when traffic congestion occurs; and the restrictions on the initial and final traffic flow states in each cell.

2. The disaster impact co-simulation method for the entire chain of events, power grid, lifeline, and users as described in claim 1, is characterized in that... The coupling elements include electric compressors, electric gas sources and gas turbines for the power-natural gas network, traffic lights for the power-road transportation network, water pumps for the power-water supply network, base stations / switches for the power-public communication network, and trains for the power-rail transit system.

3. A joint simulation system for the entire chain of disaster impacts, encompassing events, power grids, lifelines, and users, characterized in that... The system is used to implement the event-grid-lifeline-user-disaster impact full-chain joint simulation method as described in any one of claims 1-2, the system comprising: The extreme event fault scenario generation module is used to generate extreme event scenarios, analyze the impact of extreme events on the urban power grid, and determine the initial fault parameters of the power grid. The urban lifeline system modeling module is used to model the urban power grid, natural gas pipeline network, road traffic network, water supply network, public communication network and rail transit system respectively, and obtain the dynamic model of the urban lifeline system; The coupling element modeling module is used to model multiple coupling elements between urban lifeline systems to obtain a parameter correlation model; The resource dependency modeling module is used to model the resource dependencies of critical users on the lifeline system; The urban lifeline simulation module is used to perform iterative simulations of the power grid-natural gas pipeline network and power grid-road transportation network with bidirectional coupling characteristics in the first stage, and to exchange coupling point information at a fixed step size. In the second stage, it performs cross-sectional simulations of the water supply network and public communication network that are affected by the power grid in one direction, calculates functional loss based on fault parameters, realizes the interaction of time-driven and event-driven approaches, and outputs joint simulation results, including the urban power grid fault evolution process, functional loss rate of each lifeline system, and service availability indicators for important users.

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