A weak element identification method for integrated energy system considering fault propagation

Abstract

The present application belongs to the technical field of integrated energy system, and particularly relates to a weak element identification method of integrated energy system considering fault propagation, comprising: S1, constructing an integrated energy system; S2, initializing the operation parameters of the integrated energy system; S3, sampling a plurality of elements; performing fault propagation simulation on each element to obtain a corresponding fault event; S4, for each fault event, calculating the contribution value of each element in the fault event to the reliability index of the fault event; S5, for each element, accumulating the contribution value of each element to the reliability index of each fault event to obtain the contribution value of the element to the reliability index of the integrated energy system; and selecting the top n elements with the highest contribution values as weak elements. The present method can accurately quantify the contribution of each element to the reliability index of the integrated energy system on the basis of fully considering the mutual dependence of subsystems and the fault propagation across subsystems, thereby effectively identifying the weak elements of the system.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system technology, and in particular relates to a method for identifying weak components in integrated energy systems that takes into account fault propagation. Background Technology

[0002] In modern energy systems, integrated energy systems, as highly integrated energy supply networks, combine multiple subsystems such as natural gas systems, power systems, and heating systems, achieving complementarity and synergy among various energy forms. These systems contain numerous different types of components, such as generators, transformers, natural gas pipelines, gas turbines, and heat pumps, all playing crucial roles. However, the impact of failures in different components on the overall system reliability varies significantly. In recent years, with the large-scale integration of natural gas units, gas boilers, and heat pumps, and the gradual maturation of power-to-gas (P2G) technology, the coupling between the three subsystems has deepened, forming a complex and interdependent energy network.

[0003] In such highly coupled integrated energy systems, the failure of one component often propagates across subsystem boundaries, continuously spreading throughout the natural gas, electricity, and heat systems, triggering a series of chain reactions. This fault propagation can not only lead to new risks of load shedding or equipment failure, but also cause greater load losses and energy waste, severely impacting the overall reliability and stability of the system. Therefore, in-depth research into the fault propagation mechanism within integrated energy systems is of great significance for improving system power supply reliability and for the timely detection and repair of vulnerable components.

[0004] However, current research on the identification of weak components in integrated energy systems still has many shortcomings. A significant problem is that most studies neglect the interdependence between subsystems and fail to fully consider the impact of fault propagation across subsystems on the reliability of the integrated energy system. This often leads to an inability to accurately assess the actual role of each component in the fault propagation process when identifying weak components, making it difficult to quantify their contribution to system reliability indicators. Furthermore, existing research methods suffer from the problem of averaging reliability indicators, simply distributing the overall system reliability index across individual components. This approach ignores the actual position and importance of each component within the system, easily leading to allocation errors and affecting the accuracy and validity of the identification results. The difficulty in solving this problem lies in the fact that the complexity and high coupling of integrated energy systems make it difficult to accurately simulate and predict the fault propagation process. At the same time, the roles and positions of each component in the system vary, and the impact of their failures on system reliability differs greatly, making it exceptionally difficult to quantify the contribution of each component to the system reliability index.

[0005] Therefore, how to accurately quantify the contribution of each component to the reliability index of the integrated energy system, while fully considering the interdependence of subsystems and the propagation of faults across subsystems, and thus effectively identify the weak components of the system, has become an urgent problem to be solved. Summary of the Invention

[0006] To address the shortcomings of the prior art, this invention provides a method for identifying weak components in an integrated energy system that considers fault propagation. This method can accurately quantify the contribution of each component to the reliability index of the integrated energy system, based on a full consideration of the interdependence of subsystems and the propagation of faults across subsystems, thereby effectively identifying the weak components of the system.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0008] A method for identifying weak components in a comprehensive energy system considering fault propagation includes the following steps:

[0009] S1. Construct a comprehensive energy system for fault propagation simulation and weak component identification; comprehensive energy analysis includes natural gas systems, power systems, and thermal systems;

[0010] S2. Based on actual parameter data, initialize the operating parameters of the integrated energy system and calculate the initial variable values;

[0011] S3. Sample multiple components; for each component, use it as the initial faulty component, and use the integrated energy system to simulate fault propagation to obtain the corresponding fault events. The content of the fault events includes the faulty components involved in the fault propagation simulation process.

[0012] S4. For each fault event, calculate the contribution value of each component to the reliability index of the fault event according to the preset method.

[0013] S5. For each component that fails during the fault propagation simulation, sum up its contribution to the reliability index of each fault event to obtain the contribution of that component to the reliability index of the integrated energy system.

[0014] S6. Sort each component in descending order of its contribution to the reliability index of the integrated energy system, and select the top n components as weak components.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] 1. By integrating natural gas, electricity, and heating systems, a comprehensive integrated energy system model was constructed. This integration allows the system to more accurately reflect the complexity and interdependence of real-world energy networks, providing a more realistic environment for subsequent fault propagation simulation and weak component identification. Compared to traditional single-system analysis or simple multi-system parallel analysis, this method comprehensively considers the interdependence of multiple subsystems and the propagation of faults across subsystems, resulting in more accurate and comprehensive analysis results. Furthermore, by quantifying and ranking the reliability contributions of components, this technical solution can more effectively identify weak components in the system, providing strong support for subsequent maintenance and optimization.

[0017] 2. Initializing the system based on actual parameter data ensures that the starting point of the simulation matches the actual operating conditions. This helps improve the accuracy and reliability of the simulation results, making subsequent analyses more realistic. By sampling multiple components as initial failure points and conducting fault propagation simulations, the propagation path and impact range of the fault in the system can be comprehensively captured. This simulation considers not only the failure of a single component but also the propagation and impact of the fault between multiple subsystems, thus more accurately assessing the overall reliability of the system.

[0018] 3. For each failure event, the reliability contribution value of each component is calculated according to a preset method, achieving a quantitative assessment of the component's role in the failure event. This quantitative assessment helps to more accurately identify components with a significant impact on system reliability, providing a basis for subsequent optimization and improvement. By accumulating the reliability contribution values ​​of components in each failure event, the contribution value of each component to the overall energy system reliability index is obtained, and they are sorted from largest to smallest contribution value. This method can effectively identify weak components in the system, namely those components with the greatest impact on system reliability. Selecting the top n components as weak components facilitates subsequent focused maintenance and optimization, thereby improving the overall reliability of the system.

[0019] In summary, this invention can accurately quantify the contribution of each component to the reliability index of the integrated energy system, based on a full consideration of the interdependence of subsystems and the propagation of faults across subsystems, thereby effectively identifying the weak components of the system.

[0020] Preferably, S3 includes:

[0021] S31. Use Monte Carlo sampling to determine the initial components and use them as the initial faulty components;

[0022] S32. In conjunction with the integrated energy system, determine the location of the faulty component;

[0023] S33. Determine whether the fault continues to propagate. If the fault continues to propagate, then advance the simulation time step based on the integrated energy system. The evolution simulation is completed, and then the process returns to S32; if the fault propagation has stopped, the corresponding fault event is recorded. The content of the fault event includes the components involved in the current evolution simulation that have failed, and then the process returns to S34.

[0024] S34. Determine whether the variance coefficient of the Monte Carlo sampling has converged; if it has not converged, go to S31 and use Monte Carlo sampling again to determine the new initial components; if it has converged, go to S4; where, when the variance coefficient is less than the preset threshold, it is determined that it has converged.

[0025] This setup ensures the comprehensiveness of fault simulation. By using Monte Carlo sampling, components in the system can be randomly selected as initial fault points, thus simulating various possible fault scenarios. This method comprehensively considers uncertainties in the system, improving the accuracy and comprehensiveness of fault simulation.

[0026] 2. It enables dynamic capture of fault propagation. Combined with integrated energy systems, it can determine the location of faulty components in real time and dynamically capture the fault propagation process within the system by advancing the evolution simulation based on the simulation time step. This helps to gain a deeper understanding of the fault propagation mechanism and its impact on the system, providing strong support for subsequent fault analysis and handling. When fault propagation stops, the corresponding fault event is recorded in detail, including all faulty components involved in the current evolution simulation. This provides important data support for subsequent fault analysis and weak component identification, helping to discover potential problems and weaknesses in the system.

[0027] 3. By determining whether the variance coefficients of the Monte Carlo sampling converge, the stability and reliability of the simulation results can be evaluated. If the variance coefficients do not converge, Monte Carlo sampling is continued to determine new initial components until convergence. This ensures the accuracy and reliability of the simulation results, providing a solid foundation for subsequent analysis and processing.

[0028] Preferably, in S33, if the output and load shearing of the coupled equipment do not change between the two consecutive moments, it is determined that the fault propagation has stopped;

[0029] In S33, the simulation time step is based on the integrated energy system. The evolutionary simulation process includes:

[0030] Step 0: Determine whether the fault occurs on the natural gas system side or the power system side; if the fault occurs on the natural gas system side, proceed to Step 1; otherwise, proceed to Step 2.

[0031] Step 1: Based on the operating status of the natural gas system at time t and the natural gas production of the electric drive gas source and P2G unit determined by the power system at time t, calculate the following based on the natural gas system: Real-time natural gas load reduction determines the natural gas supply to natural gas units and gas-fired boilers;

[0032] Step 2: Based on the obtained natural gas unit power and the power system fault conditions, determine whether a cascading fault has occurred within the power system; when the unit output changes or a component fails within the power system, the line power is redistributed, and power flow is transferred; when the grid cascading fault simulation process terminates, the power system is calculated to reflect the current situation. The amount of load reduction at any given time determines the power supply for the electric drive gas source, P2G unit, and heat pump;

[0033] Step 3: Calculate the thermal power provided by the coupled components, including the gas boiler, heat pump, and advanced adiabatic compressed air energy storage (AA-CAES). The amount of heat load reduction at any given time.

[0034] This setup ensures the accuracy of fault propagation simulation. By simulating the interactions between the natural gas system, power system, and heating system, the propagation process of faults within the integrated energy system can be accurately reflected. Considering the roles of coupling devices such as natural gas units, gas boilers, electric gas sources, P2G units, heat pumps, and AA-CAES, the simulation results are more closely aligned with reality.

[0035] 2. Using changes in the output and load shedding of the coupling equipment as the criterion for judging the cessation of fault propagation not only meets the requirements of system stability but also facilitates rapid judgment during simulation.

[0036] 3. During the simulation, the occurrence of cascading faults within the power system and the redistribution of line power can be captured, reflecting the dynamic response of the system under fault conditions. By calculating the load reduction and equipment supply at different times, the impact of the fault on the system can be assessed.

[0037] 4. Through evolutionary simulation, the reliability of integrated energy systems under fault conditions can be assessed, including system stability, resilience, and component vulnerability. This helps identify weaknesses in the system and provides guidance for optimal system design and operational strategies.

[0038] Preferably, in step 2, the process of redistributing line power includes:

[0039] First, analyze the current system topology. If islanding occurs and the power supply and demand within the island are unbalanced, the unit output and load demand within the island will be automatically balanced through automatic generation control and low-frequency load shedding devices. The islanding adjustment and balancing strategies include: ① If the sum of unit outputs is lower than the load, the units will first increase their output proportionally based on their available remaining output; if the sum of the outputs of the largest units is still lower than the load, the load will be reduced sequentially according to the power deficit ratio; ② If the sum of unit outputs is greater than the load, the output should be reduced proportionally to match the load; if the sum of the outputs of the smallest units is still greater than the load, the units will be shut down sequentially from smallest to largest capacity.

[0040] Then, a line cascading fault simulation is performed, and the power flow of each isolated island is updated using a DC power flow model. Specifically, a latent fault probability model is used to simulate the line cascading fault, and the calculation formula is as follows:

[0041] ;

[0042] In the formula, Indicates the probability of random failure; and These represent the current power and rated capacity of the line, respectively. If a new line fault occurs, the line power will be redistributed; otherwise, proceed to step 3.

[0043] This setup allows for: 1. Rapid restoration of power balance within the island. Through the coordinated action of automatic generation control and low-frequency load shedding devices, the output of units within the island can be quickly adjusted and loads reduced to achieve a balance between supply and demand. This helps reduce the risk of power outages caused by power imbalances within the island, improving system stability and reliability.

[0044] 2. It can optimize unit output and load reduction strategies. The adjustment and balancing strategy within the island takes into account the actual situation of unit output and load. By increasing output or reducing load proportionally, and shutting down units according to capacity, it achieves optimal resource allocation. This helps reduce unnecessary energy waste and improve energy efficiency.

[0045] 3. It ensures the accuracy of cascading fault simulation. Updating the power flow of each isolated system using a DC power flow model more accurately reflects the power flow distribution under different fault conditions. This helps assess the impact of faults on the system and provides a scientific basis for subsequent fault handling and recovery strategies.

[0046] 4. Simulating line cascading faults using a latent fault probability model can take into account factors such as the line's current power and rated capacity, predicting the probability of line faults. This helps to identify potential risks in the system in advance, providing strong support for preventing faults.

[0047] 5. It ensures flexibility in fault handling procedures. If a new line fault occurs during the simulation, the line power is redistributed. This flexible fault handling procedure ensures that the system can respond quickly to different fault conditions, improving the system's adaptability and robustness.

[0048] Preferably, in S4, the preset method includes: for fault event E j The contribution of component i to the reliability index is calculated according to the following formula. :

[0049] ;

[0050] ;

[0051] ;

[0052] ;

[0053] In the formula, Indicates that element i responds to fault event E j The contribution value of reliability indicators; Indicates fault event E j Reliability metrics, x i Indicates the performance parameters of component i; Indicates the reliability index of component i Contributions; Indicates the unavailability rate of component i; r i (E j ) indicates that component i responds to fault event E j The contribution of the consequences; e represents E j The sub-events do not include the event of element i. and These represent the weight of sub-event e and the failure consequence, respectively. This indicates the fault consequence obtained after adding component i to sub-event e; This indicates the number of components in e. E represents j The number of components in it.

[0054] This setup allows for the precise calculation of each component's contribution to the reliability metrics of a specific failure event. This quantitative assessment helps identify which components play a critical role in the failure event, thus providing strong data support for subsequent failure analysis and optimization.

[0055] 2. Comprehensive Consideration of Component Impact. This method not only considers the direct consequences of components in failure events, but also comprehensively considers the indirect impact of components during failure propagation through risk allocation weighting and sub-event analysis. This makes the assessment results more comprehensive and accurate, and can more realistically reflect the actual role of components in the integrated energy system.

[0056] 3. Supports complex system analysis. This method is applicable to fault analysis of complex systems such as integrated energy systems. By considering the interactions between multiple sub-events and components, this method can handle complex fault propagation and consequence assessment problems, providing an effective tool for improving system reliability and preventing faults.

[0057] Preferably, the objective function of the natural gas system is as follows:

[0058] ;

[0059] In the formula, Indicates the simulation time step; Represents the set of gas source nodes; and These represent the output of the electrically driven gas source and the output of the natural gas driven gas source, respectively. and These represent the production costs of electrically driven gas sources and naturally driven gas sources, respectively. Represents the set of gas network nodes; and These represent the load reduction amount and load reduction penalty coefficient for natural gas, respectively.

[0060] The constraints of a natural gas system include equality constraints and inequality constraints;

[0061] The equality constraints include: if there are no faults between the x and y sides of the natural gas pipeline, then the following equality constraints are satisfied:

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] In the formula, and This represents the inflow and outflow flow rates of pipe x and y; This represents the natural gas production of the P2G device at node k at time t; Indicates gas load. This indicates the flow rate through the compressor piping. This indicates the amount of natural gas consumed by the compressor; This represents the amount of natural gas stored in the pipeline at time t. This represents the air pressure at node x at time t. and These represent the length and inner diameter of the natural gas pipeline, respectively. For the absolute roughness of the natural gas pipeline, The compressibility factor of natural gas T represents the specific gas constant of natural gas, and T represents the temperature. This represents the coefficient of friction of the gas pipeline (xy). Indicates the initial flow rate of natural gas. Indicates the speed of sound; This represents the air pressure at node y at time t. Let Q be the activation function for the flow Q.

[0069] If there is a fault at point m between the x and y natural gas pipelines, then the following equation constraint must be satisfied:

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] ;

[0075] ;

[0076] ;

[0077] ;

[0078] ;

[0079] ;

[0080] ;

[0081] ;

[0082] ;

[0083] In the formula, This is the isentropic index of natural gas; This represents the air pressure at node m at time t+1; This represents the air pressure at node x at time t+1; and Let x and t represent the inflow and outflow rates of pipe xm at time t, respectively. The equation constraints representing the friction coefficient of the gas pipeline xm and the coupling device include:

[0084] ;

[0085] ;

[0086] In the above formula, This represents the power generation capacity of the natural gas generator unit connected to node x at time t; Represents the state variables of a natural gas generator unit; This represents the thermal power of the gas-fired boiler connected to node x at time t. Represents the state variables of a gas-fired boiler. and These represent the conversion efficiencies of natural gas units and gas-fired boilers, respectively. Indicates gas load; This represents the natural gas load reduction penalty coefficient;

[0087] Inequality constraints in natural gas systems include:

[0088] ;

[0089] ;

[0090] ;

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] In the formula, This represents the pressure at the outlet of the pipe at time t at point x; This represents the pressure at the air inlet at point t on the pipeline at point y; and Let x represent the initial flow rate and pressure at time t at point x in the pipeline, respectively. Indicates the boost ratio. Let a 0, 1 variable represent the direction of flow, when When it is 1, when The value is 0; N is a theoretically infinite value. and These represent the maximum and minimum pressure values ​​at point x in the pipe, respectively. and These represent the maximum and minimum output values ​​of the electric drive power supply, respectively. and These represent the maximum and minimum output values ​​of the natural gas-driven gas source, respectively. and These represent the maximum and minimum flow rates in the pipeline, respectively. and These represent the maximum and minimum flow rates of the compressor, respectively. , , and These are the state variables for the electric-driven gas source, the natural gas-driven gas source, the pipeline, and the compressor, respectively.

[0098] This setup, through the construction of a complex yet precise mathematical model of the natural gas system, optimizes natural gas production and distribution. The model not only considers cost-effectiveness but also fully takes into account the system's safety, flexibility, and coupling with other systems, providing strong support for the operation and management of the natural gas system.

[0099] Preferably, the objective function of the power system is as follows:

[0100] ;

[0101] In the formula, Represents the set of nodes for coal-fired power units; This indicates the cost of a coal-fired power unit; Indicates the power output of the coal-fired unit; Represents the set of power nodes; Indicates the load penalty factor for electrical discharge; Indicates the amount of electricity load reduction; Represents the set of wind turbine nodes; This represents the penalty coefficient for wind power reduction; This indicates the amount of wind power reduction;

[0102] The equality constraints of the power system include:

[0103] ;

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] ;

[0110] ;

[0111] ;

[0112] ;

[0113] ;

[0114] ;

[0115] ;

[0116] ;

[0117] ;

[0118] In the above formula, Represents nodes The connected wind turbine units Power generation at any given moment Indicates the amount of wind power reduced. Indicates the power of the coal-fired unit. This represents the amount of electricity generated at time t during the expansion cycle. This represents the total power consumption at time t during the compression cycle. Indicates the amount of electricity load reduction. Indicates electrical load. Indicates power flow in power lines; Indicates the line reactance. Indicates the phase angle of the node; Indicates the specific heat ratio of air. This indicates the specific heat capacity of air. and These represent the number of stages in the compressor and expander, respectively. and Let represent the air mass flow rate at time t during the compression and expansion cycles, respectively. and These represent the inlet and outlet temperatures of the i1-th stage compressor, respectively. and Let represent the inlet and outlet temperatures of the i2th stage expander at time t, respectively; and Let represent the compression ratio and adiabatic efficiency of the i1-th stage compressor, respectively. and These represent the i2th stage expander and the adiabatic efficiency, respectively. This represents the mass flow rate of water entering the compressor at time t during the compression process. This indicates the specific heat capacity of water; This indicates the temperature of the cold water in the cryogenic storage tank. This indicates the hot water temperature at the outlet of the i1-th stage compressor; This represents the temperature of the hot water in the high-temperature thermal storage tank at time t. Indicates the efficiency of the heat exchanger. This represents the total hot water mass flow rate into the high-temperature thermal storage tank at time t. The mass of hot water in the high-temperature thermal storage tank at time t. This represents the total hot water mass flow rate into the heat exchanger at time t. Indicates the change in water temperature. Indicates the thermal power supplied to the user. This represents the gas pressure in the storage chamber at time t. Represents the universal gas constant. and These represent the gas pressure and volume of the gas storage chamber, respectively. Represents nodes The power generation capacity of the connected natural gas generator unit at time t; This indicates the temperature of the hot water flowing into the high-temperature thermal storage tank; Represents the state variables of the expansion subsystem; Represents the state variables of the compression subsystem;

[0119] The equality constraints of the coupling device include:

[0120] ;

[0121] ;

[0122] ;

[0123] In the formula, This represents the natural gas production of the P2G device at node k at time t. Indicates the operating status of P2G devices. This represents the conversion factor of the P2G device. Indicates the amount of electricity load reduction. Indicates electrical load; This represents the natural gas production of the electrically driven gas source at node k at time t. For the state variables of the electrically driven air source, Indicates the conversion coefficient of the electrically driven air source equipment; This represents the thermal power of the heat pump at node k at time t. Indicates the operating status of the heat pump equipment. This represents the conversion coefficient of the heat pump equipment;

[0124] Inequality constraints in power systems include:

[0125] ;

[0126] ;

[0127] ;

[0128] ;

[0129] ;

[0130] ;

[0131] ;

[0132] ;

[0133] ;

[0134] ;

[0135] ;

[0136] In the formula, This indicates the upper limit of the power line transmission capacity. and These represent the upper and lower limits of the output of the coal-fired power unit, respectively. and These are the state variables for power lines and coal-fired power units, respectively. and These are 0-1 variables representing the states of the compression and expansion cycles, respectively. and These represent the maximum and minimum power consumption, respectively. and These represent the maximum and minimum power generation values, respectively. and These represent the maximum and minimum values ​​of the hot water temperature, respectively. and These represent the maximum and minimum values ​​of the water storage, respectively. This indicates the maximum heat output provided to the user. and These represent the maximum and minimum values ​​of the gas pressure in the gas storage chamber, respectively.

[0137] This setup, by constructing a complex power system model, achieves multiple benefits, including economic optimization, power supply and demand balance, efficient equipment operation, system security and stability, and flexibility and scalability. This is of great significance for improving the operating efficiency of the power system, reducing power generation costs, and ensuring the stability of power supply.

[0138] Preferably, the objective function of the thermodynamic system is as follows:

[0139] ;

[0140] In the formula, Represents the set of nodes in a heating network; Indicates the heat load penalty coefficient; and These represent the annual and seasonal reductions in heat load, respectively.

[0141] The equality constraints of a thermal system include:

[0142] ;

[0143] ;

[0144] ;

[0145] ;

[0146] ;

[0147] ;

[0148] ;

[0149] ;

[0150] ;

[0151] ;

[0152] In the formula, Indicates the quality of the hot water flowing out of the pipe. Number The quality of the working fluid in the water supply pipeline at different times. and These are the outlet temperature and inlet temperature of the water supply pipeline, respectively. From arrive The quality of hot water injected into the pipes during the time period , and These represent the density of water, the cross-sectional area of ​​the pipe, and the length of the pipe, respectively. This indicates the outlet temperature of the water supply pipe. , and Representing ambient temperature and pipeline respectively The thermal conductivity and specific heat capacity of water, Water supply pipeline node The temperature at that location and Let represent the seasonal and perennial heat load power of the building at time t, respectively. Let be the indoor temperature of the building at time t. The equivalent thermal resistance of the building, It is the equivalent heat capacity; and These represent seasonal and perennial heat load reductions, respectively. and These are the upper and lower limits of suitable temperature inside a building. and Let represent the power generation of the natural gas generator unit and the gas-fired boiler connected to node x at time t, respectively. This represents the thermal power of the heat pump at node k at time t. A collection of pipes for the heat source outlet. Represents the set of nodes in the heating network. This refers to the collection of pipes at the inlet of the heat exchanger. This indicates the time it takes for hot water to travel from the inlet to the outlet.

[0153] Inequality constraints for thermal systems include:

[0154] ;

[0155] ;

[0156] ;

[0157] In the formula, and These represent the upper and lower limits of the pipe temperature, respectively. This indicates the perennial load power.

[0158] This setup, through the construction of an optimized thermal system model, achieves multiple benefits, including optimized heat load management, improved heat pipeline transmission efficiency, fulfillment of building heat load demands, enhanced system safety and stability, and increased energy utilization efficiency. This is of great significance for improving the operating efficiency of thermal systems, reducing energy consumption, and ensuring the thermal comfort and safety of buildings.

[0159] Preferably, reliability metrics include the expected power shortage value (EENS) and the probability of load shedding (LOELP).

[0160] ;

[0161] ;

[0162] In the formula, The actual number of simulated years; This represents the number of states where the power load is lost. For the first The duration of each unloaded state, For the first The amount of power loss at time t under a certain loss-of-load condition;

[0163] This also includes the expected natural gas supply shortfall (EGNS) and the probability of gas load reduction (LOGLP).

[0164] ;

[0165] ;

[0166] In the formula, This represents the number of states where natural gas is unloaded. Let be the amount of natural gas loss at time t under the m-th unloaded state;

[0167] This also includes the expected heat supply deficit (EHNS) and the probability of heat load reduction (LOHLP):

[0168] ;

[0169] ;

[0170] In the formula, This represents the state number of thermal unloading. and These represent the annual and seasonal thermal load loss at time t under the m-th unloaded state, respectively.

[0171] This also includes the expected amount of natural gas leakage (EGL) and the probability of natural gas leakage (PGL):

[0172] ;

[0173] ;

[0174] In the formula, This represents the state number of a natural gas leak. Let be the amount of natural gas leaked at time t under the m-th leak fault condition;

[0175] It also includes the expected value of hot water leakage (EWL) and the probability of hot water leakage (PWL):

[0176] ;

[0177] ;

[0178] In the formula, This represents the state number of a hot water leak. Let be the amount of hot water leakage at time t under the m-th leakage fault state;

[0179] This also includes the NOFC (Normally Inactive Components) indicator:

[0180] ;

[0181] In the formula, The number of states where a fault occurred. This represents the probability of a fault state. For the first The number of out-of-service components under each fault propagation state. This represents the initial number of faulty components in this state; when This indicates that the scale of the fault propagation has expanded.

[0182] This setup, along with these reliability indicators, constitutes a complete framework for assessing the reliability of energy systems and their components, facilitating a comprehensive and objective evaluation of the system's reliability level. Regular monitoring and analysis of these indicators can promptly identify potential risks and problems within the system, providing a scientific basis for developing preventative measures, emergency plans, and optimizing system configuration. These indicators also offer crucial references for the planning, design, operation, and maintenance of energy systems, contributing to improved overall system reliability, security, and stability.

[0183] In summary, a comprehensive and objective assessment of the reliability of the energy system and its components was conducted through a series of reliability indicators, providing strong support for the optimized operation and sustainable development of the system.

[0184] Preferably, in S2, the initial operating parameters include natural gas flow rate, power load, and heat demand; the calculated initial variable values ​​include natural gas pipeline storage, gas pressure in the storage chamber of the Advanced Insulated Compressed Air Energy Storage System (AA-CAES), and temperature of the heating network pipeline.

[0185] This setup, by initializing key operating parameters such as natural gas flow, electricity load, and heat demand, enables the system to more accurately simulate actual operating conditions. These parameters form the basis for normal system operation and fault analysis, and their accuracy directly affects the reliability of subsequent simulations and analyses. The initialized operating parameters and calculated initial variable values ​​are not only used for fault simulation but also for system performance evaluation. By comparing them with actual operating data, key performance indicators such as system energy efficiency, reliability, and stability can be evaluated, providing a basis for system optimization design and improvement.

[0186] It also ensures the accuracy of fault simulation. Accurate calculation of initial variable values ​​(such as natural gas pipeline inventory, AA-CAES storage chamber pressure, and heating network pipeline temperature) provides a more realistic starting point for fault propagation simulation. This helps to more accurately predict the propagation path and impact range of faults in the system, thereby improving the accuracy of fault simulation. Attached Figure Description

[0187] To make the objectives, technical solutions, and advantages of the invention clearer, the invention will now be described in further detail with reference to the accompanying drawings, wherein:

[0188] Figure 1 This is a flowchart of the method;

[0189] Figure 2 This is a schematic diagram of the fault propagation process in the integrated energy system in Example 1;

[0190] Figure 3 This is a schematic diagram illustrating the dynamic process of fault propagation in the simulated integrated energy system in Example 1;

[0191] Figure 4 This is the network diagram for the example in Example 2. Detailed Implementation

[0192] The following detailed explanation illustrates the specific implementation methods:

[0193] Example 1

[0194] like Figure 1 As shown, this embodiment discloses a method for identifying weak components in a comprehensive energy system that considers fault propagation, including the following steps:

[0195] S1. Construct a comprehensive energy system for fault propagation simulation and weak component identification; comprehensive energy analysis includes natural gas system, power system and heat system.

[0196] For ease of understanding, the propagation of faults in integrated energy systems is explained as follows.

[0197] Fault propagation in an integrated energy system can be defined as follows: when a disturbance or equipment failure occurs in the system, the output of the coupling element changes and further affects the coupled system. This effect propagates repeatedly and amplifies between the natural gas system and the power system, and is then transmitted to the thermal system.

[0198] Establish the fault propagation process of an integrated energy system. For example... Figure 2 As shown, it specifically includes the following two propagation processes:

[0199] (1) Due to factors such as pipeline quality issues, human factors, and natural disasters, natural gas systems may experience component failures. Natural gas system dispatchers need to take measures such as adjusting gas source output and reducing natural gas load to ensure the reliable operation of the natural gas system. When the maximum gas supply cannot meet the gas load, the natural gas supply to natural gas units and gas boilers will be reduced first. The reduction in natural gas load reduces the power generation capacity of natural gas units, which in turn leads to a reduction in power load, and the fault disturbance propagates from the natural gas system to the power system.

[0200] (2) Due to reduced power output of natural gas units or component failures in the power system, the power flow distribution of the power system will change. The power system will divide the system into islands of different sizes according to the location of the fault. The system will automatically balance the output of units and load demand within the islands through automatic generation control and low-frequency load shedding devices. If the power flow of the lines within the islands exceeds the limit, it may lead to the shutdown of the corresponding lines. When the power system reaches a stable state after the fault, the power system needs to self-dispatch all generator units and loads to achieve overall optimal operation. If the power regulation of the generator units still cannot achieve power system source-load balance, the power supply of P2G, electric gas source, and heat pump will be reduced first. The reduction in power supply forces the electric gas source and P2G equipment to reduce output, more natural gas load is reduced, and the fault disturbance is propagated to the natural gas system again through the coupling element. In this case, the self-dispatch process of the natural gas system and the power system will be repeated until the fault propagation stops.

[0201] This invention simulates the dynamic process of fault propagation in an integrated energy system on a unified time architecture, using a simulation time step. Promoting the evolution over time. For example... Figure 3 As shown. It is worth noting that the simulation step size is alternately passed between the subsystems. Between these subsystems, variable transfer also occurs in the self-scheduling model. For example, the natural gas subsystem needs to transfer the current gas source output and pipeline inventory; the power subsystem needs to transfer the output of the coal-fired unit and the state variables of the advanced adiabatic compressed air energy storage (including the gas pressure in the storage chamber, the temperature of the high-temperature thermal storage tank, and the amount of hot water stored); and the thermal subsystem needs to transfer the pipeline temperature and indoor temperature.

[0202] In practical implementation, the objective function of the natural gas system is shown in the following formula:

[0203] ;

[0204] In the formula, Indicates the simulation time step; Represents the set of gas source nodes; and These represent the output of the electrically driven gas source and the output of the natural gas driven gas source, respectively. and These represent the production costs of electrically driven gas sources and naturally driven gas sources, respectively. Represents the set of gas network nodes; and These represent the load reduction amount and load reduction penalty coefficient for natural gas, respectively.

[0205] The constraints of a natural gas system include equality constraints and inequality constraints;

[0206] The equality constraints include: if there are no faults between the x and y sides of the natural gas pipeline, then the following equality constraints are satisfied:

[0207] ;

[0208] ;

[0209] ;

[0210] ;

[0211] ;

[0212] ;

[0213] ;

[0214] In the formula, and This represents the inflow and outflow flow rates of pipe x and y; This represents the natural gas production of the P2G device at node k at time t; Indicates gas load. This indicates the flow rate through the compressor piping. This indicates the amount of natural gas consumed by the compressor; This represents the amount of natural gas stored in the pipeline at time t. This represents the air pressure at node x at time t. and These represent the length and inner diameter of the natural gas pipeline, respectively. For the absolute roughness of the natural gas pipeline, The compressibility factor of natural gas T represents the specific gas constant of natural gas, and T represents the temperature. This represents the coefficient of friction of the gas pipeline (xy). Indicates the initial flow rate of natural gas. Indicates the speed of sound; Let represent the air pressure at node y at time t, and let Q represent the activation function for the flow rate.

[0215] If there is a fault at point m between the x and y natural gas pipelines, then the following equation constraint must be satisfied:

[0216] ;

[0217] ;

[0218] ;

[0219] ;

[0220] ;

[0221] ;

[0222] ;

[0223] ;

[0224] ;

[0225] ;

[0226] ;

[0227] ;

[0228] ;

[0229] In the formula, This is the isentropic index of natural gas; This represents the air pressure at node m at time t+1; This represents the air pressure at node x at time t+1; and Let x and t represent the inflow and outflow rates of pipe xm at time t, respectively. This represents the coefficient of friction of the gas pipeline (xm).

[0230] The equality constraints of the coupling device include:

[0231] ;

[0232] ;

[0233] In the above formula, This represents the power generation capacity of the natural gas generator unit connected to node x at time t; Represents the state variables of a natural gas generator unit; This represents the thermal power of the gas-fired boiler connected to node x at time t. Represents the state variables of a gas-fired boiler. and These represent the conversion efficiencies of natural gas units and gas-fired boilers, respectively. Indicates gas load; This represents the natural gas load reduction penalty coefficient;

[0234] Inequality constraints in natural gas systems include:

[0235] ;

[0236] ;

[0237] ;

[0238] ;

[0239] ;

[0240] ;

[0241] ;

[0242] ;

[0243] ;

[0244] In the formula, This represents the pressure at the outlet of the pipe at time t at point x; This represents the pressure at the near-gas end of the pipe at point y at time t; and Let x represent the initial flow rate and pressure at time t at point x in the pipeline, respectively. Indicates the boost ratio. Let a 0, 1 variable represent the direction of flow, when When it is 1, when The value is 0; N is a theoretically infinite value. and These represent the maximum and minimum pressure values ​​at point x in the pipe, respectively. and These represent the maximum and minimum output values ​​of the electric drive power supply, respectively. and These represent the maximum and minimum output values ​​of the natural gas-driven gas source, respectively. and These represent the maximum and minimum flow rates in the pipeline, respectively. and These represent the maximum and minimum flow rates of the compressor, respectively. , , and These are the state variables for the electric-driven gas source, the natural gas-driven gas source, the pipeline, and the compressor, respectively.

[0245] In this way, by constructing a complex yet accurate mathematical model of the natural gas system, the optimization of natural gas production and distribution was achieved. This model not only considers cost-effectiveness but also fully takes into account the system's safety, flexibility, and coupling with other systems, providing strong support for the operation and management of the natural gas system.

[0246] The objective function of the power system is as follows:

[0247] ;

[0248] In the formula, Represents the set of nodes for coal-fired power units; This indicates the cost of a coal-fired power unit; Indicates the power output of the coal-fired unit; Represents the set of power nodes; Indicates the load penalty factor for electrical discharge; Indicates the amount of electricity load reduction; Represents the set of wind turbine nodes; This represents the penalty coefficient for wind power reduction; This indicates the amount of wind power reduction;

[0249] The equality constraints of the power system include:

[0250] ;

[0251] ;

[0252] ;

[0253] ;

[0254] ;

[0255] ;

[0256] ;

[0257] ;

[0258] ;

[0259] ;

[0260] ;

[0261] ;

[0262] ;

[0263] ;

[0264] ;

[0265] In the above formula, Represents nodes The connected wind turbine units Power generation at any given moment Indicates the amount of wind power reduced. Indicates the power of the coal-fired unit. This represents the amount of electricity generated at time t during the expansion cycle. This represents the total power consumption at time t during the compression cycle. Indicates the amount of electricity load reduction. Indicates electrical load. Indicates power flow in power lines; Indicates the line reactance. Indicates the phase angle of the node; Indicates the specific heat ratio of air. This indicates the specific heat capacity of air. and These represent the number of stages in the compressor and expander, respectively. and Let represent the air mass flow rate at time t during the compression and expansion cycles, respectively. and These represent the inlet and outlet temperatures of the i1-th stage compressor, respectively. and Let represent the inlet and outlet temperatures of the i2th stage expander at time t, respectively; and Let represent the compression ratio and adiabatic efficiency of the i1-th stage compressor, respectively. and These represent the i2th stage expander and the adiabatic efficiency, respectively. This represents the mass flow rate of water entering the compressor at time t during the compression process. This indicates the specific heat capacity of water; This indicates the temperature of the cold water in the cryogenic storage tank. This indicates the hot water temperature at the outlet of the i1-th stage compressor; This represents the temperature of the hot water in the high-temperature thermal storage tank at time t. Indicates the efficiency of the heat exchanger. This represents the total hot water mass flow rate into the high-temperature thermal storage tank at time t. The mass of hot water in the high-temperature thermal storage tank at time t. This represents the total hot water mass flow rate into the heat exchanger at time t. Indicates the change in water temperature. Indicates the thermal power supplied to the user. This represents the gas pressure in the storage chamber at time t. Represents the universal gas constant. and These represent the gas pressure and volume of the gas storage chamber, respectively. Represents nodes The power generation capacity of the connected natural gas generator unit at time t; This indicates the temperature of the hot water flowing into the high-temperature thermal storage tank; Represents the state variables of the expansion subsystem; Represents the state variables of the compression subsystem;

[0266] The equality constraints of the coupling device include:

[0267] ;

[0268] ;

[0269] ;

[0270] In the formula, This represents the natural gas production of the P2G device at node k at time t. Indicates the operating status of P2G devices. This represents the conversion factor of the P2G device. Indicates the amount of electricity load reduction. Indicates electrical load; This represents the natural gas production of the electrically driven gas source at node k at time t. For the state variables of the electrically driven air source, Indicates the conversion coefficient of the electrically driven air source equipment; This represents the thermal power of the heat pump at node k at time t. Indicates the operating status of the heat pump equipment. This represents the conversion coefficient of the heat pump equipment;

[0271] Inequality constraints in power systems include:

[0272] ;

[0273] ;

[0274] ;

[0275] ;

[0276] ;

[0277] ;

[0278] ;

[0279] ;

[0280] ;

[0281] ;

[0282] ;

[0283] In the formula, This indicates the upper limit of the power line transmission capacity. and These represent the upper and lower limits of the output of the coal-fired power unit, respectively. and These are the state variables for power lines and coal-fired power units, respectively. and These are 0-1 variables representing the states of the compression and expansion cycles, respectively. and These represent the maximum and minimum power consumption, respectively. and These represent the maximum and minimum power generation values, respectively. and These represent the maximum and minimum values ​​of the hot water temperature, respectively. and These represent the maximum and minimum values ​​of the water storage, respectively. This indicates the maximum heat output provided to the user. and These represent the maximum and minimum values ​​of the gas pressure in the gas storage chamber, respectively.

[0284] In this way, by constructing a complex power system model, multiple benefits are achieved, including economic optimization, power supply and demand balance, efficient equipment operation, system security and stability, and flexibility and scalability. This is of great significance for improving the operating efficiency of the power system, reducing power generation costs, and ensuring the stability of power supply.

[0285] The objective function of the thermodynamic system is shown in the following equation:

[0286] ;

[0287] In the formula, Represents the set of nodes in a heating network; Indicates the heat load penalty coefficient; and These represent the annual and seasonal reductions in heat load, respectively.

[0288] The equality constraints of a thermal system include:

[0289] ;

[0290] ;

[0291] ;

[0292] ;

[0293] ;

[0294] ;

[0295] ;

[0296] ;

[0297] ;

[0298] ;

[0299] In the formula, Indicates the quality of the hot water flowing out of the pipe. Number The quality of the working fluid in the water supply pipeline at different times. and These are the outlet temperature and inlet temperature of the water supply pipeline, respectively. From arrive The quality of hot water injected into the pipes during the time period , and These represent the density of water, the cross-sectional area of ​​the pipe, and the length of the pipe, respectively. This indicates the outlet temperature of the water supply pipe. , and Representing ambient temperature and pipeline respectively The thermal conductivity and specific heat capacity of water, Water supply pipeline node The temperature at that location and Let represent the seasonal and perennial heat load power of the building at time t, respectively. Let be the indoor temperature of the building at time t. The equivalent thermal resistance of the building, It is the equivalent heat capacity; and These represent seasonal and perennial heat load reductions, respectively. and These are the upper and lower limits of suitable temperature inside a building. and Let represent the power generation of the natural gas generator unit and the gas-fired boiler connected to node x at time t, respectively. This represents the thermal power of the heat pump at node k at time t. A collection of pipes for the heat source outlet. Represents the set of nodes in the heating network. This refers to the collection of pipes at the inlet of the heat exchanger. This indicates the time it takes for hot water to travel from the inlet to the outlet.

[0300] Inequality constraints for thermal systems include:

[0301] ;

[0302] ;

[0303] ;

[0304] In the formula, and These represent the upper and lower limits of the pipe temperature, respectively. This indicates the perennial load power.

[0305] In this way, by constructing an optimized model for the thermal system, multiple benefits were achieved, including optimized heat load management, improved heat pipeline transmission efficiency, fulfillment of building heat load demands, enhanced system safety and stability, and increased energy utilization efficiency. This is of great significance for improving the operating efficiency of the thermal system, reducing energy consumption, and ensuring the thermal comfort and safety of buildings.

[0306] S2. Based on the actual parameter data, initialize the operating parameters of the integrated energy system and calculate the initial variable values.

[0307] In practice, the initial operating parameters include natural gas flow rate, power load, and heat demand; the initial variable values ​​calculated include natural gas pipeline storage, gas pressure in the storage chamber of the Advanced Insulated Compressed Air Energy Storage System (AA-CAES), and temperature of the heating network pipeline.

[0308] In this way, by initializing key operating parameters such as natural gas flow, power load, and heat demand, the system can more accurately simulate actual operating conditions. These parameters are the foundation for normal system operation and fault analysis, and their accuracy directly affects the reliability of subsequent simulations and analyses. The initialized operating parameters and calculated initial variable values ​​are not only used for fault simulation but also for system performance evaluation. By comparing them with actual operating data, key performance indicators such as system energy efficiency, reliability, and stability can be evaluated, providing a basis for system optimization design and improvement. It also ensures the accuracy of fault simulation. Accurate calculation of initial variable values ​​(such as natural gas pipeline inventory, AA-CAES storage chamber pressure, and heating network pipeline temperature) provides a more realistic starting point for fault propagation simulation. This helps to more accurately predict the propagation path and impact range of faults in the system, thereby improving the accuracy of fault simulation.

[0309] S3. Sample multiple components; for each component, use it as the initial faulty component, and use the integrated energy system to simulate fault propagation to obtain the corresponding fault events. The content of the fault events includes the faulty components involved in the fault propagation simulation process.

[0310] In specific implementation, S3 includes:

[0311] S31. Use Monte Carlo sampling to determine the initial components and use them as the initial faulty components;

[0312] S32. In conjunction with the integrated energy system, determine the location of the faulty component;

[0313] S33. Determine whether the fault continues to propagate. If the fault continues to propagate, then advance the simulation time step based on the integrated energy system. The evolution simulation is completed, and then the process returns to S32; if the fault propagation has stopped, the corresponding fault event is recorded. The content of the fault event includes the components involved in the current evolution simulation that have failed, and then the process returns to S34.

[0314] S34. Determine whether the variance coefficient of the Monte Carlo sampling has converged; if it has not converged, go to S31 and use Monte Carlo sampling again to determine the new initial components; if it has converged, go to S4; where, when the variance coefficient is less than the preset threshold, it is determined that it has converged.

[0315] This ensures the comprehensiveness of the fault simulation. Using Monte Carlo sampling, components in the system can be randomly selected as initial fault points to simulate various possible fault scenarios. This method comprehensively considers uncertainties in the system, improving the accuracy and comprehensiveness of the fault simulation. It also enables dynamic capture of fault propagation. Combined with the integrated energy system, the location of the faulty component can be determined in real time, and the evolution simulation can be advanced based on the simulation time step, dynamically capturing the fault propagation process in the system. This helps to gain a deeper understanding of the fault propagation mechanism and its impact on the system, providing strong support for subsequent fault analysis and handling. When fault propagation stops, the corresponding fault event is recorded in detail, including all faulty components involved in the current evolution simulation. This provides important data support for subsequent fault analysis and weak component identification, helping to discover potential problems and weaknesses in the system. Furthermore, by determining whether the variance coefficient of the Monte Carlo sampling converges, the stability and reliability of the simulation results can be evaluated. If the variance coefficient does not converge, Monte Carlo sampling continues to determine new initial components until convergence. This ensures the accuracy and reliability of the simulation results, providing a solid foundation for subsequent analysis and processing.

[0316] In specific implementation, in S33, if the output and load shear of the coupled equipment do not change between the two consecutive moments, it is determined that the fault propagation has stopped.

[0317] In S33, the simulation time step is based on the integrated energy system. The evolutionary simulation process includes:

[0318] Step 0: Determine whether the fault occurs on the natural gas system side or the power system side; if the fault occurs on the natural gas system side, proceed to Step 1; otherwise, proceed to Step 2.

[0319] Step 1: Based on the operating status of the natural gas system at time t and the natural gas production of the electric drive gas source and P2G unit determined by the power system at time t, calculate the following based on the natural gas system: Real-time natural gas load reduction determines the natural gas supply to natural gas units and gas-fired boilers;

[0320] Step 2: Based on the obtained natural gas unit power and the power system fault conditions, determine whether a cascading fault has occurred within the power system; when the unit output changes or a component fails within the power system, the line power is redistributed, and power flow is transferred; when the grid cascading fault simulation process terminates, the power system is calculated to reflect the current situation. The amount of load reduction at any given time determines the power supply for the electric drive gas source, P2G unit, and heat pump;

[0321] Step 3: Calculate the thermal power provided by the coupled components, including the gas boiler, heat pump, and advanced adiabatic compressed air energy storage (AA-CAES). The amount of heat load reduction at any given time.

[0322] This ensures the accuracy of fault propagation simulation. By simulating the interactions between the natural gas, power, and heating systems, the propagation process of faults within the integrated energy system can be accurately reflected. Considering the roles of coupling equipment such as natural gas units, gas-fired boilers, electric-driven gas sources, P2G units, heat pumps, and AA-CAES (Automatic Energy Assisted Systems) makes the simulation results more realistic. Furthermore, using changes in the output and load shedding of coupling equipment as the criterion for stopping fault propagation meets system stability requirements and facilitates rapid judgment during simulation. Moreover, the simulation captures the occurrence of cascading faults within the power system and the redistribution of line power, reflecting the system's dynamic response under fault conditions. By calculating load shedding and equipment supply at different times, the impact of faults on the system can be assessed. Through the evolutionary simulation process, the reliability of the integrated energy system under fault conditions can be evaluated, including system stability, resilience, and component vulnerability. This helps identify weak points in the system and provides guidance for optimal system design and operation strategies.

[0323] Step 2, the process of redistributing line power includes:

[0324] First, analyze the current system topology. If islanding occurs and the power supply and demand within the island are unbalanced, the unit output and load demand within the island will be automatically balanced through automatic generation control and low-frequency load shedding devices. The islanding adjustment and balancing strategies include: ① If the sum of unit outputs is lower than the load, the units will first increase their output proportionally based on their available remaining output; if the sum of the outputs of the largest units is still lower than the load, the load will be reduced sequentially according to the power deficit ratio; ② If the sum of unit outputs is greater than the load, the output should be reduced proportionally to match the load; if the sum of the outputs of the smallest units is still greater than the load, the units will be shut down sequentially from smallest to largest capacity.

[0325] Then, a line cascading fault simulation is performed, and the power flow of each isolated island is updated using a DC power flow model. Specifically, a latent fault probability model is used to simulate the line cascading fault, and the calculation formula is as follows:

[0326] ;

[0327] In the formula, Indicates the probability of random failure; and These represent the current power and rated capacity of the line, respectively. If a new line fault occurs, the line power will be redistributed; otherwise, proceed to step 3.

[0328] This allows for rapid restoration of power balance within the island. Through the coordinated action of automatic generation control and low-frequency load shedding devices, the output of units within the island can be quickly adjusted and loads reduced to achieve a balance between supply and demand. This helps reduce the risk of power outages caused by power imbalances within the island, improving system stability and reliability. It also optimizes unit output and load reduction strategies. The island's adjustment and balancing strategy considers the actual situation of unit output and load, achieving optimal resource allocation by proportionally increasing output or reducing load, and shutting down units according to capacity. This helps reduce unnecessary energy waste and improve energy efficiency. Furthermore, it ensures the accuracy of line cascading fault simulation. Updating the power flow of each island using a DC power flow model more accurately reflects the power flow distribution under different fault conditions. This helps assess the impact of faults on the system, providing a scientific basis for subsequent fault handling and recovery strategies.

[0329] Simulating cascading line faults using a latent fault probability model allows for the prediction of the probability of a line fault, taking into account factors such as the line's current power and rated capacity. This helps identify potential risks in the system early, providing strong support for fault prevention. Furthermore, it ensures flexibility in fault handling procedures. If a new line fault occurs during the simulation, line power is redistributed. This flexible fault handling process ensures the system can respond quickly to different fault conditions, improving its adaptability and robustness.

[0330] S4. For each fault event, calculate the contribution value of each component to the reliability index of the fault event according to the preset method.

[0331] In specific implementation, the preset methods include: for fault event E j The contribution of component i to the reliability index is calculated according to the following formula. :

[0332] ;

[0333] ;

[0334] ;

[0335] ;

[0336] In the formula, Indicates that element i responds to fault event E jThe contribution value of reliability indicators; Indicates fault event E j Reliability metrics, x i Indicates the performance parameters of component i; Indicates the reliability index of component i Contributions; Indicates the unavailability rate of component i; r i (E j ) indicates that component i responds to fault event E j The contribution of the consequences; e represents E j The sub-events do not include the event of element i. and These represent the weight of sub-event e and the failure consequence, respectively. This indicates the fault consequence obtained after adding component i to sub-event e;

[0337] This indicates the number of components in e. E represents j The number of components in it.

[0338] To facilitate a better understanding of this method, sub-events are explained as follows. For example, fault event E. {1，2} This is caused by a malfunction in components 1 and 2. Therefore, E... {1，2} It includes three sub-events: event e, which only involves the failure of component 1. {1} Only the event e of component 2 failure. {2} and e without any faulty components {∅} .

[0339] This allows for the precise calculation of each component's contribution to the reliability index of a specific failure event. This quantitative assessment helps identify which components play a key role in the failure event, thus providing strong data support for subsequent failure analysis and optimization. It also comprehensively considers the impact of components. This method not only considers the direct consequences of components in failure events but also comprehensively considers the indirect impact of components in the failure propagation process through risk allocation weighting and sub-event analysis. This makes the assessment results more comprehensive and accurate, more realistically reflecting the actual role of components in the integrated energy system. Furthermore, it supports complex system analysis. This method is applicable to the failure analysis of complex systems such as integrated energy systems. By considering the interactions between multiple sub-events and components, this method can handle complex failure propagation and consequence assessment problems, providing an effective tool for improving system reliability and preventing failures.

[0340] In practical implementation, reliability indicators include the expected power shortage value (EENS) and the probability of load shedding (LOELP).

[0341] ;

[0342] ;

[0343] In the formula, The actual number of simulated years; This represents the number of states where the power load is lost. For the first The duration of each unloaded state, For the first The amount of power loss at time t under a certain loss-of-load condition;

[0344] This also includes the expected natural gas supply shortfall (EGNS) and the probability of gas load reduction (LOGLP).

[0345] ;

[0346] ;

[0347] In the formula, This represents the number of states where natural gas is unloaded. Let be the amount of natural gas loss at time t under the m-th unloaded state;

[0348] This also includes the expected heat supply deficit (EHNS) and the probability of heat load reduction (LOHLP):

[0349] ;

[0350] ;

[0351] In the formula, This represents the state number of thermal unloading. and These represent the annual and seasonal thermal load loss at time t under the m-th unloaded state, respectively.

[0352] This also includes the expected amount of natural gas leakage (EGL) and the probability of natural gas leakage (PGL):

[0353] ;

[0354] ;

[0355] In the formula, This represents the state number of a natural gas leak. Let be the amount of natural gas leaked at time t under the m-th leak fault condition;

[0356] It also includes the expected value of hot water leakage (EWL) and the probability of hot water leakage (PWL):

[0357] ;

[0358] ;

[0359] In the formula, This represents the state number of a hot water leak. Let be the amount of hot water leakage at time t under the m-th leakage fault state;

[0360] This also includes the NOFC (Normally Inactive Components) indicator:

[0361] ;

[0362] In the formula, The number of states where a fault occurred. This represents the probability of a fault state. For the first The number of out-of-service components under each fault propagation state. This represents the initial number of faulty components in this state; when This indicates that the scale of the fault propagation has expanded.

[0363] These reliability indicators collectively form a complete framework for assessing the reliability of energy systems and their components, facilitating a comprehensive and objective evaluation of the system's reliability level. Regular monitoring and analysis of these indicators allow for the timely identification of potential risks and problems within the system, providing a scientific basis for developing preventative measures, emergency plans, and optimizing system configuration. These indicators also offer crucial references for the planning, design, operation, and maintenance of energy systems, contributing to improved overall system reliability, safety, and stability. Through a series of reliability indicators, a comprehensive and objective assessment of the reliability of energy systems and their components is conducted, providing strong support for optimized system operation and sustainable development.

[0364] S5. For each component that fails during the fault propagation simulation, sum up its contribution to the reliability index of each fault event to obtain the contribution of that component to the reliability index of the integrated energy system.

[0365] S6. Sort each component in descending order of its contribution to the reliability index of the integrated energy system, and select the top n components as weak components.

[0366] This invention integrates natural gas, electricity, and heating systems to construct a comprehensive integrated energy system model. This integration allows the system to more accurately reflect the complexity and interdependence of actual energy networks, providing a more realistic environment for subsequent fault propagation simulation and weak component identification. Compared to traditional single-system analysis or simple multi-system parallel analysis, this method comprehensively considers the interdependence of multiple subsystems and the propagation of faults across subsystems, resulting in more accurate and comprehensive analysis results. Furthermore, by quantifying and ranking the reliability contribution values ​​of components, this technical solution can more effectively identify weak components in the system, providing strong support for subsequent maintenance and optimization. In addition, initializing the system based on actual parameter data ensures that the starting point of the simulation matches actual operating conditions. This helps improve the accuracy and reliability of the simulation results, making subsequent analysis more realistic. By sampling multiple components as initial fault points for fault propagation simulation, the propagation path and impact range of faults within the system can be comprehensively captured. This simulation considers not only the failure of a single component but also the propagation and impact of faults across multiple subsystems, thereby more accurately assessing the overall reliability of the system. Furthermore, for each failure event, the reliability contribution value of each component is calculated according to a preset method, achieving a quantitative assessment of the component's role in the failure event. This quantitative assessment helps to more accurately identify components that have a significant impact on system reliability, providing a basis for subsequent optimization and improvement. By accumulating the reliability contribution values ​​of components in each failure event, the contribution value of each component to the overall energy system reliability index is obtained, and they are sorted from largest to smallest contribution value. This method can effectively identify weak components in the system, namely those components that have the greatest impact on system reliability. Selecting the top n components as weak components facilitates subsequent focused maintenance and optimization, thereby improving the overall reliability of the system.

[0367] In summary, this invention can accurately quantify the contribution of each component to the reliability index of the integrated energy system, based on a full consideration of the interdependence of subsystems and the propagation of faults across subsystems, thereby effectively identifying the weak components of the system.

[0368] Example 2

[0369] To better illustrate the effectiveness of this method, the following specific example will be used.

[0370] Taking a comprehensive energy system consisting of an IEEE 30-node power system, a Belgian 20-node natural gas system, and a 16-node thermal system as an example, the weak links were identified. The network diagram of the example is shown below. Figure 4As shown. In the natural gas system, the gas source at node 14 is a P2G device (W6), which receives power from node 21 of the power system. The electrically driven gas sources (W2, W3, and W4) at natural gas nodes 2, 5, and 8 receive power from nodes 17, 24, and 30 of the power system, respectively; W1 and W5 are natural gas-driven gas sources. In the thermal system, gas boilers C1-C2 receive gas from natural gas nodes 15 and 6, respectively; heat pump C3 receives power from node 15 of the power system; and heat source C4 receives thermal power from AA-CAES. The conversion coefficients for the P2G device and the electrically driven gas source are set to 0.088 m³ / MW and 0.110 m³ / MW, respectively, and the conversion coefficient for the heat pump is set to 1.

[0371] Two scenarios are set up for comparative analysis of the weak components of the system under different coupling degrees. Scenario 1: Gas sources W1~W6 are all natural gas driven gas sources. Scenario 2: Gas sources W2, W3, and W4 are electrically driven gas sources, gas source W6 is a P2G device, and gas sources W1 and W5 are natural gas driven gas sources.

[0372] Table 1 presents the reliability indices of integrated energy systems under two scenarios. It can be seen that when considering fault propagation in integrated energy systems, the reliability indices EENS and LOHLP of the power system increase significantly. In scenario 1, EENS and LOHLP are 1.768 × 10³ MWh and 1.257 × 10⁻², respectively, while in scenario 2 they increase to 3.437 × 10³ MWh and 1.396 × 10⁻². Compared to scenario 1, the reliability indices of the natural gas system also show a significant increase. For the heating system, the reliability indices EHNS and LOHLP in scenario 2 are less different from those in scenario 1. This is because the heating network pipeline has a transmission delay characteristic; the impact of fault propagation from the heat source will be delayed in reaching heat users, meaning the impact on heat users is relatively small during fault propagation. The expected number of out-of-operation components (NOFC) indices are 2.211 × 10⁻⁴ and 1.449 × 10⁻², respectively, indicating that as the coupling between subsystems increases, the impact of fault propagation gradually increases, leading to a higher number of out-of-operation components in the system.

[0373] Table 1. Impact of Fault Propagation on System Reliability

[0374]

[0375] Tables 2 and 3 present the identification results of weak components ranked according to adequacy indices (EENS and EGNS), respectively, and analyze the propagation impact of component failures on the risk of load loss. EENS in the tables represents the index value allocated to each component; the top 10 ranked components are selected as the weakest components of the system.

[0376] Table 2 Comparison of Weak Components Ranked by Reliability Index EENS

[0377]

[0378] As shown in Table 2, power line L14 is the weakest component with the greatest impact on the EENS index, and its risk allocation result is significantly higher than that of other power lines. This is because: line L14 connects to wind turbines and bears the largest power transmission of the power system; its failure will trigger a large power flow shift. This shift may cause multiple lines to exceed their power flow limits and cause corresponding line outages, resulting in a greater risk of power loss. On the other hand, since a failure on this line will cause load reduction at coupled nodes, the risk of power loss will be transmitted to the natural gas system through the electric-driven gas source and P2G equipment, thus triggering a fault propagation effect. In addition, it can be seen that in scenario 2, the ranking of unit G5 and line L16 decreases, while the ranking of components such as line L36 and line L26 increases. This is because, after a failure on line L36 and line L26, in addition to causing power loss at coupled power node 30 (connected to gas source W4) and node 17 (connected to gas source W2), it will also reduce the gas production of the electric-driven gas source, thereby affecting the gas network's dispatching decisions and reducing the output of natural gas units. This cycle repeats, resulting in a large risk of power loss. In Scenario 1, the loss of load at a power node does not cause a fault propagation effect. Unit G5 and line L16 are important components for transmitting energy from the natural gas system to the power system. If they fail, it will directly reduce the power supply of the power system. Therefore, unit G5 and line L16 are ranked relatively high in Scenario 1.

[0379] Table 3 Comparison of Weak Components Ranked by Reliability Index EGNS

[0380]

[0381] As shown in Table 3, without considering the impact of power system fault propagation on the natural gas system, the top 10 weakest components in Scenario 1 are all natural gas pipelines and gas sources, indicating that faults in components within the natural gas subsystem have the greatest impact on natural gas load reduction. Taking pipelines B13 and B14 as examples, as shown in Table 3, the EGNS allocated to B13 and B14 in Scenario 1 are 1.414 × 10³ m³ and 3.284 × 10³ m³, respectively. The propagation impact of pipeline B14 on natural gas load loss is significantly higher than other components. This is because when pipeline B14 experiences a rupture fault, it will directly cause a complete reduction in the natural gas load at node 16. Furthermore, due to the large natural gas load at node 16, the EGNS index of pipeline B14 ranks first. Although pipeline B13 and B14 both belong to unidirectional transmission conditions, considering the dynamic characteristics of the natural gas system, the natural gas stored in the pipeline can be used to provide energy for a short period. That is, after a fault in pipeline 13, the gas stored in pipeline 14 can still continue to supply natural gas to node 16, thereby reducing the corresponding gas load reduction. Therefore, pipeline B13 is ranked relatively low. In addition, pipeline B2 is ranked 2nd and 4th in the two scenarios, respectively. This is because pipeline B2 is responsible for transmitting the gas production of gas sources W1 and W2. Once it fails, it means that the natural gas subsystem will lose the supply of two gas sources, which will cause a significant reduction in natural gas load.

[0382] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the technical solutions. Those skilled in the art should understand that any modifications or equivalent substitutions to the technical solutions of the present invention without departing from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A method for identifying weak components in a comprehensive energy system considering fault propagation, characterized in that: Includes the following steps: S1. Construct an integrated energy system for fault propagation simulation and weak component identification; Comprehensive energy analysis includes natural gas systems, power systems, and heat systems; S2. Based on actual parameter data, initialize the operating parameters of the integrated energy system and calculate the initial variable values; S3, Sampling multiple components; For each component, it is taken as the initial component that has failed, and the fault propagation simulation is performed using the integrated energy system to obtain the corresponding fault event. The content of the fault event includes the components that have failed during the fault propagation simulation. S4. For each fault event, calculate the contribution value of each component to the reliability index of the fault event according to the preset method. S5. For each component that fails during the fault propagation simulation, sum up its contribution to the reliability index of each fault event to obtain the contribution of that component to the reliability index of the integrated energy system. S6. Sort each component in descending order of its contribution to the reliability index of the integrated energy system, and select the top n components as weak components. S3 includes: S31. Use Monte Carlo sampling to determine the initial components and use them as the initial faulty components; S32. In conjunction with the integrated energy system, determine the location of the faulty component; S33. Determine whether the fault continues to propagate. If the fault continues to propagate, then advance the simulation time step based on the integrated energy system. The evolution simulation is completed, and then the process returns to S32; if the fault propagation has stopped, the corresponding fault event is recorded. The content of the fault event includes the components involved in the current evolution simulation that have failed, and then the process returns to S34. S34. Determine whether the variance coefficient of the Monte Carlo sampling has converged; if it has not converged, go to S31 and use Monte Carlo sampling again to determine the new initial components; if it has converged, go to S4; where, when the variance coefficient is less than the preset threshold, it is determined that it has converged. In S33, if the output and load shear of the coupled equipment do not change between the two consecutive moments, it is determined that the fault propagation has stopped. In S33, the simulation time step is based on the integrated energy system. The evolutionary simulation process includes: Step 0: Determine whether the fault occurs on the natural gas system side or the power system side; if the fault occurs on the natural gas system side, proceed to Step 1; otherwise, proceed to Step 2. Step 1: Based on the operating status of the natural gas system at time t and the natural gas production of the electric drive gas source and P2G unit determined by the power system at time t, calculate the following based on the natural gas system: Real-time natural gas load reduction determines the natural gas supply to natural gas units and gas-fired boilers; Step 2: Based on the obtained natural gas unit power and the power system fault conditions, determine whether a cascading fault has occurred within the power system; when the unit output changes or a component fails within the power system, the line power is redistributed, and power flow is transferred; when the grid cascading fault simulation process terminates, the power system is calculated to reflect the current situation. The amount of load reduction at any given time determines the power supply for the electric drive gas source, P2G unit, and heat pump; Step 3: Calculate the thermal power provided by the coupled components, including the gas boiler, heat pump, and advanced adiabatic compressed air energy storage (AA-CAES). The amount of heat load reduction at any given time; Step 2, the process of redistributing line power includes: First, analyze the current system topology. If islanding occurs and the power supply and demand within the island are unbalanced, the unit output and load demand within the island will be automatically balanced through automatic generation control and low-frequency load shedding devices. The islanding adjustment and balancing strategies include: ① If the sum of unit outputs is lower than the load, the units will first increase their output proportionally based on their available remaining output; if the sum of the outputs of the largest units is still lower than the load, the load will be reduced sequentially according to the power deficit ratio; ② If the sum of unit outputs is greater than the load, the output should be reduced proportionally to match the load; if the sum of the outputs of the smallest units is still greater than the load, the units will be shut down sequentially from smallest to largest capacity. Then, a line cascading fault simulation is performed, and the power flow of each isolated island is updated using a DC power flow model. Specifically, a latent fault probability model is used to simulate the line cascading fault, and the calculation formula is as follows: ； In the formula, Indicates the probability of random failure; and These represent the current power and rated capacity of the line, respectively. If a new line fault occurs, the line power will be redistributed; otherwise, proceed to step 3.

2. The method for identifying weak components in a comprehensive energy system considering fault propagation as described in claim 1, characterized in that: In S4, the preset methods include: for fault event E j The contribution of component i to the reliability index is calculated according to the following formula. : ； ； ； ； In the formula, Indicates that element i responds to fault event E j The contribution value of reliability indicators; Indicates fault event E j Reliability metrics, x i Indicates the performance parameters of component i; Indicates the reliability index of component i Contributions; Indicates the unavailability rate of component i; r i (E j ) indicates that component i responds to fault event E j The contribution of the consequences; e represents E j The sub-events do not include the event of element i. and These represent the weight of sub-event e and the failure consequence, respectively. This indicates the fault consequence obtained after adding component i to sub-event e; This indicates the number of components in e. E represents j The number of components in it.

3. The method for identifying weak components in a comprehensive energy system considering fault propagation as described in claim 1, characterized in that: The objective function of the natural gas system is shown in the following equation: ； In the formula, Indicates the simulation time step; Represents the set of gas source nodes; and These represent the output of the electrically driven gas source and the output of the natural gas driven gas source, respectively. and These represent the production costs of electrically driven gas sources and naturally driven gas sources, respectively. Represents the set of gas network nodes; and These represent the load reduction amount and load reduction penalty coefficient for natural gas, respectively. The constraints of a natural gas system include equality constraints and inequality constraints; The equality constraints include: if there are no faults between the x and y sides of the natural gas pipeline, then the following equality constraints are satisfied: ； ； ； ； ； ； ； In the formula, and This represents the inflow and outflow flow rates of pipe x and y; This represents the natural gas production of the P2G device at node k at time t; Indicates gas load. This indicates the flow rate through the compressor piping. This indicates the amount of natural gas consumed by the compressor; This represents the amount of natural gas stored in the pipeline at time t. This represents the air pressure at node x at time t. and These represent the length and inner diameter of the natural gas pipeline, respectively. For the absolute roughness of the natural gas pipeline, The compressibility factor of natural gas T represents the specific gas constant of natural gas, and T represents the temperature. This represents the coefficient of friction of the gas pipeline (xy). Indicates the initial flow rate of natural gas. Indicates the speed of sound; This represents the air pressure at node y at time t; If there is a fault at point m between the x and y natural gas pipelines, then the following equation constraint must be satisfied: ； ； ； ； ； ； ； ； ； ； ； ； ； In the formula, This is the isentropic index of natural gas; This represents the air pressure at node m at time t+1; This represents the air pressure at node x at time t+1; and Let x and t represent the inflow and outflow rates of pipe xm at time t, respectively. The friction coefficient of the gas pipeline is represented by xm. The equality constraints of the coupling device include: ； ； In the above formula, This represents the power generation capacity of the natural gas generator unit connected to node x at time t; Represents the state variables of a natural gas generator unit; This represents the thermal power of the gas-fired boiler connected to node x at time t. Represents the state variables of a gas-fired boiler. and These represent the conversion efficiencies of natural gas units and gas-fired boilers, respectively. Indicates gas load; This represents the natural gas load reduction penalty coefficient; Inequality constraints in natural gas systems include: ； ； ； ； ； ； ； ； ； In the formula, This represents the pressure at the outlet of the pipe at time t at point x; This represents the pressure at the air inlet at point t on the pipeline at point y; and Let x represent the initial flow rate and pressure at time t at point x in the pipeline, respectively. Indicates the boost ratio. Let a 0, 1 variable represent the direction of flow, when When it is 1, when The value is 0; N is a theoretically infinite value. and These represent the maximum and minimum pressure values ​​at point x in the pipe, respectively. and These represent the maximum and minimum output values ​​of the electric drive power supply, respectively. and These represent the maximum and minimum output values ​​of the natural gas-driven gas source, respectively. and These represent the maximum and minimum flow rates in the pipeline, respectively. and These represent the maximum and minimum flow rates of the compressor, respectively. , , and These are the state variables for the electric-driven gas source, the natural gas-driven gas source, the pipeline, and the compressor, respectively.

4. The method for identifying weak components in a comprehensive energy system considering fault propagation as described in claim 3, characterized in that: The objective function of the power system is as follows: ； In the formula, Represents the set of nodes for coal-fired power units; This indicates the cost of a coal-fired power unit; Indicates the power output of the coal-fired unit; Represents the set of power nodes; Indicates the load penalty factor for electrical discharge; Indicates the amount of electricity load reduction; Represents the set of wind turbine nodes; This represents the penalty coefficient for wind power reduction; This indicates the amount of wind power reduction; The equality constraints of the power system include: ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； In the above formula, Represents nodes The connected wind turbine units Power generation at any given moment Indicates the amount of wind power reduced. Indicates the power of the coal-fired unit. This represents the amount of electricity generated at time t during the expansion cycle. This represents the total power consumption at time t during the compression cycle. Indicates the amount of electricity load reduction. Indicates electrical load. Indicates power flow in power lines; Indicates the line reactance. Indicates the phase angle of the node; Indicates the specific heat ratio of air. This indicates the specific heat capacity of air. and These represent the number of stages in the compressor and expander, respectively. and Let represent the air mass flow rate at time t during the compression and expansion cycles, respectively. and These represent the inlet and outlet temperatures of the i1-th stage compressor, respectively. and Let represent the inlet and outlet temperatures of the i2th stage expander at time t, respectively; and Let represent the compression ratio and adiabatic efficiency of the i1-th stage compressor, respectively. and These represent the i2th stage expander and the adiabatic efficiency, respectively. This represents the mass flow rate of water entering the compressor at time t during the compression process. This indicates the specific heat capacity of water; This indicates the temperature of the cold water in the cryogenic storage tank. This indicates the hot water temperature at the outlet of the i1-th stage compressor; This represents the temperature of the hot water in the high-temperature thermal storage tank at time t. Indicates the efficiency of the heat exchanger. This represents the total hot water mass flow rate into the high-temperature thermal storage tank at time t. The mass of hot water in the high-temperature thermal storage tank at time t. This represents the total hot water mass flow rate into the heat exchanger at time t. Indicates the change in water temperature. Indicates the thermal power supplied to the user. This represents the gas pressure in the storage chamber at time t. Represents the universal gas constant. and These represent the gas pressure and volume of the gas storage chamber, respectively. Represents nodes The power generation capacity of the connected natural gas generator unit at time t; This indicates the temperature of the hot water flowing into the high-temperature thermal storage tank; Represents the state variables of the expansion subsystem; Represents the state variables of the compression subsystem; The equality constraints of the coupling device include: ； ； ； In the formula, This represents the natural gas production of the P2G device at node k at time t. Indicates the operating status of P2G devices. This represents the conversion factor of the P2G device. Indicates the amount of electricity load reduction. Indicates electrical load; This represents the natural gas production of the electrically driven gas source at node k at time t. For the state variables of the electrically driven air source, Indicates the conversion coefficient of the electrically driven air source equipment; This represents the thermal power of the heat pump at node k at time t. Indicates the operating status of the heat pump equipment. This represents the conversion coefficient of the heat pump equipment; Inequality constraints in power systems include: ； ； ； ； ； ； ； ； ； ； ； In the formula, This indicates the upper limit of the power line transmission capacity. and These represent the upper and lower limits of the output of the coal-fired power unit, respectively. and These are the state variables for power lines and coal-fired power units, respectively. and These are 0-1 variables representing the states of the compression and expansion cycles, respectively. and These represent the maximum and minimum power consumption, respectively. and These represent the maximum and minimum power generation values, respectively. and These represent the maximum and minimum values ​​of the hot water temperature, respectively. and These represent the maximum and minimum values ​​of the water storage, respectively. This indicates the maximum heat output provided to the user. and These represent the maximum and minimum values ​​of the gas pressure in the gas storage chamber, respectively.

5. The method for identifying weak components in a comprehensive energy system considering fault propagation as described in claim 4, characterized in that: The objective function of the thermodynamic system is shown in the following equation: ； In the formula, Represents the set of nodes in a heating network; Indicates the heat load penalty coefficient; and These represent the annual and seasonal reductions in heat load, respectively. The equality constraints of a thermal system include: ； ； ； ； ； ； ； ； ； ； In the formula, Indicates the quality of the hot water flowing out of the pipe. Number The quality of the working fluid in the water supply pipeline at different times. and These are the outlet temperature and inlet temperature of the water supply pipeline, respectively. From arrive The quality of hot water injected into the pipes during the time period , and These represent the density of water, the cross-sectional area of ​​the pipe, and the length of the pipe, respectively. This indicates the outlet temperature of the water supply pipe. , and Representing ambient temperature and pipeline respectively The thermal conductivity and specific heat capacity of water, Water supply pipeline node The temperature at that location and Let represent the seasonal and perennial heat load power of the building at time t, respectively. Let be the indoor temperature of the building at time t. The equivalent thermal resistance of the building, It is the equivalent heat capacity; and These represent seasonal and perennial heat load reductions, respectively. and These are the upper and lower limits of suitable temperature inside a building. and Let represent the power generation of the natural gas generator unit and the gas-fired boiler connected to node x at time t, respectively. This represents the thermal power of the heat pump at node k at time t. A collection of pipes for the heat source outlet. Represents the set of nodes in the heating network. This refers to the collection of pipes at the inlet of the heat exchanger. This indicates the time it takes for hot water to travel from the inlet to the outlet; Inequality constraints for thermal systems include: ； ； ； In the formula, and These represent the upper and lower limits of the pipe temperature, respectively. This indicates the perennial load power.

6. The method for identifying weak components in a comprehensive energy system considering fault propagation as described in claim 5, characterized in that: Reliability metrics include the expected power shortage (EENS) and the probability of load shedding (LOELP). ； ； In the formula, The actual number of simulated years; This represents the number of states where the power load is lost. For the first The duration of each unloaded state, For the first The amount of power loss at time t under a certain loss-of-load condition; This also includes the expected natural gas supply shortfall (EGNS) and the probability of gas load reduction (LOGLP). ； ； In the formula, This represents the number of states where natural gas is unloaded. Let be the amount of natural gas loss at time t under the m-th unloaded state; This also includes the expected heat supply deficit (EHNS) and the probability of heat load reduction (LOHLP): ； ； In the formula, This represents the state number of thermal unloading. and These represent the annual and seasonal thermal load loss at time t under the m-th unloaded state, respectively. This also includes the expected amount of natural gas leakage (EGL) and the probability of natural gas leakage (PGL): ； ； In the formula, This represents the state number of a natural gas leak. Let be the amount of natural gas leaked at time t under the m-th leak fault condition; It also includes the expected value of hot water leakage (EWL) and the probability of hot water leakage (PWL): ； ； In the formula, This represents the state number of a hot water leak. Let be the amount of hot water leakage at time t under the m-th leakage fault state; This also includes the NOFC (Normally Inactive Components) indicator: ； In the formula, The number of states where a fault occurred. This represents the probability of a fault state. For the first The number of out-of-service components under each fault propagation state. This represents the initial number of faulty components in this state; when This indicates that the scale of the fault propagation has expanded.

7. The method for identifying weak components in a comprehensive energy system considering fault propagation as described in claim 1, characterized in that: In S2, the initial operating parameters include natural gas flow rate, power load, and heat demand; the initial variable values ​​calculated include natural gas pipeline storage, gas pressure in the storage chamber of the Advanced Insulated Compressed Air Energy Storage System (AA-CAES), and temperature of the heating network pipeline.

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