Fault propagation-considered weak element identification method for integrated energy system

Abstract

The invention belongs to the technical field of integrated energy systems, and particularly relates to an integrated energy system weak element identification method considering fault propagation, which comprises the following steps: S1, constructing an integrated energy system; s2, initializing 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 the 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; elements with the first n contribution values are selected as weak elements. The 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 subsystem interdependence and fault cross-subsystem propagation, thereby effectively identifying the weak element of the system.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy systems, and in particular relates to a method for identifying weak components of an integrated energy system taking fault propagation into consideration. Background Art

[0002] In the modern energy system, the integrated energy system, as a highly integrated energy supply network, integrates multiple subsystems such as natural gas system, power system and thermal system, and realizes the complementarity and synergy of multiple energy forms. Such systems contain a large number of different types of components, such as generators, transformers, natural gas pipelines, gas turbines, heat pumps, etc., which play a vital role in the system. However, the impact of failures of different components on the reliability of the entire system varies significantly. In recent years, with the large-scale access of equipment such as natural gas units, gas boilers and heat pumps, and the gradual maturity of power-to-gas (P2G) technology, the coupling between the three subsystems has become increasingly deepened, forming a complex and interdependent energy network.

[0003] In this highly coupled integrated energy system, once a component fails, its impact often crosses the subsystem boundaries and continues to spread in the natural gas, electricity and thermal systems, triggering a series of chain reactions. This fault propagation may not only lead to new load loss risks or equipment failures, but also cause greater load losses and energy waste, seriously affecting the overall reliability and stability of the system. Therefore, in-depth research on the fault propagation mechanism within the integrated energy system is of great significance for improving the system's energy supply reliability and timely discovering and repairing weak components.

[0004] However, there are still many deficiencies in the current research on the identification of weak components in integrated energy systems. A significant problem is that most of these studies ignore the interdependence between subsystems and fail to fully consider the impact of fault propagation across subsystems on the reliability of integrated energy systems. This leads to the inability to accurately evaluate the actual role of each component in the fault propagation process when identifying weak components, and thus it is difficult to quantify its contribution to the system reliability index. In addition, the existing research methods also have the problem of averaging the reliability index, that is, simply allocating the overall reliability index of the system to each component. This approach ignores the actual status and importance of each component in the system, which is easy to cause allocation errors and affect the accuracy and effectiveness of the identification results. The difficulty in solving this problem lies in the fact that the complexity and high coupling of the integrated energy system make it difficult to accurately simulate and predict the fault propagation process. At the same time, the role and status of each component in the system are different, and the impact of its fault on the system reliability is also very different, which makes it extremely difficult to quantify the contribution of the 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 on the basis of fully considering the interdependence of subsystems and the propagation of faults across subsystems, so as to effectively identify the weak components of the system, has become a problem that needs to be solved urgently. Summary of the invention

[0006] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for identifying weak components of an integrated energy system taking into account fault propagation. This method can accurately quantify the contribution of each component to the reliability index of the integrated energy system on the basis of fully considering the interdependence of subsystems and the propagation of faults across subsystems, thereby effectively identifying the weak components of the system.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] A method for identifying weak components of an integrated energy system considering fault propagation comprises the following steps:

[0009] S1. Build an integrated energy system for fault propagation simulation and weak component identification; integrated energy analysis includes natural gas system, power system and thermal system;

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

[0011] S3, sampling multiple components; for each component, taking it as the initial faulty component, and using the integrated energy system to perform fault propagation simulation to obtain a corresponding fault event, wherein the content of the fault event includes the faulty components involved in the fault propagation simulation process;

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

[0013] S5. For each component that fails during the fault propagation simulation, the contribution value of the component to the reliability index of each fault event is accumulated to obtain the contribution value of the component to the reliability index of the integrated energy system;

[0014] S6. Sort the components from large to small according to their 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. A comprehensive integrated energy system model is constructed by integrating the natural gas system, the power system, and the thermal system. This integration enables the system to more accurately reflect the complexity and interdependence of the actual energy network, providing a more realistic environment for subsequent fault propagation simulation and weak component identification. Compared with 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, making the analysis results more accurate and comprehensive. At the same time, by quantifying the reliability contribution values ​​of components and ranking them, this technical solution can more effectively identify the weak components of 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 is consistent with the actual operating conditions. This helps to improve the accuracy and reliability of the simulation results, making subsequent analysis closer to reality. By sampling multiple components as initial fault points and performing fault propagation simulation, the propagation path and impact range of the fault in the system can be fully captured. This simulation not only considers the failure of a single component, but also the propagation and impact of the fault between multiple subsystems, thereby more accurately evaluating the overall reliability of the system.

[0018] 3. For each fault event, the reliability contribution value of each component is calculated according to the preset method, realizing the quantitative evaluation of the role of the component in the fault event. This quantitative evaluation helps to more accurately identify the components that have a greater impact on the system reliability, and provides a basis for subsequent optimization and improvement. By accumulating the reliability contribution values ​​of the components in each fault event, the contribution values ​​of the components to the reliability index of the integrated energy system are obtained, and they are sorted from large to small according to the contribution values. This method can effectively identify the weak components in the system, that is, those components that have the greatest impact on the system reliability. Selecting the top n components in the sorting as weak components will help with subsequent key maintenance and optimization, thereby improving the overall reliability of the system.

[0019] In summary, the present invention can accurately quantify the contribution of each component to the reliability index of the integrated energy system on the basis of fully considering 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, using Monte Carlo sampling to determine an initial component, and using it as an initial faulty component;

[0022] S32, combining the integrated energy system, determining the location of the failed component;

[0023] S33, judging whether the fault continues to propagate. If the fault continues to propagate, advancing the evolution simulation of the simulation time step Δt based on the integrated energy system, and then returning to S32; if the fault propagation has stopped, recording the corresponding fault event, the content of the fault event includes the faulty components involved in the current evolution simulation, and then going to S34;

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

[0025] Such a setting can ensure the comprehensiveness of fault simulation. Through the Monte Carlo sampling method, components in the system can be randomly selected as initial fault points to simulate various possible fault conditions. This method can fully consider the uncertainty factors in the system and improve the accuracy and comprehensiveness of fault simulation.

[0026] 2. Dynamic capture of fault propagation can be achieved. Combined with the integrated energy system, the location of the faulty component can be determined in real time, and the evolutionary simulation can be advanced based on the simulation time step to dynamically capture 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, and provides strong support for subsequent fault analysis and processing. When the fault propagation stops, the corresponding fault event is recorded in detail, including all faulty components involved in the current evolutionary simulation. This provides important data support for subsequent fault analysis and identification of weak components, and helps to discover potential problems and weak links in the system.

[0027] 3. By judging whether the variance coefficient of 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 is continued to determine new initial components until it converges. This ensures the accuracy and credibility of the simulation results and provides a solid foundation for subsequent analysis and processing.

[0028] Preferably, in S33, when the output of the coupling device and the load reduction amount at the previous and next moments do not change, it is determined that the fault propagation stops;

[0029] In S33, the process of simulating the evolution of the time step Δt based on the integrated energy system advancement simulation 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, go to step 1; otherwise, go 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 device determined by the power system at time t, the natural gas load reduction at time t+Δt is calculated according to the natural gas system, and the natural gas supply of the natural gas unit and the gas boiler is determined;

[0032] Step 2: Based on the obtained natural gas unit power and the power system fault situation, determine whether a cascading failure occurs in the power system; when the unit output changes or a component failure occurs in the power system, the line power is redistributed and the power flow is transferred; when the power grid cascading failure simulation process ends, the load reduction at time t+Δt is calculated based on the power system, and the power supply of the electric drive gas source, P2G device and heat pump is determined;

[0033] Step 3: The thermal system calculates the thermal load reduction at time t+Δt based on the thermal power provided by the coupling elements including the gas boiler, heat pump and advanced adiabatic compressed air energy storage AA-CAES.

[0034] Such a setting can ensure the accuracy of fault propagation simulation. By simulating the mutual influence between the natural gas system, the power system and the thermal system, the propagation process of the fault in the integrated energy system can be accurately reflected. Taking into account the role of coupling equipment such as natural gas units, gas boilers, electric drive gas sources, P2G devices, heat pumps and AA-CAES, the simulation results are closer to reality.

[0035] 2. Whether the output of the coupled equipment and the amount of load reduction have changed is used as the criterion for judging whether the fault propagation has stopped. This not only meets the requirements of system stability, but also facilitates quick judgment during the simulation process.

[0036] 3. During the simulation, the occurrence of cascading failures 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 evaluated.

[0037] 4. Through the evolutionary simulation process, the reliability of the integrated energy system under fault conditions can be evaluated, including the stability, recovery capability and vulnerability of the components of the system. It helps to identify the weak links in the system and provide guidance for the optimal design and operation strategy of the system.

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

[0039] First, the current system topology is analyzed. If an island appears and the supply and demand power in the island is unbalanced, the unit output and load demand in the island are automatically balanced through automatic power generation control and low-frequency load reduction devices; the internal adjustment and balancing strategies of the island include: ① If the sum of the unit output is lower than the load, the unit first increases its output in proportion to its available residual output; if the sum of the maximum unit output is still lower than the load, the load is reduced in turn according to the power shortage ratio; ② If the sum of the unit output is greater than the load, the output should be reduced in proportion to match the load; if the sum of the minimum unit output is still greater than the load, the units are shut down in order from small to large according to their capacity;

[0040] Then, the line cascading fault simulation is carried out, and the DC power flow model is used to update each island power flow; among them, the hidden fault probability model is used to simulate the line cascading fault, and the calculation formula is as follows:

[0041]

[0042] Where p0 represents the probability of random failure; F line and Represent the current power and rated capacity of the line respectively; if a new line fault occurs, the line power is redistributed; otherwise, go to step 3.

[0043] Such a setting can achieve rapid recovery of power balance within the island. Through the synergy of automatic power generation control and low-frequency load reduction device, the output of the units in the island can be quickly adjusted and the load can be reduced to achieve a balance between supply and demand power. This helps to reduce the risk of power outages caused by power imbalance within the island and improve the stability and reliability of the system.

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

[0045] 3. It can ensure the accuracy of line cascading fault simulation. Using the DC power flow model to update the power flow of each island can more accurately reflect the power flow distribution of the system under different fault conditions. This helps to evaluate the impact of the fault on the system and provide a scientific basis for subsequent fault handling and recovery strategies.

[0046] 4. Through the hidden fault probability model, the line cascading fault simulation can take into account factors such as the current power and rated capacity of the line and predict the probability of line failure. This helps to discover potential risks in the system in advance and provide strong support for preventing the occurrence of faults.

[0047] 5. The flexibility of the fault handling process can be guaranteed. If a new line fault occurs during the simulation, the line power will be redistributed. This flexible fault handling process can ensure that the system can respond quickly to different fault conditions and improve the adaptability and robustness of the system.

[0048] Preferably, in S4, the preset method includes: for the fault event E j , calculate the contribution value L of the component i to its reliability index according to the following formula: j (k→i):

[0049]

[0050] H j (x i )=U i r i (E j );

[0051]

[0052] Where, L j (k→i) represents the response of component i to fault event E j Contribution value of reliability index; L j (x1,x2,…,x n ) indicates fault event E j The reliability index, x i Indicates the performance parameters of component i; H j (x i ) represents the reliability index L of component i j (x1,x2,…,x n )'s contribution; i represents the unavailability rate of component i; r i (E j ) represents the response of component i to fault event E j The contribution of the consequences; e represents E j The sub-event does not contain the event of component i, w(e) and v(e) represent the weight and failure consequence of sub-event e respectively; v(e∪{i}) represents the failure consequence obtained after adding component i to sub-event e; |e| represents the number of components in e, |E j | indicates E j The number of components in .

[0053] Such a setting can 1. accurately calculate the contribution of each component to the reliability index of a specific fault event. This quantitative evaluation helps to identify which components play a key role in the fault event, thus providing strong data support for subsequent fault analysis and optimization.

[0054] 2. Comprehensively consider the impact of components. This method not only considers the direct consequence contribution of components in the fault event, but also comprehensively considers the indirect impact of components in the fault propagation process through risk allocation weights and sub-event analysis. This makes the evaluation results more comprehensive and accurate, and can more truly reflect the actual role of components in the integrated energy system.

[0055] 3. Support complex system analysis. This method is suitable for 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.

[0056] Preferably, the objective function of the natural gas system is as follows:

[0057]

[0058] Where Δt represents the simulation time step; Q(x) represents the gas source node set; and They represent the output of electric driven gas source and the output of natural gas driven gas source respectively; c gw,x and c gp,x They represent the production costs of electric driven gas source and natural gas driven gas source respectively; m(x) represents the set of gas network nodes; and c curs,x They represent the load reduction amount and load reduction penalty coefficient of natural gas respectively;

[0059] The constraints of the natural gas system include equality constraints and inequality constraints;

[0060] The equality constraints include: If there is no fault between the natural gas pipelines xy, the following equality constraints are satisfied:

[0061]

[0062] In the formula, and represents the inflow and outflow flow of pipe xy; represents the natural gas production of the P2G device at node k at time t; Indicates gas load, represents the flow rate through the compressor pipeline, Indicates the amount of natural gas consumed by the compressor; represents the amount of natural gas stored in the pipeline at time t, represents the air pressure at node x at time t, L xy and D xyRespectively represent the length and inner diameter of the natural gas pipeline, ε g is the absolute roughness of the natural gas pipeline, Z is the compression factor of the natural gas, R c represents the specific gas constant of natural gas, T represents temperature, and λ g,xy represents the friction coefficient of the gas pipeline xy, w0 represents the initial flow rate of natural gas, c w represents the speed of sound; represents the air pressure of node y at time t, and sgn(Q) represents the activation function of flow Q.

[0063] If there is a fault between the natural gas pipeline xy at point m, the following equality constraint is satisfied:

[0064]

[0065] λ g,xm =λ g,xy ;

[0066]

[0067] λ g,my =λ g,xy ;

[0068]

[0069] Where κκ is the isentropic index of natural gas; represents the air pressure at node m at time t+1; represents the air pressure at node x at time t+1; and They represent the inflow and outflow flow of pipeline xm at time t respectively; g,xm The equation constraints for the friction coefficient coupled device representing the gas pipe xm include:

[0070]

[0071] In the above formula, represents the power generation of the natural gas unit connected to node x at time t; X pe Represents the state variable of the natural gas unit; represents the thermal power of the gas boiler connected to node x at time t, X pr represents the state variable of the gas boiler, η pe and η pr Respectively represent the conversion efficiency of natural gas units and gas boilers; Indicates gas load; represents the natural gas load reduction penalty coefficient;

[0072] Inequality constraints for natural gas systems include:

[0073]

[0074]

[0075] In the formula, represents the pressure at the outlet of pipe x at time t; represents the pressure at the inlet end of pipe y at time t; and They represent the initial flow rate and pressure at time t at the pipe x, γ cp Indicates the boost ratio, is a 0, 1 variable indicating the direction of the flow, which is 1 when Q>0 and 0 when Q<0; N is a theoretically infinite value, P x,max and P x,min They represent the maximum and minimum pressures at the pipe x, respectively, Q gw,max and Q gw,min Respectively represent the maximum and minimum output of the electric drive power supply, and Q gp,min They represent the maximum and minimum output of natural gas driven gas source, Q xy,max and Q xy,min They represent the maximum and minimum values ​​of the pipeline flow, respectively, and f cp,max and f cp,min Respectively represent the maximum and minimum values ​​of the compressor flow, X gw , X gp , X gb and X gc They are the state variables of electric drive gas source, natural gas drive gas source, pipeline and compressor respectively.

[0076] This setup optimizes natural gas production and distribution by building a complex but accurate mathematical model of the natural gas system. The model not only takes into account cost-effectiveness, but also fully considers the system's safety, flexibility and coupling relationship with other systems, providing strong support for the operation and management of the natural gas system.

[0077] Preferably, the objective function of the power system is as follows:

[0078]

[0079] Where p(k) represents the node set of coal-fired units; c pg,k represents the cost of coal-fired units; represents the power of coal-fired units; m(k) represents the set of power nodes; c curl,k Indicates the load penalty coefficient for power shedding; represents the amount of electricity load reduction; w(k) represents the wind turbine node set; c curw,kIndicates the penalty coefficient for curtailing wind power; represents the amount of wind power curtailment;

[0080] The equality constraints of the power system include:

[0081]

[0082] In the above formula, represents the power generation of the wind turbine connected to node k at time t, Indicates the amount of wind power cut. represents the power of coal-fired units, represents the power generation at time t in the expansion cycle, represents the total power consumption at time t in the compression cycle, Indicates the amount of electrical load reduction, Indicates the electrical load. Indicates the power line flow; b kj represents line reactance, θ represents node phase angle; κ represents air specific heat ratio, represents the specific heat capacity of air, N c and N e Represent the number of compressor and expander stages, and denote the air mass flow rate at time t in the compression and expansion cycles, respectively, and denote the inlet and outlet temperatures of the i-th stage compressor, respectively, and represent the inlet and outlet temperatures of the i-th stage expander at time t respectively; and η c denote the compression ratio and adiabatic efficiency of the i-th stage compressor, respectively. and η e They are respectively represented as the i-th stage expander and adiabatic efficiency; represents the water mass flow rate entering the compressor at time t during the compression process, represents the specific heat capacity of water; T water,cold Indicates the cold water temperature in the low temperature heat storage tank, represents the hot water temperature at the outlet of the i-th stage compressor; represents the hot water temperature of the high-temperature heat storage tank at time t, represents the efficiency of the heat exchanger, represents the total hot water mass flow rate flowing into the high-temperature heat storage tank at time t, is the quality of hot water in the high-temperature heat storage tank at time t, represents the total hot water mass flow rate flowing into the heat exchanger in the expansion cycle at time t, Indicates the water temperature change, represents the thermal power provided to the user, represents the air pressure in the air storage chamber at time t, R g is the universal gas constant, T AT and V AT Respectively represent the air pressure and volume of the air storage chamber; represents the power generation of the natural gas unit connected to node k at time t; T storage,in Indicates the temperature of hot water flowing into the high-temperature heat storage tank; represents the state variables of the expansion subsystem; Represents the state variables of the compression subsystem.

[0083] The equality constraints for coupled devices include:

[0084]

[0085] In the formula, represents the natural gas production of the P2G device at node k at time t, X p2g Indicates the operating status of the P2G device, η p2g Indicates the conversion factor of the P2G device, Indicates the amount of electrical load reduction, Indicates the amount of electrical load; represents the natural gas production of the electric driven gas source at node k at time t, X gw is the state variable of the electric drive gas source, η gw Indicates the conversion factor of the electrically driven gas source equipment; represents the thermal power of the heat pump equipment at node k at time t, X pm Indicates the operating status of the heat pump equipment, η pm Indicates the conversion factor of the heat pump equipment;

[0086] The inequality constraints of the power system include:

[0087]

[0088]

[0089] In the formula, F kj,max Indicates the upper limit of the power line transmission capacity, PG k,max and PG k,min They represent the upper and lower limits of the output of coal-fired units, respectively. pf and X pg are the state variables of power lines and coal-fired units, and are 0-1 variables for the compression cycle and expansion cycle states, respectively, com,max and P com,min Represent the maximum and minimum power consumption, P exp,max and Pexp,min Respectively represent the maximum and minimum values ​​of the generated power, T s,max and T s,min Respectively represent the maximum and minimum values ​​of hot water temperature, M storage,max and M storage,min Represent the maximum and minimum water storage capacity, Indicates the maximum value of the thermal power provided to the user, P AT,max and P AT,min They respectively represent the maximum and minimum values ​​of the air pressure in the air storage chamber.

[0090] This setup, by building a complex power system model, achieves multiple effects such as economic optimization, power supply and demand balance, efficient equipment operation, system safety and stability, flexibility and scalability, etc. 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.

[0091] Preferably, the objective function of the thermal system is as follows:

[0092]

[0093] Where m(e) represents the set of heating network nodes; c curh,e Indicates the heat load penalty coefficient; and denote the annual and seasonal heat load reductions, respectively;

[0094] The equality constraints for the thermal system include:

[0095]

[0096] In the formula, Indicates the quality of hot water flowing out of the pipe. is the working fluid quality of the water supply pipeline numbered l at different time periods, and are the outlet temperature and inlet temperature of the water supply pipe, respectively. For The mass of hot water injected into the pipeline during the period t, ρ w , A l and L l represent the water density, the cross-sectional area of ​​the pipe, and the length of the pipe, respectively. Indicates the outlet temperature of the water delivery pipe, T e , and c represent the ambient temperature, the thermal conductivity of the pipe l and the specific heat capacity of water, respectively. is the temperature at the water delivery pipe node m, and They represent the seasonal and annual heat load power of the building at time t, is the indoor temperature of the building at time t, R is the equivalent thermal resistance of the building, and C is the equivalent heat capacity; and Represent the seasonal and annual heat load reduction, T com,max and T com,min The upper and lower limits of the suitable temperature in the building. and They represent the power generation of the natural gas unit and gas boiler connected to node x at time t, represents the thermal power of the heat pump equipment at node k at time t, n(e) is the set of pipes at the heat source outlet, m(e) represents the set of heat network nodes, and s(e) is the set of pipes at the heat exchanger inlet; Indicates the time it takes for hot water to reach the outlet from the water inlet.

[0097] The inequality constraints for the thermal system include:

[0098]

[0099] Where, T s,max and T s,min Respectively represent the upper and lower limits of the pipe temperature, Indicates the annual load power.

[0100] This setting, by constructing a thermal system optimization model, achieves multiple effects such as optimizing heat load management, improving heat pipe transmission efficiency, meeting building heat load demand, system safety and stability, and improving energy 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.

[0101] Preferably, the reliability index includes an expected value of insufficient electric power supply EENS and an electric load reduction probability LOELP:

[0102]

[0103] Where, T ra is the actual number of simulated years; ML is the number of power load loss states, D m is the duration of the mth load loss state, is the power load loss at time k under the mth load loss state;

[0104] It also includes the expected value of natural gas supply shortage EGNS and the probability of gas load reduction LOGLP:

[0105]

[0106] Where MG is the state number of natural gas load loss, is the natural gas load loss amount at time k under the mth load loss state;

[0107] It also includes the expected value of heat shortage EHNS and the heat load reduction probability LOHLP:

[0108]

[0109] Where MH is the state number of thermal load loss, and are the annual and seasonal thermal load loss at time k under the mth load loss state;

[0110] It also includes the expected value of natural gas leakage EGL and the probability of natural gas leakage PGL:

[0111]

[0112] Where MG is the state number of natural gas leakage, is the natural gas leakage at time k under the mth leakage fault state;

[0113] It also includes the expected value of hot water leakage EWL and the probability of hot water leakage PWL:

[0114]

[0115] Where MK is the state number of hot water leakage, is the amount of hot water leakage at time k under the mth leakage fault state;

[0116] It also includes the number of outage components indicator NOFC:

[0117]

[0118] Where MC is the number of states where failure occurs, prob j is the probability of failure state, N j is the number of outage components in the jth fault propagation state, J j is the initial number of faulty components in this state; when N j >I j When , it indicates that the fault propagation scale is expanding.

[0119] With this setup, these reliability indicators together form a complete framework for reliability assessment of energy systems and their components, which helps to comprehensively and objectively assess the reliability level of the system. By regularly monitoring and analyzing these indicators, potential risks and problems in the system can be discovered in a timely manner, providing a scientific basis for the formulation of preventive measures, emergency plans, and optimization of system configuration. These indicators also provide an important reference for the planning, design, operation, and maintenance of energy systems, helping to improve the overall reliability, safety, and stability of the system.

[0120] In summary, through a series of reliability indicators, a comprehensive and objective assessment of the reliability of the energy system and its components is conducted, providing strong support for the optimized operation and sustainable development of the system.

[0121] Preferably, in S2, the initialized operating parameters include natural gas flow, power load, and thermal demand; the calculated initial variable values ​​include natural gas pipeline storage, gas pressure in the gas storage chamber of the advanced adiabatic compressed air energy storage AA-CAES, and the temperature of the heat network pipeline.

[0122] With this setting, the system can more accurately simulate actual operating conditions by initializing key operating parameters such as natural gas flow, power load, and heat demand. These parameters are the basis for normal system operation and fault analysis, and their accuracy directly affects the reliability of subsequent simulation and analysis. The initialized operating parameters and calculated initial variable values ​​are not only used for fault simulation, but also for system performance evaluation. By comparing with actual operating data, key performance indicators such as energy efficiency, reliability, and stability of the system can be evaluated, providing a basis for system optimization design and improvement.

[0123] It can also ensure the accuracy of fault simulation. The accurate calculation of initial variable values ​​(such as natural gas pipeline storage, AA-CAES gas storage chamber pressure, and heat 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 the fault in the system, thereby improving the accuracy of fault simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0124] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0125] Figure 1 A flowchart of the method is shown in FIG.

[0126] Figure 2 This is a schematic diagram of the fault propagation process of the integrated energy system in Example 1;

[0127] Figure 3 It is a schematic diagram of the dynamic process of simulating the propagation of faults in the integrated energy system in the first embodiment;

[0128] Figure 4 This is the example network diagram in Example 2. DETAILED DESCRIPTION

[0129] The following is a further detailed description through specific implementation methods:

[0130] Embodiment 1

[0131] like Figure 1 As shown, this embodiment discloses a method for identifying weak components of an integrated energy system considering fault propagation, comprising the following steps:

[0132] S1. Build an integrated energy system for fault propagation simulation and weak component identification; integrated energy analysis includes natural gas system, power system and thermal system.

[0133] For ease of understanding, the fault propagation of the integrated energy system is explained as follows.

[0134] 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, and this impact is repeatedly propagated and amplified between the gas system and the power system and transferred to the thermal system.

[0135] Establish the fault propagation process of the integrated energy system. Figure 2 As shown in Figure 2, it specifically includes the following two propagation processes:

[0136] (1) Due to pipeline quality problems, human factors, and natural disasters, the natural gas system may experience component failures. Natural gas system dispatchers need to take measures such as gas source output adjustment and natural gas load reduction to ensure the reliable operation of the natural gas system. When the maximum gas source supply cannot meet the gas load, the natural gas supply to the natural gas units and gas boilers will be reduced first. The reduction in natural gas load reduces the power generation of natural gas units, further causing power load reduction, and the fault disturbance is transmitted from the natural gas system to the power system.

[0137] (2) The power flow distribution of the power system will change due to the reduction of power output of the natural gas unit or the failure of a component in the power system. The power system will divide the system into islands of different sizes according to the fault location. The system automatically balances the unit output and load demand in the island through automatic power generation control and low-frequency load reduction devices. If the line flow in the island exceeds the limit, the corresponding line may be shut down. When the power system reaches a stable state after the fault, the power system needs to self-dispatch all generators and loads to achieve the overall optimal operation. If the power regulation of the generator set still cannot achieve the source-load balance of the power system, the power supply of P2G, electric-driven gas source and heat pump will be reduced first. The reduction in power supply forces the electric-driven gas source and P2G equipment to reduce their output, more natural gas loads are reduced, and the fault disturbance is transmitted 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.

[0138] The present invention simulates the dynamic process of fault propagation in the integrated energy system on a unified time frame, and promotes time evolution with a simulation time step Δt. Figure 3 As shown. It is worth noting that between the simulation steps Δt that are alternately transmitted by each subsystem, there is also variable transmission in the subsystem self-scheduling model. For example, the natural gas subsystem needs to transmit the current gas source output and pipeline inventory; the power subsystem needs to transmit the output of the coal-fired unit and the state of the advanced adiabatic compressed air energy storage (including the gas storage chamber pressure, the high-temperature heat storage tank temperature and the hot water storage); the thermal subsystem needs to transmit the pipeline temperature and the indoor temperature.

[0139] In specific implementation, the objective function of the natural gas system is as follows:

[0140]

[0141] Where Δt represents the simulation time step; Q(x) represents the gas source node set; and They represent the output of electric driven gas source and the output of natural gas driven gas source respectively; c gw,x and c gp,x They represent the production costs of electric driven gas source and natural gas driven gas source respectively; m(x) represents the set of gas network nodes; and c curs,x They represent the load reduction amount and load reduction penalty coefficient of natural gas respectively;

[0142] The constraints of the natural gas system include equality constraints and inequality constraints;

[0143] The equality constraints include: If there is no fault between the natural gas pipelines xy, the following equality constraints are satisfied:

[0144]

[0145]

[0146] In the formula, and represents the inflow and outflow flow of pipe xy; represents the natural gas production of the P2G device at node k at time t; Indicates gas load, represents the flow rate through the compressor pipeline, Indicates the amount of natural gas consumed by the compressor; represents the amount of natural gas stored in the pipeline at time t, represents the air pressure at node x at time t, L xy and D xy Respectively represent the length and inner diameter of the natural gas pipeline, ε g is the absolute roughness of the natural gas pipeline, Z is the compression factor of the natural gas, R c represents the specific gas constant of natural gas, T represents temperature, and λ g,xy represents the friction coefficient of the gas pipeline xy, w0 represents the initial flow rate of natural gas, c w represents the speed of sound; represents the air pressure of node y at time t, and represents the activation function of flow Q.

[0147] If there is a fault between the natural gas pipeline xy at point m, the following equality constraint is satisfied:

[0148]

[0149] λ g,xm =λ g,xy ;

[0150]

[0151] λ g,my =λ g,xy ;

[0152]

[0153] Where κκ is the isentropic index of natural gas; represents the air pressure at node m at time t+1; represents the air pressure at node x at time t+1; and They represent the inflow and outflow flow of pipeline xm at time t respectively; g,xm Indicates the friction coefficient of the gas pipeline xm.

[0154] The equality constraints for coupled devices include:

[0155]

[0156] In the above formula, represents the power generation of the natural gas unit connected to node x at time t; X pe Represents the state variable of the natural gas unit; represents the thermal power of the gas boiler connected to node x at time t, X pr represents the state variable of the gas boiler, η pe and η pr Respectively represent the conversion efficiency of natural gas units and gas boilers; Indicates gas load; represents the natural gas load reduction penalty coefficient;

[0157] Inequality constraints for natural gas systems include:

[0158]

[0159]

[0160] In the formula, represents the pressure at the outlet of pipe x at time t; It represents the pressure near the gas end at time t at position y in the pipeline; and They represent the initial flow rate and pressure at time t at the pipe x, γ cp Indicates the boost ratio, is a 0, 1 variable indicating the direction of the flow, which is 1 when Q>0 and 0 when Q<0; N is a theoretically infinite value, P x,max and P x,min They represent the maximum and minimum pressures at the pipe x, respectively, Q gw,max and Q gw,min Respectively represent the maximum and minimum output of the electric drive power supply, and Q gp,min They represent the maximum and minimum output of natural gas driven gas source, Q xy,max and Q xy,min They represent the maximum and minimum values ​​of the pipeline flow, respectively, and f cp,max and f cp,min Respectively represent the maximum and minimum values ​​of the compressor flow, X gw , X gp , X gb and X gc They are the state variables of electric drive gas source, natural gas drive gas source, pipeline and compressor respectively.

[0161] In this way, by constructing a complex but accurate mathematical model of the natural gas system, the optimization of natural gas production and distribution is achieved. This model not only takes into account cost-effectiveness, but also fully considers the system's safety, flexibility and coupling relationship with other systems, providing strong support for the operation and management of the natural gas system.

[0162] The objective function of the power system is as follows:

[0163]

[0164] Where p(k) represents the node set of coal-fired units; c pg,k represents the cost of coal-fired units; represents the power of coal-fired units; m(k) represents the set of power nodes; c curl,k Indicates the load penalty coefficient for power shedding; represents the amount of electricity load reduction; w(k) represents the wind turbine node set; c curw,k Indicates the penalty coefficient for curtailing wind power; represents the amount of wind power curtailment;

[0165] The equality constraints of the power system include:

[0166]

[0167] In the above formula, represents the power generation of the wind turbine connected to node k at time t, Indicates the amount of wind power cut. represents the power of coal-fired units, represents the power generation at time t in the expansion cycle, represents the total power consumption at time t in the compression cycle, Indicates the amount of electrical load reduction, Indicates the electrical load. Indicates the power line flow; b kj represents line reactance, θ represents node phase angle; κ represents air specific heat ratio, represents the specific heat capacity of air, N c and N e Represent the number of compressor and expander stages, and denote the air mass flow rate at time t in the compression and expansion cycles, respectively, and denote the inlet and outlet temperatures of the i-th stage compressor, respectively, and represent the inlet and outlet temperatures of the i-th stage expander at time t respectively; and η c denote the compression ratio and adiabatic efficiency of the i-th stage compressor, respectively. and η e They are respectively represented as the i-th stage expander and adiabatic efficiency; represents the water mass flow rate entering the compressor at time t during the compression process, represents the specific heat capacity of water; T water,cold Indicates the cold water temperature in the low temperature heat storage tank, represents the hot water temperature at the outlet of the i-th stage compressor; represents the hot water temperature of the high-temperature heat storage tank at time t, represents the efficiency of the heat exchanger, represents the total hot water mass flow rate flowing into the high-temperature heat storage tank at time t, is the quality of hot water in the high-temperature heat storage tank at time t, represents the total hot water mass flow rate flowing into the heat exchanger in the expansion cycle at time t, Indicates the water temperature change, represents the thermal power provided to the user, represents the air pressure in the air storage chamber at time t, R g is the universal gas constant, T AT and V AT Respectively represent the air pressure and volume of the air storage chamber; represents the power generation of the gas unit connected to node k at time t; T storage,in Indicates the temperature of hot water flowing into the high-temperature heat storage tank; represents the state variables of the expansion subsystem; Represents the state variables of the compression subsystem.

[0168] The equality constraints for coupled devices include:

[0169]

[0170] In the formula, represents the natural gas production of the P2G device at node k at time t, X p2g Indicates the operating status of the P2G device, η p2g Indicates the conversion factor of the P2G device, Indicates the amount of electrical load reduction, Indicates the amount of electrical load; represents the natural gas production of the electric driven gas source at node k at time t, X gw is the state variable of the electric drive gas source, η gw Indicates the conversion factor of the electrically driven gas source equipment; represents the thermal power of the heat pump equipment at node k at time t, X pm Indicates the operating status of the heat pump equipment, η pm Indicates the conversion factor of the heat pump equipment;

[0171] The inequality constraints of the power system include:

[0172]

[0173] In the formula, F kj,max Indicates the upper limit of the power line transmission capacity, PG k,max and PG k,min They represent the upper and lower limits of the output of coal-fired units, respectively. pf and X pg are the state variables of power lines and coal-fired units, and are 0-1 variables for the compression cycle and expansion cycle states, respectively, com,max and P com,min Represent the maximum and minimum power consumption, P exp,max and P exp,min Respectively represent the maximum and minimum values ​​of the generated power, T s,max and T s,min Respectively represent the maximum and minimum values ​​of hot water temperature, M storage,max and M storage,min Represent the maximum and minimum water storage capacity, Indicates the maximum value of the thermal power provided to the user, P AT,max and P AT,min They respectively represent the maximum and minimum values ​​of the air pressure in the air storage chamber.

[0174] In this way, by building a complex power system model, we can achieve economic optimization, power supply and demand balance, efficient equipment operation, system safety and stability, flexibility and scalability, etc. 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.

[0175] The objective function of the thermal system is shown as follows:

[0176]

[0177] Where m(e) represents the set of heating network nodes; c curh,e Indicates the heat load penalty coefficient; and denote the annual and seasonal heat load reductions, respectively;

[0178] The equality constraints for the thermal system include:

[0179]

[0180] In the formula, Indicates the quality of hot water flowing out of the pipe. is the working fluid quality of the water supply pipeline numbered l at different time periods, and are the outlet temperature and inlet temperature of the water supply pipe, respectively. For The mass of hot water injected into the pipeline during the period t, ρ w , A l and L l represent the water density, the cross-sectional area of ​​the pipe, and the length of the pipe, respectively. Indicates the outlet temperature of the water delivery pipe, T e , and c represent the ambient temperature, the thermal conductivity of the pipe l and the specific heat capacity of water, respectively. is the temperature at the water delivery pipe node m, and They represent the seasonal and annual heat load power of the building at time t, is the indoor temperature of the building at time t, R is the equivalent thermal resistance of the building, and C is the equivalent heat capacity; and Represent the seasonal and annual heat load reduction, T com,max and T com,min The upper and lower limits of the suitable temperature in the building. and They represent the power generation of the natural gas unit and gas boiler connected to node x at time t, represents the thermal power of the heat pump equipment at node k at time t, n(e) is the set of pipes at the heat source outlet, m(e) represents the set of heat network nodes, and s(e) is the set of pipes at the heat exchanger inlet; Indicates the time it takes for hot water to reach the outlet from the water inlet.

[0181] The inequality constraints for the thermal system include:

[0182]

[0183] Where, T s,max and T s,min Respectively represent the upper and lower limits of the pipe temperature, Indicates the annual load power.

[0184] In this way, by constructing a thermal system optimization model, we have achieved multiple effects, including optimization of thermal load management, improvement of thermal pipeline transmission efficiency, satisfaction of building thermal load demand, system safety and stability, and improvement of 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.

[0185] S2. Based on the actual parameter data, the operating parameters of the integrated energy system are initialized and the initial variable values ​​are calculated.

[0186] During the specific implementation, the initialized operating parameters include natural gas flow, power load, and thermal demand; the calculated initial variable values ​​include natural gas pipeline storage, gas pressure in the storage chamber of advanced adiabatic compressed air energy storage AA-CAES, and temperature of the heat network pipeline.

[0187] In this way, by initializing key operating parameters such as natural gas flow, power load, and heat demand, the system can simulate the actual operating conditions more accurately. These parameters are the basis for normal operation and fault analysis of the system, and their accuracy directly affects the reliability of subsequent simulation and analysis. The initialized operating parameters and calculated initial variable values ​​are not only used for fault simulation, but also for system performance evaluation. By comparing with the actual operating data, key performance indicators such as energy efficiency, reliability, and stability of the system can be evaluated, providing a basis for the optimization design and improvement of the system. The accuracy of fault simulation can also be guaranteed. The accurate calculation of initial variable values ​​(such as natural gas pipeline storage, AA-CAES gas storage chamber pressure, and heat 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.

[0188] S3. Sampling multiple components; for each component, taking it as the initial faulty component, and using the integrated energy system to perform fault propagation simulation to obtain a corresponding fault event, the content of the fault event includes the faulty components involved in the fault propagation simulation process.

[0189] In specific implementation, S3 includes:

[0190] S31, using Monte Carlo sampling to determine an initial component, and using it as an initial faulty component;

[0191] S32, combining the integrated energy system, determining the location of the failed component;

[0192] S33, judging whether the fault continues to propagate. If the fault continues to propagate, advancing the evolution simulation of the simulation time step Δt based on the integrated energy system, and then returning to S32; if the fault propagation has stopped, recording the corresponding fault event, the content of the fault event includes the faulty components involved in the current evolution simulation, and then going to S34;

[0193] S34, determine whether the variance coefficient of Monte Carlo sampling has converged; if not, go to S31 and use Monte Carlo sampling again to determine new initial components; if converged, go to S4; wherein, when the variance coefficient is less than a preset threshold, it is determined to have converged.

[0194] In this way, the comprehensiveness of fault simulation can be guaranteed. Through the Monte Carlo sampling method, components in the system can be randomly selected as initial fault points to simulate various possible fault conditions. This method can fully consider the uncertainty factors in the system and improve the accuracy and comprehensiveness of fault simulation. It can also realize the dynamic capture of fault propagation. Combined with the integrated energy system, it can determine the location of the faulty component in real time, and advance the evolutionary simulation based on the simulation time step to dynamically capture the propagation process of the fault in the system. This helps to gain a deep understanding of the propagation mechanism of the fault and its impact on the system, and provides strong support for subsequent fault analysis and processing. When the fault propagation stops, the corresponding fault event is recorded in detail, including all faulty components involved in the current evolutionary simulation. This provides important data support for subsequent fault analysis and weak component identification, and helps to discover potential problems and weak links in the system. In addition, by judging whether the variance coefficient of 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 is continued to be used to determine new initial components until convergence. This ensures the accuracy and credibility of the simulation results and provides a solid foundation for subsequent analysis and processing.

[0195] In specific implementation, in S33, when the output of the coupling device and the load reduction amount at the previous and next moments do not change, it is determined that the fault propagation stops;

[0196] In S33, the process of simulating the evolution of the time step Δt based on the integrated energy system advancement simulation includes:

[0197] 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, go to step 1; otherwise, go to step 2;

[0198] 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 device determined by the power system at time t, the natural gas load reduction at time t+Δt is calculated according to the natural gas system, and the natural gas supply of the natural gas unit and the gas boiler is determined;

[0199] Step 2: Based on the obtained natural gas unit power and the power system fault situation, determine whether a cascading failure occurs in the power system; when the unit output changes or a component failure occurs in the power system, the line power is redistributed and the power flow is transferred; when the power grid cascading failure simulation process ends, the load reduction at time t+Δt is calculated based on the power system, and the power supply of the electric drive gas source, P2G device and heat pump is determined;

[0200] Step 3: The thermal system calculates the thermal load reduction at time t+Δt based on the thermal power provided by the coupling elements including the gas boiler, heat pump and advanced adiabatic compressed air energy storage AA-CAES.

[0201] In this way, the accuracy of fault propagation simulation can be guaranteed. By simulating the mutual influence between the natural gas system, the power system and the thermal system, the propagation process of the fault in the integrated energy system can be accurately reflected. Taking into account the role of coupling devices such as natural gas units, gas boilers, electric drive gas sources, P2G devices, heat pumps and AA-CAES, the simulation results are closer to reality. In addition, whether the output of the coupling equipment and the load reduction amount change as the criterion for judging the cessation of fault propagation not only meets the requirements of system stability, but also facilitates rapid judgment during the simulation process. Moreover, during the simulation process, the occurrence of chain failures 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 degree of impact of the fault on the system can be evaluated. Through the evolutionary simulation process, the reliability of the integrated energy system under fault conditions can be evaluated, including the stability, recovery capability and vulnerability of the components of the system. It helps to identify the weak links in the system and provide guidance for the optimal design and operation strategy of the system.

[0202] In step 2, the process of line power redistribution includes:

[0203] First, the current system topology is analyzed. If an island appears and the supply and demand power in the island is unbalanced, the unit output and load demand in the island are automatically balanced through automatic power generation control and low-frequency load reduction devices; the internal adjustment and balancing strategies of the island include: ① If the sum of the unit output is lower than the load, the unit first increases its output in proportion to its available residual output; if the sum of the maximum unit output is still lower than the load, the load is reduced in turn according to the power shortage ratio; ② If the sum of the unit output is greater than the load, the output should be reduced in proportion to match the load; if the sum of the minimum unit output is still greater than the load, the units are shut down in order from small to large according to their capacity;

[0204] Then, the line cascading fault simulation is carried out, and the DC power flow model is used to update each island power flow; among them, the hidden fault probability model is used to simulate the line cascading fault, and the calculation formula is as follows:

[0205]

[0206] Where p0 represents the probability of random failure; F line and Represent the current power and rated capacity of the line respectively; if a new line fault occurs, the line power is redistributed; otherwise, go to step 3.

[0207] In this way, the power balance in the island can be quickly restored. Through the synergy of automatic power generation control and low-frequency load reduction device, the output of the units in the island can be adjusted quickly and the load can be reduced to achieve the balance of supply and demand power. This helps to reduce the risk of power outages caused by power imbalance in the island and improve the stability and reliability of the system. The unit output and load reduction strategy can also be optimized. The adjustment and balancing strategy within the island takes into account the actual situation of the unit output and load. By increasing the output or reducing the load in proportion, and shutting down the unit according to the capacity, the optimal allocation of resources is achieved. This helps to reduce unnecessary energy waste and improve energy utilization efficiency. In addition, the accuracy of line chain fault simulation can be guaranteed. The DC power flow model is used to update the power flow of each island, which can more accurately reflect the power flow distribution of the system under different fault conditions. This helps to evaluate the impact of the fault on the system and provide a scientific basis for subsequent fault handling and recovery strategies.

[0208] By simulating line cascading failures through a hidden fault probability model, we can take into account factors such as the current power and rated capacity of the line and predict the probability of line failure. This helps to discover potential risks in the system in advance and provides strong support for preventing failures. In addition, it can also ensure the flexibility of the fault handling process. If a new line fault occurs during the simulation, the line power is redistributed. This flexible fault handling process ensures that the system can respond quickly to different fault conditions and improves the adaptability and robustness of the system.

[0209] S4. For each fault event, calculate the contribution value of each component in the fault event to the reliability index of the fault event according to a preset method.

[0210] In specific implementation, the preset method includes: for the fault event E j , calculate the contribution value L of the component i to its reliability index according to the following formula: j (k→i):

[0211]

[0212] H j (x i )=U i r i (E j );

[0213]

[0214] Where, L j (k→i) represents the response of component i to fault event E j Contribution value of reliability index; L j (x1,x2,…,xn ) indicates fault event E j The reliability index, x i Indicates the performance parameters of component i; H j (x i ) represents the reliability index L of component i j (x1,x2,…,x n )'s contribution; i represents the unavailability rate of component i; r i (E j ) represents the response of component i to fault event E j The contribution of the consequences; e represents E j The sub-events of do not contain the event of component i, w(e) and v(e) represent the weight and failure consequence of sub-event e respectively; v(e∪{i}) represents the failure consequence obtained after adding component i to sub-event e;

[0215] |e| represents the number of elements in e, |E j | indicates E j The number of components in .

[0216] To better understand this method, the sub-events are described as follows. For example, fault event E {1，2} is caused by the failure of components 1 and 2. Then E {1，2} It includes three sub-events: event e where only component 1 fails {1} , only the event e of component 2 failure {2} , and without any faulty components

[0217] In this way, the contribution of each component to the reliability index of a specific fault event can be accurately calculated. This quantitative evaluation helps to identify which components play a key role in the fault event, thereby providing strong data support for subsequent fault analysis and optimization. The impact of components is also considered comprehensively. This method not only considers the direct consequence contribution of components in fault events, but also comprehensively considers the indirect impact of components in the fault propagation process through risk sharing weights and sub-event analysis. This makes the evaluation results more comprehensive and accurate, and can more truly reflect the actual role of components in the integrated energy system. In addition, it also supports complex system analysis. This method is suitable for 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, and provides an effective tool for improving system reliability and preventing faults.

[0218] In specific implementation, the reliability indicators include the expected value of insufficient electricity supply EENS and the probability of load reduction LOELP:

[0219]

[0220] Where, T ra is the actual number of simulated years; ML is the number of power load loss states, D m is the duration of the mth load loss state, is the power load loss at time k under the mth load loss state;

[0221] It also includes the expected value of natural gas supply shortage EGNS and the probability of gas load reduction LOGLP:

[0222]

[0223] Where MG is the state number of natural gas load loss, is the natural gas load loss amount at time k under the mth load loss state;

[0224] It also includes the expected value of heat shortage EHNS and the heat load reduction probability LOHLP:

[0225]

[0226] Where MH is the state number of thermal load loss, and are the annual and seasonal thermal load loss at time k under the mth load loss state;

[0227] It also includes the expected value of natural gas leakage EGL and the probability of natural gas leakage PGL:

[0228]

[0229] Where MG is the state number of natural gas leakage, is the natural gas leakage at time k under the mth leakage fault state;

[0230] It also includes the expected value of hot water leakage EWL and the probability of hot water leakage PWL:

[0231]

[0232] Where MK is the state number of hot water leakage, is the amount of hot water leakage at time k under the mth leakage fault state;

[0233] It also includes the number of outage components indicator NOFC:

[0234]

[0235] Where MC is the number of states where failure occurs, prob j is the probability of failure state, N jis the number of outage components in the jth fault propagation state, J j is the initial number of faulty components in this state; when N j >I j When , it indicates that the fault propagation scale is expanding.

[0236] These reliability indicators together constitute a complete framework for reliability assessment of energy systems and their components, which helps to comprehensively and objectively assess the reliability level of the system. By regularly monitoring and analyzing these indicators, potential risks and problems in the system can be discovered in a timely manner, providing a scientific basis for the formulation of preventive measures, emergency plans and optimization of system configuration. These indicators also provide an important reference for the planning, design, operation and maintenance of energy systems, and help improve the overall reliability, safety and stability of the system. Through a series of reliability indicators, the reliability of the energy system and its components is comprehensively and objectively evaluated, providing strong support for the optimized operation and sustainable development of the system.

[0237] S5. For each component that fails during the fault propagation simulation, the contribution value of the component to the reliability index of each fault event is accumulated to obtain the contribution value of the component to the reliability index of the integrated energy system;

[0238] S6. Sort the components from large to small according to their contribution to the reliability index of the integrated energy system, and select the top n components as weak components.

[0239] The present invention constructs a comprehensive integrated energy system model by integrating natural gas system, power system and thermal system. This integration enables the system to more accurately reflect the complexity and interdependence of the actual energy network, and provides a more realistic environment for subsequent fault propagation simulation and weak component identification. Compared with 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, making the analysis results more accurate and comprehensive. At the same time, by quantifying the reliability contribution values ​​of components and sorting them, the technical solution can more effectively identify the weak components of the system, providing strong support for subsequent maintenance and optimization. In addition, the system is initialized based on actual parameter data to ensure that the starting point of the simulation is consistent with the actual operating conditions. This helps to improve the accuracy and reliability of the simulation results, making the subsequent analysis closer to reality. By sampling multiple components as initial fault points and performing fault propagation simulation, the propagation path and impact range of the fault in the system can be fully captured. This simulation not only considers the failure of a single component, but also considers the propagation and impact of the fault between multiple subsystems, thereby more accurately evaluating the overall reliability of the system. In addition, for each fault event, the reliability contribution value of each component is calculated according to the preset method, realizing the quantitative evaluation of the role of the component in the fault event. This quantitative evaluation helps to more accurately identify the components that have a greater impact on the system reliability, and provides a basis for subsequent optimization and improvement. By accumulating the reliability contribution values ​​of the components in each fault event, the contribution values ​​of the components to the reliability index of the integrated energy system are obtained, and they are sorted from large to small according to the contribution values. This method can effectively identify the weak components in the system, that is, those components that have the greatest impact on the system reliability. Selecting the top n components in the sorting as weak components will help with subsequent key maintenance and optimization, thereby improving the overall reliability of the system.

[0240] In summary, the present invention can accurately quantify the contribution of each component to the reliability index of the integrated energy system on the basis of fully considering the interdependence of subsystems and the propagation of faults across subsystems, thereby effectively identifying the weak components of the system.

[0241] Embodiment 2

[0242] In order to better illustrate the effect of this method, the following specific example is used for illustration.

[0243] Taking the integrated energy system consisting of IEEE 30-node power system, Belgium 20-node natural gas system and 16-node thermal system as an example, the weak links are identified. The example network diagram is shown in Figure 4As shown. The gas source at node 14 in the natural gas system is a P2G device (W6), which obtains electricity from the power system node 21. The electrically driven gas sources (W2, W3 and W4) at natural gas nodes 2, 5 and 8 obtain electricity from power system nodes 17, 24 and 30 respectively, and W1 and W5 are natural gas driven gas sources. The gas boilers C1 to C2 of the thermal system obtain gas supply from natural gas nodes 15 and 6 respectively, the heat pump C3 obtains electricity from the power node 15, and the heat source C4 is supplied with thermal power by AA-CAES. The conversion coefficients of the P2G device and the electrically driven gas source are set to 0.088m3 / MW and 0.110m3 / MW respectively, and the conversion coefficient of the heat pump is set to 1.

[0244] Two scenarios are set to compare and analyze the weak components of the system under different coupling degrees. Scenario 1: Gas sources W1 to W6 are all natural gas driven gas sources. Scenario 2: Gas sources W2, W3 and W4 are electric driven gas sources, gas source W6 is a P2G device, and gas sources W1 and W5 are natural gas driven gas sources.

[0245] Table 1 gives the reliability indicators of the integrated energy system under two scenarios. It can be seen that when the fault propagation of the integrated energy system is considered, the reliability indicators EENS and LOELP of the power system are significantly increased. The EENS and LOLP of scenario 1 are 1.768×103MWh and 1.257×10-2, respectively, and scenario 2 increases to 3.437×103MWh and 1.396×10-2. Compared with scenario 1, the reliability indicators of the natural gas system have also increased significantly. For the thermal system, the reliability indicators EHNS and LOHLP of scenario 2 are slightly different from those in scenario 1. This is because the heat network pipeline has a transmission delay characteristic, and the fault propagation impact of the heat source will be delayed to the heat user, that is, the impact on the heat user during the fault propagation process is small. The expected number of outage components NOFC is 2.211×10-4 times and 1.449×10-2 times, respectively, indicating that as the degree of coupling between subsystems increases, the impact of fault propagation gradually increases, which will increase the number of outage components of the system.

[0246] Table 1 Impact of fault propagation on system reliability

[0247]

[0248] Tables 2 and 3 show the identification results of weak components sorted by adequacy index (EENS, EGNS), respectively, and analyze the impact of each system component failure on the propagation of load loss risk. In the table, EENS is the index value allocated to the component, and the top 10 components are selected as the weak components of the system.

[0249] Table 2 Comparison of weak components sorted by reliability index EENS

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[0251] As can be seen from Table 2, power line L14 is the weakest element with the highest impact on EENS indicators, and the risk sharing 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. Once it fails, it will cause a large amount of flow transfer. Flow transfer may cause the flow of multiple lines to exceed the limit and cause the corresponding lines to stop operating, resulting in a greater risk of load loss. On the other hand, since the failure of this line will cause the load reduction of the coupling node, the risk of load loss is transmitted to the natural gas system through the electric drive gas source and P2G equipment, thereby causing the fault propagation effect. In addition, it can be seen that the ranking of unit G5 and line L16 in scenario 2 has decreased, while the ranking of elements such as line L36 and line L26 has increased. This is because after the failure of line L36 and line L26, in addition to causing the coupling power node 30 (connected to gas source W4) and node 17 (connected to gas source W2) to lose load, it will also reduce the gas production of the electric drive gas source, thereby affecting the scheduling decision of the gas network and reducing the output of the natural gas unit. This is repeated, resulting in a large amount of power load loss risk. In scenario 1, the loss of load at the power node does not cause fault propagation effects. As important components for the natural gas system to transmit energy to the power system, unit G5 and line L16 will directly reduce the power supply of the power system if they fail. Therefore, the ranking of unit G5 and line L16 is relatively high in scenario 1.

[0252] Table 3 Comparison of weak components sorted by reliability index EGNS

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[0254] As can be seen from Table 3, when the fault propagation effect of the power system on the natural gas system is not considered, the top 10 weak components in scenario 1 are all natural gas pipelines and gas sources, indicating that the fault of the internal components of the natural gas subsystem has the greatest impact on the reduction of natural gas load. Take the pipeline B13 and B14 components as an example. As shown in Table 3, the EGNS shared by B13 and B14 in scenario 1 are 1.414×103m3 and 3.284×103m3 respectively. The impact of pipeline B14 on the propagation of natural gas load loss is significantly higher than that of other components. This is because when pipeline B14 breaks, it will directly cause the natural gas load of node 16 to be completely reduced. At the same time, since the natural gas load of node 16 is large, the EGNS index of pipeline B14 is ranked first. Although pipeline B13 and pipeline B14 are both under the one-way transmission condition, 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 time. That is, after the failure of pipeline 13, the pipeline storage of pipeline 14 can continue to supply natural gas to node 16, thereby reducing the corresponding gas load reduction. Therefore, the ranking of pipeline B13 is relatively low. In addition, pipeline B2 is ranked 2nd and 4th in the two scenarios, because pipeline B2 undertakes the important task of transmitting the gas output of gas sources W1 and W2. Once it fails, it means that the natural gas subsystem loses the supply of two gas sources, which will cause a large amount of natural gas load reduction.

[0255] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A method for identifying weak components of an integrated energy system considering fault propagation, characterized in that: The following steps are involved: S1. Build an integrated energy system for fault propagation simulation and weak component identification; Comprehensive energy analysis includes natural gas systems, electricity systems, and thermal systems; S2. Initialize the operating parameters of the integrated energy system based on the actual parameter data and calculate the initial variable values; S3, sampling multiple components; For each component, it is taken as the initial faulty component, 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 faulty components involved in the fault propagation simulation process; S4. For each fault event, calculate the contribution value of each component in the fault event to the reliability index of the fault event according to a preset method; S5. For each component that fails during the fault propagation simulation, the contribution value of the component to the reliability index of each fault event is accumulated to obtain the contribution value of the component to the reliability index of the integrated energy system; S6. Sort the components from large to small according to their contribution to the reliability index of the integrated energy system, and select the top n components as weak components.

2. The method for identifying weak components of an integrated energy system considering fault propagation according to claim 1, characterized in that: S3 includes: S31, using Monte Carlo sampling to determine an initial component, and using it as an initial faulty component; S32, combining the integrated energy system, determining the location of the failed component; S33, judging whether the fault continues to propagate. If the fault continues to propagate, advancing the evolution simulation of the simulation time step Δt based on the integrated energy system, and then returning to S32; if the fault propagation has stopped, recording the corresponding fault event, the content of the fault event includes the faulty components involved in the current evolution simulation, and then going to S34; S34, determine whether the variance coefficient of Monte Carlo sampling has converged; if not, go to S31 and use Monte Carlo sampling again to determine new initial components; if converged, go to S4; wherein, when the variance coefficient is less than a preset threshold, it is determined to have converged.

3. The method for identifying weak components of an integrated energy system considering fault propagation according to claim 2, characterized in that: In S33, when the output of the coupling device and the load reduction amount at the previous and next moments do not change, it is determined that the fault propagation stops; In S33, the process of simulating the evolution of the time step Δt based on the integrated energy system advancement simulation 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, go to step 1; otherwise, go 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 device determined by the power system at time t, the natural gas load reduction at time t+Δt is calculated according to the natural gas system, and the natural gas supply of the natural gas unit and the gas boiler is determined; Step 2: Based on the obtained natural gas unit power and the power system fault situation, determine whether a cascading failure occurs in the power system; when the unit output changes or a component failure occurs in the power system, the line power is redistributed and the power flow is transferred; when the power grid cascading failure simulation process ends, the load reduction at time t+Δt is calculated based on the power system, and the power supply of the electric drive gas source, P2G device and heat pump is determined; Step 3: The thermal system calculates the thermal load reduction at time t+Δt based on the thermal power provided by the coupling elements including the gas boiler, heat pump and advanced adiabatic compressed air energy storage AA-CAES.

4. The method for identifying weak components of an integrated energy system considering fault propagation as claimed in claim 3, characterized in that: In step 2, the process of line power redistribution includes: First, the current system topology is analyzed. If an island appears and the supply and demand power in the island is unbalanced, the unit output and load demand in the island are automatically balanced through automatic power generation control and low-frequency load reduction devices. Among them, the internal adjustment and balancing strategies of the island include: ① If the sum of the unit output is lower than the load, the unit first increases its output in proportion to its available residual output; if the sum of the maximum unit output is still lower than the load, the load is reduced in turn according to the power shortage ratio; ② If the sum of the unit output is greater than the load, the output should be reduced in proportion to match the load; if the sum of the minimum unit output is still greater than the load, the units are shut down in order from small to large according to their capacity; Then, the line cascading fault simulation is carried out, and the DC power flow model is used to update each island power flow; among them, the hidden fault probability model is used to simulate the line cascading fault, and the calculation formula is as follows: Where p0 represents the probability of random failure; F line and Represent the current power and rated capacity of the line respectively; if a new line fault occurs, the line power is redistributed; otherwise, go to step 3.

5. The method for identifying weak components of an integrated energy system considering fault propagation according to claim 1, characterized in that: In S4, the preset method includes: for the fault event E j , calculate the contribution value L of the component i to its reliability index according to the following formula: j (k→i): H j (x i )=U i r i (E j ); Where, L j (k→i) represents the response of component i to fault event E j Contribution value of reliability index; L j (x1,x2,…,x n ) indicates fault event E j The reliability index, x i Indicates the performance parameters of component i; H j (x i ) represents the reliability index L of component i j (x1,x2,…,x n )'s contribution; i represents the unavailability rate of component i; r i (E j ) represents the response of component i to fault event E j The contribution of the consequences; e represents E j The sub-events of do not contain the event of component i, w(e) and v(e) represent the weight and failure consequence of sub-event e respectively; v(e∪{i}) represents the failure consequence obtained after adding component i to sub-event e; |e| represents the number of elements in e, |E j | indicates E j The number of components in .

6. The method for identifying weak components of an integrated energy system considering fault propagation according to claim 1, characterized in that: The objective function of the natural gas system is as follows: Where Δt represents the simulation time step; Q(x) represents the gas source node set; and They represent the output of electric driven gas source and the output of natural gas driven gas source respectively; c gw,x and c gp,x They represent the production costs of electric driven gas source and natural gas driven gas source respectively; m(x) represents the set of gas network nodes; and c curs,x They represent the load reduction amount and load reduction penalty coefficient of natural gas respectively; The constraints of the natural gas system include equality constraints and inequality constraints; The equality constraints include: If there is no fault between the natural gas pipelines xy, the following equality constraints are satisfied: In the formula, and represents the inflow and outflow flow of pipe xy; represents the natural gas production of the P2G device at node k at time t; Indicates gas load, represents the flow rate through the compressor pipeline, Indicates the amount of natural gas consumed by the compressor; represents the amount of natural gas stored in the pipeline at time t, represents the air pressure at node x at time t, L xy and D xy Respectively represent the length and inner diameter of the natural gas pipeline, ε g is the absolute roughness of the natural gas pipeline, Z is the compression factor of the natural gas, R c represents the specific gas constant of natural gas, T represents temperature, and λ g,xy represents the friction coefficient of the gas pipeline xy, w0 represents the initial flow rate of natural gas, c w represents the speed of sound; Represents the air pressure at node y at time t. If there is a fault between the natural gas pipeline xy at point m, the following equality constraint is satisfied: l g,xm =λ g,xy ; l g,my =λ g,xy ; Where κκ is the isentropic index of natural gas; represents the air pressure at node m at time t+1; represents the air pressure at node x at time t+1; and They represent the inflow and outflow flow of pipeline xm at time t respectively; g,xm Indicates the friction coefficient of the gas pipeline xm; The equality constraints for coupled devices include: In the above formula, represents the power generation of the natural gas unit connected to node x at time t; X pe Represents the state variable of the natural gas unit; represents the thermal power of the gas boiler connected to node x at time t, X pr represents the state variable of the gas boiler, η pe and η pr Respectively represent the conversion efficiency of natural gas units and gas boilers; Indicates gas load; represents the natural gas load reduction penalty coefficient; Inequality constraints for natural gas systems include: In the formula, represents the pressure at the outlet of pipe x at time t; represents the pressure at the inlet end of pipe y at time t; and They represent the initial flow rate and pressure at time t at the pipe x, γ cp Indicates the boost ratio, is a 0, 1 variable indicating the direction of the flow, which is 1 when Q>0 and 0 when Q<0; N is a theoretically infinite value, P x,max and P x,min They represent the maximum and minimum pressures at the pipe x, respectively, Q gw,max and Q gw,min Respectively represent the maximum and minimum output of the electric drive power supply, and Q gp,min They represent the maximum and minimum output of natural gas driven gas source, Q xy,max and Q xy,min They represent the maximum and minimum values ​​of the pipeline flow, respectively, and f cp,max and f cp,min Respectively represent the maximum and minimum values ​​of the compressor flow, X gw , X gp , X gb and X gc They are the state variables of electric drive gas source, natural gas drive gas source, pipeline and compressor respectively.

7. The method for identifying weak components of an integrated energy system considering fault propagation according to claim 6, characterized in that: The objective function of the power system is as follows: Where p(k) represents the node set of coal-fired units; c pg,k represents the cost of coal-fired units; represents the power of coal-fired units; m(k) represents the set of power nodes; c curl,k Indicates the load penalty coefficient for power shedding; represents the amount of electricity load reduction; w(k) represents the wind turbine node set; c curw,k Indicates the penalty coefficient for curtailing wind power; represents the amount of wind power curtailment; The equality constraints of the power system include: In the above formula, represents the power generation of the wind turbine connected to node k at time t, Indicates the amount of wind power cut. represents the power of coal-fired units, represents the power generation at time t in the expansion cycle, represents the total power consumption at time t in the compression cycle, Indicates the amount of electrical load reduction, Indicates the electrical load. Indicates the power line flow; b kj represents line reactance, θ represents node phase angle; κ represents air specific heat ratio, represents the specific heat capacity of air, N c and N e Represent the number of compressor and expander stages, and denote the air mass flow rate at time t in the compression and expansion cycles, respectively, and denote the inlet and outlet temperatures of the i-th stage compressor, respectively, and represent the inlet and outlet temperatures of the i-th stage expander at time t respectively; and η c denote the compression ratio and adiabatic efficiency of the i-th stage compressor, respectively. and η e They are respectively represented as the i-th stage expander and adiabatic efficiency; represents the water mass flow rate entering the compressor at time t during the compression process, represents the specific heat capacity of water; T water,cold Indicates the cold water temperature in the low temperature heat storage tank, represents the hot water temperature at the outlet of the i-th stage compressor; represents the hot water temperature of the high-temperature heat storage tank at time t, θ represents the efficiency of the heat exchanger, represents the total hot water mass flow rate flowing into the high-temperature heat storage tank at time t, is the quality of hot water in the high-temperature heat storage tank at time t, represents the total hot water mass flow rate flowing into the heat exchanger in the expansion cycle at time t, Indicates the water temperature change, represents the thermal power provided to the user, represents the air pressure in the air storage chamber at time t, R g is the universal gas constant, T AT and V AT Respectively represent the air pressure and volume of the air storage chamber; represents the power generation of the natural gas unit connected to node k at time t; T storage,in Indicates the temperature of hot water flowing into the high-temperature heat storage tank; represents the state variables of the expansion subsystem; Represents the state variables of the compression subsystem. The equality constraints for coupled devices include: In the formula, represents the natural gas production of the P2G device at node k at time t, X p2g Indicates the operating status of the P2G device, η p2g Indicates the conversion factor of the P2G device, Indicates the amount of electrical load reduction, Indicates the amount of electrical load; represents the natural gas production of the electric driven gas source at node k at time t, X gw is the state variable of the electric drive gas source, η gw Indicates the conversion factor of the electrically driven gas source equipment; represents the thermal power of the heat pump equipment at node k at time t, X pm Indicates the operating status of the heat pump equipment, η pm Indicates the conversion factor of the heat pump equipment; The inequality constraints of the power system include: In the formula, F kj,max Indicates the upper limit of the power line transmission capacity, PG k,max and PG k,min They represent the upper and lower limits of the output of coal-fired units, respectively. pf and X pg are the state variables of power lines and coal-fired units, and are 0-1 variables for the compression cycle and expansion cycle states, respectively, com,max and P com,min Represent the maximum and minimum power consumption, P exp,max and P exp,min Respectively represent the maximum and minimum values ​​of the generated power, T s,max and T s,min Respectively represent the maximum and minimum values ​​of hot water temperature, M storage,max and M storage,min Represent the maximum and minimum water storage capacity, Indicates the maximum value of the thermal power provided to the user, P AT,max and P AT,min They respectively represent the maximum and minimum values ​​of the air pressure in the air storage chamber.

8. The method for identifying weak components of an integrated energy system considering fault propagation according to claim 7, characterized in that: The objective function of the thermal system is shown as follows: Where m(e) represents the set of heating network nodes; c curh,e Indicates the heat load penalty coefficient; and denote the annual and seasonal heat load reductions, respectively; The equality constraints for the thermal system include: In the formula, Indicates the quality of hot water flowing out of the pipe. is the working fluid quality of the water supply pipeline numbered l at different time periods, and are the outlet temperature and inlet temperature of the water supply pipe, For The mass of hot water injected into the pipeline during the period t, ρ w , A l and L l represent the water density, the cross-sectional area of ​​the pipe, and the length of the pipe, respectively. Indicates the outlet temperature of the water delivery pipe, T e , and c represent the ambient temperature, the thermal conductivity of the pipe l and the specific heat capacity of water, respectively. is the temperature at the water delivery pipe node m, and They represent the seasonal and annual heat load power of the building at time t, is the indoor temperature of the building at time t, R is the equivalent thermal resistance of the building, and C is the equivalent heat capacity; and Represent the seasonal and annual heat load reduction, T com,max and T com,min The upper and lower limits of the suitable temperature in the building. and They represent the power generation of the natural gas unit and gas boiler connected to node x at time t, represents the thermal power of the heat pump equipment at node k at time t, n(e) is the set of pipes at the heat source outlet, m(e) represents the set of heat network nodes, and s(e) is the set of pipes at the heat exchanger inlet; Indicates the time it takes for hot water to reach the outlet from the water inlet. The inequality constraints for the thermal system include: Where, T s,max and T s,min Respectively represent the upper and lower limits of the pipe temperature, Indicates the annual load power.

9. The method for identifying weak components of an integrated energy system considering fault propagation according to claim 8, characterized in that: Reliability indicators include the expected value of insufficient electricity supply (EENS) and the probability of load reduction (LOELP): Where, T ra is the actual number of simulated years; ML is the number of power load loss states, D m is the duration of the mth load loss state, is the power load loss at time k under the mth load loss state; It also includes the expected value of natural gas supply shortage EGNS and the probability of gas load reduction LOGLP: Where MG is the state number of natural gas load loss, is the natural gas load loss amount at time k under the mth load loss state; It also includes the expected value of heat shortage EHNS and the heat load reduction probability LOHLP: Where MH is the state number of thermal load loss, and are the annual and seasonal thermal load loss at time k under the mth load loss state; It also includes the expected value of natural gas leakage EGL and the probability of natural gas leakage PGL: Where MG is the state number of natural gas leakage, is the natural gas leakage at time k under the mth leakage fault state; It also includes the expected value of hot water leakage EWL and the probability of hot water leakage PWL: Where MK is the state number of hot water leakage, is the amount of hot water leakage at time k under the mth leakage fault state; It also includes the number of outage components indicator NOFC: Where MC is the number of states where failure occurs, prob j is the probability of failure state, N j is the number of outage components in the jth fault propagation state, J j is the initial number of faulty components in this state; when N j >I j When , it indicates that the fault propagation scale is expanding.

10. The method for identifying weak components of an integrated energy system considering fault propagation according to claim 1, characterized in that: In S2, the initialized operating parameters include natural gas flow, power load, and thermal demand; the calculated initial variable values ​​include natural gas pipeline storage, gas pressure in the gas storage chamber of the advanced adiabatic compressed air energy storage AA-CAES, and the temperature of the heat network pipeline.

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