A comprehensive energy system reliability assessment method considering pipeline leakage failures

Abstract

This invention belongs to the field of integrated energy system technology, and particularly relates to a reliability assessment method for integrated energy systems considering pipeline leakage faults. The method includes: S0, constructing an integrated energy system and building an optimal dynamic energy flow model for the integrated energy system considering leakage faults; S1, initializing the integrated energy system; S2, using a state duration sampling algorithm to extract the state time series sequences of all components, including the natural gas network and heating network pipelines; combining the time series sequences of all components to obtain the system state transition sequence; S3, for each obtained system state transition sequence, using the optimal dynamic energy flow model for the integrated energy system considering leakage faults to determine the energy load reduction amount; recording the load reduction amount and the duration of the load reduction state; S4, performing a convergence judgment; if the convergence condition is met, the iterative simulation is stopped. This method can improve the accuracy of reliability assessment of integrated energy systems by considering pipeline leakage faults.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system technology, and in particular relates to a reliability assessment method for integrated energy systems that takes into account pipeline leakage failures. Background Technology

[0002] In integrated energy systems, natural gas networks and heating pipelines serve as critical infrastructure, undertaking the vital tasks of energy transmission and distribution. However, due to their long transmission distances and prolonged exposure to complex environments, these pipelines are highly susceptible to corrosion from energy-carrying media, airborne media, and environmental microorganisms, leading to frequent leaks. Leaks not only cause the loss of energy-carrying media, reducing the effective supply of energy, but can also disrupt the balance of existing heating systems, affecting hot water usage and room heating efficiency for users. In severe cases, they can even lead to heating outages in some areas, causing significant inconvenience to residents.

[0003] Traditional fault models for natural gas and heating networks mostly employ simple two-state models, namely normal operation and fault state, without fully considering the crucial factor of leakage faults. This simplified model cannot accurately reflect pipeline fault modes, especially regarding the impact of leakage faults on system reliability and operational optimization, exhibiting significant limitations.

[0004] In reality, leakage and rupture failures differ significantly in their impact mechanisms. After a rupture failure occurs, the monitoring system can respond quickly, closing valves at both ends of the faulty pipeline and halting the normal transport of the energy-carrying medium. Leakage failures, however, are difficult to detect in a timely manner due to their smaller impact on pipeline pressure, and typically rely on manual patrols for detection. Before a leak is repaired, the faulty pipeline can continue to transport the energy-carrying medium and supply the load, but a small amount of the medium will continuously leak into the environment, resulting not only in energy waste but also potential environmental pollution.

[0005] Furthermore, leakage failures are far more frequent than fracture failures, occurring 3 to 10 times more often. Therefore, ignoring the impact of leakage failures in the reliability assessment of integrated energy systems could lead to significant errors in the assessment results, failing to accurately reflect the system's true operating status and reliability level.

[0006] Although a wealth of research has been conducted on pipeline failure risks, most of this work has focused on analyzing the consequences of failures and simulating leakage processes, with little attention paid to the impact of leakage failures on optimal scheduling and reliability assessment in integrated electro-gas-thermal models. More importantly, existing research largely fails to consider the dynamic energy flow equations during leakage failures, which to some extent limits the accuracy and precision of integrated energy system reliability assessments.

[0007] Therefore, how to improve the accuracy of reliability assessment of integrated energy systems while taking into account pipeline leakage failures has become an urgent problem to be solved. Summary of the Invention

[0008] To address the shortcomings of the existing technology, this invention provides a comprehensive energy system reliability assessment method that considers pipeline leakage faults, which can improve the accuracy of comprehensive energy system reliability assessment based on the consideration of pipeline leakage faults.

[0009] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0010] A comprehensive energy system reliability assessment method considering pipeline leakage failures includes the following steps:

[0011] S0. Construct an integrated energy system, which includes an electricity system, a natural gas system, and a heat system; and based on the integrated energy system, consider leakage faults in the natural gas network and heat network pipelines, as well as the impact of these faults on energy flow and distribution, and construct an optimal dynamic energy flow model for the integrated energy system that considers leakage faults.

[0012] S1. Input the topology data of the integrated energy system and the parameters of each component, and initialize the state of each component of the integrated energy system to the normal operating state;

[0013] S2. Based on the integrated energy system, the state duration sampling algorithm is used to extract the state time sequence of all components, including the natural gas network and heating network pipelines; the time sequence of all components is combined to obtain the system state transition sequence, and it is divided according to days;

[0014] S3. For each system state transition sequence obtained by division, the energy load reduction amount is determined by the optimal dynamic energy flow model of the integrated energy system considering leakage faults; the load reduction amount and the duration of the load reduction state are recorded until the number of recorded days reaches the preset simulation duration.

[0015] S4. Calculate the sample mean of the recorded load reduction amount and duration; calculate the variance convergence coefficient using the standard deviation of the sample mean, and perform a convergence judgment; if the convergence condition is met, stop the iterative simulation, and calculate the reliability index of the integrated energy system based on the recorded load reduction amount and load reduction duration; if the convergence condition is not met, return to S2 to continue the iterative simulation for the next simulation duration.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] 1. Accurately simulate the impact of pipeline leakage failures. Most existing fault models employ simple two-state models, failing to consider the impact of leakage failures on the system, leading to inaccurate evaluation results. This method, by constructing an optimal dynamic energy flow model of the integrated energy system that considers leakage failures, can accurately simulate the impact of pipeline leakage failures in natural gas and heating networks on energy flow and distribution.

[0018] 2. Improve the accuracy of reliability assessment. Existing reliability assessment methods often neglect the dynamic process of leakage failures, leading to errors in the assessment results. In this method, by using a state duration sampling algorithm to extract the component state time sequence and combining it with the system state transition sequence for simulation, this method can more accurately assess the reliability of integrated energy systems.

[0019] 3. Comprehensive consideration of the complexity of integrated energy systems. Existing assessment methods often focus only on a single system or a single type of failure, neglecting the complexity and diversity of integrated energy systems. This method not only considers leakage failures in natural gas and heating pipelines, but also comprehensively considers the interactions and influences of the power, natural gas, and heating systems, thus enabling a more comprehensive assessment of the reliability of integrated energy systems.

[0020] 4. Provides a foundation for optimized scheduling. Existing assessment methods often fail to provide sufficient information and support for optimized scheduling, limiting the improvement of system performance. By accurately assessing the impact of pipeline leakage failures on the integrated energy system, this method can provide an important basis for optimized scheduling, helping to improve the system's operational efficiency and energy utilization efficiency.

[0021] In summary, this method, by considering pipeline leakage failures in the integrated energy system reliability assessment, can significantly improve the accuracy and precision of the assessment results, providing strong support for the optimized operation and reliability management of integrated energy systems.

[0022] Preferably, when constructing the optimal dynamic energy flow model of the integrated energy system considering leakage faults, the constraints include natural gas system constraints, thermal system constraints, power system constraints, gas network pipeline energy flow constraints considering leakage faults, and heat network pipeline energy flow constraints considering leakage faults.

[0023] This setup, by comprehensively considering the constraints of the natural gas, heating, and power systems, allows the model to more fully reflect the operational characteristics and interrelationships of the integrated energy system. This helps to more accurately assess the system's performance and reliability under various operating conditions.

[0024] 2. By introducing energy flow constraints for gas and heating network pipelines that account for leakage failures, the model can more accurately simulate the impact of pipeline leaks on energy flow and distribution. This helps to reveal the potential threat of leakage failures to the overall system performance and provides a scientific basis for taking corresponding countermeasures.

[0025] Preferably, the natural gas system constraints include:

[0026] The relationship between the amount of natural gas consumed by the compressor and the flow rate through the compressor:

[0027]

[0028] The pressure relationship between the compressor's inlet and outlet:

[0029]

[0030] Gas pressure at natural gas nodes Upper and lower limit constraints:

[0031]

[0032] in, This indicates the flow rate through the compressor piping. γ represents the amount of natural gas consumed by the compressor. cp For boost ratio, This indicates the near-gas pressure of the compressor. P represents the near-gas pressure of the compressor. x,min P represents the lower limit of natural gas node pressure. x,max Γ represents the upper limit of the natural gas node; Γ represents the coefficient relating the amount of natural gas consumed by the compressor to the flow rate through the compressor.

[0033] Operating constraints of CHP cogeneration units:

[0034]

[0035]

[0036] In the formula, This refers to the operating power of the CHP unit; and These refer to the power generation and heating capacity of the extraction-type thermal power unit, respectively; c v c is the rate of change of power generation as heating power increases, assuming the total steam intake of the unit remains constant. m Here, r is the back pressure operating flexibility coefficient of the CHP unit; r is a constant. PN represents the operating status of the CHP unit at time t, where 1 represents normal operation and 0 represents a fault. max,x and PN min,xThese represent the upper and lower limits of the power generation capacity of the CHP unit under pure condensing conditions; PH max,x The upper limit of the heating capacity of the CHP unit;

[0037] Natural gas balance equation:

[0038] Node traffic balancing:

[0039]

[0040] Upper and lower limits of load shedding:

[0041]

[0042] Upper and lower limits of gas source output constraints:

[0043]

[0044] Pipeline flow rate upper and lower limit constraints:

[0045]

[0046] Wherein, mass flow rate Q = ρwA represents the amount of natural gas passing through any cross-section of the pipe per unit time, where A is the cross-sectional area of ​​the pipe, ρ is the density, and w is the flow velocity; the ideal gas law P = ρZR c T represents the relationship between gas pressure, density, and temperature, where Z is the compressibility factor of natural gas, and R... c Here, T is the specific gas constant of natural gas, and T is the temperature.

[0047] In the formula, n(x) represents the set of pipes connected to node x; and This represents the outflow and inflow flow rates of pipe x and y; S represents the gas supply volume. x t Indicates gas load. Indicates the air cutting load; This indicates the flow rate through the compressor piping; This indicates the amount of natural gas consumed by the compressor; and Represent the electrical power of the gas turbine and the thermal power of the gas boiler, respectively; m(x) represents the set of gas network nodes; η gas ,η chp and η heat These represent the conversion efficiencies of the gas turbine, CHP unit, and gas boiler, respectively; cp represents the compressor branch.

[0048] Linear equations for dynamic energy flow model of natural gas pipelines:

[0049] The pipeline xy is divided into several segments with an interval of Δx; in terms of time, time t is the initial point of the operating cycle. and Let x be the initial flow rate and pressure at time t at pipe x.

[0050] Natural gas pipeline storage equation:

[0051]

[0052] In the formula, This represents the amount of natural gas stored in the pipeline of length Δx at time t; Δt represents the differential time interval; Δx represents the differential length interval; time t is the initial point of the operating cycle. and Let x be the initial flow rate and pressure at time t at pipe x.

[0053] Linear equation for dynamic energy flow of natural gas:

[0054]

[0055] In the formula, λ is the hydraulic friction coefficient, and D is the inner diameter of the natural gas pipeline;

[0056] The expression for sgn(Q) is:

[0057]

[0058] The friction coefficient λ of a natural gas pipeline is:

[0059]

[0060] In the formula, ε g This refers to the absolute roughness of the natural gas pipeline.

[0061] And introduce 0-1 variables to establish flow direction constraints:

[0062]

[0063] In the formula, The variable is used to determine the x and y directions of the pipeline; it is 1 when Q > 0 and 0 when Q < 0; N is a preset value.

[0064] The stored data is restored every 24 hours.

[0065]

[0066] In the formula, I is an integer and δ is the range of variation.

[0067] This setup allows for: 1. Accurate simulation of natural gas system operation. By considering the relationship between the amount of natural gas consumed by the compressor and its flow rate, as well as the pressure relationship between the compressor's inlet and outlet, the model can accurately simulate the transmission process of natural gas in the pipeline, including the compressor's energy consumption and pressure changes, thus more accurately reflecting the actual operating status of the natural gas system.

[0068] 2. Improve the accuracy of pipeline dynamic energy flow simulation. The introduction of linear equations into the natural gas pipeline dynamic energy flow model allows the model to consider the dynamic flow process of natural gas within the pipeline, including pipeline storage, flow rate changes, and pressure changes. This helps to more accurately simulate the transmission and distribution process of natural gas in the pipeline, improving the model's simulation accuracy.

[0069] 3. Consideration of pipeline storage recovery characteristics. The setting of restoring pipeline storage every 24 hours takes into account the recovery characteristics of natural gas stored in pipelines. This helps to more accurately simulate the storage and release process of natural gas in pipelines, providing an important reference for the long-term operation and scheduling of the system.

[0070] Preferably, the thermal system constraints include:

[0071] Hot water temperature transfer equation:

[0072] hot water quality flowing out of the pipe From arrive The mass composition of the hot water flowing into the pipes during a given period:

[0073]

[0074] in, The working fluid mass of water supply pipeline number l at different times; ρ w A l and L l Let represent the density of water, the cross-sectional area of ​​the pipe, and the length of the pipe, respectively; the mass of hot water flowing into the pipe during the time interval from t to t+Δt is . The mass of hot water flowing out of the pipe during the time interval Δt is equal to the mass of hot water flowing into the pipe.

[0075] Hot water temperature at the outlet of the supply and return water pipes It consists of a linearly weighted average of the mass temperature of hot water injected over several time periods:

[0076]

[0077]

[0078] The pipe outlet temperature, taking heat loss into account, is corrected using the Sukhov temperature drop model:

[0079]

[0080] in, and These represent the outlet and inlet temperatures of the water supply pipe at time t, respectively. and These represent the outlet and inlet temperatures of the return water pipe at time t, respectively. Indicates in The quality of hot water injected into the pipeline during time period t; Indicates from The quality of hot water injected into the pipeline during time period t; and T represents the outlet temperature of the supply and return water pipes, respectively. e , λ l ther c and c represent the ambient temperature, the thermal conductivity of pipe l, and the specific heat capacity of water, respectively.

[0081] Pipeline hot water temperature fusion constraint:

[0082] Hot water temperature mixing constraints at pipe junctions:

[0083]

[0084] The inlet temperature of all pipes flowing out of node m is equal to this mixing temperature:

[0085]

[0086] Pipeline temperature upper and lower limit constraints:

[0087]

[0088] in, and T represents the temperature at node m in the supply and return water pipes, respectively. s,min T indicates the lower limit of the water supply pipeline temperature. s,max Indicates the upper limit of water supply pipe temperature, T r,min T represents the lower limit of the temperature of the return water pipe. r,max Indicates the upper limit of the return water pipe temperature;

[0089] Dynamic model of heat load:

[0090] Indoor temperature was simulated using a building heat load model:

[0091]

[0092] In the formula, Let t be the seasonal heat load power of the user at time t. Let R be the indoor temperature of the user at time t, R be the equivalent thermal resistance of the building, and C be the equivalent heat capacity.

[0093] Seasonal heat load reduction for:

[0094]

[0095] In the formula, T com,min and T com,max These refer to the minimum and maximum suitable indoor temperatures of the building, respectively.

[0096] Equilibrium equations of a thermodynamic system:

[0097] The relationship between heat source output and hot water temperature in the pipes:

[0098]

[0099] Relationship between year-round and seasonal loads and pipe hot water temperature:

[0100]

[0101] Annual load reduction limits:

[0102]

[0103] in, This indicates the heating capacity of the CHP units in the heating network. This indicates the thermal power of the gas-fired boilers in the heating network. Let n(e) represent the heat pump power in the heating network, n(e) represent the set of pipes at the heat source outlet, and m(e) represent the set of nodes in the heating network. Indicates the year-round heat load. This represents the annual heat load reduction, and s(e) represents the piping assembly at the heat exchanger inlet. This indicates the upper limit of the annual load reduction.

[0104] This setup allows for two key advantages: 1. Accurate simulation of the hot water transfer process. Using the hot water temperature transfer equation, the model can precisely simulate the transfer of hot water in the pipes, including changes in the mass and temperature of hot water flowing into and out of the pipes at different times. This helps to more accurately reflect the actual operating state of the thermal system.

[0105] 2. Consideration of heat loss and correction for pipe outlet temperature. The Sukhov temperature drop model is used to correct the pipe outlet temperature after taking heat loss into account, so that the model can more accurately simulate the heat loss of hot water during transmission, and thus calculate the pipe outlet temperature more accurately.

[0106] 3. By using a building heat load model to simulate indoor temperature and considering seasonal heat load reductions, the model can more accurately simulate the dynamic changes in indoor temperature and heat load. This helps to evaluate the performance of the thermal system under different operating conditions.

[0107] 4. Optimize heat load reduction strategies. By setting upper and lower limits for year-round load reduction, the model can optimize heat load reduction strategies, ensuring that the amount of heat load reduction is minimized while guaranteeing the safe and stable operation of the system, thereby improving user satisfaction.

[0108] Preferably, power system constraints include:

[0109] Node power balancing:

[0110]

[0111] The relationship between trend and node angle:

[0112]

[0113] Node phase angle constraints:

[0114]

[0115] Load shearing upper and lower limit constraints:

[0116]

[0117] Power line transmission capacity constraints:

[0118]

[0119] Upper and lower limits of electrical power output for coal-fired units, gas turbines, and CHP units:

[0120]

[0121] in, Indicates the electrical power of the gas turbine. This indicates the electrical power of the extraction-type thermal power unit. This indicates the electrical power of the coal-fired power unit. This represents the heat pump power, and j represents the power branch connected to node k. Indicates power flow in power lines; η represents the amount of load reduction, m(k) represents the set of power nodes; hp Indicates the heat pump's conversion efficiency, b kl PG represents the line reactance, θ represents the node phase angle, and PG represents the line reactance. k,min PG represents the lower limit of the electrical power output of a coal-fired power unit. k,max PE represents the upper limit of electrical power output of a coal-fired power unit.k,min PE represents the lower limit of the electrical power output of the gas turbine. k,max PN indicates the upper limit of the electrical power output of the gas turbine. k,min PN indicates the lower limit of the electrical power output of the CHP unit. k,max Indicates the upper limit of electrical power output of the CHP unit; F kj,max Indicates the upper limit of power flow in the power line; This indicates the upper limit of load reduction.

[0122] This setup allows for: 1. Accurate simulation of the relationship between power flow and node phase angles. The constraint on the relationship between power flow and node phase angles enables the model to simulate the distribution of power flow in the power system, as well as the mathematical relationship between power flow and node phase angles. This helps to more accurately reflect the actual operating state of the power system.

[0123] 2. Optimize load shedding strategies. The upper and lower limits of load shedding enable the model to optimize load shedding strategies while ensuring the safe and stable operation of the system, minimizing the amount of power load reduction, and improving user satisfaction and system reliability.

[0124] 3. Improve the overall operating efficiency of the power system. By comprehensively considering the above constraints, the model can achieve accurate simulation and optimized scheduling of the overall operating state of the power system. This helps to improve the operating efficiency of the power system, reduce operating costs, and provide a scientific basis for the planning, design, and operation of the power system.

[0125] Preferably, the energy flow constraints of the gas network pipeline after a leakage fault include:

[0126] The energy flow is calculated by splitting a pipe segment xy into two segments xm and my. The energy flow equation for the xm segment is as follows:

[0127]

[0128] λ g,xm =λ g,xy ;

[0129] In the formula, L xm and D xy They represent natural gas pipeline L xm The length and inner diameter of the xy segment; This represents the air pressure at pipe node x at time t; and λ represents the flow rate of natural gas flowing into and out of the natural gas pipeline, respectively. g,xm The friction coefficient of the natural gas pipeline segment xm is represented by w0; the initial flow velocity of the natural gas is represented by c. w Indicates the speed of sound;

[0130] The energy flow equation for pipe segment my is:

[0131]

[0132]

[0133] λ g,my =λ g,xy ;

[0134] The energy flow balance equation must be satisfied at the virtual gas load:

[0135]

[0136] Among them, virtual gas load The calculation formula:

[0137]

[0138] In the formula, R c κ is the gas constant of natural gas, and κ is the isentropic index of natural gas.

[0139] This setup allows for two key advantages: 1) It enables more refined simulation of pipeline energy flow. Dividing a pipeline into two segments (xy segment and my segment) and calculating the energy flow separately allows for a more accurate simulation of the natural gas flow within the pipeline. This refined modeling helps to more accurately reflect the actual flow state of natural gas within the pipeline, especially when considering leakage faults.

[0140] 2. The impact of leakage faults on energy flow was considered. By introducing relevant parameters (such as pipe length, inner diameter, gas pressure, and flow rate) into the energy flow equations and combining them with the characteristics of leakage faults, the impact of leakage faults on pipeline energy flow can be simulated. This helps to assess the degree of impact of leakage faults on the overall operating status of the gas network and provides a scientific basis for emergency response and recovery strategies after a fault.

[0141] 3. Considering the energy flow constraints of gas network pipelines after a leak helps in the timely detection and handling of potential leak risks, thereby avoiding or reducing the impact of leaks on the safe operation of the gas network. This contributes to improving the overall safety and reliability of the gas network. Based on refined modeling and energy flow balance analysis, more optimized gas network operation strategies can be formulated. For example, after a leak occurs, parameters such as pipeline flow rate and gas pressure can be adjusted according to energy flow constraints to restore the gas network to normal operation as quickly as possible.

[0142] Preferably, the energy flow constraints of the heating network pipeline after a leakage fault include:

[0143]

[0144] In the formula, The pressure at node i of the heating network pipeline; λ represents the pressure at node i of the heating network pipeline; h,l The frictional resistance coefficient of the heating network pipeline; w h,l D represents the hot water flow velocity in the pipe. l This refers to the diameter of the heating network pipe.

[0145] The coefficient of frictional resistance is:

[0146]

[0147] In the formula, λ h,l D is the coefficient of frictional resistance of the heating network pipeline; l Where is the diameter of the heating network pipe; Δ is the equivalent roughness of the pipe, usually taken as 1 mm; Re is the Reynolds number.

[0148]

[0149] In the formula, υ is the viscosity coefficient;

[0150] The formula for calculating pressure drop is:

[0151]

[0152] L l Indicates the length of the natural gas pipeline; ρ w Indicates the density of water; This indicates the working fluid quality of the water supply pipe numbered l at different times;

[0153] When a leak occurs in the heating network pipeline, the original pipeline is split into two sections for calculation, and the transmission delay time is redefined.

[0154]

[0155] In the formula, A l This indicates the cross-sectional area of ​​the pipe;

[0156] The water flow rate caused by the pipeline leak is:

[0157]

[0158] In the formula, C2 is the flow coefficient, and A h,leak The area of ​​the leak hole;

[0159] The relationship between the virtual hot water load and the hot water flow rate at both ends of the pipe is as follows:

[0160]

[0161] The hot water temperature can be calculated based on the changed hot water flow rate:

[0162]

[0163]

[0164] in,

[0165]

[0166] To ensure the normal operation of the heating system, a centralized water replenishment method is adopted, with water uniformly produced at the inlet of the heat source return water pipeline or condensate used for replenishment and pressure stabilization; the temperature fusion equation for the water replenishment process is:

[0167]

[0168] In the formula, The return water temperature after water is added to the heat source; The water replenishment volume is the difference between the flow rate of the inlet water pipe and the flow rate of the return water pipe at the heat source. The water replenishment temperature is usually set to ambient temperature.

[0169] This setup allows for two key advantages: 1) It enables more precise simulation of the impact of leaks on the heating network. By splitting the existing pipeline into two segments at the leak point for calculation and redefining the transmission delay time, the impact of leaks on energy flow in the heating network pipelines can be simulated more accurately. This refined modeling helps to more accurately reflect the operating status of the heating network after a leak.

[0170] 2. By introducing parameters such as the frictional resistance coefficient and Reynolds number, as well as the formula for calculating pressure drop, the frictional resistance and pressure drop in the heating network pipeline can be accurately calculated. This helps assess the impact of leakage faults on the flow characteristics of the heating network pipeline, providing a scientific basis for subsequent fault handling. By calculating the leakage flow rate and virtual hot water load, the impact of leakage faults on the hot water flow rate of the heating network can be quantified. This helps assess the degree of impact of leakage faults on the overall performance of the heating system and take corresponding measures to reduce leakage losses and restore the normal operation of the heating system. Based on the changed hot water flow rate, the hot water temperature can be calculated. This helps assess the impact of leakage faults on the hot water temperature, ensuring that the heating system can provide the required hot water temperature.

[0171] 3. By adopting a centralized water replenishment method, with unified water production at the inlet of the heat source return water pipeline or using condensate for replenishment and pressure stabilization, the water replenishment strategy can be optimized. This helps reduce energy loss during the water replenishment process and improves the overall efficiency of the heating system. Considering the energy flow constraints of the heating network pipeline after a leakage fault helps to promptly detect and address potential leakage risks, thereby avoiding or reducing the impact of leakage faults on the reliability and stability of the heating system. This contributes to improving the overall performance of the heating system and user satisfaction.

[0172] Preferably, when constructing the optimal dynamic energy flow model of the integrated energy system considering leakage faults, the objective function is:

[0173]

[0174] In the formula, c gw,x Indicates gas source cost; c curs,x c represents the air reduction load penalty coefficient; pg,k Indicates the output cost of a coal-fired power unit; c curl,k c represents the load penalty factor for electrical discharge; curh,e Indicates the heat load penalty coefficient; Indicates the gas supply volume; Indicates the air cutting load; Indicates the electrical power of the coal-fired power unit; Indicates the amount of electricity load reduction; This indicates the amount of annual heat load reduction; denoted by d, m(x) represents the seasonal heat load reduction; m(k) represents the set of gas network nodes; m(k) represents the set of power nodes; Q(x) represents the set of gas source nodes; p(x) represents the set of coal-fired unit nodes; and n(d) represents the set of pipelines connected to node d.

[0175] This setup ensures that: 1. The impact of leakage failures on the integrated energy system is considered when constructing the objective function. Leakage failures may lead to a reduction in gas supply, a decrease in electrical and thermal loads, and an increase in corresponding penalty costs. By incorporating these factors into the objective function, it can be ensured that the model accurately reflects the impact of leakage failures on the system's economics.

[0176] 2. The optimal dynamic energy flow model for a comprehensive energy system considering leakage faults needs to be adaptable to different operating scenarios and fault conditions. By constructing a flexible objective function, it can be ensured that the model can quickly adjust energy supply and demand strategies when facing leakage faults, in order to minimize costs and meet energy demands.

[0177] 3. All costs in the objective function are closely related to the energy supply and demand balance. For example, gas source costs and gas load reduction penalty coefficients reflect the tightness of natural gas supply and demand; coal-fired unit output costs and electricity load reduction penalty coefficients reflect the balance of electricity supply and demand; and heat load reduction penalty coefficients reflect the supply and demand relationship of heat load. By optimizing these costs, it can be ensured that the integrated energy system can maintain the energy supply and demand balance even under leakage failures.

[0178] Preferably, in S2, the process of extracting the element state timing sequence using the state duration sampling algorithm includes:

[0179] Step 2.1: Set the initial state of all components;

[0180] Step 2.2: Determine the set of states Ω that the current state of element m (m = 1, 2, ..., M) can transition to.m Where M is the total number of all components;

[0181] Step 2.3: Transition rate based on the set of transitional states The duration τ of each element remaining in the current state before transitioning to the next state is obtained by sampling according to the following formula. m =(τ 1 m ,τ m 2 ,...,τ m j );

[0182]

[0183] in, Given a uniformly distributed random number in the range [0,1] under state j;

[0184] Step 2.4: Select the minimum value in the duration set as the duration of the current state of each element, and simultaneously use the transition state corresponding to the minimum value as the element state to be transitioned to next:

[0185]

[0186] In step 2.4, if the transferred state involves a leakage fault in the natural gas network and / or heating network pipeline, a random sampling method is also used to determine the orifice size and location of the pipeline leakage fault. The process includes:

[0187] First, for a random number Q between [0,1] is drawn from the pipeline, the distance from the leak fault location to the beginning of the pipeline is calculated as L′=LQ, where L is the length of the pipeline;

[0188] Then, random numbers following a normal distribution are used. The diameter of the simulated leakage orifice is given, where μ and σ are related to the type of pipe.

[0189] This setup allows us to: 1) set the initial state of all components and sample each component based on the transition rate of the transitional state set, thus obtaining the set of durations a component remains in its current state before transitioning to the next state. This process can accurately simulate changes in component states, providing a foundation for subsequent energy system analysis and optimization.

[0190] 2. When the transferred state involves leakage faults in natural gas networks and / or heating network pipelines, a random sampling method is used to determine the orifice size and location of the pipeline leakage fault. This process not only considers the probability of leakage faults but also further refines the specific characteristics of leakage faults (such as orifice size and location), making the simulation results closer to the actual situation.

[0191] 3. By accurately simulating component states and leakage fault characteristics, this method can support operational decisions for energy systems. For example, when a leakage fault occurs, the location and size of the fault can be quickly determined based on the simulation results, allowing for appropriate emergency measures to be taken to reduce the impact of the fault on the system.

[0192] 4. Considering the state duration sampling algorithm for leakage faults helps in the reliability analysis of energy systems. By simulating the system's operating state under different fault conditions, the reliability and stability of the system can be evaluated, providing a scientific basis for the optimal design and operation of the system.

[0193] Preferably, in S4, the process of determining whether the convergence condition is met includes:

[0194] The variance convergence coefficient σ is calculated using the standard deviation of the sample mean. k The calculation formula is:

[0195] σ ele,k = std(EENS) / EENS;

[0196] σ gas,k = std(EGNS) / EGNS;

[0197] σ heat,k = std(EHNS) / EHNS;

[0198] Among them, EENS represents the expected value of insufficient electricity supply; EGNS represents the expected value of insufficient natural gas supply; and EHNS represents the expected value of insufficient heat supply.

[0199] Choose the maximum value of the three coefficients as the convergence criterion:

[0200] σ k =max{σ ele,k ,σ gas,k ,σ heat,k};

[0201] Where std represents the standard deviation; if σ k If the value is ≤0.02 or the total simulation time reaches the preset value, the simulation is considered to meet the convergence condition and is stopped; otherwise, the simulation is considered not to meet the convergence condition.

[0202] This setup has two advantages: 1. It improves the reliability of simulation results. Choosing the maximum value among the three coefficients (EENS, EGNS, and EHNS, corresponding to variance convergence coefficients) as the convergence criterion ensures that convergence requirements are met across multiple key metrics. This helps improve the reliability of simulation results, making them more reflective of actual conditions.

[0203] 2. Supports simulation analysis of complex systems. For energy systems containing multiple components and complex relationships, this method provides an effective convergence determination approach. By continuously monitoring and judging convergence conditions, it can be ensured that the simulation process accurately reflects the operating state and characteristics of the system. Attached Figure Description

[0204] To make the objectives, technical solutions, and advantages of the invention clearer, the invention will now be described in further detail with reference to the accompanying drawings, wherein:

[0205] Figure 1 This is a flowchart of the method;

[0206] Figure 2 This is a schematic diagram of the electrothermal characteristics of the extraction-type thermal power unit in Example 1;

[0207] Figure 3 This is a schematic diagram of the flow process in the heating network pipeline at time t in Example 1;

[0208] Figure 4 This is a schematic diagram of a natural gas pipeline leakage fault in Example 1;

[0209] Figure 5 This is a schematic diagram of a leakage fault in the heating network pipeline in Example 1;

[0210] Figure 6 This is a schematic diagram of the pipeline state transition in Example 1;

[0211] Figure 7 This is a schematic diagram of the integrated energy system in the example of Example 2;

[0212] Figure 8 This is a schematic diagram of the load curve of the integrated energy system in the example of Example 2. Detailed Implementation

[0213] The following detailed explanation illustrates the specific implementation methods:

[0214] Example 1

[0215] like Figure 1 As shown, this embodiment discloses a comprehensive energy system reliability assessment method considering pipeline leakage failures, including the following steps:

[0216] S0. Construct an integrated energy system, which includes an electricity system, a natural gas system, and a heat system; and based on the integrated energy system, consider leakage faults in the natural gas network and heat network pipelines, as well as the impact of these faults on energy flow and distribution, and construct an optimal dynamic energy flow model for the integrated energy system that considers leakage faults.

[0217] The optimal dynamic energy flow model for the integrated energy system, considering leakage faults, incorporates constraints related to the natural gas system, heating system, power system, and energy flow constraints for both gas and heating pipelines, taking leakage faults into account. By comprehensively considering the constraints of these systems, the model can more fully reflect the operational characteristics and interrelationships of the integrated energy system. This facilitates a more accurate assessment of the system's performance and reliability under various operating conditions. Introducing energy flow constraints for gas and heating pipelines, considering leakage faults, allows the model to more accurately simulate the impact of pipeline leaks on energy flow and distribution. This helps reveal the potential threats of leakage faults to the overall system performance and provides a scientific basis for taking appropriate countermeasures.

[0218] In practice, natural gas system constraints include:

[0219] 1) Compressor-related constraints: The main function of a compressor is to compensate for the pressure loss during transmission and maintain the pipeline pressure within the normal range.

[0220] The relationship between the amount of natural gas consumed by the compressor and the flow rate through the compressor:

[0221]

[0222] The pressure relationship between the compressor's inlet and outlet:

[0223]

[0224] Pressure upper and lower limits constraints for natural gas nodes:

[0225]

[0226] in, This indicates the flow rate through the compressor piping. γ represents the amount of natural gas consumed by the compressor. cp For boost ratio, This indicates the near-gas pressure of the compressor. P represents the near-gas pressure of the compressor. x,min P represents the lower limit of natural gas node pressure. x,max Γ represents the upper limit of the natural gas node; Γ represents the coefficient relating the amount of natural gas consumed by the compressor to the flow rate through the compressor.

[0227] 2) Currently, the most widely used combined heat and power (CHP) units in China are extraction-type CHP units. During unit operation, the steam entering the turbine can either generate electricity or be partially extracted to supply the heat load. The power generation capacity of extraction-type CHP units is... and heating power The correlation and coupling relationship between them is called electrothermal characteristics, such as Figure 2 As shown in the figure. PN max,x and PN min,x These represent the upper and lower limits of the power generation capacity of the CHP unit under pure condensing operation (power generation only). PH max,x This refers to the upper limit of the heating capacity of the CHP unit. PH pm,x This represents the heating capacity corresponding to the minimum power generation of the unit. v This represents the rate of change of power generation as heating power increases, assuming the total steam intake of the unit remains constant. The operating constraints of the CHP unit are as follows:

[0228]

[0229]

[0230] In the formula, For the operating power of the CHP unit, c m Here, r is the back pressure operating flexibility coefficient of the CHP unit; r is a constant. The operating status of the CHP unit at time t ("1" represents normal status, "0" represents fault status).

[0231] 3) Natural Gas Balance Equation:

[0232] Node traffic balancing:

[0233]

[0234] Upper and lower limits of load shedding:

[0235]

[0236] Upper and lower limits of gas source output constraints:

[0237]

[0238] Pipeline flow rate upper and lower limit constraints:

[0239]

[0240] Here, the mass flow rate Q = ρwA represents the amount of natural gas passing through any cross-section of the pipe per unit time, where A is the cross-sectional area of ​​the pipe, ρ is the density, and w is the flow velocity. Simultaneously, the ideal gas law P = ρZR is introduced. c T describes the relationship between gas pressure, density, and temperature, where Z is the compressibility factor of natural gas, and R... c Let η be the specific gas constant of natural gas, and T be the temperature. n(x) represents the set of pipelines connected to node x, and m(x) represents the set of gas network nodes; ηgas ,η chp and η heat These represent the conversion efficiencies of the gas turbine, CHP unit, and gas boiler, respectively. and These represent the electrical power of the gas turbine and the thermal power of the gas boiler, respectively; cp represents the compressor branch; and This represents the outflow and inflow flow rates of pipe x and y. Indicates the gas supply volume. Indicates gas load. This indicates the air cutting load.

[0241] 4) Linear equations for the dynamic energy flow model of natural gas pipelines:

[0242] The pipeline xy is divided into several segments with an interval of Δx. In terms of time, time t is the initial point of the operating cycle. and Let x be the initial flow rate and pressure at time t at pipe x.

[0243] The natural gas pipeline storage equation is:

[0244]

[0245]

[0246] In the formula, Δt represents the difference time interval, and Δx represents the difference length interval. This represents the amount of natural gas stored in a pipeline of length Δx at time t. Time t is the initial point of the operating cycle. and Let x be the initial flow rate and pressure at time t at pipe x.

[0247] Linear equation for dynamic energy flow of natural gas:

[0248]

[0249] In the formula, λ is the hydraulic friction coefficient, and D is the inner diameter of the natural gas pipeline.

[0250] The expression for sgn(Q) is:

[0251]

[0252] The friction coefficient λ of a natural gas pipeline can be expressed as:

[0253]

[0254] In the formula, ε g This refers to the absolute roughness of the natural gas pipeline.

[0255] At any given moment, the flow direction is the same at all points along the same pipe. To ensure accuracy in the differential process, 0-1 variables are introduced to establish flow direction constraints:

[0256]

[0257] In the formula, The xy direction of the pipeline is determined by the variable, which is 1 when Q > 0 and 0 when Q < 0. N is theoretically an infinitely large value and needs to be adjusted according to the actual flow rate of the natural gas system to reduce the computational burden.

[0258] Pipeline storage provides significant flexibility to natural gas systems. However, because natural gas systems lack the diverse regulation facilities found in power systems, pipeline storage must be used cautiously as a regulatory tool and restored after use. This invention considers pipeline storage to be restored every 24 hours.

[0259]

[0260] In the formula, I is an integer and δ is the range of variation.

[0261] In this way, by considering the relationship between the amount of natural gas consumed by the compressor and the flow rate, as well as the pressure relationship between the compressor's inlet and outlet, the model can accurately simulate the transmission process of natural gas in the pipeline, including the compressor's energy consumption and pressure changes, thus more accurately reflecting the actual operating state of the natural gas system. Furthermore, the introduction of linear equations into the natural gas pipeline dynamic energy flow model allows the model to consider the dynamic flow process of natural gas within the pipeline, including pipeline storage, flow rate changes, and pressure changes. This helps to more accurately simulate the transmission and distribution process of natural gas in the pipeline, improving the model's simulation accuracy. Moreover, the setting of restoring pipeline storage every 24 hours considers the recovery characteristics of natural gas stored in the pipeline. This helps to more accurately simulate the storage and release process of natural gas in the pipeline, providing important reference for the long-term operation and scheduling of the system.

[0262] Thermal system constraints include:

[0263] 1) Hot water temperature transfer equation:

[0264] The flow process in the heating network pipeline at time t is as follows Figure 3 As shown, the shaded area represents the pipe wall. The working fluid mass of water supply pipeline number l at different times; ρ w A l and L l Let ρ represent the density of water, the cross-sectional area of ​​the pipe, and the length of the pipe, respectively. The total mass of hot water contained in the pipe is equal to ρ. w A l L lThe section in the middle of the pipe represents the amount of hot water flowing into the pipe over a continuous time period. During the time period from t to t+Δt, the mass of hot water flowing into the pipe is... The red portion represents the mass of hot water flowing out of the pipe during the time interval Δt; these two masses should remain equal. After supplying heat to the load, the outflowing hot water returns to the starting point through the return pipe for reheating.

[0265] hot water quality flowing out of the pipe From arrive The mass composition of the hot water flowing into the pipes during a given period:

[0266]

[0267] This invention regulates the output of the heat source through a mass flow regulation method to maintain a supply-demand balance between the user and the heat source, and sets the mass flow rate of the pipeline as a constant. The hot water temperature at the outlet of the supply and return water pipelines is a linearly weighted average of the mass temperatures of the hot water injected over several time periods.

[0268]

[0269] The pipe outlet temperature, taking heat loss into account, is corrected using the Sukhov temperature drop model:

[0270]

[0271] in, and These represent the outlet temperature and inlet temperature of the water supply pipeline, respectively. and These represent the outlet temperature and inlet temperature of the return water pipe, respectively. Indicates in The quality of hot water injected into the pipeline during time period t; Indicates from The quality of hot water injected into the pipeline during the time period up to t. and T represents the outlet temperature of the supply and return water pipes, respectively. e , λ l ther c and c represent the ambient temperature, the thermal conductivity of pipe l, and the specific heat capacity of water, respectively.

[0272] 2) Pipeline hot water temperature fusion constraint:

[0273] Hot water temperature mixing constraints at pipe junctions:

[0274]

[0275] The inlet temperature of all pipes flowing out of node m is equal to this mixing temperature:

[0276]

[0277] Pipeline temperature upper and lower limit constraints:

[0278]

[0279] in, and T represents the temperature at node m in the supply and return water pipes, respectively. s,min T indicates the lower limit of the water supply pipeline temperature. s,max Indicates the upper limit of water supply pipe temperature, T r,min T represents the lower limit of the temperature of the return water pipe. r,max This indicates the upper limit of the return water pipe temperature.

[0280] 3) Dynamic model of heat load:

[0281] This invention uses a building heat load model to simulate indoor temperature, as shown below:

[0282]

[0283] In the formula, Let t be the seasonal heat load power of the user at time t. Let R be the indoor temperature of the user at time t, R be the equivalent thermal resistance of the building, and C be the equivalent heat capacity.

[0284] Seasonal heat load reduction for:

[0285]

[0286] In the formula, T com,min and T com,max These refer to the minimum and maximum suitable indoor temperatures of the building, respectively.

[0287] 4) Equilibrium equations of a thermodynamic system:

[0288] The relationship between heat source output and hot water temperature in the pipes:

[0289]

[0290] Relationship between year-round and seasonal loads and pipe hot water temperature:

[0291]

[0292] Annual load reduction limits:

[0293]

[0294] in, This indicates the heating capacity of the CHP units in the heating network. This indicates the thermal power of the gas-fired boilers in the heating network. Let n(e) represent the heat pump power in the heating network, n(e) represent the set of pipes at the heat source outlet, and m(e) represent the set of nodes in the heating network. Indicates the year-round heat load. This represents the annual heat load reduction, and s(e) represents the piping assembly at the heat exchanger inlet. This indicates the upper limit of the annual load reduction.

[0295] Thus, through the hot water temperature transfer equation, the model can accurately simulate the hot water transfer process in the pipes, including the changes in the mass and temperature of hot water flowing into and out of the pipes at different times. This helps to more accurately reflect the actual operating state of the thermal system. Furthermore, by using the Sukhov temperature drop model to correct the pipe outlet temperature after considering heat loss, the model can more accurately simulate the heat loss during hot water transfer, thereby calculating the pipe outlet temperature more precisely. In addition, a building heat load model is used to simulate indoor temperature, considering seasonal heat load reductions, allowing the model to more accurately simulate the dynamic changes in indoor temperature and heat load. This helps to evaluate the performance of the thermal system under different operating conditions. By setting upper and lower limits for year-round load reduction, the model can optimize heat load reduction strategies, ensuring that the reduction in heat load is minimized while guaranteeing the safe and stable operation of the system, thereby improving user satisfaction.

[0296] Power system constraints include:

[0297] Node power balancing:

[0298]

[0299] The relationship between trend and node angle:

[0300]

[0301] Node phase angle constraints:

[0302]

[0303] Load shearing upper and lower limit constraints:

[0304]

[0305] Power line transmission capacity constraints:

[0306]

[0307] Upper and lower limits of electrical power output for coal-fired units, gas turbines, and CHP units:

[0308]

[0309] in, Indicates the electrical power of the gas turbine. This indicates the electrical power of the extraction-type thermal power unit. This indicates the electrical power of the coal-fired power unit. This represents the heat pump power, and j represents the power branch connected to node k. Indicates power flow in power lines; η represents the amount of load reduction, m(k) represents the set of power nodes; hp Indicates the heat pump's conversion efficiency, b kl PG represents the line reactance, θ represents the node phase angle, and PG represents the line reactance. k,min PG represents the lower limit of the electrical power output of a coal-fired power unit. k,max PE represents the upper limit of electrical power output of a coal-fired power unit. k,min PE represents the lower limit of the electrical power output of the gas turbine. k,max PN indicates the upper limit of the electrical power output of the gas turbine. k,min PN indicates the lower limit of the electrical power output of the CHP unit. k,max Indicates the upper limit of electrical power output of the CHP unit; F kj,max Indicates the upper limit of power flow in the power line; This indicates the upper limit of load reduction.

[0310] This allows for accurate simulation of the relationship between power flow and node phase angles. The constraints on this relationship enable the model to simulate the distribution of power flow in the power system and the mathematical relationship between power flow and node phase angles. This helps to more accurately reflect the actual operating state of the power system. Furthermore, it allows for optimization of load shedding strategies. Upper and lower limits for load shedding enable the model to optimize load shedding strategies while ensuring the safe and stable operation of the system, minimizing the amount of load reduction, improving user satisfaction and system reliability. It also improves the overall operating efficiency of the power system. By comprehensively considering the above constraints, the model can achieve accurate simulation and optimized scheduling of the overall operating state of the power system. This helps to improve the operating efficiency of the power system, reduce operating costs, and provide a scientific basis for the planning, design, and operation of the power system.

[0311] Energy flow constraints in gas pipelines after a leak failure include:

[0312] like Figure 4 As shown, a pipe segment xy is split into two segments xm and my to calculate the energy flow separately. The energy flow equation for the xm segment is as follows:

[0313]

[0314] λg,xm =λ g,xy ;

[0315] In the formula, L xm and D xy They represent natural gas pipeline L xm The length and inner diameter of the xy segment; This represents the air pressure at pipe node x at time t; and λ represents the flow rate of natural gas flowing into and out of the natural gas pipeline, respectively. g,xm The friction coefficient of the natural gas pipeline segment xm is represented by w0; the initial flow velocity of the natural gas is represented by c. w Indicates the speed of sound;

[0316] The energy flow equation for pipe segment my is:

[0317]

[0318] λ g,my =λ g,xy ;

[0319] The energy flow balance equation must be satisfied at the virtual gas load:

[0320]

[0321] Among them, virtual gas load The calculation formula:

[0322]

[0323] In the formula, R c κ is the gas constant of natural gas, and κ is the isentropic exponent of natural gas. At room temperature, the value of κ for an ideal gas can be approximated as a constant, and κ is taken as 1.29.

[0324] This allows for a more refined simulation of pipeline energy flow. Dividing a pipeline into two segments (xy and my) and calculating the energy flow separately allows for a more accurate simulation of natural gas flow within the pipeline. This refined modeling helps to more accurately reflect the actual flow state of natural gas within the pipeline, especially when considering leakage faults. Furthermore, the impact of leakage faults on energy flow is considered. By introducing relevant parameters (such as pipeline length, inner diameter, gas pressure, and flow rate) into the energy flow equations and combining them with the characteristics of leakage faults, the impact of leakage faults on pipeline energy flow can be simulated. This helps to assess the degree of impact of leakage faults on the overall operation of the gas network, providing a scientific basis for emergency response and recovery strategies after a fault. In addition, considering the energy flow constraints of the gas network pipelines after a leakage fault helps to promptly identify and address potential leakage risks, thereby avoiding or reducing the impact of leakage faults on the safe operation of the gas network. This contributes to improving the overall safety and reliability of the gas network. Based on refined modeling and energy flow balance analysis, more optimized gas network operation strategies can be formulated. For example, after a leakage fault occurs, parameters such as pipeline flow rate and gas pressure can be adjusted according to energy flow constraints to restore the normal operation of the gas network as quickly as possible.

[0325] Energy flow constraints in heating network pipelines after a leak failure include:

[0326]

[0327] In the formula, The pressure at node i of the heating network pipeline; λ represents the pressure at node i of the heating network pipeline; h,l The frictional resistance coefficient of the heating network pipeline; w h,l D represents the hot water flow velocity in the pipe. l This refers to the diameter of the heating network pipe.

[0328] The coefficient of frictional resistance is:

[0329]

[0330] In the formula, λ h,l D is the coefficient of frictional resistance of the heating network pipeline; l Where is the diameter of the heating network pipe; Δ is the equivalent roughness of the pipe, usually taken as 1 mm; Re is the Reynolds number.

[0331]

[0332] In the formula, υ is the viscosity coefficient;

[0333] The formula for calculating pressure drop is:

[0334]

[0335] L l Indicates the length of the natural gas pipeline; ρw Indicates the density of water; This indicates the working fluid quality of the water supply pipe numbered l at different times;

[0336] When a leak occurs in the heating network pipeline, a virtual hot water load can be assumed to replace the hot water lost due to the leak, such as... Figure 5 As shown. The mass of hot water flowing out of the pipe. equal to from arrive The composition of the hot water mass injected into the pipeline during a given period. Hot water takes time to travel through the pipeline, known as the transmission delay. When a leak occurs, the hot water flow rate in the pipeline changes, causing a change in the transmission delay.

[0337] The original pipeline was split into two segments for calculation, and the transmission delay time was redefined.

[0338]

[0339] In the formula, A l This indicates the cross-sectional area of ​​the pipe;

[0340] The water flow rate caused by the pipeline leak is:

[0341]

[0342] In the formula, C2 is the flow coefficient, and A h,leak The area of ​​the leak hole;

[0343] The relationship between the virtual hot water load and the hot water flow rate at both ends of the pipe is as follows:

[0344]

[0345] The hot water temperature can be calculated based on the changed hot water flow rate:

[0346]

[0347] in,

[0348]

[0349] A leak at any point in the heating system will affect the amount of hot water returning to the heat source, causing hydraulic imbalance and consequently affecting the stable operation of the heat source. To ensure the normal operation of the heating system, this invention adopts a centralized water replenishment method, that is, unified water production at the inlet of the heat source return water pipe or using condensate for replenishment and pressure stabilization. The temperature fusion equation for the water replenishment process is:

[0350]

[0351] In the formula, The return water temperature after water is added to the heat source; The water replenishment volume is the difference between the flow rate of the inlet water pipe and the flow rate of the return water pipe at the heat source. The water replenishment temperature is usually set to ambient temperature.

[0352] This allows for a more refined simulation of the impact of leaks on the heating network. By splitting the existing pipeline into two segments at the leak point and redefining the transmission delay time, the impact of leaks on energy flow in the heating network pipelines can be simulated more accurately. This refined modeling helps to more accurately reflect the operating status of the heating network after a leak. Furthermore, by introducing parameters such as the friction resistance coefficient and Reynolds number, as well as the formula for calculating pressure drop, the friction resistance and pressure drop in the heating network pipelines can be accurately calculated. This helps to assess the impact of leaks on the flow characteristics of the heating network pipelines, providing a scientific basis for subsequent fault handling. By calculating the leakage flow rate and virtual hot water load, the impact of leaks on the hot water flow rate of the heating network can be quantified. This helps to assess the degree of impact of leaks on the overall performance of the heating system and to take corresponding measures to reduce leakage losses and restore the normal operation of the heating system. Based on the changed hot water flow rate, the hot water temperature can be calculated. This helps to assess the impact of leaks on the hot water temperature and ensure that the heating system can provide the required hot water temperature.

[0353] Furthermore, by adopting a centralized water replenishment method, with unified water production at the inlet of the heat source return water pipeline or using condensate for replenishment and pressure stabilization, the water replenishment strategy can be optimized. This helps reduce energy loss during the water replenishment process and improves the overall efficiency of the heating system. Considering the energy flow constraints of the heating network pipelines after a leakage fault helps to promptly detect and address potential leakage risks, thereby avoiding or reducing the impact of leakage faults on the reliability and stability of the heating system. This contributes to improving the overall performance of the heating system and user satisfaction.

[0354] When constructing the optimal dynamic energy flow model of a comprehensive energy system considering leakage faults, the objective function is as follows:

[0355]

[0356] In the formula, c gw,x Indicates gas source cost; c curs,x c represents the air reduction load penalty coefficient; pg,k Indicates the output cost of a coal-fired power unit; c curl,k c represents the load penalty factor for electrical discharge; curh,e Indicates the heat load penalty coefficient; Indicates the gas supply volume; Indicates the air cutting load; Indicates the electrical power of the coal-fired power unit; Indicates the amount of electricity load reduction; This indicates the amount of annual heat load reduction; denoted by d, m(x) represents the seasonal heat load reduction; m(k) represents the set of gas network nodes; m(k) represents the set of power nodes; Q(x) represents the set of gas source nodes; p(x) represents the set of coal-fired unit nodes; and n(d) represents the set of pipelines connected to node d.

[0357] Thus, the impact of leakage failures on the integrated energy system is considered when constructing the objective function. Leakage failures may lead to a reduction in gas supply, a decrease in electrical and thermal loads, and an increase in corresponding penalty costs. By incorporating these factors into the objective function, it can be ensured that the model accurately reflects the impact of leakage failures on the system's economics. Furthermore, the optimal dynamic energy flow model of the integrated energy system considering leakage failures needs to be adaptable to different operating scenarios and failure conditions. By constructing a flexible objective function, it can be ensured that the model can quickly adjust energy supply and demand strategies in the face of leakage failures to minimize costs and meet energy demand. In addition, all costs in the objective function are closely related to the energy supply and demand balance. For example, gas source costs and gas load reduction penalty coefficients reflect the tightness of natural gas supply and demand; coal-fired unit output costs and electrical load reduction penalty coefficients reflect the balance of electricity supply and demand; and thermal load reduction penalty coefficients reflect the supply and demand relationship of thermal load. By optimizing these costs, it can be ensured that the integrated energy system can still maintain the balance of energy supply and demand under leakage failures.

[0358] S1. Input the topology data of the integrated energy system and the parameters of each component, and initialize the state of each component of the integrated energy system to the normal operating state;

[0359] S2. Based on the integrated energy system, the state duration sampling algorithm is used to extract the state time sequence of all components, including the natural gas network and heating network pipelines; the time sequence of all components is combined to obtain the system state transition sequence, and it is divided according to days.

[0360] In practice, the process of extracting the component state timing sequence using the state duration sampling algorithm includes:

[0361] Step 2.1: Set the initial state of all components;

[0362] Step 2.2: According to Figure 6 Determine the set of states Ω that an element m (m = 1, 2, ..., M) can transition from its current state. m Where M is the total number of all components; Figure 6 (a) is a two-state transition diagram of a pipeline without considering pipeline leakage faults; Figure 6 (b) A three-state Markov transition diagram for pipeline considering pipeline leakage failure.

[0363] Step 2.3: Transition rate based on the set of transitional states The set of durations in the current state before transitioning to the next state is obtained by sampling each element according to the following formula.

[0364]

[0365] in, Given a uniformly distributed random number in the range [0,1] under state j;

[0366] Step 2.4: Select the minimum value in the duration set as the duration of the current state of each element, and simultaneously use the transition state corresponding to the minimum value as the element state to be transitioned to next:

[0367]

[0368] In step 2.4, if the transferred state involves a leakage fault in the natural gas network and / or heating network pipeline, a random sampling method is also used to determine the orifice size and location of the pipeline leakage fault. The process includes:

[0369] First, for a random number Q between [0,1] is drawn from the pipeline, the distance from the leak fault location to the beginning of the pipeline is calculated as L′=LQ, where L is the length of the pipeline;

[0370] Then, the diameter of the leakage orifice is simulated using random numbers d ~ N(μ,σ) that follow a normal distribution, where μ and σ are related to the type of pipe.

[0371] By setting the initial state of all components and sampling each component based on the transition rate of the transitional state set, the duration for which an component remains in its current state before transitioning to the next state can be obtained. This process accurately simulates changes in component states, providing a foundation for subsequent energy system analysis and optimization. When the transitioned state involves a leak fault state in a natural gas network and / or heating network pipeline, a random sampling method is used to determine the orifice size and location of the pipeline leak fault. This process not only considers the probability of a leak fault but also further refines the specific characteristics of the leak fault (such as orifice size and location), making the simulation results closer to reality. Furthermore, by accurately simulating component states and leak fault characteristics, this method can support operational decisions for the energy system. For example, when a leak fault occurs, the fault location and orifice size can be quickly determined based on the simulation results, allowing for appropriate emergency measures to be taken to reduce the impact of the fault on the system. Moreover, considering the state duration sampling algorithm for leak faults helps in analyzing the reliability of the energy system. By simulating the system operating states under different fault conditions, the reliability and stability of the system can be evaluated, providing a scientific basis for the optimal design and operation of the system.

[0372] S3. For each system state transition sequence obtained by division, use the optimal dynamic energy flow model of the integrated energy system considering leakage faults to determine the energy load reduction amount; record the load reduction amount and the duration of the load reduction state until the number of recorded days reaches the preset simulation duration (e.g., 365 days).

[0373] S4. Calculate the sample mean of the recorded load reduction amount and duration; calculate the variance convergence coefficient using the standard deviation of the sample mean, and perform a convergence judgment; if the convergence condition is met, stop the iterative simulation, and calculate the reliability index of the integrated energy system based on the recorded load reduction amount and load reduction duration; if the convergence condition is not met, return to S2 to continue the iterative simulation for the next simulation duration.

[0374] In practice, the process of determining whether the convergence condition is met includes:

[0375] The variance convergence coefficient σ is calculated using the standard deviation of the sample mean. k The calculation formula is:

[0376] σ ele,k = std(EENS) / EENS;

[0377] σ gas,k = std(EGNS) / EGNS;

[0378] σ heat,k = std(EHNS) / EHNS;

[0379] Among them, EENS represents the expected value of insufficient electricity supply; EGNS represents the expected value of insufficient natural gas supply; and EHNS represents the expected value of insufficient heat supply.

[0380] Choose the maximum value of the three coefficients as the convergence criterion:

[0381] σ k =max{σ ele,k ,σ gas,k ,σ heat,k};

[0382] Where std represents the standard deviation; if σ k If the value is ≤0.02 or the total simulation time reaches the preset value, the simulation is considered to meet the convergence condition and is stopped; otherwise, the simulation is considered not to meet the convergence condition.

[0383] The reliability index of the integrated energy system can be calculated using existing technical means. The calculation process is not a creative aspect of this method and will not be elaborated here.

[0384] This improves the reliability of simulation results. Choosing the maximum value among the three coefficients (EENS, EGNS, and EHNS corresponding to variance convergence coefficients) as the convergence criterion ensures that convergence requirements are met across multiple key indicators. This contributes to the reliability of simulation results, making them more reflective of reality. Furthermore, it supports simulation analysis of complex systems. For energy systems containing multiple components and complex relationships, this method provides an effective convergence determination approach. By continuously monitoring and judging convergence conditions, it ensures that the simulation process accurately reflects the system's operating state and characteristics.

[0385] Most existing fault models employ simple two-state models, neglecting the impact of leakage faults on the system, leading to inaccurate assessment results. This method constructs an optimal dynamic energy flow model for the integrated energy system that considers leakage faults, accurately simulating the impact of leakage faults in natural gas and heating pipelines on energy flow and distribution. Furthermore, existing reliability assessment methods often ignore the dynamic process of leakage faults, resulting in errors in the assessment results. In this method, by employing a state duration sampling algorithm to extract the component state time sequence and combining it with the system state transition sequence for simulation, this approach can more accurately assess the reliability of the integrated energy system.

[0386] Furthermore, this method can comprehensively consider the complexity of integrated energy systems. Existing assessment methods often focus only on a single system or a single type of failure, neglecting the complexity and diversity of integrated energy systems. This method not only considers leakage failures in natural gas and heating pipelines but also comprehensively considers the interactions and influences of the power, natural gas, and heating systems, thus enabling a more comprehensive assessment of the reliability of integrated energy systems. In addition, existing assessment methods often fail to provide sufficient information and support for optimal scheduling, limiting the improvement of system performance. By accurately assessing the impact of pipeline leakage failures on integrated energy systems, this method can provide important basis for optimal system scheduling, contributing to improved system operating efficiency and energy utilization efficiency.

[0387] This method, by considering pipeline leakage failures in the integrated energy system reliability assessment, can significantly improve the accuracy and precision of the assessment results, providing strong support for the optimized operation and reliability management of integrated energy systems.

[0388] Example 2

[0389] To better understand this method, the following example is provided.

[0390] Example system such as Figure 7 As shown, an integrated energy system consisting of an IEEE 30-node power system, a Belgian 20-node natural gas system, and a 16-node thermal system is used to verify the reliability assessment method proposed in this invention that considers leakage failures in gas and thermal pipelines, and to analyze the impact of leakage failures in natural gas and thermal pipelines on the reliability assessment of the integrated energy system.

[0391] This integrated energy system includes 3 coal-fired power units, 1 combined heat and power (CHP) unit, 2 gas turbines, 41 power lines, 6 gas sources, 2 compressors, 17 natural gas pipelines, 2 gas-fired boilers, 1 heat pump, and 14 sets of heating network pipelines. To improve computational efficiency, a modified typical daily curve is used to represent the annual level, such as... Figure 8 As shown. Furthermore, a leak can be considered a fault when the ratio of the leak orifice diameter to the pipe diameter is less than 0.1. Therefore, for leak faults in gas and heating network pipelines, it is assumed that the average orifice diameter of gas network leaks is 10 mm with a standard deviation of 4 mm. The average orifice diameter of heating network leaks is 65 mm with a standard deviation of 10 mm. Reliability parameters for natural gas and power systems are available in the literature. The duration of faults in heating network pipeline components generally exceeds 10 hours, and can even reach tens of hours. The reliability numbers of the three-state Markov model for natural gas and heating network pipelines are shown in Tables 1 and 2. Given the fault transition rate from a leak state to a shutdown state, the other two fault transition rates can be calculated using the following formulas.

[0392]

[0393] Table 1. Markov Model Parameters for Natural Gas Network Pipelines

[0394]

[0395] Table 2 Markov Model Parameters for Heating Network Pipelines

[0396]

[0397] Two scenarios are set up to verify the impact of pipeline leakage faults in natural gas and heating networks on the reliability assessment of integrated energy systems. Scenario 1 uses a traditional two-state reliability model without considering leakage faults, while Scenario 2 uses a three-state Markov reliability model for pipelines that considers leakage faults.

[0398] Table 3 presents the reliability indices of the integrated energy system (electricity, natural gas, and heat) in two scenarios. As can be seen from the table, compared to Scenario 1, the changes in the power system reliability indices EENS and LOGLP in Scenario 2 are smaller. This is because, when considering pipeline leakage faults, the power system, due to its highest load importance, is the last to be disconnected during integrated dispatching. For the natural gas system, EENS and LOGLP in Scenario 1 are 6.6319 × 10⁻⁶. 2 m 3 and 5.4201×10 -5 Scene 2 increased to 1.9834×10 3 m 3 and 1.6209×10 -4 The trends in LOGLP and EGNS are consistent. For the thermal system, the reliability indices EHNS and LOHLP in Scenario 2 are 2.04 and 1.45 times higher than those in Scenario 1, respectively. The results show that the reliability indices of both the natural gas and thermal systems in Scenario 2 are significantly higher. This is because natural gas and thermal pipelines are mostly radial networks, and some load points can only receive unidirectional energy transmission. The multi-state model considering leakage faults has a significant impact on the system reliability indices. This also indicates that in integrated energy systems, ignoring leakage faults in component reliability modeling will greatly underestimate the system reliability indices. Therefore, accurate reliability modeling of natural gas and thermal pipelines is necessary to improve the accuracy of reliability assessment for integrated energy systems.

[0399] Table 3 also shows that the expected natural gas leakage rate (EGL) is 5.2 times that of the expected natural gas supply shortage rate (EGNS), and the natural gas leakage probability rate (PGL) is 4.1 times that of the natural gas system efficiency rate (LOGLP). This indicates that the risk of natural gas leakage is far greater than the risk of system gas load reduction. The expected hot water leakage rate (EWL) reaches 4.1327 × 10⁻⁶. 3 m 3This indicates a significant waste of water resources, which will further lead to hydraulic imbalance in the heating system and poor heating performance for users.

[0400] Table 3 Considering the impact of leakage failures on the reliability index of the integrated energy system

[0401]

[0402]

[0403] Tables 4 and 5 show the node reliability indices for natural gas and heating systems, respectively, investigating the impact of leakage failures on the reliability of different nodes in power, natural gas, and heating systems. The node reliability indices are calculated using the following formula:

[0404]

[0405] Replace the system load loss in the formula with the node load loss. Since the power system reliability indicators change relatively little, node reliability analysis is not performed here. It should be noted that the load reduction of gas-coupled nodes, such as load reductions at nodes 6, 7, 10, 15, and 16 of the natural gas system, is not included in the total reduction and therefore will not be analyzed.

[0406] Table 4 shows that the EGNS changes at natural gas nodes 19 and 20 in Scenario 2 increase dramatically, approximately three times that in Scenario 6. The change rates at natural gas nodes 3 and 12 are relatively smaller, at 1.28 and 1.08 times respectively. This phenomenon is because the loads at nodes 19 and 20 are supplied with natural gas via unidirectional pipelines B15 to B17. If these pipelines experience leaks, in addition to the leaked gas load, the pipeline inventory is reduced, increasing the load reduction at nodes 19 and 20—that is, the magnitude of the load reduction is greater. Furthermore, the EGNS value at node 20 is relatively large, at 4.5571 × 10⁻⁶ in both Scenario 1 and Scenario 2. 2 m 3 and 1.3633×10 3 m 3 This is mainly because the natural gas load at node 20 is relatively far from the gas source. Considering the pressure operating limitations of the natural gas system, in an emergency, natural gas is more easily transported from the gas source to nearby gas loads rather than to more distant locations.

[0407] Table 4 considers the impact of leakage failures on the EGNS (Engineering Reliability Index) of natural gas system nodes.

[0408]

[0409] Table 5 considers the impact of leakage failures on the EHNS (Electronic Highness Reliability Index) of the thermal system.

[0410]

[0411]

[0412] As shown in Table 5, nodes 6, 7, 14, and 15 have relatively high EHNS indices due to their larger heat loads. Compared to scenario 1, the improvement ratios of nodes 5, 6, 7, and 8 in scenario 2 increase sequentially, while nodes 13, 14, 15, and 16 show the same trend, with all improvement ratios being larger than those of nodes at the same topological location. The results indicate that the increase in reliability indices is greater for nodes further down the heat network topology. This is because pipe leaks have a cumulative impact on later-downward ... Furthermore, the reason why the right-side thermal system (nodes 13-16) has a slightly larger boost ratio than the left-side thermal system (nodes 5-8) is that the heat source C4 of the right-side thermal system is supplied with natural gas by natural gas node 15, and the pipeline connected to natural gas node 15 can only transmit natural gas in one direction. Therefore, a natural gas leak will have a greater impact on the right-side thermal system.

[0413] The simulation results show that after adopting the three-state reliability model of the pipeline that includes leakage faults, the reliability index values ​​of both the natural gas and heating systems increased significantly, indicating that leakage faults have a significant impact on system reliability indexes. This also demonstrates that ignoring leakage faults in component reliability modeling will significantly overestimate the overall reliability level of the energy system.

[0414] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the technical solutions. Those skilled in the art should understand that any modifications or equivalent substitutions to the technical solutions of the present invention without departing from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A comprehensive energy system reliability assessment method considering pipeline leakage failures, characterized in that, Includes the following steps: S0. Construct an integrated energy system, which includes an electricity system, a natural gas system, and a heat system; and based on the integrated energy system, consider leakage faults in the natural gas network and heat network pipelines, as well as the impact of these faults on energy flow and distribution, and construct an optimal dynamic energy flow model for the integrated energy system that considers leakage faults. S1. Input the topology data of the integrated energy system and the parameters of each component, and initialize the state of each component of the integrated energy system to the normal operating state; S2. Based on the integrated energy system, the state duration sampling algorithm is used to extract the state time sequence of all components, including the natural gas network and heating network pipelines; the time sequence of all components is combined to obtain the system state transition sequence, and it is divided according to days; S3. For each system state transition sequence obtained by division, the energy load reduction amount is determined by the optimal dynamic energy flow model of the integrated energy system considering leakage faults; the load reduction amount and the duration of the load reduction state are recorded until the number of recorded days reaches the preset simulation duration. S4. Calculate the sample mean of the recorded load reduction amount and duration; The variance convergence coefficient is calculated using the standard deviation of the sample mean, and a convergence test is performed. If the convergence condition is met, the iterative simulation stops, and the reliability index of the integrated energy system is calculated based on the recorded load reduction amount and load reduction duration. If the convergence condition is not met, the simulation returns to S2 to continue the iterative simulation for the next simulation duration. Among them, when constructing the optimal dynamic energy flow model of the integrated energy system considering leakage faults, the constraints include natural gas system constraints, thermal system constraints, power system constraints, gas network pipeline energy flow constraints considering leakage faults, and thermal network pipeline energy flow constraints considering leakage faults. Natural gas system constraints include: The relationship between the amount of natural gas consumed by the compressor and the flow rate through the compressor: The pressure relationship between the compressor's inlet and outlet: Gas pressure at natural gas nodes Upper and lower limit constraints: in, This indicates the flow rate through the compressor piping. γ represents the amount of natural gas consumed by the compressor. cp For boost ratio, This indicates the near-gas pressure of the compressor. P represents the near-gas pressure of the compressor. x,min P represents the lower limit of natural gas node pressure. x,max Γ represents the upper limit of the natural gas node; Γ represents the coefficient relating the amount of natural gas consumed by the compressor to the flow rate through the compressor. Operating constraints of CHP cogeneration units: In the formula, This refers to the operating power of the CHP unit; and These refer to the power generation and heating capacity of the extraction-type thermal power unit, respectively; c v c is the rate of change of power generation as heating power increases, assuming the total steam intake of the unit remains constant. m Here, r is the back pressure operating flexibility coefficient of the CHP unit; r is a constant. PN represents the operating status of the CHP unit at time t, where 1 represents normal operation and 0 represents a fault. max,x and PN min,x These represent the upper and lower limits of the power generation capacity of the CHP unit under pure condensing conditions; PH max,x The upper limit of the heating capacity of the CHP unit; Natural gas balance equation: Node traffic balancing: Upper and lower limits of load shedding: Upper and lower limits of gas source output constraints: Pipeline flow rate upper and lower limit constraints: Wherein, mass flow rate Q = ρwA represents the amount of natural gas passing through any cross-section of the pipe per unit time, where A is the cross-sectional area of ​​the pipe, ρ is the density, and w is the flow velocity; the ideal gas law P = ρZR c T represents the relationship between gas pressure, density, and temperature, where Z is the compressibility factor of natural gas, and R... c Here, T is the specific gas constant of natural gas, and T is the temperature. In the formula, n(x) represents the set of pipes connected to node x; and This represents the outflow and inflow flow rates of pipe x and y; Indicates the gas supply volume. Indicates gas load. Indicates the air cutting load; This indicates the flow rate through the compressor piping; This indicates the amount of natural gas consumed by the compressor; and Represent the electrical power of the gas turbine and the thermal power of the gas boiler, respectively; m(x) represents the set of gas network nodes; η gas ,η chp and η heat These represent the conversion efficiencies of the gas turbine, CHP unit, and gas boiler, respectively; cp represents the compressor branch. Linear equations for dynamic energy flow model of natural gas pipelines: The pipeline xy is divided into several segments with an interval of Δx; in terms of time, time t is the initial point of the operating cycle. and Let x be the initial flow rate and pressure at time t at pipe x; Natural gas pipeline storage equation: In the formula, This represents the amount of natural gas stored in the pipeline of length Δx at time t; Δt represents the differential time interval; Δx represents the differential length interval; time t is the initial point of the operating cycle. and Let x be the initial flow rate and pressure at time t at pipe x; Linear equation for dynamic energy flow of natural gas: In the formula, λ is the hydraulic friction coefficient, and D is the inner diameter of the natural gas pipeline; The expression for sgn(Q) is: The friction coefficient λ of a natural gas pipeline is: In the formula, ε g This refers to the absolute roughness of the natural gas pipeline. And introduce 0-1 variables to establish flow direction constraints: In the formula, The variable is used to determine the x and y directions of the pipeline; it is 1 when Q > 0 and 0 when Q < 0; N is a preset value. The stored data is restored every 24 hours. In the formula, I is an integer, and δ is the range of variation; Thermal system constraints include: Hot water temperature transfer equation: Hot water quality flowing out of the pipe From arrive The mass composition of the hot water flowing into the pipes during a given period: in, The working fluid mass of water supply pipeline number l at different times; ρ w A l and L l Let represent the density of water, the cross-sectional area of ​​the pipe, and the length of the pipe, respectively; the mass of hot water flowing into the pipe during the time interval from t to t+Δt is . The mass of hot water flowing out of the pipe during the time interval Δt is equal to the mass of hot water flowing into the pipe. Hot water temperature at the outlet of the supply and return water pipes It consists of a linearly weighted average of the mass temperature of hot water injected over several time periods: The pipe outlet temperature, taking heat loss into account, is corrected using the Sukhov temperature drop model: in, and These represent the outlet and inlet temperatures of the water supply pipe at time t, respectively. and These represent the outlet and inlet temperatures of the return water pipe at time t, respectively. Indicates in The quality of hot water injected into the pipeline during time period t; Indicates from The quality of hot water injected into the pipeline during time period t; and T represents the outlet temperature of the supply and return water pipes, respectively. e , c and c represent the ambient temperature, the thermal conductivity of pipe l, and the specific heat capacity of water, respectively. Pipeline hot water temperature fusion constraint: Hot water temperature mixing constraints at pipe junctions: The inlet temperature of all pipes flowing out of node m is equal to this mixing temperature: Pipeline temperature upper and lower limit constraints: in, and T represents the temperature at node m in the supply and return water pipes, respectively. s,min T indicates the lower limit of the water supply pipeline temperature. s,max Indicates the upper limit of water supply pipe temperature, T r,min T represents the lower limit of the temperature of the return water pipe. r,max Indicates the upper limit of the return water pipe temperature; Dynamic model of heat load: Indoor temperature was simulated using a building heat load model: In the formula, Let t be the seasonal heat load power of the user at time t. Let R be the indoor temperature of the user at time t, R be the equivalent thermal resistance of the building, and C be the equivalent heat capacity. Seasonal heat load reduction for: In the formula, T com,min and T com,max These refer to the minimum and maximum suitable indoor temperatures of the building, respectively. Equilibrium equations of a thermodynamic system: The relationship between heat source output and hot water temperature in the pipes: Relationship between year-round and seasonal loads and pipe hot water temperature: Annual load reduction limits: in, This indicates the heating capacity of the CHP units in the heating network. This indicates the thermal power of the gas-fired boilers in the heating network. Let n(e) represent the heat pump power in the heating network, n(e) represent the set of pipes at the heat source outlet, and m(e) represent the set of nodes in the heating network. Indicates the year-round heat load. This represents the annual heat load reduction, and s(e) represents the piping assembly at the heat exchanger inlet. This indicates the upper limit of the annual load reduction; Power system constraints include: Node power balancing: The relationship between trend and node angle: Node phase angle constraints: Load shearing upper and lower limit constraints: Power line transmission capacity constraints: Upper and lower limits of electrical power output for coal-fired units, gas turbines, and CHP units: in, Indicates the electrical power of the gas turbine. This indicates the electrical power of the extraction-type thermal power unit. This indicates the electrical power of the coal-fired power unit. This represents the heat pump power, and j represents the power branch connected to node k. Indicates power flow in power lines; η represents the amount of load reduction, m(k) represents the set of power nodes; hp Indicates the heat pump's conversion efficiency, b kl PG represents the line reactance, θ represents the node phase angle, and PG represents the line reactance. k,min PG represents the lower limit of the electrical power output of a coal-fired power unit. k,max PE represents the upper limit of electrical power output of a coal-fired power unit. k,min PE represents the lower limit of the electrical power output of the gas turbine. k,max PN indicates the upper limit of the electrical power output of the gas turbine. k,min PN indicates the lower limit of the electrical power output of the CHP unit. k,max Indicates the upper limit of electrical power output of the CHP unit; F kj,max Lt represents the upper limit of power flow in a power line. k This indicates the upper limit of load reduction.

2. The comprehensive energy system reliability assessment method considering pipeline leakage faults as described in claim 1, characterized in that: Energy flow constraints in gas pipelines after a leak failure include: The energy flow is calculated by splitting a pipe segment xy into two segments xm and my. The energy flow equation for the xm segment is as follows: l g,xm =λ g,xy ; In the formula, L xm and D xy They represent natural gas pipeline L xm The length and inner diameter of the xy segment; This represents the air pressure at pipe node x at time t; and λ represents the flow rate of natural gas flowing into and out of the natural gas pipeline, respectively. g,xm The friction coefficient of the natural gas pipeline segment xm is represented by w0; the initial flow velocity of the natural gas is represented by c. w Indicates the speed of sound; The energy flow equation for pipe segment my is: l g,my =λ g,xy ; The energy flow balance equation must be satisfied at the virtual gas load: Among them, virtual gas load The calculation formula: In the formula, R c κ is the gas constant of natural gas, and κ is the isentropic index of natural gas.

3. The comprehensive energy system reliability assessment method considering pipeline leakage faults as described in claim 2, characterized in that: Energy flow constraints in heating network pipelines after a leak failure include: In the formula, The pressure at node i of the heating network pipeline; λ represents the pressure at node i of the heating network pipeline; h,l The frictional resistance coefficient of the heating network pipeline; w h,l D represents the hot water flow velocity in the pipe. l The diameter of the heating network pipe; The coefficient of frictional resistance is: In the formula, λ h,l D is the coefficient of frictional resistance of the heating network pipeline; l Where is the diameter of the heating network pipe; Δ is the equivalent roughness of the pipe, usually taken as 1 mm; Re is the Reynolds number. In the formula, υ is the viscosity coefficient; The formula for calculating pressure drop is: L l Indicates the length of the natural gas pipeline; ρ w Indicates the density of water; This indicates the working fluid quality of the water supply pipe numbered l at different times; When a leak occurs in the heating network pipeline, the original pipeline is split into two sections for calculation, and the transmission delay time is redefined. In the formula, A l This indicates the cross-sectional area of ​​the pipe; The water flow rate caused by the pipeline leak is: In the formula, C2 is the flow coefficient, and A h,leak The area of ​​the leak hole; The relationship between the virtual hot water load and the hot water flow rate at both ends of the pipe is as follows: The hot water temperature can be calculated based on the changed hot water flow rate: in, To ensure the normal operation of the heating system, a centralized water replenishment method is adopted, with water uniformly produced at the inlet of the heat source return water pipeline or condensate used for replenishment and pressure stabilization; the temperature fusion equation for the water replenishment process is: In the formula, The return water temperature after water is added to the heat source; The water replenishment volume is the difference between the flow rate of the inlet water pipe and the flow rate of the return water pipe at the heat source. The water replenishment temperature is usually set to ambient temperature.

4. The comprehensive energy system reliability assessment method considering pipeline leakage faults as described in claim 3, characterized in that: When constructing the optimal dynamic energy flow model of a comprehensive energy system considering leakage faults, the objective function is as follows: In the formula, c gw,x Indicates gas source cost; c curs,x c represents the air reduction load penalty coefficient; pg,k Indicates the output cost of a coal-fired power unit; c curl,k c represents the load penalty factor for electrical discharge; curh,e Indicates the heat load penalty coefficient; Indicates the gas supply volume; Indicates the air cutting load; Indicates the electrical power of the coal-fired power unit; Indicates the amount of electricity load reduction; This indicates the amount of annual heat load reduction; denoted by d, m(x) represents the seasonal heat load reduction; m(k) represents the set of gas network nodes; m(k) represents the set of power nodes; Q(x) represents the set of gas source nodes; p(x) represents the set of coal-fired unit nodes; and n(d) represents the set of pipelines connected to node d.

5. The comprehensive energy system reliability assessment method considering pipeline leakage faults as described in claim 1, characterized in that: In S2, the process of extracting the element state timing sequence using the state duration sampling algorithm includes: Step 2.1: Set the initial state of all components; Step 2.2: Determine the set of states Ω that the current state of element m (m = 1, 2, ..., M) can transition to. m Where M is the total number of all components; Step 2.3: Transition rate based on the set of transitional states The set of durations in the current state before transitioning to the next state is obtained by sampling each element according to the following formula. in, Given a uniformly distributed random number in the range [0,1] under state j; Step 2.4: Select the minimum value in the duration set as the duration of the current state of each element, and simultaneously use the transition state corresponding to the minimum value as the element state to be transitioned to next: In step 2.4, if the transferred state involves a leakage fault in the natural gas network and / or heating network pipeline, a random sampling method is also used to determine the orifice size and location of the pipeline leakage fault. The process includes: First, for a random number Q between [0,1] is drawn from the pipeline, the distance from the leak fault location to the beginning of the pipeline is calculated as L′=LQ, where L is the length of the pipeline; Then, the diameter of the leakage orifice is simulated using random numbers d ~ N(μ,σ) that follow a normal distribution, where μ and σ are related to the type of pipe.

6. The integrated energy system reliability assessment method considering pipeline leakage faults as described in claim 1, characterized in that: In S4, the process of determining whether the convergence condition is met includes: The variance convergence coefficient σ is calculated using the standard deviation of the sample mean. k The calculation formula is: σ ele,k =std(AGREE) / AGREE; σ gas,k =std(EGNS) / EGNS; σ heat,k =std(EHNS) / EHNS; Wherein, EENS represents the expected value of insufficient electricity supply; EGNS represents the expected value of insufficient natural gas supply; and EHNS represents the expected value of insufficient heat supply. Choose the maximum value of the three coefficients as the convergence criterion: s k =max{σ ele,k ,s gas,k ,s heat,k }; Where std represents the standard deviation; if σ k If the value is ≤0.02 or the total simulation time reaches the preset value, the simulation is considered to meet the convergence condition and is stopped; otherwise, the simulation is considered not to meet the convergence condition.

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