Comprehensive energy system reliability evaluation method considering pipeline leakage fault

Abstract

The invention belongs to the technical field of integrated energy systems, and particularly relates to an integrated energy system reliability evaluation method considering a pipeline leakage fault, which comprises the following steps: S0, constructing an integrated energy system; constructing an optimal dynamic energy flow model of the integrated energy system considering the leakage fault; s1, initializing an integrated energy system; s2, adopting a state duration sampling algorithm to extract all element state time sequence sequences including natural gas network and heat supply network pipelines; combining the sequential sequences of all the elements to obtain a system state transition sequence; s3, for each divided system state transition sequence, determining an energy load reduction amount by adopting an optimal dynamic energy flow model of the integrated energy system considering a leakage fault; recording the load reduction amount and the duration of the load reduction state; s4, carrying out convergence judgment; and if the convergence condition is met, stopping iterative simulation. According to the method, the reliability evaluation precision of the integrated energy system can be improved on the basis of considering the pipeline leakage fault.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy systems, and in particular relates to a reliability assessment method for integrated energy systems taking pipeline leakage failures into consideration. Background Art

[0002] In the integrated energy system, natural gas network and heat network pipelines, as key infrastructure, undertake the important task of energy transmission and distribution. However, due to the long transmission distance and long-term exposure to complex environments, these pipelines are extremely susceptible to corrosion from energy-carrying media, air media and environmental microorganisms, resulting in frequent leakage failures. Leakage failures will not only cause the loss of energy-carrying media and reduce the effective supply of energy, but may also damage the balance of the existing heating system, thereby affecting the use of hot water and room temperature heating effects by heat users. In severe cases, it may even cause heating to stop in some areas, causing great inconvenience to residents' lives.

[0003] Traditional pipeline failure models for natural gas and heat networks mostly use a simple two-state model, namely the normal operation state and the fault state, without fully considering the important factor of leakage failure. This simplified model cannot accurately reflect the failure mode of the pipeline, especially in terms of the impact of leakage failure on system reliability and operation optimization, and has significant limitations.

[0004] In fact, there are significant differences in the impact mechanisms between leakage failures and rupture failures. After a rupture failure occurs, the monitoring system can respond quickly, close the valves at both ends of the faulty pipeline, and block the normal delivery of the energy-carrying medium. However, leakage failures are difficult to detect in time because of their small impact on pipeline pressure, and can usually only be detected by human patrols. Before the leakage failure is repaired, the faulty pipeline can continue to transfer the energy-carrying medium and supply the load, but a small amount of the energy-carrying medium will continue to leak into the environment, which not only causes a waste of energy, but may also cause environmental pollution.

[0005] In addition, the frequency of leakage failures is much higher than that of fracture failures, and its probability of occurrence is 3-10 times that of fracture failures. Therefore, if the impact of leakage failures in the reliability assessment of integrated energy systems is ignored, it is likely to lead to large errors in the assessment results and fail to accurately reflect the actual operating status and reliability level of the system.

[0006] Although a lot of research has been done on pipeline failure risk, most of these studies focus on the analysis of failure consequences and the simulation of leakage processes, while little attention has been paid to the impact of leakage failures on optimal scheduling and reliability assessment in the electricity-gas-heat joint model. More importantly, most of the existing studies do not consider the dynamic energy flow equations during leakage failures, which to some extent limits the accuracy and precision of reliability assessment of integrated energy systems.

[0007] Therefore, how to improve the reliability assessment accuracy of the integrated energy system on the basis of considering pipeline leakage failures has become a problem that needs to be solved urgently. Summary of the invention

[0008] In view of the above-mentioned deficiencies in the prior art, the present invention provides a reliability assessment method for an integrated energy system taking pipeline leakage failures into consideration, which can improve the reliability assessment accuracy of the integrated energy system on the basis of taking pipeline leakage failures into consideration.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0010] A reliability assessment method for an integrated energy system considering pipeline leakage failures comprises the following steps:

[0011] S0. Build an integrated energy system, which includes power system, natural gas system and thermal system; and on the basis of the integrated energy system, consider the leakage failures of natural gas network and thermal network pipelines, as well as the impact of these failures on energy flow and distribution, and build an optimal dynamic energy flow model of the integrated energy system considering leakage failures;

[0012] S1. Input the topological structure 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 a normal operating state;

[0013] S2. Based on the integrated energy system, the state duration sampling algorithm is used to extract the time series of all component states including the natural gas network and the heat network pipeline; the time series of all components are combined to obtain the system state transition sequence, and it is divided according to the day;

[0014] S3. For each system state transition sequence obtained by the division, the energy load reduction amount is determined by using the optimal dynamic energy flow model of the integrated energy system considering leakage failures; 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; use the standard deviation of the sample mean to calculate the variance convergence coefficient and make 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 of the fault models in the prior art use a simple two-state model, which does not consider the impact of leakage failures on the system, resulting in inaccurate evaluation results. This method can accurately simulate the impact of natural gas and heat network pipeline leakage failures on energy flow and distribution by constructing an optimal dynamic energy flow model for a comprehensive energy system that considers leakage failures.

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

[0019] 3. Comprehensively consider the complexity of the integrated energy system. The evaluation methods in the existing technology often only focus on a single system or a single fault type, ignoring the complexity and diversity of the integrated energy system. This method not only considers the leakage failures of the natural gas network and the heat network pipeline, but also comprehensively considers the interaction and influence of the power system, the natural gas system and the thermal system, so as to more comprehensively evaluate the reliability of the integrated energy system.

[0020] 4. Provide a basis for optimized scheduling. The evaluation methods in the existing technology often cannot provide sufficient information and support for optimized scheduling, which limits the improvement of system performance. By accurately evaluating the impact of pipeline leakage failures on the integrated energy system, this method can provide an important basis for the optimized scheduling of the system, which helps to improve the operating efficiency and energy utilization efficiency of the system.

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

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

[0023] With this setup, 1. By comprehensively considering the constraints of the natural gas system, thermal system, and power system, the model can more comprehensively reflect the operating characteristics and interrelationships of the integrated energy system. This helps to more accurately evaluate the performance and reliability of the system under various operating conditions.

[0024] 2. The introduction of energy flow constraints for gas and heat network pipelines after leakage failure enables the model to more accurately simulate the impact of pipeline leakage on energy flow and distribution. This helps to reveal the potential threat of leakage failure to the overall performance of the system and provide a scientific basis for taking corresponding countermeasures.

[0025] Preferably, natural gas system constraints include:

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

[0027]

[0028] The relationship between the air pressure at the inlet and outlet of the compressor:

[0029]

[0030] Gas pressure at the gas node Upper and lower limit constraints:

[0031]

[0032] in, represents the flow rate through the compressor pipeline, represents the amount of natural gas consumed by the compressor, γ cp is the boost ratio, Indicates the pressure at the compressor near the gas end. Indicates the pressure at the compressor near the gas end, P x,min Indicates the lower limit of natural gas node pressure, P x,max represents the upper limit of the natural gas node; Γ represents the coefficient of the relationship between the amount of natural gas consumed by the compressor and the flow rate through the compressor.

[0033] Operation constraints of CHP units:

[0034]

[0035]

[0036] In the formula, is the operating power of the CHP unit; and are the power generation and heating power of the air extraction thermal power unit respectively; c v is the rate of change of power generation with increase of heating power when the total steam inlet of the unit does not change; c m is the back pressure operation elastic coefficient of the CHP unit; r is a constant; is the operating status of the CHP unit at time t, 1 represents normal status, and 0 represents fault status; PN max,x and PN min,xare 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 power of the CHP unit;

[0037] Natural gas balance equation:

[0038] Node traffic balancing:

[0039]

[0040] Cut the upper and lower limits of the load:

[0041]

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

[0043]

[0044] Pipeline flow upper and lower limit constraints:

[0045]

[0046] Among them, the mass flow rate Q = ρwA represents the amount of natural gas passing through any section of the pipeline per unit time, where A is the cross-sectional area of ​​the pipeline, ρ is the density, and w is the flow rate; the ideal gas state equation P = ρZR c T represents the relationship between gas pressure, density and temperature, where Z is the compression factor of natural gas, R c is the specific gas constant of natural gas, T is the temperature;

[0047] Where n(x) represents the set of pipelines connected to node x; and represents the outflow and inflow flow of pipe xy; Indicates the gas supply volume, S x t Indicates gas load, Indicates the amount of gas cutting load; Indicates the flow rate through the compressor pipeline; Indicates the amount of natural gas consumed by the compressor; and They represent the gas turbine electrical power and gas boiler thermal power respectively; m(x) represents the gas grid node set; η gas ,η chp and η heat They represent the conversion efficiency of gas turbine, CHP unit and gas boiler respectively; cp represents the compressor branch;

[0048] Linear equation of dynamic energy flow model of natural gas pipeline:

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

[0050] Natural gas pipeline storage equation:

[0051]

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

[0053] The linear equation of natural gas dynamic energy flow is:

[0054]

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

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

[0057]

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

[0059]

[0060] In the formula, ε g is the absolute roughness of the natural gas pipeline;

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

[0062]

[0063] In the formula, It is the variable for judging the direction of the pipeline xy, which is 1 when Q>0 and 0 when Q<0; N is the preset value;

[0064] The storage is restored every 24 hours:

[0065]

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

[0067] Such a setting can 1. accurately simulate the operation of the natural gas system. By considering the relationship between the amount of natural gas consumed by the compressor and the flow rate, the relationship between the air pressure at the inlet and outlet of the compressor, etc., the model can accurately simulate the transmission process of natural gas in the pipeline, including the energy consumption of the compressor and the change in air pressure, thereby more accurately reflecting the actual operation status of the natural gas system.

[0068] 2. Improve the simulation accuracy of pipeline dynamic energy flow. The introduction of linear equations in the natural gas pipeline dynamic energy flow model enables the model to consider the dynamic flow process of natural gas in the pipeline, including pipeline storage, flow changes, pressure changes, etc. This helps to more accurately simulate the transmission and distribution process of natural gas in the pipeline and improve the simulation accuracy of the model.

[0069] 3. Consider the pipeline storage recovery characteristics. The setting of pipeline storage recovery 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] Quality of hot water flowing out of the pipe From arrive The mass composition of the hot water flowing into the pipeline during the period is:

[0073]

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

[0075] Hot water temperature at the outlet of the water supply and return pipes It is composed of the linear weighted temperature of the hot water mass injected in several time periods:

[0076]

[0077]

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

[0079]

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

[0081] Pipeline hot water temperature fusion constraints:

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

[0083]

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

[0085]

[0086] Pipeline temperature upper and lower limit constraints:

[0087]

[0088] in, and Respectively represent the temperature at the node m of the water supply pipe and the return pipe, T s,min Indicates the lower limit of water supply pipe temperature, T s,max Indicates the upper limit of the water supply pipe temperature, T r,min Indicates the lower limit of return pipe temperature, T r,max Indicates the upper limit of the return pipe temperature;

[0089] Heat load dynamic model:

[0090] Use the building heat load model to simulate indoor temperature:

[0091]

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

[0093] Seasonal heat load reduction for:

[0094]

[0095] Where, T com,min and T com,max They are the minimum and maximum suitable indoor temperatures of the building, respectively;

[0096] Thermodynamic system balance equation:

[0097] Relationship between heat source output and pipeline hot water temperature:

[0098]

[0099] Relationship between annual load and seasonal load and pipeline hot water temperature:

[0100]

[0101] Annual load reduction upper and lower limits:

[0102]

[0103] in, Indicates the heating power of the CHP unit in the heating network, Indicates the thermal power of the gas boiler in the heating network, represents the heat pump power in the heat network, n(e) represents the pipe set of the heat source outlet, m(e) represents the heat network node set, represents the annual heat load, represents the annual heat load reduction, s(e) represents the pipe set at the heat exchanger inlet, Indicates the upper limit of annual load reduction.

[0104] This setting can 1. accurately simulate the hot water transmission process. Through the hot water temperature transmission equation, the model can accurately simulate the transmission process of hot water in the pipeline, including the changes in the quality and temperature of hot water flowing into and out of the pipeline at different time periods. This helps to more accurately reflect the actual operating status of the thermal system.

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

[0106] 3. Using the building heat load model to simulate indoor temperature and taking into account the reduction of seasonal heat load, the model can more accurately simulate the dynamic changes of indoor temperature and heat load. This helps to evaluate the performance of the thermal system under different working conditions.

[0107] 4. Optimize the heat load reduction strategy. By setting the upper and lower limits of the annual load reduction, the model can optimize the heat load reduction strategy to ensure that the heat load reduction is minimized as much as possible while ensuring the safe and stable operation of the system, thereby improving user satisfaction.

[0108] Preferably, the power system constraints include:

[0109] Node power balance:

[0110]

[0111] The relationship between the tidal current and the node phase angle is:

[0112]

[0113] Node phase angle constraint:

[0114]

[0115] Cut load upper and lower limit constraints:

[0116]

[0117] Power line transmission capacity constraints:

[0118]

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

[0120]

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

[0122] Such a setting can 1. accurately simulate the relationship between power flow and node phase angle. The relationship constraint between power flow and node phase angle enables the model to simulate the distribution of power flow in the power system and the mathematical relationship between power flow and node phase angle. This helps to more accurately reflect the actual operating status of the power system.

[0123] 2. Optimize the load reduction strategy. The upper and lower limit constraints of load reduction enable the model to optimize the load reduction strategy under the premise of ensuring the safe and stable operation of the system, minimize the reduction of power load, and improve 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 accurately simulate and optimize the overall operating status 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 the leakage failure are considered include:

[0126] Split a xy segment pipeline into two segments xm and my to calculate the energy flow separately. The energy flow equation in the xm segment pipeline is as follows:

[0127]

[0128] λ g,xm =λ g,xy ;

[0129] Where, L xm and D xy Represents natural gas pipeline L xm The length and inner diameter of the xy segment; represents the air pressure at pipeline node x at time t; and Respectively represent the natural gas flow rates flowing into and out of the natural gas pipeline; g,xm represents the friction coefficient of the natural gas pipeline in section xm; w0 represents the initial flow rate of natural gas; c w represents the speed of sound;

[0130] The energy flow equation in the my section of the pipeline is:

[0131]

[0132]

[0133] λ g,my =λ g,xy ;

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

[0135]

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

[0137]

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

[0139] This setting can 1. simulate pipeline energy flow in a refined manner. Splitting a pipeline into two segments (xy segment and my segment) to calculate energy flow separately can more accurately simulate the flow of natural gas in the pipeline. This refined modeling helps to more accurately reflect the actual flow state of natural gas in the pipeline, especially when considering leakage failures.

[0140] 2. The impact of leakage faults on energy flow is considered. By introducing relevant parameters (such as pipeline length, inner diameter, air pressure, flow rate, etc.) into the energy flow equation and combining the characteristics of leakage faults, the impact of leakage faults on pipeline energy flow can be simulated. This helps to evaluate the impact of leakage faults on the overall operating status of the gas network and provide a scientific basis for emergency response and recovery strategies after faults.

[0141] 3. Considering the energy flow constraints of the gas network pipeline after the leakage failure will help to timely discover and deal with potential leakage risks, thereby avoiding or reducing the impact of leakage failures on the safe operation of the gas network. This will help improve the overall safety and reliability of the gas network. Based on refined modeling and energy flow balance analysis, a more optimized gas network operation strategy can be formulated. For example, after a leakage failure occurs, the pipeline flow, gas pressure and other parameters can be adjusted according to the energy flow constraints to restore the normal operation of the gas network as soon as possible.

[0142] Preferably, the energy flow constraints of the heat network pipeline after the leakage failure are considered include:

[0143]

[0144] In the formula, is the pressure of node i in the heat network pipeline; is the pressure of node i in the heat network pipeline; h,l is the friction resistance coefficient of the heating network pipeline; w h,l is the hot water flow rate in the pipeline; D l is the diameter of the heating network pipe.

[0145] The friction coefficient is:

[0146]

[0147] In the formula, λ h,l is the friction resistance coefficient of the heat network pipeline; D l is the diameter of the heat network pipeline; Δ is the equivalent roughness of the pipeline, usually 1 mm; Re is the Reynolds number:

[0148]

[0149] Where, υ is the viscosity coefficient;

[0150] The pressure drop calculation formula is:

[0151]

[0152] L l represents the length of the natural gas pipeline; ρ w Indicates the density of water; It indicates the working medium quality of the water supply pipeline numbered l at different time periods;

[0153] When a leakage occurs in the heat 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 Indicates the cross-sectional area of ​​the pipe;

[0156] The leakage flow caused by pipeline leakage is:

[0157]

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

[0159] The relationship between the virtual hot water load and the hot water flow rate of the two sections of the pipeline is:

[0160]

[0161] Based on the changed hot water flow rate, the hot water temperature can be calculated as:

[0162]

[0163]

[0164] in,

[0165]

[0166] In order to ensure the normal operation of the heating system, a centralized water replenishment method is adopted, and water is uniformly produced at the inlet of the heat source return pipe or condensed water is used for water replenishment and constant pressure; the temperature fusion equation of the water replenishment process is:

[0167]

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

[0169] This setting can 1. finely simulate the impact of leakage failure on the heating network. By splitting the original pipeline into two sections at the leakage failure point for calculation and redefining the transmission delay time, the impact of leakage failure on the energy flow of the heating network pipeline can be more accurately simulated. This refined modeling helps to more accurately reflect the operating status of the heating network after the leakage failure.

[0170] 2. By introducing parameters such as friction coefficient and Reynolds number, as well as the calculation formula for pressure drop, the friction resistance and pressure drop in the heating network pipeline can be accurately calculated. This helps to evaluate the impact of leakage failures on the flow characteristics of the heating network pipeline and provide a scientific basis for subsequent fault handling. By calculating the leakage flow and virtual hot water load, the impact of leakage failures on the hot water flow of the heating network can be quantified. This helps to evaluate the impact of leakage failures 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, the hot water temperature can be calculated. This helps to evaluate the impact of leakage failures on the hot water temperature and ensure that the heating system can provide hot water temperatures that meet the requirements.

[0171] 3. By adopting a centralized water replenishment method, unified water production at the entrance of the heat source return water pipe or using condensed water for water replenishment and constant pressure, the water replenishment strategy can be optimized. This helps to reduce energy loss during the water replenishment process and improve the overall efficiency of the heating system. Considering the energy flow constraints of the heat network pipeline after a leakage failure helps to promptly discover and deal with potential leakage risks, thereby avoiding or reducing the impact of leakage failures on the reliability and stability of the heating system. This helps to improve 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 failures, the objective function constructed is:

[0173]

[0174] In the formula, c gw,x represents the gas source cost; c curs,x represents the gas cutting load penalty coefficient; c pg,k represents the output cost of coal-fired units; c curl,k represents the load penalty coefficient for power reduction; c curh,e Indicates the heat load penalty coefficient; Indicates the gas supply volume of the gas source; Indicates the amount of gas cutting load; Indicates the electrical power of coal-fired units; Indicates the amount of electrical load reduction; Indicates the annual heat load reduction; represents the seasonal heat load reduction; m(x) represents the gas grid node set; m(k) represents the power node set; Q(x) represents the gas source node set; p(x) represents the coal-fired unit node set; n(d) represents the pipeline set connected to node d.

[0175] Such a setting, 1. When constructing the objective function, the impact of leakage failure on the integrated energy system is considered. Leakage failure may lead to a reduction in gas supply, a reduction in electrical load and thermal load, and a corresponding increase in penalty costs. By incorporating these factors into the objective function, it can be ensured that the model can accurately reflect the impact of leakage failure on the system economy.

[0176] 2. The optimal dynamic energy flow model of the integrated energy system considering leakage failures needs to be able to adapt to different operating scenarios and failure conditions. By constructing a flexible objective function, it can be ensured that the model can quickly adjust the energy supply and demand strategy in the face of leakage failures to minimize costs and meet energy needs.

[0177] 3. All costs in the objective function are closely related to the balance of energy supply and demand. For example, the gas source cost and the gas load reduction penalty coefficient reflect the tension of natural gas supply and demand; the coal-fired unit output cost and the power load reduction penalty coefficient reflect the balance of power supply and demand; the heat load reduction penalty coefficient reflects the supply and demand relationship of the heat 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 failure.

[0178] Preferably, in S2, the process of extracting the component 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 state set Ω to which the current state of element m (m = 1, 2, ... M) may transferm ; Where M is the number of all components;

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

[0182]

[0183] in, is 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 each component's current state, and use the state after the transition corresponding to the minimum value as the component state to be transferred next time:

[0185]

[0186] Among them, in step 2.4, if the state after the transfer involves the leakage fault state of the natural gas network and / or heat network pipeline, a random sampling method is also used to determine the aperture size and location of the pipeline leakage fault, and the process includes:

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

[0188] Then, we use a random number that follows a normal distribution The diameter of the simulated leakage aperture, where μ and σ are related to the pipe type.

[0189] Such a setting, 1. By setting the initial state of all components and sampling each component based on the transition rate of the transferable state set, the duration set of the component staying in the current state before transferring to the next state can be obtained. This process can accurately simulate the change of component state and provide a basis for subsequent energy system analysis and optimization.

[0190] 2. When the transferred state involves leakage failure of the natural gas network and / or heat network pipeline, a random sampling method is used to determine the aperture size and location of the pipeline leakage failure. This process not only takes into account the possibility of leakage failure, but also further refines the specific characteristics of the leakage failure (such as aperture size and location), making the simulation results closer to the actual situation.

[0191] 3. By accurately simulating the component status and leakage fault characteristics, this method can provide support for the operation decision-making of the energy system. For example, when a leakage fault occurs, the fault location and aperture size can be quickly determined based on the simulation results, so that corresponding emergency measures can be taken to reduce the impact of the fault on the system.

[0192] 4. The state duration sampling algorithm considering leakage faults is helpful for analyzing the reliability of energy systems. By simulating the system operation status 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 electricity supply shortage; EGNS represents the expected value of natural gas supply shortage; EHNS represents the expected value of heat supply shortage.

[0199] The maximum of the three coefficients is chosen 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 ≤0.02 or the total simulation time reaches the preset value, the convergence condition is determined to be met and the state simulation is stopped; otherwise, the convergence condition is determined to be not met.

[0202] Such a setting can, 1. improve the reliability of simulation results. Selecting the maximum value of the three coefficients (variance convergence coefficients corresponding to EENS, EGNS, and EHNS) as the convergence criterion can ensure that convergence requirements are met on multiple key indicators. This helps to improve the reliability of simulation results and make the simulation results more reflective of the actual situation.

[0203] 2. Support simulation analysis of complex systems. For energy systems containing multiple components and complex relationships, this method provides an effective convergence judgment method. By continuously monitoring and judging the convergence conditions, it can ensure that the simulation process can accurately reflect the operating status and characteristics of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0204] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0205] Figure 1 A flowchart of the method is shown in FIG.

[0206] Figure 2 It is a schematic diagram of the electric and thermal characteristics of the air extraction type thermal power unit in the first embodiment;

[0207] Figure 3 Schematic diagram of the flow process of the heat network pipeline at time t in Example 1;

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

[0209] Figure 5 This is a schematic diagram of a leakage failure of a heat network pipeline in Example 1;

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

[0211] Figure 7 It is a schematic diagram of the integrated energy system of the example in the second embodiment;

[0212] Figure 8 Schematic diagram of the load curve of the integrated energy system in the example of Example 2. DETAILED DESCRIPTION

[0213] The following is a further detailed description through specific implementation methods:

[0214] Embodiment 1

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

[0216] S0. Build an integrated energy system, which includes power system, natural gas system and thermal system. On the basis of the integrated energy system, consider the leakage failures of natural gas network and thermal network pipelines, as well as the impact of these failures on energy flow and distribution, and build the optimal dynamic energy flow model of the integrated energy system considering leakage failures.

[0217] Among them, when constructing the optimal dynamic energy flow model of the integrated energy system considering leakage failures, the constraints constructed include natural gas system constraints, thermal system constraints, power system constraints, gas network pipeline energy flow constraints after leakage failures, and heat network pipeline energy flow constraints after leakage failures. In this way, by comprehensively considering the constraints of the natural gas system, thermal system, and power system, the model can more comprehensively reflect the operating characteristics and interrelationships of the integrated energy system. This helps to more accurately evaluate the performance and reliability of the system under various working conditions. The introduction of energy flow constraints for gas network pipelines and heat network pipelines after leakage failures enables the model to more accurately simulate the impact of pipeline leakage on energy flow and distribution. This helps to reveal the potential threat of leakage failures to the overall performance of the system and provide a scientific basis for taking corresponding countermeasures.

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

[0219] 1) Compressor-related constraints The main function of the compressor is to compensate for the reduced air pressure due to loss during the transmission process and to keep the pipeline air pressure within the normal range.

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

[0221]

[0222] The relationship between the air pressure at the inlet and outlet of the compressor:

[0223]

[0224] The upper and lower pressure constraints of natural gas nodes:

[0225]

[0226] in, represents the flow rate through the compressor pipeline, represents the amount of natural gas consumed by the compressor, γ cp is the boost ratio, Indicates the pressure at the compressor near the gas end. Indicates the pressure at the compressor near the gas end, P x,min Indicates the lower limit of natural gas node pressure, P x,max represents the upper limit of the natural gas node; Γ represents the coefficient of the relationship between the amount of natural gas consumed by the compressor and the flow rate through the compressor.

[0227] 2) The combined heat and power (CHP) units widely used in China are extraction-type heat and power units. During the operation of the unit, the steam entering the steam turbine can be used to generate electricity through the steam turbine, or partially extracted to supply heat load. Among them, the power generation capacity of the extraction-type heat and power unit is and heating power The associated coupling relationship between them is called electrothermal characteristics, such as Figure 2 As shown in the figure, PN max,x and PN min,x They are the upper and lower limits of the power generation capacity of the CHP unit under pure condensing conditions (power generation only). max,x It is the upper limit of the heating power of CHP unit. pm,x It is the heating power corresponding to the minimum generating power of the unit. v is the rate of change of power generation as heating power increases when the total steam inlet of the unit does not change. The operating constraints of the CHP unit are as follows:

[0228]

[0229]

[0230] In the formula, is the operating power of the CHP unit, c m is the back pressure operation elastic coefficient of the CHP unit; r is a constant; is the operating status of the CHP unit at time t (“1” represents normal status, and “0” represents fault status).

[0231] 3) Natural gas balance equation:

[0232] Node traffic balancing:

[0233]

[0234] Cut the upper and lower limits of the load:

[0235]

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

[0237]

[0238] Pipeline flow upper and lower limit constraints:

[0239]

[0240] Among them, the mass flow rate Q = ρwA represents the amount of natural gas passing through any section of the pipeline per unit time, where A is the cross-sectional area of ​​the pipeline, ρ is the density, and w is the flow rate. At the same time, the ideal gas state equation P = ρZR is introduced c T describes the relationship between gas pressure, density and temperature, where Z is the compression factor of natural gas, R c is the specific gas constant of natural gas, T is the temperature. n(x) represents the set of pipelines connected to node x, m(x) represents the set of gas network nodes; ηgas ,η chp and η heat They represent the conversion efficiency of gas turbine, CHP unit and gas boiler respectively; and They represent the gas turbine electrical power and the gas boiler thermal power respectively; cp represents the compressor branch; and represents the outflow and inflow flow of pipe xy, Indicates the gas supply volume. Indicates gas load, Indicates the air cutting load.

[0241] 4) Linear equation of natural gas pipeline dynamic energy flow model:

[0242] Pipeline xy is divided into several segments with intervals of Δx. In terms of time, time t is the initial point of the operation cycle. and are the initial flow rate and pressure at time t in pipeline x.

[0243] The natural gas pipeline storage equation is:

[0244]

[0245]

[0246] In the formula, Δt represents the differential time interval, Δx represents the differential length interval, It represents the amount of natural gas stored in the pipeline with a length of Δx at time t. Time t is the initial point of the operation cycle. and are the initial flow rate and pressure at time t in pipeline x.

[0247] The linear equation of natural gas dynamic energy flow is:

[0248]

[0249] Where λ is the hydraulic friction factor and D is the inner diameter of the natural gas pipeline.

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

[0251]

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

[0253]

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

[0255] At the same time, the flow direction at each point in the same pipe is the same. To ensure the accuracy of the difference process, a 0-1 variable is introduced to establish the flow direction constraint:

[0256]

[0257] In the formula, is the variable for judging the direction of pipeline xy. It is 1 when Q>0 and 0 when Q<0. N is an infinite value in theory and needs to be adjusted according to the actual flow rate of the natural gas system to reduce the calculation burden.

[0258] Pipeline storage provides great flexibility for natural gas systems. However, since natural gas systems do not have multiple regulation facilities like those in power systems, it is necessary to use pipeline storage as a means of regulation with caution and restore it after use. The present invention considers that pipeline storage should be restored once every 24 hours.

[0259]

[0260] Wherein, 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, the relationship between the air pressure at the inlet and outlet of the compressor, etc., the model can accurately simulate the transmission process of natural gas in the pipeline, including the energy consumption and air pressure changes of the compressor, thereby more accurately reflecting the actual operating status of the natural gas system. In addition, the introduction of the linear equation of the natural gas pipeline dynamic energy flow model enables the model to consider the dynamic flow process of natural gas in the pipeline, including pipeline storage, flow changes, pressure changes, etc. This helps to more accurately simulate the transmission and distribution process of natural gas in the pipeline and improve the simulation accuracy of the model. In addition, the setting of restoring the pipeline storage every 24 hours takes into account 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 an important reference for the long-term operation and scheduling of the system.

[0262] Thermal system constraints include:

[0263] 1) Hot water temperature transmission equation:

[0264] The flow process of the heat network pipeline at time t is as follows Figure 3 As shown, the shaded area represents pipeline barriers. is the working fluid quality of the water supply pipeline numbered l at different time periods; ρ w , A l and L l denote the density of water, the cross-sectional area of ​​the pipe, and the length of the pipe respectively. The total mass of hot water in the pipe is equal to ρ w A l L lThe block in the middle of the pipe represents the amount of hot water flowing into the pipe in a continuous period of time. In the period from t to t+Δt, the mass of hot water flowing into the pipe is The red part represents the quality of hot water flowing out of the pipe during the Δt period, and the two should remain equal. After the hot water flows out, it supplies heat to the load and then returns to the starting point through the return pipe for reheating.

[0265] Quality of hot water flowing out of the pipe From arrive The mass composition of the hot water flowing into the pipeline during the period is:

[0266]

[0267] The present invention adjusts the output of the heat source by mass regulation to maintain the supply and demand balance between the user and the heat source, and sets the mass flow rate of the pipeline to be constant. The hot water temperature at the outlet of the water supply and return pipes is composed of the linear weighted mass temperature of the hot water injected in several time periods:

[0268]

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

[0270]

[0271] in, and Respectively represent the outlet temperature and inlet temperature of the water supply pipe; and They represent the outlet temperature and inlet temperature of the return pipe respectively; Indicated in The quality of hot water injected into the pipeline during the period t; Indicates from The mass of hot water injected into the pipeline during period t. and Respectively represent the outlet temperature of the water supply and return pipes; T e ,λ l ther 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 constraints:

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

[0274]

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

[0276]

[0277] Pipeline temperature upper and lower limit constraints:

[0278]

[0279] in, and Respectively represent the temperature at the node m of the water supply pipe and the return pipe, T s,min Indicates the lower limit of water supply pipe temperature, T s,max Indicates the upper limit of the water supply pipe temperature, T r,min Indicates the lower limit of return pipe temperature, T r,max Indicates the upper limit of return pipe temperature.

[0280] 3) Heat load dynamic model:

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

[0282]

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

[0284] Seasonal heat load reduction for:

[0285]

[0286] Where, T com,min and T com,max They are the minimum suitable indoor temperature and the maximum suitable indoor temperature of the building respectively.

[0287] 4) Thermodynamic system balance equation:

[0288] Relationship between heat source output and pipeline hot water temperature:

[0289]

[0290] Relationship between annual load and seasonal load and pipeline hot water temperature:

[0291]

[0292] Annual load reduction upper and lower limits:

[0293]

[0294] in, Indicates the heating power of the CHP unit in the heating network, Indicates the thermal power of the gas boiler in the heating network, represents the heat pump power in the heat network, n(e) represents the pipe set of the heat source outlet, m(e) represents the heat network node set, Indicates the annual heat load, represents the annual heat load reduction, s(e) represents the pipe set at the heat exchanger inlet, Indicates the upper limit of annual load reduction.

[0295] In this way, through the hot water temperature transmission equation, the model can accurately simulate the transmission process of hot water in the pipeline, including the changes in the quality and temperature of hot water flowing into and out of the pipeline in different periods of time. This helps to more accurately reflect the actual operating status of the thermal system. In addition, the Sukhov temperature drop model is used to correct the pipeline outlet temperature after taking into account the heat loss, so that the model can more accurately simulate the heat loss of hot water during the transmission process, thereby more accurately calculating the pipeline outlet temperature. In addition, the building heat load model is used to simulate the indoor temperature, and the reduction of seasonal heat load is considered, so that the model can more accurately simulate the dynamic changes of indoor temperature and heat load. This helps to evaluate the performance of the thermal system under different working conditions. By setting the upper and lower limits of the perennial load reduction, the model can optimize the heat load reduction strategy to ensure that the heat load reduction is minimized as much as possible while ensuring the safe and stable operation of the system, thereby improving user satisfaction.

[0296] Power system constraints include:

[0297] Node power balance:

[0298]

[0299] The relationship between the tidal current and the node phase angle is:

[0300]

[0301] Node phase angle constraint:

[0302]

[0303] Cut load upper and lower limit constraints:

[0304]

[0305] Power line transmission capacity constraints:

[0306]

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

[0308]

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

[0310] In this way, the relationship between the power flow and the node phase angle can be accurately simulated. The relationship constraint between the power flow and the node phase angle enables the model to simulate the distribution of the power flow in the power system, as well as the mathematical relationship between the power flow and the node phase angle. This helps to more accurately reflect the actual operating status of the power system. In addition, the load reduction strategy can also be optimized. The upper and lower limit constraints of load reduction enable the model to optimize the load reduction strategy under the premise of ensuring the safe and stable operation of the system, minimize the reduction of power load, improve user satisfaction and system reliability. In addition, the overall operating efficiency of the power system can also be improved. By comprehensively considering the above constraints, the model can achieve accurate simulation and optimal scheduling of the overall operating status 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] The energy flow constraints of the gas network pipeline after considering leakage failure include:

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

[0313]

[0314] λg,xm =λ g,xy ;

[0315] Where, L xm and D xy Represents natural gas pipeline L xm The length and inner diameter of the xy segment; represents the air pressure at pipeline node x at time t; and Respectively represent the natural gas flow rates flowing into and out of the natural gas pipeline; g,xm represents the friction coefficient of the natural gas pipeline in section xm; w0 represents the initial flow rate of natural gas; c w represents the speed of sound;

[0316] The energy flow equation in the my section of the pipeline is:

[0317]

[0318] λ g,my =λ g,xy ;

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

[0320]

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

[0322]

[0323] In the formula, R c is the gas constant of natural gas, κκ is the isentropic index of natural gas. At room temperature, the κκ value of an ideal gas can be approximately regarded as a constant, and κκ is 1.29.

[0324] In this way, the pipeline energy flow can be simulated in a refined manner. By splitting a pipeline into two segments (xy segment and my segment) to calculate the energy flow separately, the flow of natural gas in the pipeline can be simulated more accurately. This refined modeling helps to more accurately reflect the actual flow state of natural gas in the pipeline, especially when considering leakage faults. In addition, the impact of leakage faults on energy flow is considered. By introducing relevant parameters (such as pipeline length, inner diameter, air pressure, flow rate, etc.) into the energy flow equation and combining the characteristics of leakage faults, the impact of leakage faults on pipeline energy flow can be simulated. This helps to evaluate the impact of leakage faults on the overall operating state of the gas network and provide a scientific basis for emergency response and recovery strategies after the fault. In addition, the energy flow constraints of the gas network pipeline after the leakage fault are also considered, which helps to timely discover and deal with potential leakage risks, thereby avoiding or reducing the impact of leakage faults on the safety of gas network operation. This helps to improve the overall safety and reliability of the gas network. Based on refined modeling and energy flow balance analysis, a more optimized gas network operation strategy can be formulated. For example, after a leakage fault occurs, parameters such as pipeline flow and air pressure can be adjusted according to the energy flow constraints to restore the normal operating state of the gas network as soon as possible.

[0325] The energy flow constraints of the heat network pipeline after considering the leakage failure include:

[0326]

[0327] In the formula, is the pressure of node i in the heat network pipeline; is the pressure of node i in the heat network pipeline; h,l is the friction resistance coefficient of the heating network pipeline; w h,l is the hot water flow rate in the pipeline; D l is the diameter of the heating network pipe.

[0328] The friction coefficient is:

[0329]

[0330] In the formula, λ h,l is the friction resistance coefficient of the heat network pipeline; D l is the diameter of the heat network pipeline; Δ is the equivalent roughness of the pipeline, usually 1 mm; Re is the Reynolds number:

[0331]

[0332] Where, υ is the viscosity coefficient;

[0333] The pressure drop calculation formula is:

[0334]

[0335] L l represents the length of the natural gas pipeline; ρw Indicates the density of water; It indicates the working medium quality of the water supply pipeline numbered l at different time periods;

[0336] When a leakage fault occurs in the heat network pipeline, it can also be assumed that the virtual hot water load replaces the hot water lost by the leakage fault, such as Figure 5 The quality of hot water flowing out of the pipe Equal to arrive The mass of the hot water injected into the pipeline during the period. It takes time for hot water to be transmitted in the pipeline, which is the transmission delay time. After the leakage occurs, the hot water flow in the pipeline also changes, causing the transmission delay time to change.

[0337] Split the original pipeline into two sections for calculation and redefine the transmission delay time

[0338]

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

[0340] The leakage flow caused by pipeline leakage is:

[0341]

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

[0343] The relationship between the virtual hot water load and the hot water flow rate of the two sections of the pipeline is:

[0344]

[0345] Based on the changed hot water flow rate, the hot water temperature can be calculated as:

[0346]

[0347] in,

[0348]

[0349] A leakage accident at any location in the thermal system will affect the amount of hot water returned to the heat source, causing hydraulic imbalance in the system, and further affecting the stable operation of the heat source. In order to ensure the normal operation of the heating system, the present invention adopts a centralized water replenishment method, that is, unified water production at the inlet of the heat source return water pipeline or using condensed water for water replenishment and constant pressure. The temperature fusion equation of the water replenishment process is:

[0350]

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

[0352] In this way, the impact of leakage faults on the heating network can be simulated in a refined manner. By splitting the original pipeline into two sections at the leakage fault point for calculation and redefining the transmission delay time, the impact of leakage faults on the energy flow of the heating network pipeline can be simulated more accurately. This refined modeling helps to more accurately reflect the operating status of the heating network after the leakage fault. In addition, by introducing parameters such as friction resistance coefficient and Reynolds number, as well as the calculation formula of pressure drop, the friction resistance and pressure drop in the heating network pipeline can be accurately calculated. This helps to evaluate the impact of leakage faults on the flow characteristics of the heating network pipeline and provide a scientific basis for subsequent fault handling. By calculating the leakage flow and virtual hot water load, the impact of leakage faults on the hot water flow of the heating network can be quantified. This helps to evaluate the 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, the hot water temperature can be calculated. This helps to evaluate the impact of leakage faults on hot water temperature and ensure that the heating system can provide hot water temperature that meets the requirements.

[0353] In addition, by adopting a centralized water replenishment method, unified water production at the entrance of the heat source return pipe or using condensed water for water replenishment and constant pressure, the water replenishment strategy can be optimized. This helps to reduce energy loss during the water replenishment process and improve the overall efficiency of the heating system. Considering the energy flow constraints of the heat network pipeline after a leakage failure helps to promptly discover and deal with potential leakage risks, thereby avoiding or reducing the impact of leakage failures on the reliability and stability of the heating system. This helps to improve the overall performance of the heating system and user satisfaction.

[0354] When constructing the optimal dynamic energy flow model of the integrated energy system considering leakage failures, the objective function constructed is:

[0355]

[0356] In the formula, c gw,x represents the gas source cost; c curs,x represents the gas cutting load penalty coefficient; c pg,k represents the output cost of coal-fired units; c curl,k represents the load penalty coefficient for power reduction; c curh,e Indicates the heat load penalty coefficient; Indicates the gas supply volume of the gas source; Indicates the amount of gas cutting load; Indicates the electrical power of coal-fired units; Indicates the amount of electrical load reduction; Indicates the annual heat load reduction; represents the seasonal heat load reduction; m(x) represents the gas grid node set; m(k) represents the power node set; Q(x) represents the gas source node set; p(x) represents the coal-fired unit node set; n(d) represents the pipeline set connected to node d.

[0357] In this way, the impact of leakage failure on the integrated energy system is considered when constructing the objective function. Leakage failure may lead to a reduction in gas supply, a reduction in electrical load and thermal load, and an increase in the corresponding penalty cost. By incorporating these factors into the objective function, it can be ensured that the model can accurately reflect the impact of leakage failure on the system economy. In addition, the optimal dynamic energy flow model of the integrated energy system considering leakage failure needs to be able to adapt to different operating scenarios and failure conditions. By constructing a flexible objective function, it can be ensured that the model can quickly adjust the energy supply and demand strategy when facing a leakage failure to minimize costs and meet energy demand. In addition, each cost in the objective function is closely related to the balance of energy supply and demand. For example, the gas source cost and the gas load reduction penalty coefficient reflect the tension of natural gas supply and demand; the coal-fired unit output cost and the power load reduction penalty coefficient reflect the balance of power supply and demand; the heat load reduction penalty coefficient reflects the supply and demand relationship of the heat 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 failure.

[0358] S1. Input the topological structure 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 a normal operating state;

[0359] S2. Based on the integrated energy system, the state duration sampling algorithm is used to extract the time series of all component states including the natural gas network and the heat network pipeline; the time series of all components are combined to obtain the system state transfer sequence, and it is divided according to days.

[0360] In specific implementation, 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 state set Ω to which the current state of element m (m = 1, 2, ... M) may transfer m ; Where M is the number of all components; Figure 6 (a) is the two-state transition diagram of the pipeline without considering pipeline leakage failure; Figure 6 (b) is the three-state Markov transition diagram of the pipeline considering pipeline leakage failure.

[0363] Step 2.3: Transition rate based on the set of transferable states The duration set of each element staying in the current state before transferring to the next state is sampled according to the following formula

[0364]

[0365] in, is 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 each component's current state, and use the state after the transition corresponding to the minimum value as the component state to be transferred next time:

[0367]

[0368] Among them, in step 2.4, if the state after the transfer involves the leakage fault state of the natural gas network and / or heat network pipeline, a random sampling method is also used to determine the aperture size and location of the pipeline leakage fault, and the process includes:

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

[0370] Then, the random numbers d~N(μ,σ) that obey the normal distribution are used to simulate the diameter of the leakage aperture, where μ and σ are related to the type of pipeline.

[0371] In this way, by setting the initial state of all components and sampling each component based on the transfer rate of the transferable state set, the duration set of the component staying in the current state before transferring to the next state can be obtained. This process can accurately simulate the change of component state and provide a basis for subsequent energy system analysis and optimization. When the state after the transfer involves the leakage fault state of the natural gas network and / or heat network pipeline, the random sampling method is used to determine the aperture size and location of the pipeline leakage fault. This process not only considers the possibility of leakage fault, but also further refines the specific characteristics of the leakage fault (such as aperture size and location), making the simulation results closer to the actual situation. In addition, by accurately simulating the component state and leakage fault characteristics, this method can provide support for the operation decision of the energy system. For example, when a leakage fault occurs, the fault location and aperture size can be quickly determined according to the simulation results, so that corresponding emergency measures can be taken to reduce the impact of the fault on the system. In addition, the state duration sampling algorithm considering the leakage fault is helpful for analyzing the reliability of the energy system. By simulating the system operation 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.

[0372] S3. For each system state transition sequence obtained by the division, the energy load reduction amount is determined by using the optimal dynamic energy flow model of the integrated energy system considering leakage failures; 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 (e.g., 365 days);

[0373] S4. Calculate the sample mean of the recorded load reduction amount and duration; use the standard deviation of the sample mean to calculate the variance convergence coefficient and make 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 specific implementation, 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 electricity supply shortage; EGNS represents the expected value of natural gas supply shortage; EHNS represents the expected value of heat supply shortage.

[0380] The maximum of the three coefficients is chosen 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 ≤0.02 or the total simulation time reaches the preset value, the convergence condition is determined to be met and the state simulation is stopped; otherwise, the convergence condition is determined to be not met.

[0383] The reliability index of the integrated energy system can be calculated using existing technical means. The calculation process does not belong to the creative content of this method and will not be repeated here.

[0384] In this way, the reliability of the simulation results can be improved. Selecting the maximum value of the three coefficients (the variance convergence coefficients corresponding to EENS, EGNS, and EHNS) as the convergence criterion can ensure that the convergence requirements are met on multiple key indicators. This helps to improve the reliability of the simulation results and make the simulation results more reflective of the actual situation. In addition, it also supports simulation analysis of complex systems. For energy systems containing multiple components and complex relationships, this method provides an effective convergence judgment method. By continuously monitoring and judging the convergence conditions, it can be ensured that the simulation process can accurately reflect the operating status and characteristics of the system.

[0385] Most fault models in the prior art use a simple two-state model, which does not consider the impact of leakage faults on the system, resulting in inaccurate evaluation results. This method can accurately simulate the impact of natural gas network and heat network pipeline leakage faults on energy flow and distribution by constructing an optimal dynamic energy flow model for an integrated energy system that takes leakage faults into account. In addition, reliability assessment methods in the prior art often ignore the dynamic process of leakage faults, resulting in errors in the assessment results. In this method, by using a state duration sampling algorithm to extract the timing sequence of component states, and combining it with the system state transition sequence for simulation, this method can more accurately evaluate the reliability of the integrated energy system.

[0386] Moreover, the method can comprehensively consider the complexity of the integrated energy system. The evaluation methods in the prior art often only focus on a single system or a single fault type, ignoring the complexity and diversity of the integrated energy system. This method not only considers the leakage failures of the natural gas network and the heat network pipelines, but also comprehensively considers the interaction and influence of the power system, the natural gas system and the thermal system, so as to more comprehensively evaluate the reliability of the integrated energy system. In addition, the evaluation methods in the prior art often cannot provide sufficient information and support for optimal scheduling, which limits the improvement of system performance. By accurately evaluating the impact of pipeline leakage failures on the integrated energy system, this method can provide an important basis for the optimal scheduling of the system, which helps to improve the operating efficiency and energy utilization efficiency of the system.

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

[0388] Embodiment 2

[0389] In order to better understand this method, the following example is given.

[0390] Example system Figure 7 As shown in the figure, an integrated energy system is composed of an IEEE 30-node power system, a Belgian 20-node natural gas system and a 16-node thermal system. The reliability assessment method considering the leakage failure of gas and heat network pipelines proposed in the present invention is verified, and the impact of leakage failures of natural gas network pipelines and heat network pipelines on the reliability assessment of the integrated energy system is analyzed.

[0391] The integrated energy system includes 3 coal-fired units, 1 cogeneration unit, 2 gas turbines, 41 power lines, 6 gas sources, 2 compressors, 17 natural gas pipelines, 2 gas boilers, 1 heat pump and 14 heat network pipelines. In order to improve the calculation efficiency, the modified typical daily curve is used to represent the annual level, such as Figure 8 As shown. In addition, when the ratio of the leakage aperture to the pipeline is less than 0.1, it can be considered as a leakage fault. Therefore, for the leakage faults of the gas network and heat network pipelines, it is assumed that the average diameter of the leakage aperture of the gas network is 10mm and the standard deviation is 4mm. The average diameter of the leakage aperture of the heat network is 65mm and the standard deviation is 10mm. The reliability parameters of the natural gas system and the power system can be found in the literature. The duration of the failure of the heat network pipeline component generally exceeds 10 hours, and even reaches dozens of hours. The reliability numbers of the three-state Markov model of the natural gas network and the heat network pipeline are shown in Tables 1 and 2. Given the failure transition rate from the leakage state to the shutdown state, the other two failure transition rates can be calculated according to the following formulas.

[0392]

[0393] Table 1 Parameters of the natural gas network pipeline Markov model

[0394]

[0395] Table 2 Parameters of Markov model for heat network pipeline

[0396]

[0397] Two scenarios are set up to verify the impact of pipeline leakage failures in the natural gas network and heat network on the reliability assessment of the integrated energy system. Scenario 1 is a traditional two-state reliability model without considering leakage failures, and scenario 2 is a three-state Markov reliability model of pipelines considering leakage failures.

[0398] Table 3 gives the reliability indicators of the integrated energy system of electricity, natural gas and heat in the two scenarios. It can be seen from the table that compared with scenario 1, the changes in the reliability indicators EENS and LOOLP of the power system in scenario 2 are smaller. This is because when considering pipeline leakage failures, the power system is the last to be cut off during comprehensive scheduling due to its highest load importance. For the natural gas system, the EGNS and LOGLP of scenario 1 are 6.6319×10 2 m 3 and 5.4201×10 -5 , scenario 2 increases to 1.9834×10 3 m 3 and 1.6209×10 -4 , the change trend of LOGLP is consistent with that of EGNS. For the thermal system, the reliability indicators EHNS and LOHLP of scenario 2 are 2.04 and 1.45 times of those in scenario 1, respectively. The results show that the reliability indicators of the natural gas and thermal systems in scenario 2 have increased significantly. This is because most natural gas and thermal pipelines are radial networks, and some load points can only accept one-way energy transmission. The multi-state model considering leakage faults has a greater impact on the system reliability indicators. It also shows that in the integrated energy system, if leakage faults are ignored in the component reliability modeling, the system reliability indicators will be greatly underestimated. Therefore, it is necessary to accurately model the reliability of the natural gas network and the thermal network pipelines to improve the reliability assessment accuracy of the integrated energy system.

[0399] At the same time, it can be seen from Table 3 that the expected index of natural gas leakage EGL is 5.2 times the expected index of natural gas supply shortage EGNS, and the natural gas leakage probability index PGL is 4.1 times the natural gas system index LOGLP, which shows that the risk of natural gas leakage is much greater than the risk of system gas load reduction. The expected index of hot water leakage EWL is 4.1327×10 3 m 3, indicating that a large amount of water resources are wasted, which will further cause hydraulic imbalance in the thermal system and poor heating effect for users.

[0400] Table 3 Impact of leakage failure on reliability index of integrated energy system

[0401]

[0402]

[0403] Tables 4 and 5 are the node reliability indices of the natural gas system and the thermal system, respectively, to study the impact of leakage failures on the reliability of different nodes in the power, natural gas and thermal systems. The node reliability index is calculated as follows:

[0404]

[0405] Replace the system load loss in the formula with the node load loss. Since the reliability index of the power system changes little, the node reliability index analysis is not performed here. It should be noted that the gas load reduction at the system coupling node, such as the load reduction at load nodes 6, 7, 10, 15 and 16 of the natural gas system, is not included in the total reduction, so it is not analyzed.

[0406] Table 4 shows that the EGNS changes of natural gas nodes 19 and 20 in scenario 2 increase sharply, and their values ​​are about 3 times that in scenario 6. The change ratios of natural gas nodes 3 and 12 are relatively small, 1.28 and 1.08 times, respectively. The reason for this phenomenon is that the loads of nodes 19 and 20 are supplied by one-way pipelines B15 to B17. Once these pipelines leak, they will cause the leaked gas load and reduce the pipe storage in the pipeline, increasing the load reduction of nodes 19 and 20, that is, the load reduction increases by a greater margin. In addition, the EGNS value of node 20 is relatively large, 4.5571×10 2 m 3 and 1.3633×10 3 m 3 , mainly because the natural gas load at node 20 is relatively far from the gas source. Considering the gas pressure operating limit of the natural gas system, in an emergency, it is easier to transport natural gas from the gas source to the adjacent gas load rather than to a more distant location.

[0407] Table 4 Impact of leakage failure on the reliability index EGNS of natural gas system nodes

[0408]

[0409] Table 5 Impact of leakage failure on the node reliability index EHNS of the thermal system

[0410]

[0411]

[0412] As shown in Table 5, the EHNS index of the heat nodes 6, 7, 14 and 15 is relatively large due to the larger heat load. Compared with scenario 1, the improvement ratios of the heat nodes 5, 6, 7 and 8 in scenario 2 increase successively, and the heat nodes 13, 14, 15 and 16 have the same situation, and the improvement ratios are larger than the nodes corresponding to the same topological position. The results show that the reliability index of the node at the back of the heat network topology structure increases more, because the pipeline leakage failure will have a cumulative impact on the back heat nodes. For example, assuming that pipelines H9 and H10 have leakage failures, the hot water passing through the H13 and H14 pipelines will reduce two parts of the flow due to two leakage failures, while the hot water passing through the H12 pipeline will only reduce a part of the flow due to the H9 leakage. Therefore, compared with load node 14, the hot water used for heat exchange at load nodes 15 and 16 is reduced, resulting in a reduction in heat supply and more heat load reduction. In other words, the load reduction of the back heat nodes is more affected by the leakage failure. In addition, the reason why the right thermal system (nodes 13-16) has a slightly larger improvement ratio than the left thermal system (nodes 5-8) is that the heat source C4 of the right 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, when a natural gas leak occurs, the impact on the right thermal system is greater.

[0413] The results of the example show that after adopting the three-state pipeline reliability model including leakage faults, the reliability index values ​​of natural gas and thermal systems have increased significantly, that is, leakage faults have a greater impact on system reliability indicators. It also shows that if leakage faults are ignored in component reliability modeling, the reliability level of the integrated energy system will be significantly overestimated.

[0414] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A reliability assessment method for an integrated energy system considering pipeline leakage failures, characterized in that: The following steps are involved: S0. Build an integrated energy system, which includes power system, natural gas system and thermal system; and on the basis of the integrated energy system, consider the leakage failures of natural gas network and thermal network pipelines, as well as the impact of these failures on energy flow and distribution, and build an optimal dynamic energy flow model of the integrated energy system considering leakage failures; S1. Input the topological structure 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 a normal operating state; S2. Based on the integrated energy system, the state duration sampling algorithm is used to extract the time series of all component states including the natural gas network and the heat network pipeline; the time series of all components are combined to obtain the system state transition sequence, and it is divided according to the day; S3. For each system state transition sequence obtained by the division, the energy load reduction amount is determined by using the optimal dynamic energy flow model of the integrated energy system considering leakage failures; 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 standard deviation of the sample mean is used to calculate the variance convergence coefficient and perform convergence judgment; if the convergence condition is met, the iterative simulation is stopped, 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, return to S2 to continue the iterative simulation for the next simulation duration.

2. The reliability assessment method of the integrated energy system considering pipeline leakage failure according to claim 1 is characterized in that: When constructing the optimal dynamic energy flow model of the integrated energy system considering leakage failures, the constructed constraints include natural gas system constraints, thermal system constraints, power system constraints, gas network pipeline energy flow constraints after considering leakage failures, and heat network pipeline energy flow constraints after considering leakage failures.

3. The reliability assessment method of the integrated energy system considering pipeline leakage failure according to claim 2 is characterized in that: Natural gas system constraints include: The relationship between the amount of natural gas consumed by the compressor and the flow through the compressor: The relationship between the air pressure at the inlet and outlet of the compressor: Gas pressure at the gas node Upper and lower limit constraints: in, represents the flow rate through the compressor pipeline, represents the amount of natural gas consumed by the compressor, γ cp is the boost ratio, Indicates the pressure at the compressor near the gas end. Indicates the pressure at the compressor near the gas end, P x,min Indicates the lower limit of natural gas node pressure, P x,max represents the upper limit of the natural gas node; Γ represents the coefficient of the relationship between the amount of natural gas consumed by the compressor and the flow rate through the compressor; Operation constraints of CHP units: In the formula, is the operating power of the CHP unit; and are the power generation and heating power of the air extraction thermal power unit respectively; c v is the rate of change of power generation with increase of heating power when the total steam inlet of the unit does not change; c m is the back pressure operation elastic coefficient of the CHP unit; r is a constant; is the operating status of the CHP unit at time t, 1 represents normal status, and 0 represents fault status; PN max,x and PN min,x are 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 power of the CHP unit; Balanced equation for natural gas: Node traffic balancing: Cut the upper and lower limits of the load: Upper and lower limits of gas source output: Pipeline flow upper and lower limit constraints: Among them, the mass flow rate Q = ρwA represents the amount of natural gas passing through any section of the pipeline per unit time, where A is the cross-sectional area of ​​the pipeline, ρ is the density, and w is the flow rate; the ideal gas state equation P = ρZR c T represents the relationship between gas pressure, density and temperature, where Z is the compression factor of natural gas, R c is the specific gas constant of natural gas, T is the temperature; Where n(x) represents the set of pipelines connected to node x; and represents the outflow and inflow flow of pipe xy; Indicates the gas supply volume. Indicates gas load, Indicates the amount of gas cutting load; Indicates the flow rate through the compressor pipeline; Indicates the amount of natural gas consumed by the compressor; and They represent the gas turbine electrical power and gas boiler thermal power respectively; m(x) represents the gas grid node set; η gas ,η chp and η heat They represent the conversion efficiency of gas turbine, CHP unit and gas boiler respectively; cp represents the compressor branch; Linear equation of dynamic energy flow model of natural gas pipeline: The pipeline xy is divided into several segments with intervals of Δx; in terms of time, time t is the initial point of the operation cycle. and is the initial flow rate and pressure at time t at pipe x; Natural gas pipeline storage equation: In the formula, represents the amount of natural gas stored in the pipeline with a length of Δx at time t; Δt represents the differential time interval; Δx represents the differential length interval; time t is the initial point of the operation cycle, and is the initial flow rate and pressure at time t at pipe x; The linear equation of natural gas dynamic energy flow is: In the formula, λ is the hydraulic friction factor, and D is the inner diameter of the natural gas pipeline; The expression of sgn(Q) is: The friction coefficient λ of the natural gas pipeline is: In the formula, ε g is the absolute roughness of the natural gas pipeline; And introduce 0-1 variables to establish traffic direction constraints: In the formula, It is the variable for judging the direction of the pipeline xy, which is 1 when Q>0 and 0 when Q<0; N is the preset value; The storage is restored every 24 hours: Wherein, I is an integer and δ is the range of variation.

4. The reliability assessment method of the integrated energy system considering pipeline leakage failure according to claim 3 is characterized in that: Thermal system constraints include: Hot water temperature transfer equation: Quality of hot water flowing out of the pipe From arrive The mass composition of the hot water flowing into the pipeline during the period is: in, is the working fluid quality of the water supply pipeline numbered l at different time periods; ρ w , A l and L l represent the water density, the cross-sectional area of ​​the pipe, and the length of the pipe respectively; during the period from t to t+Δt, the mass of hot water flowing into the pipe is The mass of hot water flowing out of the pipe during the Δt period is equal to the mass of hot water flowing into the pipe; Hot water temperature at the outlet of the water supply and return pipes It is composed of the linear weighted temperature of the hot water mass injected in several time periods: The pipe outlet temperature after taking into account heat loss is corrected using the Sukhov temperature drop model: in, and They represent the outlet temperature and inlet temperature of the water supply pipe at time t respectively; and They represent the outlet temperature and inlet temperature of the return pipe at time t respectively; Indicated in The quality of hot water injected into the pipeline during the period t; Indicates from The quality of hot water injected into the pipeline during the period t; and Respectively represent the outlet temperature of the water supply and return pipes; T e , and c represent the ambient temperature, the thermal conductivity of the pipe l and the specific heat capacity of water respectively; Pipeline hot water temperature fusion constraints: Hot water temperature mixing constraints at pipe junctions: The inlet temperature of all pipes leaving node m is equal to this mixing temperature: Pipeline temperature upper and lower limit constraints: in, and Respectively represent the temperature at the node m of the water supply pipe and the return pipe, T s,min Indicates the lower limit of water supply pipe temperature, T s,max Indicates the upper limit of water supply pipe temperature, T r,min Indicates the lower limit of return pipe temperature, T r,max Indicates the upper limit of the return pipe temperature; Heat load dynamic model: Use the building heat load model to simulate indoor temperature: In the formula, is the seasonal heat load power of the user at time t, is the indoor temperature of the user at time t, R is the equivalent thermal resistance of the building, and C is the equivalent heat capacity; Seasonal heat load reduction for: Where, T com,min and T com,max They are the minimum and maximum suitable indoor temperatures of the building, respectively; Thermodynamic system balance equation: Relationship between heat source output and pipeline hot water temperature: Relationship between annual load and seasonal load and pipeline hot water temperature: Annual load reduction upper and lower limits: in, Indicates the heating power of the CHP unit in the heating network, Indicates the thermal power of the gas boiler in the heating network, represents the heat pump power in the heat network, n(e) represents the pipe set of the heat source outlet, m(e) represents the heat network node set, represents the annual heat load, represents the annual heat load reduction, s(e) represents the pipe set at the heat exchanger inlet, Indicates the upper limit of annual load reduction.

5. The reliability assessment method of the integrated energy system considering pipeline leakage failure according to claim 4 is characterized in that: Power system constraints include: Node power balance: The relationship between the tidal current and the node phase angle is: Node phase angle constraint: Cut load upper and lower limit constraints: Power line transmission capacity constraints: Upper and lower limits of power output for coal-fired units, gas turbines and CHP units: in, represents the gas turbine electrical power, Indicates the electric power of the extraction type thermal power unit, Represents the electrical power of coal-fired units, represents the heat pump power, j represents the power branch connected to node k, Indicates the power line flow; represents the amount of electric load reduction, m(k) represents the set of power nodes; η hp represents the conversion efficiency of the heat pump, b kl represents line reactance, θ represents node phase angle, PG k,min Indicates the lower limit of the power output of the coal-fired unit, PG k,max Indicates the upper limit of the electric power output of the coal-fired unit, PE k,min Indicates the lower limit of gas turbine electrical power output, PE k,max Indicates the upper limit of gas turbine electrical power output, PN k,min Indicates the lower limit of the CHP unit power output, PN k,max Indicates the upper limit of the CHP unit power output; F kj,max It indicates the upper limit of power line flow; Indicates the upper limit of load reduction.

6. The reliability assessment method of the integrated energy system considering pipeline leakage failure according to claim 5 is characterized in that: The energy flow constraints of the gas network pipeline after considering leakage failure include: Split a xy segment pipeline into two segments xm and my to calculate the energy flow separately. The energy flow equation in the xm segment pipeline is as follows: l g,xm =λ g,xy ; Where, L xm and D xy Represents natural gas pipeline L xm The length and inner diameter of the xy segment; represents the air pressure at pipeline node x at time t; and Respectively represent the natural gas flow rates flowing into and out of the natural gas pipeline; g,xm represents the friction coefficient of the natural gas pipeline in section xm; w0 represents the initial flow rate of natural gas; c w represents the speed of sound; The energy flow equation in the my section of the pipeline is: l g,my =λ g,xy ; The energy flow balance equation needs to be satisfied at the virtual gas load: Among them, virtual gas load The calculation formula is: In the formula, R c is the gas constant of natural gas, κκ is the isentropic index of natural gas.

7. The reliability assessment method of the integrated energy system considering pipeline leakage failure according to claim 6 is characterized in that: The energy flow constraints of the heat network pipeline after considering the leakage failure include: In the formula, is the pressure of node i in the heat network pipeline; is the pressure of node i in the heat network pipeline; h,l is the friction resistance coefficient of the heat network pipeline; w h,l is the hot water flow rate in the pipeline; D l is the diameter of the heating network pipe. The friction coefficient is: In the formula, λ h,l is the friction resistance coefficient of the heat network pipeline; D l is the diameter of the heat network pipeline; Δ is the equivalent roughness of the pipeline, usually 1 mm; Re is the Reynolds number: Where, υ is the viscosity coefficient; The pressure drop calculation formula is: L l represents the length of the natural gas pipeline; ρ w Indicates the density of water; It indicates the working medium quality of the water supply pipeline numbered l at different time periods; When a leakage occurs in the heat network pipeline, the original pipeline is split into two sections for calculation and the transmission delay time is redefined. In the formula, A l Indicates the cross-sectional area of ​​the pipe; The leakage flow caused by pipeline leakage is: In the formula, C2 is the flow coefficient, A h,leak is the area of ​​the leakage hole; The relationship between the virtual hot water load and the hot water flow rate of the two sections of the pipeline is: Based on the changed hot water flow rate, the hot water temperature can be calculated as: in, In order to ensure the normal operation of the heating system, a centralized water replenishment method is adopted, and water is uniformly produced at the inlet of the heat source return pipe or condensed water is used for water replenishment and constant pressure; the temperature fusion equation of the water replenishment process is: In the formula, The return water temperature after replenishment at the heat source; is the replenishment amount, which is the difference between the flow rate of the water inlet pipe and the flow rate of the return pipe at the heat source; This is the water filling temperature, usually set to the ambient temperature.

8. The reliability assessment method of the integrated energy system considering pipeline leakage failure according to claim 7 is characterized in that: When constructing the optimal dynamic energy flow model of the integrated energy system considering leakage failures, the objective function constructed is: In the formula, c gw,x represents the gas source cost; c curs,x represents the gas cutting load penalty coefficient; c pg,k represents the output cost of coal-fired units; c curl,k represents the load penalty coefficient for power reduction; c curh,e Indicates the heat load penalty coefficient; Indicates the gas supply volume of the gas source; Indicates the amount of gas cutting load; Indicates the electrical power of coal-fired units; Indicates the amount of electrical load reduction; Indicates the annual heat load reduction; represents the seasonal heat load reduction; m(x) represents the gas grid node set; m(k) represents the power node set; Q(x) represents the gas source node set; p(x) represents the coal-fired unit node set; n(d) represents the pipeline set connected to node d.

9. The reliability assessment method of the integrated energy system considering pipeline leakage failure according to claim 1, characterized in that: In S2, the process of extracting the component 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 state set Ω to which the current state of element m (m = 1, 2, ... M) may transfer m ; Where M is the number of all components; Step 2.3: Transition rate based on the set of transferable states The duration set of each element staying in the current state before transferring to the next state is sampled according to the following formula in, is 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 each component's current state, and use the state after the transition corresponding to the minimum value as the component state to be transferred next time: Among them, in step 2.4, if the state after the transfer involves the leakage fault state of the natural gas network and / or heat network pipeline, a random sampling method is also used to determine the aperture size and location of the pipeline leakage fault, and the process includes: First, a random number Q between [0,1] is extracted for the pipeline, and the length from the leakage fault location to the beginning of the pipeline is calculated as L′=LQ, where L is the length of the pipeline; Then, the random numbers d~N(μ,σ) that obey the normal distribution are used to simulate the diameter of the leakage aperture, where μ and σ are related to the type of pipeline.

10. The reliability assessment method of an integrated energy system considering pipeline leakage failure according to 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; Among them, EENS represents the expected value of electricity supply shortage; EGNS represents the expected value of natural gas supply shortage; EHNS represents the expected value of heat supply shortage. The maximum of the three coefficients is chosen 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 ≤0.02 or the total simulation time reaches the preset value, the convergence condition is determined to be met and the state simulation is stopped; otherwise, the convergence condition is determined to be not met.

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