Comprehensive energy system reliability evaluation method considering compressed air energy storage

Abstract

The invention belongs to the technical field of comprehensive energy system intelligent analysis and evaluation, and particularly relates to a comprehensive energy system reliability evaluation method considering compressed air energy storage, which comprises the following steps: S1, establishing an AA-CAES reliability evaluation model considering a partial function failure state; wherein the AA-CAES topological structure is divided into a compression subsystem, an expansion subsystem and a storage subsystem according to functions; s2, constructing an electricity-gas-heat comprehensive energy system model based on the AA-CAES reliability evaluation model filtering partial function failure states, and performing reliability analysis processing on the comprehensive energy system; s3, initializing the integrated energy system model based on the actual parameters of the integrated energy system; and S4, performing reliability analysis processing on the integrated energy system by using the integrated energy system model to obtain a corresponding reliability index. According to the method, the influence of the AA-CAES system on the reliability of the integrated energy system can be comprehensively considered, and the overall performance and reliability of the integrated energy system can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent analysis and evaluation of integrated energy systems, and in particular relates to a reliability evaluation method for an integrated energy system taking compressed air energy storage into consideration. Background Art

[0002] With the increasing depletion of fossil fuel resources and the continuous increase in greenhouse gas emissions, human society is facing unprecedented challenges of environmental degradation and resource scarcity. In order to meet these challenges, the development and utilization of renewable energy has become an important trend in global energy development. As an effective way to integrate multiple energy forms (such as electricity, gas, heat, etc.), the integrated energy system has attracted much attention due to its flexible and efficient energy supply characteristics. However, the intermittent and instability of renewable energy has become a key factor restricting the reliability of the integrated energy system.

[0003] In this context, the advanced adiabatic compressed air energy storage (AA-CAES) technology has shown great potential in improving the reliability of integrated energy systems due to its many advantages such as green environmental protection, strong peak-shaving capability and high dispatchability. The AA-CAES system compresses the air and stores it in the air storage chamber by using an electric motor to drive a multi-stage compressor, and uses a heat exchanger to store the heat generated during the compression process in a heat storage tank. When energy needs to be released, the high-temperature and high-pressure air drives the multi-stage expander to work, which in turn drives the generator to generate electricity. At the same time, the heat in the heat storage tank is used to heat the air to improve the power generation efficiency. This technology can not only effectively solve the problems of intermittent and instability of renewable energy, but also realize efficient storage and conversion of energy.

[0004] However, although AA-CAES technology has broad application prospects in integrated energy systems, existing research still has many shortcomings in its reliability assessment.

[0005] First, existing studies often ignore the impact of temperature changes in the working medium of the AA-CAES thermal storage tank on its heating and power generation capabilities. In fact, temperature changes in the working medium in the thermal storage tank will directly affect the overall performance and reliability of the AA-CAES system. If this impact is not accurately assessed, the reliability assessment results of the electric-gas-heat system may be overly optimistic, thus failing to truly reflect the actual operating status of the system.

[0006] Secondly, existing studies usually only consider the normal state and fault state when evaluating the reliability of AA-CAES systems, while ignoring the partial functional failure state. In actual operation, the AA-CAES system may experience partial functional failure due to various reasons (such as equipment aging, improper maintenance, etc.). Although this partial functional failure will not cause the system to completely stop operating, it will seriously affect its performance and reliability. If this state is not included in the reliability assessment model, the assessment results will be inaccurate and unable to provide strong support for the optimal design and operation of the integrated energy system.

[0007] Therefore, how to comprehensively consider the impact of the AA-CAES system on the reliability of the integrated energy system, including the impact of the temperature change of the working medium of the heat storage tank and the impact of the failure of some functions, so as to establish a more accurate and reliable evaluation model and method, has become an urgent problem to be solved. This will not only help improve the overall performance and reliability of the integrated energy system, but also provide a strong guarantee for the widespread application of renewable energy and the optimization and transformation of the energy structure. 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 into account compressed air energy storage, which can comprehensively consider the impact of the AA-CAES system on the reliability of the integrated energy system, and help to improve the overall performance and reliability of the integrated energy system.

[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 compressed air energy storage comprises the following steps:

[0011] S1. Based on the topological structure and working mode of AA-CAES, analyze the multi-state operation mode of AA-CAES, wherein the multi-state operation mode includes a normal operation state mode, a fault state mode and a partial function failure state mode; establish a reliability evaluation model of AA-CAES considering the partial function failure state;

[0012] The topological structure of AA-CAES is divided into compression subsystem, expansion subsystem and storage subsystem according to function; the compression subsystem includes an electric motor and a multi-stage compressor; the expansion subsystem includes a multi-stage expander and a generator; the storage subsystem includes a heat exchanger and a high-temperature / low-temperature heat storage tank and a gas storage chamber for storing the working medium; the three subsystems use the same working medium to form independent compression cycles and expansion cycles;

[0013] S2. Based on the reliability assessment model of AA-CAES considering partial functional failure, a comprehensive energy system model of electricity, gas and heat is constructed to perform reliability analysis on the comprehensive energy system;

[0014] S3. Initializing the integrated energy system model based on the actual parameters of the integrated energy system;

[0015] S4. Use the integrated energy system model to perform reliability analysis on the integrated energy system and obtain corresponding reliability indicators.

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

[0017] 1. Comprehensively consider the impact of temperature changes of the working medium in the heat storage tank. This method fully considers the dynamic process of temperature changes of the working medium in the heat storage tank when establishing the AA-CAES reliability assessment model. Compared with the existing technology, this improvement can more accurately reflect the performance fluctuations of the AA-CAES system caused by temperature changes during operation. By accurately simulating the heating, cooling and storage process of the working medium in the heat storage tank, this method can evaluate the impact of temperature changes on the system's heating efficiency, power generation capacity and overall reliability, thereby providing more accurate reliability assessment results.

[0018] 2. Incorporate consideration of partial functional failure states. Unlike existing technologies that mainly focus on the normal state and complete failure state of the AA-CAES system, this method innovatively introduces the evaluation of partial functional failure states. This means that in the reliability analysis, not only the situation of complete system failure is considered, but also the performance of the system when the performance of one or some components is degraded but still partially operational. This improvement significantly improves the accuracy and comprehensiveness of the reliability assessment, making the assessment results closer to the actual operating conditions. By incorporating partial functional failure states, this method can more accurately predict the performance of the system under different operating conditions, providing stronger support for system maintenance and optimization.

[0019] 3. Improve the accuracy and practicality of reliability assessment of integrated energy systems. Combined with the above two improvements, this method can more accurately reflect the AA-CAES system and its interaction with other energy conversion and storage devices when constructing an integrated energy system model of electricity, gas and heat. This helps to more comprehensively evaluate the overall reliability of the integrated energy system, including energy supply stability, system recovery capability and fault response capability under different operating scenarios. Compared with the existing technology, the reliability assessment results provided by this method are more accurate and practical, and can provide more valuable reference for the planning, design and optimization of integrated energy systems.

[0020] In summary, this method significantly improves the accuracy and practicality of the reliability assessment of the integrated energy system by comprehensively considering the impact of the temperature change of the working medium of the heat storage tank and incorporating the consideration of the partial functional failure state. This improvement not only helps to optimize the system design and operation strategy, but also provides a more reliable guarantee for the safe and stable operation of the energy system. This method can comprehensively consider the impact of the AA-CAES system on the reliability of the integrated energy system, which helps to improve the overall performance and reliability of the integrated energy system.

[0021] Preferably, in S1, the reliability assessment model of AA-CAES considering partial functional failure state is constructed, and its constraints include the operation constraints of AA-CAES and the linearization constraints of the thermal energy storage model; wherein, the operation constraints of AA-CAES include the operation constraints of the compression subsystem, the operation constraints of the expansion subsystem, and the operation constraints of the storage subsystem.

[0022] Such a setting can, 1. improve the accuracy of the AA-CAES reliability assessment model. In S1, by constructing an AA-CAES reliability assessment model that takes into account partial functional failure states and introducing the operating constraints of AA-CAES (including the operating constraints of the compression subsystem, expansion subsystem, and storage subsystem), it is possible to more accurately simulate the various states of AA-CAES in actual operation, including normal state, partial functional failure state, and fault state. This detailed simulation helps capture the behavioral characteristics of the system when partial functional failure occurs, thereby more accurately assessing the reliability of the system.

[0023] 2. It can enhance the practicality of the thermal energy storage model. The reliability assessment model also includes linear constraints on the thermal energy storage model. This constraint allows the dynamic behavior of the thermal energy storage system to be effectively represented in the model while maintaining the computational efficiency of the model. Linear constraints help simplify complex nonlinear relationships, making the model solution more efficient and thus more practical in practical applications.

[0024] Preferably, the compression subsystem operation constraints include:

[0025] Total power consumption at time t during the compression cycle for:

[0026]

[0027] Where, κ is the specific heat ratio of air; is the air mass flow rate entering the compressor at time t during the compression process; is the specific heat capacity of air; and is the inlet and outlet temperature of the i-th stage compressor; N c is the number of compressor stages;

[0028] The outlet temperature of the i-th stage compressor is calculated as:

[0029]

[0030] In the formula, is the compression ratio of the i-th stage compressor, η c is the adiabatic efficiency of the compressor;

[0031] The upper and lower limits of the total power consumption of AA-CAES are:

[0032]

[0033] In the formula, Represents a 0-1 variable describing the compression cycle state; P com,max and P com,min Indicates the maximum and minimum values ​​of power consumption.

[0034] Such a setting can 1. accurately simulate the energy consumption of the compression subsystem. By providing a calculation formula for the total power consumption at time t during the compression cycle, this technical content can accurately simulate the energy consumption of the compression subsystem in the AA-CAES system. The formula takes into account multiple factors such as air specific heat ratio, air mass flow rate, air specific heat capacity, compressor inlet and outlet temperatures, and compressor stages, so as to fully reflect the energy consumption characteristics of the compression process.

[0035] 2. The calculation of compressor interstage temperature can be refined. The technical content provides a calculation formula for the outlet temperature of the i-th compressor, which takes into account the compression ratio and adiabatic efficiency of the compressor and can accurately calculate the air temperature at the outlet of each compressor stage. This refined calculation helps to more accurately evaluate the heat exchange and energy loss between compressor stages, providing an important basis for optimizing compressor design and operation strategies.

[0036] 3. By setting the upper and lower limits of the total power consumption of AA-CAES, this technical content can ensure that the compression subsystem does not exceed its design range during operation, thereby protecting the system equipment from damage. At the same time, this setting also helps to consider the performance of the system at different power consumption levels in the reliability assessment, providing strong support for system design and optimization. Taking into account the above technical contents, by accurately simulating the energy consumption and temperature characteristics of the compression subsystem and setting reasonable upper and lower limits of power consumption, this technical content helps to improve the reliability and energy efficiency of the AA-CAES system. By optimizing the compressor design and operation strategy, the system energy consumption can be reduced and the energy conversion efficiency can be improved, thereby extending the system life and reducing operating costs.

[0037] Preferably, the expansion subsystem operation constraints include:

[0038] The power generation of the expander at time t The calculation formula is:

[0039]

[0040] In the formula, is the air mass flow rate entering the expander at time t in the expansion cycle; and are the inlet and outlet temperatures of the i-th stage expander at time t; N e is the number of expander stages; is the specific heat capacity of air; κ is the specific heat ratio of air;

[0041] The outlet temperature of the i-th stage expander at time t The calculation is:

[0042]

[0043] In the formula, is the expansion ratio of the i-th stage expander, η e is the adiabatic efficiency of the expander;

[0044] Total power generation of AA-CAES The upper and lower limits are:

[0045]

[0046] In the formula, is a 0-1 variable describing the expansion cycle state; P exp,max and P exp,min are the maximum and minimum values ​​of the generated power respectively;

[0047] The compression subsystem and expansion subsystem in AA-CAES cannot run at the same time:

[0048]

[0049] In the formula, Represents a 0-1 variable describing the state of the compression cycle; is a 0-1 variable describing the state of the expansion cycle.

[0050] With this setting, 1. The calculation formula for the power generation of the expander at time t provided can accurately simulate the power generation performance of the expansion subsystem under different working conditions. By considering multiple factors such as the mass flow rate of air entering the expander, the inlet and outlet temperatures of each stage of the expander, the number of expander stages, the specific heat capacity and specific heat ratio of the air, the calculation formula can fully reflect the energy conversion efficiency in the expansion process and provide accurate data support for system design and optimization. The calculation formula for the outlet temperature of the i-th stage expander given takes into account the expansion ratio and adiabatic efficiency of the expander, and can accurately calculate the air temperature at the outlet of each stage of the expander. This detailed calculation helps to evaluate the heat loss and energy utilization efficiency during the expansion process, and provides an important basis for optimizing the design and operation strategy of the expander.

[0051] 2. By setting the upper and lower limits of the total power generation of AA-CAES, it is ensured that the expansion subsystem will not exceed its design range during operation, thereby protecting the system equipment from damage. At the same time, this setting also helps to consider the performance of the system at different power generation levels in the reliability assessment, providing strong support for system design and optimization. The introduced constraint that the compression subsystem and the expansion subsystem cannot operate at the same time is an important guarantee for the safe and stable operation of the AA-CAES system. By ensuring that the two subsystems are not in working state at any time, potential energy conflicts and equipment damage risks are avoided, and the stability and reliability of the system are improved.

[0052] 3. Taking the above technical contents into consideration, the overall performance and reliability of the AA-CAES system can be significantly improved by accurately calculating the power generation of the expansion subsystem, refining the temperature calculation between the expander stages, setting the upper and lower limits of the total power generation, and ensuring the mutually exclusive operation of the compression and expansion subsystems. This will not only help reduce system energy consumption and improve energy conversion efficiency, but also extend the system life and reduce operating costs, contributing to the construction of a more efficient and environmentally friendly energy system.

[0053] Preferably, the storage subsystem operation constraints include:

[0054] The temperatures of water and air in the heat exchanger are expressed as:

[0055]

[0056] In the formula, is the mass flow rate of hot water entering the compressor at time t during the compression process; is the specific heat capacity of water; T water,cold is the cold water temperature in the low-temperature heat storage tank; is the hot water temperature at the outlet of the i-th compressor; T s tis the hot water temperature of the high-temperature heat storage tank at time t; θ is the efficiency of the heat exchanger. The above expressions all assume that the heat exchange between the hot medium and the cold medium in the heat exchanger is equal; is the air mass flow rate entering the compressor at time t during the compression process; is the specific heat capacity of air; and are the inlet and outlet temperatures of the i-th stage compressor; and are the inlet and outlet temperatures of the i-th stage expander at time t, respectively;

[0057] In the compression cycle, the temperature of the hot water flowing into the high-temperature heat storage tank is T storage,in The calculation formula is:

[0058]

[0059] In the formula, represents the total hot water mass flow rate flowing into the high-temperature heat storage tank at time t;

[0060] The calculation formula for the water storage capacity and hot water temperature in the high-temperature heat storage tank is:

[0061]

[0062] In the formula, represents the quality of hot water in the high-temperature heat storage tank at time t; represents the total hot water mass flow rate flowing into the heat exchanger in the expansion cycle at time t; Indicates the change in water temperature; The thermal power provided to the user; represents the total hot water mass flow rate flowing into the high-temperature heat storage tank at time t; represents the hot water temperature of the high-temperature heat storage tank at time t; Δt represents the time interval between two consecutive calculation times;

[0063] The ranges of hot water temperature, water storage capacity and thermal power provided to users are:

[0064]

[0065] Where, T s,min and T s,max are the minimum and maximum values ​​of hot water temperature respectively; M storage,min and M storage,max are the minimum and maximum water storage capacity respectively; is the maximum user thermal power;

[0066] The high pressure air pressure in the air storage chamber is:

[0067]

[0068] In the formula, represents the air pressure in the air storage chamber at time t; R g represents the universal gas constant; T AT and V AT Respectively represent the temperature and volume of the gas storage chamber; P AT,max and P AT,min Respectively represent the maximum and minimum values ​​of the air pressure in the air storage chamber; is the air mass flow rate entering the compressor at time t during the compression process; Represents a 0-1 variable describing the state of the compression cycle; is the air mass flow rate entering the expander at time t in the expansion cycle; is a 0-1 variable describing the expansion cycle state;

[0069] With this setup, 1. The temperature calculation formulas for water and air in the heat exchanger provided can accurately simulate the heat exchange in the heat exchanger during compression and expansion. By considering multiple factors such as hot water mass flow rate, specific heat capacity of water, cold water temperature, compressor outlet hot water temperature, expander inlet and outlet temperatures, and heat exchanger efficiency, these formulas can fully reflect the energy conversion and temperature changes in the heat exchange process, providing accurate data support for system design and optimization.

[0070] 2. By calculating the temperature of hot water flowing into the high-temperature heat storage tank, the water storage volume and the hot water temperature in the heat storage tank, the technical content can optimize the storage and management of thermal energy. This includes determining key parameters such as the quality of hot water in the heat storage tank, the change in water temperature, and the thermal power provided to the user, thereby ensuring the effective storage and efficient use of thermal energy. At the same time, by setting the range of change of hot water temperature, water storage volume and thermal power provided to the user, the stable operation and safety of the system can be further guaranteed.

[0071] 3. The formula for calculating the high-pressure air pressure in the air storage chamber is given to accurately control the pressure change in the air storage chamber. By considering multiple factors such as the gas constant, the temperature and volume of the air storage chamber, the air mass flow rate of the compressor and expander, and the 0-1 variable describing the cycle state, these formulas can fully reflect the dynamic changes in the pressure in the air storage chamber, providing important guarantees for the stable operation of the system. At the same time, by setting the maximum and minimum values ​​of the air pressure in the air storage chamber, the safety and reliability of the system can be further ensured.

[0072] 4. Taking the above technical contents into consideration, the overall performance and efficiency of the AA-CAES system can be significantly improved by accurately simulating the heat exchange process, optimizing thermal energy storage and management, and accurately controlling the pressure of the gas storage chamber. This not only helps to reduce system energy consumption and improve energy conversion efficiency, but also prolongs the system life, reduces operating costs, and provides users with a more stable and reliable thermal energy supply.

[0073] Preferably, the linearization constraint of the thermal energy storage model is obtained by converting the dynamic temperature equation into a linear model through the Taylor series expansion formula and the big M method, and the process includes:

[0074] 1) Expand the nonlinear terms through Taylor series:

[0075] First, the nonlinear term is defined as The linear term is defined as

[0076]

[0077] Then, according to the Taylor series, f1 t exist Nearby expansion is:

[0078]

[0079] Indicates the initial value of water storage; Indicates the initial value of the water temperature change;

[0080] 2) The domain is divided into multiple grids by approximating the actual value by the average value:

[0081] The water storage capacity and water temperature change are divided into m and n segments respectively. The average value of each segment is used to approximately replace the actual value of each segment and serve as the initial expansion point of the Taylor series; f1 t The domain is divided into m*n grids; two sets of integer variables are introduced and Used to indicate and Segments within the range;

[0082]

[0083] In the formula, represents an integer variable describing the water storage capacity in the segmented state at time t; represents an integer variable describing the change in water temperature at time t in the segmented state; M storage,j,min and M storage,j,max are the minimum and maximum water storage capacity under segmented state; T rate,k,min and T rate,k,max are the minimum and maximum values ​​of water temperature variation under segmented state respectively;

[0084] Introduce two additional integer variables and a set of constraints Perform linearization;

[0085]

[0086] In the formula, represents an integer variable describing the mass flow rate of hot water entering the compressor at time t during the compression process in a segmented state; Represents an integer variable describing the hot water temperature of the high-temperature heat storage tank in the segmented state at time t; m com,total,water,h,min and m com,total,water,h,max are the minimum and maximum values ​​of the hot water mass flow rate entering the compressor at time t in the compression process under the segmented state; T s,g,min and T s,g,max are the minimum and maximum values ​​of the hot water temperature of the high-temperature heat storage tank under the segmented state;

[0087] By omitting the higher-order terms, the dynamic temperature equation is averaged over the segments. Nearby expansion is:

[0088]

[0089] In the formula, represents the water storage function ignoring higher-order terms; It represents the water temperature variation function ignoring higher-order terms;

[0090] 3) The nonlinear terms in the equation after Taylor expansion are linearized by the big M method:

[0091]

[0092] Where DM, GM, FM and AM are constants and are limited by the following formula:

[0093]

[0094] Such a setting, 1. Linearization of nonlinear equations. Thermal energy storage models usually contain complex nonlinear equations, which are difficult to use directly in optimization and control algorithms. Through the above technical content, using the Taylor series expansion formula, these nonlinear terms are successfully converted into linear terms, thereby greatly simplifying the model. This linearization not only retains the main features of the original model, but also makes the model easier to handle and analyze. Linear models are more computationally efficient than nonlinear models. Through linearization processing, the calculation time can be significantly reduced, making real-time optimization and control possible. This is crucial for the operation and management of thermal energy storage systems, especially in scenarios that require fast response and precise control.

[0095] 2. The linearized thermal energy storage model provides strong support for optimization decisions. Through optimization methods such as linear programming and integer programming, the optimal storage strategy, scheduling scheme, etc. can be solved more efficiently, thereby maximizing the utilization of thermal energy and minimizing costs. Linearized models usually have better robustness. Because linear models are simpler and clearer, they have stronger adaptability to fluctuations and uncertainties in input data. This makes the model more reliable in practical applications and can better cope with various complex situations.

[0096] 3. By dividing the domain of water storage and water temperature changes into multiple grids and introducing integer variables to represent the segments, the model is more flexible in dealing with continuous variables. This processing method not only helps linearization, but also better captures the dynamic characteristics of the system. The Big M method is an effective linearization method that converts nonlinear terms into linear terms by introducing additional constraints and constants. This method is particularly effective in dealing with models containing complex nonlinear terms, and can significantly improve the linearization degree and computational efficiency of the model.

[0097] Preferably, in S2, the objective function of the integrated energy system model is:

[0098]

[0099] In the formula, Indicates the gas supply volume of the gas source; Indicates the amount of natural gas load reduction; Indicates the power of coal-fired units; Indicates the amount of electrical load reduction; and They represent seasonal and annual heat load reduction respectively; Indicates wind power reduction; c curw,k represents the penalty coefficient for wind power reduction; w(k) represents the set of wind turbine nodes; T represents the total time; Q(x) represents the set of gas source nodes; c gw,x represents the gas source cost; m(x) represents the gas network node set; c curs,x represents the gas load penalty coefficient; p(k) represents the coal-fired unit node set; c pg,k represents the output cost of coal-fired units; m(k) represents the set of power nodes; c curl,k represents the load penalty coefficient for power reduction; n(d) represents the set of heating network nodes; c curh,e Represents the heat load penalty coefficient.

[0100] Such a setting, 1. The objective function aims to achieve the overall performance optimization of the integrated energy system by comprehensively considering multiple aspects such as gas supply, power production, heat load satisfaction, and wind power utilization. This helps to improve the energy utilization efficiency of the system, reduce operating costs, and enhance the reliability and stability of the system.

[0101] 2. The objective function helps improve the economic benefits of the system by minimizing the amount of natural gas load reduction, electricity load reduction, thermal load reduction, and the penalty cost of wind power reduction. This helps reduce the energy costs of users and increase the profits of energy suppliers.

[0102] 3. The objective function contains multiple variables and parameters, which can be adjusted and optimized according to the actual situation. This enables the model to flexibly adapt to changes in external conditions such as different energy demands, energy prices and energy policies.

[0103] Preferably, in S4, the process of performing reliability analysis on the integrated energy system includes:

[0104] S41, setting the initial state of each component of the integrated energy system to a normal operating state;

[0105] S42, using a state duration sampling method to obtain a state sequence of each component of the system, and then jointly obtaining a corresponding system state;

[0106] S43, analyzing the system status within a preset period using a combined energy system model to determine whether the system is in a load reduction state;

[0107] S44, calculating a system reliability index based on the analysis result of the load reduction;

[0108] S45, calculate the variance convergence coefficient of the system reliability index and determine whether the convergence condition is met; if not, return to step S42; if met, output the system reliability index; wherein the convergence condition is that the variance convergence coefficient is less than a preset threshold or the simulation cycle reaches a preset maximum number of times.

[0109] Such a setting, 1. The process sets the initial state of each component of the system to the normal operating state, and uses the state duration sampling method to simulate the state sequence of each component of the system, so that the random failure and repair process of the system components can be fully considered. This helps to more accurately evaluate the reliability level of the system. By using the integrated energy system model to analyze the system state within the preset period, it is possible to accurately determine whether the system is in a load reduction state. This helps to identify the operating bottlenecks and potential risks of the system under specific conditions.

[0110] 2. System reliability indicators can be quantified. Based on the analysis results of load reduction, system reliability indicators such as power supply reliability rate and load loss probability are calculated. These indicators can quantify the reliability level of the system and provide an important basis for system optimization and decision-making.

[0111] 3. By calculating the variance convergence coefficient of the system reliability index and judging whether the convergence condition is met, the stability and reliability of the analysis results can be ensured. If the convergence condition is not met, return to the step and re-simulate the analysis until the convergence condition is met. This helps to avoid errors caused by insufficient simulation times or randomness. By setting parameters such as the preset cycle and the maximum number of simulations, the process can complete the system reliability analysis within a reasonable time. At the same time, by adopting efficient algorithms such as the state duration sampling method, the analysis efficiency is further improved.

[0112] Preferably, in S42, the state sequence of each component includes a multi-state sequence of AA-CAES, a multi-state sequence of natural gas network and heat network pipelines, and a two-state sequence of other components; in S44, the system reliability index includes the system load loss probability, load loss frequency, and load loss duration;

[0113] In S42, when the state sequence of each component of the system is obtained by the state duration sampling method, the state sequence is determined by using the transition rate or steady-state probability between each mode; wherein the steady-state probability of each state is calculated by the frequency and duration method; and the transition rate between each mode is calculated according to the frequency balance principle.

[0114] Such a setting can 1. refine the component state sequence. By subdividing the state sequence of each component into a multi-state sequence of AA-CAES (Advanced Adiabatic Compressed Air Energy Storage System), a multi-state sequence of natural gas network and heat network pipelines, and a two-state sequence of other components, the actual operating state of each component of the system can be more accurately reflected. This refinement helps to more accurately evaluate the overall performance and reliability of the system.

[0115] 2. Improve the accuracy of state sequence generation. Using the transition rate or steady-state probability between modes to determine the state sequence can more realistically simulate the random failure and repair process of system components. The steady-state probability of each state is calculated by the frequency and duration method, and the transition rate between modes is calculated according to the frequency balance principle, which ensures the accuracy and reliability of state sequence generation.

[0116] 3. It can ensure a comprehensive assessment of system reliability. System reliability indicators include load loss probability, load loss frequency and load loss duration, which can fully reflect the reliability level of the system under specific conditions. By calculating these indicators, the risk of the system can be quantified, providing an important basis for system optimization and decision-making.

[0117] 4. Enhance the practicality of system reliability analysis. This technical content not only provides methods for state sequence generation and reliability index calculation, but also emphasizes the practical application value of these methods. By applying these methods to the reliability analysis of integrated energy systems, it can provide strong support for the optimal design and operation management of the system, thereby improving the reliability and economy of the system.

[0118] Preferably, the working medium includes air and water; the normal operating state mode is that the compression subsystem, the expansion subsystem and the storage subsystem are all operating normally; the partial function failure state mode includes the expansion only failure mode and the compression only failure mode; the expansion only failure mode is that only the expansion subsystem fails; the compression only failure mode is that only the compression subsystem fails; other operating states all belong to the failure state mode.

[0119] Such a setting, 1. By defining normal operating state, partial functional failure state (including expansion-only failure mode and compression-only failure mode) and other fault state modes, the technical content clearly defines the various possible operating states of the AA-CAES system. This helps system operation and maintenance personnel to quickly identify the current state of the system and take corresponding maintenance or repair measures. Through the clear division of different operating state modes, the reliability of the system can be evaluated more specifically. In particular, the identification of partial functional failure states enables the system to continue to operate when certain components fail, improving the overall reliability and fault tolerance of the system.

[0120] 2. Understanding the performance of the system under different operating conditions helps optimize the system design and operation strategy. For example, in the expansion-only failure mode, the system may still be able to provide some power output through other means; while in the compression-only failure mode, the operation of the storage subsystem may need to be adjusted to maintain the stable operation of the system. This in-depth understanding of system performance helps to develop more efficient operation plans and maintenance strategies. By analyzing the behavioral characteristics of the system under different operating conditions, a fault prediction and diagnosis model can be established. This helps to detect potential faults in advance, reduce system downtime, and improve system availability and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] 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:

[0122] Figure 1 A flowchart of the method is shown in FIG.

[0123] Figure 2 Schematic diagram of the topological structure of AA-CAES in Example 1;

[0124] Figure 3Schematic diagram of the 8-state Markov model of AA-CAES in Example 1;

[0125] Figure 4 It is a simplified schematic diagram of the 8-Markov model of AA-CAES in Example 1;

[0126] Figure 5 Schematic diagram of the dynamic temperature characteristics of AA-CAES in Example 1;

[0127] Figure 6 A reliability analysis process flow chart of the integrated energy system in Example 1;

[0128] Figure 7 This is an example diagram of the topology of the integrated energy system in the example of the second embodiment;

[0129] Figure 8 This is a schematic diagram of wind abandonment in an example of Embodiment 2;

[0130] Fig. 9 This is an example diagram of the optimal operation scheme of the power system in scenario 1 in the example of embodiment 2;

[0131] Fig.10 It is a schematic diagram of the optimal operation scheme of the power system in scenario 2 in the example of the second embodiment;

[0132] Fig.11 It is a schematic diagram of gas source production in the example of embodiment 2;

[0133] Fig.12 It is a schematic diagram of the pressure of the gas storage chamber in the example of the second embodiment;

[0134] Fig.13 This is a schematic diagram of the hot water temperature in the high-temperature heat storage tank in the second embodiment;

[0135] Fig.14 It is a schematic diagram of the compression and expansion power of AA-CAES in the example of the second embodiment;

[0136] Fig.15 Schematic diagram of heat generation of AA-CAES in Example 2. DETAILED DESCRIPTION

[0137] The following is a further detailed description through specific implementation methods:

[0138] Embodiment 1

[0139] like Figure 1 As shown, this embodiment discloses a reliability assessment method for an integrated energy system considering compressed air energy storage, comprising the following steps:

[0140] S1. Based on the topological structure and working mode of AA-CAES, analyze the multi-state operation mode of AA-CAES, wherein the multi-state operation mode includes a normal operation state mode, a fault state mode and a partial function failure state mode; establish a reliability evaluation model of AA-CAES considering the partial function failure state;

[0141] Among them, the topological structure of AA-CAES is divided into compression subsystem, expansion subsystem and storage subsystem according to function; the compression subsystem includes an electric motor and a multi-stage compressor; the expansion subsystem includes a multi-stage expander and a generator; the storage subsystem includes a heat exchanger and a high-temperature / low-temperature heat storage tank and a gas storage chamber for storing the working medium; the three subsystems use the same working medium to form independent compression cycles and expansion cycles.

[0142] In specific implementation, the working medium includes air and water; the normal operating state mode is that the compression subsystem, the expansion subsystem and the storage subsystem are all operating normally; the partial function failure state mode includes the expansion only failure mode and the compression only failure mode; the expansion only failure mode is that only the expansion subsystem fails; the compression only failure mode is that only the compression subsystem fails; other operating states all belong to the failure state mode.

[0143] In this way, by defining normal operating state, partial functional failure state (including expansion failure mode and compression failure mode) and other fault state modes, the technical content clearly defines various possible operating states of the AA-CAES system. This helps system operation and maintenance personnel to quickly identify the current state of the system and take corresponding maintenance or repair measures. Through the clear division of different operating state modes, the reliability of the system can be evaluated more targeted. In particular, the identification of partial functional failure state enables the system to continue to operate when some components fail, improving the overall reliability and fault tolerance of the system. In addition, understanding the performance of the system under different operating states helps to optimize the design and operation strategy of the system. For example, in the expansion failure mode, the system may still be able to provide partial power output through other means; while in the compression failure mode, the operation of the storage subsystem may need to be adjusted to maintain the stable operation of the system. This in-depth understanding of system performance helps to develop more efficient operation plans and maintenance strategies. By analyzing the behavioral characteristics of the system under different operating states, a fault prediction and diagnosis model can be established. This helps to detect potential faults in advance, reduce system downtime, and improve system availability and reliability.

[0144] In order to facilitate those skilled in the art to better understand the above technical content, the following explanation is given.

[0145] First, the topological structure and working mode of AA-CAES are comprehensively analyzed. The specific analysis process is as follows:

[0146] 1) Topological structure of AA-CAES. Figure 2 As shown in the figure, the AA-CAES structure is divided into three parts according to function: compression subsystem, expansion subsystem and storage subsystem. The compression subsystem mainly includes an electric motor and a multi-stage compressor. The expansion subsystem mainly includes a multi-stage expander and a generator. The storage subsystem contains a heat exchanger and a high-temperature / low-temperature heat storage tank and a gas storage chamber for storing working media (air and water). These three subsystems use the same working medium and form two independent circulation loops, namely the compression cycle and the expansion cycle.

[0147] 2) Working mode of AA-CAES. Its main working modes are as follows: ① When the integrated energy system is in the low energy consumption period or the wind power is in surplus, the compression cycle starts, and the surplus electricity is used to drive the compressor to compress the normal pressure and temperature air into high pressure and high temperature air; the high pressure and high temperature air is cooled by the heat exchanger to high pressure and normal temperature air and stored in the gas storage chamber (underground cave or pressure vessel). At the same time, the cold water in the low temperature heat storage tank is heated by the heat exchanger and stored in the high temperature heat storage tank to recover the compression heat. ② When the integrated energy system is in the peak energy consumption period or the system has an energy shortage due to a fault, the expansion cycle starts, and the high pressure gas stored in the gas storage chamber is heated by the hot water in the high temperature heat storage tank through the heat exchanger. At the same time, the hot water in the high temperature heat storage tank is cooled by the heat exchanger and stored in the low temperature heat storage tank. Then, the high temperature and high pressure compressed gas enters the expander, and the expander drives the generator to output electrical power to the system. It is worth noting that the residual heat energy in the high temperature heat storage tank can be transmitted to users through the heat exchanger and the heat network pipeline.

[0148] Secondly, a multi-state Markov model of AA-CAES is established based on the topological structure and working mode of AA-CAES. The specific modeling steps are as follows:

[0149] 1) Model the components of each subsystem separately. According to the topological structure and working principle of AA-CAES, the components in each subsystem can be regarded as series and independent. Once any component in the subsystem fails, the subsystem will stop running. Similarly, the three subsystems can be equivalent to a series logical relationship, and each subsystem can be modeled using a two-state fault model. Let the equivalent failure rate and equivalent repair rate of the compression subsystem, expansion subsystem, and storage subsystem be λ1, λ2, λ3 and μ1, μ2, μ3 respectively. Taking the compression subsystem as an example, the calculation of λ1 and μ1 is as follows:

[0150]

[0151] In the formula, λ c,m and r c,m are the failure rate and mean repair time of component m in the compression subsystem respectively; M cis the number of components in the compression subsystem.

[0152] 2) Establish the 8-state Markov model and 4 simplified operation mode models of AA-CAES. The operation state of AA-CAES can be determined according to the independent operation state of the three subsystems, as shown in Table 1. "1" represents the normal operation of the subsystem, and "0" represents the failure of the subsystem.

[0153] Table 1AA-CAES status table

[0154]

[0155] Based on this table, an 8-state Markov model of AA-CAES is established, such as Figure 3 The states of the compression, expansion, and storage subsystems are marked from top to bottom in each block diagram, with "UP" and "DN" representing the normal operation and shutdown of the subsystems, respectively. The number in the lower right corner of the block diagram represents the AA-CAES state number, and the number in the dotted box in the upper right corner represents its operating mode.

[0156] like Figure 3 As shown in the figure, AA-CAES has eight operating states and four operating modes. The storage subsystem is an essential part of the compression and expansion process. When it fails, AA-CAES will stop operating completely. States 1, 2, 3, 5, and 6 cannot compress air and output power. They are fault states and are classified as mode 0. Mode 1 is the normal operating state, which includes state 8, that is, all subsystems are operating normally. Mode 2 and mode 3 represent two different partial functional failure states: state 4 and state 7, respectively. In state 4, the compression subsystem fails, while the expansion and storage subsystems can operate normally. Therefore, AA-CAES cannot compress air and store heat, but can only release heat and output electrical power. In state 7, when the expansion subsystem fails, AA-CAES can only compress air and store heat. The steady-state probabilities p1-p8 of states 1-8 can be calculated by the frequency and duration method. In reliability assessment state sampling, the state sequence can be determined using the transition rate or steady-state probability between each mode.

[0157] Therefore, the 8-state Markov model of AA-CAES can be simplified into 4 operating modes. Figure 4 As shown, the transfer rates λ′1-λ′4 and u′1-u′4 between the four modes can be calculated according to the frequency balance principle.

[0158] In the specific implementation, the reliability assessment model of AA-CAES considering the partial functional failure state is constructed, and its constraints include the operation constraints of AA-CAES and the linearization constraints of the thermal energy storage model; among them, the operation constraints of AA-CAES include the operation constraints of the compression subsystem, the operation constraints of the expansion subsystem, and the operation constraints of the storage subsystem. In this way, the accuracy of the AA-CAES reliability assessment model can be improved. In S1, by constructing the reliability assessment model of AA-CAES considering the partial functional failure state and introducing the operation constraints of AA-CAES (including the operation constraints of the compression subsystem, the expansion subsystem, and the storage subsystem), the various states of AA-CAES in actual operation can be more accurately simulated, including the normal state, the partial functional failure state, and the fault state. This detailed simulation helps to capture the behavioral characteristics of the system when the partial functional failure occurs, so as to more accurately evaluate the reliability of the system. It can also enhance the practicality of the thermal energy storage model. The reliability assessment model also includes the linearization constraints of the thermal energy storage model. This constraint condition enables the dynamic behavior of the thermal energy storage system to be effectively represented in the model while maintaining the computational efficiency of the model. Linearization constraints help simplify complex nonlinear relationships, making model solving more efficient and thus more practical in practical applications.

[0159] Among them, the compression subsystem operation constraints include:

[0160] Total power consumption at time t during the compression cycle for:

[0161]

[0162] Where, κ is the specific heat ratio of air; is the air mass flow rate entering the compressor at time t during the compression process; is the specific heat capacity of air; and is the inlet and outlet temperature of the i-th stage compressor; N c is the number of compressor stages;

[0163] The outlet temperature of the i-th stage compressor is calculated as:

[0164]

[0165] In the formula, is the compression ratio of the i-th stage compressor, η c is the adiabatic efficiency of the compressor;

[0166] The upper and lower limits of the total power consumption of AA-CAES are:

[0167]

[0168] In the formula, Represents a 0-1 variable describing the compression cycle state; P com,max and P com,min Indicates the maximum and minimum values ​​of power consumption.

[0169] Such a setting can accurately simulate the energy consumption of the compression subsystem. By providing a calculation formula for the total power consumption at time t during the compression cycle, this technical content can accurately simulate the energy consumption of the compression subsystem in the AA-CAES system. The formula takes into account multiple factors such as air specific heat ratio, air mass flow rate, air specific heat capacity, compressor inlet and outlet temperatures, and the number of compressor stages, so as to fully reflect the energy consumption characteristics during the compression process. The calculation of the compressor interstage temperature can also be refined. The technical content provides a calculation formula for the outlet temperature of the i-th stage compressor, which takes into account the compression ratio and adiabatic efficiency of the compressor and can accurately calculate the air temperature at the outlet of each compressor stage. This detailed calculation helps to more accurately evaluate the heat exchange and energy loss between compressor stages, and provides an important basis for optimizing the design and operation strategy of the compressor. In addition, by setting the upper and lower limits of the total power consumption of AA-CAES, this technical content can ensure that the compression subsystem does not exceed its design range during operation, thereby protecting the system equipment from damage. At the same time, this setting also helps to consider the performance of the system at different power consumption levels in reliability assessment, providing strong support for system design and optimization. Taking the above technical contents into consideration, by accurately simulating the energy consumption and temperature characteristics of the compression subsystem and setting reasonable upper and lower limits of power consumption, this technical content helps to improve the reliability and energy efficiency of the AA-CAES system. By optimizing the compressor design and operation strategy, the system energy consumption can be reduced, the energy conversion efficiency can be improved, thereby extending the system life and reducing the operating cost.

[0170] The operating constraints of the expansion subsystem include:

[0171] The power generation of the expander at time t The calculation formula is:

[0172]

[0173] In the formula, is the air mass flow rate entering the expander at time t in the expansion cycle; and are the inlet and outlet temperatures of the i-th stage expander at time t; N e is the number of expander stages; is the specific heat capacity of air; κ is the specific heat ratio of air;

[0174] The outlet temperature of the i-th stage expander at time t The calculation is:

[0175]

[0176] In the formula, is the expansion ratio of the i-th stage expander, η e is the adiabatic efficiency of the expander;

[0177] Total power generation of AA-CAES The upper and lower limits are:

[0178]

[0179] In the formula, is a 0-1 variable describing the expansion cycle state; P exp,max and P exp,min are the maximum and minimum values ​​of the generated power respectively;

[0180] The compression subsystem and expansion subsystem in AA-CAES cannot run at the same time:

[0181]

[0182] In the formula, Represents a 0-1 variable describing the state of the compression cycle; is a 0-1 variable describing the state of the expansion cycle.

[0183] In this way, the calculation formula for the power generation of the expander at time t can accurately simulate the power generation performance of the expansion subsystem under different working conditions. By considering multiple factors such as the mass flow rate of air entering the expander, the inlet and outlet temperatures of each level of expanders, the number of expander stages, the specific heat capacity and specific heat ratio of air, the calculation formula can fully reflect the energy conversion efficiency in the expansion process and provide accurate data support for system design and optimization. The calculation formula for the outlet temperature of the i-th stage expander takes into account the expansion ratio and adiabatic efficiency of the expander, and can accurately calculate the air temperature at the outlet of each stage of the expander. This detailed calculation helps to evaluate the heat loss and energy utilization efficiency during the expansion process, and provides an important basis for optimizing the design and operation strategy of the expander. In addition, by setting the upper and lower limits of the total power generation of AA-CAES, it is ensured that the expansion subsystem will not exceed its design range during operation, thereby protecting the system equipment from damage. At the same time, this setting also helps to consider the performance of the system at different power generation levels in reliability evaluation, providing strong support for system design and optimization. The constraint that the compression subsystem and the expansion subsystem cannot operate at the same time introduced is an important guarantee for the safe and stable operation of the AA-CAES system. By ensuring that the two subsystems are not in operation at the same time at any time, potential energy conflicts and equipment damage risks are avoided, and the stability and reliability of the system are improved. By accurately calculating the power generation of the expansion subsystem, refining the temperature calculation between the expander stages, setting the upper and lower limits of the total power generation, and ensuring the mutually exclusive operation of the compression and expansion subsystems, the overall performance and reliability of the AA-CAES system can be significantly improved. This not only helps to reduce system energy consumption and improve energy conversion efficiency, but also prolongs the system life and reduces operating costs, contributing to the construction of a more efficient and environmentally friendly energy system.

[0184] The compression heat generated by AA-CAES is converted into hot water by the heat exchanger and stored in a high-temperature heat storage tank. The temperature and quality of the water change over time, which is called the dynamic temperature characteristics of AA-CAES, such as Figure 5 As shown. In the compression cycle, the cold water in the low-temperature heat storage tank is heated by the heat exchanger and flows into the high-temperature heat storage tank to recover the heat of the compressed air process, and the temperature and quality of the hot water are increased. In the expansion cycle, the hot water from the high-temperature heat storage tank is used to heat the high-pressure air flowing through the heat exchanger, and the quality of the hot water is reduced. In addition, the water temperature drops when the thermal power is supplied to the heat user. When the water temperature in the high-temperature heat storage tank rises to the upper limit, the water intake decreases and the compression and storage process is affected. On the other hand, the reduction in water temperature weakens the working capacity of the unit high-pressure air and reduces the inlet temperature of the expander, and the expansion power generation is affected. When the water temperature drops to the lower limit, the thermal power that AA-CAES can generate is 0.

[0185] Therefore, in specific implementation, the storage subsystem operation constraints include:

[0186] The temperatures of water and air in the heat exchanger are expressed as:

[0187]

[0188] In the formula, is the mass flow rate of hot water entering the compressor at time t during the compression process; is the specific heat capacity of water; T water,cold is the cold water temperature in the low-temperature heat storage tank; is the hot water temperature at the outlet of the i-th compressor; T s t is the hot water temperature of the high-temperature heat storage tank at time t; θ is the efficiency of the heat exchanger. The above expressions all assume that the heat exchange between the hot medium and the cold medium in the heat exchanger is equal; is the air mass flow rate entering the compressor at time t during the compression process; is the specific heat capacity of air; and are the inlet and outlet temperatures of the i-th stage compressor; and are the inlet and outlet temperatures of the i-th stage expander at time t, respectively;

[0189] In the compression cycle, the temperature of the hot water flowing into the high-temperature heat storage tank is T storage,in The calculation formula is:

[0190]

[0191] In the formula, represents the total hot water mass flow rate flowing into the high-temperature heat storage tank at time t;

[0192] The calculation formula for the water storage capacity and hot water temperature in the high-temperature heat storage tank is:

[0193]

[0194]

[0195] In the formula, represents the quality of hot water in the high-temperature heat storage tank at time t; represents the total hot water mass flow rate flowing into the heat exchanger in the expansion cycle at time t; Indicates the change in water temperature; The thermal power provided to the user; represents the total hot water mass flow rate flowing into the high-temperature heat storage tank at time t; represents the hot water temperature of the high-temperature heat storage tank at time t; Δt represents the time interval between two consecutive calculation times;

[0196] The ranges of hot water temperature, water storage capacity and thermal power provided to users are:

[0197]

[0198] Where, T s,min and T s,max are the minimum and maximum values ​​of hot water temperature respectively; M storage,min and M storage,max are the minimum and maximum water storage capacity respectively; is the maximum user thermal power;

[0199] The high pressure air pressure in the air storage chamber is:

[0200]

[0201] In the formula, represents the air pressure in the air storage chamber at time t; R g represents the universal gas constant; T AT and V AT Respectively represent the temperature and volume of the gas storage chamber; P AT,max and P AT,min Respectively represent the maximum and minimum values ​​of the air pressure in the air storage chamber; is the air mass flow rate entering the compressor at time t during the compression process; Represents a 0-1 variable describing the state of the compression cycle; is the air mass flow rate entering the expander at time t in the expansion cycle; is a 0-1 variable describing the expansion cycle state;

[0202] In this way, the temperature calculation formulas for water and air in the heat exchanger provided in 1 can accurately simulate the heat exchange in the heat exchanger during compression and expansion. By considering multiple factors such as hot water mass flow rate, specific heat capacity of water, cold water temperature, hot water temperature at the compressor outlet, expander inlet and outlet temperatures, and heat exchanger efficiency, these formulas can fully reflect the energy conversion and temperature changes in the heat exchange process, and provide accurate data support for system design and optimization. In addition, by calculating the hot water temperature flowing into the high-temperature heat storage tank and the water storage volume and hot water temperature in the heat storage tank, the technical content can optimize thermal energy storage and management. This includes determining key parameters such as the hot water quality of the heat storage tank, the water temperature change, and the thermal power provided to the user, thereby ensuring the effective storage and efficient use of thermal energy. At the same time, by setting the range of changes in hot water temperature, water storage volume, and thermal power provided to the user, the stable operation and safety of the system can be further guaranteed. In addition, the calculation formula for the high-pressure air pressure in the air storage chamber is given, which can accurately control the pressure change in the air storage chamber. By considering multiple factors such as gas constants, air storage chamber temperature and volume, air mass flow rate of compressors and expanders, and 0-1 variables describing the cycle state, these formulas can fully reflect the dynamic changes in the pressure in the air storage chamber, providing important guarantees for the stable operation of the system. At the same time, by setting the maximum and minimum values ​​of the air pressure in the air storage chamber, the safety and reliability of the system can be further ensured. Taking into account the above technical contents, the overall performance and efficiency of the AA-CAES system can be significantly improved by accurately simulating the heat exchange process, optimizing thermal energy storage and management, and accurately controlling the air storage chamber pressure. This not only helps to reduce system energy consumption and improve energy conversion efficiency, but also prolongs the service life of the system, reduces operating costs, and provides users with a more stable and reliable heat supply.

[0203] In specific implementation, the linearization constraint of the thermal energy storage model is obtained by converting the dynamic temperature equation into a linear model through the Taylor series expansion formula and the big M method. The process includes:

[0204] 1) Expand the nonlinear terms through Taylor series:

[0205] First, the nonlinear term is defined as The linear term is defined as

[0206]

[0207] Then, according to the Taylor series, f1 t exist Nearby expansion is:

[0208]

[0209] Indicates the initial value of water storage; Indicates the initial value of the water temperature change;

[0210] 2) The domain is divided into multiple grids by approximating the actual value by the average value:

[0211] The water storage capacity and water temperature change are divided into m and n segments respectively. The average value of each segment is used to approximately replace the actual value of each segment and serve as the initial expansion point of the Taylor series; f1 t The domain is divided into m*n grids; two sets of integer variables are introduced and Used to indicate and Segments within the range;

[0212]

[0213]

[0214] In the formula, represents an integer variable describing the water storage capacity in the segmented state at time t; represents an integer variable describing the change in water temperature at time t in the segmented state; M storage,j,min and M storage,j,max are the minimum and maximum water storage capacity under segmented state; T rate,k,min and T rate,k,max are the minimum and maximum values ​​of water temperature variation under segmented state respectively;

[0215] Introduce two additional integer variables and a set of constraints Perform linearization;

[0216]

[0217] In the formula, represents an integer variable describing the mass flow rate of hot water entering the compressor at time t during the compression process in a segmented state; m represents an integer variable describing the hot water temperature of the high-temperature heat storage tank in the segmented state at time t; com,total,water,h,min and m com,total,water,h,max are the minimum and maximum values ​​of the hot water mass flow rate entering the compressor at time t in the compression process under the segmented state; T s,g,min and T s,g,max are the minimum and maximum values ​​of the hot water temperature of the high-temperature heat storage tank under the segmented state;

[0218] By omitting the higher-order terms, the dynamic temperature equation is averaged over the segments. Nearby expansion is:

[0219]

[0220] In the formula, represents the water storage function ignoring higher-order terms; It represents the water temperature variation function ignoring higher-order terms;

[0221] 3) The nonlinear terms in the equation after Taylor expansion are linearized by the big M method:

[0222]

[0223] Where DM, GM, FM and AM are constants and are limited by the following formula:

[0224]

[0225] Thus, thermal energy storage models usually contain complex nonlinear equations, which are difficult to use directly in optimization and control algorithms. Through the above technical content, these nonlinear terms are successfully converted into linear terms using the Taylor series expansion formula, thereby greatly simplifying the model. This linearization not only retains the main features of the original model, but also makes the model easier to process and analyze. Compared with nonlinear models, linear models are more efficient in calculation. Through linearization processing, the calculation time can be significantly reduced, making real-time optimization and control possible. This is crucial for the operation and management of thermal energy storage systems, especially in scenarios that require fast response and precise control. In addition, linearized thermal energy storage models provide strong support for optimization decisions. Through optimization methods such as linear programming and integer programming, the optimal storage strategy, scheduling scheme, etc. can be solved more efficiently, thereby maximizing the utilization of thermal energy and minimizing costs. Linearized models usually have better robustness. Because linear models are simpler and clearer, they have stronger adaptability to fluctuations and uncertainties in input data. This makes the model more reliable in practical applications and better able to cope with various complex situations. In addition, by dividing the domain of water storage and water temperature changes into multiple grids and introducing integer variables to represent the segments, the model is more flexible in dealing with continuous variables. This processing method not only helps linearization, but also better captures the dynamic characteristics of the system. The Big M method is an effective linearization method that converts nonlinear terms into linear terms by introducing additional constraints and constants. This method is particularly effective in dealing with models containing complex nonlinear terms, and can significantly improve the linearization degree and computational efficiency of the model.

[0226] S2. Based on the reliability assessment model of AA-CAES considering partial functional failure, an integrated energy system model of electricity, gas and heat is constructed to perform reliability analysis on the integrated energy system.

[0227] In specific implementation, the objective function of the integrated energy system model is:

[0228]

[0229] In the formula, Indicates the gas supply volume of the gas source; Indicates the amount of natural gas load reduction; Indicates the power of coal-fired units; Indicates the amount of electrical load reduction; and They represent seasonal and annual heat load reduction respectively; Indicates wind power reduction; c curw,k represents the penalty coefficient for wind power reduction; w(k) represents the set of wind turbine nodes; T represents the total time; Q(x) represents the set of gas source nodes; c gw,x represents the gas source cost; m(x) represents the gas network node set; c curs,x represents the gas load penalty coefficient; p(k) represents the coal-fired unit node set; c pg,k represents the output cost of coal-fired units; m(k) represents the set of power nodes; c curl,k represents the load penalty coefficient for power reduction; n(d) represents the set of heating network nodes; c curh,e Represents the heat load penalty coefficient.

[0230] When constructing the model, wind curtailment power and wind curtailment penalty costs are introduced into the objective function to measure the wind power absorption capacity of the integrated energy system. The operation constraints of AA-CAES and the linearization constraints of the thermal energy storage model considered in the optimization model have been modeled in the above steps.

[0231] In this way, the objective function aims to achieve the overall performance optimization of the integrated energy system by comprehensively considering multiple aspects such as gas supply, power production, heat load satisfaction, and wind power utilization. This helps to improve the energy utilization efficiency of the system, reduce operating costs, and enhance the reliability and stability of the system. In addition, the objective function helps to improve the economic benefits of the system by minimizing the penalty costs of natural gas load reduction, electricity load reduction, heat load reduction, and wind power reduction. This helps to reduce users' energy costs while increasing the benefits of energy suppliers. In addition, the objective function contains multiple variables and parameters, which can be adjusted and optimized according to actual conditions. This allows the model to flexibly adapt to changes in external conditions such as different energy demands, energy prices, and energy policies.

[0232] S3. Initialize the integrated energy system model based on the actual parameters of the integrated energy system.

[0233] S4. Use the integrated energy system model to perform reliability analysis on the integrated energy system and obtain corresponding reliability indicators.

[0234] Among them, Figure 6 As shown in Figure 1, the process of reliability analysis of the integrated energy system includes the following steps. Figure 6 The optimal energy flow model of the integrated energy system in is the integrated energy system model in this method.

[0235] S41, setting the initial state of each component of the integrated energy system to a normal operating state;

[0236] S42, using a state duration sampling method to obtain a state sequence of each component of the system, and then jointly obtaining a corresponding system state;

[0237] In specific implementation, the state sequence of each component includes the multi-state sequence of AA-CAES, the multi-state sequence of the natural gas network and the heat network pipeline, and the two-state sequence of other components; when the state sequence of each component of the system is obtained by the state duration sampling method, the state sequence is determined by the transfer rate or steady-state probability between each mode; wherein, the steady-state probability of each state is calculated by the frequency and duration method; and the transfer rate between each mode is calculated according to the frequency balance principle. In this way, the component state sequence can be refined. By subdividing the state sequence of each component into the multi-state sequence of AA-CAES (advanced adiabatic compressed air energy storage system), the multi-state sequence of the natural gas network and the heat network pipeline, and the two-state sequence of other components, the actual operating state of each component of the system can be more accurately reflected. This refinement helps to more accurately evaluate the overall performance and reliability of the system. In addition, the accuracy of state sequence generation can also be improved. Using the transfer rate or steady-state probability between each mode to determine the state sequence can more realistically simulate the random failure and repair process of system components. The steady-state probability of each state is calculated by the frequency and duration method, and the transfer rate between each mode is calculated according to the frequency balance principle, which ensures the accuracy and reliability of state sequence generation.

[0238] S43, analyzing the system status within a preset period using a combined energy system model to determine whether the system is in a load reduction state;

[0239] S44, based on the analysis results of load reduction, calculate the system reliability index; wherein the system reliability index includes the system load loss probability, load loss frequency, and load loss duration. These indexes can fully reflect the reliability level of the system under specific conditions. By calculating these indexes, the risk of the system can be quantified, providing an important basis for system optimization and decision-making.

[0240] S45, calculate the variance convergence coefficient of the system reliability index and determine whether the convergence condition is met; if not, return to step S42; if met, output the system reliability index; wherein the convergence condition is that the variance convergence coefficient is less than a preset threshold or the simulation cycle reaches a preset maximum number of times.

[0241] The process sets the initial state of each component of the system as the normal operating state, and uses the state duration sampling method to simulate the state sequence of each component of the system, so as to fully consider the random failure and repair process of the system components. This helps to more accurately evaluate the reliability level of the system. By using the integrated energy system model to analyze the system state within the preset period, it is possible to accurately determine whether the system is in a load reduction state. This helps to identify the operating bottlenecks and potential risks of the system under specific conditions. In addition, the system reliability indicators can be quantified. Based on the analysis results of load reduction, the system reliability indicators such as power supply reliability and load loss probability are calculated. These indicators can quantify the reliability level of the system and provide an important basis for system optimization and decision-making. In addition, by calculating the variance convergence coefficient of the system reliability indicator and judging whether the convergence conditions are met, the stability and reliability of the analysis results can be ensured. If the convergence conditions are not met, the steps are returned to re-simulate the analysis until the convergence conditions are met. This helps to avoid errors caused by insufficient simulation times or randomness. By setting parameters such as the preset period and the maximum number of simulations, the process can complete the system reliability analysis within a reasonable time. At the same time, by adopting efficient algorithms such as the state duration sampling method, the analysis efficiency is further improved.

[0242] When establishing the AA-CAES reliability assessment model, this method fully considers the dynamic process of the temperature change of the working medium in the heat storage tank. Compared with the existing technology, this improvement can more accurately reflect the performance fluctuations of the AA-CAES system caused by temperature changes during operation. By accurately simulating the heating, cooling and storage process of the working medium in the heat storage tank, this method can evaluate the impact of temperature changes on the heating efficiency, power generation capacity and overall reliability of the system, thereby providing more accurate reliability assessment results. In addition, unlike the existing technology that mainly focuses on the normal state and complete failure state of the AA-CAES system, this method innovatively introduces the assessment of partial functional failure state. This means that in the reliability analysis, not only the situation of complete failure of the system is considered, but also the performance of the system in the state where the performance of one or some components is reduced but still partially operational. This improvement significantly improves the accuracy and comprehensiveness of the reliability assessment, making the assessment results closer to the actual operation. By incorporating the partial functional failure state, this method can more accurately predict the performance of the system under different operating conditions, providing more powerful support for system maintenance and optimization. Combined with the above two improvements, this method can more accurately reflect the AA-CAES system and its interaction with other energy conversion and storage devices when constructing an integrated energy system model of electricity, gas and heat. This helps to more comprehensively evaluate the overall reliability of the integrated energy system, including energy supply stability, system recovery capability and fault response capability under different operating scenarios. Compared with the existing technology, the reliability assessment results provided by this method are more accurate and practical, and can provide more valuable reference for the planning, design and optimization of the integrated energy system.

[0243] In summary, this method significantly improves the accuracy and practicality of the reliability assessment of the integrated energy system by comprehensively considering the impact of the temperature change of the working medium of the heat storage tank and incorporating the consideration of the partial functional failure state. This improvement not only helps to optimize the system design and operation strategy, but also provides a more reliable guarantee for the safe and stable operation of the energy system. This method can comprehensively consider the impact of the AA-CAES system on the reliability of the integrated energy system, which helps to improve the overall performance and reliability of the integrated energy system.

[0244] Embodiment 2

[0245] In order to better illustrate the effect of the present invention, the following examples are given.

[0246] Taking the integrated energy system consisting of IEEE 30-node power system, Belgium 20-node natural gas system and 16-node thermal system as an example, the dynamic thermal energy storage model considering compressed air energy storage proposed in this chapter is verified, and the impact of compressed air energy storage on the reliability assessment of the integrated energy system is demonstrated. Among them, the original coal-fired unit at node 11 in the test system is replaced with AA-CAES (A1), and a new wind turbine unit (D1) is added to the node, and the original heat pump at node 9 in the thermal system is replaced with a heat pump provided by AA-CAES, such as Figure 7 In order to ensure that the power between the power system and the thermal system is equivalent, the total thermal load is reduced by 28MW. The operating parameters of AA-CAES are shown in Table 2.

[0247] Table 2 Design parameters of AA-CAES

[0248]

[0249]

[0250] Firstly, two scenarios are set up to compare and analyze the impact of AA-CAES on the integrated energy system.

[0251] Scenario 1: System without considering AA-CAES.

[0252] Scenario 2: System considering AA-CAES multiple fault state operation mode and using dynamic temperature model.

[0253] Table 3 System operation costs for scenario 1 and scenario 2

[0254]

[0255] Table 4 Impact of AA-CAES on system reliability

[0256]

[0257] Table 3 gives the system operating costs of scenario 1 and scenario 2. It can be seen from Table 3 that when AA-CAES participates in the optimization of the integrated energy system, the operating cost of the coal-fired units in scenario 2 increases, the cost of the gas source decreases, and there is no wind abandonment. Compared with scenario 1 without considering AA-CAES, the total system operating cost of scenario 2 is significantly reduced by 21.98%. AA-CAES can obtain economic benefits by transferring electricity from non-peak load periods and wind power generation peak periods to peak load periods. This shows that the energy storage and heating capabilities of AA-CAES can effectively improve the operating economy of the integrated energy system.

[0258] The reliability indicators under scenario 1 and scenario 2 are shown in Table 4. The results show that the reliability indicators EENS, EHNS, LEOLP and LOHLP of the power system and thermal system are significantly reduced, while the reliability indicators EGNS and LOGLP of the natural gas system have hardly improved. This is because electricity is a high-quality energy source, and the optimal scheduling model tends to ensure the priority of electricity supply. At the same time, AA-CAES can also provide thermal power to the thermal system to alleviate the reduction of thermal load. Although AA-CAES has brought great benefits to the integrated energy system, its own failure will also increase the risk of load reduction. Therefore, improving the reliability of AA-CAES is the focus of improving the reliability of the integrated energy system.

[0259] Figure 8 The wind curtailment is shown, and it can be seen that during the peak period of wind power generation, most of the power load is supplied by wind power. However, in scenario 1, wind power is significantly reduced from 1:00 to 10:00 and from 18:00 to 24:00. There are two main reasons for this phenomenon: on the one hand, it is difficult for coal-fired units to quickly reduce or increase output due to their limited regulation characteristics; on the other hand, there are congestion in power lines. AA-CAES can quickly adjust power generation or switch to a compressed state, absorb excess energy from the integrated energy system, and thus reduce wind curtailment, so that the flexibility of AA-CAES is better utilized.

[0260] Fig. 9 and Fig.10 The optimal operation schemes of the power system in scenarios 1 and 2 are shown respectively, where the negative power of AA-CAES indicates that it is currently in a compressed state. In scenario 1, natural gas and coal-fired units increase power generation during peak load periods and wind power trough periods (11:00 to 17:00) to quickly respond to load fluctuations. In scenario 2, AA-CAES starts to store electricity from 1:00 to 10:00 and releases electricity from 11:00 to 17:00 during the peak power load period, thereby reducing the power generation of natural gas and coal-fired units and reducing power generation costs. From 19:00 to 24:00, the power generation of coal-fired units in scenario 2 is higher than that in scenario 1 because AA-CAES needs to store energy and return to its initial state to meet the scheduling requirements of the next cycle.

[0261] The coal-fired units in Scenario 1 maintain lower power generation because their ramp rate is lower than that of the natural gas units. In Scenario 2, the AA-CAES compression process can store excess power, which allows the coal-fired units to replace the natural gas units to generate more power. Fig.11 It can be seen that the gas output in scenario 2 is mostly lower than that in scenario 1, and the total system operating cost is reduced.

[0262] Afterwards, the effectiveness of the dynamic thermal energy storage model (DTESM) is illustrated and the impact of the dynamic thermal energy storage model in AA-CAES is analyzed by setting scenario 3. In scenario 3, the AA-CAES static thermal energy storage model (STESM) is considered, that is, the changes in the hot water temperature and water storage capacity in the thermal storage tank are not considered.

[0263] Table 5 Operating costs in scenarios 2 and 3

[0264]

[0265] Table 5 gives the operating costs of the integrated energy system in scenarios 2 and 3. It can be seen from Table 5 that when the dynamic changes in hot water temperature are not considered, the total cost of STESM is reduced by 6.64% compared with DTESM. Table 6 gives the reliability indicators of the integrated energy system. In scenario 3, EENS, EDHNS and LOELP are significantly reduced. The above shows that ignoring the dynamic changes in hot water temperature will have a great impact on the operating cost and reliability of the integrated energy system. Since the DTESM proposed by this method more accurately describes the operating characteristics of the heat storage tank, the established heat storage constraints are more stringent, and the feasible domain of the optimization problem is narrowed to a certain extent, which often leads to a greater risk of load reduction. Therefore, the scheduling scheme is inferior to the results of STESM.

[0266] Table 6 Impact of dynamic thermal energy storage model on IES reliability

[0267]

[0268] Fig.12 and Fig.13 The changes of hot water temperature and gas storage chamber pressure in the high-temperature heat storage tank calculated by DTESM and STESM are shown respectively. Fig.14 represents the compression and expansion power of AA-CAES obtained by the two models. Fig.14 As shown, due to the absence of temperature constraints, the compression and expansion powers in the STESM are greater than those in the DTESM.

[0269] Fig.12 and Fig.13It describes that during the peak period of wind power generation, AA-CAES quickly stores energy from 1:00 to 6:00, and the air pressure in the air storage chamber gradually increases; during the peak period of power load (11:00-17:00), the air storage chamber releases high-pressure air to the expander, and the air pressure decreases; from 19:00 to 24:00, AA-CAES needs to return to the initial state, so the air pressure has a small rebound. In addition, when the water temperature of the heat storage tank is about to reach the upper limit, the flow of hot water into the heat storage tank is reduced or even stopped. Therefore, compared with STESM, the compression power of the power station in DTESM is relatively low, and the pressure of the air storage chamber increases relatively slowly from 1:00 to 6:00. During the period from 12:00 to 19:00, the hot water temperature of the heat storage tank has reached the minimum temperature limit.

[0270] Fig.15 Describe the heat generation power of AA-CAES under different scenarios. As can be seen from the figure, AA-CAES can take on more heat load supply while ensuring the power supply load, which improves the operating economy of the integrated energy system. In addition, STESM ignores the hot water temperature and storage constraints in the heat storage tank and adopts more relaxed heat constraints, thereby expanding the feasible domain of the optimization problem. Therefore, the heat generation of AA-CAES in STESM is greatly increased, resulting in overly optimistic scheduling results. Fig.15 As shown in Figure 2, when the hot water temperature in the thermal storage tank is close to its minimum limit, the heat generation in DTESM is 0. Through comparative studies, the necessity of considering DTESM in AA-CAES is demonstrated.

[0271] 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 present method 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 compressed air energy storage, characterized in that: The following steps are involved: S1. Based on the topological structure and working mode of AA-CAES, analyze the multi-state operation mode of AA-CAES, wherein the multi-state operation mode includes a normal operation state mode, a fault state mode and a partial function failure state mode; establish a reliability evaluation model of AA-CAES considering the partial function failure state; The topological structure of AA-CAES is divided into compression subsystem, expansion subsystem and storage subsystem according to function; the compression subsystem includes an electric motor and a multi-stage compressor; the expansion subsystem includes a multi-stage expander and a generator; the storage subsystem includes a heat exchanger and a high-temperature / low-temperature heat storage tank and a gas storage chamber for storing the working medium; the three subsystems use the same working medium to form independent compression cycles and expansion cycles; S2. Based on the reliability assessment model of AA-CAES considering partial functional failure, a comprehensive energy system model of electricity, gas and heat is constructed to perform reliability analysis on the comprehensive energy system; S3. Initializing the integrated energy system model based on the actual parameters of the integrated energy system; S4. Use the integrated energy system model to perform reliability analysis on the integrated energy system and obtain corresponding reliability indicators.

2. The reliability assessment method of the integrated energy system considering compressed air energy storage as claimed in claim 1, characterized in that: In S1, a reliability assessment model of AA-CAES considering partial functional failure is constructed, and its constraints include the operation constraints of AA-CAES and the linearization constraints of the thermal energy storage model; among them, the operation constraints of AA-CAES include the operation constraints of the compression subsystem, the operation constraints of the expansion subsystem, and the operation constraints of the storage subsystem.

3. The reliability assessment method of the integrated energy system considering compressed air energy storage as claimed in claim 2, characterized in that: The compression subsystem operating constraints include: Total power consumption at time t during the compression cycle for: Where, κ is the specific heat ratio of air; is the air mass flow rate entering the compressor at time t during the compression process; is the specific heat capacity of air; and is the inlet and outlet temperature of the i-th stage compressor; N c is the number of compressor stages; The outlet temperature of the i-th stage compressor is calculated as: In the formula, is the compression ratio of the i-th stage compressor, η c is the adiabatic efficiency of the compressor; The upper and lower limits of the total power consumption of AA-CAES are: In the formula, Represents a 0-1 variable describing the compression cycle state; P com,max and P com,min Indicates the maximum and minimum values ​​of power consumption.

4. The reliability assessment method of the integrated energy system considering compressed air energy storage as claimed in claim 3, characterized in that: The operating constraints of the expansion subsystem include: The power generation of the expander at time t The calculation formula is: In the formula, is the air mass flow rate entering the expander at time t in the expansion cycle; and are the inlet and outlet temperatures of the i-th stage expander at time t; N e is the number of expander stages; is the specific heat capacity of air; κ is the specific heat ratio of air; The outlet temperature of the i-th stage expander at time t The calculation is: In the formula, is the expansion ratio of the i-th stage expander, η e is the adiabatic efficiency of the expander; Total power generation of AA-CAES The upper and lower limits are: In the formula, is a 0-1 variable describing the expansion cycle state; P exp,max and P exp,min are the maximum and minimum values ​​of the generated power respectively; The compression subsystem and expansion subsystem in AA-CAES cannot run at the same time: In the formula, Represents a 0-1 variable describing the state of the compression cycle; is a 0-1 variable describing the state of the expansion cycle.

5. The reliability assessment method of the integrated energy system considering compressed air energy storage as claimed in claim 4, characterized in that: Storage subsystem operating constraints include: The temperatures of water and air in the heat exchanger are expressed as: In the formula, is the mass flow rate of hot water entering the compressor at time t during the compression process; is the specific heat capacity of water; T water,cold is the cold water temperature in the low-temperature heat storage tank; is the hot water temperature at the outlet of the i-th compressor; T s t is the hot water temperature of the high-temperature heat storage tank at time t; is the efficiency of the heat exchanger. The above expressions all assume that the heat exchange between the hot medium and the cold medium in the heat exchanger is equal; is the air mass flow rate entering the compressor at time t during the compression process; is the specific heat capacity of air; and are the inlet and outlet temperatures of the i-th stage compressor; and are the inlet and outlet temperatures of the i-th stage expander at time t, respectively; In the compression cycle, the temperature of the hot water flowing into the high-temperature heat storage tank is T storage,in The calculation formula is: In the formula, represents the total hot water mass flow rate flowing into the high-temperature heat storage tank at time t; The calculation formula for the water storage capacity and hot water temperature in the high-temperature heat storage tank is: In the formula, represents the quality of hot water in the high-temperature heat storage tank at time t; represents the total hot water mass flow rate flowing into the heat exchanger in the expansion cycle at time t; Indicates the change in water temperature; The thermal power provided to the user; represents the total hot water mass flow rate flowing into the high-temperature heat storage tank at time t; represents the hot water temperature of the high-temperature heat storage tank at time t; Δt represents the time interval between two consecutive calculation times; The ranges of hot water temperature, water storage capacity and thermal power provided to users are: Where, T s,min and T s,max are the minimum and maximum values ​​of hot water temperature respectively; M storage,min and M storage,max are the minimum and maximum water storage capacity respectively; is the maximum user thermal power; The high pressure air pressure in the air storage chamber is: In the formula, represents the air pressure in the air storage chamber at time t; R g represents the universal gas constant; T AT and V AT Respectively represent the temperature and volume of the gas storage chamber; P AT,max and P AT,min Respectively represent the maximum and minimum values ​​of the air pressure in the air storage chamber; is the air mass flow rate entering the compressor at time t during the compression process; Represents a 0-1 variable describing the state of the compression cycle; is the air mass flow rate entering the expander at time t in the expansion cycle; is a 0-1 variable describing the state of the expansion cycle.

6. The reliability assessment method of the integrated energy system considering compressed air energy storage as claimed in claim 5, characterized in that: The linearization constraint of the thermal energy storage model is obtained by converting the dynamic temperature equation into a linear model through the Taylor series expansion formula and the big M method. The process includes: 1) Expand the nonlinear terms through Taylor series: First, the nonlinear term is defined as The linear term is defined as Then, according to the Taylor series, f1 t exist Nearby expansion is: Indicates the initial value of water storage; Indicates the initial value of the water temperature change; 2) The domain is divided into multiple grids by approximating the actual value by the average value: The water storage capacity and water temperature change are divided into m and n segments respectively. The average value of each segment is used to approximately replace the actual value of each segment and serve as the initial expansion point of the Taylor series. The domain of f1t is divided into m*n grids. Two sets of integer variables are introduced. and Used to indicate and Segments within the range; In the formula, represents an integer variable describing the water storage capacity in the segmented state at time t; represents an integer variable describing the change in water temperature at time t in the segmented state; M storage,j,min and M storage,j,max are the minimum and maximum water storage capacity under segmented state; T rate,k,min and T rate,k,max are the minimum and maximum values ​​of water temperature variation under segmented state respectively; Introduce two additional integer variables and a set of constraints Perform linearization; In the formula, represents an integer variable describing the mass flow rate of hot water entering the compressor at time t during the compression process in a segmented state; Represents an integer variable describing the hot water temperature of the high-temperature heat storage tank in the segmented state at time t; m com,total,water,h,min and m com,total,water,h,max are the minimum and maximum values ​​of the hot water mass flow rate entering the compressor at time t in the compression process under the segmented state; T s,g,min and T s,g,max They are the minimum and maximum values ​​of the hot water temperature of the high-temperature heat storage tank in the segmented state; By omitting the higher-order terms, the dynamic temperature equation is averaged over the segments. Nearby expansion is: In the formula, represents the water storage function ignoring higher-order terms; It represents the water temperature variation function ignoring higher-order terms; 3) The nonlinear terms in the equation after Taylor expansion are linearized by the big M method: Where DM, GM, FM and AM are constants and are limited by the following formula:

7. The reliability assessment method of the integrated energy system considering compressed air energy storage as claimed in claim 6, characterized in that: In S2, the objective function of the integrated energy system model is: In the formula, Indicates the gas supply volume of the gas source; Indicates the amount of natural gas load reduction; Indicates the power of coal-fired units; Indicates the amount of electrical load reduction; and They represent seasonal and annual heat load reduction respectively; Indicates wind power reduction; c curw,k represents the penalty coefficient for wind power reduction; w(k) represents the set of wind turbine nodes; T represents the total time; Q(x) represents the set of gas source nodes; c gw,x represents the gas source cost; m(x) represents the gas network node set; c curs,x represents the gas load penalty coefficient; p(k) represents the coal-fired unit node set; c pg,k represents the output cost of coal-fired units; m(k) represents the set of power nodes; c curl,k represents the load penalty coefficient for power reduction; n(d) represents the set of heating network nodes; c curh,e Represents the heat load penalty coefficient.

8. The reliability assessment method of the integrated energy system considering compressed air energy storage as claimed in claim 1, characterized in that: In S4, the process of reliability analysis of the integrated energy system includes: S41, setting the initial state of each component of the integrated energy system to a normal operating state; S42, using a state duration sampling method to obtain a state sequence of each component of the system, and then jointly obtaining a corresponding system state; S43, analyzing the system status within a preset period using a combined energy system model to determine whether the system is in a load reduction state; S44, calculating a system reliability index based on the analysis result of the load reduction; S45, calculate the variance convergence coefficient of the system reliability index and determine whether the convergence condition is met; if not, return to step S42; if met, output the system reliability index; wherein the convergence condition is that the variance convergence coefficient is less than a preset threshold or the simulation cycle reaches a preset maximum number of times.

9. The reliability assessment method of the integrated energy system considering compressed air energy storage as claimed in claim 8, characterized in that: In S42, the state sequence of each component includes the multi-state sequence of AA-CAES, the multi-state sequence of the natural gas network and the heat network pipeline, and the two-state sequence of other components; in S44, the system reliability index includes the system load loss probability, load loss frequency, and load loss duration.

10. The reliability assessment method of the integrated energy system considering compressed air energy storage according to claim 1, characterized in that: The working medium includes air and water; the normal operating state mode is that the compression subsystem, expansion subsystem and storage subsystem are all operating normally; the partial function failure state mode includes expansion-only failure mode and compression-only failure mode; the expansion-only failure mode is that only the expansion subsystem fails; the compression-only failure mode is that only the compression subsystem fails; other operating states all belong to failure state modes.

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