A reliability assessment method for integrated energy systems considering compressed air energy storage

Abstract

This invention belongs to the field of intelligent analysis and evaluation technology for integrated energy systems, and particularly relates to a reliability evaluation method for integrated energy systems considering compressed air energy storage. The method includes: S1, establishing a reliability evaluation model of an AA-CAES system considering partial functional failure states; wherein the topology of the AA-CAES is functionally divided into a compression subsystem, an expansion subsystem, and a storage subsystem; S2, based on the reliability evaluation model of the AA-CAES system considering partial functional failure states, constructing an integrated energy system model of electricity, gas, and heat for reliability analysis of the integrated energy system; S3, initializing the integrated energy system model based on the actual parameters of the integrated energy system; S4, using the integrated energy system model to perform reliability analysis of the integrated energy system and obtain the corresponding reliability indicators. This method can comprehensively consider the impact of the AA-CAES system on the reliability of the integrated energy system, helping to improve the overall performance and reliability of the integrated energy system.

Description

Technical Field

[0001] This invention belongs to the field of intelligent analysis and evaluation technology of integrated energy systems, and particularly relates to a reliability evaluation method for integrated energy systems that considers compressed air energy storage. Background Technology

[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. To address these challenges, the development and utilization of renewable energy has become a significant trend in global energy development. Integrated energy systems, as an effective way to integrate multiple energy forms (such as electricity, gas, and heat), have attracted much attention due to their flexible and efficient energy supply characteristics. However, the intermittency and instability of renewable energy sources have become key factors restricting the reliability of integrated energy systems.

[0003] Against this backdrop, Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) technology has demonstrated significant potential in enhancing the reliability of integrated energy systems due to its numerous advantages, including being environmentally friendly, having strong peak-shaving capabilities, and being highly dispatchable. The AA-CAES system uses an electric motor to drive a multi-stage compressor to compress air and store it in an air storage chamber, while simultaneously using a heat exchanger to store the heat generated during compression in a thermal storage tank. When energy needs to be released, the high-temperature, high-pressure air drives a multi-stage expander to generate electricity, which in turn powers a generator. Simultaneously, the heat from the thermal storage tank is used to reheat the air, improving power generation efficiency. This technology not only effectively addresses the intermittency and instability of renewable energy sources but also achieves highly efficient energy storage and conversion.

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

[0005] First, existing research often overlooks the impact of temperature variations in the working medium of the AA-CAES thermal storage tank on its heating and power generation capabilities. In reality, temperature changes in the working medium within the storage tank directly affect the overall performance and reliability of the AA-CAES system. Failure to accurately assess this impact may lead to overly optimistic reliability assessments of the electro-gas-thermal system, thus failing to accurately reflect the system's actual operating status.

[0006] Secondly, existing research assessing the reliability of AA-CAES systems typically only considers normal and fault states, neglecting partial functional failure states. In actual operation, AA-CAES systems may experience partial functional failures due to various reasons (such as equipment aging, improper maintenance, etc.). While such partial functional failures do not cause the system to completely stop operating, they severely impact its performance and reliability. Failure to incorporate this state into the reliability assessment model will result in inaccurate assessment results, failing to provide strong support for the optimized design and operation of integrated energy systems.

[0007] Therefore, how to comprehensively consider the impact of the AA-CAES system on the reliability of integrated energy systems, including the effects of temperature changes in the working medium of the thermal storage tank and the impact of partial functional failure states, and thus establish more accurate and reliable evaluation models and methods, has become an urgent problem to be solved. This will not only help improve the overall performance and reliability of integrated energy systems, but also provide strong support for the widespread application of renewable energy and the optimized transformation of the energy structure. Summary of the Invention

[0008] To address the shortcomings of the existing technologies, this invention provides a reliability assessment method for integrated energy systems that considers compressed air energy storage. This method can comprehensively consider the impact of the AA-CAES system on the reliability of the integrated energy system, thereby helping to improve the overall performance and reliability of the integrated energy system.

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

[0010] A reliability assessment method for an integrated energy system considering compressed air energy storage includes the following steps:

[0011] S1. Based on the topology and working mode of AA-CAES, analyze the multi-state operation mode of AA-CAES, which includes normal operation mode, fault mode and partial functional failure mode; establish a reliability assessment model of AA-CAES that considers partial functional failure mode.

[0012] The topology of AA-CAES is divided into a compression subsystem, an expansion subsystem, and a 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; and 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 and form independent compression and expansion cycles.

[0013] S2. Based on the AA-CAES reliability assessment model that considers partial functional failure states, a comprehensive energy system model of electricity, gas and heat is constructed for reliability analysis of the comprehensive energy system.

[0014] S3. Initialize 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 the corresponding reliability indicators.

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

[0017] 1. Comprehensive consideration of the impact of working medium temperature variations in the thermal storage tank. This method fully considers the dynamic process of working medium temperature variations within the thermal storage tank when establishing the AA-CAES reliability assessment model. Compared to existing technologies, this improvement more accurately reflects the performance fluctuations of the AA-CAES system caused by temperature changes during operation. By precisely simulating the heating, cooling, and storage processes of the working medium in the thermal storage tank, this method can assess the impact of temperature variations on system heating efficiency, power generation capacity, and overall reliability, thus providing more accurate reliability assessment results.

[0018] 2. Incorporation of Partial Functional Failure States. Unlike existing technologies that primarily focus on the normal and complete failure states of the AA-CAES system, this method innovatively introduces the assessment of partial functional failure states. This means that reliability analysis considers not only the scenario of complete system failure but also the system's performance in a state where the performance of one or more components degrades but still allows for partial operation. This improvement significantly enhances the accuracy and comprehensiveness of reliability assessment, making the results closer to actual operating conditions. By incorporating partial functional failure states, this method can more accurately predict the system's performance under different operating states, providing stronger support for system maintenance and optimization.

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

[0020] In summary, this method significantly improves the accuracy and practicality of integrated energy system reliability assessment by comprehensively considering the impact of temperature changes in the working medium of the thermal storage tank and incorporating considerations of partial functional failure states. This improvement not only helps optimize system design and operation strategies 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 integrated energy systems, contributing to the improvement of the overall performance and reliability of integrated energy systems.

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

[0022] This setup has two advantages: 1. It improves the accuracy of the AA-CAES reliability assessment model. In S1, by constructing an AA-CAES reliability assessment model that considers partial functional failure states and introducing operational constraints of AA-CAES (including operational constraints of the compression subsystem, expansion subsystem, and storage subsystem), it is possible to more accurately simulate various states of AA-CAES in actual operation, including normal state, partial functional failure state, and fault state. This detailed simulation helps to capture the behavioral characteristics of the system when partial functional failure occurs, thereby more accurately assessing the system's reliability.

[0023] 2. It can enhance the practicality of thermal energy storage models. The reliability assessment model also includes linearization 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 computational efficiency. Linearization constraints help simplify complex nonlinear relationships, making model solving more efficient and thus more practical in real-world applications.

[0024] Preferably, the operational constraints of the compression subsystem include:

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

[0026]

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

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

[0029]

[0030] In the formula, Let η be the compression ratio of the i-th stage compressor. c 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, P represents a 0-1 variable describing the state of the compression loop; com,max and P com,min This indicates the maximum and minimum power consumption.

[0034] This setup allows for: 1. Accurate simulation of the energy consumption of the compression subsystem. By providing a formula for calculating the total power consumption at time t during the compression cycle, this technology can accurately simulate the energy consumption of the compression subsystem in the AA-CAES system. This formula considers 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, thus comprehensively reflecting the energy consumption characteristics during the compression process.

[0035] 2. It allows for more refined calculations of compressor stage temperatures. The technical content provides a formula for calculating the outlet temperature of the i-th stage compressor. This formula considers the compressor's compression ratio and adiabatic efficiency, enabling accurate calculation of the air temperature at the outlet of each compressor stage. This refined calculation helps to more accurately assess heat exchange and energy loss between compressor stages, providing an important basis for optimizing compressor design and operating strategies.

[0036] 3. By setting upper and lower limits for the total power consumption of AA-CAES, this technology ensures that the compression subsystem does not exceed its design range during operation, thus protecting the system equipment from damage. Simultaneously, this setting helps to consider the system's performance at different power consumption levels during reliability assessments, providing strong support for system design and optimization. Taking into account the above technical aspects, by accurately simulating the energy consumption and temperature characteristics of the compression subsystem and setting reasonable upper and lower limits for power consumption, this technology helps improve the reliability and energy efficiency of the AA-CAES system. By optimizing compressor design and operating strategies, system energy consumption can be reduced, energy conversion efficiency can be improved, thereby extending system lifespan and reducing operating costs.

[0037] Preferably, the operational constraints of the expansion subsystem include:

[0038] The amount of electricity generated by the expander at time t. The formula for calculation is:

[0039]

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

[0041] The outlet temperature of the l-stage expander at time t The calculation is as follows:

[0042]

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

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

[0045]

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

[0047] In AA-CAES, the compression subsystem and the expansion subsystem cannot operate simultaneously:

[0048]

[0049] In the formula, Represents a 0-1 variable describing the state of the compression loop; These are 0-1 variables that describe the state of the expansion cycle.

[0050] This setup, 1. provides a formula for calculating the power generation of the expander at time t, which can accurately simulate the power generation performance of the expansion subsystem under different operating conditions. By considering multiple factors such as the mass flow rate of the air entering the expander, the inlet and outlet temperatures of each stage of the expander, the number of expander stages, and the specific heat capacity and specific heat ratio of the air, this formula can comprehensively reflect the energy conversion efficiency during the expansion process, providing accurate data support for system design and optimization. The formula for calculating the outlet temperature of the l-th stage expander considers the expander's expansion ratio and adiabatic efficiency, and can accurately calculate the air temperature at the outlet of each stage expander. This refined calculation helps to evaluate heat loss and energy utilization efficiency during the expansion process, providing an important basis for optimizing expander design and operation strategies.

[0051] 2. By setting upper and lower limits for the total power generation of the AA-CAES system, it is ensured that the expansion subsystem does not exceed its design range during operation, thus protecting system equipment from damage. Simultaneously, this setting helps to consider the system's performance at different power generation levels during reliability assessments, providing strong support for system design and optimization. The introduced constraint that the compression and expansion subsystems cannot operate simultaneously is a crucial guarantee for the safe and stable operation of the AA-CAES system. By ensuring that these two subsystems are never simultaneously operational, potential energy conflicts and equipment damage risks are avoided, improving system stability and reliability.

[0052] 3. Taking into account the above technical aspects, by accurately calculating the power generation of the expansion subsystem, refining the calculation of interstage temperatures in the expander, setting upper and lower limits for total power generation, and ensuring 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 reduce system energy consumption and improve energy conversion efficiency, but also extends system lifespan and reduces operating costs, contributing to the construction of a more efficient and environmentally friendly energy system.

[0053] Preferably, the operational constraints of the storage subsystem include:

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

[0055]

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

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

[0058]

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

[0060] The formulas for calculating the water storage capacity and hot water temperature in a high-temperature thermal storage tank are as follows:

[0061]

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

[0063] The ranges of variation for hot water temperature, water storage capacity, and heat power supplied to users are as follows:

[0064]

[0065]

[0066] In the formula, T s,min and T s,max These represent the minimum and maximum temperatures of the hot water, respectively; M storage,min and M storage,max These represent the minimum and maximum water storage capacity, respectively. Maximum user thermal power;

[0067] The high-pressure air pressure in the gas storage chamber is:

[0068]

[0069] In the formula, R represents the gas pressure in the storage chamber at time t; g T represents the universal gas constant;AT and V AT P represents the temperature and volume of the gas storage chamber, respectively; AT,max and P AT,min These represent the maximum and minimum values ​​of the gas pressure in the gas storage chamber, respectively. Let be the mass flow rate of air entering the compressor at time t during the compression process; Represents a 0-1 variable describing the state of the compression loop; Let be the mass flow rate of air entering the expander at time t during the expansion cycle; These are 0-1 variables describing the state of the expansion cycle;

[0070] This setup, along with the provided formulas for calculating the temperatures of water and air in the heat exchanger, can accurately simulate the heat exchange process within the heat exchanger during compression and expansion. By considering multiple factors such as the hot water mass flow rate, the specific heat capacity of water, the cold water temperature, the hot water temperature at the compressor outlet, the inlet and outlet temperatures of the expander, and the efficiency of the heat exchanger, these formulas comprehensively reflect the energy conversion and temperature changes during the heat exchange process, providing accurate data support for system design and optimization.

[0071] 2. By calculating the temperature of the hot water flowing into the high-temperature thermal storage tank, as well as the water volume and temperature within the tank, this technology optimizes thermal energy storage and management. This includes determining key parameters such as the quality of the hot water in the storage tank, the temperature variation, and the heat power supplied to users, thereby ensuring effective storage and efficient utilization of thermal energy. Furthermore, by setting the range of variation for the hot water temperature, storage volume, and heat power supplied to users, the stable operation and safety of the system can be further guaranteed.

[0072] 3. The given formula for calculating the high-pressure air pressure in the gas storage chamber enables precise control of pressure changes within the chamber. By considering multiple factors such as the gas constant, gas storage chamber temperature and volume, air mass flow rate of the compressor and expander, and 0-1 variables describing the cycle state, these formulas comprehensively reflect the dynamic changes in pressure within the gas storage chamber, providing crucial assurance for the stable operation of the system. Furthermore, by setting the maximum and minimum values ​​of the gas storage chamber pressure, the safety and reliability of the system can be further ensured.

[0073] 4. Taking into account the above technical aspects, by accurately simulating the heat exchange process, optimizing heat energy storage and management, and precisely controlling the pressure in the gas storage chamber, the overall performance and efficiency of the AA-CAES system can be significantly improved. This not only helps reduce system energy consumption and improve energy conversion efficiency, but also extends system lifespan, reduces operating costs, and provides users with a more stable and reliable heat energy supply.

[0074] Preferably, the linearization constraints of the thermal energy storage model are obtained by converting the dynamic temperature equation into a linear model using Taylor series expansion and the Big M method. The process includes:

[0075] 1) Expand the nonlinear terms using Taylor series:

[0076] First, define the nonlinear term as Define the linear term as

[0077]

[0078] Then, according to the Taylor series, exist The surrounding area expands to:

[0079]

[0080] This represents the initial value of the water storage capacity; This represents the initial value of the water temperature change;

[0081] 2) The domain is divided into multiple grids by approximating the actual values ​​with average values:

[0082] The water storage capacity and water temperature change are divided into m segments and n segments respectively. The average value of each segment is used to approximate the actual value of each segment and serves as the initial expansion point of the Taylor series. The domain is divided into m*n grids; two sets of integer variables are introduced. and Used to indicate in and Segmentation within the range;

[0083]

[0084] In the formula, This represents an integer variable describing the water storage volume at time t in a piecewise state; M represents the integer variable describing the change in water temperature at time t in a piecewise state; storage,j,min and M storage,j,max These represent the minimum and maximum water storage values ​​under segmented conditions; T rate,k,min and T rate,k,max These represent the minimum and maximum values ​​of water temperature change under segmented conditions, respectively.

[0085] Introducing two additional integer variables and a set of constraints Perform linearization;

[0086]

[0087] In the formula, This represents an integer variable describing the mass flow rate of hot water entering the compressor at time t during the compression process in a piecewise state; m represents an integer variable describing the temperature of the hot water in the high-temperature thermal storage tank at time t under segmented conditions; com,total,water,h,min and m com,total,water,h,max T represents the minimum and maximum values ​​of the hot water mass flow rate entering the compressor at time t during the compression process, respectively, under the segmented state. s,g,min and T s,g,max These are the minimum and maximum values ​​of the hot water temperature in the high-temperature thermal storage tank under segmented conditions, respectively.

[0088] By omitting higher-order terms, the dynamic temperature equation yields average values ​​for each segment. The surrounding area expands to:

[0089]

[0090] In the formula, This represents a water storage function that ignores higher-order terms. This represents a function that ignores higher-order terms related to water temperature change.

[0091] 3) Linearize the nonlinear terms in the Taylor expansion equations using the Big M method:

[0092]

[0093] In the formula, DM, GM, FM, and AM are constants, and are constrained by the following formula:

[0094]

[0095] This setup achieves several key benefits: 1. Linearization of Nonlinear Equations. Thermal energy storage models typically contain complex nonlinear equations that are difficult to directly apply to optimization and control algorithms. By utilizing the Taylor series expansion formula, these nonlinear terms are successfully transformed into linear terms, significantly simplifying the model. This linearization not only preserves the main characteristics of the original model but also makes it easier to process and analyze. Linear models are computationally more efficient than nonlinear models. Linearization significantly reduces computation time, enabling real-time optimization and control. This is crucial for the operation and management of thermal energy storage systems, especially in scenarios requiring rapid response and precise control.

[0096] 2. Linearized thermal energy storage models provide strong support for optimization decisions. Through optimization methods such as linear programming and integer programming, optimal storage strategies and scheduling schemes can be solved more efficiently, thereby maximizing thermal energy utilization and minimizing costs. Linearized models typically exhibit better robustness. Because linear models are simpler and clearer, they are more adaptable to fluctuations and uncertainties in input data. This makes the model more reliable in practical applications and better able to handle various complex situations.

[0097] 3. By dividing the domains of water storage and temperature changes into multiple grids and introducing integer variables to represent the segments, the model becomes more flexible in handling continuous variables. This approach not only facilitates linearization but also better captures the dynamic characteristics of the system. The Big M method is an effective linearization method that transforms nonlinear terms into linear terms by introducing additional constraints and constants. This method is particularly effective when dealing with models containing complex nonlinear terms, significantly improving the linearization degree and computational efficiency of the model.

[0098] Preferably, in S2, the objective function of the integrated energy system model is:

[0099]

[0100] In the formula, Q(x1) represents the set of gas source nodes; c gw,x1 Indicates the cost of the gas source; The gas supply volume is represented by m(x2); the set of gas network nodes is represented by c. curs,x2 Indicates the air reduction load penalty coefficient; Represents the natural gas load reduction; p(k1) represents the set of nodes for coal-fired power units; c pg,k1 This indicates the output cost of a coal-fired power unit; The power of the coal-fired unit is represented by m(k2); the set of power nodes is represented by c. curl,k2 Indicates the load penalty factor for electrical discharge; Represents the amount of electricity load reduction; w(k3) represents the set of wind turbine nodes; c curw,k3 This represents the penalty coefficient for wind power reduction; Represents the amount of wind power cut; n(d) represents the set of heating network nodes; c curh,e Indicates the heat load penalty coefficient; and These represent the seasonal and perennial heat load reductions, respectively; T represents the total time period.

[0101] This setup, 1. The objective function, by comprehensively considering multiple aspects such as gas supply, electricity generation, heat load fulfillment, and wind power utilization, aims to optimize the overall performance of the integrated energy system. This helps improve the system's energy efficiency, reduce operating costs, and enhance its reliability and stability.

[0102] 2. The objective function helps improve the system's economic efficiency by minimizing the penalties for natural gas load reduction, electricity load reduction, heat load reduction, and wind power reduction. This helps reduce energy costs for users while increasing revenue for energy suppliers.

[0103] 3. The objective function includes 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 energy demand, energy prices, and energy policies.

[0104] Preferably, in S4, the process of performing reliability analysis on the integrated energy system includes:

[0105] S41, Set the initial state of each component of the integrated energy system to the normal operating state;

[0106] S42, the state sequence of each component of the system is obtained by using the state duration sampling method, and then the corresponding system state is obtained by combining them;

[0107] S43 uses a combined energy system model to analyze the system status within a preset period and determine whether the system is in a load reduction phase.

[0108] S44, based on the analysis results of load reduction, calculate the system reliability index;

[0109] 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 period reaches a preset maximum number of times.

[0110] This setup allows for comprehensive consideration of random failures and recovery processes by setting the initial state of each system component to normal operating condition and simulating the state sequence of each component using a state duration sampling method. This helps to more accurately assess the system's reliability level. By analyzing the system state within a preset period using a comprehensive energy system model, it is possible to accurately determine whether the system is in a load-cutting state. This helps to identify system bottlenecks and potential risks under specific conditions.

[0111] 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 failure probability can be calculated. These indicators can quantify the system's reliability level, providing important basis for system optimization and decision-making.

[0112] 3. By calculating the variance convergence coefficient of the system reliability index and determining whether the convergence condition is met, the stability and reliability of the analysis results can be ensured. If the convergence condition is not met, the process returns to the previous step and repeats the simulation analysis until the convergence condition is met. This helps avoid errors caused by insufficient simulation runs or randomness. By setting parameters such as the preset period and maximum number of simulations, the system reliability analysis can be completed within a reasonable time. Furthermore, the use of efficient algorithms such as the state duration sampling method further improves the analysis efficiency.

[0113] Preferably, 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 heating network pipeline, and the two-state sequence of other components; in S44, the system reliability index includes the system's failure probability, failure frequency, and failure duration.

[0114] 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 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.

[0115] This setup allows for: 1. Refining the component state sequences. By subdividing the state sequences of each component into multi-state sequences for AA-CAES (Advanced Adiabatic Compressed Air Energy Storage System), multi-state sequences for natural gas and heating network pipelines, and two-state sequences for other components, the actual operating states of each component in the system can be reflected more accurately. This refinement helps to more accurately assess the overall performance and reliability of the system.

[0116] 2. Improve the accuracy of state sequence generation. Using transition rates or steady-state probabilities between modes to determine state sequences enables a more realistic simulation of random failures and repair processes of system components. Calculating the steady-state probability of each state using the frequency and duration method, and calculating the transition rates between modes based on the frequency balance principle, ensures the accuracy and reliability of state sequence generation.

[0117] 3. It allows for a comprehensive assessment of system reliability. System reliability metrics include the probability of load failure, the frequency of load failure, and the duration of load failure. These metrics comprehensively reflect the system's reliability level under specific conditions. By calculating these metrics, system risk can be quantified, providing crucial information for system optimization and decision-making.

[0118] 4. Enhancing the practicality of system reliability analysis. This technical content not only provides methods for generating state sequences and calculating reliability indicators, 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 system's reliability and economy.

[0119] Preferably, 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; expansion-only failure mode is that only the expansion subsystem fails; compression-only failure mode is that only the compression subsystem fails; other operating states are all failure state modes.

[0120] This configuration, by defining normal operation, partial functional failure (including expansion-only and compression-only failure modes), and other fault state modes, clearly defines the various possible operating states of the AA-CAES system. This helps system maintenance personnel quickly identify the current state of the system and take appropriate maintenance or repair measures. The clear division of different operating state modes allows for more targeted assessment of system reliability. In particular, the identification of partial functional failure states enables the system to continue operating even when certain components fail, improving the overall reliability and fault tolerance of the system.

[0121] 2. Understanding system performance under different operating conditions helps optimize system design and operational strategies. For example, in an expansion-only failure mode, the system may still be able to provide some power output through other means; while in a compression-only failure mode, adjustments to the storage subsystem's operation may be necessary to maintain stable system operation. This in-depth understanding of system performance helps in developing more efficient operational plans and maintenance strategies. By analyzing the system's behavioral characteristics under different operating conditions, fault prediction and diagnostic models can be established. This helps in the early detection of potential faults, reducing system downtime and improving system availability and reliability. Attached Figure Description

[0122] To make the objectives, technical solutions, and advantages of the invention clearer, the invention will now be described in further detail with reference to the accompanying drawings, wherein:

[0123] Figure 1 This is a flowchart of the method;

[0124] Figure 2 This is a schematic diagram of the topology of AA-CAES in Example 1;

[0125] Figure 3This is a schematic diagram of the 8-state Markov model of AA-CAES in Example 1;

[0126] Figure 4 This is a simplified schematic diagram of the 8 Markov model of AA-CAES in Example 1;

[0127] Figure 5 This is a schematic diagram of the dynamic temperature characteristics of AA-CAES in Example 1;

[0128] Figure 6 This is a flowchart of the reliability analysis process for the integrated energy system in Example 1;

[0129] Figure 7 This is an example diagram of the integrated energy system topology in Example 2;

[0130] Figure 8 This is a schematic diagram of the wind curtailment situation in Example 2;

[0131] Figure 9 This is an example diagram of the optimal operation scheme of the power system in scenario 1 of Example 2;

[0132] Figure 10 This is a schematic diagram of the optimal operation scheme of the power system in scenario 2 of Example 2;

[0133] Figure 11 This is a schematic diagram of the gas source output in Example 2;

[0134] Figure 12 This is a schematic diagram of the gas storage chamber pressure in Example 2;

[0135] Figure 13 This is a schematic diagram of the hot water temperature in the high-temperature thermal storage tank in Example 2;

[0136] Figure 14 This is a schematic diagram of the compression and expansion power of AA-CAES in Example 2;

[0137] Figure 15 This is a schematic diagram of the heat generation of AA-CAES in Example 2. Detailed Implementation

[0138] The following detailed explanation illustrates the specific implementation methods:

[0139] Example 1

[0140] like Figure 1 As shown, this embodiment discloses a comprehensive energy system reliability assessment method considering compressed air energy storage, including the following steps:

[0141] S1. Based on the topology and working mode of AA-CAES, analyze the multi-state operation mode of AA-CAES, which includes normal operation mode, fault mode and partial functional failure mode; establish a reliability assessment model of AA-CAES that considers partial functional failure mode.

[0142] The topology of AA-CAES is divided into a compression subsystem, an expansion subsystem, and a 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 and form independent compression and expansion cycles.

[0143] In specific implementation, 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; expansion-only failure mode is that only the expansion subsystem is faulty; compression-only failure mode is that only the compression subsystem is faulty; other operating states are all failure state modes.

[0144] By defining normal operating states, partial functional failure states (including expansion-only failure mode and compression-only failure mode), and other fault state modes, this technical content clearly defines the various possible operating states of the AA-CAES system. This helps system maintenance personnel quickly identify the current state of the system and take corresponding maintenance or repair measures. The clear division of different operating state modes allows for more targeted assessment of system reliability. In particular, the identification of partial functional failure states enables the system to continue operating even when certain components fail, improving the overall reliability and fault tolerance of the system. Furthermore, understanding the system's performance under different operating states helps optimize system design and operating strategies. For example, in expansion-only failure mode, the system may still be able to provide some power output through other means; while in compression-only failure mode, it may be necessary to adjust the operation of the storage subsystem to maintain stable system operation. This in-depth understanding of system performance helps in developing more efficient operating plans and maintenance strategies. By analyzing the behavioral characteristics of the system under different operating states, fault prediction and diagnostic models can be established. This helps to detect potential faults in advance, reduce system downtime, and improve system availability and reliability.

[0145] To facilitate a better understanding of the above technical content by those skilled in the art, the following explanation is provided.

[0146] First, the topology and operating mode of AA-CAES are comprehensively analyzed. The specific analysis process is as follows:

[0147] 1) The topology of AA-CAES. For example... Figure 2 As shown, the AA-CAES structure is functionally divided into three parts: a compression subsystem, an expansion subsystem, and a 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 high-temperature / low-temperature storage tanks and a gas storage chamber for storing the working medium (air and water). These three subsystems use the same working medium and form two independent circulation loops: the compression loop and the expansion loop.

[0148] 2) AA-CAES Operating Modes. Its main operating modes are as follows: ① When the integrated energy system is in a low-energy consumption period or when wind power is surplus, the compression cycle starts, using the surplus electricity to drive the compressor to compress normal-pressure, normal-temperature air into high-pressure, high-temperature air; the high-pressure, high-temperature air is cooled by a heat exchanger to high-pressure, normal-temperature air and stored in a gas storage chamber (underground cave or pressure vessel). Simultaneously, the cold water in the low-temperature storage tank is heated by a heat exchanger and stored in the high-temperature storage tank to recover the heat of compression. ② When the integrated energy system is in a high-energy consumption period or when the system experiences an energy shortage due to a fault, the expansion cycle starts. The high-pressure gas stored in the gas storage chamber is heated by hot water in the high-temperature storage tank through a heat exchanger. Simultaneously, the hot water in the high-temperature storage tank is cooled by a heat exchanger and stored in the low-temperature storage tank. Then, the high-temperature, high-pressure compressed gas enters the expander, which drives the generator to output electrical power to the system. It is worth noting that the residual heat energy in the high-temperature storage tank can be transferred to users through the heat exchanger and heating network pipelines.

[0149] Secondly, a multi-state Markov model of AA-CAES is established based on its topology and operating mode. The specific modeling steps are as follows:

[0150] 1) Model each subsystem component individually. Based on the topology and working principle of AA-CAES, the components in each subsystem can be considered as connected in series and independent. Once any component in a subsystem fails, the subsystem will stop operating. Similarly, the three subsystems can be represented as a series logical relationship, and each subsystem can be modeled using a two-state fault model. Let the equivalent failure rates and equivalent repair rates 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, λ1 and μ1 are calculated as follows:

[0151]

[0152] In the formula, λ c,m and r c,m These represent the failure rate and mean time to repair (MTBT) of component m within the compression subsystem, respectively; M cThis represents the number of components in the compression subsystem.

[0153] 2) Establish an 8-state Markov model and a simplified model with 4 operating modes for AA-CAES. The operating state of AA-CAES can be determined based on the independent operating states of the three subsystems, as shown in Table 1. "1" represents normal operation of the subsystem, and "0" represents subsystem failure.

[0154] Table 1 AA-CAES Status Table

[0155]

[0156] Based on this table, an 8-state Markov model of AA-CAES is established, as follows: Figure 3 As shown in the diagram, the states of the compression, expansion, and storage subsystems are labeled from top to bottom in each block diagram. "UP" and "DN" represent the normal operation and shutdown states of the subsystem, respectively. The numbers in the lower right corner of the block diagram represent the AA-CAES state number, and the numbers in the dashed box in the upper right corner represent the operating mode.

[0157] like Figure 3 As shown, the AA-CAES has eight operating states and four operating modes. The storage subsystem, essential for compression and expansion, will completely stop the AA-CAES if it fails. States 1, 2, 3, 5, and 6 cannot compress air or output power; these are fault states and are classified as Mode 0. Mode 1 is the normal operating state, including state 8, where all subsystems operate normally. Modes 2 and 3 represent two different partial 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, the AA-CAES cannot compress air or store heat; it can only release heat and output electrical power. In state 7, when the expansion subsystem fails, the AA-CAES can only compress air and store heat. The steady-state probabilities p1-p8 of states 1-8 can be calculated using 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.

[0158] Therefore, the 8-state Markov model of AA-CAES can be simplified to 4 operating modes. For example... 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.

[0159] In practical implementation, the constructed AA-CAES reliability assessment model, which considers partial functional failure states, includes constraints on the AA-CAES operation and linearization constraints on the thermal energy storage model. The AA-CAES operation constraints include those for the compression subsystem, expansion subsystem, and storage subsystem. This improves the accuracy of the AA-CAES reliability assessment model. In S1, by constructing an AA-CAES reliability assessment model that considers partial functional failure states and introducing AA-CAES operation constraints (including those for the compression, expansion, and storage subsystems), it is possible to more accurately simulate 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, thus more accurately assessing the system's reliability. It also enhances the practicality of the thermal energy storage model. The reliability assessment model also includes linearization 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 model's computational efficiency. Linearization constraints help simplify complex nonlinear relationships, making model solving more efficient and thus more practical in real-world applications.

[0160] The operational constraints of the compression subsystem include:

[0161] Total power consumption at time t during the compression cycle for:

[0162]

[0163] In the formula, k is the specific heat ratio of air; Let be the mass flow rate of air entering the compressor at time t during the compression process; The specific heat capacity of air; and N represents the inlet and outlet temperatures of the i-th stage compressor; c The number of compressor stages;

[0164] The outlet temperature of the i-th stage compressor is calculated as follows:

[0165]

[0166] In the formula, Let η be the compression ratio of the i-th stage compressor. c The adiabatic efficiency of the compressor;

[0167] The upper and lower limits of the total power consumption of AA-CAES are:

[0168]

[0169] In the formula, P represents a 0-1 variable describing the state of the compression loop; com,max and P com,min This indicates the maximum and minimum power consumption.

[0170] This setup allows for precise simulation of the energy consumption of the compression subsystem. By providing a formula for calculating the total power consumption at time t during the compression cycle, this technology can accurately simulate the energy consumption of the compression subsystem in an AA-CAES system. This formula considers multiple factors, including air specific heat ratio, air mass flow rate, air specific heat capacity, compressor inlet and outlet temperatures, and the number of compressor stages, thus comprehensively reflecting the energy consumption characteristics during compression. It also refines the calculation of interstage compressor temperatures. The technology provides a formula for calculating the outlet temperature of the i-th stage compressor, which considers the compressor's compression ratio and adiabatic efficiency, accurately calculating the air temperature at the outlet of each compressor stage. This refined calculation helps to more accurately assess heat exchange and energy loss between compressor stages, providing an important basis for optimizing compressor design and operating strategies. Furthermore, by setting upper and lower limits for the total power consumption of AA-CAES, this technology ensures that the compression subsystem does not exceed its design range during operation, thus protecting system equipment from damage. Simultaneously, this setting also helps to consider the system's performance at different power consumption levels in reliability assessments, providing strong support for system design and optimization. Taking into account the above technical aspects, this technology helps improve the reliability and energy efficiency of the AA-CAES system by accurately simulating the energy consumption and temperature characteristics of the compression subsystem and setting reasonable upper and lower limits for power consumption. By optimizing compressor design and operating strategies, system energy consumption can be reduced, energy conversion efficiency can be improved, thereby extending system lifespan and reducing operating costs.

[0171] The operational constraints of the expansion subsystem include:

[0172] The amount of electricity generated by the expander at time t. The formula for calculation is:

[0173]

[0174] In the formula, Let be the mass flow rate of air entering the expander at time t during the expansion cycle; and These are the inlet and outlet temperatures of the l-th stage expander at time t, respectively; N e The number of stages in the expander; κ is the specific heat capacity of air; κ is the specific heat ratio of air.

[0175] The outlet temperature of the l-stage expander at time t The calculation is as follows:

[0176]

[0177] In the formula, η is the expansion ratio of the l-th stage expander. e The adiabatic efficiency of the expander;

[0178] Total power generation of AA-CAES The upper and lower limits are:

[0179]

[0180] In the formula, P is a 0-1 variable describing the state of the expansion cycle; exp,max and P exp,min These represent the maximum and minimum power generation values, respectively.

[0181] In AA-CAES, the compression subsystem and the expansion subsystem cannot operate simultaneously:

[0182]

[0183] In the formula, Represents a 0-1 variable describing the state of the compression loop; These are 0-1 variables that describe the state of the expansion cycle.

[0184] Thus, the formula for calculating the power generation of the expander at time t can accurately simulate the power generation performance of the expansion subsystem under different operating conditions. By considering multiple factors such as the mass flow rate of the air entering the expander, the inlet and outlet temperatures of each stage of the expander, the number of expander stages, and the specific heat capacity and specific heat ratio of the air, this formula can comprehensively reflect the energy conversion efficiency during the expansion process, providing accurate data support for system design and optimization. The formula for calculating the outlet temperature of the l-th stage expander takes into account the expander's expansion ratio and adiabatic efficiency, and can accurately calculate the air temperature at the outlet of each stage expander. This refined calculation helps to evaluate heat loss and energy utilization efficiency during the expansion process, providing an important basis for optimizing expander design and operation strategies. In addition, by setting upper and lower limits for 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 system's performance at different power generation levels in reliability assessment, providing strong support for system design and optimization. The introduced constraint that the compression subsystem and expansion subsystem cannot operate simultaneously is an important guarantee for the safe and stable operation of the AA-CAES system. By ensuring that these two subsystems never operate simultaneously, potential energy conflicts and equipment damage risks are avoided, thus improving system stability and reliability. Through precise calculations of the expansion subsystem's power generation, refined calculations of interstage temperatures in the expander, setting upper and lower limits for total power generation, and ensuring 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 reduce system energy consumption and improve energy conversion efficiency but also extends system lifespan and reduces operating costs, contributing to the construction of a more efficient and environmentally friendly energy system.

[0185] The heat of compression generated by AA-CAES is converted into hot water by a heat exchanger and stored in a high-temperature storage tank. The temperature and mass of the water change over time; this is known as the dynamic temperature characteristic of AA-CAES. Figure 5 As shown. In the compression cycle, cold water in the low-temperature thermal storage tank is heated through a heat exchanger and flows into the high-temperature thermal storage tank to recover heat from the compressed air process, thus increasing the temperature and mass of the hot water. In the expansion cycle, hot water from the high-temperature thermal storage tank is used to heat the high-pressure air flowing through the heat exchanger, reducing the mass of the hot water. Furthermore, the water temperature decreases when heat power is supplied to heat users. When the water temperature in the high-temperature thermal storage tank rises to its upper limit, the incoming water decreases, affecting the compression and storage process. On the other hand, the decrease in water temperature weakens the working capacity per unit of high-pressure air and lowers the inlet temperature of the expander, thus affecting the expansion power generation. When the water temperature drops to its lower limit, the heat power that the AA-CAES can generate is 0.

[0186] Therefore, in practical implementation, the operational constraints of the storage subsystem include:

[0187] The temperatures of the water and air in the heat exchanger are expressed as:

[0188]

[0189] In the formula, Let be the mass flow rate of hot water entering the compressor at time t during the compression process; T is the specific heat capacity of water. water,cold The temperature of the cold water in the low-temperature thermal storage tank; The temperature of the hot water at the outlet of the i-th stage compressor; Let be the temperature of the hot water in the high-temperature thermal storage tank at time t; θ be the efficiency of the heat exchanger. The above expressions all assume that the heat exchange between the hot and cold media in the heat exchanger is equal. Let be the mass flow rate of air entering the compressor at time t during the compression process; The specific heat capacity of air; and These are the inlet and outlet temperatures of the i-th stage compressor;

[0190] During the compression cycle, the temperature T of the hot water flowing into the high-temperature heat storage tank is... storage,in The formula for calculation is:

[0191]

[0192] In the formula, This represents the total hot water mass flow rate flowing into the high-temperature thermal storage tank at time t;

[0193] The formulas for calculating the water storage capacity and hot water temperature in a high-temperature thermal storage tank are as follows:

[0194]

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

[0196] The ranges of variation for hot water temperature, water storage capacity, and heat power supplied to users are as follows:

[0197]

[0198] In the formula, T s,min and T s,maxThese represent the minimum and maximum temperatures of the hot water, respectively; M storage,min and M storage,max These represent the minimum and maximum water storage capacity, respectively. Maximum user thermal power;

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

[0200]

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

[0202] Thus, the temperature calculation formulas for water and air in the heat exchanger provided in section 1 can accurately simulate the heat exchange within 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 comprehensively reflect the energy conversion and temperature changes during the heat exchange process, providing accurate data support for system design and optimization. Furthermore, by calculating the temperature of the hot water flowing into the high-temperature heat storage tank, as well as the water volume and temperature in the tank, this technology can optimize thermal energy storage and management. This includes determining key parameters such as the hot water mass in the heat storage tank, the water temperature change, and the heat power provided to the user, thereby ensuring effective storage and efficient utilization of thermal energy. Simultaneously, by setting the range of variation for hot water temperature, water volume, and heat power provided to the user, the stable operation and safety of the system can be further guaranteed. In addition, the given formula for calculating the high-pressure air pressure in the air storage chamber can precisely control the pressure changes in the air storage chamber. By considering multiple factors such as the gas constant, the temperature and volume of the gas storage chamber, the air mass flow rate of the compressor and expander, and the 0-1 variables describing the cycle state, these formulas can comprehensively reflect the dynamic changes in the pressure within the gas storage chamber, providing crucial assurance for the stable operation of the system. Furthermore, by setting the maximum and minimum values ​​of the gas pressure in the gas storage chamber, the safety and reliability of the system can be further ensured. Taking all the above technical aspects into account, through precise simulation of the heat exchange process, optimization of heat energy storage and management, and precise control of the gas storage chamber pressure, the overall performance and efficiency of the AA-CAES system can be significantly improved. This not only helps reduce system energy consumption and improve energy conversion efficiency but also extends system lifespan, reduces operating costs, and provides users with a more stable and reliable heat energy supply.

[0203] In practice, the linearization constraints of the thermal energy storage model are obtained by converting the dynamic temperature equation into a linear model using Taylor series expansion and the Big M method. The process includes:

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

[0205] First, define the nonlinear term as Define the linear term as

[0206]

[0207] Then, according to the Taylor series, exist The surrounding area expands to:

[0208]

[0209] This represents the initial value of the water storage capacity; This represents the initial value of the water temperature change;

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

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

[0212]

[0213] In the formula, This represents an integer variable describing the water storage volume at time t in a piecewise state; M represents the integer variable describing the change in water temperature at time t in a piecewise state; storage,j,min and M storage,j,max These represent the minimum and maximum water storage values ​​under segmented conditions; T rate,k,min and T rate,k,max These represent the minimum and maximum values ​​of water temperature change under segmented conditions, respectively.

[0214] Introducing two additional integer variables and a set of constraints Perform linearization;

[0215]

[0216] In the formula, This represents an integer variable describing the mass flow rate of hot water entering the compressor at time t during the compression process in a piecewise state; m represents an integer variable describing the temperature of the hot water in the high-temperature thermal storage tank at time t under segmented conditions; com,total,water,h,min and m com,total,water,h,max T represents the minimum and maximum values ​​of the hot water mass flow rate entering the compressor at time t during the compression process, respectively, under the segmented state. s,g,min and T s,g,max These are the minimum and maximum values ​​of the hot water temperature in the high-temperature thermal storage tank under segmented conditions, respectively.

[0217] By omitting higher-order terms, the dynamic temperature equation yields average values ​​for each segment. The surrounding area expands to:

[0218]

[0219] In the formula, This represents a water storage function that ignores higher-order terms. This represents a function that ignores higher-order terms related to water temperature change.

[0220] 3) Linearize the nonlinear terms in the Taylor expansion equations using the Big M method:

[0221]

[0222]

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

[0224]

[0225] Thus, thermal energy storage models typically contain complex nonlinear equations that are difficult to directly apply to optimization and control algorithms. By employing the techniques described above and utilizing Taylor series expansion formulas, these nonlinear terms are successfully transformed into linear terms, significantly simplifying the model. This linearization not only preserves the main characteristics of the original model but also makes it easier to process and analyze. Linear models are computationally more efficient than nonlinear models. Linearization significantly reduces computation time, enabling real-time optimization and control. This is crucial for the operation and management of thermal energy storage systems, especially in scenarios requiring rapid response and precise control. Furthermore, the linearized thermal energy storage model provides strong support for optimization decisions. Optimal storage strategies and scheduling schemes can be solved more efficiently using linear programming, integer programming, and other optimization methods, thereby maximizing thermal energy utilization and minimizing costs. Linearized models generally exhibit better robustness. Due to their simplicity and clarity, linear models are more adaptable to fluctuations and uncertainties in input data. This makes the model more reliable in practical applications and better able to handle various complex situations. In addition, by dividing the domains of water storage and temperature changes into multiple grids and introducing integer variables to represent the segments, the model becomes more flexible in handling continuous variables. This approach not only facilitates linearization but also better captures the dynamic characteristics of the system. The Big M method is an effective linearization method that transforms nonlinear terms into linear terms by introducing additional constraints and constants. This method is particularly effective when dealing with models containing complex nonlinear terms, significantly improving the linearization degree and computational efficiency of the model.

[0226] S2. Based on the AA-CAES reliability assessment model that considers partial functional failure states, a comprehensive energy system model of electricity, gas, and heat is constructed for reliability analysis of the comprehensive energy system.

[0227] In practical implementation, the objective function of the integrated energy system model in S2 is:

[0228]

[0229] In the formula, Q(x1) represents the set of gas source nodes; c gw,x1 Indicates the cost of the gas source; The gas supply volume is represented by m(x2); the set of gas network nodes is represented by c. curs,x2 Indicates the air reduction load penalty coefficient; Represents the natural gas load reduction; p(k1) represents the set of nodes for coal-fired power units; c pg,k1 This indicates the output cost of a coal-fired power unit; The power of the coal-fired unit is represented by m(k2); the set of power nodes is represented by c. curl,k2 Indicates the load penalty factor for electrical discharge; Represents the amount of electricity load reduction; w(k3) represents the set of wind turbine nodes; c curw,k3 This represents the penalty coefficient for wind power reduction; Represents the amount of wind power cut; n(d) represents the set of heating network nodes; c curh,e Indicates the heat load penalty coefficient; and These represent the seasonal and perennial heat load reductions, respectively; T represents the total time period.

[0230] In this model, wind-cutting power and wind-cutting penalty cost are incorporated into the objective function to measure the wind power absorption capacity of the integrated energy system. The operational constraints of AA-CAES and the linearization constraints of the thermal energy storage model considered in this optimization model have already been modeled in the aforementioned steps.

[0231] Thus, the objective function, by comprehensively considering multiple aspects such as gas supply, electricity generation, heat load fulfillment, and wind power utilization, aims to optimize the overall performance of the integrated energy system. This helps improve the system's energy efficiency, reduce operating costs, and enhance its reliability and stability. Furthermore, by minimizing the penalties for natural gas load reduction, electricity load reduction, heat load reduction, and wind power reduction, the objective function helps improve the system's economic efficiency. This helps reduce energy costs for users while increasing the revenue of energy suppliers. In addition, the objective function includes multiple variables and parameters that can be adjusted and optimized according to actual conditions. This allows the model to flexibly adapt to changes in external conditions such as energy demand, 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 the corresponding reliability indicators.

[0234] Among them, such as Figure 6 As shown, the process of performing reliability analysis on an integrated energy system includes the following steps. It should be noted that... Figure 6 The optimal energy flow model of the integrated energy system in the above is the integrated energy system model in this method.

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

[0236] S42, the state sequence of each component of the system is obtained by using the state duration sampling method, and then the corresponding system state is obtained by combining them;

[0237] In practical implementation, the state sequences of each component include multi-state sequences of AA-CAES (Advanced Adiabatic Compressed Air Energy Storage System), multi-state sequences of the natural gas network and heating network pipelines, and two-state sequences of other components. When obtaining the state sequences of each system component using the state duration sampling method, the transition rate or steady-state probability between each mode is used to determine the state sequence. Specifically, the steady-state probability of each state is calculated using the frequency and duration method; the transition rate between each mode is calculated according to the frequency balance principle. This refines the component state sequences. By subdividing the state sequences of each component into multi-state sequences of AA-CAES, multi-state sequences of the natural gas network and heating network pipelines, and two-state sequences of other components, the actual operating states of each system component can be reflected more accurately. This refinement helps to more accurately assess the overall performance and reliability of the system. Furthermore, it improves the accuracy of state sequence generation. Using the transition rate or steady-state probability between each mode to determine the state sequence allows for a more realistic simulation of random failures and repair processes of system components. Calculating the steady-state probability of each state using the frequency and duration method, and calculating the transition rate between each mode according to the frequency balance principle, ensures the accuracy and reliability of the state sequence generation.

[0238] S43 uses a combined energy system model to analyze the system status within a preset period and determine whether the system is in a load reduction phase.

[0239] S44. Based on the analysis results of load reduction, calculate the system reliability indicators; wherein, the system reliability indicators include the system's failure probability, failure frequency, and failure duration. These indicators can comprehensively reflect the system's reliability level under specific conditions. By calculating these indicators, the system's risk 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 period reaches a preset maximum number of times.

[0241] This process sets the initial state of each system component as normal operation and uses state duration sampling to simulate the state sequence of each component, thus comprehensively considering random failures and repair processes of system components. This helps to more accurately assess the system's reliability level. By analyzing the system state within a preset period using a comprehensive energy system model, it is possible to accurately determine whether the system is in a load-cutting state. This helps to identify operational bottlenecks and potential risks under specific conditions. Furthermore, system reliability indicators can be quantified. Based on the analysis results of load cutting, system reliability indicators such as power supply reliability rate and load failure probability are calculated. These indicators can quantify the system's reliability level, providing important basis for system optimization and decision-making. In addition, by calculating the variance convergence coefficient of the system reliability indicators and determining whether the convergence condition is met, the stability and reliability of the analysis results can be ensured. If the convergence condition is not met, the process returns to the previous step and repeats the simulation 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 period and the maximum number of simulations, this process can complete the system reliability analysis within a reasonable time. At the same time, the use of efficient algorithms such as state duration sampling further improves the analysis efficiency.

[0242] This method fully considers the dynamic process of temperature change of the working medium in the thermal storage tank when establishing the AA-CAES reliability assessment model. Compared with existing technologies, 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 processes of the working medium in the thermal storage tank, this method can assess the impact of temperature changes on the system's heating efficiency, power generation capacity, and overall reliability, thus providing more accurate reliability assessment results. Furthermore, unlike existing technologies that primarily focus on the normal and complete failure states of the AA-CAES system, this method innovatively introduces the assessment of partial functional failure states. This means that the reliability analysis considers not only the case of complete system failure but also the system's performance in a state where the performance of one or more components degrades but still partially functions. This improvement significantly enhances the accuracy and comprehensiveness of the reliability assessment, making the assessment results closer to actual operating conditions. By incorporating partial functional failure states, this method can more accurately predict the system's performance under different operating conditions, providing stronger support for system maintenance and optimization. Combining the two improvements mentioned above, this method can more accurately reflect the AA-CAES system and its interactions with other energy conversion and storage devices when constructing an integrated energy system model of electricity, gas, and heat. This helps to more comprehensively assess the overall reliability of the integrated energy system, including energy supply stability, system resilience, and fault response capabilities under different operating scenarios. Compared with existing technologies, the reliability assessment results provided by this method are more accurate and practical, offering more valuable references for the planning, design, and optimization of integrated energy systems.

[0243] In summary, this method significantly improves the accuracy and practicality of integrated energy system reliability assessment by comprehensively considering the impact of temperature changes in the working medium of the thermal storage tank and incorporating considerations of partial functional failure states. This improvement not only helps optimize system design and operation strategies 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 integrated energy systems, contributing to the improvement of the overall performance and reliability of integrated energy systems.

[0244] Example 2

[0245] To better illustrate the effects of the present invention, the following examples are provided.

[0246] This paper uses an integrated energy system consisting of an IEEE 30-node power system, a Belgian 20-node natural gas system, and a 16-node thermal system as an example to verify the dynamic thermal energy storage model considering compressed air energy storage proposed in this chapter, and to demonstrate the impact of compressed air energy storage on the reliability assessment of the integrated energy system. Specifically, at node 11 of the test system, the original coal-fired unit was replaced with an AA-CAES unit (A1), and a new wind turbine (D1) was added to that node. In the thermal system, the original heat pump at node 9 was replaced with one powered by an AA-CAES unit. Figure 7 As shown in Table 2, to ensure power parity between the power system and the thermal system, 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] First, two scenarios are set up to compare and analyze the impact of AA-CAES on integrated energy systems.

[0251] Scenario 1: Systems that do not consider AA-CAES.

[0252] Scenario 2: A system that considers the AA-CAES multiple fault state operation mode and adopts a dynamic temperature model.

[0253] Table 3 System operating costs for scenarios 1 and 2

[0254]

[0255] Table 4. Impact of AA-CAES on System Reliability

[0256]

[0257] Table 3 presents the system operating costs for Scenario 1 and Scenario 2. As shown in Table 3, when AA-CAES is involved in integrated energy system optimization, the operating cost of coal-fired units increases in Scenario 2, while the cost of gas sources decreases, and there is no wind curtailment. Compared to Scenario 1 without considering AA-CAES, the total system operating cost of Scenario 2 is significantly reduced by 21.98%. AA-CAES achieves economic benefits by shifting electricity from off-peak load periods and peak wind power periods to peak load periods. This indicates that the energy storage and heating capabilities of AA-CAES can effectively improve the operational economy of integrated energy systems.

[0258] Table 4 shows the reliability indices for scenarios 1 and 2. The results indicate that the reliability indices EENS, EHNS, LOULP, and LOHLP of the power and heating systems significantly decrease, while the reliability indices EGNS and LOGLP of the natural gas system show almost no improvement. This is because electricity is a high-quality energy source, and the optimal dispatch model tends to prioritize electricity supply. Simultaneously, AA-CAES can also provide thermal power to the heating system to mitigate load reduction. Although AA-CAES brings significant benefits to the integrated energy system, its own failures can also increase the risk of load reduction; therefore, improving the reliability of AA-CAES is key to enhancing the overall reliability of the integrated energy system.

[0259] Figure 8 The data shows wind curtailment, indicating that during peak wind power generation periods, most of the electricity load is supplied by wind power. However, in Scenario 1, wind power output is significantly reduced from 1:00 to 10:00 and from 18:00 to 24:00. This phenomenon is mainly due to two factors: firstly, coal-fired power units have limited adjustment characteristics, making it difficult to quickly reduce or increase output; secondly, there are power line congestion issues. AA-CAES can quickly adjust power generation or switch to compression mode to absorb excess energy from the integrated energy system, thereby reducing wind curtailment and better utilizing the flexibility of AA-CAES.

[0260] Figure 9 and Figure 10 The optimal operating schemes for the power system are shown in Scenario 1 and Scenario 2, 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 off-peak periods (11:00 to 17:00) to quickly respond to load fluctuations. In Scenario 2, AA-CAES begins storing electricity from 1:00 to 10:00 and releases it during peak load periods from 11:00 to 17:00, thereby reducing the power generation of natural gas and coal-fired units and lowering power generation costs. From 19:00 to 24:00, the power generation of coal-fired units in Scenario 2 is higher than in Scenario 1 because AA-CAES needs to store energy and return to its initial state to meet the dispatch requirements of the next cycle.

[0261] In Scenario 1, the coal-fired unit maintains a lower power generation rate due to its lower ramp rate compared to the natural gas unit. In Scenario 2, the AA-CAES compression process allows excess electrical energy to be stored, enabling the coal-fired unit to generate more electricity instead of the natural gas unit. Figure 11 As can be seen, the gas output of Scenario 2 is mostly lower than that of Scenario 1, thus reducing the total system operating cost.

[0262] Next, scenario 3 is used to illustrate the effectiveness of the dynamic thermal energy storage model (DTESM) and analyze its impact on AA-CAES. Scenario 3 considers the AA-CAES static thermal energy storage model (STESM), which does not consider changes in the temperature and volume of hot water in the storage tank.

[0263] Table 5 shows the operating costs for scenarios 2 and 3.

[0264]

[0265] Table 5 presents the operating costs of the integrated energy system in scenarios 2 and 3. As can be seen from Table 5, without considering dynamic changes in hot water temperature, the total cost of STESM is reduced by 6.64% compared to DTESM. Table 6 shows the reliability indices of the integrated energy system; in scenario 3, EENS, EDHNS, and LOULP are significantly reduced. This indicates that ignoring dynamic changes in hot water temperature will have a significant impact on the operating cost and reliability of the integrated energy system. Because the DTESM proposed in this method more accurately describes the operating characteristics of the thermal storage tank and establishes stricter thermal storage constraints, the feasible region of the optimization problem is narrowed to some extent. This often leads to a greater risk of load shedding, 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] Figure 12 and Figure 13 The changes in hot water temperature and gas chamber pressure of the high-temperature thermal storage tank, calculated by DTESM and STESM respectively, are shown. Figure 14 This represents the compression and expansion power of AA-CAES derived from two models. For example... Figure 14 As shown, since there is no temperature constraint, the compression and expansion power in STESM is greater than that in DTESM.

[0269] Figure 12 and Figure 13The description illustrates that during peak wind power generation, the AA-CAES rapidly stores energy from 1:00 to 6:00, with the pressure in the storage chamber gradually increasing. During peak power load (11:00-17:00), the storage chamber releases high-pressure air to the expander, causing the pressure to decrease. From 19:00 to 24:00, the AA-CAES needs to return to its initial state, resulting in a slight pressure rebound. Furthermore, when the water temperature in the storage tank is about to reach its upper limit, the flow of hot water into the tank decreases or even stops. Therefore, compared to STESM, the power plant compression power in DTESM is relatively low, and the pressure in the storage chamber increases relatively slowly from 1:00 to 6:00. Between 12:00 and 19:00, the hot water temperature in the storage tank has reached its minimum temperature limit.

[0270] Figure 15 The diagram describes the heat generation power of AA-CAES under different scenarios. As shown in the figure, AA-CAES can handle more heat load while ensuring power supply, thus improving the operational economy of the integrated energy system. Furthermore, STESM neglects the hot water temperature and storage constraints in the storage tank, employing more relaxed heat constraints, thereby expanding the feasible region of the optimization problem. Therefore, the heat generation of AA-CAES is significantly increased in STESM, leading to overly optimistic scheduling results. Figure 15 As shown, when the hot water temperature in the thermal storage tank approaches its minimum limit, the heat generation in the DTESM is zero. Comparative studies demonstrate the necessity of incorporating the DTESM into AA-CAES.

[0271] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the technical solutions. Those skilled in the art should understand that any modifications or equivalent substitutions to the technical solutions of the present invention without departing from the spirit and scope of the method should be covered within the scope of the claims of the present invention.

Claims

1. A reliability assessment method for a comprehensive energy system considering compressed air energy storage, characterized in that, Includes the following steps: S1. Based on the topology and working mode of AA-CAES, analyze the multi-state operation mode of AA-CAES, which includes normal operation mode, fault mode and partial functional failure mode; establish a reliability assessment model of AA-CAES that considers partial functional failure mode. The topology of AA-CAES is divided into a compression subsystem, an expansion subsystem, and a 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; and 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 and form independent compression and expansion cycles. S2. Based on the AA-CAES reliability assessment model that considers partial functional failure states, a comprehensive energy system model of electricity-gas-heat is constructed for reliability analysis of the comprehensive energy system. S3. Initialize 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 the corresponding reliability indicators; 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 failure state mode includes expansion failure mode only and compression failure mode only; expansion failure mode only means that only the expansion subsystem is faulty; compression failure mode only means that only the compression subsystem is faulty; other operating states are all failure state modes. In S1, the reliability assessment model of AA-CAES, which considers partial functional failure states, is constructed. Its constraints include the operational constraints of AA-CAES and the linearization constraints of the thermal energy storage model. Among them, the operational constraints of AA-CAES include the operational constraints of the compression subsystem, the operational constraints of the expansion subsystem, and the operational constraints of the storage subsystem. The operational constraints of the compression subsystem include: Total power consumption at time t during the compression cycle for: In the formula, κ is the specific heat ratio of air; Let be the mass flow rate of air entering the compressor at time t during the compression process; The specific heat capacity of air; and N represents the inlet and outlet temperatures of the i-th stage compressor; c The number of compressor stages; The outlet temperature of the i-th stage compressor is calculated as follows: In the formula, Let η be the compression ratio of the i-th stage compressor. c The adiabatic efficiency of the compressor; The upper and lower limits of the total power consumption of AA-CAES are: In the formula, P represents a 0-1 variable describing the state of the compression loop; com,max and P com,min This indicates the maximum and minimum power consumption. The operational constraints of the expansion subsystem include: The amount of electricity generated by the expander at time t. The formula for calculation is: In the formula, Let be the mass flow rate of air entering the expander at time t during the expansion cycle; and These are the inlet and outlet temperatures of the l-th stage expander at time t, respectively; N e The number of stages in the expander; κ is the specific heat capacity of air; κ is the specific heat ratio of air. The outlet temperature of the l-stage expander at time t The calculation is as follows: In the formula, η is the expansion ratio of the l-th stage expander. e The adiabatic efficiency of the expander; Total power generation of AA-CAES The upper and lower limits are: In the formula, P is a 0-1 variable describing the state of the expansion cycle; exp,max and P exp,min These represent the maximum and minimum power generation values, respectively. In AA-CAES, the compression subsystem and the expansion subsystem cannot operate simultaneously: In the formula, Represents a 0-1 variable describing the state of the compression loop; These are 0-1 variables describing the state of the expansion cycle; The operational constraints of the storage subsystem include: The temperatures of the water and air in the heat exchanger are expressed as: In the formula, Let be the mass flow rate of hot water entering the compressor at time t during the compression process; T is the specific heat capacity of water. water,cold The temperature of the cold water in the low-temperature thermal storage tank; The temperature of the hot water at the outlet of the i-th stage compressor; Let t be the temperature of the hot water in the high-temperature thermal storage tank at time t; θ be the efficiency of the heat exchanger; the above expressions all assume that the heat exchange between the hot and cold media in the heat exchanger is equal; Let be the mass flow rate of air entering the compressor at time t during the compression process; The specific heat capacity of air; and These are the inlet and outlet temperatures of the i-th stage compressor; During the compression cycle, the temperature T of the hot water flowing into the high-temperature heat storage tank is... storage,in The formula for calculation is: In the formula, This represents the total hot water mass flow rate flowing into the high-temperature thermal storage tank at time t; The formulas for calculating the water storage capacity and hot water temperature in a high-temperature thermal storage tank are as follows: In the formula, This represents the mass of hot water in the high-temperature thermal storage tank at time t; This represents the total hot water mass flow rate into the heat exchanger at time t; Indicates the change in water temperature; The heat power provided to the user; This represents the total hot water mass flow rate flowing into the high-temperature thermal storage tank at time t; Δt represents the temperature of the hot water in the high-temperature thermal storage tank at time t; Δt represents the time interval between two consecutive calculated times. The ranges of variation for hot water temperature, water storage capacity, and heat power supplied to users are as follows: In the formula, T s,min and T s,max These represent the minimum and maximum temperatures of the hot water, respectively; M storage,min and M storage,max These represent the minimum and maximum water storage capacity, respectively. Maximum user thermal power; The high-pressure air pressure in the gas storage chamber is: In the formula, R represents the gas pressure in the storage chamber at time t; g T represents the universal gas constant; AT and V AT P represents the temperature and volume of the gas storage chamber, respectively; AT,max and P AT,min These represent the maximum and minimum values ​​of the gas pressure in the gas storage chamber, respectively. Let be the mass flow rate of air entering the compressor at time t during the compression process; Represents a 0-1 variable describing the state of the compression loop; Let be the mass flow rate of air entering the expander at time t during the expansion cycle; These are 0-1 variables describing the state of the expansion cycle; The linearization constraints of the thermal energy storage model are obtained by converting the dynamic temperature equation into a linear model using Taylor series expansion and the Big M method. The process includes: 1) Expand the nonlinear terms using Taylor series: First, define the nonlinear term as Define the linear term as Then, according to the Taylor series, f1 t exist The surrounding area expands to: This represents the initial value of the water storage capacity; This represents the initial value of the water temperature change; 2) The domain is divided into multiple grids by approximating the actual values ​​with average values: The water storage capacity and water temperature change are divided into m segments and n segments respectively. The average value of each segment is used to approximate the actual value of each segment and serves as the initial expansion point for the Taylor series; f1 t The domain is divided into m*n grids; two sets of integer variables are introduced. and Used to indicate in and Segmentation within the range; In the formula, This represents an integer variable describing the water storage volume at time t in a piecewise state; M represents the integer variable describing the change in water temperature at time t in a piecewise state; storage,j,min and M storage,j,max These represent the minimum and maximum water storage values ​​under segmented conditions; T rate,k,min and T rate,k,max These represent the minimum and maximum values ​​of water temperature change under segmented conditions, respectively. Introducing two additional integer variables and a set of constraints Perform linearization; In the formula, This represents an integer variable describing the mass flow rate of hot water entering the compressor at time t during the compression process in a piecewise state; m represents an integer variable describing the temperature of the hot water in the high-temperature thermal storage tank at time t under segmented conditions; com,total,water,h,min and m com,total,water,h,max T represents the minimum and maximum values ​​of the hot water mass flow rate entering the compressor at time t during the compression process, respectively, under the segmented state. s,g,min and T s,g,max These are the minimum and maximum values ​​of the hot water temperature in the high-temperature thermal storage tank under segmented conditions, respectively. By omitting higher-order terms, the dynamic temperature equation yields average values ​​for each segment. The surrounding area expands to: In the formula, This represents a water storage function that ignores higher-order terms. This represents a function that ignores higher-order terms related to water temperature change. 3) Linearize the nonlinear terms in the Taylor expansion equations using the Big M method: In the formula, DM, GM, FM, and AM are constants, and are constrained by the following formula: In S2, the objective function of the integrated energy system model is: In the formula, Q(x1) represents the set of gas source nodes; c gw,x1 Indicates the cost of the gas source; The gas supply volume is represented by m(x2); the set of gas network nodes is represented by c. curs,x2 Indicates the air reduction load penalty coefficient; Represents the natural gas load reduction; p(k1) represents the set of nodes for coal-fired power units; c pg,k1 This indicates the output cost of a coal-fired power unit; The power of the coal-fired unit is represented by m(k2); the set of power nodes is represented by c. curl,k2 Indicates the load penalty factor for electrical discharge; Represents the amount of electricity load reduction; w(k3) represents the set of wind turbine nodes; c curw,k3 This represents the penalty coefficient for wind power reduction; Represents the amount of wind power cut; n(d) represents the set of heating network nodes; c curh,e Indicates the heat load penalty coefficient; and These represent the seasonal and perennial heat load reductions, respectively; T represents the total time period.

2. The reliability assessment method for a comprehensive energy system considering compressed air energy storage as described in claim 1, characterized in that: In S4, the process of performing reliability analysis on the integrated energy system includes: S41, Set the initial state of each component of the integrated energy system to the normal operating state; S42, the state sequence of each component of the system is obtained by using the state duration sampling method, and then the corresponding system state is obtained by combining them; S43 uses a combined energy system model to analyze the system status within a preset period and determine whether the system is in a load reduction phase. S44, based on the analysis results of load reduction, calculate the system reliability index; 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 period reaches a preset maximum number of times.

3. The reliability assessment method for a comprehensive energy system considering compressed air energy storage as described in claim 2, 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 heating network pipeline, and the two-state sequence of other components; in S44, the system reliability index includes the system's failure probability, failure frequency, and failure duration.

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