Multi-energy coupling integrated energy system fast multi-state reliability improvement method

Abstract

The application discloses a kind of multi-energy coupling's comprehensive energy system fast multi-state reliability improvement method.For the comprehensive energy system, a multi-state reliability model considering multi-energy coupling is established;Gaussian approximation is used to obtain a multi-state Gaussian approximation reliability model by approximating the reliability of the multi-state reliability model; a multi-state sampling approximation reliability model is obtained by approximating the reliability of the multi-state Gaussian approximation reliability model using sampling approximation; the approximate reliability of the comprehensive energy system under different multi-energy load requirements is calculated using a fast reliability evaluation algorithm for the multi-state sampling approximation reliability model; the approximate reliability is used to establish a reliability constraint condition for comprehensive energy system structure planning and standby optimization scheduling, thereby improving the reliability of the comprehensive energy system.The application improves the traditional reliability evaluation method in terms of time and accuracy, reduces the calculation time, and effectively improves the system reliability.

Description

Technical Field

[0001] This invention relates to a system reliability processing method in the field of integrated energy systems, specifically a method for rapidly improving the multi-state reliability of integrated energy systems that considers multi-energy coupling. Background Technology

[0002] An integrated energy system refers to a new type of integrated energy system that utilizes advanced physical information technology and innovative management models within a specific region to integrate various energy sources such as coal, oil, natural gas, electricity, and heat. This system achieves coordinated planning, optimized operation, collaborative management, interactive response, and mutual support among multiple heterogeneous energy subsystems. While meeting the diverse energy needs within the system, it effectively improves energy utilization efficiency and promotes sustainable energy development.

[0003] Reliability technology was first developed in the aerospace and electronics industries after World War II. The task of an integrated energy system is to provide users with a continuous supply of qualified energy, specifically including gas, heat, and electricity. Because various equipment in an integrated energy system, including energy coupling equipment (such as combined heat and power units, natural gas units, and combined cooling, heating, and power units), energy transmission equipment (such as urban utility tunnels), circuit breakers, and other primary and associated secondary equipment, can experience different types of failures, thus affecting the normal operation of the integrated energy system and the normal energy supply to users. Failures in integrated energy systems can cause varying degrees of economic losses to energy companies, users, and certain sectors of the national economy. With the acceleration of social modernization, production and daily life are increasingly dependent on various types of gas, heat, cooling, and electricity energy, and the losses caused by the interruption of various energy sources are also increasing. Therefore, integrated energy systems are required to have high reliability.

[0004] Reliability assessment involves calculating and analyzing the probability and consequences of possible failure states to derive a series of indicators reflecting the system's reliability level. However, in a real-world system with hundreds or even thousands of components, the number of possible failure states is enormous. Due to limitations in computation time and resources, it is impossible to assess all possible failure states in practical evaluations. Therefore, state enumeration methods only select failure states that contribute significantly to system reliability for evaluation. The most commonly used selection method is the failure severity cutoff, which selects failure states with 2 or fewer severity levels and ignores those with higher severity levels. The advantage of this method is that the sum of probabilities of the selected states is close to 1, and the number is relatively small. However, in real-world systems, due to the different outage probabilities of components, some high-severity failures have a higher probability of occurrence than low-severity failures. These high-severity failure states have high probabilities and severe consequences, significantly impacting system reliability. State selection by the failure severity cutoff ignores these high-probability high-severity failures. As can be seen, in reliability analysis, the large number of selected states, whether retained or deleted, will significantly affect the final result. This means that rapid and accurate reliability analysis of systems is a necessary research direction.

[0005] Current reliability improvement algorithms primarily focus on power system reliability. However, with the introduction of multiple coupled energy sources, power system reliability improvement techniques that only assess electrical energy are no longer suitable for improving the reliability of integrated energy systems with multiple energy couplings. The reliability of multiple energy sources, such as gas, heat, cooling, and electricity, needs to be reflected simultaneously. Therefore, this invention proposes a new reliability improvement algorithm that can be used to improve the reliability of integrated energy systems considering multiple energy couplings.

[0006] Existing reliability improvement algorithms can be used to enhance the reliability of integrated energy systems. However, these algorithms treat the operation of the integrated energy system as having two states: complete failure or perfect operation, without considering the intermediate states of the integrated energy system's operation. Therefore, due to insufficient modeling of the integrated energy system's operation, the resulting reliability improvement models cannot accurately reflect the system's reliability. This invention proposes a multi-state reliability improvement model for integrated energy systems that considers multi-energy coupling, incorporating multiple intermediate states of the integrated energy system's operation into the system's reliability.

[0007] Existing algorithms for improving the reliability of integrated energy systems often face the challenge of drastically increasing reliability calculation time as system scales up, thus reducing the practicality of reliability assessment. Accurate reliability assessment and rapid computation are often mutually exclusive. However, from a practical perspective, a certain range of error is acceptable during system operation; the most important thing is to obtain system reliability that meets accuracy requirements as quickly as possible. Therefore, this invention proposes a rapid multi-state reliability improvement method for integrated energy systems considering multi-energy coupling, enabling rapid reliability assessment of integrated energy systems.

[0008] The shortcomings of existing technologies are summarized as follows:

[0009] Disadvantage of existing technology 1: Traditional reliability improvement algorithms mainly focus on improving the reliability of power systems. However, with the introduction of multiple coupled energy sources, the reliability improvement of power systems that can only evaluate electrical energy is no longer applicable to the reliability improvement of integrated energy systems with multiple energy couplings.

[0010] Disadvantage 2 of existing technology: Existing improvement technologies treat the operation of integrated energy systems as having two states: complete failure or perfect operation, without considering the intermediate states of the integrated energy system's operation. Therefore, due to insufficient modeling of the integrated energy system's operation, the resulting reliability improvement model cannot accurately reflect the reliability of the integrated energy system.

[0011] Disadvantage 3 of existing technology: When existing technologies improve the reliability of integrated energy systems, they often face the problem that as the system scale increases, the reliability calculation time also increases dramatically, thereby reducing the practicality of reliability assessment. Accurate reliability assessment and rapid calculation often cannot be satisfied at the same time. Summary of the Invention

[0012] To address the shortcomings of existing technologies, this invention proposes a fast multi-state reliability improvement method for integrated energy systems considering multi-energy coupling. This method is applied to the reliability calculation of integrated energy systems considering multi-energy coupling. First, a multi-state reliability model of the integrated energy system considering multi-energy coupling is proposed. Then, fast reliability calculation is performed using two methods: Gaussian approximation and sampling approximation. This method can obtain an approximate reliability of the integrated energy system that meets the reliability accuracy requirements, and the time required is much shorter than that of previous traditional algorithms.

[0013] like Figure 1 As shown, the technical solution of the present invention is as follows:

[0014] Step 1: Establish a multi-state reliability model for the integrated energy system that considers the coupling of multiple energy sources, and characterize it using a multi-dimensional general generating function.

[0015] Step 2: Apply Gaussian approximation to the multi-state reliability model obtained in Step 1 to obtain a multi-state Gaussian approximation reliability model, and represent it in the form of an approximate multidimensional general generating function;

[0016] Step 3: Use sampling approximation to perform reliability approximation on the multi-state Gaussian approximation reliability model obtained in step 2 to obtain a multi-state sampling approximation reliability model, and characterize it in the form of an approximate multidimensional general generating function;

[0017] Step 4: Based on the multi-state sampling approximate reliability model obtained in Step 3, the approximate reliability of the integrated energy system that meets the requirements of different multi-energy loads is quickly calculated using a fast reliability assessment algorithm.

[0018] Step 5: Use approximate reliability to establish reliability constraints for integrated energy system structure planning and backup optimization scheduling, thereby improving the reliability of the integrated energy system.

[0019] The integrated energy system of the present invention is a multi-state system. A multi-state system is defined as a system and its components that may exhibit multiple operating levels.

[0020] In this invention, the bolded letter variables all represent vectors. The integrated energy system considers the coupling of multiple energy sources, and the output of the integrated energy system is extended from one-dimensional output to multi-dimensional output, ensuring that multiple energy sources can be universally represented through a single output form.

[0021] When the number of states of multi-energy coupled components is too large, it is often difficult to calculate the number of states of the integrated energy system after a large number of multi-energy coupled components are connected to the grid. The method of this invention reduces the number of states of components and the system by merging two types of approximation processing, thereby accelerating the calculation.

[0022] The first step, the multi-state reliability model, specifically includes:

[0023] The integrated energy system is equivalent to a structure consisting of a generator subsystem and a transmission line subsystem connected in series. Both the generator subsystem and the transmission line subsystem are composed of multi-energy coupling elements connected in parallel, and each multi-energy coupling element has k states. The multi-energy coupling elements are divided into multi-energy coupling generators and multi-energy coupling transmission lines. The generator subsystem is composed of multi-energy coupling generators connected in parallel, and the transmission line subsystem is composed of multi-energy coupling transmission lines connected in parallel.

[0024] The functional relationship between the generator unit subsystem and the transmission line subsystem follows the following sequence, equivalent to a series structure: the multi-energy coupled generator unit must first generate various energy sources before these energy sources can be transmitted to users via the multi-energy coupled transmission line. Energy sources include electrical energy parameters, thermal energy, cooling energy, natural gas energy, etc.

[0025] The integrated energy system has multiple multi-energy coupled units operating simultaneously to generate output power. The multi-energy coupled units are connected in parallel to form a unit subsystem. Because the output power of the multi-energy coupled units is too large, the transmission capacity of a single multi-energy coupled transmission line is limited. Therefore, multiple multi-energy coupled transmission lines are set up to transmit the output power of the units simultaneously, so that the multi-energy coupled transmission lines are connected in parallel to form a line subsystem.

[0026] Multi-energy coupled units include combined heat and power (CHP) units, natural gas units, and combined cooling, heating and power (CCHP) units, while multi-energy coupled transmission lines include energy transmission lines (urban integrated utility tunnels). In this invention, the equipment of a comprehensive energy system considering multi-energy coupling (including CHP units, natural gas units, CCHP units, urban integrated utility tunnels, etc.) is uniformly regarded as multi-energy coupling elements.

[0027] The state of a multi-energy coupling element is used to characterize its operating features. For example, if the state ordinal number of a cogeneration unit is 1, it indicates that the cogeneration unit is in a completely failed state and cannot operate at all, with an output of 0. If the state ordinal number of a cogeneration unit is k, it indicates that the cogeneration unit is in a perfect operating state, with an output of 100% of the rated output of the cogeneration unit. If the state ordinal number of a cogeneration unit is x, x = 1 to k, it indicates that the cogeneration unit is in a partially failed state, with an output of (x / k)% of the rated output of the cogeneration unit.

[0028] The state of each multi-energy coupling element is represented by parameter p. i,j and w i,j In this representation, i represents the sequence number of the multi-energy coupling element, j represents the state sequence number, and p... i,j w represents the probability that multi-energy coupled element i is in state j. i,j Let w represent the output force of multi-energy coupling element i when it is in state j; and the output force of each multi-energy coupling element is characterized by multiple energy parameters. The output force of any multi-energy coupling element can be completely characterized by V energy parameters. i,j Characterized as in This represents the output power corresponding to energy parameter v when the multi-energy coupling element i is in state j, where v represents the sequence number of the energy parameter. The output power of a multi-energy coupling element i is formed by the superposition of multiple energy parameters.

[0029] In practice, the energy parameters are specifically categorized into electrical energy parameters, thermal energy parameters, cold energy parameters, and natural gas energy parameters, but are not limited to these. For example, the output power of a combined cooling, heating, and power (CCHP) unit needs to be characterized by three energy parameters: cold energy parameters, thermal energy parameters, and electrical energy parameters. CCHP units include both electrical and thermal energy parameters, while natural gas units include only electrical energy parameters.

[0030] In the first step, the multi-state reliability model is represented by a multi-dimensional general generating function, including:

[0031] Reliability of individual multi-energy coupling element i in the unit subsystem and line subsystem:

[0032]

[0033] In the formula, u i (z) represents the multi-state reliability function of the multi-energy coupled element i, characterized by a general generating function, p i,j Let i represent the probability that the multi-energy coupled element i is in state j. The output power of the multi-energy coupling element i in state j is represented by the z-transformation form, w i,j Let z represent the output power of the multi-energy coupling element i when it is in state j, z represent the z transformation parameter, and k represent the total number of states of the multi-energy coupling element i.

[0034] The reliability of a single multi-energy coupled element i under the output power corresponding to a single energy parameter v in the unit subsystem and the line subsystem is transformed from a multi-dimensional variable to a one-dimensional variable:

[0035]

[0036] In the formula, This represents the multi-state reliability function of the multi-energy coupled element i under the output power corresponding to the energy parameter v, characterized by a general generating function. This represents the output power corresponding to the energy parameter v when the multi-energy coupling element i is in state j.

[0037] In the prior art, a one-dimensional variable is used to represent the state of a comprehensive energy system. However, in the processing of comprehensive energy systems in this invention, the state of the comprehensive energy system is represented by V energy parameters, using multi-dimensional variables. The general generating function is extended from the existing one-dimensional function to a multi-dimensional function.

[0038] In the second step, the Gaussian approximation is used to approximate the reliability of the multi-state reliability model of the integrated energy system. The reliability of both the unit subsystem and the transmission subsystem is approximated as a multidimensional Gaussian function, forming a multi-state Gaussian approximation reliability model, including the following:

[0039] The multidimensional Gaussian function of the reliability of the unit subsystem:

[0040]

[0041]

[0042] In the formula, This represents the probability density function of the output power distribution of the unit subsystem; This represents the output power of the unit subsystem characterized by V energy parameters. This represents the output power of the unit subsystem under the corresponding energy parameter v; μ W1 This represents the average output power of the n multi-energy coupled units contained in the unit subsystem. This represents the average output of the n multi-energy coupled units contained in the unit subsystem under the output power corresponding to the energy parameter v. ∑1 represents the mean output of one multi-energy coupled unit within the unit subsystem under the output power corresponding to energy parameter v; ∑1 represents the variance of the output power of n multi-energy coupled units within the unit subsystem. This represents the variance of the output power of the n multi-energy coupled units contained in the unit subsystem under the output power corresponding to the energy parameter v; This represents the variance of the output power of a multi-energy coupled unit contained in the unit subsystem under the output power corresponding to the energy parameter v.

[0043] The multidimensional Gaussian function of the reliability of the transmission subsystem:

[0044]

[0045]

[0046] In the formula, This represents the probability density function of the output force distribution of the transmission subsystem; This represents the output power of the transmission subsystem characterized by V energy parameters. This represents the output power of the transmission subsystem under the corresponding output power of the energy parameter v; μ W2 This represents the average output power of the n multi-energy coupled transmission lines contained in the transmission subsystem. This represents the average output power of the n multi-energy coupled transmission lines contained in the transmission subsystem under the output power corresponding to the energy parameter v. ∑2 represents the mean output power of a single multi-energy coupled transmission line in the transmission subsystem under the output power corresponding to energy parameter v; ∑2 represents the variance of the output power of n multi-energy coupled transmission lines in the transmission subsystem. This represents the variance of the output power of the n multi-energy coupled transmission lines contained in the transmission subsystem under the output power corresponding to the energy parameter v. This represents the variance of the output power of a multi-energy coupled transmission line included in the transmission subsystem under the output power corresponding to the energy parameter v.

[0047] The mean value of the unit subsystem and variance The following formula is used to calculate:

[0048]

[0049]

[0050] In the formula, Let represent the derivative of the general generating function of multi-energy coupled unit i within the unit subsystem when the z-transform parameter is 1, after taking the first derivative. This represents the derivative of the general generating function of multi-energy coupled unit i within the unit subsystem, with the z-transform parameter set to 1 after taking the second derivative; | z＝1 This means that the z-transform parameter is equal to 1 and substituted.

[0051] The mean value of the line subsystem and variance The following formula is used to calculate:

[0052]

[0053]

[0054] In the formula, Let represent the derivative of the general generating function of the multi-energy coupled transmission line i within the line subsystem when the z-transform parameter is 1, after taking the first derivative. It represents the derivative of the general generating function of multi-energy coupled transmission line i within the line subsystem when the z-transform parameter is 1, after taking the second derivative.

[0055] In the third step, a sampling approximation is used to approximate the reliability of the multi-state Gaussian approximation reliability model, thus forming a multi-state sampling approximation reliability model, as follows:

[0056] Using the 3σ sampling method based on the sampling principle, the multidimensional Gaussian function of the unit subsystem reliability is sampled, and the reliability of the unit subsystem is sampled as follows: There are several states, and the output force in each state is represented as... in Represents the unit subsystem A set of states, This represents the output power of the j-th state of the unit subsystem. This represents the output power of the unit subsystem in the j-th state under the output power corresponding to the energy parameter v.

[0057] The probability that the unit subsystem is in the j-th state at the same time The following formula is used to calculate:

[0058]

[0059] In the formula, Indicates output force Equal to the output force of the j-th state The numerical value of the probability density function of the output power distribution of the unit subsystem at that time; This represents the total number of states in the unit's subsystems, where 's' represents the state number.

[0060] Similarly, using the 3σ sampling principle, the multidimensional Gaussian function of the transmission subsystem reliability is sampled, and the reliability of the transmission subsystem is sampled as follows: There are several states, and the output force in each state is represented as... in Represents the transmission subsystem A set of states, This represents the output power of the j-th state of the transmission subsystem. This represents the output power of the transmission subsystem in the j-th state under the output power corresponding to the energy parameter v.

[0061] The probability that the transmission subsystem is in the j-th state at the same time The following formula is used to calculate:

[0062]

[0063] In the formula, Indicates output force Equal to the output force of the j-th state The numerical value of the probability density function of the output power distribution of the transmission subsystem at that time.

[0064] The number of states of multi-energy coupling elements is excessive. After a large number of multi-energy coupling elements are connected to the grid, the number of states of the generator subsystem and the transmission subsystem of the integrated energy system is often difficult to calculate. Therefore, this invention reduces the number of states of the generator subsystem and the transmission subsystem, i.e., the number of samples, through Gaussian approximation and sampling approximation procedures. The number of states is much smaller than the original, unprocessed states of the unit subsystem and the line subsystem, thus accelerating the computation.

[0065] In the fourth step, a modified general generating function method is used to quickly calculate the reliability of the integrated energy system that meets different multi-energy load requirements, as detailed below:

[0066] Based on the multi-state reliability model of the multi-energy coupling element i obtained in the first step, the output power and probability of the unit subsystem and the transmission subsystem under each state are obtained based on the Gaussian approximation and sampling approximation in the second and third steps. The output power and probability of the unit subsystem and the transmission subsystem under each state are approximate and are not the exact states of the two subsystems. Furthermore, the states of the two subsystems are multi-dimensional variables. The exact representation is further corrected to an approximate multi-dimensional representation.

[0067] In this invention, the generator subsystem and transmission subsystem are connected in series. The output power of the entire integrated energy system should be determined by the system with the smaller output power in order to ensure the safe operation of each subsystem.

[0068] Based on the multi-state sampling approximate reliability models of the unit subsystem and transmission subsystem obtained in step three, a multi-state approximate reliability model of the integrated energy system considering multi-energy coupling is established as follows:

[0069]

[0070] In the formula, This represents a multi-state approximate reliability function characterized by an approximate multidimensional universal generating function for a comprehensive energy system considering multi-energy coupling, where K represents the total number of states of the comprehensive energy system after processing, and p sys,j W represents the probability that the integrated energy system is in state j. sys,j This represents the output power of the integrated energy system characterized by V energy parameters in state j. This represents the output power of the integrated energy system in state j under the output power corresponding to energy parameter v.

[0071] The following relationship is obtained using a multi-state approximate reliability model of an integrated energy system, and the probability p is calculated. sys,j and output power W sys,j :

[0072]

[0073]

[0074] According to probability p sys,j and output power W sys,jThe output of the integrated energy system to meet the multi-energy load demand is calculated using the following formula. The sum of probabilities corresponding to the approximate state As an approximate reliability of a comprehensive energy system:

[0075]

[0076]

[0077] In the formula, Given the known multi-energy load demand, w *v This represents the load demand under the output power corresponding to the energy parameter v; The function representing the comparison between multi-energy load demand and output power is given if and only if the output power W under each energy parameter is given. sys,j All greater than or equal to When, function Select 1, otherwise function All values ​​are set to 0.

[0078] The multi-energy load demand and output comparison function Represented as:

[0079]

[0080] In the formula, ∧ represents the logical operation and sum.

[0081] The beneficial effects of this invention are:

[0082] The multi-state reliability model of the integrated energy system of the present invention integrates multiple energy sources into the state of the system, and corrects the one-dimensional state into a multi-dimensional state.

[0083] The multi-state approximate reliability model for integrated energy systems of the present invention represents the state of the system as an approximate state, thereby solving the problem of complex reliability calculation of integrated energy systems caused by an excessive number of components.

[0084] The multi-state approximate reliability assessment method for integrated energy systems of the present invention uses a multi-dimensional approximate general generating function method to characterize the multi-state approximate reliability model of multi-energy coupled components and integrated energy systems, thereby realizing rapid calculation of system reliability.

[0085] This invention enables accurate calculation of the reliability of integrated energy systems that consider multi-energy coupling, reducing computation time. Attached Figure Description

[0086] Figure 1 This is a flowchart of the present invention.

[0087] Figure 2 This is a schematic diagram of the equivalent system structure of an embodiment. Detailed Implementation

[0088] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0089] The embodiments of the present invention are as follows:

[0090] Step 1: Propose a multi-state reliability model for an integrated energy system considering multi-energy coupling, and characterize the multi-state reliability model of the integrated energy system in the form of a multi-dimensional general generating function;

[0091] In the integrated energy system, the unit subsystem comprises 15 identical combined heat and power (CHP) units, and the transmission subsystem comprises 15 identical urban utility tunnels. The entire integrated energy system is characterized by two energy parameters: heat and electricity (elec), with units in megawatts (MW). Each CHP unit and urban utility tunnel has six states. A general generating function characterizes the multi-state reliability model of a single CHP unit or urban utility tunnel considering only the electricity or heat energy parameters, as follows:

[0092]

[0093]

[0094]

[0095]

[0096] Step 2: Use Gaussian approximation to approximate the reliability of the multi-state reliability model of the integrated energy system, and characterize the multi-state approximate reliability model of the integrated energy system in the form of an approximate multidimensional general generating function.

[0097] By applying the law of large numbers and Gauss's law, the reliability of the unit subsystem can be approximated as a multidimensional Gaussian function:

[0098]

[0099] By applying the law of large numbers and Gauss's law, the reliability of the circuit subsystem can be approximated as a multidimensional Gaussian function:

[0100]

[0101] Step 3: Use sampling approximation to approximate the reliability of the multi-state reliability model of the integrated energy system, and characterize the multi-state approximate reliability model of the integrated energy system in the form of an approximate multidimensional general generating function.

[0102] The multidimensional Gaussian function of the unit subsystem reliability is sampled using the 3σ sampling principle, and the unit subsystem reliability is sampled into 2001 states. The multidimensional Gaussian function of the line subsystem reliability is also sampled, and the line subsystem reliability is sampled into 2001 states.

[0103] Step 4: Based on the multi-state approximate reliability model obtained in Step 2 and Step 3, a fast reliability assessment algorithm is proposed to quickly calculate the reliability of the integrated energy system that meets different multi-energy load requirements.

[0104] The system's multi-energy load requirements are That is, at least 150MW of thermal energy and 380MW of electrical energy are required for the system to operate reliably. Based on the multi-state approximate reliability model of the integrated energy system considering multi-energy coupling, the approximate reliability of the integrated energy system can be calculated to be 0.9806.

[0105] The following table compares the calculation accuracy of the implementation results of this embodiment with that of the traditional method (Monte Carlo simulation method):

[0106] Table 1 Accuracy Comparison

[0107]

[0108] Therefore, it can be seen that the method of the present invention has similar computational accuracy to the traditional method, meets the requirements of reliability computational accuracy, and is practical.

[0109] The results of this implementation are compared with those of the traditional method (Monte Carlo simulation) in terms of computation time, as shown in the table below:

[0110] Table 2 Time Comparison

[0111]

[0112] Therefore, the method of the present invention is faster in terms of time and is more suitable for reliability calculation of large-scale systems.

Claims

1. A method for rapidly improving the multi-state reliability of a multi-energy coupled integrated energy system, characterized in that: Step 1: Establish a multi-state reliability model for the integrated energy system that considers the coupling of multiple energy sources, and characterize it using a multi-dimensional general generating function. Step 2: Apply Gaussian approximation to the multi-state reliability model obtained in Step 1 to obtain a multi-state Gaussian approximation reliability model, and represent it in the form of an approximate multidimensional general generating function; Step 3: Use sampling approximation to perform reliability approximation on the multi-state Gaussian approximation reliability model obtained in step 2 to obtain a multi-state sampling approximation reliability model, and characterize it in the form of an approximate multidimensional general generating function; Step 4: Based on the multi-state sampling approximate reliability model obtained in Step 3, the approximate reliability of the integrated energy system that meets the requirements of different multi-energy loads is calculated using a fast reliability assessment algorithm; Step 5: Utilize approximate reliability to establish reliability constraints for integrated energy system structure planning and backup optimization scheduling, thereby improving the reliability of the integrated energy system; In the second step, the Gaussian approximation is used to approximate the reliability of the multi-state reliability model of the integrated energy system. The reliability of both the unit subsystem and the transmission subsystem is approximated as a multidimensional Gaussian function, forming a multi-state Gaussian approximation reliability model, including the following: The multidimensional Gaussian function of the reliability of the unit subsystem: In the formula, This represents the probability density function of the output power distribution of the unit subsystem; This represents the output power of the unit subsystem characterized by V energy parameters. , This indicates the output power of the unit subsystem under the corresponding energy parameter v. This represents the average output power of the n multi-energy coupled units contained in the unit subsystem. , This represents the average output of the n multi-energy coupled units contained in the unit subsystem under the output power corresponding to the energy parameter v. This represents the average output of a multi-energy coupled unit within the unit subsystem under the output power corresponding to the energy parameter v. This represents the variance of the output power of the n multi-energy coupled units contained in the unit subsystem. This represents the variance of the output power of the n multi-energy coupled units contained in the unit subsystem under the output power corresponding to the energy parameter v; This represents the variance of the output power of a multi-energy coupled unit contained in the unit subsystem under the output power corresponding to the energy parameter v; The multidimensional Gaussian function of the reliability of the transmission subsystem: In the formula, This represents the probability density function of the output force distribution of the transmission subsystem; This represents the output power of the transmission subsystem characterized by V energy parameters. , This represents the output power of the transmission subsystem under the corresponding output power of energy parameter v; This represents the average output power of the n multi-energy coupled transmission lines contained in the transmission subsystem. , This represents the average output power of the n multi-energy coupled transmission lines contained in the transmission subsystem under the output power corresponding to the energy parameter v. This represents the average output of a single multi-energy coupled transmission line within the transmission subsystem, corresponding to the output power of the energy parameter v. This represents the variance of the output power of the n multi-energy coupled transmission lines contained in the transmission subsystem. This represents the variance of the output power of the n multi-energy coupled transmission lines contained in the transmission subsystem under the output power corresponding to the energy parameter v. This represents the variance of the output power of a single multi-energy coupled transmission line contained in the transmission subsystem under the output power corresponding to the energy parameter v. In the third step, a sampling approximation is used to approximate the reliability of the multi-state Gaussian approximation reliability model, thus forming a multi-state sampling approximation reliability model, as follows: The multidimensional Gaussian function of the unit subsystem reliability is sampled, and the reliability of the unit subsystem is sampled as follows: There are several states, and the output force in each state is represented as... ,in Represents the unit subsystem A set of states, This represents the output power of the j-th state of the unit subsystem. , This represents the output power of the unit subsystem in the j-th state under the output power corresponding to the energy parameter v. The probability that the unit subsystem is in the j-th state at the same time The following formula is used to calculate: In the formula, Indicates output force Equal to the output force of the j-th state The numerical value of the probability density function of the output power distribution of the unit subsystem at that time; This represents the total number of states in the unit's subsystems, where 's' represents the state number. The reliability of the transmission subsystem is sampled using a multidimensional Gaussian function, and the reliability of the transmission subsystem is sampled as follows: There are several states, and the output force in each state is represented as... ,in Represents the transmission subsystem A set of states, This represents the output power of the j-th state of the transmission subsystem. , This represents the output power of the transmission subsystem in the j-th state under the output power corresponding to the energy parameter v. The probability that the transmission subsystem is in the j-th state at the same time The following formula is used to calculate: In the formula, Indicates output force Equal to the output force of the j-th state The numerical value of the probability density function of the output force distribution of the transmission subsystem at that time; In the fourth step, a modified general generating function method is used to quickly calculate the reliability of the integrated energy system that meets different multi-energy load requirements, as detailed below: Based on the multi-state sampling approximate reliability models of the unit subsystem and transmission subsystem obtained in step three, a multi-state approximate reliability model of the integrated energy system considering multi-energy coupling is established as follows: In the formula, This represents a multi-state approximate reliability function for a comprehensive energy system considering multi-energy coupling, characterized by an approximate multidimensional universal generating function. K represents the total number of states in the comprehensive energy system after processing. Let represent the probability that the integrated energy system is in state j. This represents the output power of the integrated energy system characterized by V energy parameters in state j. , This represents the output power of the integrated energy system in state j under the output power corresponding to energy parameter v. The following relationships were obtained using a multi-state approximate reliability model of an integrated energy system, and the probabilities were calculated. and output power : According to probability and output power The output of the integrated energy system to meet the multi-energy load demand is calculated using the following formula. The sum of probabilities corresponding to the approximate state As an approximate reliability of a comprehensive energy system: In the formula, To meet the needs of multi-energy loads, This represents the load demand under the output power corresponding to the energy parameter v; This function represents a comparison between demand and output of multi-energy loads. The multi-energy load demand and output comparison function Represented as: In the formula, This represents logical operations and.

2. The method for rapidly improving the multi-state reliability of a multi-energy coupled integrated energy system according to claim 1, characterized in that: The first step, the multi-state reliability model, specifically includes: The integrated energy system is equivalent to a structure consisting of a generator subsystem and a transmission line subsystem connected in series. Both the generator subsystem and the transmission line subsystem are composed of multi-energy coupling elements connected in parallel, and each multi-energy coupling element has k states. The multi-energy coupling elements are divided into multi-energy coupling generators and multi-energy coupling transmission lines. The generator subsystem is composed of multi-energy coupling generators connected in parallel, and the transmission line subsystem is composed of multi-energy coupling transmission lines connected in parallel. The state of each multi-energy coupling element is represented by parameters. and express, Let i represent the probability that the multi-energy coupled element i is in state j. This represents the output force of multi-energy coupling element i when it is in state j; and the output force of each multi-energy coupling element is characterized by multiple energy parameters. Characterized as ,in This represents the output power corresponding to energy parameter v when the multi-energy coupling element i is in state j, where v represents the sequence number of the energy parameter.

3. The method for rapidly improving the multi-state reliability of a multi-energy coupled integrated energy system according to claim 1, characterized in that: In the first step, the multi-state reliability model is represented by a multi-dimensional general generating function, including: Reliability of individual multi-energy coupling element i in the unit subsystem and line subsystem: In the formula, This represents the multi-state reliability function characterized by a general generating function for the multi-energy coupled element i. Let i represent the probability that the multi-energy coupled element i is in state j. This represents the output force of the multi-energy coupling element i in state j, expressed as a z-transform. Let z represent the output power of the multi-energy coupling element i when it is in state j, z represent the z transformation parameter, and k represent the total number of states of the multi-energy coupling element i. Reliability of a single multi-energy coupled element i under the output power corresponding to a single energy parameter v in the unit subsystem and the line subsystem: In the formula, This represents the multi-state reliability function of the multi-energy coupled element i under the output power corresponding to the energy parameter v, characterized by a general generating function. This represents the output power corresponding to the energy parameter v when the multi-energy coupling element i is in state j.

4. The method for rapidly improving the multi-state reliability of a multi-energy coupled integrated energy system according to claim 1, characterized in that: The mean value of the unit subsystem and variance The following formula is used to calculate: In the formula, Let represent the derivative of the general generating function of multi-energy coupled unit i within the unit subsystem when the z-transform parameter is 1, after taking the first derivative. This represents the derivative of the general generating function of multi-energy coupled unit i within the unit subsystem when the z-transform parameter is 1 after taking the second derivative. This means that the z-transform parameter is equal to 1 and substituted. The mean value of the line subsystem and variance The following formula is used to calculate: In the formula, Let represent the derivative of the general generating function of the multi-energy coupled transmission line i within the line subsystem when the z-transform parameter is 1, after taking the first derivative. It represents the derivative of the general generating function of multi-energy coupled transmission line i within the line subsystem when the z-transform parameter is 1, after taking the second derivative.

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