Multi-energy coupling and conversion integrated energy system multi-state reliability evaluation method

Abstract

The application discloses a kind of multi-energy coupling and conversion comprehensive energy system multi-state reliability evaluation method.Establish the multi-state reliability model of multi-energy conversion element considering multi-energy conversion, using clustering algorithm reliability aggregation, and mapping formation aggregated multi-state reliability model;Multi-state reliability model of multi-energy coupling element considering multi-energy coupling is established, and then using Gaussian approximation and sampling approximation reliability approximation processing in sequence obtains multi-state approximate reliability model;The aggregated multi-state reliability model and multi-state approximate reliability model are processed according to the fast reliability evaluation method to obtain the reliability of the comprehensive energy system, and the reliability evaluation is realized.The application improves the traditional reliability evaluation method from time and accuracy, reduces the calculation time, and quickly and effectively realizes the estimation of system reliability.

Description

Technical Field

[0001] This invention relates to a system reliability extraction method in the field of integrated energy systems, specifically a rapid multi-state reliability assessment method for integrated energy systems that considers multi-energy coupling and conversion. 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 within 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 management systems), 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 its ability to supply energy 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 accelerating pace of modernization, production and daily life are increasingly reliant on various types of gas, heat, cooling, and electricity energy, and the losses caused by the interruption of these energy sources are also growing. Therefore, integrated energy systems are required to have high reliability.

[0004] Reliability assessment involves calculating and analyzing the probabilities 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 to cut off the failure severity, i.e., selecting failure states with 2 or fewer severity levels and ignoring 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. Taking an IEEE-RTS system with 71 components as an example, when the components adopt a 2-state model, the number of system states considering N-3 is 57226, and the sum of probabilities is 0.95110503. In reality, the top 57,226 states with higher probabilities include 16,786 states with 0 to 3 faults and 40,440 states with 4 to 6 faults, with a sum of probabilities of 0.98976138. These high-probability fault states have high probabilities and severe consequences, significantly impacting system reliability. State selection based on the cutoff fault count ignores these high-probability faults. Therefore, in reliability analysis, the number of states selected, whether retained or deleted, greatly affects the final result. This underscores the necessity of researching rapid and accurate reliability analysis for systems.

[0005] Current reliability assessment methods mainly focus on power system reliability analysis. However, with the introduction of multiple coupled energy sources, power system reliability analysis that can only assess electrical energy is no longer applicable to the reliability analysis 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 assessment method that can be used to calculate the reliability of integrated energy systems considering multiple energy couplings.

[0006] Meanwhile, with the introduction of multiple coupled energy sources, multi-energy conversion is a typical feature of integrated energy systems. Different energy sources can be transformed into other energy sources through multi-energy conversion processes, thereby achieving complex coupling between energy sources. Reliability analysis of power systems that can only assess electrical energy is no longer applicable to the reliability analysis of integrated energy systems involving multi-energy conversion processes. The conversion and coupling of multiple energy sources such as gas, heat, cooling, and electricity need to be reflected in the reliability assessment simultaneously. Traditional reliability assessment methods cannot evaluate the reliability of integrated energy systems with multi-energy conversion. Therefore, this invention proposes a new reliability assessment method that can be used to calculate the reliability of integrated energy systems considering multi-energy conversion.

[0007] Existing algorithms can be used to calculate the reliability of integrated energy systems, but 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 model cannot accurately reflect the system's reliability. This invention proposes a multi-state reliability model for integrated energy systems that considers multi-energy coupling and conversion, incorporating multiple intermediate states of the integrated energy system's operation into the system's reliability.

[0008] Current algorithms for calculating the reliability of integrated energy systems often face the challenge of drastically increasing computation time as system size expands, thus reducing the practicality of reliability assessments. 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 fast multi-state reliability algorithm for integrated energy systems that considers multi-energy coupling and conversion, enabling rapid reliability assessment of integrated energy systems.

[0009] The shortcomings of existing technologies are summarized as follows:

[0010] Disadvantage of existing technology 1: Traditional reliability calculation algorithms mainly focus on power system reliability analysis. However, with the introduction of multiple coupled energy sources, power system reliability analysis that can only evaluate electrical energy is no longer applicable to the reliability analysis of integrated energy systems containing multiple energy conversions.

[0011] Disadvantage 2 of existing technology: Traditional reliability calculation algorithms mainly focus on power system reliability analysis. However, with the increasing complexity of the conversion process of multiple coupled energy sources, power system reliability analysis that can only evaluate electrical energy is no longer applicable to the reliability analysis of integrated energy systems containing multiple energy conversion processes.

[0012] Disadvantage 3 of existing technology: Existing technology treats 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 model cannot accurately reflect the reliability of the integrated energy system.

[0013] Disadvantage 4 of existing technology: When calculating the reliability of integrated energy systems, existing technologies 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

[0014] To address the shortcomings of existing technologies, this invention proposes a rapid multi-state reliability assessment method for integrated energy systems that considers multi-energy coupling and conversion. This method improves upon traditional reliability assessment methods in terms of both time and accuracy, reduces computation time, and enables rapid and effective estimation of system reliability.

[0015] like Figure 1 As shown, the technical solution of the present invention is as follows:

[0016] Step 1: Establish a multi-state reliability model of multi-energy conversion components for a comprehensive energy system considering multi-energy conversion, and represent it using a matrix-based general generating function; use a clustering algorithm to aggregate the reliability of the multi-state reliability model of multi-energy conversion components, and map it to form an aggregated multi-state reliability model, which is also represented using a matrix-based general generating function.

[0017] The second step is to establish a multi-state reliability model of the multi-energy coupling components of the integrated energy system that considers multi-energy coupling, and to represent it in the form of a multi-dimensional general generating function; then, the reliability approximation model of the multi-energy coupling components is successively processed by Gaussian approximation and sampling approximation to obtain a multi-state approximate reliability model, and to represent it in the form of an approximate multi-dimensional general generating function.

[0018] The third step is to aggregate the multi-state reliability model and the multi-state approximate reliability model, and then process them quickly using a rapid reliability assessment method to obtain the reliability of the integrated energy system that meets the needs of different multi-energy loads, thus achieving reliability assessment.

[0019] In practice, reliability can be further utilized to establish reliability constraints for the structural planning and backup optimization scheduling of integrated energy systems, thereby improving the reliability of integrated energy systems.

[0020] 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.

[0021] In this invention, the bolded letter variables all represent vectors. The multi-energy coupling element takes into account multi-energy coupling, and the output of the multi-energy coupling element is extended from one-dimensional output to multi-dimensional output, ensuring that multiple energy sources can be universally represented through a single output form.

[0022] The multi-energy conversion element takes into account multiple energy conversions. The variables of the multi-energy conversion element are extended from one-dimensional variables to matrix variables, ensuring that multiple energy sources can be universally represented through matrix elements.

[0023] When the number of states of multiple energy conversion elements or multiple energy coupling elements is too large, it is often difficult to calculate the number of states of the multiple energy conversion subsystem and the multiple energy coupling subsystem after a large number of multiple energy conversion elements or multiple energy coupling elements are connected in parallel. Therefore, this invention reduces the number of states of the elements by combining Gaussian approximation, sampling approximation and clustering algorithm, thereby accelerating the calculation.

[0024] The integrated energy system is equivalent to a structure consisting of a series connection of multiple energy conversion subsystems and multiple energy coupling subsystems. The multiple energy conversion subsystems are composed of multiple energy conversion elements connected in parallel, and the multiple energy coupling subsystems are composed of multiple energy coupling elements connected in parallel.

[0025] Integrated energy system equipment is divided into two main categories: multi-energy conversion equipment (i.e., multi-energy conversion elements, the most common of which are various multi-energy conversion units, such as cogeneration units, natural gas units, and combined cooling, heating and power units) and multi-energy coupling equipment (i.e., multi-energy coupling elements, the most common of which are various multi-energy production equipment and multi-energy transmission lines, such as natural gas sources and urban integrated pipe corridors).

[0026] The functions of the multi-energy conversion subsystem and the multi-energy coupling subsystem operate sequentially, equivalent to a series structure: within an integrated energy system, multiple energy conversion devices must first convert and generate various energy sources before these energy sources can be transmitted to users via energy coupling devices. Energy sources include electrical energy parameters, thermal energy, cooling energy, natural gas energy, etc.

[0027] Because multiple energy conversion devices can operate simultaneously within an integrated energy system, generating output power, these devices are connected in parallel to form a multi-energy conversion subsystem. If the output power of the energy conversion devices is too large, and the transmission capacity of a single energy transmission line is limited, multiple energy coupling devices are needed to simultaneously transmit the unit's output power. Therefore, these multiple energy coupling devices are connected in parallel to form an energy coupling subsystem.

[0028] The first step is specifically as follows:

[0029] The energy conversion equipment in an integrated energy system (including combined heat and power units, natural gas units, and combined cooling, heating and power units) is collectively referred to as multi-energy conversion elements. Multi-energy conversion elements involve multi-energy conversion processes. These processes refer to the conversion of one type of input energy capacity into other forms of output energy through the multi-energy conversion process. For example, in a combined heat and power unit, natural gas, as the input energy, is transformed into both electricity and heat as output energy through the multi-energy conversion process.

[0030] As multi-energy conversion elements age, their energy conversion capacity also decreases, and their operating state becomes multi-state, with each element having *s* states. These states characterize the operational features of the multi-energy conversion element. For example, a state number of 1 indicates complete failure, meaning the element is completely inoperable and has an output of 0. A state number of *s* indicates perfect operation, with an output of 100% of its rated capacity. A state number of *x*, where *x* = 1 to *s*, indicates partial failure, with an output of (x / s)% of its rated capacity.

[0031] S11. In the established multi-energy conversion element multi-state reliability model, the multi-energy conversion elements are connected in parallel to form a multi-energy conversion subsystem, and the state of each multi-energy conversion element is represented by parameters. and C i,j This indicates that i represents the serial number of the multi-energy conversion element, and j represents the state serial number. C represents the probability that the multi-energy conversion element i is in state j. i,j The energy conversion matrix represents the energy conversion capability between the input and output energy parameters of the multi-energy conversion element i when it is in state j.

[0032] The input energy capacity of each multi-energy conversion element is characterized by V energy parameters, and the output energy output is also characterized by V energy parameters. The energy parameters represent the type of energy, such as natural gas, cold energy, heat energy, and electrical energy. For example, the input energy capacity of a multi-energy conversion element is characterized by two energy parameters, natural gas and electricity, indicating that the element inputs natural gas energy and electrical energy.

[0033] The energy conversion capability from the energy parameter v of the input multi-energy conversion element to the energy parameter v' of the output multi-energy conversion element is expressed as c. v,v' The energy conversion capability refers to the probability that the energy parameter v of the input multi-energy conversion element is converted into the energy parameter v' of the output multi-energy conversion element. Therefore, the energy conversion capability between the various input and output energy parameters of the multi-energy conversion element i is determined by the energy conversion matrix C. i express, This represents the energy conversion capability from the energy parameter v of the multi-energy conversion element i to the energy parameter v' of the output multi-energy conversion element. V×V The matrix C represents the set of V×V elements contained in the matrix, i.e., the energy conversion matrix. i There are V rows and V columns, and each matrix element uses... This indicates that v = 1 to V and v' = 1 to V.

[0034] In S11, the states of the multiple energy conversion elements are represented by a matrix-based energy conversion matrix. The general generating function is extended from a one-dimensional function to a matrix-represented function. The multi-state reliability model of the multiple energy conversion element i is represented by a matrix-based general generating function, i.e.:

[0035]

[0036] In the formula, Let represent the multi-state reliability function characterized by a matrix-based general generating function for the multi-energy conversion element i, and s represent the total number of states of the multi-energy conversion element.

[0037] S12,

[0038] For each multi-energy conversion element, all states of the multi-energy conversion element are aggregated using the K-means clustering algorithm, so that a total of s original states of the multi-energy conversion element are clustered into K aggregate sets;

[0039] The aggregation process is as follows: First, randomly select K original states from the s original states as the initial centers of K aggregation sets. Second, calculate the center of each original state and each initial center, and assign each original state to the aggregation set corresponding to its nearest initial center, thus dividing the s original states into K aggregation sets. Third, take the average value of the energy conversion matrix corresponding to the original states contained in each aggregation set as the new center of that aggregation set, return to the second step, and divide the s original states into K aggregation sets again. Repeat the third and second steps until the center of each aggregation set no longer changes, then stop the iteration and output the K aggregation sets.

[0040] For each aggregate set a, each aggregate set a contains K a The original state, namely 'a' represents the index of the aggregate set. An aggregate state is composed of all the original states contained in aggregate set 'a'. The average value of the energy conversion matrices corresponding to the original states contained in aggregate set 'a' is used as the clustering energy conversion matrix of the aggregate state. The probability of the aggregate state is the sum of the probabilities of the original states contained in the aggregate set a. The sum of the probabilities of the aggregate states corresponding to all aggregate sets a is equal to 1;

[0041] After K-means clustering, the energy conversion capability of multiple energy conversion elements is characterized as K clustered energy conversion matrices. and their corresponding probabilities

[0042]

[0043] In the formula, This represents the energy conversion capability from input energy parameter v to output energy parameter v' of multiple energy conversion element i in cluster state j.

[0044] The input energy capacity of an integrated energy system only requires one energy conversion process. The input energy capacity can be divided into several parts as the input energy capacity of different multi-energy conversion elements, which are then input into different multi-energy conversion elements. The sum of the output energy of these different multi-energy conversion elements is the output energy of the integrated energy system. That is, the multi-energy conversion elements are connected in parallel, and the output energy of the multi-energy conversion subsystem of the integrated energy system is the sum of the output energy of each element.

[0045] If the number of states of a multi-energy conversion element is too large, the number of states of the multi-energy conversion subsystem is often difficult to calculate after a large number of multi-energy conversion elements are connected in parallel. This invention uses the K-means clustering algorithm to cluster the s-state reliability of the multi-energy conversion element into K states. Usually, K is much smaller than s, thereby reducing the number of states of the element and accelerating the calculation.

[0046] In step S12, the clustering states of the multiple energy conversion elements and their corresponding probabilities are known. The aggregated multi-state reliability model of the multiple energy conversion element i is characterized by a matrix-based general generating function, namely:

[0047]

[0048] In the formula, The aggregated multi-state reliability function represents the multi-energy conversion element i using a matrix-based general generating function, where K represents the total number of aggregated states.

[0049] Following the first step, the following aggregated multi-state reliability model considering multiple energy conversions is established:

[0050]

[0051] In the formula, This represents the aggregated multi-state reliability function of the multi-energy conversion subsystem considering multiple energy conversions, characterized by a matrix general generating function. G represents the total number of aggregated states of the multi-energy conversion subsystem after processing. This represents the probability that the multi-energy conversion subsystem is in aggregation state j. The clustered energy conversion matrix represents the multi-energy conversion subsystem in aggregation state j, where n represents the number of multi-energy conversion elements contained in the multi-energy conversion subsystem; j i This represents the aggregation state number of the i-th multi-energy conversion element, where i is the number of n elements contained in the multi-energy conversion subsystem. oThe serial number of each energy conversion element, i.e. n o This indicates the number of multi-energy conversion elements contained in the multi-energy conversion subsystem. This indicates that the i-th multi-energy conversion element is in aggregation state j. i Clustering energy conversion matrix at time, This indicates that the i-th multi-energy conversion element is in the aggregation state j. i 4. The multi-state reliability assessment method for a multi-energy coupling and conversion integrated energy system according to claim 1, characterized in that:

[0052] The second step is as follows:

[0053] The energy coupling equipment of an integrated energy system (including urban integrated pipe corridors, etc.) is collectively referred to as a multi-energy coupling element. The multi-energy coupling element takes the output energy from the multi-energy conversion element as its input transmission capacity, and after passing through the multi-energy coupling element, it becomes the output energy of the multi-energy coupling element and is transmitted to the user.

[0054] The second step is as follows:

[0055] The states of multi-energy coupling elements are used to characterize their operational features. Each multi-energy coupling element has k states. For example, if the state number of an urban integrated utility tunnel is 1, it indicates that the tunnel is in a completely failed state, unable to operate, and its output power is 0. If the state number of an urban integrated utility tunnel is k, it indicates that the tunnel is in a perfectly operating state, and its output power is 100% of the tunnel's rated output power. If the state number of an urban integrated utility tunnel is x, x = 1 to k, it indicates that the tunnel is in a partially failed state, and its output power is (x / k)% of the rated output power of the cogeneration unit.

[0056] S21. In the established multi-energy coupling element multi-state reliability model, the multi-energy coupling elements are connected in parallel to form a multi-energy coupling subsystem, and 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 power of multi-energy coupling element i when it is in state j; and the output power of each multi-energy coupling element is characterized by multiple energy parameters (for example, for an urban integrated utility tunnel that transmits three types of energy, namely cold energy, heat energy and electricity, its output power needs to be characterized by three parameters: cold energy, heat energy and electricity). The output power of any multi-energy coupling element can be completely characterized by V energy parameters. The output power w of multi-energy coupling element i when it is in state j i,jCharacterized 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.

[0057] In practice, the energy parameters are specifically divided into electrical energy parameters, thermal energy parameters, and natural gas energy parameters, but are not limited to these.

[0058] In S21, the multi-state reliability model of multi-energy coupled components is characterized by a multi-dimensional general generating function, including:

[0059] Reliability of a single multi-energy coupling element i:

[0060]

[0061] 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 represents the output force of the multi-energy coupling element i when it is in state j, and z represents the z transformation parameter;

[0062] The reliability of a single multi-energy coupled element i under the output power corresponding to a single energy parameter v is transformed from a multi-dimensional variable to a one-dimensional variable:

[0063]

[0064] 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.

[0065] In existing technologies, one-dimensional variables are used to characterize the system state. However, in the processing of multi-energy coupled subsystems in this invention, the state of the multi-energy coupled subsystem is characterized by V energy parameters, which are multi-dimensional variables. The general generating function is extended from the existing one-dimensional function to a multi-dimensional function.

[0066] S22. The Gaussian approximation is used to approximate the reliability of the multi-state reliability model of the multi-energy coupled components. The reliability of the multi-energy coupled subsystem is approximated as a multidimensional Gaussian function, forming a multi-state Gaussian approximation reliability model. The multidimensional Gaussian function of the reliability of the multi-energy coupled subsystem is as follows:

[0067]

[0068]

[0069]

[0070] In the formula, This represents the probability density function of the output power distribution of a multi-energy coupled subsystem. This represents the output power of a multi-energy coupled subsystem characterized by V energy parameters. μ represents the output power of the multi-energy coupled subsystem under the corresponding output power of the energy parameter v; W1 This represents the average output power of the n multi-energy coupling elements contained in the multi-energy coupling subsystem. This represents the average output power of the n multi-energy coupling elements contained in the multi-energy coupling subsystem under the output power corresponding to the energy parameter v; ∑1 represents the mean output of one multi-energy coupling element in the unit subsystem under the output power corresponding to energy parameter v; ∑1 represents the variance of the output power of n multi-energy coupling elements in the multi-energy coupling subsystem. This represents the variance of the output power of the n multi-energy coupling elements contained in the multi-energy coupling 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 element contained in the unit subsystem under the output power corresponding to the energy parameter v.

[0071] The mean value of the multi-energy coupling subsystem and variance The following formula is used to calculate:

[0072]

[0073]

[0074] In the formula, Let represent the derivative of the general generating function of the multi-energy coupling element i in the multi-energy coupling subsystem when the z-transform parameter is 1 after taking the first derivative. This represents the derivative of the general generating function of the multi-energy coupled element i within the unit subsystem when the z-transform parameter is 1, after taking the second derivative; z＝1 This means that the z-transform parameter is equal to 1 and substituted.

[0075] S23. A sampling approximation is used to approximate the reliability of the multi-state Gaussian approximation reliability model, forming a multi-state sampling approximation reliability model, as follows:

[0076] Using a 3σ sampling method based on the sampling principle, the multidimensional Gaussian function of the reliability of the multi-energy coupled subsystem is sampled, and the reliability of the multi-energy coupled subsystem is sampled as follows: There are several states, and the output force in each state is represented as... in Represents a multi-energy coupled subsystem A set of states, This represents the output power of the j-th state of the multi-energy coupled subsystem. This represents the output power of the multi-energy coupled subsystem in the j-th state under the output power corresponding to the energy parameter v.

[0077] The probability that the multi-energy coupled subsystem is in the j-th state. The following formula is used to calculate:

[0078]

[0079] In the formula, Indicates output force Equal to the output force of the j-th state The numerical value of the output power distribution probability density function of the multi-energy coupled subsystem at that time; This represents the total number of states in the multi-energy coupled subsystem, where s represents the state number.

[0080] The output power and probability of the multi-energy coupled subsystem under each state are obtained by sequentially processing the Gaussian approximation and the sampling approximation. The output power and probability of the multi-energy coupled subsystem under each state are approximate and are not the exact state of the subsystem. Moreover, the state of the subsystem is a multi-dimensional variable. The general generating function method corrects the exact representation to an approximate multi-dimensional representation.

[0081] The multi-state sampling approximate reliability model of the multi-energy coupled subsystem finally obtained in S23 is characterized by an approximate multidimensional universal generating function as follows:

[0082]

[0083] In the formula, The multi-state approximate reliability function represents a multi-energy coupled subsystem and is characterized by an approximate multidimensional universal generating function.

[0084] The fifth step employs a rapid reliability assessment algorithm to quickly calculate the reliability of the integrated energy system that meets different multi-energy load requirements, as detailed below:

[0085] The preceding steps yielded an aggregated multi-state reliability model for the multi-energy conversion subsystem considering multiple energy conversions. Since the multi-energy conversion process refers to the conversion of one input energy capacity into other output energy outputs through multiple energy conversion processes, based on the energy conversion capability of the multi-energy conversion subsystem, the input energy capacity of the entire multi-energy conversion subsystem needs to be processed, and then the output energy output of the multi-energy conversion subsystem is calculated, thereby calculating the reliability.

[0086] By combining a multi-state reliability model of a multi-energy conversion subsystem with its input energy capacity, an output energy model of the multi-energy conversion subsystem can be established.

[0087]

[0088]

[0089] In the formula, The output energy of a multi-energy conversion subsystem is represented by a general matrix generating function, which is an aggregated multi-state function. The output energy of the multi-energy conversion subsystem in aggregation state j is represented by V energy parameters. This represents the output energy of the integrated energy system considering energy parameter v under aggregation state j; w in The input energy capacity of the multi-energy conversion subsystem is characterized by V energy parameters. G represents the input energy capacity when the energy parameter is v; G represents the total number of aggregate states of the multi-energy conversion subsystem. This represents the probability that the multi-energy conversion subsystem is in aggregation state j. The clustered energy conversion matrix represents the aggregation state j of the multi-energy conversion subsystem;

[0090] Then, the output energy output of the multi-energy conversion subsystem under the aggregation state j is calculated using the output energy output model of the multi-energy conversion subsystem. Sum of probabilities Thus, the multi-energy conversion subsystem is in a convergent state j, where the output energy is produced. Sum of probabilities It can be calculated as follows:

[0091]

[0092] The multi-energy conversion subsystem and the multi-energy coupling subsystem are connected in series, and the output energy of the multi-energy conversion subsystem is equal to the input transmission capacity of the multi-energy coupling subsystem. Using the output energy of the multi-energy conversion subsystem and the multi-state approximate reliability model of the multi-energy coupling subsystem, a multi-state reliability model of the integrated energy system considering multi-energy coupling and conversion is established as follows:

[0093]

[0094] In the formula, This represents the multi-state reliability function of a comprehensive energy system considering multi-energy coupling and conversion, characterized by an approximate multidimensional universal generating function. M represents the total number of states of the comprehensive energy system after the fusion and rearrangement of the multi-energy conversion subsystem and the multi-energy coupling subsystem. 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 indicates the output power of the integrated energy system in state j under only energy parameter v;

[0095] The number of states of multi-energy conversion elements and multi-energy coupling elements is greatly reduced through the third and fourth steps. This greatly reduces the number of aggregated states of the multi-energy conversion subsystem and the number of approximate states of the multi-energy coupling subsystem, thereby reducing the number of states corresponding to the output power of the integrated energy system. The output power of each state can be calculated quickly.

[0096] Then, using the output energy model of the multi-energy conversion subsystem, the following relationship is obtained, and the probability p of the integrated energy system is calculated. sys,j and output power W sys,j Therefore, the output energy of the multi-energy conversion subsystem can be calculated as follows:

[0097]

[0098] According to probability p sys,j and output power W sys,j 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 system states As a measure of the reliability of an integrated energy system:

[0099]

[0100]

[0101] In the formula, Given the known multi-energy load demand, 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.

[0102] The multi-energy load demand and output comparison function Represented as:

[0103]

[0104] In the formula, ^ represents the logical operation and sum.

[0105] This invention is applied to the reliability calculation of integrated energy systems considering multi-energy coupling and conversion. First, a multi-state reliability model for the integrated energy system considering multi-energy coupling is proposed. Second, a multi-state reliability model for the integrated energy system considering multi-energy conversion is proposed. Then, Gaussian approximation and sampling approximation methods are used to quickly calculate the reliability of the multi-state reliability model of the integrated energy system considering multi-energy coupling. A clustering algorithm is then used to quickly calculate the reliability of the multi-state reliability model of the integrated energy system considering multi-energy conversion. Finally, by integrating the energy coupling and conversion characteristics, the reliability of the integrated energy system is quickly obtained, and the time required is significantly less than that of previous traditional algorithms.

[0106] The beneficial effects of this invention are:

[0107] The integrated energy system multi-state reliability model of the present invention integrates multi-energy conversion into the system state, corrects the one-dimensional state into a matrix state, and also integrates multi-energy coupling into the system state, correcting the one-dimensional state into a multi-dimensional state.

[0108] The integrated energy system aggregation multi-state reliability model of the present invention represents the system state as a clustered state, thereby solving the problem of complex reliability calculation of integrated energy systems caused by too many components; it also represents the system state as an approximate state, thereby solving the problem of complex reliability calculation of integrated energy systems caused by too many components.

[0109] The rapid multi-state reliability assessment method for integrated energy systems of the present invention can accurately calculate the reliability of integrated energy systems that consider multiple energy conversions, thereby reducing calculation time. Attached Figure Description

[0110] Figure 1 This is a flowchart of the present invention.

[0111] Figure 2 This is a schematic diagram of the equivalent system structure of an embodiment. Detailed Implementation

[0112] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0113] The embodiments of the present invention are as follows:

[0114] Step 1: Establish a multi-state reliability model of multi-energy conversion components for a comprehensive energy system that considers multi-energy conversion, and characterize the multi-state reliability model of multi-energy conversion components using a matrix-based general generating function.

[0115] In the integrated energy system of this embodiment, its multi-energy conversion subsystem comprises 10 identical combined heat and power (CHP) units. The entire multi-energy conversion subsystem is characterized by three energy parameters: natural gas (gas), heat (heat), and electricity (elec), with the unit being megawatts (MW). Each CHP unit has six states, and a multi-state reliability model for a single CHP unit is characterized using a general generating function of a matrix, as follows:

[0116]

[0117] Step 2: Establish a multi-state reliability model of multi-energy coupling components of a comprehensive energy system that considers multi-energy coupling, and characterize the multi-state reliability model of multi-energy coupling components in the form of a multi-dimensional general generating function;

[0118] In the integrated energy system of this embodiment, its multi-energy coupled subsystem comprises 10 identical urban integrated utility tunnels. Each urban integrated utility tunnel has 6 states. A general generating function is used to characterize the multi-state reliability model of a single urban integrated utility tunnel considering only gas, electricity, or heat energy parameters, as follows:

[0119]

[0120]

[0121]

[0122] Step 3: Use clustering algorithm to aggregate the reliability of multi-state reliability models of multi-energy conversion components and map them into aggregated multi-state reliability models of multi-energy conversion subsystems considering multi-energy conversion. The aggregated multi-state reliability model of multi-energy conversion subsystems is represented by a matrix-based general generating function.

[0123] Since a single cogeneration unit has only 6 states, and the number of states is not large, there is no need to cluster its states. Two cogeneration units connected in parallel will generate 36 states, which is a large number. Therefore, this embodiment considers the two cogeneration units as a whole and uses a clustering algorithm to cluster their states.

[0124] The multi-state reliability model of the two cogeneration units, characterized by a general generating function of matrices, is as follows:

[0125]

[0126] Since the system has three energy parameters, the multi-energy conversion matrix contains nine elements. To better illustrate the process, the embodiment uses the energy conversion capacity c of natural gas to electricity. elec,gas As an example, after K-means clustering, c elec,gas A state with 36 states can be aggregated into 6 states, namely c. elec,gas = (0, 0.01, 0.12, 0.2743, 0.4055, 0.6662).

[0127] Thus, the two cogeneration units, each with 36 states, are aggregated into a 6-state element. The aggregated multi-state reliability model, characterized by a matrix universal generating function, is as follows:

[0128]

[0129] Since the multiple energy conversion components are connected in parallel, the output energy of the multi-energy conversion subsystem of the integrated energy system should be the sum of the output energy of each component. Therefore, the aggregated multi-state reliability model of the multi-energy conversion subsystem containing 15 cogeneration units can be obtained.

[0130] Step 4: Use Gaussian approximation and sampling approximation to approximate the reliability of the multi-state reliability model of the multi-energy coupled subsystem containing multiple energy coupling elements, and characterize the multi-state approximate reliability model of the multi-energy coupled subsystem in the form of an approximate multidimensional general generating function.

[0131] By applying the law of large numbers and Gauss's law, the reliability of a multi-energy coupled subsystem can be approximated as a multidimensional Gaussian function:

[0132]

[0133] ∑2=diag(1067.475,1090.6,1553)

[0134] The 3σ principle of sampling is used to sample the multidimensional Gaussian function of the reliability of the multi-energy coupled subsystem, and the reliability of the multi-energy coupled subsystem is sampled into 2001 states.

[0135] Step 5: Based on the aggregated multi-state reliability model obtained in Step 3 and the multi-state approximate reliability model obtained in Step 4, a fast reliability assessment algorithm is proposed to quickly calculate the reliability of the integrated energy system that meets different multi-energy load requirements.

[0136] The system's input energy capacity is That is, 150MW of natural gas will be supplied. The multi-energy load demand is... That is, at least 10MW of thermal energy and 10MW of electrical energy are required for the system to operate reliably. Based on the integrated energy system aggregation multi-state reliability model and multi-state approximate reliability model that considers multiple energy conversion and coupling, the approximate reliability of the integrated energy system can be obtained as 0.7541.

[0137] The following table compares the calculation accuracy of the implementation results of this embodiment with that of the traditional method (Monte Carlo simulation method):

[0138] Table 1 Accuracy Comparison

[0139] 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.

[0140] 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:

[0141] Table 2 Time Comparison

[0142] 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 multi-state reliability assessment method for a comprehensive energy system involving multi-energy coupling and conversion, characterized in that: Step 1: Establish a multi-state reliability model of multi-energy conversion components for a comprehensive energy system that considers multi-energy conversion. Use the K-means clustering algorithm to aggregate the reliability of the multi-state reliability model of multi-energy conversion components and map it to form an aggregated multi-state reliability model. The second step is to establish a multi-state reliability model of the multi-energy coupling components of the integrated energy system that considers multi-energy coupling, and then use Gaussian approximation and sampling approximation to perform reliability approximation processing on the multi-state reliability model of the multi-energy coupling components in turn to obtain a multi-state approximate reliability model. Step 3: Combine the multi-state reliability model and the multi-state approximate reliability model, process them according to the rapid reliability assessment method to obtain the reliability of the integrated energy system that meets the requirements of different multi-energy loads, and realize the reliability assessment. Following the first step, the following aggregated multi-state reliability model is established: ； In the formula, This represents the aggregated multi-state reliability function of the multi-energy conversion subsystem considering multiple energy conversions, characterized by a matrix general generating function. G represents the total number of aggregated states of the multi-energy conversion subsystem after processing. This represents the probability that the multi-energy conversion subsystem is in aggregation state j. K represents the clustered energy conversion matrix of the multi-energy conversion subsystem in aggregation state j, where K is the number of aggregation sets; This represents the aggregation state number of the i-th multi-energy conversion element, where i is the number of elements contained in the multi-energy conversion subsystem. The serial number of each energy conversion element, i.e. ; This indicates the number of multi-energy conversion elements contained in the multi-energy conversion subsystem. This indicates that the i-th multi-energy conversion element is in the aggregation state. Clustering energy conversion matrix at time, This indicates that the i-th multi-energy conversion element is in a convergent state. The probability of; The second step is as follows: S21. In the established multi-energy coupling element multi-state reliability model, the multi-energy coupling elements are connected in parallel to form a multi-energy coupling subsystem, and 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 the multi-energy coupling element i when it is in state j; Furthermore, the output power of each multi-energy coupling element is characterized by multiple energy parameters, and the output power of multi-energy coupling element i when it is in state j is... 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. S22. The reliability of the multi-state reliability model of the multi-energy coupled components is approximated using Gaussian approximation. The reliability of the multi-energy coupled subsystem is approximated as a multidimensional Gaussian function, forming a multi-state Gaussian approximation reliability model, as follows: ； In the formula, This represents the probability density function of the output power distribution of a multi-energy coupled subsystem. This represents the output power of a multi-energy coupled subsystem characterized by V energy parameters. , This represents the output power of the multi-energy coupled subsystem under the corresponding output power of energy parameter v; This represents the average output power of the n multi-energy coupling elements contained in the multi-energy coupling subsystem. , This represents the average output power of the n multi-energy coupling elements contained in the multi-energy coupling subsystem under the output power corresponding to the energy parameter v; This represents the average output of a single multi-energy coupling element within the unit subsystem under the output power corresponding to energy parameter v. This represents the variance of the output power of the n multi-energy coupling elements contained in the multi-energy coupling subsystem. This represents the variance of the output power of the n multi-energy coupling elements contained in the multi-energy coupling 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 element contained in the unit subsystem under the output power corresponding to the energy parameter v. S23. A sampling approximation is used to approximate the reliability of the multi-state Gaussian approximation reliability model, forming a multi-state sampling approximation reliability model, as follows: The reliability of the multi-energy coupled subsystem is sampled using a multidimensional Gaussian function, and the reliability of the multi-energy coupled subsystem is sampled as follows: There are several states, and the output force in each state is represented as... ,in Represents a multi-energy coupled subsystem A set of states, This represents the output power of the j-th state of the multi-energy coupled subsystem. , This represents the output power of the multi-energy coupled subsystem in the j-th state under the output power corresponding to the energy parameter v. The probability that the multi-energy coupled subsystem is in the j-th state. 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 output power distribution probability density function of the multi-energy coupled subsystem at that time; This represents the total number of states in a multi-energy coupled subsystem, where s represents the state number. The third step employs a fast reliability assessment algorithm to quickly calculate the reliability of the integrated energy system that meets different multi-energy load requirements, as detailed below: By combining a multi-state reliability model of a multi-energy conversion subsystem with its input energy capacity, an output energy model of the multi-energy conversion subsystem can be established. ； ； In the formula, The output energy of a multi-energy conversion subsystem is represented by a general matrix generating function, which is an aggregated multi-state function. The output energy of the multi-energy conversion subsystem in aggregation state j is represented by V energy parameters. , This represents the output energy of the integrated energy system under aggregate state j, considering energy parameter v. The input energy capacity of the multi-energy conversion subsystem is characterized by V energy parameters. Indicates the input energy capacity when the energy parameter is v; Using the output energy of the multi-energy conversion subsystem and the multi-state approximate reliability model of the multi-energy coupling subsystem, the multi-state reliability model of the integrated energy system is established as follows: ； In the formula, This represents the multi-state reliability function of the integrated energy system considering multi-energy coupling and conversion, characterized by an approximate multidimensional universal generating function, where M represents the total number of states of the integrated 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 indicates the output power of the integrated energy system in state j under only energy parameter v; 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 system states As a measure of the reliability of an integrated 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 multi-state reliability assessment method for a multi-energy coupling and conversion integrated energy system according to claim 1, characterized in that: The integrated energy system is equivalent to a structure consisting of a series connection of multiple energy conversion subsystems and multiple energy coupling subsystems. The multiple energy conversion subsystems are composed of multiple energy conversion elements connected in parallel, and the multiple energy coupling subsystems are composed of multiple energy coupling elements connected in parallel.

3. The multi-state reliability assessment method for a comprehensive energy system involving multi-energy coupling and conversion according to claim 1, characterized in that: The first step is specifically as follows: S11. In the established multi-energy conversion element multi-state reliability model, the multi-energy conversion elements are connected in parallel to form a multi-energy conversion subsystem, and the state of each multi-energy conversion element is represented by parameters. and This indicates that i represents the serial number of the multi-energy conversion element, and j represents the state serial number. Let i represent the probability that the multi-energy conversion element i is in state j. The energy conversion matrix represents the energy conversion capability between the input and output energy parameters of the multi-energy conversion element i when it is in state j. S12、 For each multi-energy conversion element, all states of the multi-energy conversion element are aggregated using the K-means clustering algorithm, so that a total of s original states of the multi-energy conversion element are clustered into K aggregate sets; After K-means clustering, the energy conversion capability of multiple energy conversion elements is characterized as K clustered energy conversion matrices. and their corresponding probabilities : ； In the formula, This represents the energy conversion capability from input energy parameter v to output energy parameter v' of multiple energy conversion element i in cluster state j.

4. The multi-state reliability assessment method for a multi-energy coupling and conversion integrated energy system according to claim 3, characterized in that: For each aggregate set a, each aggregate set a contains The original state, namely 'a' represents the index of the aggregate set. An aggregate state is composed of all the original states contained in aggregate set 'a'. The average value of the energy conversion matrices corresponding to the original states contained in aggregate set 'a' is used as the clustering energy conversion matrix of the aggregate state. The probability of the aggregate state is the sum of the probabilities corresponding to the original states contained in the aggregate set a. .

5. The multi-state reliability assessment method for a multi-energy coupling and conversion integrated energy system according to claim 1, characterized in that: In S21, the multi-state reliability model of multi-energy coupled components is characterized by a multi-dimensional general generating function, including: Reliability of a single multi-energy coupling element i: ； 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. represents the output force of the multi-energy coupling element i when it is in state j, and z represents the z transformation parameter; Reliability of a single multi-energy coupled element i under the output power corresponding to a single energy parameter v: ； 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.

6. The multi-state reliability assessment method for a multi-energy coupling and conversion integrated energy system according to claim 1, characterized in that: In S22, the mean value in the multi-energy coupling 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 coupling element i in the multi-energy coupling subsystem when the z-transform parameter is 1 after taking the first derivative. The derivative of the general generating function of the multi-energy coupled element i in the unit subsystem is given by taking the second derivative when the z-transform parameter is 1. This means that the z-transform parameter is equal to 1 and substituted.

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