Monte Carlo based reliability evaluation method for IES containing power distribution network

Abstract

The application belongs to the technical field of power systems, and provides a Monte Carlo-based reliability evaluation method for an IES-containing distribution network, comprising: firstly, generating a system fault state sequence covering three scenarios of a comprehensive energy system, a distribution network and simultaneous faults of both by using Markov chain Monte Carlo simulation combined with Gibbs sampling; secondly, constructing a differentiated load reduction model with tie-line power as a coupling variable for different fault types, and iteratively solving an optimal reduction scheme by using a hierarchical distributed optimization strategy and a target cascade analysis method; thirdly, aggregating and calculating expected power supply shortage, average power outage frequency and average power outage duration based on the scheme results to obtain three reliability indexes; and finally, judging the indexes by using a variance coefficient as a convergence criterion, outputting an evaluation result if the accuracy is met, or continuing iteration until convergence. The application significantly improves the accuracy and efficiency of the reliability evaluation of the IES-containing distribution network, and provides direct technical support for system configuration and dispatching strategy optimization.

Description

Technical Field

[0001] This application belongs to the field of power system technology, and in particular relates to a Monte Carlo-based method, system, terminal equipment and storage medium for reliability assessment of distribution networks with IES. Background Technology

[0002] With the deepening of energy transition, the integration of Integrated Energy Systems (IES) with distribution networks has become an important way to improve the efficiency and reliability of comprehensive energy utilization. IES integrates multiple energy forms such as electricity, heat, and gas, and has the capabilities of multi-energy complementarity, cascade utilization, and islanded operation, enabling distribution networks to shift from traditional single-energy supply to coordinated operation of electricity, heat, and gas. Against this backdrop, accurately assessing the operational reliability of distribution networks containing IES has become a key issue in system planning and safe operation. Currently, the Monte Carlo simulation method is widely used in this field for reliability assessment. This method simulates the failure states of system components through extensive random sampling and calculates indicators such as system outage frequency and outage time based on probability statistics. It features strong model adaptability and ease of handling complex systems.

[0003] However, current methods suffer from problems such as low sampling efficiency, inaccurate characterization of multi-energy coupling and islanding operation characteristics, and lack of differentiated load shedding strategies when assessing the reliability of distribution networks containing IES. Summary of the Invention

[0004] This application provides a Monte Carlo-based method, system, terminal equipment, and storage medium for assessing the reliability of distribution networks containing IES. This method addresses the problems of low sampling efficiency, inaccurate characterization of multi-energy coupling and islanding operation, and lack of differentiated load shedding strategies in current methods for assessing the reliability of distribution networks containing IES.

[0005] In a first aspect, embodiments of this application provide a Monte Carlo-based reliability assessment method for distribution networks containing IES, comprising: S1, constructing a component state probability model of the distribution network and integrated energy system using the Markov chain Monte Carlo simulation method, and generating a Markov chain following a stationary distribution through Gibbs sampling, and randomly sampling the operating states of each component of the system to obtain a system fault state sample sequence; S2, determining the corresponding fault scenario type based on the system fault state sample sequence obtained in step S1, and calling a load reduction optimization model matching the different fault scenarios; wherein, the load reduction optimization model adopts a power consumption method based on the interconnection line power consumption of the distribution network and integrated energy system. A hierarchical distributed optimization strategy with coupled variables is adopted, and iterative solutions are performed based on the objective cascade analysis method to take into account the multi-energy coupling characteristics and islanding operation capability of the integrated energy system, so as to obtain the optimal load reduction scheme corresponding to the current fault state; S3, based on the optimal load reduction schemes corresponding to the fault states of each system obtained in step S2, the results of multiple samplings are statistically analyzed to calculate the reliability indicators of the overall expected power shortage, average power outage frequency, and average power outage duration; S4, the variance coefficient of the reliability indicators is calculated as the convergence criterion. When the variance coefficient reaches the preset accuracy or the number of simulations reaches the set threshold, the reliability assessment result is output; otherwise, steps S1-S3 are continued.

[0006] In one possible implementation of the first aspect, S1 above employs Markov chain Monte Carlo simulation to construct a component state probability model of the distribution network and integrated energy system, and generates a Markov chain following a stationary distribution through Gibbs sampling. The operating states of each component in the system are then randomly sampled to obtain a system fault state sample sequence, specifically including:

[0007] Assume that each component in the distribution network and integrated energy system has two states: operating state and failure state, and that the states of each component are independent of each other. Construct a corresponding state probability model for each component.

[0008] The Markov chain Monte Carlo simulation method is used to sample the states of all components, letting Indicates the state of element i. Let be the failure probability of component i. Then, generate a random number uniformly distributed in the interval [0,1] for component i. , so that:

[0009]

[0010] in, A value of 0 indicates normal. A value of 1 indicates failure;

[0011] Construct the system state vector based on the states of each component:

[0012]

[0013] Where m is the number of components, X (k) This indicates the operating state of the i-th element at the k-th sampling.

[0014] The Gibbs sampling method is used to generate a Markov chain that follows a stationary distribution. Specifically, in the k-th sampling, the chain is generated from a fully conditional distribution. Extraction From the complete conditional distribution Extraction By analogy, a system fault state sample sequence is obtained. ;

[0015] The system fault state sample sequence is pre-sampled, and the first m samples that do not reach a stationary distribution are removed.

[0016] Optionally, in another possible implementation of the first aspect, S2, based on the system fault state sample sequence obtained in step S1, determines the corresponding fault scenario type, and calls the corresponding load reduction optimization model for different fault scenarios; wherein, the load reduction optimization model adopts a hierarchical distributed optimization strategy with the power of the tie line between the distribution network and the integrated energy system as the coupling variable, and iteratively solves the problem based on the objective cascade analysis method, taking into account the multi-energy coupling characteristics and islanding operation capability of the integrated energy system, to obtain the optimal load reduction scheme corresponding to the current fault state, specifically including:

[0017] Based on the system fault state sample sequence obtained in step S1, the system type to which the faulty component belongs is identified, and the fault scenario type corresponding to the system fault state is determined according to whether the faulty component is located in the integrated energy system, the distribution network, or both the integrated energy system and the distribution network.

[0018] For each fault scenario type identified, a corresponding joint load reduction optimization model for the distribution network and integrated energy system is constructed. The power of the tie line between the distribution network and the integrated energy system is used as a coupling variable to coordinate the power exchange relationship between the two sides.

[0019] A hierarchical distributed optimization strategy is adopted to solve the joint load reduction optimization model. The upper-level optimization aims to minimize the overall load reduction of the system, while the lower-level optimization is based on the operation constraints of the distribution network and the integrated energy system, and is iteratively updated based on the tie-line power.

[0020] In the event of a failure in the integrated energy system, different load supply and demand combinations are distinguished based on the supply and demand status of electrical and thermal loads. By adjusting the output and load weight ratio of multi-energy coupling equipment such as electric boilers in the integrated energy system, the coordinated optimization of load reduction is achieved.

[0021] In the event of a distribution network fault, determine whether the system is in a state of islanding operation. If it is in a state of islanding operation, increase the output of the integrated energy system equipment and allocate power according to the weight ratio of the distribution network load and the integrated energy system load. The power supplied by the integrated energy system to the distribution network shall not exceed the upper limit of the tie line power.

[0022] The target cascade analysis method is used to iteratively solve the upper-level optimization and lower-level optimization until the tie-line power satisfies the consistency constraint condition in the upper and lower-level optimization results, thereby obtaining the optimal load reduction scheme corresponding to the current fault state.

[0023] Optionally, in another possible implementation of the first aspect, the above-mentioned S3, based on the optimal load reduction scheme corresponding to each system fault state obtained in step S2, statistically analyzes the results of multiple samplings to calculate the overall expected power shortage, average power outage frequency, and average power outage duration reliability indicators of the system, specifically including:

[0024] Based on the optimal load reduction scheme corresponding to the current fault state, calculate the corresponding load reduction amount for the i-th system fault state sampling. , For experimental functions;

[0025] Statistical analysis was performed on multiple system fault state sampling results to calculate the overall expected power shortage of the system. The estimated value M is:

[0026]

[0027] Where N is the number of times the system fault state is sampled;

[0028] Based on the number of power outages at each load point in the system fault state sample sequence, the system average power outage frequency reliability index is statistically analyzed.

[0029] Based on the outage duration of each load point in the system fault state sample sequence, the reliability index of the system's average outage duration is statistically analyzed.

[0030] Optionally, in another possible implementation of the first aspect, the aforementioned S4, calculating the variance coefficient of the reliability index, is used as a convergence criterion. When the variance coefficient reaches a preset accuracy or the number of simulations reaches a set threshold, the reliability assessment result is output, specifically including:

[0031] Based on the estimated value M and the corresponding experimental function The variance of the reliability index is calculated as follows:

[0032] ;

[0033] Based on the estimated value and variance, the variance coefficient is calculated as a convergence criterion. The variance coefficient is:

[0034]

[0035] The variance coefficient is compared with a preset accuracy threshold. When the variance coefficient is less than or equal to the preset accuracy threshold, or when the number of samplings of system fault states reaches the set upper limit, the reliability assessment process is determined to have converged, and the reliability assessment result is output.

[0036] Secondly, embodiments of this application provide a Monte Carlo-based reliability assessment system for a distribution network containing an Integrated Energy System (IES), comprising: a state sampling module, used to construct a component state probability model of the distribution network and the integrated energy system using the Markov chain Monte Carlo simulation method, and to generate a Markov chain following a stationary distribution through Gibbs sampling to randomly sample the operating states of each component of the system to obtain a system fault state sample sequence; and a load reduction calculation module, used to determine the corresponding fault scenario type based on the system fault state sample sequence, and to call a matching load reduction optimization model for different fault scenarios; wherein, the load reduction optimization model adopts a coupling variable with the power of the tie line between the distribution network and the integrated energy system. A hierarchical distributed optimization strategy is employed, and iterative solutions are obtained based on the objective cascade analysis method to take into account the multi-energy coupling characteristics and islanding operation capability of the integrated energy system, thus obtaining the optimal load reduction scheme corresponding to the current fault state. A reliability index calculation module is used to statistically analyze multiple sampling results based on the optimal load reduction scheme corresponding to each system fault state, calculating the overall expected power shortage, average outage frequency, and average outage duration reliability indices. The module also calculates the variance coefficient of the reliability indices as a convergence criterion. When the variance coefficient reaches a preset precision or the number of simulations reaches a set threshold, the reliability assessment result is output; otherwise, the system state sampling, load reduction calculation, and reliability index calculation process continues.

[0037] Thirdly, embodiments of this application provide a terminal device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aforementioned Monte Carlo-based IES-based distribution network reliability assessment method.

[0038] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned Monte Carlo-based reliability assessment method for distribution networks containing IES.

[0039] Beneficial effects: First, Markov chain Monte Carlo simulation combined with Gibbs sampling is used to generate system fault state sequences covering three scenarios: integrated energy system, distribution network, and simultaneous failure of both. Then, for different fault types, a differentiated load reduction model with tie line power as the coupling variable is constructed. The optimal load reduction scheme is iteratively solved using a hierarchical distributed optimization strategy and target cascade analysis method. Then, based on the scheme results, three types of reliability indicators are aggregated and calculated: expected power shortage, average outage frequency, and average outage duration. Finally, the variance coefficient of the indicators is used as the convergence criterion. If the accuracy is met, the evaluation result is output; otherwise, iterative sampling continues until convergence. This application significantly improves sampling efficiency by integrating Markov chain Monte Carlo simulation and Gibbs sampling, achieving reliable convergence with fewer simulations. By constructing a differentiated load reduction model covering three fault scenarios, it accurately characterizes the multi-energy coupling and islanded operation characteristics of IES, greatly improving evaluation accuracy. By employing hierarchical distributed optimization and target cascade analysis, it coordinates different stakeholders and achieves globally optimal load reduction. Finally, the multi-dimensional reliability indicators provided can offer direct and quantitative technical support for the planning, configuration, and scheduling strategies of distribution networks containing IES. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a schematic flowchart of a Monte Carlo-based reliability assessment method for distribution networks containing IES, provided in one embodiment of this application.

[0042] Figure 2 This is a topology diagram of a computational system provided in an embodiment of this application;

[0043] Figure 3 This is a schematic diagram of load point parameters of a power distribution system provided in an embodiment of this application;

[0044] Figure 4 This is a schematic diagram of a typical daily source-load power curve provided in an embodiment of this application;

[0045] Figure 5 This is a schematic diagram of the variance coefficient convergence curve provided in an embodiment of this application;

[0046] Figure 6This is a schematic diagram of the structure of a Monte Carlo-based distribution network reliability assessment system with IES provided in one embodiment of this application;

[0047] Figure 7 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. Detailed Implementation

[0048] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0049] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0050] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0051] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0052] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0053] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0054] The following description, with reference to the accompanying drawings, details a Monte Carlo-based method, system, terminal equipment, and storage medium for reliability assessment of distribution networks containing IES.

[0055] Figure 1 A schematic flowchart of a Monte Carlo-based reliability assessment method for distribution networks containing IES, provided in an embodiment of this application, is shown.

[0056] like Figure 1 As shown, this Monte Carlo-based method for reliability assessment of distribution networks containing IES includes the following steps:

[0057] S1. Using the Markov chain Monte Carlo simulation method, a component state probability model of the distribution network and integrated energy system is constructed. A Markov chain following a stationary distribution is generated by Gibbs sampling. The operating state of each component in the system is randomly sampled to obtain a system fault state sample sequence.

[0058] Furthermore, in this embodiment of the application, step S1 includes:

[0059] Assume that each component in the distribution network and integrated energy system has two states: operating state and failure state, and that the states of each component are independent of each other. Construct a corresponding state probability model for each component.

[0060] The Markov chain Monte Carlo simulation method is used to sample the states of all components, letting Indicates the state of element i. Let be the failure probability of component i. Then, generate a random number uniformly distributed in the interval [0,1] for component i. , so that:

[0061]

[0062] in, A value of 0 indicates normal. A value of 1 indicates failure;

[0063] Construct the system state vector based on the states of each component:

[0064]

[0065] Where m is the number of components, X (k) This indicates the operating state of the i-th element at the k-th sampling.

[0066] The Gibbs sampling method is used to generate a Markov chain that follows a stationary distribution. Specifically, in the k-th sampling, the chain is generated from a fully conditional distribution. Extraction From the complete conditional distribution Extraction By analogy, a system fault state sample sequence is obtained. ;

[0067] The system fault state sample sequence is pre-sampled, and the first m samples that do not reach a stationary distribution are removed.

[0068] S2. Based on the system fault state sample sequence obtained in step S1, determine the corresponding fault scenario type, and call the load reduction optimization model that matches the different fault scenarios. The load reduction optimization model adopts a hierarchical distributed optimization strategy with the power of the tie line between the distribution network and the integrated energy system as the coupling variable, and iteratively solves the problem based on the target cascade analysis method to take into account the multi-energy coupling characteristics and islanding operation capability of the integrated energy system, and obtain the optimal load reduction scheme corresponding to the current fault state.

[0069] Furthermore, in this embodiment of the application, step S2 includes:

[0070] Based on the system fault state sample sequence obtained in step S1, the system type to which the faulty component belongs is identified, and the fault scenario type corresponding to the system fault state is determined according to whether the faulty component is located in the integrated energy system, the distribution network, or both the integrated energy system and the distribution network.

[0071] For each fault scenario type identified, a corresponding joint load reduction optimization model for the distribution network and integrated energy system is constructed. The power of the tie line between the distribution network and the integrated energy system is used as a coupling variable to coordinate the power exchange relationship between the two sides.

[0072] A hierarchical distributed optimization strategy is adopted to solve the joint load reduction optimization model. The upper-level optimization aims to minimize the overall load reduction of the system, while the lower-level optimization is based on the operation constraints of the distribution network and the integrated energy system, and is iteratively updated based on the tie-line power.

[0073] In the event of a failure in the integrated energy system, different load supply and demand combinations are distinguished based on the supply and demand status of electrical and thermal loads. By adjusting the output and load weight ratio of multi-energy coupling equipment such as electric boilers in the integrated energy system, the coordinated optimization of load reduction is achieved.

[0074] In the event of a distribution network fault, determine whether the system is in a state of islanding operation. If it is in a state of islanding operation, increase the output of the integrated energy system equipment and allocate power according to the weight ratio of the distribution network load and the integrated energy system load. The power supplied by the integrated energy system to the distribution network shall not exceed the upper limit of the tie line power.

[0075] The target cascade analysis method is used to iteratively solve the upper-level optimization and lower-level optimization until the tie-line power satisfies the consistency constraint condition in the upper and lower-level optimization results, thereby obtaining the optimal load reduction scheme corresponding to the current fault state.

[0076] S3. Based on the optimal load reduction schemes corresponding to each system fault state obtained in step S2, the results of multiple samplings are statistically analyzed to calculate the overall expected power shortage, average power outage frequency, and average power outage duration reliability indicators of the system.

[0077] Furthermore, in this embodiment of the application, step S3 includes:

[0078] Based on the optimal load reduction scheme corresponding to the current fault state, calculate the corresponding load reduction amount for the i-th system fault state sampling. , For experimental functions;

[0079] Statistical analysis was performed on multiple system fault state sampling results to calculate the overall expected power shortage of the system. The estimated value M is:

[0080]

[0081] Where N is the number of times the system fault state is sampled;

[0082] Based on the number of power outages at each load point in the system fault state sample sequence, the system average power outage frequency reliability index is statistically analyzed.

[0083] Based on the outage duration of each load point in the system fault state sample sequence, the reliability index of the system's average outage duration is statistically analyzed.

[0084] S4. Calculate the variance coefficient of the reliability index as a convergence criterion. When the variance coefficient reaches the preset accuracy or the number of simulations reaches the set threshold, output the reliability assessment result; otherwise, continue to execute steps S1-S3.

[0085] Furthermore, in this embodiment of the application, step S4 includes:

[0086] Based on the estimated value M and the corresponding experimental function The variance of the reliability index is calculated as follows:

[0087] ;

[0088] Based on the estimated value and variance, the variance coefficient is calculated as a convergence criterion. The variance coefficient is:

[0089]

[0090] The variance coefficient is compared with a preset accuracy threshold. When the variance coefficient is less than or equal to the preset accuracy threshold, or when the number of samplings of system fault states reaches the set upper limit, the reliability assessment process is determined to have converged, and the reliability assessment result is output.

[0091] The following verifies the progressiveness of the method provided in this application.

[0092] 1. Example parameters:

[0093] System configuration: 30 feeders, 23 distribution transformers, 23 load points, topology as follows: Figure 2 As shown, the number of users and average load at each load point are as follows: Figure 3 As shown.

[0094] Source load data: The distribution network and IES time-series load curves are generated by superimposing normally distributed random variables on typical daily loads;

[0095] The photovoltaic output curve is calculated based on typical solar irradiance and beta-distributed random variables;

[0096] The wind power output curve is calculated based on typical daily wind speed and Weibull distributed random variables. The typical daily source-load power curve is shown below. Figure 4 As shown.

[0097] Simulation settings: 2000 simulation years, convergence accuracy threshold is variance coefficient ≤ 0.05.

[0098] 2. Verification results:

[0099] Convergence: When the simulation reaches 500 years, the variance coefficients of the EENS indices of both the distribution network and IES converge to 0.05, with the distribution network's EENS index converging faster (e.g., Figure 5 (As shown).

[0100] Accuracy: The proposed MCMC simulation method achieves high-precision evaluation within a finite simulation period, verifying the rationality of the load reduction model and solution strategy.

[0101] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0102] Corresponding to the Monte Carlo-based reliability assessment method for distribution networks containing IES in the above embodiment, Figure 6 The diagram shows a structural block diagram of a Monte Carlo-based distribution network reliability assessment system with IES provided in an embodiment of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0103] Reference Figure 6 The system 600 includes:

[0104] The state sampling module 601 is used to construct the component state probability model of the distribution network and integrated energy system using the Markov chain Monte Carlo simulation method, and generate a Markov chain that follows a stationary distribution through Gibbs sampling to randomly sample the operating state of each component of the system to obtain the system fault state sample sequence.

[0105] The load reduction calculation module 602 is used to determine the corresponding fault scenario type based on the system fault state sample sequence, and call the matching load reduction optimization model for different fault scenarios. The load reduction optimization model adopts a hierarchical distributed optimization strategy with the power of the tie line between the distribution network and the integrated energy system as the coupling variable, and performs iterative solution based on the objective cascade analysis method to take into account the multi-energy coupling characteristics and islanding operation capability of the integrated energy system, so as to obtain the optimal load reduction scheme corresponding to the current fault state.

[0106] The reliability index calculation module 603 is used to calculate the overall system reliability indices, including the expected power shortage, average outage frequency, and average outage duration, based on the optimal load reduction scheme corresponding to each system fault state and statistically analyzing multiple sampling results.

[0107] The variance coefficient of the reliability index is calculated as a convergence criterion. When the variance coefficient reaches the preset accuracy or the number of simulations reaches the set threshold, the reliability assessment result is output; otherwise, the system state sampling, load reduction calculation and reliability index calculation process continues.

[0108] In practical use, the Monte Carlo-based IES-based distribution network reliability assessment system provided in this application embodiment can be configured in any terminal device to execute the aforementioned Monte Carlo-based IES-based distribution network reliability assessment method.

[0109] This application provides a Monte Carlo-based reliability assessment system for distribution networks with IES (Environmental Engineering Systems). First, it uses Markov chain Monte Carlo simulation combined with Gibbs sampling to generate a system fault state sequence covering three scenarios: integrated energy system, distribution network, and simultaneous failure of both. Then, for different fault types, it constructs differentiated load reduction models with tie-line power as the coupling variable, and uses a hierarchical distributed optimization strategy and target cascade analysis to iteratively solve for the optimal load reduction scheme. Next, based on the scheme results, it aggregates and calculates three reliability indicators: expected power shortage, average outage frequency, and average outage duration. Finally, it uses the variance coefficient of the indicators as the convergence criterion; if the accuracy is met, the assessment result is output; otherwise, iterative sampling continues until convergence. This application significantly improves sampling efficiency by integrating Markov chain Monte Carlo simulation and Gibbs sampling, achieving reliable convergence with fewer simulations. By constructing a differentiated load reduction model covering three fault scenarios, it accurately characterizes the multi-energy coupling and islanded operation characteristics of IES, greatly improving evaluation accuracy. By employing hierarchical distributed optimization and target cascade analysis, it coordinates different stakeholders and achieves globally optimal load reduction. Finally, the multi-dimensional reliability indicators provided can offer direct and quantitative technical support for the planning, configuration, and scheduling strategies of distribution networks containing IES.

[0110] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0111] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0112] To implement the above embodiments, this application also proposes a terminal device.

[0113] Figure 7 This is a schematic diagram of the structure of a terminal device according to an embodiment of this application.

[0114] like Figure 7 As shown, the terminal device 200 includes:

[0115] The system includes a memory 210 and at least one processor 220, and a bus 230 connecting different components (including the memory 210 and the processor 220). The memory 210 stores a computer program, which, when executed by the processor 220, implements the Monte Carlo-based reliability assessment method for distribution networks with IES as described in the embodiments of this application.

[0116] Bus 230 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0117] Terminal device 200 typically includes various electronically readable media. These media can be any available media that can be accessed by terminal device 200, including volatile and non-volatile media, removable and non-removable media.

[0118] Memory 210 may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 240 and / or cache memory 250. Terminal device 200 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 260 may be used to read and write non-removable, non-volatile magnetic media (… Figure 7 Not shown; usually referred to as a "hard drive"). Although Figure 7 As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 230 via one or more data media interfaces. Memory 210 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0119] A program / utility 280 having a set (at least one) of program modules 270 may be stored in, for example, memory 210. Such program modules 270 include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 270 typically perform the functions and / or methods described in the embodiments of this application.

[0120] Terminal device 200 can also communicate with one or more external devices 290 (e.g., keyboard, pointing device, display 291, etc.), and with one or more devices that enable a user to interact with terminal device 200, and / or with any device that enables terminal device 200 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 292. Furthermore, terminal device 200 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 293. As shown, network adapter 293 communicates with other modules of terminal device 200 via bus 230. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with terminal device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0121] The processor 220 performs various functional applications and data processing by running programs stored in the memory 210.

[0122] It should be noted that the implementation process and technical principles of the terminal device in this embodiment are explained in the foregoing description of a Monte Carlo-based reliability assessment method for distribution networks containing IES, and will not be repeated here.

[0123] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0124] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0125] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0126] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0127] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0128] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0129] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0130] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A Monte Carlo-based reliability assessment method for distribution networks containing IES, characterized in that, Includes the following steps: S1. Using the Markov chain Monte Carlo simulation method, a component state probability model of the distribution network and integrated energy system is constructed. A Markov chain following a stationary distribution is generated by Gibbs sampling. The operating state of each component in the system is randomly sampled to obtain a system fault state sample sequence. S2. Based on the system fault state sample sequence obtained in step S1, determine the corresponding fault scenario type, and call the load reduction optimization model that matches the different fault scenarios; wherein, the load reduction optimization model adopts a hierarchical distributed optimization strategy with the power of the tie line between the distribution network and the integrated energy system as the coupling variable, and performs iterative solution based on the target cascade analysis method to take into account the multi-energy coupling characteristics and islanding operation capability of the integrated energy system, and obtain the optimal load reduction scheme corresponding to the current fault state. S3. Based on the optimal load reduction schemes corresponding to each system fault state obtained in step S2, the results of multiple samplings are statistically analyzed to calculate the overall expected power shortage, average power outage frequency, and average power outage duration reliability indicators of the system. S4. Calculate the variance coefficient of the reliability index as a convergence criterion. When the variance coefficient reaches the preset accuracy or the number of simulations reaches the set threshold, output the reliability evaluation result; otherwise, continue to execute steps S1-S3.

2. The method as described in claim 1, characterized in that, S1 employs the Markov chain Monte Carlo simulation method to construct a component state probability model of the distribution network and integrated energy system. A Markov chain following a stationary distribution is generated through Gibbs sampling. The operating states of each component in the system are randomly sampled to obtain a system fault state sample sequence, specifically including: Assume that each component in the distribution network and integrated energy system has two states: operating state and failure state, and that the states of each component are independent of each other. Construct a corresponding state probability model for each component. The Markov chain Monte Carlo simulation method is used to sample the states of all components, letting Indicates the state of element i. Let be the failure probability of component i. Then, generate a random number uniformly distributed in the interval [0,1] for component i. , so that: ； in, A value of 0 indicates normal. A value of 1 indicates failure; Construct the system state vector based on the states of each component: ； Where m is the number of components, X (k) This indicates the operating state of the i-th element at the k-th sampling. The Gibbs sampling method is used to generate a Markov chain that follows a stationary distribution. Specifically, in the k-th sampling, the chain is generated from a fully conditional distribution. Extraction From the complete conditional distribution Extraction By analogy, a system fault state sample sequence is obtained. ; The system fault state sample sequence is pre-sampled, and the first m samples that do not reach a stationary distribution are removed.

3. The method as described in claim 2, characterized in that, Step S2, based on the system fault state sample sequence obtained in step S1, determines the corresponding fault scenario type and calls the corresponding load reduction optimization model for different fault scenarios; wherein, the load reduction optimization model adopts a hierarchical distributed optimization strategy with the power of the tie line between the distribution network and the integrated energy system as the coupling variable, and iteratively solves the problem based on the objective cascade analysis method, taking into account the multi-energy coupling characteristics and islanding operation capability of the integrated energy system, to obtain the optimal load reduction scheme corresponding to the current fault state, specifically including: Based on the system fault state sample sequence obtained in step S1, the system type to which the faulty component belongs is identified, and the fault scenario type corresponding to the system fault state is determined according to whether the faulty component is located in the integrated energy system, the distribution network, or both the integrated energy system and the distribution network. For each fault scenario type identified, a corresponding joint load reduction optimization model for the distribution network and integrated energy system is constructed. The power of the tie line between the distribution network and the integrated energy system is used as a coupling variable to coordinate the power exchange relationship between the two sides. A hierarchical distributed optimization strategy is adopted to solve the joint load reduction optimization model. The upper-level optimization aims to minimize the overall load reduction of the system, while the lower-level optimization is based on the operation constraints of the distribution network and the integrated energy system, and is iteratively updated based on the tie-line power. In the event of a failure in the integrated energy system, different load supply and demand combinations are distinguished based on the supply and demand status of electrical and thermal loads. By adjusting the output and load weight ratio of multi-energy coupling equipment such as electric boilers in the integrated energy system, the coordinated optimization of load reduction is achieved. In the event of a distribution network fault, determine whether the system is in a state of islanding operation. If it is in a state of islanding operation, increase the output of the integrated energy system equipment and allocate power according to the weight ratio of the distribution network load and the integrated energy system load. The power supplied by the integrated energy system to the distribution network shall not exceed the upper limit of the tie line power. The upper-level optimization and lower-level optimization are iteratively solved based on the target cascade analysis method until the tie line power satisfies the consistency constraint condition in the upper and lower-level optimization results, thereby obtaining the optimal load reduction scheme corresponding to the current fault state.

4. The method as described in claim 3, characterized in that, Step S3 involves statistically analyzing the optimal load reduction schemes corresponding to each system fault state obtained in step S2, and calculating the overall expected power shortage, average outage frequency, and average outage duration reliability indicators of the system. Specifically, this includes: Based on the optimal load reduction scheme corresponding to the current fault state, calculate the corresponding load reduction amount for the i-th system fault state sampling. , For experimental functions; Statistical analysis was performed on multiple system fault state sampling results to calculate the overall expected power shortage of the system. The estimated value M is: ； Where N is the number of times the system fault state is sampled; Based on the number of power outages at each load point in the system fault state sample sequence, the system average power outage frequency reliability index is statistically analyzed. Based on the outage duration of each load point in the system fault state sample sequence, the reliability index of the system's average outage duration is statistically analyzed.

5. The method as described in claim 4, characterized in that, Step S4 involves calculating the variance coefficient of the reliability index as a convergence criterion. When the variance coefficient reaches a preset accuracy or the number of simulations reaches a set threshold, the reliability assessment result is output, specifically including: Based on the estimated value M and the corresponding experimental function The variance of the reliability index is calculated as follows: ； Based on the estimated value and variance, the variance coefficient is calculated as a convergence criterion, and the variance coefficient is: ； The variance coefficient is compared with a preset accuracy threshold. When the variance coefficient is less than or equal to the preset accuracy threshold, or when the number of samplings of system fault states reaches the set upper limit, the reliability assessment process is determined to have converged, and the reliability assessment result is output.

6. A Monte Carlo-based reliability assessment system for distribution networks containing IES, characterized in that, include: The state sampling module is used to construct the component state probability model of the distribution network and integrated energy system using the Markov chain Monte Carlo simulation method, and generate a Markov chain that follows a stationary distribution through Gibbs sampling to randomly sample the operating state of each component of the system to obtain the system fault state sample sequence. The load reduction calculation module is used to determine the corresponding fault scenario type based on the system fault state sample sequence, and call the matching load reduction optimization model for different fault scenarios. The load reduction optimization model adopts a hierarchical distributed optimization strategy with the power of the tie line between the distribution network and the integrated energy system as the coupling variable, and performs iterative solution based on the target cascade analysis method to take into account the multi-energy coupling characteristics and islanding operation capability of the integrated energy system, so as to obtain the optimal load reduction scheme corresponding to the current fault state. The reliability index calculation module is used to statistically analyze multiple sampling results based on the optimal load reduction scheme corresponding to each system failure state, and calculate the overall system reliability indexes, including expected power shortage, average outage frequency, and average outage duration; and, The variance coefficient of the reliability index is calculated as a convergence criterion. When the variance coefficient reaches the preset accuracy or the number of simulations reaches the set threshold, the reliability assessment result is output; otherwise, the system state sampling, load reduction calculation and reliability index calculation process continues.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 5.

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