A power distribution network reliability evaluation method and system

Abstract

This invention discloses a method and system for assessing the reliability of a distribution network, belonging to the technical field of distribution network reliability assessment. The method includes: acquiring the grid topology, historical wind speed data, historical node load active power, and historical node load reactive power of the distribution network to be assessed; generating load power data and wind power output power data based on the above data; constructing an extended grid topology based on the above data and acquiring branch weighted transmission power, node weighted injection power, and grid fault component data; establishing a distribution network reliability simulation model based on the above data for simulation; if a preset simulation period is reached, acquiring branch power flow indicators and node power flow indicators; and acquiring the reliability indicators of the distribution network to be assessed based on the above data. This invention provides a method for assessing the reliability of a distribution network that can identify vulnerable nodes and vulnerable branches, thereby achieving accurate assessment of the distribution network's reliability.

Description

Technical Field

[0001] This invention relates to the field of reliability assessment technology for power distribution networks, and in particular to a method and system for assessing the reliability of power distribution networks. Background Technology

[0002] The reliability of the distribution network is a crucial component of the power system. With the large-scale integration of renewable energy sources such as wind power, the uncertainty of wind power output and load fluctuations pose significant challenges to the stability and reliability of the distribution network. Currently, various reliability assessment methods have been proposed in related research: To address the issue of inaccurate assessment results due to changes in the distribution network's operating state after the integration of new energy sources, this study analyzes big data to obtain the influencing factors on the short-term operational reliability of the distribution network under the influence of fluctuations, constructs a simulation system model of the distribution network to obtain its real-time operating state, and uses the ant colony algorithm to calculate various indicators at load points, thereby achieving a short-term operational reliability assessment of the distribution network; Taking flexible distribution networks as the research object, a reliability assessment model for AC / DC interconnected distribution networks based on sequential inertial Monte Carlo simulation has been established. The study found that the integration of flexible power electronic devices significantly reduces the reliability of the AC side in the event of a power outage. The system and users experience power outages following road faults. Considering the impact of distributed power sources and flexible loads, the reliability of power supply to loads within islands is effectively improved by dividing power supply islands and using an improved minimum path method for reliability assessment. An island division model is constructed using mixed-integer linear programming, considering various constraints, and the Monte Carlo method is used to assess the reliability of the distribution network, maximizing power restoration and improving the overall reliability level. By establishing a distribution network reliability assessment index system, using principal component analysis to obtain index weights, and constructing a comprehensive assessment function, the system can assess the power supply reliability of multiple regions or multiple objects to be assessed at different times within the same region, identifying weak areas.

[0003] Under the current technological background, although existing research has proposed various methods for assessing the reliability of distribution networks, most of them ignore the random fluctuations in wind power output and load, resulting in assessment results that cannot fully reflect the reliability of the actual power system. Furthermore, with the development of smart grids and the increasing complexity of distribution network topologies, traditional power flow calculation methods often cannot effectively handle complex network structures and those integrating large-scale renewable energy sources. Summary of the Invention

[0004] The present invention aims to provide a method and system for evaluating the reliability of distribution networks, in order to solve the above-mentioned technical problems, avoid the distortion of evaluation results caused by ignoring the random fluctuations of wind power output and load in the reliability evaluation of distribution networks, identify the vulnerable nodes and vulnerable branches of the distribution network, and achieve accurate evaluation of the reliability of the distribution network.

[0005] To address the aforementioned technical problems, this invention provides a method for evaluating the reliability of a power distribution network, comprising: Based on the distribution network to be evaluated, obtain the network topology, historical wind speed data, historical node load active power, and historical node load reactive power; Based on historical wind speed data, historical node load active power and historical node load reactive power, several load power data and several wind power output power data are generated. Based on the power grid topology, several load power data and several wind power output power data, an extended power grid topology is constructed. Based on the power grid topology, several load power data and several wind power output power data, obtain several branch weighted transmission power, several node weighted injection power and power grid fault element data; A distribution network reliability simulation model is established based on the extended topology of the power grid, the weighted transmission power of branches, the weighted injection power of nodes, and the data of faulty components in the power grid. The distribution network reliability simulation model is then used to simulate the model. If the preset simulation period is reached, several branch power flow indicators and several node power flow indicators are obtained. Vulnerable nodes and vulnerable branches are determined based on several branch power flow indicators, several node power flow indicators, and preset vulnerability screening rules, and the reliability indicators of the distribution network to be evaluated are obtained based on the vulnerable nodes and vulnerable branches.

[0006] In the above scheme, several load power data and several wind power output power data are generated by using historical wind speed data, historical node load active power, and historical node load reactive power of the distribution network to be evaluated. This quantifies the random fluctuations of wind power output and load, avoiding the distortion of evaluation results caused by ignoring the random fluctuations of wind power output and load in the distribution network reliability assessment. It provides random power samples that are close to the actual operating conditions for subsequent distribution network power analysis. Next, an extended grid topology is constructed by using the grid topology, several load power data, and several wind power output power data. This integrates the random fluctuations of wind power output and load into the distribution network topology and power constraint system, enabling the quantification of the actual operating topology and power transmission risks of the distribution network. At the same time, by identifying the status of faulty components in the distribution network, it can provide a realistic extended grid topology, several branch weighted transmission power, several node weighted injection power, and grid faulty component data for subsequent power flow index calculations.

[0007] Then, a distribution network reliability simulation model is established using the extended topology of the power grid, weighted transmission power of several branches, weighted injected power of several nodes, and data from faulty components in the power grid. This model is used to simulate the distribution network reliability, quantifying the power transmission contribution of each branch and node in actual operation by combining source-load uncertainty and the fault state of distribution network components. It calculates power flow indices for several branches and nodes, providing quantitative indicators for identifying vulnerable parts of the distribution network and laying the computational foundation for screening vulnerable nodes and branches. Finally, through the power flow indices for several branches and nodes, and preset vulnerability screening rules, a comprehensive assessment of distribution network reliability considering source-load uncertainty can be performed. This enables accurate identification of vulnerable parts of the distribution network, determining vulnerable nodes and branches, and obtaining reliability indices reflecting the actual operating state of the distribution network under assessment based on these indices. This effectively solves the problem of distorted assessment results caused by neglecting wind power output and random load fluctuations in traditional assessment methods, achieving accurate assessment of distribution network reliability.

[0008] Furthermore, the generation of several load power data and several wind power output power data based on historical wind speed data, historical node load active power, and historical node load reactive power includes: Wind speed shape parameters and wind speed scale parameters are obtained based on historical wind speed data; A wind speed probability density model is constructed based on historical wind speed data, wind speed shape parameters, and wind speed scale parameters. A load probability density model is constructed based on the historical node load active power and historical node load reactive power, and several load power data are generated based on the load probability density model. Several wind speed data are generated based on the wind speed probability density function, and wind power output power data corresponding to the several wind speed data are generated based on the several wind speed data and the preset wind power output power probability model.

[0009] In the above scheme, wind speed shape and scale parameters are obtained from historical wind speed data, providing parameter support for the subsequent construction of a wind speed probability density model and ensuring that the model accurately reflects the statistical characteristics of actual wind speed. Next, a wind speed probability density model is constructed using historical wind speed data, wind speed shape parameters, and wind speed scale parameters. This model accurately describes the random fluctuations in wind speed, addressing the problem of models failing to accurately represent wind speed uncertainty. Then, a load probability density model is constructed using historical node load active and reactive power data. This quantifies the load volatility affected by seasonal and climatic factors, generating several load power data points that conform to actual load characteristics. Finally, several wind speed data points are generated using the wind speed probability density model, and combined with a pre-defined wind power output probability model, corresponding wind power output data for each wind speed is generated, achieving a quantitative representation of the uncertainty in wind power output.

[0010] Furthermore, the acquisition of branch-weighted transmission power, node-weighted injection power, and grid fault component data based on the power grid topology, several load power data, and several wind power output power data includes: Based on the power grid topology, several load power data, several wind power output power data, a preset risk probability threshold, and a preset distribution network risk model, the initial transmission power of several branches and the initial injection power of several nodes are obtained. Based on the initial transmission power of several branches, the initial injection power of several nodes, the preset VaR model, and the preset information level value, the line transmission power threshold and the node injection power threshold are obtained. Based on the power grid topology, line transmission power threshold, and node injection power threshold, we obtain the weighted transmission power of several branches, the weighted injection power of several nodes, and data on faulty components in the power grid.

[0011] In the above scheme, by using the power grid topology, several load power data, several wind power output power data, preset risk probability thresholds, and a preset distribution network risk model, the initial transmission power of several branches and the initial injected power of several nodes are obtained. This allows for a preliminary integration of source load random fluctuations with the distribution network topology, providing basic data for subsequent risk quantification and power threshold calculation. Next, by using the initial transmission power of several branches, the initial injected power of several nodes, a preset VaR model, and preset confidence levels, line transmission power thresholds and node injected power thresholds are obtained. This transforms the potential risks caused by source load uncertainty into quantifiable power constraint indicators, defining power boundaries for the safe operation of the distribution network. Then, by using the power grid topology, line transmission power thresholds, and node injected power thresholds, risk constraints are deeply integrated with the power grid topology. Simultaneously, the operating status of components in the distribution network is clarified, and weighted transmission power of several branches, weighted injected power of several nodes, and data on faulty components in the power grid are obtained, providing input for the subsequent establishment of a distribution network reliability simulation model.

[0012] Furthermore, the acquisition of weighted transmission power of several branches, weighted injection power of several nodes, and grid fault component data based on the power grid topology, line transmission power threshold, and node injection power threshold includes: Based on the power grid topology, line transmission power threshold, and node injection power threshold, the weighted transmission power of several branches and the weighted injection power of several nodes are obtained. Based on the distribution network to be evaluated, obtain historical failure rate and historical repair rate; Based on historical failure rates and historical repair rates, construct power grid component operating time models and power grid component repair time models; Data on faulty power grid components are obtained based on power grid component operating time models, power grid component repair time models, and extended power grid topology.

[0013] In the above scheme, by binding risk quantification indicators to the distribution network topology through the power grid topology, line transmission power threshold, and node injection power threshold, the power data on the power grid topology can be made more consistent with the risk constraints of actual operation, obtaining weighted transmission power of several branches and weighted injection power of several nodes. Next, by retrieving historical failure rates and historical repair rates from the distribution network to be evaluated, realistic data support can be provided for subsequent component operation status modeling, enabling objective identification of faulty components. Then, by constructing normal operation duration models and fault repair time models for power grid components using historical failure rates and historical repair rates, the randomness of the operating status of power grid components can be accurately characterized, solving the problem of ignoring component failure uncertainty in existing technologies for assessing distribution network reliability. Finally, through the power grid component operating time model, power grid component repair time model, and extended power grid topology, power grid components in a faulty state can be identified, and power grid faulty component data can be obtained, providing realistic component failure scenarios for distribution network reliability simulation.

[0014] Furthermore, a distribution network reliability simulation model is established based on the extended topology of the power grid, branch weighted transmission power, node weighted injection power, and data on faulty components in the power grid. This model is then used to simulate the distribution network reliability. If a preset simulation period is reached, several branch power flow indicators and several node power flow indicators are obtained, including: A distribution network reliability simulation model is established based on the extended topology of the power grid, the branch weighted transmission power, the node weighted injection power, and the data of faulty components in the power grid. The distribution network reliability simulation model is then used to simulate the model. If the preset simulation period is reached, the effective branch weighted transmission power and the effective node weighted injection power are determined. Calculate several branch power flow indices based on the extended topology of the power grid and the weighted transmission power of effective branches. Calculate power flow indices for several nodes based on the extended topology of the power grid and the weighted injected power of effective nodes.

[0015] In the above scheme, a distribution network reliability simulation model is established and simulated using the extended grid topology, branch weighted transmission power, node weighted injection power, and grid fault component data. A preset simulation period is used as the simulation termination condition. This allows for the selection of effective branch weighted transmission power and effective node weighted injection power that meet risk constraints and actual operating conditions, ensuring the accuracy and rationality of subsequent power flow index calculations. Next, several branch power flow indices are calculated using the extended grid topology and effective branch weighted transmission power, quantifying the contribution and importance of each branch in power transmission and providing a basis for identifying vulnerable branches. Finally, several node power flow indices are calculated using the node connection relationships of the extended grid topology and effective node weighted injection power, providing a basis for identifying vulnerable nodes.

[0016] Furthermore, the step of determining vulnerable nodes and vulnerable branches based on several branch power flow indicators, several node power flow indicators, and preset vulnerability screening rules, and obtaining the reliability indicators of the distribution network to be evaluated based on the vulnerable nodes and vulnerable branches, includes: Based on the aforementioned branch power flow indices and node power flow indices, several branch vulnerability power flow expectation values ​​and several node vulnerability power flow expectation values ​​are obtained respectively. Based on preset vulnerability screening rules, the expected values ​​of vulnerability power flow for the several branches and the expected values ​​of vulnerability power flow for the several nodes are sorted to determine vulnerable nodes and vulnerable branches, and the reliability indicators of the distribution network to be evaluated are obtained based on the vulnerable nodes and vulnerable branches.

[0017] In the above scheme, several branch vulnerability power flow expectation values ​​and several node vulnerability power flow expectation values ​​are obtained by using several branch power flow indicators and several node power flow indicators, respectively. This enables comprehensive quantification of the vulnerability of branches and nodes in the distribution network under multiple scenarios, ensuring the comprehensiveness of the vulnerability assessment. Next, the expected values ​​of branch vulnerability power flow and several node vulnerability power flow are sorted according to preset vulnerability screening rules to accurately locate vulnerable nodes and vulnerable branches. Then, the reliability indicators of the distribution network to be assessed are obtained by combining the vulnerable nodes and vulnerable branches, thus realizing the identification and reliability assessment of vulnerable parts of the distribution network.

[0018] Furthermore, it also includes: A distribution network reliability simulation model is established based on the extended topology of the power grid, the weighted transmission power of branches, the weighted injection power of nodes, and the data of faulty components in the power grid. The model is then used to simulate the distribution network reliability. If the preset simulation period has not been reached, the extended topology of the power grid is reconstructed, and the weighted transmission power of several branches, the weighted injection power of several nodes, and the data of faulty components in the power grid are obtained. The distribution network reliability simulation model is then updated and simulated based on the extended topology of the power grid, the weighted transmission power of several branches, the weighted injection power of several nodes, and the data of faulty components in the power grid, until the preset simulation period is reached.

[0019] In the above scheme, when the simulation has not reached the preset simulation period, the power grid extended topology is reconstructed and the weighted transmission power of several branches, the weighted injection power of several nodes, and the data of faulty components in the power grid are obtained to update the distribution network reliability simulation model and continue the simulation. This can realize reliability simulation in multiple scenarios and for a long period of time, fully cover the uncertainty of source load and the randomness of component failure, and ensure the comprehensiveness and accuracy of the distribution network reliability assessment results.

[0020] This invention provides a distribution network reliability assessment system, comprising a data acquisition module, a source-load random data generation module, a power grid extended topology construction module, a data acquisition module, an iterative simulation module, and a distribution network reliability index acquisition module, specifically: The data acquisition module is used to acquire, based on the distribution network to be evaluated, the power grid topology, historical wind speed data, historical node load active power and historical node load reactive power. The source-load random data generation module is used to generate several load power data and several wind power output power data based on historical wind speed data, historical node load active power and historical node load reactive power. The power grid extended topology construction module is used to construct a power grid extended topology based on the power grid topology, several load power data, and several wind power output power data. The data acquisition module is used to acquire several branch weighted transmission power, several node weighted injection power and grid fault component data based on the power grid topology, several load power data and several wind power output power data. The iterative simulation module is used to establish a distribution network reliability simulation model based on the extended topology of the power grid, the branch weighted transmission power, the node weighted injection power, and the data of faulty components in the power grid, so as to simulate the distribution network reliability simulation model. If the preset simulation period is reached, several branch power flow indicators and several node power flow indicators are obtained. The distribution network reliability index acquisition module is used to determine vulnerable nodes and vulnerable branches based on several branch power flow indices, several node power flow indices and preset vulnerability screening rules, and to acquire the distribution network reliability index to be evaluated based on the vulnerable nodes and vulnerable branches.

[0021] This invention provides a distribution network reliability assessment system. In practical applications, it only requires a source-load random data generation module to generate several load power data and several wind power output power data using historical wind speed data, historical node load active power, and historical node load reactive power of the distribution network to be assessed. This quantifies the random fluctuations of wind power output and load, avoiding the distortion of assessment results caused by ignoring the random fluctuations of wind power output and load in distribution network reliability assessments. It provides random power samples that closely reflect the actual operating conditions of the distribution network for subsequent power analysis. Next, a grid extended topology construction module is used to construct a grid extended topology structure using the grid topology structure, several load power data, and several wind power output power data. A data acquisition module integrates the random fluctuations of wind power output and load into the distribution network topology and power constraint system, enabling the quantification of the actual operating topology and power transmission risks of the distribution network. Simultaneously, it identifies the status of faulty components in the distribution network, providing a realistic grid extended topology structure, several branch weighted transmission power, several node weighted injection power, and grid faulty component data for subsequent power flow index calculations.

[0022] Then, an iterative simulation module is used to establish a distribution network reliability simulation model by using the extended topology of the power grid, weighted transmission power of several branches, weighted injected power of several nodes, and data from faulty components in the power grid. This model is used to simulate the distribution network reliability, quantifying the power transmission contribution of each branch and node in actual operation by combining source-load uncertainty and the fault state of distribution network components. It calculates several branch power flow indices and several node power flow indices, providing quantitative indicators for identifying vulnerable parts of the distribution network and laying the computational foundation for screening vulnerable nodes and branches. Finally, a distribution network reliability index acquisition module is used. By using several branch power flow indices, several node power flow indices, and preset vulnerability screening rules, it can comprehensively evaluate the reliability of the distribution network considering source-load uncertainty. This allows for accurate identification of vulnerable parts of the distribution network, determining vulnerable nodes and branches, and obtaining reliability indices reflecting the actual operating state of the distribution network under evaluation based on these vulnerable nodes and branches. This effectively solves the problem of distorted evaluation results caused by neglecting wind power output and random load fluctuations in traditional evaluation methods, achieving accurate evaluation of distribution network reliability.

[0023] Furthermore, the source-load random data generation module is used to generate several load power data and several wind power output power data based on historical wind speed data, historical node load active power, and historical node load reactive power, including: Wind speed shape parameters and wind speed scale parameters are obtained based on historical wind speed data; A wind speed probability density model is constructed based on historical wind speed data, wind speed shape parameters, and wind speed scale parameters. A load probability density model is constructed based on the historical node load active power and historical node load reactive power, and several load power data are generated based on the load probability density model. Several wind speed data are generated based on the wind speed probability density function, and wind power output power data corresponding to the several wind speed data are generated based on the several wind speed data and the preset wind power output power probability model.

[0024] In the above scheme, wind speed shape and scale parameters are obtained from historical wind speed data, providing parameter support for the subsequent construction of a wind speed probability density model and ensuring that the model accurately reflects the statistical characteristics of actual wind speed. Next, a wind speed probability density model is constructed using historical wind speed data, wind speed shape parameters, and wind speed scale parameters. This model accurately describes the random fluctuations in wind speed, addressing the problem of models failing to accurately represent wind speed uncertainty. Then, a load probability density model is constructed using historical node load active and reactive power data. This quantifies the load volatility affected by seasonal and climatic factors, generating several load power data points that conform to actual load characteristics. Finally, several wind speed data points are generated using the wind speed probability density model, and combined with a pre-defined wind power output probability model, corresponding wind power output data for each wind speed is generated, achieving a quantitative representation of the uncertainty in wind power output.

[0025] Furthermore, the data acquisition module is used to acquire, based on the power grid topology, several load power data, and several wind power output power data, several branch weighted transmission power, several node weighted injection power, and power grid fault component data, including: Based on the power grid topology, several load power data, several wind power output power data, a preset risk probability threshold, and a preset distribution network risk model, the initial transmission power of several branches and the initial injection power of several nodes are obtained. Based on the initial transmission power of several branches, the initial injection power of several nodes, the preset VaR model, and the preset information level value, the line transmission power threshold and the node injection power threshold are obtained. Based on the power grid topology, line transmission power threshold, and node injection power threshold, we obtain the weighted transmission power of several branches, the weighted injection power of several nodes, and data on faulty components in the power grid.

[0026] In the above scheme, by using the power grid topology, several load power data, several wind power output power data, preset risk probability thresholds, and a preset distribution network risk model, the initial transmission power of several branches and the initial injected power of several nodes are obtained. This allows for a preliminary integration of source load random fluctuations with the distribution network topology, providing basic data for subsequent risk quantification and power threshold calculation. Next, by using the initial transmission power of several branches, the initial injected power of several nodes, a preset VaR model, and preset confidence levels, line transmission power thresholds and node injected power thresholds are obtained. This transforms the potential risks caused by source load uncertainty into quantifiable power constraint indicators, defining power boundaries for the safe operation of the distribution network. Then, by using the power grid topology, line transmission power thresholds, and node injected power thresholds, risk constraints are deeply integrated with the power grid topology. Simultaneously, the operating status of components in the distribution network is clarified, and weighted transmission power of several branches, weighted injected power of several nodes, and data on faulty components in the power grid are obtained, providing input for the subsequent establishment of a distribution network reliability simulation model. Attached Figure Description

[0027] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments 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 from these drawings without creative effort.

[0028] Figure 1 A flowchart illustrating a method for evaluating the reliability of a power distribution network, as provided in an embodiment of the present invention; Figure 2 This is an architecture diagram of a power distribution network reliability assessment system provided in an embodiment of the present invention; Figure 3 This is an IEEE 33-node diagram of a power distribution network provided in one embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0031] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0032] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0033] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0034] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0035] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0036] See Figure 1To avoid distorted evaluation results due to neglecting wind power output and random load fluctuations in distribution network reliability assessment, and to accurately assess distribution network reliability by identifying vulnerable nodes and branches, this embodiment provides a method for evaluating distribution network reliability. The flowchart of this method can be found in [link to flowchart]. Figure 1 ,include: Step S1: Based on the distribution network to be evaluated, obtain the power grid topology, historical wind speed data, historical node load active power, and historical node load reactive power; Step S2: Based on historical wind speed data, historical node load active power and historical node load reactive power, generate several load power data and several wind power output power data. Step S3: Based on the power grid topology, several load power data and several wind power output power data, construct an extended power grid topology; Step S4: Based on the power grid topology, several load power data and several wind power output power data, obtain several branch weighted transmission power, several node weighted injection power and power grid fault element data; Step S5: Establish a distribution network reliability simulation model based on the extended topology of the power grid, branch weighted transmission power, node weighted injection power and power grid fault component data, and simulate the distribution network reliability simulation model. If the preset simulation period is reached, obtain several branch power flow indicators and several node power flow indicators. Step S6: Based on several branch power flow indicators, several node power flow indicators and preset vulnerability screening rules, determine vulnerable nodes and vulnerable branches, and obtain the reliability indicators of the distribution network to be evaluated based on vulnerable nodes and vulnerable branches.

[0037] In this embodiment, historical wind speed data of the distribution network to be evaluated can be obtained through a meteorological data network. Historical node load active power and reactive power of the distribution network to be evaluated are obtained through basic data at the power grid dispatch level. Several load power data and several wind power output power data are generated from the historical wind speed data, historical node load active power, and historical node load reactive power of the distribution network to be evaluated. This quantifies the random fluctuations of wind power output and load, avoiding distortion of evaluation results due to ignoring the random fluctuations of wind power output and load in distribution network reliability assessments, and providing random power samples that closely reflect actual operating conditions for subsequent distribution network power analysis.

[0038] Next, an extended grid topology is constructed using the grid topology, several load power data, and several wind power output power data. Specifically, the grid topology of the distribution network to be evaluated contains M nodes and l lines, which can typically be described by an undirected connectivity graph G={N, E}, where N is a non-empty finite set of nodes, including tie nodes, generator nodes, and load nodes, and E is the set of all transmission lines in the distribution network. This allows the random fluctuations of wind power output and load to be integrated into the distribution network topology and power constraint system, enabling the quantification of the actual operating topology and power transmission risks of the distribution network. At the same time, by identifying the status of faulty components in the distribution network, it can provide a realistic extended grid topology, weighted transmission power of several branches, weighted injected power of several nodes, and grid faulty component data for subsequent power flow index calculations.

[0039] Then, a distribution network reliability simulation model is established using the extended topology of the power grid, weighted transmission power of several branches, weighted injected power of several nodes, and data from faulty components in the power grid. This model is used to simulate the distribution network reliability, quantifying the power transmission contribution of each branch and node in actual operation by combining source-load uncertainty and the fault state of distribution network components. It calculates power flow indices for several branches and nodes, providing quantitative indicators for identifying vulnerable parts of the distribution network and laying the computational foundation for screening vulnerable nodes and branches. Finally, through the power flow indices for several branches and nodes, and preset vulnerability screening rules, a comprehensive assessment of distribution network reliability considering source-load uncertainty can be performed. This enables accurate identification of vulnerable parts of the distribution network, determining vulnerable nodes and branches, and obtaining reliability indices reflecting the actual operating state of the distribution network under assessment based on these indices. This effectively solves the problem of distorted assessment results caused by neglecting wind power output and random load fluctuations in traditional assessment methods, achieving accurate assessment of distribution network reliability.

[0040] Furthermore, the generation of several load power data and several wind power output power data based on historical wind speed data, historical node load active power, and historical node load reactive power includes: Wind speed shape parameters and wind speed scale parameters are obtained based on historical wind speed data; A wind speed probability density model is constructed based on historical wind speed data, wind speed shape parameters, and wind speed scale parameters. A load probability density model is constructed based on the historical node load active power and historical node load reactive power, and several load power data are generated based on the load probability density model. Several wind speed data are generated based on the wind speed probability density function, and wind power output power data corresponding to the several wind speed data are generated based on the several wind speed data and the preset wind power output power probability model.

[0041] In this embodiment, since the uncertainty of wind power output is affected by various environmental factors, such as wind speed and direction, and the deterministic models commonly used in existing technologies are difficult to accurately describe its characteristics, the maximum likelihood estimation method is used to obtain wind speed shape parameters and wind speed scale parameters through historical wind speed data. This provides parameter support for the subsequent construction of a wind speed probability density model, ensuring that the wind speed probability density model closely matches the statistical characteristics of actual wind speed. Next, a wind speed probability density model is constructed using the Weibull distribution based on historical wind speed data, wind speed shape parameters, and wind speed scale parameters, specifically as follows: Where v is wind speed, k is the wind speed shape parameter, and A is the wind speed scale parameter, this model can accurately describe the random fluctuations of wind speed and solve the problem that the model cannot accurately represent the uncertainty of wind speed. Since load is usually affected by multiple factors, such as seasonal variations, climate conditions, and differences between weekdays and holidays, the load fluctuation exhibits characteristics similar to a normal distribution due to the combined effect of these factors. Therefore, a load probability density model is constructed using historical node load active power and historical node load reactive power, specifically: in, For historical node load active power, This is the average active power of the load at historical nodes, which is calculated from the active power of the load at historical nodes. This is the standard deviation of the active power of the historical node load, which is obtained by calculating the active power of the historical node load. For historical node load reactive power, This is the average reactive power of historical node loads, which is obtained through calculation of the reactive power of historical node loads. The standard deviation of reactive power at historical nodes is calculated using the reactive power of historical node loads. After estimating the normal distribution parameters, the fluctuation of load due to seasonal and climatic factors can be quantified, and several load power data points conforming to the actual load characteristics are generated according to this normal distribution. Finally, several wind speed data points are generated according to the Weibull distribution using the wind speed probability density model, and combined with a preset wind power output power probability model, wind power output power data corresponding to each wind speed data is generated. Specifically, the preset wind power output power probability model is: in, , , These are the preset fan inlet velocity, preset fan rated velocity, and preset fan outlet velocity, respectively. This is the preset rated active power of the fan. This refers to wind power output data. By generating wind power output data corresponding to each wind speed through a preset wind power output power probability model, the uncertainty of wind power output is quantitatively characterized.

[0042] Furthermore, the acquisition of branch-weighted transmission power, node-weighted injection power, and grid fault component data based on the power grid topology, several load power data, and several wind power output power data includes: Based on the power grid topology, several load power data, several wind power output power data, a preset risk probability threshold, and a preset distribution network risk model, the initial transmission power of several branches and the initial injection power of several nodes are obtained. Based on the initial transmission power of several branches, the initial injection power of several nodes, the preset VaR model, and the preset information level value, the line transmission power threshold and the node injection power threshold are obtained. Based on the power grid topology, line transmission power threshold, and node injection power threshold, we obtain the weighted transmission power of several branches, the weighted injection power of several nodes, and data on faulty components in the power grid.

[0043] In this embodiment, by using the power grid topology, several load power data, several wind power output power data, a preset risk probability threshold, and a preset distribution network risk model, the initial transmission power of several branches and the initial injection power of several nodes are obtained. This allows for a preliminary integration of source load random fluctuations with the distribution network topology, providing foundational data for subsequent risk quantification and power threshold calculation. Next, by using the initial transmission power of several branches, the initial injection power of several nodes, a preset VaR model, and a preset confidence level value, the line transmission power threshold and the node injection power threshold are obtained. This transforms the potential risks arising from source load uncertainty into quantifiable power constraint indicators, defining power boundaries for the safe operation of the distribution network. Where y represents the power grid topology, and z represents a random scenario composed of the aforementioned load power data and wind power output power data. This represents the risk value in the (y,z) scenario obtained after inputting y and z into a preset VaR model, where a is the preset risk threshold, and p(z) is the joint probability distribution function obtained by fitting the probability distribution of scenario z. This represents the probability that, under the influence of several load power data and several wind power output power data, the risk is no greater than 'a'. Then, the calculation is performed under a preset confidence level. Risk indicator VAR: ,in, The default information level value is set.

[0044] Subsequently passed Determine the line transmission power threshold and the node injection power threshold, where, and They are respectively The following risk indicators are used. From this formula, the threshold values ​​for line transmitted active power, line transmitted reactive power, node injected active power, and node injected reactive power can be derived respectively. That is, the maximum active power passing through the line does not exceed the line transmitted active power threshold, the maximum reactive power passing through the line does not exceed the line transmitted reactive power threshold, the maximum active power injected into the node does not exceed the node injected active power threshold, and the maximum reactive power injected into the node does not exceed the node injected reactive power threshold. The line transmitted power threshold is obtained from the line transmitted active power threshold and the line transmitted reactive power threshold, and the node injected power threshold is obtained from the node injected active power threshold and the node injected reactive power threshold.

[0045] Then, based on the power grid topology, line transmission power threshold, and node injection power threshold, specifically: defining line weights. Line transmission power threshold With line impedance value Construct a weighted adjacency matrix A, whose elements satisfy... This allows for deep integration of risk constraints with the power grid topology, while simultaneously clarifying the operating status of components in the distribution network. It also enables the acquisition of weighted transmission power of several branches, weighted injection power of several nodes, and data on faulty components in the power grid, providing input for the subsequent establishment of a distribution network reliability simulation model. The extended power grid topology is represented as G′={N, E, W}.

[0046] Furthermore, the acquisition of weighted transmission power of several branches, weighted injection power of several nodes, and grid fault component data based on the power grid topology, line transmission power threshold, and node injection power threshold includes: Based on the power grid topology, line transmission power threshold, and node injection power threshold, the weighted transmission power of several branches and the weighted injection power of several nodes are obtained. Based on the distribution network to be evaluated, obtain historical failure rate and historical repair rate; Based on historical failure rates and historical repair rates, construct power grid component operating time models and power grid component repair time models; Data on faulty power grid components are obtained based on power grid component operating time models, power grid component repair time models, and extended power grid topology.

[0047] In this embodiment, by using the power grid topology, line transmission power threshold, and node injection power threshold, risk quantification indicators can be bound to the distribution network topology. This allows the power data on the power grid topology to better reflect the actual operational risk constraints, obtaining weighted transmission power of several branches and weighted injection power of several nodes. Next, by retrieving annual power outage event data from the power supply bureau's statistics for the distribution network to be evaluated, and by sampling the status of each component (such as transformers and circuit breakers) using Monte Carlo methods, the number of failures and repairs of each component are statistically analyzed. Historical failure rates and historical repair rates are calculated, providing real data support for subsequent component operation status modeling and enabling objective identification of faulty components.

[0048] Then, models for the normal operating time and fault repair time of power grid components are constructed using historical failure rates and historical repair rates, specifically as follows: ,in, Historical failure rate For historical restoration rate, and All are random numbers uniformly distributed between [0,1]. For the operating time model of power grid components, This invention provides a repair time model for power grid components, which can accurately characterize the randomness of the operating state of power grid components and solve the problem of neglecting the uncertainty of component failures in the existing technology for assessing the reliability of distribution networks. Finally, by using the power grid component operating time model, the power grid component repair time model, and the extended topology of the power grid, it is possible to identify power grid components in a faulty state, obtain power grid fault component data, and provide realistic component failure scenarios to support the reliability simulation of distribution networks.

[0049] Furthermore, a distribution network reliability simulation model is established based on the extended topology of the power grid, branch weighted transmission power, node weighted injection power, and data on faulty components in the power grid. This model is then used to simulate the distribution network reliability. If a preset simulation period is reached, several branch power flow indicators and several node power flow indicators are obtained, including: A distribution network reliability simulation model is established based on the extended topology of the power grid, the branch weighted transmission power, the node weighted injection power, and the data of faulty components in the power grid. The distribution network reliability simulation model is then used to simulate the model. If the preset simulation period is reached, the effective branch weighted transmission power and the effective node weighted injection power are determined. Calculate several branch power flow indices based on the extended topology of the power grid and the weighted transmission power of effective branches. Calculate power flow indices for several nodes based on the extended topology of the power grid and the weighted injected power of effective nodes.

[0050] In this embodiment, since multiple branches in the power grid typically transmit power in parallel, the amount of power carried by a particular branch determines its impact on the stability of the entire network. The more power transmitted through a branch, the more important its role in the power transmission process from the source to the load. Therefore, to comprehensively evaluate the importance of branches in the distribution network, a distribution network reliability simulation model is established and simulated using the extended topology of the power grid, branch weighted transmission power, node weighted injection power, and data on faulty components in the power grid. A preset simulation period is used as the simulation termination condition. This allows for the selection of effective branch weighted transmission power and effective node weighted injection power that meet risk constraints and actual operating conditions, ensuring the accuracy and rationality of subsequent power flow index calculations.

[0051] Next, several branch power flow indices are calculated using the extended grid topology and effective branch weighted transmission power, specifically: Where G represents the power source group in the extended grid topology, and D represents the load group in the extended grid topology. The weighting factor represents the minimum difference between the actual output power of power supply g in the power supply group and the actual power of load d in the load group, reflecting the maximum power transfer between the power supply and the load. This represents the active power transferred between power source g and load d through branch l. This represents the active power transferred from power source g to load d. The branch power flow index of branch l is represented by the branch power flow index, since It is not only related to the actual output of power sources and loads, but also reflects the operating status of the power system and the network topology. Therefore, it can provide a comprehensive understanding of the importance of branches in the power grid, quantify the contribution and importance of each branch in power transmission, and provide a basis for identifying vulnerable branches.

[0052] Then, according to Kirchhoff's laws, several node power flow indices are calculated based on the node connectivity relationships and effective node weighted injection power of the extended power grid topology, specifically: ,in, Let i be the node power flow index. This represents the set of branches connected to node i in the extended topology of the power grid. This represents the power flow in branch k. By injecting power into the effective nodes of node i with weights, the calculated node power flow index can accurately reflect the power flow characteristics and importance of nodes in the network, providing a basis for the identification of vulnerable nodes.

[0053] Furthermore, the step of determining vulnerable nodes and vulnerable branches based on several branch power flow indicators, several node power flow indicators, and preset vulnerability screening rules, and obtaining the reliability indicators of the distribution network to be evaluated based on the vulnerable nodes and vulnerable branches, includes: Based on the aforementioned branch power flow indices and node power flow indices, several branch vulnerability power flow expectation values ​​and several node vulnerability power flow expectation values ​​are obtained respectively. Based on preset vulnerability screening rules, the expected values ​​of vulnerability power flow for the several branches and the expected values ​​of vulnerability power flow for the several nodes are sorted to determine vulnerable nodes and vulnerable branches, and the reliability indicators of the distribution network to be evaluated are obtained based on the vulnerable nodes and vulnerable branches.

[0054] In this embodiment, Latin Hypercube Sampling (LHS) is used for stratified random sampling in an equally probable interval to simulate the wind power output scenario. Expected values ​​of vulnerability power flow for several branches and nodes are obtained through several branch power flow indices and several node power flow indices, respectively. Specifically: In the formula: N is the number of wind power output scenarios generated by the LHS method. Let be the probability of the i-th node occurring in the r-th wind power output scenario. Let be the probability of the l-th branch occurring in the r-th wind power output scenario. Right now This represents the expected value of the node vulnerability power flow for the i-th node. Right now , representing the expected vulnerability power flow value of the l-th branch, can comprehensively quantify the vulnerability of branches and nodes in the distribution network under multiple scenarios, ensuring the comprehensiveness of vulnerability assessment. Next, the expected vulnerability power flow values ​​of several branches and several nodes are sorted according to preset vulnerability screening rules. The nodes and branches with the highest ranking are identified as vulnerable parts and the vulnerable nodes and branches are output. Then, the normal or fault state of each wind power output scenario is calculated one by one based on the vulnerable nodes and branches.

[0055] When a vulnerable node or branch fails, the power outage load and load downtime of the distribution network are determined according to a preset power outage load judgment rule. Specifically, this preset power outage load judgment rule states that after a vulnerable node or branch fails, its downstream loads lose power due to the loss of upstream power supply. After the power flow redistribution is triggered by the vulnerable node or branch failure, the weighted transmission power of the branch is calculated based on the line transmission power threshold under a preset confidence level. If some branches are overloaded, some loads need to be disconnected to restore normal power system operation. The power outage load and load downtime data calculated through the vulnerable node and branch failure scenarios will serve as the basic data for calculating the distribution network reliability indicators for that year, enabling the identification and reliability assessment of vulnerable parts of the distribution network. The preset vulnerability screening rule can employ a bubble sort method.

[0056] The reliability indices of the distribution network to be evaluated in this embodiment include the system average interruption frequency index (SAIFI), the system average interruption duration index (SAIDI), the average service availability index (ASAI), and the average energy not supplied (AENS), with the specific expressions as follows: In the formula: The average number of power outages per year at node i is obtained through simulation statistics; This represents the total number of users at node i. This represents the set of busbars in the distribution network to be evaluated. and All of these are basic data for the distribution network areas to be evaluated; The average annual number of power outage hours for node i is obtained through simulation statistics; The average load of node i is represented by the value obtained through simulation statistics.

[0057] Furthermore, it also includes: A distribution network reliability simulation model is established based on the extended topology of the power grid, the weighted transmission power of branches, the weighted injection power of nodes, and the data of faulty components in the power grid. The model is then used to simulate the distribution network reliability. If the preset simulation period has not been reached, the extended topology of the power grid is reconstructed, and the weighted transmission power of several branches, the weighted injection power of several nodes, and the data of faulty components in the power grid are obtained. The distribution network reliability simulation model is then updated and simulated based on the extended topology of the power grid, the weighted transmission power of several branches, the weighted injection power of several nodes, and the data of faulty components in the power grid, until the preset simulation period is reached.

[0058] In this embodiment, when the simulation has not reached the preset simulation period, the extended topology of the power grid is reconstructed and the weighted transmission power of several branches, the weighted injection power of several nodes, and the data of faulty components of the power grid are obtained to update the distribution network reliability simulation model and continue the simulation. This can realize reliability simulation in multiple scenarios and over a long period of time, fully cover the uncertainty of source load and the randomness of component failure, and ensure the comprehensiveness and accuracy of the distribution network reliability assessment results.

[0059] This embodiment provides a system for evaluating the reliability of a power distribution network, such as... Figure 2 As shown, it includes a data acquisition module, a source-load random data generation module, a power grid extended topology construction module, a data acquisition module, an iterative simulation module, and a distribution network reliability index acquisition module, specifically: The data acquisition module is used to acquire, based on the distribution network to be evaluated, the power grid topology, historical wind speed data, historical node load active power and historical node load reactive power. The source-load random data generation module is used to generate several load power data and several wind power output power data based on historical wind speed data, historical node load active power and historical node load reactive power. The power grid extended topology construction module is used to construct a power grid extended topology based on the power grid topology, several load power data, and several wind power output power data. The data acquisition module is used to acquire several branch weighted transmission power, several node weighted injection power and grid fault component data based on the power grid topology, several load power data and several wind power output power data. The iterative simulation module is used to establish a distribution network reliability simulation model based on the extended topology of the power grid, the branch weighted transmission power, the node weighted injection power, and the data of faulty components in the power grid, so as to simulate the distribution network reliability simulation model. If the preset simulation period is reached, several branch power flow indicators and several node power flow indicators are obtained. The distribution network reliability index acquisition module is used to determine vulnerable nodes and vulnerable branches based on several branch power flow indices, several node power flow indices and preset vulnerability screening rules, and to acquire the distribution network reliability index to be evaluated based on the vulnerable nodes and vulnerable branches.

[0060] This embodiment provides a distribution network reliability assessment system. In practical applications, it only requires a source-load random data generation module to generate several load power data and several wind power output power data through historical wind speed data, historical node load active power, and historical node load reactive power of the distribution network to be assessed. This can quantify the random fluctuations of wind power output and load, avoid the distortion of assessment results due to ignoring the random fluctuations of wind power output and load in the distribution network reliability assessment, and provide random power samples that fit the actual operating conditions for subsequent distribution network power analysis.

[0061] Next, a grid extended topology construction module is used to construct an extended grid topology using the grid topology structure, several load power data, and several wind power output power data. A data acquisition module integrates the random fluctuations of wind power output and load into the distribution network topology and power constraint system, enabling the quantification of the actual operating topology and power transmission risks of the distribution network. Simultaneously, it identifies the status of faulty components in the distribution network, providing a realistic grid extended topology structure, weighted transmission power of several branches, weighted injected power of several nodes, and data on faulty components for subsequent power flow index calculations. Then, an iterative simulation module is used to establish a distribution network reliability simulation model using the grid extended topology structure, weighted transmission power of several branches, weighted injected power of several nodes, and data on faulty components. This model is then used to simulate the distribution network reliability simulation, combining source-load uncertainty and the fault status of distribution network components to quantify the power transmission contribution of each branch and node in actual operation. It calculates several branch power flow indices and several node power flow indices, providing quantitative indicators for identifying vulnerable parts of the distribution network and laying the computational foundation for screening vulnerable nodes and branches.

[0062] Finally, a distribution network reliability index acquisition module is adopted. Through several branch power flow indices, several node power flow indices, and preset vulnerability screening rules, the reliability of the distribution network can be comprehensively evaluated considering the uncertainty of source load. It can accurately identify the vulnerable parts of the distribution network, determine the vulnerable nodes and vulnerable branches, and obtain the reliability index of the distribution network under evaluation based on the vulnerable nodes and vulnerable branches, which reflects the actual operating status of the distribution network under evaluation. It can effectively solve the problem of distortion of evaluation results caused by the neglect of wind power output and random load fluctuations in traditional evaluation methods, and achieve accurate evaluation of distribution network reliability.

[0063] Furthermore, the source-load random data generation module is used to generate several load power data and several wind power output power data based on historical wind speed data, historical node load active power, and historical node load reactive power, including: Wind speed shape parameters and wind speed scale parameters are obtained based on historical wind speed data; A wind speed probability density model is constructed based on historical wind speed data, wind speed shape parameters, and wind speed scale parameters. A load probability density model is constructed based on the historical node load active power and historical node load reactive power, and several load power data are generated based on the load probability density model. Several wind speed data are generated based on the wind speed probability density function, and wind power output power data corresponding to the several wind speed data are generated based on the several wind speed data and the preset wind power output power probability model.

[0064] In this embodiment, wind speed shape parameters and wind speed scale parameters are obtained through historical wind speed data, providing parameter support for the subsequent construction of a wind speed probability density model and ensuring that the model accurately reflects the statistical characteristics of actual wind speed. Next, a wind speed probability density model is constructed using historical wind speed data, wind speed shape parameters, and wind speed scale parameters. This model accurately describes the random fluctuations in wind speed, addressing the problem of models failing to accurately represent wind speed uncertainty. Then, a load probability density model is constructed using historical node load active power and historical node load reactive power. This quantifies the load volatility affected by seasonal and climatic factors, generating several load power data points that conform to actual load characteristics. Finally, several wind speed data points are generated using the wind speed probability density model, and combined with a preset wind power output probability model, wind power output data corresponding to each wind speed data point is generated, achieving a quantitative representation of the uncertainty in wind power output.

[0065] Furthermore, the data acquisition module is used to acquire, based on the power grid topology, several load power data, and several wind power output power data, several branch weighted transmission power, several node weighted injection power, and power grid fault component data, including: Based on the power grid topology, several load power data, several wind power output power data, a preset risk probability threshold, and a preset distribution network risk model, the initial transmission power of several branches and the initial injection power of several nodes are obtained. Based on the initial transmission power of several branches, the initial injection power of several nodes, the preset VaR model, and the preset information level value, the line transmission power threshold and the node injection power threshold are obtained. Based on the power grid topology, line transmission power threshold, and node injection power threshold, we obtain the weighted transmission power of several branches, the weighted injection power of several nodes, and data on faulty components in the power grid.

[0066] In this embodiment, by using the power grid topology, several load power data, several wind power output power data, a preset risk probability threshold, and a preset distribution network risk model, the initial transmission power of several branches and the initial injected power of several nodes are obtained. This allows for a preliminary integration of source load random fluctuations with the distribution network topology, providing foundational data for subsequent risk quantification and power threshold calculation. Next, by using the initial transmission power of several branches, the initial injected power of several nodes, a preset VaR model, and a preset confidence level value, the line transmission power threshold and the node injected power threshold are obtained. This transforms the potential risks arising from source load uncertainty into quantifiable power constraint indicators, defining power boundaries for the safe operation of the distribution network. Then, by using the power grid topology, the line transmission power threshold, and the node injected power threshold, the risk constraints are deeply integrated with the power grid topology. Simultaneously, the operating status of components in the distribution network is clarified, and the weighted transmission power of several branches, the weighted injected power of several nodes, and data on faulty components in the power grid are obtained, providing input for the subsequent establishment of a distribution network reliability simulation model.

[0067] To more intuitively and fully illustrate that the distribution network reliability assessment method provided in this embodiment can identify vulnerable nodes and vulnerable branches in the distribution network, thereby achieving an accurate assessment of the distribution network reliability, the following embodiments are provided for specific explanation: For a certain region's distribution network, the operating voltage level is 12.66kV, the basic capacity is 10MVA, and after wind power integration, the wind power penetration rate is 48%. The wind turbine cut-in wind speed is 4m / s, the cut-out wind speed is 25m / s, and the rated wind speed is 10m / s. Its IEEE 33-node diagram is as follows: Figure 3 As shown, W represents a wind turbine generator. Using the power distribution network reliability assessment method provided in this embodiment, the reliability of this power distribution network is assessed, resulting in Tables 1, 2, 3, and 4 below.

[0068] As shown in Table 1, with the increase of the preset confidence level value, the number of power outages, the duration of power outages, and the amount of power supply shortage also increase. That is, the higher the preset confidence level value, the wider the range of risks considered, covering more extreme but less probable risk events. In other words, the power system may experience greater power shortages in extreme cases, directly leading to an increase in the number of power outages, the duration of power outages, and the amount of power supply shortage. This method, which comprehensively considers the probability of exceeding limits, can reasonably account for most cases of load reduction exceeding limits without overemphasizing extremely low-probability scenarios, making the reliability assessment of the distribution network more reasonable. In this embodiment, 90% was selected as the preset confidence level value for testing.

[0069] Table 1 Distribution network reliability indicators under different preset confidence levels As shown in Table 2, the top six vulnerable nodes were selected in the wind power output scenario. Furthermore, failures in some vulnerable nodes will lead to a loss of load. To verify the accuracy of the vulnerable node identification, possible node or branch failures were simulated, and the load survival rate (LY) was introduced as a quantitative performance indicator. , This represents the total load of the distribution network during normal operation. This is the load shedding caused by a node or branch failure. This refers to the load that continues to supply power after a fault occurs. Load survival rate reflects the distribution network's ability to maintain power supply after a fault. A value closer to 1 indicates stronger power supply capability and lower vulnerability; conversely, a lower value indicates weaker power supply capability and higher vulnerability. A lower load survival rate indicates that the vulnerability identification strategy of the distribution network is more effective. Assuming a load survival rate LY less than 0.4, accurate vulnerable nodes are identified. The data in Table 2 shows that the top three nodes have lower load survival rate LY values, indicating that these nodes have a more severe impact on the vulnerability of the distribution network—when these nodes fail, the distribution network struggles to maintain a stable and reliable operating state. This verifies that the distribution network reliability assessment method provided in this embodiment can accurately identify vulnerable nodes.

[0070] Table 2 Node Vulnerability Assessment Results As shown in Table 3, in the wind power output scenario, the top six vulnerable branches were selected. The results obtained by LY verification show that the top four branches have a serious impact on the vulnerability of the distribution network and cannot maintain the reliable operation of the distribution network.

[0071] Table 3. Branch vulnerability assessment results As shown in Table 4, two scenarios were set up: Scenario 1, a distribution network without wind power output; and Scenario 2, a distribution network considering wind power output (wind power penetration rate of 48%). Table 4 shows that after integrating distributed energy sources such as wind power, when components fail, islanding occurs. Distributed power sources like wind power supply these islands, thereby reducing the number of power outages, the duration of outages, and the amount of power loss for users, thus improving the reliability of the distribution network. Therefore, the reasonable integration of distributed power sources such as wind power into the distribution network is of great significance for improving the operational reliability of the distribution network.

[0072] Table 4 Reliability index results of distribution network systems under different scenarios This embodiment also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the functions of the system as described above.

[0073] It is understood that the above system embodiments correspond to the method embodiments of the present invention, and can implement the method embodiment of the present invention to provide a method for evaluating the reliability of a power distribution network.

[0074] It should be noted that the system embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0075] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating the reliability of a power distribution network, characterized in that, include: Based on the distribution network to be evaluated, obtain the network topology, historical wind speed data, historical node load active power, and historical node load reactive power; Based on historical wind speed data, historical node load active power and historical node load reactive power, several load power data and several wind power output power data are generated. Based on the power grid topology, several load power data and several wind power output power data, an extended power grid topology is constructed. Based on the power grid topology, several load power data and several wind power output power data, obtain several branch weighted transmission power, several node weighted injection power and power grid fault element data; A distribution network reliability simulation model is established based on the extended topology of the power grid, the weighted transmission power of branches, the weighted injection power of nodes, and the data of faulty components in the power grid. The distribution network reliability simulation model is then used to simulate the model. If the preset simulation period is reached, several branch power flow indicators and several node power flow indicators are obtained. Vulnerable nodes and vulnerable branches are determined based on several branch power flow indicators, several node power flow indicators, and preset vulnerability screening rules, and the reliability indicators of the distribution network to be evaluated are obtained based on the vulnerable nodes and vulnerable branches.

2. The method for evaluating the reliability of a power distribution network according to claim 1, characterized in that, The process generates several load power data points and several wind power output power data points based on historical wind speed data, historical node load active power, and historical node load reactive power, including: Wind speed shape parameters and wind speed scale parameters are obtained based on historical wind speed data; A wind speed probability density model is constructed based on historical wind speed data, wind speed shape parameters, and wind speed scale parameters. A load probability density model is constructed based on the historical node load active power and historical node load reactive power, and several load power data are generated based on the load probability density model. Several wind speed data are generated based on the wind speed probability density function, and wind power output power data corresponding to the several wind speed data are generated based on the several wind speed data and the preset wind power output power probability model.

3. The method for evaluating the reliability of a power distribution network according to claim 1, characterized in that, The process of obtaining weighted transmission power of several branches, weighted injection power of several nodes, and grid fault component data based on the power grid topology, several load power data, and several wind power output power data includes: Based on the power grid topology, several load power data, several wind power output power data, a preset risk probability threshold, and a preset distribution network risk model, the initial transmission power of several branches and the initial injection power of several nodes are obtained. Based on the initial transmission power of several branches, the initial injection power of several nodes, the preset VaR model, and the preset information level value, the line transmission power threshold and the node injection power threshold are obtained. Based on the power grid topology, line transmission power threshold, and node injection power threshold, we obtain the weighted transmission power of several branches, the weighted injection power of several nodes, and data on faulty components in the power grid.

4. The method for evaluating the reliability of a power distribution network according to claim 3, characterized in that, The process of obtaining weighted transmission power of several branches, weighted injection power of several nodes, and grid fault component data based on grid topology, line transmission power threshold, and node injection power threshold includes: Based on the power grid topology, line transmission power threshold, and node injection power threshold, the weighted transmission power of several branches and the weighted injection power of several nodes are obtained. Based on the distribution network to be evaluated, obtain historical failure rate and historical repair rate; Based on historical failure rates and historical repair rates, construct power grid component operating time models and power grid component repair time models; Data on faulty power grid components are obtained based on power grid component operating time models, power grid component repair time models, and extended power grid topology.

5. The method for evaluating the reliability of a power distribution network according to claim 1, characterized in that, The distribution network reliability simulation model is established based on the extended topology of the power grid, branch weighted transmission power, node weighted injection power, and power grid fault component data. This model is then used to simulate the distribution network reliability. If a preset simulation period is reached, several branch power flow indicators and several node power flow indicators are obtained, including: A distribution network reliability simulation model is established based on the extended topology of the power grid, the branch weighted transmission power, the node weighted injection power, and the data of faulty components in the power grid. The distribution network reliability simulation model is then used to simulate the model. If the preset simulation period is reached, the effective branch weighted transmission power and the effective node weighted injection power are determined. Calculate several branch power flow indices based on the extended topology of the power grid and the weighted transmission power of effective branches. Calculate power flow indices for several nodes based on the extended topology of the power grid and the weighted injected power of effective nodes.

6. The method for evaluating the reliability of a distribution network according to claim 1, characterized in that, The process of determining vulnerable nodes and vulnerable branches based on several branch power flow indicators, several node power flow indicators, and preset vulnerability screening rules, and obtaining the reliability indicators of the distribution network to be evaluated based on the vulnerable nodes and vulnerable branches, includes: Based on the aforementioned branch power flow indices and node power flow indices, several branch vulnerability power flow expectation values ​​and several node vulnerability power flow expectation values ​​are obtained respectively. Based on preset vulnerability screening rules, the expected values ​​of vulnerability power flow for the several branches and the expected values ​​of vulnerability power flow for the several nodes are sorted to determine vulnerable nodes and vulnerable branches, and the reliability indicators of the distribution network to be evaluated are obtained based on the vulnerable nodes and vulnerable branches.

7. The method for evaluating the reliability of a power distribution network according to claim 6, characterized in that, Also includes: A distribution network reliability simulation model is established based on the extended topology of the power grid, the weighted transmission power of branches, the weighted injection power of nodes, and the data of faulty components in the power grid. The model is then used to simulate the distribution network reliability. If the preset simulation period has not been reached, the extended topology of the power grid is reconstructed, and the weighted transmission power of several branches, the weighted injection power of several nodes, and the data of faulty components in the power grid are obtained. The distribution network reliability simulation model is then updated and simulated based on the extended topology of the power grid, the weighted transmission power of several branches, the weighted injection power of several nodes, and the data of faulty components in the power grid, until the preset simulation period is reached.

8. A system for evaluating the reliability of a power distribution network, characterized in that, It includes a data acquisition module, a source-load random data generation module, a power grid extended topology construction module, a data acquisition module, an iterative simulation module, and a distribution network reliability index acquisition module, specifically: The data acquisition module is used to acquire, based on the distribution network to be evaluated, the power grid topology, historical wind speed data, historical node load active power and historical node load reactive power. The source-load random data generation module is used to generate several load power data and several wind power output power data based on historical wind speed data, historical node load active power and historical node load reactive power. The power grid extended topology construction module is used to construct a power grid extended topology based on the power grid topology, several load power data, and several wind power output power data. The data acquisition module is used to acquire several branch weighted transmission power, several node weighted injection power and grid fault component data based on the power grid topology, several load power data and several wind power output power data. The iterative simulation module is used to establish a distribution network reliability simulation model based on the extended topology of the power grid, the branch weighted transmission power, the node weighted injection power, and the data of faulty components in the power grid, so as to simulate the distribution network reliability simulation model. If the preset simulation period is reached, several branch power flow indicators and several node power flow indicators are obtained. The distribution network reliability index acquisition module is used to determine vulnerable nodes and vulnerable branches based on several branch power flow indices, several node power flow indices and preset vulnerability screening rules, and to acquire the distribution network reliability index to be evaluated based on the vulnerable nodes and vulnerable branches.

9. The power distribution network reliability assessment system according to claim 8, characterized in that, The source-load random data generation module is used to generate several load power data and several wind power output power data based on historical wind speed data, historical node load active power, and historical node load reactive power, including: Wind speed shape parameters and wind speed scale parameters are obtained based on historical wind speed data; A wind speed probability density model is constructed based on historical wind speed data, wind speed shape parameters, and wind speed scale parameters. A load probability density model is constructed based on the historical node load active power and historical node load reactive power, and several load power data are generated based on the load probability density model. Several wind speed data are generated based on the wind speed probability density function, and wind power output power data corresponding to the several wind speed data are generated based on the several wind speed data and the preset wind power output power probability model.

10. The power distribution network reliability assessment system according to claim 8, characterized in that, The data acquisition module is used to acquire, based on the power grid topology, several load power data, and several wind power output power data, several branch weighted transmission power, several node weighted injection power, and power grid fault component data, including: Based on the power grid topology, several load power data, several wind power output power data, a preset risk probability threshold, and a preset distribution network risk model, the initial transmission power of several branches and the initial injection power of several nodes are obtained. Based on the initial transmission power of several branches, the initial injection power of several nodes, the preset VaR model, and the preset information level value, the line transmission power threshold and the node injection power threshold are obtained. Based on the power grid topology, line transmission power threshold, and node injection power threshold, we obtain the weighted transmission power of several branches, the weighted injection power of several nodes, and data on faulty components in the power grid.

Images