Power distribution network operation characteristic evaluation method and device, computer equipment and storage medium

Abstract

The invention discloses a power distribution network operation characteristic evaluation method and device, computer equipment and a storage medium, relates to the technical field of power distribution network operation maintenance, can improve the objectivity and accuracy of evaluation index calculation, provides a comprehensive and reliable basis for optimal scheduling and safe operation of a power distribution network, and improves the reliability of power distribution network operation. And the requirements of intelligent and refined management of a modern power distribution network are met. The method comprises the following steps: acquiring historical operation data of a target power distribution network to be subjected to operation characteristic evaluation, and generating distributed resource scene data with spatial-temporal correlation by performing distributed resource spatial-temporal distribution characteristic analysis on the historical operation data; determining a plurality of basic evaluation indexes, and performing load flow calculation on the distributed resource scene data by referring to the plurality of basic evaluation indexes to obtain a plurality of basic evaluation results; and calculating the plurality of basic evaluation results by adopting an entropy weight method according to the power distribution network operation evaluation indexes to obtain a power distribution network operation overall evaluation result and outputting the power distribution network operation overall evaluation result.

Description

Technical Field

[0001] This application relates to the field of power distribution network operation and maintenance technology, and in particular to a method, apparatus, computer equipment and storage medium for evaluating the operating characteristics of a power distribution network. Background Technology

[0002] With the ongoing energy transition, the integration of distributed renewable energy sources such as distributed photovoltaic and wind power into the power system is expanding rapidly. Meanwhile, electric vehicles, as an emerging product of the integration of transportation and energy, are also experiencing rapid growth in their charging load. However, the output of distributed renewable energy is significantly affected by natural conditions, exhibiting intermittency and randomness; the charging load of electric vehicles is closely related to users' travel habits and charging time choices, displaying spatiotemporal uncertainty. These complex characteristics bring numerous challenges to the operation and management of the distribution network, placing higher demands on power flow calculations, operational assessments, and dispatch scheme formulation.

[0003] In related technologies, in the evaluation method of distribution network operation characteristics, some evaluation index system is usually constructed to measure the operation characteristics of the distribution network, such as voltage over-limit and line overload, so as to reflect the operation characteristics of the distribution network and thus realize the evaluation of the operation characteristics of the distribution network.

[0004] However, the applicant recognizes that the relevant technology has at least the following technical problems in its implementation: The indicator system constructed by related technologies focuses on the security and reliability of individual nodes. The calculation results of power flow in the distribution network deviate significantly from the actual operating conditions, making it difficult to accurately reflect the real-time operating status of the distribution network. This affects the scientificity and effectiveness of scheduling decisions based on the calculation results, resulting in the evaluation results failing to fully and accurately reflect the comprehensive impact of distributed resource access on the operating characteristics of the distribution network. This reduces the objectivity and accuracy of the evaluation, and fails to provide a comprehensive and reliable basis for the optimized scheduling and safe operation of the distribution network, making it difficult to meet the needs of intelligent and refined management of modern distribution networks. Summary of the Invention

[0005] In view of this, this application provides a method, device, computer equipment, and storage medium for evaluating the operating characteristics of a distribution network. The main purpose is to solve the problem that the current system is unable to accurately reflect the real-time operating status of the distribution network, which affects the scientificity and effectiveness of scheduling decisions based on calculation results. As a result, the evaluation results cannot fully and accurately reflect the comprehensive impact of distributed resource access on the operating characteristics of the distribution network, reducing the objectivity and accuracy of the evaluation. This makes it impossible to provide a comprehensive and reliable basis for the optimized scheduling and safe operation of the distribution network, and it is difficult to meet the needs of intelligent and refined management of modern distribution networks.

[0006] According to a first aspect of this application, a method for evaluating the operating characteristics of a distribution network is provided, the method comprising: Historical operating data of the target distribution network to be evaluated is obtained, and distributed resource scenario data with spatiotemporal correlation is generated by analyzing the spatiotemporal distribution characteristics of the historical operating data. Multiple basic evaluation indicators are determined, and power flow calculations are performed on the distributed resource scenario data with reference to the multiple basic evaluation indicators to obtain multiple basic evaluation results. The multiple basic evaluation indicators are used to evaluate the operation of nodes in the target distribution network. Based on the power distribution network operation evaluation indicators, the entropy weight method is used to calculate the multiple basic evaluation results to obtain and output the overall power distribution network operation evaluation results.

[0007] According to a second aspect of this application, a distribution network operation characteristic assessment device is provided, the device comprising: The analysis module is used to acquire historical operating data of the target distribution network to be evaluated for its operating characteristics, and to generate distributed resource scenario data with spatiotemporal correlation by performing distributed resource spatiotemporal distribution characteristic analysis on the historical operating data. The basic evaluation module is used to determine multiple basic evaluation indicators, and to perform power flow calculations on the distributed resource scenario data with reference to the multiple basic evaluation indicators to obtain multiple basic evaluation results. The multiple basic evaluation indicators are used to evaluate the operation of nodes in the target distribution network. The overall evaluation module is used to calculate the multiple basic evaluation results based on the distribution network operation evaluation indicators and the entropy weight method, so as to obtain and output the overall evaluation result of the distribution network operation.

[0008] According to a third aspect of this application, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the first aspects above.

[0009] According to a fourth aspect of this application, a storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects above.

[0010] By employing the above technical solutions, this application provides a method, apparatus, computer equipment, and storage medium for evaluating the operating characteristics of a distribution network. This application acquires historical operating data of the target distribution network to be evaluated, analyzes the spatiotemporal distribution characteristics of distributed resources on the historical operating data, generates spatiotemporally correlated distributed resource scenario data, determines multiple basic evaluation indicators, performs power flow calculations on the distributed resource scenario data with reference to these indicators, obtains multiple basic evaluation results, and calculates the multiple basic evaluation results using the entropy weight method based on the distribution network operation evaluation indicators, obtaining and outputting the overall distribution network operation evaluation result. By analyzing the spatiotemporal distribution characteristics of distributed resources on the historical operating data of the target distribution network, this ensures that the generated distributed resource scenario data can accurately, reasonably, and comprehensively reflect the overall comprehensive operating characteristics of the target distribution network. Furthermore, the use of the entropy weight method for comprehensive evaluation of multiple basic evaluation indicators reduces the impact of human factors on indicator calculation, improves the objectivity and accuracy of indicator calculation, and provides a comprehensive and reliable basis for the optimized scheduling and safe operation of the distribution network, meeting the needs of intelligent and refined management of modern distribution networks.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0012] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This illustration shows a flowchart of a method for evaluating the operating characteristics of a power distribution network according to an embodiment of this application. Figure 2 This paper illustrates a flowchart of another method for evaluating the operating characteristics of a power distribution network provided in an embodiment of this application. Figure 3 This paper illustrates a schematic diagram of a power distribution network topology provided in an embodiment of this application. Figure 4 This illustration shows a schematic diagram of the technical route for a distribution network operation characteristic evaluation method provided in an embodiment of this application; Figure 5 This illustration shows a schematic diagram of the structure of a power distribution network operation characteristic evaluation device provided in an embodiment of this application; Figure 6 A schematic diagram of the device structure of a computer device provided in an embodiment of this application is shown. Detailed Implementation

[0013] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0014] This application provides a method for evaluating the operating characteristics of a power distribution network, such as... Figure 1 As shown, the method includes: S10: Obtain historical operating data of the target distribution network to be evaluated for its operating characteristics, and generate distributed resource scenario data with spatiotemporal correlation by analyzing the spatiotemporal distribution characteristics of the historical operating data.

[0015] In this embodiment, the target distribution network uses a voltage level of 10kV. First, historical operating data of the target distribution network needs to be collected. This data includes, but is not limited to, voltage, current, active power, and reactive power of each node, as well as real-time output data of distributed renewable energy (distributed resources include distributed renewable energy and electric vehicles, where distributed renewable energy encompasses distributed photovoltaic and wind power generation) and electric vehicle charging loads. Electric vehicles can serve as mobile energy storage devices and also provide regulation capabilities through the charging network. Subsequently, using this historical data, a time-dependent model based on a Copula (connection function) transfer kernel-Continuous State Space Markov Chain (CSMC) and a spatial-dependent model based on a multivariate normal distribution function are constructed to deeply analyze the spatiotemporal distribution characteristics of distributed resources. The Copula function can flexibly describe the correlation between variables, while the CSMC model can effectively capture the time-dimensional transfer characteristics of distributed resources without increasing complexity while maintaining high modeling accuracy. Simultaneously, the multivariate normal distribution function is used to describe the spatial correlation of distributed resources, and the covariance matrix is ​​constructed to quantify the synchronicity of distributed resource output in different geographical locations. Spatiotemporal correlation of distributed resources includes the temporal correlation, spatial correlation, spatiotemporal correlation scenario generation, and scenario evaluation of distributed resources. Natural energy sources such as wind speed and solar intensity exhibit fluctuations and indirectness, resulting in a certain temporal correlation in the output power of a single photovoltaic power station or wind farm within adjacent time periods. Wind and solar energy characteristics are relatively consistent within the same or surrounding regions, and unit output typically shows the same trend, exhibiting spatial correlation. The higher the spatial correlation of distributed renewable energy, the stronger the synchronicity (i.e., simultaneous low or high output) of different wind farms and photovoltaic power stations, leading to greater fluctuations in distributed renewable energy. Due to the continuous nature of electric vehicle charging behavior, charging station loads are correlated in adjacent time periods. Furthermore, due to the spatial shifting characteristics of electric vehicle locations, electric vehicle charging stations in different regions within the same time period exhibit spatial correlation. Ultimately, spatiotemporally correlated distributed resource scenario data is generated. This data more closely reflects actual operating conditions, providing a solid foundation for subsequent power flow calculations and evaluations.

[0016] In this way, by deeply analyzing the spatiotemporal distribution characteristics of distributed resources, the generated distributed resource scenario data can accurately, reasonably, and comprehensively reflect the overall integrated operation characteristics of the target distribution network, providing more accurate data support for subsequent power flow calculations and assessments. For example, taking a regional distribution network as an example, this network is connected to a certain scale of distributed photovoltaic and electric vehicle charging stations. By collecting historical operation data of this network and using the Copula transfer kernel-CSMC model and multivariate normal distribution function to analyze its spatiotemporal distribution characteristics, multiple distributed resource scenario data with spatiotemporal correlations were successfully generated. This data not only considers the temporal fluctuations and spatial correlations of photovoltaic output but also incorporates the spatiotemporal variation characteristics of electric vehicle charging loads, providing strong support for subsequent distribution network operation characteristic assessments.

[0017] In step S10, historical operating data of the target distribution network to be evaluated is obtained. Distributed resource scenario data with spatiotemporal correlation is generated by analyzing the spatiotemporal distribution characteristics of the historical operating data, including: S11: Load the historical operating data of the target distribution network and preprocess the historical operating data.

[0018] In this embodiment, the historical operating data of the target distribution network must first be loaded from a database or file. This historical operating data covers key parameters such as distributed renewable energy (e.g., wind power, photovoltaic) output, load consumption, and voltage fluctuations. Since the original data may contain missing or outlier values, preprocessing is required: for missing values, linear interpolation or filling based on the average of nearby data can be used; for outliers, they are identified and removed by setting a reasonable threshold (e.g., based on the 3σ principle of normal distribution), and then corrected using data from adjacent time periods. The core purpose of data preprocessing is to eliminate noise interference, ensuring that subsequent analysis is based on a complete and reliable data foundation, and avoiding deviations in analysis results due to data defects.

[0019] In this way, by handling missing and outlier values ​​through the above process, the quality of historical operational data can be improved, providing an accurate data foundation for subsequent analysis of the spatiotemporal distribution characteristics of distributed resources. For example, in a distribution network connected to distributed photovoltaic (PV) systems, the PV output record for 12:00 noon on a certain day is missing. By using linear interpolation, the output data at 11:00 and 13:00 can be used to estimate and fill in the missing value, ensuring data continuity and providing a complete dataset for subsequent analysis.

[0020] S12: The kernel density estimation method is used to analyze the pattern of distributed resource output in each time period to form the marginal distribution of a single time period.

[0021] In this embodiment, combining preprocessed historical operating data, the kernel density estimation method is used to call the ksdensity (Kernel Density Estimation) function in MATLAB (Matrix Laboratory) to analyze the pattern of distributed resource output in each time period, forming the marginal distribution of a single time period. Kernel density estimation is a non-parametric probability density estimation method that constructs a probability density function by smoothing data points. It does not require a pre-defined distribution form and is suitable for capturing the complex time-varying characteristics of distributed renewable energy output. Specifically, the distributed resource output values ​​from historical data are used as input, and a Gaussian kernel function with appropriate bandwidth is selected to perform kernel density estimation on the output data for each time period (e.g., per hour), forming the marginal distribution of a single time period. This flexibly adapts to the randomness and volatility of renewable energy output, accurately characterizing its output probability distribution features.

[0022] Thus, by employing kernel density estimation in the above process, the limitations imposed by traditional parameterization methods on the assumptions about the distribution pattern are avoided, allowing for a more realistic reflection of the actual distribution patterns of distributed resource output and improving the accuracy of edge distribution. For example, analyzing the historical output data of a wind farm at 1:00 AM using kernel density estimation reveals a bimodal characteristic (both low and high output probabilities are relatively high), which aligns with nighttime wind speed variation patterns, providing accurate edge distribution input for subsequent time correlation analysis.

[0023] S13: Using a time correlation model, perform distributed resource time autocorrelation analysis on the marginal distribution of a single time period to obtain a time correlation model that indicates the impact of distributed resource output on adjacent time periods.

[0024] In this embodiment, a time-related model is first constructed based on the transfer kernel function. The transfer kernel function quantifies the correlation in the time dimension by describing the transfer probability of distributed resource output in adjacent time periods. Then, using the time-related model, distributed resource time autocorrelation analysis is performed on the marginal distribution of a single time period to obtain a time-related model that indicates the impact of distributed resource output in adjacent time periods. The specific process is as follows: Specifically, the joint probability of power output values ​​in adjacent time periods is calculated using the transition kernel function in the formula, and a continuous state-space Markov chain model is constructed. This model can capture the time-series dependence of distributed resource output, such as the persistence of photovoltaic power output under consecutive sunny weather or the fluctuation characteristics of wind power output in short time scales.

[0025] First, the transfer kernel function shown in Formula 1 is selected, where the transfer kernel function is the Copula function. The Frank Copula function can flexibly describe the entire range from negative to positive correlation, has good symmetry, and its parameter estimation is simple and easy to implement. Formula 1:

[0026] in, This indicates a way to describe two random variables. and The cumulative probability distribution function of the correlation structure between them and These represent the marginal distribution functions of the random variables of wind and solar power, respectively. Represent two random variables and The relevant parameters between them. In the embodiments of this application, , express and Positive correlation, express and Negative correlation Approaching 0 indicates and Tend to independence.

[0027] Then, an initial correlation model is constructed, which is a continuous-state-space Markov chain model. In the time dimension, the continuous-state-space Markov chain model can be... The values ​​at adjacent time points are considered as a binary joint distribution, so their transition characteristics can be modeled using a Markov chain after discretization. For a first-order Markov chain, this embodiment uses a Copula function to replace its state transition matrix, forming a CSMC model. Its advantage lies in maintaining high modeling accuracy without increasing complexity. Specifically, the original state transition kernel in the initial correlation model is represented by the following Equation 2. Formula 2:

[0028] in, This represents the conditional probability distribution function in the original state transition kernel. Indicates in The output value of distributed resources at any given moment. Indicates in The output value of distributed resources at any given moment. and Continuous values ​​can be taken, as shown in Formula 2. Under the conditions The probability of.

[0029] Next, in the initial correlation model, a transition kernel function is used to express the conditional probability distribution function in the original state transition kernel. To obtain the time-dependent model, where the conditional probability distribution function in the time-dependent model is... The expression is shown in Formula 3 below. Formula 3:

[0030] in, Indicates in time The corresponding marginal cumulative distribution function value, Indicates in time The corresponding marginal cumulative distribution function value.

[0031] Finally, the marginal distribution of a single time period is input into the time correlation model for analysis. The time correlation model analyzes the probability of influence between distributed resource outputs in adjacent time periods to obtain the time correlation model. The time correlation model reveals the dynamic evolution law of distributed resource output in the time dimension, providing theoretical support for generating scenario data that conforms to actual time series characteristics and enhancing the temporal rationality of scenario data.

[0032] For example, analyzing the power output data of a distributed photovoltaic system in adjacent time periods reveals a strong transition probability between the high power output state at 10:00 AM and the high power output state at 11:00 AM, indicating that photovoltaic power output has temporal continuity under sunny weather conditions, and the time-related model successfully captures this characteristic.

[0033] S14: Using the multivariate normal distribution function, perform distributed resource spatial correlation analysis on historical operating data to obtain a spatial correlation model that indicates the impact of distributed resource output at different locations.

[0034] In this embodiment, a multivariate normal distribution function is used to perform spatial correlation analysis on historical operating data to obtain a spatial correlation model indicating the impact of distributed resource output at different locations. This model reveals the mutual influence relationships of distributed resources in their geographical distribution, such as the synchronous changes in output of adjacent wind farms due to wind speed propagation effects. Specifically, the implementation first uses the multivariate normal distribution function to calculate the covariance between the output of distributed resources at each pair of locations, quantifying the spatial correlation. Then, the calculated covariance is used to construct the covariance matrix shown in Formula 4 below to obtain the spatial correlation model. Formula 4:

[0035] in, Represents the covariance matrix. Indicate location With location Covariance between , and The value ranges from 1 to .

[0036] S15: Based on time-related and space-related models, multiple simulated power distribution network operation scenarios are randomly generated to obtain scenario-related sampling data.

[0037] In this embodiment, firstly, based on a spatial correlation model, a covariance matrix at the first time step is determined. This matrix quantifies the coordinated changes in the output of distributed resources in different geographical locations (such as the output synchronization of adjacent wind farms due to wind speed propagation effects), generating first spatial sampling data that follows a uniform distribution. This first spatial sampling data reflects the initial random distribution state in the spatial dimension. Subsequently, based on the spatial correlation model, a covariance matrix at the second time step is determined, and second spatial sampling data that follows a uniform distribution is generated using the covariance matrix at the second time step.

[0038] Next, to establish temporal correlation, it is necessary to query the joint probability function corresponding to the first and second time points in the temporal correlation model. This function describes the transfer pattern of distributed resource output in adjacent time periods (such as the continuity or abrupt change of photovoltaic output in continuous time periods). The joint probability function is used to process the first spatial sample data to obtain the temporal correlation sample data of the second time point, thereby realizing the coupled generation of spatiotemporal data.

[0039] Furthermore, using a time-related model, based on the first spatial sampling data, the second spatial sampling data, and the time-related sampling data, scenario simulation descriptions are performed simultaneously in both the time and spatial dimensions (e.g., simulating the output fluctuation of distributed resources in a certain area under wind-solar complementary conditions), resulting in a simulated scenario data set. This is then processed at the third time point to obtain another simulated scenario data set, and so on, iteratively generating multiple sets of simulated scenario data set time by time until all time points are traversed. Finally, all the obtained simulated scenario data sets are integrated into a scenario-related sampling dataset covering the entire time period. Through the synergistic effect of the spatiotemporal models, it is ensured that the generated scenario data not only conforms to the spatial correlation of geographical distribution but also reflects the dynamic characteristics of temporal evolution, forming a multi-dimensional simulation sample of power distribution network operation.

[0040] In this way, through the joint driving of spatiotemporal models, the generated scenario data maintains inherent consistency in both time and space dimensions, comprehensively reflecting the complex operational characteristics of the distribution network after the integration of distributed renewable energy, and providing high-fidelity data support for subsequent assessments. For example, when assessing a distribution network containing wind and solar power, initial spatial sampling data of different wind farms and solar power plants are generated through a spatial correlation model, revealing that adjacent wind farms exhibit output synchronicity due to terrain effects; a temporal correlation model is used to capture the sustained high-intensity characteristics of solar power output during midday. In the simulation scenario, wind power output shows a coordinated increase in space due to increased wind speed on a certain morning, and subsequently, as sunlight intensifies, solar power output gradually follows in the temporal dimension, forming a spatiotemporally coupled scenario of complementary wind and solar power output. This simulation data effectively supports the assessment of voltage fluctuations and power flow distribution in the distribution network under multi-energy synergy.

[0041] S16: Perform inverse transformation sampling on the scene-related sampling data to obtain distributed resource scene data.

[0042] In this embodiment, inverse transformation sampling is a method for converting uniformly distributed random numbers into target distributed random numbers. By inputting the generated uniformly distributed scene-related sampling data into the constructed spatiotemporal correlation model, the inverse cumulative distribution function is used for transformation to generate distributed resource scene data that conforms to the spatiotemporal distribution characteristics of actual distributed resources. This ensures that the generated distributed resource scene data is consistent with the real data in terms of probability distribution, thereby improving the rationality and practicality of the scene data.

[0043] In this way, through the inverse transformation sampling process described above, the scenario data is statistically matched with the actual distribution network operation data, providing reliable input for subsequent evaluation and meeting the data quality requirements of intelligent distribution network management. For example, uniformly distributed random sampling data can be transformed into scenario data that conforms to the actual output distribution of a distributed photovoltaic system through inverse transformation sampling, ensuring that the generated scenario data is consistent with the real system in terms of temporal continuity and spatial synchronization, thus providing high-quality data support for the evaluation of distribution network operation characteristics.

[0044] In addition, in practical applications, the Energy Score (ES) metric can be used in Formula 5 to perform a fit analysis on distributed resource scenario data, determine the degree of fit between the scenario and historical data, and use the obtained fit analysis results to label the distributed resource scenario. Formula 5:

[0045] in, This refers to the ES index used to indicate fit. This indicates the total number of scenarios indicated by the distributed resource scenario. This represents measured data of the scenery. The scenario indicating a distributed resource scenario of efforts, The scenario indicating a distributed resource scenario of efforts, The scenario indicating a distributed resource scenario The probability of occurrence, The scenario indicating a distributed resource scenario The probability of occurrence.

[0046] S20: Determine multiple basic evaluation indicators, and perform power flow calculations on distributed resource scenario data with reference to these indicators to obtain multiple basic evaluation results.

[0047] In this embodiment, multiple basic evaluation indicators are used to assess the operational status of nodes in the target distribution network. These indicators constitute a comprehensive evaluation system that reflects the operational status of distribution network nodes. These indicators include, but are not limited to, node voltage compliance rate, node voltage deviation index, and single-segment line overload index. Specifically, the node voltage compliance rate quantifies the probability that the node voltage amplitude is within the acceptable range at a specific time; the node voltage deviation index assesses the degree of deviation in voltage drop or rise; and the single-segment line overload index monitors whether the current carrying capacity of a line segment exceeds the limit.

[0048] Subsequently, based on these basic evaluation indicators, power flow calculations are performed on the generated distributed resource scenario data to obtain the operating parameters of each node under different scenarios, such as voltage and current. Then, the specific values ​​of each basic evaluation indicator are calculated, resulting in multiple basic evaluation results.

[0049] In this way, by establishing a scientific and reasonable basic evaluation index system and referring to these indicators to perform power flow calculations on distributed resource scenario data, the operation of the distribution network under different scenarios can be comprehensively and accurately evaluated, providing rich data support for subsequent comprehensive evaluation. Continuing with the example of the distribution network in the aforementioned region, node voltage qualification rate, node voltage deviation index, and single-segment line overload index were determined as basic evaluation indicators. Subsequently, power flow calculations were performed using the generated distributed resource scenario data to obtain the voltage and current data of each node under different scenarios, providing an important basis for subsequent comprehensive evaluation.

[0050] Specifically, the process of determining multiple basic evaluation indicators and performing power flow calculations on distributed resource scenario data based on these indicators to obtain multiple basic evaluation results is as follows: First, referring to several basic evaluation indicators, the node voltage qualification rate is calculated using the following formula 6 and used as a basic evaluation result. The node voltage qualification rate quantifies the probability distribution of voltage amplitude at a specific node in the distribution network at a specific time. For example, in The node voltage qualification rate index at any time See Formula 6 below. Formula 6:

[0051] in, This represents the calculated node voltage qualification rate. Represents a node exist The cumulative probability distribution function of voltage amplitude at time step. This indicates the upper limit of the acceptable voltage. This indicates the lower limit of acceptable voltage. When calculating voltage, a standard value can be used, which can be referenced from the currently established industry standard power quality-supply voltage deviation voltage acceptable range.

[0052] Subsequently, referring to several basic evaluation indicators, the node voltage deviation is calculated using the following formula 7 and used as a basic evaluation result. The node voltage deviation includes voltage drop deviation and voltage rise deviation, which can be specifically calculated using the confidence interval from the probabilistic power flow results using the following formula 7. Calculate using boundary data. Formula 7:

[0053] in, Represents a node exist Voltage drop deviation at time, Represents a node exist Voltage rise deviation at any given time, Represents a node exist Voltage at time 1 at node The corresponding probability distribution confidence interval The upper limit, Represents a node exist Voltage at time 1 at node The corresponding probability distribution confidence interval The lower limit, This indicates the preset standard voltage.

[0054] Next, referring to several basic evaluation indicators, the overload value of a single line segment is calculated using the following formula 8 and used as a basic evaluation result. A single line segment refers to the line segment between two nodes in the distribution network. During the operation of the distribution network, when the load power is large and the output power of distributed generation is too high, the current carrying capacity of some line segments may exceed the limit. Therefore, it is necessary to evaluate the line overload index. Formula 8:

[0055] in, This represents the calculated single line segment. l exist The single-segment line load value at any given time. Indicates a single line segment l exist The power accumulation probability distribution function at time t. Indicates a single line segment l The standard value of heavy-load power, 80% of the line's maximum carrying capacity can be used.

[0056] Finally, by integrating all the basic evaluation results calculated in the above process, multiple basic evaluation results can be obtained.

[0057] S30: Based on the distribution network operation evaluation indicators, the entropy weight method is used to calculate multiple basic evaluation results to obtain the overall evaluation result of the distribution network operation and output it.

[0058] In this embodiment, distribution network operation evaluation indicators are first determined. Based on these indicators, an objective weighting method, the entropy weight method, is used to comprehensively calculate multiple basic evaluation results. The entropy weight method determines the weight of each indicator by calculating its information entropy. A smaller information entropy indicates a more concentrated distribution of the indicator's data and a greater impact on the overall objective, thus resulting in a larger weight. The weights of each indicator calculated using the entropy weight method can further yield the overall evaluation result of the distribution network operation. Finally, the overall evaluation result is output in the form of a report or visual chart, providing a comprehensive and reliable basis for the optimized scheduling and safe operation of the distribution network.

[0059] In this way, by using the entropy weight method to comprehensively calculate multiple basic evaluation results through the above process, the influence of human factors on the calculation of indicators can be reduced, and the objectivity and accuracy of the evaluation indicator calculation can be improved. Moreover, by outputting the overall evaluation results, a comprehensive and reliable basis can be provided for the optimized scheduling and safe operation of the distribution network, meeting the needs of intelligent and refined management of modern distribution networks.

[0060] In the example of the distribution network in the aforementioned region, based on the distribution network operation evaluation indicators, the entropy weight method was used to comprehensively calculate multiple basic evaluation results, obtaining the objective weight coefficients of each indicator. The overall evaluation result of the distribution network operation was obtained by weighted summation and visualized in the form of charts. The evaluation results show that the distribution network is operating well overall after the access of distributed new energy and electric vehicles, but there are operational risks in some nodes and line sections, which need to be improved by taking corresponding measures. The overall evaluation result provides strong support for the optimized scheduling and safe operation of the distribution network.

[0061] The specific process of calculating and outputting the overall evaluation result of the distribution network operation based on the distribution network operation evaluation indicators and using the entropy weight method to calculate multiple basic evaluation results is as follows: In this embodiment, the voltage qualification rate of the distribution network line needs to be calculated by feature weighting of the node voltage qualification rate. Considering that node voltage exceedance depends not only on the injected power of the node but also on the load of the entire distribution network line, the feature weighting needs to reflect the node load and the backfeed power of the node. The node load and the distributed generation power of the node are used as feature weights. Therefore, the following formula 9 is used to calculate... Time Node The weighting coefficient corresponding to the node voltage qualification rate , Formula 9:

[0062] in, This represents the set of load nodes in a distribution network. Represents a node exist The load of time, Represents a node exist Distributed resource output at all times.

[0063] Then, referring to the distribution network operation evaluation index, the node voltage qualification rate in multiple basic evaluation results is calculated using the following formula 10 to obtain the overall voltage qualification index of the distribution network lines. , Formula 10:

[0064] in, This represents the set of load nodes in a distribution network. express Time Node The weighting coefficient corresponding to the node voltage qualification rate This represents the calculated node voltage qualification rate.

[0065] Meanwhile, the voltage deviation index of the distribution network lines is used to determine the confidence interval. The overall voltage quality of the line is assessed, and expressed as the confidence interval for the voltage at all nodes along the line. The difference between the boundary value and the reference voltage is used for feature weighting in the calculation, with the same weights as the voltage qualification index. Specifically, it is necessary to refer to the distribution network operation evaluation index and use the following formula 11 to calculate the node voltage deviation in multiple basic evaluation results to obtain the distribution network line voltage deviation rate index. , Formula 11:

[0066] in, express Time Node The weighting coefficient corresponding to the node voltage qualification rate Represents a node exist Voltage drop deviation at time, Represents a node exist Voltage rise deviation at any given moment.

[0067] Furthermore, distribution networks typically have a radial topology, and heavily loaded lines mainly occur on two types of line segments: the initial line segments of the radial network and branches connected to load nodes with significant backhaul power. If these two types of lines are not heavily loaded, other lines will also not be heavily loaded. To avoid the problem of some lines having excessively high load rates, making it difficult for overall indicators to reflect the situation effectively, feature weighting is applied to these two types of lines, using the total load of the lower-level branches of the line as the feature weight. Therefore, the following formula 12 is used to calculate the load of a single line segment. l exist Line load weight corresponding to the single-segment line load value at a given time. , Formula 12:

[0068] in, Indicates a section of the line l The expected sum of the power of all load nodes in the lower-level network. This represents the expected value operation. Indicates a section of the line l The set of subordinate network nodes, P j This represents the expected power value of the load node.

[0069] Thus, by referring to the distribution network operation evaluation index, the following formula 13 can be used to calculate the heavy load value of a single line segment from multiple basic evaluation results, thereby obtaining the distribution line heavy load index. , Formula 13:

[0070] in, Indicates a single line segment l exist The line load weight corresponding to the single-segment line load value at a given time. This represents the calculated single line segment. l exist The single-segment line load value at any given time.

[0071] After completing all the above calculations, the original calculated values ​​of the indicators need to be standardized to evaluate the physical information entropy of each indicator on the same calculation scale. The line voltage qualification indicator is a positive indicator, meaning that the larger the indicator value, the more positive the evaluation. On the other hand, the line voltage deviation indicator and the distribution line heavy load indicator are negatively correlated indicators, meaning that the larger the indicator value, the more negative the evaluation. Therefore, in the standardization of evaluation indicators, the range method expressions for positive and negative indicators are shown in Formula 14 below. That is, Formula 14 can be used to standardize the calculated distribution network line overall voltage qualification indicator, distribution network line voltage deviation rate indicator, and distribution line heavy load indicator. Formula 14:

[0072] in, Indicators exist Standardized metrics for each time period It refers to any one of the following indicators: overall voltage qualification index of distribution network lines, voltage deviation rate index of distribution network lines, and heavy load index of distribution lines. Indicators exist Indicators before standardization Indicators A set of values ​​taken at different times. Indicators The corresponding preset coefficients.

[0073] Next, using Formula 15 below, we will calculate the physical information entropy for each of the following indicators: overall voltage qualification index of distribution network lines, voltage deviation rate index of distribution network lines, and heavy load index of distribution lines. Formula 15:

[0074] in, Indicators The physical information entropy This indicates the number of time periods analyzed in a day. As an intermediate variable, Indicators exist Standardized metrics at any given time.

[0075] Then, the physical information entropy of each indicator is weighted using Formula 16 below. Based on the information entropy value, the information utility value (or variability) of each indicator is calculated. The larger the information utility value, the greater the contribution of the indicator to distinguishing different planning schemes. Finally, the indicator type weight corresponding to each indicator is obtained. Formula 16:

[0076] in, Indicators The corresponding indicator type weights, Indicates the number of indicators. Indicators The physical information entropy.

[0077] Finally, Formula 17 is used to calculate the overall voltage qualification index, voltage deviation rate index, overload index, and corresponding index type weight of the distribution network lines, thus obtaining the overall evaluation index of the distribution network lines. This overall evaluation index is then used as the overall evaluation result of the distribution network operation and output. The overall evaluation result of the distribution network operation can quantitatively reflect the overall quality at various times, thereby enabling effective scheduling of the distribution network. Formula 17:

[0078] in, This indicates the overall evaluation indicators for the distribution network lines. Indicators The corresponding indicator type weights, Indicates the number of indicators. Represents the standardized Time indicators .

[0079] In summary, the logical process of the technical solution proposed in this application is summarized as follows: Figure 2As shown, historical data of distributed resources is first input, and the dynamic transfer characteristics of distributed resource output in the time dimension are captured by the Copula transfer kernel-CSMC model. At the same time, the covariance matrix is ​​constructed using a multivariate normal distribution function to quantify the output correlation between different access points in the spatial dimension. The two are combined to generate distributed resource scenario data with both spatiotemporal correlation to reflect the actual operation characteristics. Then, these scenario data are connected to the distribution network topology nodes (a chain network including photovoltaic and EV load access points) to carry out power flow calculation to obtain basic operating parameters such as node voltage and line load. Based on this, basic evaluation indicators such as voltage qualification rate, node voltage deviation, and single-segment line overload are calculated. Finally, the line-level indicators are integrated by feature weighting method, and the voltage qualification rate (positive indicator), voltage deviation, and line overload (negative indicator) are standardized and weighted by entropy weight method. The distribution concentration and influence weight of each indicator data are determined based on the information entropy value of each indicator data, so as to objectively synthesize the indicators to obtain the overall operation characteristic evaluation result of the distribution network, and provide a basis for optimized scheduling.

[0080] To illustrate the effectiveness and accuracy of the generated spatiotemporal correlation scenarios, three schemes are compared and analyzed below: First, the Copula transfer kernel-CSMC function is used to construct the temporal correlation relationship of flexible resources, generating distributed resource time-related scenarios. Second, a corresponding multivariate normal distribution is constructed using the covariance matrix, and distributed resource scenarios considering only spatial correlation are sampled and generated. Third, distributed resource scenarios fully considering spatiotemporal correlation are generated by combining the Copula transfer kernel-CSMC function and the multivariate normal distribution function. Simultaneously, the ES index is used to calculate the distance between the generated distributed resource scenarios and the cumulative distribution function of the measured values. The smaller the ES value, the closer the distance between the two, and the more the generated scenario conforms to the characteristics of distributed resources. In this case, the distributed resources are four distributed photovoltaic power stations. The ES index calculation results of the above three schemes are shown in Table 1 below: Table 1

[0081] Table 1 presents the ES index calculation results of three scenario generation methods (time-related, space-related, and spatiotemporal-related) under four distributed generation scenarios (DPV1-DPV4). The data in Table 1 shows that the ES index values ​​of different DPVs under the same scenario generation method are completely identical. For example, the ES value of all DPVs in the time-related scenario is 0.04806, indicating that the scenario generation method is the core factor affecting the ES index, while the differences in DPV scenarios do not differentiate the results. Based on the above description, three basic evaluation indicators are constructed: voltage qualification rate, node voltage deviation, and line overload. The line-specific indicators are calculated using the feature weighting method, and the original indicators need to be standardized when using the entropy weight method for overall evaluation—the voltage qualification rate is a positive indicator (the larger the value, the more positive the evaluation), while voltage deviation and line overload are negative indicators (the larger the value, the more negative the evaluation). During the calculation process, the physical information entropy value reflects the concentration of indicator data distribution. The smaller the entropy value, the more concentrated the data distribution and the greater the impact on the overall target, and the higher the corresponding indicator weight. Finally, the weights of each indicator are derived through information entropy to support the comprehensive evaluation of the distribution network operation characteristics.

[0082] Furthermore, voltage qualification rate, node voltage deviation index, and line heavy load index are selected as basic evaluation indicators for distribution network operation characteristics. The distribution network example topology diagram is as follows: Figure 3 As shown, the topology includes the main power network, photovoltaic (PV) access points, and electric vehicle (EV) load access points. The main power network is connected through node 1 and extends laterally to node 33, forming a chain-like distribution network. Black nodes represent conventional distribution network nodes, orange nodes (such as 22, 26, 29, etc.) are marked as PV access points, and red nodes (such as 2, 3, 10, 23, etc.) are EV load access points. Their spatial distribution reflects the geographical embedding characteristics of distributed renewable energy and flexible loads in the distribution network. This topology is directly related to the analysis of the spatiotemporal distribution characteristics of distributed resources—the clustered distribution of PV access points may cause local voltage fluctuations, while the spatiotemporal aggregation of EV load access points will affect the risk of line overload. It is necessary to quantify spatial correlation through covariance matrix and characterize time-series dependence through time-related models. At the same time, the calculation of evaluation indicators such as node voltage qualification rate, voltage deviation, and line overload needs to be based on this type of topology data, combined with scenario generation methods (such as spatiotemporal correlation scenario simulation) and entropy weight method for comprehensive evaluation, so as to reflect the combined impact of distributed resource access on the operating characteristics of the distribution network.

[0083] It should be noted that, when using the entropy weight method, in order to evaluate the physical information entropy of each indicator on the same computational scale, the original indicator calculation values ​​need to be standardized. Since the original indicators have already fully considered the weights of different nodes and lines on specific indicators, linear standardization can be directly used in data standardization. The voltage qualification indicator is a positive indicator, meaning that the larger the indicator value, the more positive the evaluation. On the other hand, the voltage deviation indicator and the distribution line overload indicator are negatively correlated indicators, meaning that the larger the indicator value, the more negative the evaluation. The calculated physical information entropy value can describe the amount of information contained in each indicator. The smaller the information entropy value, the more concentrated the distribution of the indicator data, and the greater the impact on the overall target. Therefore, the weight of the indicator is also greater. Based on the obtained information entropy value, the indicator weights of each level of indicators can be further calculated.

[0084] In practical applications, the technical route of the power distribution network operation characteristic evaluation method proposed in the embodiments of this application is as follows: Figure 4 As shown, this paper takes the uncertainty sampling scenario of distributed new energy and electric vehicle charging load as the core foundation, and realizes the evaluation of distribution network operation characteristics by integrating spatiotemporal dimension analysis: First, based on historical data, the Copula transfer kernel-CSMC model and the multivariate normal distribution function are used to characterize the temporal transfer characteristics and spatial coupling relationship of distributed resources, respectively, to generate scenario data with spatiotemporal correlation. On this basis, basic probability evaluation indicators such as node voltage qualification rate, node voltage deviation index, and single-segment line overload are calculated to reflect the static impact of distributed resource access on the local power grid. Then, at the distribution network layer, feature weighted aggregation is used to form the distribution network voltage qualification rate, distribution network voltage deviation index, and distribution network overload index, which comprehensively characterize the distribution network operation status. Finally, the entropy weight method is introduced to automatically determine the weights according to the information entropy value of the data distribution of each indicator, effectively eliminating subjective weighting bias, objectively integrating multi-dimensional evaluation results, and forming an overall probability evaluation index of distribution network voltage quality, load capacity, and new energy absorption characteristics, providing data support for intelligent scheduling decisions.

[0085] The method provided in this application acquires historical operating data of the target distribution network to be evaluated for operational characteristics. It analyzes the spatiotemporal distribution characteristics of distributed resources on the historical operating data to generate spatiotemporally correlated distributed resource scenario data. Multiple basic evaluation indicators are determined, and power flow calculations are performed on the distributed resource scenario data with reference to these indicators to obtain multiple basic evaluation results. Based on the distribution network operation evaluation indicators, the entropy weight method is used to calculate the multiple basic evaluation results, resulting in an overall evaluation result for the distribution network operation, which is then output. By analyzing the spatiotemporal distribution characteristics of distributed resources on the historical operating data of the target distribution network, the method ensures that the generated distributed resource scenario data accurately, reasonably, and comprehensively reflects the overall operational characteristics of the target distribution network. Furthermore, the use of the entropy weight method for comprehensive evaluation of multiple basic evaluation indicators reduces the impact of human factors on indicator calculations, improves the objectivity and accuracy of indicator calculations, and provides a comprehensive and reliable basis for the optimized scheduling and safe operation of the distribution network, meeting the needs of intelligent and refined management of modern distribution networks.

[0086] Furthermore, as Figure 1 To specifically implement the method, this application provides a distribution network operation characteristic evaluation device, such as... Figure 5 As shown, the device includes: an analysis module 501, a basic evaluation module 502, and an overall evaluation module 503.

[0087] Analysis module 501 is used to acquire historical operating data of the target distribution network to be evaluated for operating characteristics, and to generate distributed resource scenario data with spatiotemporal correlation by performing distributed resource spatiotemporal distribution characteristic analysis on the historical operating data. The basic evaluation module 502 is used to determine multiple basic evaluation indicators, perform power flow calculation on the distributed resource scenario data with reference to the multiple basic evaluation indicators, and obtain multiple basic evaluation results. The multiple basic evaluation indicators are used to evaluate the operation of nodes in the target distribution network. The overall evaluation module 503 is used to calculate the multiple basic evaluation results based on the distribution network operation evaluation indicators and the entropy weight method to obtain and output the overall evaluation result of the distribution network operation.

[0088] In a specific application scenario, the analysis module 501 is used to load the historical operating data of the target distribution network, preprocess the historical operating data, including handling missing and outlier values; analyze the distribution resource output pattern of each time period using kernel density estimation to form the marginal distribution of a single time period; perform distributed resource time autocorrelation analysis on the marginal distribution of the single time period using a time correlation model to obtain a time correlation model indicating the impact of distributed resource output in adjacent time periods; perform distributed resource spatial correlation analysis on the historical operating data using a multivariate normal distribution function to obtain a spatial correlation model indicating the impact of distributed resource output in different locations; based on the time correlation model and the spatial correlation model, randomly generate multiple simulated distribution network operation scenarios to obtain scenario-related sampling data; and perform inverse transform sampling processing on the scenario-related sampling data to obtain the distributed resource scenario data. The analysis module is further configured to perform a fit analysis on the distributed resource scenario data using the following formula, and to annotate the distributed resource scenario using the obtained fit analysis results.

[0089] in, This refers to the ES index used to indicate fit. This indicates the total number of scenarios indicated by the distributed resource scenario. This represents measured data of the scenery. The scenario indicated by the distributed resource scenario of efforts, The scenario indicated by the distributed resource scenario of efforts, The scenario indicated by the distributed resource scenario The probability of occurrence, The scenario indicated by the distributed resource scenario The probability of occurrence.

[0090] In specific application scenarios, analysis module 501 is used to select the transfer kernel function shown in the following formula.

[0091] in, This indicates a way to describe two random variables. and The cumulative probability distribution function of the correlation structure between them Represent two random variables and The relevant parameters between them are determined; an initial correlation model is constructed, wherein the initial correlation model is a continuous state-space Markov chain model, and the original state transition kernel in the initial correlation model is represented by the following formula.

[0092] in, This represents the conditional probability distribution function in the original state transition kernel. Indicates in The output value of distributed resources at any given moment. Indicates in The distributed resource output value at time t; in the initial correlation model, the transition kernel function is used to express the conditional probability distribution function in the original state transition kernel. To obtain the time correlation model, wherein the conditional probability distribution function in the time correlation model is... The expression is shown in the following formula.

[0093] in, Indicates in time The corresponding marginal cumulative distribution function value, Indicates in time The corresponding edge cumulative distribution function value; the edge distribution of the single time period is input into the time correlation model for analysis, and the time correlation model is used to analyze the probability of influence between distributed resource output in adjacent time periods to obtain the time correlation model.

[0094] In specific application scenarios, the analysis module 501 is used to calculate the covariance between the distributed resource output of every two locations using the multivariate normal distribution function, and to construct the following covariance matrix using the calculated covariance to obtain the spatial correlation model.

[0095] in, Let the covariance matrix be represented. Indicate location With location Covariance between and The value ranges from 1 to .

[0096] In a specific application scenario, the analysis module 501 is used to determine the covariance matrix of the first time step based on the spatial correlation model, and to generate first spatial sampling data following a uniform distribution using the covariance matrix of the first time step; to determine the covariance matrix of the second time step based on the spatial correlation model, and to generate second spatial sampling data following a uniform distribution using the covariance matrix of the second time step; to query the joint probability function corresponding to the first time step and the second time step in the temporal correlation model, and to process the first spatial sampling data using the joint probability function to obtain the temporal correlation sampling data of the second time step; to use the temporal correlation model to simultaneously perform scene simulation description in the temporal and spatial dimensions based on the first spatial sampling data, the second spatial sampling data, and the temporal correlation sampling data, to obtain a simulated scene data, and to continue processing the third time step to obtain another simulated scene data, and so on, until all time steps are traversed, and to integrate all the obtained simulated scene data to obtain the scene-related sampling data.

[0097] In specific application scenarios, the basic evaluation module 502 is used to calculate the node voltage qualification rate using the following formula, referring to the multiple basic evaluation indicators, and use this as a basic evaluation result.

[0098] in, This represents the calculated node voltage pass rate. Represents a node exist The cumulative probability distribution function of voltage amplitude at time step. This indicates the upper limit of the acceptable voltage. This indicates the lower limit of acceptable voltage; referring to the aforementioned multiple basic evaluation indicators, the node voltage deviation is calculated using the following formula and used as a basic evaluation result, wherein the node voltage deviation includes voltage drop deviation and voltage rise deviation.

[0099] in, Represents a node exist Voltage drop deviation at time, Represents a node exist Voltage rise deviation at any given time, Represents a node exist Voltage at time 1 at node The corresponding probability distribution is the upper bound of the confidence interval. Represents a node exist Voltage at time 1 at node The lower bound of the corresponding probability distribution confidence interval. The preset standard voltage is used; referring to the aforementioned multiple basic evaluation indicators, the overload value of a single line segment is calculated using the following formula and used as a basic evaluation result.

[0100] in, This represents the calculated single line segment. l exist The single-segment line load value at any given time. Indicates a single line segment l exist The power accumulation probability distribution function at time t. Indicates a single line segment l The heavy-load power standard value; integrate all the basic evaluation results calculated in the above process to obtain the multiple basic evaluation results.

[0101] In specific application scenarios, the overall evaluation module 503 is used to calculate using the following formula. Time Node The weighting coefficient corresponding to the node voltage qualification rate ,

[0102] in, This represents the set of load nodes in a distribution network. Represents a node exist The load of time, Represents a node exist The distributed resource output at any given time; referring to the aforementioned distribution network operation evaluation indicators, the node voltage qualification rate in the multiple basic evaluation results is calculated using the following formula to obtain the overall voltage qualification index of the distribution network lines. ,

[0103] in, This represents the set of load nodes in a distribution network. express Time Node The weighting coefficient corresponding to the node voltage qualification rate This represents the calculated node voltage qualification rate; referring to the distribution network operation evaluation index, the node voltage deviation in multiple basic evaluation results is calculated using the following formula to obtain the distribution network line voltage deviation rate index. ,

[0104] in, express Time Node The weighting coefficient corresponding to the node voltage qualification rate Represents a node exist Voltage drop deviation at time, Represents a node exist The voltage rise deviation at any given time; calculate the voltage rise deviation for a single line segment using the following formula. l exist Line load weight corresponding to the single-segment line load value at a given time. ,

[0105] in, Indicates a section of the line l The expected sum of the power of all load nodes in the lower-level network. This represents the expected value operation. Indicates a section of the line l The set of subordinate network nodes, P j This represents the expected power value of the load node; referring to the aforementioned distribution network operation evaluation index, the reload value of a single line segment in multiple basic evaluation results is calculated using the following formula to obtain the distribution line reload index. ,

[0106] in, Indicates a single line segment l exist The line load weight corresponding to the single-segment line load value at a given time. This represents the calculated single line segment. l exist The single-segment line overload value at a given time; the following formulas are used to standardize the calculated overall voltage qualification index, voltage deviation rate index, and overload index of the distribution network lines.

[0107] in, Indicators exist Standardized metrics for each time period The voltage qualification index of the distribution network line, the voltage deviation rate index of the distribution network line, and the heavy load index of the distribution line are all among the following indicators: Indicators exist Indicators before standardization Indicators A set of values ​​taken at different times. Indicators The corresponding preset coefficients are used; the physical information entropy of each of the following indicators—the overall voltage qualification index of the distribution network line, the voltage deviation rate index of the distribution network line, and the heavy load index of the distribution line—is calculated using the following formula.

[0108] in, Indicators The physical information entropy This indicates the number of time periods analyzed in a day. As an intermediate variable, Indicators exist The time-standardized indicators; the physical information entropy of each indicator is weighted using the following formula to obtain the indicator type weight corresponding to each indicator.

[0109] in, Indicators The corresponding indicator type weights, Indicates the number of indicators. Indicators The physical information entropy is calculated; the overall voltage qualification index, voltage deviation rate index, overload index, and corresponding index type weight of the distribution network lines are calculated using the following formula to obtain the overall evaluation index of the distribution network lines; and the overall evaluation index of the distribution network lines is used as the overall evaluation result of the distribution network operation and output.

[0110] in, This represents the overall evaluation index of the power distribution network lines. Indicators The corresponding indicator type weights, Indicates the number of indicators. Represents the standardized Time indicators .

[0111] The apparatus provided in this application acquires historical operating data of a target distribution network to be evaluated for operational characteristics. It analyzes the spatiotemporal distribution characteristics of distributed resources on the historical operating data to generate spatiotemporally correlated distributed resource scenario data. Multiple basic evaluation indicators are determined, and power flow calculations are performed on the distributed resource scenario data with reference to these indicators to obtain multiple basic evaluation results. Based on the distribution network operation evaluation indicators, the entropy weight method is used to calculate the multiple basic evaluation results, resulting in an overall distribution network operation evaluation result, which is then output. By analyzing the spatiotemporal distribution characteristics of distributed resources on the historical operating data of the target distribution network, the apparatus ensures that the generated distributed resource scenario data accurately, reasonably, and comprehensively reflects the overall operational characteristics of the target distribution network. Furthermore, the use of the entropy weight method for comprehensive evaluation of multiple basic evaluation indicators reduces the impact of human factors on indicator calculations, improves the objectivity and accuracy of indicator calculations, and provides a comprehensive and reliable basis for the optimized scheduling and safe operation of the distribution network, meeting the needs of intelligent and refined management of modern distribution networks.

[0112] It should be noted that other corresponding descriptions of the functional units involved in the distribution network operation characteristic evaluation device provided in this application embodiment can be found by referring to... Figures 1 to 4 The corresponding descriptions in [the document] will not be repeated here.

[0113] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0114] The above embodiments and the technical features in the embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

[0116] In an exemplary embodiment, see Figure 6The invention also provides a computer device including a bus, a processor, a memory, and a communication interface. It may also include an input / output interface and a display device, wherein the various functional units can communicate with each other via the bus. The memory stores a computer program, and the processor executes the program stored in the memory to perform the power distribution network operation characteristic evaluation method described in the above embodiments.

[0117] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the power distribution network operation characteristic evaluation method.

[0118] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented in hardware or by using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0119] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application.

[0120] Those skilled in the art will understand that the modules in the apparatus of the implementation scenario can be distributed within the apparatus of the implementation scenario as described, or they can be located in one or more apparatuses different from this implementation scenario, with corresponding changes. The modules of the above-described implementation scenario can be combined into one module, or they can be further divided into multiple sub-modules.

[0121] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of the implementation scenario.

[0122] The above disclosures are only a few specific implementation scenarios of this application. However, this application is not limited to these. Any variations that can be conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A method for evaluating the operating characteristics of a power distribution network, characterized in that, include: Historical operating data of the target distribution network to be evaluated is obtained, and distributed resource scenario data with spatiotemporal correlation is generated by analyzing the spatiotemporal distribution characteristics of the historical operating data. Multiple basic evaluation indicators are determined, and power flow calculations are performed on the distributed resource scenario data with reference to the multiple basic evaluation indicators to obtain multiple basic evaluation results. The multiple basic evaluation indicators are used to evaluate the operation of nodes in the target distribution network. Based on the power distribution network operation evaluation indicators, the entropy weight method is used to calculate the multiple basic evaluation results to obtain and output the overall power distribution network operation evaluation results.

2. The method according to claim 1, characterized in that, The process of acquiring historical operational data of the target distribution network to be evaluated, and generating distributed resource scenario data with spatiotemporal correlation by performing distributed resource spatiotemporal distribution characteristic analysis on the historical operational data, includes: Load the historical operating data of the target distribution network, and preprocess the historical operating data, wherein the preprocessing includes handling missing values ​​and outliers in the historical operating data; The kernel density estimation method is used to analyze the pattern of distributed resource output in each time period, forming the marginal distribution of a single time period; Using a time correlation model, distributed resource time autocorrelation analysis is performed on the marginal distribution of the single time period to obtain a time correlation model for indicating the impact of distributed resource output on adjacent time periods; Using the multivariate normal distribution function, a distributed resource spatial correlation analysis is performed on the historical operating data to obtain a spatial correlation model that indicates the impact of distributed resource output at different locations. Based on the time-related model and the spatial-related model, multiple simulated power distribution network operation scenarios are randomly generated to obtain scenario-related sampling data. The scene-related sampling data is subjected to inverse transform sampling processing to obtain the distributed resource scene data; The method further includes: The following formula is used to perform a fit analysis on the distributed resource scenario data, and the obtained fit analysis results are used to label the distributed resource scenario. in, This refers to the ES index used to indicate fit. This indicates the total number of scenarios indicated by the distributed resource scenario. This represents measured data of the scenery. The scenario indicated by the distributed resource scenario of efforts, The scenario indicated by the distributed resource scenario of efforts, The scenario indicated by the distributed resource scenario The probability of occurrence, The scenario indicated by the distributed resource scenario The probability of occurrence.

3. The method according to claim 2, characterized in that, The method utilizes a time correlation model to perform distributed resource time autocorrelation analysis on the marginal distribution of a single time period, resulting in a time correlation model used to indicate the impact of distributed resource output on adjacent time periods, including: Choose the transfer kernel function shown in the following formula, in, This indicates a way to describe two random variables. and The cumulative probability distribution function of the correlation structure between them Represent two random variables and The relevant parameters between them; An initial correlation model is constructed, wherein the initial correlation model is a continuous state-space Markov chain model, and the original state transition kernel in the initial correlation model is represented by the following formula. in, This represents the conditional probability distribution function in the original state transition kernel. Indicates in The output value of distributed resources at any given moment. Indicates in The output value of distributed resources at any given moment; In the initial correlation model, the transition kernel function is used to express the conditional probability distribution function in the original state transition kernel. To obtain the time correlation model, wherein the conditional probability distribution function in the time correlation model is... The expression is shown in the following formula. in, Indicates in time The corresponding marginal cumulative distribution function value, Indicates in time The corresponding marginal cumulative distribution function value; The marginal distribution of a single time period is input into the time correlation model for analysis. The time correlation model is used to analyze the probability of influence between distributed resource outputs in adjacent time periods to obtain the time correlation model.

4. The method according to claim 2, characterized in that, The method of using a multivariate normal distribution function to perform distributed resource spatial correlation analysis on the historical operational data yields a spatial correlation model indicating the impact of distributed resource output at different locations, including: Using the aforementioned multivariate normal distribution function, the covariance between the distributed resource outputs of every two locations is calculated from the historical operational data. The calculated covariance is then used to construct the following covariance matrix to obtain the spatial correlation model. in, Let the covariance matrix be represented. Indicate location With location Covariance between and The value ranges from 1 to .

5. The method according to claim 2, characterized in that, Based on the time correlation model and the spatial correlation model, multiple simulated power distribution network operation scenarios are randomly generated to obtain scenario-related sampling data, including: Based on the spatial correlation model, the covariance matrix at the first time step is determined, and the first spatial sampling data following a uniform distribution is generated using the covariance matrix at the first time step. Based on the spatial correlation model, the covariance matrix at the second time step is determined, and the second spatial sampling data following a uniform distribution is generated using the covariance matrix at the second time step. Query the joint probability function corresponding to the first time point and the second time point in the time correlation model, and use the joint probability function to process the first spatial sample number to obtain the time correlation sample data of the second time point; Using the time-related model, based on the first spatial sampling data, the second spatial sampling data, and the time-related sampling data, a scene simulation description is performed simultaneously in the time and spatial dimensions to obtain a simulated scene data. Then, the third time point is processed to obtain another simulated scene data, and so on, until all time points are traversed. All the obtained simulated scene data are integrated to obtain the scene-related sampling data.

6. The method according to claim 1, characterized in that, The process involves determining multiple basic evaluation indicators, and then performing power flow calculations on the distributed resource scenario data based on these indicators to obtain multiple basic evaluation results, including: Referring to the aforementioned basic evaluation indicators, the node voltage qualification rate is calculated using the following formula and used as a basic evaluation result. in, This represents the calculated node voltage pass rate. Represents a node exist The cumulative probability distribution function of voltage amplitude at time step. This indicates the upper limit of the acceptable voltage. Indicates the lower limit of acceptable voltage; Referring to the aforementioned basic evaluation indicators, the node voltage deviation is calculated using the following formula and used as a basic evaluation result, wherein the node voltage deviation includes voltage drop deviation and voltage rise deviation. in, Represents a node exist Voltage drop deviation at time, Represents a node exist Voltage rise deviation at any given time, Represents a node exist Voltage at time 1 at node The corresponding probability distribution is the upper bound of the confidence interval. Represents a node exist Voltage at time 1 at node The lower bound of the corresponding probability distribution confidence interval. This indicates the preset standard voltage; Referring to the aforementioned basic evaluation indicators, the overload value of a single line segment is calculated using the following formula and used as a basic evaluation result. in, This represents the calculated single line segment. l exist The single-segment line load value at any given time. Indicates a single line segment l exist The power accumulation probability distribution function at time t. Indicates a single line segment l The standard value for heavy-load power; By integrating all the basic evaluation results calculated in the above process, the multiple basic evaluation results are obtained.

7. The method according to claim 1, characterized in that, Based on the distribution network operation evaluation indicators, the entropy weight method is used to calculate the multiple basic evaluation results to obtain and output the overall distribution network operation evaluation result, including: Calculate using the following formula Time Node The weighting coefficient corresponding to the node voltage qualification rate , in, This represents the set of load nodes in a distribution network. Represents a node exist The load of time, Represents a node exist Distributed resource output in real time; Referring to the aforementioned distribution network operation evaluation indicators, the node voltage qualification rate in the multiple basic evaluation results is calculated using the following formula to obtain the overall voltage qualification index of the distribution network lines. , in, This represents the set of load nodes in a distribution network. express Time Node The weighting coefficient corresponding to the node voltage qualification rate This represents the calculated node voltage pass rate; Referring to the aforementioned distribution network operation evaluation indicators, the following formula is used to calculate the node voltage deviation in multiple basic evaluation results, resulting in the distribution network line voltage deviation rate index. , in, express Time Node The weighting coefficient corresponding to the node voltage qualification rate Represents a node exist Voltage drop deviation at time, Represents a node exist Voltage rise deviation at any given moment; Calculate a single line segment using the following formula. l exist Line load weight corresponding to the single-segment line load value at a given time. , in, Indicates a section of the line l The expected sum of the power of all load nodes in the lower-level network. This represents the expected value operation. Indicates a section of the line l The set of subordinate network nodes, P j This represents the expected power value of the load node; Referring to the aforementioned power distribution network operation evaluation indicators, the following formula is used to calculate the single-segment line overload value from multiple basic evaluation results, thus obtaining the power distribution line overload index. , in, Indicates a single line segment l exist The line load weight corresponding to the single-segment line load value at a given time. This represents the calculated single line segment. l exist The single-segment line overload value at any given time; The following formulas are used to standardize the calculated overall voltage qualification index, voltage deviation rate index, and heavy load index of the distribution network lines. in, Indicators exist Standardized metrics for each time period The voltage qualification index of the distribution network line, the voltage deviation rate index of the distribution network line, and the heavy load index of the distribution line are all among the following indicators: Indicators exist Indicators before standardization Indicators A set of values ​​taken at different times. Indicators The corresponding preset coefficients; The physical information entropy of each of the following indicators—the overall voltage qualification index of the distribution network line, the voltage deviation rate index of the distribution network line, and the heavy load index of the distribution line—is calculated using the following formula. in, Indicators The physical information entropy This indicates the number of time periods analyzed in a day. As an intermediate variable, Indicators exist Standardized metrics for each moment; The physical information entropy of each indicator is weighted using the following formula to obtain the indicator type weight corresponding to each indicator. in, Indicators The corresponding indicator type weights, Indicates the number of indicators. Indicators The physical information entropy; The following formulas are used to calculate the overall voltage qualification index, voltage deviation rate index, overload index, and corresponding index type weight of the distribution network lines, resulting in an overall evaluation index for the distribution network lines. This overall evaluation index is then used as the overall evaluation result for the distribution network operation and output. in, This represents the overall evaluation index of the power distribution network lines. Indicators The corresponding indicator type weights, Indicates the number of indicators. Represents the standardized Time indicators .

8. A distribution network operation characteristic evaluation device, characterized in that, include: The analysis module is used to acquire historical operating data of the target distribution network to be evaluated for its operating characteristics, and to generate distributed resource scenario data with spatiotemporal correlation by performing distributed resource spatiotemporal distribution characteristic analysis on the historical operating data. The basic evaluation module is used to determine multiple basic evaluation indicators, and to perform power flow calculations on the distributed resource scenario data with reference to the multiple basic evaluation indicators to obtain multiple basic evaluation results. The multiple basic evaluation indicators are used to evaluate the operation of nodes in the target distribution network. The overall evaluation module is used to calculate the multiple basic evaluation results based on the distribution network operation evaluation indicators and the entropy weight method, so as to obtain and output the overall evaluation result of the distribution network operation.

9. The apparatus according to claim 8, characterized in that, The analysis module is used to load the historical operating data of the target distribution network, preprocess the historical operating data, including handling missing and outlier values; analyze the distribution resource output patterns of each time period using kernel density estimation to form the marginal distribution of a single time period; perform distributed resource time autocorrelation analysis on the marginal distribution of the single time period using a time correlation model to obtain a time correlation model indicating the impact of distributed resource output on adjacent time periods; perform distributed resource spatial correlation analysis on the historical operating data using a multivariate normal distribution function to obtain a spatial correlation model indicating the impact of distributed resource output on different locations; based on the time correlation model and the spatial correlation model, randomly generate multiple simulated distribution network operation scenarios to obtain scenario-related sampling data; and perform inverse transform sampling processing on the scenario-related sampling data to obtain the distributed resource scenario data. The analysis module is further configured to perform a fit analysis on the distributed resource scenario data using the following formula, and to annotate the distributed resource scenario using the obtained fit analysis results. in, This refers to the ES index used to indicate fit. This indicates the total number of scenarios indicated by the distributed resource scenario. This represents measured data of the scenery. The scenario indicated by the distributed resource scenario of efforts, The scenario indicated by the distributed resource scenario of efforts, The scenario indicated by the distributed resource scenario The probability of occurrence, The scenario indicated by the distributed resource scenario The probability of occurrence.

10. The apparatus according to claim 9, characterized in that, The analysis module is used to select the transfer kernel function shown in the following formula. in, This indicates a way to describe two random variables. and The cumulative probability distribution function of the correlation structure between them Represent two random variables and The relevant parameters between them are determined; an initial correlation model is constructed, wherein the initial correlation model is a continuous state-space Markov chain model, and the original state transition kernel in the initial correlation model is represented by the following formula. in, This represents the conditional probability distribution function in the original state transition kernel. Indicates in The output value of distributed resources at any given moment. Indicates in The distributed resource output value at time t; in the initial correlation model, the transition kernel function is used to express the conditional probability distribution function in the original state transition kernel. To obtain the time correlation model, wherein the conditional probability distribution function in the time correlation model is... The expression is shown in the following formula. in, Indicates in time The corresponding marginal cumulative distribution function value, Indicates in time The corresponding edge cumulative distribution function value; the edge distribution of the single time period is input into the time correlation model for analysis, and the time correlation model is used to analyze the probability of influence between distributed resource output in adjacent time periods to obtain the time correlation model.

11. The apparatus according to claim 9, characterized in that, The analysis module is used to calculate the covariance between the distributed resource output of every two locations using the multivariate normal distribution function, and to construct the following covariance matrix using the calculated covariance to obtain the spatial correlation model. in, Let the covariance matrix be represented. Indicate location With location Covariance between and The value ranges from 1 to .

12. The apparatus according to claim 9, characterized in that, The analysis module is used to determine the covariance matrix at the first time step based on the spatial correlation model, and to generate first spatial sampling data that follows a uniform distribution using the covariance matrix at the first time step. Based on the spatial correlation model, the covariance matrix at the second time step is determined, and the second spatial sampling data following a uniform distribution is generated using the covariance matrix at the second time step. The joint probability function corresponding to the first and second time steps in the temporal correlation model is queried, and the first spatial sampling data is processed using the joint probability function to obtain the temporal correlation sampling data at the second time step. Using the temporal correlation model, based on the first spatial sampling data, the second spatial sampling data, and the temporal correlation sampling data, a scene simulation description is performed simultaneously in the temporal and spatial dimensions to obtain a simulated scene data. This process is then continued to the third time step to obtain another simulated scene data, and so on, until all time steps are traversed. All the obtained simulated scene data are then integrated to obtain the scene-related sampling data.

13. The apparatus according to claim 8, characterized in that, The basic evaluation module is used to calculate the node voltage qualification rate using the following formula, referring to the multiple basic evaluation indicators, and to obtain a basic evaluation result. in, This represents the calculated node voltage pass rate. Represents a node exist The cumulative probability distribution function of voltage amplitude at time step. This indicates the upper limit of the acceptable voltage. This indicates the lower limit of acceptable voltage; referring to the aforementioned multiple basic evaluation indicators, the node voltage deviation is calculated using the following formula and used as a basic evaluation result, wherein the node voltage deviation includes voltage drop deviation and voltage rise deviation. in, Represents a node exist Voltage drop deviation at time, Represents a node exist Voltage rise deviation at any given time, Represents a node exist Voltage at time 1 at node The corresponding probability distribution is the upper bound of the confidence interval. Represents a node exist Voltage at time 1 at node The lower bound of the corresponding probability distribution confidence interval. The preset standard voltage is used; referring to the aforementioned multiple basic evaluation indicators, the overload value of a single line segment is calculated using the following formula and used as a basic evaluation result. in, This represents the calculated single line segment. l exist The single-segment line load value at any given time. Indicates a single line segment l exist The power accumulation probability distribution function at time t. Indicates a single line segment l The heavy-load power standard value; integrate all the basic evaluation results calculated in the above process to obtain the multiple basic evaluation results.

14. The apparatus according to claim 8, characterized in that, The overall evaluation module is used to calculate using the following formula. Time Node The weighting coefficient corresponding to the node voltage qualification rate , in, This represents the set of load nodes in a distribution network. Represents a node exist The load of time, Represents a node exist The distributed resource output at any given time; referring to the aforementioned distribution network operation evaluation indicators, the node voltage qualification rate in the multiple basic evaluation results is calculated using the following formula to obtain the overall voltage qualification index of the distribution network lines. , in, This represents the set of load nodes in a distribution network. express Time Node The weighting coefficient corresponding to the node voltage qualification rate This represents the calculated node voltage qualification rate; referring to the distribution network operation evaluation index, the node voltage deviation in multiple basic evaluation results is calculated using the following formula to obtain the distribution network line voltage deviation rate index. , in, express Time Node The weighting coefficient corresponding to the node voltage qualification rate Represents a node exist Voltage drop deviation at time, Represents a node exist The voltage rise deviation at any given time; calculate the voltage rise deviation for a single line segment using the following formula. l exist Line load weight corresponding to the single-segment line load value at a given time. , in, Indicates a section of the line l The expected sum of the power of all load nodes in the lower-level network. This represents the expected value operation. Indicates a section of the line l The set of subordinate network nodes, P j This represents the expected power value of the load node; referring to the aforementioned distribution network operation evaluation index, the reload value of a single line segment in multiple basic evaluation results is calculated using the following formula to obtain the distribution line reload index. , in, Indicates a single line segment l exist The line load weight corresponding to the single-segment line load value at a given time. This represents the calculated single line segment. l exist The single-segment line overload value at a given time; the following formulas are used to standardize the calculated overall voltage qualification index, voltage deviation rate index, and overload index of the distribution network lines. in, Indicators exist Standardized metrics for each time period The voltage qualification index of the distribution network line, the voltage deviation rate index of the distribution network line, and the heavy load index of the distribution line are all among the following indicators: Indicators exist Indicators before standardization Indicators A set of values ​​taken at different times. Indicators The corresponding preset coefficients are used; the physical information entropy of each of the following indicators—the overall voltage qualification index of the distribution network line, the voltage deviation rate index of the distribution network line, and the heavy load index of the distribution line—is calculated using the following formula. in, Indicators The physical information entropy This indicates the number of time periods analyzed in a day. As an intermediate variable, Indicators exist The time-standardized indicators; the physical information entropy of each indicator is weighted using the following formula to obtain the indicator type weight corresponding to each indicator. in, Indicators The corresponding indicator type weights, Indicates the number of indicators. Indicators The physical information entropy is calculated; the overall voltage qualification index, voltage deviation rate index, overload index, and corresponding index type weight of the distribution network lines are calculated using the following formula to obtain the overall evaluation index of the distribution network lines; and the overall evaluation index of the distribution network lines is used as the overall evaluation result of the distribution network operation and output. in, This represents the overall evaluation index of the power distribution network lines. Indicators The corresponding indicator type weights, Indicates the number of indicators. Represents the standardized Time indicators .

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

16. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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