A power distribution network networking form planning method and device, equipment and medium

Abstract

This application discloses a method, apparatus, equipment, and medium for planning the network configuration of a distribution network, belonging to the field of distribution network planning. The method includes: acquiring historical operating data of the distribution network in the target area and a set of evaluation indicators reflecting the development status of the distribution network; establishing a causal loop diagram representing the causal feedback relationship of each evaluation indicator; establishing a simulation model based on the historical operating data and the causal loop diagram, and performing dynamic simulation on the historical state to obtain historical simulation results; calculating the relative deviation based on the historical simulation results and historical operating data; if both meet preset conditions, inputting the current value into the simulation model to obtain the predicted value of the evaluation indicator, and planning the network configuration of the distribution network accordingly. Therefore, by implementing this application, the problem of low accuracy in the planning of distribution network configurations in the prior art can be solved.

Description

Technical Field

[0001] This application relates to the field of distribution network topology planning, and more particularly to a planning method, apparatus, equipment, and medium for distribution network topology. Background Technology

[0002] Scientific and rational network topology planning can ensure that the distribution network can support the access of a high proportion of distributed energy sources with the optimal topology, support flexible resource regulation across regions, and meet the increasingly diversified electricity needs of users. It is the cornerstone for building a safe, reliable, green and efficient new distribution system.

[0003] Currently, the planning of distribution network topology mainly relies on radial structure design based on typical power supply modes and static empirical rules, supplemented by multi-segment moderate interconnections in local areas to meet differentiated reliability requirements. This planning method often adopts the traditional model of current status analysis, load forecasting, and scheme comparison, determining the network structure through manual experience or simple technical and economic comparisons. However, the above methods have significant shortcomings in accuracy: First, their static architecture is difficult to characterize the random fluctuations on both the source and load sides after a high proportion of distributed energy is added in the future, lacking the ability to simulate the dynamic evolution of the system; second, traditional planning focuses on verification at a single time segment, failing to reveal the long-term dynamic feedback relationships between multiple evaluation dimensions; therefore, existing methods suffer from low accuracy in planning distribution network topology. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and medium for planning the network configuration of a power distribution network, which can solve the problem of low accuracy in the planning of the network configuration of a power distribution network in the prior art.

[0005] In a first aspect, embodiments of the present invention provide a method for planning the network topology of a distribution network, comprising: Acquire historical operational data of the distribution network in the target area and a set of pre-defined evaluation indicators to reflect the development status of the distribution network; Based on each evaluation index in the first preset evaluation index set, a causal loop diagram is established to characterize the causal feedback relationship between each evaluation index. Based on the historical operating data and the causal loop diagram, a simulation model is established between the evaluation indicators. Then, the historical state of the target area distribution network is dynamically simulated based on the simulation model to obtain the historical simulation results of each evaluation indicator. Based on the historical simulation results and the historical operation data, the relative deviation of each evaluation index at each preset time point is calculated; if each relative deviation meets the first preset condition, the current value of each evaluation index is input into the simulation model for simulation to obtain the predicted value of each evaluation index of the target area distribution network, and the distribution network configuration is planned according to the predicted value of each evaluation index.

[0006] This application provides an objective data foundation for subsequent analysis by acquiring historical operational data and a set of preset evaluation indicators. Secondly, a causal loop diagram representing the causal feedback relationships between various evaluation indicators is established, presenting the complex interaction logic of the distribution network system in a structured manner, revealing the inherent laws of system operation, and laying an accurate logical foundation for subsequent modeling. Thirdly, a simulation model is established based on historical data and the causal loop diagram, and dynamic simulation of historical states is performed, achieving a leap from qualitative logic to quantitative simulation. This enables the model to accurately capture the dynamic evolution characteristics of the distribution network, overcoming the deficiency of static planning in reflecting the time-varying characteristics of the system. Furthermore, by calculating the relative deviation between historical simulation results and actual data, and only determining the model's validity when all deviations meet preset conditions, it ensures that only models that have been historically tested and can accurately simulate the real system can be used for prediction, fundamentally eliminating the risk of model inaccuracy. Finally, current values ​​are input into the validated model to obtain predicted values ​​through simulation, which guide planning decisions, enabling the planning scheme to proactively adapt to complex future scenarios.

[0007] As a preferred example of the first aspect, the step of establishing a causal loop diagram to characterize the causal feedback relationship between the evaluation indicators based on each evaluation indicator in the first preset evaluation indicator set includes: For any first evaluation indicator and second evaluation indicator in the first preset evaluation indicator set, determine whether the first evaluation indicator and the second evaluation indicator meet the second preset condition. If so, determine the first causal chain used to characterize the causal feedback relationship between the first evaluation indicator and the second evaluation indicator. Connect all the first causal chains end to end to obtain the causal loop diagram.

[0008] In this preferred example, by determining whether any two evaluation indicators meet preset conditions, only indicators with genuine interactions are included in the causal chain, avoiding interference from invalid or spurious relationships and ensuring the reliability of the modeling foundation. Secondly, all selected causal chains are connected end-to-end to form a causal loop diagram, constructing a closed-loop structure that reflects the complex feedback mechanism within the distribution network system, enabling subsequent simulations to accurately capture the dynamic influence relationships between indicators. These methods collectively lay a rigorous logical foundation for establishing a high-fidelity simulation model, thereby improving the accuracy of the planning scheme.

[0009] As a preferred example of the first aspect, the step of establishing a simulation model between various evaluation indicators based on the historical operating data and the causal loop diagram includes: Based on the historical operating data, determine the state variables, rate variables, auxiliary variables, and exogenous variables corresponding to each of the evaluation indicators; Based on the state variables, rate variables, auxiliary variables, and exogenous variables corresponding to each evaluation index, construct the system dynamics equations corresponding to each evaluation index, and establish the simulation model based on each system dynamics equation; wherein, the system dynamics equations include state variable equations, rate equations, and auxiliary equations.

[0010] In this preferred example, state variables, rate variables, auxiliary variables, and exogenous variables are clearly distinguished based on historical operating data, achieving a scientific classification of different attribute variables in the distribution network system and laying an accurate structural foundation for subsequent modeling. Secondly, based on the above classification, state variable equations, rate equations, and auxiliary equations are constructed respectively, forming a complete system of system dynamic equations. This allows the simulation model to simultaneously reflect the cumulative effects, rates of change, and intermediate calculation processes of the variables. These methods together achieve a precise mapping from static indicators to dynamic models, ensuring that the simulation model can realistically depict the evolution of the distribution network system, thereby improving the accuracy of planning and prediction.

[0011] As a preferred example of the first aspect, the step of dynamically simulating the historical state of the target area distribution network based on the simulation model to obtain the historical simulation results of each of the evaluation indicators includes: Based on the historical operating data, the actual values ​​of each evaluation index of the target area distribution network at the preset initial time are determined, and each of the actual values ​​is input into the simulation model; Iterative calculations are performed according to a preset simulation step size. At the end of each simulation step size, the simulation calculation values ​​of each evaluation index are recorded until the simulation time covers the entire preset historical period, thus obtaining the historical simulation results of each evaluation index.

[0012] In this preferred example, the actual values ​​at the initial moment are input into the simulation model as the starting point for calculation, ensuring that the simulation process is accurately aligned with the historical state of the real system and avoiding the accumulation of initial deviations. Secondly, iterative calculations are performed according to a preset simulation step size, and the simulation calculation values ​​are recorded at the end of each step, enabling continuous tracking and complete recording of the dynamic evolution of the distribution network. This allows the historical simulation results to truly reflect the system's trajectory over time. These methods collectively ensure the accuracy and completeness of the historical state simulation, providing a reliable benchmark for subsequent model verification, thereby improving the accuracy of planning and prediction.

[0013] As a preferred example of the first aspect, based on the historical simulation results and the historical operating data, the relative deviation of each evaluation index at each preset time point is calculated, including: Based on the historical simulation results and the historical operation data, determine the simulation results and corresponding historical actual values ​​of each evaluation indicator at each preset time point; Subtract the historical actual value of each evaluation indicator from the simulation results of each evaluation indicator within each preset time point to obtain the first difference of each evaluation indicator within each preset time point. Divide the first difference of each evaluation indicator within each preset time point by the historical actual value of each evaluation indicator within each preset time point to obtain the relative deviation of each evaluation indicator within each preset time point.

[0014] In this preferred example, the simulation results and historical actual values ​​at each time point are determined, establishing an accurate benchmark for model validation. Secondly, the absolute error between the model output and the real system is quantified by calculating the difference between the simulated and actual values. Finally, the relative deviation is obtained by dividing the difference by the historical actual value, achieving error normalization and eliminating comparability barriers between indicators of different dimensions, enabling a unified standard to judge the model's effectiveness. These methods collectively construct a rigorous model validation mechanism, ensuring that only simulation models that have undergone quantitative verification and whose errors are within acceptable limits can be used for future predictions, thereby significantly improving the reliability of planning decisions.

[0015] As a preferred example of the first aspect, the condition that each of the relative deviations satisfies the first preset condition includes: If any of the relative deviations is less than a first preset threshold, then the relative deviation is determined to satisfy the first preset condition. Count the number of all relative deviations that meet the conditions to obtain the number of qualified points, and count the total number of points. Divide the number of qualified points by the total number of points to obtain the proportion of qualified points. If the proportion of qualified points is greater than the second preset threshold and any one of the relative deviations is not greater than the third preset threshold, then it is determined that all the relative deviations meet the first preset condition.

[0016] In this preferred example, a single-point threshold is used to ensure that the relative deviation at each time point is less than a first preset threshold, controlling model error at the local level. Secondly, the number of compliant points is counted and the proportion of compliant points is calculated; the overall proportion threshold is used to determine the model's effectiveness over the entire time period. Finally, a third preset threshold is set as an upper limit control to prevent individual points from meeting the proportion requirements but exhibiting serious deviations. These multi-level judgment mechanisms collectively construct a scientifically rigorous model validation standard, ensuring both the model's accuracy at local time points and its stability over the entire time period, thereby significantly improving the reliability of the simulation model and the accuracy of planning decisions.

[0017] As a preferred example of the first aspect, the step of inputting the current values ​​of each evaluation index into the simulation model to perform simulation and obtain the predicted values ​​of each evaluation index of the target area distribution network includes: The current values ​​are input into the simulation model, and iterative calculations are performed according to the preset simulation step size until the current simulation time reaches the preset time. The simulation calculation values ​​of each evaluation index are recorded, and then the simulation calculation values ​​of each evaluation index are used as the predicted values ​​of each evaluation index of the target area distribution network.

[0018] Secondly, the present invention provides a planning device for the network configuration of a power distribution network, comprising: a data acquisition module, a first planning module, a second planning module and a third planning module; The data acquisition module is used to acquire historical operating data of the distribution network in the target area and a first set of preset evaluation indicators to reflect the development status of the distribution network. The first planning module is used to establish a causal loop diagram to characterize the causal feedback relationship between each evaluation indicator in the first preset evaluation indicator set. The second planning module is used to establish a simulation model between various evaluation indicators based on the historical operating data and the causal loop diagram, and then to perform dynamic simulation of the historical state of the distribution network in the target area based on the simulation model to obtain the historical simulation results of each evaluation indicator. The third planning module is used to calculate the relative deviation of each evaluation index at each preset time point based on the historical simulation results and the historical operation data; if each relative deviation meets the first preset condition, the current value of each evaluation index is input into the simulation model for simulation to obtain the predicted value of each evaluation index of the target area distribution network, and the distribution network configuration is planned based on the predicted value of each evaluation index.

[0019] As a preferred example of the second aspect, the step of establishing a causal loop diagram to characterize the causal feedback relationship between the evaluation indicators based on each evaluation indicator in the first preset evaluation indicator set includes: For any first evaluation indicator and second evaluation indicator in the first preset evaluation indicator set, determine whether the first evaluation indicator and the second evaluation indicator meet the second preset condition. If so, determine the first causal chain used to characterize the causal feedback relationship between the first evaluation indicator and the second evaluation indicator. Connect all the first causal chains end to end to obtain the causal loop diagram.

[0020] As a preferred example of the second aspect, the step of establishing a simulation model between various evaluation indicators based on the historical operating data and the causal loop diagram includes: Based on the historical operating data, determine the state variables, rate variables, auxiliary variables, and exogenous variables corresponding to each of the evaluation indicators; Based on the state variables, rate variables, auxiliary variables, and exogenous variables corresponding to each evaluation index, construct the system dynamics equations corresponding to each evaluation index, and establish the simulation model based on each system dynamics equation; wherein, the system dynamics equations include state variable equations, rate equations, and auxiliary equations.

[0021] As a preferred example of the second aspect, the step of dynamically simulating the historical state of the target area distribution network based on the simulation model to obtain the historical simulation results of each evaluation index includes: Based on the historical operating data, the actual values ​​of each evaluation index of the target area distribution network at the preset initial time are determined, and each of the actual values ​​is input into the simulation model; Iterative calculations are performed according to a preset simulation step size. At the end of each simulation step size, the simulation calculation values ​​of each evaluation index are recorded until the simulation time covers the entire preset historical period, thus obtaining the historical simulation results of each evaluation index.

[0022] As a preferred example of the second aspect, based on the historical simulation results and the historical operating data, the relative deviation of each evaluation index at each preset time point is calculated, including: Based on the historical simulation results and the historical operation data, determine the simulation results and corresponding historical actual values ​​of each evaluation indicator at each preset time point; Subtract the historical actual value of each evaluation indicator from the simulation results of each evaluation indicator within each preset time point to obtain the first difference of each evaluation indicator within each preset time point. Divide the first difference of each evaluation indicator within each preset time point by the historical actual value of each evaluation indicator within each preset time point to obtain the relative deviation of each evaluation indicator within each preset time point.

[0023] As a preferred example of the second aspect, the condition that each of the relative deviations satisfies the first preset condition includes: If any of the relative deviations is less than a first preset threshold, then the relative deviation is determined to satisfy the first preset condition. Count the number of all relative deviations that meet the conditions to obtain the number of qualified points, and count the total number of points. Divide the number of qualified points by the total number of points to obtain the proportion of qualified points. If the proportion of qualified points is greater than the second preset threshold and any one of the relative deviations is not greater than the third preset threshold, then it is determined that all the relative deviations meet the first preset condition.

[0024] As a preferred example of the second aspect, the step of inputting the current values ​​of each evaluation index into the simulation model to perform simulation and obtain the predicted values ​​of each evaluation index of the target area distribution network includes: The current values ​​are input into the simulation model, and iterative calculations are performed according to the preset simulation step size until the current simulation time reaches the preset time. The simulation calculation values ​​of each evaluation index are recorded, and then the simulation calculation values ​​of each evaluation index are used as the predicted values ​​of each evaluation index of the target area distribution network.

[0025] In summary, this application provides an objective data foundation for subsequent analysis by acquiring historical operational data and a set of preset evaluation indicators. Secondly, by establishing a causal loop diagram representing the causal feedback relationships between various evaluation indicators, the complex interaction logic of the distribution network system is presented in a structured manner, revealing the inherent laws of system operation and laying an accurate logical foundation for subsequent modeling. Thirdly, by establishing a simulation model based on historical data and the causal loop diagram and performing dynamic simulation of historical states, a leap from qualitative logic to quantitative simulation is achieved, enabling the model to accurately capture the dynamic evolution characteristics of the distribution network and overcoming the deficiency of static planning in reflecting the time-varying characteristics of the system. Furthermore, by calculating the relative deviation between historical simulation results and actual data, and only determining the model's validity when all deviations meet preset conditions, it ensures that only models that have been historically tested and can accurately simulate the real system can be used for prediction, fundamentally eliminating the risk of model inaccuracy. Finally, current values ​​are input into the validated model to obtain predicted values ​​through simulation, which guide planning decisions, enabling the planning scheme to proactively adapt to complex future scenarios.

[0026] Another embodiment of this application provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the planning method for the distribution network configuration of this application.

[0027] Another embodiment of this application also provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the planning method of the power distribution network configuration of this application. Attached Figure Description

[0028] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0029] Figure 1 A flowchart illustrating an embodiment of the power distribution network configuration planning method provided by the present invention; Figure 2 A cause-effect diagram illustrating an embodiment of a power distribution network configuration planning method provided by the present invention; Figure 3 The simulation result diagram of line margin is shown in one embodiment of the planning method for distribution network configuration provided by the present invention. Figure 4This is a module structure diagram of one embodiment of a power distribution network configuration planning device provided by the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0032] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0033] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0034] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0035] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0036] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0037] Example 1 Please refer to Figure 1 To address the issue of low accuracy in the planning of distribution network topology in existing technologies, this application provides a method for planning distribution network topology, comprising: S1. Obtain historical operation data of the distribution network in the target area and a set of first preset evaluation indicators to reflect the development status of the distribution network; For example, each evaluation indicator in the first preset evaluation indicator set can be as follows: From four aspects—power supply security, efficiency and effectiveness, flexible interaction, and carrying capacity—14 key indicators that are typical and representative of the development status of medium-voltage distribution networks were selected to establish a distribution network evaluation index system. The selection of indicators is shown in Table 1 below. Among them, the indicators most closely related to the network structure are power supply reliability, fault load transfer capability, unit load investment cost, maximum accommodative load imbalance, line margin, and distributed generation conversion index.

[0038] Table 1 Examples of Evaluation Indicators S2. Based on each evaluation index in the first preset evaluation index set, establish a causal loop diagram to characterize the causal feedback relationship between each evaluation index. It should be noted that System Dynamics (SD) is a computer simulation-based approach designed to study and solve problems in complex dynamic feedback systems. Its core idea is that "system structure determines system function," emphasizing exploring the root causes of problems by examining the system's internal structure (especially the causal feedback relationships between its constituent elements). Therefore, System Dynamics modeling focuses on the system's microstructure, analyzing the logical connections between internal elements using Causal Loop Diagrams (CLDs) and Stock and Flow Diagrams (SFDs), quantifying the quantitative relationships between variables using structural equations, and finally employing simulation software for calculation and verification.

[0039] This application utilizes system dynamics to characterize the development and evolution of the distribution network state in a specific region, clarifies the interaction logic between various evaluation dimensions, and precisely designs variables to describe the relationships within and between subsystems. Through simplification and quantification, system dynamic relationship equations between variables are established, thereby simulating the dynamic development of the distribution network system in this region and predicting its future indicators.

[0040] Causal loop diagrams (CLDs) form the logical framework of a system dynamics model. Based on a defined system boundary, they depict the overall causal feedback loops composed of system elements and their interaction chains (causal chains) by analyzing the internal logical relationships of the system. The key to drawing a CLD lies in accurately identifying causal chains, recognizing feedback loops (loops), and analyzing their behavioral patterns. CLDs provide the basic framework for subsequent quantitative models (such as SFDs and equations). In the diagram, variables are connected by arrows marked with causal polarity ("+" indicates positive influence, and "-" indicates negative influence). An exemplary causal relationship diagram of the overall system established in this embodiment is shown below. Figure 2 As shown.

[0041] As a preferred embodiment, the step of establishing a causal loop diagram to characterize the causal feedback relationship between the evaluation indicators based on each evaluation indicator in the first preset evaluation indicator set includes: For any first evaluation indicator and second evaluation indicator in the first preset evaluation indicator set, determine whether the first evaluation indicator and the second evaluation indicator meet the second preset condition. If so, determine the first causal chain used to characterize the causal feedback relationship between the first evaluation indicator and the second evaluation indicator. Connect all the first causal chains end to end to obtain the causal loop diagram.

[0042] As a preferred implementation, the step of establishing a simulation model between various evaluation indicators based on the historical operating data and the causal loop diagram includes: Based on the historical operating data, determine the state variables, rate variables, auxiliary variables, and exogenous variables corresponding to each of the evaluation indicators; Based on the state variables, rate variables, auxiliary variables, and exogenous variables corresponding to each evaluation index, construct the system dynamics equations corresponding to each evaluation index, and establish the simulation model based on each system dynamics equation; wherein, the system dynamics equations include state variable equations, rate equations, and auxiliary equations.

[0043] For example, taking the line margin index in the carrying capacity subsystem as an example, the system dynamic equation is established as follows: State variable equations: in, It is the state variable line margin. The value at time; Line margin The value at time; It is the rate of change of the line margin; This refers to the input rate of the line margin, which is related to 110N-1L, 110N-1T, 35N-1L, 35N-1T, 10N-1L, and 10N-1T. It is the output rate of the line margin, and its output is connected to the carrying capacity subsystem.

[0044] Rate equation: in, It is the rate of change of the line margin; yes The value of the line margin at any given time; It is the value of the auxiliary variable; It is the value of the exogenous variable; It is a constant.

[0045] Auxiliary equation: in, These are auxiliary variable values; It is the value of the line margin at any given time; Besides auxiliary variables Other auxiliary variables; It is the value of the exogenous variable; It is a constant.

[0046] S3. Based on the historical operating data and the causal loop diagram, establish a simulation model between the evaluation indicators, and then perform dynamic simulation of the historical state of the target area distribution network based on the simulation model to obtain the historical simulation results of each evaluation indicator. As a preferred implementation, the step of dynamically simulating the historical state of the target area's distribution network based on the simulation model to obtain the historical simulation results of each evaluation index includes: Based on the historical operating data, the actual values ​​of each evaluation index of the target area distribution network at the preset initial time are determined, and each of the actual values ​​is input into the simulation model; Iterative calculations are performed according to a preset simulation step size. At the end of each simulation step size, the simulation calculation values ​​of each evaluation index are recorded until the simulation time covers the entire preset historical period, thus obtaining the historical simulation results of each evaluation index.

[0047] S4. Based on the historical simulation results and the historical operation data, calculate the relative deviation of each evaluation index at each preset time point; if each relative deviation meets the first preset condition, input the current value of each evaluation index into the simulation model for simulation, obtain the predicted value of each evaluation index of the target area distribution network, and plan the distribution network configuration based on the predicted value of each evaluation index.

[0048] As a preferred implementation, the step of calculating the relative deviation of each evaluation index at each preset time point based on the historical simulation results and the historical operating data includes: Based on the historical simulation results and the historical operation data, determine the simulation results and corresponding historical actual values ​​of each evaluation indicator at each preset time point; Subtract the historical actual value of each evaluation indicator from the simulation results of each evaluation indicator within each preset time point to obtain the first difference of each evaluation indicator within each preset time point. Divide the first difference of each evaluation indicator within each preset time point by the historical actual value of each evaluation indicator within each preset time point to obtain the relative deviation of each evaluation indicator within each preset time point.

[0049] As a preferred embodiment, the step of ensuring that each of the relative deviations satisfies a first preset condition includes: If any of the relative deviations is less than a first preset threshold, then the relative deviation is determined to satisfy the first preset condition. Count the number of all relative deviations that meet the conditions to obtain the number of qualified points, and count the total number of points. Divide the number of qualified points by the total number of points to obtain the proportion of qualified points. If the proportion of qualified points is greater than and any of the relative deviations is not greater than the third preset threshold, then it is determined that all the relative deviations meet the first preset condition.

[0050] For example, the preferred value of the first preset threshold may be 5%, the preferred value of the second preset threshold may be 70%, and the preferred value of the third preset threshold may be 10%.

[0051] As a preferred implementation, the step of inputting the current values ​​of each evaluation index into the simulation model to perform simulation and obtain the predicted values ​​of each evaluation index of the target area distribution network includes: The current values ​​are input into the simulation model, and iterative calculations are performed according to the preset simulation step size until the current simulation time reaches the preset time. The simulation calculation values ​​of each evaluation index are recorded, and then the simulation calculation values ​​of each evaluation index are used as the predicted values ​​of each evaluation index of the target area distribution network.

[0052] Furthermore, to verify the effectiveness of the proposed method, simulation verification and prediction were conducted on the line margin index of a certain region from 2015 to 2030. The system model equations were mainly established based on historical data of the region from 2015 to 2024, and SPSS regression analysis was used to find the correlation between variables. Figure 3 This is a simulation result diagram of the line margin index for this region from 2015 to 2030.

[0053] Calculations show that the average relative error of the line margin is 7.05%, less than 10%, which is within an acceptable error range. More than 70% of the variables have a relative deviation of less than 5%, and the relative deviation does not exceed 10%. Therefore, the model's behavior demonstrates good simulation performance. Similarly, the 2030 forecast values ​​for other secondary indicators closely related to the grid structure can be obtained, as shown in Table 2 below.

[0054] Table 2 Predicted Values It can be seen that future power distribution networks need to have high reliability, high flexibility and high adaptability to cope with the safe and reliable power supply problems brought about by the rapid growth of new energy sources and loads such as distributed energy and electric vehicles.

[0055] In summary, this application provides an objective data foundation for subsequent analysis by acquiring historical operational data and a set of preset evaluation indicators. Secondly, by establishing a causal loop diagram representing the causal feedback relationships between various evaluation indicators, the complex interaction logic of the distribution network system is presented in a structured manner, revealing the inherent laws of system operation and laying an accurate logical foundation for subsequent modeling. Thirdly, by establishing a simulation model based on historical data and the causal loop diagram and performing dynamic simulation of historical states, a leap from qualitative logic to quantitative simulation is achieved, enabling the model to accurately capture the dynamic evolution characteristics of the distribution network and overcoming the deficiency of static planning in reflecting the time-varying characteristics of the system. Furthermore, by calculating the relative deviation between historical simulation results and actual data, and only determining the model's validity when all deviations meet preset conditions, it ensures that only models that have been historically tested and can accurately simulate the real system can be used for prediction, fundamentally eliminating the risk of model inaccuracy. Finally, current values ​​are input into the validated model to obtain predicted values ​​through simulation, which guide planning decisions, enabling the planning scheme to proactively adapt to complex future scenarios.

[0056] Example 2 like Figure 4 As shown, based on the above method embodiments, corresponding device embodiments are provided; An embodiment of the present invention provides a planning device for the network configuration of a power distribution network, comprising: a data acquisition module 41, a first planning module 42, a second planning module 43, and a third planning module 44; Data acquisition module 41 is used to acquire historical operation data of the distribution network in the target area and a first set of preset evaluation indicators to reflect the development status of the distribution network; The first planning module 42 is used to establish a causal loop diagram to characterize the causal feedback relationship between each evaluation index in the first preset evaluation index set. The second planning module 43 is used to establish a simulation model between various evaluation indicators based on the historical operation data and the causal loop diagram, and then to perform dynamic simulation of the historical state of the distribution network in the target area based on the simulation model to obtain the historical simulation results of each evaluation indicator. The third planning module 44 is used to calculate the relative deviation of each evaluation index at each preset time point based on the historical simulation results and the historical operation data; if each relative deviation meets the first preset condition, the current value of each evaluation index is input into the simulation model for simulation to obtain the predicted value of each evaluation index of the target area distribution network, and the distribution network configuration is planned based on the predicted value of each evaluation index.

[0057] As a preferred embodiment, the step of establishing a causal loop diagram to characterize the causal feedback relationship between the evaluation indicators based on each evaluation indicator in the first preset evaluation indicator set includes: For any first evaluation indicator and second evaluation indicator in the first preset evaluation indicator set, determine whether the first evaluation indicator and the second evaluation indicator meet the second preset condition. If so, determine the first causal chain used to characterize the causal feedback relationship between the first evaluation indicator and the second evaluation indicator. Connect all the first causal chains end to end to obtain the causal loop diagram.

[0058] As a preferred implementation, the step of establishing a simulation model between various evaluation indicators based on the historical operating data and the causal loop diagram includes: Based on the historical operating data, determine the state variables, rate variables, auxiliary variables, and exogenous variables corresponding to each of the evaluation indicators; Based on the state variables, rate variables, auxiliary variables, and exogenous variables corresponding to each evaluation index, construct the system dynamics equations corresponding to each evaluation index, and establish the simulation model based on each system dynamics equation; wherein, the system dynamics equations include state variable equations, rate equations, and auxiliary equations.

[0059] As a preferred implementation, the step of dynamically simulating the historical state of the target area's distribution network based on the simulation model to obtain the historical simulation results of each evaluation index includes: Based on the historical operating data, the actual values ​​of each evaluation index of the target area distribution network at the preset initial time are determined, and each of the actual values ​​is input into the simulation model; Iterative calculations are performed according to a preset simulation step size. At the end of each simulation step size, the simulation calculation values ​​of each evaluation index are recorded until the simulation time covers the entire preset historical period, thus obtaining the historical simulation results of each evaluation index.

[0060] As a preferred implementation, the step of calculating the relative deviation of each evaluation index at each preset time point based on the historical simulation results and the historical operating data includes: Based on the historical simulation results and the historical operation data, determine the simulation results and corresponding historical actual values ​​of each evaluation indicator at each preset time point; Subtract the historical actual value of each evaluation indicator from the simulation results of each evaluation indicator within each preset time point to obtain the first difference of each evaluation indicator within each preset time point. Divide the first difference of each evaluation indicator within each preset time point by the historical actual value of each evaluation indicator within each preset time point to obtain the relative deviation of each evaluation indicator within each preset time point.

[0061] As a preferred embodiment, the step of ensuring that each of the relative deviations satisfies a first preset condition includes: If any of the relative deviations is less than a first preset threshold, then the relative deviation is determined to satisfy the first preset condition. Count the number of all relative deviations that meet the conditions to obtain the number of qualified points, and count the total number of points. Divide the number of qualified points by the total number of points to obtain the proportion of qualified points. If the proportion of qualified points is greater than the second preset threshold and any one of the relative deviations is not greater than the third preset threshold, then it is determined that all the relative deviations meet the first preset condition.

[0062] As a preferred implementation, the step of inputting the current values ​​of each evaluation index into the simulation model to perform simulation and obtain the predicted values ​​of each evaluation index of the target area distribution network includes: The current values ​​are input into the simulation model, and iterative calculations are performed according to the preset simulation step size until the current simulation time reaches the preset time. The simulation calculation values ​​of each evaluation index are recorded, and then the simulation calculation values ​​of each evaluation index are used as the predicted values ​​of each evaluation index of the target area distribution network.

[0063] For more detailed steps and working principles of this embodiment, please refer to the relevant description in Embodiment 1, but not limited to these descriptions.

[0064] In summary, this application provides an objective data foundation for subsequent analysis by acquiring historical operational data and a set of preset evaluation indicators. Secondly, by establishing a causal loop diagram representing the causal feedback relationships between various evaluation indicators, the complex interaction logic of the distribution network system is presented in a structured manner, revealing the inherent laws of system operation and laying an accurate logical foundation for subsequent modeling. Thirdly, by establishing a simulation model based on historical data and the causal loop diagram and performing dynamic simulation of historical states, a leap from qualitative logic to quantitative simulation is achieved, enabling the model to accurately capture the dynamic evolution characteristics of the distribution network and overcoming the deficiency of static planning in reflecting the time-varying characteristics of the system. Furthermore, by calculating the relative deviation between historical simulation results and actual data, and only determining the model's validity when all deviations meet preset conditions, it ensures that only models that have been historically tested and can accurately simulate the real system can be used for prediction, fundamentally eliminating the risk of model inaccuracy. Finally, current values ​​are input into the validated model to obtain predicted values ​​through simulation, which guide planning decisions, enabling the planning scheme to proactively adapt to complex future scenarios.

[0065] It is understood that the above-described device embodiments correspond to the method embodiments of this application, and can implement the planning method for the distribution network configuration provided by any of the above-described method embodiments of this application.

[0066] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided in this application, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0067] Example 3 Based on the above embodiments of the distribution network configuration planning method, another embodiment of this application provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the distribution network configuration planning method of any embodiment of this application.

[0068] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more module units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0069] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0070] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0071] Example 4 Based on the above-described method embodiments, another embodiment of this application provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the power distribution network configuration planning method described in any of the above-described method embodiments of this application.

[0072] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

Claims

1. A planning method for distribution network topology, characterized in that, include: Acquire historical operational data of the distribution network in the target area and a set of pre-defined evaluation indicators to reflect the development status of the distribution network; Based on each evaluation index in the first preset evaluation index set, a causal loop diagram is established to characterize the causal feedback relationship between each evaluation index. Based on the historical operating data and the causal loop diagram, a simulation model is established between the evaluation indicators. Then, the historical state of the target area distribution network is dynamically simulated based on the simulation model to obtain the historical simulation results of each evaluation indicator. Based on the historical simulation results and the historical operation data, the relative deviation of each evaluation index at each preset time point is calculated. If all the relative deviations meet the first preset condition, the current values ​​of each evaluation index are input into the simulation model for simulation to obtain the predicted values ​​of each evaluation index of the target area distribution network, and the distribution network configuration is planned according to the predicted values ​​of each evaluation index.

2. The method for planning a distribution network configuration as described in claim 1, characterized in that, The step of establishing a causal loop diagram to characterize the causal feedback relationship between the evaluation indicators based on each evaluation indicator in the first preset evaluation indicator set includes: For any first evaluation indicator and second evaluation indicator in the first preset evaluation indicator set, determine whether the first evaluation indicator and the second evaluation indicator meet the second preset condition. If so, determine the first causal chain used to characterize the causal feedback relationship between the first evaluation indicator and the second evaluation indicator. Connect all the first causal chains end to end to obtain the causal loop diagram.

3. The method for planning the network configuration of a power distribution network as described in claim 2, characterized in that, The step of establishing a simulation model for the relationships between various evaluation indicators based on the historical operating data and the causal loop diagram includes: Based on the historical operating data, determine the state variables, rate variables, auxiliary variables, and exogenous variables corresponding to each of the evaluation indicators; Based on the state variables, rate variables, auxiliary variables, and exogenous variables corresponding to each evaluation index, construct the system dynamics equations corresponding to each evaluation index, and establish the simulation model based on each system dynamics equation; wherein, the system dynamics equations include state variable equations, rate equations, and auxiliary equations.

4. The method for planning a distribution network topology as described in claim 1, characterized in that, The step of dynamically simulating the historical state of the target area's power distribution network based on the simulation model to obtain the historical simulation results of each evaluation index includes: Based on the historical operating data, the actual values ​​of each evaluation index of the target area distribution network at the preset initial time are determined, and each of the actual values ​​is input into the simulation model; Iterative calculations are performed according to a preset simulation step size. At the end of each simulation step size, the simulation calculation values ​​of each evaluation index are recorded until the simulation time covers the entire preset historical period, thus obtaining the historical simulation results of each evaluation index.

5. The method for planning a distribution network configuration as described in claim 1, characterized in that, The step of calculating the relative deviation of each evaluation index at each preset time point based on the historical simulation results and the historical operation data includes: Based on the historical simulation results and the historical operation data, determine the simulation results and corresponding historical actual values ​​of each evaluation indicator at each preset time point; Subtract the historical actual value of each evaluation indicator from the simulation results of each evaluation indicator within each preset time point to obtain the first difference of each evaluation indicator within each preset time point. Divide the first difference of each evaluation indicator within each preset time point by the historical actual value of each evaluation indicator within each preset time point to obtain the relative deviation of each evaluation indicator within each preset time point.

6. The method for planning a distribution network configuration as described in claim 5, characterized in that, If all the relative deviations satisfy the first preset condition, including: If any of the relative deviations is less than a first preset threshold, then the relative deviation is determined to satisfy the first preset condition. Count the number of all relative deviations that meet the conditions to obtain the number of qualified points, and count the total number of points. Divide the number of qualified points by the total number of points to obtain the proportion of qualified points. If the proportion of qualified points is greater than the second preset threshold and any one of the relative deviations is not greater than the third preset threshold, then it is determined that all the relative deviations meet the first preset condition.

7. The method for planning the network configuration of a power distribution network as described in claim 1, characterized in that, The step of inputting the current values ​​of each evaluation index into the simulation model to perform simulation and obtain the predicted values ​​of each evaluation index of the target area distribution network includes: The current values ​​are input into the simulation model, and iterative calculations are performed according to the preset simulation step size until the current simulation time reaches the preset time. The simulation calculation values ​​of each evaluation index are recorded, and then the simulation calculation values ​​of each evaluation index are used as the predicted values ​​of each evaluation index of the target area distribution network.

8. A planning device for a power distribution network configuration, characterized in that, include: The system comprises a data acquisition module, a first planning module, a second planning module, and a third planning module. The data acquisition module is used to acquire historical operating data of the distribution network in the target area and a first set of preset evaluation indicators to reflect the development status of the distribution network. The first planning module is used to establish a causal loop diagram to characterize the causal feedback relationship between each evaluation indicator in the first preset evaluation indicator set. The second planning module is used to establish a simulation model between various evaluation indicators based on the historical operating data and the causal loop diagram, and then to perform dynamic simulation of the historical state of the target area distribution network based on the simulation model to obtain the historical simulation results of each evaluation indicator. The third planning module is used to calculate the relative deviation of each evaluation index at each preset time point based on the historical simulation results and the historical operation data. If all the relative deviations meet the first preset condition, the current values ​​of each evaluation index are input into the simulation model for simulation to obtain the predicted values ​​of each evaluation index of the target area distribution network, and the distribution network configuration is planned according to the predicted values ​​of each evaluation index.

9. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a planning method for a power distribution network configuration as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform a planning method for a power distribution network configuration as described in any one of claims 1 to 7.

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