A novel power distribution system primary and secondary equipment collaborative planning method

Abstract

The application discloses a novel power distribution system primary and secondary equipment collaborative planning method, and relates to the technical field of intelligent power distribution network planning, which comprises the following steps: S1, a static adaptive primary and secondary equipment collaborative typical network topology library is constructed; S2, typical network topology matching is performed on power distribution network operation data based on the typical network topology library, and a candidate typical network topology matched with a current operation state is obtained; S3, corresponding fault scenarios and corresponding fault parameters are obtained according to the candidate typical network topology; S4, a target function of power distribution network primary and secondary equipment system planning is constructed, and a target value corresponding to each candidate typical network topology is calculated based on the fault parameters; and S5, an optimal primary and secondary equipment collaborative network topology is selected according to the target value, and configuration parameters corresponding to the topology are output. The application can effectively improve the convenience and reliability of the primary and secondary equipment collaborative system planning.

Description

Technical Field

[0001] This invention relates to the field of smart power distribution network planning technology, and more specifically to a novel collaborative planning method for primary and secondary equipment in a power distribution system. Background Technology

[0002] With the deepening of the construction of new power systems, distribution networks are facing multiple challenges, including multi-dimensional interaction among power sources, grids, loads, and storage, high proportion of distributed energy access, and diversified user electricity demands. Traditional distribution network planning methods typically design and configure primary equipment (such as transformers, switches, and lines) and secondary equipment (such as protection devices, automation terminals, and communication units) separately and independently, lacking systematic coordination. This leads to problems in actual operation, such as uncoordinated protection, low fault isolation efficiency, weak self-healing capabilities, and redundant or insufficient investment.

[0003] New power distribution systems face numerous new challenges and opportunities, demanding more flexible, collaborative, and intelligent planning methods. The traditional power distribution network planning approach—"primary system determines secondary system, secondary system determines communication"—is no longer sufficient to meet the development needs of new power distribution systems. Power distribution network planning methods are gradually changing. As the cornerstone of power grid operation, the exploration and research into the collaborative planning and design of primary and secondary systems is becoming a core driving force for the intelligent development of power distribution networks. In an intelligent environment, the planning and design of primary and secondary systems are no longer completely independent but need to be integrated and coordinated to improve the reliability and power quality of the power grid.

[0004] Currently, secondary system planning has not yet formed a standardized and typical collaborative planning scheme with primary system planning, resulting in a disconnect and fragmented planning in practical work at the grassroots level. The boundary conditions of primary, secondary, and communication system planning are considered one-sidedly, lacking standardized and typical scenario-based principles, technical guidelines, and indicator systems to support primary and secondary collaborative planning. This makes it impossible to fully meet the requirements of the transformation of distribution networks into flexible resource aggregation platforms for sources, grids, and loads under market conditions.

[0005] Therefore, there is an urgent need for a new collaborative planning method for primary and secondary equipment that is based on data-driven approaches, uses typical topologies as carriers, and aims at fault resilience. Summary of the Invention

[0006] In view of the above problems, this invention is proposed to provide a novel collaborative planning method for primary and secondary equipment in a power distribution system that overcomes or at least partially solves the above problems. By constructing a statically adaptable typical network topology library and combining it with intelligent matching based on real-time operating status, the method realizes the scenario-based, quantitative, and optimal collaborative planning of primary and secondary equipment, significantly improving the fault resilience and planning accuracy of the power distribution network, and effectively supporting the safe and reliable operation under a high proportion of distributed energy access.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] In a first aspect, embodiments of the present invention provide a novel method for collaborative planning of primary and secondary equipment in a power distribution system, comprising the following steps: S1: Construct a statically adaptable typical network topology library for primary and secondary equipment collaboration; S2: Based on the typical network topology library, perform typical network topology matching on the distribution network operation data to obtain candidate typical network topologies that match the current operation status; S3: Obtain the corresponding fault scenarios and corresponding fault parameters based on the candidate typical network topology; S4: Construct the objective function for the planning of the primary and secondary equipment systems of the power distribution network, and calculate the objective value corresponding to each candidate typical network topology based on the fault parameters; S5: Select the optimal primary and secondary equipment collaborative network topology based on the target value, and output the configuration parameters corresponding to the topology.

[0009] Preferably, S1 includes: S1-1: Define a multi-dimensional boundary condition system, with the physical attributes of the distribution network, operating conditions, environmental constraints and market demand as the core dimensions, and establish a boundary condition classification standard containing 11 graded indicators; S1-2: Based on the six core quantitative indicators in the boundary condition classification standard, the existing network topology is clustered and grouped using the K-means clustering algorithm. One or two core topologies are extracted from each group as typical templates, and standardized parameter specifications are formulated to form the typical network topology library and the corresponding boundary condition feature vector B.

[0010] Preferably, the six core quantitative indicators include: grid structure, equipment service life, load fluctuation, geographical conditions, risks and disasters, and user type.

[0011] Preferably, S2 includes: S2-1: Collect distribution network operation data in real time, construct the boundary condition feature vector A under the current operating state based on the K-means clustering method, and perform Z-score standardization processing; S2-2: Using the cosine similarity algorithm, the boundary condition feature vector A and the boundary condition feature vector B are compared, and typical topologies with a similarity of not less than a preset threshold are selected as the candidate typical network topologies.

[0012] Preferably, the fault parameters include: fault frequency, fault recovery time, fault power loss, average load within the fault area, fault range, and electricity price.

[0013] Preferably, the objective function for the planning of the primary and secondary equipment systems of the power distribution network is constructed as follows:

[0014] in, Let be the objective function. For the first i Fault recovery time for each fault scenario For the first i Hierarchical weighting of fault recovery time for each fault scenario For the first i The magnitude of failure loss in each failure scenario. For the first i Hierarchical weights for the magnitude of failure loss in each failure scenario. For the first i The fault range of each fault scenario For the first i The hierarchical weights of the fault range for each fault scenario. n The number of fault scenarios, For the first i Fault frequency of each fault scenario;

[0015] in, For the fault recovery time, This is the moment the fault begins;

[0016] in, For the first i The average load across the range of each fault scenario For the first i Electricity price characteristics for each fault scenario; in, The average load of residential electricity consumption, This represents the average load of industrial electricity consumption. This represents the average load for commercial electricity use. For the first i The number of industrial electricity users within the fault range of each fault scenario. For the first i The number of commercial electricity users within the fault range of each fault scenario. For the first i The number of residential electricity users within the fault range of each fault scenario;

[0017] in, The hierarchical weights are assigned based on fault recovery time, fault loss magnitude, or fault range exceeding a preset threshold. The basic weights for fault recovery time, fault loss magnitude, or fault range.

[0018] Secondly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a novel method for collaborative planning of primary and secondary equipment in a power distribution system.

[0019] Thirdly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a novel method for collaborative planning of primary and secondary equipment in a power distribution system.

[0020] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a novel method for collaborative planning of primary and secondary equipment in a power distribution system, which has the following effects: 1. This invention constructs multiple typical network topologies for primary and secondary equipment collaboration, and selects the optimal network topology based on existing typical network topologies, which can effectively improve the convenience and reliability of planning primary and secondary equipment collaborative systems.

[0021] 2. This invention improves the accuracy and reliability of network topology selection by constructing an objective function based on three dimensions: fault recovery time, fault loss magnitude, and fault range. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0023] Figure 1 This is a flowchart of a novel collaborative planning method for primary and secondary equipment in a power distribution system, provided in an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] This invention discloses a novel collaborative planning method for primary and secondary equipment in a power distribution system, such as... Figure 1 As shown, it includes the following steps: S1: Construct a statically adaptable typical network topology library for primary and secondary equipment collaboration; S2: Based on the typical network topology library, perform typical network topology matching on the distribution network operation data to obtain candidate typical network topologies that match the current operation status; S3: Obtain the corresponding fault scenarios and fault parameters based on the candidate typical network topology; S4: Construct the objective function for the planning of primary and secondary equipment systems in the distribution network, and calculate the objective value corresponding to each candidate typical network topology based on fault parameters; S5: Select the optimal primary and secondary equipment collaborative network topology based on the target value, and output the configuration parameters corresponding to the topology.

[0026] The specific implementation process of this invention is described in detail below. S1 includes the following steps: S1-1: Define a multi-dimensional boundary condition system, using the physical attributes of the distribution network (network structure, equipment service life), operating conditions (load fluctuations, renewable energy penetration rate), environmental constraints (geographical conditions, risks and disasters), and market demand (user type) as core dimensions, and establish a 4-level, 11-item boundary condition classification standard. The graded indicators, quantification methods, and value ranges for each dimension are as follows: Table 1. Boundary Condition Classification Criteria

[0027] S1-2: Topology Clustering and Standardization. Based on the S1-1 boundary condition classification standard, the K-means clustering algorithm is used to group existing network topologies according to boundary condition similarity. Six core quantitative indicators from the boundary condition classification standard (network structure, equipment operating years, load fluctuation, geographical conditions, risk hazards, and user type) are selected to construct cluster feature vectors. Standardized Euclidean distance is used to calculate similarity (to avoid the influence of dimensional differences on the results). The number of clusters k is determined through elbow rule analysis, and 1-2 core topologies are extracted from each group as typical templates. Standardized parameter specifications are formulated for different templates to form a directly reusable typical network topology library, resulting in the corresponding boundary condition feature vector B.

[0028] S2 includes the following steps: S2-1: Real-time boundary condition perception and quantification. This involves collecting current load fluctuation data, renewable energy output, equipment operating years, and disaster early warning information through systems such as Distribution Network Power Consumption Information Acquisition 2.0, PMS 2.0, and the enterprise-level meteorological service center. The data collection frequency is 15 minutes per instance. Based on the S1-2 clustering method and combined with actual operating data, a boundary condition feature vector A=(a1,a2,...,a6) is formed and standardized using Z-score. The formula is as follows: (Where μ is the historical data mean and σ is the standard deviation), ensuring balanced weights for each feature. The boundary condition feature vector A and boundary condition feature vector B have the same data type.

[0029] S2-2: Multi-objective topology matching. A cosine similarity algorithm is used to filter candidate topologies. Based on the boundary condition feature vector A=(a1,a2,...,a6) calculated in S2-1, and the boundary condition feature vector B=(b1,b2,...,b6) from the typical network topology library calculated in S1-2, the similarity calculation formula is as follows:

[0030] in, This represents the value of the j-th index in the boundary condition feature vector A (calculated by S2-1). The value of the j-th index in the boundary condition feature vector B (from the typical network topology library) is represented. Based on the calculation results, typical topology matching is performed on the existing data. The similarity threshold is set to 0.8, and topologies with a matching degree ≥ 0.8 are selected. If there are less than 3, the 3 with the highest matching degree are selected as candidate topologies.

[0031] In S3, the corresponding fault scenarios and corresponding fault parameters are obtained based on the candidate typical network topology. The fault parameters include power supply area type, fault type, number of faults, fault recovery time, power loss due to fault, average load of the fault range, fault range, and electricity price.

[0032] In S4, the objective function for the planning of the primary and secondary equipment systems of the distribution network is:

[0033] in, Let be the objective function. For the first i Fault recovery time for each fault scenario For the first i Hierarchical weighting of fault recovery time for each fault scenario For the first i The magnitude of failure loss in each failure scenario. For the first i Hierarchical weights for the magnitude of failure loss in each failure scenario. For the first i The fault range of each fault scenario For the first i The hierarchical weights of the fault range for each fault scenario. n The number of fault scenarios, For the first i Fault frequency of each fault scenario; Specifically, for a given network topology, the objective function can comprehensively consider the fault recovery time, fault loss magnitude, and fault range under a fault scenario, with the goal of minimizing the combined value of these three indicators. The values ​​of fault recovery time, fault loss magnitude, and fault range are derived from the average / median values ​​of historical data in the network topology. Furthermore, since fault recovery time, fault loss magnitude, and fault range are parameters with different dimensions, the three parameters can be standardized and normalized to obtain their respective characteristics, including fault recovery time characteristics, fault loss magnitude characteristics, and fault range characteristics.

[0034] Furthermore, the fault recovery time can be obtained using the following formula:

[0035] in, For the fault recovery time, This is the moment the fault begins; The magnitude of the failure loss can be obtained using the following formula:

[0036] in, The average load across the range of the i-th fault scenario includes industrial, residential, and commercial applications. Let i be the fault recovery time feature for the i-th fault scenario. Let be the electricity price characteristic (unit price) for the i-th fault scenario.

[0037] The scope of the fault can be determined based on the number of users affected by the power outage. However, considering the differences in economic losses due to power outages affecting industrial, commercial, and residential users, the scope of the fault can be determined using the following formula: in, The average load of residential electricity consumption, This represents the average load of industrial electricity consumption. This represents the average load for commercial electricity use. For the first i The number of industrial electricity users within the fault range of each fault scenario. For the first i The number of commercial electricity users within the fault range of each fault scenario. For the first i The number of residential electricity users within the fault range of each fault scenario; Furthermore, the hierarchical weights for fault recovery time, fault loss magnitude, and fault range can be understood as setting different weights based on the values ​​of different parameter indicators. Specifically, if the fault recovery time, fault loss magnitude, and fault range are too large, it indicates that the reliability of the primary and secondary equipment collaborative network topology is poor and the configuration is unreasonable. Therefore, if the fault recovery time, fault loss magnitude, and fault range exceed a preset range threshold, the corresponding hierarchical weights increase exponentially, and the base weight can include 1, as shown below:

[0038] If the fault recovery time, the magnitude of the fault loss, and the fault range are less than or equal to the preset range threshold, the fault range can be divided into equal proportions, and the layer weights can be determined according to the ratio of 0 to 1.

[0039] The fault frequency for each fault scenario, that is, the frequency of occurrence of this type of fault under this network topology, can be specifically represented by the following formula.

[0040] in, For the first i The number of failures in each failure scenario, where M is the total number of failures under the network topology.

[0041] In S5, after obtaining the target value for each typical network topology, the network topology with the smallest target value is selected as the optimal primary and secondary equipment collaborative network topology. Based on this optimal primary and secondary equipment collaborative network topology, the corresponding configuration components and parameters are obtained. The primary and secondary equipment collaborative network topology is planned according to these configuration components and parameters.

[0042] Based on the same inventive concept, this invention also provides a computer device, including: a memory and a processor, wherein the memory stores a computer program that can run on the processor, and when the processor executes the computer program, it implements a novel method for collaborative planning of primary and secondary equipment in a power distribution system.

[0043] This embodiment provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements a novel method for collaborative planning of primary and secondary equipment in a power distribution system.

[0044] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0045] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0046] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A novel collaborative planning method for primary and secondary equipment in a power distribution system, characterized in that, Includes the following steps: S1: Construct a statically adaptable typical network topology library for primary and secondary equipment collaboration; S2: Based on the typical network topology library, perform typical network topology matching on the distribution network operation data to obtain candidate typical network topologies that match the current operation status; S3: Obtain the corresponding fault scenarios and corresponding fault parameters based on the candidate typical network topology; S4: Construct the objective function for the planning of the primary and secondary equipment systems of the power distribution network, and calculate the objective value corresponding to each candidate typical network topology based on the fault parameters; S5: Select the optimal primary and secondary equipment collaborative network topology based on the target value, and output the configuration parameters corresponding to the topology.

2. The method as described in claim 1, characterized in that, S1 includes: S1-1: Define a multi-dimensional boundary condition system, with the physical attributes of the distribution network, operating conditions, environmental constraints and market demand as the core dimensions, and establish a boundary condition classification standard containing 11 graded indicators; S1-2: Based on the six core quantitative indicators in the boundary condition classification standard, the existing network topology is clustered and grouped using the K-means clustering algorithm. One or two core topologies are extracted from each group as typical templates, and standardized parameter specifications are formulated to form the typical network topology library and the corresponding boundary condition feature vector B.

3. The method as described in claim 2, characterized in that, The six core quantitative indicators include: grid structure, equipment service life, load fluctuation, geographical conditions, risks and disasters, and user type.

4. The method as described in claim 3, characterized in that, S2 includes: S2-1: Collect distribution network operation data in real time, construct the boundary condition feature vector A under the current operating state based on the K-means clustering method, and perform Z-score standardization processing; S2-2: Using the cosine similarity algorithm, the boundary condition feature vector A and the boundary condition feature vector B are compared, and typical topologies with a similarity of not less than a preset threshold are selected as the candidate typical network topologies.

5. The method as described in claim 1, characterized in that, The fault parameters include: fault frequency, fault recovery time, fault power loss, average load within the fault area, fault range, and electricity price.

6. The method as described in claim 1, characterized in that, Construct the objective function for the planning of primary and secondary equipment systems in a power distribution network: in, Let be the objective function. For the first i Fault recovery time for each fault scenario For the first i Hierarchical weighting of fault recovery time for each fault scenario For the first i The magnitude of failure loss in each failure scenario. For the first i Hierarchical weights for the magnitude of failure loss in each failure scenario. For the first i The fault range of each fault scenario For the first i The hierarchical weights of the fault range for each fault scenario. n The number of fault scenarios, For the first i Fault frequency of each fault scenario; in, For the fault recovery time, This is the moment the fault begins; in, For the first i The average load across the range of each fault scenario For the first i Electricity price characteristics for each fault scenario; in, The average load of residential electricity consumption, This represents the average load of industrial electricity consumption. This represents the average load for commercial electricity use. For the first i The number of industrial electricity users within the fault range of each fault scenario. For the first i The number of commercial electricity users within the fault range of each fault scenario. For the first i The number of residential electricity users within the fault range of each fault scenario; in, The hierarchical weights are assigned based on fault recovery time, fault loss magnitude, or fault range exceeding a preset threshold. The basic weights for fault recovery time, fault loss magnitude, or fault range.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements a novel collaborative planning method for primary and secondary equipment in a power distribution system as described in any one of claims 1 to 6.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a novel collaborative planning method for primary and secondary equipment in a power distribution system as described in any one of claims 1 to 6.

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