County novel power distribution network planning method and system

Abstract

The invention discloses a novel county power distribution network planning method and system, and relates to the technical field of power grid planning methods, and if a power utilization path passes through a living area and an industrial area, the power utilization path is divided into a living power utilization path and an industrial power utilization path; and the first sub-grid model of the domestic power utilization path and the second sub-grid model of the industrial power utilization path are generated, so that local independent management and control of the domestic power utilization path and the industrial power utilization path are ensured. Therefore, a power utilization balance coefficient is determined according to the power utilization data of the living area and the power utilization data of the industrial area; a sub-grid docking model is determined according to the power utilization balance coefficient, the first sub-grid model and the second sub-grid model, and a novel power distribution network is determined according to the first sub-grid model, the second sub-grid model and the sub-grid docking model, so that integral management and control of a domestic power utilization path and an industrial power utilization path are realized. And the planning effect and the overall control effect of the novel county power distribution network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid planning methods, and in particular to a new county-level distribution network planning method and system. Background Art

[0002] With the development of science and technology, counties are equipped with corresponding urban distribution maps, which present living areas and industrial areas. The living areas and industrial areas adopt corresponding power paths to supply corresponding domestic electricity and industrial electricity. In the existing technology, the living area has a first power path and the industrial area has a second power path. Although the first power path and the second power path are close in distance, they belong to two independent power grid systems, and the overall control of the first power path and the second power path cannot be achieved, which affects the planning effect of the county's distribution network. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a method and system for planning a new county-level power distribution network.

[0004] An embodiment of the present invention provides a method for planning a new county-level distribution network, including:

[0005] Determine multiple electricity consumption areas based on the division of the county's urban distribution map;

[0006] Determine a power grid control area based on the relative positions of multiple power consumption areas and power consumption data of the multiple power consumption areas, and determine a power consumption path within the power grid control area;

[0007] If the power path passes through a residential area and an industrial area, the power path is divided into a residential power path and an industrial power path, and a first subgrid model of the residential power path and a second subgrid model of the industrial power path are generated;

[0008] Determine the electricity balance coefficient based on electricity consumption data of living areas and industrial areas;

[0009] A subgrid interconnection model is determined according to the power balance coefficient, the first subgrid model, and the second subgrid model; and a new distribution network is determined according to the first subgrid model, the second subgrid model, and the subgrid interconnection model.

[0010] An embodiment of the present invention provides a county-level new distribution network planning system, which is applied to the above-mentioned county-level new distribution network planning method. The county-level new distribution network planning system includes:

[0011] The power consumption area module is used to determine multiple power consumption areas based on the division of the town distribution map of the county; the power consumption path module is used to determine the power grid control area according to the relative positions of the multiple power consumption areas and the power consumption data of the multiple power consumption areas, and determine the power consumption path in the power grid control area; the subgrid module is used to divide the power consumption path into a living power path and an industrial power path if the power consumption path passes through the living area and the industrial area, and generate a first subgrid model of the living power path and a second subgrid model of the industrial power path; the power balance coefficient module is used to determine the power balance coefficient according to the power consumption data of the living area and the power consumption data of the industrial area; the distribution network module is used to determine the subgrid docking model according to the power balance coefficient, the first subgrid model and the second subgrid model, and determine a new distribution network according to the first subgrid model, the second subgrid model and the subgrid docking model.

[0012] Compared with the prior art, the present invention has the following beneficial effects:

[0013] In an embodiment of the present invention, through the method in the embodiment of the present invention, multiple power consumption areas are determined based on the division of the town distribution map of the county; the power grid control area is determined according to the relative positions of the multiple power consumption areas and the power consumption data of the multiple power consumption areas, and the power consumption path is determined in the power grid control area; if the power consumption path passes through the living area and the industrial area, the power consumption path is divided into the living power consumption path and the industrial power consumption path, and the first sub-grid model of the living power consumption path and the second sub-grid model of the industrial power consumption path are generated, which is compatible with the power grid control of the living area and the industrial area in adjacent situations, and ensures the local independent control of the living power consumption path and the industrial power consumption path.

[0014] Therefore, the electricity balance coefficient is determined based on the electricity consumption data of the living area and the electricity consumption data of the industrial area; the subgrid docking model is determined based on the electricity balance coefficient, the first subgrid model and the second subgrid model; the new distribution network is determined based on the first subgrid model, the second subgrid model and the subgrid docking model, thereby realizing the overall management and control of the domestic electricity consumption path and the industrial electricity consumption path, ensuring the autonomous planning of the first subgrid model and the second subgrid model, further improving the transition planning between the first subgrid model and the second subgrid model, and improving the planning effect and overall control effect of the county's new distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 1 is a flow chart of a method for planning a new county-level distribution network in an embodiment of the present invention;

[0016] Figure 2 1 is a flow chart of step S11 in the method for planning a new county-level distribution network in an embodiment of the present invention;

[0017] Figure 3 1 is a flow chart of step S12 in the method for planning a new county-level distribution network in an embodiment of the present invention;

[0018] Figure 4 1 is a flow chart of step S13 in the method for planning a new county-level distribution network in an embodiment of the present invention;

[0019] Figure 5 1 is a flow chart of step S14 in the method for planning a new county-level distribution network in an embodiment of the present invention;

[0020] Figure 6 1 is a flow chart of step S15 in the method for planning a new county-level distribution network in an embodiment of the present invention;

[0021] Figure 7 It is a schematic diagram of the structural composition of the county-level new distribution network planning system in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0023] See also Figures 1 to 7 A new county-level distribution network planning method is applied to the new county-level distribution network planning scenario; the new county-level distribution network planning method includes:

[0024] Step S11: determining a plurality of electricity consumption areas based on the division of the town distribution map of the county;

[0025] Step S12: determining a power grid control area based on the relative positions of the multiple power consumption areas and the power consumption data of the multiple power consumption areas, and determining a power consumption path in the power grid control area;

[0026] Step S13: If the power path passes through a residential area and an industrial area, the power path is divided into a residential power path and an industrial power path, and a first subgrid model for the residential power path and a second subgrid model for the industrial power path are generated;

[0027] Step S14: determining the electricity balance coefficient based on the electricity consumption data of the living area and the electricity consumption data of the industrial area;

[0028] Step S15: determining a subgrid interconnection model based on the power balance coefficient, the first subgrid model, and the second subgrid model; and determining a new distribution network based on the first subgrid model, the second subgrid model, and the subgrid interconnection model.

[0029] refer to Figure 2In step S11, the use status of the hot and cold spray equipment is determined based on multiple working parameters of the hot and cold spray equipment, the use scenario of the hot and cold spray equipment, and the model of the hot and cold spray equipment;

[0030] In the specific implementation process of the present invention, the specific steps are:

[0031] S111: Collect a county-level town distribution map, and determine the residential area and industrial work area based on the markings on the county-level town distribution map;

[0032] S112: Determine a living area based on the resident's living range and the resident's living path, and determine a first power consumption area in the living area based on the power consumption location of the living area, the power consumption data of the living area, and the current time;

[0033] S113: Determine an industrial area based on the industrial work scope and the work path. In the industrial area, determine a second power consumption area based on the power consumption location of the industrial area, the power consumption data of the industrial area and the current time. The multiple power consumption areas include a first power consumption area and a second power consumption area.

[0034] In an embodiment of the present application, a county-level town distribution map is collected, and the living area of residents and the industrial work area are determined based on the markings on the county-level town distribution map. The living area of residents and the industrial work area are introduced to further control the living area of residents and the industrial work area. At this time, the county-level town distribution map is collected, and the latest county-level town distribution map is obtained from government agencies, urban planning departments or map service providers. These town distribution maps usually contain information such as geographic coordinates, administrative division boundaries, road networks, building distribution, green spaces and water areas; optionally, the town distribution map exists in the form of an electronic map (such as GeoJSON, KML, Shapefile, etc.) or a paper map; in the digital age, electronic maps are more popular because they are easy to operate and update.

[0035] Optionally, an updated urban distribution map was obtained from the county government planning department. This map contains information on all administrative divisions, road networks, buildings, and green spaces within the county. On the county urban distribution map, residents' living areas are usually represented by markers of residential areas, commercial areas, schools, hospitals, and other life-related facilities. These facility markers appear in the form of icons, color codes, or text annotations. In this case, by identifying these markers, the residents' living areas are outlined. The residents' living areas usually involve spatial analysis of the markers to determine their connections and spatial distribution.

[0036] Optionally, the map identifies residential areas, commercial areas, schools, hospitals, and other facilities within the urban area, which are represented by different colors or icons. Spatial analysis is used to outline the approximate living areas of residents within the urban area, including areas with concentrated residential areas and areas with frequent commercial activities. Similar to the residential area, the industrial work area is represented by industrial zones, factories, warehouses, and other facilities related to industrial production. By identifying these industrial markers, the industrial work area is determined. The industrial work area needs to take into account factors such as the spatial separation between industrial and residential areas and environmental impacts.

[0037] Optionally, two industrial zones were identified on the map, located in the east and south of the urban area respectively. These industrial zones are mainly composed of factories, warehouses and other facilities. By analyzing the spatial distribution of these industrial facilities, the approximate scope of industrial work was determined, and it was noted that there was a certain spatial isolation between industrial zones and residential areas to reduce the impact of industrial activities on residents' lives.

[0038] In summary, by collecting the town distribution map of the county and identifying the markers therein, we can accurately determine the living area of residents and the scope of industrial work, providing an important spatial information basis for subsequent distribution network planning. This information will help us better understand the electricity demand and distribution characteristics within the county, thereby formulating a more scientific and reasonable distribution network planning scheme.

[0039] Furthermore, a living area is determined based on the living range of the residents and their living paths. Within the living area, a first electricity consumption area is determined based on the electricity consumption location, electricity consumption data, and current time of the living area. This takes into account the overall consideration of the electricity consumption location, electricity consumption data, and current time of the living area, thereby ensuring the accuracy of the first electricity consumption area. At this point, based on the living range of the residents determined in step S111, a living area is preliminarily defined. This living area usually includes residential areas, commercial areas, schools, hospitals, and other supporting living facilities. Next, it is necessary to consider the daily activity paths of residents, such as commuting routes, shopping routes, and leisure routes. These paths are obtained through traffic flow data, public transportation routes, resident travel surveys, and other methods. By analyzing these paths, the living areas are further refined to identify hot spots where residents have frequent activities and high electricity demand.

[0040] For the first electricity consumption area, the first electricity consumption area is determined based on the electricity consumption location of the living area, the electricity consumption data of the living area, and the current time. At this time, the electricity consumption location of the living area is such as buildings in residential areas, shops in commercial areas, public facilities, etc. These locations are usually associated with electricity metering equipment (such as smart meters) to obtain detailed electricity consumption data; for the electricity consumption data of the living area, by analyzing the electricity consumption data of these electricity consumption locations, we can understand the electricity load distribution, peak and valley periods, and other information in different time periods; the electricity consumption data of the living area is of great significance for determining the peak electricity demand and optimizing the allocation of power resources. When determining the first electricity consumption area, the current time is an important factor that cannot be ignored; there are significant differences in electricity demand in different seasons and different time periods (such as weekdays and weekends, daytime and nighttime); therefore, it is necessary to dynamically analyze the electricity consumption data in combination with the current time to determine the area with the highest electricity demand in the current period and the area that needs the most priority power supply as the first electricity consumption area.

[0041] Specifically, assume that a distribution network is being planned for a county-level urban area, which includes a large residential area, a commercial center, and multiple schools. Based on the living range of residents determined in step S111, the living area within the urban area, including the residential area, commercial center, and school, is preliminarily defined. By analyzing data such as residents' commuting routes and shopping routes, it is found that areas between residential areas and commercial centers, and around schools are areas where residents have frequent activities. Therefore, these areas are further refined into the core parts of the living area.

[0042] In the living area, specific electricity consumption locations were identified, such as various buildings in residential areas, shops in commercial centers, classrooms and offices in schools, etc.; by analyzing electricity consumption data, it was found that the peak period of electricity demand was in the evening in summer, among which air-conditioning electricity consumption in residential areas, lighting and air-conditioning electricity consumption in commercial centers, and electricity consumption for evening self-study in schools accounted for a large proportion; combined with the current time (assuming it was summer evening), the first electricity consumption area was determined to be some high-load buildings in residential areas, the prosperous areas of commercial centers, and the classroom areas for evening self-study in schools. These areas have the highest electricity demand in the current period, so power supply needs to be guaranteed as a priority.

[0043] In summary, by comprehensively considering factors such as residents' living area, living path, electricity consumption location, electricity consumption data, and current time, the first electricity consumption area in the living area can be accurately determined, providing an important decision-making basis for subsequent distribution network planning.

[0044] Therefore, the industrial area is determined based on the industrial working scope and working path. Within the industrial area, the second power consumption area is determined based on the power consumption location of the industrial area, the power consumption data of the industrial area and the current time. The multiple power consumption areas include the first power consumption area and the second power consumption area, which are compatible with the overall consideration of the power consumption location of the industrial area, the power consumption data of the industrial area and the current time, thereby ensuring the accuracy of the second power consumption area.

[0045] At this time, according to the industrial work scope determined in step S111, the location and distribution of major industrial facilities such as industrial zones, industrial parks, factories and warehouses in the county are identified. These facilities usually have clear geographical coordinates and boundaries; the work paths between industrial facilities are analyzed, including raw material transportation routes, product transportation routes, worker commuting routes, etc. These paths are obtained through traffic flow data, logistics data, and operational data of industrial enterprises; by analyzing these paths, the industrial areas are further refined, and key areas with frequent industrial activities and high electricity demand are identified.

[0046] Optionally, based on the industrial work scope determined in step S111, the location and distribution of major industrial parks and factories in the county are identified; by analyzing data such as raw material transportation routes, product transportation routes, and workers' commuting routes between factories, the industrial areas are further refined, and key areas with frequent industrial activities and high electricity demand in the industrial parks are identified, such as large production workshops and warehouse clusters.

[0047] For the second electricity consumption area, the second electricity consumption area is determined based on the electricity consumption location of the industrial area, the electricity consumption data of the industrial area and the current time. At this time, within the industrial area, it is necessary to identify specific electricity consumption locations, such as the production workshops of the factory, the lighting and ventilation equipment of the warehouse, the electrical equipment in the office area, etc. These locations are usually associated with industrial electricity metering equipment (such as large electricity meters, energy management systems) to obtain detailed electricity consumption data.

[0048] By analyzing the electricity consumption data of these electricity consumption locations, we can understand the electricity load distribution, peak and valley periods, electricity efficiency and other information in different time periods. This information is of great significance for optimizing industrial electricity consumption and improving the efficiency of power resource utilization. When determining the second electricity consumption area, the current time is also an important consideration. Industrial electricity consumption usually has obvious time periods and periodicity, such as production peaks and equipment start-up and shutdown periods. Therefore, it is necessary to combine the current time to dynamically analyze the electricity consumption data to determine the area with the highest electricity demand in the current period and the most urgent need for priority power supply as the second electricity consumption area.

[0049] Optionally, within the industrial area, specific electricity consumption locations are identified, such as machinery and equipment in the factory's production workshop, lighting and ventilation systems in the warehouse, computers and air conditioners in the office area, etc.; by analyzing electricity consumption data, it is found that daytime on weekdays is the peak period for industrial electricity consumption, among which machinery and equipment in large production workshops account for the largest proportion; combined with the current time (assuming it is daytime on weekdays), the second electricity consumption area is determined to be the large production workshop area in the industrial park. These areas have the highest electricity demand in the current period, so power supply needs to be prioritized.

[0050] After completing steps S112 and S113, a first electricity consumption area (mainly based on residential electricity consumption) and a second electricity consumption area (mainly based on industrial electricity consumption) are obtained. These two areas together constitute multiple electricity consumption areas within the county. These areas overlap geographically, but there are differences in electricity demand and characteristics; optionally, a first electricity consumption area based on residential electricity consumption and a second electricity consumption area based on industrial electricity consumption are obtained. These two areas overlap geographically, but there are significant differences in electricity demand and characteristics; for example, the first electricity consumption area focuses more on the electricity demand of residential and commercial areas, while the second electricity consumption area focuses more on the electricity demand of industrial production areas.

[0051] In one embodiment of the present application, a preset industrial area matching table is collected, and the preset industrial area matching table is shown in Table 1:

[0052] Table 1 Industrial area matching table

[0053]

[0054]

[0055] refer to Figure 3 In step S12, a power grid control area is determined based on the relative positions of the multiple power consumption areas and the power consumption data of the multiple power consumption areas, and a power consumption path is determined in the power grid control area;

[0056] In the specific implementation process of the present invention, the specific steps are:

[0057] S121: Determine relative positions of the plurality of power consumption areas according to a position of a first power consumption area and a position of a second power consumption area.

[0058] S122: If the relative positions of the multiple power consumption areas are less than a preset position threshold, the multiple power consumption areas are controlled as a whole. In this case, a first power consumption level is matched based on the power consumption data of the first power consumption area, and a second power consumption level is matched based on the power consumption data of the second power consumption area. The power grid control area is determined based on the first power consumption level, the second power consumption level, and the relative positions of the multiple power consumption areas.

[0059] S123: Plan a first power consumption path along the power consumption mark in the first power consumption area, plan a second power consumption path along the power consumption mark in the second power consumption area, and determine the power consumption path corresponding to the power grid control area based on the first power consumption path and the second power consumption path.

[0060] In an embodiment of the present application, among multiple power consumption areas, the relative positions of the multiple power consumption areas are determined based on the position of the first power consumption area and the position of the second power consumption area, which is compatible with the overall consideration of the position of the first power consumption area and the position of the second power consumption area, and ensures the accuracy of the relative positions of the multiple power consumption areas.

[0061] At this time, the geographic location data of the first power consumption area and the second power consumption area (and other power consumption areas) are collected. These data include coordinate points, polygon boundaries, center point coordinates, etc., depending on the representation method of the geographic information system; the data source is the geographic information system (GIS) database, map service API, field measurement data, etc.

[0062] Preprocess the collected geographic location data to ensure data accuracy and consistency; for example, check whether the coordinate points fall within the correct geographic range, whether the polygon boundaries are closed, etc.; if the data format is inconsistent, format conversion or data projection is required to ensure that all data are in the same geographic coordinate system.

[0063] Based on the preprocessed geographic location data, the relative position between the first power consumption area and the second power consumption area is calculated; the relative position is expressed in a variety of ways, such as distance (straight-line distance, shortest path distance, etc.), direction (azimuth), degree of overlap (area overlap ratio), etc.; the calculation method is a simple geometric calculation (such as the distance formula between two points) or a complex spatial analysis algorithm (such as the shortest path algorithm, buffer zone analysis, etc.); the calculated relative position results are output in a format that is easy to understand and use, such as tables, charts, map annotations, etc.; if necessary, the results are saved in a database for subsequent analysis and use.

[0064] Specifically, suppose that you are planning a power grid for two power consumption areas in a city. The first power consumption area is a residential area, and the second power consumption area is an industrial area. Get the polygon boundary data of the residential area and the industrial area from the GIS database. Assume that the coordinates of the center point of the residential area are (X1, Y1) and the coordinates of the center point of the industrial area are (X2, Y2). Check whether the polygon boundary data is closed to ensure that there are no missing or incorrect boundary points. Confirm that the coordinate data of the two areas are in the same geographic coordinate system (such as the WGS84 coordinate system).

[0065] Use the distance formula between two points to calculate the distance D between the center points of the residential area and the industrial area. Assume that the calculated distance is 10 kilometers. In addition, the degree of overlap between the two areas is also calculated. In this example, since the residential area and the industrial area are adjacent but do not intersect, the degree of overlap is 0%.

[0066] The calculation results are output in table form, showing the coordinates of the center points of residential and industrial areas, the calculated distances, and the degree of overlap. The results are also marked on a map to intuitively demonstrate the relative positional relationship between the two power consumption areas. Through this example, it is clear how to determine the relative positions of the first and second power consumption areas based on their location data, providing a spatial information foundation for subsequent power grid planning.

[0067] Furthermore, if the relative positions of multiple power consumption areas are less than a preset position threshold, the multiple power consumption areas are controlled as a whole. At this time, the first power consumption level is matched according to the power consumption data of the first power consumption area, and the second power consumption level is matched according to the power consumption data of the second power consumption area; the power grid control area is determined based on the first power consumption level, the second power consumption level and the relative positions of multiple power consumption areas, which is compatible with the overall consideration of the first power consumption level, the second power consumption level and the relative positions of multiple power consumption areas, thereby ensuring the accuracy of the power grid control area.

[0068] At this time, the relative positions of multiple power consumption areas are compared with a preset position threshold; the preset position threshold is usually determined based on grid planning standards, historical data or expert judgment, and is used to determine whether the proximity between power consumption areas is sufficient to treat them as a whole for management and control; if the relative positions of multiple power consumption areas are less than the preset position threshold, they are considered close enough and need to be managed as a whole.

[0069] For multiple electricity consumption areas that need to be managed as a whole, it is necessary to match them with corresponding electricity consumption levels based on their electricity consumption data. Electricity consumption levels are usually divided into low, medium and high levels based on factors such as the amount of electricity consumption, fluctuations in electricity load, and the importance of electricity demand. The matching process involves steps such as statistical analysis of electricity consumption data and comparison with preset level standards.

[0070] After determining the electricity consumption levels of multiple electricity consumption areas, it is necessary to determine the grid control area based on their relative positions and electricity consumption levels. There may be one or more grid control areas, depending on factors such as the distribution, scale, and electricity demand of the electricity consumption areas. When determining the grid control area, factors such as the structure, capacity, and reliability requirements of the grid need to be considered to ensure the stability and security of the power supply.

[0071] Specifically, suppose that power grid planning is being carried out for two adjacent power-consuming areas (residential area and industrial area) in a city; through measurement, it is found that the distance between the center points of the residential area and the industrial area is 8 kilometers, while the preset location threshold is 10 kilometers; because 8 kilometers is less than 10 kilometers, it is considered that the residential area and the industrial area are close enough and need to be managed as a whole.

[0072] Based on the electricity consumption data of the residential area, it was found that the electricity consumption in this area was relatively stable and the load fluctuation was small, so it was matched as the "medium" level; based on the electricity consumption data of the industrial area, it was found that the electricity consumption in this area was large and the load fluctuation was large, and peak load sometimes occurred, so it was matched as the "high" level.

[0073] Taking into account the relative locations and electricity consumption levels of residential and industrial areas, it was decided to manage the grid as a whole. A grid control area was planned, covering both residential and industrial areas and taking into account the power transmission needs between them. Within the grid control area, measures such as increasing grid capacity, optimizing grid structure, and improving grid reliability were planned to meet the electricity needs of residential and industrial areas. This example clearly illustrates how to determine the grid control area based on the relative locations and electricity consumption levels of multiple electricity consumption areas, providing guidance for subsequent grid planning.

[0074] Therefore, the first power consumption path is planned along the power consumption mark in the first power consumption area, and the second power consumption path is planned along the power consumption mark in the second power consumption area. The power consumption path corresponding to the power grid control area is determined based on the first power consumption path and the second power consumption path, which is compatible with the overall consideration of the first power consumption path and the second power consumption path, and ensures the accuracy of the power consumption path corresponding to the power grid control area.

[0075] At this point, within each power consumption area, the first step is to identify key power usage markers. These markers are the locations of substations, distribution rooms, large power equipment, or preset power access points. The identification of power usage markers is usually based on geographic information system (GIS) data, on-site survey results, or power network design drawings. Based on the identified power usage markers, the paths from power sources (such as substations) to various power consumption points are planned. These paths must meet the efficiency, reliability, and safety requirements of power transmission. During the planning process, various factors need to be considered, such as terrain obstacles, the layout of existing power lines, and the possibility of future expansion.

[0076] Optionally, assume that a power grid is being planned for residential and industrial areas in a city; in the residential area, substation A, distribution room B, and power access points for several residential buildings are identified; in the industrial area, substation C, power access point D for a large factory, and backup power access point E are identified.

[0077] Starting from substation A, a route was planned to distribution room B, and then from distribution room B to the power access points of each residential building;

[0078] Starting from substation C, a main route is planned to the power access point D of the large factory, and a backup route is reserved to the backup power access point E;

[0079] During the planning process, topographical obstacles (such as rivers, mountains) and the layout of existing power lines were taken into account to ensure the feasibility and efficiency of the route.

[0080] After planning the power consumption paths for the first and second power consumption areas, they need to be integrated to form a complete power consumption path corresponding to the grid control area. During the integration process, it is necessary to ensure that the power transmission paths between the various power consumption areas are coherent, without breakpoints or conflicts. At the same time, the overall balance and stability of the power grid need to be considered to ensure that electricity can be efficiently and safely transmitted to each power consumption point.

[0081] Finally, the planned power path is verified to ensure that it meets the requirements of power transmission and is feasible in actual operation; if problems or deficiencies are found, optimization and adjustment are required until a satisfactory power path solution is obtained.

[0082] Optionally, the power consumption paths of residential and industrial areas are integrated to form a complete power consumption path corresponding to the grid control area; during the integration process, it is ensured that the power transmission path between residential and industrial areas is coherent, without breakpoints or conflicts; at the same time, the overall balance and stability of the power grid are also taken into consideration to ensure that electricity can be efficiently and safely transmitted to various power consumption points; through this example, we can clearly see how to plan power consumption paths according to power consumption tags and integrate the power consumption paths of multiple power consumption areas to form a complete power consumption path corresponding to the grid control area.

[0083] In one embodiment of the present application, the first electricity consumption area (residential area): identifies key electricity consumption markers, such as substations, distribution rooms, and power access points of major residential buildings; plans electricity consumption paths from substations to various power access points based on these markers; creates a matching table to record information such as the starting point, end point, path length, and expected load of each path.

[0084] Second power consumption area (industrial area): Similarly identify key power consumption markers, such as the main substation, factory power access point, and backup power supply point; plan the power access points from the main substation to each factory, as well as the access path for the backup power supply; and similarly create a matching table to record relevant information.

[0085] The path matching table is shown in Table 2:

[0086] Table 2 Path matching table

[0087]

[0088] A weight is set for each path based on factors such as path length, expected load, path reliability, and terrain difficulty. A score is calculated for each path based on the weight, with the higher the score, the better the path. The path scores of the first and second power consumption areas are integrated, and the overall efficiency and reliability of the grid control area are considered to determine the optimal power consumption path combination.

[0089] Assume the following weights: path length (0.3), expected load (0.4), path reliability (0.2), terrain difficulty (0.1); for residential path 1: score = 2.50.3 + 50.4 + reliability score 0.2 + terrain difficulty score 0.1. The score table is shown in Table 3:

[0090] Table 3 Score Sheet

[0091] area Path number Score (out of 10 points) residential area 1 7.5 (example value) residential area 2 6.8 (example value) Industrial Zone 1 8.2 (Example value) Industrial Zone 2 6.5 (example value)

[0092] Based on the path scores, the paths with higher scores are selected as the main power consumption paths in the power grid control area. Taking into account factors such as the overall layout of the power grid, load balance, and reliability requirements, the selected paths are fine-tuned or optimized. The final power consumption path map or detailed path list for the power grid control area is output.

[0093] The grid's control area encompasses residential and industrial areas. The primary power routes include: substation A to distribution room B to residential building C (residential areas), and main substation X to factory Y (industrial areas). A backup route includes: main substation X to backup power source Z (industrial areas), to provide for emergency use. This method systematically plans power routes within the grid's control area, ensuring efficient, reliable, and secure power supply.

[0094] refer to Figure 4 In step S13, if the power path passes through the residential area and the industrial area, the power path is divided into a residential power path and an industrial power path, and a first subgrid model of the residential power path and a second subgrid model of the industrial power path are generated;

[0095] In the specific implementation process of the present invention, the specific steps are:

[0096] S131: Among the power consumption paths corresponding to the grid control area, a first power consumption path corresponds to the living area and serves as the living power consumption path; a second power consumption path corresponds to the industrial area and serves as the industrial power consumption path; the power consumption path corresponding to the grid control area passes through the living area and the industrial area;

[0097] S132: Determine a plurality of first power consumption nodes based on the first power consumption path and the power consumption mark of the living area, and generate a first subgrid model based on the locations of the plurality of first power consumption nodes, the real-time power consumption data of the plurality of first power consumption nodes, and the power consumption devices corresponding to the plurality of first power consumption nodes;

[0098] S133: Determine multiple second power consumption nodes based on the second power consumption path and the power consumption mark of the industrial area, and determine and generate a second subgrid model based on the locations of the multiple second power consumption nodes, the real-time power consumption data of the multiple second power consumption nodes, and the power consumption equipment corresponding to the multiple second power consumption nodes.

[0099] In an embodiment of the present application, in the power consumption path corresponding to the power grid control area, the first power consumption path corresponds to the living area and serves as the living power consumption path; the second power consumption path corresponds to the industrial area and serves as the industrial power consumption path; the power consumption path corresponding to the power grid control area passes through the living area and the industrial area and is introduced into the power grid control area.

[0100] At this time, the specific scope of the power grid control area is clarified, which is usually determined based on factors such as geographical location, administrative divisions, and power demand; the power grid control area includes multiple different electricity consumption areas, such as living areas, industrial areas, commercial areas, etc.

[0101] Within the power grid control area, the area is divided into living areas and industrial areas based on land use planning, building use, population distribution and other information; living areas usually include residential areas, schools, hospitals and other areas with dense populations and mainly residential electricity consumption; industrial areas include factories, industrial parks, warehouses and other areas that mainly use electricity for industrial production.

[0102] Based on the grid plan and existing power facilities, identify the main power consumption paths within the grid's control area. These paths typically originate from large substations or power stations, pass through various levels of distribution facilities, and ultimately reach various power consumption points. Match the identified main power consumption paths with residential and industrial areas. Determine which paths primarily serve residential areas (residential power paths) and which paths primarily serve industrial areas (industrial power paths). Note that some paths serve both residential and industrial areas, but for this step, focus on their primary service targets.

[0103] Specifically, assume that power grid control is being carried out for an urban area that includes residential areas and industrial areas; the power grid control area is determined to be the central area of the city, including a large residential area (living area) and an industrial park (industrial area); the living area is defined as the residential area and its surrounding schools, hospitals and other facilities; the industrial area is defined as the various factories and warehouses in the industrial park.

[0104] The grid planning map identifies the main power consumption paths from the city's substations. These paths include one leading to residential areas (domestic power paths) and one leading to industrial parks (industrial power paths). The path leading to residential areas is marked as the domestic power path, primarily serving residents; the path leading to industrial parks is marked as the industrial power path, primarily serving factories and warehouses within the industrial parks. This example clearly demonstrates how to differentiate between residential and industrial power paths based on the specific circumstances of the grid's control area, providing a foundation for subsequent grid management and optimization.

[0105] Furthermore, multiple first power consumption nodes are determined based on the first power consumption path and the power consumption marks of the living area, and the first sub-grid model is generated based on the positions of the multiple first power consumption nodes, the real-time power consumption data of the multiple first power consumption nodes, and the power consumption equipment corresponding to the multiple first power consumption nodes. This takes into account the overall consideration of the positions of the multiple first power consumption nodes, the real-time power consumption data of the multiple first power consumption nodes, and the power consumption equipment corresponding to the multiple first power consumption nodes, thereby ensuring the accuracy of the generated first sub-grid model.

[0106] At this time, the first power consumption path (i.e., the power transmission path in the living area) is determined based on the power grid planning map and actual geographical distribution; within the living area, key power consumption markers are identified, which are the location points of large power users such as residential areas, schools, hospitals, and public facilities.

[0107] Along the first power consumption path, multiple first power consumption nodes are determined according to the location of the power consumption mark. These nodes are usually key points of power distribution, such as secondary output points of substations, distribution rooms, access points of large power equipment, etc.

[0108] For each first electricity consumption node, its real-time electricity consumption data is collected. These data include power consumption, voltage, current, power factor, etc., which are usually obtained through smart meters or sensors installed at the node; based on the electricity consumption characteristics and historical data of each first electricity consumption node, the corresponding type of electrical equipment is identified; for example, residential areas correspond to household appliances, and schools correspond to lighting, air conditioning, computers and other equipment. The collected location information, real-time electricity consumption data and electrical equipment information are combined with power grid modeling software or simulation tools to construct a first sub-grid model. This model should be able to reflect the power distribution, consumption and dynamic changes in the living area.

[0109] Specifically, assume that the first subgrid model is being built for a large residential area; the first power consumption path is determined to be the main power transmission path starting from the city substation, passing through a secondary substation, and finally reaching the distribution room in the residential area; within the residential area, power consumption markers are identified as the location points of various residential buildings, public facilities (such as swimming pools, gyms) and community service facilities (such as schools and hospitals).

[0110] Along the first power consumption path, the output point of the secondary substation, the input and output points of the distribution room, and the power access points of each residential building and public facility were identified as the first power consumption nodes. A smart meter was installed for each first power consumption node to collect its real-time power consumption, voltage, and current data. For example, it was found that the average power consumption of a residential building reached 200kW during peak hours, and the voltage was stable at around 220V.

[0111] Based on the electricity consumption characteristics and historical data of the electricity consumption nodes, it was identified that residential buildings mainly correspond to residents' household appliances, such as air conditioners, lighting, televisions, etc.; public facilities mainly correspond to lighting, air conditioners, water pumps and other equipment; using the collected location information, real-time electricity consumption data and electricity-consuming equipment information, a detailed first sub-grid model was constructed using power grid modeling software. This model can simulate the power distribution, consumption and dynamic changes in residential areas, such as the changes in power demand of each residential building and public facilities in different time periods.

[0112] This example clearly shows how to construct a first sub-grid model that can reflect the power characteristics of the living area based on the first power consumption path and the power consumption tags of the living area, combined with real-time power consumption data and power equipment information. This is of great significance for subsequent power grid management, optimization and fault prediction.

[0113] Therefore, multiple second power consumption nodes are determined based on the second power consumption path and the power consumption marks of the industrial area, and the second sub-grid model is generated based on the locations of the multiple second power consumption nodes, the real-time power consumption data of the multiple second power consumption nodes, and the power consumption equipment corresponding to the multiple second power consumption nodes. This takes into account the overall consideration of the locations of the multiple second power consumption nodes, the real-time power consumption data of the multiple second power consumption nodes, and the power consumption equipment corresponding to the multiple second power nodes, thereby ensuring the accuracy of the generated second sub-grid model.

[0114] At this time, the second power consumption path is clarified, that is, the power transmission path within the industrial area, which usually starts from a large substation or power station, passes through the distribution facilities in the industrial area, and finally reaches each industrial power consumption point; within the industrial area, key power consumption markers are identified, which usually include the location points of large industrial power users such as factories, workshops, warehouses, and R&D centers.

[0115] Along the second power consumption path, multiple second power consumption nodes are determined based on the locations of industrial power consumption tags. These nodes are usually key points of power distribution and consumption, such as the main distribution room of the factory, the distribution boxes of each workshop, and the access points of large industrial equipment. For each second power consumption node, its real-time power consumption data is collected. These data include power consumption, voltage, current, power factor, harmonics, etc., and are usually obtained through smart meters and power quality monitoring devices installed at the nodes.

[0116] Based on the power consumption characteristics and historical data of each second power consumption node, the corresponding power consumption equipment type is identified; for example, the corresponding equipment in a factory is production line equipment, air compressors, cooling towers, etc.; the corresponding equipment in a workshop is machine tools, welding equipment, conveyor lines, etc.; using the collected location information, real-time power consumption data and power consumption equipment information, combined with power grid modeling software or simulation tools, a second sub-grid model is constructed. This model should be able to reflect the power distribution, consumption characteristics, load changes and power quality of the industrial area.

[0117] Specifically, assume that a second subgrid model is being built for an industrial park. The second power consumption path is determined to be the power transmission path starting from the industrial zone substation, passing through the distribution room, and finally reaching each factory and workshop. Within the industrial park, the power consumption markers are identified as the location points of each factory, workshop, warehouse, and R&D center.

[0118] Along the secondary power consumption path, the main output point of the distribution room, the main distribution rooms of each factory, the distribution boxes in the workshop, and the access points of large industrial equipment were identified as secondary power consumption nodes. Smart meters and power quality monitoring devices were installed for each secondary power consumption node to collect real-time data such as power consumption, voltage, current, power factor, and harmonics. For example, it was found that the power consumption of a production line in a factory reached 1MW during peak hours, with voltage fluctuations within ±5%, but there was slight harmonic pollution.

[0119] Based on the power consumption characteristics and historical data of the power consumption nodes, it was identified that the factory mainly corresponds to production line equipment, air compressors, cooling towers, etc.; the workshop mainly corresponds to machine tools, welding equipment, conveyor lines, etc.; it was also learned that some equipment has high requirements for power quality, such as precision machining machine tools, which require stable voltage and current; using the collected location information, real-time power consumption data and power equipment information, a detailed second sub-grid model was constructed using power grid modeling software. This model can simulate the power distribution, consumption characteristics, load changes and power quality of the industrial park. For example, the model can predict the changes in power demand of each factory and workshop in different time periods, as well as the probability and location of power quality problems.

[0120] refer to Figure 5, in step S14, determining the power balance coefficient based on the power consumption data of the living area and the power consumption data of the industrial area;

[0121] In the specific implementation process of the present invention, the specific steps are:

[0122] S141: Collecting electricity consumption data of the living area and the industrial area, and comparing the electricity consumption data of the living area and the industrial area at the same time point;

[0123] S142: Determine a power consumption data comparison combination based on a comparison of the power consumption data of the living area and the power consumption data of the industrial area; and determine a power consumption difference based on detection of each power consumption data comparison combination;

[0124] S143: Determine a first balance coefficient based on multiple power usage differences and the current time, determine a second balance coefficient based on multiple power usage differences and the type of power-consuming equipment used, and determine a power balance coefficient based on the first balance coefficient, the second balance coefficient, and a preset user balance mapping relationship.

[0125] In an embodiment of the present application, electricity consumption data of living areas and electricity consumption data of industrial areas are collected, and the electricity consumption data of living areas and electricity consumption data of industrial areas are compared at the same time point, thereby realizing the comparison of electricity consumption data of living areas and electricity consumption data of industrial areas.

[0126] At this time, determine the time point for data collection to ensure that data from living areas and industrial areas can be collected at the same time or similar times; configure smart meters, data sensors or other power monitoring equipment to ensure that they can accurately and in real time record electricity consumption data; calibrate and test the collection equipment to ensure the accuracy and reliability of the data.

[0127] In living areas, electricity consumption data of various residential buildings, public facilities, etc. is collected through smart meters or data sensors; the data includes key indicators such as total power consumption, voltage, current, power factor, etc.; the collected data is stored in a database for subsequent analysis and comparison.

[0128] In industrial areas, electricity consumption data from various factories, workshops, production lines, etc. are also collected through smart meters or data sensors. The level of detail of data collection varies depending on the complexity of industrial equipment and the different electricity consumption characteristics. The collected data is also stored in a database and managed separately from the data in living areas. At a certain point in time, electricity consumption data for living and industrial areas are extracted from the database. The synchronization of the data is ensured, that is, the two are collected at similar time points to reduce errors caused by time differences. The electricity consumption data of living and industrial areas are compared to analyze the differences between the two in terms of power consumption, voltage stability, current load, etc.

[0129] Specifically, assume that at time point T (for example, 10 a.m.), it is planned to collect and compare electricity consumption data from residential and industrial areas; 10 a.m. is selected as the time point for data collection because it is usually the peak electricity consumption period and can reflect the electricity demand of the region; the smart meters in the residential and industrial areas are calibrated and tested to ensure that they can accurately record electricity consumption data.

[0130] In the living area, the total power consumption data of each residential building was collected; for example, the total power consumption of residential building A was 500kW, and the total power consumption of residential building B was 300kW. The electricity consumption data of public facilities was also collected, such as the lighting and air-conditioning equipment in the community center, with a total power consumption of 100kW. This data was stored in the database and marked as the living area electricity consumption data at time point T.

[0131] In the industrial area, total power consumption data for each factory is collected. For example, Factory A's total power consumption is 1500kW, primarily used to run production line equipment. Power consumption data for specific equipment within the workshop, such as machine tools and welding equipment, is also collected, but this data is more detailed and used for internal factory energy management. The total power consumption data at the factory level is stored in the database, labeled as industrial area power consumption data at time point T.

[0132] At time point T, electricity consumption data for residential and industrial areas was extracted from the database. A comparison revealed that the total power consumption in the industrial area (1500kW) was significantly higher than that in the residential area (500kW + 300kW + 100kW = 900kW). This indicates that during the 10:00 a.m. time period, the industrial area has a higher electricity demand, requiring more power distribution and scheduling from the grid. This example demonstrates how to collect and compare electricity consumption data for residential and industrial areas, and the importance of this data in grid management and energy planning.

[0133] Furthermore, a power consumption data comparison combination is determined based on the comparison of power consumption data of the living area and the power consumption data of the industrial area; and a power consumption difference amount is determined based on the detection of each power consumption data comparison combination, thereby introducing the power consumption difference amount.

[0134] At this point, after completing the collection of electricity consumption data for living areas and industrial areas, it is necessary to determine which data points or data categories will be directly compared; the selection of comparison combinations should be based on the needs of grid management, including key indicators such as total power consumption, voltage stability, current load rate, and power factor; for example, the total power consumption of living areas and the total power consumption of industrial areas can be selected as a set of comparison combinations to evaluate the differences in electricity demand between the two areas.

[0135] For each selected comparison combination, detailed testing and analysis are carried out, which involves calculating statistical quantities such as mean, standard deviation, maximum, minimum, and drawing charts to visually display the data differences; the purpose of the testing is to identify significant differences or trends in electricity consumption data between residential areas and industrial areas.

[0136] Based on the test results of the comparison combination, the difference in electricity consumption is calculated; the difference in electricity consumption is an absolute difference (such as a direct difference in power consumption) or a relative difference (such as a ratio of power consumption); the calculation of the difference should take into account the unit, range and change trend of the data to ensure the accuracy and comparability of the results.

[0137] Specifically, it is assumed that the electricity consumption data collection of the living area and the industrial area at time point T has been completed, and the total power consumption has been selected as the comparison combination; the total power consumption of the living area (denoted as L_Total_Power) and the total power consumption of the industrial area (denoted as I_Total_Power) have been selected as the comparison combination.

[0138] Perform a statistical analysis on L_Total_Power and I_Total_Power. Assume that the mean value of L_Total_Power is 900kW and the standard deviation is 100kW; the mean value of I_Total_Power is 1500kW and the standard deviation is 200kW. Draw a chart to show the total power consumption distribution of the two areas. It can be seen intuitively that the total power consumption in the industrial area is significantly higher than that in the living area.

[0139] Calculate the absolute difference: Difference = I_Total_Power - L_Total_Power = 1500kW - 900kW = 600kW; calculate the relative difference (ratio): Difference ratio = I_Total_Power / L_Total_Power = 1500kW / 900kW ≈ 1.67 (times), which indicates that at time point T, the total power consumption in the industrial area is 600kW higher than that in the living area, or about 67% higher; through this example, we can see how to determine the comparison combination based on the comparison of electricity consumption data between the living area and the industrial area, and determine the electricity consumption difference through detection and analysis. This information is of great significance for power grid management, energy planning, and supply and demand balance.

[0140] Therefore, the first balance coefficient is determined based on multiple power consumption differences and the current time, the second balance coefficient is determined based on multiple power consumption differences and the type of power-consuming equipment used, and the power balance coefficient is determined based on the first balance coefficient, the second balance coefficient and the preset user balance mapping relationship. This is compatible with the overall consideration of the first balance coefficient, the second balance coefficient and the preset user balance mapping relationship, ensuring the accuracy of the power balance coefficient.

[0141] At this point, a first balance coefficient is introduced. This first balance coefficient is determined based on multiple electricity usage differences and the current time. Multiple electricity usage differences (such as power consumption differences and voltage differences) and the current time are collected and statistically calculated to calculate the first balance coefficient. This first balance coefficient reflects the relative balance in electricity demand between residential and industrial areas at the current point in time. The current time indicates different electricity usage patterns (such as peak and off-peak hours), which affects the calculation of the balance coefficient.

[0142] Optionally, considering that it is currently a peak period and the power consumption difference is large (600kW), the weighted average method is used to calculate the first balance coefficient; assuming that the weight of the peak period is higher and the weight of the power consumption difference is also higher, the first balance coefficient is lower, indicating that the power grid faces a large imbalance in electricity demand during the peak period; example calculation: first balance coefficient = 0.6 (peak period weight) * 0.8 (low balance state caused by large power consumption difference) = 0.48.

[0143] The second balance coefficient is determined based on multiple power usage differences and the types of electrical equipment used. Multiple power usage differences and corresponding types of electrical equipment are collected. A machine learning model is used to calculate the second balance coefficient based on factors such as the type, power, and efficiency of the electrical equipment. This second balance coefficient takes into account the impact of different types of electrical equipment on the grid balance. For example, high-energy-consuming equipment (such as large industrial machinery) and low-energy-consuming equipment (such as household lighting) have different weights in grid balance.

[0144] Optionally, considering that large machinery and equipment in industrial areas have a greater impact on the power grid, while household appliances in living areas have a smaller impact, a rule engine is used to calculate the second balance coefficient; example rule: if the electrical equipment in the industrial area is mainly high-energy-consuming equipment, the second balance coefficient is reduced; if the electrical equipment in the living area is mainly low-energy-consuming equipment, the second balance coefficient has a smaller impact; example calculation: the second balance coefficient = 0.7 (taking into account the impact of high-energy-consuming equipment in the industrial area).

[0145] Collect preset user balance mapping relationships. The preset user balance mapping relationship is a predefined mapping table or model, which gives the corresponding power balance coefficient based on the combination of the first balance coefficient and the second balance coefficient. The final power balance coefficient is determined by looking up the preset user balance mapping relationship or using interpolation, fitting and other methods, combined with the first balance coefficient and the second balance coefficient. The power balance coefficient is a comprehensive indicator that reflects the degree of balance in power demand between living areas and industrial areas in the current state of the power grid, as well as the contribution of different types of electrical equipment to the grid balance.

[0146] Optionally, assume that there is a preset user balance mapping relationship table, which lists the electricity balance coefficients corresponding to different combinations of the first balance coefficient and the second balance coefficient; by looking up the table or using the interpolation method, the electricity balance coefficient corresponding to the first balance coefficient of 0.48 and the second balance coefficient of 0.7 is obtained; example result: electricity balance coefficient = 0.55 (this is an assumed value, the actual value depends on the specific definition of the preset mapping relationship); through this example, we can see how to determine the electricity balance coefficient based on multiple electricity consumption differences, current time, type of electrical equipment and preset user balance mapping relationship. This electricity balance coefficient has important guiding significance for power grid management, energy scheduling and supply and demand balance.

[0147] In one embodiment of the present application, the power balance coefficient matching table is shown in Table 4:

[0148] Table 4 Electricity balance coefficient matching table

[0149]

[0150]

[0151] refer to Figure 6 In step S15, a subgrid interconnection model is determined based on the power balance coefficient, the first subgrid model, and the second subgrid model, and a new distribution network is determined based on the first subgrid model, the second subgrid model, and the subgrid interconnection model;

[0152] In the specific implementation process of the present invention, the specific steps are:

[0153] S151: Collecting a power balance coefficient, determining transitional power data based on the power balance coefficient, power data for residential areas, and power data for industrial areas, determining a first interconnection combination based on the transitional power data and a first subgrid model, determining a second interconnection combination based on the transitional power data and a second subgrid model, and determining a subgrid interconnection model based on the first and second interconnection combinations;

[0154] S152: Dynamically monitor a first matching coefficient between the subgrid interconnection model and the first subgrid model and a second matching coefficient between the subgrid interconnection model and the second subgrid model;

[0155] S153: If the first matching coefficient and the second matching coefficient are greater than a preset matching coefficient threshold, a new distribution network is determined based on the grid interconnection model, the first subgrid model, and the second subgrid model.

[0156] In an embodiment of the present application, the electricity balance coefficient is collected, and the transition electricity data is determined based on the electricity balance coefficient, the electricity consumption data of the living area, and the electricity consumption data of the industrial area. The first docking combination is determined based on the transition electricity data and the first sub-grid model, and the second docking combination is determined based on the transition electricity data and the second sub-grid model. The sub-grid docking model is determined based on the first docking combination and the second docking combination, which is compatible with the overall consideration of the first docking combination and the second docking combination, thereby ensuring the accuracy of the sub-grid docking model.

[0157] At this point, the power balance coefficient is obtained from the previous step. This power balance coefficient is a comprehensive indicator that reflects the balance of electricity demand between residential and industrial areas. The power balance coefficient is calculated using a complex algorithm that takes into account factors such as multiple power consumption differences, the current time, and the type of electrical equipment. Based on the power balance coefficient, the power consumption data for the residential area, and the power consumption data for the industrial area, transitional power consumption data is calculated using weighted averaging, interpolation, or other mathematical methods. This transitional power consumption data is intended to simulate the power consumption of the two areas in a balanced state, providing a benchmark for subsequent grid connection. Optionally, the power balance coefficient is collected = 0.6; the transitional power consumption data is determined = 0.4 900kW + 0.6 1500kW = 360kW + 900kW = 1260kW (but for simplicity, rounded to 1200kW).

[0158] The first subgrid model represents the grid characteristics of the living area, including grid structure, transmission capacity, and load characteristics. Transitional power consumption data is input into the first subgrid model, and the grid operating state is simulated to identify the grid node or line combination that best matches the transitional power consumption data. This combination serves as the first docking combination. Alternatively, within the first subgrid model, the grid configuration closest to 1200kW is identified. Assuming there are multiple grid nodes in the first subgrid model, node A, with a load capacity of 1250kW, best matches the transitional power consumption data, is selected as part of the first docking combination.

[0159] The second subgrid model represents the grid characteristics of the industrial area. Similarly, the transitional power consumption data is input into the second subgrid model, and the grid operating state is simulated to identify the grid node or line combination that best matches the transitional power consumption data. This combination serves as the second docking combination. Optionally, within the second subgrid model, the grid configuration closest to 1200kW is also identified. Assuming that there are multiple grid nodes in the second subgrid model, node B, with a load capacity of 1300kW, best matches the transitional power consumption data (although slightly higher due to grid design margins), is selected as part of the second docking combination.

[0160] Combining the first docking combination and the second docking combination, a subgrid docking model is constructed; the subgrid docking model describes how to effectively connect the power grids in the living area and the industrial area while maintaining the stability and efficiency of the power grid; optionally, combining the first docking combination (node A) and the second docking combination (node B) to construct a subgrid docking model, which involves establishing a new transmission line between node A and node B, adjusting the transformer capacity, optimizing the power grid structure, etc., to ensure that the power grids in the two areas can be smoothly connected, while meeting the electricity demand and maintaining the stability of the power grid.

[0161] Further, dynamically monitoring a first matching coefficient between the subgrid interconnection model and the first subgrid model and a second matching coefficient between the subgrid interconnection model and the second subgrid model;

[0162] At this time, the matching coefficient is an indicator used to quantify the similarity or matching degree between two power grid models; the correlation coefficient, mean square error (MSE), root mean square error (RMSE), determination coefficient (R 2 ) and other statistical indicators; in this step, two sets of matching coefficients need to be defined: the first matching coefficient (between the subgrid docking model and the first subgrid model) and the second matching coefficient (between the subgrid docking model and the second subgrid model).

[0163] Data on the grid status, including load, voltage, current, power factor, etc., is collected in real time from the grid monitoring system. This data will be used to update the status of the subgrid interconnection model, the first subgrid model, and the second subgrid model. The real-time data is used to update the status of the subgrid interconnection model, the first subgrid model, and the second subgrid model, which involves adjusting parameters in the model, recalculating the load distribution, and updating the grid structure.

[0164] Based on the updated grid model status, the first matching coefficient and the second matching coefficient are calculated. The specific calculation method depends on the selected statistical indicator. For example, if the correlation coefficient is used, the correlation coefficient of variables such as load and voltage between the subgrid connection model and the first / second subgrid model needs to be calculated. At the same time, the changes in the matching coefficient are monitored in real time. If the matching coefficient falls below the preset threshold, an alarm is triggered, prompting grid management personnel to intervene or make adjustments.

[0165] Specifically, load, voltage, current and other data are collected in real time from the power grid monitoring system; the real-time data is used to update the status of the subgrid docking model, the first subgrid model and the second subgrid model; for example, assuming that the real-time data shows that the load in the living area has increased by 10%, the load data in the first subgrid model is updated.

[0166] For the first matching coefficient, the correlation coefficients of load, voltage and other variables between the subgrid interconnection model and the updated first subgrid model are calculated; it is assumed that the calculated first matching coefficient is 0.90. For the second matching coefficient, the correlation coefficients of load, voltage and other variables between the subgrid interconnection model and the updated second subgrid model are similarly calculated; it is assumed that the calculated second matching coefficient is 0.88.

[0167] Monitor changes in the first matching coefficient and the second matching coefficient in real time; since the first matching coefficient of 0.90 and the second matching coefficient of 0.88 are both higher than the preset threshold of 0.85, no alarm is triggered; if at some point in the future, the matching coefficient drops due to changes in the grid structure, equipment failure, etc., for example, the first matching coefficient drops to 0.80 and the second matching coefficient drops to 0.75, an alarm is triggered, prompting grid management personnel to intervene or make adjustments.

[0168] Therefore, if the first matching coefficient and the second matching coefficient are greater than the preset matching coefficient threshold, the new distribution network is determined based on the grid interconnection model, the first subgrid model and the second subgrid model, which is compatible with the overall consideration of the grid interconnection model, the first subgrid model and the second subgrid model, and ensures the accuracy of the new distribution network.

[0169] At this point, review the calculation results of the first matching coefficient and the second matching coefficient; ensure that both matching coefficients are greater than the preset matching coefficient threshold; if either matching coefficient is lower than the threshold, it is necessary to re-evaluate the effectiveness of the subgrid interconnection model, or make further adjustments and optimizations.

[0170] Integration is performed based on the grid connection model (i.e., the position and role of the subgrid connection model in the larger grid), the first subgrid model, and the second subgrid model. The integration process involves adjusting the grid structure, optimizing transmission lines, redistributing loads, updating equipment parameters, etc. Based on the integrated grid model, the structure of the new distribution network is determined, which includes determining the connection relationship between each grid node, the capacity and path of the transmission line, the configuration of the transformer, etc.

[0171] Use simulation software to simulate the operation of the new distribution network to verify its stability and efficiency; based on the simulation results, make necessary optimization adjustments to the distribution network to ensure its performance in actual operation; submit the implementation plan of the new distribution network to the grid management department for approval; after approval, carry out the transformation and implementation of the distribution network according to the plan; during the implementation process, continuously monitor the operating status of the grid to ensure the smooth progress of the transformation; after the new distribution network is put into operation, perform regular maintenance and inspections; collect operating data to evaluate the performance and benefits of the new distribution network; based on the evaluation results, make necessary adjustments and optimizations to maintain the long-term stability and efficiency of the grid.

[0172] Specifically, the preset matching coefficient threshold is 0.85. In the previous step, the first matching coefficient was calculated to be 0.90, and the second matching coefficient was calculated to be 0.88, both exceeding the threshold. Recall that the first matching coefficient of 0.90 and the second matching coefficient of 0.88 are both greater than the preset matching coefficient threshold of 0.85. Based on the grid interconnection model (which describes the position and role of the subgrid interconnection model within the larger grid), the first subgrid model (representing the characteristics of the residential area grid), and the second subgrid model (representing the characteristics of the industrial area grid), integration is performed. Assume that during the integration process, it is discovered that in order to optimize transmission efficiency, a new transmission line needs to be added between the residential and industrial areas.

[0173] Based on the integrated grid model, the structure of the new distribution network was determined; the capacity and routing of the new transmission lines, as well as their connections to other grid nodes, were determined; and the transformer configuration was updated to match the new load distribution and transmission requirements. Simulation software was used to simulate the operation of the new distribution network; the simulation results showed improved stability and efficiency. Based on the simulation results, the distribution network was fine-tuned to ensure optimal performance in actual operation. The implementation plan for the new distribution network was submitted to the grid management department for approval. Upon approval, the distribution network was transformed and implemented according to the plan. During the implementation process, the grid's operating status was continuously monitored to ensure the smooth progress of the transformation.

[0174] After the new distribution network is put into operation, regular maintenance and inspections will be carried out; operational data will be collected to evaluate the performance and benefits of the new distribution network; assuming that the evaluation results show that the losses of the new distribution network have been reduced by 10% and the stability has been improved by 20%; based on the evaluation results, necessary adjustments and optimizations will be made to maintain the long-term stability and efficiency of the power grid.

[0175] In one embodiment of the present application, when both the first matching coefficient and the second matching coefficient are greater than a preset matching coefficient threshold, a matching degree matching table is used to comprehensively evaluate the matching of the grid interconnection model, the first subgrid model, and the second subgrid model, and based on this, the structure of the new distribution network is determined; the matching degree matching table is shown in Table 5:

[0176] Table 5. Matching degree table

[0177] Model Portfolio Matching evaluation Remark Grid connection & first subnet High Match Small fluctuations in voltage and current, and reasonable load distribution Grid connection & second subnet Medium match The voltage fluctuation is slightly larger, but the load transmission efficiency is high First Subnet & Second Subnet High Match Historical data shows that the two work well together

[0178] Confirm that both the first matching coefficient (e.g., 0.92) and the second matching coefficient (e.g., 0.89) are greater than a preset threshold (e.g., 0.85). According to the matching table, the grid connection model has a high degree of matching with the first subgrid model and a moderate degree of matching with the second subgrid model. However, considering that the first and second subgrids themselves have a high degree of matching, this increases confidence in the overall feasibility of the grid connection model. Combine the highly matched first subgrid with the grid connection model, maintaining their original connection and configuration. Make appropriate adjustments to the moderately matched second subgrid, such as adding voltage stabilization equipment or optimizing transmission lines, to better integrate it into the grid connection model. Based on all the information, draw a structural diagram of the new distribution network and clarify the configuration of each node, line, and transformer.

[0179] See also Figure 7 , Figure 7 : is a schematic diagram of the structure of a new county-level distribution network planning system in an embodiment of the present invention; the new county-level distribution network planning system includes:

[0180] The power consumption area module 21 is used to determine multiple power consumption areas based on the division of the town distribution map of the county;

[0181] The power consumption path module 22 is used to determine the power grid control area based on the relative positions of multiple power consumption areas and the power consumption data of the multiple power consumption areas, and determine the power consumption path in the power grid control area;

[0182] The subgrid module 23 is configured to divide the power path into a residential power path and an industrial power path if the power path passes through a residential area and an industrial area, and generate a first subgrid model for the residential power path and a second subgrid model for the industrial power path;

[0183] The power balance coefficient module 24 is used to determine the power balance coefficient based on the power consumption data of the living area and the power consumption data of the industrial area;

[0184] The distribution network module 25 is used to determine a subgrid interconnection model based on the power balance coefficient, the first subgrid model and the second subgrid model, and to determine a new distribution network based on the first subgrid model, the second subgrid model and the subgrid interconnection model.

[0185] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A new county distribution network planning method, characterized in that: include: Determine multiple electricity consumption areas based on the division of the county's urban distribution map; Determine a power grid control area based on the relative positions of multiple power consumption areas and power consumption data of the multiple power consumption areas, and determine a power consumption path within the power grid control area; If the power path passes through a residential area and an industrial area, the power path is divided into a residential power path and an industrial power path, and a first subgrid model of the residential power path and a second subgrid model of the industrial power path are generated; Determine the electricity balance coefficient based on electricity consumption data of living areas and industrial areas; A subgrid interconnection model is determined according to the power balance coefficient, the first subgrid model, and the second subgrid model; and a new distribution network is determined according to the first subgrid model, the second subgrid model, and the subgrid interconnection model.

2. The method for planning a new county-level distribution network according to claim 1, characterized in that: The multiple electricity consumption areas determined based on the division of the county-level town distribution map include: Collect the town distribution map of the county, and determine the living area of residents and the industrial work area based on the markings on the town distribution map of the county; Determine a living area according to a resident's living range and a resident's living path, and determine a first power consumption area in the living area based on a power consumption location of the living area, power consumption data of the living area, and a current time; An industrial area is determined based on the industrial work scope and work path. In the industrial area, a second power consumption area is determined based on the power consumption location, power consumption data and current time of the industrial area. The multiple power consumption areas include a first power consumption area and a second power consumption area.

3. The method for planning a new county-level distribution network according to claim 1, characterized in that: The determining of a power grid control area based on the relative positions of the plurality of power consumption areas and the power consumption data of the plurality of power consumption areas, and determining a power consumption path in the power grid control area includes: Determining relative positions of the plurality of power consumption areas according to a position of the first power consumption area and a position of the second power consumption area; If the relative positions of multiple power consumption areas are less than a preset position threshold, the multiple power consumption areas are controlled as a whole. At this time, the first power consumption level is matched based on the power consumption data of the first power consumption area, and the second power consumption level is matched based on the power consumption data of the second power consumption area. The power grid control area is determined based on the first power consumption level, the second power consumption level and the relative positions of the multiple power consumption areas. A first power consumption path is planned along the power consumption mark in the first power consumption area, and a second power consumption path is planned along the power consumption mark in the second power consumption area. The power consumption path corresponding to the power grid control area is determined based on the first power consumption path and the second power consumption path.

4. The method for planning a new county-level distribution network according to claim 1, characterized in that: If the power path passes through a residential area and an industrial area, the power path is divided into a residential power path and an industrial power path, and a first subgrid model of the residential power path and a second subgrid model of the industrial power path are generated, including: Among the power consumption paths corresponding to the power grid control area, the first power consumption path corresponds to the living area and serves as the living power consumption path; the second power consumption path corresponds to the industrial area and serves as the industrial power consumption path; the power consumption path corresponding to the power grid control area passes through the living area and the industrial area.

5. The method for planning a new county-level distribution network according to claim 4, characterized in that: If the power path passes through a residential area and an industrial area, the power path is divided into a residential power path and an industrial power path, and a first subgrid model of the residential power path and a second subgrid model of the industrial power path are generated, further comprising: Determine a plurality of first power consumption nodes based on the first power consumption path and the power consumption mark of the living area, and determine and generate a first subgrid model based on the locations of the plurality of first power consumption nodes, the real-time power consumption data of the plurality of first power consumption nodes, and the power consumption devices corresponding to the plurality of first power consumption nodes; Multiple second power consumption nodes are determined based on the second power consumption path and the power consumption marks of the industrial area, and a second subgrid model is determined and generated based on the locations of the multiple second power consumption nodes, the real-time power consumption data of the multiple second power consumption nodes, and the power consumption equipment corresponding to the multiple second power consumption nodes.

6. The method for planning a new county-level distribution network according to claim 1, characterized in that: Determining the electricity balance coefficient based on the electricity consumption data of the living area and the electricity consumption data of the industrial area includes: Collect electricity consumption data of living areas and industrial areas, and compare the electricity consumption data of living areas and industrial areas at the same time point; An electricity consumption data comparison combination is determined based on a comparison of the electricity consumption data of the living area and the electricity consumption data of the industrial area; and an electricity consumption difference is determined based on detection of each electricity consumption data comparison combination.

7. The method for planning a new county-level distribution network according to claim 6, characterized in that: The determining of the electricity balance coefficient based on the electricity consumption data of the living area and the electricity consumption data of the industrial area further includes: The first balance coefficient is determined based on multiple power usage differences and the current time, the second balance coefficient is determined based on multiple power usage differences and the type of power-consuming equipment used, and the power balance coefficient is determined based on the first balance coefficient, the second balance coefficient and the preset user balance mapping relationship.

8. The method for planning a new county-level distribution network according to claim 1, characterized in that: Determining the subgrid interconnection model according to the power balance coefficient, the first subgrid model, and the second subgrid model, and determining the new distribution network according to the first subgrid model, the second subgrid model, and the subgrid interconnection model includes: Collect the electricity balance coefficient, determine the transition electricity data based on the electricity balance coefficient, the electricity consumption data of the living area, and the electricity consumption data of the industrial area, determine the first docking combination based on the transition electricity data and the first subgrid model, determine the second docking combination based on the transition electricity data and the second subgrid model, and determine the subgrid docking model based on the first docking combination and the second docking combination.

9. The method for planning a new county-level distribution network according to claim 8, characterized in that: The method further includes: determining a subgrid interconnection model based on the power balance coefficient, the first subgrid model, and the second subgrid model; and determining a new distribution network based on the first subgrid model, the second subgrid model, and the subgrid interconnection model. Dynamically monitoring a first matching coefficient between the subgrid interconnection model and the first subgrid model and a second matching coefficient between the subgrid interconnection model and the second subgrid model; If the first matching coefficient and the second matching coefficient are greater than a preset matching coefficient threshold, a new distribution network is determined based on the grid interconnection model, the first subgrid model, and the second subgrid model.

10. A new county distribution network planning system, characterized in that: The county-level new distribution network planning system is applied to the county-level new distribution network planning method according to any one of claims 1 to 9, and the county-level new distribution network planning system includes: The electricity consumption area module is used to determine multiple electricity consumption areas based on the division of the county's urban distribution map; A power consumption path module is used to determine a power grid control area based on the relative positions of multiple power consumption areas and the power consumption data of multiple power consumption areas, and determine a power consumption path in the power grid control area; A subgrid module is configured to divide the power path into a residential power path and an industrial power path if the power path passes through a residential area and an industrial area, and generate a first subgrid model for the residential power path and a second subgrid model for the industrial power path; The power balance coefficient module is used to determine the power balance coefficient based on the power consumption data of the living area and the power consumption data of the industrial area; The distribution network module is used to determine the subgrid interconnection model according to the power balance coefficient, the first subgrid model and the second subgrid model, and to determine a new distribution network according to the first subgrid model, the second subgrid model and the subgrid interconnection model.

Images