Power distribution network peak-facing safety supervision method and system

Abstract

The invention discloses a peak-facing safety supervision method and system for a power distribution network, and relates to the technical field of power grid supervision, and the method comprises the steps: 1, creating a regional visual information graph, which comprises the steps: obtaining the position data of a power supply function station in a supervision region, respectively importing a pre-uploaded GIS map for marking, and generating corresponding nodes; acquiring power grid distribution data among the nodes, importing the GIS map of the marked nodes, and generating a regional power grid simulation graph; distributing a storage space for any node, and recording corresponding site associated data; step 2, security supervision implementation, which comprises the following steps: acquiring energy consumption supervision statistical data of authority units in a supervision area; respectively associating and binding the corresponding energy consumption supervision statistical data to corresponding blocks in the regional power grid simulation graph by taking an administrative region as a unit, and calling and displaying the data; and determining the daily / weekly energy consumption peak value of each administrative region according to the historical energy consumption supervision statistical data. According to the invention, related workers can conveniently carry out comprehensive overall management on the peak-facing work of the power grid.

Description

Technical Field

[0001] The present application relates to the technical field of power grid supervision, and in particular to a method and system for inspecting the peak safety of distribution networks. Background Art

[0002] Affected by many factors such as the increase of greenhouse gases and the urban heat island effect, summer temperatures in some parts of the country have repeatedly hit new highs, which has led to an increased dependence on refrigeration equipment such as air conditioners. Daily electricity consumption in summer has also reached a peak, and the safety and reliability of the distribution network has faced a major test during this stage.

[0003] In order to ensure the safety and reliability of the power grid during the peak period, work can be carried out from the following aspects: 1. Promote the smooth construction of power grid projects and complete the commissioning of expansion and new construction projects before the peak season; Second, allocate electricity to various regions and optimize resource allocation, that is, transmit electricity from areas with sufficient power generation and energy storage sites through ultra-high voltage transmission projects to areas with high electricity consumption, industrial centers, active economies and large populations.

[0004] In the process of carrying out the above work, there is currently a lack of technology that can manage the entire power distribution peak load, and information islands are formed between various units and departments. The coordination and management of the peak load for relevant staff is complicated and takes up a lot of manpower. Therefore, this application proposes a new technical solution. Summary of the invention

[0005] In order to facilitate relevant staff to carry out comprehensive and coordinated management of power grid peak load handling work, this application provides a distribution network peak load safety supervision method and system.

[0006] In the first aspect, the present application provides a method for inspecting the peak safety of a distribution network, which adopts the following technical solution: A method for inspecting the peak load safety of a distribution network, comprising: Step 1: Create a regional visualization infographic, which includes: Obtain the location data of power supply function sites in the inspection area, and import them into the pre-uploaded GIS map for marking to generate corresponding nodes; wherein the power supply function sites include power stations, energy storage stations, and substations that are in operation, new construction, expansion, and maintenance; Obtain the power grid distribution data between each node, import the GIS map of the marked nodes, and generate a regional power grid simulation map; Allocate a storage space for any node and record the corresponding site-related data; wherein the site-related data includes one or more of site operation data, construction data, and maintenance data; Step 2: Safety inspection implementation, which includes: Obtain energy consumption supervision statistics of authorized units within the inspection area; The corresponding energy consumption supervision statistics are associated and bound to the corresponding blocks in the regional power grid simulation diagram based on administrative districts, and used for calling and display; Determine the daily / weekly peak energy consumption of each administrative area based on historical energy consumption regulatory statistics; Analyze and evaluate the energy load rate of the peak day, week, month and quarter based on the historical site association data of the nodes belonging to each administrative district; If the power supply load rate reaches the preset power restriction threshold, the power restriction duration is calculated, and the corresponding energy consumption supervision statistics and power supply load rate are summarized to generate resource optimization allocation recommendation data; Perform AI diagnosis on the site-related data of any node, and conduct risk investigation and tracking, as well as peak planning and analysis for projects under construction based on the diagnosis results.

[0007] Optionally, the safety inspection implementation further includes: Obtain the production plans of enterprises that meet the statistical conditions in the area corresponding to the node; Energy forecasting based on production plans and calculation of expected energy consumption increase and decrease parameters based on historical energy consumption supervision statistics; Generate recommended expansion information based on energy consumption increase and decrease parameters and pre-stored design parameters of power supply function sites of various specifications; The expected energy consumption increase and decrease parameters and recommended expansion information are summarized as resource optimization allocation recommendation data.

[0008] Optionally, the performing AI diagnosis on the site-related data of any node includes: Obtain the operating data of the running node, compare it with the preset standard data, and generate the diagnosis result; Acquire images of power facilities during inspections at operational nodes; Identify risk features based on images, obtain machine recognition results and send them to bound users, receive feedback, and generate diagnosis results; Among them, the image acquisition method includes collecting and uploading images through a drone / personal terminal carrying a visual unit.

[0009] Optionally, the performing AI diagnosis on the site-related data of any node includes: Call the construction data of the node under construction; Analyze construction progress based on construction data and estimate project completion time; Identify the downstream nodes of the node under construction and determine the administrative area of ​​electricity consumption; Search historical data to determine the historical energy consumption peak time of the electricity-consuming administrative area or receive uploaded custom energy consumption peak time; Determine whether the project is completed before the energy consumption peak time based on the project completion time. If yes, maintain project progress tracking; if not, output project delay interference peak information as the diagnosis result.

[0010] Optionally, the peak planning analysis of the projects under construction may include: Obtain product parameters and quantities of private, self-owned power generation facilities in the electricity-consuming administrative area; The maximum self-generated electricity is calculated based on product parameters and quantity, and output as peak response plan data.

[0011] Optionally, the peak planning analysis of the projects under construction may also include: Obtain and identify solutions to project delays; If the solution is to adjust the transmission route of the original grid structure, the estimated transmission loss is obtained and the economic loss is calculated based on the current electricity price; Determine the power shortage according to the energy consumption increase and decrease parameters; If the difference between the power shortage and the maximum self-generated power is less than the preset approximate threshold, the unit power loss is calculated based on the maximum self-generated power and the economic loss, and is defined as the self-generated power compensation data output as the peak response plan data.

[0012] Optionally, the safety inspection implementation further includes: Calculate the power transmission loss data between nodes based on the site operation data corresponding to each operating node; The power transmission loss data is bound to the corresponding power grid segment and used for calling and display.

[0013] In the second aspect, the present application provides a distribution network peak safety inspection system, which adopts the following technical solution: A distribution network peak load safety inspection system includes a cloud server, wherein the cloud server stores a computer program that can be loaded by a processor and execute any of the distribution network peak load safety inspection methods described above.

[0014] In summary, this application includes the following beneficial technical effects: 1. It allows staff to understand the distribution of power grids in the inspection area on an electronic map, and staff can check the status of any node (power station, energy storage station, substation) at any time; 2. Analyze historical data to obtain the historical energy supply load rate of each administrative district, and generate resource optimization allocation recommendation data for staff reference to determine whether to expand substations and adjust power distribution; 3. Carry out AI supervision on nodes to promptly detect risks in running nodes and conduct investigations; track and analyze projects under construction, and make intelligent plans to ensure the safety and reliability of peak work. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the main process of the method of the present application; Figure 2 It is a flowchart of the steps for creating a regional visualization infographic; Figure 3 It is a flowchart of the steps for implementing safety inspections. DETAILED DESCRIPTION

[0016] The following is combined with Figure 1-3 This application is described in further detail.

[0017] The embodiment of the present application discloses a method for inspecting the safety of power distribution network during peak hours.

[0018] Reference Figure 1 , the distribution network peak safety inspection methods include: Step 1: Create a regional visualization infographic; Step 2: Implement safety inspection.

[0019] Among them, the visual information graph is established on a cloud platform, which is connected to the corresponding online management systems of the Energy Bureau, power companies, power project contractors, etc. through various protocols to implement this method.

[0020] Reference Figure 2 , regarding step 1, creating a regional visualization infographic, it includes: S11. Obtain location data of power supply function sites within the inspection area, and import them into the pre-uploaded GIS map for marking to generate corresponding nodes.

[0021] Among them, power supply function sites include power stations, energy storage stations, and substations that are in operation, newly built, expanded, and under maintenance. It can be understood that when the platform capacity is surplus, the lower-level communities, street box-type substations, and various power towers can all be used as sites to meet the needs of high-granularity and refined management.

[0022] S12. Obtain the power grid distribution data between each node, import the GIS map of the marked nodes, and generate a regional power grid simulation map.

[0023] Application example: any node is bound to the cable directly connected to it. If the user selects a node in the UI interface, the node and the cable directly connected to it will be highlighted and displayed in another distinguishing color. The advantage of this design is that it can help relevant personnel understand the distribution relationship more clearly and intuitively, especially after marking the current direction arrows for each cable based on the node function attributes, the power resource allocation information can be read more intuitively.

[0024] S13. Allocate a storage space for any node and record the corresponding site-related data.

[0025] Among them, site-related data includes one or more of site operation data, construction data, and maintenance data. Examples of operation data include: real-time power generation, load rate, and fault details of the power station; construction data, that is, project data of any power engineering project under construction. The source of this data can be the project party's BIM management platform, the project party's active reporting, or analysis of cameras at the project site.

[0026] Reference Figure 3 , Step 2: Safety inspection implementation, which includes: S21. Obtain energy consumption supervision statistical data, i.e. electricity consumption data, from the authorized units (energy bureau, power company, statistics bureau, etc.) within the inspection area.

[0027] S22. Based on administrative areas (districts, counties, and cities), the corresponding energy consumption supervision statistical data are associated and bound to the corresponding blocks in the regional power grid simulation diagram, and used for calling and display.

[0028] If energy consumption supervision statistics are displayed in each block in a UI interface of the data large screen, the displayed content is updated according to the latest data obtained.

[0029] S23. Determine the daily / weekly peak energy consumption of each administrative area based on historical energy consumption supervision statistics; The energy load rate on the peak day, week, month and quarter is evaluated based on the historical site association data of the nodes belonging to each administrative district.

[0030] It is understandable that the power supply range of each area is relatively fixed when the facilities remain unchanged. For example, a comprehensive industrial zone consumes 2 million kWh of electricity per month, of which the daily electricity consumption is less than 60,000 kWh for 7 days, while the electricity consumption at other times is 1.58 million kWh and the node operation data in the area indicates full load operation, then the load rate of 90% for 7 days and the full load rate for other times will be evaluated.

[0031] S24. If the power supply load rate reaches the preset power restriction threshold, the power restriction duration is calculated, and the corresponding energy consumption supervision statistical data and the power supply load rate are summarized to generate resource optimization allocation recommendation data.

[0032] It is understandable that the power restriction policy is issued by the relevant management unit. If this method cannot obtain the latest information in real time to obtain the power restriction threshold, it is necessary to upload the design parameters of the power facilities in advance and set a power restriction threshold by the background to perform energy load rate analysis, resource optimization suggestions, etc.

[0033] S25. Perform AI diagnosis on the site-related data of any node, and conduct risk investigation and tracking, as well as peak planning and analysis for ongoing projects based on the diagnosis results.

[0034] Based on the above settings, this method: 1. It allows staff to understand the distribution of power grids in the inspection area on an electronic map, and staff can check the status of any node (power station, energy storage station, substation) at any time; 2. Analyze historical data to obtain the historical energy supply load rate of each administrative district, and generate resource optimization allocation recommendation data for staff reference to determine whether to expand substations and adjust power distribution; 3. Carry out AI supervision on nodes to promptly detect risks in running nodes and conduct investigations; track and analyze projects under construction, and make intelligent plans to ensure the safety and reliability of peak work.

[0035] In addition to energy consumption supervision statistics, energy load rate and power restriction duration, the above resource optimization allocation recommendation data also includes: Get the production plans of enterprises that meet the statistical conditions (quarterly electricity consumption meets the standard or is above the standard) in the area corresponding to the node; Example: Open account registration permissions for electricity consumption in the area, standardize and guide enterprises to actively report quarterly and annual production plans.

[0036] Energy forecasts are made based on production plans, and the expected energy consumption increase or decrease parameters are calculated based on historical energy consumption supervision statistics. For example, if the production plan for the third quarter of 2023 is 1.5 times that of the second quarter, the expected energy consumption will be 1.5 times that of the second quarter. The expected energy consumption increase or decrease parameters can be calculated based on historical electricity consumption.

[0037] Generate recommended expansion information based on energy consumption increase and decrease parameters and pre-stored design parameters of power supply function sites of various specifications; for example: if the estimated energy consumption increase is greater than the designed power transmission capacity of a minimum-specification substation, it is recommended to expand a substation of a higher level.

[0038] The expected energy consumption increase and decrease parameters and recommended expansion information are summarized as resource optimization allocation recommendation data.

[0039] According to the above settings, this method can link high-power-consuming enterprises in the administrative region and give more accurate suggestions based on their production plans, so as to ensure smooth industrial production during the peak period and reduce the restrictions on economic growth caused by power rationing.

[0040] In another embodiment of the method, AI diagnosis is performed on the site-related data of any node, which includes: Obtain the operating data of the running node, compare it with the preset standard data, and generate the diagnosis result; Acquire images of power facilities during inspections at operational nodes; Perform risk feature recognition based on the image, obtain machine recognition results and send them to the bound user, receive feedback, and generate diagnosis results; that is, recognize the feedback and determine whether the recognition results need to be adjusted.

[0041] The image acquisition method includes collecting and uploading images through drones / personal terminals carrying visual units. Special risk examples: damage to cables and cracked features of electrical structures.

[0042] According to the above settings, on the one hand, it can facilitate on-site workers to quickly complete the inspection work. On the other hand, the inspection process can be recorded in real time, and image recognition technology can be used to find leaks. At the same time, it can also be archived to facilitate accountability after a safety accident occurs.

[0043] On the basis of the above, the aforementioned risk tracking is to conduct maintenance tracking after the safety risk is discovered, recording the maintenance person, maintenance time, and maintenance results.

[0044] The above targets nodes that are already in operation. For power projects under construction, AI diagnosis is performed on the site-related data of the corresponding nodes, which includes: Call the construction data of the node under construction; Estimated project completion time based on construction data through construction progress analysis; Example: Call the progress tracking record of the construction party's BIM system. The original construction period is 5 months. The current time is 3 months after the project start time, and the project progress is 30%. It is estimated that the project will be completed in the seventh month after the current time. Identify the downstream nodes of the node under construction and determine the administrative area for electricity consumption; wherein the downstream node refers to the node that receives the electricity sent by the node under construction; Search historical data to determine the historical energy consumption peak time of the electricity-consuming administrative area or receive uploaded custom energy consumption peak time; Determine whether the project is completed before the energy consumption peak time based on the project completion time. If yes, maintain project progress tracking; if not, output project delay interference peak information as the diagnosis result.

[0045] According to the above settings, this method can use the construction data uploaded by the under-construction nodes to analyze the project completion time, and combine the historical energy consumption time of the corresponding electricity-consuming administrative area to diagnose whether the project will affect the peak work.

[0046] In another embodiment of the method, after the project delay interferes with the peak load, the peak load planning analysis of the project under construction includes: Obtain product parameters and quantities of private, self-owned power generation / energy storage facilities in the electricity-consuming administrative area; It is understandable that enterprises, especially large-scale manufacturing enterprises, often provide their own generators to ensure production safety, and even use flow battery technology to build energy storage stations.

[0047] The maximum self-generated electricity is calculated based on product parameters and quantity, and output as peak response plan data.

[0048] According to the above settings, this method will count and output the self-generated electricity capacity of the corresponding administrative area after discovering the project delay, so that the staff can use it as a reference when arranging how to solve the impact of the project delay. It will help to make full use of private resources and coordinate in advance to ensure the implementation of peak work.

[0049] On the basis of the above, the method further includes: Obtain the solutions for project extensions uploaded by staff members and perform (image, text, and audio) recognition; If the solution is to adjust the transmission route of the original grid structure, the estimated transmission loss is obtained and the economic loss is calculated based on the current electricity price. It is understandable that there will be multiple routes in the transmission node, but generally some will not be switched because the transmission loss is relatively large due to factors such as long-distance deployment.

[0050] In this embodiment, the line with the largest loss is defined, or about 5%-15% of its power transmission capacity is not included in the node full load parameter.

[0051] On the basis of the above: determine the power shortage according to the energy consumption increase and decrease parameters; because the energy consumption increase and decrease parameters are mainly industrial electricity consumption, and the increase and decrease of civil consumption are not considered, the power shortage here is an estimated value, such as: estimated daily / weekly / monthly power shortage.

[0052] If the difference between the (daily average, weekly average, monthly average) deficit electricity and the (daily average, weekly average, monthly average) maximum self-generated electricity is less than the preset similar threshold, the unit electricity loss is calculated based on the maximum self-generated electricity and economic loss, for example: economic loss / maximum self-generated electricity = unit electricity loss, and the self-generated electricity compensation data output is defined as the peak response plan data.

[0053] The above is a reasonable suggestion to distribute the economic losses caused by long-distance power transmission to private enterprises in the form of subsidies, fully encourage the rational use of private resources and reduce power supply pressure; after all, the cross-regional allocation process involves many things and will have an impact on electricity consumption in other regions. A typical example is: the mainland’s power generation is allocated to the east, but the electricity consumption of many local enterprises is restricted.

[0054] In another embodiment of the method, considering the above-mentioned power transmission loss, the safety inspection implementation is configured to further include: The power transmission loss data of the power grid between nodes is calculated based on the site operation data corresponding to each operating node; for example: the K1 node transmits 100,000 kWh of power per week, and the K2 node receives 96,000 kWh, so the loss is 4,000 kWh, a loss of 4%.

[0055] The power transmission loss data is bound to the corresponding power grid segment and used for calling and display.

[0056] On the one hand, the above data can be used to present the corresponding power grid segment on the data screen, so that the observation staff can understand the losses of each dispatching network; on the other hand, it can also be provided to the aforementioned analysis self-generated point compensation.

[0057] The embodiment of the present application also discloses a distribution network peak safety inspection system.

[0058] A distribution network peak load safety inspection system includes a cloud server, wherein the cloud server stores a computer program that can be loaded by a processor and execute any of the distribution network peak load safety inspection methods described above.

[0059] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for inspecting the peak load safety of a distribution network, characterized in that: include: Step 1: Create a regional visualization infographic, which includes: Obtain the location data of power supply function sites in the inspection area, and import them into the pre-uploaded GIS map for marking to generate corresponding nodes; wherein the power supply function sites include power stations, energy storage stations, and substations that are in operation, new construction, expansion, and maintenance; Obtain the power grid distribution data between each node, import the GIS map of the marked nodes, and generate a regional power grid simulation map; Allocate a storage space for any node and record the corresponding site-related data; wherein the site-related data includes one or more of site operation data, construction data, and maintenance data; Step 2: Safety inspection implementation, which includes: Obtain energy consumption supervision statistics of authorized units within the inspection area; The corresponding energy consumption supervision statistics are associated and bound to the corresponding blocks in the regional power grid simulation diagram based on administrative districts, and used for calling and display; Determine the daily / weekly peak energy consumption of each administrative area based on historical energy consumption regulatory statistics; Analyze and evaluate the energy load rate of the peak day, week, month and quarter based on the historical site association data of the nodes belonging to each administrative district; If the power supply load rate reaches the preset power restriction threshold, the power restriction duration is calculated, and the corresponding energy consumption supervision statistics and power supply load rate are summarized to generate resource optimization allocation recommendation data; Perform AI diagnosis on the site-related data of any node, and conduct risk investigation and tracking, as well as peak planning and analysis for projects under construction based on the diagnosis results.

2. The method for inspecting the peak load safety of the distribution network according to claim 1, characterized in that: The safety inspection implementation also includes: Obtain the production plans of enterprises that meet the statistical conditions in the area corresponding to the node; Energy forecasting based on production plans and calculation of expected energy consumption increase and decrease parameters based on historical energy consumption supervision statistics; Generate recommended expansion information based on energy consumption increase and decrease parameters and pre-stored design parameters of power supply function sites of various specifications; The expected energy consumption increase and decrease parameters and recommended expansion information are summarized as resource optimization allocation recommendation data.

3. The method for inspecting the peak load safety of distribution network according to claim 1, characterized in that: The AI ​​diagnosis of the site-related data of any node includes: Obtain the operating data of the running node, compare it with the preset standard data, and generate the diagnosis result; Acquire images of power facilities during inspections at operational nodes; Identify risk features based on images, obtain machine recognition results and send them to bound users, receive feedback, and generate diagnosis results; Among them, the image acquisition method includes collecting and uploading images through a drone / personal terminal carrying a visual unit.

4. The method for inspecting the peak load safety of the distribution network according to claim 1, characterized in that: The AI ​​diagnosis of the site-related data of any node includes: Call the construction data of the node under construction; Analyze construction progress based on construction data and estimate project completion time; Identify the downstream nodes of the node under construction and determine the administrative area of ​​electricity consumption; Search historical data to determine the historical energy consumption peak time of the electricity-consuming administrative area or receive uploaded custom energy consumption peak time; Determine whether the project is completed before the energy consumption peak time based on the project completion time. If yes, maintain project progress tracking; if not, output project delay interference peak information as the diagnosis result.

5. The method for inspecting the peak load safety of the distribution network according to claim 4, characterized in that: The peak planning analysis of the projects under construction includes: Obtain product parameters and quantities of private, self-owned power generation facilities in the electricity-consuming administrative area; The maximum self-generated electricity is calculated based on product parameters and quantity, and output as peak response plan data.

6. The method for inspecting the peak load safety of the distribution network according to claim 5, characterized in that: The peak planning analysis of the projects under construction also includes: Obtain and identify solutions to project delays; If the solution is to adjust the transmission route of the original grid structure, the estimated transmission loss is obtained and the economic loss is calculated based on the current electricity price; Determine the power shortage according to the energy consumption increase and decrease parameters; If the difference between the power shortage and the maximum self-generated power is less than the preset approximate threshold, the unit power loss is calculated based on the maximum self-generated power and the economic loss, and is defined as the self-generated power compensation data output as the peak response plan data.

7. The method for inspecting the peak load safety of the distribution network according to claim 6, characterized in that: The safety inspection implementation also includes: Calculate the power transmission loss data between nodes based on the site operation data corresponding to each operating node; The power transmission loss data is bound to the corresponding power grid segment and used for calling and display.

8. A distribution network peak safety inspection system, characterized by: It includes a cloud server, which stores a computer program that can be loaded by a processor and executes a distribution network peak safety inspection method as described in any one of claims 1 to 7.

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