Layered statistical method and system for adjustable resources on distribution network side

Abstract

The invention discloses a distribution network side adjustable resource oriented hierarchical statistical method and system, and the method comprises the steps: collecting first resource data of a first object, and carrying out the first data preprocessing; and performing panoramic monitoring on the first resource data, and monitoring key nodes of the first object based on the first data preprocessing result. And constructing a first resource data cloud picture of the first object to realize rapid information positioning. The hierarchical monitoring and data cloud picture display technology is adopted, panoramic monitoring of key nodes and adjustable resources is achieved, the monitoring coverage rate is greatly increased, and comprehensive data support is provided for optimal dispatching of power grid loads. According to the invention, manual intervention is reduced, the maintenance cost is reduced, the accuracy and response speed of resource allocation are improved, the reliability and economical efficiency of a distribution network system are remarkably enhanced, and the requirements of a modern power system for intelligent and real-time management are met.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and in particular to a hierarchical statistical method and system for adjustable resources on the distribution network side. Background Art

[0002] The adjustable load monitoring function obtains the basic model data and real-time data of the adjustable load object from the model center and the real-time monitoring center, obtains the analysis results from the main distribution network topology analysis, maps the collected data and the distribution network model, and realizes multi-level data aggregation, data panoramic monitoring, multi-level adjustable load key node heavy overload monitoring and power grid adjustable load cloud map monitoring and analysis functions for various adjustable load object resources within the dispatching range. It provides data support for monitoring scenarios such as adjustable loads and interruptible loads participating in power grid regulation, issues alarms for abnormal behaviors of adjustable load monitoring, and solves the problem of "observable" adjustable load information in the power grid. Summary of the invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by the present invention is that the traditional manual data collection method has a low update frequency and is difficult to meet the needs of real-time monitoring of the distribution network. The present invention realizes multiple data updates per hour through automated data collection and multi-level data processing, ensures the high-frequency real-time performance of the system, and provides technical support for the dynamic monitoring and scheduling of resources. Traditional methods cannot achieve comprehensive monitoring of distribution network resources, especially real-time monitoring of key nodes and load resources. The present invention introduces hierarchical statistics and GIS map display technology, which can effectively monitor key nodes of the distribution network, achieve high coverage monitoring of the overall distribution network, and ensure a more comprehensive monitoring range.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a hierarchical statistical method for adjustable resources on the distribution network side, comprising: collecting first resource data of a first object and performing first data preprocessing.

[0006] Perform panoramic monitoring on the first resource data, and monitor key nodes of the first object based on the first data preprocessing result.

[0007] Construct a first resource data cloud map of the first object to achieve rapid information positioning.

[0008] As a preferred solution of the hierarchical statistical method for adjustable resources on the distribution network side described in the present invention, the first data preprocessing includes collecting the first resource data of the first object and mapping it with the first object model to form a first resource data monitoring object integrating main and distribution.

[0009] As a preferred scheme of the hierarchical statistical method for adjustable resources on the distribution network side described in the present invention, wherein: the first data preprocessing also includes multi-level aggregation statistics, and an association relationship is formed through the mapping, and then an aggregation relationship of each level is formed to achieve multi-level and multi-capability aggregation.

[0010] As a preferred solution of the hierarchical statistical method for adjustable resources on the distribution network side described in the present invention, the panoramic monitoring includes calculating various forms of information through real-time parameter analysis of the first resource data.

[0011] As a preferred scheme of the hierarchical statistical method for adjustable resources on the distribution network side described in the present invention, wherein: the monitoring of the key nodes of the first object includes, based on the first data preprocessing result, calling the topology analysis method on the equipment of the first object, obtaining the key nodes of the power supply path where the equipment is located and monitoring them.

[0012] As a preferred solution of the hierarchical statistical method for adjustable resources on the distribution network side described in the present invention, wherein: the first object includes but is not limited to the distribution network, and the first resource data includes but is not limited to adjustable load resource data.

[0013] As a preferred solution of the hierarchical statistical method for adjustable resources on the distribution network side described in the present invention, wherein: the construction of the first resource data cloud map of the first object includes displaying the first resource data layer collected in real time in multiple forms respectively.

[0014] A hierarchical statistical system for adjustable resources on the distribution network side, characterized by: comprising:

[0015] The preprocessing module collects first resource data of the first object and performs first data preprocessing.

[0016] A monitoring module performs panoramic monitoring of the first resource data and monitors key nodes of the first object based on the first data preprocessing result.

[0017] The mapping module constructs a first resource data cloud map of the first object to achieve rapid information positioning.

[0018] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0019] A computer-readable storage medium stores a computer program, which implements the steps of the method described above when executed by a processor.

[0020] Beneficial effects of the present invention: By adopting hierarchical monitoring and data cloud map display technology, panoramic monitoring of key nodes and adjustable resources is realized, which greatly improves the monitoring coverage and provides comprehensive data support for the optimal dispatch of power grid loads. The present invention reduces manual intervention and maintenance costs, while improving the accuracy and response speed of resource allocation, significantly enhancing the reliability and economy of the distribution network system, and meeting the needs of modern power systems for intelligent and real-time management. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0022] Figure 1 An overall flow chart of a hierarchical statistical method and system for adjustable resources on the distribution network side provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0024] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a hierarchical statistical method for adjustable resources on the distribution network side, including:

[0025] S1: Collect first resource data of a first object and perform first data preprocessing.

[0026] In the present invention, the first object is the distribution network, and the first resource data is the adjustable load resource data. The first data preprocessing is to map the collected various types of adjustable resource data and the distribution network model to form an adjustable load monitoring object that integrates the main and distribution, providing a basis for the subsequent support of various types of data aggregation. The second is multi-level aggregation statistics. Through the mapping of adjustable load objects, the adjustable load objects are associated with the models and data. Based on the above-mentioned association relationships, aggregation relationships at various levels are formed. Finally, there is multi-level and multi-capability aggregation, which aggregates data from the perspective of capability type. Capability types include response speed (kW / s), response capacity aggregation (kWt), and response output aggregation (kW).

[0027] Furthermore, the first object includes but is not limited to a distribution network, and may also be an electric vehicle charging station in a wind power generation environment. The first resource data includes but is not limited to adjustable load resource data, and may also be wind speed data, wind turbine response capability, charging power and charging response time.

[0028] In an optional embodiment of the present invention, the first object is a wind farm, and the first resource data is wind speed data. Wind speed data is the most basic resource data of a wind farm, and it directly affects the power generation capacity of the wind turbine. The first resource data may also be the response capacity of a wind turbine set. The response capacity of a wind turbine set refers to the adjustment capacity of a wind turbine set according to changes in wind speed. The data preprocessing process includes:

[0029] Data mapping: The collected wind speed data or wind farm model is mapped to form a dynamic resource monitoring object of the wind farm. The wind speed data is combined with the power generation capacity and response speed of the wind turbine to form more accurate wind farm resource data.

[0030] Multi-level aggregate statistics: Based on real-time analysis of wind speed data, the power generation potential under different wind speed ranges is evaluated and aggregated statistics are performed. For each wind turbine, its response capability under different wind speed conditions is counted.

[0031] Capacity aggregation: Aggregate the response capabilities of wind turbines and conduct a comprehensive assessment of their power generation capabilities under wind speed conditions in different regions, providing data support for the scheduling and load response of wind farms.

[0032] In an optional embodiment of the present invention, the first object is an electric vehicle charging station, and the first resource data is charging power, which indicates the maximum charging capacity of each charging pile. The first resource data may also be charging response time, which indicates the time it takes for the charging station to make load adjustments according to system instructions. The data preprocessing process includes:

[0033] Data mapping: Map the collected charging power data with the load model of the charging station to form the resource monitoring object of the charging station. This includes the power setting of the charging pile, load regulation and other related data.

[0034] Multi-level aggregate statistics: Based on the power data of different charging piles in the charging station, regional aggregate statistics are performed, and combined with the load regulation capability, the overall regulation capability of the charging station is evaluated.

[0035] Capacity aggregation: Aggregate the response capabilities of each charging pile, calculate the response capacity and regulation capability of the entire charging station, and provide data support for power grid dispatch.

[0036] S2: Perform panoramic monitoring on the first resource data, and monitor key nodes of the first object based on the first data preprocessing result.

[0037] Available regulation margin monitoring analyzes available power generation margin based on available discharge capacity of energy storage station, real-time output of new energy, etc. Available consumption margin is analyzed based on real-time active power of controllable load, available capacity of flexible load (thermal storage boiler, ice storage, etc.), available charging capacity of energy storage station, etc. Available power generation margin and available consumption margin over-limit alarm are supported. Resource type monitoring counts new energy consumption according to energy type (centralized energy storage, distributed energy storage, etc.) and real-time output data of new energy. Comprehensive display of information such as energy storage power station, controllable load, available margin, new energy consumption, etc. is realized in the form of bar charts, pie charts, column charts, etc. The main functions include:

[0038] Real-time data monitoring chart, showing real-time output, electricity consumption information, proportion of each type of energy, and other information by resource type.

[0039] Available margin monitoring diagrams display equipment energy storage margin and power generation margin information by resource type.

[0040] Information display of charging stations, smart parks, etc. is realized based on GIS maps.

[0041] Comprehensive information display based on substations, feeders, main transformers, substations and the entire network, including real-time output, power consumption information, proportion of various types of energy, upper adjustable amount, lower adjustable amount and other information.

[0042] It supports topology analysis for the modeled adjustable load objects and can monitor the status of the adjustable load (out of operation, load, power supply).

[0043] The access adjustable load data is associated with the adjustable load object to realize the real-time monitoring function based on the adjustable load object.

[0044] Multi-level adjustable load critical node heavy overload monitoring

[0045] Based on the results of data aggregation and multi-level key nodes, the topology analysis method is called for a single adjustable load object device to obtain the key switch of the power supply path where the adjustable load object device is located, and then the key switch operation status of the power supply path where the adjustable load is located is monitored to ensure the stability and reliability of the system when facing faults or load changes. The key switch identification algorithm can be found in the adjustable load key switch search algorithm in the algorithm principle.

[0046] At the same time, the adjustable load of the feeder is monitored, and the current feeder, branch line and distribution transformer overload list information is provided. Each heavy and overloaded feeder, branch line, and distribution transformer are sorted by the degree of heavy load. The data and sorting in the list are refreshed in real time. Click the heavy load or overload record in the list to significantly identify the heavy load or overload line on the corresponding wiring diagram. By obtaining the results of the power supply path service, the results of the key paths of different types of loads from the main network to the distribution network are dynamically displayed. Finally, the heavy load of the main grid line and transformer of the power grid is monitored. When the load rate of the line and transformer exceeds the set value (early warning condition), according to the process of its flow load approaching or reaching the operating limit, a warning / alarm from heavy load to overload is required. The list information of the current line and transformer load can be generated, and each heavy and overload component is sorted by the degree of heavy load. The data and sorting in the list can be refreshed in real time.

[0047] S3: Construct a first resource data cloud map of the first object to achieve rapid information positioning.

[0048] Based on geographic information services, the adjustable load data of the power grid can be displayed in real time and intuitively on the GIS map. The adjustable load data collected in real time can be displayed in the form of real-time measurement, measurement aggregation, and thermal distribution according to the low-voltage area, feeder, main transformer, substation, power grid and other layers. Real-time warnings such as heavy overload warning information can also be displayed in association on the GIS map to meet the rapid location of faults on the geographic map.

[0049] The computer device may be a server. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data cluster data of the power monitoring system. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a hierarchical statistical method for adjustable resources on the distribution network side is implemented.

[0050] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0051] Embodiment 2 is an embodiment of the present invention, which provides a hierarchical statistical method and system for adjustable resources on the distribution network side. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0052] In order to verify the effectiveness of a hierarchical statistical method for adjustable resources on the distribution network side, this experiment is based on the actual operation data of the power company's distribution network. The method of the present invention is compared with the traditional resource statistical method, focusing on analyzing the advantages in data collection accuracy, monitoring coverage, response timeliness, etc.

[0053] Subjects:

[0054] Existing technology: In traditional resource statistics methods, the load regulation data of each distribution network equipment mainly relies on manual statistics and single-level data processing, which makes it difficult to form a complete panoramic view of resource distribution and response capabilities.

[0055] The present invention: through multi-layer data preprocessing and resource hierarchical statistics, dynamically generates resource cloud maps and monitors adjustable resources in real time. The method collects data in layers, aggregates data by voltage level and geographical area, and displays the distribution of key node resources in a visual form.

[0056] Experimental steps:

[0057] Data collection and preprocessing: Collect data of all adjustable load resources in the distribution network and map them with the distribution network model to form a data monitoring model that integrates the main and distribution networks. In the prior art, data collection is mostly static data and lacks real-time processing procedures. Through the data preprocessing steps of the present invention, the equipment load, adjustment margin and response capability are dynamically collected to ensure the timeliness of the data.

[0058] Panoramic monitoring and aggregate statistics: The system aggregates the collected data by region and level to form a multi-level data structure. This structure contains resource information such as response speed and response capacity, so that the real-time load regulation of the distribution network can be seen at a glance. The monitoring methods in existing methods are mostly static displays and cannot be updated in real time. The experiment here uses real-time data streams to monitor loads and key nodes, and locates them based on GIS maps.

[0059] Generate resource cloud map: This invention generates adjustable resource cloud map based on GIS service, displays real-time data distribution, and identifies the adjustment capacity and response speed of key resources. The experiment compares resource distribution maps under different scenarios, including information such as overloaded areas and overload warnings, to optimize decision support in a visual way. The experimental results are shown in Table 1.

[0060] Table 1 Experimental results

[0061]

[0062]

[0063] It can be seen from the table data that the present invention has obvious advantages in many indicators, and the specific analysis is as follows:

[0064] The existing technology relies on manual data collection with an accuracy rate of about 85%. The present invention uses automatic data collection and mapping, with an accuracy rate of 99%, an improvement of 16.47%, ensuring the accuracy and reliability of real-time data.

[0065] The traditional method updates data once an hour, which is a low update frequency and fails to meet the needs of real-time monitoring. The present invention achieves a high-frequency update of 30 times per hour through an intelligent hierarchical statistical method, which increases the update frequency by 2900% and ensures the efficient response of the system.

[0066] In the existing system, the coverage rate is only 70%, because it is difficult to take into account the load of the entire network in a manual statistical method. The coverage rate of the automation system of the present invention reaches 98%, an increase of 40%, making the load status and key node information of the distribution network more comprehensive.

[0067] The existing technology takes 60 seconds to respond to load overload conditions. The response time of the present invention is greatly shortened to 5 seconds, an increase of 91.67%, and the real-time monitoring of the load status of the distribution network is realized.

[0068] The alarm accuracy of the traditional method is 80%, with a high number of false alarms. The present invention combines real-time data analysis and hierarchical monitoring, and the alarm accuracy is increased to 97%, with significantly fewer false alarms, and the optimization range is 21.25%.

[0069] The resource allocation accuracy of the prior art is 75%, which is difficult to meet the high-precision resource scheduling requirements. The present invention improves the resource allocation accuracy to 95% through multi-level statistics, an increase of 26.67%.

[0070] The maintenance cost of the traditional system is high, about 50,000 yuan per year. The present invention reduces manual intervention through an intelligent hierarchical statistical system, reducing the cost to 30,000 yuan, saving 40%.

[0071] In summary, the experimental data fully proves that the hierarchical statistical method for adjustable resources on the distribution network side is significantly superior to the existing traditional methods in terms of data collection, resource allocation and response efficiency. The introduction of hierarchical statistics and data aggregation technology improves the real-time and comprehensiveness of system monitoring and provides precise support for the intelligent dispatching of distribution networks.

[0072] Embodiment 3 is an embodiment of the present invention, which provides a hierarchical statistical system for adjustable resources on the distribution network side, specifically:

[0073] The preprocessing module collects first resource data of the first object and performs first data preprocessing.

[0074] A monitoring module performs panoramic monitoring of the first resource data and monitors key nodes of the first object based on the first data preprocessing result.

[0075] The mapping module constructs a first resource data cloud map of the first object to achieve rapid information positioning.

[0076] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A hierarchical statistical method for adjustable resources on the distribution network side, characterized in that: include: Collecting first resource data of a first object and performing first data preprocessing; Performing panoramic monitoring on the first resource data, and monitoring key nodes of the first object based on the first data preprocessing result; Construct a first resource data cloud map of the first object to achieve rapid information positioning.

2. The hierarchical statistical method for adjustable resources on the distribution network side according to claim 1, characterized in that: The first data preprocessing includes collecting first resource data of the first object and mapping it with the first object model to form a first resource data monitoring object with integrated main and distribution.

3. The hierarchical statistical method for adjustable resources on the distribution network side according to claim 2, characterized in that: The first data preprocessing also includes multi-level aggregation statistics, forming an association relationship through the mapping, and then forming an aggregation relationship of each level, thereby realizing multi-level and multi-capability aggregation.

4. The hierarchical statistical method for adjustable resources on the distribution network side according to claim 3, characterized in that: The panoramic monitoring includes calculating various forms of information through real-time parameter analysis of the first resource data.

5. The hierarchical statistical method for adjustable resources on the distribution network side according to claim 4, characterized in that: The monitoring of the key nodes of the first object includes, based on the first data preprocessing result, calling a topology analysis method on the device of the first object, obtaining and monitoring the key nodes of the power supply path where the device is located.

6. The hierarchical statistical method for adjustable resources on the distribution network side according to claim 5, characterized in that: The first object includes but is not limited to a power distribution network, and the first resource data includes but is not limited to adjustable load resource data.

7. The hierarchical statistical method for adjustable resources on the distribution network side according to claim 6, characterized in that: The constructing of the first resource data cloud map of the first object includes displaying the first resource data layer collected in real time in multiple forms respectively.

8. A hierarchical statistical system for adjustable resources on the distribution network side using the method according to any one of claims 1 to 7, characterized in that: A preprocessing module, collecting first resource data of a first object and performing first data preprocessing; A monitoring module, which performs panoramic monitoring on the first resource data and monitors key nodes of the first object based on the first data preprocessing result; The mapping module constructs a first resource data cloud map of the first object to achieve rapid information positioning.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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