New energy power control method and device based on distributed power supply access

Abstract

The invention relates to the technical field of power systems, and discloses a new energy power control method and device based on distributed power supply access, and the method comprises the steps: constructing a unified topology model comprising distributed source loads, energy storage equipment and charging piles based on an access mode of a distributed power supply; acquiring operation data of the distributed power supply through the unified topology model, and performing data preprocessing on the operation data to generate a standardized operation data set; monitoring the real-time operation state and operation index of the power grid in real time based on the standardized operation data set, dynamically generating a corresponding power control instruction, and issuing the power control instruction to the distributed power supply to execute a power regulation task; and generating a power balance instruction after the occurrence of the power grid abnormal event is detected, decomposing the power balance instruction based on the unified topology model and the standardized operation data set, and executing a power balance task. According to the invention, real-time monitoring and dynamic power adjustment of the distributed power supply can be realized, and the power grid stability and the power balance capability are improved.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method and device for controlling new energy power based on access of distributed power sources. Background Art

[0002] With the rapid development of new energy technologies, the scale of distributed power sources (such as photovoltaic power generation, energy storage systems, and electric vehicle charging facilities) integrated into distribution networks continues to expand. Distributed power sources, characterized by their dispersed distribution, large number, and dynamic and changing operating conditions, present new challenges for power system power control. Traditional power control methods, primarily based on the regulation and control model of centralized power sources, are unable to adapt to the complex and changing operating environment of massive distributed power sources, placing severe challenges on the stability and efficiency of power systems.

[0003] Existing power control methods for distributed power sources (DGs) often focus on local optimization or static configuration. For example, some solutions optimize power configuration by building uncertainty models or adjust DG output based on probabilistic power flow calculations. However, existing technologies suffer from the following drawbacks: First, they lack global monitoring of the real-time operating status of DGs, making it difficult to promptly respond to sudden grid issues such as line overloads and voltage anomalies, and their dynamic adjustment capabilities are insufficient. Second, data collection suffers from security and reliability deficiencies, and mechanisms for cleaning, verifying, and fusing heterogeneous multi-source data are imperfect, resulting in inconsistent data quality for control decisions. Furthermore, the coordinated control mechanisms between power sources, grids, loads, and storage are weak, making it difficult to effectively coordinate DGs with load demand, especially in the event of a fault or power outage, making it difficult to achieve rapid power transfer and power balance. Consequently, existing technologies suffer from insufficient real-time monitoring and dynamic adjustment capabilities, low data security, and insufficient coordination between power sources, grids, loads, and storage, failing to meet the power control needs of distribution networks after the integration of massive DGs.

[0004] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the Invention

[0005] The embodiments of the present application provide a new energy power control method and device based on distributed power supply access, which solves the problems of insufficient real-time monitoring and dynamic adjustment capabilities, low data security, and insufficient source-grid-load-storage coordination capabilities in the existing technology, realizes real-time monitoring and dynamic power adjustment of distributed power sources, and improves power grid stability and power balancing capabilities.

[0006] In a first aspect, an embodiment of the present application provides a new energy power control method based on distributed power supply access, including:

[0007] Based on the access mode of distributed power sources, a unified topology model including distributed sources and loads, energy storage equipment and charging piles is constructed. The unified topology model integrates device attributes and electrical associations;

[0008] Acquiring operating data of distributed power sources through the unified topology model, performing data preprocessing on the operating data, and generating a standardized operating data set;

[0009] Based on the standardized operation data set, real-time monitoring of the real-time operation status and operation indicators of the power grid is carried out, corresponding power control instructions are dynamically generated, and the power control instructions are sent to the distributed power source to perform power regulation tasks;

[0010] After detecting that an abnormal event occurs in the power grid, a power balancing instruction is generated, and based on the unified topology model and the standardized operation data set, the power balancing instruction is decomposed and a power balancing task is executed.

[0011] Optionally, in some embodiments of the present application, the access mode based on the distributed power supply is used to construct a unified topology model including distributed sources and loads, energy storage devices, and charging piles, including:

[0012] According to the voltage level and grid connection mode of the distributed source and load, a basic model is constructed using classification extension technology, and device attributes and distribution network feeders are bound using data association technology;

[0013] According to the access type of the energy storage device, the corresponding dynamic access point information is recorded, and the electrical topology relationship between the energy storage device and the power grid is constructed based on the geographic information system;

[0014] The geographical location and power attributes of the charging piles are integrated into a unified topology type to form a charging facility sub-model including spatial distribution characteristics.

[0015] Optionally, in some embodiments of the present application, obtaining the operating data of the distributed power supply through the unified topology model, performing data preprocessing on the operating data, and generating a standardized operating data set include:

[0016] Collecting operating data of the distributed power supply through a preset communication protocol and an encrypted channel, the operating data including electrical parameters of the distributed source and load, the charge and discharge status of the energy storage device, and real-time power data of the charging pile;

[0017] Noise filtering, format standardization, and redundant data elimination are performed on the operating data, and quality codes are marked on non-measured data to generate a standardized operating data set;

[0018] The operation data is analyzed for deviations by using a verification model pre-trained based on historical data. When it is detected that the deviation rate exceeds a preset threshold, an abnormality warning is triggered and the standardized operation data set is updated.

[0019] Optionally, in some embodiments of the present application, performing deviation analysis on the operating data using a verification model pre-trained based on historical data, and triggering an abnormality warning and updating the standardized operating data set after detecting that the deviation rate exceeds a preset threshold, includes:

[0020] Based on historical data, a regression model of distributed generation output and grid parameters is trained as a verification model;

[0021] Inputting the real-time detected operating data into the verification model to calculate the corresponding deviation rate;

[0022] After detecting that the deviation rate exceeds the preset threshold, the abnormal data is marked and the equipment status is checked to generate the verification results;

[0023] According to the verification results, abnormal data is frozen or the preset threshold is modified, and the standardized operation data set is updated.

[0024] Optionally, in some embodiments of the present application, the real-time monitoring of the real-time operating status and operating indicators of the power grid based on the standardized operating data set, dynamically generating corresponding power control instructions, and sending the power control instructions to the distributed power source to perform the power regulation task includes:

[0025] Based on the standardized operating data set, the line load rate, voltage amplitude and power factor indicators of the power grid are analyzed in real time. If any indicator exceeds a preset safety range, a control strategy is triggered;

[0026] Decompose the adjustment amount into output adjustment sub-instructions for distributed sources and loads, energy storage equipment, and charging piles according to priority, and send the output adjustment sub-instructions to each distributed power source through the group adjustment and group control interface to perform power adjustment tasks;

[0027] The power grid operation data after power regulation is collected in real time. If it is detected that the abnormal indicators have not returned to a safe range, power adjustment instructions are iteratively generated based on the updated standardized operation data set until the power grid stabilizes.

[0028] Optionally, in some embodiments of the present application, the step of sending the output adjustment sub-command to each of the distributed power sources through the group adjustment and control interface to perform the power adjustment task includes:

[0029] Divide the distributed generation into regional clusters based on the distribution network topology and calculate the severity of the over-limit of each regional cluster in real time;

[0030] The corresponding output adjustment sub-instructions are issued according to the priority of the severity of the limit violation, and the upper-level power grid instructions are dynamically coordinated.

[0031] Optionally, in some embodiments of the present application, generating a power balancing instruction after detecting a power grid abnormality event, decomposing the power balancing instruction based on the unified topology model and the standardized operating data set, and executing a power balancing task includes:

[0032] After a power outage is detected, the available distributed power sources and energy storage devices are located according to the unified topology model, and the matching degree between the power transfer capacity and the load demand is calculated;

[0033] If the power transfer capacity is insufficient, the output of the distributed power source is dynamically adjusted or the load is reduced based on the power balancing algorithm to generate a temporary power supply plan;

[0034] After the temporary power supply plan is sent to the target adjustment device through the communication interface, the execution result of the target adjustment device is monitored in real time and fed back to the main control system.

[0035] Optionally, in some embodiments of the present application, if the power transfer capacity is insufficient, dynamically adjusting the output of the distributed power source or reducing the load based on a power balancing algorithm to generate a temporary power supply plan includes:

[0036] Generate a dynamic capacity pool based on the real-time location and output forecast data of mobile energy storage;

[0037] After detecting that the dynamic capacity pool is insufficient, the load is reduced or third-party output is purchased according to priority, a temporary plan is generated, and the temporary power supply plan is updated periodically.

[0038] Optionally, in some embodiments of the present application, the method further includes:

[0039] Assign a key pair to each distributed power source, use an asymmetric encryption algorithm to encrypt operating data, and digitally sign power control instructions;

[0040] Verify the validity of the signature of the power control command at the command receiving end, trigger an alarm and terminate the command processing if the verification fails;

[0041] Periodically update key pairs and dynamically adjust key distribution policies based on device status.

[0042] In a second aspect, an embodiment of the present application provides a new energy power control device based on distributed power supply access, including:

[0043] A model building module is used to build a unified topology model including distributed sources and loads, energy storage devices, and charging piles based on the access mode of distributed power sources. The unified topology model integrates device attributes and electrical associations;

[0044] A data processing module, configured to obtain operating data of the distributed power source through the unified topology model, perform data preprocessing on the operating data, and generate a standardized operating data set;

[0045] A power regulation module, configured to monitor the real-time operating status and operating indicators of the power grid based on the standardized operating data set, dynamically generate corresponding power control instructions, and issue the power control instructions to the distributed power source to perform power regulation tasks;

[0046] The power balancing module is used to generate a power balancing instruction after detecting an abnormal power grid event, decompose the power balancing instruction based on the unified topology model and the standardized operation data set, and execute a power balancing task.

[0047] The present application provides a new energy power control method and device based on distributed power supply access. First, based on the access mode of the distributed power supply, a unified topology model including distributed sources and loads, energy storage equipment and charging piles is constructed, and the unified topology model integrates device attributes and electrical associations; then, the operating data of the distributed power supply is obtained through the unified topology model, and the operating data is preprocessed to generate a standardized operating data set; then, the real-time operating status and operating indicators of the power grid are monitored in real time based on the standardized operating data set, and corresponding power control instructions are dynamically generated, and the power control instructions are sent to the distributed power supply to perform power regulation tasks; after detecting an abnormal event in the power grid, a power balancing instruction is generated, and based on the unified topology model and the standardized operating data set, the power balancing instruction is decomposed and the power balancing task is performed. In the new energy power control solution based on distributed power supply access provided in this application, a unified topology model is constructed to integrate the device properties and electrical correlations of distributed sources and loads, energy storage equipment and charging piles, and these devices are comprehensively monitored and controlled. A standardized operation data set is then generated through data preprocessing. The grid operation status is monitored in real time and power control instructions are dynamically generated and sent to distributed power sources to perform power adjustment tasks. When the grid is abnormal, power balancing instructions are generated and the power balancing tasks are decomposed to achieve stable operation and power balance control of the grid, solving the problems of insufficient real-time monitoring and dynamic adjustment capabilities, low data security, and insufficient source-grid-load-storage coordination capabilities in the existing technology, realizing real-time monitoring and dynamic power adjustment of distributed power sources, improving grid stability and power balancing capabilities, and thus meeting the power control needs of the distribution network after the access of massive distributed power sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 This is a diagram of the application environment of the new energy power control method based on distributed power access provided by an embodiment of the present application;

[0050] Figure 2 This is a flow chart of a new energy power control method based on distributed power access provided by an embodiment of the present application;

[0051] Figure 3 This is another flow chart of a new energy power control method based on distributed power access provided by an embodiment of the present application;

[0052] Figure 4 This is a schematic diagram of the process of constructing a distributed source-load model provided in an embodiment of the present application;

[0053] Figure 5 This is a flow chart of distributed source-load data collection and processing provided by an embodiment of the present application;

[0054] Figure 6 This is a flow chart of distributed source-load monitoring and group dispatching and control provided by an embodiment of the present application;

[0055] Figure 7 This is a flow chart of auxiliary analysis and disposal of source-grid-load-storage coordinated control provided by an embodiment of the present application;

[0056] Figure 8 This is a structural diagram of a new energy power control device based on distributed power access provided by an embodiment of the present application;

[0057] Figure 9 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of systems and methods consistent with aspects of the present application, as detailed in the appended claims.

[0059] It should be noted that, in this document, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive descriptions such as inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0060] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0061] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of this application and have no specific meaning. Therefore, "module", "component" or "unit" can be used interchangeably.

[0062] With the widespread integration of distributed power sources (DGs) into distribution networks, traditional power control methods are struggling to meet new demands, presenting a series of pressing technical challenges. Firstly, the dispersed and numerous nature of DGs makes monitoring their operating status difficult. The lack of a unified and effective modeling approach for distributed sources and loads with varying access methods makes it difficult to accurately acquire operational data and achieve comprehensive, real-time monitoring of these sources and loads. Secondly, data processing and control present challenges. During the collection of distributed source and load data, ensuring accuracy, security, and timeliness is difficult. Data collection protocols and the choice of communication channels impact data transmission quality, and encryption and verification mechanisms for collected data are incomplete. Data processing involves complex processing of analog quantities, computational complexity, and non-measured data. In addition, an incomplete data verification and evaluation system prevents effective identification of abnormal data. Furthermore, efficient strategies for coordinated control and group scheduling of distributed sources and loads are lacking, making it difficult to coordinate active and reactive power control. Consequently, when grid anomalies occur, distributed source and load output cannot be quickly and appropriately adjusted to ensure safe and stable grid operation. In addition, the coordination between the source, grid, load and storage is poor, the new distributed energy model ledger is not sufficiently linked to the existing system, the efficiency is low when decomposing and executing power control instructions, and the power supply analysis after the distributed power source is connected to the distribution network is not comprehensive, and the role of the distributed power source in the event of a power outage cannot be fully utilized.

[0063] In order to solve the above technical problems and overcome the defects of the existing technology, the embodiment of the present application provides a new energy power control method and device based on distributed power supply access, which can realize real-time monitoring and dynamic power regulation of distributed power supplies, and improve the stability of the power grid and the power balancing capability.

[0064] Figure 1 FIG is an application environment diagram of a new energy power control method based on distributed power access in an embodiment. Figure 1 The new energy power control method based on distributed power access is applied to a new energy power control system based on distributed power access. The new energy power control system based on distributed power access includes a terminal 110 and a server 120. Terminal 110 and server 120 are connected via a network. Terminal 110 can be a desktop terminal or a mobile terminal. The mobile terminal can be at least one of a mobile phone, a tablet computer, a laptop computer, etc. Server 120 can be implemented as a standalone server or a server cluster consisting of multiple servers. Server 120 is used to construct a unified topology model based on the distributed power access method, including distributed sources and loads, energy storage devices, and charging piles. The unified topology model integrates device attributes and electrical relationships; obtains operating data of the distributed power sources through the unified topology model, performs data preprocessing on the operating data, and generates a standardized operating data set; monitors the real-time operating status and operating indicators of the power grid in real time based on the standardized operating data set, dynamically generates corresponding power control instructions, and issues the power control instructions to the distributed power sources to execute power regulation tasks; generates power balancing instructions after detecting a power grid abnormality event, decomposes the power balancing instructions based on the unified topology model and the standardized operating data set, and executes the power balancing tasks.

[0065] See also Figure 2 , Figure 2 This is a flow chart of a new energy power control method based on distributed power access provided by an embodiment of the present application. This embodiment mainly uses the new energy power control method based on distributed power access applied to computer equipment as an example for illustration. The new energy power control method based on distributed power access provided by an embodiment of the present application may specifically include the following steps:

[0066] S1. Based on the access method of distributed power sources, a unified topology model is constructed, including distributed sources and loads, energy storage devices, and charging piles. The unified topology model integrates device attributes and electrical relationships;

[0067] Specifically, in step S1, based on the access methods of different distributed power sources (such as distributed sources and loads, energy storage devices, and charging piles), a unified topology model containing device attributes and electrical relationships is constructed using classification and extension technologies, data association technologies, and topology construction technologies. Classification and extension technologies can expand the basic model for distributed sources and loads with different voltage levels and grid connection methods; data association technologies can bind device attributes and distribution network feeders to achieve information association and fusion; and topology construction technologies, based on geographic information systems, construct the electrical topology relationship between energy storage devices and the grid, and form a charging facility sub-model that includes spatial distribution characteristics.

[0068] S2. Obtain the operating data of the distributed power generation through the unified topology model, perform data preprocessing on the operating data, and generate a standardized operating data set;

[0069] Specifically, in step S2, the distributed power supply's operating data, including the electrical parameters of distributed sources and loads, the charge and discharge status of energy storage devices, and real-time power data from charging piles, is collected through a preset communication protocol and encrypted channel. This operating data is then preprocessed through noise filtering, format standardization, and redundant data removal. Non-measured data is also tagged with a quality code to generate a standardized operating data set. The preset communication protocol ensures the accuracy and reliability of data transmission, while the encrypted channel ensures data security. The data preprocessing process utilizes data cleaning methods from data mining technology to remove noise, duplication, and erroneous information, completing format conversion and standardization. Statistical analysis functions are also used to analyze real-time quantized analog values ​​and resource information based on regional and zoning attribute statistics, establishing statistical models to understand operating status and patterns.

[0070] S3. Monitor the real-time operating status and operating indicators of the power grid based on a standardized operating data set, dynamically generate corresponding power control instructions, and issue the power control instructions to the distributed power generation to perform power regulation tasks;

[0071] Specifically, in step S3, based on the standardized operating data set, the grid's line load rate, voltage amplitude, and power factor are analyzed in real time. Once any indicator is found to exceed the preset safety range, the control strategy is triggered. The adjustment amount is decomposed into output adjustment sub-instructions for distributed sources and loads, energy storage equipment, and charging piles according to priority. These sub-instructions are then sent to each distributed power source through the group adjustment and control interface to perform the power adjustment task. At the same time, the grid operation data after power adjustment is collected in real time. If the abnormal indicator has not returned to the safe range, the power adjustment instruction is iteratively generated based on the updated standardized operating data set until the grid stabilizes. When analyzing grid indicators in real time, intelligent algorithms can be used to quickly and accurately determine whether the indicator exceeds the limit. When triggering the generation of the control strategy, corresponding active and reactive power control strategies are formulated based on different over-limit situations and grid operation status. The group adjustment and control interface can realize the coordinated control of multiple distributed power sources, accurately adjusting the output of each power source according to certain priorities and control logic.

[0072] S4. generating a power balancing instruction after detecting a power grid abnormality event, decomposing the power balancing instruction and executing the power balancing task based on a unified topology model and a standardized operating data set;

[0073] Specifically, in step S4, after detecting an abnormal event such as a power outage, a power balancing instruction is generated. Based on a unified topology model and a standardized operating data set, the instruction is decomposed and the power balancing task is executed. For example, when a power outage occurs, the available distributed power sources and energy storage devices are located according to the unified topology model, and the matching degree between the transfer power supply capacity and the load demand is calculated. If the transfer power supply capacity is insufficient, the output of the distributed power source is dynamically adjusted or the load is reduced based on the power balancing algorithm. A temporary power supply plan is generated and sent to the target adjustment device via a communication interface. The execution results are monitored in real time and fed back to the main control system. The power balancing algorithm can flexibly adjust the output or load of the distributed power source according to different power grid abnormalities and the characteristics of the distributed power source to achieve power balance. The communication interface ensures that the temporary power supply plan can be accurately and promptly transmitted to the relevant equipment, ensuring that each device is adjusted according to the plan.

[0074] This embodiment achieves comprehensive management of distributed power sources by constructing a unified topology model. After data preprocessing to ensure data quality and security, it monitors the power grid status in real time and dynamically adjusts the power accordingly. In the event of an abnormality, power balancing instructions are generated and efficiently executed, thereby solving the problems of real-time monitoring and dynamic adjustment, data processing, and source-grid-load-storage coordination in the existing technology, significantly improving the stability, efficiency, and reliability of the power grid, and ensuring the safe, efficient, and stable operation of the distribution network with the access of massive distributed power sources.

[0075] Optionally, in some embodiments, step S1 of “building a unified topology model including distributed sources and loads, energy storage devices, and charging piles based on the access mode of the distributed power source” may specifically include:

[0076] S11. Based on the voltage levels and grid connection methods of distributed sources and loads, a basic model is constructed using classification and extension technology, and device attributes and distribution network feeders are bound using data association technology.

[0077] Specifically, in step S11, classification extension technology is used to expand the basic model based on the voltage level and grid connection method of the distributed source and load. This basic model is then constructed, and data association technology is used to bind device attributes to distribution network feeders, enabling unified management and monitoring of devices. Classification extension technology flexibly customizes and expands the basic model based on different access scenarios and device types to accommodate complex distribution network structures and operational requirements. Data association technology establishes relationships between devices and feeders, enabling precise device location and information integration.

[0078] S12. According to the access type of the energy storage device, record the corresponding dynamic access point information, and build the electrical topology relationship between the energy storage device and the grid based on the geographic information system;

[0079] Specifically, in step S12, dynamic access point information is recorded based on the access type of different energy storage devices (e.g., fixed or mobile). The electrical topology relationship between the energy storage device and the power grid is established using technologies such as geographic information systems (GIS). For fixed energy storage, GIS technology is used to accurately establish its electrical connection relationship with the distribution network, ensuring that the location and function of the energy storage device in the power grid are clearly visible. For mobile energy storage, access point information must be updated in real time to reflect its dynamic access status, while also considering the impact of changes in access points during movement on the power grid.

[0080] S13. Integrate the geographic location and power attributes of charging piles into a unified topology type to form a charging facility sub-model that includes spatial distribution characteristics;

[0081] Specifically, in step S13, key information such as the charging pile's geographic location and power attributes are integrated into a unified topology type, thereby forming a charging facility submodel that includes spatial distribution characteristics. During the modeling process, in addition to geographic location and power attributes, multi-dimensional information such as the charging pile's usage status and charging power variation characteristics can also be considered to more comprehensively reflect the charging pile's operation and its impact on the power grid. At the same time, a geographic information system (GIS) is used to analyze the spatial distribution of charging piles and optimize their layout planning.

[0082] This embodiment builds a comprehensive and accurate unified topology model by conducting targeted modeling of distributed sources and loads, energy storage equipment, and charging piles, and integrating relevant technologies and information. This significantly improves the accuracy and efficiency of the management and control of various distributed energy devices, enhances the adaptability and compatibility of the power grid to the access of massive distributed power sources, and provides a solid foundation for achieving efficient control of new energy power and stable operation of the power grid.

[0083] Optionally, in some embodiments, step S2 of "obtaining operating data of distributed power sources through a unified topology model, performing data preprocessing on the operating data, and generating a standardized operating data set" may specifically include:

[0084] S21. Collect the operating data of the distributed power supply through the preset communication protocol and encrypted channel. The operating data includes the electrical parameters of the distributed source and load, the charge and discharge status of the energy storage device, and the real-time power data of the charging pile;

[0085] Specifically, for step S21, the operating data of the distributed power supply is collected through a preset communication protocol (such as 104 or 101 protocol) and an encrypted channel. The operating data includes the electrical parameters of the distributed source and load (such as voltage, current, power, etc.), the charge and discharge status of the energy storage device (such as charging capacity, discharging capacity, charging and discharging rate, etc.), and the real-time power data of the charging pile (such as charging power, discharging power, etc.). The selection of the communication protocol must take into account the efficiency, compatibility, and reliability of data transmission to ensure that the data can be accurately transmitted from the distributed power supply to the data acquisition system; the encrypted channel uses encryption technologies such as asymmetric encryption algorithms to encrypt the transmitted data to prevent the data from being stolen or tampered with during transmission, thereby ensuring the security of the data.

[0086] S22. Filter the operating data for noise, standardize its format, and remove redundant data. Also, mark non-measured data with a quality code to generate a standardized operating data set.

[0087] Specifically, in step S22, the collected operational data undergoes noise filtering, format standardization, and redundant data elimination to remove noise, outliers, and duplicate information. The data is then converted to a standardized format, and non-measured data is labeled with a quality code to generate a standardized operational data set. Noise filtering can utilize filtering algorithms and data cleaning techniques to remove noise and outliers. Format standardization converts and organizes different types of data according to unified data format specifications. Redundant data elimination uses data deduplication algorithms to identify and remove duplicate data. Non-measured data is assigned a specific quality code to distinguish measured data from unmeasured data during subsequent data processing and analysis, ensuring data accuracy and reliability.

[0088] S23. Perform deviation analysis on the operating data using a pre-trained validation model based on historical data. If the deviation rate exceeds a preset threshold, an abnormality warning is triggered and the standardized operating data set is updated.

[0089] Specifically, for step S23, a verification model (such as a regression model) pre-trained based on historical data is used to perform deviation analysis on the operating data, the real-time detected operating data is input into the verification model, and the deviation rate between it and the model prediction value is calculated. If the deviation rate exceeds the preset threshold, an abnormal warning is triggered and the standardized operating data set is updated. The training of the verification model requires the selection of appropriate historical data samples and the use of effective regression algorithms (such as linear regression, nonlinear regression, etc.) to construct a model to accurately reflect the relationship between the distributed power supply operating data and the grid parameters. The calculation of the deviation rate can use different indicators such as absolute deviation and relative deviation, and the determination of the preset threshold needs to be reasonably set based on actual operating experience and data quality requirements. The abnormal warning mechanism can be combined with an alarm system, a notification system, etc. to promptly inform relevant personnel of abnormal situations so that appropriate measures can be taken.

[0090] This embodiment comprehensively ensures the accuracy, reliability, and security of data by standardizing operational data collection and strengthening preprocessing and verification steps. It builds a solid data foundation for precise monitoring of grid operating status and rational power regulation, and effectively improves the performance of new energy power control systems and the operation and maintenance management level of the grid.

[0091] Optionally, in some embodiments, step S23 of "performing deviation analysis on the operating data using a verification model pre-trained based on historical data, and triggering an abnormality warning and updating the standardized operating data set upon detecting that the deviation rate exceeds a preset threshold" may specifically include:

[0092] S231. Based on historical data, a regression model of distributed power output and grid parameters is trained as a verification model;

[0093] Specifically, a large amount of historical operating data, including distributed generation output data and grid parameter data, is collected. Machine learning methods such as regression analysis are then used to train a regression model that correlates output with grid parameters, which serves as a validation model. During training, data can be preprocessed, such as removing outliers and filling missing values, to improve the model's accuracy and stability. Appropriate regression algorithms, such as linear regression, ridge regression, and LASSO regression, are selected, and the optimal algorithm is determined based on the data characteristics and modeling requirements. Furthermore, the model can be cross-validated to evaluate its performance and optimize its parameters.

[0094] S232. The real-time detected operation data is input into the verification model to calculate the corresponding deviation rate;

[0095] Specifically, real-time operational data is fed into the verification model, and the deviation rate between it and the model's predicted value is calculated. The deviation rate can be expressed as absolute deviation, relative deviation, or other methods. When calculating the deviation rate, the time series characteristics of the data can be considered, and methods such as moving average can be used to smooth the data to avoid the impact of short-term fluctuations on the deviation rate. At the same time, a reasonable deviation rate threshold can be set, and the threshold range can be determined based on the distribution of historical data and operational experience to ensure the sensitivity and accuracy of deviation detection.

[0096] S233. After detecting that the deviation rate exceeds the preset threshold, mark the abnormal data and perform device status verification to generate verification results;

[0097] Specifically, when the deviation rate exceeds a preset threshold, an abnormality warning is triggered, the abnormal data is marked, the status of the relevant equipment is verified, and a verification result is generated. Abnormal warnings can be implemented through various means, such as SMS notifications, audio and visual alarms, and system pop-up windows, to promptly alert operation and maintenance personnel to abnormal conditions. Equipment status verification can combine remote monitoring systems with on-site inspection methods to conduct a comprehensive assessment of the equipment's operating status and parameter settings to determine the cause of the abnormality.

[0098] S234. Freeze abnormal data or modify preset thresholds based on the verification results, and update the standardized operating data set;

[0099] Specifically, based on the verification results, abnormal data is processed, such as freezing abnormal data and correcting erroneous data, and the standardized operating data set is updated to ensure data accuracy and reliability. During the data update process, data fusion technology can be used to merge the corrected data with historical data to maintain data consistency and integrity. At the same time, data sets can be versioned, recording the content and time of each update for easy traceability and analysis.

[0100] This embodiment forms a closed-loop management mechanism for operating data through verification model training based on historical data, real-time deviation monitoring, abnormal warning and verification, and data set updating, effectively ensuring the quality and reliability of data, and improving the accuracy of power grid operation monitoring and the timeliness of fault response.

[0101] Optionally, in some embodiments, step S3 of "monitoring the real-time operating status and operating indicators of the power grid in real time based on the standardized operating data set, dynamically generating corresponding power control instructions, and issuing the power control instructions to the distributed power sources to perform power regulation tasks" may specifically include:

[0102] S31. Real-time analysis of the grid's line load rate, voltage amplitude, and power factor indicators based on a standardized operational data set. If any of these indicators exceeds a preset safety range, a control strategy is generated.

[0103] Specifically, based on standardized operational data sets, key grid indicators, such as line load rate, voltage amplitude, and power factor, are analyzed in real time. If any indicator exceeds a preset safety range, a control strategy is triggered. Real-time analysis of grid indicators can be used with the help of intelligent algorithms (such as machine learning) to quickly and accurately determine whether an indicator exceeds a limit. The determination of the preset safety range requires a comprehensive consideration of the grid's operating characteristics, the equipment's load capacity, and relevant standards and regulations.

[0104] S32. The adjustment amount is decomposed into output adjustment sub-commands for distributed source loads, energy storage equipment and charging piles according to priority, and the output adjustment sub-commands are sent to each distributed power source to perform power adjustment tasks through the group adjustment group control interface;

[0105] Specifically, once an abnormality in the grid operation indicators is detected, the control strategy generation mechanism is immediately activated, and corresponding active and reactive power control strategies are formulated based on the grid topology, operating status, and characteristics of distributed power sources. Advanced control algorithms (such as model predictive control and fuzzy control) can be used to generate the optimal control strategy to achieve precise regulation of the grid. At the same time, the response characteristics of different distributed power sources are taken into consideration, and the adjustment amount is reasonably allocated to ensure the feasibility and effectiveness of the control strategy. The formulated adjustment amount is decomposed into output adjustment sub-instructions for distributed sources and loads, energy storage equipment, and charging piles according to priority, and is sent to each distributed power source through the group adjustment and group control interface to perform power adjustment tasks. The priority can be determined based on a comprehensive assessment of factors such as the equipment's adjustment capability, operating cost, and response speed; the group adjustment and group control interface must have good compatibility and communication performance to ensure that the sub-instructions can be accurately transmitted to each distributed power source.

[0106] S33. Real-time collection of power grid operation data after power regulation. If it is detected that the abnormal indicator has not returned to a safe range, iteratively generates power adjustment instructions based on the updated standardized operation data set until the grid is stable;

[0107] Specifically, after executing the power regulation task, grid operating data is collected in real time to monitor whether grid operating indicators have returned to a safe range. High-speed, high-precision data acquisition equipment, combined with real-time data transmission technology, ensures that the collected data promptly and accurately reflects the grid's operating status. If abnormal indicators are detected that have not returned to a safe range, power adjustment instructions are iteratively generated based on the updated standardized operating data set until the grid stabilizes. During this iterative process, an adaptive control algorithm is employed to automatically adjust the control strategy based on the grid's real-time status, achieving dynamic optimization and regulation of the grid. Furthermore, the iterative process is optimized by combining historical data and operational experience to improve the convergence speed and stability of the regulation.

[0108] This embodiment achieves precise and dynamic power regulation control of the power grid by real-time monitoring of the power grid operation status, triggering control strategy generation, disassembling the regulation quantity to generate sub-instructions, real-time collection of power grid operation data after power regulation, and iterative generation of power adjustment instructions. It effectively ensures the safe and stable operation of the power grid, improves the adaptability and regulation capability of the power grid to the access of distributed power sources, and optimizes the operation efficiency and reliability of the power grid.

[0109] Optionally, in some embodiments, the step S3 of "issuing the output adjustment sub-command to each distributed power source through the group adjustment and group control interface to execute the power adjustment task" may specifically include:

[0110] Divide the distributed generation into regional clusters based on the distribution network topology and calculate the severity of the over-limit of each regional cluster in real time;

[0111] The corresponding output adjustment sub-instructions are issued according to the priority of the severity of the limit violation, and the upper-level power grid instructions are dynamically coordinated.

[0112] Specifically, distributed generation (DGs) are divided into regional clusters based on the topology of the distribution network. The DGs within each regional cluster have certain electrical connections and proximity. Topology analysis techniques can be used to identify key nodes and connections in the distribution network, thereby rationally dividing the regional clusters. Furthermore, factors such as the grid's operating characteristics, load distribution, and the type and capacity of DGs are considered to ensure that the regional cluster divisions meet power regulation and control requirements.

[0113] For each regional cluster, the operating status is monitored in real time, and the severity of over-limit conditions for indicators such as line load rate, voltage amplitude, and power factor is calculated. The real-time monitoring system requires high-precision data acquisition and rapid data processing capabilities to promptly acquire and analyze grid operating data. Advanced algorithms, such as fuzzy logic-based assessment methods, can be used to calculate over-limit severity, to more accurately reflect the actual operating status of the grid.

[0114] Based on the severity of the over-limit indicator, the corresponding output adjustment sub-command is issued first to the regional clusters with the most severe over-limit indicators. The determination of priority requires a comprehensive consideration of factors such as the severity of the over-limit indicator, its development trend, and its potential impact on grid security. Furthermore, the generation of output adjustment sub-commands should be based on the regulation capabilities and response characteristics of the distributed generation (DGs) to ensure their feasibility and effectiveness.

[0115] During the issuance of power adjustment sub-commands, dynamic coordination with the upper-level power grid is performed to ensure that the regional cluster's power regulation is consistent with the upper-level grid's overall dispatch. This dynamic coordination mechanism requires robust communication and information exchange capabilities to obtain real-time dispatch commands and grid operation information from the upper-level grid. Furthermore, intelligent coordination algorithms, such as multi-agent coordinated control algorithms, are employed to achieve effective collaboration between the regional cluster and the upper-level grid.

[0116] This embodiment achieves precise and efficient control of distributed power sources by dividing regional clusters according to the distribution network topology, calculating the severity of over-limit situations in real time, issuing power adjustment sub-instructions in a priority manner, and dynamically coordinating instructions from the upper-level power grid. This effectively improves the power regulation capability and operational stability of the power grid, ensures the coordinated operation of distributed power sources and the upper-level power grid, and enhances the overall performance and reliability of the power grid.

[0117] Optionally, in some embodiments, step S4 of "generating a power balancing instruction after detecting a power grid abnormality event, decomposing the power balancing instruction based on the unified topology model and the standardized operating data set, and executing a power balancing task" may specifically include:

[0118] S41. After a power outage is detected, locate available distributed power sources and energy storage devices based on the unified topology model and calculate the matching degree between power transfer capacity and load demand;

[0119] Specifically, the grid's operating status is monitored in real time, and when an abnormal event such as a grid outage is detected, a power balancing instruction is immediately generated. Advanced fault detection algorithms, such as data-driven fault diagnosis methods, can be used to analyze grid operating data in real time using machine learning models to quickly and accurately detect abnormal grid events. At the same time, a reasonable instruction generation mechanism should be designed to ensure that power balancing instructions can be issued in a timely and accurate manner. Based on the unified topology model, the available distributed power sources and energy storage devices in the grid are located to determine their real-time status and available power. With the help of a geographic information system (GIS) and a real-time monitoring system, the location of distributed power sources and energy storage devices can be quickly located, and their operating status information can be obtained. At the same time, the available power capacity of the equipment is evaluated based on its performance parameters and current operating conditions.

[0120] S42. If the power transfer capacity is insufficient, dynamically adjust the output of the distributed generation or reduce the load based on the power balancing algorithm to generate a temporary power supply plan;

[0121] Specifically, the degree of match between the available distributed power generation and energy storage capacity and the current load demand is calculated. A power balance calculation algorithm can be used to comprehensively consider the output characteristics of the distributed power generation, the charge and discharge status of the energy storage equipment, and the changes in load demand to accurately calculate the match between the power generation capacity and the load demand. Furthermore, a forecasting model is introduced to predict the changing trends of load demand to improve the accuracy of the matching calculation.

[0122] If the transfer capacity is insufficient, the output of distributed generation (DGs) is dynamically adjusted or load is reduced based on a power balancing algorithm to generate a temporary power supply plan. This power balancing algorithm can flexibly adjust the output of DGs or appropriately reduce loads based on varying grid operating conditions and resource conditions. For example, an optimization algorithm (such as particle swarm optimization) can be used to optimize the output of DGs to achieve power balance. Furthermore, a reasonable load reduction strategy can be developed based on the importance and elasticity of the loads. The generation of a temporary power supply plan must fully consider the operational safety of the equipment and the reliability of the power supply to ensure its feasibility and effectiveness.

[0123] S43. After the temporary power supply plan is sent to the target adjustment device through the communication interface, the execution results of the target adjustment device are monitored in real time and fed back to the main control system;

[0124] Specifically, the temporary power supply plan is distributed to the target devices through a communication interface. The execution results of these devices are monitored in real time and fed back to the main control system. The communication interface must be highly reliable and timely to ensure that the temporary power supply plan is accurately and promptly transmitted to the target devices. Furthermore, a comprehensive execution result monitoring system should be established to collect real-time device operating data and execution status information to evaluate and adjust the plan's effectiveness. Advanced sensor technology and data acquisition systems can be used to accurately monitor device execution results.

[0125] This embodiment effectively improves the grid's emergency response and power balance adjustment capabilities under abnormal circumstances by detecting abnormal grid events and generating power balancing instructions, locating available distributed power sources and energy storage equipment, calculating the matching degree between power transfer capacity and load demand, adjusting the output of distributed power sources or reducing loads, and issuing temporary power supply plans and monitoring the execution results. This ensures the stable operation and power supply reliability of the grid, enhances the grid's adaptability and flexibility to the access of distributed power sources, and provides a strong guarantee for the control of new energy power.

[0126] Optionally, in some embodiments, step S42, "if the power transfer capacity is insufficient, dynamically adjusting the output of the distributed power source or reducing the load based on the power balancing algorithm to generate a temporary power supply plan," may specifically include:

[0127] S421. Generate a dynamic capacity pool based on the real-time location and output forecast data of mobile energy storage;

[0128] Specifically, the dynamic capacity pool is generated by collecting real-time information such as the real-time location and output forecast data of mobile energy storage devices. Geographic Information Systems (GIS) and real-time positioning technologies (such as GPS) can be used to obtain the location information of mobile energy storage devices. This information is then combined with their output forecast model to dynamically update the dynamic capacity pool. The output forecast model can be based on historical data and machine learning algorithms, taking into account factors such as the device's charge and discharge status and battery health to improve forecast accuracy.

[0129] S422. After detecting that the dynamic capacity pool is insufficient, reduce the load or purchase third-party output according to priority, generate a temporary plan, and periodically iterate and update the temporary power supply plan;

[0130] Specifically, when insufficient capacity is detected in the dynamic capacity pool, a temporary solution is generated by prioritizing load reduction or purchasing additional third-party power. For load reduction, a priority strategy can be developed based on the importance and resilience of the loads, prioritizing the reduction of non-critical or interruptible loads. For purchasing additional third-party power, market mechanisms and transaction costs must be considered to establish a cost-effective power procurement mechanism. The generation of the temporary solution requires considering the real-time grid operating status and resource conditions, using an optimization algorithm to determine the optimal adjustment plan.

[0131] After the temporary power supply plan is issued and implemented, the execution results of the target adjustment equipment are monitored in real time. These results are fed back to the main control system, allowing for periodic, iterative updates of the temporary power supply plan. A closed-loop plan execution monitoring and feedback mechanism is established to collect real-time device execution data and evaluate the effectiveness of the temporary plan based on changes in grid operating conditions. Adaptive control algorithms are employed to dynamically adjust plan parameters based on feedback data, enabling continuous optimization of the plan. Furthermore, the plan update strategy is intelligently upgraded based on historical data and operational experience, improving the plan's adaptability and effectiveness.

[0132] This embodiment effectively improves the grid's emergency response and power balance adjustment capabilities in the event of insufficient dynamic capacity through dynamic capacity pool management of mobile energy storage devices, coordinated control of load reduction and third-party power purchases, and periodic iterative updates of temporary power supply plans. This enhances the flexibility, stability, and economy of grid operation, and provides more reliable protection for renewable energy power control.

[0133] Optionally, in some embodiments, the method provided in this embodiment may further include:

[0134] Assign a key pair to each distributed power source, use an asymmetric encryption algorithm to encrypt operating data, and digitally sign power control instructions;

[0135] Specifically, each distributed power source (DG) is equipped with a unique key pair, consisting of a public key and a private key, for data encryption and digital signatures. Asymmetric encryption algorithms (such as RSA and ECC) are used to generate and manage key pairs based on the number and type of DGs. A key management system is established to manage key generation, distribution, storage, update, and revocation.

[0136] The public key in the key pair is used to encrypt the distributed power supply operation data to ensure the confidentiality of the data during transmission. A suitable asymmetric encryption algorithm is selected, taking into account the data size and encryption efficiency. For large amounts of operation data, a hybrid encryption mode can be used, that is, using a symmetric encryption algorithm to encrypt the data and then using an asymmetric encryption algorithm to encrypt the symmetric key.

[0137] Power control commands are digitally signed using the sender's private key to ensure command integrity and sender identity authentication. A digital signature algorithm (such as DSA or ECDSA) is used to generate a digital signature for the power control command and append it to the command before sending it. Upon receiving the command, the receiver verifies the validity of the digital signature using the sender's public key.

[0138] Verify the validity of the signature of the power control command at the command receiving end, trigger an alarm and terminate the command processing if the verification fails;

[0139] Specifically, at the command receiving end, the sender's public key is used to verify the validity of the digital signature of the power control command. A digital signature verification mechanism is established to automatically verify received power control commands. If verification fails, the system triggers an alarm and terminates command processing.

[0140] Periodically update key pairs and dynamically adjust key distribution strategies based on device status;

[0141] Specifically, key pairs for distributed power sources should be regularly updated, and key distribution policies should be dynamically adjusted based on device status and security requirements. A key update plan should be established to regularly update keys. Furthermore, key distribution and management policies should be dynamically adjusted based on factors such as device operating status and security threat levels, such as increasing or decreasing the key update frequency for certain devices.

[0142] This embodiment ensures data security and command reliability by encrypting the transmission of distributed power generation operating data and verifying digital signatures for power control commands. This effectively prevents data leakage and the execution of illegal commands, ensuring the safe and stable operation of the power grid. Furthermore, by regularly updating key pairs and dynamically adjusting key distribution strategies, the system's security and adaptability are further enhanced, providing a solid security guarantee for renewable energy power control.

[0143] In order to facilitate understanding of the new energy power control method based on distributed power access provided by this application, as Figure 3 As shown, this embodiment also provides a specific implementation of a new energy power control method based on distributed power generation access. First, a distributed source-load model is constructed. Using classification-based extended modeling, data association, and topology construction techniques, distributed source-load, energy storage, and charging piles are modeled separately based on different access methods and management requirements, providing a foundational architecture for subsequent steps. Next, distributed source-load data is collected and processed. During collection, various operational data are acquired using specific devices, protocols, and communication channels. During processing, data mining techniques are used for cleaning, analysis, calculation, and verification evaluation, providing reliable data support for the system. Distributed source-load monitoring and group control utilize data communication, strategy optimization, and human-computer interaction technologies. Through centralized monitoring, coordinated control, group control, and regional control, all-round management and control of distributed sources and loads is achieved, ensuring stable system operation. Finally, source-grid-load-storage coordinated control assists with analysis and resolution. Building on previous achievements, data processing techniques are used to construct a new distributed energy model association. Communication and data processing techniques are used to decompose and execute control instructions. Power balancing algorithms are used to analyze power transfer issues, ensuring system stability and power supply and demand balance.

[0144] like Figure 4 As shown, Figure 4 A schematic diagram of the process of constructing a distributed source-load graph model provided in this embodiment is provided. The specific process is as follows:

[0145] Distributed source-load graph modeling. By integrating classification-based extended modeling, data association, and topology construction techniques, a precise graph model is constructed based on the different access methods and management requirements of distributed sources and loads, energy storage, and charging piles in the distribution network. By establishing targeted models for distributed sources and loads, distributed energy storage, and distributed charging piles, effective monitoring, control, and analysis of various distributed energy resources and related facilities in the distribution network are achieved, improving the level of automatic power control for new energy sources.

[0146] (1) Distributed source and load modeling. The construction of distributed source and load diagrams aims to meet the needs of effective management and in-depth analysis of distributed source and load in the distribution network. It comprehensively uses classification extension modeling, data association and topology construction technology to carry out work according to different access methods and management requirements. For distributed source and load modeling, according to its access method in the distribution network and the distribution network scheduling and management requirements, the classification extension modeling technology is used to expand the basic model to support applications such as monitoring and control. When the high-voltage side of the user's dedicated transformer is connected to the 10kV distribution network or the low-voltage distributed power source is connected to the 220 / 400V distribution network, the data association technology is used to expand the distributed power source high-voltage users on the distribution network medium-voltage feeder model and associate related data to achieve unified management and monitoring. For distributed source and load power stations directly connected to the 10kV distribution network line, the topology construction technology is used to model the equipment in the station in detail and establish the electrical topology relationship with the distribution network to provide a basis for operation analysis and control.

[0147] (2) Distributed energy storage modeling. Distributed energy storage modeling is based on both fixed and mobile access types to the distribution network. It uses classification extension modeling, data association and topology construction technology to comprehensively model key equipment such as energy storage stations, batteries, energy storage converters, and grid-connected and off-grid fast switching switches. Data association technology is used to record relevant information about energy storage stations and batteries and associate their relationships. For fixed energy storage, topology construction technology is used in combination with GIS to establish an electrical topology relationship with the distribution network. Mobile energy storage records access point information and updates the topology after access to ensure its stable operation in the distribution network.

[0148] (3) Distributed charging pile modeling. When adding distributed electric vehicle charging station and charging pile models to the system, the distributed charging pile modeling uses classification extension modeling, data association, and geographic information fusion technology. For the distributed charging station model, geographic information fusion technology is used to combine its various types of information with geographic information to fully describe its characteristics and location to facilitate management and analysis of the impact on the distribution network. For the charging pile model, data association technology is used to define information and associate ownership relationships. In combination with geographic information, the attributes and locations are accurately grasped to provide data support for control and scheduling.

[0149] like Figure 5 As shown, Figure 5 The following is a flow chart of distributed source-load data collection and processing provided in this embodiment.

[0150] Distributed source-load data collection and processing is the foundation for effective monitoring and precise control of distributed source-load data. It not only inherits the model system established by the distributed source-load graph construction and provides data support for subsequent analysis and control, but also involves distributed source-load monitoring and group dispatching and control, providing a key basis for realizing intelligent operation of the system. Specifically, it covers the following three key parts:

[0151] (1) Distributed source and load data collection. The purpose of this step is to comprehensively collect the operating status data of distributed source and load and distributed energy storage, covering a variety of data types, and using a secure data interaction method to realize the collection of distributed source and load data and distributed energy storage data.

[0152] 1) Distributed Source-Load Data Collection: Utilizing the PV station's data collection and control device, data collection and control interaction are performed using wired channels such as optical fiber or wireless networks, in accordance with 104 or 101 protocols. Grid-connected switch measurements include electrical parameters such as voltage and current, and status data such as switch position and alarm information. The grid-connected point collects forward and reverse active and reactive energy data. Regulation measurements include adjustable capacity and regulation rate, and regulation status includes information such as controllability and system status. The power station collects operational data such as total active and reactive power, as well as status data such as grid-connected switch status and control completion. Inverters collect operational data such as voltage and temperature. Status data includes various operating modes and abnormal alarms, while control data includes active, reactive, and voltage control, as well as inverter start and stop operations. To ensure data access and control security at distributed sites, a key pair is assigned to each site, and identity authentication is performed using an asymmetric cryptographic algorithm. Symmetric encryption is used for real-time operational data exchange between devices within the site. Control commands issued by the master station are digitally signed and verified with random numbers, and similar verification is performed on uplinked control results.

[0153] 2) Distributed energy storage data collection: Using edge-side intelligent gateway devices or direct acquisition mode, data is collected through wired or wireless channels, also in accordance with 104 or 101 protocols. The collected content includes operating data such as voltage, current, charge and discharge capacity of the energy storage inverter, as well as charging and discharging status data, as well as regulation measurement data such as active and reactive power and power factor settings.

[0154] (2) Distributed source and load operation data processing. This is mainly accomplished with the help of data mining technology. First, data collection and preprocessing are carried out. After collecting the analog quantity, switch quantity and multi-state digital quantity of the distributed source and load primary equipment, the data cleaning method of data mining is used to remove noise, duplication and error information, and complete format conversion and standardization. Then, in terms of analog quantity and calculation quantity processing, the statistical analysis function of data mining is used to analyze the real-time quantitative value of the analog quantity and resource information based on regional and partition attribute statistics, and a statistical model is established to understand the operation status and rules. Formula calculation processing uses the rule engine technology of data mining to support multiple operations, can start calculations periodically or trigger, can automatically adjust the calculation order to ensure the accuracy of the results, and automatically update related calculations when a single calculation component changes. For non-measured data, after assigning a specific quality code, the same data mining processing process as the measured data is used to ensure consistency and availability. Multi-source data processing uses data mining data fusion and evaluation methods, sets thresholds and rule verification, builds an evaluation model, selects the best one, and stores it in the real-time database, providing a unified interface for other applications. Finally, the storage optimization strategy of data mining is used to store key data in the historical database, classify and store them, compress them, and establish efficient indexes to facilitate subsequent statistical analysis and historical curve drawing.

[0155] (3) Distributed source and load data verification and evaluation. Distributed source and load data verification and evaluation are crucial to ensuring data quality, supporting stable system operation and accurate decision-making. Regression analysis algorithms can be used to strictly review and evaluate data from multiple dimensions. When conducting data validity and rationality verification and centralized monitoring of relevant data points, first collect a large amount of historical data such as distributed source and load power output, grid voltage, frequency, light intensity, online status, output data, etc., build a regression model, and clarify the quantitative relationship and correlation pattern between each data; for real-time collected data, substitute it into the regression model to calculate the predicted value and compare it with the actual value. If the deviation exceeds the preset reasonable range, such as the power output deviation exceeds 15% and the correlation deviation between light radiation and output data exceeds 10%, it is determined that the data validity is questionable and the operating status may be abnormal, and further verification or triggering of an early warning is required. At the same time, the regression equation is used to determine the reasonable range of data values ​​under different operating conditions. Data that deviates from this range is marked as suspicious and can be corrected by referring to similar historical situations. During the data evaluation phase, a data quality evaluation model is constructed based on regression analysis, taking into account situations such as abnormal interruptions in the collection device and data non-refresh. By studying a large amount of historical data, the quantitative relationship between abnormal situations and data quality degradation is clarified. Data quality is scored based on the degree of fit between real-time data and the regression model. For data with low scores, the root cause of the problem, such as collection device failure or communication interference, is thoroughly analyzed, and a defect handling process is promptly initiated to ensure data reliability and availability.

[0156] like Figure 6 As shown, Figure 6The process of distributed source-load monitoring and group dispatching and control provided in this embodiment is as follows:

[0157] After the distributed source and load data are collected and processed, they need to be further effectively monitored and precisely controlled, and strategic optimization calculations, human-computer interaction and other technologies should be used to achieve comprehensive management and control of the distributed source and load, providing a direct basis for subsequent system optimization scheduling and intelligent decision-making.

[0158] (1) Distributed source-load monitoring and distributed source-load collaborative control. In terms of distributed source-load centralized monitoring, the monitoring interface displays the medium-voltage distributed source-load operating data (daily power generation, real-time power, etc.) and alarm items, supports querying according to multiple conditions, and displays the content of the power station in the responsibility area by default. You can also specify the power station to view real-time telemetry values ​​and statistical curves, and display key information next to the power station icon on the single-line diagram; for distributed source-load collaborative control, the distribution network OCS distribution network master station communicates and interacts with the centralized control system to obtain operating data, and sends control instructions to the centralized control center, which forwards them to specific source loads for execution. At the same time, it supports data upload, instruction parsing, adjustment quantity allocation and inverter adjustment.

[0159] (2) Distributed source-load group control. Cluster division control is adopted. Active power auxiliary control is triggered when the power grid exceeds the limit, generates and executes the adjustment strategy, stores the query strategy information, and monitors at the same time. Reactive power auxiliary control is triggered when the voltage or power factor exceeds the limit. After calculation, filtering and analysis, voltage and power factor control is performed, supporting coordinated control and monitoring plans with the upper power grid. Remote control management issues instructions to the master station and receives feedback based on different situations. Start and stop operations support query, input of adjustment quantities and remote control operations.

[0160] (3) Low-voltage intelligent substation control and medium-voltage regional autonomous control. Low-voltage intelligent substation distributed source-load collaborative control relies on intelligent fusion terminals to monitor the operating status of the substation and internal equipment, and can remotely adjust the adjustable capacity. Medium-voltage regional autonomous distributed source-load control is targeted at medium-voltage microgrids, etc., and monitors the operating status of the region and internal equipment on the basis of regional autonomy, and remotely adjusts the adjustable capacity to meet the overall feeder operation objectives and the safe and stable operation requirements of the power grid.

[0161] like Figure 7 As shown, Figure 7 The flowchart of the auxiliary analysis and disposal of the source-grid-load-storage coordinated control provided in this embodiment is as follows:

[0162] Based on the previous data collection, processing, and monitoring and control results, we further conduct in-depth analysis of new distributed energy, accurately decompose and execute control instructions, and conduct power supply analysis for special situations where distributed power sources are connected to the distribution network to ensure stable operation of the system and balance power supply and demand. Specifically, we cover the following three aspects:

[0163] (1) Generalization analysis and association construction of new distributed energy model ledgers. Using data processing technology, the source-grid-load-storage model data related to the distribution network OCS system and the new distributed energy model ledger are analyzed and processed. Key information such as various names, codes, quantities, and power are extracted from model data such as the Internet of Vehicles, electric vehicle charging stations and charging piles, smart energy service platforms and their individual loads, and third-party independent entity aggregators and their individual loads. By establishing data association rules and based on the logical relationships between models, such as affiliation and grid connection, the association relationships between the models are constructed to form a unified data system, providing a clear data structure for subsequent collaborative control.

[0164] (2) Decomposition and execution of automatic power collaborative control instructions for low-distribution sources, grids, loads, and storage. With the help of communication and data processing technologies, the processing of automatic power collaborative control instructions for low-distribution sources, grids, loads, and storage is realized. The distribution network OCS master station receives power control commands from the regional main grid and the control cloud through the communication interface, uses data processing technology to parse the command content, and splits the instructions into sub-instructions for each individual load object based on the operating characteristics of the access load aggregator. Using communication technology, the sub-instructions are sent to the corresponding individual load to execute the control instructions according to different platform interfaces. At the same time, the execution status of the instructions of each control object is obtained through real-time data monitoring technology, and the execution results are fed back to the instruction source through communication technology to ensure the effective execution and timely feedback of the control instructions.

[0165] (3) Analysis of power transfer when distributed power sources are connected to the distribution network. Using the power balance algorithm, an in-depth analysis is conducted on the power transfer problem of distributed power sources connected to the distribution network in special circumstances such as planned maintenance outages and fault outages. When there is a power transfer feeder and a distributed power source, the distributed source load output is considered when calculating the power transfer load capacity and the power transfer power side capacity according to the power calculation method. By monitoring power changes in real time, if the output of the power transfer feeder is greater than the load, the system will issue an operating mode change warning to ensure power balance. When there is no power transfer feeder, the power balance calculation method is used to determine whether the distributed power source and mobile energy storage can be balanced with the power load. If it is unbalanced, the strategy is adjusted to reduce the output or load to achieve temporary power supply to the power outage users and maintain stable power supply and demand.

[0166] In summary, the new energy automatic power control (APC) method provided in this embodiment has built a complete and efficient new energy power management system through a series of closely related technical links such as distributed source-load diagram construction, data collection and processing, monitoring and group dispatching and control, and source-grid-load-storage collaborative control auxiliary analysis and disposal. The construction of the distributed source-load diagram accurately depicts the layout and relationship of various distributed energy sources and facilities in the distribution network, laying a solid data model foundation for the entire system. Data collection and processing ensure the accurate acquisition and in-depth mining of massive data, providing key support for subsequent decision-making. Monitoring and group dispatching and control realize real-time monitoring and intelligent regulation of distributed sources and loads, ensuring the stable operation of the power system. Source-grid-load-storage collaborative control auxiliary analysis and disposal further optimizes system operation and improves the accuracy and reliability of power supply and demand balance.

[0167] It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0168] To facilitate better implementation of the new energy power control method based on distributed power access according to an embodiment of the present application, an embodiment of the present invention further provides a new energy power control device based on distributed power access, which is based on the above-mentioned new energy power control method based on distributed power access. The meanings of the terms herein are the same as those in the above-mentioned new energy power control method based on distributed power access. For specific implementation details, please refer to the description in the method embodiment.

[0169] See also Figure 8 , Figure 8 This is a schematic diagram of the structure of a new energy power control device based on distributed power access provided in an embodiment of the present application. The new energy power control device based on distributed power access may specifically include a model building module 201, a data processing module 202, a power regulation module 203, and a power balancing module 204, as follows:

[0170] The model building module 201 is used to build a unified topology model including distributed sources and loads, energy storage devices and charging piles based on the access mode of distributed power sources. The unified topology model integrates device attributes and electrical associations;

[0171] The data processing module 202 is used to obtain the operating data of the distributed power source through the unified topology model, perform data preprocessing on the operating data, and generate a standardized operating data set;

[0172] The power regulation module 203 is used to monitor the real-time operation status and operation indicators of the power grid based on the standardized operation data set, dynamically generate corresponding power control instructions, and send the power control instructions to the distributed power generation to perform power regulation tasks;

[0173] The power balancing module 204 is configured to generate a power balancing instruction after detecting an abnormal power grid event, decompose the power balancing instruction based on a unified topology model and a standardized operation data set, and execute a power balancing task.

[0174] For the specific definition of the new energy power control device based on distributed power access, please refer to the definition of the new energy power control method based on distributed power access above, which will not be repeated here. The various modules in the above-mentioned new energy power control device based on distributed power access can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0175] The new energy power control device based on distributed power supply access provided in this embodiment integrates the device attributes and electrical associations of distributed sources and loads, energy storage equipment, and charging piles by constructing a unified topology model, comprehensively monitors and controls these devices, and then generates a standardized operation data set through data preprocessing. It monitors the operation status of the power grid in real time and dynamically generates power control instructions to be sent to distributed power supplies to perform power adjustment tasks. When the power grid is abnormal, it generates power balancing instructions and decomposes and executes power balancing tasks to achieve stable operation and power balance control of the power grid, solves the problems of insufficient real-time monitoring and dynamic adjustment capabilities, low data security, and insufficient source-grid-load-storage coordination capabilities in the existing technology, realizes real-time monitoring and dynamic power adjustment of distributed power sources, improves power grid stability and power balancing capabilities, and thus meets the power control needs of the distribution network after the access of massive distributed power sources.

[0176] In addition, the present invention also provides an electronic device, such as Figure 9 , which shows a schematic structural diagram of an electronic device involved in an embodiment of the present application, specifically:

[0177] The electronic device may include one or more processors 301 of processing cores, one or more computer-readable storage media memories 302, a power supply 303, an input unit 304 and other components. Those skilled in the art will appreciate that Figure 9 The electronic device structure shown in the figure does not constitute a limitation of the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently.

[0178] The processor 301 is the control center of the electronic device. It connects all parts of the electronic device using various interfaces and lines. By running or executing software programs and / or modules stored in the memory 302 and accessing data stored in the memory 302, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor primarily processes the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into the processor 301.

[0179] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and a new energy power control method based on distributed power access by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0180] The electronic device also includes a power supply 303 for supplying power to various components. Preferably, the power supply 303 can be logically connected to the processor 301 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The power supply 303 can also include one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0181] The electronic device may further include an input unit 304, which may be configured to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0182] Although not shown, the electronic device may further include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302 to implement various functions as follows:

[0183] Based on the access mode of distributed power sources, a unified topology model including distributed sources and loads, energy storage equipment and charging piles is constructed. The unified topology model integrates device attributes and electrical associations; the operating data of distributed power sources is obtained through the unified topology model, and the operating data is preprocessed to generate a standardized operating data set; based on the standardized operating data set, the real-time operating status and operating indicators of the power grid are monitored in real time, and the corresponding power control instructions are dynamically generated. The power control instructions are sent to the distributed power sources to perform power regulation tasks; after detecting an abnormal event in the power grid, a power balancing instruction is generated. Based on the unified topology model and the standardized operating data set, the power balancing instruction is decomposed and the power balancing task is executed.

[0184] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0185] The embodiment of the present application integrates the device attributes and electrical associations of distributed sources and loads, energy storage devices and charging piles by constructing a unified topology model, comprehensively monitors and controls these devices, and then generates a standardized operation data set through data preprocessing. It monitors the operation status of the power grid in real time and dynamically generates power control instructions to be sent to distributed power sources to perform power regulation tasks. When the power grid is abnormal, it generates power balancing instructions and decomposes and executes power balancing tasks to achieve stable operation and power balance control of the power grid, solves the problems of insufficient real-time monitoring and dynamic adjustment capabilities, low data security, and insufficient source-grid-load-storage coordination capabilities in the existing technology, realizes real-time monitoring and dynamic power adjustment of distributed power sources, improves power grid stability and power balancing capabilities, and thus meets the power control needs of the distribution network after the access of massive distributed power sources.

[0186] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0187] To this end, an embodiment of the present application provides a storage medium storing a plurality of instructions that can be loaded by a processor to execute the steps of any of the new energy power control methods based on distributed power access provided in the embodiments of the present application. For example, the instructions can execute the following steps:

[0188] Based on the access mode of distributed power sources, a unified topology model including distributed sources and loads, energy storage equipment and charging piles is constructed. The unified topology model integrates device attributes and electrical associations; the operating data of distributed power sources is obtained through the unified topology model, and the operating data is preprocessed to generate a standardized operating data set; based on the standardized operating data set, the real-time operating status and operating indicators of the power grid are monitored in real time, and the corresponding power control instructions are dynamically generated. The power control instructions are sent to the distributed power sources to perform power regulation tasks; after detecting an abnormal event in the power grid, a power balancing instruction is generated. Based on the unified topology model and the standardized operating data set, the power balancing instruction is decomposed and the power balancing task is executed.

[0189] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0190] The storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0191] Since the instructions stored in the storage medium can execute the steps of any of the new energy power control methods based on distributed power supply access provided in the embodiments of the present application, the beneficial effects that can be achieved by any of the new energy power control methods based on distributed power supply access provided in the embodiments of the present application can be achieved. Please see the previous embodiments for details and will not be repeated here.

[0192] The above is a detailed introduction to a new energy power control method and device based on distributed power supply access provided in an embodiment of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, based on the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A new energy power control method based on distributed power access, characterized in that: The steps include: Based on the access mode of distributed power sources, a unified topology model including distributed sources and loads, energy storage equipment and charging piles is constructed. The unified topology model integrates device attributes and electrical associations; Acquiring operating data of distributed power sources through the unified topology model, performing data preprocessing on the operating data, and generating a standardized operating data set; Based on the standardized operation data set, real-time monitoring of the real-time operation status and operation indicators of the power grid is carried out, corresponding power control instructions are dynamically generated, and the power control instructions are sent to the distributed power source to perform power regulation tasks; After detecting that an abnormal event occurs in the power grid, a power balancing instruction is generated, and based on the unified topology model and the standardized operation data set, the power balancing instruction is decomposed and a power balancing task is executed.

2. The new energy power control method based on distributed power access according to claim 1 is characterized in that: The access method based on distributed power sources constructs a unified topology model including distributed sources and loads, energy storage equipment and charging piles, including: According to the voltage level and grid connection mode of the distributed source and load, a basic model is constructed using classification extension technology, and device attributes and distribution network feeders are bound using data association technology; According to the access type of the energy storage device, the corresponding dynamic access point information is recorded, and the electrical topology relationship between the energy storage device and the power grid is constructed based on the geographic information system; The geographical location and power attributes of the charging piles are integrated into a unified topology type to form a charging facility sub-model including spatial distribution characteristics.

3. The new energy power control method based on distributed power access according to claim 1 is characterized in that: The step of obtaining the operating data of the distributed power source through the unified topology model, performing data preprocessing on the operating data, and generating a standardized operating data set includes: Collecting operating data of the distributed power supply through a preset communication protocol and an encrypted channel, the operating data including electrical parameters of the distributed source and load, the charge and discharge status of the energy storage device, and real-time power data of the charging pile; Noise filtering, format standardization, and redundant data elimination are performed on the operating data, and quality codes are marked on non-measured data to generate a standardized operating data set; The operation data is analyzed for deviations by using a verification model pre-trained based on historical data. When it is detected that the deviation rate exceeds a preset threshold, an abnormality warning is triggered and the standardized operation data set is updated.

4. The new energy power control method based on distributed power access according to claim 1 is characterized in that: The method of performing deviation analysis on the operating data using a verification model pre-trained based on historical data, triggering an abnormality warning and updating the standardized operating data set upon detecting that the deviation rate exceeds a preset threshold, includes: Based on historical data, a regression model of distributed generation output and grid parameters is trained as a verification model; Inputting the real-time detected operating data into the verification model to calculate the corresponding deviation rate; After detecting that the deviation rate exceeds the preset threshold, the abnormal data is marked and the equipment status is checked to generate the verification results; According to the verification results, abnormal data is frozen or the preset threshold is modified, and the standardized operation data set is updated.

5. The new energy power control method based on distributed power access according to claim 1 is characterized in that: The real-time monitoring of the real-time operating status and operating indicators of the power grid based on the standardized operating data set, dynamically generating corresponding power control instructions, and sending the power control instructions to the distributed power supply to perform power regulation tasks, includes: Based on the standardized operating data set, the line load rate, voltage amplitude and power factor indicators of the power grid are analyzed in real time. If any indicator exceeds a preset safety range, a control strategy is triggered; Decompose the adjustment amount into output adjustment sub-instructions for distributed sources and loads, energy storage equipment, and charging piles according to priority, and send the output adjustment sub-instructions to each distributed power source through the group adjustment and group control interface to perform power adjustment tasks; The power grid operation data after power regulation is collected in real time. If it is detected that the abnormal indicators have not returned to a safe range, power adjustment instructions are iteratively generated based on the updated standardized operation data set until the power grid stabilizes.

6. The new energy power control method based on distributed power access according to claim 5 is characterized in that: The step of sending the output adjustment sub-command to each of the distributed power sources through the group adjustment and control interface to execute the power adjustment task includes: Divide the distributed generation into regional clusters based on the distribution network topology and calculate the severity of the over-limit of each regional cluster in real time; The corresponding output adjustment sub-instructions are issued according to the priority of the severity of the limit violation, and the upper-level power grid instructions are dynamically coordinated.

7. The new energy power control method based on distributed power access according to claim 1 is characterized in that: The generating of a power balancing instruction after detecting an abnormal power grid event, decomposing the power balancing instruction based on the unified topology model and the standardized operation data set, and executing a power balancing task, includes: After a power outage is detected, the available distributed power sources and energy storage devices are located according to the unified topology model, and the matching degree between the power transfer capacity and the load demand is calculated; If the power transfer capacity is insufficient, the output of the distributed power source is dynamically adjusted or the load is reduced based on the power balancing algorithm to generate a temporary power supply plan; After the temporary power supply plan is sent to the target adjustment device through the communication interface, the execution result of the target adjustment device is monitored in real time and fed back to the main control system.

8. The new energy power control method based on distributed power access according to claim 1 is characterized in that: If the power transfer capacity is insufficient, the output of the distributed power source is dynamically adjusted or the load is reduced based on a power balancing algorithm to generate a temporary power supply plan, including: Generate a dynamic capacity pool based on the real-time location and output forecast data of mobile energy storage; After detecting that the dynamic capacity pool is insufficient, the load is reduced or third-party output is purchased according to priority, a temporary plan is generated, and the temporary power supply plan is updated periodically.

9. The new energy power control method based on distributed power access according to claim 1, characterized in that: The method further comprises: Assign a key pair to each distributed power source, use an asymmetric encryption algorithm to encrypt operating data, and digitally sign power control instructions; Verify the validity of the signature of the power control command at the command receiving end, trigger an alarm and terminate the command processing if the verification fails; Periodically update key pairs and dynamically adjust key distribution policies based on device status.

10. A new energy power control device based on distributed power access, characterized in that: include: A model building module is used to build a unified topology model including distributed sources and loads, energy storage devices, and charging piles based on the access mode of distributed power sources. The unified topology model integrates device attributes and electrical associations; A data processing module, configured to obtain operating data of the distributed power source through the unified topology model, perform data preprocessing on the operating data, and generate a standardized operating data set; A power regulation module, configured to monitor the real-time operating status and operating indicators of the power grid based on the standardized operating data set, dynamically generate corresponding power control instructions, and issue the power control instructions to the distributed power source to perform power regulation tasks; The power balancing module is used to generate a power balancing instruction after detecting an abnormal power grid event, decompose the power balancing instruction based on the unified topology model and the standardized operation data set, and execute a power balancing task.

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