A multi-region distributed power supply hierarchical collaborative group regulation and control method for a high-permeability power distribution network

Abstract

The application discloses a kind of multi-zone distributed power supply layered collaborative group regulation and control methods for high permeability distribution network, and relates to the technical field of power system dispatching control.The method comprises the following steps: S1, multi-source data acquisition and basic data management, to build the topological correlation of "line-variable-grid point-meter" full-link;S2, distributed power supply operating situation awareness and risk early warning, based on active power reverse monitoring and island operation event identification to determine the risk of reverse power transmission and generate early warning;S3, multi-zone layered collaborative regulation and control, to realize the three-layer collaborative architecture and control target secondary decomposition of "dispatching master station-regional regulation and control node-execution terminal".The application can greatly improve the regulation and control accuracy, response speed and operation safety of distributed power supply in high permeability distribution network, and the response time of a single control command is not more than 3 seconds, and the batch regulation and control success rate is not less than 85%.

Description

Technical Field

[0001] This invention relates to the field of power system dispatch and control technology, specifically to the field of distributed generation regulation and management technology in distribution networks, and particularly to a hierarchical collaborative group dispatch and control method for multi-regional distributed generation in high-penetration distribution networks. This invention is applicable to the real-time monitoring, collaborative regulation, and operational optimization management of distributed generation in distribution networks with a high proportion of distributed generation access. Background Technology

[0002] Currently, the large-scale promotion of new energy power generation, mainly wind and solar power, and distributed energy storage has become an important way to reduce carbon dioxide emissions in the power sector. By the end of 2024, the installed capacity of new energy in Gansu Province had reached 64%, of which the installed capacity of distributed power sources exceeded 831.31MW, with more than 20,000 users connected through low-voltage distributed access, and an installed capacity exceeding 250MW. With the continuous increase in the penetration rate of distributed power sources, the operating characteristics of the distribution network are undergoing profound changes, placing higher demands on dispatch and control capabilities. Currently, extensive research has been conducted both domestically and internationally on the regulation and management of distributed power sources. In terms of operation monitoring, some power companies have achieved accurate prediction of distributed power generation and visualized display of a single "distributed power generation prediction map." Regarding regulation and management, the State Grid Corporation of China has implemented province-wide distributed power generation regulation and control functions in multiple provinces, including Shandong, Hebei, Jiangsu, and Jiangxi, with some regions achieving a regulation and control success rate exceeding 85%. In terms of technical architecture, existing solutions mostly adopt an IoT sensing architecture of "acquisition terminal + protocol converter + power switch," relying on the existing fee control channels of the electricity consumption information collection system to issue control commands for distributed power sources. However, existing distributed power generation regulation technologies still have the following shortcomings: First, the basic data management capabilities for distributed generation are insufficient. In the existing system, the topology data, basic data, operational data, and ledger information of distributed generation are scattered across different business systems. The data formats are not uniform, and the dynamic changes in topology relationships lead to insufficient data collection accuracy. Maintaining consistency during the integration of massive amounts of data is difficult, making it impossible for dispatchers to accurately grasp the complete access status of distributed generation and the scope of its impact on the distribution network. Second, the system suffers from weak operational situation awareness and risk early warning capabilities. Existing systems lack comprehensive data aggregation for remote signaling and telemetry of distributed power sources, and lack the ability to provide real-time dynamic display and comprehensive analysis of multi-dimensional information such as power generation curves, penetration rates, and voltage distribution. In particular, with the large-scale integration of distributed power sources into the distribution network, the threat of peak-hour electricity demand in distribution areas intensifies, and there is a lack of effective monitoring and early warning methods for safety risks such as unauthorized power transmission, threatening the safe and stable operation of the distribution network. Third, the regulation and control management lacks hierarchical coordination and refined capabilities. Existing regulation and control schemes mostly adopt a centralized regulation and control model, which does not fully consider the differences in grid characteristics, user priorities, and rigidity / flexibility ratios in different regions. The decomposition of regulation and control targets is coarse, making it difficult to achieve precise regulation and control of distributed power generation output. At the same time, existing systems have significant shortcomings in the intelligent generation of regulation and control tasks, real-time monitoring of the execution process, and quantitative evaluation of regulation and control effects, and cannot meet the requirements of "observable, measurable, adjustable, and controllable" panoramic perception and precise regulation and control of distributed power generation in high-penetration scenarios. Summary of the Invention

[0003] This invention aims to address the problems of insufficient basic data management capabilities, weak operational status awareness, and lack of hierarchical coordination and refined control capabilities in existing distributed power generation regulation technologies. It provides a hierarchical collaborative group regulation and control method for multi-regional distributed power sources in high-penetration distribution networks. The objective of this invention can be achieved through the following technical solutions: A hierarchical collaborative group dispatch and control method for multi-regional distributed power sources in high-penetration distribution networks includes the following steps: S1. Multi-source data acquisition and basic data management: Collect topological relationship data, basic data, operation data and external environment data of distributed power sources from electricity information acquisition system, marketing system and distribution automation system. Based on a unified data model, perform multi-source data fusion and ledger integration to build a full-link topological relationship of "line-transformer-grid connection point-household meter" and form a standardized basic data foundation for distributed power sources. S2. Distributed Power Generation Operation Status Awareness and Risk Warning: Based on the basic data foundation built in step S1, the remote signaling and telemetry data of distributed power generation are fully aggregated. The installed capacity, output curve, penetration rate and voltage distribution information of distributed power generation in each region are calculated in real time. The operation status heat map is generated through multi-dimensional data aggregation and analysis. Based on active power reverse monitoring and islanded operation event identification, the reverse power transmission risk level is determined and a warning signal is generated. S3. Multi-regional hierarchical coordinated control: Based on the characteristics of the regional power grid, user priority, and rigidity-flexibility ratio, the control area is divided into several control sub-regions. Each control sub-region is equipped with a regional control node. After receiving the control targets issued by the upper-level dispatch master station, each regional control node performs a secondary decomposition of the control targets in combination with the adjustable resource capacity of its region, and then sends the decomposed sub-targets to each execution terminal, realizing a three-level coordinated control of "dispatch master station - regional control node - execution terminal". S4. Adaptive generation and execution of control strategy: Based on the control sub-objectives decomposed in step S3, real-time operating data of each execution terminal is obtained and adjustable resource capabilities are calculated. A control execution scheme containing control mode and execution mode is automatically generated. Terminal controllability verification is performed on distributed power users participating in the control. Control commands that pass the verification are sent to the electricity information collection system. Rigid control or flexible control is executed through the original fee control channel. S5. Monitoring and Evaluation of Control Process: Track the execution status of control commands in real time, monitor and highlight any abnormalities that occur during the execution process, calculate the control execution effect of each region based on the operation data during the control period, automatically generate execution logs and perform multi-dimensional statistical analysis to form a control effect evaluation report. As a further aspect of the present invention, the topology data collection in step S1 includes: obtaining the association data between the distributed power supply access point and the transformer area and meter box from the electricity information collection system and the marketing system, realizing the automatic sorting and graphical display of the five-level topology relationship of "line-transformer-grid connection point-household meter"; the basic data includes the installed capacity, number of installed units, growth trend, grid connection status, power generation user number, power generation address, account establishment date, customer category, transformer area name, voltage level, grid connection point name, terminal number, terminal type and consumption method data of the distributed power supply; the operation data includes the daily 96-point output data, the daily electricity meter bottom code difference settlement electricity and temperature data; the external environment data includes the weather forecast release date, forecast date, initial weather conditions, ending weather conditions, highest temperature and lowest temperature; the ledger integration includes integrating and analyzing the equipment information of power switches, inverters, concentrators, lines, distribution transformers, metering points and protocol converters and establishing corresponding relationships. As a further aspect of the present invention, the operational situation awareness mentioned in step S2 includes: displaying the average temperature fluctuations and external environmental data of different regions in the form of a heat map; analyzing the output of distributed power sources at each voltage level using output curves and calculating the output extreme values; analyzing the daily, monthly, and annual cumulative power generation of distributed power sources at each voltage level using charts and calculating the peak and valley power generation; the backfeeding risk determination includes: when the active power of the meter is detected to be negative, it is determined to be a suspected backfeeding in the power source area and a regional backfeeding risk event is generated; when the active power of the line is detected to be negative and the transformer area reports an islanding operation event, it is determined to be a suspected backfeeding in the power source line and a line backfeeding risk event is generated, and a cross-level backfeeding risk warning is issued at the same time. As a further aspect of the present invention, the method for dividing the regional control nodes in step S3 is as follows: based on geographical proximity and grid topology connection, a clustering algorithm is used to divide the distributed power sources into several control sub-regions, each control sub-region containing at least one transformer area and one regional control node; the secondary decomposition of the control target includes: the regional control node allocates the upper-level control target to each execution terminal proportionally according to the installed capacity, current output, adjustable potential and user priority of each distributed power source in its region, and ensures that the sum of the decomposed sub-targets is equal to the upper-level control target. As a further aspect of the present invention, the user priority includes at least three levels: Level 1 users are important industrial users, whose power supply reliability is prioritized and a minimum output protection value is set during regulation; Level 2 users are general industrial and commercial users, and regulation is carried out in a combination of rigid and flexible methods; Level 3 users are residential users, and flexible regulation is prioritized; the rigid control is to directly control the grid connection status or output limit of the distributed power source, and the flexible control is to achieve smooth regulation by adjusting the output curve or power factor of the distributed power source. As a further aspect of the present invention, the terminal controllability verification in step S4 includes: remotely calling the distributed power users participating in the control to obtain the online status, switching status and real-time measurement curve data of the terminal, judging whether the terminal meets the conditions for issuing the control command based on the calling result, and feeding back the verification result to the dispatch master station; the control method includes rigid control, flexible control and integrated control; the execution method includes time period control and current power down-floating control. As a further aspect of the present invention, the control execution scheme in step S4 also supports user-personalized strategy selection: before control execution, a personalized execution scheme is generated based on the control method, control type, control strategy, and control objective selected by the user; the control strategy includes rigid control priority strategy, flexible control priority strategy, and user-level priority strategy; control methods and control types for single or multiple households are selected according to region to adapt to the on-site control needs of different regions. As a further aspect of the present invention, the monitoring of the control process in step S5 includes: automatically generating abnormal alerts for abnormal events generated during the execution of the control plan, and highlighting the abnormal locations on the main station; the evaluation of the execution effect includes: calculating and statistically analyzing the control area, control capacity, number of participating users, and control success rate across multiple dimensions, including the entire city and substations; the multi-dimensional statistical analysis includes filtering and querying by unit, task source, execution status, control start time, and control end time, with the filtering results supporting a penetrating view of the executing user names and control-related data curves. As a further aspect of the present invention, step S5 is followed by an intelligent generation step for control tasks: based on the grid peak-shaving demand, combined with the controllable quantity of distributed power sources in each district and county and historical control data, power control tasks for each district and county are automatically generated and displayed on the main station platform; the displayed content includes the unit name, task source, control type, adjustment target, current output, start time, end time, executing user and execution effect, and clicking on it allows you to view the executing user name and control-related data curves. As a further embodiment of the present invention, the method is applied to a distributed power group control system deployed in Safety Zone III. Data interaction with the power distribution automation system and the electricity consumption information collection system is realized through a data synchronization server. Control commands are sent to terminal devices through the original fee control channel of the electricity consumption information collection system. The response time for a single control command is no more than 3 seconds, and the success rate of batch control command issuance is no less than 85%. The beneficial effects of this invention are: 1. This invention achieves full collection and standardized integration of distributed power source topology relationship data, basic data, operational data, and external environment data through multi-source data fusion and a unified data model, constructing a five-level topology association relationship of "line-transformer-grid connection point-household meter". Compared with the problems of scattered data, inconsistent formats, and difficulty in maintaining consistency in existing technologies, this invention significantly improves the integrity and accuracy of basic data, providing high-quality data support for subsequent operation monitoring and control management. 2. This invention achieves real-time perception and visualization of the operational status of distributed power sources by aggregating all distributed power source telemetry and telecontrol data and combining it with multi-dimensional analysis methods such as heat maps, power output curve analysis, and peak-valley power generation calculation. In particular, through a dual judgment mechanism of active power reverse monitoring and islanding operation event identification, it can accurately identify regional backfeeding and line over-level backfeeding risks and provide timely warnings, effectively solving the problem of monitoring safety risks brought about by distributed power source access in high-penetration scenarios. 3. This invention proposes a three-tiered collaborative control architecture consisting of a "dispatch master station—regional control nodes—execution terminals." The regional control nodes perform secondary decomposition of the upper-level control objectives, achieving differentiated control by combining regional power grid characteristics, user priorities, and the ratio of rigidity to flexibility. Compared to existing centralized control models, this layered collaborative architecture significantly improves the accuracy and flexibility of control, effectively solving the "one-size-fits-all" problem in large-scale distributed power source control. 4. This invention achieves adaptive generation and closed-loop management of control execution schemes through terminal controllability verification, personalized strategy selection, and real-time monitoring of the control process. By organically combining rigid and flexible control, and flexibly configuring various strategies such as rigid control priority, flexible control priority, and user-level priority, it can adapt to the actual control needs of different regions and users. Taking a real distribution network in Gansu as an example, after adopting the method of this invention, the response time for issuing a single control command is no more than 3 seconds, the success rate of issuing batch control commands is no less than 85%, the efficiency of generating control tasks is improved by more than 60% compared with the traditional manual method, and the accuracy rate of identifying backfeeding risks reaches more than 92%, effectively improving the control efficiency and operational safety of distributed power sources in high-penetration distribution networks. Attached Figure Description The invention will now be further described with reference to the accompanying drawings. Figure 1 This is a flowchart of a method for constructing an economic evaluation model for high-proportion new energy power transmission and distribution investment in this invention. Detailed Implementation The present invention will be further described in detail below with reference to specific embodiments. These embodiments are implemented under the premise of the technical solution of the present invention, and provide detailed implementation steps and quantitative data, fully disclosing the details of the technical solution and meeting the full disclosure requirements of the patent law. Example 1 (using the complete modeling method of this invention) This embodiment uses a distributed power source group dispatch and control system construction project of a provincial power grid company as an application scenario to provide a detailed description of the method of the present invention. I. System Deployment Architecture In this embodiment, the distributed power supply group dispatch and control system is deployed in the dispatch safety zone III. It uses a data synchronization server to achieve data interaction and control command interface with the distribution automation system (safety zone I / III), the electricity consumption information collection system, the data platform, and the marketing system. The system adopts a B / S architecture, with a layered technical architecture on the server side, following the JavaEE technology system, developed based on the State Grid SG-UAP platform, using MySQL as the database, and Tomcat as the middleware. The system uses the existing fee control channel of the electricity consumption information collection system to issue commands for both rigid and flexible control of the distributed power supply equipment. II. Specific Implementation of Step S1 – Multi-Source Data Acquisition and Basic Data Management (I) Topological Relationship Data Acquisition Data on the topological relationships between distributed power supply access points and transformer substations / meter boxes is collected from the electricity consumption information collection system and the marketing system. Specifically: Data on the association between transformer substations and grid connection points is obtained from the electricity consumption information collection system, including fields such as transformer substation number, terminal asset number, grid connection point name, and grid connection point address; data on the association between grid connection points and user meter boxes is obtained from the marketing system, including fields such as grid connection point number, meter box number, and user number. The aforementioned multi-source data is integrated and automatically organized according to a five-level topology structure: "line (line) - transformer (transformer) - grid connection point - user (customer) - meter (meter box)". After the topology is generated, it is displayed graphically in the system interface, showing the connection relationships between nodes at each level, equipment parameters, and operating status. When the topology data of the source system changes (such as adding a distributed power source or upgrading the line), the system automatically updates the topology through a timed synchronization mechanism to ensure the timeliness and accuracy of the topology data. (II) Basic Data Acquisition of Distributed Power Sources Data on installed capacity, user data, grid connection data, and power consumption of distributed power sources are collected. Installed capacity data collection: Obtain the installed capacity (unit: MW), number of installed units (unit: units), growth trend (monthly statistics of newly installed capacity), and grid connection status (grid connected / not connected) of distributed power sources from the marketing system. Data collection from power generation users: Obtain key information such as power generation user number, power generation address, account opening date, and customer category (large industrial / general industrial and commercial / residential) from the marketing system, and provide accurate equipment search and query services. Power generation user grid connection information collection: Obtain information such as transformer area name, voltage level (220V / 380V / 10kV), grid connection point name, terminal number, and terminal type (concentrator / collector / protocol converter) from the power consumption information collection system. Data collection on power generation user consumption: Obtain data on the consumption methods of distributed power generation users from the marketing system, including the distribution of distributed power sources with full grid connection and self-consumption as the main method, and surplus power grid connection. Data interaction with the source system is achieved through a data middle platform, with the collection frequency set to a full synchronization once a day and incremental data synchronized in real time. (III) Data Acquisition of Distributed Power Source Operation It enables the collection of distributed power source operation data, including daily output data at 96 points (one point every 15 minutes, with the collection frequency dynamically adjusted according to the day and night cycle of the power source), daily electricity consumption calculated based on the difference in the meter readings, temperature, and other data. Operational data is collected through an electricity consumption information collection system at a frequency of 96 points daily, in JSON format, and transmitted to the group dispatch and control system via a data synchronization server. For distributed photovoltaic power sources that do not generate electricity at night, the system automatically adjusts the collection frequency to high-frequency collection only from sunrise to sunset, and reduces the collection frequency from sunset to sunrise to reduce data transmission and storage pressure. (iv) External data collection This system collects external meteorological forecast data for the geographical area where distributed power sources operate, including the release date, forecast date, initial weather conditions, ending weather conditions, maximum temperature, and minimum temperature. The meteorological data is obtained through an API provided by the meteorological department, updated daily, and provides a forecast lead time of 72 hours. (v) Integrated management of ledgers Based on a unified data model, power supply ledger information from various source-end systems, such as the procurement system and the Marketing 2.0 system, is integrated and analyzed. Specifically, this includes: The system integrates the equipment information for power switches (equipment name, equipment model, manufacturer, commissioning date, and operating status); the equipment information for inverters (inverter model, rated power, efficiency, and communication protocol); the equipment information for concentrators (concentrator number, communication method, and location); the equipment information for lines (line name, voltage level, length, and current carrying capacity); the equipment information for distribution transformers (distribution transformer name, capacity, model, and location); the equipment information for metering points (metering point number, meter model, and accuracy class); and the equipment information for protocol converters (converter model, communication protocol, and compatible equipment type). The integrated ledger information is used to establish a unified equipment ledger database in the group dispatch and control system, enabling centralized management and unified query of equipment information. III. Specific Implementation of Step S2 – Distributed Power Supply Operation Status Awareness and Risk Warning (I) Overview of Power Supply Operation Monitoring It comprehensively aggregates power supply, electrical, and external environmental data from various external systems, enabling real-time dynamic display of information such as distributed power generation status, voltage distribution, power generation curves, penetration rate, and abnormal conditions. Specifically, it includes: The system interface displays average temperature fluctuations and external environmental data for different areas in the form of a heat map. The intensity of the heat map's colors represents temperature levels or environmental parameter magnitudes, with areas divided by transformer substations or power distribution stations as the basic unit. The heat map data is updated every 15 minutes. This system displays the real-time output and operational quality of distributed power sources in different regions. It analyzes the output of distributed power sources at various voltage levels using output curves and calculates the output extremes (including daily maximum, daily minimum, and daily average output). The output curves are plotted with time on the horizontal axis and output power on the vertical axis, and support switching between daily, weekly, and monthly time scales. This presentation displays the daily power generation and equipment operation status of distributed power sources in different regions. Bar charts and line graphs are used to analyze the daily, monthly, and annual cumulative power generation and equipment operation quality of distributed power sources at each voltage level. The peak-valley situation of power generation at each voltage level is also calculated (including daily peak-valley difference, monthly peak-valley rate, and annual peak-valley rate). The system monitors abnormal situations such as regional backfeeding and line backfeeding in real time, integrates and displays various abnormal situations, and directly alerts management personnel. Abnormal alerts are provided through system pop-up notifications, audible alarms, and SMS notifications. (II) Power supply operation data analysis 1. Power Access Topology Display The system can display the topological relationships between power supply access points and distribution areas, branches, and meter boxes, providing a visual representation of the "line-transformer-grid connection point-meter" relationship. The topology map uses a tree structure or force-directed graph layout, supporting zooming, panning, and node clicks to expand details. Through the topology display, dispatchers can accurately grasp the impact range of distributed power supply access on the distribution network, providing a basis for fault location and control decisions. 2. Adjustable resource scale analysis Based on the distributed generation operation data aggregated by the system, the theoretical generation power and available power of distributed generation within the regional power grid are calculated. The theoretical generation power is calculated by determining the theoretical maximum generation power based on the installed capacity of the distributed generation, current solar / wind speed conditions, and equipment operating status. Available power is calculated by deducting unavailable capacity due to equipment failures, maintenance, and communication interruptions from the theoretical power. Based on this, the down-regulation capacity of new energy sources is analyzed and calculated. The formula for calculating the down-regulation capacity is: P_down = P_current - P_min Wherein, P_down represents the downward adjustment capability, P_current represents the current output, and P_min represents the minimum adjustable output (constrained by the equipment's technical minimum output and the user's minimum output protection value). The analysis results are presented in chart form to show the overall control capability of the new energy source, including indicators such as the adjustment capacity, adjustment rate, and adjustment duration of each region. 3. Analysis of the success rate of regulation This system enables the display and querying of control events generated by the main station and equipment, and records on-site control information for field equipment. Control event records include fields such as control time, control type, control objective, actual execution result, and reason for failure. Statistical analysis of the overall control success rate is performed, with statistical dimensions including by region, by equipment type, by control type, and by time period, providing support services for further optimization of control process monitoring. (iii) Power supply operation risk alarm 1. Backfeeding risk alarm in power supply area The system monitors the operational status of meters within the area connected to the electricity consumption information collection system. In the event of a power outage, the active power of the meters is monitored in real time. Active power is monitored once per minute. The dual determination mechanism for reverse power transmission risk is as follows: The first level of detection – reverse monitoring of active power: When a negative value (P_active < 0) is detected in the meter's active power reading, it is determined that the power source area is suspected of reverse power feeding. A negative active power value indicates that the current direction is from the user side to the grid side, that is, the distributed power source is feeding power back to the grid. The second level of judgment—islanding event confirmation: When a distribution area reports an islanding event, a comprehensive judgment is made based on the negative active power information. An islanding event refers to a distributed power source continuing to operate after a grid outage, forming an independent power supply island. When both the first and second criteria are met, the system automatically generates a backfeeding risk event in the power supply area and issues an alarm to prevent backfeeding accidents within the area. The alarm levels for backfeeding risk events are divided into three levels: Level 1 (minor backfeeding, with the absolute value of the negative active power less than 10% of the rated power), Level 2 (moderate backfeeding, with the absolute value of the negative active power between 10% and 30% of the rated power), and Level 3 (severe backfeeding, with the absolute value of the negative active power greater than 30% of the rated power). 2. Power line backfeed risk alarm Monitor the active power information of relevant lines. When a line shows a negative active power value, and this is combined with an islanding operation event transmitted from the transformer area, the system will determine that the power supply line is suspected of backfeeding. The logic for determining reverse power transmission from the line is as follows: If the line active power P_line < 0 AND the islanding event flag of the transformer area = TRUE, then a line reverse power transmission risk event is generated. Line backfeeding risk is more serious than regional backfeeding risk because it can lead to cascading faults and affect the safe operation of the upstream power grid. Therefore, when line backfeeding is detected, the system automatically generates a power line backfeeding risk event and issues an alarm, while simultaneously issuing a cascading backfeeding risk warning to remind dispatchers to take immediate action. IV. Specific Implementation of Step S3 – Multi-Regional Layered Coordinated Regulation (I) Division of Regional Regulation Nodes Based on geographical proximity and grid topology, the K-means clustering algorithm is used to divide distributed power sources into several control sub-regions. The input features of the clustering algorithm include: the geographical coordinates (longitude and latitude) of the distributed power source, its substation number, voltage level, and installed capacity. The specific steps of the clustering algorithm are as follows: The first step is to determine the number of clusters, K. Depending on the size of the regulated area and management needs, K ranges from 3 to 10. In this embodiment, a prefecture-level city is used as an example, comprising 5 districts and counties. K is set to 5, meaning each district and county constitutes a regulated sub-region. The second step is to initialize the cluster centers. K cluster centers are randomly selected from all distributed sources. The third step is to assign samples. Calculate the Euclidean distance from each distributed source to each cluster center, and assign it to the cluster containing the nearest cluster center. The fourth step is to update the cluster centers. Calculate the mean coordinates of all distributed sources within each cluster, and use these as the new cluster centers. Fifth, iterate through steps three and four until the cluster centers no longer change or the maximum number of iterations is reached (100 times in this example). After clustering, each control sub-region contains at least one transformer substation and one regional control node. The regional control node is either the highest-performing transformer substation terminal in that region or an independently deployed edge computing device. (ii) Secondary decomposition of control objectives After receiving the control targets from the superior dispatching station, the regional control node performs a secondary decomposition of the control targets based on the available resources in the region. The specific steps for the secondary decomposition of the control objective are as follows: The first step is to obtain real-time operating data of each distributed power source in this area, including current output P_i, installed capacity C_i, adjustable potential U_i (adjustable upwards and downwards) and user priority level L_i. The second step is to calculate the total adjustable potential of this region: U_total = Σ(U_i_down) Where U_i_down represents the downward adjustable potential of the i-th distributed power source. The third step is to determine whether the higher-level control target ΔP exceeds the total adjustable potential of the region. If ΔP > U_total, feedback is sent to the higher-level dispatching master station that it cannot be fully executed, and a request is made to adjust the control target; if ΔP ≤ U_total, the target is decomposed. The fourth step is to allocate control targets proportionally. The allocation weights take into account both installed capacity and user priority. w_i = α × (C_i / ΣC_i) + β × (1 / L_i) Where α and β are weighting coefficients (α=0.6, β=0.4 in this embodiment), and L_i is the user priority level (the larger the value, the lower the priority). The control amount for each terminal after allocation is: ΔP_i = ΔP × w_i Fifth, verify the allocation results. Ensure that the adjustment amount of each terminal does not exceed its adjustable potential, and that ΣΔP_i = ΔP. If the allocation amount of any terminal exceeds its adjustable potential, then the allocation amount of that terminal is set to its maximum adjustable potential, and the difference is redistributed to other terminals. After decomposition, the sub-targets are distributed to each execution terminal. V. Specific Implementation of Step S4 – Adaptive Generation and Execution of Control Strategies (a) Terminal controllability verification For distributed power users involved in control, the system provides a remote call-to-test function, which can determine whether the terminal is controllable by remotely calling the online status, switch status and real-time measurement curve data of some key power users. The specific steps for terminal controllability verification are as follows: The first step is for the system to send a call command to the target terminal. The command includes the call type (online status / on / off status / measurement curve) and the call period. The second step is that after receiving the call command, the terminal sends the corresponding data back to the system. The third step is for the system to determine the controllability of the terminal based on the returned data: If the terminal is online and communication is normal, it is determined to be "online and controllable"; If the terminal is online but its switch status is "locked" or "faulty", it is determined to be "online but uncontrollable". If the terminal is offline or communication times out (no response for more than 5 seconds), it is judged as "offline and uncontrollable". The fourth step is to send the verification results back to the main station and mark the controllable status of each terminal in the control execution plan. For terminals that are "online but uncontrollable", the system automatically removes them from the current control execution plan and recalculates the allocation for other terminals. (ii) Automatic generation of execution plan Develop the optimal execution plan based on the received execution plan, including the selection of control methods and execution methods. Control methods include: Rigid control: Directly controls the grid connection status or output limit of distributed power sources. Suitable for general users other than emergency control scenarios and critical industrial users. The advantages of rigid control are fast response and reliable execution; the disadvantage is its significant impact on users. Flexible control: Smooth regulation is achieved by adjusting the output curve or power factor of distributed power sources. It is suitable for routine control scenarios for residential and general industrial and commercial users. The advantages of flexible control are minimal impact on users and a good user experience; the disadvantage is a relatively slow response time. Integrated control: a combination of rigid and flexible control. Flexible control is used for smooth adjustment in the initial stage of regulation. When flexible control fails to achieve the regulation target, it automatically switches to rigid control to complete the remaining adjustment. Execution methods include: Time-period control: Executes control within a specified time period, suitable for control scenarios that can be planned in advance (such as day-ahead control plans based on the next day's load forecast). Current power down-adjustment control: Adjusts output proportionally based on current output, suitable for real-time control scenarios (such as rapid response when the power grid experiences an emergency power shortage). The priority logic for automatically generated execution plans is as follows: First, the urgency of the control task is determined. Urgent tasks prioritize rigid control and current power reduction control; non-urgent tasks prioritize flexible control and time-based control. Based on this, differentiated configurations are made according to user priority: Level 1 users prioritize flexible control, Level 2 users use comprehensive control, and Level 3 users can use rigid control. (III) Issuance and Execution of Control Orders According to the execution plan, the system sends control instructions to the electricity information collection system to issue single or batch control commands. The process for issuing a single control command is as follows: the system generates a control command for a single terminal → sends it to the electricity information collection system through the data synchronization server → the electricity information collection system issues the command to the terminal device through the existing fee control channel → the terminal device executes the command and returns the execution result → the system records the execution result and updates the control status. The process for issuing batch control commands is as follows: The system generates a list of control commands for multiple terminals → sends them in batches to the electricity information collection system through the data synchronization server → the electricity information collection system issues commands one by one to each terminal device through the existing fee control channel → each terminal device executes the commands and returns the execution results → the system summarizes the execution results of each terminal and updates the control status. After receiving the order, the district and county company dispatch and management personnel will simultaneously coordinate with marketing and control personnel to implement the low-voltage distributed power supply control scheme, ensuring the accurate transmission and effective execution of the control instructions. VI. Specific Implementation of Step S5 – Monitoring and Evaluation of the Control Process (a) Control of abnormal monitoring The system automatically generates alerts for any anomalies that occur during the execution of the control plan. The system highlights and displays the locations of anomalies on the main station, prompting personnel to pay close attention and take appropriate action. Exception types include: Command issuance timeout: No response is received from the terminal more than 5 seconds after the control command is issued; Terminal execution failed: The terminal returns an execution failure status after receiving the instruction; Deviation in control effect: The actual control amount after terminal execution deviates from the target control amount by more than ±10%; Terminal communication interruption: The terminal suddenly went offline during the control process. The anomaly alert is displayed as follows: on the control task monitoring page of the main system interface, the anomaly location is highlighted in red, and the anomaly type, occurrence time, and suggested handling measures are displayed. Simultaneously, the system automatically sends an anomaly SMS notification to the on-duty dispatcher. (II) Demonstration of Implementation Results The effectiveness of the control measures is calculated and displayed based on the operational data during the user's control period. The calculation method for the effectiveness is as follows: Calculate and statistically analyze data related to the implementation effect of regulation across multiple dimensions, including the city as a whole and the district, such as the regulation area, capacity, number of users, and regulation success rate. The formula for calculating the success rate of regulation is: Control success rate = (Number of successfully executed commands / Total number of commands issued) × 100% Among them, "successfully executed instructions" are defined as instructions in which the terminal returns a successful execution status and the actual control amount reaches more than 90% of the target control amount. The main website homepage displays the issuance time, return time, and issue events of all scheduling and peak-shaving commands, and supports data penetration viewing. Penetration viewing means that clicking on a record allows you to expand and view detailed information about that record, including the name of the executing user, the output data curves at various time points, and terminal status change records. Based on a preset template, the system automatically generates execution logs for completed tasks. The execution logs include: task name, adjustment period, number of participating users, total adjustment target, actual adjustment amount, adjustment success rate, and records of abnormal events. The execution logs are displayed on the main website and can be exported to Excel or PDF format. VII. Intelligent Generation of Control Tasks (Optional Step) Based on the grid's peak-shaving needs and combined with the specific control quantity information of subordinate district and county companies, power supply control tasks are intelligently generated to optimize power supply operation. The specific steps for intelligently generating control tasks are as follows: The first step is to obtain the power grid peak-shaving demand. This involves obtaining the current power deficit or peak-shaving demand ΔP_need of the power grid from the dispatch master station. The second step is to obtain the controllable resource information for each district and county. This involves querying the current controllable distributed power supply capacity U_region_j (j=1,2,...,M, where M is the number of districts and counties) for each district and county from the system. The third step is to allocate the control tasks proportionally. The control tasks for each district and county are as follows: ΔP_region_j = ΔP_need × (U_region_j / ΣU_region) The fourth step is to generate control task sheets for each district and county. The task sheet includes: unit name, task source (e.g., "provincial control and peak shaving instruction"), control type (e.g., "rigid control" or "flexible control"), adjustment target (i.e., ΔP_region_j), current output, start time, and end time. The fifth step is to provide an overall overview on the main platform. This will display the control measures for all districts and counties within the city. Clicking on a specific task will allow users to view the executing user's name and related data curves. The control tasks can be filtered and queried by unit, task source, execution status, control start time, and control end time. The filtered results can also be viewed in detail. VIII. Application Effect Data Response time of a single control command ≤2.8 seconds Includes the entire process of instruction generation, transmission, execution, and result return. Success rate of batch control command issuance 87.3% A total of 12,847 commands were issued, of which 11,216 were successful. Accuracy of reverse power transmission risk identification 93.6% Based on statistics of 43 reverse power transmission incidents that actually occurred within 3 months Improved efficiency of task generation It improves efficiency by approximately 65% ​​compared to traditional manual methods. Traditional methods take an average of 2.5 hours per session, while this method takes an average of 0.9 hours per session. Data collection completeness rate 98.2% Of the 96 data points collected daily, more than 94.3 points were actually successfully collected. The reasons for achieving the above results are analyzed as follows: The response time for a single control command is no more than 3 seconds, mainly due to: (1) the system is deployed in Security Zone III, and the network delay between it and the electricity information collection system is controlled within 50ms; (2) the command is transmitted in a lightweight JSON format, with small data volume and fast parsing speed; (3) the fee control channel of the electricity information collection system is a dedicated channel with a priority transmission guarantee mechanism. The success rate of batch control command issuance is no less than 85%, mainly due to: (1) the terminal controllability verification in step S4 pre-excludes uncontrollable terminals, thus avoiding issuance failure; (2) the system has a command retransmission mechanism, which automatically retryes up to 3 times for commands that fail to be issued for the first time; (3) the fee control channel of the electricity information collection system has been optimized over many years of operation and has high transmission reliability. The accuracy rate of reverse power transmission risk identification reached over 92%, mainly due to the dual-judgment mechanism in step S2—combining active power reverse monitoring with islanding operation event identification. Single active power monitoring is prone to false alarms due to metering errors or temporary power fluctuations, while single islanding operation event identification may miss some events due to communication delays. The dual-judgment mechanism significantly reduces both false alarm and missed alarm rates through cross-validation from two independent information sources. Comparative Example 1 To verify the technical effectiveness of the method of the present invention, Comparative Example 1 is set up. Comparative Example 1 adopts a traditional centralized distributed power supply regulation method, that is, it does not include the multi-region hierarchical collaborative regulation architecture of the present invention (the three-layer collaborative architecture and secondary decomposition of regional regulation nodes in step S3). The regulation target is directly issued from the scheduling master station to each execution terminal, without regional division and secondary target decomposition. Other conditions (data acquisition method, operation monitoring method, regulation command issuance channel, etc.) are the same as those in Example 1. Response time of a single control command 2.8 seconds 3.5 seconds Increased by 20.0% Success rate of batch control command issuance 87.3% 71.6% An increase of 15.7 percentage points Achievement rate of regulatory targets 92.1% 78.5% An increase of 13.6 percentage points Adjusting task generation efficiency 0.9 hours / time 2.5 hours / session An increase of 64.0% User complaint rate 1.2% 4.8% Reduced by 75.0% The test results of Comparative Example 1 show that: First, due to the lack of secondary decomposition of regional control nodes, the dispatch master station needs to communicate directly with tens of thousands of terminals, which increases the communication burden and extends the response time of a single command from 2.8 seconds to 3.5 seconds. More importantly, when some terminals experience communication abnormalities, the master station needs to identify and eliminate abnormal terminals one by one, causing the overall success rate of batch control to drop significantly from 87.3% to 71.6%. Second, due to the lack of regional coordination and optimization, the allocation of control targets cannot fully consider the differences in power grid characteristics and user priorities in each region, resulting in over-regulation in some regions and under-regulation in others. The overall control target achievement rate is only 78.5%, which is far lower than the 92.1% of the method of this invention. Third, due to the lack of intelligent generation function for control tasks, the formulation of control tasks still relies on manual analysis and door-to-door notification by dispatchers. On average, each control task takes 2.5 hours to complete, which is far less efficient than the 0.9 hours / time of the method of this invention. Fourth, due to the lack of personalized user strategy selection and flexible control measures, the control of residential users often adopts a rigid switching method, resulting in a user complaint rate as high as 4.8%. However, the method of this invention reduces the complaint rate to 1.2% through flexible control and user priority strategy. The above comparative examples fully demonstrate the significant advantages of the method of the present invention in terms of regulation efficiency, regulation success rate, regulation accuracy, and user experience. Example 2 This embodiment applies the method of the present invention to a rural power distribution network scenario with high penetration of distributed photovoltaic (PV). The characteristics of this scenario are: large installed capacity of distributed PV (accounting for 78% of the total installed capacity in the area), a large number of users (approximately 8,500 households), small installed capacity per household (average 5-20kW), and relatively poor communication conditions (some remote areas only have 2G / 3G signal coverage). In this embodiment, the data acquisition frequency in step S1 was adaptively adjusted. Because the output of distributed photovoltaic power in rural areas is highly predictable (working at sunrise and resting at sunset), the fixed acquisition frequency of 96 points per day was optimized to a dynamic acquisition frequency: an acquisition frequency of once every 5 minutes from 1 hour before sunrise to 1 hour after sunset (approximately 120 points per day), and an acquisition frequency of once every 30 minutes from sunset to the next day's sunrise (approximately 24 points per day). The dynamic acquisition frequency reduces the amount of data collected by approximately 25% compared to the fixed 96-point mode, effectively reducing the data transmission pressure on wireless communication in remote areas, while ensuring data integrity during periods of rapid output change after sunrise and before sunset. In step S3, the regional control node allocation takes into account the dispersed distribution of power distribution areas in rural areas. The cluster number K is set to 8 (there are 8 townships in the region, with one regional control node set up in each township). Each regional control node covers approximately 1,000 distributed power users. The regional control nodes are deployed on edge computing devices in each township power supply station and communicate with the dispatch master station via a 4G wireless network. In the generation of the control strategy in step S4, considering the high proportion of residential users in rural areas (approximately 92%), a flexible control strategy is prioritized. For users with an installed capacity of less than 10kW per household, power factor adjustment is used to achieve flexible control; for users with an installed capacity of 10kW or more per household, a downward shift of the output curve is used to achieve flexible control. Rigid control is only activated when flexible control cannot meet the control target. Data collection completeness rate 96.7% Response time of a single control command 3.8 seconds (affected by wireless communication delay) Flexible control ratio 84.2% Success rate of batch control command issuance 83.1% Accuracy of reverse power transmission risk identification 91.2% User complaint rate 0.8% This embodiment demonstrates the good adaptability of the method of the present invention under different communication conditions and user structures. Even in rural areas with relatively poor communication conditions, high data acquisition integrity and control success rates can still be achieved through dynamic acquisition frequency optimization and the edge computing capabilities of regional control nodes. In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A hierarchical collaborative group dispatch and control method for multi-regional distributed power sources in high-penetration distribution networks, characterized in that, Includes the following steps: S1. Multi-source data acquisition and basic data management: Collect topological relationship data, basic data, operation data and external environment data of distributed power sources from electricity information acquisition system, marketing system and distribution automation system. Based on a unified data model, perform multi-source data fusion and ledger integration to build a full-link topological relationship of "line-transformer-grid connection point-household meter" and form a standardized distributed power source basic data foundation. S2. Distributed power source operation status perception and risk warning: Based on the basic data foundation built in step S1, the remote signaling and telemetry data of distributed power sources are fully aggregated, and the installed capacity, output curve, penetration rate and voltage distribution information of distributed power sources in each region are calculated in real time. The operation status heat map is generated through multi-dimensional data aggregation and analysis, and the reverse monitoring of active power and the identification of islanded operation events are used to determine the reverse power transmission risk level and generate warning signals. S3. Multi-regional hierarchical collaborative control: Based on the characteristics of the regional power grid, user priority, and rigidity-flexibility ratio, the control area is divided into several control sub-regions. Each control sub-region is equipped with a regional control node. After receiving the control target issued by the upper-level dispatch master station, each regional control node performs a secondary decomposition of the control target in combination with the adjustable resource capacity of its own region, and then issues the decomposed sub-targets to each execution terminal, thereby realizing a three-layer collaborative control of "dispatch master station - regional control node - execution terminal". S4. Adaptive generation and execution of control strategy: Based on the control sub-objectives decomposed in step S3, real-time operating data of each execution terminal is obtained and adjustable resource capabilities are calculated. A control execution scheme containing control mode and execution mode is automatically generated. Terminal controllability verification is performed on the distributed power users participating in the control. Control commands that pass the verification are sent to the electricity information collection system. Rigid control or flexible control is executed through the original fee control channel. S5. Monitoring and Evaluation of Control Process: Track the execution status of control commands in real time, monitor and highlight any abnormalities that occur during the execution process, calculate the control execution effect of each region based on the operation data during the control period, automatically generate execution logs and perform multi-dimensional statistical analysis to form a control effect evaluation report.

2. The method according to claim 1, characterized in that, The topology data collection in step S1 includes: obtaining the association data between distributed power supply access points and transformer areas / meter boxes from the electricity information collection system and marketing system, realizing the automatic sorting and graphical display of the five-level topology relationship of "line-transformer-grid connection point-household meter"; the basic data includes the installed capacity, number of installed units, growth trend, grid connection status, power generation user number, power generation address, account establishment date, customer category, transformer area name, voltage level, grid connection point name, terminal number, terminal type, and consumption method data of distributed power supply; the operation data includes the daily output data of 96 points, the daily electricity meter bottom code difference settlement electricity and temperature data; the external environment data includes the weather forecast release date, forecast date, initial weather conditions, ending weather conditions, highest temperature, and lowest temperature; the ledger integration includes integrating and analyzing the equipment information of power switches, inverters, concentrators, lines, distribution transformers, metering points, and protocol converters and establishing corresponding relationships.

3. The method according to claim 1, characterized in that, The operational situation awareness mentioned in step S2 includes: displaying average temperature fluctuations and external environmental data of different regions in the form of heat maps; analyzing the output of distributed power sources at each voltage level using output curves and calculating output extreme values; analyzing the daily, monthly, and annual cumulative power generation of distributed power sources at each voltage level using charts and calculating peak and valley power generation; the backfeed risk determination includes: when a negative active power value is detected in the meter, it is determined as a suspected backfeed in the power source area and a regional backfeed risk event is generated; when a negative active power value is detected in the line and the transformer area reports an islanding operation event, it is determined as a suspected backfeed in the power source line and a line backfeed risk event is generated, and a cross-level backfeed risk warning is issued at the same time.

4. The method according to claim 1, characterized in that, The method for dividing the regional control nodes in step S3 is as follows: based on geographical proximity and grid topology connection, a clustering algorithm is used to divide the distributed power source into several control sub-regions, each control sub-region containing at least one transformer area and one regional control node. The secondary decomposition of the control target includes: the regional control node allocates the superior control target to each execution terminal according to the installed capacity, current output, adjustable potential and user priority of each distributed power source in the region, and ensures that the sum of the decomposed sub-targets is equal to the superior control target.

5. The method according to claim 4, characterized in that, The user priority includes at least three levels: Level 1 users are important industrial users, whose power supply reliability is prioritized and a minimum output protection value is set during regulation; Level 2 users are general industrial and commercial users, and regulation is carried out in a combination of rigid and flexible methods; Level 3 users are residential users, and flexible regulation is prioritized; the rigid control is to directly control the grid connection status or output limit of the distributed power source, and the flexible control is to achieve smooth regulation by adjusting the output curve or power factor of the distributed power source.

6. The method according to claim 1, characterized in that, The terminal controllability verification in step S4 includes: remotely calling the distributed power users participating in the control to obtain the online status, switching status and real-time measurement curve data of the terminal, judging whether the terminal meets the conditions for issuing the control command based on the calling result, and feeding back the verification result to the dispatch master station; the control method includes rigid control, flexible control and integrated control; the execution method includes time period control and current power down-floating control.

7. The method according to claim 1, characterized in that, The control execution scheme described in step S4 also supports personalized strategy selection by users: before control execution, a personalized execution scheme is generated based on the control method, control type, control strategy and control objective selected by the user; the control strategies include rigid control priority strategy, flexible control priority strategy and user level priority strategy; Choose the control method and regulation type for single or multiple households according to the region, and adapt to the on-site regulation needs of different regions.

8. The method according to claim 1, characterized in that, The monitoring of the control process in step S5 includes: automatically generating abnormal alerts for abnormal events generated during the execution of the control plan, and highlighting the abnormal locations on the main station; the evaluation of the execution effect includes: calculating and statistically analyzing the control area, control capacity, number of participating users, and control success rate across multiple dimensions, including the city and substations; the multi-dimensional statistical analysis includes filtering and querying by unit, task source, execution status, control start time, and control end time, with the filtering results supporting a penetrating view of the executing user names and control-related data curves.

9. The method according to claim 1, characterized in that, Step S5 is followed by a step of intelligent generation of control tasks: based on the grid peak-shaving demand, combined with the number of controllable distributed power sources and historical control data of each district and county, the power control tasks of each district and county are automatically generated and displayed on the main station platform; the displayed content includes the unit name, task source, control type, adjustment target, current output, start time, end time, executing user and execution effect. After clicking, you can see through to view the name of the executing user and control-related data curves.

10. The method according to claim 1, characterized in that, The method is applied to a distributed power group dispatch and control system deployed in Safety Zone III, and achieves data interaction with the power distribution automation system and the electricity consumption information collection system through a data synchronization server; Control commands are sent to terminal devices through the existing fee control channel of the electricity information collection system.

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