A power distribution network partition collaborative control method and system based on boundary node attribution

Abstract

The application discloses a power distribution network partition collaborative control method and system based on boundary node attribution, and belongs to the technical field of power distribution network control. The application can effectively identify the boundary node which produces power shock after the power synchronization is damaged according to the power synchronization damage critical period and the historical voltage deviation sequence. The power supply area attribution of each power shock boundary node of the adjacent power supply area is evaluated through the historical area reconstruction times and the historical voltage deviation sequence, so that the dynamic division of the power supply area attribution of the power shock boundary node can be realized. Finally, the electrical data of each power supply area is configured through the power supply area attribution of the power shock boundary node, the partition collaborative control of the power distribution network is realized, and the adaptability and accuracy of the power distribution network partition collaborative control are improved.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network control and processing technology, and in particular relates to a method and system for coordinated control of power distribution network zones based on boundary node affiliation. Background Technology

[0002] Stable control of the distribution network is a crucial link in ensuring the safe and reliable operation of the power system. With the high proportion of distributed photovoltaic and other intermittent renewable energy sources integrated into the network, the power distribution and voltage fluctuation characteristics of the distribution network are becoming increasingly complex. Against this backdrop, zoned coordinated control of the distribution network has become an important solution. Zoned coordinated control of the distribution network refers to dividing the distribution network into multiple relatively independent power supply areas, thereby enabling individual control within each area and coordinated control between these areas, achieving both rapid local response and optimal global control.

[0003] In existing distribution network zone collaborative control schemes, after the distribution network is divided into multiple power supply areas, there are multiple electrical devices at the boundaries between adjacent power supply areas. These electrical devices are usually referred to as boundary nodes in the distribution network. The power supply area affiliation of boundary nodes is mostly determined based on electrical distance and remains unchanged after determination. However, in scenarios with high-penetration photovoltaic (PV) integration, the randomness and anti-peak-shaving characteristics of PV output cause the electrical characteristics of the power supply area to change continuously. The power synchronization of adjacent power supply areas is complex and variable. This causes conflicts between control commands for boundary nodes with fixed power supply area affiliations and actual operating conditions, severely restricting the accuracy of distribution network zone collaborative control. Summary of the Invention

[0004] This invention aims to provide a distribution network zone collaborative control method and system based on boundary node affiliation, in order to solve the technical problem of inaccurate distribution network zone collaborative control in the prior art. By identifying the critical period of power synchronization loss and screening power oscillation boundary nodes, the power supply area affiliation of the power oscillation boundary nodes is divided, thereby improving the accuracy of distribution network zone collaborative control.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a distribution network zone collaborative control method based on boundary node affiliation, comprising: The active power time-series curves and photovoltaic injection power data of each power supply area in the distribution network are obtained, as well as the historical voltage deviation sequence and historical area reconstruction times of each boundary node of the adjacent power supply areas are obtained. The power change direction of adjacent power supply areas is identified based on the active power time-series curve; and the critical period of synchronous power loss of adjacent power supply areas is determined based on the power change direction and the photovoltaic injection power data. Based on the critical period of power synchronization loss and the historical voltage deviation sequence, the boundary nodes of adjacent power supply areas are screened to determine several power oscillation boundary nodes of adjacent power supply areas. The power supply area affiliation of each power oscillation boundary node in adjacent power supply areas is determined based on the number of historical area reconstructions and the historical voltage deviation sequence. The electrical data of each power supply area is configured according to the power supply area affiliation of each power oscillation boundary node to complete the zoned coordinated control of the power distribution network.

[0006] It is understood that this invention acquires active power time-series curves and photovoltaic injected power data for each power supply area in a multi-dimensional distribution network, as well as historical voltage deviation sequences and historical region reconstruction times for each boundary node of adjacent power supply areas. Then, by using the critical period of power synchronization loss in adjacent power supply areas based on the active power time-series curves, it can accurately capture the time characteristics of power synchronization loss between adjacent power supply areas caused by photovoltaic output fluctuations. Subsequently, based on the critical period of power synchronization loss and the historical voltage deviation sequence, it can effectively identify boundary nodes that generate power oscillations after power synchronization loss. Then, by evaluating the power supply area affiliation of each power oscillation boundary node in adjacent power supply areas using the historical region reconstruction times and historical voltage deviation sequences, it can achieve dynamic allocation of the power supply area affiliation of power oscillation boundary nodes, avoiding control command conflicts caused by fixed power supply area affiliation. Finally, by configuring the electrical data of each power supply area based on the power supply area affiliation of the power oscillation boundary nodes, it realizes zoned collaborative control of the distribution network, thereby improving the adaptability and accuracy of zoned collaborative control of the distribution network, enhancing the distribution network's ability to cope with random fluctuations in distributed photovoltaic power, and providing reliable technical support for voltage stability control.

[0007] As a preferred embodiment, the step of identifying the power change direction of adjacent power supply areas based on the active power time-series curve; and determining the critical period of power synchronization loss in adjacent power supply areas based on the power change direction and the photovoltaic injection power data, includes: Perform differential operations on the active power time-series curve to determine the power change sequence for each power supply area; Based on the power change sequence, determine the power change trend identifier for each power supply area at each sampling time; By comparing the power change trend indicators of adjacent power supply areas at each sampling time, the direction of power change of adjacent power supply areas at each sampling time is determined; Based on the power change direction and the photovoltaic injection power data, the critical period of synchronous power loss in adjacent power supply areas is determined.

[0008] The above scheme processes the active power time-series curve through differential operations, transforming continuous active power data into an intuitive sequence of power changes, thus clearly depicting the power change trend of each power supply area at different sampling times. By comparing the power change trend of adjacent areas at each sampling time, it can accurately and dynamically monitor the changes in power synchronization between adjacent power supply areas, thereby obtaining the direction of power change. Finally, by using the direction of power change and the photovoltaic injected power data, it determines the critical period of power synchronization loss in adjacent power supply areas, accurately capturing the time characteristics of power synchronization loss between adjacent power supply areas caused by photovoltaic output fluctuations, thus providing a data basis for subsequent power supply area attribution, and improving the accuracy of distribution network zone collaborative control.

[0009] As a preferred embodiment, determining the critical period of power synchronization loss in adjacent power supply areas based on the power change direction and the photovoltaic injection power data includes: Based on the power change direction of adjacent power supply areas at each sampling time, several power synchronization change times of adjacent power supply areas are determined; Based on the photovoltaic injection power data and preset photovoltaic injection power threshold of each power supply area, the period of photovoltaic injection power exceeding the limit in each power supply area is determined; Based on the photovoltaic injection power exceeding the limit period of each of the power supply areas, several power synchronization change times of adjacent power supply areas are screened to determine several photovoltaic power synchronization change times of adjacent power supply areas. Based on a preset time window and the time of photovoltaic power synchronization change, several photovoltaic power synchronization change periods are determined for adjacent power supply areas; Based on the photovoltaic power synchronization transition period and the active power time series curve, the active power similarity score of adjacent power supply areas in each photovoltaic power synchronization transition period is determined, and several active power synchronization change periods of adjacent power supply areas are determined based on the active power similarity score. Based on the timing of photovoltaic power synchronization changes and the time period of active power synchronization changes in adjacent power supply areas, the critical time period of power synchronization loss in adjacent power supply areas is determined.

[0010] The above scheme can accurately identify several power synchronization change moments in adjacent power supply areas by analyzing the power change direction of adjacent power supply areas at each sampling time. Then, it identifies photovoltaic power injection exceeding the limit period by using photovoltaic power injection data and photovoltaic power injection thresholds. By filtering power synchronization change moments based on these exceeding periods, it can accurately locate the photovoltaic power synchronization change moments caused by large-scale excessive photovoltaic power injection. Finally, it determines the active power similarity score of the power supply area based on the photovoltaic power synchronization change moments and the active power time series curve, thus identifying the critical period of power synchronization loss. This multi-dimensional comprehensive judgment, combining power synchronization change moments, photovoltaic power injection exceeding the limit period, and similarity scores, accurately defines the critical period of power synchronization loss, providing an accurate basis for subsequent screening of power oscillation boundary nodes, thereby improving the accuracy of distribution network zone collaborative control.

[0011] As a preferred embodiment, the step of filtering the boundary nodes of adjacent power supply areas based on the critical period of power synchronization loss and the historical voltage deviation sequence to determine several power oscillation boundary nodes of adjacent power supply areas includes: Based on the critical period of power synchronization loss and the historical voltage deviation sequence, the voltage fluctuation amplitude of each boundary node of the adjacent power supply area during the critical period of power synchronization loss is determined. Based on the voltage fluctuation amplitude, a voltage fluctuation vector is constructed for each boundary node of the adjacent power supply area; Based on the voltage fluctuation vector, the boundary nodes of the adjacent power supply areas are clustered, and combined with the historical voltage deviation sequence, several power fluctuation boundary nodes of the adjacent power supply areas are obtained. The rated voltage regulating equipment operating time of the power distribution network is obtained. Based on the historical voltage deviation sequence and the rated voltage regulating equipment operating time, the power fluctuation boundary nodes are screened to determine several power oscillation boundary nodes in adjacent power supply areas.

[0012] The above scheme calculates the voltage fluctuation amplitude of boundary nodes during each critical period of power synchronization loss using the historical voltage deviation sequence. This accurately and intuitively quantifies the voltage instability of boundary nodes during periods of electrical synchronization mismatch in electrical regions. Next, a voltage fluctuation vector is constructed based on the voltage fluctuation amplitude. By clustering boundary nodes, power fluctuation boundary nodes with significantly high volatility and severe impact from synchronization disruption can be accurately and efficiently identified from a large number of boundary nodes. Finally, the key parameter of the rated voltage regulating equipment operating time of the distribution network is introduced, and a secondary screening is performed using the historical voltage deviation sequence to obtain power oscillation boundary nodes. This further accurately identifies power oscillation boundary nodes that require reassignment of power supply areas, thereby improving the accuracy of subsequent distribution network zone-based coordinated control.

[0013] As a preferred embodiment, the step of obtaining the rated voltage regulating equipment operating time of the distribution network, filtering the power fluctuation boundary nodes based on the historical voltage deviation sequence and the rated voltage regulating equipment operating time, and determining several power oscillation boundary nodes in adjacent power supply areas includes: Based on the historical voltage deviation sequence, determine several positive voltage deviation peak times, several positive voltage deviation peak times, several negative voltage deviation peak times, and several negative voltage deviation peak times for each power fluctuation boundary node; The oscillation period of each power fluctuation boundary node is determined based on the peak time of the positive voltage deviation and the peak time of the negative voltage deviation. Obtain the rated voltage regulating equipment operating time of the power distribution network, and determine the voltage regulation response result of each power fluctuation boundary node based on the oscillation period and the rated voltage regulating equipment operating time; Based on several positive voltage deviation peaks and several negative voltage deviation peaks of each power fluctuation boundary node, the voltage oscillation characteristics of each power fluctuation boundary node are determined. Based on the voltage oscillation characteristics and voltage regulation response results of each power fluctuation boundary node, the power fluctuation boundary nodes are screened to determine several power oscillation boundary nodes in adjacent power supply areas.

[0014] The above scheme extracts several positive voltage deviation peak times, several positive voltage deviation peak times, several negative voltage deviation peak times, and several negative voltage deviation peak times for each power fluctuation boundary node through historical voltage deviation sequences. Then, it determines the oscillation period of each power fluctuation boundary node using the positive and negative voltage deviation peak times, accurately quantifying the speed of voltage fluctuations at the power fluctuation boundary node. Next, by comparing the oscillation period with the rated operating time of the voltage regulating equipment, it can determine whether the voltage fluctuations at the power fluctuation boundary node exceed the dynamic response capability range of the voltage regulating equipment. Then, it determines the voltage oscillation characteristics using the positive and negative voltage deviation peak times, characterizing the voltage oscillations at the power fluctuation boundary node. Finally, it identifies power oscillation boundary nodes with significant volatility that are difficult to control using voltage regulating equipment based on the voltage oscillation characteristics and voltage regulation response results, thereby improving the accuracy of subsequent distribution network zone coordinated control.

[0015] As a preferred embodiment, determining the power supply region affiliation of each power oscillation boundary node of adjacent power supply regions based on the number of historical region reconstructions and the historical voltage deviation sequence includes: Based on the number of historical region reconstructions and the preset threshold for the number of region affiliation changes, several power oscillation boundary nodes of adjacent power supply regions are divided to determine several affiliated swing boundary nodes and several non-affiliated swing boundary nodes of adjacent power supply regions. Based on the power change trend identifier of each power supply area at each sampling time, and combined with several positive voltage deviation peak times and several negative voltage deviation peak times of each of the assigned swing boundary nodes, the power correlation degree of each of the assigned swing boundary nodes in the adjacent power supply areas is determined. The power supply area affiliation of each of the affiliated swing boundary nodes is determined based on the power correlation, while the power supply area affiliation of the non-affiliated swing boundary nodes remains unchanged, so as to determine the power supply area affiliation of each of the power oscillation boundary nodes of the adjacent power supply areas.

[0016] The above scheme identifies several home-swing boundary nodes and several non-home-swing boundary nodes by using the number of historical region reconstructions and the preset threshold for the number of region ownership changes. For non-home-swing boundary nodes, their power supply region ownership remains unchanged, avoiding redundant adjustments and maintaining the relative stability of the system structure. For home-swing boundary nodes, the power correlation is calculated by using the peak time of positive voltage deviation, the peak time of negative voltage deviation, and power change trend indicators. This avoids the inaccuracies in zone-based coordinated control caused by traditional calculation of power supply region ownership based on electrical distance. It can better adapt to the actual electrical characteristics of distribution networks with high-penetration photovoltaic access, thereby improving the accuracy of zone-based coordinated control of the distribution network.

[0017] As a preferred embodiment, configuring the electrical data of each power supply area according to the power supply area affiliation of each power oscillation boundary node to complete the coordinated control of the distribution network includes: Each power oscillation boundary node is taken as an electrical node of the corresponding power supply area. Each power oscillation boundary node is assigned to the corresponding power supply area according to the power supply area affiliation of each power oscillation boundary node, so as to determine several electrical nodes of each power supply area. Obtain the electrical distance between any two electrical nodes within each power supply area; and determine the electrical influence weight between any two electrical nodes within each power supply area based on the electrical distance; Obtain the sensitivity coefficient of any electrical node and any voltage regulating device within each power supply area; determine the sensitivity weight of each electrical node within each power supply area based on the sensitivity coefficient; The electrical influence weight and sensitivity weight are weighted and summed to determine the voltage regulation priority of each electrical node in each power supply area; The rated action delay of each power oscillation boundary node in each power supply area is obtained, and the regional action delay parameter of each power oscillation boundary node in each power supply area is determined based on the rated action delay and the pre-acquired voltage regulation device action delay sequence. The electrical data of each power supply area is configured based on the regional action delay parameters and the voltage regulation priority to complete the zoned coordinated control of the power distribution network.

[0018] The above scheme quantifies the mutual coupling relationship of electrical nodes in the network structure by calculating the electrical distance between electrical nodes within the power supply area and determining the electrical influence weight accordingly. It quantifies the degree to which electrical nodes receive voltage regulation control from the voltage regulating equipment by determining the sensitivity weight of electrical nodes through the sensitivity coefficients of electrical nodes and voltage regulating equipment. By weighted summing of the electrical influence weight and sensitivity weight, the voltage regulation priority takes into account both the network topology and the accessibility of control resources, ensuring that control commands are preferentially applied to electrical nodes that have a significant impact on the overall voltage level of the power supply area and are easily adjustable. Finally, by setting regional action delay parameters for power oscillation boundary nodes, orderly voltage regulation coordination can be formed, improving the accuracy of regional coordinated control of the distribution network.

[0019] As a preferred embodiment, after performing zoned coordinated control on the distribution network, the method further includes: The distribution network is pre-operated, and the pre-operation voltage deviation sequence, the number of pre-operation area reconstructions, and the number of pre-operation voltage regulation command conflicts are obtained for each of the home swing boundary nodes within a preset pre-operation period. The voltage deviation improvement rate of each of the home swing boundary nodes is determined based on the pre-run voltage deviation sequence and the historical voltage deviation sequence of each of the home swing boundary nodes; The voltage regulation command conflict improvement rate of each of the home swing boundary nodes is determined based on the number of pre-run voltage regulation command conflicts and the number of pre-acquired historical line voltage regulation command conflicts. The improvement rate of the number of regional reconstructions for each of the home swing boundary nodes is determined based on the number of pre-running regional reconstructions and the number of historical regional reconstructions for each of the home swing boundary nodes. The voltage deviation improvement rate, voltage regulation command conflict improvement rate, and regional reconfiguration number improvement rate of each of the home swing boundary nodes are weighted and summed to determine the cooperative control improvement result of each of the home swing boundary nodes; Based on the collaborative control improvement results of each of the aforementioned home swing boundary nodes, the evaluation results of the zoned collaborative control of the distribution network are determined.

[0020] The above scheme calculates the voltage deviation improvement rate, voltage regulation command conflict improvement rate, and regional reconfiguration number improvement rate after performing zoned coordinated control on the distribution network. The voltage deviation improvement rate characterizes the voltage stability of the zoned coordinated control, the voltage regulation command conflict improvement rate characterizes the command control stability of the zoned coordinated control, and the regional reconfiguration number improvement rate characterizes the distribution network topology control stability of the zoned coordinated control. A comprehensive and accurate evaluation result of the zoned coordinated control is obtained by weighted summation, which can provide a basis for improvement of subsequent zoned coordinated control.

[0021] Accordingly, this invention provides a distribution network zone collaborative control system based on boundary node affiliation, including: a collaborative control data acquisition module, a power synchronization loss critical time period determination module, a boundary node screening module, a power supply area affiliation determination module, and a distribution network zone collaborative control module; The collaborative control data acquisition module is used to acquire the active power time-series curve and photovoltaic injection power data of each power supply area in the distribution network, as well as the historical voltage deviation sequence and historical area reconstruction number of each boundary node of the adjacent power supply area. The power synchronization loss critical period determination module is used to identify the power change direction of adjacent power supply areas based on the active power time series curve; and to determine the power synchronization loss critical period of adjacent power supply areas based on the power change direction and the photovoltaic injection power data. The boundary node screening module is used to screen the boundary nodes of adjacent power supply areas based on the critical period of power synchronization loss and the historical voltage deviation sequence, and to determine a number of power oscillation boundary nodes of adjacent power supply areas. The power supply area attribution determination module is used to determine the power supply area attribution of each power oscillation boundary node of adjacent power supply areas based on the number of historical area reconstructions and the historical voltage deviation sequence. The power distribution network zone collaborative control module is used to configure the electrical data of each power supply area according to the power supply area affiliation of each power oscillation boundary node, so as to complete the zone collaborative control of the power distribution network.

[0022] As a preferred embodiment, the power synchronization loss critical period determination module includes: a power synchronization loss critical period determination unit; The power synchronization loss critical period determination unit is used to perform differential operation on the active power time series curve to determine the power change sequence of each power supply area; Based on the power change sequence, determine the power change trend identifier for each power supply area at each sampling time; By comparing the power change trend indicators of adjacent power supply areas at each sampling time, the direction of power change of adjacent power supply areas at each sampling time is determined; Based on the power change direction and the photovoltaic injection power data, the critical period of synchronous power loss in adjacent power supply areas is determined.

[0023] It is understood that this invention acquires active power time-series curves and photovoltaic injected power data for each power supply area in a multi-dimensional distribution network, as well as historical voltage deviation sequences and historical region reconstruction times for each boundary node of adjacent power supply areas. Then, by using the critical period of power synchronization loss in adjacent power supply areas based on the active power time-series curves, it can accurately capture the time characteristics of power synchronization loss between adjacent power supply areas caused by photovoltaic output fluctuations. Subsequently, based on the critical period of power synchronization loss and the historical voltage deviation sequence, it can effectively identify boundary nodes that generate power oscillations after power synchronization loss. Then, by evaluating the power supply area affiliation of each power oscillation boundary node in adjacent power supply areas using the historical region reconstruction times and historical voltage deviation sequences, it can achieve dynamic allocation of the power supply area affiliation of power oscillation boundary nodes, avoiding control command conflicts caused by fixed power supply area affiliation. Finally, by configuring the electrical data of each power supply area based on the power supply area affiliation of the power oscillation boundary nodes, it realizes zoned collaborative control of the distribution network, thereby improving the adaptability and accuracy of zoned collaborative control of the distribution network, enhancing the distribution network's ability to cope with random fluctuations in distributed photovoltaic power, and providing reliable technical support for voltage stability control. Attached Figure Description

[0024] Figure 1A flowchart illustrating the steps of a distribution network zone collaborative control method based on boundary node affiliation provided in this embodiment of the invention; Figure 2 This is a schematic diagram of a distribution network zone collaborative control system based on boundary node affiliation, provided as an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1 To address the technical issue of inaccurate alignment of electricity marketing operations in existing technologies, please refer to... Figure 1 , Figure 1 The flowchart of a distribution network zone collaborative control method based on boundary node affiliation provided in this embodiment of the invention includes steps S101 to S105.

[0027] Step S101: Obtain the active power time-series curve and photovoltaic injection power data of each power supply area in the distribution network, and obtain the historical voltage deviation sequence and historical area reconstruction number of each boundary node of the adjacent power supply area.

[0028] Step S102: Identify the power change direction of adjacent power supply areas based on the active power time-series curve; and determine the critical period of power synchronous loss of adjacent power supply areas based on the power change direction and the photovoltaic injection power data.

[0029] Step S103: Based on the critical period of power synchronization loss and the historical voltage deviation sequence, the boundary nodes of the adjacent power supply areas are screened to determine a number of power oscillation boundary nodes of the adjacent power supply areas.

[0030] Step S104: Determine the power supply area affiliation of each power oscillation boundary node of the adjacent power supply areas based on the number of historical area reconstructions and the historical voltage deviation sequence.

[0031] Step S105: Configure the electrical data of each power supply area according to the power supply area affiliation of each power oscillation boundary node to complete the zoned coordinated control of the power distribution network.

[0032] In one optional embodiment, acquiring the active power time-series curves and photovoltaic injected power data of each power supply area in the distribution network, and acquiring the historical voltage deviation sequence and historical area reconstruction number of each boundary node of adjacent power supply areas, includes: collecting three-phase current and voltage data of each power supply area through the monitoring platform of the distribution network, obtaining active power sampling values ​​according to mature three-phase power calculation formulas in the electrical field; then recording the active power sampling values ​​once every 5 minutes or 10 minutes to form the active power time-series curve of the power supply area. Configuring RS485 or Ethernet communication interfaces on the photovoltaic inverters in the power supply area to collect photovoltaic injected power data of the power supply area. The photovoltaic injected power data refers to the photovoltaic injected power amplitude, which reflects the maximum output capacity of photovoltaic power generation in the power supply area. Deploying voltage monitoring sensors at the boundary nodes of adjacent power supply areas to collect the actual voltage values ​​of the boundary nodes, subtracting the actual voltage values ​​from the rated voltage values ​​of the boundary nodes to obtain the voltage deviation values ​​of the boundary nodes, and forming historical voltage deviation sequences according to sampling intervals of 5 minutes or 10 minutes. For example, for a 10kV distribution network, when the actual voltage is 10.3kV, the voltage deviation is 0.3kV; when the actual voltage is 9.7kV, the voltage deviation is -0.3kV. Simultaneously, the number of times the power supply area affiliation of boundary nodes changes each time the power supply area topology changes is obtained through the database within the distribution network, forming the historical area reconstruction count.

[0033] In this embodiment, the step of identifying the power change direction of adjacent power supply areas based on the active power time-series curve, and determining the critical period of power synchronization loss in adjacent power supply areas based on the power change direction and the photovoltaic injection power data, includes: Perform differential operations on the active power time-series curve to determine the power change sequence for each power supply area; Based on the power change sequence, determine the power change trend identifier for each power supply area at each sampling time; By comparing the power change trend indicators of adjacent power supply areas at each sampling time, the direction of power change of adjacent power supply areas at each sampling time is determined; Based on the power change direction and the photovoltaic injection power data, the critical period of synchronous power loss in adjacent power supply areas is determined.

[0034] The above embodiments process the active power time-series curve through differential operations, which can transform continuous active power data into an intuitive sequence of power changes, thereby clearly depicting the power change trend of each power supply area at different sampling times. By comparing the power change trend of adjacent areas at each sampling time, the changes in power synchronization between adjacent power supply areas can be accurately and dynamically monitored, thereby obtaining the direction of power change. Finally, by using the power change direction and the photovoltaic injected power data, the critical period of power synchronization loss in adjacent power supply areas can be determined. This can accurately capture the time characteristics of power synchronization loss between adjacent power supply areas caused by photovoltaic output fluctuations, thereby providing a data basis for subsequent power supply area attribution and improving the accuracy of distribution network zone collaborative control.

[0035] In this embodiment, determining the critical period of power synchronization loss in adjacent power supply areas based on the power change direction and the photovoltaic injection power data includes: Based on the power change direction of adjacent power supply areas at each sampling time, several power synchronization change times of adjacent power supply areas are determined; Based on the photovoltaic injection power data and preset photovoltaic injection power threshold of each power supply area, the period of photovoltaic injection power exceeding the limit in each power supply area is determined; Based on the photovoltaic injection power exceeding the limit period of each of the power supply areas, several power synchronization change times of adjacent power supply areas are screened to determine several photovoltaic power synchronization change times of adjacent power supply areas. Based on a preset time window and the time of photovoltaic power synchronization change, several photovoltaic power synchronization change periods are determined for adjacent power supply areas; Based on the photovoltaic power synchronization transition period and the active power time series curve, the active power similarity score of adjacent power supply areas in each photovoltaic power synchronization transition period is determined, and several active power synchronization change periods of adjacent power supply areas are determined based on the active power similarity score. Based on the timing of photovoltaic power synchronization changes and the time period of active power synchronization changes in adjacent power supply areas, the critical time period of power synchronization loss in adjacent power supply areas is determined.

[0036] The above embodiments can accurately identify several power synchronization change moments of adjacent power supply areas by analyzing the power change direction of adjacent power supply areas at each sampling time. Then, by using photovoltaic power injection data and photovoltaic power injection thresholds to identify photovoltaic power over-limit periods, and by filtering power synchronization change moments based on these over-limit periods, the embodiments can accurately locate photovoltaic power synchronization change moments caused by large-scale excessive photovoltaic power injection. Finally, by determining the active power similarity score of the power supply area based on the photovoltaic power synchronization change moments and the active power time series curve, the embodiments determine the critical period of power synchronization loss. Through multi-dimensional comprehensive judgment of power synchronization change moments, photovoltaic power over-limit periods, and similarity scores, the embodiments accurately define the critical period of power synchronization loss, providing an accurate basis for the subsequent screening of power oscillation boundary nodes, thereby improving the accuracy of distribution network zone coordinated control.

[0037] In an optional embodiment, a differential operation is performed on the active power time-series curve to calculate the power change at each sampling time. The change in power can be expressed by the formula calculate, For the power supply area at the sampling time The active power; For the power supply area at the sampling time The active power; for each sampling time, if the number of power changes greater than 0 in the previous 5 consecutive sampling times exceeds the number of power changes less than 0, the power change trend of the power supply area at that sampling time is marked as positive, otherwise it is negative.

[0038] Next, the power change trend indicators of adjacent power supply areas at each sampling time are compared. If the power change trend indicator of one power supply area is positive and the power change trend indicator of the other power supply area is negative, then the power change direction is set to be opposite; if the power change trend indicator of one power supply area is positive and the power change trend indicator of the other power supply area is positive, then the power change direction is set to be the same; if the power change trend indicator of one power supply area is negative and the power change trend indicator of the other power supply area is negative, then the power change direction is set to be the same.

[0039] Furthermore, when the power change direction of adjacent power supply areas changes from the same to opposite at the sampling time, the sampling time when the direction changes to opposite is recorded as the power synchronization change time, thereby obtaining several power synchronization change times; then, the photovoltaic injection power threshold is obtained (its specific value can be set according to the actual load of the power supply area, and in this embodiment it is set to 30% of the actual load of the power supply area), the sampling time when the photovoltaic injection power data exceeds the photovoltaic injection power threshold is marked, and all sampling times are counted to form the photovoltaic injection power over-limit period.

[0040] It's important to note that the Pearson product-moment correlation coefficient quantifies the degree of linear association between two variables. Its value ranges from 1 to -1: a positive value indicates a positive correlation (one variable increases as the other increases), a negative value indicates a negative correlation (one variable decreases as the other increases), and 0 indicates no linear correlation. This coefficient is calculated as the ratio of the covariance to the product of the standard deviations of the two variables.

[0041] In this embodiment, the moment of power synchronization change is defined as the moment when the photovoltaic (PV) power injection exceeds the limit. This indicates that the power synchronization change is caused by excessive PV power injection, rather than normal load fluctuations. Next, a preset time window of 15 minutes is set. The time window is extended forward and backward from the moment of PV power synchronization change to form PV power synchronization transition periods. These transition periods can be represented as [PV power synchronization change moment - time window, PV power synchronization change moment + time window]. Then, the active power time series curve of the power supply area is obtained for the active power sequence during the PV power synchronization transition periods. The Pearson correlation coefficient method is used to calculate the Pearson correlation coefficient of the active power sequence for each PV power synchronization transition period, which serves as the active power similarity score for adjacent power supply areas during each PV power synchronization transition period. Then, the period during which the photovoltaic power synchronization change occurs when the active power similarity score decreases from above 0.7 to below -0.3, or when the active power similarity score changes from a positive value to a negative value, is defined as the active power synchronization change period. Finally, the intersection of the photovoltaic power synchronization change time and the active power synchronization change period is taken as the critical period for power synchronization loss in the adjacent power supply area.

[0042] In this embodiment, the step of filtering the boundary nodes of adjacent power supply areas based on the critical period of power synchronization loss and the historical voltage deviation sequence to determine several power oscillation boundary nodes of adjacent power supply areas includes: Based on the critical period of power synchronization loss and the historical voltage deviation sequence, the voltage fluctuation amplitude of each boundary node of the adjacent power supply area during the critical period of power synchronization loss is determined. Based on the voltage fluctuation amplitude, a voltage fluctuation vector is constructed for each boundary node of the adjacent power supply area; Based on the voltage fluctuation vector, the boundary nodes of the adjacent power supply areas are clustered, and combined with the historical voltage deviation sequence, several power fluctuation boundary nodes of the adjacent power supply areas are obtained. The rated voltage regulating equipment operating time of the power distribution network is obtained. Based on the historical voltage deviation sequence and the rated voltage regulating equipment operating time, the power fluctuation boundary nodes are screened to determine several power oscillation boundary nodes in adjacent power supply areas.

[0043] The above embodiments calculate the voltage fluctuation amplitude of boundary nodes during each critical period of power synchronization loss using the critical period of power synchronization loss and historical voltage deviation sequences. This allows for accurate and intuitive quantification of the voltage instability of boundary nodes during periods of electrical synchronization mismatch in electrical regions. Next, a voltage fluctuation vector is constructed using the voltage fluctuation amplitude, and by clustering the boundary nodes, power fluctuation boundary nodes with significantly high volatility and severe impact from synchronization disruption can be accurately and efficiently identified from a large number of boundary nodes. Finally, the key parameter of the rated voltage regulating equipment operating time of the distribution network is introduced, and a secondary screening is performed using the historical voltage deviation sequence to obtain power oscillation boundary nodes. This further accurately identifies power oscillation boundary nodes that require re-determination of their power supply area affiliation, thereby improving the accuracy of subsequent distribution network zone-based coordinated control.

[0044] In this embodiment, obtaining the rated voltage regulating equipment operating time of the distribution network, filtering the power fluctuation boundary nodes based on the historical voltage deviation sequence and the rated voltage regulating equipment operating time, and determining several power oscillation boundary nodes in adjacent power supply areas includes: Based on the historical voltage deviation sequence, determine several positive voltage deviation peak times, several positive voltage deviation peak times, several negative voltage deviation peak times, and several negative voltage deviation peak times for each power fluctuation boundary node; The oscillation period of each power fluctuation boundary node is determined based on the peak time of the positive voltage deviation and the peak time of the negative voltage deviation. Obtain the rated voltage regulating equipment operating time of the power distribution network, and determine the voltage regulation response result of each power fluctuation boundary node based on the oscillation period and the rated voltage regulating equipment operating time; Based on several positive voltage deviation peaks and several negative voltage deviation peaks of each power fluctuation boundary node, the voltage oscillation characteristics of each power fluctuation boundary node are determined. Based on the voltage oscillation characteristics and voltage regulation response results of each power fluctuation boundary node, the power fluctuation boundary nodes are screened to determine several power oscillation boundary nodes in adjacent power supply areas.

[0045] The above embodiment extracts several positive voltage deviation peak times, several positive voltage deviation peak times, several negative voltage deviation peak times, and several negative voltage deviation peak times for each power fluctuation boundary node through historical voltage deviation sequences. Then, it determines the oscillation period of each power fluctuation boundary node using the positive and negative voltage deviation peak times, accurately quantifying the speed of voltage fluctuations at the power fluctuation boundary node. Next, by comparing the oscillation period with the rated voltage regulating device's operating time, it can be determined whether the voltage fluctuations at the power fluctuation boundary node exceed the dynamic response capability range of the voltage regulating device. Then, it determines the voltage oscillation characteristics using the positive and negative voltage deviation peak times, characterizing the voltage oscillations at the power fluctuation boundary node. Finally, it identifies power oscillation boundary nodes with significant volatility that are difficult to control using voltage regulating devices based on the voltage oscillation characteristics and voltage regulation response results, thereby improving the accuracy of subsequent distribution network zone coordinated control.

[0046] In one optional embodiment, voltage deviation values ​​at the critical period of power synchronization loss are selected from the historical voltage deviation sequence, and the difference between the maximum and minimum voltage deviation values ​​is used to obtain the voltage fluctuation amplitude. Simultaneously, the standard deviation of the historical voltage deviation sequence is calculated. The standard deviation reflects the dispersion of the voltage deviation values ​​around the mean; a larger standard deviation indicates more severe voltage fluctuations. The voltage fluctuation amplitude and standard deviation are combined to form a two-dimensional voltage fluctuation vector, reflecting both the extreme range of the voltage deviation and the stability characteristics of the fluctuation. For example, if the maximum voltage deviation value is 0.5kV and the minimum voltage deviation value is -0.4kV, the difference yields a voltage fluctuation amplitude of 0.9kV; if the standard deviation of the historical voltage deviation sequence is 0.25kV at this time, then the voltage fluctuation vector is [0.9, 0.25].

[0047] Next, the K-means clustering algorithm is used to cluster the boundary nodes. Specifically, the implementation of the K-means clustering algorithm includes three stages: initialization, iterative allocation, and center update. In the initialization stage, the voltage fluctuation vector with the largest voltage fluctuation amplitude is selected as one cluster center, and the voltage fluctuation vector with the smallest voltage fluctuation amplitude is selected as the other cluster center. In the iterative allocation stage, the Euclidean distance from the voltage fluctuation vector of each boundary node to the two cluster centers is calculated. The Euclidean distance is calculated based on the formula for the straight-line distance between two points in two-dimensional space, i.e., the square root of the sum of the squares of the difference in voltage fluctuation amplitude and the squares of the difference in standard deviation. Based on the principle of closest proximity, the boundary nodes are assigned to the corresponding clusters. In the center update stage, the mean of all voltage fluctuation vectors within each cluster is recalculated as the new cluster center. The iterative allocation and center update process is repeated until the preset maximum number of iterations is reached (50 times in this embodiment). After clustering, two cluster centers are obtained: one for the high-fluctuation group with both large voltage fluctuation amplitude and standard deviation, and the other for the low-fluctuation group with both small voltage fluctuation amplitude and standard deviation. At this point, for each boundary node in the high fluctuation group, starting from the second data point (i.e., the second voltage deviation value) in its corresponding historical voltage deviation sequence, the sign of the current data point is compared with that of the previous data point. If the previous data point is positive and the current data point is negative, or vice versa, it is recorded as a positive-to-negative switch. If, within a 30-minute time window, the boundary nodes in the high fluctuation group experience more than 10 positive-to-negative switches, they are marked as power fluctuation boundary nodes.

[0048] Furthermore, in the historical voltage deviation sequence, the peak value of a positive voltage deviation is defined as a local maximum, where the absolute value of the peak value is greater than the absolute values ​​of the two adjacent data points; the sampling time corresponding to the peak value of the positive voltage deviation is the peak value time of the positive voltage deviation. Similarly, the peak value of a negative voltage deviation is defined as a local minimum, where the absolute value of the peak value is greater than the absolute values ​​of the two adjacent data points; the sampling time corresponding to the peak value of the negative voltage deviation is the peak value time of the negative voltage deviation. Then, the absolute value of the time difference between the first peak value of the positive voltage deviation and the first peak value of the negative voltage deviation is obtained as the oscillation half-cycle, thus the oscillation period is twice the oscillation half-cycle. For example, the first peak value of the positive voltage deviation is at the 5th minute, the first peak value of the negative voltage deviation is at the 8th minute, the oscillation half-cycle is 3 minutes, and the oscillation period is 6 minutes.

[0049] Next, the rated operating time of the voltage regulating equipment in the distribution network is obtained. The rated operating time refers to the total time required for the voltage regulating equipment in the distribution network to complete its voltage regulating action. The rated operating time includes the tap changer switching time of the on-load tap changer and the switching time of the reactive power compensation device. The tap changer switching time of the on-load tap changer includes the time for a single voltage regulating action (usually 3-5 seconds) and a control delay (usually 7-15 seconds), therefore, the tap changer switching time of the on-load tap changer is typically 10-20 seconds. The switching time of the reactive power compensation device is generally 1-2 seconds, but to avoid frequent operation of the reactive power compensation device, a 30-second blocking time is usually set. Therefore, it can be understood that the rated operating time of the voltage regulating equipment in the distribution network is 30 seconds. The specific rated operating time of the voltage regulating equipment in the distribution network can be set according to the actual distribution network.

[0050] When the oscillation period is less than the rated operating time of the voltage regulator, it indicates that the voltage fluctuation rate at the power fluctuation boundary node exceeds the response capability of the voltage regulator. In this case, the voltage regulation response result is set as voltage regulation response exceeding the limit. When the oscillation period is greater than or equal to the rated operating time of the voltage regulator, the voltage regulation response result is set as voltage regulation response not exceeding the limit. Next, the peak values ​​of the positive voltage deviations at the power fluctuation boundary node are summed and averaged to obtain the average peak value of the positive voltage deviation. The absolute values ​​of the peak values ​​of the negative voltage deviations at the power fluctuation boundary node are summed and averaged to obtain the average peak value of the negative voltage deviation. The voltage oscillation characteristic is determined based on the ratio of the average peak value of the positive voltage deviation to the average peak value of the negative voltage deviation. If the voltage oscillation characteristic is close to 1, it indicates that the oscillation exhibits symmetrical characteristics. Symmetrical oscillation usually indicates that the boundary node is simultaneously subjected to control actions of opposite directions and similar in intensity. Finally, the voltage oscillation characteristic threshold is set to 0.9. If the voltage regulation response result is voltage regulation response exceeding the limit and the voltage oscillation characteristic exceeds 0.9, then the power fluctuation boundary node is a power oscillation boundary node, thus obtaining several power oscillation boundary nodes.

[0051] It should be noted that K-means clustering is a classic partition-based unsupervised machine learning algorithm. It iteratively divides the dataset into K mutually exclusive clusters, aiming to minimize the sum of squared distances from each data point to the center of its cluster. First, K points are randomly initialized as the centroids of the clusters. Then, two steps are repeated until convergence: (1) Assignment step: Calculate the distance (usually Euclidean distance) from each data point to each centroid and assign it to the cluster containing the nearest centroid; (2) Update step: Recalculate the centroid of each cluster (i.e., the mean of all points) based on all the data points assigned to each cluster. After multiple iterations, the algorithm stops when the position of the centroid no longer changes significantly. Finally, all data points are divided into K clusters, with high similarity within clusters and low similarity between clusters.

[0052] In this embodiment, determining the power supply region affiliation of each power oscillation boundary node of adjacent power supply regions based on the number of historical region reconstructions and the historical voltage deviation sequence includes: Based on the number of historical region reconstructions and the preset threshold for the number of region affiliation changes, several power oscillation boundary nodes of adjacent power supply regions are divided to determine several affiliated swing boundary nodes and several non-affiliated swing boundary nodes of adjacent power supply regions. Based on the power change trend identifier of each power supply area at each sampling time, and combined with several positive voltage deviation peak times and several negative voltage deviation peak times of each of the assigned swing boundary nodes, the power correlation degree of each of the assigned swing boundary nodes in the adjacent power supply areas is determined. The power supply area affiliation of each of the affiliated swing boundary nodes is determined based on the power correlation, while the power supply area affiliation of the non-affiliated swing boundary nodes remains unchanged, so as to determine the power supply area affiliation of each of the power oscillation boundary nodes of the adjacent power supply areas.

[0053] In one optional embodiment, a threshold of 5 changes in regional affiliation is set, and a time window of 30 days is set for these changes. If the number of historical regional reconstructions exceeds 5 in the past 30 days, the power oscillation boundary node is considered a swaying boundary node; otherwise, it is considered a non-swaying boundary node. Swaying boundary nodes reflect the instability of boundary nodes at the power supply area boundary. This instability typically stems from differences in power characteristics between adjacent power supply areas. Particularly when one side has high photovoltaic penetration while the other is dominated by loads, the electrical influence on the boundary node exhibits bidirectional characteristics, making its affiliation difficult to determine.

[0054] Next, since the peak times of positive and negative voltage deviations, and a single swing boundary node belongs to two power supply regions, this embodiment sets two adjacent power supply regions as the first power supply region and the second power supply region. Therefore, based on the power change trend indicators of each power supply region obtained above, the power change trend indicators of the first and second power supply regions corresponding to the peak times of positive voltage deviations can be directly obtained; similarly, the power change trend indicators of the first and second power supply regions corresponding to the peak times of negative voltage deviations can also be directly obtained. For the peak times of positive voltage deviations, if the power change trend indicator of the power supply region is positive, the matching count between the boundary node and the power supply region is incremented by one. For the peak times of negative voltage deviations, if the power change trend indicator of the power supply region is negative, the matching count between the boundary node and the power supply region is incremented by one. The power correlation degree of the assigned swing boundary node among the adjacent power supply areas is obtained by counting the number of matches. Further, if an assigned swing boundary node is located among N adjacent power supply areas, then the assigned swing boundary node has a power correlation degree with each of the N power supply areas. The power supply area with the highest power correlation degree is selected as the assigned power supply area of ​​the assigned swing boundary node, while the assigned power supply areas of non-assigned swing boundary nodes remain unchanged. For example, assuming the assigned swing boundary node matches power supply area A 15 times (i.e., power correlation degree is 15), matches power supply area B 8 times (i.e., power correlation degree is 8), and matches power supply area C 3 times (i.e., power correlation degree is 3), then power supply area A is the assigned power supply area of ​​the assigned swing boundary node.

[0055] The above embodiments identify several home swing boundary nodes and several non-home swing boundary nodes by using the number of historical region reconstructions and the preset threshold for the number of region ownership changes. For non-home swing boundary nodes, their power supply region ownership remains unchanged, avoiding redundant adjustments and maintaining the relative stability of the system structure. For home swing boundary nodes, the power correlation is calculated by using the peak time of positive voltage deviation, the peak time of negative voltage deviation, and the power change trend indicator. This avoids the inaccuracies in zoned collaborative control caused by traditional calculation of power supply region ownership based on electrical distance. It can better adapt to the actual electrical characteristics of distribution networks with high-penetration photovoltaic access, thereby improving the accuracy of distribution network zoned collaborative control.

[0056] In this embodiment, configuring the electrical data of each power supply area according to the power supply area affiliation of each power oscillation boundary node to complete the coordinated control of the distribution network includes: Each power oscillation boundary node is taken as an electrical node of the corresponding power supply area. Each power oscillation boundary node is assigned to the corresponding power supply area according to the power supply area affiliation of each power oscillation boundary node, so as to determine several electrical nodes of each power supply area. Obtain the electrical distance between any two electrical nodes within each power supply area; and determine the electrical influence weight between any two electrical nodes within each power supply area based on the electrical distance; Obtain the sensitivity coefficient of any electrical node and any voltage regulating device within each power supply area; determine the sensitivity weight of each electrical node within each power supply area based on the sensitivity coefficient; The electrical influence weight and sensitivity weight are weighted and summed to determine the voltage regulation priority of each electrical node in each power supply area; The rated action delay of each power oscillation boundary node in each power supply area is obtained, and the regional action delay parameter of each power oscillation boundary node in each power supply area is determined based on the rated action delay and the pre-acquired voltage regulation device action delay sequence. The electrical data of each power supply area is configured based on the regional action delay parameters and the voltage regulation priority to complete the zoned coordinated control of the power distribution network.

[0057] In one optional embodiment, each power oscillation boundary node is designated as an electrical node corresponding to a power supply area. Based on the power supply area affiliation of each power oscillation boundary node, the electrical node list for the power supply area is updated in the distribution network's operating station, thereby assigning each power oscillation boundary node to its corresponding power supply area. Then, combined with the existing electrical nodes in each power supply area, several electrical nodes for each power supply area are determined. For example, the distribution network's operating station stores a list of electrical nodes for each power supply area. This list contains a control parameter configuration file for each electrical node, including parameters such as the zone identifier, voltage regulation priority, action delay time, and voltage dead zone range. When a power oscillation boundary node is transferred from power supply area A to power supply area B, its status is first marked as "pending transfer" in the electrical node list of power supply area A. Simultaneously, a new record for this node is created in the electrical node list of power supply area B, with its status marked as "pending reception." Then, the corresponding electrical data for the power supply area is configured as described below.

[0058] For any given power supply area, obtain any two electrical nodes (nodes) and nodes Electrical distance between (In this embodiment, the line impedance per unit value between two electrical nodes is set), therefore the electrical influence weight between any two electrical nodes is... Expressed as electrical distance The reciprocal of, that is Meanwhile, to facilitate subsequent calculations, it is necessary to assign weights to the electrical influence. Normalization is performed. Then, any electrical node (node) is obtained. ) to any voltage regulating device (voltage regulating device) Sensitivity coefficient , Indicates the first The voltage of each electrical node; Indicates the first The reactive power output of a voltage regulating device; This represents partial derivative operations. A larger sensitivity coefficient indicates a more direct impact of the voltage regulating equipment on the electrical node, and its priority should be higher. The sensitivity weight of the electrical node is obtained by summing all its sensitivity coefficients and normalizing them to the range [0,1]. Finally, the electrical influence weights and sensitivity weights are weighted and summed to obtain the voltage regulation priority. Voltage regulation priority The specific calculation formula is as follows: ; and These are the weighting coefficients. The specific value can be set according to actual needs; It is the first Load level of each electrical node (set according to actual needs). Voltage regulation priority. A larger value indicates that the electrical node has a higher priority during voltage regulation.

[0059] Obtain the action delay sequence of voltage regulating equipment in the power supply area The system collects the operating delay of each voltage regulating device within the power supply area and forms an operating delay sequence in ascending order; simultaneously, it acquires the rated operating delay of the power oscillation boundary node. The rated operating delay of the voltage regulator and the rated operating delay of the power oscillation boundary node can both be obtained from the equipment manual. Next, the rated operating delays are inserted into the voltage regulator operating delay sequence in ascending order, and the smallest voltage regulator operating delay in the sequence that is greater than the rated operating delay is obtained. Delay the action of the minimum voltage regulating device With a preset protection interval (Set to 5 seconds) Add them together to obtain the regional action delay parameter of the power oscillation boundary node. Furthermore, the area motion delay parameter can be calculated using the following formula: ; A fixed coordination time window (30 seconds). To divide the coordinated time window into segments, the current approach is to ensure that the action of the power oscillation boundary node falls within the idle window of the existing voltage regulation equipment.

[0060] It's important to note that Min-Max Normalization uses the maximum and minimum values ​​of the data and a linear transformation formula to proportionally scale the original data to a new range. Essentially, it calculates the relative position of each data point within the entire data range. The processed data not only eliminates the influence of the original dimensions and orders of magnitude, making different features comparable, but also completely preserves the relative relationships between the values ​​in the original data (the distribution shape remains unchanged). It is one of the most commonly used and simplest normalization techniques in the data preparation stage.

[0061] The above embodiments quantify the mutual coupling relationship of electrical nodes in the network structure by calculating the electrical distance between electrical nodes within the power supply area and determining the electrical influence weight accordingly; by determining the sensitivity weight of electrical nodes through the sensitivity coefficients of electrical nodes and voltage regulating equipment, the degree to which electrical nodes receive voltage regulation control from voltage regulating equipment can be quantified; by weighted summing of electrical influence weight and sensitivity weight, the voltage regulation priority takes into account both network topology and accessibility of control resources, ensuring that control commands can be preferentially applied to electrical nodes that have a significant impact on the overall voltage level of the power supply area and are easy to regulate; finally, by setting regional action delay parameters for power oscillation boundary nodes, orderly voltage regulation coordination can be formed, improving the accuracy of distribution network zone coordinated control.

[0062] In this embodiment, after performing zoned coordinated control on the distribution network, the method further includes: The distribution network is pre-operated, and the pre-operation voltage deviation sequence, the number of pre-operation area reconstructions, and the number of pre-operation voltage regulation command conflicts are obtained for each of the home swing boundary nodes within a preset pre-operation period. The voltage deviation improvement rate of each of the home swing boundary nodes is determined based on the pre-run voltage deviation sequence and the historical voltage deviation sequence of each of the home swing boundary nodes; The voltage regulation command conflict improvement rate of each of the home swing boundary nodes is determined based on the number of pre-run voltage regulation command conflicts and the number of pre-acquired historical line voltage regulation command conflicts. The improvement rate of the number of regional reconstructions for each of the home swing boundary nodes is determined based on the number of pre-running regional reconstructions and the number of historical regional reconstructions for each of the home swing boundary nodes. The voltage deviation improvement rate, voltage regulation command conflict improvement rate, and regional reconfiguration number improvement rate of each of the home swing boundary nodes are weighted and summed to determine the cooperative control improvement result of each of the home swing boundary nodes; Based on the collaborative control improvement results of each of the aforementioned home swing boundary nodes, the evaluation results of the zoned collaborative control of the distribution network are determined.

[0063] In an optional embodiment, after performing zoned coordinated control on the distribution network, the distribution network is pre-run to obtain the pre-run voltage deviation sequence, the number of pre-run area reconstructions, and the number of pre-run voltage regulation command conflicts for each of the belonging swing boundary nodes within a preset pre-run period; The pre-operation period is preset to 7 days. Voltage values ​​at the swing boundary nodes are recorded every 5 minutes, resulting in 2016 data points and forming a pre-operation voltage deviation sequence. The arithmetic mean of the absolute values ​​of the voltage deviations in this sequence is then calculated to obtain the pre-operation average voltage deviation. Similarly, the arithmetic mean of the absolute values ​​of the voltage deviations in the historical voltage deviation sequence is calculated to obtain the historical average voltage deviation. The voltage deviation improvement rate is obtained by subtracting the pre-operation average voltage deviation from the historical average voltage deviation and dividing the result by the historical average voltage deviation. For example, assuming a historical average voltage deviation of 0.35kV and a pre-operation average voltage deviation of 0.21kV, the voltage deviation improvement rate is 40%.

[0064] Within the pre-running period, the number of regional reconstructions for the assigned swing boundary node is counted to form the pre-running regional reconstruction count. Simultaneously, the historical regional reconstruction count for the assigned swing boundary node is retrieved from the database. The difference between the pre-running regional reconstruction count and the historical regional reconstruction count is divided by the historical regional reconstruction count to obtain the regional reconstruction count improvement rate. For example, if the historical regional reconstruction count is 8, and within the pre-running period after the above-described partitioned collaborative control, the pre-running regional reconstruction count is 2, then the regional reconstruction count improvement rate is 75%.

[0065] During the pre-run cycle, the voltage regulation commands received by the home swing boundary node are counted. These commands include command type (boost and buck commands), target voltage value, and transmission time. If boost and buck commands are sent alternately within 10 minutes, this is recorded as one pre-run voltage regulation command conflict. Simultaneously, the historical line voltage regulation command conflict count for this home swing boundary node is retrieved from the database. The difference between the pre-run voltage regulation command conflict count and the historical line voltage regulation command conflict count is divided by the historical line voltage regulation command conflict count to obtain the voltage regulation command conflict improvement rate. For example, if the pre-run voltage regulation command conflict count is 3 and the historical line voltage regulation command conflict count is 10, the voltage regulation command conflict improvement rate is 70%.

[0066] The improvement rate of coordinated control is obtained by summing and averaging the improvement rates of voltage regulation command conflict and regional reconfiguration times. Then, the weight of the voltage deviation improvement rate is set to 0.6, and the weight of the coordinated control improvement rate is set to 0.4. The weighted sum of the coordinated control improvement rate and the voltage deviation improvement rate is then obtained to obtain the overall improvement rate. The overall improvement rate is compared with an improvement rate threshold. If it is greater than the threshold, the coordinated control improvement result is considered improved; otherwise, it is considered decreased. For example, if the voltage deviation improvement rate is 35%, the coordinated control improvement rate is 45%, and the overall improvement rate is 35% × 0.6 + 45% × 0.4 = 39%, and the improvement rate threshold is set to 30%, then the coordinated control improvement result is considered improved.

[0067] The above embodiments calculate the voltage deviation improvement rate, voltage regulation command conflict improvement rate, and regional reconfiguration number improvement rate after performing zoned coordinated control on the distribution network. The voltage deviation improvement rate characterizes the voltage stability of the zoned coordinated control, the voltage regulation command conflict improvement rate characterizes the command control stability of the zoned coordinated control, and the regional reconfiguration number improvement rate characterizes the distribution network topology control stability of the zoned coordinated control. A comprehensive and accurate zoned coordinated control evaluation result is obtained by weighted summation, which can provide a basis for improvement of subsequent zoned coordinated control.

[0068] This embodiment acquires the active power time-series curves and photovoltaic injected power data of each power supply area in the multi-dimensional distribution network, as well as the historical voltage deviation sequence and historical region reconstruction number of each boundary node of adjacent power supply areas. Then, by using the critical period of power synchronization loss in adjacent power supply areas through the active power time-series curves, it can accurately capture the time characteristics of power synchronization loss between adjacent power supply areas caused by photovoltaic output fluctuations. Subsequently, based on the critical period of power synchronization loss and the historical voltage deviation sequence, it can effectively identify the boundary nodes that generate power oscillations after power synchronization loss. Then, by evaluating the power supply area affiliation of each power oscillation boundary node in adjacent power supply areas using the historical region reconstruction number and historical voltage deviation sequence, it can achieve dynamic division of the power supply area affiliation of power oscillation boundary nodes, avoiding control command conflicts caused by fixed power supply area affiliation. Finally, by configuring the electrical data of each power supply area based on the power supply area affiliation of the power oscillation boundary nodes, it realizes the zoned collaborative control of the distribution network, thereby improving the adaptability and accuracy of the zoned collaborative control of the distribution network, enhancing the distribution network's ability to cope with random fluctuations in distributed photovoltaic power, and providing reliable technical support for voltage stability control.

[0069] Example 2 Please refer to Figure 2 , Figure 2A schematic diagram of a distribution network zone collaborative control system based on boundary node affiliation provided in an embodiment of the present invention includes: a collaborative control data acquisition module 201, a power synchronization loss critical time period determination module 202, a boundary node screening module 203, a power supply area affiliation determination module 204, and a distribution network zone collaborative control module 205. The collaborative control data acquisition module 201 is used to acquire the active power time-series curve and photovoltaic injection power data of each power supply area in the distribution network, as well as the historical voltage deviation sequence and historical area reconstruction number of each boundary node of the adjacent power supply area. The power synchronization loss critical period determination module 202 is used to identify the power change direction of adjacent power supply areas based on the active power time series curve; and to determine the power synchronization loss critical period of adjacent power supply areas based on the power change direction and the photovoltaic injection power data. The boundary node screening module 203 is used to screen the boundary nodes of adjacent power supply areas based on the critical period of power synchronization loss and the historical voltage deviation sequence, and to determine a number of power oscillation boundary nodes of adjacent power supply areas. The power supply area attribution determination module 204 is used to determine the power supply area attribution of each power oscillation boundary node of adjacent power supply areas based on the number of historical area reconstructions and the historical voltage deviation sequence. The power distribution network zone collaborative control module 205 is used to configure the electrical data of each power supply area according to the power supply area affiliation of each power oscillation boundary node, so as to complete the zone collaborative control of the power distribution network.

[0070] In this embodiment, the power synchronization loss critical period determination module 202 includes: a power synchronization loss critical period determination unit; The power synchronization loss critical period determination unit is used to perform differential operation on the active power time series curve to determine the power change sequence of each power supply area; Based on the power change sequence, determine the power change trend identifier for each power supply area at each sampling time; By comparing the power change trend indicators of adjacent power supply areas at each sampling time, the direction of power change of adjacent power supply areas at each sampling time is determined; Based on the power change direction and the photovoltaic injection power data, the critical period of synchronous power loss in adjacent power supply areas is determined.

[0071] In this embodiment, the power synchronization loss critical period determination unit includes: a power synchronization loss critical period determination subunit; The power synchronization loss critical period determination subunit is used to determine several power synchronization change times of adjacent power supply areas based on the power change direction of adjacent power supply areas at each sampling time. Based on the photovoltaic injection power data and preset photovoltaic injection power threshold of each power supply area, the period of photovoltaic injection power exceeding the limit in each power supply area is determined; Based on the photovoltaic injection power exceeding the limit period of each of the power supply areas, several power synchronization change times of adjacent power supply areas are screened to determine several photovoltaic power synchronization change times of adjacent power supply areas. Based on a preset time window and the time of photovoltaic power synchronization change, several photovoltaic power synchronization change periods are determined for adjacent power supply areas; Based on the photovoltaic power synchronization transition period and the active power time series curve, the active power similarity score of adjacent power supply areas in each photovoltaic power synchronization transition period is determined, and several active power synchronization change periods of adjacent power supply areas are determined based on the active power similarity score. Based on the timing of photovoltaic power synchronization changes and the time period of active power synchronization changes in adjacent power supply areas, the critical time period of power synchronization loss in adjacent power supply areas is determined.

[0072] In this embodiment, the boundary node filtering module 203 includes: a boundary node filtering unit; The boundary node screening unit is used to determine the voltage fluctuation amplitude of each boundary node of the adjacent power supply area during the critical period of power synchronization loss based on the critical period of power synchronization loss and the historical voltage deviation sequence. Based on the voltage fluctuation amplitude, a voltage fluctuation vector is constructed for each boundary node of the adjacent power supply area; Based on the voltage fluctuation vector, the boundary nodes of the adjacent power supply areas are clustered, and combined with the historical voltage deviation sequence, several power fluctuation boundary nodes of the adjacent power supply areas are obtained. The rated voltage regulating equipment operating time of the power distribution network is obtained. Based on the historical voltage deviation sequence and the rated voltage regulating equipment operating time, the power fluctuation boundary nodes are screened to determine several power oscillation boundary nodes in adjacent power supply areas.

[0073] In this embodiment, the boundary node filtering unit includes: a power oscillation boundary node acquisition subunit; The power oscillation boundary node acquisition subunit is used to determine, based on the historical voltage deviation sequence, several positive voltage deviation peak times, several positive voltage deviation peak times, and several negative voltage deviation peak times for each power fluctuation boundary node. The oscillation period of each power fluctuation boundary node is determined based on the peak time of the positive voltage deviation and the peak time of the negative voltage deviation. Obtain the rated voltage regulating equipment operating time of the power distribution network, and determine the voltage regulation response result of each power fluctuation boundary node based on the oscillation period and the rated voltage regulating equipment operating time; Based on several positive voltage deviation peaks and several negative voltage deviation peaks of each power fluctuation boundary node, the voltage oscillation characteristics of each power fluctuation boundary node are determined. Based on the voltage oscillation characteristics and voltage regulation response results of each power fluctuation boundary node, the power fluctuation boundary nodes are screened to determine several power oscillation boundary nodes in adjacent power supply areas.

[0074] In this embodiment, the power supply area attribution determination module 204 includes: a power supply area attribution determination unit; The power supply area attribution determination unit is used to divide several power oscillation boundary nodes of adjacent power supply areas based on the number of historical area reconstructions and a preset threshold for the number of area attribution changes, and to determine several attribution swing boundary nodes and several non-attribution swing boundary nodes of adjacent power supply areas. Based on the power change trend identifier of each power supply area at each sampling time, and combined with several positive voltage deviation peak times and several negative voltage deviation peak times of each of the assigned swing boundary nodes, the power correlation degree of each of the assigned swing boundary nodes in the adjacent power supply areas is determined. The power supply area affiliation of each of the affiliated swing boundary nodes is determined based on the power correlation, while the power supply area affiliation of the non-affiliated swing boundary nodes remains unchanged, so as to determine the power supply area affiliation of each of the power oscillation boundary nodes of the adjacent power supply areas.

[0075] In this embodiment, the power distribution network zone coordination control module 205 includes: a power distribution network zone coordination control unit; The power distribution network partition coordination control unit is used to classify each power oscillation boundary node as an electrical node corresponding to the power supply area, and to divide each power oscillation boundary node into the corresponding power supply area according to the power supply area affiliation of each power oscillation boundary node, so as to determine several electrical nodes in each power supply area; Obtain the electrical distance between any two electrical nodes within each power supply area; and determine the electrical influence weight between any two electrical nodes within each power supply area based on the electrical distance; Obtain the sensitivity coefficient of any electrical node and any voltage regulating device within each power supply area; determine the sensitivity weight of each electrical node within each power supply area based on the sensitivity coefficient; The electrical influence weight and sensitivity weight are weighted and summed to determine the voltage regulation priority of each electrical node in each power supply area; The rated action delay of each power oscillation boundary node in each power supply area is obtained, and the regional action delay parameter of each power oscillation boundary node in each power supply area is determined based on the rated action delay and the pre-acquired voltage regulation device action delay sequence. The electrical data of each power supply area is configured based on the regional action delay parameters and the voltage regulation priority to complete the zoned coordinated control of the power distribution network.

[0076] In this embodiment, the power distribution network zone coordination control unit includes: a zone coordination control evaluation subunit; The partitioned collaborative control evaluation subunit is used to perform pre-operation of the distribution network and obtain the pre-operation voltage deviation sequence, the number of pre-operation area reconstructions, and the number of pre-operation voltage regulation command conflicts for each of the belonging swing boundary nodes within a preset pre-operation period. The voltage deviation improvement rate of each of the home swing boundary nodes is determined based on the pre-run voltage deviation sequence and the historical voltage deviation sequence of each of the home swing boundary nodes; The voltage regulation command conflict improvement rate of each of the home swing boundary nodes is determined based on the number of pre-run voltage regulation command conflicts and the number of pre-acquired historical line voltage regulation command conflicts. The improvement rate of the number of regional reconstructions for each of the home swing boundary nodes is determined based on the number of pre-running regional reconstructions and the number of historical regional reconstructions for each of the home swing boundary nodes. The voltage deviation improvement rate, voltage regulation command conflict improvement rate, and regional reconfiguration number improvement rate of each of the home swing boundary nodes are weighted and summed to determine the cooperative control improvement result of each of the home swing boundary nodes; Based on the collaborative control improvement results of each of the aforementioned home swing boundary nodes, the evaluation results of the zoned collaborative control of the distribution network are determined.

[0077] This embodiment acquires the active power time-series curves and photovoltaic injected power data of each power supply area in the multi-dimensional distribution network, as well as the historical voltage deviation sequence and historical region reconstruction number of each boundary node of adjacent power supply areas. Then, by using the critical period of power synchronization loss in adjacent power supply areas through the active power time-series curves, it can accurately capture the time characteristics of power synchronization loss between adjacent power supply areas caused by photovoltaic output fluctuations. Subsequently, based on the critical period of power synchronization loss and the historical voltage deviation sequence, it can effectively identify the boundary nodes that generate power oscillations after power synchronization loss. Then, by evaluating the power supply area affiliation of each power oscillation boundary node in adjacent power supply areas using the historical region reconstruction number and historical voltage deviation sequence, it can achieve dynamic division of the power supply area affiliation of power oscillation boundary nodes, avoiding control command conflicts caused by fixed power supply area affiliation. Finally, by configuring the electrical data of each power supply area based on the power supply area affiliation of the power oscillation boundary nodes, it realizes the zoned collaborative control of the distribution network, thereby improving the adaptability and accuracy of the zoned collaborative control of the distribution network, enhancing the distribution network's ability to cope with random fluctuations in distributed photovoltaic power, and providing reliable technical support for voltage stability control.

[0078] In summary, this invention acquires multi-dimensional active power time-series curves and photovoltaic injected power data for each power supply area in the distribution network, as well as historical voltage deviation sequences and historical region reconstruction counts for each boundary node of adjacent power supply areas. Then, by using the critical period of power synchronization loss in adjacent power supply areas based on the active power time-series curves, it can accurately capture the temporal characteristics of power synchronization loss between adjacent power supply areas caused by photovoltaic output fluctuations. Subsequently, based on the critical period of power synchronization loss and the historical voltage deviation sequence, it can effectively identify boundary nodes that generate power oscillations after power synchronization loss. Then, by evaluating the power supply area affiliation of each power oscillation boundary node in adjacent power supply areas using the historical region reconstruction count and historical voltage deviation sequence, it can achieve dynamic allocation of the power supply area affiliation of power oscillation boundary nodes, avoiding control command conflicts caused by fixed power supply area affiliation. Finally, by configuring the electrical data of each power supply area based on the power supply area affiliation of the power oscillation boundary nodes, it realizes zoned collaborative control of the distribution network, thereby improving the adaptability and accuracy of zoned collaborative control of the distribution network, enhancing the distribution network's ability to cope with random fluctuations in distributed photovoltaic power, and providing reliable technical support for voltage stability control.

[0079] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A distribution network zone collaborative control method based on boundary node affiliation, characterized in that, include: The active power time-series curves and photovoltaic injection power data of each power supply area in the distribution network are obtained, as well as the historical voltage deviation sequence and historical area reconstruction times of each boundary node of the adjacent power supply areas are obtained. The power change direction of adjacent power supply areas is identified based on the active power time-series curve; and the critical period of synchronous power loss of adjacent power supply areas is determined based on the power change direction and the photovoltaic injection power data. Based on the critical period of power synchronization loss and the historical voltage deviation sequence, the boundary nodes of adjacent power supply areas are screened to determine several power oscillation boundary nodes of adjacent power supply areas. Based on the historical region reconstruction count and a preset threshold for the number of region affiliation changes, several power oscillation boundary nodes of adjacent power supply regions are divided to determine several affiliated swing boundary nodes and several non-affiliated swing boundary nodes of adjacent power supply regions. Based on the power change trend identifier of each power supply region at each sampling time, combined with several positive voltage deviation peak times and several negative voltage deviation peak times of each affiliated swing boundary node, the power correlation degree of each affiliated swing boundary node in the adjacent power supply regions is determined. If the power change trend identifier of the power supply region is positive at the positive voltage deviation peak time of the affiliated swing boundary node, the matching count of the affiliated swing boundary node with the power supply region is incremented by one; if the power change trend identifier of the power supply region is negative at the negative voltage deviation peak time of the affiliated swing boundary node, the matching count of the affiliated swing boundary node with the power supply region is incremented by one. The power correlation degree of the assigned swing boundary node in the adjacent power supply area is obtained by counting the number of matching. The power supply area affiliation of each of the affiliated swing boundary nodes is determined based on the power correlation, while the power supply area affiliation of the non-affiliated swing boundary nodes remains unchanged, so as to determine the power supply area affiliation of each of the power oscillation boundary nodes of the adjacent power supply areas. The electrical data of each power supply area is configured according to the power supply area affiliation of each power oscillation boundary node to complete the zoned coordinated control of the power distribution network.

2. The distribution network zone collaborative control method based on boundary node affiliation as described in claim 1, characterized in that, The step of identifying the power change direction of adjacent power supply areas based on the active power time-series curve, and determining the critical period of power synchronous loss in adjacent power supply areas based on the power change direction and the photovoltaic injection power data, includes: Perform differential operations on the active power time-series curve to determine the power change sequence for each power supply area; Based on the power change sequence, determine the power change trend identifier for each power supply area at each sampling time; By comparing the power change trend indicators of adjacent power supply areas at each sampling time, the direction of power change of adjacent power supply areas at each sampling time is determined; Based on the power change direction and the photovoltaic injection power data, the critical period of synchronous power loss in adjacent power supply areas is determined.

3. The distribution network zone collaborative control method based on boundary node affiliation as described in claim 2, characterized in that, The determination of the critical period of power synchronous loss in adjacent power supply areas based on the power change direction and the photovoltaic injection power data includes: Based on the power change direction of adjacent power supply areas at each sampling time, several power synchronization change times of adjacent power supply areas are determined; Based on the photovoltaic injection power data and preset photovoltaic injection power threshold of each power supply area, the period of photovoltaic injection power exceeding the limit for each power supply area is determined; Based on the photovoltaic injection power exceeding the limit period of each of the power supply areas, several power synchronization change times of adjacent power supply areas are screened to determine several photovoltaic power synchronization change times of adjacent power supply areas. Based on a preset time window and the time of photovoltaic power synchronization change, several photovoltaic power synchronization change periods are determined for adjacent power supply areas; Based on the photovoltaic power synchronization transition period and the active power time series curve, the active power similarity score of adjacent power supply areas in each photovoltaic power synchronization transition period is determined, and several active power synchronization change periods of adjacent power supply areas are determined based on the active power similarity score. Based on the timing of photovoltaic power synchronization changes and the time period of active power synchronization changes in adjacent power supply areas, the critical time period of power synchronization loss in adjacent power supply areas is determined.

4. The distribution network zone collaborative control method based on boundary node affiliation as described in claim 2, characterized in that, The step of filtering the boundary nodes of adjacent power supply areas based on the critical period of power synchronization loss and historical voltage deviation sequence to determine several power oscillation boundary nodes of adjacent power supply areas includes: Based on the critical period of power synchronization loss and the historical voltage deviation sequence, the voltage fluctuation amplitude of each boundary node of the adjacent power supply area during the critical period of power synchronization loss is determined. Based on the voltage fluctuation amplitude, a voltage fluctuation vector is constructed for each boundary node of the adjacent power supply area; Based on the voltage fluctuation vector, the boundary nodes of the adjacent power supply areas are clustered, and combined with the historical voltage deviation sequence, several power fluctuation boundary nodes of the adjacent power supply areas are obtained. The rated voltage regulating equipment operating time of the power distribution network is obtained. Based on the historical voltage deviation sequence and the rated voltage regulating equipment operating time, the power fluctuation boundary nodes are screened to determine several power oscillation boundary nodes in adjacent power supply areas.

5. The distribution network zone collaborative control method based on boundary node affiliation as described in claim 4, characterized in that, The step involves obtaining the rated voltage regulating equipment operating time of the distribution network, filtering the power fluctuation boundary nodes based on the historical voltage deviation sequence and the rated voltage regulating equipment operating time, and determining several power oscillation boundary nodes in adjacent power supply areas, including: Based on the historical voltage deviation sequence, determine several positive voltage deviation peak times, several positive voltage deviation peak times, several negative voltage deviation peak times, and several negative voltage deviation peak times for each power fluctuation boundary node; The oscillation period of each power fluctuation boundary node is determined based on the peak time of the positive voltage deviation and the peak time of the negative voltage deviation. Obtain the rated voltage regulating equipment operating time of the power distribution network, and determine the voltage regulation response result of each power fluctuation boundary node based on the oscillation period and the rated voltage regulating equipment operating time; Based on several positive voltage deviation peaks and several negative voltage deviation peaks of each power fluctuation boundary node, the voltage oscillation characteristics of each power fluctuation boundary node are determined. Based on the voltage oscillation characteristics and voltage regulation response results of each power fluctuation boundary node, the power fluctuation boundary nodes are screened to determine several power oscillation boundary nodes in adjacent power supply areas.

6. The distribution network zone collaborative control method based on boundary node affiliation as described in claim 1, characterized in that, The configuration of electrical data for each power supply area based on the power supply area affiliation of each power oscillation boundary node, in order to complete the zoned coordinated control of the distribution network, includes: Each power oscillation boundary node is taken as an electrical node of the corresponding power supply area. Each power oscillation boundary node is assigned to the corresponding power supply area according to the power supply area affiliation of each power oscillation boundary node, so as to determine several electrical nodes of each power supply area. Obtain the electrical distance between any two electrical nodes within each power supply area; and determine the electrical influence weight between any two electrical nodes within each power supply area based on the electrical distance; Obtain the sensitivity coefficient of any electrical node and any voltage regulating device within each power supply area; determine the sensitivity weight of each electrical node within each power supply area based on the sensitivity coefficient; The electrical influence weight and sensitivity weight are weighted and summed to determine the voltage regulation priority of each electrical node in each power supply area; The rated action delay of each power oscillation boundary node in each power supply area is obtained, and the regional action delay parameter of each power oscillation boundary node in each power supply area is determined based on the rated action delay and the pre-acquired voltage regulation device action delay sequence. The electrical data of each power supply area is configured based on the regional action delay parameters and the voltage regulation priority to complete the zoned coordinated control of the power distribution network.

7. The distribution network zone collaborative control method based on boundary node affiliation as described in claim 6, characterized in that, The process of performing zoned coordinated control of the power distribution network further includes: The distribution network is pre-operated, and the pre-operation voltage deviation sequence, the number of pre-operation area reconstructions, and the number of pre-operation voltage regulation command conflicts are obtained for each of the home swing boundary nodes within a preset pre-operation period. The voltage deviation improvement rate of each of the home swing boundary nodes is determined based on the pre-run voltage deviation sequence and the historical voltage deviation sequence of each of the home swing boundary nodes; The voltage regulation command conflict improvement rate of each of the home swing boundary nodes is determined based on the number of pre-run voltage regulation command conflicts and the number of pre-acquired historical line voltage regulation command conflicts. The improvement rate of the number of regional reconstructions for each of the home swing boundary nodes is determined based on the number of pre-running regional reconstructions and the number of historical regional reconstructions for each of the home swing boundary nodes. The voltage deviation improvement rate, voltage regulation command conflict improvement rate, and regional reconfiguration number improvement rate of each of the home swing boundary nodes are weighted and summed to determine the cooperative control improvement result of each of the home swing boundary nodes; Based on the collaborative control improvement results of each of the aforementioned home swing boundary nodes, the evaluation results of the zoned collaborative control of the distribution network are determined.

8. A distribution network zone collaborative control system based on boundary node affiliation, characterized in that, include: The system includes a collaborative control data acquisition module, a power synchronization loss critical time period determination module, a boundary node screening module, a power supply area attribution determination module, and a distribution network collaborative control module. The collaborative control data acquisition module is used to acquire the active power time-series curve and photovoltaic injection power data of each power supply area in the distribution network, as well as the historical voltage deviation sequence and historical area reconstruction number of each boundary node of the adjacent power supply area. The power synchronization loss critical period determination module is used to identify the power change direction of adjacent power supply areas based on the active power time series curve; and to determine the power synchronization loss critical period of adjacent power supply areas based on the power change direction and the photovoltaic injection power data. The boundary node screening module is used to screen the boundary nodes of adjacent power supply areas based on the critical period of power synchronization loss and the historical voltage deviation sequence, and to determine a number of power oscillation boundary nodes of adjacent power supply areas. The power supply area attribution determination module is used to determine the power supply area attribution of each power oscillation boundary node of adjacent power supply areas based on the historical area reconstruction count and historical voltage deviation sequence. The power supply area attribution determination module includes a power supply area attribution determination unit. The power supply area attribution determination unit is used to divide several power oscillation boundary nodes of adjacent power supply areas based on the historical area reconstruction count and a preset threshold for the number of area attribution changes, determining several attribution swing boundary nodes and several non-attribution swing boundary nodes of adjacent power supply areas. Based on the power change trend indicator of each power supply area at each sampling time, combined with several positive voltage deviation peak times and several negative voltage deviation peak times of each attribution swing boundary node, the attribution swing boundary node is determined. The power correlation degree of the swing boundary node in the adjacent power supply areas is determined as follows: if the power change trend indicator of the power supply area is positive at the peak of the positive voltage deviation of the swing boundary node, the matching number between the swing boundary node and the power supply area is incremented by one; if the power change trend indicator of the power supply area is negative at the peak of the negative voltage deviation of the swing boundary node, the matching number between the swing boundary node and the power supply area is incremented by one; the power correlation degree of the swing boundary node in the adjacent power supply areas is obtained by counting the matching number; the power supply area affiliation of each swing boundary node is determined based on the power correlation degree, while keeping the power supply area affiliation of the non-swing boundary nodes unchanged, so as to determine the power supply area affiliation of each power oscillation boundary node in the adjacent power supply areas; The power distribution network collaborative control module is used to configure the electrical data of each power supply area according to the power supply area affiliation of each power oscillation boundary node, so as to complete the partitioned collaborative control of the power distribution network.

9. A distribution network zone collaborative control system based on boundary node affiliation as described in claim 8, characterized in that, The power synchronization loss critical period determination module includes: a power synchronization loss critical period determination unit; The power synchronization loss critical period determination unit is used to perform differential operation on the active power time series curve to determine the power change sequence of each power supply area; Based on the power change sequence, determine the power change trend identifier for each power supply area at each sampling time; By comparing the power change trend indicators of adjacent power supply areas at each sampling time, the direction of power change of adjacent power supply areas at each sampling time is determined; Based on the power change direction and the photovoltaic injection power data, the critical period of synchronous power loss in adjacent power supply areas is determined.

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