Microgrid power supply control method and system based on load monitoring

Abstract

The invention relates to the technical field of power supply systems, in particular to a micro-grid power supply control method and system based on load monitoring, and the method comprises the steps: obtaining the load data and voltage data of each power utilization node in a micro-grid; dividing the micro-grid into a plurality of power utilization areas; determining a likelihood index that load reduction belongs to active load reduction; and determining the power supply index of each power utilization node based on the historical synchronous load data, and adjusting the power supply priority in combination with the possibility index to perform local power supply regulation and control of the micro-grid. According to the method, the load in the micro-grid is monitored in real time, dynamic partitioning is implemented based on the load association between the power utilization nodes, and the power supply priority is adjusted in combination with the distinguishing result of active and passive load reduction and the historical load fluctuation characteristics, so that accurate local power supply regulation and control considering the electrical association and the time sequence characteristics is realized; and the reliability and the electric energy quality of micro-grid power supply are improved.

Description

Technical Field

[0001] This invention relates to the field of power supply system technology, and specifically to a microgrid power supply control method and system based on load monitoring. Background Technology

[0002] A microgrid is a small-scale power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, loads, monitoring and protection devices, capable of self-control and self-management. During microgrid operation, load monitoring-based power supply control is a key technology for ensuring stable operation. This involves obtaining the actual electricity demand within the microgrid by sensing real-time dynamic changes in the load, and then dynamically adjusting and optimizing the power supply method accordingly to achieve power balance between distributed power sources and loads. However, existing microgrid power supply control methods typically only perform independent predictive analysis based on the total load of the microgrid, ignoring the correlation between local load fluctuations and power supply fluctuations, making it difficult to accurately determine the power demand of local areas.

[0003] Existing methods have significant shortcomings in practical applications: when local voltage fluctuations occur in a microgrid (such as the starting of large generators, a drop in bus voltage, or a sudden decrease in photovoltaic output), the load will passively decrease due to changes in external power supply conditions; conversely, power-consuming nodes may actively decrease their load due to changes in their own demand. Current technologies lack effective means to distinguish between these two types of load reduction, often confusing passive load changes caused by local voltage fluctuations with active load reductions caused by changes in power demand. This leads to misjudgments of local control needs within the microgrid, hindering precise local power supply control and consequently affecting the microgrid's power supply reliability and power quality. Summary of the Invention

[0004] This invention provides a microgrid power supply control method and system based on load monitoring to solve existing problems.

[0005] The microgrid power supply control method and system based on load monitoring of the present invention adopts the following technical solution: One embodiment of the present invention provides a microgrid power supply control method based on load monitoring. The method includes: acquiring load data and voltage data of each power consumption node in the microgrid; dividing the microgrid into several power consumption areas according to the correlation characteristics of load fluctuations of each power consumption node; determining the probability index of the load reduction being an active load reduction based on the spatial distribution characteristics of load reduction and its correlation with voltage fluctuations; determining the power supply index of each power consumption node based on historical load data of the same period, and adjusting the power supply priority in combination with the probability index to perform local power supply regulation of the microgrid; wherein, the power supply index is used to characterize the temporal stability of the power supply demand of the power consumption node.

[0006] Further, the step of dividing the microgrid into several power consumption areas based on the correlation characteristics of load fluctuations at each power consumption node includes: determining the load decline index of each power consumption node based on the time-series variation characteristics of load data at each power consumption node; wherein the load decline index is used to characterize the trend and magnitude of load decline at the power consumption node within a corresponding time period; determining the electrical correlation index between each power consumption node at a corresponding time based on the similarity of the load decline index among the power consumption nodes; wherein the electrical correlation index is used to determine the degree of similarity of load decline trends among different power consumption nodes; and merging power consumption nodes whose electrical correlation index meets preset conditions into the same power consumption area.

[0007] Furthermore, determining the load decline index of each power consumption node based on the time-series variation characteristics of the load data of each power consumption node includes: arranging the load data of the power consumption node in time sequence and calculating the first-order differential amplitude; determining segmentation points based on the deviation of the first-order differential amplitude from the average value of its monitoring period to divide the load data into several load segments; marking the moments with negative first-order differential values ​​within the load segments as effective load fluctuation moments; and determining the load decline index based on the maximum number of consecutive occurrences of effective load fluctuation moments within the load segments, the duration of the load segments, and the power difference between the start and end times of the load segments.

[0008] Further, determining the electrical correlation index between the power consumption nodes at a corresponding time based on the similarity of the load drop index between the power consumption nodes includes: calculating the difference in the load drop index of each power consumption node at the corresponding time and the spatial distance between the power consumption nodes; determining the electrical correlation index based on the difference in the load drop index and the spatial distance; wherein the electrical correlation index decreases as the difference in the load drop index increases, and the electrical correlation index decreases as the spatial distance increases.

[0009] Furthermore, determining the probability index that the load reduction belongs to active load reduction based on the spatial distribution characteristics of load reduction and its correlation with voltage fluctuations includes: determining the load trend difference index between the power consumption area where the power consumption node is located and other power consumption areas within the microgrid; wherein, the load trend difference index is used to characterize the degree of load trend difference between the power consumption area where the power consumption node is located and other power consumption areas within the microgrid; determining the number of power consumption areas to which the power consumption node belongs in the current monitoring period, and the correlation coefficient between the load change of the power consumption node and the corresponding bus voltage change; and determining the probability index that the load reduction belongs to active load reduction based on the trend difference index, the number of power consumption areas, and the correlation coefficient.

[0010] Further, determining the load trend difference index between the power consumption area where the power consumption node is located and other power consumption areas within the microgrid includes: obtaining the number of power consumption nodes in the power consumption area where the power consumption node is located, and the average load decline index of all power consumption nodes in the power consumption area; obtaining the average electrical correlation index between the power consumption node and power consumption nodes in other power consumption areas within the microgrid; determining the load trend difference index based on the number of power consumption nodes, the average load decline index, and the average electrical correlation index; wherein the load trend difference index decreases as the number of power consumption nodes increases, increases as the average load decline index increases, and decreases as the average electrical correlation index increases.

[0011] Furthermore, determining the power supply index of each power consumption node based on historical load data includes: obtaining the load decline index of the power consumption node at the same time in multiple historical monitoring periods; wherein the load decline index is used to characterize the trend and magnitude of load decline within the corresponding period; determining the power supply index of the power consumption node based on the variance and mean of the load decline index; wherein the power supply index is negatively correlated with the variance and negatively correlated with the mean.

[0012] Furthermore, the step of adjusting the power supply priority in conjunction with the probability index to perform local power supply regulation of the microgrid includes: adjusting the power supply priority of each power consumption node according to the power supply index; determining the power transmission index of each power consumption area based on the adjusted power supply priority of the power consumption nodes in each power consumption area and the probability index; wherein the power transmission index is used to characterize the degree of power transmission demand of the power consumption area; and performing power transmission scheduling between each power consumption area based on the power transmission index to perform the local power supply regulation.

[0013] Furthermore, the step of scheduling power transmission between different power consumption areas based on the power transmission index includes: when the load of the target power consumption area suddenly increases, prioritizing the transmission of power from the power consumption area with the smaller power transmission index to the target power consumption area.

[0014] Another embodiment of the present invention provides a microgrid power supply control system based on load monitoring, including a data monitoring unit, an edge computing node, and a central control unit. The data monitoring unit is communicatively connected to the edge computing node, and the edge computing node is communicatively connected to the central control unit, wherein: Data monitoring units are distributed at the beginning of each bus and at each power consumption node of the microgrid to collect load data of each power consumption node and voltage data of the corresponding bus. An edge computing node is used to receive and process the data collected by the data monitoring unit, and send the processed data to the central control unit. The central control unit is used to acquire load and voltage data of each power consumption node in the microgrid; divide the microgrid into several power consumption areas according to the correlation characteristics of load fluctuations of each power consumption node; determine the probability index of the load reduction being an active load reduction based on the spatial distribution characteristics of load reduction and its correlation with voltage fluctuations; determine the power supply index of each power consumption node based on historical load data of the same period, and adjust the power supply priority in combination with the probability index to perform local power supply regulation of the microgrid; wherein, the power supply index is used to characterize the temporal stability of the power supply demand of the power consumption node.

[0015] The beneficial effects of the technical solution of the present invention are: In this embodiment of the invention, load data and voltage data of each power consumption node in the microgrid are acquired; the microgrid is divided into several power consumption areas according to the correlation characteristics of load fluctuations of each power consumption node; based on the spatial distribution characteristics of load reduction and its correlation with voltage fluctuations, the probability index of load reduction belonging to active load reduction is determined; the power supply index of each power consumption node is determined based on historical load data of the same period, and the power supply priority is adjusted in combination with the probability index to carry out local power supply regulation of the microgrid; wherein, the power supply index is used to characterize the temporal stability of the power supply demand of the power consumption node.

[0016] This invention achieves precise local power supply control considering electrical correlation and temporal characteristics by real-time monitoring of loads within a microgrid and dynamic zoning based on load correlations between power consumption nodes. It also adjusts power supply priorities by combining the distinction between active and passive load reductions with historical load fluctuation characteristics. This improves the reliability and power quality of microgrid power supply. Furthermore, by dynamically zoning the microgrid based on the spatiotemporal response characteristics of load fluctuations, closely correlated power consumption nodes are merged into the same power consumption area. This provides a spatial dimension for identifying the propagation range and impact of local load fluctuations, enhancing the spatial accuracy of power supply control. Additionally, by analyzing the differences in load trends between the area where the power consumption node is located and other areas, as well as the correlation between load changes and voltage fluctuations, active and passive load reductions are distinguished, avoiding misjudging passive load changes caused by voltage disturbances as demand reductions and improving the accuracy of judging local microgrid control needs. Finally, by determining the power supply index based on the statistical characteristics of historical load data from the same period and dynamically adjusting power supply priorities, power supply control can adapt to the changing patterns of power demand in different time periods, achieving optimized allocation of power supply resources in time and space. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic flowchart of a microgrid power supply control method based on load monitoring provided in an embodiment of this application; Figure 2 This is a schematic diagram of the architecture of a microgrid power supply control system based on load monitoring, provided in an embodiment of this application. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a microgrid power supply control method and system based on load monitoring proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] The following description, in conjunction with the accompanying drawings, details the specific scheme of a microgrid power supply control method and system based on load monitoring provided by the present invention.

[0022] like Figure 1 As shown in the figure, this application provides a microgrid power supply control method based on load monitoring, including: Step S110: Obtain load data and voltage data of each power consumption node in the microgrid.

[0023] The aforementioned microgrid refers to a small-scale power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, loads, monitoring and protection devices, etc. It possesses self-control and self-protection capabilities and can operate as an independent grid island or connect to the main grid through a grid connection point. The microgrid achieves power supply and demand matching in a local area through an internal power balancing mechanism. Electricity consumption nodes are the basic units of power consumption in a microgrid, distributed across load buses and load branches, representing specific sets of electrical equipment or loads. Their electrical operating status directly reflects the real-time power demand characteristics and load fluctuations of the microgrid.

[0024] Load data refers to the real-time active power consumed by each power consumption node, which is used to quantitatively characterize the actual power demand intensity and changing trend of the power consumption node. Voltage data refers to the instantaneous voltage value at the beginning of each bus in the microgrid, which is used to characterize the voltage quality and power supply capacity of the power supply node. Together, they constitute the basic data source for evaluating the supply and demand balance and electrical interconnection characteristics of the microgrid.

[0025] The aforementioned load and voltage data can be acquired by various types of sensors deployed within the microgrid. These sensors may include voltage sensors installed at the beginning of each bus, power sensors installed at each power consumption node and load branch, and switch status sensors used to monitor the on / off status of the grid connection point switches. The raw data collected by the sensors, after being timestamped and filtered for noise reduction, is then aggregated to edge computing nodes for storage and analysis via wired transmission.

[0026] Step S120: Based on the correlation characteristics of load fluctuations at each power consumption node, the microgrid is divided into several power consumption areas.

[0027] It is understandable that during actual operation, microgrids exhibit spatial heterogeneity in their internal electrical state due to factors such as distributed power generation output fluctuations, load switching behavior, and topology adjustments. Load fluctuations at different power consumption nodes may be electrically coupled or independent. Step S120, by dividing the microgrid into several power consumption areas based on the correlation characteristics of load fluctuations, can group power consumption nodes with close electrical connections and similar load change trends into a unified control unit. This allows for precise spatial characterization of the propagation range and impact boundary of local load fluctuations, providing a spatial comparison benchmark to distinguish between passive load reductions caused by local voltage disturbances and active load reductions caused by changes in user demand. This enables differentiated perception and precise control of power supply demand in different electrical areas within the microgrid, avoiding the control inaccuracies caused by ignoring the correlation of local power supply fluctuations when performing independent forecasting analysis based solely on total load.

[0028] The load fluctuation of the aforementioned power consumption nodes refers to the dynamic change characteristics of the active power of the node over time during operation. This is manifested as a rise, fall, or smooth transition in power values ​​over time, reflecting changes in electricity demand intensity caused by active switching of electrical equipment, adjustments in operating conditions, or influence from external power supply conditions. The correlation characteristics of load fluctuations at power consumption nodes refer to the degree of interrelationship between the load change trends of different power consumption nodes. This characterizes the synchronicity or similarity of load changes between nodes due to proximity in electrical distance, close topological connections, or the influence of the same electrical disturbance source, reflecting the differences in the strength of electrical coupling relationships between power consumption nodes at different locations within the microgrid.

[0029] The above step S120 can divide the microgrid into several power consumption areas in at least one of the following ways: The first method: dividing power consumption areas based on a collaborative zoning approach that considers electrical topology and load correlation. In this implementation, by analyzing the electrical distance between each power-consuming node in the microgrid topology and combining it with the load fluctuation similarity metric obtained from real-time monitoring, power-consuming nodes that are electrically close and have consistent load change trends can be grouped into the same power-consuming area. This approach takes into account both the tightness of physical connections and the similarity of operating states, and is suitable for microgrids with relatively stable topologies.

[0030] The second method: Divide the power consumption area based on the influence domain zoning method of the disturbance propagation range; In the implementation method, by identifying the propagation characteristics of load fluctuations, the impact range of local disturbances on power nodes at different locations can be analyzed, and power nodes affected by the same disturbance source or with the same response characteristics can be divided into the same power consumption area. This method emphasizes the correlation of electrical events and is suitable for microgrids that need to accurately identify passive load reduction scenarios.

[0031] The third method: Divide electricity consumption areas based on the load decline index and the electrical correlation index; Optionally, step S120 may include: determining the load decline index of each power consumption node based on the time-series variation characteristics of the load data of each power consumption node; wherein the load decline index is used to characterize the trend and magnitude of the load decline of the power consumption node in the corresponding time period; determining the electrical correlation index between each power consumption node at the corresponding time according to the similarity of the load decline index between each power consumption node; wherein the electrical correlation index is used to determine the similarity of the load decline trend between different power consumption nodes; merging power consumption nodes whose electrical correlation index meets the preset conditions into the same power consumption area.

[0032] The time-series variation characteristics of the aforementioned electricity node load data refer to the dynamic law of the evolution of active power of the electricity node over time, manifested as the increasing or decreasing trend, rate of change, and persistence characteristics of power values ​​in a discrete time series. These time-series variation characteristics can characterize the instantaneous fluctuation direction and intensity of load by analyzing the differential changes in power values ​​between adjacent moments. By identifying the degree of deviation of the differential amplitude from the statistical characteristics of the overall monitoring period, the phase boundaries of load fluctuations can be defined. This reflects the duration, cumulative magnitude, and approximation of the load decline trend within a specific time period, providing a structured representation for quantitatively assessing the dynamic load behavior of electricity nodes in different time periods.

[0033] Optionally, the above-mentioned determination of the load decline index of each power consumption node based on the time-series variation characteristics of the load data of each power consumption node includes: arranging the load data of the power consumption node in time sequence and calculating the first-order differential amplitude; determining the segmentation point based on the degree of deviation of the first-order differential amplitude from the average value of its monitoring period to divide the load data into several load segments; marking the time when the first-order differential value is negative within the load segment as the effective load fluctuation time; and determining the load decline index based on the maximum number of consecutive occurrences of the effective load fluctuation time within the load segment, the duration of the load segment, and the power difference between the start and end times of the load segment.

[0034] The aforementioned load decline index is a dimensionless indicator used to quantify the strength and magnitude of the load decline trend at a power consumption node within a specific time period. By comprehensively evaluating the persistence of load reduction and the cumulative power change within that period, it provides a numerical basis for identifying the load fluctuation characteristics of power consumption nodes and for subsequent electrical correlation analysis.

[0035] Taking a single power consumption node as an example (such as the first...) (Each electricity consumption node) is segmented into time sequences based on its load fluctuations, specifically:

[0036] The active power obtained from real-time monitoring is arranged chronologically, with a 24-hour monitoring cycle, and the corresponding first-order differential values ​​are obtained. If the differential amplitude at a certain moment is larger than the average of all first-order differential amplitudes within the same cycle, then that moment is marked as a segment point for that power consumption node, and the time interval between two adjacent segment points is a load segment for that node. The probability index of the next power consumption node being a segmentation point for: ; in, For a moment The corresponding first-order difference magnitude, This represents the average of the first-order difference amplitudes within the monitoring period at the current moment. Based on Min-Max normalization Normalize to (0,1), mark the time when the normalization result is greater than 0.7 as the segment point of the power consumption node in the current cycle, and mark the load segments (so that the fluctuation degree in the obtained individual load segments is more approximate).

[0037] If the first-order difference value of the power consumption node is negative at a certain moment, then the power consumption node has a load decrease, and this value is marked as the effective load fluctuation value.

[0038] Taking a single load segment corresponding to this power consumption node as an example (e.g., the first...) (A load segment), if the active power of a power consumption node decreases more frequently and by a greater magnitude within that segment, the load decrease index of that power consumption node in the current segment is... The larger: ; in, This is the maximum duration for which the effective load fluctuation value appears continuously in this load segment (the larger the value, the more obvious the load reduction trend in the current load segment). The duration of the current load segment; This represents the active power of the power consumption node at the start of the current load segment; This represents the active power of the power consumption node at the end of the current load segment; The larger the value, the greater the load decrease in the current segment. Using the above calculation method, the load decrease index for all load segments at all power consumption nodes can be obtained.

[0039] The above load reduction index pass and The ratio reflects the duration of the load decrease process within a given time period, through... The cumulative power difference reflects the load decrease. and The product of these terms comprehensively characterizes the overall intensity of the load decrease during that period; when When the value is greater than zero, it indicates that there is a net load decrease within the load segment of the power consumption node. The larger the value, the more significant the downward trend and the greater the decrease. Conversely, it indicates that the load change is relatively stable or shows an upward trend.

[0040] It is understood that the Min-Max normalization described above is a mature normalization algorithm in the prior art. For its specific implementation and working principle, please refer to the relevant technologies. The embodiments in this application will not be repeated here.

[0041] Optionally, the above-mentioned determination of the electrical correlation index between each power consumption node at a corresponding time based on the similarity of the load drop index between each power consumption node includes: calculating the difference in the load drop index of each power consumption node at the corresponding time and the spatial distance between each power consumption node; determining the electrical correlation index based on the difference in the load drop index and the spatial distance; wherein, the electrical correlation index decreases as the difference in the load drop index increases, and the electrical correlation index decreases as the spatial distance increases.

[0042] The aforementioned electrical correlation index is a dimensionless indicator that quantifies the similarity of load fluctuation trends and the strength of electrical coupling between different power-consuming nodes in a microgrid. By integrating the numerical differences in load decline trends with spatial topological distance, it reflects the synchronicity of load changes between nodes due to electrical connections or common disturbances, and provides a basis for identifying groups of power-consuming nodes with close electrical connections.

[0043] If a certain power consumption node (e.g.) The current power consumption node is on the same load bus and is relatively close to it. At the current moment, this power consumption node and the current power consumption node... If the load decrease trends are similar, then the two power consumption nodes have a high electrical correlation at the current moment. Therefore, the electrical correlation index of the two power consumption nodes at the current moment is... for: ; in, The nodes after normalization With nodes The load decline index difference at the current moment is the normalized value of the numerical difference in the intensity of the load decline trend between the two electricity consumption nodes in the corresponding time period. The normalized spatial distance between the two power-consuming nodes reflects the degree of electrical connection between them in the microgrid topology; the negative sign in the exponential function ensures that the electrical correlation index decreases monotonically as the difference and distance increase.

[0044] It is understandable that when the load reduction indices of the two electricity consumption nodes are closer (i.e., The smaller the size and the closer the spatial distance (i.e.) When the value is smaller, the electrical correlation index is closer to 1, indicating that the load fluctuation trends of the two nodes are highly similar and may be in the same electrical influence domain or affected by the same disturbance source. Conversely, when the difference or distance increases, the index value decays exponentially and approaches 0, indicating that the load changes of the two nodes are independent or the electrical connection is weak. Therefore, by setting a threshold, nodes whose electrical correlation index meets the preset conditions can be grouped into the same power consumption area.

[0045] In the same load bus Electricity consumption nodes and nodes greater than 0.5 The nodes are merged into a single power consumption area. Since the time segments of each node within this area may differ, the earliest time within the current time period of each node in that area is recorded as the start time of the approximate load area, and the latest time as the end time, thus obtaining the target time period for that node. Power generation units (e.g., photovoltaic power generation) and energy storage units are matched with the power consumption area whose topological distance is closest to them.

[0046] Step S130: Based on the spatial distribution characteristics of load reduction and its correlation with voltage fluctuations, determine the probability index that the load reduction belongs to active load reduction.

[0047] The aforementioned spatial distribution characteristics of load reduction refer to the distribution pattern and impact range of load decline phenomena within a microgrid in terms of geographical topology and electrical connections. Specifically, this manifests as whether the load reduction is concentrated in a few local power consumption nodes or widely distributed across multiple power consumption areas, and the similarity or difference in load change trends among affected areas. When load reduction exhibits spatial isolation, i.e., it is limited to a single or a few power consumption nodes and its trend differs significantly from that of surrounding areas, it indicates that the load change may originate from proactive adjustments in local power demand. Conversely, when load reduction spans multiple power consumption areas and exhibits large-scale synchronicity, it suggests the presence of external factors such as voltage disturbances affecting the entire bus or system.

[0048] The spatial distribution characteristics of load reduction and its correlation with voltage fluctuations together constitute a dual discriminant dimension for distinguishing between active and passive load reduction. The former characterizes the scope of load reduction and inter-regional trend differences from a spatial topology perspective, while the latter characterizes the degree of correlation between load changes and bus voltage fluctuations from an electrical causality perspective. When load reduction exhibits a spatially isolated distribution and low correlation with voltage fluctuations, it indicates that the load reduction is mainly actively caused by changes in the demand of the power consumption nodes themselves. When load reduction exhibits a spatially widespread distribution and high correlation with voltage drops, it indicates that the load reduction is mainly passively caused by changes in external power supply conditions. By comprehensively analyzing the coupling relationship between the two, the true cause type of load reduction can be accurately identified.

[0049] Step S130 above determines the probability index of load reduction being an active load reduction, aiming to accurately characterize the physical nature and generation mechanism of load decline, thereby providing a classification decision basis for differentiated power supply regulation. In actual microgrid operation, active load reduction caused by the demand adjustment of the user nodes themselves and passive load reduction caused by the deterioration of external conditions such as voltage disturbances have distinctly different electrical characteristics and regulation requirements: active reduction reflects the actual change in the user's willingness to consume electricity, requiring no additional power supply compensation and may even release power resources; passive reduction, on the other hand, indicates abnormal power supply quality, requiring priority implementation of voltage support or power compensation to restore power supply capacity. By quantitatively assessing the probability of active load reduction, it is possible to avoid misjudging passive load caused by voltage drops as demand contraction and reducing power supply, or misjudging active power saving by users as power supply failure and making unnecessary power allocation, thereby ensuring that power supply priority adjustment and power transmission strategy can accurately match the real causes of load changes, improving the accuracy of microgrid power supply resource allocation and regulation effectiveness in the spatiotemporal dimensions.

[0050] Optionally, step S130 may include: determining the load trend difference index between the power consumption area where the power consumption node is located and other power consumption areas within the microgrid; wherein, the load trend difference index is used to characterize the degree of load trend difference between the power consumption area where the power consumption node is located and other power consumption areas within the microgrid; determining the number of power consumption areas to which the power consumption node belongs in the current monitoring period, and the correlation coefficient between the load change of the power consumption node and the corresponding bus voltage change; and determining the probability index that the load reduction belongs to the active load reduction based on the trend difference index, the number of power consumption areas, and the correlation coefficient.

[0051] The aforementioned load trend difference index is an indicator that quantifies the degree of deviation between the load change trend of the electricity consumption area where the electricity consumption node is located and other electricity consumption areas in the microgrid. By comparing the average load decline index of the target area and the external area at the same time and the electrical correlation strength between the areas, it reflects the spatial isolation of the load fluctuation of the node. When the index value is large, it indicates that the load trend of the target area is significantly different from that of the surrounding area, and the load reduction shows localized characteristics, which is consistent with the spatial distribution law of active load reduction.

[0052] The number of power consumption areas to which the aforementioned power consumption node belongs during the current monitoring period refers to the total frequency at which the node is assigned to different power consumption areas at different times due to dynamic changes in load fluctuation characteristics, characterizing the spatiotemporal stability of the node's load behavior. When this number is significantly higher than the average level of the entire network, it indicates that the boundary of the area to which the node belongs changes frequently, and the load fluctuation has strong randomness and instability, which is consistent with the characteristics of active load reduction caused by users' random switching behavior. The correlation coefficient between the load change of the power consumption node and the corresponding bus voltage change is usually quantified using the Pearson correlation coefficient, which characterizes the degree of linear correlation between the active power time series and the bus voltage time series. When the value of this coefficient is low, it indicates that the load change and voltage fluctuation are independent of each other, and the load reduction is not caused by external power supply condition deterioration such as voltage drop, further supporting the judgment of active load reduction. It is understood that the Pearson correlation coefficient is prior art, and its calculation method and working principle can be found in the prior art, which will not be repeated in the embodiments of this application.

[0053] The aforementioned probability index of load reduction belonging to active load reduction is constructed by integrating the characteristic parameters of the three dimensions mentioned above to build a comprehensive discrimination model, organically combining the degree of spatial trend difference, regional attribution stability, and voltage coupling. Specifically, the load trend difference index and the number of electricity-consuming areas characterize the isolation and randomness of load reduction from the dimensions of spatial distribution and temporal evolution, while the correlation coefficient excludes the influence of external disturbances from the dimension of electrical and physical causality. The three work together to form a quantitative assessment of the probability of active load reduction, providing a classification basis for subsequent dynamic adjustment of power supply priority. When calculating the probability index of load reduction belonging to active load reduction, a weighted fusion method can be used to assign differentiated weight coefficients to different characteristic parameters to adapt to the discrimination needs of microgrids with different topologies; alternatively, a classification model can be trained based on historical operating data, and the discrimination threshold of each parameter can be automatically optimized through machine learning algorithms; or, in specific scenarios, the correlation coefficient can be used alone as the dominant discrimination factor. When the voltage correlation is lower than the preset threshold and accompanied by regional voltage drop, it is directly judged as passive load reduction, and vice versa, thus simplifying the calculation complexity and improving real-time response capability.

[0054] Optionally, determining the load trend difference index between the power consumption area where the power consumption node is located and other power consumption areas within the microgrid includes: obtaining the number of power consumption nodes in the power consumption area where the power consumption node is located, and the average load decline index of all power consumption nodes in the power consumption area; obtaining the average electrical correlation index between the power consumption node and power consumption nodes in other power consumption areas within the microgrid; determining the load trend difference index based on the number of power consumption nodes, the average load decline index, and the average electrical correlation index; wherein the load trend difference index decreases as the number of power consumption nodes increases, increases as the average load decline index increases, and decreases as the average electrical correlation index increases.

[0055] The load segment at a certain time (e.g., time t) Taking a single power consumption node as an example (e.g., node) Since active load fluctuations are determined by the electricity demand of the individual power-consuming nodes and have a certain degree of randomness, their respective power-consuming areas may be unstable, and the load of power-consuming nodes within the area will decrease smoothly (while passive load fluctuations are usually caused by the passive coupling of each node, and their corresponding power-consuming range is relatively fixed), and their impact range is usually small (passive load fluctuations usually affect the load fluctuations of the entire bus or even a larger area), and the correlation between load changes and voltage fluctuations is low, then the possibility that the load reduction of the power-consuming node at the current moment is an active load reduction caused by changes in the node's electricity demand is high. Significantly large. The load trend difference index between the electricity consumption area of ​​a single electricity node and the rest of the microgrid. for: ; in, For nodes At any moment The number of power consumption nodes within the power consumption area; This refers to the load segment of all power-consuming nodes within the power-consuming area of ​​this point at time t during the target time period. The mean of (the larger the value, the greater the decrease); The electrical correlation index between the current node and other nodes outside its region. The average value. If the electricity consumption node The fewer the number of electrical nodes in an electrical consumption area at a given moment (e.g., moment t), the less similar the load trend changes of each electrical node in that area are to the changes in other electrical consumption areas at the same moment (i.e., the smaller the electrical correlation index), and the larger the load drop, the greater the difference in load trend from other areas. The larger.

[0056] It is understandable that the greater the decrease in load within the electricity consumption area (i.e., The larger the area, the fewer the number of nodes in the region (i.e., The smaller the size and the weaker the electrical connection with the external area (i.e., When the value is smaller, the load trend difference index is higher. The larger the value, the more significant the difference between the load change in this power consumption area and the trend in other areas within the microgrid, exhibiting obvious spatial isolation characteristics. This indicates that the load reduction is more likely to be due to proactive adjustments in power demand within the area. Conversely, when the area is large or closely connected to the outside world, the index value approaches zero, indicating that the load change has broad spatial synchronicity and is more likely to be passively caused by external factors such as voltage disturbances.

[0057] If the electricity consumption area of ​​this node changes frequently within the current cycle, and the voltage changes and its own load changes are less similar during the current period, then its corresponding... The larger: ; in, For nodes The number of electricity consumption areas corresponding to the current monitoring period; This is the average number of electricity consumption areas corresponding to all electricity consumption nodes in the microgrid during the current monitoring period; The Pearson correlation coefficient is the measured active power time series and the corresponding bus voltage time series of the node in the current load segment. For time t node Load trend difference index between the area and other electricity consumption areas The larger the average value, the more it conforms to the characteristics of proactive changes in the electricity demand of the current electricity consumption area.

[0058] It is understandable that the more frequently the electricity consumption area changes (i.e., the more frequent the change), the more likely the electricity consumption node will be to become more active. The larger the value, the lower the correlation with bus voltage fluctuations (i.e., the larger the value). The smaller the value, and the more significant the difference in load trends compared to the surrounding areas (i.e., The larger the probability index, the higher the calculated probability index value, indicating that the current load reduction of the node is more likely to be caused by its own active adjustment of electricity demand; conversely, when the node area is stable, the load change and voltage drop are highly synchronized and consistent with the trend of the surrounding area, the probability index approaches zero, indicating that the load reduction is more likely to be caused by passive factors such as external voltage disturbances, thus realizing the quantitative distinction of the physical causes of load reduction.

[0059] Finally, based on Min-Max normalization, Normalize to (0,1), and denote the normalization result as .

[0060] Step S140: Determine the power supply index of each power consumption node based on historical load data of the same period, and adjust the power supply priority in combination with the probability index to carry out local power supply regulation of the microgrid; wherein, the power supply index is used to characterize the temporal stability of the power supply demand of the power consumption node.

[0061] The aforementioned historical load data refers to load data collected from time periods corresponding to the current moment, extracted from multiple historical monitoring cycles. It typically covers the load decline index sequence at the same time each day within a span of nearly one week or longer. By statistically analyzing these data with time correspondence, we can characterize the regularity of electricity consumption behavior and the stability of demand at specific times, providing a data foundation for identifying the time-series patterns of power supply demand.

[0062] The aforementioned power supply index is a dimensionless parameter calculated based on the statistical characteristics of historical load data for the same period. It is used to quantify the temporal stability of the power supply demand of a power consumption node, that is, whether the node exhibits regular, uniform, and low load decay characteristics at the same historical time. The larger the index value, the smaller the load fluctuation and the more controllable the decline of the power consumption node at the same historical time. The stronger the regularity and the higher the stability of its current power supply demand, the stronger the rigidity of power supply maintenance or the more certain load adjustment margin.

[0063] In one optional implementation, when adjusting the power supply priority, the preset initial static priority can be dynamically corrected based on the power supply index. The initial static priority can be preset to different levels according to the type of power consumption node. When the power consumption node is currently in a state of sudden load increase, the larger the power supply index, the greater the priority increase, so as to ensure the power load with high stability requirements. When the load is in a state of sudden load decrease, the larger the power supply index, the greater the priority decrease, so as to allow the implementation of regular power saving behavior. At the same time, the probability index is combined to distinguish between active and passive load reduction types. For areas determined to be active load reduction, the power supply weight is appropriately reduced to release power resources. For areas determined to be passive load reduction, the power supply priority is maintained or increased to implement power supply compensation, thereby realizing differentiated and time-varying power supply resource allocation.

[0064] Local power supply regulation of microgrids is a refined control process based on adjusted power supply priorities. It uses the power transmission index of each power consumption area to characterize the urgency of its power transmission demand and schedules power transmission between power consumption areas accordingly. When the target area experiences a sudden load increase and has a high priority, power is preferentially transmitted from the power consumption area with the lower power transmission index to that area. At the same time, the state of charge constraints and power limitations of energy storage devices are taken into account to achieve optimized redistribution of power resources in the spatiotemporal dimension, ensuring the reliability of power supply to critical loads and the overall power balance of the system.

[0065] Optionally, step S140 above determines the power supply index of each power consumption node based on historical load data of the same period, including: obtaining the load decline index of the power consumption node at the same time in multiple historical monitoring periods; wherein, the load decline index is used to characterize the trend and magnitude of load decline in the corresponding period; and determining the power supply index of the power consumption node according to the variance and mean of the load decline index; wherein, the power supply index is negatively correlated with the variance and negatively correlated with the mean.

[0066] Taking a single electricity consumption node at a certain moment as an example (e.g., time...) The following power consumption nodes ), analyze its priority adjustment index: if the power consumption node has a similar, uniform and high load (low load attenuation) at an approximate time in the recent historical monitoring period to the current time, then its power supply demand at the current time is relatively stable, and its power supply index The larger (using monitoring data from the past week as historical data): ; in, Indicates the first The power supply index of each power consumption node is used to quantitatively characterize the temporal stability of the power supply demand of that node; This refers to the corresponding point in the historical data at the same time across all monitoring periods. The variance of represents the degree of dispersion of load fluctuations during the same historical period; This refers to the corresponding point in the historical data at the same time across all monitoring periods. The average value represents the rate of decline in the average load for the same period in history.

[0067] It is understandable that the smaller the variance, the smaller the fluctuation in the load decline of the electricity consumption node at the same historical time, and the stronger the regularity of electricity consumption behavior; the smaller the mean, the smaller the average load decline in the same historical period, and the higher and more stable the electricity demand; when the product of variance and mean is smaller, the power supply index obtained after negative exponential transformation is larger, indicating that the power supply demand of the electricity consumption node at the current time has a high degree of temporal stability and predictability, and its power supply priority adjustment should reflect stronger maintenance rigidity; conversely, when the variance or mean is large, the power supply index approaches zero, indicating that the electricity demand fluctuates drastically or has low regularity, and the power supply priority can be adjusted flexibly accordingly.

[0068] In one alternative implementation, the selection of the historical monitoring period can be adjusted according to the operating characteristics of the microgrid, such as using monitoring data from the past three days, the past month, or seasonal periods; the calculation of statistical moments can also use standard deviation instead of variance, or assign higher weight to recent data through weighted averaging to highlight the impact of recent electricity consumption patterns; in addition, the calculation of the power supply index can introduce a correction coefficient, and set differentiated sensitivity parameters for different types of electricity consumption nodes to adapt to differentiated power supply reliability requirements.

[0069] Optionally, step S140 above combines the probability index to adjust the power supply priority and perform local power supply regulation of the microgrid, including: adjusting the power supply priority of each power consumption node according to its power supply index; determining the power transmission index of each power consumption area based on the adjusted power supply priority of the power consumption nodes and the probability index; wherein, the power transmission index is used to characterize the degree of power transmission demand of the power consumption area; and performing power transmission scheduling between the power consumption areas based on the power transmission index to perform local power supply regulation.

[0070] The aforementioned power transmission index is a comprehensive indicator that quantitatively represents the urgency of power transmission demand in a power-consuming area. Based on the obtained power supply index, the power supply priority of each power-consuming node is adjusted. The initial priority of each power-consuming node is the static priority of the existing method; for example, the priority of a hospital power-consuming node is 3, the industrial priority is 2, and the residential area priority is 1, etc. Current node power supply priority. for: ; in, This refers to the power supply priority of this node before the adjustment.

[0071] The load segment where the current node is located If the value is negative, the priority is adjusted according to the above formula; otherwise, it is... .

[0072] Based on the priority obtained by each node, power supply is regulated to its respective power consumption area. If the load in a certain area suddenly increases at the current moment, then each node... When the mean is negative, the following analysis is performed: the greater the load surge at each node within the region and the higher its priority, and the lower the current power supply capacity within the region, the greater the current demand for power transmission in that region, and the corresponding power transmission index... Larger: ; in, For the first Electricity transmission index for each electricity consumption area; For nodes All power consumption nodes in the area The mean; For nodes The average power supply priority of all power-consuming nodes in the area; This refers to the current power supply capacity within the region (i.e., the difference between the region's power generation capacity during the corresponding target time period, the dispatchable power of energy storage devices, and the current total load power of the region). It can be understood that the greater the sudden increase in regional load, the higher the power supply priority, and the more insufficient the power supply capacity, the more urgent the region's demand for power input, the higher the power transmission index, and the more preferentially it will obtain power supplementation from other regions.

[0073] If the load in a certain area suddenly drops at the current moment, i.e. The mean is positive, and the priority of each node in the region is relatively low, the degree of reduction in active load is small, the power supply capacity is better, and the current demand for power transmission in the region is smaller, corresponding to the power transmission index. Smaller: ; in, This represents the maximum value of the probability index of active load reduction for all nodes in the region where the current node is located. This represents the average load decline index of all electricity-consuming nodes within the region. It can be understood that when the power supply priority within a region is low, the likelihood of active load reduction is small, power supply capacity is ample, and the load decline is significant, the greater the margin for power output from that region, the smaller the power transmission index, and the more preferentially it is as a power output to other regions.

[0074] Based on Min-Max normalization Normalize to (0,1), and denote the normalization result as .

[0075] In one alternative implementation, the calculation of the power transmission index can incorporate weighting coefficients to differentiate the weighting of load surges, power supply priorities, and power supply capacity; it can also be modified by combining real-time electricity price signals, line transmission capacity limitations, or network loss factors; at the control execution level, multi-objective optimization algorithms can be used to balance power supply fairness and economy, or model predictive control methods can be used to predict the power supply and demand status in future periods and adjust the power transmission strategy in advance to improve the economy and robustness of microgrid operation.

[0076] Optionally, the above-mentioned power transmission scheduling between various power consumption areas based on the power transmission index includes: when the load of the target power consumption area suddenly increases, priority is given to transmitting power from the power consumption area with the smaller power transmission index to the target power consumption area.

[0077] The power transmission index, as a quantitative indicator of the degree of power transmission demand in each power-consuming area, directly reflects whether the area is currently in a state of power shortage or has the margin to output power to the outside. When a target power-consuming area experiences a sudden increase in load, power is preferentially transmitted from the power-consuming area with a lower power transmission index. This is because the area with a lower index indicates that its current demand for maintaining its own power supply is less urgent, or it is in a state of sudden load drop and is judged to be actively reducing load (i.e., the user side actively reduces demand). Therefore, it has the conditions to release power resources to the outside. By establishing a dispatch mechanism based on the urgency of demand, the directed flow of power from surplus nodes to shortage nodes within the microgrid can be realized, optimizing local power balance and global energy allocation efficiency.

[0078] In addition, the above scheduling process must strictly follow the physical boundary conditions such as the state of charge constraints of energy storage devices (e.g., when the remaining energy storage capacity is lower than a preset threshold, such as 20%, discharge is prohibited), line transmission capacity limits, and converter power output limits, to ensure the feasibility and safety of power transmission.

[0079] Optionally, the above can be further modified by weighting the power transmission index based on the transmission loss caused by line impedance, the real-time marginal power supply cost of each region, or the voltage stability margin, or by using model predictive control methods to predict the supply and demand evolution trend in future periods, and dynamically optimize the power transmission path and power allocation sequence, so as to achieve coordinated optimization of the economy and robustness of microgrid operation.

[0080] like Figure 2 As shown, based on the same inventive concept, this application also provides a microgrid power supply control system 200 based on load monitoring, including: Data monitoring units 210 are distributed at the beginning of each bus and each power consumption node of the microgrid, and are used to collect load data of each power consumption node and voltage data of the corresponding bus. Edge computing node 220 is used to receive and process data collected by data monitoring unit 210, and send the processed data to central control unit 230; The central control unit 230 is used to acquire load and voltage data of each power consumption node in the microgrid; divide the microgrid into several power consumption areas according to the correlation characteristics of load fluctuations of each power consumption node; determine the probability index of load reduction being an active load reduction based on the spatial distribution characteristics of load reduction and its correlation with voltage fluctuations; determine the power supply index of each power consumption node based on historical load data of the same period, and adjust the power supply priority in combination with the probability index to carry out local power supply regulation of the microgrid; wherein, the power supply index is used to characterize the temporal stability of the power supply demand of the power consumption node.

[0081] The aforementioned data monitoring unit 210, as the front-end device of the system perception layer, is distributed at the head end of each bus and at each power consumption node within the microgrid. It may include voltage sensors deployed at the head end of the bus, power sensors installed at the power consumption nodes and load branches, and switch status sensors used to monitor the switch status at the grid connection point. This unit is used to collect load data (i.e., active power data) of each power consumption node and voltage data of the corresponding bus in real time, providing the original data basis for subsequent zoning, active and passive load identification, and priority adjustment.

[0082] The aforementioned edge computing node 220 serves as the regional data processing hub of the system. It can be deployed at a physical location adjacent to the data monitoring unit and receive the raw data collected by the data monitoring unit via wired transmission (such as fieldbus or industrial Ethernet). The node performs preprocessing operations such as timestamp alignment and filtering to clean and standardize the format of the raw monitoring data. It then sends the processed data to the central control unit via wireless transmission technology to reduce communication bandwidth usage and improve the real-time performance of data processing.

[0083] The central control unit 230, as the core decision-making module of the system, is equipped with a processor, memory, and control command output interface, and is responsible for executing the microgrid power supply control method based on load monitoring provided in the embodiments of this application. It is understood that the central control unit 230 can realize any one of the functions of the microgrid power supply control method based on load monitoring provided in the embodiments of this application. For the implementation methods and working principles of each function, please refer to the above method embodiments; the system embodiments will not be repeated here.

[0084] Optionally, the aforementioned microgrid power supply control system 200 based on load monitoring can adopt a multi-level edge computing architecture, setting up multiple edge computing nodes within the microgrid to form a distributed processing network. Each node is responsible for local data processing in a specific area, only reporting key characteristic data or abnormal events to the central control unit, thereby improving the system's scalability and fault tolerance. Communication between the data monitoring unit 210 and the edge computing node 220 can use industrial-grade wired protocols such as Modbus, CAN bus, or fiber optic communication. Communication between the edge computing node 220 and the central control unit 230 can use wireless or wired communication methods such as 4G / 5G, LoRa, or power line carrier. The central control unit 230 can adopt hardware forms such as an industrial controller, embedded industrial computer, or cloud server, and can provide operation status monitoring and parameter configuration functions through a human-machine interface.

[0085] This invention is now complete.

[0086] In summary, in this embodiment of the invention, load data and voltage data of each power-consuming node within the microgrid are acquired; the microgrid is divided into several power-consuming areas based on the correlation characteristics of load fluctuations at each power-consuming node; based on the spatial distribution characteristics of load reduction and its correlation with voltage fluctuations, the probability index of load reduction belonging to active load reduction is determined; the power supply index of each power-consuming node is determined based on historical load data of the same period, and the power supply priority is adjusted in combination with the probability index to perform local power supply regulation of the microgrid; wherein, the power supply index is used to characterize the temporal stability of the power supply demand of the power-consuming node. This invention achieves precise local power supply regulation considering electrical correlation and temporal characteristics by real-time monitoring of loads within a microgrid and dynamic zoning based on load correlations between power consumption nodes. It combines the differentiation between active and passive load reductions with historical load fluctuation characteristics to adjust power supply priorities, thereby improving the reliability and power quality of the microgrid. Furthermore, by dynamically zoning the microgrid based on the spatiotemporal response characteristics of load fluctuations, closely correlated power consumption nodes are merged into the same power consumption area. This provides a spatial dimension for identifying the propagation range and impact of local load fluctuations, enhancing the spatial accuracy of power supply regulation. Moreover, by analyzing the differences in load trends between the area where the power consumption node is located and other areas, as well as the correlation between load changes and voltage fluctuations, it distinguishes between active and passive load reductions, avoiding misjudging passive load changes caused by voltage disturbances as demand reductions, thus improving the accuracy of judging local microgrid regulation needs. Finally, by determining the power supply index based on the statistical characteristics of historical load data from the same period and dynamically adjusting power supply priorities, power supply regulation can adapt to the changing patterns of power demand at different times, achieving optimized allocation of power supply resources in time and space.

[0087] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A microgrid power supply control method based on load monitoring, characterized in that, The method includes: Obtain load and voltage data for each power consumption node within the microgrid; Based on the correlation characteristics of load fluctuations at each power consumption node, the microgrid is divided into several power consumption areas; Based on the spatial distribution characteristics of load reduction and its correlation with voltage fluctuations, the probability index of the load reduction belonging to active load reduction is determined. The power supply index of each power consumption node is determined based on historical load data of the same period, and the power supply priority is adjusted in combination with the probability index to carry out local power supply regulation of the microgrid; wherein, the power supply index is used to characterize the temporal stability of the power supply demand of the power consumption node.

2. The microgrid power supply control method based on load monitoring according to claim 1, characterized in that, Based on the correlation characteristics of load fluctuations at each power consumption node, the microgrid is divided into several power consumption areas, including: Based on the time-series variation characteristics of the load data of each power consumption node, the load decline index of each power consumption node is determined; wherein, the load decline index is used to characterize the trend and magnitude of the load decline of the power consumption node in the corresponding time period; Based on the similarity of the load decline index among the various power consumption nodes, an electrical correlation index among the power consumption nodes at a corresponding time is determined; wherein, the electrical correlation index is used to determine the similarity of the load decline trends among different power consumption nodes; The power consumption nodes that meet the preset conditions of the electrical correlation index are merged into the same power consumption area.

3. The microgrid power supply control method based on load monitoring according to claim 2, characterized in that, The determination of the load decline index for each power consumption node based on the time-series variation characteristics of load data at each node includes: The load data of the power consumption nodes are arranged in time sequence and the first-order differential amplitude is calculated. Based on the degree of deviation of the first-order differential amplitude from the average value of its monitoring period, the segmentation point is determined so as to divide the load data into several load segments. The moments within the load segment where the first-order difference value is negative are marked as effective load fluctuation moments; The load drop index is determined based on the maximum number of consecutive occurrences of effective load fluctuation moments within the load segment, the duration of the load segment, and the power difference between the start and end times of the load segment.

4. The microgrid power supply control method based on load monitoring according to claim 2, characterized in that, The step of determining the electrical correlation index between the electricity consumption nodes at a corresponding time based on the similarity of the load decline index among the electricity consumption nodes includes: Calculate the difference in the load decrease index of each power consumption node at the corresponding time and the spatial distance between each power consumption node; An electrical correlation index is determined based on the difference in the load drop index and the spatial distance; wherein the electrical correlation index decreases as the difference in the load drop index increases, and the electrical correlation index decreases as the spatial distance increases.

5. The microgrid power supply control method based on load monitoring according to claim 2, characterized in that, The determination of the probability index that the load reduction belongs to active load reduction based on the spatial distribution characteristics of the load reduction and its correlation with voltage fluctuations includes: Determine the load trend difference index between the power consumption area where the power consumption node is located and other power consumption areas within the microgrid; wherein, the load trend difference index is used to characterize the degree of load trend difference between the power consumption area where the power consumption node is located and other power consumption areas within the microgrid; Determine the number of power consumption areas to which the power consumption node belongs in the current monitoring period, and the correlation coefficient between the load change of the power consumption node and the corresponding bus voltage change; Based on the trend difference index, the number of electricity consumption areas, and the correlation coefficient, the probability index of the load reduction being an active load reduction is determined.

6. The microgrid power supply control method based on load monitoring according to claim 5, characterized in that, Determining the load trend difference index between the power consumption area where the power consumption node is located and other power consumption areas within the microgrid includes: Obtain the number of power consumption nodes in the power consumption area where the power consumption node is located, and the average load reduction index of all power consumption nodes in the power consumption area; Obtain the average value of the electrical correlation index between the power consumption node and other power consumption nodes in other power consumption areas within the microgrid; The load trend difference index is determined based on the number of power consumption nodes, the average of the load decline index, and the average of the electrical correlation index; wherein the load trend difference index decreases as the number of power consumption nodes increases, increases as the average of the load decline index increases, and decreases as the average of the electrical correlation index increases.

7. The microgrid power supply control method based on load monitoring according to claim 1, characterized in that, The determination of the power supply index for each electricity consumption node based on historical load data includes: Obtain the load decline index of the electricity consumption node at the same time in multiple historical monitoring periods; wherein, the load decline index is used to characterize the trend and magnitude of load decline within the corresponding time period; The power supply index of the power consumption node is determined based on the variance and mean of the load decrease index; wherein the power supply index is negatively correlated with the variance and negatively correlated with the mean.

8. The microgrid power supply control method based on load monitoring according to claim 1, characterized in that, The method of adjusting power supply priority based on the probability index to perform local power supply regulation of the microgrid includes: The power supply priority of each power consumption node is adjusted according to the power supply index of each node. Based on the adjusted power supply priority and the probability index of each power consumption node in each power consumption area, the power transmission index of each power consumption area is determined; wherein, the power transmission index is used to characterize the degree of power transmission demand of the power consumption area. Based on the power transmission index, power transmission scheduling is carried out between various power consumption areas to perform the local power supply regulation.

9. The microgrid power supply control method based on load monitoring according to claim 8, characterized in that, The step of scheduling power transmission between different power consumption areas based on the power transmission index includes: When the load in the target power consumption area suddenly increases, power is preferentially supplied from the power consumption area with the smaller power transmission index to the target power consumption area.

10. A microgrid power supply control system based on load monitoring, characterized in that, It includes a data monitoring unit, an edge computing node, and a central control unit. The data monitoring unit is communicatively connected to the edge computing node, and the edge computing node is communicatively connected to the central control unit, wherein: Data monitoring units are distributed at the beginning of each bus and at each power consumption node of the microgrid to collect load data of each power consumption node and voltage data of the corresponding bus. An edge computing node is used to receive and process the data collected by the data monitoring unit, and send the processed data to the central control unit. The central control unit is used to acquire load and voltage data of each power consumption node in the microgrid; divide the microgrid into several power consumption areas according to the correlation characteristics of load fluctuations of each power consumption node; determine the probability index of the load reduction being an active load reduction based on the spatial distribution characteristics of load reduction and its correlation with voltage fluctuations; determine the power supply index of each power consumption node based on historical load data of the same period, and adjust the power supply priority in combination with the probability index to perform local power supply regulation of the microgrid; wherein, the power supply index is used to characterize the temporal stability of the power supply demand of the power consumption node.

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