Micro-grid energy flow visualization and optimization decision method and system

Abstract

This invention discloses a method and system for visualizing and optimizing energy flow in microgrids. The method includes: obtaining an oscillation index based on the original power characteristic data of microgrid monitoring points; selecting several first monitoring points when the oscillation index is less than a first preset threshold; and repeatedly performing detailed judgments on the monitoring points when the oscillation index fluctuation coefficient is greater than or equal to a second preset threshold, until the oscillation index fluctuation coefficient is less than the second preset threshold or the number of iterations reaches a preset upper limit; selecting several second monitoring points to obtain an oscillation risk cumulative deviation coefficient; and increasing the monitoring frequency of the smallest circuit loop to which the second monitoring point belongs to a preset frequency and issuing an early warning when the coefficient is greater than or equal to a third preset threshold. This invention can automatically switch power control modes, adjust monitoring frequencies, inject damping current, balance reactive power output, and dynamically optimize carbon flow management according to different early warning levels, realizing multi-level risk inspection and adaptive collaborative control throughout the entire microgrid grid connection process.

Description

Technical Field

[0001] This invention belongs to the field of microgrid data processing technology, and specifically relates to a method and system for visualizing and optimizing energy flow in microgrids. Background Technology

[0002] With the deepening of the "dual carbon" goals and the continuous increase in the proportion of renewable energy, the safety and stability issues of microgrids, as an important carrier for integrating distributed energy and improving power supply reliability, are becoming increasingly prominent when operating in conjunction with the main grid. Although existing microgrid grid-connected control technologies can ensure the completion of basic grid-connected operations to a certain extent, they expose systemic defects when dealing with complex and ever-changing actual operating environments, especially in special scenarios such as post-disaster recovery, making it difficult to meet the dual requirements of high-reliability power supply and refined carbon management.

[0003] Existing technologies typically focus on verifying local electrical quantities at the moment of grid connection, such as checking the synchronization of voltage, frequency, and phase. This static and isolated judgment mode lacks a coherent assessment of risks throughout the entire process before and after grid connection. Systems often can only respond passively after obvious faults such as circulating currents or oscillations occur, and cannot accurately predict or actively suppress risks before grid connection. More importantly, after a single microgrid passes grid connection verification, existing systems generally lack monitoring mechanisms for the systemic and cumulative risks caused by the sequential connection of multiple subsequent microgrids. For example, continuous grid connection operations may cause frequency drift or harmonic superposition across the entire grid. These potential stability degradation processes are difficult to detect in the monitoring data of a single connection point, but may ultimately lead to a system-wide operational crisis. In addition, in existing monitoring systems, the functional modules such as data acquisition, risk assessment, and control execution are loosely coupled, forming information barriers, resulting in delayed operation and maintenance decisions and making it difficult to achieve closed-loop management from local risk warning to global situational awareness and then to adaptive and collaborative control.

[0004] To address the aforementioned issues, there is an urgent need in this field for a comprehensive solution that can span the entire grid connection process, consider both local and global risks, and achieve intelligent early warning and adaptive control. Existing microgrid grid connection technologies, when facing complex scenarios such as post-disaster recovery involving the sequential connection of multiple microgrids, suffer from limitations due to their singular risk perception dimensions, fragmented prevention and control mechanisms, and lack of systematic cumulative risk early warning capabilities. This makes it difficult to guarantee the local transient safety of the microgrid grid connection process. Summary of the Invention

[0005] The purpose of this invention is to provide a visualization and optimization decision-making method and system for microgrid energy flow. By acquiring raw power characteristic data from microgrid monitoring points, an oscillation index is calculated. Based on the oscillation index and its derived fluctuation coefficient and risk accumulation deviation coefficient, multi-level judgment and recursive monitoring are performed to achieve accurate identification of oscillation risks from a global to a local perspective. This invention can automatically switch power control modes, adjust monitoring frequencies, inject damping current, balance reactive power output, and dynamically optimize carbon flow management according to different warning levels. Finally, an integrated visualization interface provides a panoramic view of risk distribution and carbon flow status. This solves the problems of single-dimensional risk perception, fragmented prevention and control links, and disconnected carbon flow management in existing microgrid grid-connected control systems. It realizes multi-level risk inspection and adaptive collaborative control throughout the entire microgrid grid connection process, significantly improving the system's operational safety, stability, and the level of carbon management refinement.

[0006] To achieve the above objectives, the solution of the present invention is:

[0007] A method for visualizing and optimizing energy flow in microgrids includes the following steps:

[0008] Step 1: Obtain the oscillation index based on the original power characteristic data of the microgrid monitoring points. When the oscillation index is less than the first preset threshold, select several first monitoring points on the circuit loop to which the microgrid monitoring points belong to form the first monitoring point set.

[0009] Step 2: Based on the oscillation index of any two first monitoring points in the first monitoring point set, obtain the oscillation index fluctuation coefficient of the first monitoring point set. Compare the oscillation index fluctuation coefficient with the second preset threshold. If the oscillation index fluctuation coefficient is greater than or equal to the second preset threshold, proceed to step 3; otherwise, proceed to step 4.

[0010] Step 3: When the oscillation index fluctuation coefficient is greater than or equal to the second preset threshold, locate the pair of first monitoring points with the largest difference in oscillation index in the first monitoring point set, and select several secondary monitoring points in the circuit loop segment between the pair of first monitoring points to form a new monitoring point set. Based on the new monitoring point set and the current system operating status, obtain a new oscillation index fluctuation coefficient. If the new oscillation index fluctuation coefficient is greater than or equal to the second preset threshold, repeat step 3 until the recalculated oscillation index fluctuation coefficient is less than the second preset threshold or the number of iterations reaches the preset upper limit. At this time, proceed to step 4.

[0011] Step 4: Select several second monitoring points on the circuit loop between each first monitoring point in the first monitoring point set, obtain the oscillation index of the second monitoring point and compare it with the oscillation index of the corresponding first monitoring point to calculate the cumulative deviation coefficient of oscillation risk.

[0012] Step 5: When the cumulative deviation coefficient of the oscillation risk is greater than or equal to the third preset threshold, the monitoring frequency of the smallest circuit loop to which the second monitoring point belongs is increased to the preset frequency and an early warning is issued.

[0013] Step 3 further includes,

[0014] When the oscillation index fluctuation coefficient is greater than or equal to the second preset threshold, record the oscillation index fluctuation coefficient of the first monitoring point on the circuit loop to which each execution belongs during the repeated execution process.

[0015] If the difference between the maximum and minimum values ​​of the oscillation index fluctuation coefficient under a preset number of consecutive cycles is greater than the fourth preset threshold, then the output power oscillation data of each distributed power source on the circuit loop is collected, and the dominant oscillation mode and the corresponding oscillation frequency and damping ratio are extracted by the modal analysis algorithm.

[0016] Based on the dominant oscillation mode, the participation factor of each distributed power source is calculated. The distributed power source with the largest participation factor is identified as the dominant oscillation source and the following adjustment operations are performed: the damping ratio of the dominant oscillation source is extracted from the microgrid database and increased according to the preset gain coefficient, and the oscillation index fluctuation coefficient is recalculated.

[0017] If the oscillation index fluctuation coefficient of the first monitoring point in the circuit loop is still greater than or equal to the second preset threshold, the adjustment operation is repeated until the oscillation index fluctuation coefficient is less than the second preset threshold or the number of adjustment operations reaches the preset upper limit.

[0018] In step 1, the oscillation index is obtained based on the original power characteristic data of the microgrid monitoring points, including:

[0019] Based on the phase angles of the output voltages of each power source in the microgrid, the phase angle difference relative to the central bus of the microgrid is obtained, and then the standard deviation of the relative phase angle difference of each distributed power source on the circuit loop to which the microgrid monitoring point belongs is obtained. ;

[0020] Based on the voltage data from the microgrid monitoring points, the deviation rate from the rated voltage is calculated and averaged to obtain the average voltage amplitude deviation rate of the microgrid monitoring points. ;

[0021] Based on the frequency data from each monitoring point, the standard deviation of the frequency data from the microgrid monitoring points was obtained. ;

[0022] The total harmonic distortion rate of the voltage at the microgrid monitoring point was obtained based on the voltage harmonic spectrum data. ;

[0023] The maximum absolute value of the power change rate at the microgrid monitoring point is obtained by differentiating the active power data. ;

[0024] The formula for calculating the oscillation index is as follows:

[0025] ,

[0026] in, The weighting coefficients are the first, second, third, fourth, and fifth weighting coefficients, respectively, satisfying... ; It is an oscillation index.

[0027] In step 2, the specific process for obtaining the oscillation index fluctuation coefficient of the first monitoring point is as follows:

[0028] Obtain the oscillation index of each pair of the first monitoring points in the first detection point set, and obtain the oscillation index of different first monitoring points on the same circuit loop. and ;

[0029] Obtain the number of pairs of the first monitoring point. ;

[0030] Calculate the arithmetic mean of the oscillation indices of all first monitoring points. ;

[0031] Calculate and sum the squared differences of the oscillation indices of all pairwise pairs of the first monitoring points. ;

[0032] The formula for calculating the volatility coefficient of the oscillator index is as follows:

[0033] ,

[0034] in, This is the volatility coefficient of the oscillation index.

[0035] In step 4, the specific process for obtaining the cumulative deviation coefficient of oscillation risk is as follows:

[0036] Calculate the arithmetic mean of the oscillation indices of all second monitoring points. ;

[0037] Calculate the standard deviation of the oscillation index for all second monitoring points. ;

[0038] Calculate the standard deviation of the oscillation index for all first monitoring points. ;

[0039] Based on the damping ratio of the dominant oscillation source in the smallest circuit loop to which the second monitoring point belongs and the preset mapping relationship, the corresponding proportional coefficient is obtained based on the damping ratio through the preset mapping relationship. ;

[0040] The formula for calculating the cumulative deviation coefficient of oscillation risk is as follows.

[0041] ,

[0042] in, This is the cumulative deviation coefficient for oscillation risk.

[0043] In step 5, when the cumulative deviation coefficient of oscillation risk is less than the third preset threshold, the voltage harmonic spectrum amplitude data of the second monitoring point with the longest electrical distance from the microgrid's grid-connected electrical point is obtained, and denoted as... And acquire the current harmonic spectrum amplitude data of all first monitoring points that are electrically closest to the microgrid's grid-connected electrical point, denoted as ;

[0044] Based on the voltage harmonic spectrum data and current harmonic spectrum data, calculate the preset major harmonic order. The harmonic impedance amplification factor is below ;

[0045] When the harmonic impedance amplification factor exceeds the fifth preset threshold, in the harmonic impedance frequency characteristic curve of the smallest circuit loop at the second monitoring point, [the following will be included / removed / indicated / etc.]. The frequency corresponding to the maximum value is identified as the resonant frequency point. ;

[0046] Based on the resonant frequency point Harmonic impedance amplification factor Calculate the injection current setpoint of the active power filter. , ,in, This is the preset damping gain coefficient;

[0047] Control the active power filter to inject a damping current of a set value at the resonant frequency point;

[0048] Based on the resonant frequency point, calculate the switching adjustment amount of the reactive power compensation equipment. , ,in, The equivalent inductance of the smallest circuit loop to which the second monitoring point belongs. The preset switching capacitor value;

[0049] The reactive power compensation equipment on the smallest circuit loop to which the second monitoring point belongs adjusts its switching capacity according to the switching adjustment amount.

[0050] The method further includes,

[0051] When the oscillation index is less than the first preset threshold and the oscillation index fluctuation coefficient is less than the second preset threshold, the total current data of the second monitoring point is acquired. The sum of the output current data of each distributed power node in the smallest circuit loop to which the second monitoring point belongs. ;

[0052] Calculate the superposition coefficient of the circulation Determine whether the circulating superposition coefficient exceeds the sixth preset threshold;

[0053] If so, calculate the reactive power deviation of each distributed power source. ,in, This represents the current reactive power output value of each distributed power source. This represents the average reactive power output of each distributed power source.

[0054] Based on the reactive power deviation, a reactive power balancing control command is generated: the distributed power source corresponding to the maximum value of the reactive power deviation is identified as the adjustment target, and its reactive power output adjustment amount is calculated. ,in, This is the preset maximum adjustment amount per cycle;

[0055] The distributed power source is controlled to adjust its original reactive power output value according to the reactive power output adjustment amount.

[0056] In step 1, when the oscillation index is greater than or equal to the first preset threshold, the control mode of all distributed power sources on the circuit loop to which the microgrid monitoring point belongs is switched to the current source control mode, and a special warning is issued.

[0057] In step 3, when the oscillation index fluctuation coefficient is greater than or equal to the second preset threshold, the monitoring frequency of the circuit loop between a group of first monitoring points is increased to the first preset frequency, and an advanced warning is issued.

[0058] In step 5, when the cumulative deviation coefficient of oscillation risk is greater than or equal to the third preset threshold, an intermediate warning is issued; when the cumulative deviation coefficient of oscillation risk is less than the third preset threshold, the maximum fluctuation similarity between the cumulative deviation coefficient of oscillation risk of the main network central bus corresponding to the smallest circuit loop to which the second monitoring point belongs and the cumulative deviation coefficient of oscillation risk of the second monitoring point within a preset time period is calculated, and it is determined whether the maximum fluctuation similarity exceeds the fourth preset threshold. If so, a primary warning is issued.

[0059] The method further includes,

[0060] When a special warning is issued, the carbon flow monitoring frequency of the corresponding distributed power source will be increased to the first carbon monitoring frequency.

[0061] When a high-level warning is issued, the carbon flow monitoring frequency of the corresponding circuit loop is increased to the second carbon monitoring frequency, and high-carbon risk areas are marked based on high-frequency carbon flow data.

[0062] When a medium-level warning is issued, the carbon flux density of the relevant circuit loops is recalculated.

[0063] Based on the type of warning issued, the corresponding circuit loop and the carbon flow monitoring frequency of the circuit loop are dynamically displayed.

[0064] A microgrid energy flow visualization and optimization decision-making system, comprising,

[0065] The first judgment module is configured to obtain the oscillation index based on the original power characteristic data of the microgrid monitoring points. When the oscillation index is less than the first preset threshold, a number of first monitoring points are selected on the circuit loop to which the microgrid monitoring points belong to form a first monitoring point set.

[0066] The second judgment module is configured to obtain the oscillation index fluctuation coefficient of the first monitoring point set based on the oscillation index of any two first monitoring points in the first monitoring point set, and compare the oscillation index fluctuation coefficient with a second preset threshold.

[0067] The first execution module is configured to locate a pair of first monitoring points with the largest difference in oscillation index when the oscillation index fluctuation coefficient is greater than or equal to a second preset threshold. It then selects several secondary monitoring points within the circuit loop segment between the pair of first monitoring points to form a new monitoring point set. Based on the new monitoring point set and the current system operating state, it obtains a new oscillation index fluctuation coefficient. If the new oscillation index fluctuation coefficient is greater than or equal to the second preset threshold, it repeats the action of the first execution module until the recalculated oscillation index fluctuation coefficient is less than the second preset threshold or the number of iterations reaches a preset upper limit.

[0068] The second execution module is configured to, when the oscillation index fluctuation coefficient is less than a second preset threshold or when the iteration number of the first execution module reaches a preset upper limit, select several second monitoring points on the circuit loop between the first monitoring points in the first monitoring point set, obtain the oscillation index of the second monitoring points, and compare it with the oscillation index of the corresponding first monitoring points to calculate the cumulative deviation coefficient of oscillation risk; and,

[0069] The third execution module is configured to raise the monitoring frequency of the smallest circuit loop to which the second monitoring point belongs to a preset frequency and issue an early warning when the cumulative deviation coefficient of the oscillation risk is greater than or equal to a third preset threshold.

[0070] By adopting the above solution, the present invention has at least the following technical effects or advantages:

[0071] (1) This invention proactively narrows the monitoring range spatially and traces the risk evolution temporally through a progressive judgment logic of oscillation index analysis, first-level monitoring point deployment, fluctuation coefficient calculation, second-level monitoring point expansion, and cumulative deviation assessment. This changes the static mode of traditional technology that relies solely on fixed monitoring point threshold alarms, and can discover "hidden risks" that appear normal at a single monitoring point but only manifest after propagation and accumulation through the network, thus achieving a deeper level of assessment of system operational stability.

[0072] (2) This invention not only issues early warnings, but also automatically performs functions such as identifying oscillation sources based on modal analysis and adjusting their damping, suppressing harmonic resonance by injecting damping current, eliminating circulating current by balancing reactive power output, and verifying the control effect based on a recalculated risk index, and performing iterative optimization when necessary. This integrated capability of "diagnosis-treatment-review" significantly reduces the dependence on external manual intervention and improves the system's autonomy and response speed.

[0073] (3) This invention enables maintenance personnel to intuitively identify the nature, location, and severity of risks on the global topology map by using different levels of warnings triggered by the hierarchical judgment structure. By dynamically marking risk circuit loops with different colors and highlighting methods, abstract data is transformed into intuitive graphics, which greatly reduces the complexity of decision-making, facilitates root cause analysis and countermeasure formulation, and provides multi-dimensional decision support from "precise local positioning" to "global panoramic visualization".

[0074] (4) This invention uses the level of oscillation risk as a switch to trigger carbon flow monitoring and analysis at different granularities, realizing the linkage between operational safety status and carbon emission management. This enables the system to not only ensure grid stability but also proactively understand the impact of stability disturbances on carbon flow, or use carbon flow data to assist in analyzing the root causes of stability problems such as equipment energy efficiency degradation. Ultimately, it achieves the unity of safety and low-carbon goals at the decision-making level, and achieves cross-goal synergy between "safe and stable" and "low-carbon operation". Attached Figure Description

[0075] Figure 1 This is a schematic diagram of the structure of the microgrid energy flow visualization and optimization decision-making system provided in an embodiment of the present invention;

[0076] Figure 2 This is a schematic diagram of the adjustment operation logic flow of the first execution module provided in an embodiment of the present invention;

[0077] Figure 3 This is a schematic diagram of the logical flow of the fourth execution module provided in an embodiment of the present invention;

[0078] Figure 4 This is a schematic diagram of the logic flow of the fifth execution module provided in an embodiment of the present invention;

[0079] Figure 5 This is a schematic diagram of the overall process of the microgrid energy flow visualization and optimization decision-making method provided in the embodiments of the present invention. Detailed Implementation

[0080] The embodiments of the present invention provide a microgrid energy flow visualization and optimization decision-making system, which solves the problems in the prior art of single risk perception dimension, fragmented prevention and control links and disconnected carbon flow management in microgrid grid connection control.

[0081] In existing technologies, microgrid grid-connected control mainly relies on static electrical parameter verification, lacking the ability to dynamically perceive risks throughout the entire grid connection process. Current systems typically perform isolated checks on parameters such as voltage and frequency at the moment of grid connection, failing to effectively identify systemic risks arising from the continuous connection of multiple microgrids. Especially in post-disaster recovery scenarios, when multiple microgrids are sequentially connected to the main grid, existing technologies struggle to capture the cumulative effects of circulating current superposition and resonance risks, potentially causing local oscillations to evolve into grid-wide stability issues. For example, in one regional power grid, after the continuous connection of three microgrids, the failure to promptly identify harmonic impedance coupling between the connection points ultimately led to a bus voltage collapse accident.

[0082] To address the aforementioned issues, a dynamic monitoring mechanism spanning the entire grid connection process is needed to achieve early warning of potential faults through a layered and progressive risk identification strategy. Firstly, a multi-dimensional oscillation assessment index is introduced at the initial monitoring points to accurately reflect the system's stability state through multi-parameter fusion calculations. When minor oscillations are detected, a layered and refined monitoring point deployment strategy is adopted, dynamically selecting key nodes in the circuit loop for data collection. For detected abnormal fluctuations, an iterative judgment process is designed to distinguish between instantaneous disturbances and persistent risks through multiple rounds of data comparison. Simultaneously, a risk accumulation deviation model is established to quantify the impact of local anomalies on the overall system, providing a quantitative basis for early warning classification.

[0083] To address the above problems, this invention proposes a method for visualizing and optimizing energy flow in microgrids, such as... Figure 5 As shown, the process includes: acquiring raw power characteristic data of microgrid monitoring points and calculating the oscillation index; selecting several first monitoring points when the oscillation index is less than a set threshold; calculating the oscillation index fluctuation coefficient of the first monitoring points; repeatedly performing detailed judgment of monitoring points when the fluctuation coefficient exceeds the threshold; selecting second monitoring points and calculating the risk cumulative deviation coefficient when the fluctuation coefficient is less than the threshold; dynamically adjusting the monitoring frequency and triggering graded early warning based on the deviation coefficient judgment result.

[0084] Specifically, the system first acquires the phase angle of each power supply output voltage using a synchronous phasor measurement unit, and then collects multi-dimensional power data using devices such as voltage transformers and harmonic analyzers. After calculating the oscillation index of the initial monitoring points, if the index does not reach the warning threshold, at least three first monitoring points are selected at preset intervals on the corresponding circuit loop. The fluctuation coefficients of these three monitoring points are calculated in pairs. When a set of fluctuation coefficients exceeds the threshold, at least three secondary monitoring points are deployed again in the corresponding section for iterative judgment. After at least three iterations, a second monitoring point is deployed in the finally determined stable section. By comparing the data differences between the second monitoring point and the original monitoring point, the risk cumulative deviation coefficient is calculated. When this coefficient exceeds a set threshold, the monitoring frequency of the minimum circuit unit in the corresponding loop is automatically increased, and the warning information is pushed to the visualization interface.

[0085] Compared to existing technologies, which only perform a single parameter check at the moment of grid connection, this invention constructs a dynamic monitoring network covering the entire grid connection process. Existing technologies employ fixed monitoring point layouts, making it difficult to capture differences in the operating status of different sections of the circuit loop. This invention, through a progressive monitoring point deployment strategy, can accurately locate areas of abnormal fluctuations. Traditional early warning mechanisms trigger alarms based on a single parameter threshold, while this invention, through the quantitative calculation of the risk accumulation deviation coefficient, achieves correlation analysis from local anomalies to system risks.

[0086] Through the above technical solutions, this invention effectively solves the risk accumulation problem caused by the sequential connection of multiple microgrids. The progressive monitoring point deployment strategy can dynamically capture the differences in operating status of different sections of the circuit loop, avoiding monitoring blind spots caused by fixed monitoring point layouts. The iterative judgment mechanism can distinguish between instantaneous disturbances and continuous anomalies, reducing the false alarm rate. The introduction of a risk accumulation deviation coefficient enables a quantitative assessment of the impact of local anomalies on the overall system, providing a reliable basis for tiered early warning. The dynamic status display function of the visualization module allows maintenance personnel to intuitively grasp the risk distribution of the entire network and take timely and targeted control measures.

[0087] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0088] like Figure 1 The diagram shown is a structural schematic of the microgrid energy flow visualization and optimization decision-making system provided in an embodiment of the present invention, including:

[0089] First judgment module: used to acquire the original power characteristic data of the microgrid monitoring point and calculate the oscillation index accordingly. When the oscillation index is less than the first preset threshold, several first monitoring points are selected in the circuit loop to which the microgrid monitoring point belongs.

[0090] The second judgment module is used to obtain the oscillation index of all first monitoring points and calculate the total oscillation index fluctuation coefficient of the first monitoring points in pairs, and to determine whether the oscillation index fluctuation coefficient is greater than or equal to the second preset threshold.

[0091] The first execution module is configured to, when the judgment result of the second judgment module is yes, locate the pair of first monitoring points with the largest difference in oscillation index in the first monitoring point set, and densify and select a number of secondary monitoring points in the circuit loop segment between the pair of first monitoring points to form a new monitoring point set; based on the new monitoring point set, repeatedly execute the second judgment module and the first execution module, and synchronously trigger the microgrid operation status adjustment operation during the iteration process until the recalculated oscillation index fluctuation coefficient is less than the second preset threshold or the number of iterations reaches the preset upper limit;

[0092] The second execution module is used to select several second monitoring points in the circuit loop between a group of first monitoring points if the judgment result is negative, obtain the oscillation index of the second monitoring points and compare it with the oscillation index of the corresponding group of first monitoring points to calculate the cumulative deviation coefficient of oscillation risk.

[0093] The third execution module is used to determine whether the cumulative deviation coefficient of oscillation risk is greater than or equal to the third preset threshold. If so, the monitoring frequency of the smallest circuit loop to which the second monitoring point belongs is increased to the preset frequency and an early warning is issued.

[0094] In this embodiment, the second judgment module is used to obtain the oscillation index of several first monitoring points and calculate the actual oscillation index of all selected first monitoring points in pairs, and calculate the oscillation index fluctuation coefficient of the entire group of first monitoring points in pairs, and judge whether the oscillation index fluctuation coefficient is greater than or equal to the second preset threshold.

[0095] If the judgment result is yes, the first execution module is used to re-select several secondary monitoring points in the circuit loop between the two monitoring points with the largest fluctuation difference in this group of first monitoring points; at the same time, it combines the damping ratio adjustment operation automatically executed by the system to change the microgrid operating state, and then, based on the newly selected monitoring points and the new operating state, it repeats the execution of the second judgment module and the first execution module until the recalculated oscillation index fluctuation coefficient is less than the second preset threshold or the number of repeated executions reaches the upper limit.

[0096] The oscillation index is a multi-dimensional evaluation index that integrates the standard deviation of phase angle difference, voltage deviation rate, frequency standard deviation, harmonic distortion rate, and power change rate. Specifically, it can be implemented using a weighted summation algorithm to comprehensively reflect the stability of the microgrid's operating status.

[0097] Taking a 10kV microgrid monitoring point as an example, assuming the acquisition period is 1 second and the number of sampling points N=100, the calculation yields: , , , , ; Extract weight coefficients from the database , , , =0.15、 =0.10. The calculation process for the oscillation index is as follows:

[0098] If the first preset threshold is 4.0, then the current oscillation index is lower than the preset threshold, and the oscillation risk of the monitoring point is determined to be within a controllable range. The system will continue to execute the first monitoring point selection and subsequent progressive judgment process.

[0099] The first monitoring point refers to the observation node selected at equal intervals along the circuit loop based on electrical distance. Specifically, a topology analysis algorithm is used to extract the network topology diagram of the microgrid circuit loop, abstracting each distributed power source and load node as vertices of the graph, and the connecting lines as edges. Based on the impedance parameters of the lines, the total equivalent electrical distance of the loop is calculated, and at least three nodes are selected as the first monitoring points along the edges according to the principle of equal distribution of electrical distance. For example, if the loop is long, 5-7 nodes can be set to ensure monitoring granularity, but at least 3 nodes are required to support subsequent pairwise comparison calculations. This method is used to accurately capture the differences in operating status caused by uneven line impedance in different sections of the circuit loop.

[0100] The oscillation index fluctuation coefficient refers to the dispersion index of the oscillation index of multiple monitoring points within the same loop. Specifically, it can be implemented using the ratio algorithm of standard deviation to mean, and is used to identify local abnormal fluctuation areas.

[0101] The risk cumulative deviation coefficient is a quantitative indicator of the difference in status between new monitoring points and original monitoring points. Specifically, it can be implemented using the ratio of mean difference to standard deviation algorithm, and is used to assess the potential impact of local anomalies on the overall system.

[0102] Furthermore, it also includes a visualization module: when the oscillation index is greater than or equal to a first preset threshold, the control mode of all distributed power sources in the target microgrid is switched to current source control mode, and a high-level warning is issued; when the oscillation index fluctuation coefficient is greater than or equal to a second preset threshold, the monitoring frequency of the circuit loops between a group of first monitoring points is increased to a first preset frequency, and a high-level warning is issued; when the oscillation risk cumulative deviation coefficient is greater than or equal to a third preset threshold, a medium-level warning is issued; when the oscillation risk cumulative deviation coefficient is less than the third preset threshold, the maximum fluctuation similarity between the oscillation risk cumulative deviation coefficient of the main grid central bus and the oscillation risk cumulative deviation coefficient of the second monitoring point within a preset time period is calculated, and the most... If the similarity of large fluctuations exceeds the fourth preset threshold, a primary warning is issued. The topology diagram of all microgrid circuit loops is integrated and displayed on the visualization interface, and the corresponding visualization status is dynamically set for each circuit loop according to the warning type. The special warning corresponds to highlighting the distributed power nodes and their circuit loops of the target microgrid in the first color, and simultaneously displaying the control mode switching status. The advanced warning corresponds to highlighting the circuit loops between a group of first monitoring points in the second color, and marking the monitoring frequency increase information. The intermediate warning corresponds to displaying the smallest circuit loop belonging to the second monitoring point in the third color. The primary warning corresponds to flashing the associated circuit loops between the main grid central bus and the second monitoring point in the fourth color.

[0103] In this embodiment, the current source control mode refers to switching the output characteristics of the distributed power source from voltage source mode to current follower mode. Specifically, this can be achieved by using a digital signal processor to modify the reference signal generation logic of the inverter control loop, thereby suppressing system oscillations by changing the control strategy.

[0104] The first to fourth colors refer to the visual identifiers corresponding to different warning levels. Specifically, they can be implemented in the graphical interface rendering engine using four color systems: red, orange, yellow, and blue, to quickly distinguish risk levels through color differences.

[0105] Monitoring frequency increase information refers to the operation prompt of increasing the data sampling rate of a specific circuit loop. Specifically, dynamic label overlay technology can be used to display the frequency value change status on the topology diagram.

[0106] Maximum fluctuation similarity refers to the degree of matching between the trends of the cumulative deviation coefficient of oscillation risk between two monitoring points over time. Specifically, the waveform similarity index can be calculated using the dynamic time warping algorithm.

[0107] The visualization module combines a multi-level early warning triggering mechanism with dynamic interface rendering to achieve a hierarchical representation of the risk situation. When a high-level early warning is triggered, the system automatically switches the distributed power supply control mode and highlights relevant nodes and loops in red on the topology map, enabling maintenance personnel to immediately identify key fault points. For areas covered by high-level early warnings, orange highlighted loops, along with real-time updates of monitoring frequency values, assist in determining the oscillation propagation path. Intermediate early warnings use yellow to indicate the smallest circuit loops to help locate local anomaly sources. Primary early warnings use blue flashing indicators of associated loops to provide early warning of potential risk transmission. The calculation of the similarity of fluctuations in the main grid central bus can capture cross-regional risk coupling characteristics and can still trigger preventative prompts even if the intermediate early warning threshold is not reached. This achieves multi-dimensional real-time perception and hierarchical handling guidance for microgrid operation risks, enabling maintenance personnel to quickly locate high-risk areas based on color markings. Combined with dynamically displayed monitoring frequency changes and mode switching status, it effectively shortens fault diagnosis time. The introduction of the similarity of fluctuations in the main grid central bus enhances the early identification capability of cross-microgrid risk transmission and prevents local anomalies from evolving into systemic failures.

[0108] Furthermore, the first execution module also includes: when the oscillation index fluctuation coefficient is greater than or equal to the second preset threshold, recording the oscillation index fluctuation coefficient of the first monitoring point on the circuit loop to which each execution belongs during the repeated execution process; if the difference between the maximum and minimum values ​​of the oscillation index fluctuation coefficient under a preset number of consecutive executions is greater than the fourth preset threshold, then collecting the output power oscillation data of each distributed power source on the circuit loop, extracting the dominant oscillation mode and the corresponding oscillation frequency and damping ratio through a modal analysis algorithm; calculating the participation factor of each distributed power source based on the dominant oscillation mode, identifying the distributed power source with the largest participation factor as the dominant oscillation source and performing the following adjustment operations;

[0109] The adjustment operation is as follows: the damping ratio of the dominant oscillation source is increased by extracting from the microgrid database and according to the preset gain coefficient, and the oscillation index fluctuation coefficient is recalculated; if the oscillation index fluctuation coefficient of the first monitoring point on the circuit loop is still greater than or equal to the second preset threshold, the adjustment operation is repeated until the oscillation index fluctuation coefficient is less than the second preset threshold or the number of adjustment operations reaches the preset upper limit.

[0110] In this embodiment, Figure 2 This is a schematic diagram of the adjustment operation logic flow of the first execution module provided in an embodiment of the present invention.

[0111] When the system damping is insufficient, multiple distributed power sources will generate a negative damping effect through grid coupling, resulting in continuous low-frequency power oscillations. These oscillations will intensify with changes in operating mode, and traditional local control cannot effectively suppress them.

[0112] The oscillation index fluctuation coefficient difference refers to the maximum change in the fluctuation coefficient during multiple consecutive executions. Specifically, it can be achieved by calculating the range of the results of the most recent executions using a sliding window algorithm, and is used to determine whether the oscillation trend shows a divergent state.

[0113] Modal analysis algorithms refer to time-domain or frequency-domain eigenvalue decomposition methods based on output power oscillation data. Specifically, they can be implemented using the Prony algorithm or random subspace identification algorithm to separate the oscillation components that have the greatest impact on system stability.

[0114] The participation factor is a quantitative indicator that characterizes the contribution of each distributed power source to the dominant oscillation mode. Specifically, it can be calculated through the orthogonality analysis of the eigenvectors of the state-space model and is used to accurately locate the power nodes that need to be adjusted first.

[0115] Damping ratio adjustment refers to changing the dynamic response characteristics of a system by adjusting the control parameters of power electronic equipment. Specifically, it can be achieved by using virtual synchronous machine control or additional damping controllers to enhance the system's ability to suppress oscillations at specific frequencies.

[0116] By continuously monitoring the changing trend of the fluctuation coefficient, temporary fluctuations can be distinguished from persistent oscillations, thus avoiding malfunctions. Existing technologies lack the ability to precisely locate the oscillation source, often requiring the simultaneous adjustment of multiple devices. This embodiment, however, achieves precise location through participation factor calculation, significantly improving adjustment efficiency. A closed-loop adjustment mechanism is employed to ensure adjustment accuracy while avoiding excessive intervention, maintaining the stability of the microgrid operation. Through dynamic adjustment and iterative optimization, the risk of secondary oscillations caused by parameter mismatch is significantly reduced, enhancing adaptive control capabilities under complex operating conditions.

[0117] Furthermore, the raw power characteristic data of the microgrid monitoring points are obtained and the oscillation index is calculated accordingly. Specifically, this includes: obtaining the phase angle of the output voltage of each power source in the microgrid through the synchronous phasor measurement unit and calculating the phase angle difference relative to the central bus of the microgrid; then, obtaining the standard deviation of the relative phase angle difference of each distributed power source on the circuit loop to which the microgrid monitoring point belongs. The average voltage amplitude deviation rate of the microgrid monitoring points is obtained by averaging the voltage data obtained from the voltage transformers at the monitoring points and calculating the deviation rate from the rated voltage. The standard deviation of the frequency data at each monitoring point in the microgrid is calculated after acquiring the frequency data using a frequency measurement device. The total harmonic distortion rate of the voltage at the microgrid monitoring point was calculated after obtaining voltage harmonic spectrum data using a harmonic analyzer. The maximum absolute value of the power change rate at the microgrid monitoring point is obtained by calculating the differential after acquiring active power data through a power transmitter. Weighting coefficients, including the first weighting coefficient, are extracted from the microgrid database. Second weighting coefficient Third weighting coefficient Fourth weighting coefficient and the fifth weighting coefficient , The formula for calculating the oscillator index is as follows: , It is an oscillation index.

[0118] In this embodiment, the synchronization phasor measurement unit refers to a device used to measure the voltage phase in real time. Specifically, it can be implemented using a PMU device, and its function is to capture the phase synchronization status between the distributed power source and the main grid.

[0119] The average voltage amplitude deviation rate refers to the average percentage of voltage deviation from the rated value at each monitoring point. It is calculated after the starting voltage is collected by the voltage transformer and is used to reflect voltage stability.

[0120] The standard deviation of frequency data is a quantitative indicator of the degree of frequency fluctuation at each monitoring point. It is obtained by taking the square root of the variance after continuous sampling by a frequency measuring device and is used to characterize frequency consistency.

[0121] Total harmonic distortion (THD) of voltage refers to the ratio of harmonic components to fundamental components in a voltage waveform. It is calculated by performing spectral decomposition using a harmonic analyzer and is used to assess power quality.

[0122] The maximum absolute value of the power change rate refers to the peak value of the rate at which active power changes over time. It is obtained by performing differential calculations on power data collected by a power transmitter and is used to identify the risk of power surges.

[0123] The weighting coefficient refers to the pre-set parameter allocation ratio, which is obtained by training with historical operating data in the microgrid database and is used to weight and fuse different types of electrical quantities.

[0124] By integrating five key indicators—phase angle synchronization, voltage stability, frequency consistency, harmonic purity, and power mutation—and introducing a dynamic weighting mechanism, the operational risks of microgrids can be assessed more comprehensively. For example, when a monitoring point simultaneously experiences an increase in phase angle difference and harmonic distortion, existing technologies may overlook the risk because a single indicator does not exceed the limit. However, this embodiment can identify potential composite fault hazards in advance through multi-dimensional weighted calculations.

[0125] Furthermore, the specific process for obtaining the oscillation index fluctuation coefficient of a set of first monitoring points is as follows: obtain the oscillation index of each pair of first monitoring points, and obtain the oscillation index of different first monitoring points on the same circuit loop. and ; Obtain the number of pairs of the first monitoring point ; Calculate the arithmetic mean of the oscillation indices of all first monitoring points. ; Calculate and sum the squared differences of the oscillation indices of all pairs of the first monitoring points. The formula for calculating the volatility coefficient of the oscillator index is as follows: ,in, This is the volatility coefficient of the oscillation index.

[0126] like Figure 2 The diagram shown is a logical flow diagram of the microgrid energy flow visualization and optimization decision-making system provided in an embodiment of the present invention.

[0127] In this embodiment, the arithmetic mean refers to the central tendency measure of the oscillation index of the second monitoring point. Specifically, it can be achieved by summing and dividing by the number of samples, and is used to reflect the overall oscillation level of the second monitoring point.

[0128] Standard deviation is a quantitative indicator of the dispersion of an oscillation index. It can be achieved by taking the square root of the squared average of the differences between the sample values ​​and the mean, and is used to characterize the fluctuation range of the oscillation state between monitoring points.

[0129] The proportional coefficient is a weighting factor that is dynamically adjusted according to the damping ratio. Specifically, it can be obtained from a preset mapping relationship by using a lookup table method or interpolation method, and is used to adapt to the different sensitivity of different circuit loops to oscillation risk.

[0130] The dominant oscillation source refers to the main power node that causes the oscillation, as determined by participation factor analysis. Specifically, it can be achieved by using modal analysis to extract the dominant oscillation mode and calculate the contribution of each power source, which is used to locate the interference source that needs to be adjusted first.

[0131] In the multi-level monitoring process of microgrids, once the second monitoring point is selected, the system first calculates the statistical distribution characteristics of its oscillation index. Through joint analysis of the arithmetic mean and standard deviation, it can simultaneously capture the average level and spatial variability of oscillation intensity. Introducing a proportional gain allows the system to automatically adjust the calculation weight of risk accumulation deviation based on the current loop damping characteristics, avoiding insufficient sensitivity caused by a single threshold judgment. The absolute value term in the calculation formula focuses on monitoring the overall deviation trend, while the ratio term focuses on the amplification effect of local fluctuations; the combination of both forms a comprehensive assessment of potential risk accumulation paths. For example, when a sudden change in the output of a distributed power source causes the mean oscillation index of the second monitoring point to deviate significantly from that of the first monitoring point, even if the standard deviation does not increase significantly, the system can still trigger an early warning mechanism in a timely manner through an increase in the ACD value.

[0132] Existing technologies typically rely on threshold comparisons at a single monitoring point for risk assessment, failing to distinguish between different risk types such as mean shift and fluctuation spread. Furthermore, the fixed proportional coefficients commonly used in existing technologies are ill-suited to the dynamic changes in loop damping characteristics under varying topologies, easily leading to misjudgments.

[0133] This embodiment establishes a two-dimensional evaluation model of mean and standard deviation, combined with an adaptive damping ratio coefficient, which can more accurately identify the accumulation of hidden risks caused by power supply imbalance or loop parameter mismatch. This provides differentiated decision-making basis for subsequent frequency adjustment and early warning strategies, avoiding the risk of redundant monitoring or missed detection due to misjudgment.

[0134] Furthermore, the specific process for obtaining the cumulative deviation coefficient of oscillation risk is as follows: calculate the arithmetic mean of the oscillation index of all second monitoring points. ; Calculate the standard deviation of the oscillation index for all second monitoring points. ; Calculate the standard deviation of the oscillation index for all first monitoring points. Based on the microgrid database, the damping ratio of the dominant oscillation source on the smallest circuit loop to which the second monitoring point belongs and the preset mapping relationship are extracted, and the corresponding proportional coefficient is obtained based on the damping ratio through the preset mapping relationship. The formula for calculating the cumulative deviation coefficient of oscillation risk is as follows: ;in, This is the cumulative deviation coefficient for oscillation risk.

[0135] In this embodiment, the harmonic impedance amplification factor refers to the ratio of the voltage harmonic amplitude to the current harmonic amplitude at a specific harmonic order. Specifically, it can be calculated by synchronously collecting harmonic data with voltage transformers and current transformers and then performing spectrum analysis, which is used to quantitatively assess the risk of harmonic resonance.

[0136] The resonant frequency identification operation refers to locating the frequency point corresponding to the maximum impedance in the harmonic impedance frequency characteristic curve. This can be achieved through frequency sweep measurement or an impedance frequency characteristic tester, and is used to accurately identify the system resonant frequency.

[0137] The active power filter injection current setting value refers to the damping current amplitude determined based on the harmonic impedance amplification factor and preset gain. Specifically, it can be calculated and generated in real time by a digital signal processor to actively cancel the resonant current.

[0138] The switching adjustment of reactive power compensation equipment refers to the capacitor capacity adjustment value calculated based on the resonant frequency point. Specifically, it can be achieved by executing switching commands through the capacitor bank controller, which is used to change the equivalent impedance characteristics of the circuit.

[0139] When the cumulative deviation coefficient of oscillation risk does not reach the warning threshold, the impedance amplification factor of the main harmonic order is calculated by comparing the far-end voltage harmonics and near-end current harmonics data. If this factor exceeds the safety threshold, it indicates a potential harmonic resonance risk. At this time, the system automatically performs resonant frequency point location and determines the maximum resonant frequency based on the impedance frequency curve. The damping current amplitude to be injected into the active filter is calculated according to this frequency, and a reverse harmonic current is generated in real time to offset the resonant energy. At the same time, the capacitor switching of the reactive power compensation equipment is recalculated, and the circuit impedance characteristics are changed by adjusting the equivalent capacitance value, so that the system resonant frequency deviates from the original dangerous frequency band.

[0140] This system can identify potential harmonic resonance hazards before the system oscillation risk reaches the warning threshold. Through coordinated control of active filtering and reactive power compensation, it effectively suppresses the amplitude of resonant current and changes the system's resonant frequency distribution. This proactive defense mechanism avoids the lag of traditional passive filtering and solves the harmonic superposition problem caused by the connection of multiple microgrids.

[0141] Furthermore, it also includes a fourth execution module, used to: when the cumulative deviation coefficient of oscillation risk is less than a third preset threshold, acquire the voltage harmonic spectrum amplitude data of the second monitoring point with the longest electrical distance from the microgrid's grid-connected electrical point among all the second monitoring points, denoted as... And acquire the current harmonic spectrum amplitude data of all first monitoring points that are electrically closest to the microgrid's grid-connected electrical point, denoted as Based on voltage harmonic spectrum data and current harmonic spectrum data, calculate the preset main harmonic order. The harmonic impedance amplification factor is as follows: , ; Determine if the harmonic impedance amplification factor exceeds the fifth preset threshold; if so, perform the resonant frequency point identification operation: in the harmonic impedance frequency characteristic curve of the smallest circuit loop to which the second monitoring point belongs, The frequency corresponding to the maximum value is identified as the resonant frequency point. Based on the resonant frequency point Harmonic impedance amplification factor Calculate the injection current setpoint of the active power filter. , ,in, The damping gain coefficient is preset; the active power filter is controlled to inject a damping current of a set value at the resonant frequency point; based on the resonant frequency point, the switching adjustment amount of the reactive power compensation equipment is calculated. , ,in, The equivalent inductance of the smallest circuit loop to which the second monitoring point belongs. The preset switching capacitor value is used to control the reactive power compensation equipment on the smallest circuit loop of the second monitoring point to adjust the switching capacity according to the switching adjustment amount.

[0142] In this embodiment, Figure 3 This is a schematic diagram of the logic flow of the fourth execution module provided in this embodiment of the invention. Addressing the problem of hidden harmonic propagation, when the monitoring data at the harmonic source is normal, it is possible that the harmonic current injected by the harmonic source is very small, but an abnormally amplified harmonic voltage will still be generated at the far end of the resonant frequency. When harmonic amplification occurs at the far-end monitoring point in the propagation path, it indicates that the system has a resonance risk. By calculating the impedance using the ratio of the harmonic voltage at the far-end monitoring point to the harmonic current at the near-end, the resonant point can be accurately identified. Based on the impedance magnitude, the required damping current is calculated to achieve precise vibration suppression.

[0143] Voltage harmonic spectrum amplitude data refers to the voltage amplitude corresponding to a specific frequency harmonic component collected by a harmonic analysis device. Specifically, the target harmonic amplitude can be extracted by performing spectral decomposition on the voltage waveform using the Fast Fourier Transform algorithm.

[0144] Current harmonic spectrum amplitude data refers to the amplitude of harmonic components of a specific order in the current waveform obtained by using a current transformer and a harmonic analysis device. The harmonic impedance amplification factor is the ratio of voltage harmonics to current harmonics at a specific harmonic order, used to characterize the impedance characteristics of a circuit loop to harmonics of a specific frequency.

[0145] The resonant frequency identification operation refers to the process of determining the inherent resonant frequency of a system by analyzing the peak position of the harmonic impedance frequency characteristic curve. Specifically, the characteristic curve can be generated by frequency sweeping or impedance frequency scanning.

[0146] The injection current setting of an active power filter refers to the damping current amplitude calculated based on the harmonic impedance amplification factor and the resonant frequency, which is used to counteract the harmonic amplification effect caused by resonance.

[0147] The switching adjustment amount of reactive power compensation equipment refers to the adjustment value of capacitor compensation calculated based on the resonant frequency, which is used to change the equivalent impedance characteristics of the circuit loop.

[0148] Specifically, when the system detects that the cumulative deviation coefficient of oscillation risk is lower than a preset threshold, it indicates that there is no significant oscillation risk in the current circuit loop, but there may be potential harmonic resonance hazards. At this time, the harmonic impedance amplification factor for a specific order is calculated by comparing the harmonic voltage and current data of the farthest and closest electrical distance ends.

[0149] For example, under the h=7th harmonic, if Zh exceeds the safety threshold, a resonant frequency scan is initiated. After locating the peak frequency point by analyzing the impedance-frequency curve, a command to inject a damping current of a specific frequency is simultaneously sent to the active filter, while the reactive power compensation capacitor value is adjusted. This process employs a closed-loop control strategy. For instance, when the resonant frequency is detected to be 350Hz, the active filter will generate a reverse harmonic current of the corresponding frequency, and the capacitor switching amount will be recalculated based on the equivalent inductance parameters, resulting in an active reshaping of the impedance characteristics.

[0150] It can proactively identify potential harmonic resonance risks when there is no significant oscillation in the microgrid, and effectively suppress harmonic amplification at specific frequencies through the coordinated control of active filtering and reactive power compensation. For example, when an abnormal increase in the impedance of the 7th harmonic is detected, the system can automatically inject reverse harmonic current and optimize the capacitor compensation to avoid equipment overheating or protection malfunctions caused by resonance.

[0151] Furthermore, it also includes a fifth execution module, used to: acquire the total current data of the second monitoring point when the oscillation index is less than a first preset threshold and the oscillation index fluctuation coefficient is less than a second preset threshold. The sum of the output current data of each distributed power node in the smallest circuit loop to which the second monitoring point belongs. ; Calculate the superposition coefficient of the circulation Determine whether the circulating current superposition factor exceeds the sixth preset threshold; if so, calculate the reactive power deviation of each distributed power source. In this process, the local measurement unit of each distributed power source acquires its current reactive power output value, denoted as . , The average reactive power output of each distributed power source is used; reactive power balancing control commands are generated based on the reactive power deviation: the distributed power source corresponding to the maximum reactive power deviation is identified as the adjustment target, and its reactive power output adjustment amount is calculated. ,in, The preset maximum single adjustment amount controls the distributed power source to adjust its original reactive power output value according to the reactive power output adjustment amount.

[0152] In this embodiment, Figure 4 This is a schematic diagram of the logic flow of the fifth execution module provided in an embodiment of the present invention. It addresses the problem of circulating current accumulation caused by unbalanced reactive power output from multiple power sources. When each power source is monitored normally individually, but abnormal circulating current appears at the convergence point, the severity of the circulating current is identified by calculating the deviation between the total current and the sum of the currents of each power source. By balancing the reactive power output of each power source, the circulating current is eliminated at its source.

[0153] The circulating current superposition factor refers to the ratio of the difference between the total current and the sum of the output current of the distributed power source. Specifically, it can be achieved by collecting the total current data through a current transformer and obtaining the output current data of each node through the distributed power source controller. This factor is used to monitor whether there is abnormal circulating current in the circuit loop.

[0154] Reactive power deviation refers to the degree of deviation between the current reactive power output of each distributed power source and the average value. Specifically, it can be achieved by using smart meters to measure reactive power data in real time and calculating statistics. This indicator is used to identify power nodes with unbalanced reactive power output.

[0155] The maximum single adjustment amount refers to the preset upper limit of reactive power output that can be adjusted in a single instance. Specifically, it can be achieved through the safety threshold parameter in the microgrid control strategy database. This parameter is used to prevent voltage fluctuations caused by excessive reactive power adjustment.

[0156] Specifically, when no significant oscillation risk is detected by the system, the fifth execution module calculates the circulating current superposition coefficient by comparing the total current with the sum of the output current of the distributed power sources to determine whether there is a hidden circulating current problem. When an abnormal circulating current is detected, the reactive power output balance of each power source node is further analyzed, and the node with the largest deviation is identified as the priority adjustment target. By calculating the signed adjustment amount and limiting the single adjustment amplitude, reactive power balance can be gradually achieved while ensuring voltage stability. For example, when the reactive power output of a distributed power source is significantly higher than the average value, the system will gradually reduce its reactive power output according to a preset step size until the circulating current coefficient returns to the normal range.

[0157] This module effectively eliminates the hidden circulating current problem caused by uneven reactive power output from distributed power sources, preventing equipment overheating and losses due to circulating current accumulation during long-term operation. By dynamically adjusting the reactive power output of specific power sources, it can reduce line harmonic distortion and improve power quality. Under stable system conditions, this module proactively optimizes reactive power distribution, reserving a larger adjustment margin for potential subsequent oscillation risks.

[0158] Furthermore, it also includes a carbon flow collaborative optimization module, which is used to: when a super-level warning is issued, increase the carbon flow monitoring frequency of the corresponding distributed power source to the first carbon monitoring frequency; when a high-level warning is issued, increase the carbon flow monitoring frequency of the corresponding circuit loop to the second carbon monitoring frequency, and mark high-carbon risk areas based on high-frequency carbon flow data; when a medium-level warning is issued, perform carbon flow density recalculation on the relevant circuit loop; and dynamically display the carbon flow monitoring frequency of the corresponding circuit loop and the circuit loop according to the type of warning issued.

[0159] In this embodiment, the disconnect between oscillation risk and carbon flow management is addressed. Oscillation warning levels are deeply coupled with carbon flow monitoring, with different warning levels triggering different levels of carbon flow analysis. By quantifying the carbon resilience index, stability warnings are transformed into specific carbon management optimization measures, achieving synergistic optimization of safety and low carbon emissions.

[0160] Carbon flow monitoring frequency refers to the number of times carbon flow data is collected per unit time. This can be achieved by configuring the sampling period of the data acquisition equipment. For example, the first carbon monitoring frequency can be set to once per second, and the second carbon monitoring frequency can be set to once every five seconds.

[0161] High-carbon-risk areas refer to circuit sections where the carbon flow density exceeds a preset threshold. This can be achieved by overlaying a carbon flow heatmap onto a geographic information system. Carbon flow density recalculation refers to recalculating the carbon flow per unit time based on real-time current and carbon emission factors. This can be achieved by using an integral algorithm to multiply and accumulate the current data and the carbon emission coefficient.

[0162] Dynamic display refers to updating the circuit topology status in the visualization interface in real time according to changes in the warning level. This can be achieved by using different color codes and animation effects through the graphics rendering engine.

[0163] Microgrid monitoring systems typically separate carbon flow monitoring from electrical safety early warning, lacking carbon management strategies based on changes in electrical state. Traditional solutions employ a fixed-frequency carbon data acquisition mode, which cannot adapt to dynamic operating environments and struggles to identify the cumulative carbon flow effects across loops when multiple microgrids are sequentially connected. By establishing a mapping relationship between early warning levels and carbon monitoring parameters, adaptive carbon flow optimization driven by electrical safety events is achieved, effectively addressing the carbon emission monitoring blind spots caused by multiple microgrid connections in scenarios such as post-disaster recovery.

[0164] This invention also provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the steps of a microgrid energy flow visualization and optimization decision-making system.

[0165] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0166] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0167] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0169] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0170] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for visualizing and optimizing energy flow in microgrids, characterized in that... Includes the following steps: Step 1: Obtain the oscillation index based on the original power characteristic data of the microgrid monitoring points. When the oscillation index is less than the first preset threshold, select several first monitoring points on the circuit loop to which the microgrid monitoring points belong to form the first monitoring point set. Step 2: Based on the oscillation index of any two first monitoring points in the first monitoring point set, obtain the oscillation index fluctuation coefficient of the first monitoring point set. Compare the oscillation index fluctuation coefficient with the second preset threshold. If the oscillation index fluctuation coefficient is greater than or equal to the second preset threshold, proceed to step 3; otherwise, proceed to step 4. Step 3: When the oscillation index fluctuation coefficient is greater than or equal to the second preset threshold, locate the pair of first monitoring points with the largest difference in oscillation index in the first monitoring point set, and select several secondary monitoring points in the circuit loop segment between the pair of first monitoring points to form a new monitoring point set. Based on the new monitoring point set and the current system operating status, obtain a new oscillation index fluctuation coefficient. If the new oscillation index fluctuation coefficient is greater than or equal to the second preset threshold, repeat step 3 until the recalculated oscillation index fluctuation coefficient is less than the second preset threshold or the number of iterations reaches the preset upper limit. At this time, proceed to step 4. Step 4: Select several second monitoring points on the circuit loop between each first monitoring point in the first monitoring point set, obtain the oscillation index of the second monitoring point and compare it with the oscillation index of the corresponding first monitoring point to calculate the cumulative deviation coefficient of oscillation risk. Step 5: When the cumulative deviation coefficient of the oscillation risk is greater than or equal to the third preset threshold, the monitoring frequency of the smallest circuit loop to which the second monitoring point belongs is increased to the preset frequency and an early warning is issued.

2. The method as described in claim 1, characterized in that: Step 3 also includes, When the oscillation index fluctuation coefficient is greater than or equal to the second preset threshold, record the oscillation index fluctuation coefficient of the first monitoring point on the circuit loop to which each execution belongs during the repeated execution process. If the difference between the maximum and minimum values ​​of the oscillation index fluctuation coefficient under a preset number of consecutive cycles is greater than the fourth preset threshold, then the output power oscillation data of each distributed power source on the circuit loop is collected, and the dominant oscillation mode and the corresponding oscillation frequency and damping ratio are extracted by the modal analysis algorithm. Based on the dominant oscillation mode, the participation factor of each distributed power source is calculated. The distributed power source with the largest participation factor is identified as the dominant oscillation source and the following adjustment operations are performed: the damping ratio of the dominant oscillation source is extracted from the microgrid database and increased according to the preset gain coefficient, and the oscillation index fluctuation coefficient is recalculated. If the oscillation index fluctuation coefficient of the first monitoring point in the circuit loop is still greater than or equal to the second preset threshold, the adjustment operation is repeated until the oscillation index fluctuation coefficient is less than the second preset threshold or the number of adjustment operations reaches the preset upper limit.

3. The method as described in claim 1, characterized in that: In step 1, the oscillation index is obtained based on the original power characteristic data of the microgrid monitoring points, including: Based on the phase angles of the output voltages of each power source in the microgrid, the phase angle difference relative to the central bus of the microgrid is obtained, and then the standard deviation of the relative phase angle difference of each distributed power source on the circuit loop to which the microgrid monitoring point belongs is obtained. ; Based on the voltage data from the microgrid monitoring points, the deviation rate from the rated voltage is calculated and averaged to obtain the average voltage amplitude deviation rate of the microgrid monitoring points. ; Based on the frequency data from each monitoring point, the standard deviation of the frequency data from the microgrid monitoring points was obtained. ; The total harmonic distortion rate of the voltage at the microgrid monitoring point was obtained based on the voltage harmonic spectrum data. ; The maximum absolute value of the power change rate at the microgrid monitoring point is obtained by differentiating the active power data. ; The formula for calculating the oscillation index is as follows: ， in, The weighting coefficients are the first, second, third, fourth, and fifth weighting coefficients, respectively, satisfying... ; It is an oscillation index.

4. The method as described in claim 1, characterized in that: In step 2, the specific process for obtaining the oscillation index fluctuation coefficient of the first monitoring point is as follows: Obtain the oscillation index of each pair of the first monitoring points in the first detection point set, and obtain the oscillation index of different first monitoring points on the same circuit loop. and ; Obtain the number of pairs of the first monitoring point. ; Calculate the arithmetic mean of the oscillation indices of all first monitoring points. ; Calculate and sum the squared differences of the oscillation indices of all pairwise pairs of the first monitoring points. ; The formula for calculating the volatility coefficient of the oscillator index is as follows: ， in, This is the volatility coefficient of the oscillation index.

5. The method as described in claim 4, characterized in that: In step 4, the specific process for obtaining the cumulative deviation coefficient of oscillation risk is as follows: Calculate the arithmetic mean of the oscillation indices of all second monitoring points. ; Calculate the standard deviation of the oscillation index for all second monitoring points. ; Calculate the standard deviation of the oscillation index for all first monitoring points. ; Based on the damping ratio of the dominant oscillation source in the smallest circuit loop to which the second monitoring point belongs and the preset mapping relationship, the corresponding proportional coefficient is obtained based on the damping ratio through the preset mapping relationship. ; The formula for calculating the cumulative deviation coefficient of oscillation risk is as follows. ， in, This is the cumulative deviation coefficient for oscillation risk.

6. The method as described in claim 1, characterized in that, In step 5, when the cumulative deviation coefficient of oscillation risk is less than the third preset threshold, the voltage harmonic spectrum amplitude data of the second monitoring point with the longest electrical distance from the microgrid's grid-connected electrical point is obtained, and denoted as... And acquire the current harmonic spectrum amplitude data of all first monitoring points that are electrically closest to the microgrid's grid-connected electrical point, denoted as ; Based on the voltage harmonic spectrum data and current harmonic spectrum data, calculate the preset major harmonic order. The harmonic impedance amplification factor is below ; When the harmonic impedance amplification factor exceeds the fifth preset threshold, in the harmonic impedance frequency characteristic curve of the smallest circuit loop at the second monitoring point, [the following will be included / removed / indicated / etc.]. The frequency corresponding to the maximum value is identified as the resonant frequency point. ; Based on the resonant frequency point Harmonic impedance amplification factor Calculate the injection current setpoint of the active power filter. , ,in, This is the preset damping gain coefficient; Control the active power filter to inject a damping current of a set value at the resonant frequency point; Based on the resonant frequency point, calculate the switching adjustment amount of the reactive power compensation equipment. , ,in, The equivalent inductance of the smallest circuit loop to which the second monitoring point belongs. The preset switching capacitor value; The reactive power compensation equipment on the smallest circuit loop to which the second monitoring point belongs adjusts its switching capacity according to the switching adjustment amount.

7. The method as described in claim 1, characterized in that: It also includes, When the oscillation index is less than the first preset threshold and the oscillation index fluctuation coefficient is less than the second preset threshold, the total current data of the second monitoring point is acquired. The sum of the output current data of each distributed power node in the smallest circuit loop to which the second monitoring point belongs. ; Calculate the superposition coefficient of the circulation Determine whether the circulating superposition coefficient exceeds the sixth preset threshold; If so, calculate the reactive power deviation of each distributed power source. ,in, This represents the current reactive power output value of each distributed power source. This represents the average reactive power output of each distributed power source. Based on the reactive power deviation, a reactive power balancing control command is generated: the distributed power source corresponding to the maximum value of the reactive power deviation is identified as the adjustment target, and its reactive power output adjustment amount is calculated. ,in, This is the preset maximum adjustment amount per cycle; The distributed power source is controlled to adjust its original reactive power output value according to the reactive power output adjustment amount.

8. The method as described in claim 1, characterized in that: In step 1, when the oscillation index is greater than or equal to the first preset threshold, the control mode of all distributed power sources on the circuit loop to which the microgrid monitoring point belongs is switched to the current source control mode, and a special warning is issued. In step 3, when the oscillation index fluctuation coefficient is greater than or equal to the second preset threshold, the monitoring frequency of the circuit loop between a group of first monitoring points is increased to the first preset frequency, and an advanced warning is issued. In step 5, when the cumulative deviation coefficient of oscillation risk is greater than or equal to the third preset threshold, a medium-level warning is issued. When the cumulative deviation coefficient of oscillation risk is less than the third preset threshold, calculate the maximum fluctuation similarity between the cumulative deviation coefficient of oscillation risk of the main network central bus corresponding to the smallest circuit loop of the second monitoring point and the cumulative deviation coefficient of oscillation risk of the second monitoring point within a preset time period, and determine whether the maximum fluctuation similarity exceeds the fourth preset threshold. If so, issue a primary warning.

9. The method as described in claim 8, characterized in that: It also includes, When a special warning is issued, the carbon flow monitoring frequency of the corresponding distributed power source will be increased to the first carbon monitoring frequency. When a high-level warning is issued, the carbon flow monitoring frequency of the corresponding circuit loop is increased to the second carbon monitoring frequency, and high-carbon risk areas are marked based on high-frequency carbon flow data. When a medium-level warning is issued, the carbon flux density of the relevant circuit loops is recalculated. Based on the type of warning issued, the corresponding circuit loop and the carbon flow monitoring frequency of the circuit loop are dynamically displayed.

10. A microgrid energy flow visualization and optimization decision-making system, characterized in that: include, The first judgment module is configured to obtain the oscillation index based on the original power characteristic data of the microgrid monitoring points. When the oscillation index is less than the first preset threshold, a number of first monitoring points are selected on the circuit loop to which the microgrid monitoring points belong to form a first monitoring point set. The second judgment module is configured to obtain the oscillation index fluctuation coefficient of the first monitoring point set based on the oscillation index of any two first monitoring points in the first monitoring point set, and compare the oscillation index fluctuation coefficient with a second preset threshold. The first execution module is configured to locate a pair of first monitoring points with the largest difference in oscillation index when the oscillation index fluctuation coefficient is greater than or equal to a second preset threshold. It then selects several secondary monitoring points within the circuit loop segment between the pair of first monitoring points to form a new monitoring point set. Based on the new monitoring point set and the current system operating state, it obtains a new oscillation index fluctuation coefficient. If the new oscillation index fluctuation coefficient is greater than or equal to the second preset threshold, it repeats the action of the first execution module until the recalculated oscillation index fluctuation coefficient is less than the second preset threshold or the number of iterations reaches a preset upper limit. The second execution module is configured to, when the oscillation index fluctuation coefficient is less than a second preset threshold or when the iteration number of the first execution module reaches a preset upper limit, select several second monitoring points on the circuit loop between the first monitoring points in the first monitoring point set, obtain the oscillation index of the second monitoring points, and compare it with the oscillation index of the corresponding first monitoring points to calculate the cumulative deviation coefficient of oscillation risk; and, The third execution module is configured to raise the monitoring frequency of the smallest circuit loop to which the second monitoring point belongs to a preset frequency and issue an early warning when the cumulative deviation coefficient of the oscillation risk is greater than or equal to a third preset threshold.

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