Power resource management method and system for micro-grid

Abstract

The invention discloses a micro-grid power resource management method and system, and the method comprises the steps: obtaining the operation data of each node in a micro-grid in real time, and calculating the comprehensive power quality index of each node based on the operation data; predicting operation data of each node in the micro-grid, and calculating an abnormal index of each node based on the operation data obtained and predicted in real time; calculating a resource scheduling index of each node according to the comprehensive power quality index, the abnormal index and the load change rate of each node; and scheduling the power resource of each node based on the resource scheduling index of each node. The quality of the power system is comprehensively evaluated by using the comprehensive power quality index, power abnormity can be quickly identified, the accuracy of voltage load monitoring is improved, abnormal fluctuation of the microgrid is detected by calculating the abnormal index, potential problems are identified in advance, and major faults caused by voltage abnormity or load fluctuation are avoided. And accurate micro-grid management is realized.

Description

Technical Field

[0001] The present invention relates to microgrid management, and in particular to a microgrid power resource management method and system. Background Art

[0002] In the field of distribution network load monitoring, the currently commonly used voltage load monitoring method mainly relies on fixed sensor equipment at power substations and microgrid nodes. These sensors usually transmit the monitored data to the central control system through wired or wireless means for data collection, analysis and fault warning. In addition, traditional voltage load monitoring is mainly concentrated on key nodes and large equipment, relying on regular manual inspections and maintenance to ensure the normal operation of equipment. This method is more effective when the power supply system is operating normally, and can promptly detect and solve some equipment failures.

[0003] However, due to the high installation cost of traditional monitoring equipment and the relatively fixed monitoring points, non-critical nodes and marginal areas in the power system are not fully monitored. In addition, it is difficult for manual regular inspections to detect load fluctuations and voltage anomalies in a timely manner, which can easily lead to greater safety hazards and economic losses. With the growth of electricity demand and the complexity of grid loads, traditional management methods are difficult to meet the current needs for real-time monitoring of the grid, data refinement processing, and intelligent operation and maintenance. Especially in emergencies, the response speed is slow, and it is impossible to dynamically perceive and quickly respond to voltage and load changes. Summary of the invention

[0004] Purpose of the invention: In view of the above shortcomings, the present invention provides an accurate and timely power resource management method and system for a microgrid.

[0005] Technical solution: To solve the above problems, the present invention adopts a power resource management method for a microgrid, comprising the following steps:

[0006] Obtain the operating data of each node in the microgrid in real time, and calculate the comprehensive power quality index of each node based on the operating data;

[0007] Predict the operation data of each node in the microgrid, and calculate the abnormal index of each node based on the real-time operation data and predicted operation data;

[0008] Calculate the load change rate of each node, and calculate the resource scheduling index of each node based on the comprehensive power quality index, abnormal index, and load change rate of each node;

[0009] Based on the resource scheduling index of each node, the power resources of each node are scheduled.

[0010] Furthermore, the operation data includes voltage, current, power factor and frequency, and the calculation of the comprehensive power quality index of each node based on the operation data includes preprocessing the operation data acquired in real time to obtain the measured value of the operation data; the comprehensive power quality index CPQI is expressed as:

[0011]

[0012] Where V is the measured value of voltage, V n is the rated value of voltage; I is the measured value of current, I n is the rated value of the current; is the measured value of power factor; f is the measured value of frequency, f n is the rated value of the frequency, and α, β, γ, δ, ∈, ζ, η, and θ are all weight coefficients.

[0013] Furthermore, the abnormal index AI is expressed as:

[0014]

[0015] Among them, X i is the measured value of the ith running data, is the predicted value of the ith running data, σ historical is the standard deviation of historical operating data, ω i is the weight coefficient and ξ is the adjustment factor.

[0016] Furthermore, the resource scheduling index RDC is expressed as:

[0017]

[0018] in, is the load change rate, κ, π, and χ are all weight coefficients.

[0019] Furthermore, after the operation data of each node in the microgrid is acquired in real time, the operation data is compressed and transmitted, and the data compression ratio CR of the operation data of each node is expressed as:

[0020]

[0021] Among them, σ is the standard deviation of the running data, σ t is the maximum standard deviation allowed after compression, λ and μ are adjustment parameters;

[0022] The transmission priority TP of the operation data of each node is expressed as:

[0023]

[0024] in, It represents the rate of change of the comprehensive power quality index CPQI over time, BW represents the available bandwidth of the current network, BW max It represents the maximum bandwidth that the current network can provide, and q, ρ and τ are all weight coefficients.

[0025] The present invention also adopts a power resource management system of a microgrid, comprising:

[0026] A data acquisition module is used to obtain the operating data of each node in the microgrid in real time;

[0027] The data calculation module is used to calculate the comprehensive power quality index of each node based on the operating data; it is also used to predict the operating data of each node in the microgrid, and calculate the abnormal index of each node based on the real-time acquired operating data and the predicted operating data; calculate the load change rate of each node, and calculate the resource scheduling index of each node according to the comprehensive power quality index, abnormal index, and load change rate of each node;

[0028] The execution module is used to schedule the power resources of each node based on the resource scheduling index of each node.

[0029] Furthermore, the operating data includes voltage, current, power factor and frequency, and the comprehensive power quality index CPQI is expressed as:

[0030]

[0031] Where V is the measured value of voltage, V n is the rated value of voltage; I is the measured value of current, I n is the rated value of the current; is the measured value of power factor; f is the measured value of frequency, f n is the rated value of the frequency, and α, β, γ, δ, ∈, ζ, η, and θ are all weight coefficients.

[0032] The abnormality index AI is expressed as:

[0033]

[0034] Among them, X i is the measured value of the ith running data, is the predicted value of the ith running data, σ historical is the standard deviation of historical operating data, ω i is the weight coefficient and ξ is the adjustment factor.

[0035] The resource scheduling index RDC is expressed as:

[0036]

[0037] Among them, AI is the anomaly index, is the load change rate, k, π, and χ are all weight coefficients.

[0038] Furthermore, it also includes a data compression transmission module for compressing and transmitting the operation data;

[0039] The data compression ratio CR of the running data of each node is expressed as:

[0040]

[0041] Among them, σ is the standard deviation of the running data, σ t is the maximum standard deviation allowed after compression, λ and μ are adjustment parameters;

[0042] The transmission priority TP of the operation data of each node is expressed as:

[0043]

[0044] in, It represents the rate of change of the comprehensive power quality index CPQI over time, BW represents the available bandwidth of the current network, BW max It represents the maximum bandwidth that the current network can provide, and q, ρ and τ are all weight coefficients.

[0045] The present invention also adopts a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0046] The present invention also adopts a computer-readable storage medium on which a computer program is stored, and the computer program implements the steps of the above method when executed by a processor.

[0047] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0048] (1) By deploying intelligent sensor networks at each node of the microgrid to collect parameters such as voltage, current, and power factor in real time, the present invention can perform real-time monitoring in a wider area and more detailed nodes, breaking through the limitation of traditional methods that are limited to key nodes. At the same time, the comprehensive power quality index (CPQI) is used to comprehensively evaluate the quality of the power system, which can quickly identify power anomalies, improve the accuracy of voltage and load monitoring, and achieve precise microgrid management.

[0049] (2) By calculating the anomaly index (AI) and combining it with a machine learning algorithm, it is possible to predict and detect abnormal fluctuations in voltage and load in real time, identify potential problems in advance, and avoid major failures caused by abnormal voltage or load fluctuations. The introduction of historical standard deviation enables the system to dynamically adjust the detection sensitivity, reduce false alarms, and ensure timely warnings in abnormal situations.

[0050] (3) The bandwidth occupied by data transmission is reduced through edge computing and compression algorithms, which significantly improves the data transmission efficiency. The data compression ratio (CR) calculation formula can reduce data redundancy while ensuring data integrity. At the same time, the dynamic adjustment of the transmission priority (TP) ensures that key data is transmitted first, so that the system can still maintain efficient operation when the network conditions are poor. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of the power resource management method in the present invention. DETAILED DESCRIPTION

[0052] Example 1

[0053] like Figure 1 As shown, a power resource management method for a microgrid in this embodiment includes the following steps:

[0054] Step 1: Collect and monitor the operating data of each node in the microgrid in real time.

[0055] The operation data may include at least voltage, current and power factor. Operation data refers to the actual power parameters collected by sensors or other monitoring devices at each node of the microgrid, which are used to evaluate the operation status and power quality of the node.

[0056] Real-time acquisition means that the system continuously obtains power operation parameters from each node through the intelligent sensor network without obvious delay, ensuring the immediacy of data. The data collected in real time can reflect the dynamic operation of the microgrid at every moment, providing an accurate basis for subsequent data analysis and decision support. Real-time monitoring means that the system not only collects data, but also continuously monitors the data and can quickly identify any abnormal situation. Through real-time monitoring, potential problems in the operation of the microgrid can be discovered and corresponding measures can be taken in time to avoid failures.

[0057] It can be understood that voltage reflects the voltage level of each node in the microgrid. Voltage stability is a basic requirement for grid operation. Too high or too low voltage will affect the normal operation of power equipment.

[0058] Current refers to the amount of electricity passing through a node. The current is directly related to the electrical load, and monitoring the current can help understand the node's load and potential overload risks.

[0059] The power factor refers to the ratio of the active power to the apparent power of a node, reflecting the efficiency of power use. When the power factor is low, the energy efficiency of the system is reduced, which may result in higher energy loss. Therefore, monitoring the power factor helps improve the efficiency of power grid operation.

[0060] Through real-time monitoring of operating data such as voltage, current and power factor, the present invention enables the system to dynamically evaluate the power quality of each node, promptly identify abnormal phenomena such as voltage fluctuations and load changes, provide real-time power load and operating status information, help operation and maintenance personnel make adjustment decisions, and provide key data support for subsequent fault diagnosis, load forecasting and resource optimization allocation.

[0061] Through the above real-time data collection and monitoring, the present invention can ensure the stable operation of the microgrid and help improve the management and maintenance efficiency of the power system.

[0062] Step 2: Preprocess the operating data to obtain preprocessed data, and calculate the comprehensive power quality index of each node based on the preprocessed data.

[0063] In some embodiments, step 2 may include:

[0064] obtaining a rated value of each of the preprocessed data;

[0065] The comprehensive power quality index of each node is determined according to the rated value and the measured value of each pre-processed data.

[0066] In some embodiments, the comprehensive power quality index is expressed as:

[0067]

[0068] Among them, V is the measured value of voltage, I is the measured value of current, is the measured value of power factor, f is the measured value of frequency, V n ,I n 、f n are the corresponding rated values, and α, β, γ, δ, ∈, ζ, η, and θ are the first weight, second weight, third weight, fourth weight, fifth weight, sixth weight, seventh weight, and eighth weight, respectively.

[0069] In the specific implementation, V is the measured value of the voltage, which is the voltage level when the system is currently running.

[0070] I is the measured value of current, which reflects the actual load status of the system.

[0071] It is the measured value of power factor, which is the ratio between active power and apparent power in the power system and reflects the efficiency of power transmission.

[0072] f is the measured value of frequency. The power system is usually maintained at 50Hz or 60Hz. The stability of frequency is an important indicator of power grid quality. n ,I n 、f n is the corresponding rated value, V n The standard rated voltage designed for the system, usually the reference voltage for stable operation in the power grid. This value is used to evaluate the deviation of the measured voltage V from the standard voltage. n The standard rated current expected in the power distribution system. This value is used to compare with the actual current I to measure the load on the system.

[0073] f n The standard frequency designed for the system, usually 50Hz or 60Hz. The actual frequency f is relative to f n The deviation can be used to measure the stability of the power grid. α, β, γ, δ, ∈, ζ, η, and θ are the first weight, the second weight, the third weight, the fourth weight, the fifth weight, the sixth weight, the seventh weight, and the eighth weight, respectively, which are used to adjust the contribution of each parameter to the comprehensive power quality index (CPQI).

[0074] The goal of this formula is to calculate the comprehensive power quality index (CPQI) of each node by combining real-time monitoring data of multiple key electrical parameters (voltage, current, power factor and frequency). Each parameter is combined with different powers and weights to reflect the overall operating quality of the power system.

[0075] The ratio of voltage V to current I reflects the electrical load condition of the node. Voltage that is too high or too low, or current that is unstable, may affect the safety and stability of the system. It directly reflects the energy efficiency of the system. A lower power factor will lead to increased energy loss in the power system, so it is a key factor affecting power quality. Frequency stability is the core parameter of power grid stability, and frequency fluctuations will affect the synchronization of the entire power network.

[0076] By reasonably setting these weight coefficients, the present invention can flexibly adjust the calculation method of the power quality index according to the actual operation of the microgrid, ensuring that the power system can maintain high power quality under various circumstances.

[0077] Step 3: According to the comprehensive power quality index of each node, each operation data is preliminarily analyzed and compressed, and the data compression ratio of each operation data is calculated.

[0078] In some embodiments, step 3 may include:

[0079] Obtaining a standard deviation of the operating data;

[0080] Obtaining a maximum standard deviation allowed after compression corresponding to the operating data;

[0081] Obtaining adjustment parameters for calculating the data compression ratio;

[0082] A data compression ratio of the operating data is determined according to the adjustment parameter, and a standard deviation and a maximum standard deviation of the operating data.

[0083] In some embodiments, the data compression ratio is expressed as:

[0084]

[0085] Among them, CR is the data compression ratio, σ is the standard deviation of the original data of the running data, and σ t is the maximum standard deviation allowed after compression, λ and μ are the first adjustment parameter and the second adjustment parameter respectively.

[0086] In the specific implementation, CR is the data compression ratio, which reflects the efficiency of the compression algorithm. A higher CR means that the data volume is reduced as much as possible while maintaining data accuracy. When compressing data in edge computing devices, the data volume and data accuracy are balanced. By adjusting CR, the data transmitted to the central control system is sufficiently representative, and bandwidth usage and storage costs are reduced.

[0087] σ is the standard deviation of the uncompressed original data, which is used to measure the degree of dispersion or volatility of the original data. The standard deviation σ of the original data is an important basic indicator for calculating the compression ratio. The greater the data volatility, the greater the standard deviation, which means that the data contains more volatility information, and more caution is needed when compressing to avoid losing key information.

[0088] σ t is the maximum standard deviation allowed after compression, representing the tolerance of the compressed data in terms of accuracy. t Indicates that the data accuracy requirement is lower and the compression strength can be greater; a lower σ t It means that the data must retain more details, and the compression ratio is correspondingly smaller. The standard deviation of the compressed data is σ t Determines the allowable difference between the compressed data and the original data. By controlling σ t , the system can balance data accuracy and compression effect.

[0089] λ and μ are the first adjustment parameter and the second adjustment parameter, respectively. λ is used to adjust the overall amplitude of the compression ratio in the formula, and μ is used to adjust the influence of the data compression ratio CR as the comprehensive power quality index (CPQI) changes.

[0090] The logarithm of the ratio of the standard deviation of the original data to the standard deviation of the compressed data is calculated, indicating the degree of data compression. The ratio indicates that the original data has large volatility, but the volatility after compression is small, indicating that the data compression is strong. This logarithmic term reflects the magnitude of data compression. By calculating the standard deviation σ of the original data and the maximum standard deviation σ allowed for the compressed data t The system can quantify the loss of data accuracy after compression by taking the logarithm of the ratio.

[0091] 1-e -μ×CPQI It reflects the impact of the comprehensive power quality index (CPQI) on the compression ratio. When the CPQI is high, the index term e -μ×CPQI When the CPQI is close to 0, the compression ratio CR becomes larger; when the CPQI is low, the exponential term is close to 1, and the compression ratio is relatively small. The higher the comprehensive power quality index (CPQI), the more stable the system operation state is, and the compressed data can reduce more details without significantly affecting the validity of the data. Therefore, a higher CPQI will result in a larger compression ratio CR; when the CPQI is low, the system will retain more data details and the compression ratio will be relatively small.

[0092] The formula calculates the data compression ratio CR by combining the standard deviation of the data, the allowable accuracy loss, and the system power quality comprehensive assessment value (CPQI). This process ensures that the amount of transmitted data is minimized while ensuring data quality. The term determines the basic degree of data compression, which depends on the standard deviation ratio of the original data and the compressed data. By adjusting the compression ratio by CPQI, when the power quality is good, the system can increase the data compression; when the power quality is poor, the system will retain more data details to improve accuracy. This formula realizes the intelligent adjustment of data compression, optimizes data transmission efficiency, reduces data volume, and reduces bandwidth and storage costs without affecting monitoring and early warning functions.

[0093] As can be seen from the above, the data compression ratio CR of the present invention is obtained by comprehensive evaluation of the volatility of the original data, the allowable standard deviation and the power quality index (CPQI). The various parameters in the formula work together to achieve efficient data compression without losing key data. λ and μ are key factors in adjusting the sensitivity of the formula, controlling the overall amplitude of data compression and the adjustment effect with the change of CPQI, ensuring the flexibility and adaptability of data compression.

[0094] Step 4: According to the network status and the comprehensive power quality index of each node, the transmission priority of each operation data is calculated, and each operation data is compressed and transmitted based on the data compression ratio and the transmission priority.

[0095] In some embodiments, step 4 may include:

[0096] Get the current available bandwidth and maximum bandwidth of the network;

[0097] Determining a rate of change of a comprehensive power quality index of a node where the operating data is located;

[0098] The transmission priority of each of the operating data is calculated according to the available bandwidth corresponding to each of the operating data, the maximum bandwidth and the change rate of the comprehensive power quality index.

[0099] In some embodiments, the transmission priority of the operation data can be expressed as:

[0100]

[0101] TP is a quantification of the priority of data transmission of each node at a given moment. The higher the value, the more important the data of the node is and should be transmitted first. TP aims to intelligently determine which nodes' data should be sent first under the current network conditions by comprehensively considering power quality, data change rate and network bandwidth, so as to ensure the overall operation efficiency of the system and data validity.

[0102] The change rate of CPQI indicates the change rate of the comprehensive power quality index CPQI over time. It reflects the fluctuation speed of power quality. The larger the value, the faster the power quality of the node changes. The system may face an unstable situation, so it is necessary to obtain the data of the node more timely for early warning and processing. Therefore, the data of nodes with a larger change rate should be transmitted first.

[0103] BW is the current available bandwidth, which indicates the available bandwidth of the current network. It is the amount of bandwidth that nodes in the system can use for data transmission. In the case of limited bandwidth, the system needs to allocate resources reasonably to ensure that data of key nodes can be transmitted first. The size of the available bandwidth BW directly affects the possibility of node data transmission.

[0104] BW max It is the maximum bandwidth, which is the maximum bandwidth that the network can provide, indicating the data transmission capacity of the system under optimal conditions. BW is close to BW max When BW is large, network resources are sufficient and data can be transmitted more freely; when BW is small and close to the bandwidth limit, the system must filter the transmitted data according to priority.

[0105] Indicates the current bandwidth usage. It is the ratio of the current available bandwidth to the maximum bandwidth, reflecting the bandwidth utilization. Indicates the urgency of the current bandwidth occupancy. When the bandwidth is close to saturation, the transmission priority TP increases, indicating that key data needs to be transmitted first when bandwidth resources are tight; when BW is large and bandwidth is sufficient, TP can be appropriately reduced. q, ρ, and τ are the ninth, tenth, and eleventh weights, respectively.

[0106] The present invention combines power quality index (CPQI), power quality change rate The transmission priority TP of each node data is dynamically calculated by combining the CPQI and network bandwidth utilization. This ensures that when the power system is running and bandwidth resources are limited, the data of important nodes can be transmitted first to avoid data delay or loss, thereby improving the real-time monitoring and response capabilities of the system. The larger the value, the higher the power status of the network, and the higher the data transmission rate. The item will increase, so that the system can still give priority to transmitting data of key nodes when the bandwidth is tight, ensuring that important data will not be lost.

[0107] TP is a priority index that comprehensively considers node power quality, fluctuations, and bandwidth resources.

[0108] Through the flexible adjustment of weight parameters q, ρ and τ, the system can dynamically optimize the data transmission strategy to ensure that key data is transmitted first when bandwidth resources are tight, thereby ensuring the monitoring efficiency and stability of the microgrid.

[0109] Step 5: Predict the operating data of each node, detect the abnormality of the operating status of each node in real time, and calculate the corresponding abnormality index.

[0110] In some embodiments, step 5 may include:

[0111] Obtaining measured values ​​and predicted values ​​of the operating data monitored by each of the nodes;

[0112] Get the standard deviation of historical operation data;

[0113] The abnormality index is determined according to a first difference between the measured value and the predicted value, and a standard deviation of the historical operation data.

[0114] In some embodiments, the anomaly index may be expressed as:

[0115]

[0116] Among them, AI is used to measure the degree of deviation between the operating status of an electric node and the predicted value. It reflects the difference between the measured data and the predicted value, and is used to determine whether the node is in an abnormal state. A higher AI means that the data of the node deviates greatly from the normal range, and there may be a fault or anomaly; a lower AI means that the node is running stably and there is no obvious deviation. By calculating AI, the system can detect abnormal changes early, provide early warning and fault diagnosis.

[0117] X i It is the measured value of the ith operating data, indicating the real-time measured value of the ith parameter (such as voltage, current, power factor, etc.) in the power system. It is the data collected by the sensor at a certain point in time. As the current actual operating data, it is compared with the predicted value to measure the abnormality of the node operation status.

[0118] is the predicted value of the ith operating data, which is the predicted value of the ith parameter calculated by machine learning algorithms or other prediction models. It reflects the power parameter value that the system expects the node to reach under normal circumstances. As a reference value, it is compared with the measured value X i Compare to see if the system is off track.

[0119] ω i is the twelfth weight of the ith running data. historical It is the standard deviation of historical operation data. It is the standard deviation of historical data in the system. It is used to measure the fluctuation of the node in historical operation. It reflects the stability of the data of the node under normal operating conditions. By using the historical standard deviation, the system can set a benchmark to determine whether the fluctuation of the current data exceeds the normal historical fluctuation range. A larger historical standard deviation means that the operation fluctuation of the node is larger, and the system has a higher tolerance for the current fluctuation; a smaller historical standard deviation means that the node has been relatively stable in history and has a lower tolerance. The current small fluctuation may trigger an abnormal alarm.

[0120] ξ is the adjustment factor. The system can control the sensitivity of the anomaly index. A smaller ξ value will increase the AI ​​value, making the system more sensitive to detecting anomalies, and even small deviations may trigger an anomaly alarm; while a larger ξ value will make the system react more slowly to fluctuations, and only trigger an alarm when the deviation reaches a certain level.

[0121] This formula combines the deviation between the measured value and the predicted value, the fluctuation of historical data and the importance of each parameter, and is used to evaluate whether a node in the current power system is abnormal by calculating the abnormal index AI. When the measured value of a node deviates greatly from the predicted value, it means that the operating status of the node has deviated from the normal range, which will cause the abnormal index AI to rise.

[0122] By introducing the historical standard deviation, the system can refer to the historical operation status of the node. The node's volatility is compared with its historical stability to help the system determine whether the current volatility is abnormal. If the current volatility exceeds the historical range, the system will consider the node to be abnormal. i The adjustment factor ξ provides flexibility, and can flexibly adjust the AI ​​calculation according to the importance of different parameters to the system and the characteristics of the node's historical data, making anomaly detection more accurate.

[0123] Step 6: According to the comprehensive power quality index and abnormal index of each node, dynamically adjust the power resource allocation of the microgrid, calculate the corresponding resource scheduling coefficient, and schedule the power resources of each node based on the resource scheduling coefficient to reduce the abnormal voltage load.

[0124] In some embodiments, step 6 may include:

[0125] Obtaining a load change rate of the node;

[0126] A resource scheduling index of the node is determined according to the load change rate, an abnormality index and a comprehensive power quality index of the node.

[0127] In some embodiments, the resource scheduling index may be expressed as:

[0128]

[0129] RDC is a dynamic indicator for allocating system resources (such as power, bandwidth, etc.) among various nodes. It reflects the resource priority allocated to each node based on its power quality and operating status. The larger the value, the more resource support the node should receive. Through RDC calculation, the system can dynamically adjust the allocation of power resources in the event of load fluctuations and power anomalies, ensuring that key nodes can obtain sufficient resources and improving the overall operating efficiency and stability of the system.

[0130] It is the load change rate, which indicates the change of the node load in unit time. It reflects the speed and amplitude of load fluctuation in the power system. The load change rate is used to measure the load fluctuation of the node. This indicates that the load of the node changes rapidly. Load fluctuations may cause system instability. Therefore, it is necessary to adjust resource allocation in time to cope with fluctuations and ensure the normal operation of the system. v, π, and χ are the thirteenth, fourteenth, and fifteenth weights, respectively.

[0131] The present invention calculates the resource scheduling coefficient RDC of each node by combining the comprehensive power quality index (CPQI), the abnormal index (AI) and the load change rate, thereby dynamically allocating system resources to each node. The CPQI and AI terms in the formula are combined to measure the current power quality and abnormal conditions of the node. If the power quality of a node is high or the abnormal index is large, the system will prioritize the allocation of resources to ensure the normal operation of the node. Indicates the load change of the node. When the load fluctuation of the node is large, The item will increase the resource scheduling coefficient RDC, ensuring that nodes with severe load fluctuations obtain more resources to cope with fluctuations and maintain stability.

[0132] For nodes with good power quality, low abnormal index, and small load fluctuation, the CPQI in the formula is high, but AI and If the load of a node fluctuates greatly, even if the power quality CPQI is low, the system will provide appropriate resources for the node to ensure its normal operation. The item will also significantly improve the resource scheduling coefficient RDC, ensuring that the node has sufficient resource support when the load fluctuates.

[0133] The RDC of the present invention is a dynamic resource allocation index comprehensively evaluated based on power quality, abnormal conditions and load fluctuation rate. Through the adjustment of weight parameters, the system can flexibly allocate power resources dynamically according to the operating status and load changes of different nodes to ensure the stability and efficient operation of the entire microgrid.

[0134] In some embodiments, the present invention may further include: presenting the preprocessed data in a multi-dimensional visualization manner and providing an interactive decision support tool for the preprocessed data;

[0135] In some embodiments, the multi-dimensional visualization method may include heat maps, time series diagrams, relationship network diagrams, and geographic information system integrated displays. Among them, heat maps can be used to intuitively display the distribution of power parameters in the power grid. The heat map reflects the differences in parameters such as voltage and load in different regions through color gradients. For example, the darker the color, the higher the load, or the greater the voltage fluctuation. The heat map is suitable for global monitoring of the operation status of the power grid, especially identifying areas with heavy loads.

[0136] The timing diagram can show how the parameters of each node in the power system change over time. The timing diagram is suitable for monitoring the fluctuation trend of voltage, current or load, and identifying the moment of abnormal fluctuation. For example, the timing diagram of voltage can help managers find the time period of voltage instability and take timely measures.

[0137] The relationship network diagram can be used to show the relationship between different nodes in the microgrid. For example, the load distribution and resource sharing between different nodes. Through the relationship network diagram, users can understand the mutual influence of each node in the system, so as to optimize resource allocation and load management.

[0138] By integrating the nodes and data of the power system with the geographic information system, users can intuitively view the operating status of each substation or node on the map. This integration method is very suitable for large-scale power grid management, especially for power networks with wide geographical distribution. GIS can combine real-time data to help users quickly locate problem areas and improve response efficiency.

[0139] In some embodiments, the interactive decision support tool may include load forecasting, fault diagnosis, maintenance suggestions, and resource optimization allocation functions. Among them, the load forecasting tool predicts the future trend of power load changes based on historical data, real-time monitoring data, and prediction algorithms (such as machine learning algorithms). This can help managers to make load scheduling in advance to avoid overload or insufficient power supply. For example, before the peak power period, the system can increase resource allocation or adjust the load in advance according to the load forecast results to ensure smooth operation of the system.

[0140] The fault diagnosis tool compares real-time monitoring data with historical data and calculates the abnormal index (AI) to identify potential fault points in the system. The fault diagnosis tool can automatically analyze the possible causes of the fault and provide repair suggestions. When the power parameters (such as voltage and current) of a node deviate from the normal range, the system will automatically issue an alarm and analyze it in combination with historical fault patterns to help managers quickly locate the problem and develop solutions.

[0141] The resource optimization allocation tool provides managers with resource allocation optimization solutions based on real-time monitoring data, load forecasting results, and power quality parameters (CPQI). The tool can help dynamically adjust the resource allocation of each node to ensure stable operation of the power grid when load fluctuates or resources are limited. For example, when bandwidth or power resources are limited, the system will prioritize resource allocation to key nodes based on the power quality and load conditions of the nodes to avoid system crashes or performance degradation.

[0142] Maintenance suggestions are based on system operation status and equipment usage. The maintenance suggestion tool can provide managers with a timetable and specific suggestions for equipment maintenance. The tool comprehensively considers the equipment's load, operating time, and historical fault records to generate the optimal maintenance plan.

[0143] The combination of multi-dimensional visualization and interactive decision support tools enables managers to grasp complex power data in an intuitive way and make efficient management decisions quickly based on the decision suggestions provided by the system. This approach not only improves the automation and intelligence level of the power system, but also reduces the risk of human intervention and improves the efficiency and stability of power operation and maintenance.

[0144] The present invention can help users observe the operation status of microgrids from multiple perspectives by presenting preprocessed data in a multi-dimensional visualization manner, especially by multi-dimensional display methods such as heat maps, time series diagrams, relationship network diagrams, and GIS integrated displays, to intuitively discover potential problems in the power system. At the same time, with the help of interactive decision support tools, managers can make more intelligent and scientific decisions based on the system's load forecasting, fault diagnosis, maintenance suggestions, and resource optimization allocation functions, thereby improving the stability, reliability, and operation and maintenance efficiency of the microgrid.

[0145] In summary, the present invention provides a real-time, accurate and efficient voltage load monitoring and optimization scheduling solution by introducing technologies such as intelligent sensor networks, edge computing and machine learning, which effectively improves the safety, stability and management efficiency of the microgrid.

[0146] Example 2

[0147] In this embodiment, a power resource management system of a microgrid includes:

[0148] A data acquisition module is used to collect and monitor the operating data of each node in the microgrid in real time; the operating data includes at least voltage, current and power factor;

[0149] A data calculation module, used to pre-process the operating data to obtain pre-processed data, and calculate the comprehensive power quality index of each node based on the pre-processed data; used to predict the voltage load of each node during data compression and transmission, and detect the abnormal situation of the voltage load of each node in real time, and calculate the corresponding abnormal index; used to dynamically adjust the power resource allocation of the microgrid according to the comprehensive power quality index and abnormal index of each node, and calculate the corresponding resource scheduling coefficient;

[0150] A data compression and transmission module, configured to perform preliminary analysis and compression on each of the operation data according to the comprehensive power quality index of each of the nodes, and calculate the data compression ratio of each of the operation data; calculate the transmission priority of each of the operation data according to the network status and the comprehensive power quality index of each of the nodes, and compress and transmit each of the operation data based on the data compression ratio and the transmission priority;

[0151] An execution module is used to schedule the power resources of each of the nodes based on the resource scheduling coefficient to reduce abnormal voltage load.

[0152] Example 3

[0153] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0154] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0155] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0157] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for managing power resources in a microgrid, characterized in that: The following steps are involved: Obtain the operating data of each node in the microgrid in real time, and calculate the comprehensive power quality index of each node based on the operating data; Predict the operation data of each node in the microgrid, and calculate the abnormal index of each node based on the real-time operation data and predicted operation data; Calculate the load change rate of each node, and calculate the resource scheduling index of each node based on the comprehensive power quality index, abnormal index, and load change rate of each node; Based on the resource scheduling index of each node, the power resources of each node are scheduled.

2. The power resource management method of a microgrid according to claim 1, characterized in that: The operation data includes voltage, current, power factor and frequency. The calculation of the comprehensive power quality index of each node based on the operation data includes preprocessing the operation data acquired in real time to obtain the measured value of the operation data. The comprehensive power quality index CPQI is expressed as: Where V is the measured value of voltage, V n is the rated value of voltage; I is the measured value of current, I n is the rated value of the current; is the measured value of power factor; f is the measured value of frequency, f n is the rated value of frequency, and α, β, γ, δ, ∩, ζ, η, and θ are all weight coefficients.

3. The power resource management method of the microgrid according to claim 2, characterized in that: The abnormality index AI is expressed as: Among them, X i is the measured value of the ith running data, is the predicted value of the ith running data, σ historical is the standard deviation of historical operating data, ω i is the weight coefficient and ξ is the adjustment factor.

4. The power resource management method of a microgrid according to claim 3, characterized in that: The resource scheduling index RDC is expressed as: in, is the load change rate, κ, π, and χ are all weight coefficients.

5. The power resource management method of a microgrid according to claim 2, characterized in that: After the operation data of each node in the microgrid is acquired in real time, the operation data is compressed and transmitted. The data compression ratio CR of the operation data of each node is expressed as: Among them, σ is the standard deviation of the running data, σ t is the maximum standard deviation allowed after compression, λ and μ are adjustment parameters; The transmission priority TP of the operation data of each node is expressed as: in, It represents the rate of change of the comprehensive power quality index CPQI over time, BW represents the available bandwidth of the current network, BW max It represents the maximum bandwidth that the current network can provide, and q, ρ and τ are all weight coefficients.

6. A power resource management system for a microgrid, characterized in that: include: A data acquisition module is used to obtain the operating data of each node in the microgrid in real time; A data calculation module, used to calculate the comprehensive power quality index of each node based on the operation data; It is also used to predict the operation data of each node in the microgrid, calculate the abnormal index of each node based on the real-time operation data and the predicted operation data; calculate the load change rate of each node, and calculate the resource scheduling index of each node based on the comprehensive power quality index, abnormal index and load change rate of each node; The execution module is used to schedule the power resources of each node based on the resource scheduling index of each node.

7. The power resource management system of the microgrid according to claim 6, characterized in that: The operating data includes voltage, current, power factor and frequency, and the comprehensive power quality index CPQI is expressed as: Where V is the measured value of voltage, V n is the rated value of voltage; I is the measured value of current, I n is the rated value of the current; is the measured value of power factor; f is the measured value of frequency, f n is the rated value of the frequency, α, β, γ, δ, ∈, ζ, η, θ are all weight coefficients; The abnormal index AI is expressed as: Among them, X i is the measured value of the ith running data, is the predicted value of the ith running data, σ historical is the standard deviation of historical operating data, ω i is the weight coefficient, ξ is the adjustment factor; The resource scheduling index RDC is expressed as: Among them, AI is the anomaly index, is the load change rate, κ, π, and χ are all weight coefficients.

8. The power resource management system for a microgrid according to claim 7, characterized in that: It also includes a data compression transmission module, which is used to compress and transmit the operation data; The data compression ratio CR of the running data of each node is expressed as: Among them, σ is the standard deviation of the running data, σ t is the maximum standard deviation allowed after compression, λ and μ are adjustment parameters; The transmission priority TP of the operation data of each node is expressed as: in, It represents the rate of change of the comprehensive power quality index CPQI over time, BW represents the available bandwidth of the current network, BW max It represents the maximum bandwidth that the current network can provide, and q, ρ and τ are all weight coefficients.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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