Intelligent power distribution method and system for distribution station area based on distributed energy storage device

Abstract

The present invention discloses an intelligent power distribution method and system for a distribution station area based on a distributed energy storage device, which relates to the field of smart grids. The main scheme is: configuring a distributed energy storage device in a distribution station area, and maintaining communication with a distribution automation terminal in the distribution station area; load demand data, power grid status data and transformer load rate data of the distribution station area are obtained; predictive analysis is performed on the load demand data of the distribution station area to generate a predicted load; predictive analysis is performed on the real-time power grid status of the distribution station area to generate a predicted power grid status HI; predictive analysis is performed on the transformer load rate to generate a predicted transformer load rate λ; when the distributed energy storage device receives a charge or discharge instruction, the corresponding instruction is executed; the operating status of the distributed energy storage device is monitored in real time, and an alarm signal is sent when an abnormal data signal is detected.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a smart power distribution method and system for a distribution station area based on a distributed energy storage device. Background Art

[0002] Distributed energy storage technology has developed rapidly in recent years. With the continuous growth of electricity demand, a large number of distributed energy sources have been connected to the distribution network, and new loads such as electric vehicles, virtual power plants, integrated energy, and charging facilities have continued to emerge. Distribution substations are facing problems such as voltage over-limit, load regulation, insufficient new energy absorption capacity, and three-phase imbalance. In addition, traditional distribution substations have low information and digitalization levels in intelligent distribution methods, insufficient reliability of communication systems, weak fault diagnosis and early warning capabilities, insufficient load management and optimization capabilities, and low operation and maintenance management efficiency. These problems have seriously affected the power supply quality and reliability of distribution substations. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] In view of the deficiencies in the prior art, the present invention provides an intelligent power distribution method and system for distribution station areas based on distributed energy storage devices. According to the real-time monitoring of the load demand data of the distribution station, the charging and discharging plan of the distributed energy storage device is adjusted, thereby effectively balancing the load fluctuations in the distribution station area. This helps to reduce the peak-to-valley difference of the power grid and improve the operating efficiency of the power grid. By predicting and monitoring the load conditions of transformers and lines in real time, measures can be taken in advance to prevent transformers and lines from being overloaded and to protect power grid equipment from damage. According to real-time monitoring data and preset control strategies, the charging and discharging plan of the distributed energy storage device is automatically calculated and determined, realizing adaptive control of the energy storage device. It solves the problems of low degree of informatization and digitization, weak fault diagnosis and early warning capabilities, insufficient load management and optimization capabilities, and excessive energy consumption.

[0005] (II) Technical solution

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for intelligent power distribution in a distribution area based on a distributed energy storage device, comprising:

[0007] Configure distributed energy storage devices in the distribution substation area and maintain communication with the distribution automation terminal in the distribution substation area;

[0008] Obtain load demand data, grid status data and transformer load rate data of distribution substations;

[0009] Forecast and analyze the load demand data of the distribution station area to generate a forecast load index The preset distribution station load charge and discharge threshold Z will be used to predict the load index Compare it with the distribution station load charge and discharge threshold Z to obtain judgment result 1;

[0010] Predict and analyze the real-time grid state of the distribution station area to generate a predicted grid state HI; preset a grid state charge and discharge threshold B, compare the predicted grid state HI with the preset grid state charge and discharge threshold B, and obtain a second judgment result;

[0011] Predict and analyze the transformer load rate to generate a predicted transformer load rate λ; preset a transformer load rate charge and discharge threshold R, compare the predicted transformer load rate λ with the preset transformer load rate charge and discharge threshold R, and obtain a third judgment result;

[0012] When one or more of the judgment results 1, 2 and 3 meet the charging instruction triggering conditions, the charging instruction is executed; when one or more of the judgment results 1, 2 and 3 meet the discharging instruction triggering conditions, the discharging instruction is executed;

[0013] Monitor the operating status of distributed energy storage devices in real time and send an alarm signal when abnormal data signals are detected.

[0014] In the above-mentioned preferred embodiment of the intelligent power distribution method for a distribution station area based on a distributed energy storage device: judgment result one, judgment result two and judgment result three all include a charging trigger instruction, a normal state instruction and a discharging trigger instruction.

[0015] In the preferred embodiment of the intelligent power distribution method for a distribution area based on a distributed energy storage device: generating a predicted load index The method is as follows:

[0016] Submit the load data into the random forest regression model to calculate the predicted load index The formula is:

[0017]

[0018] in, is the predicted load at time t; M is the number of decision trees in the random forest;

[0019] T m (x t ) is the load value x in the past period of time for the mth decision tree t The predicted value of x t is the load value at time t.

[0020] In the above preferred solution of the intelligent power distribution method for a distribution station area based on a distributed energy storage device: when judging whether the charging instruction triggering condition or the discharging instruction triggering condition is met according to the judgment result 1, the method is:

[0021] The matrix Z0 of the preset distribution station load charge and discharge threshold Z, Z0 (Z1, Z2), and Z1 < Z2; Z1 is the charge influence coefficient 1, and Z2 is the discharge influence coefficient 1;

[0022] when When , it is determined that the charging trigger instruction 1 is satisfied;

[0023] when When , it is judged that the normal state instruction is satisfied;

[0024] when , it is determined that the discharge trigger instruction is satisfied.

[0025] In the above preferred embodiment of the intelligent power distribution method for a distribution station area based on a distributed energy storage device: the method for generating the predicted power grid state HI is as follows:

[0026] The grid status data includes: actual voltage Vactual, rated voltage Vrated, actual current Iactual, rated current Irated, power loss Ploss, total power Ptotal, actual temperature T, optimal temperature Topt, actual humidity H, optimal humidity Hopt;

[0027] The above data is used to calculate the predicted power grid status HI, based on the following formula:

[0028]

[0029] Among them, Trange is the tolerance range of temperature; Hrange is the tolerance range of humidity;

[0030] w1 is the weight coefficient of voltage parameter in grid status assessment; w2 is the weight coefficient of current parameter in grid status assessment; w3 is the weight coefficient of power loss parameter in grid status assessment; w4 is the weight coefficient of temperature and humidity comprehensive parameters.

[0031] In the above preferred solution of the intelligent power distribution method for a distribution station area based on a distributed energy storage device: the method for judging whether the charging instruction trigger condition or the discharging instruction trigger condition is met according to the second judgment result is:

[0032] A matrix B0 of the charge and discharge threshold B of the power distribution station grid state is preset, B0(B1, B2), and B1<B2; B1 is the charging influence coefficient 2, and B2 is the discharging influence coefficient 2;

[0033] When the predicted grid state HI < charging influence coefficient B1, it is determined that the charging trigger instruction is met;

[0034] When the charging influence coefficient B1≤predicted grid state HI≤discharging influence coefficient B2, it is judged that the normal state instruction is met;

[0035] When the predicted grid state HI> the discharge influence coefficient B2, it is determined that the discharge trigger instruction is satisfied.

[0036] In the above preferred solution of the intelligent power distribution method for a distribution station area based on a distributed energy storage device: the method for generating the transformer load rate λ is as follows:

[0037] The calculation formula of transformer load factor λ is as follows:

[0038]

[0039] Among them, Pactual is the actual load power; Prated is the rated capacity;

[0040] The method for judging whether the charging instruction triggering condition or the discharging instruction triggering condition is met according to the judgment result three is:

[0041] The matrix R0 of the preset distribution station load charge and discharge threshold R, R0 (R1, R2), and R1 < R2; R1 is the charging influence coefficient three, and R2 is the discharging influence coefficient three;

[0042] When the transformer load rate λ<charging influence coefficient R1, it is judged that the charging trigger instruction is met;

[0043] When the charging influence coefficient III R1≤transformer load rate λ≤discharging influence coefficient III R2, it is judged that the normal state instruction is met;

[0044] When the transformer load rate λ>discharge influence coefficient R2, it is determined that the discharge trigger instruction is met.

[0045] In the preferred embodiment of the intelligent power distribution method for a distribution station area based on a distributed energy storage device: when at least one of the first judgment result, the second judgment result and the third judgment result satisfies the charging trigger instruction; and when the other items satisfy the normal state instruction, the charging instruction is executed;

[0046] When at least one of the first, second and third judgment results satisfies the discharge trigger instruction, and the other items satisfy the normal state instruction, the discharge instruction is executed;

[0047] When at least one of the first, second and third judgment results satisfies the charge trigger instruction, and the remaining items satisfy the discharge trigger instruction, it is determined to be an abnormal state and a fault prompt instruction is issued.

[0048] In the above preferred solution of the intelligent power distribution method for a distribution station area based on a distributed energy storage device: the method for detecting abnormal data signals is as follows:

[0049] Get the current signal value Xi;

[0050] Preset the normal value Xnormal and the warning threshold ΔXthreshold of the current signal;

[0051] When the condition Xi-Xnormal|>ΔXthreshold is met, it is judged as abnormal data signal 1;

[0052] Get the current voltage signal value Vc;

[0053] Preset a normal value Vnormal and a warning threshold ΔVthreshold of the voltage signal;

[0054] When the condition of Vc-Vnormal>ΔVthreshold is met, it is judged as abnormal data signal 2;

[0055] Get the current temperature signal value Tc;

[0056] Preset the normal value Tnormal and the warning threshold ΔTthreshold of the temperature signal;

[0057] When the condition of Tc-Tnormal>ΔTthreshold is met, it is judged as abnormal data signal three;

[0058] When one or more of abnormal data signal 1, abnormal data signal 2 and abnormal data signal 3 occur, an alarm signal is sent.

[0059] The present invention also discloses an intelligent power distribution system for a power distribution area based on a distributed energy storage device, comprising:

[0060] A communication module, used to enable the distributed energy storage device to maintain communication with the distribution automation terminal in the distribution station area;

[0061] Data acquisition module, used to obtain load demand data, power grid status data and transformer load rate data of the distribution station area;

[0062] Intelligent prediction module, used to predict and analyze the load demand data of the distribution station area and generate the predicted load index The preset distribution station load charge and discharge threshold Z will be used to predict the load index Compare it with the distribution station load charge and discharge threshold Z to obtain judgment result 1;

[0063] Predict and analyze the real-time grid state of the distribution station area to generate a predicted grid state HI; preset a grid state charge and discharge threshold B, compare the predicted grid state HI with the preset grid state charge and discharge threshold B, and obtain a second judgment result;

[0064] Predict and analyze the transformer load rate to generate a predicted transformer load rate λ; preset a transformer load rate charge and discharge threshold R, compare the predicted transformer load rate λ with the preset transformer load rate charge and discharge threshold R, and obtain a third judgment result;

[0065] An execution module, when one or more of the judgment results 1, 2 and 3 meet the charging instruction triggering condition, executes the charging instruction; when one or more of the judgment results 1, 2 and 3 meet the discharging instruction triggering condition, executes the discharging instruction;

[0066] The fault warning module is used to monitor the operating status of the distributed energy storage device in real time and send an alarm signal when an abnormal data signal is detected.

[0067] (III) Beneficial effects

[0068] The present invention provides a method and system for intelligent power distribution in a distribution area based on a distributed energy storage device, which has the following beneficial effects:

[0069] (1) By real-time monitoring and analysis of the load demand, grid status and transformer load rate of the distribution station area, it is possible to predict and take corresponding measures in advance, effectively alleviate the pressure on the grid, reduce the risk of power outages caused by sudden load changes or grid failures, and improve the reliability and stability of power supply. By using load forecasting and grid status prediction technology, the charging and discharging process of the energy storage device can be controlled more accurately to achieve efficient use of energy. Charging during low-load periods and discharging during peak periods will help balance the grid load and reduce the impact of the peak-valley electricity price difference.

[0070] (2) By properly controlling the load rate of the transformer and avoiding it from operating in an overload or underload state for a long time, it helps to reduce equipment wear and aging, thereby extending the service life of key equipment such as transformers; distributed energy storage devices can quickly respond to changes in the state of the power grid, adjust the power balance of the power grid through rapid charging and discharging, enhance the flexibility and response speed of the power grid, and improve the power grid's ability to respond to emergencies.

[0071] (3) Through accurate load forecasting and energy management, the storage and release capacity of energy storage devices can be maximized to achieve peak-valley arbitrage and reduce electricity costs. At the same time, the economic losses caused by power outages can be reduced and the overall economic benefits can be improved. This method combines advanced forecasting and analysis technology, automated control technology, and real-time monitoring technology to achieve intelligent management of distribution stations. It not only improves work efficiency, but also reduces the need for manual intervention, and improves the automation and intelligence level of the system.

[0072] (4) By real-time monitoring of the operating status of distributed energy storage devices, including key parameters such as battery pack voltage, current, and temperature, combined with intelligent algorithms for data analysis, potential faults or anomalies can be discovered in a timely manner, and early warning signals can be issued in advance. This greatly shortens the time to discover faults, reduces the impact of faults on the stable operation of the system, and reduces the economic losses caused by downtime due to faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a schematic diagram of the steps of the intelligent power distribution method and system for a distribution station area based on a distributed energy storage device of the present invention. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] See also Figure 1 , the present invention provides a method for intelligent power distribution in a distribution substation based on a distributed energy storage device, comprising: configuring a distributed energy storage device in the distribution substation, and maintaining communication with a distribution automation terminal in the distribution substation;

[0076] Obtain load demand data, grid status data and transformer load rate data of distribution substations;

[0077] Forecast and analyze the load demand data of the distribution station area to generate a forecast load index The preset distribution station load charge and discharge threshold Z will be used to predict the load index Compare it with the distribution station load charge and discharge threshold Z to obtain judgment result 1;

[0078] Predict and analyze the real-time grid state of the distribution station area to generate a predicted grid state HI; preset a grid state charge and discharge threshold B, compare the predicted grid state HI with the preset grid state charge and discharge threshold B, and obtain a second judgment result;

[0079] Predict and analyze the transformer load rate to generate a predicted transformer load rate λ; preset a transformer load rate charge and discharge threshold R, compare the predicted transformer load rate λ with the preset transformer load rate charge and discharge threshold R, and obtain a third judgment result;

[0080] When one or more of the judgment results 1, 2 and 3 meet the charging instruction triggering conditions, the charging instruction is executed; when one or more of the judgment results 1, 2 and 3 meet the discharging instruction triggering conditions, the discharging instruction is executed;

[0081] Monitor the operating status of distributed energy storage devices in real time and send an alarm signal when abnormal data signals are detected.

[0082] Step 1: Configure a distributed energy storage device in the distribution substation area and maintain communication with the distribution automation terminal in the distribution substation area;

[0083] Step one includes configuring distributed energy storage devices in the distribution station area, selecting efficient, safe, and long-life battery packs, and equipping them with advanced battery management systems and power conversion systems to ensure the safe and stable operation of the energy storage devices.

[0084] Select DTU / FTU that supports international standard protocols such as IEC61850 and has powerful data processing and communication capabilities. Select the appropriate communication method according to the site environment and configure the corresponding communication equipment. The components inside the energy storage device communicate using protocols such as CAN bus or Modbus. The energy storage device and the distribution automation terminal communicate using the IEC61850 protocol to ensure data standardization and interoperability.

[0085] Remote monitoring is achieved through VPN or cloud platform, and data transmission is carried out using protocols such as MQTT and HTTP.

[0086] Step 2: Obtain load demand data, grid status data and transformer load rate data of the distribution station area;

[0087] Step 2 includes: obtaining load demand data, installing current sensors and voltage sensors at key load points in the distribution area to monitor current and voltage values ​​in real time. If conditions permit, existing meters can be replaced or upgraded with smart meters, which can provide more accurate load data and support remote communication. Set an appropriate collection frequency to obtain load data in real time through the data collector or the communication module built into the smart meter. Ensure data synchronization between the data collection system and the distribution management system or data center for timely analysis and processing.

[0088] Obtain grid status data and connect the SCADA system of the distribution station area to the data center or cloud platform to realize real-time monitoring of the grid operation status; establish a visual interface on the data center or cloud platform to display the voltage, current, power factor and other status data of the grid in real time.

[0089] Obtain transformer load rate data, select high-precision, high-reliability transformer monitoring devices that can measure the actual load current and rated current of the transformer in real time; install monitoring devices on transformers in distribution stations, and configure and debug them to ensure accurate data collection and transmission to data centers or cloud platforms. The monitoring device regularly collects actual load current and rated current data of the transformer, and transmits it to the data center or cloud platform via wired or wireless means. Calculate the load rate of the transformer using the load rate calculation formula. Display the real-time and historical load rate data of the transformer on the data center or cloud platform.

[0090] Step 3: Forecast and analyze the load demand data of the distribution station area to generate a forecast load index The preset distribution station load charge and discharge threshold Z will be used to predict the load index Compare it with the distribution station load charge and discharge threshold Z to obtain judgment result 1;

[0091] Step 3 includes: generating a forecast load index The method is as follows:

[0092] Submit the load data into the random forest regression model to calculate the predicted load The formula is:

[0093]

[0094] in, is the predicted load at time t; M is the number of decision trees in the random forest;

[0095] T m (x t ) is the load value x in the past period of time for the mth decision tree t The predicted value of x t is the load value at time t.

[0096] The method for judging whether the charging instruction triggering condition or the discharging instruction triggering condition is met according to the judgment result 1 is as follows:

[0097] The matrix Z0 of the preset distribution station load charge and discharge threshold Z, Z0 (Z1, Z2), and Z1 < Z2; Z1 is the charge influence coefficient 1, and Z2 is the discharge influence coefficient 1;

[0098] when When , it is determined that the charging trigger instruction is met;

[0099] when When , it is judged that the normal state instruction is satisfied;

[0100] when When , it is judged that the discharge trigger instruction is satisfied;

[0101] Formula formation principle

[0102] Random forest regression reduces overfitting and improves the robustness of predictions by building multiple decision trees and letting them independently learn a random subset of the training data and a random subset of features, and then taking the average of the predictions of these trees.

[0103] Parameter source and value range

[0104] The value of M usually ranges from tens to hundreds, depending on the complexity of the data and the computing resources.

[0105] x t The various components of are derived from the load demand data obtained by the real-time monitoring system of the distribution station area, and the value range is x t ∈[0,1] MW or higher.

[0106] Step 4: predict and analyze the real-time grid state of the distribution station area to generate a predicted grid state HI; preset a grid state charge and discharge threshold B, compare the predicted grid state HI with the preset grid state charge and discharge threshold B, and obtain a second judgment result;

[0107] Step 4 includes: the method of generating the predicted power grid state HI is as follows:

[0108] The grid status data includes: actual voltage Vactual, rated voltage Vrated, actual current Iactual, rated current Irated, power loss Ploss, total power Ptotal, actual temperature T, optimal temperature Topt, actual humidity H, optimal humidity Hopt;

[0109] The above data is used to calculate the predicted power grid status HI, based on the following formula:

[0110]

[0111] in,

[0112] Trange is the tolerance range of temperature; Hrange is the tolerance range of humidity;

[0113] w1 is the weight coefficient of voltage parameter in grid status assessment; w2 is the weight coefficient of current parameter in grid status assessment; w3 is the weight coefficient of power loss parameter in grid status assessment; w4 is the weight coefficient of temperature and humidity comprehensive parameters.

[0114] The method for judging whether the charging instruction triggering condition or the discharging instruction triggering condition is met according to the second judgment result is:

[0115] A matrix B0 of the charge and discharge threshold B of the power distribution station grid state is preset, B0(B1, B2), and B1<B2; B1 is the charging influence coefficient 2, and B2 is the discharging influence coefficient 2;

[0116] When the predicted grid state HI < charging influence coefficient B1, it is determined that the charging trigger instruction is met;

[0117] When the charging influence coefficient B1≤predicted grid state HI≤discharging influence coefficient B2, it is judged that the normal state instruction is met;

[0118] When the predicted grid state HI> the discharge influence coefficient B2, it is determined that the discharge trigger instruction is satisfied.

[0119] Formula formation principle

[0120] Voltage and current: Evaluate the load of the grid by comparing the actual voltage Vactual with the rated voltage Vrated, and the actual current Iactual with the rated current Irated. A high ratio may indicate that the grid is close to full load or overloaded.

[0121] Power loss: The efficiency of the power grid is evaluated by calculating the ratio of power loss Ploss in the power grid to total power Ptotal. A lower loss ratio indicates higher efficiency.

[0122] Temperature and humidity: The impact of the environment on power grid equipment is evaluated by calculating the deviation between the actual temperature T and the optimal temperature Topt, and the deviation between the actual humidity H and the optimal humidity Hopt. A higher deviation value may indicate that the equipment is operating in a poor environment, which may affect the life and performance of the equipment.

[0123] Parameter source and value range

[0124] Electrical parameters: usually obtained in real time through measuring equipment in the power grid.

[0125] Actual voltage Vactual: about 230V; Rated voltage Vrated: about 240V;

[0126] Actual current Vactual: about 10A; rated current Vrated: about 12A;

[0127] Power loss Ploss: about 2%-5% of total power; total power Ptotal: about 5kW;

[0128] Temperature and humidity: obtained through environmental monitoring equipment installed at key locations in the power grid.

[0129] The actual temperature T: may be between 20℃ and 50℃, depending on the ambient temperature and heat dissipation conditions.

[0130] Optimum temperature Topt: Usually slightly lower than the upper limit of the actual temperature range to ensure good heat dissipation and efficiency.

[0131] Actual humidity H: may be between 30%RH to 70%RH, depending on geographic location and weather conditions.

[0132] Optimum humidity Hopt: Usually close to but not exceeding the comfortable range of ambient humidity to avoid problems caused by excessive humidity or dryness, approximately 45% RH.

[0133] Weights w1, w2, w3, w4: are set according to the specific needs and concerns of the power grid, usually determined through expert evaluation or data analysis. The sum of the weights should be equal to 1 to ensure that HI is a dimensionless indicator.

[0134] Step 5: predict and analyze the transformer load rate to generate a predicted transformer load rate λ; preset a transformer load rate charge and discharge threshold R, compare the predicted transformer load rate λ with the preset transformer load rate charge and discharge threshold R, and obtain a judgment result 3;

[0135] Step five includes: the method of generating the transformer load factor λ is as follows:

[0136] The calculation formula of transformer load factor is as follows:

[0137]

[0138] Among them, Pactual is the actual load power; Prated is the rated capacity;

[0139] The method for judging whether the charging instruction triggering condition or the discharging instruction triggering condition is met according to the judgment result three is:

[0140] The matrix R0 of the preset distribution station load charge and discharge threshold R, R0 (R1, R2), and R1 < R2; R1 is the charging influence coefficient three, and R2 is the discharging influence coefficient three;

[0141] When the transformer load rate λ<charging influence coefficient R1, it is judged that the charging trigger instruction is met;

[0142] When the charging influence coefficient III R1≤transformer load rate λ≤discharging influence coefficient III R2, it is judged that the normal state instruction is met;

[0143] When the transformer load rate λ>discharge influence coefficient R2, it is determined that the discharge trigger instruction is met.

[0144] Step 6: When one or more of the judgment results 1, 2 and 3 meet the charging instruction triggering condition, execute the charging instruction; when one or more of the judgment results 1, 2 and 3 meet the discharging instruction triggering condition, execute the discharging instruction;

[0145] Step six includes: when at least one of the first judgment result, the second judgment result and the third judgment result satisfies the charging trigger instruction; and when the other items satisfy the normal state instruction, executing the charging instruction;

[0146] When at least one of the first, second and third judgment results satisfies the discharge trigger instruction, and the other items satisfy the normal state instruction, the discharge instruction is executed;

[0147] When at least one of the first, second and third judgment results satisfies the charge trigger instruction, and the remaining items satisfy the discharge trigger instruction, it is determined to be an abnormal state and a fault prompt instruction is issued.

[0148] Step 7: monitor the operating status of the distributed energy storage device in real time, and send an alarm signal when an abnormal data signal is detected;

[0149] Step seven includes: setting a reasonable data collection frequency according to the operating characteristics and monitoring requirements of the distributed energy storage device; achieving stable communication between the data acquisition module and the monitoring center to ensure that data can be transmitted to the monitoring center in real time and accurately; considering the use of wired or wireless transmission methods, and flexibly selecting according to the on-site environment.

[0150] The method to detect abnormal data signals is as follows:

[0151] Get the current signal value Xi;

[0152] Preset the normal value Xnormal and warning threshold ΔXthreshold of the current signal

[0153] When the condition Xi-Xnormal|>ΔXthreshold is met, it is judged as abnormal data signal 1;

[0154] It should be noted that the determination of the normal value Xnormal is mainly based on equipment specifications, industry standards and actual application scenarios; and the acquisition of the warning threshold ΔXthreshold can be achieved through various methods such as calculation based on safety margin, historical data analysis, expert experience and judgment, and risk management principles. This safety margin is usually determined based on factors such as the material properties, heat dissipation conditions, and protection mechanisms of the equipment. The normal value Xnormal of the current signal and the warning threshold ΔXthreshold need to be adjusted according to specific circumstances in actual applications.

[0155] Get the current voltage signal value Vc;

[0156] Preset a normal value Vnormal and a warning threshold ΔVthreshold of the voltage signal;

[0157] When the condition of Vc-Vnormal>ΔVthreshold is met, it is judged as abnormal data signal 2;

[0158] It should be noted that the normal value Vnormal of the preset voltage signal is usually determined based on equipment specifications and design parameters, industry standards and specifications, and actual operating data; and the warning threshold ΔVthreshold can be obtained through calculations based on safety margins, historical data analysis, expert experience and judgment, principles based on risk management, and standards and specifications. The size of this safety margin depends on multiple factors such as equipment reliability requirements, material properties, heat dissipation conditions, and protection mechanisms. The normal value Vnormal of the preset voltage signal and the warning threshold ΔVthreshold need to be adjusted according to specific circumstances in actual applications.

[0159] Get the current temperature signal value Tc;

[0160] Preset the normal value Tnormal and the warning threshold ΔTthreshold of the temperature signal;

[0161] When the condition of Tc-Tnormal>ΔTthreshold is met, it is judged as abnormal data signal three;

[0162] It should be noted that the normal value Tnormal of the preset temperature signal is usually determined based on equipment specifications and design parameters, industry standards and specifications, and actual operating data; while the warning threshold ΔTthreshold can be obtained through calculation based on safety margin, historical data analysis, expert experience and judgment, risk management principles, standards and specifications, etc. The size of this safety margin depends on multiple factors such as equipment reliability requirements, material properties, heat dissipation conditions, and protection mechanisms. The normal value Tnormal of the preset temperature signal and the warning threshold ΔTthreshold need to be adjusted according to specific circumstances in actual applications.

[0163] When one or more of abnormal data signal 1, abnormal data signal 2 and abnormal data signal 3 occur, an alarm signal is sent.

[0164] After receiving the alarm signal, relevant personnel should immediately confirm the authenticity and accuracy of the alarm information; classify and record the confirmed alarm information for subsequent tracking and processing.

[0165] Develop corresponding emergency response plans according to the alarm type and urgency. Quickly organize relevant personnel and equipment to inspect and maintain the distributed energy storage device; conduct in-depth investigation and analysis of the causes of abnormalities; take corresponding treatment measures based on the investigation results to restore the normal operation of the distributed energy storage device; record and archive the treatment process for subsequent analysis and improvement.

[0166] Feedback the alarm processing results and problems found during the troubleshooting process to the data acquisition and processing system. Adjust the monitoring parameters, thresholds, and judgment logic based on the feedback results to improve the accuracy and reliability of the system. Regularly evaluate and optimize the monitoring system to ensure that it meets the operation and monitoring requirements of the energy storage device. Introduce new technologies and methods to continuously improve the intelligence and automation level of the system.

[0167] On the other hand, the present invention also discloses an intelligent power distribution system for a distribution station area based on a distributed energy storage device, comprising:

[0168] A communication module, used to enable the distributed energy storage device to maintain communication with the distribution automation terminal in the distribution station area;

[0169] Data acquisition module, used to obtain load demand data, power grid status data and transformer load rate data of the distribution station area;

[0170] Intelligent prediction module, used to predict and analyze the load demand data of the distribution station area and generate the predicted load index The preset distribution station load charge and discharge threshold Z will be used to predict the load index Compare it with the distribution station load charge and discharge threshold Z to obtain judgment result 1;

[0171] Predict and analyze the real-time grid state of the distribution station area to generate a predicted grid state HI; preset a grid state charge and discharge threshold B, compare the predicted grid state HI with the preset grid state charge and discharge threshold B, and obtain a second judgment result;

[0172] Predict and analyze the transformer load rate to generate a predicted transformer load rate λ; preset a transformer load rate charge and discharge threshold R, compare the predicted transformer load rate λ with the preset transformer load rate charge and discharge threshold R, and obtain a third judgment result;

[0173] An execution module, when one or more of the judgment results 1, 2 and 3 meet the charging instruction triggering condition, executes the charging instruction; when one or more of the judgment results 1, 2 and 3 meet the discharging instruction triggering condition, executes the discharging instruction;

[0174] The fault warning module is used to monitor the operating status of the distributed energy storage device in real time and send an alarm signal when an abnormal data signal is detected.

[0175] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware or in combination with computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0176] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0177] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An intelligent power distribution method for a distribution area based on a distributed energy storage device, characterized in that: The following steps are involved: Configure distributed energy storage devices in the distribution substation area and maintain communication with the distribution automation terminal in the distribution substation area; Obtain load demand data, grid status data and transformer load rate data of distribution substations; Forecast and analyze the load demand data of the distribution station area to generate a forecast load index The preset distribution station load charge and discharge threshold Z will be used to predict the load index Compare it with the distribution station load charge and discharge threshold Z to obtain judgment result 1; Predict and analyze the real-time grid state of the distribution station area to generate a predicted grid state HI; preset a grid state charge and discharge threshold B, compare the predicted grid state HI with the preset grid state charge and discharge threshold B, and obtain a second judgment result; Performing prediction analysis on the transformer load factor to generate a predicted transformer load factor λ; A transformer load rate charge and discharge threshold R is preset, and the predicted transformer load rate λ is compared with the preset transformer load rate charge and discharge threshold R to obtain a third judgment result; When one or more of the judgment results 1, 2 and 3 meet the charging instruction triggering conditions, the charging instruction is executed; when one or more of the judgment results 1, 2 and 3 meet the discharging instruction triggering conditions, the discharging instruction is executed; Monitor the operating status of distributed energy storage devices in real time and send an alarm signal when abnormal data signals are detected.

2. According to claim 1, a method for intelligent power distribution in a distribution area based on a distributed energy storage device is characterized in that: The first judgment result, the second judgment result and the third judgment result all include a charge trigger instruction, a normal state instruction and a discharge trigger instruction.

3. According to claim 2, a method for intelligent power distribution in a distribution area based on a distributed energy storage device is characterized in that: Generate forecast load index The method is as follows: Submit the load data into the random forest regression model to calculate the predicted load index The formula is: in, is the predicted load at time t; M is the number of decision trees in the random forest; T m (x t ) is the load value x in the past period of time for the mth decision tree t The predicted value of x t is the load value at time t.

4. According to claim 3, a method for intelligent power distribution in a distribution area based on a distributed energy storage device is characterized in that: The method for judging whether the charging instruction triggering condition or the discharging instruction triggering condition is met according to the judgment result 1 is as follows: The matrix Z0 of the preset distribution station load charge and discharge threshold Z, Z0 (Z1, Z2), and Z1 < Z2; Z1 is the charge influence coefficient 1, and Z2 is the discharge influence coefficient 1; when When , it is determined that the charging trigger instruction 1 is satisfied; when When , it is judged that the normal state instruction is satisfied; when , it is determined that the discharge trigger instruction is satisfied.

5. According to claim 4, a method for intelligent power distribution in a distribution area based on a distributed energy storage device is characterized in that: The method for generating the predicted power grid status HI is as follows: The grid status data includes: actual voltage Vactual, rated voltage Vrated, actual current Iactual, rated current Irated, power loss Ploss, total power Ptotal, actual temperature T, optimal temperature Topt, actual humidity H, optimal humidity Hopt; The above data is used to calculate the predicted power grid status HI, based on the following formula: Among them, Trange is the tolerance range of temperature; Hrange is the tolerance range of humidity; w1 is the weight coefficient of voltage parameter in grid status assessment; w2 is the weight coefficient of current parameter in grid status assessment; w3 is the weight coefficient of power loss parameter in grid status assessment; w4 is the weight coefficient of temperature and humidity comprehensive parameters.

6. The intelligent power distribution method for a distribution station area based on a distributed energy storage device according to claim 5 is characterized in that: The method for judging whether the charging instruction triggering condition or the discharging instruction triggering condition is met according to the second judgment result is: A matrix B0 of the charge and discharge threshold B of the power distribution station grid state is preset, B0(B1, B2), and B1<B2; B1 is the charging influence coefficient 2, and B2 is the discharging influence coefficient 2; When the predicted grid state HI < charging influence coefficient B1, it is determined that the charging trigger instruction is met; When the charging influence coefficient B1≤predicted grid state HI≤discharging influence coefficient B2, it is judged that the normal state instruction is met; When the predicted grid state HI> the discharge influence coefficient B2, it is determined that the discharge trigger instruction is satisfied.

7. The intelligent power distribution method for a distribution station area based on a distributed energy storage device according to claim 6 is characterized in that: The method for generating the transformer load factor λ is as follows: The calculation formula of transformer load factor λ is as follows: Among them, Pactual is the actual load power; Prated is the rated capacity; The method for judging whether the charging instruction triggering condition or the discharging instruction triggering condition is met according to the judgment result three is: The matrix R0 of the preset distribution station load charge and discharge threshold R, R0 (R1, R2), and R1 < R2; R1 is the charging influence coefficient three, and R2 is the discharging influence coefficient three; When the transformer load rate λ<charging influence coefficient R1, it is judged that the charging trigger instruction is met; When the charging influence coefficient III R1≤transformer load rate λ≤discharging influence coefficient III R2, it is judged that the normal state instruction is met; When the transformer load rate λ>discharge influence coefficient R2, it is determined that the discharge trigger instruction is met.

8. The intelligent power distribution method for a distribution station area based on a distributed energy storage device according to claim 7 is characterized in that: When at least one of the first, second and third judgment results satisfies the charging trigger instruction, and the other items satisfy the normal state instruction, the charging instruction is executed; When at least one of the judgment result 1, the judgment result 2 and the judgment result 3 satisfies the discharge trigger instruction; When the other items meet the normal state instructions, the discharge instruction is executed; When at least one of the first, second and third judgment results satisfies the charge trigger instruction, and the remaining items satisfy the discharge trigger instruction, it is determined to be an abnormal state and a fault prompt instruction is issued.

9. The intelligent power distribution method for a distribution station area based on a distributed energy storage device according to claim 8, characterized in that: The method to detect abnormal data signals is as follows: Get the current signal value Xi; Preset the normal value Xnormal and the warning threshold ΔXthreshold of the current signal; When the condition Xi-Xnormal|>ΔXthreshold is met, it is judged as abnormal data signal 1; Get the current voltage signal value Vc; Preset a normal value Vnormal and a warning threshold ΔVthreshold of the voltage signal; When the condition of Vc-Vnormal>ΔVthreshold is met, it is judged as abnormal data signal 2; Get the current temperature signal value Tc; Preset the normal value Tnormal and the warning threshold ΔTthreshold of the temperature signal; When the condition of Tc-Tnormal>ΔTthreshold is met, it is judged as abnormal data signal three; When one or more of abnormal data signal 1, abnormal data signal 2 and abnormal data signal 3 occur, an alarm signal is sent.

10. An intelligent power distribution system for distribution stations based on distributed energy storage devices, characterized in that: include: A communication module, used to enable the distributed energy storage device to maintain communication with the distribution automation terminal in the distribution station area; Data acquisition module, used to obtain load demand data, power grid status data and transformer load rate data of the distribution station area; Intelligent prediction module, used to predict and analyze the load demand data of the distribution station area and generate the predicted load index The preset distribution station load charge and discharge threshold Z will be used to predict the load index Compare it with the distribution station load charge and discharge threshold Z to obtain judgment result 1; Predict and analyze the real-time grid state of the distribution station area to generate a predicted grid state HI; preset a grid state charge and discharge threshold B, compare the predicted grid state HI with the preset grid state charge and discharge threshold B, and obtain a second judgment result; Performing prediction analysis on the transformer load factor to generate a predicted transformer load factor λ; A transformer load rate charge and discharge threshold R is preset, and the predicted transformer load rate λ is compared with the preset transformer load rate charge and discharge threshold R to obtain a third judgment result; An execution module, when one or more of the judgment results 1, 2 and 3 meet the charging instruction triggering condition, executes the charging instruction; when one or more of the judgment results 1, 2 and 3 meet the discharging instruction triggering condition, executes the discharging instruction; The fault warning module is used to monitor the operating status of the distributed energy storage device in real time and send an alarm signal when an abnormal data signal is detected.

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